From e1e305369552b82d3f09f0c113ea8b54d5c90658 Mon Sep 17 00:00:00 2001
From: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com>
Date: Wed, 16 Jul 2025 10:49:24 +0200
Subject: [PATCH] fix: fix HTML table parser and JATS backend bugs (#1948)
Fix a bug in parsing HTML tables in HTML backend.
Fix a bug in test file that prevented JATS backend tests.
Ensure that the JATS backend creates headings with the right level.
Remove unnecessary data files for testing JATS backend.
Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com>
---
docling/backend/html_backend.py | 42 +-
docling/backend/xml/jats_backend.py | 16 +-
.../docling_v2/bmj_sample.xml.itxt | 70 -
.../docling_v2/bmj_sample.xml.json | 1080 --
.../groundtruth/docling_v2/bmj_sample.xml.md | 105 -
...e-56337.xml.itxt => elife-56337.nxml.itxt} | 0
.../docling_v2/elife-56337.nxml.json | 7265 ++++++++
...elife-56337.xml.md => elife-56337.nxml.md} | 26 +-
.../docling_v2/example_8.html.itxt | 8 -
.../docling_v2/example_8.html.json | 2008 ---
.../groundtruth/docling_v2/example_8.html.md | 29 -
.../docling_v2/pnas_sample.xml.itxt | 148 -
.../docling_v2/pnas_sample.xml.json | 6353 -------
.../groundtruth/docling_v2/pnas_sample.xml.md | 258 -
...008301.xml.itxt => pntd.0008301.nxml.itxt} | 0
.../docling_v2/pntd.0008301.nxml.json | 7237 ++++++++
...td.0008301.xml.md => pntd.0008301.nxml.md} | 24 +-
...234687.xml.itxt => pone.0234687.nxml.itxt} | 0
.../docling_v2/pone.0234687.nxml.json | 14628 ++++++++++++++++
...ne.0234687.xml.md => pone.0234687.nxml.md} | 0
tests/data/jats/bmj_sample.xml | 842 -
tests/data/jats/pnas_sample.xml | 3089 ----
tests/data/jats/pntd.0008301.txt | 96 -
tests/data/jats/pntd.0008301.xml | 96 -
tests/data/jats/pone.0234687.txt | 60 -
tests/data/jats/pone.0234687.xml | 60 -
tests/test_backend_jats.py | 28 +-
27 files changed, 29206 insertions(+), 14362 deletions(-)
delete mode 100644 tests/data/groundtruth/docling_v2/bmj_sample.xml.itxt
delete mode 100644 tests/data/groundtruth/docling_v2/bmj_sample.xml.json
delete mode 100644 tests/data/groundtruth/docling_v2/bmj_sample.xml.md
rename tests/data/groundtruth/docling_v2/{elife-56337.xml.itxt => elife-56337.nxml.itxt} (100%)
create mode 100644 tests/data/groundtruth/docling_v2/elife-56337.nxml.json
rename tests/data/groundtruth/docling_v2/{elife-56337.xml.md => elife-56337.nxml.md} (98%)
delete mode 100644 tests/data/groundtruth/docling_v2/example_8.html.itxt
delete mode 100644 tests/data/groundtruth/docling_v2/example_8.html.json
delete mode 100644 tests/data/groundtruth/docling_v2/example_8.html.md
delete mode 100644 tests/data/groundtruth/docling_v2/pnas_sample.xml.itxt
delete mode 100644 tests/data/groundtruth/docling_v2/pnas_sample.xml.json
delete mode 100644 tests/data/groundtruth/docling_v2/pnas_sample.xml.md
rename tests/data/groundtruth/docling_v2/{pntd.0008301.xml.itxt => pntd.0008301.nxml.itxt} (100%)
create mode 100644 tests/data/groundtruth/docling_v2/pntd.0008301.nxml.json
rename tests/data/groundtruth/docling_v2/{pntd.0008301.xml.md => pntd.0008301.nxml.md} (96%)
rename tests/data/groundtruth/docling_v2/{pone.0234687.xml.itxt => pone.0234687.nxml.itxt} (100%)
create mode 100644 tests/data/groundtruth/docling_v2/pone.0234687.nxml.json
rename tests/data/groundtruth/docling_v2/{pone.0234687.xml.md => pone.0234687.nxml.md} (100%)
delete mode 100644 tests/data/jats/bmj_sample.xml
delete mode 100644 tests/data/jats/pnas_sample.xml
delete mode 100644 tests/data/jats/pntd.0008301.txt
delete mode 100644 tests/data/jats/pntd.0008301.xml
delete mode 100644 tests/data/jats/pone.0234687.txt
delete mode 100644 tests/data/jats/pone.0234687.xml
diff --git a/docling/backend/html_backend.py b/docling/backend/html_backend.py
index 3b9a55a5..4488360a 100644
--- a/docling/backend/html_backend.py
+++ b/docling/backend/html_backend.py
@@ -379,6 +379,25 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
else:
_log.debug(f"list-item has no text: {element}")
+ @staticmethod
+ def _get_cell_spans(cell: Tag) -> tuple[int, int]:
+ """Extract colspan and rowspan values from a table cell tag.
+
+ This function retrieves the 'colspan' and 'rowspan' attributes from a given
+ table cell tag.
+ If the attribute does not exist or it is not numeric, it defaults to 1.
+ """
+ raw_spans: tuple[str, str] = (
+ str(cell.get("colspan", "1")),
+ str(cell.get("rowspan", "1")),
+ )
+ int_spans: tuple[int, int] = (
+ int(raw_spans[0]) if raw_spans[0].isnumeric() else 1,
+ int(raw_spans[1]) if raw_spans[0].isnumeric() else 1,
+ )
+
+ return int_spans
+
@staticmethod
def parse_table_data(element: Tag) -> Optional[TableData]: # noqa: C901
nested_tables = element.find("table")
@@ -398,10 +417,9 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
if not isinstance(row, Tag):
continue
cell_tag = cast(Tag, cell)
- val = cell_tag.get("colspan", "1")
- colspan = int(val) if (isinstance(val, str) and val.isnumeric()) else 1
- col_count += colspan
- if cell_tag.name == "td" or cell_tag.get("rowspan") is None:
+ col_span, row_span = HTMLDocumentBackend._get_cell_spans(cell_tag)
+ col_count += col_span
+ if cell_tag.name == "td" or row_span == 1:
is_row_header = False
num_cols = max(num_cols, col_count)
if not is_row_header:
@@ -428,10 +446,11 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
row_header = True
for html_cell in cells:
if isinstance(html_cell, Tag):
+ _, row_span = HTMLDocumentBackend._get_cell_spans(html_cell)
if html_cell.name == "td":
col_header = False
row_header = False
- elif html_cell.get("rowspan") is None:
+ elif row_span == 1:
row_header = False
if not row_header:
row_idx += 1
@@ -456,18 +475,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
text = html_cell.text
# label = html_cell.name
- col_val = html_cell.get("colspan", "1")
- col_span = (
- int(col_val)
- if isinstance(col_val, str) and col_val.isnumeric()
- else 1
- )
- row_val = html_cell.get("rowspan", "1")
- row_span = (
- int(row_val)
- if isinstance(row_val, str) and row_val.isnumeric()
- else 1
- )
+ col_span, row_span = HTMLDocumentBackend._get_cell_spans(html_cell)
if row_header:
row_span -= 1
while (
diff --git a/docling/backend/xml/jats_backend.py b/docling/backend/xml/jats_backend.py
index f286504c..8a85e0c2 100755
--- a/docling/backend/xml/jats_backend.py
+++ b/docling/backend/xml/jats_backend.py
@@ -93,8 +93,8 @@ class JatsDocumentBackend(DeclarativeDocumentBackend):
# Initialize the root of the document hierarchy
self.root: Optional[NodeItem] = None
-
- self.valid = False
+ self.hlevel: int = 0
+ self.valid: bool = False
try:
if isinstance(self.path_or_stream, BytesIO):
self.path_or_stream.seek(0)
@@ -147,6 +147,7 @@ class JatsDocumentBackend(DeclarativeDocumentBackend):
binary_hash=self.document_hash,
)
doc = DoclingDocument(name=self.file.stem or "file", origin=origin)
+ self.hlevel = 0
# Get metadata XML components
xml_components: XMLComponents = self._parse_metadata()
@@ -304,7 +305,9 @@ class JatsDocumentBackend(DeclarativeDocumentBackend):
title: str = abstract["label"] or DEFAULT_HEADER_ABSTRACT
if not text:
continue
- parent = doc.add_heading(parent=self.root, text=title)
+ parent = doc.add_heading(
+ parent=self.root, text=title, level=self.hlevel + 1
+ )
doc.add_text(
parent=parent,
text=text,
@@ -637,7 +640,10 @@ class JatsDocumentBackend(DeclarativeDocumentBackend):
elif child.tag == "ack":
text = DEFAULT_HEADER_ACKNOWLEDGMENTS
if text:
- new_parent = doc.add_heading(text=text, parent=parent)
+ self.hlevel += 1
+ new_parent = doc.add_heading(
+ text=text, parent=parent, level=self.hlevel
+ )
elif child.tag == "list":
new_parent = doc.add_group(
label=GroupLabel.LIST, name="list", parent=parent
@@ -694,6 +700,8 @@ class JatsDocumentBackend(DeclarativeDocumentBackend):
new_text = self._walk_linear(doc, new_parent, child)
if not (node.getparent().tag == "p" and node.tag in flush_tags):
node_text += new_text
+ if child.tag in ("sec", "ack") and text:
+ self.hlevel -= 1
# pick up the tail text
node_text += child.tail.replace("\n", " ") if child.tail else ""
diff --git a/tests/data/groundtruth/docling_v2/bmj_sample.xml.itxt b/tests/data/groundtruth/docling_v2/bmj_sample.xml.itxt
deleted file mode 100644
index 88a44483..00000000
--- a/tests/data/groundtruth/docling_v2/bmj_sample.xml.itxt
+++ /dev/null
@@ -1,70 +0,0 @@
-item-0 at level 0: unspecified: group _root_
- item-1 at level 1: title: Evolving general practice consul ... Britain: issues of length and context
- item-2 at level 2: paragraph: George K Freeman, John P Horder, ... on P Hill, Nayan C Shah, Andrew Wilson
- item-3 at level 2: paragraph: Centre for Primary Care and Soci ... ersity of Leicester, Leicester LE5 4PW
- item-4 at level 2: text: In 1999 Shah1 and others said th ... per consultation in general practice?
- item-5 at level 2: text: We report on the outcome of exte ... review identified 14 relevant papers.
- item-6 at level 2: section_header: Summary points
- item-7 at level 3: list: group list
- item-8 at level 4: list_item: Longer consultations are associa ... ith a range of better patient outcomes
- item-9 at level 4: list_item: Modern consultations in general ... th more serious and chronic conditions
- item-10 at level 4: list_item: Increasing patient participation ... interaction, which demands extra time
- item-11 at level 4: list_item: Difficulties with access and wit ... e and lead to further pressure on time
- item-12 at level 4: list_item: Longer consultations should be a ... t to maximise interpersonal continuity
- item-13 at level 4: list_item: Research on implementation is needed
- item-14 at level 2: section_header: Longer consultations: benefits for patients
- item-15 at level 3: text: The systematic review consistent ... ther some doctors insist on more time.
- item-16 at level 3: text: A national survey in 1998 report ... s the effects of their own experience.
- item-17 at level 2: section_header: Context of modern consultations
- item-18 at level 3: text: Shorter consultations were more ... potential length of the consultation.
- item-19 at level 2: section_header: Participatory consultation style
- item-20 at level 3: text: The most effective consultations ... style usually lengthens consultations.
- item-21 at level 2: section_header: Extended professional agenda
- item-22 at level 3: text: The traditional consultation in ... agerial expectations of good practice.
- item-23 at level 3: text: Adequate time is essential. It m ... inevitably leads to pressure on time.
- item-24 at level 2: section_header: Access problems
- item-25 at level 3: text: In a service free at the point o ... ort notice squeeze consultation times.
- item-26 at level 3: text: While appointment systems can an ... for the inadequate access to doctors.
- item-27 at level 3: text: In response to perception of del ... ntation is currently being negotiated.
- item-28 at level 3: text: Virtually all patients think tha ... e that is free at the point of access.
- item-29 at level 3: text: A further government initiative ... ealth advice and first line treatment.
- item-30 at level 2: section_header: Loss of interpersonal continuity
- item-31 at level 3: text: If a patient has to consult seve ... unning and professional frustration.18
- item-32 at level 3: text: Mechanic described how loss of l ... patient and professional satisfaction.
- item-33 at level 2: section_header: Health service reforms
- item-34 at level 3: text: Finally, for the past 15 years t ... ents and staff) and what is delivered.
- item-35 at level 2: section_header: The future
- item-36 at level 3: text: We think that the way ahead must ... p further the care of chronic disease.
- item-37 at level 3: text: The challenge posed to general p ... ermedicalisation need to be exploited.
- item-38 at level 3: text: We must ensure better communicat ... between planned and ad hoc consulting.
- item-39 at level 2: section_header: Next steps
- item-40 at level 3: text: General practitioners do not beh ... ailable time in complex consultations.
- item-41 at level 3: text: Devising appropriate incentives ... and interpersonal knowledge and trust.
- item-42 at level 2: section_header: Acknowledgments
- item-43 at level 3: text: We thank the other members of th ... Practitioners for administrative help.
- item-44 at level 2: section_header: References
- item-45 at level 3: list: group list
- item-46 at level 4: list_item: Shah NC. Viewpoint: Consultation ... y men!”. Br J Gen Pract 49:497 (1999).
- item-47 at level 4: list_item: Mechanic D. How should hamsters ... BMJ 323:266–268 (2001). PMID: 11485957
- item-48 at level 4: list_item: Howie JGR, Porter AMD, Heaney DJ ... n Pract 41:48–54 (1991). PMID: 2031735
- item-49 at level 4: list_item: Howie JGR, Heaney DJ, Maxwell M, ... BMJ 319:738–743 (1999). PMID: 10487999
- item-50 at level 4: list_item: Kaplan SH, Greenfield S, Ware JE ... c disease. Med Care 27:110–125 (1989).
- item-51 at level 4: list_item: Airey C, Erens B. National surve ... e, 1998. London: NHS Executive (1999).
- item-52 at level 4: list_item: Hart JT. Expectations of health ... h Expect 1:3–13 (1998). PMID: 11281857
- item-53 at level 4: list_item: Tuckett D, Boulton M, Olson C, W ... London: Tavistock Publications (1985).
- item-54 at level 4: list_item: General Medical Council. Draft r ... ctors/index.htm (accessed 2 Jan 2002).
- item-55 at level 4: list_item: Balint M. The doctor, his patien ... the illness. London: Tavistock (1957).
- item-56 at level 4: list_item: Stott NCH, Davies RH. The except ... J R Coll Gen Pract 29:210–205 (1979).
- item-57 at level 4: list_item: Hill AP, Hill AP. Challenges for ... nium. London: King's Fund75–86 (2000).
- item-58 at level 4: list_item: National service framework for c ... . London: Department of Health (2000).
- item-59 at level 4: list_item: Hart JT. A new kind of doctor: t ... ommunity. London: Merlin Press (1988).
- item-60 at level 4: list_item: Morrison I, Smith R. Hamster hea ... J 321:1541–1542 (2000). PMID: 11124164
- item-61 at level 4: list_item: Arber S, Sawyer L. Do appointmen ... BMJ 284:478–480 (1982). PMID: 6800503
- item-62 at level 4: list_item: Hjortdahl P, Borchgrevink CF. Co ... MJ 303:1181–1184 (1991). PMID: 1747619
- item-63 at level 4: list_item: Howie JGR, Hopton JL, Heaney DJ, ... Pract 42:181–185 (1992). PMID: 1389427
- item-64 at level 4: list_item: Freeman G, Shepperd S, Robinson ... ), Summer 2000. London: NCCSDO (2001).
- item-65 at level 4: list_item: Wilson A, McDonald P, Hayes L, C ... Pract 41:184–187 (1991). PMID: 1878267
- item-66 at level 4: list_item: De Maeseneer J, Hjortdahl P, Sta ... J 320:1616–1617 (2000). PMID: 10856043
- item-67 at level 4: list_item: Freeman G, Hjortdahl P. What fut ... MJ 314:1870–1873 (1997). PMID: 9224130
- item-68 at level 4: list_item: Kibbe DC, Bentz E, McLaughlin CP ... Pract 36:304–308 (1993). PMID: 8454977
- item-69 at level 4: list_item: Williams M, Neal RD. Time for a ... ct 48:1783–1786 (1998). PMID: 10198490
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/bmj_sample.xml.json b/tests/data/groundtruth/docling_v2/bmj_sample.xml.json
deleted file mode 100644
index cd98e064..00000000
--- a/tests/data/groundtruth/docling_v2/bmj_sample.xml.json
+++ /dev/null
@@ -1,1080 +0,0 @@
-{
- "schema_name": "DoclingDocument",
- "version": "1.0.0",
- "name": "bmj_sample",
- "origin": {
- "mimetype": "application/xml",
- "binary_hash": 2961779396863376371,
- "filename": "bmj_sample.xml"
- },
- "furniture": {
- "self_ref": "#/furniture",
- "children": [],
- "name": "_root_",
- "label": "unspecified"
- },
- "body": {
- "self_ref": "#/body",
- "children": [
- {
- "$ref": "#/texts/0"
- }
- ],
- "name": "_root_",
- "label": "unspecified"
- },
- "groups": [
- {
- "self_ref": "#/groups/0",
- "parent": {
- "$ref": "#/texts/5"
- },
- "children": [
- {
- "$ref": "#/texts/6"
- },
- {
- "$ref": "#/texts/7"
- },
- {
- "$ref": "#/texts/8"
- },
- {
- "$ref": "#/texts/9"
- },
- {
- "$ref": "#/texts/10"
- },
- {
- "$ref": "#/texts/11"
- }
- ],
- "name": "list",
- "label": "list"
- },
- {
- "self_ref": "#/groups/1",
- "parent": {
- "$ref": "#/texts/42"
- },
- "children": [
- {
- "$ref": "#/texts/43"
- },
- {
- "$ref": "#/texts/44"
- },
- {
- "$ref": "#/texts/45"
- },
- {
- "$ref": "#/texts/46"
- },
- {
- "$ref": "#/texts/47"
- },
- {
- "$ref": "#/texts/48"
- },
- {
- "$ref": "#/texts/49"
- },
- {
- "$ref": "#/texts/50"
- },
- {
- "$ref": "#/texts/51"
- },
- {
- "$ref": "#/texts/52"
- },
- {
- "$ref": "#/texts/53"
- },
- {
- "$ref": "#/texts/54"
- },
- {
- "$ref": "#/texts/55"
- },
- {
- "$ref": "#/texts/56"
- },
- {
- "$ref": "#/texts/57"
- },
- {
- "$ref": "#/texts/58"
- },
- {
- "$ref": "#/texts/59"
- },
- {
- "$ref": "#/texts/60"
- },
- {
- "$ref": "#/texts/61"
- },
- {
- "$ref": "#/texts/62"
- },
- {
- "$ref": "#/texts/63"
- },
- {
- "$ref": "#/texts/64"
- },
- {
- "$ref": "#/texts/65"
- },
- {
- "$ref": "#/texts/66"
- }
- ],
- "name": "list",
- "label": "list"
- }
- ],
- "texts": [
- {
- "self_ref": "#/texts/0",
- "parent": {
- "$ref": "#/body"
- },
- "children": [
- {
- "$ref": "#/texts/1"
- },
- {
- "$ref": "#/texts/2"
- },
- {
- "$ref": "#/texts/3"
- },
- {
- "$ref": "#/texts/4"
- },
- {
- "$ref": "#/texts/5"
- },
- {
- "$ref": "#/texts/12"
- },
- {
- "$ref": "#/texts/15"
- },
- {
- "$ref": "#/texts/17"
- },
- {
- "$ref": "#/texts/19"
- },
- {
- "$ref": "#/texts/22"
- },
- {
- "$ref": "#/texts/28"
- },
- {
- "$ref": "#/texts/31"
- },
- {
- "$ref": "#/texts/33"
- },
- {
- "$ref": "#/texts/37"
- },
- {
- "$ref": "#/texts/40"
- },
- {
- "$ref": "#/texts/42"
- }
- ],
- "label": "title",
- "prov": [],
- "orig": "Evolving general practice consultation in Britain: issues of length and context",
- "text": "Evolving general practice consultation in Britain: issues of length and context"
- },
- {
- "self_ref": "#/texts/1",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [],
- "label": "paragraph",
- "prov": [],
- "orig": "George K Freeman, John P Horder, John G R Howie, A Pali Hungin, Alison P Hill, Nayan C Shah, Andrew Wilson",
- "text": "George K Freeman, John P Horder, John G R Howie, A Pali Hungin, Alison P Hill, Nayan C Shah, Andrew Wilson"
- },
- {
- "self_ref": "#/texts/2",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [],
- "label": "paragraph",
- "prov": [],
- "orig": "Centre for Primary Care and Social Medicine, Imperial College of Science, Technology and Medicine, London W6 8RP; Royal College of General Practitioners, London SW7 1PU; Department of General Practice, University of Edinburgh, Edinburgh EH8 9DX; Centre for Health Studies, University of Durham, Durham DH1 3HN; Kilburn Park Medical Centre, London NW6; Department of General Practice and Primary Health Care, University of Leicester, Leicester LE5 4PW",
- "text": "Centre for Primary Care and Social Medicine, Imperial College of Science, Technology and Medicine, London W6 8RP; Royal College of General Practitioners, London SW7 1PU; Department of General Practice, University of Edinburgh, Edinburgh EH8 9DX; Centre for Health Studies, University of Durham, Durham DH1 3HN; Kilburn Park Medical Centre, London NW6; Department of General Practice and Primary Health Care, University of Leicester, Leicester LE5 4PW"
- },
- {
- "self_ref": "#/texts/3",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "In 1999 Shah1 and others said that the Royal College of General Practitioners should advocate longer consultations in general practice as a matter of policy. The college set up a working group chaired by A P Hungin, and a systematic review of literature on consultation length in general practice was commissioned. The working group agreed that the available evidence would be hard to interpret without discussion of the changing context within which consultations now take place. For many years general practitioners and those who have surveyed patients' opinions in the United Kingdom have complained about short consultation time, despite a steady increase in actual mean length. Recently Mechanic pointed out that this is also true in the United States.2 Is there any justification for a further increase in mean time allocated per consultation in general practice?",
- "text": "In 1999 Shah1 and others said that the Royal College of General Practitioners should advocate longer consultations in general practice as a matter of policy. The college set up a working group chaired by A P Hungin, and a systematic review of literature on consultation length in general practice was commissioned. The working group agreed that the available evidence would be hard to interpret without discussion of the changing context within which consultations now take place. For many years general practitioners and those who have surveyed patients' opinions in the United Kingdom have complained about short consultation time, despite a steady increase in actual mean length. Recently Mechanic pointed out that this is also true in the United States.2 Is there any justification for a further increase in mean time allocated per consultation in general practice?"
- },
- {
- "self_ref": "#/texts/4",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "We report on the outcome of extensive debate among a group of general practitioners with an interest in the process of care, with reference to the interim findings of the commissioned systematic review and our personal databases. The review identified 14 relevant papers.",
- "text": "We report on the outcome of extensive debate among a group of general practitioners with an interest in the process of care, with reference to the interim findings of the commissioned systematic review and our personal databases. The review identified 14 relevant papers."
- },
- {
- "self_ref": "#/texts/5",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [
- {
- "$ref": "#/groups/0"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Summary points",
- "text": "Summary points",
- "level": 1
- },
- {
- "self_ref": "#/texts/6",
- "parent": {
- "$ref": "#/groups/0"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Longer consultations are associated with a range of better patient outcomes",
- "text": "Longer consultations are associated with a range of better patient outcomes",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/7",
- "parent": {
- "$ref": "#/groups/0"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Modern consultations in general practice deal with patients with more serious and chronic conditions",
- "text": "Modern consultations in general practice deal with patients with more serious and chronic conditions",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/8",
- "parent": {
- "$ref": "#/groups/0"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Increasing patient participation means more complex interaction, which demands extra time",
- "text": "Increasing patient participation means more complex interaction, which demands extra time",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/9",
- "parent": {
- "$ref": "#/groups/0"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Difficulties with access and with loss of continuity add to perceived stress and poor performance and lead to further pressure on time",
- "text": "Difficulties with access and with loss of continuity add to perceived stress and poor performance and lead to further pressure on time",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/10",
- "parent": {
- "$ref": "#/groups/0"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Longer consultations should be a professional priority, combined with increased use of technology and more flexible practice management to maximise interpersonal continuity",
- "text": "Longer consultations should be a professional priority, combined with increased use of technology and more flexible practice management to maximise interpersonal continuity",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/11",
- "parent": {
- "$ref": "#/groups/0"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Research on implementation is needed",
- "text": "Research on implementation is needed",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/12",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [
- {
- "$ref": "#/texts/13"
- },
- {
- "$ref": "#/texts/14"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Longer consultations: benefits for patients",
- "text": "Longer consultations: benefits for patients",
- "level": 1
- },
- {
- "self_ref": "#/texts/13",
- "parent": {
- "$ref": "#/texts/12"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "The systematic review consistently showed that doctors with longer consultation times prescribe less and offer more advice on lifestyle and other health promoting activities. Longer consultations have been significantly associated with better recognition and handling of psychosocial problems3 and with better patient enablement.4 Also clinical care for some chronic illnesses is better in practices with longer booked intervals between one appointment and the next.5 It is not clear whether time is itself the main influence or whether some doctors insist on more time.",
- "text": "The systematic review consistently showed that doctors with longer consultation times prescribe less and offer more advice on lifestyle and other health promoting activities. Longer consultations have been significantly associated with better recognition and handling of psychosocial problems3 and with better patient enablement.4 Also clinical care for some chronic illnesses is better in practices with longer booked intervals between one appointment and the next.5 It is not clear whether time is itself the main influence or whether some doctors insist on more time."
- },
- {
- "self_ref": "#/texts/14",
- "parent": {
- "$ref": "#/texts/12"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "A national survey in 1998 reported that most (87%) patients were satisfied with the length of their most recent consultation.6 Satisfaction with any service will be high if expectations are met or exceeded. But expectations are modified by previous experience.7 The result is that primary care patients are likely to be satisfied with what they are used to unless the context modifies the effects of their own experience.",
- "text": "A national survey in 1998 reported that most (87%) patients were satisfied with the length of their most recent consultation.6 Satisfaction with any service will be high if expectations are met or exceeded. But expectations are modified by previous experience.7 The result is that primary care patients are likely to be satisfied with what they are used to unless the context modifies the effects of their own experience."
- },
- {
- "self_ref": "#/texts/15",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [
- {
- "$ref": "#/texts/16"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Context of modern consultations",
- "text": "Context of modern consultations",
- "level": 1
- },
- {
- "self_ref": "#/texts/16",
- "parent": {
- "$ref": "#/texts/15"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "Shorter consultations were more appropriate when the population was younger, when even a brief absence from employment due to sickness required a doctor's note, and when many simple remedies were available only on prescription. Recently at least five important influences have increased the content and hence the potential length of the consultation.",
- "text": "Shorter consultations were more appropriate when the population was younger, when even a brief absence from employment due to sickness required a doctor's note, and when many simple remedies were available only on prescription. Recently at least five important influences have increased the content and hence the potential length of the consultation."
- },
- {
- "self_ref": "#/texts/17",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [
- {
- "$ref": "#/texts/18"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Participatory consultation style",
- "text": "Participatory consultation style",
- "level": 1
- },
- {
- "self_ref": "#/texts/18",
- "parent": {
- "$ref": "#/texts/17"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "The most effective consultations are those in which doctors most directly acknowledge and perhaps respond to patients' problems and concerns. In addition, for patients to be committed to taking advantage of medical advice they must agree with both the goals and methods proposed. A landmark publication in the United Kingdom was Meetings Between Experts, which argued that while doctors are the experts about medical problems in general patients are the experts on how they themselves experience these problems.8 New emphasis on teaching consulting skills in general practice advocated specific attention to the patient's agenda, beliefs, understanding, and agreement. Currently the General Medical Council, aware that communication difficulties underlie many complaints about doctors, has further emphasised the importance of involving patients in consultations in its revised guidance to medical schools.9 More patient involvement should give a better outcome, but this participatory style usually lengthens consultations.",
- "text": "The most effective consultations are those in which doctors most directly acknowledge and perhaps respond to patients' problems and concerns. In addition, for patients to be committed to taking advantage of medical advice they must agree with both the goals and methods proposed. A landmark publication in the United Kingdom was Meetings Between Experts, which argued that while doctors are the experts about medical problems in general patients are the experts on how they themselves experience these problems.8 New emphasis on teaching consulting skills in general practice advocated specific attention to the patient's agenda, beliefs, understanding, and agreement. Currently the General Medical Council, aware that communication difficulties underlie many complaints about doctors, has further emphasised the importance of involving patients in consultations in its revised guidance to medical schools.9 More patient involvement should give a better outcome, but this participatory style usually lengthens consultations."
- },
- {
- "self_ref": "#/texts/19",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [
- {
- "$ref": "#/texts/20"
- },
- {
- "$ref": "#/texts/21"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Extended professional agenda",
- "text": "Extended professional agenda",
- "level": 1
- },
- {
- "self_ref": "#/texts/20",
- "parent": {
- "$ref": "#/texts/19"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "The traditional consultation in general practice was brief.2 The patient presented symptoms and the doctor prescribed treatment. In 1957 Balint gave new insights into the meaning of symptoms.10 By 1979 an enhanced model of consultation was presented, in which the doctors dealt with ongoing as well as presenting problems and added health promotion and education about future appropriate use of services.11 Now, with an ageing population and more community care of chronic illness, there are more issues to be considered at each consultation. Ideas of what constitutes good general practice are more complex.12 Good practice now includes both extended care of chronic medical problems\u2014for example, coronary heart disease13\u2014and a public health role. At first this model was restricted to those who lead change (\u201cearly adopters\u201d) and enthusiasts14 but now it is embedded in professional and managerial expectations of good practice.",
- "text": "The traditional consultation in general practice was brief.2 The patient presented symptoms and the doctor prescribed treatment. In 1957 Balint gave new insights into the meaning of symptoms.10 By 1979 an enhanced model of consultation was presented, in which the doctors dealt with ongoing as well as presenting problems and added health promotion and education about future appropriate use of services.11 Now, with an ageing population and more community care of chronic illness, there are more issues to be considered at each consultation. Ideas of what constitutes good general practice are more complex.12 Good practice now includes both extended care of chronic medical problems\u2014for example, coronary heart disease13\u2014and a public health role. At first this model was restricted to those who lead change (\u201cearly adopters\u201d) and enthusiasts14 but now it is embedded in professional and managerial expectations of good practice."
- },
- {
- "self_ref": "#/texts/21",
- "parent": {
- "$ref": "#/texts/19"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "Adequate time is essential. It may be difficult for an elderly patient with several active problems to undress, be examined, and get adequate professional consideration in under 15 minutes. Here the doctor is faced with the choice of curtailing the consultation or of reducing the time available for the next patient. Having to cope with these situations often contributes to professional dissatisfaction.15 This combination of more care, more options, and more genuine discussion of those options with informed patient choice inevitably leads to pressure on time.",
- "text": "Adequate time is essential. It may be difficult for an elderly patient with several active problems to undress, be examined, and get adequate professional consideration in under 15 minutes. Here the doctor is faced with the choice of curtailing the consultation or of reducing the time available for the next patient. Having to cope with these situations often contributes to professional dissatisfaction.15 This combination of more care, more options, and more genuine discussion of those options with informed patient choice inevitably leads to pressure on time."
- },
- {
- "self_ref": "#/texts/22",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [
- {
- "$ref": "#/texts/23"
- },
- {
- "$ref": "#/texts/24"
- },
- {
- "$ref": "#/texts/25"
- },
- {
- "$ref": "#/texts/26"
- },
- {
- "$ref": "#/texts/27"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Access problems",
- "text": "Access problems",
- "level": 1
- },
- {
- "self_ref": "#/texts/23",
- "parent": {
- "$ref": "#/texts/22"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "In a service free at the point of access, rising demand will tend to increase rationing by delay. But attempts to improve access by offering more consultations at short notice squeeze consultation times.",
- "text": "In a service free at the point of access, rising demand will tend to increase rationing by delay. But attempts to improve access by offering more consultations at short notice squeeze consultation times."
- },
- {
- "self_ref": "#/texts/24",
- "parent": {
- "$ref": "#/texts/22"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "While appointment systems can and should reduce queuing time for consultations, they have long tended to be used as a brake on total demand.16 This may seriously erode patients' confidence in being able to see their doctor or nurse when they need to. Patients are offered appointments further ahead but may keep these even if their symptoms have remitted \u201cjust in case.\u201d Availability of consultations is thus blocked. Receptionists are then inappropriately blamed for the inadequate access to doctors.",
- "text": "While appointment systems can and should reduce queuing time for consultations, they have long tended to be used as a brake on total demand.16 This may seriously erode patients' confidence in being able to see their doctor or nurse when they need to. Patients are offered appointments further ahead but may keep these even if their symptoms have remitted \u201cjust in case.\u201d Availability of consultations is thus blocked. Receptionists are then inappropriately blamed for the inadequate access to doctors."
- },
- {
- "self_ref": "#/texts/25",
- "parent": {
- "$ref": "#/texts/22"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "In response to perception of delay, the government has set targets in the NHS plan of \u201cguaranteed access to a primary care professional within 24 hours and to a primary care doctor within 48 hours.\u201d Implementation is currently being negotiated.",
- "text": "In response to perception of delay, the government has set targets in the NHS plan of \u201cguaranteed access to a primary care professional within 24 hours and to a primary care doctor within 48 hours.\u201d Implementation is currently being negotiated."
- },
- {
- "self_ref": "#/texts/26",
- "parent": {
- "$ref": "#/texts/22"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "Virtually all patients think that they would not consult unless it was absolutely necessary. They do not think they are wasting NHS time and do not like being made to feel so. But underlying general practitioners' willingness to make patients wait several days is their perception that few of the problems are urgent. Patients and general practitioners evidently do not agree about the urgency of so called minor problems. To some extent general practice in the United Kingdom may have scored an \u201cown goal\u201d by setting up perceived access barriers (appointment systems and out of hours cooperatives) in the attempt to increase professional standards and control demand in a service that is free at the point of access.",
- "text": "Virtually all patients think that they would not consult unless it was absolutely necessary. They do not think they are wasting NHS time and do not like being made to feel so. But underlying general practitioners' willingness to make patients wait several days is their perception that few of the problems are urgent. Patients and general practitioners evidently do not agree about the urgency of so called minor problems. To some extent general practice in the United Kingdom may have scored an \u201cown goal\u201d by setting up perceived access barriers (appointment systems and out of hours cooperatives) in the attempt to increase professional standards and control demand in a service that is free at the point of access."
- },
- {
- "self_ref": "#/texts/27",
- "parent": {
- "$ref": "#/texts/22"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "A further government initiative has been to bypass general practice with new services\u2014notably, walk-in centres (primary care clinics in which no appointment is needed) and NHS Direct (a professional telephone helpline giving advice on simple remedies and access to services). Introduced widely and rapidly, these services each potentially provide significant features of primary care\u2014namely, quick access to skilled health advice and first line treatment.",
- "text": "A further government initiative has been to bypass general practice with new services\u2014notably, walk-in centres (primary care clinics in which no appointment is needed) and NHS Direct (a professional telephone helpline giving advice on simple remedies and access to services). Introduced widely and rapidly, these services each potentially provide significant features of primary care\u2014namely, quick access to skilled health advice and first line treatment."
- },
- {
- "self_ref": "#/texts/28",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [
- {
- "$ref": "#/texts/29"
- },
- {
- "$ref": "#/texts/30"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Loss of interpersonal continuity",
- "text": "Loss of interpersonal continuity",
- "level": 1
- },
- {
- "self_ref": "#/texts/29",
- "parent": {
- "$ref": "#/texts/28"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "If a patient has to consult several different professionals, particularly over a short period of time, there is inevitable duplication of stories, risk of naive diagnoses, potential for conflicting advice, and perhaps loss of trust. Trust is essential if patients are to accept the \u201cwait and see\u201d management policy which is, or should be, an important part of the management of self limiting conditions, which are often on the boundary between illness and non-illness.17 Such duplication again increases pressure for more extra (unscheduled) consultations resulting in late running and professional frustration.18",
- "text": "If a patient has to consult several different professionals, particularly over a short period of time, there is inevitable duplication of stories, risk of naive diagnoses, potential for conflicting advice, and perhaps loss of trust. Trust is essential if patients are to accept the \u201cwait and see\u201d management policy which is, or should be, an important part of the management of self limiting conditions, which are often on the boundary between illness and non-illness.17 Such duplication again increases pressure for more extra (unscheduled) consultations resulting in late running and professional frustration.18"
- },
- {
- "self_ref": "#/texts/30",
- "parent": {
- "$ref": "#/texts/28"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "Mechanic described how loss of longitudinal (and perhaps personal and relational19) continuity influences the perception and use of time through an inability to build on previous consultations.2 Knowing the doctor well, particularly in smaller practices, is associated with enhanced patient enablement in shorter time.4 Though Mechanic pointed out that three quarters of UK patients have been registered with their general practitioner five years or more, this may be misleading. Practices are growing, with larger teams and more registered patients. Being registered with a doctor in a larger practice is usually no guarantee that the patient will be able to see the same doctor or the doctor of his or her choice, who may be different. Thus the system does not encourage adequate personal continuity. This adds to pressure on time and reduces both patient and professional satisfaction.",
- "text": "Mechanic described how loss of longitudinal (and perhaps personal and relational19) continuity influences the perception and use of time through an inability to build on previous consultations.2 Knowing the doctor well, particularly in smaller practices, is associated with enhanced patient enablement in shorter time.4 Though Mechanic pointed out that three quarters of UK patients have been registered with their general practitioner five years or more, this may be misleading. Practices are growing, with larger teams and more registered patients. Being registered with a doctor in a larger practice is usually no guarantee that the patient will be able to see the same doctor or the doctor of his or her choice, who may be different. Thus the system does not encourage adequate personal continuity. This adds to pressure on time and reduces both patient and professional satisfaction."
- },
- {
- "self_ref": "#/texts/31",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [
- {
- "$ref": "#/texts/32"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Health service reforms",
- "text": "Health service reforms",
- "level": 1
- },
- {
- "self_ref": "#/texts/32",
- "parent": {
- "$ref": "#/texts/31"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "Finally, for the past 15 years the NHS has experienced unprecedented change with a succession of major administrative reforms. Recent reforms have focused on an NHS led by primary care, including the aim of shifting care from the secondary specialist sector to primary care. One consequence is increased demand for primary care of patients with more serious and less stable problems. With the limited piloting of reforms we do not know whether such major redirection can be achieved without greatly altering the delicate balance between expectations (of both patients and staff) and what is delivered.",
- "text": "Finally, for the past 15 years the NHS has experienced unprecedented change with a succession of major administrative reforms. Recent reforms have focused on an NHS led by primary care, including the aim of shifting care from the secondary specialist sector to primary care. One consequence is increased demand for primary care of patients with more serious and less stable problems. With the limited piloting of reforms we do not know whether such major redirection can be achieved without greatly altering the delicate balance between expectations (of both patients and staff) and what is delivered."
- },
- {
- "self_ref": "#/texts/33",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [
- {
- "$ref": "#/texts/34"
- },
- {
- "$ref": "#/texts/35"
- },
- {
- "$ref": "#/texts/36"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "The future",
- "text": "The future",
- "level": 1
- },
- {
- "self_ref": "#/texts/34",
- "parent": {
- "$ref": "#/texts/33"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "We think that the way ahead must embrace both longer mean consultation times and more flexibility. More time is needed for high quality consultations with patients with major and complex problems of all kinds. But patients also need access to simpler services and advice. This should be more appropriate (and cost less) when it is given by professionals who know the patient and his or her medical history and social circumstances. For doctors, the higher quality associated with longer consultations may lead to greater professional satisfaction and, if these longer consultations are combined with more realistic scheduling, to reduced levels of stress.20 They will also find it easier to develop further the care of chronic disease.",
- "text": "We think that the way ahead must embrace both longer mean consultation times and more flexibility. More time is needed for high quality consultations with patients with major and complex problems of all kinds. But patients also need access to simpler services and advice. This should be more appropriate (and cost less) when it is given by professionals who know the patient and his or her medical history and social circumstances. For doctors, the higher quality associated with longer consultations may lead to greater professional satisfaction and, if these longer consultations are combined with more realistic scheduling, to reduced levels of stress.20 They will also find it easier to develop further the care of chronic disease."
- },
- {
- "self_ref": "#/texts/35",
- "parent": {
- "$ref": "#/texts/33"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "The challenge posed to general practice by walk-in centres and NHS Direct is considerable, and the diversion of funding from primary care is large. The risk of waste and duplication increases as more layers of complexity are added to a primary care service that started out as something familiar, simple, and local and which is still envied in other developed countries.21 Access needs to be simple, and the advantages of personal knowledge and trust in minimising duplication and overmedicalisation need to be exploited.",
- "text": "The challenge posed to general practice by walk-in centres and NHS Direct is considerable, and the diversion of funding from primary care is large. The risk of waste and duplication increases as more layers of complexity are added to a primary care service that started out as something familiar, simple, and local and which is still envied in other developed countries.21 Access needs to be simple, and the advantages of personal knowledge and trust in minimising duplication and overmedicalisation need to be exploited."
- },
- {
- "self_ref": "#/texts/36",
- "parent": {
- "$ref": "#/texts/33"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "We must ensure better communication and access so that patients can more easily deal with minor issues and queries with someone they know and trust and avoid the formality and inconvenience of a full face to face consultation. Too often this has to be with a different professional, unfamiliar with the nuances of the case. There should be far more managerial emphasis on helping patients to interact with their chosen practitioner22; such a programme has been described.23 Modern information systems make it much easier to record which doctor(s) a patient prefers to see and to monitor how often this is achieved. The telephone is hardly modern but is underused. Email avoids the problems inherent in arranging simultaneous availability necessary for telephone consultations but at the cost of reducing the communication of emotions. There is a place for both.2 Access without prior appointment is a valued feature of primary care, and we need to know more about the right balance between planned and ad hoc consulting.",
- "text": "We must ensure better communication and access so that patients can more easily deal with minor issues and queries with someone they know and trust and avoid the formality and inconvenience of a full face to face consultation. Too often this has to be with a different professional, unfamiliar with the nuances of the case. There should be far more managerial emphasis on helping patients to interact with their chosen practitioner22; such a programme has been described.23 Modern information systems make it much easier to record which doctor(s) a patient prefers to see and to monitor how often this is achieved. The telephone is hardly modern but is underused. Email avoids the problems inherent in arranging simultaneous availability necessary for telephone consultations but at the cost of reducing the communication of emotions. There is a place for both.2 Access without prior appointment is a valued feature of primary care, and we need to know more about the right balance between planned and ad hoc consulting."
- },
- {
- "self_ref": "#/texts/37",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [
- {
- "$ref": "#/texts/38"
- },
- {
- "$ref": "#/texts/39"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Next steps",
- "text": "Next steps",
- "level": 1
- },
- {
- "self_ref": "#/texts/38",
- "parent": {
- "$ref": "#/texts/37"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "General practitioners do not behave in a uniform way. They can be categorised as slow, medium, and fast and react in different ways to changes in consulting speed.18 They are likely to have differing views about a widespread move to lengthen consultation time. We do not need further confirmation that longer consultations are desirable and necessary, but research could show us the best way to learn how to introduce them with minimal disruption to the way in which patients and practices like primary care to be provided.24 We also need to learn how to make the most of available time in complex consultations.",
- "text": "General practitioners do not behave in a uniform way. They can be categorised as slow, medium, and fast and react in different ways to changes in consulting speed.18 They are likely to have differing views about a widespread move to lengthen consultation time. We do not need further confirmation that longer consultations are desirable and necessary, but research could show us the best way to learn how to introduce them with minimal disruption to the way in which patients and practices like primary care to be provided.24 We also need to learn how to make the most of available time in complex consultations."
- },
- {
- "self_ref": "#/texts/39",
- "parent": {
- "$ref": "#/texts/37"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "Devising appropriate incentives and helping practices move beyond just reacting to demand in the traditional way by working harder and faster is perhaps our greatest challenge in the United Kingdom. The new primary are trusts need to work together with the growing primary care research networks to carry out the necessary development work. In particular, research is needed on how a primary care team can best provide the right balance of quick access and interpersonal knowledge and trust.",
- "text": "Devising appropriate incentives and helping practices move beyond just reacting to demand in the traditional way by working harder and faster is perhaps our greatest challenge in the United Kingdom. The new primary are trusts need to work together with the growing primary care research networks to carry out the necessary development work. In particular, research is needed on how a primary care team can best provide the right balance of quick access and interpersonal knowledge and trust."
- },
- {
- "self_ref": "#/texts/40",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [
- {
- "$ref": "#/texts/41"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Acknowledgments",
- "text": "Acknowledgments",
- "level": 1
- },
- {
- "self_ref": "#/texts/41",
- "parent": {
- "$ref": "#/texts/40"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "We thank the other members of the working group: Susan Childs, Paul Freeling, Iona Heath, Marshall Marinker, and Bonnie Sibbald. We also thank Fenny Green of the Royal College of General Practitioners for administrative help.",
- "text": "We thank the other members of the working group: Susan Childs, Paul Freeling, Iona Heath, Marshall Marinker, and Bonnie Sibbald. We also thank Fenny Green of the Royal College of General Practitioners for administrative help."
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- "self_ref": "#/texts/42",
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- "self_ref": "#/texts/43",
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- "orig": "Shah NC. Viewpoint: Consultation time\u2014time for a change? Still the \u201cperfunctory work of perfunctory men!\u201d. Br J Gen Pract 49:497 (1999).",
- "text": "Shah NC. Viewpoint: Consultation time\u2014time for a change? Still the \u201cperfunctory work of perfunctory men!\u201d. Br J Gen Pract 49:497 (1999).",
- "enumerated": false,
- "marker": "-"
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- "self_ref": "#/texts/44",
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- "prov": [],
- "orig": "Mechanic D. How should hamsters run? Some observations about sufficient patient time in primary care. BMJ 323:266\u2013268 (2001). PMID: 11485957",
- "text": "Mechanic D. How should hamsters run? Some observations about sufficient patient time in primary care. BMJ 323:266\u2013268 (2001). PMID: 11485957",
- "enumerated": false,
- "marker": "-"
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- "self_ref": "#/texts/45",
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- "$ref": "#/groups/1"
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- "label": "list_item",
- "prov": [],
- "orig": "Howie JGR, Porter AMD, Heaney DJ, Hopton JL. Long to short consultation ratio: a proxy measure of quality of care for general practice. Br J Gen Pract 41:48\u201354 (1991). PMID: 2031735",
- "text": "Howie JGR, Porter AMD, Heaney DJ, Hopton JL. Long to short consultation ratio: a proxy measure of quality of care for general practice. Br J Gen Pract 41:48\u201354 (1991). PMID: 2031735",
- "enumerated": false,
- "marker": "-"
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- {
- "self_ref": "#/texts/46",
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- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Howie JGR, Heaney DJ, Maxwell M, Walker JJ, Freeman GK, Rai H. Quality at general practice consultations: cross-sectional survey. BMJ 319:738\u2013743 (1999). PMID: 10487999",
- "text": "Howie JGR, Heaney DJ, Maxwell M, Walker JJ, Freeman GK, Rai H. Quality at general practice consultations: cross-sectional survey. BMJ 319:738\u2013743 (1999). PMID: 10487999",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/47",
- "parent": {
- "$ref": "#/groups/1"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Kaplan SH, Greenfield S, Ware JE. Assessing the effects of physician-patient interactions on the outcome of chronic disease. Med Care 27:110\u2013125 (1989).",
- "text": "Kaplan SH, Greenfield S, Ware JE. Assessing the effects of physician-patient interactions on the outcome of chronic disease. Med Care 27:110\u2013125 (1989).",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/48",
- "parent": {
- "$ref": "#/groups/1"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Airey C, Erens B. National surveys of NHS patients: general practice, 1998. London: NHS Executive (1999).",
- "text": "Airey C, Erens B. National surveys of NHS patients: general practice, 1998. London: NHS Executive (1999).",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/49",
- "parent": {
- "$ref": "#/groups/1"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Hart JT. Expectations of health care: promoted, managed or shared?. Health Expect 1:3\u201313 (1998). PMID: 11281857",
- "text": "Hart JT. Expectations of health care: promoted, managed or shared?. Health Expect 1:3\u201313 (1998). PMID: 11281857",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/50",
- "parent": {
- "$ref": "#/groups/1"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Tuckett D, Boulton M, Olson C, Williams A. Meetings between experts: an approach to sharing ideas in medical consultations. London: Tavistock Publications (1985).",
- "text": "Tuckett D, Boulton M, Olson C, Williams A. Meetings between experts: an approach to sharing ideas in medical consultations. London: Tavistock Publications (1985).",
- "enumerated": false,
- "marker": "-"
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- {
- "self_ref": "#/texts/51",
- "parent": {
- "$ref": "#/groups/1"
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- "label": "list_item",
- "prov": [],
- "orig": "General Medical Council. Draft recommendations on undergraduate medical education. July 2001. www.gmc-uk.org/med_ed/tomorrowsdoctors/index.htm (accessed 2 Jan 2002).",
- "text": "General Medical Council. Draft recommendations on undergraduate medical education. July 2001. www.gmc-uk.org/med_ed/tomorrowsdoctors/index.htm (accessed 2 Jan 2002).",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/52",
- "parent": {
- "$ref": "#/groups/1"
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- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Balint M. The doctor, his patient and the illness. London: Tavistock (1957).",
- "text": "Balint M. The doctor, his patient and the illness. London: Tavistock (1957).",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/53",
- "parent": {
- "$ref": "#/groups/1"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Stott NCH, Davies RH. The exceptional potential in each primary care consultation. J R Coll Gen Pract 29:210\u2013205 (1979).",
- "text": "Stott NCH, Davies RH. The exceptional potential in each primary care consultation. J R Coll Gen Pract 29:210\u2013205 (1979).",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/54",
- "parent": {
- "$ref": "#/groups/1"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Hill AP, Hill AP. Challenges for primary care. What's gone wrong with health care? Challenges for the new millennium. London: King's Fund75\u201386 (2000).",
- "text": "Hill AP, Hill AP. Challenges for primary care. What's gone wrong with health care? Challenges for the new millennium. London: King's Fund75\u201386 (2000).",
- "enumerated": false,
- "marker": "-"
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- {
- "self_ref": "#/texts/55",
- "parent": {
- "$ref": "#/groups/1"
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- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "National service framework for coronary heart disease. London: Department of Health (2000).",
- "text": "National service framework for coronary heart disease. London: Department of Health (2000).",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/56",
- "parent": {
- "$ref": "#/groups/1"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Hart JT. A new kind of doctor: the general practitioner's part in the health of the community. London: Merlin Press (1988).",
- "text": "Hart JT. A new kind of doctor: the general practitioner's part in the health of the community. London: Merlin Press (1988).",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/57",
- "parent": {
- "$ref": "#/groups/1"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Morrison I, Smith R. Hamster health care. BMJ 321:1541\u20131542 (2000). PMID: 11124164",
- "text": "Morrison I, Smith R. Hamster health care. BMJ 321:1541\u20131542 (2000). PMID: 11124164",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/58",
- "parent": {
- "$ref": "#/groups/1"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Arber S, Sawyer L. Do appointment systems work?. BMJ 284:478\u2013480 (1982). PMID: 6800503",
- "text": "Arber S, Sawyer L. Do appointment systems work?. BMJ 284:478\u2013480 (1982). PMID: 6800503",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/59",
- "parent": {
- "$ref": "#/groups/1"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Hjortdahl P, Borchgrevink CF. Continuity of care: influence of general practitioners' knowledge about their patients on use of resources in consultations. BMJ 303:1181\u20131184 (1991). PMID: 1747619",
- "text": "Hjortdahl P, Borchgrevink CF. Continuity of care: influence of general practitioners' knowledge about their patients on use of resources in consultations. BMJ 303:1181\u20131184 (1991). PMID: 1747619",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/60",
- "parent": {
- "$ref": "#/groups/1"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Howie JGR, Hopton JL, Heaney DJ, Porter AMD. Attitudes to medical care, the organization of work, and stress among general practitioners. Br J Gen Pract 42:181\u2013185 (1992). PMID: 1389427",
- "text": "Howie JGR, Hopton JL, Heaney DJ, Porter AMD. Attitudes to medical care, the organization of work, and stress among general practitioners. Br J Gen Pract 42:181\u2013185 (1992). PMID: 1389427",
- "enumerated": false,
- "marker": "-"
- },
- {
- "self_ref": "#/texts/61",
- "parent": {
- "$ref": "#/groups/1"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Freeman G, Shepperd S, Robinson I, Ehrich K, Richards SC, Pitman P. Continuity of care: report of a scoping exercise for the national co-ordinating centre for NHS Service Delivery and Organisation R&D (NCCSDO), Summer 2000. London: NCCSDO (2001).",
- "text": "Freeman G, Shepperd S, Robinson I, Ehrich K, Richards SC, Pitman P. Continuity of care: report of a scoping exercise for the national co-ordinating centre for NHS Service Delivery and Organisation R&D (NCCSDO), Summer 2000. London: NCCSDO (2001).",
- "enumerated": false,
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- "prov": [],
- "orig": "Wilson A, McDonald P, Hayes L, Cooney J. Longer booking intervals in general practice: effects on doctors' stress and arousal. Br J Gen Pract 41:184\u2013187 (1991). PMID: 1878267",
- "text": "Wilson A, McDonald P, Hayes L, Cooney J. Longer booking intervals in general practice: effects on doctors' stress and arousal. Br J Gen Pract 41:184\u2013187 (1991). PMID: 1878267",
- "enumerated": false,
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- "prov": [],
- "orig": "De Maeseneer J, Hjortdahl P, Starfield B. Fix what's wrong, not what's right, with general practice in Britain. BMJ 320:1616\u20131617 (2000). PMID: 10856043",
- "text": "De Maeseneer J, Hjortdahl P, Starfield B. Fix what's wrong, not what's right, with general practice in Britain. BMJ 320:1616\u20131617 (2000). PMID: 10856043",
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- "prov": [],
- "orig": "Freeman G, Hjortdahl P. What future for continuity of care in general practice?. BMJ 314:1870\u20131873 (1997). PMID: 9224130",
- "text": "Freeman G, Hjortdahl P. What future for continuity of care in general practice?. BMJ 314:1870\u20131873 (1997). PMID: 9224130",
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- "orig": "Kibbe DC, Bentz E, McLaughlin CP. Continuous quality improvement for continuity of care. J Fam Pract 36:304\u2013308 (1993). PMID: 8454977",
- "text": "Kibbe DC, Bentz E, McLaughlin CP. Continuous quality improvement for continuity of care. J Fam Pract 36:304\u2013308 (1993). PMID: 8454977",
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- "prov": [],
- "orig": "Williams M, Neal RD. Time for a change? The process of lengthening booking intervals in general practice. Br J Gen Pract 48:1783\u20131786 (1998). PMID: 10198490",
- "text": "Williams M, Neal RD. Time for a change? The process of lengthening booking intervals in general practice. Br J Gen Pract 48:1783\u20131786 (1998). PMID: 10198490",
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\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/bmj_sample.xml.md b/tests/data/groundtruth/docling_v2/bmj_sample.xml.md
deleted file mode 100644
index fd3d3739..00000000
--- a/tests/data/groundtruth/docling_v2/bmj_sample.xml.md
+++ /dev/null
@@ -1,105 +0,0 @@
-# Evolving general practice consultation in Britain: issues of length and context
-
-George K Freeman, John P Horder, John G R Howie, A Pali Hungin, Alison P Hill, Nayan C Shah, Andrew Wilson
-
-Centre for Primary Care and Social Medicine, Imperial College of Science, Technology and Medicine, London W6 8RP; Royal College of General Practitioners, London SW7 1PU; Department of General Practice, University of Edinburgh, Edinburgh EH8 9DX; Centre for Health Studies, University of Durham, Durham DH1 3HN; Kilburn Park Medical Centre, London NW6; Department of General Practice and Primary Health Care, University of Leicester, Leicester LE5 4PW
-
-In 1999 Shah1 and others said that the Royal College of General Practitioners should advocate longer consultations in general practice as a matter of policy. The college set up a working group chaired by A P Hungin, and a systematic review of literature on consultation length in general practice was commissioned. The working group agreed that the available evidence would be hard to interpret without discussion of the changing context within which consultations now take place. For many years general practitioners and those who have surveyed patients' opinions in the United Kingdom have complained about short consultation time, despite a steady increase in actual mean length. Recently Mechanic pointed out that this is also true in the United States.2 Is there any justification for a further increase in mean time allocated per consultation in general practice?
-
-We report on the outcome of extensive debate among a group of general practitioners with an interest in the process of care, with reference to the interim findings of the commissioned systematic review and our personal databases. The review identified 14 relevant papers.
-
-## Summary points
-
-- Longer consultations are associated with a range of better patient outcomes
-- Modern consultations in general practice deal with patients with more serious and chronic conditions
-- Increasing patient participation means more complex interaction, which demands extra time
-- Difficulties with access and with loss of continuity add to perceived stress and poor performance and lead to further pressure on time
-- Longer consultations should be a professional priority, combined with increased use of technology and more flexible practice management to maximise interpersonal continuity
-- Research on implementation is needed
-
-## Longer consultations: benefits for patients
-
-The systematic review consistently showed that doctors with longer consultation times prescribe less and offer more advice on lifestyle and other health promoting activities. Longer consultations have been significantly associated with better recognition and handling of psychosocial problems3 and with better patient enablement.4 Also clinical care for some chronic illnesses is better in practices with longer booked intervals between one appointment and the next.5 It is not clear whether time is itself the main influence or whether some doctors insist on more time.
-
-A national survey in 1998 reported that most (87%) patients were satisfied with the length of their most recent consultation.6 Satisfaction with any service will be high if expectations are met or exceeded. But expectations are modified by previous experience.7 The result is that primary care patients are likely to be satisfied with what they are used to unless the context modifies the effects of their own experience.
-
-## Context of modern consultations
-
-Shorter consultations were more appropriate when the population was younger, when even a brief absence from employment due to sickness required a doctor's note, and when many simple remedies were available only on prescription. Recently at least five important influences have increased the content and hence the potential length of the consultation.
-
-## Participatory consultation style
-
-The most effective consultations are those in which doctors most directly acknowledge and perhaps respond to patients' problems and concerns. In addition, for patients to be committed to taking advantage of medical advice they must agree with both the goals and methods proposed. A landmark publication in the United Kingdom was Meetings Between Experts, which argued that while doctors are the experts about medical problems in general patients are the experts on how they themselves experience these problems.8 New emphasis on teaching consulting skills in general practice advocated specific attention to the patient's agenda, beliefs, understanding, and agreement. Currently the General Medical Council, aware that communication difficulties underlie many complaints about doctors, has further emphasised the importance of involving patients in consultations in its revised guidance to medical schools.9 More patient involvement should give a better outcome, but this participatory style usually lengthens consultations.
-
-## Extended professional agenda
-
-The traditional consultation in general practice was brief.2 The patient presented symptoms and the doctor prescribed treatment. In 1957 Balint gave new insights into the meaning of symptoms.10 By 1979 an enhanced model of consultation was presented, in which the doctors dealt with ongoing as well as presenting problems and added health promotion and education about future appropriate use of services.11 Now, with an ageing population and more community care of chronic illness, there are more issues to be considered at each consultation. Ideas of what constitutes good general practice are more complex.12 Good practice now includes both extended care of chronic medical problems—for example, coronary heart disease13—and a public health role. At first this model was restricted to those who lead change (“early adopters”) and enthusiasts14 but now it is embedded in professional and managerial expectations of good practice.
-
-Adequate time is essential. It may be difficult for an elderly patient with several active problems to undress, be examined, and get adequate professional consideration in under 15 minutes. Here the doctor is faced with the choice of curtailing the consultation or of reducing the time available for the next patient. Having to cope with these situations often contributes to professional dissatisfaction.15 This combination of more care, more options, and more genuine discussion of those options with informed patient choice inevitably leads to pressure on time.
-
-## Access problems
-
-In a service free at the point of access, rising demand will tend to increase rationing by delay. But attempts to improve access by offering more consultations at short notice squeeze consultation times.
-
-While appointment systems can and should reduce queuing time for consultations, they have long tended to be used as a brake on total demand.16 This may seriously erode patients' confidence in being able to see their doctor or nurse when they need to. Patients are offered appointments further ahead but may keep these even if their symptoms have remitted “just in case.” Availability of consultations is thus blocked. Receptionists are then inappropriately blamed for the inadequate access to doctors.
-
-In response to perception of delay, the government has set targets in the NHS plan of “guaranteed access to a primary care professional within 24 hours and to a primary care doctor within 48 hours.” Implementation is currently being negotiated.
-
-Virtually all patients think that they would not consult unless it was absolutely necessary. They do not think they are wasting NHS time and do not like being made to feel so. But underlying general practitioners' willingness to make patients wait several days is their perception that few of the problems are urgent. Patients and general practitioners evidently do not agree about the urgency of so called minor problems. To some extent general practice in the United Kingdom may have scored an “own goal” by setting up perceived access barriers (appointment systems and out of hours cooperatives) in the attempt to increase professional standards and control demand in a service that is free at the point of access.
-
-A further government initiative has been to bypass general practice with new services—notably, walk-in centres (primary care clinics in which no appointment is needed) and NHS Direct (a professional telephone helpline giving advice on simple remedies and access to services). Introduced widely and rapidly, these services each potentially provide significant features of primary care—namely, quick access to skilled health advice and first line treatment.
-
-## Loss of interpersonal continuity
-
-If a patient has to consult several different professionals, particularly over a short period of time, there is inevitable duplication of stories, risk of naive diagnoses, potential for conflicting advice, and perhaps loss of trust. Trust is essential if patients are to accept the “wait and see” management policy which is, or should be, an important part of the management of self limiting conditions, which are often on the boundary between illness and non-illness.17 Such duplication again increases pressure for more extra (unscheduled) consultations resulting in late running and professional frustration.18
-
-Mechanic described how loss of longitudinal (and perhaps personal and relational19) continuity influences the perception and use of time through an inability to build on previous consultations.2 Knowing the doctor well, particularly in smaller practices, is associated with enhanced patient enablement in shorter time.4 Though Mechanic pointed out that three quarters of UK patients have been registered with their general practitioner five years or more, this may be misleading. Practices are growing, with larger teams and more registered patients. Being registered with a doctor in a larger practice is usually no guarantee that the patient will be able to see the same doctor or the doctor of his or her choice, who may be different. Thus the system does not encourage adequate personal continuity. This adds to pressure on time and reduces both patient and professional satisfaction.
-
-## Health service reforms
-
-Finally, for the past 15 years the NHS has experienced unprecedented change with a succession of major administrative reforms. Recent reforms have focused on an NHS led by primary care, including the aim of shifting care from the secondary specialist sector to primary care. One consequence is increased demand for primary care of patients with more serious and less stable problems. With the limited piloting of reforms we do not know whether such major redirection can be achieved without greatly altering the delicate balance between expectations (of both patients and staff) and what is delivered.
-
-## The future
-
-We think that the way ahead must embrace both longer mean consultation times and more flexibility. More time is needed for high quality consultations with patients with major and complex problems of all kinds. But patients also need access to simpler services and advice. This should be more appropriate (and cost less) when it is given by professionals who know the patient and his or her medical history and social circumstances. For doctors, the higher quality associated with longer consultations may lead to greater professional satisfaction and, if these longer consultations are combined with more realistic scheduling, to reduced levels of stress.20 They will also find it easier to develop further the care of chronic disease.
-
-The challenge posed to general practice by walk-in centres and NHS Direct is considerable, and the diversion of funding from primary care is large. The risk of waste and duplication increases as more layers of complexity are added to a primary care service that started out as something familiar, simple, and local and which is still envied in other developed countries.21 Access needs to be simple, and the advantages of personal knowledge and trust in minimising duplication and overmedicalisation need to be exploited.
-
-We must ensure better communication and access so that patients can more easily deal with minor issues and queries with someone they know and trust and avoid the formality and inconvenience of a full face to face consultation. Too often this has to be with a different professional, unfamiliar with the nuances of the case. There should be far more managerial emphasis on helping patients to interact with their chosen practitioner22; such a programme has been described.23 Modern information systems make it much easier to record which doctor(s) a patient prefers to see and to monitor how often this is achieved. The telephone is hardly modern but is underused. Email avoids the problems inherent in arranging simultaneous availability necessary for telephone consultations but at the cost of reducing the communication of emotions. There is a place for both.2 Access without prior appointment is a valued feature of primary care, and we need to know more about the right balance between planned and ad hoc consulting.
-
-## Next steps
-
-General practitioners do not behave in a uniform way. They can be categorised as slow, medium, and fast and react in different ways to changes in consulting speed.18 They are likely to have differing views about a widespread move to lengthen consultation time. We do not need further confirmation that longer consultations are desirable and necessary, but research could show us the best way to learn how to introduce them with minimal disruption to the way in which patients and practices like primary care to be provided.24 We also need to learn how to make the most of available time in complex consultations.
-
-Devising appropriate incentives and helping practices move beyond just reacting to demand in the traditional way by working harder and faster is perhaps our greatest challenge in the United Kingdom. The new primary are trusts need to work together with the growing primary care research networks to carry out the necessary development work. In particular, research is needed on how a primary care team can best provide the right balance of quick access and interpersonal knowledge and trust.
-
-## Acknowledgments
-
-We thank the other members of the working group: Susan Childs, Paul Freeling, Iona Heath, Marshall Marinker, and Bonnie Sibbald. We also thank Fenny Green of the Royal College of General Practitioners for administrative help.
-
-## References
-
-- Shah NC. Viewpoint: Consultation time—time for a change? Still the “perfunctory work of perfunctory men!”. Br J Gen Pract 49:497 (1999).
-- Mechanic D. How should hamsters run? Some observations about sufficient patient time in primary care. BMJ 323:266–268 (2001). PMID: 11485957
-- Howie JGR, Porter AMD, Heaney DJ, Hopton JL. Long to short consultation ratio: a proxy measure of quality of care for general practice. Br J Gen Pract 41:48–54 (1991). PMID: 2031735
-- Howie JGR, Heaney DJ, Maxwell M, Walker JJ, Freeman GK, Rai H. Quality at general practice consultations: cross-sectional survey. BMJ 319:738–743 (1999). PMID: 10487999
-- Kaplan SH, Greenfield S, Ware JE. Assessing the effects of physician-patient interactions on the outcome of chronic disease. Med Care 27:110–125 (1989).
-- Airey C, Erens B. National surveys of NHS patients: general practice, 1998. London: NHS Executive (1999).
-- Hart JT. Expectations of health care: promoted, managed or shared?. Health Expect 1:3–13 (1998). PMID: 11281857
-- Tuckett D, Boulton M, Olson C, Williams A. Meetings between experts: an approach to sharing ideas in medical consultations. London: Tavistock Publications (1985).
-- General Medical Council. Draft recommendations on undergraduate medical education. July 2001. www.gmc-uk.org/med\_ed/tomorrowsdoctors/index.htm (accessed 2 Jan 2002).
-- Balint M. The doctor, his patient and the illness. London: Tavistock (1957).
-- Stott NCH, Davies RH. The exceptional potential in each primary care consultation. J R Coll Gen Pract 29:210–205 (1979).
-- Hill AP, Hill AP. Challenges for primary care. What's gone wrong with health care? Challenges for the new millennium. London: King's Fund75–86 (2000).
-- National service framework for coronary heart disease. London: Department of Health (2000).
-- Hart JT. A new kind of doctor: the general practitioner's part in the health of the community. London: Merlin Press (1988).
-- Morrison I, Smith R. Hamster health care. BMJ 321:1541–1542 (2000). PMID: 11124164
-- Arber S, Sawyer L. Do appointment systems work?. BMJ 284:478–480 (1982). PMID: 6800503
-- Hjortdahl P, Borchgrevink CF. Continuity of care: influence of general practitioners' knowledge about their patients on use of resources in consultations. BMJ 303:1181–1184 (1991). PMID: 1747619
-- Howie JGR, Hopton JL, Heaney DJ, Porter AMD. Attitudes to medical care, the organization of work, and stress among general practitioners. Br J Gen Pract 42:181–185 (1992). PMID: 1389427
-- Freeman G, Shepperd S, Robinson I, Ehrich K, Richards SC, Pitman P. Continuity of care: report of a scoping exercise for the national co-ordinating centre for NHS Service Delivery and Organisation R&D (NCCSDO), Summer 2000. London: NCCSDO (2001).
-- Wilson A, McDonald P, Hayes L, Cooney J. Longer booking intervals in general practice: effects on doctors' stress and arousal. Br J Gen Pract 41:184–187 (1991). PMID: 1878267
-- De Maeseneer J, Hjortdahl P, Starfield B. Fix what's wrong, not what's right, with general practice in Britain. BMJ 320:1616–1617 (2000). PMID: 10856043
-- Freeman G, Hjortdahl P. What future for continuity of care in general practice?. BMJ 314:1870–1873 (1997). PMID: 9224130
-- Kibbe DC, Bentz E, McLaughlin CP. Continuous quality improvement for continuity of care. J Fam Pract 36:304–308 (1993). PMID: 8454977
-- Williams M, Neal RD. Time for a change? The process of lengthening booking intervals in general practice. Br J Gen Pract 48:1783–1786 (1998). PMID: 10198490
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/elife-56337.xml.itxt b/tests/data/groundtruth/docling_v2/elife-56337.nxml.itxt
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rename to tests/data/groundtruth/docling_v2/elife-56337.nxml.itxt
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+ "content_layer": "body",
+ "label": "title",
+ "prov": [],
+ "orig": "KRAB-zinc finger protein gene expansion in response to active retrotransposons in the murine lineage",
+ "text": "KRAB-zinc finger protein gene expansion in response to active retrotransposons in the murine lineage"
+ },
+ {
+ "self_ref": "#/texts/1",
+ "parent": {
+ "$ref": "#/texts/0"
+ },
+ "children": [],
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+ "label": "paragraph",
+ "prov": [],
+ "orig": "Gernot Wolf, Alberto de Iaco, Ming-An Sun, Melania Bruno, Matthew Tinkham, Don Hoang, Apratim Mitra, Sherry Ralls, Didier Trono, Todd S Macfarlan",
+ "text": "Gernot Wolf, Alberto de Iaco, Ming-An Sun, Melania Bruno, Matthew Tinkham, Don Hoang, Apratim Mitra, Sherry Ralls, Didier Trono, Todd S Macfarlan"
+ },
+ {
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+ "parent": {
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+ "orig": "The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health, Bethesda, United States; School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland",
+ "text": "The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health, Bethesda, United States; School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland"
+ },
+ {
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+ "parent": {
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+ "orig": "The Krüppel-associated box zinc finger protein (KRAB-ZFP) family diversified in mammals. The majority of human KRAB-ZFPs bind transposable elements (TEs), however, since most TEs are inactive in humans it is unclear whether KRAB-ZFPs emerged to suppress TEs. We demonstrate that many recently emerged murine KRAB-ZFPs also bind to TEs, including the active ETn, IAP, and L1 families. Using a CRISPR/Cas9-based engineering approach, we genetically deleted five large clusters of KRAB-ZFPs and demonstrate that target TEs are de-repressed, unleashing TE-encoded enhancers. Homozygous knockout mice lacking one of two KRAB-ZFP gene clusters on chromosome 2 and chromosome 4 were nonetheless viable. In pedigrees of chromosome 4 cluster KRAB-ZFP mutants, we identified numerous novel ETn insertions with a modest increase in mutants. Our data strongly support the current model that recent waves of retrotransposon activity drove the expansion of KRAB-ZFP genes in mice and that many KRAB-ZFPs play a redundant role restricting TE activity.",
+ "text": "The Krüppel-associated box zinc finger protein (KRAB-ZFP) family diversified in mammals. The majority of human KRAB-ZFPs bind transposable elements (TEs), however, since most TEs are inactive in humans it is unclear whether KRAB-ZFPs emerged to suppress TEs. We demonstrate that many recently emerged murine KRAB-ZFPs also bind to TEs, including the active ETn, IAP, and L1 families. Using a CRISPR/Cas9-based engineering approach, we genetically deleted five large clusters of KRAB-ZFPs and demonstrate that target TEs are de-repressed, unleashing TE-encoded enhancers. Homozygous knockout mice lacking one of two KRAB-ZFP gene clusters on chromosome 2 and chromosome 4 were nonetheless viable. In pedigrees of chromosome 4 cluster KRAB-ZFP mutants, we identified numerous novel ETn insertions with a modest increase in mutants. Our data strongly support the current model that recent waves of retrotransposon activity drove the expansion of KRAB-ZFP genes in mice and that many KRAB-ZFPs play a redundant role restricting TE activity."
+ },
+ {
+ "self_ref": "#/texts/5",
+ "parent": {
+ "$ref": "#/texts/0"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/6"
+ },
+ {
+ "$ref": "#/texts/7"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Introduction",
+ "text": "Introduction",
+ "level": 1
+ },
+ {
+ "self_ref": "#/texts/6",
+ "parent": {
+ "$ref": "#/texts/5"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Nearly half of the human and mouse genomes consist of transposable elements (TEs). TEs shape the evolution of species, serving as a source for genetic innovation (Chuong et al., 2016; Frank and Feschotte, 2017). However, TEs also potentially harm their hosts by insertional mutagenesis, gene deregulation and activation of innate immunity (Maksakova et al., 2006; Kano et al., 2007; Brodziak et al., 2012; Hancks and Kazazian, 2016). To protect themselves from TE activity, host organisms have developed a wide range of defense mechanisms targeting virtually all steps of the TE life cycle (Dewannieux and Heidmann, 2013). In tetrapods, KRAB zinc finger protein (KRAB-ZFP) genes have amplified and diversified, likely in response to TE colonization (Thomas and Schneider, 2011; Najafabadi et al., 2015; Wolf et al., 2015a; Wolf et al., 2015b; Imbeault et al., 2017). Conventional ZFPs bind DNA using tandem arrays of C2H2 zinc finger domains, each capable of specifically interacting with three nucleotides, whereas some zinc fingers can bind two or four nucleotides and include DNA backbone interactions depending on target DNA structure (Patel et al., 2018). This allows KRAB-ZFPs to flexibly bind to large stretches of DNA with high affinity. The KRAB domain binds the corepressor KAP1, which in turn recruits histone modifying enzymes including the NuRD histone deacetylase complex and the H3K9-specific methylase SETDB1 (Schultz et al., 2002; Sripathy et al., 2006), which induces persistent and heritable gene silencing (Groner et al., 2010). Deletion of KAP1 (Rowe et al., 2010) or SETDB1 (Matsui et al., 2010) in mouse embryonic stem (ES) cells induces TE reactivation and cell death, but only minor phenotypes in differentiated cells, suggesting KRAB-ZFPs are most important during early embryogenesis where they mark TEs for stable epigenetic silencing that persists through development. However, SETDB1-containing complexes are also required to repress TEs in primordial germ cells (Liu et al., 2014) and adult tissues (Ecco et al., 2016), indicating KRAB-ZFPs are active beyond early development.",
+ "text": "Nearly half of the human and mouse genomes consist of transposable elements (TEs). TEs shape the evolution of species, serving as a source for genetic innovation (Chuong et al., 2016; Frank and Feschotte, 2017). However, TEs also potentially harm their hosts by insertional mutagenesis, gene deregulation and activation of innate immunity (Maksakova et al., 2006; Kano et al., 2007; Brodziak et al., 2012; Hancks and Kazazian, 2016). To protect themselves from TE activity, host organisms have developed a wide range of defense mechanisms targeting virtually all steps of the TE life cycle (Dewannieux and Heidmann, 2013). In tetrapods, KRAB zinc finger protein (KRAB-ZFP) genes have amplified and diversified, likely in response to TE colonization (Thomas and Schneider, 2011; Najafabadi et al., 2015; Wolf et al., 2015a; Wolf et al., 2015b; Imbeault et al., 2017). Conventional ZFPs bind DNA using tandem arrays of C2H2 zinc finger domains, each capable of specifically interacting with three nucleotides, whereas some zinc fingers can bind two or four nucleotides and include DNA backbone interactions depending on target DNA structure (Patel et al., 2018). This allows KRAB-ZFPs to flexibly bind to large stretches of DNA with high affinity. The KRAB domain binds the corepressor KAP1, which in turn recruits histone modifying enzymes including the NuRD histone deacetylase complex and the H3K9-specific methylase SETDB1 (Schultz et al., 2002; Sripathy et al., 2006), which induces persistent and heritable gene silencing (Groner et al., 2010). Deletion of KAP1 (Rowe et al., 2010) or SETDB1 (Matsui et al., 2010) in mouse embryonic stem (ES) cells induces TE reactivation and cell death, but only minor phenotypes in differentiated cells, suggesting KRAB-ZFPs are most important during early embryogenesis where they mark TEs for stable epigenetic silencing that persists through development. However, SETDB1-containing complexes are also required to repress TEs in primordial germ cells (Liu et al., 2014) and adult tissues (Ecco et al., 2016), indicating KRAB-ZFPs are active beyond early development."
+ },
+ {
+ "self_ref": "#/texts/7",
+ "parent": {
+ "$ref": "#/texts/5"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "TEs, especially long terminal repeat (LTR) retrotransposons, also known as endogenous retroviruses (ERVs), can affect expression of neighboring genes through their promoter and enhancer functions (Macfarlan et al., 2012; Wang et al., 2014; Thompson et al., 2016). KAP1 deletion in mouse ES cells causes rapid gene deregulation (Rowe et al., 2013), indicating that KRAB-ZFPs may regulate gene expression by recruiting KAP1 to TEs. Indeed, Zfp809 knock-out (KO) in mice resulted in transcriptional activation of a handful of genes in various tissues adjacent to ZFP809-targeted VL30-Pro elements (Wolf et al., 2015b). It has therefore been speculated that KRAB-ZFPs bind to TE sequences to domesticate them for gene regulatory innovation (Ecco et al., 2017). This idea is supported by the observation that many human KRAB-ZFPs target TE groups that have lost their coding potential millions of years ago and that KRAB-ZFP target sequences within TEs are in some cases under purifying selection (Imbeault et al., 2017). However, there are also clear signs of an evolutionary arms-race between human TEs and KRAB-ZFPs (Jacobs et al., 2014), indicating that some KRAB-ZFPs may limit TE mobility for stretches of evolutionary time, prior to their ultimate loss from the genome or adaptation for other regulatory functions. Here we use the laboratory mouse, which has undergone a recent expansion of the KRAB-ZFP family, to determine the in vivo requirement of the majority of evolutionarily young KRAB-ZFP genes.",
+ "text": "TEs, especially long terminal repeat (LTR) retrotransposons, also known as endogenous retroviruses (ERVs), can affect expression of neighboring genes through their promoter and enhancer functions (Macfarlan et al., 2012; Wang et al., 2014; Thompson et al., 2016). KAP1 deletion in mouse ES cells causes rapid gene deregulation (Rowe et al., 2013), indicating that KRAB-ZFPs may regulate gene expression by recruiting KAP1 to TEs. Indeed, Zfp809 knock-out (KO) in mice resulted in transcriptional activation of a handful of genes in various tissues adjacent to ZFP809-targeted VL30-Pro elements (Wolf et al., 2015b). It has therefore been speculated that KRAB-ZFPs bind to TE sequences to domesticate them for gene regulatory innovation (Ecco et al., 2017). This idea is supported by the observation that many human KRAB-ZFPs target TE groups that have lost their coding potential millions of years ago and that KRAB-ZFP target sequences within TEs are in some cases under purifying selection (Imbeault et al., 2017). However, there are also clear signs of an evolutionary arms-race between human TEs and KRAB-ZFPs (Jacobs et al., 2014), indicating that some KRAB-ZFPs may limit TE mobility for stretches of evolutionary time, prior to their ultimate loss from the genome or adaptation for other regulatory functions. Here we use the laboratory mouse, which has undergone a recent expansion of the KRAB-ZFP family, to determine the in vivo requirement of the majority of evolutionarily young KRAB-ZFP genes."
+ },
+ {
+ "self_ref": "#/texts/8",
+ "parent": {
+ "$ref": "#/texts/0"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/9"
+ },
+ {
+ "$ref": "#/texts/17"
+ },
+ {
+ "$ref": "#/texts/20"
+ },
+ {
+ "$ref": "#/texts/24"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Results",
+ "text": "Results",
+ "level": 1
+ },
+ {
+ "self_ref": "#/texts/9",
+ "parent": {
+ "$ref": "#/texts/8"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/10"
+ },
+ {
+ "$ref": "#/texts/11"
+ },
+ {
+ "$ref": "#/pictures/0"
+ },
+ {
+ "$ref": "#/tables/0"
+ },
+ {
+ "$ref": "#/texts/14"
+ },
+ {
+ "$ref": "#/texts/15"
+ },
+ {
+ "$ref": "#/texts/16"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Mouse KRAB-ZFPs target retrotransposons",
+ "text": "Mouse KRAB-ZFPs target retrotransposons",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/10",
+ "parent": {
+ "$ref": "#/texts/9"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "We analyzed the RNA expression profiles of mouse KRAB-ZFPs across a wide range of tissues to identify candidates active in early embryos/ES cells. While the majority of KRAB-ZFPs are expressed at low levels and uniformly across tissues, a group of KRAB-ZFPs are highly and almost exclusively expressed in ES cells (Figure 1—figure supplement 1A). About two thirds of these KRAB-ZFPs are physically linked in two clusters on chromosome 2 (Chr2-cl) and 4 (Chr4-cl) (Figure 1—figure supplement 1B). These two clusters encode 40 and 21 KRAB-ZFP annotated genes, respectively, which, with one exception on Chr4-cl, do not have orthologues in rat or any other sequenced mammals (Supplementary file 1). The KRAB-ZFPs within these two genomic clusters also group together phylogenetically (Figure 1—figure supplement 1C), indicating these gene clusters arose by a series of recent segmental gene duplications (Kauzlaric et al., 2017).",
+ "text": "We analyzed the RNA expression profiles of mouse KRAB-ZFPs across a wide range of tissues to identify candidates active in early embryos/ES cells. While the majority of KRAB-ZFPs are expressed at low levels and uniformly across tissues, a group of KRAB-ZFPs are highly and almost exclusively expressed in ES cells (Figure 1—figure supplement 1A). About two thirds of these KRAB-ZFPs are physically linked in two clusters on chromosome 2 (Chr2-cl) and 4 (Chr4-cl) (Figure 1—figure supplement 1B). These two clusters encode 40 and 21 KRAB-ZFP annotated genes, respectively, which, with one exception on Chr4-cl, do not have orthologues in rat or any other sequenced mammals (Supplementary file 1). The KRAB-ZFPs within these two genomic clusters also group together phylogenetically (Figure 1—figure supplement 1C), indicating these gene clusters arose by a series of recent segmental gene duplications (Kauzlaric et al., 2017)."
+ },
+ {
+ "self_ref": "#/texts/11",
+ "parent": {
+ "$ref": "#/texts/9"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "To determine the binding sites of the KRAB-ZFPs within these and other gene clusters, we expressed epitope-tagged KRAB-ZFPs using stably integrating vectors in mouse embryonic carcinoma (EC) or ES cells (Table 1, Supplementary file 1) and performed chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We then determined whether the identified binding sites are significantly enriched over annotated TEs and used the non-repetitive peak fraction to identify binding motifs. We discarded 7 of 68 ChIP-seq datasets because we could not obtain a binding motif or a target TE and manual inspection confirmed low signal to noise ratio. Of the remaining 61 KRAB-ZFPs, 51 significantly overlapped at least one TE subfamily (adjusted p-value<1e-5). Altogether, 81 LTR retrotransposon, 18 LINE, 10 SINE and one DNA transposon subfamilies were targeted by at least one of the 51 KRAB-ZFPs (Figure 1A and Supplementary file 1). Chr2-cl KRAB-ZFPs preferably bound IAPEz retrotransposons and L1-type LINEs, while Chr4-cl KRAB-ZFPs targeted various retrotransposons, including the closely related MMETn (hereafter referred to as ETn) and ETnERV (also known as MusD) elements (Figure 1A). ETn elements are non-autonomous LTR retrotransposons that require trans-complementation by the fully coding ETnERV elements that contain Gag, Pro and Pol genes (Ribet et al., 2004). These elements have accumulated to ~240 and~100 copies in the reference C57BL/6 genome, respectively, with ~550 solitary LTRs (Baust et al., 2003). Both ETn and ETnERVs are still active, generating polymorphisms and mutations in several mouse strains (Gagnier et al., 2019). The validity of our ChIP-seq screen was confirmed by the identification of binding motifs - which often resembled the computationally predicted motifs (Figure 1—figure supplement 2A) - for the majority of screened KRAB-ZFPs (Supplementary file 1). Moreover, predicted and experimentally determined motifs were found in targeted TEs in most cases (Supplementary file 1), and reporter repression assays confirmed KRAB-ZFP induced silencing for all the tested sequences (Figure 1—figure supplement 2B). Finally, we observed KAP1 and H3K9me3 enrichment at most of the targeted TEs in wild type ES cells, indicating that most of these KRAB-ZFPs are functionally active in the early embryo (Figure 1A).",
+ "text": "To determine the binding sites of the KRAB-ZFPs within these and other gene clusters, we expressed epitope-tagged KRAB-ZFPs using stably integrating vectors in mouse embryonic carcinoma (EC) or ES cells (Table 1, Supplementary file 1) and performed chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We then determined whether the identified binding sites are significantly enriched over annotated TEs and used the non-repetitive peak fraction to identify binding motifs. We discarded 7 of 68 ChIP-seq datasets because we could not obtain a binding motif or a target TE and manual inspection confirmed low signal to noise ratio. Of the remaining 61 KRAB-ZFPs, 51 significantly overlapped at least one TE subfamily (adjusted p-value<1e-5). Altogether, 81 LTR retrotransposon, 18 LINE, 10 SINE and one DNA transposon subfamilies were targeted by at least one of the 51 KRAB-ZFPs (Figure 1A and Supplementary file 1). Chr2-cl KRAB-ZFPs preferably bound IAPEz retrotransposons and L1-type LINEs, while Chr4-cl KRAB-ZFPs targeted various retrotransposons, including the closely related MMETn (hereafter referred to as ETn) and ETnERV (also known as MusD) elements (Figure 1A). ETn elements are non-autonomous LTR retrotransposons that require trans-complementation by the fully coding ETnERV elements that contain Gag, Pro and Pol genes (Ribet et al., 2004). These elements have accumulated to ~240 and~100 copies in the reference C57BL/6 genome, respectively, with ~550 solitary LTRs (Baust et al., 2003). Both ETn and ETnERVs are still active, generating polymorphisms and mutations in several mouse strains (Gagnier et al., 2019). The validity of our ChIP-seq screen was confirmed by the identification of binding motifs - which often resembled the computationally predicted motifs (Figure 1—figure supplement 2A) - for the majority of screened KRAB-ZFPs (Supplementary file 1). Moreover, predicted and experimentally determined motifs were found in targeted TEs in most cases (Supplementary file 1), and reporter repression assays confirmed KRAB-ZFP induced silencing for all the tested sequences (Figure 1—figure supplement 2B). Finally, we observed KAP1 and H3K9me3 enrichment at most of the targeted TEs in wild type ES cells, indicating that most of these KRAB-ZFPs are functionally active in the early embryo (Figure 1A)."
+ },
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+ "self_ref": "#/texts/12",
+ "parent": {
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+ "prov": [],
+ "orig": "Figure 1. Genome-wide binding patterns of mouse KRAB-ZFPs. (A) Probability heatmap of KRAB-ZFP binding to TEs. Blue color intensity (main field) corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher’s exact test). The green/red color intensity (top panel) represents mean KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) enrichment (respectively) at peaks overlapping significantly targeted TEs (adjusted p-value<1e-5) in WT ES cells. (B) Summarized ChIP-seq signal for indicated KRAB-ZFPs and previously published KAP1 and H3K9me3 in WT ES cells across 127 intact ETn elements. (C) Heatmaps of KRAB-ZFP ChIP-seq signal at ChIP-seq peaks. For better comparison, peaks for all three KRAB-ZFPs were called with the same parameters (p<1e-10, peak enrichment >20). The top panel shows a schematic of the arrangement of the contact amino acid composition of each zinc finger. Zinc fingers are grouped and colored according to similarity, with amino acid differences relative to the five consensus fingers highlighted in white.",
+ "text": "Figure 1. Genome-wide binding patterns of mouse KRAB-ZFPs. (A) Probability heatmap of KRAB-ZFP binding to TEs. Blue color intensity (main field) corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher’s exact test). The green/red color intensity (top panel) represents mean KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) enrichment (respectively) at peaks overlapping significantly targeted TEs (adjusted p-value<1e-5) in WT ES cells. (B) Summarized ChIP-seq signal for indicated KRAB-ZFPs and previously published KAP1 and H3K9me3 in WT ES cells across 127 intact ETn elements. (C) Heatmaps of KRAB-ZFP ChIP-seq signal at ChIP-seq peaks. For better comparison, peaks for all three KRAB-ZFPs were called with the same parameters (p<1e-10, peak enrichment >20). The top panel shows a schematic of the arrangement of the contact amino acid composition of each zinc finger. Zinc fingers are grouped and colored according to similarity, with amino acid differences relative to the five consensus fingers highlighted in white."
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+ "self_ref": "#/texts/13",
+ "parent": {
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+ "orig": "Table 1. KRAB-ZFP genes clusters in the mouse genome that were investigated in this study. * Number of protein-coding KRAB-ZFP genes identified in a previously published screen (Imbeault et al., 2017) and the ChIP-seq data column indicates the number of KRAB-ZFPs for which ChIP-seq was performed in this study.",
+ "text": "Table 1. KRAB-ZFP genes clusters in the mouse genome that were investigated in this study. * Number of protein-coding KRAB-ZFP genes identified in a previously published screen (Imbeault et al., 2017) and the ChIP-seq data column indicates the number of KRAB-ZFPs for which ChIP-seq was performed in this study."
+ },
+ {
+ "self_ref": "#/texts/14",
+ "parent": {
+ "$ref": "#/texts/9"
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+ "prov": [],
+ "orig": "We generally observed that KRAB-ZFPs present exclusively in mouse target TEs that are restricted to the mouse genome, indicating KRAB-ZFPs and their targets emerged together. For example, several mouse-specific KRAB-ZFPs in Chr2-cl and Chr4-cl target IAP and ETn elements which are only found in the mouse genome and are highly active. This is the strongest data to date supporting that recent KRAB-ZFP expansions in these young clusters is a response to recent TE activity. Likewise, ZFP599 and ZFP617, both conserved in Muroidea, bind to various ORR1-type LTRs which are present in the rat genome (Supplementary file 1). However, ZFP961, a KRAB-ZFP encoded on a small gene cluster on chromosome 8 that is conserved in Muroidea targets TEs that are only found in the mouse genome (e.g. ETn), a paradox we have previously observed with ZFP809, which also targets TEs that are evolutionarily younger than itself (Wolf et al., 2015b). The ZFP961 binding site is located at the 5’ end of the internal region of ETn and ETnERV elements, a sequence that usually contains the primer binding site (PBS), which is required to prime retroviral reverse transcription. Indeed, the ZFP961 motif closely resembles the PBSLys1,2 (Figure 1—figure supplement 3A), which had been previously identified as a KAP1-dependent target of retroviral repression (Yamauchi et al., 1995; Wolf et al., 2008). Repression of the PBSLys1,2 by ZFP961 was also confirmed in reporter assays (Figure 1—figure supplement 2B), indicating that ZFP961 is likely responsible for this silencing effect.",
+ "text": "We generally observed that KRAB-ZFPs present exclusively in mouse target TEs that are restricted to the mouse genome, indicating KRAB-ZFPs and their targets emerged together. For example, several mouse-specific KRAB-ZFPs in Chr2-cl and Chr4-cl target IAP and ETn elements which are only found in the mouse genome and are highly active. This is the strongest data to date supporting that recent KRAB-ZFP expansions in these young clusters is a response to recent TE activity. Likewise, ZFP599 and ZFP617, both conserved in Muroidea, bind to various ORR1-type LTRs which are present in the rat genome (Supplementary file 1). However, ZFP961, a KRAB-ZFP encoded on a small gene cluster on chromosome 8 that is conserved in Muroidea targets TEs that are only found in the mouse genome (e.g. ETn), a paradox we have previously observed with ZFP809, which also targets TEs that are evolutionarily younger than itself (Wolf et al., 2015b). The ZFP961 binding site is located at the 5’ end of the internal region of ETn and ETnERV elements, a sequence that usually contains the primer binding site (PBS), which is required to prime retroviral reverse transcription. Indeed, the ZFP961 motif closely resembles the PBSLys1,2 (Figure 1—figure supplement 3A), which had been previously identified as a KAP1-dependent target of retroviral repression (Yamauchi et al., 1995; Wolf et al., 2008). Repression of the PBSLys1,2 by ZFP961 was also confirmed in reporter assays (Figure 1—figure supplement 2B), indicating that ZFP961 is likely responsible for this silencing effect."
+ },
+ {
+ "self_ref": "#/texts/15",
+ "parent": {
+ "$ref": "#/texts/9"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "To further test the hypothesis that KRAB-ZFPs target sites necessary for retrotransposition, we utilized previously generated ETn and ETnERV retrotransposition reporters in which we mutated KRAB-ZFP binding sites (Ribet et al., 2004). Whereas the ETnERV reporters are sufficient for retrotransposition, the ETn reporter requires ETnERV genes supplied in trans. We tested and confirmed that the REX2/ZFP600 and GM13051 binding sites within these TEs are required for efficient retrotransposition (Figure 1—figure supplement 3B). REX2 and ZFP600 both bind a target about 200 bp from the start of the internal region (Figure 1B), a region that often encodes the packaging signal. GM13051 binds a target coding for part of a highly structured mRNA export signal (Legiewicz et al., 2010) near the 3’ end of the internal region of ETn (Figure 1—figure supplement 3C). Both signals are characterized by stem-loop intramolecular base-pairing in which a single mutation can disrupt loop formation. This indicates that at least some KRAB-ZFPs evolved to bind functionally essential target sequences which cannot easily evade repression by mutation.",
+ "text": "To further test the hypothesis that KRAB-ZFPs target sites necessary for retrotransposition, we utilized previously generated ETn and ETnERV retrotransposition reporters in which we mutated KRAB-ZFP binding sites (Ribet et al., 2004). Whereas the ETnERV reporters are sufficient for retrotransposition, the ETn reporter requires ETnERV genes supplied in trans. We tested and confirmed that the REX2/ZFP600 and GM13051 binding sites within these TEs are required for efficient retrotransposition (Figure 1—figure supplement 3B). REX2 and ZFP600 both bind a target about 200 bp from the start of the internal region (Figure 1B), a region that often encodes the packaging signal. GM13051 binds a target coding for part of a highly structured mRNA export signal (Legiewicz et al., 2010) near the 3’ end of the internal region of ETn (Figure 1—figure supplement 3C). Both signals are characterized by stem-loop intramolecular base-pairing in which a single mutation can disrupt loop formation. This indicates that at least some KRAB-ZFPs evolved to bind functionally essential target sequences which cannot easily evade repression by mutation."
+ },
+ {
+ "self_ref": "#/texts/16",
+ "parent": {
+ "$ref": "#/texts/9"
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+ "children": [],
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+ "label": "text",
+ "prov": [],
+ "orig": "Our KRAB-ZFP ChIP-seq dataset also provided unique insights into the emergence of new KRAB-ZFPs and binding patterns. The Chr4-cl KRAB-ZFPs REX2 and ZFP600 bind to the same target within ETn but with varying affinity (Figure 1C). Comparison of the amino acids responsible for DNA contact revealed a high similarity between REX2 and ZFP600, with the main differences at the most C-terminal zinc fingers. Additionally, we found that GM30910, another KRAB-ZFP encoded in the Chr4-cl, also shows a strong similarity to both KRAB-ZFPs yet targets entirely different groups of TEs (Figure 1C and Supplementary file 1). Together with previously shown data (Ecco et al., 2016), this example highlights how addition of a few new zinc fingers to an existing array can entirely shift the mode of DNA binding.",
+ "text": "Our KRAB-ZFP ChIP-seq dataset also provided unique insights into the emergence of new KRAB-ZFPs and binding patterns. The Chr4-cl KRAB-ZFPs REX2 and ZFP600 bind to the same target within ETn but with varying affinity (Figure 1C). Comparison of the amino acids responsible for DNA contact revealed a high similarity between REX2 and ZFP600, with the main differences at the most C-terminal zinc fingers. Additionally, we found that GM30910, another KRAB-ZFP encoded in the Chr4-cl, also shows a strong similarity to both KRAB-ZFPs yet targets entirely different groups of TEs (Figure 1C and Supplementary file 1). Together with previously shown data (Ecco et al., 2016), this example highlights how addition of a few new zinc fingers to an existing array can entirely shift the mode of DNA binding."
+ },
+ {
+ "self_ref": "#/texts/17",
+ "parent": {
+ "$ref": "#/texts/8"
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+ "prov": [],
+ "orig": "Genetic deletion of KRAB-ZFP gene clusters leads to retrotransposon reactivation",
+ "text": "Genetic deletion of KRAB-ZFP gene clusters leads to retrotransposon reactivation",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/18",
+ "parent": {
+ "$ref": "#/texts/17"
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+ "orig": "The majority of KRAB-ZFP genes are harbored in large, highly repetitive clusters that have formed by successive complex segmental duplications (Kauzlaric et al., 2017), rendering them inaccessible to conventional gene targeting. We therefore developed a strategy to delete entire KRAB-ZFP gene clusters in ES cells (including the Chr2-cl and Chr4-cl as well as two clusters on chromosome 13 and a cluster on chromosome 10) using two CRISPR/Cas9 gRNAs targeting unique regions flanking each cluster, and short single-stranded repair oligos with homologies to both sides of the projected cut sites. Using this approach, we generated five cluster KO ES cell lines in at least two biological replicates and performed RNA sequencing (RNA-seq) to determine TE expression levels. Strikingly, four of the five cluster KO ES cells exhibited distinct TE reactivation phenotypes (Figure 2A). Chr2-cl KO resulted in reactivation of several L1 subfamilies as well as RLTR10 (up to more than 100-fold as compared to WT) and IAPEz ERVs. In contrast, the most strongly upregulated TEs in Chr4-cl KO cells were ETn/ETnERV (up to 10-fold as compared to WT), with several other ERV groups modestly reactivated. ETn/ETnERV elements were also upregulated in Chr13.2-cl KO ES cells while the only upregulated ERVs in Chr13.1-cl KO ES cells were MMERVK10C elements (Figure 2A). Most reactivated retrotransposons were targeted by at least one KRAB-ZFP that was encoded in the deleted cluster (Figure 2A and Supplementary file 1), indicating a direct effect of these KRAB-ZFPs on TE expression levels. Furthermore, we observed a loss of KAP1 binding and H3K9me3 at several TE subfamilies that are targeted by at least one KRAB-ZFP within the deleted Chr2-cl and Chr4-cl (Figure 2B, Figure 2—figure supplement 1A), including L1, ETn and IAPEz elements. Using reduced representation bisulfite sequencing (RRBS-seq), we found that a subset of KRAB-ZFP bound TEs were partially hypomethylated in Chr4-cl KO ES cells, but only when grown in genome-wide hypomethylation-inducing conditions (Blaschke et al., 2013; Figure 2C and Supplementary file 2). These data are consistent with the hypothesis that KRAB-ZFPs/KAP1 are not required to establish DNA methylation, but under certain conditions they protect specific TEs and imprint control regions from genome-wide demethylation (Leung et al., 2014; Deniz et al., 2018).",
+ "text": "The majority of KRAB-ZFP genes are harbored in large, highly repetitive clusters that have formed by successive complex segmental duplications (Kauzlaric et al., 2017), rendering them inaccessible to conventional gene targeting. We therefore developed a strategy to delete entire KRAB-ZFP gene clusters in ES cells (including the Chr2-cl and Chr4-cl as well as two clusters on chromosome 13 and a cluster on chromosome 10) using two CRISPR/Cas9 gRNAs targeting unique regions flanking each cluster, and short single-stranded repair oligos with homologies to both sides of the projected cut sites. Using this approach, we generated five cluster KO ES cell lines in at least two biological replicates and performed RNA sequencing (RNA-seq) to determine TE expression levels. Strikingly, four of the five cluster KO ES cells exhibited distinct TE reactivation phenotypes (Figure 2A). Chr2-cl KO resulted in reactivation of several L1 subfamilies as well as RLTR10 (up to more than 100-fold as compared to WT) and IAPEz ERVs. In contrast, the most strongly upregulated TEs in Chr4-cl KO cells were ETn/ETnERV (up to 10-fold as compared to WT), with several other ERV groups modestly reactivated. ETn/ETnERV elements were also upregulated in Chr13.2-cl KO ES cells while the only upregulated ERVs in Chr13.1-cl KO ES cells were MMERVK10C elements (Figure 2A). Most reactivated retrotransposons were targeted by at least one KRAB-ZFP that was encoded in the deleted cluster (Figure 2A and Supplementary file 1), indicating a direct effect of these KRAB-ZFPs on TE expression levels. Furthermore, we observed a loss of KAP1 binding and H3K9me3 at several TE subfamilies that are targeted by at least one KRAB-ZFP within the deleted Chr2-cl and Chr4-cl (Figure 2B, Figure 2—figure supplement 1A), including L1, ETn and IAPEz elements. Using reduced representation bisulfite sequencing (RRBS-seq), we found that a subset of KRAB-ZFP bound TEs were partially hypomethylated in Chr4-cl KO ES cells, but only when grown in genome-wide hypomethylation-inducing conditions (Blaschke et al., 2013; Figure 2C and Supplementary file 2). These data are consistent with the hypothesis that KRAB-ZFPs/KAP1 are not required to establish DNA methylation, but under certain conditions they protect specific TEs and imprint control regions from genome-wide demethylation (Leung et al., 2014; Deniz et al., 2018)."
+ },
+ {
+ "self_ref": "#/texts/19",
+ "parent": {
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+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Figure 2. Retrotransposon reactivation in KRAB-ZFP cluster KO ES cells. (A) RNA-seq analysis of TE expression in five KRAB-ZFP cluster KO ES cells. Green and grey squares on top of the panel represent KRAB-ZFPs with or without ChIP-seq data, respectively, within each deleted gene cluster. Reactivated TEs that are bound by one or several KRAB-ZFPs are indicated by green squares in the panel. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. (B) Differential KAP1 binding and H3K9me3 enrichment at TE groups (summarized across all insertions) in Chr2-cl and Chr4-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in blue (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (C) DNA methylation status of CpG sites at indicated TE groups in WT and Chr4-cl KO ES cells grown in serum containing media or in hypomethylation-inducing media (2i + Vitamin C). P-values were calculated using paired t-test.",
+ "text": "Figure 2. Retrotransposon reactivation in KRAB-ZFP cluster KO ES cells. (A) RNA-seq analysis of TE expression in five KRAB-ZFP cluster KO ES cells. Green and grey squares on top of the panel represent KRAB-ZFPs with or without ChIP-seq data, respectively, within each deleted gene cluster. Reactivated TEs that are bound by one or several KRAB-ZFPs are indicated by green squares in the panel. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. (B) Differential KAP1 binding and H3K9me3 enrichment at TE groups (summarized across all insertions) in Chr2-cl and Chr4-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in blue (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (C) DNA methylation status of CpG sites at indicated TE groups in WT and Chr4-cl KO ES cells grown in serum containing media or in hypomethylation-inducing media (2i + Vitamin C). P-values were calculated using paired t-test."
+ },
+ {
+ "self_ref": "#/texts/20",
+ "parent": {
+ "$ref": "#/texts/8"
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+ "children": [
+ {
+ "$ref": "#/texts/21"
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+ "prov": [],
+ "orig": "KRAB-ZFP cluster deletions license TE-borne enhancers",
+ "text": "KRAB-ZFP cluster deletions license TE-borne enhancers",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/21",
+ "parent": {
+ "$ref": "#/texts/20"
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+ "orig": "We next used our RNA-seq datasets to determine the effect of KRAB-ZFP cluster deletions on gene expression. We identified 195 significantly upregulated and 130 downregulated genes in Chr4-cl KO ES cells, and 108 upregulated and 59 downregulated genes in Chr2-cl KO ES cells (excluding genes on the deleted cluster) (Figure 3A). To address whether gene deregulation in Chr2-cl and Chr4-cl KO ES cells is caused by nearby TE reactivation, we determined whether genes near certain TE subfamilies are more frequently deregulated than random genes. We found a strong correlation of gene upregulation and TE proximity for several TE subfamilies, of which many became transcriptionally activated themselves (Figure 3B). For example, nearly 10% of genes that are located within 100 kb (up- or downstream of the TSS) of an ETn element are upregulated in Chr4-cl KO ES cells, as compared to 0.8% of all genes. In Chr2-cl KO ES cells, upregulated genes were significantly enriched near various LINE groups but also IAPEz-int and RLTR10-int elements, indicating that TE-binding KRAB-ZFPs in these clusters limit the potential activating effects of TEs on nearby genes.",
+ "text": "We next used our RNA-seq datasets to determine the effect of KRAB-ZFP cluster deletions on gene expression. We identified 195 significantly upregulated and 130 downregulated genes in Chr4-cl KO ES cells, and 108 upregulated and 59 downregulated genes in Chr2-cl KO ES cells (excluding genes on the deleted cluster) (Figure 3A). To address whether gene deregulation in Chr2-cl and Chr4-cl KO ES cells is caused by nearby TE reactivation, we determined whether genes near certain TE subfamilies are more frequently deregulated than random genes. We found a strong correlation of gene upregulation and TE proximity for several TE subfamilies, of which many became transcriptionally activated themselves (Figure 3B). For example, nearly 10% of genes that are located within 100 kb (up- or downstream of the TSS) of an ETn element are upregulated in Chr4-cl KO ES cells, as compared to 0.8% of all genes. In Chr2-cl KO ES cells, upregulated genes were significantly enriched near various LINE groups but also IAPEz-int and RLTR10-int elements, indicating that TE-binding KRAB-ZFPs in these clusters limit the potential activating effects of TEs on nearby genes."
+ },
+ {
+ "self_ref": "#/texts/22",
+ "parent": {
+ "$ref": "#/body"
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+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Figure 3. TE-dependent gene activation in KRAB-ZFP cluster KO ES cells. (A) Differential gene expression in Chr2-cl and Chr4-cl KO ES cells. Significantly up- and downregulated genes (adjusted p-value<0.05) are highlighted in red and green, respectively, KRAB-ZFP genes within the deleted clusters are shown in blue. (B) Correlation of TEs and gene deregulation. Plots show enrichment of TE groups within 100 kb of up- and downregulated genes relative to all genes. Significantly overrepresented LTR and LINE groups (adjusted p-value<0.1) are highlighted in blue and red, respectively. (C) Schematic view of the downstream region of Chst1 where a 5’ truncated ETn insertion is located. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). (D) RT-qPCR analysis of Chst1 mRNA expression in Chr4-cl WT and KO ES cells with or without the CRISPR/Cas9 deleted ETn insertion near Chst1. Values represent mean expression (normalized to Gapdh) from three biological replicates per sample (each performed in three technical replicates) in arbitrary units. Error bars represent standard deviation and asterisks indicate significance (p<0.01, Student’s t-test). n.s.: not significant. (E) Mean coverage of ChIP-seq data (Input subtracted from ChIP) in Chr4-cl WT and KO ES cells over 127 full-length ETn insertions. The binding sites of the Chr4-cl KRAB-ZFPs Rex2 and Gm13051 are indicated by dashed lines.",
+ "text": "Figure 3. TE-dependent gene activation in KRAB-ZFP cluster KO ES cells. (A) Differential gene expression in Chr2-cl and Chr4-cl KO ES cells. Significantly up- and downregulated genes (adjusted p-value<0.05) are highlighted in red and green, respectively, KRAB-ZFP genes within the deleted clusters are shown in blue. (B) Correlation of TEs and gene deregulation. Plots show enrichment of TE groups within 100 kb of up- and downregulated genes relative to all genes. Significantly overrepresented LTR and LINE groups (adjusted p-value<0.1) are highlighted in blue and red, respectively. (C) Schematic view of the downstream region of Chst1 where a 5’ truncated ETn insertion is located. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). (D) RT-qPCR analysis of Chst1 mRNA expression in Chr4-cl WT and KO ES cells with or without the CRISPR/Cas9 deleted ETn insertion near Chst1. Values represent mean expression (normalized to Gapdh) from three biological replicates per sample (each performed in three technical replicates) in arbitrary units. Error bars represent standard deviation and asterisks indicate significance (p<0.01, Student’s t-test). n.s.: not significant. (E) Mean coverage of ChIP-seq data (Input subtracted from ChIP) in Chr4-cl WT and KO ES cells over 127 full-length ETn insertions. The binding sites of the Chr4-cl KRAB-ZFPs Rex2 and Gm13051 are indicated by dashed lines."
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+ "orig": "While we generally observed that TE-associated gene reactivation is not caused by elongated or spliced transcription starting at the retrotransposons, we did observe that the strength of the effect of ETn elements on gene expression is stronger on genes in closer proximity. About 25% of genes located within 20 kb of an ETn element, but only 5% of genes located at a distance between 50 and 100 kb from the nearest ETn insertion, become upregulated in Chr4-cl KO ES cells. Importantly however, the correlation is still significant for genes that are located at distances between 50 and 100 kb from the nearest ETn insertion, indicating that ETn elements can act as long-range enhancers of gene expression in the absence of KRAB-ZFPs that target them. To confirm that Chr4-cl KRAB-ZFPs such as GM13051 block ETn-borne enhancers, we tested the ability of a putative ETn enhancer to activate transcription in a reporter assay. For this purpose, we cloned a 5 kb fragment spanning from the GM13051 binding site within the internal region of a truncated ETn insertion to the first exon of the Cd59a gene, which is strongly activated in Chr4-cl KO ES cells (Figure 2—figure supplement 1B). We observed strong transcriptional activity of this fragment which was significantly higher in Chr4-cl KO ES cells. Surprisingly, this activity was reduced to background when the internal segment of the ETn element was not included in the fragment, suggesting the internal segment of the ETn element, but not its LTR, contains a Chr4-cl KRAB-ZFP sensitive enhancer. To further corroborate these findings, we genetically deleted an ETn element that is located about 60 kb from the TSS of Chst1, one of the top-upregulated genes in Chr4-cl KO ES cells (Figure 3C). RT-qPCR analysis revealed that the Chst1 upregulation phenotype in Chr4-cl KO ES cells diminishes when the ETn insertion is absent, providing direct evidence that a KRAB-ZFP controlled ETn-borne enhancer regulates Chst1 expression (Figure 3D). Furthermore, ChIP-seq confirmed a general increase of H3K4me3, H3K4me1 and H3K27ac marks at ETn elements in Chr4-cl KO ES cells (Figure 3E). Notably, enhancer marks were most pronounced around the GM13051 binding site near the 3’ end of the internal region, confirming that the enhancer activity of ETn is located on the internal region and not on the LTR.",
+ "text": "While we generally observed that TE-associated gene reactivation is not caused by elongated or spliced transcription starting at the retrotransposons, we did observe that the strength of the effect of ETn elements on gene expression is stronger on genes in closer proximity. About 25% of genes located within 20 kb of an ETn element, but only 5% of genes located at a distance between 50 and 100 kb from the nearest ETn insertion, become upregulated in Chr4-cl KO ES cells. Importantly however, the correlation is still significant for genes that are located at distances between 50 and 100 kb from the nearest ETn insertion, indicating that ETn elements can act as long-range enhancers of gene expression in the absence of KRAB-ZFPs that target them. To confirm that Chr4-cl KRAB-ZFPs such as GM13051 block ETn-borne enhancers, we tested the ability of a putative ETn enhancer to activate transcription in a reporter assay. For this purpose, we cloned a 5 kb fragment spanning from the GM13051 binding site within the internal region of a truncated ETn insertion to the first exon of the Cd59a gene, which is strongly activated in Chr4-cl KO ES cells (Figure 2—figure supplement 1B). We observed strong transcriptional activity of this fragment which was significantly higher in Chr4-cl KO ES cells. Surprisingly, this activity was reduced to background when the internal segment of the ETn element was not included in the fragment, suggesting the internal segment of the ETn element, but not its LTR, contains a Chr4-cl KRAB-ZFP sensitive enhancer. To further corroborate these findings, we genetically deleted an ETn element that is located about 60 kb from the TSS of Chst1, one of the top-upregulated genes in Chr4-cl KO ES cells (Figure 3C). RT-qPCR analysis revealed that the Chst1 upregulation phenotype in Chr4-cl KO ES cells diminishes when the ETn insertion is absent, providing direct evidence that a KRAB-ZFP controlled ETn-borne enhancer regulates Chst1 expression (Figure 3D). Furthermore, ChIP-seq confirmed a general increase of H3K4me3, H3K4me1 and H3K27ac marks at ETn elements in Chr4-cl KO ES cells (Figure 3E). Notably, enhancer marks were most pronounced around the GM13051 binding site near the 3’ end of the internal region, confirming that the enhancer activity of ETn is located on the internal region and not on the LTR."
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+ "orig": "ETn retrotransposition in Chr4-cl KO and WT mice",
+ "text": "ETn retrotransposition in Chr4-cl KO and WT mice",
+ "level": 2
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+ "orig": "IAP, ETn/ETnERV and MuLV/RLTR4 retrotransposons are highly polymorphic in inbred mouse strains (Nellåker et al., 2012), indicating that these elements are able to mobilize in the germ line. Since these retrotransposons are upregulated in Chr2-cl and Chr4-cl KO ES cells, we speculated that these KRAB-ZFP clusters evolved to minimize the risks of insertional mutagenesis by retrotransposition. To test this, we generated Chr2-cl and Chr4-cl KO mice via ES cell injection into blastocysts, and after germ line transmission we genotyped the offspring of heterozygous breeding pairs. While the offspring of Chr4-cl KO/WT parents were born close to Mendelian ratios in pure C57BL/6 and mixed C57BL/6 129Sv matings, one Chr4-cl KO/WT breeding pair gave birth to significantly fewer KO mice than expected (p-value=0.022) (Figure 4—figure supplement 1A). Likewise, two out of four Chr2-cl KO breeding pairs on mixed C57BL/6 129Sv matings failed to give birth to a single KO offspring (p-value<0.01) while the two other mating pairs produced KO offspring at near Mendelian ratios (Figure 4—figure supplement 1A). Altogether, these data indicate that KRAB-ZFP clusters are not absolutely essential in mice, but that genetic and/or epigenetic factors may contribute to reduced viability.",
+ "text": "IAP, ETn/ETnERV and MuLV/RLTR4 retrotransposons are highly polymorphic in inbred mouse strains (Nellåker et al., 2012), indicating that these elements are able to mobilize in the germ line. Since these retrotransposons are upregulated in Chr2-cl and Chr4-cl KO ES cells, we speculated that these KRAB-ZFP clusters evolved to minimize the risks of insertional mutagenesis by retrotransposition. To test this, we generated Chr2-cl and Chr4-cl KO mice via ES cell injection into blastocysts, and after germ line transmission we genotyped the offspring of heterozygous breeding pairs. While the offspring of Chr4-cl KO/WT parents were born close to Mendelian ratios in pure C57BL/6 and mixed C57BL/6 129Sv matings, one Chr4-cl KO/WT breeding pair gave birth to significantly fewer KO mice than expected (p-value=0.022) (Figure 4—figure supplement 1A). Likewise, two out of four Chr2-cl KO breeding pairs on mixed C57BL/6 129Sv matings failed to give birth to a single KO offspring (p-value<0.01) while the two other mating pairs produced KO offspring at near Mendelian ratios (Figure 4—figure supplement 1A). Altogether, these data indicate that KRAB-ZFP clusters are not absolutely essential in mice, but that genetic and/or epigenetic factors may contribute to reduced viability."
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+ "orig": "We reasoned that retrotransposon activation could account for the reduced viability of Chr2-cl and Chr4-cl KO mice in some matings. However, since only rare matings produced non-viable KO embryos, we instead turned to the viable KO mice to assay for increased transposon activity. RNA-seq in blood, brain and testis revealed that, with a few exceptions, retrotransposons upregulated in Chr2 and Chr4 KRAB-ZFP cluster KO ES cells are not expressed at higher levels in adult tissues (Figure 4—figure supplement 1B). Likewise, no strong transcriptional TE reactivation phenotype was observed in liver and kidney of Chr4-cl KO mice (data not shown) and ChIP-seq with antibodies against H3K4me1, H3K4me3 and H3K27ac in testis of Chr4-cl WT and KO mice revealed no increase of active histone marks at ETn elements or other TEs (data not shown). This indicates that Chr2-cl and Chr4-cl KRAB-ZFPs are primarily required for TE repression during early development. This is consistent with the high expression of these KRAB-ZFPs uniquely in ES cells (Figure 1—figure supplement 1A). To determine whether retrotransposition occurs at a higher frequency in Chr4-cl KO mice during development, we screened for novel ETn (ETn/ETnERV) and MuLV (MuLV/RLTR4_MM) insertions in viable Chr4-cl KO mice. For this purpose, we developed a capture-sequencing approach to enrich for ETn/MuLV DNA and flanking sequences from genomic DNA using probes that hybridize with the 5’ and 3’ ends of ETn and MuLV LTRs prior to deep sequencing. We screened genomic DNA samples from a total of 76 mice, including 54 mice from ancestry-controlled Chr4-cl KO matings in various strain backgrounds, the two ES cell lines the Chr4-cl KO mice were generated from, and eight mice from a Chr2-cl KO mating which served as a control (since ETn and MuLVs are not activated in Chr2-cl KO ES cells) (Supplementary file 4). Using this approach, we were able to enrich reads mapping to ETn/MuLV LTRs about 2,000-fold compared to genome sequencing without capture. ETn/MuLV insertions were determined by counting uniquely mapped reads that were paired with reads mapping to ETn/MuLV elements (see materials and methods for details). To assess the efficiency of the capture approach, we determined what proportion of a set of 309 largely intact (two LTRs flanking an internal sequence) reference ETn elements could be identified using our sequencing data. 95% of these insertions were called with high confidence in the majority of our samples (data not shown), indicating that we are able to identify ETn insertions at a high recovery rate.",
+ "text": "We reasoned that retrotransposon activation could account for the reduced viability of Chr2-cl and Chr4-cl KO mice in some matings. However, since only rare matings produced non-viable KO embryos, we instead turned to the viable KO mice to assay for increased transposon activity. RNA-seq in blood, brain and testis revealed that, with a few exceptions, retrotransposons upregulated in Chr2 and Chr4 KRAB-ZFP cluster KO ES cells are not expressed at higher levels in adult tissues (Figure 4—figure supplement 1B). Likewise, no strong transcriptional TE reactivation phenotype was observed in liver and kidney of Chr4-cl KO mice (data not shown) and ChIP-seq with antibodies against H3K4me1, H3K4me3 and H3K27ac in testis of Chr4-cl WT and KO mice revealed no increase of active histone marks at ETn elements or other TEs (data not shown). This indicates that Chr2-cl and Chr4-cl KRAB-ZFPs are primarily required for TE repression during early development. This is consistent with the high expression of these KRAB-ZFPs uniquely in ES cells (Figure 1—figure supplement 1A). To determine whether retrotransposition occurs at a higher frequency in Chr4-cl KO mice during development, we screened for novel ETn (ETn/ETnERV) and MuLV (MuLV/RLTR4_MM) insertions in viable Chr4-cl KO mice. For this purpose, we developed a capture-sequencing approach to enrich for ETn/MuLV DNA and flanking sequences from genomic DNA using probes that hybridize with the 5’ and 3’ ends of ETn and MuLV LTRs prior to deep sequencing. We screened genomic DNA samples from a total of 76 mice, including 54 mice from ancestry-controlled Chr4-cl KO matings in various strain backgrounds, the two ES cell lines the Chr4-cl KO mice were generated from, and eight mice from a Chr2-cl KO mating which served as a control (since ETn and MuLVs are not activated in Chr2-cl KO ES cells) (Supplementary file 4). Using this approach, we were able to enrich reads mapping to ETn/MuLV LTRs about 2,000-fold compared to genome sequencing without capture. ETn/MuLV insertions were determined by counting uniquely mapped reads that were paired with reads mapping to ETn/MuLV elements (see materials and methods for details). To assess the efficiency of the capture approach, we determined what proportion of a set of 309 largely intact (two LTRs flanking an internal sequence) reference ETn elements could be identified using our sequencing data. 95% of these insertions were called with high confidence in the majority of our samples (data not shown), indicating that we are able to identify ETn insertions at a high recovery rate."
+ },
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+ "orig": "Using this dataset, we first confirmed the polymorphic nature of both ETn and MuLV retrotransposons in laboratory mouse strains (Figure 4—figure supplement 2A), highlighting the potential of these elements to retrotranspose. To identify novel insertions, we filtered out insertions that were supported by ETn/MuLV-paired reads in more than one animal. While none of the 54 ancestry-controlled mice showed a single novel MuLV insertion, we observed greatly varying numbers of up to 80 novel ETn insertions in our pedigree (Figure 4A).",
+ "text": "Using this dataset, we first confirmed the polymorphic nature of both ETn and MuLV retrotransposons in laboratory mouse strains (Figure 4—figure supplement 2A), highlighting the potential of these elements to retrotranspose. To identify novel insertions, we filtered out insertions that were supported by ETn/MuLV-paired reads in more than one animal. While none of the 54 ancestry-controlled mice showed a single novel MuLV insertion, we observed greatly varying numbers of up to 80 novel ETn insertions in our pedigree (Figure 4A)."
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+ "orig": "Figure 4. ETn retrotransposition in Chr4-cl KO mice. (A) Pedigree of mice used for transposon insertion screening by capture-seq in mice of different strain backgrounds. The number of novel ETn insertions (only present in one animal) are indicated. For animals whose direct ancestors have not been screened, the ETn insertions are shown in parentheses since parental inheritance cannot be excluded in that case. Germ line insertions are indicated by asterisks. All DNA samples were prepared from tail tissues unless noted (-S: spleen, -E: ear, -B:Blood) (B) Statistical analysis of ETn insertion frequency in tail tissue from 30 Chr4-cl KO, KO/WT and WT mice that were derived from one Chr4-c KO x KO/WT and two Chr4-cl KO/WT x KO/WT matings. Only DNA samples that were collected from juvenile tails were considered for this analysis. P-values were calculated using one-sided Wilcoxon Rank Sum Test. In the last panel, KO, WT and KO/WT mice derived from all matings were combined for the statistical analysis.",
+ "text": "Figure 4. ETn retrotransposition in Chr4-cl KO mice. (A) Pedigree of mice used for transposon insertion screening by capture-seq in mice of different strain backgrounds. The number of novel ETn insertions (only present in one animal) are indicated. For animals whose direct ancestors have not been screened, the ETn insertions are shown in parentheses since parental inheritance cannot be excluded in that case. Germ line insertions are indicated by asterisks. All DNA samples were prepared from tail tissues unless noted (-S: spleen, -E: ear, -B:Blood) (B) Statistical analysis of ETn insertion frequency in tail tissue from 30 Chr4-cl KO, KO/WT and WT mice that were derived from one Chr4-c KO x KO/WT and two Chr4-cl KO/WT x KO/WT matings. Only DNA samples that were collected from juvenile tails were considered for this analysis. P-values were calculated using one-sided Wilcoxon Rank Sum Test. In the last panel, KO, WT and KO/WT mice derived from all matings were combined for the statistical analysis."
+ },
+ {
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+ "$ref": "#/texts/24"
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+ "orig": "To validate some of the novel ETn insertions, we designed specific PCR primers for five of the insertions and screened genomic DNA of the mice in which they were identified as well as their parents. For all tested insertions, we were able to amplify their flanking sequence and show that these insertions are absent in their parents (Figure 4—figure supplement 3A). To confirm their identity, we amplified and sequenced three of the novel full-length ETn insertions. Two of these elements (Genbank accession: MH449667-68) resembled typical ETnII elements with identical 5’ and 3’ LTRs and target site duplications (TSD) of 4 or 6 bp, respectively. The third sequenced element (MH449669) represented a hybrid element that contains both ETnI and MusD (ETnERV) sequences. Similar insertions can be found in the B6 reference genome; however, the identified novel insertion has a 2.5 kb deletion of the 5’ end of the internal region. Additionally, the 5’ and 3’ LTR of this element differ in one nucleotide near the start site and contain an unusually large 248 bp TSD (containing a SINE repeat) indicating that an improper integration process might have truncated this element.",
+ "text": "To validate some of the novel ETn insertions, we designed specific PCR primers for five of the insertions and screened genomic DNA of the mice in which they were identified as well as their parents. For all tested insertions, we were able to amplify their flanking sequence and show that these insertions are absent in their parents (Figure 4—figure supplement 3A). To confirm their identity, we amplified and sequenced three of the novel full-length ETn insertions. Two of these elements (Genbank accession: MH449667-68) resembled typical ETnII elements with identical 5’ and 3’ LTRs and target site duplications (TSD) of 4 or 6 bp, respectively. The third sequenced element (MH449669) represented a hybrid element that contains both ETnI and MusD (ETnERV) sequences. Similar insertions can be found in the B6 reference genome; however, the identified novel insertion has a 2.5 kb deletion of the 5’ end of the internal region. Additionally, the 5’ and 3’ LTR of this element differ in one nucleotide near the start site and contain an unusually large 248 bp TSD (containing a SINE repeat) indicating that an improper integration process might have truncated this element."
+ },
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+ "orig": "Besides novel ETn insertions that were only identified in one specific animal, we also observed three ETn insertions that could be detected in several siblings but not in their parents or any of the other screened mice. This strongly indicates that these retrotransposition events occurred in the germ line of the parents from which they were passed on to some of their offspring. One of these germ line insertions was evidently passed on from the offspring to the next generation (Figure 4A). As expected, the read numbers supporting these novel germ line insertions were comparable to the read numbers that were found in the flanking regions of annotated B6 ETn insertions (Figure 4—figure supplement 3B). In contrast, virtually all novel insertions that were only found in one animal were supported by significantly fewer reads (Figure 4—figure supplement 3B). This indicates that these elements resulted from retrotransposition events in the developing embryo and not in the zygote or parental germ cells. Indeed, we detected different sets of insertions in various tissues from the same animal (Figure 4—figure supplement 3C). Even between tail samples that were collected from the same animal at different ages, only a fraction of the new insertions were present in both samples, while technical replicates from the same genomic DNA samples showed a nearly complete overlap in insertions (Figure 4—figure supplement 3D).",
+ "text": "Besides novel ETn insertions that were only identified in one specific animal, we also observed three ETn insertions that could be detected in several siblings but not in their parents or any of the other screened mice. This strongly indicates that these retrotransposition events occurred in the germ line of the parents from which they were passed on to some of their offspring. One of these germ line insertions was evidently passed on from the offspring to the next generation (Figure 4A). As expected, the read numbers supporting these novel germ line insertions were comparable to the read numbers that were found in the flanking regions of annotated B6 ETn insertions (Figure 4—figure supplement 3B). In contrast, virtually all novel insertions that were only found in one animal were supported by significantly fewer reads (Figure 4—figure supplement 3B). This indicates that these elements resulted from retrotransposition events in the developing embryo and not in the zygote or parental germ cells. Indeed, we detected different sets of insertions in various tissues from the same animal (Figure 4—figure supplement 3C). Even between tail samples that were collected from the same animal at different ages, only a fraction of the new insertions were present in both samples, while technical replicates from the same genomic DNA samples showed a nearly complete overlap in insertions (Figure 4—figure supplement 3D)."
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+ {
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+ "orig": "Finally, we asked whether there were more novel ETn insertions in mice lacking the Chr4-cl relative to their wild type and heterozygous littermates in our pedigree. Interestingly, only one out of the eight Chr4-cl KO mice in a pure C57BL/6 strain background and none of the eight offspring from a Chr2-cl mating carried a single novel ETn insertion (Figure 4A). When crossing into a 129Sv background for a single generation before intercrossing heterozygous mice (F1), we observed 4 out of 8 Chr4-cl KO mice that contained at least one new ETn insertion, whereas none of 3 heterozygous mice contained any insertions. After crossing to the 129Sv background for a second generation (F2), we determined the number of novel ETn insertions in the offspring of one KO/WT x KO and two KO/WT x KO/WT matings, excluding all samples that were not derived from juvenile tail tissue. Only in the offspring of the KO/WT x KO mating, we observed a statistically significant higher average number of ETn insertions in KO vs. KO/WT animals (7.3 vs. 29.6, p=0.045, Figure 4B). Other than that, only a non-significant trend towards greater average numbers of ETn insertions in KO (11 vs. 27.8, p=0.192, Figure 4B) was apparent in one of the WT/KO x KO/WT matings whereas no difference in ETn insertion numbers between WT and KO mice could be observed in the second mating WT/KO x KO/WT (26 vs. 31, p=0.668, Figure 4B). When comparing all KO with all WT and WT/KO mice from these three matings, a trend towards more ETn insertions in KO remained but was not supported by strong significance (26 vs. 13, p=0.057, Figure 4B). Altogether, we observed a high variability in the number of new ETn insertions in both KO and WT but our data suggest that the Chr4-cl KRAB-ZFPs may have a modest effect on ETn retrotransposition rates in some mouse strains but other genetic and epigenetic effects clearly also play an important role.",
+ "text": "Finally, we asked whether there were more novel ETn insertions in mice lacking the Chr4-cl relative to their wild type and heterozygous littermates in our pedigree. Interestingly, only one out of the eight Chr4-cl KO mice in a pure C57BL/6 strain background and none of the eight offspring from a Chr2-cl mating carried a single novel ETn insertion (Figure 4A). When crossing into a 129Sv background for a single generation before intercrossing heterozygous mice (F1), we observed 4 out of 8 Chr4-cl KO mice that contained at least one new ETn insertion, whereas none of 3 heterozygous mice contained any insertions. After crossing to the 129Sv background for a second generation (F2), we determined the number of novel ETn insertions in the offspring of one KO/WT x KO and two KO/WT x KO/WT matings, excluding all samples that were not derived from juvenile tail tissue. Only in the offspring of the KO/WT x KO mating, we observed a statistically significant higher average number of ETn insertions in KO vs. KO/WT animals (7.3 vs. 29.6, p=0.045, Figure 4B). Other than that, only a non-significant trend towards greater average numbers of ETn insertions in KO (11 vs. 27.8, p=0.192, Figure 4B) was apparent in one of the WT/KO x KO/WT matings whereas no difference in ETn insertion numbers between WT and KO mice could be observed in the second mating WT/KO x KO/WT (26 vs. 31, p=0.668, Figure 4B). When comparing all KO with all WT and WT/KO mice from these three matings, a trend towards more ETn insertions in KO remained but was not supported by strong significance (26 vs. 13, p=0.057, Figure 4B). Altogether, we observed a high variability in the number of new ETn insertions in both KO and WT but our data suggest that the Chr4-cl KRAB-ZFPs may have a modest effect on ETn retrotransposition rates in some mouse strains but other genetic and epigenetic effects clearly also play an important role."
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+ "orig": "Discussion",
+ "text": "Discussion",
+ "level": 1
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+ {
+ "self_ref": "#/texts/33",
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+ "prov": [],
+ "orig": "C2H2 zinc finger proteins, about half of which contain a KRAB repressor domain, represent the largest DNA-binding protein family in mammals. Nevertheless, most of these factors have not been investigated using loss-of-function studies. The most comprehensive characterization of human KRAB-ZFPs revealed a strong preference to bind TEs (Imbeault et al., 2017; Najafabadi et al., 2015) yet their function remains unknown. In humans, very few TEs are capable of retrotransposition yet many of them, often tens of million years old, are bound by KRAB-ZFPs. While this suggests that human KRAB-ZFPs mainly serve to control TE-borne enhancers and may have potentially transcription-independent functions, we were interested in the biological significance of KRAB-ZFPs in restricting potentially active TEs. The mouse is an ideal model for such studies since the mouse genome contains several active TE families, including IAP, ETn and L1 elements. We found that many of the young KRAB-ZFPs present in the genomic clusters of KRAB-ZFPs on chromosomes 2 and 4, which are highly expressed in a restricted pattern in ES cells, bound redundantly to these three active TE families. In several cases, KRAB-ZFPs bound to functionally constrained sequence elements we and others have demonstrated to be necessary for retrotransposition, including PBS and viral packaging signals. Targeting such sequences may help the host defense system keep pace with rapidly evolving mouse transposons. This provides strong evidence that many young KRAB-ZFPs are indeed expanding in response to TE activity. But do these young KRAB-ZFP genes limit the mobilization of TEs? Despite the large number of polymorphic ETn elements in mouse strains (Nellåker et al., 2012) and several reports of phenotype-causing novel ETn germ line insertions, no new ETn insertions were reported in recent screens of C57BL/6 mouse genomes (Richardson et al., 2017; Gagnier et al., 2019), indicating that the overall rate of ETn germ line mobilization in inbred mice is rather low. We have demonstrated that Chr4-cl KRAB-ZFPs control ETn/ETnERV expression in ES cells, but this does not lead to widespread ETn mobility in viable C57BL/6 mice. In contrast, we found numerous novel, including several germ line, ETn insertions in both WT and Chr4-cl KO mice in a C57BL/6 129Sv mixed genetic background, with generally more insertions in KO mice and in mice with more 129Sv DNA. This is consistent with a report detecting ETn insertions in FVB.129 mice (Schauer et al., 2018). Notably, there was a large variation in the number of new insertions in these mice, possibly caused by hyperactive polymorphic ETn insertions that varied from individual to individual, epigenetic variation at ETn insertions between individuals and/or the general stochastic nature of ETn mobilization. Furthermore, recent reports have suggested that KRAB-ZFP gene content is distinct in different strains of laboratory mice (Lilue et al., 2018; Treger et al., 2019), and reduced KRAB-ZFP gene content could contribute to increased activity in individual mice. Although we have yet to find obvious phenotypes in the mice carrying new insertions, novel ETn germ line insertions have been shown to cause phenotypes from short tails (Lugani et al., 2013; Semba et al., 2013; Vlangos et al., 2013) to limb malformation (Kano et al., 2007) and severe morphogenetic defects including polypodia (Lehoczky et al., 2013) depending upon their insertion site.",
+ "text": "C2H2 zinc finger proteins, about half of which contain a KRAB repressor domain, represent the largest DNA-binding protein family in mammals. Nevertheless, most of these factors have not been investigated using loss-of-function studies. The most comprehensive characterization of human KRAB-ZFPs revealed a strong preference to bind TEs (Imbeault et al., 2017; Najafabadi et al., 2015) yet their function remains unknown. In humans, very few TEs are capable of retrotransposition yet many of them, often tens of million years old, are bound by KRAB-ZFPs. While this suggests that human KRAB-ZFPs mainly serve to control TE-borne enhancers and may have potentially transcription-independent functions, we were interested in the biological significance of KRAB-ZFPs in restricting potentially active TEs. The mouse is an ideal model for such studies since the mouse genome contains several active TE families, including IAP, ETn and L1 elements. We found that many of the young KRAB-ZFPs present in the genomic clusters of KRAB-ZFPs on chromosomes 2 and 4, which are highly expressed in a restricted pattern in ES cells, bound redundantly to these three active TE families. In several cases, KRAB-ZFPs bound to functionally constrained sequence elements we and others have demonstrated to be necessary for retrotransposition, including PBS and viral packaging signals. Targeting such sequences may help the host defense system keep pace with rapidly evolving mouse transposons. This provides strong evidence that many young KRAB-ZFPs are indeed expanding in response to TE activity. But do these young KRAB-ZFP genes limit the mobilization of TEs? Despite the large number of polymorphic ETn elements in mouse strains (Nellåker et al., 2012) and several reports of phenotype-causing novel ETn germ line insertions, no new ETn insertions were reported in recent screens of C57BL/6 mouse genomes (Richardson et al., 2017; Gagnier et al., 2019), indicating that the overall rate of ETn germ line mobilization in inbred mice is rather low. We have demonstrated that Chr4-cl KRAB-ZFPs control ETn/ETnERV expression in ES cells, but this does not lead to widespread ETn mobility in viable C57BL/6 mice. In contrast, we found numerous novel, including several germ line, ETn insertions in both WT and Chr4-cl KO mice in a C57BL/6 129Sv mixed genetic background, with generally more insertions in KO mice and in mice with more 129Sv DNA. This is consistent with a report detecting ETn insertions in FVB.129 mice (Schauer et al., 2018). Notably, there was a large variation in the number of new insertions in these mice, possibly caused by hyperactive polymorphic ETn insertions that varied from individual to individual, epigenetic variation at ETn insertions between individuals and/or the general stochastic nature of ETn mobilization. Furthermore, recent reports have suggested that KRAB-ZFP gene content is distinct in different strains of laboratory mice (Lilue et al., 2018; Treger et al., 2019), and reduced KRAB-ZFP gene content could contribute to increased activity in individual mice. Although we have yet to find obvious phenotypes in the mice carrying new insertions, novel ETn germ line insertions have been shown to cause phenotypes from short tails (Lugani et al., 2013; Semba et al., 2013; Vlangos et al., 2013) to limb malformation (Kano et al., 2007) and severe morphogenetic defects including polypodia (Lehoczky et al., 2013) depending upon their insertion site."
+ },
+ {
+ "self_ref": "#/texts/34",
+ "parent": {
+ "$ref": "#/texts/32"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Despite a lack of widespread ETn activation in Chr4-cl KO mice, it still remains to be determined whether other TEs, like L1, IAP or other LTR retrotransposons are activated in any of the KRAB-ZFP cluster KO mice, which will require the development of additional capture-seq based assays. Notably, two of the heterozygous matings from Chr2-cl KO mice failed to produce viable knockout offspring, which could indicate a TE-reactivation phenotype. It may also be necessary to generate compound homozygous mutants of distinct KRAB-ZFP clusters to eliminate redundancy before TEs become unleashed. The KRAB-ZFP cluster knockouts produced here will be useful reagents to test such hypotheses. In sum, our data supports that a major driver of KRAB-ZFP gene expansion in mice is recent retrotransposon insertions, and that redundancy within the KRAB-ZFP gene family and with other TE restriction pathways provides protection against widespread TE mobility, explaining the non-essential function of the majority of KRAB-ZFP genes.",
+ "text": "Despite a lack of widespread ETn activation in Chr4-cl KO mice, it still remains to be determined whether other TEs, like L1, IAP or other LTR retrotransposons are activated in any of the KRAB-ZFP cluster KO mice, which will require the development of additional capture-seq based assays. Notably, two of the heterozygous matings from Chr2-cl KO mice failed to produce viable knockout offspring, which could indicate a TE-reactivation phenotype. It may also be necessary to generate compound homozygous mutants of distinct KRAB-ZFP clusters to eliminate redundancy before TEs become unleashed. The KRAB-ZFP cluster knockouts produced here will be useful reagents to test such hypotheses. In sum, our data supports that a major driver of KRAB-ZFP gene expansion in mice is recent retrotransposon insertions, and that redundancy within the KRAB-ZFP gene family and with other TE restriction pathways provides protection against widespread TE mobility, explaining the non-essential function of the majority of KRAB-ZFP genes."
+ },
+ {
+ "self_ref": "#/texts/35",
+ "parent": {
+ "$ref": "#/texts/0"
+ },
+ "children": [
+ {
+ "$ref": "#/tables/1"
+ },
+ {
+ "$ref": "#/texts/37"
+ },
+ {
+ "$ref": "#/texts/39"
+ },
+ {
+ "$ref": "#/texts/41"
+ },
+ {
+ "$ref": "#/texts/43"
+ },
+ {
+ "$ref": "#/texts/46"
+ },
+ {
+ "$ref": "#/texts/48"
+ },
+ {
+ "$ref": "#/texts/50"
+ },
+ {
+ "$ref": "#/texts/52"
+ },
+ {
+ "$ref": "#/texts/54"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Materials and methods",
+ "text": "Materials and methods",
+ "level": 1
+ },
+ {
+ "self_ref": "#/texts/36",
+ "parent": {
+ "$ref": "#/body"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Key resources table",
+ "text": "Key resources table"
+ },
+ {
+ "self_ref": "#/texts/37",
+ "parent": {
+ "$ref": "#/texts/35"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/38"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Cell lines and transgenic mice",
+ "text": "Cell lines and transgenic mice",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/38",
+ "parent": {
+ "$ref": "#/texts/37"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Mouse ES cells and F9 EC cells were cultivated as described previously (Wolf et al., 2015b) unless stated otherwise. Chr4-cl KO ES cells originate from B6;129‐ Gt(ROSA)26Sortm1(cre/ERT)Nat/J mice (Jackson lab), all other KRAB-ZFP cluster KO ES cell lines originate from JM8A3.N1 C57BL/6N-Atm1Brd ES cells (KOMP Repository). Chr2-cl KO and WT ES cells were initially grown in serum-containing media (Wolf et al., 2015b) but changed to 2i media (De Iaco et al., 2017) for several weeks before analysis. To generate Chr4-cl and Chr2-cl KO mice, the cluster deletions were repeated in B6 ES (KOMP repository) or R1 (Nagy lab) ES cells, respectively, and heterozygous clones were injected into B6 albino blastocysts. Chr2-cl KO mice were therefore kept on a mixed B6/Svx129/Sv-CP strain background while Chr4-cl KO mice were initially derived on a pure C57BL/6 background. For capture-seq screens, Chr4-cl KO mice were crossed with 129 × 1/SvJ mice (Jackson lab) to produce the founder mice for Chr4-cl KO and WT (B6/129 F1) offspring. Chr4-cl KO/WT (B6/129 F1) were also crossed with 129 × 1/SvJ mice to get Chr4-cl KO/WT (B6/129 F1) mice, which were intercrossed to give rise to the parents of Chr4-cl KO/KO and KO/WT (B6/129 F2) offspring.",
+ "text": "Mouse ES cells and F9 EC cells were cultivated as described previously (Wolf et al., 2015b) unless stated otherwise. Chr4-cl KO ES cells originate from B6;129‐ Gt(ROSA)26Sortm1(cre/ERT)Nat/J mice (Jackson lab), all other KRAB-ZFP cluster KO ES cell lines originate from JM8A3.N1 C57BL/6N-Atm1Brd ES cells (KOMP Repository). Chr2-cl KO and WT ES cells were initially grown in serum-containing media (Wolf et al., 2015b) but changed to 2i media (De Iaco et al., 2017) for several weeks before analysis. To generate Chr4-cl and Chr2-cl KO mice, the cluster deletions were repeated in B6 ES (KOMP repository) or R1 (Nagy lab) ES cells, respectively, and heterozygous clones were injected into B6 albino blastocysts. Chr2-cl KO mice were therefore kept on a mixed B6/Svx129/Sv-CP strain background while Chr4-cl KO mice were initially derived on a pure C57BL/6 background. For capture-seq screens, Chr4-cl KO mice were crossed with 129 × 1/SvJ mice (Jackson lab) to produce the founder mice for Chr4-cl KO and WT (B6/129 F1) offspring. Chr4-cl KO/WT (B6/129 F1) were also crossed with 129 × 1/SvJ mice to get Chr4-cl KO/WT (B6/129 F1) mice, which were intercrossed to give rise to the parents of Chr4-cl KO/KO and KO/WT (B6/129 F2) offspring."
+ },
+ {
+ "self_ref": "#/texts/39",
+ "parent": {
+ "$ref": "#/texts/35"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/40"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Generation of KRAB-ZFP expressing cell lines",
+ "text": "Generation of KRAB-ZFP expressing cell lines",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/40",
+ "parent": {
+ "$ref": "#/texts/39"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "KRAB-ZFP ORFs were PCR-amplified from cDNA or synthesized with codon-optimization (Supplementary file 1), and stably expressed with 3XFLAG or 3XHA tags in F9 EC or ES cells using Sleeping beauty transposon-based (Wolf et al., 2015b) or lentiviral expression vectors (Imbeault et al., 2017; Supplementary file 1). Cells were selected with puromycin (1 µg/ml) and resistant clones were pooled and further expanded for ChIP-seq.",
+ "text": "KRAB-ZFP ORFs were PCR-amplified from cDNA or synthesized with codon-optimization (Supplementary file 1), and stably expressed with 3XFLAG or 3XHA tags in F9 EC or ES cells using Sleeping beauty transposon-based (Wolf et al., 2015b) or lentiviral expression vectors (Imbeault et al., 2017; Supplementary file 1). Cells were selected with puromycin (1 µg/ml) and resistant clones were pooled and further expanded for ChIP-seq."
+ },
+ {
+ "self_ref": "#/texts/41",
+ "parent": {
+ "$ref": "#/texts/35"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/42"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "CRISPR/Cas9 mediated deletion of KRAB-ZFP clusters and an MMETn insertion",
+ "text": "CRISPR/Cas9 mediated deletion of KRAB-ZFP clusters and an MMETn insertion",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/42",
+ "parent": {
+ "$ref": "#/texts/41"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "All gRNAs were expressed from the pX330-U6-Chimeric_BB-CBh-hSpCas9 vector (RRID:Addgene_42230) and nucleofected into 106 ES cells using Amaxa nucleofection in the following amounts: 5 µg of each pX330-gRNA plasmid, 1 µg pPGK-puro and 500 pmoles single-stranded repair oligos (Supplementary file 3). One day after nucleofection, cells were kept under puromycin selection (1 µg/ml) for 24 hr. Individual KO and WT clones were picked 7–8 days after nucleofection and expanded for PCR genotyping (Supplementary file 3).",
+ "text": "All gRNAs were expressed from the pX330-U6-Chimeric_BB-CBh-hSpCas9 vector (RRID:Addgene_42230) and nucleofected into 106 ES cells using Amaxa nucleofection in the following amounts: 5 µg of each pX330-gRNA plasmid, 1 µg pPGK-puro and 500 pmoles single-stranded repair oligos (Supplementary file 3). One day after nucleofection, cells were kept under puromycin selection (1 µg/ml) for 24 hr. Individual KO and WT clones were picked 7–8 days after nucleofection and expanded for PCR genotyping (Supplementary file 3)."
+ },
+ {
+ "self_ref": "#/texts/43",
+ "parent": {
+ "$ref": "#/texts/35"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/44"
+ },
+ {
+ "$ref": "#/texts/45"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "ChIP-seq analysis",
+ "text": "ChIP-seq analysis",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/44",
+ "parent": {
+ "$ref": "#/texts/43"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "For ChIP-seq analysis of KRAB-ZFP expressing cells, 5–10 × 107 cells were crosslinked and immunoprecipitated with anti-FLAG (Sigma-Aldrich Cat# F1804, RRID:AB_262044) or anti-HA (Abcam Cat# ab9110, RRID:AB_307019 or Covance Cat# MMS-101P-200, RRID:AB_10064068) antibody using one of two previously described protocols (O'Geen et al., 2010; Imbeault et al., 2017) as indicated in Supplementary file 1. H3K9me3 distribution in Chr4-cl, Chr10-cl, Chr13.1-cl and Chr13.2-cl KO ES cells was determined by native ChIP-seq with anti-H3K9me3 serum (Active Motif Cat# 39161, RRID:AB_2532132) as described previously (Karimi et al., 2011). In Chr2-cl KO ES cells, H3K9me3 and KAP1 ChIP-seq was performed as previously described (Ecco et al., 2016). In Chr4-cl KO and WT ES cells KAP1 binding was determined by endogenous tagging of KAP1 with C-terminal GFP (Supplementary file 3), followed by FACS to enrich for GFP-positive cells and ChIP with anti-GFP (Thermo Fisher Scientific Cat# A-11122, RRID:AB_221569) using a previously described protocol (O'Geen et al., 2010). For ChIP-seq analysis of active histone marks, cross-linked chromatin from ES cells or testis (from two-week old mice) was immunoprecipitated with antibodies against H3K4me3 (Abcam Cat# ab8580, RRID:AB_306649), H3K4me1 (Abcam Cat# ab8895, RRID:AB_306847) and H3K27ac (Abcam Cat# ab4729, RRID:AB_2118291) following the protocol developed by O'Geen et al., 2010 or Khil et al., 2012 respectively.",
+ "text": "For ChIP-seq analysis of KRAB-ZFP expressing cells, 5–10 × 107 cells were crosslinked and immunoprecipitated with anti-FLAG (Sigma-Aldrich Cat# F1804, RRID:AB_262044) or anti-HA (Abcam Cat# ab9110, RRID:AB_307019 or Covance Cat# MMS-101P-200, RRID:AB_10064068) antibody using one of two previously described protocols (O'Geen et al., 2010; Imbeault et al., 2017) as indicated in Supplementary file 1. H3K9me3 distribution in Chr4-cl, Chr10-cl, Chr13.1-cl and Chr13.2-cl KO ES cells was determined by native ChIP-seq with anti-H3K9me3 serum (Active Motif Cat# 39161, RRID:AB_2532132) as described previously (Karimi et al., 2011). In Chr2-cl KO ES cells, H3K9me3 and KAP1 ChIP-seq was performed as previously described (Ecco et al., 2016). In Chr4-cl KO and WT ES cells KAP1 binding was determined by endogenous tagging of KAP1 with C-terminal GFP (Supplementary file 3), followed by FACS to enrich for GFP-positive cells and ChIP with anti-GFP (Thermo Fisher Scientific Cat# A-11122, RRID:AB_221569) using a previously described protocol (O'Geen et al., 2010). For ChIP-seq analysis of active histone marks, cross-linked chromatin from ES cells or testis (from two-week old mice) was immunoprecipitated with antibodies against H3K4me3 (Abcam Cat# ab8580, RRID:AB_306649), H3K4me1 (Abcam Cat# ab8895, RRID:AB_306847) and H3K27ac (Abcam Cat# ab4729, RRID:AB_2118291) following the protocol developed by O'Geen et al., 2010 or Khil et al., 2012 respectively."
+ },
+ {
+ "self_ref": "#/texts/45",
+ "parent": {
+ "$ref": "#/texts/43"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "ChIP-seq libraries were constructed and sequenced as indicated in Supplementary file 4. Reads were mapped to the mm9 genome using Bowtie (RRID:SCR_005476; settings: --best) or Bowtie2 (Langmead and Salzberg, 2012) as indicated in Supplementary file 4. Under these settings, reads that map to multiple genomic regions are assigned to the top-scored match and, if a set of equally good choices is encountered, a pseudo-random number is used to choose one location. Peaks were called using MACS14 (RRID:SCR_013291) under high stringency settings (p<1e-10, peak enrichment >20) (Zhang et al., 2008). Peaks were called both over the Input control and a FLAG or HA control ChIP (unless otherwise stated in Supplementary file 4) and only peaks that were called in both settings were kept for further analysis. In cases when the stringency settings did not result in at least 50 peaks, the settings were changed to medium (p<1e-10, peak enrichment >10) or low (p<1e-5, peak enrichment >10) stringency (Supplementary file 4). For further analysis, all peaks were scaled to 200 bp regions centered around the peak summits. The overlap of the scaled peaks to each repeat element in UCSC Genome Browser (RRID:SCR_005780) were calculated by using the bedfisher function (settings: -f 0.25) from BEDTools (RRID:SCR_006646). The right-tailed p-values between pair-wise comparison of each ChIP-seq peak and repeat element were extracted, and then adjusted using the Benjamini-Hochberg approach implemented in the R function p.adjust(). Binding motifs were determined using only nonrepetitive (<10% repeat content) peaks with MEME (Bailey et al., 2009). MEME motifs were compared with in silico predicted motifs (Najafabadi et al., 2015) using Tomtom (Bailey et al., 2009) and considered as significantly overlapping with a False Discovery Rate (FDR) below 0.1. To find MEME and predicted motifs in repetitive peaks, we used FIMO (Bailey et al., 2009). Differential H3K9me3 and KAP1 distribution in WT and Chr2-cl or Chr4-cl KO ES cells at TEs was determined by counting ChIP-seq reads overlapping annotated insertions of each TE group using BEDTools (MultiCovBed). Additionally, ChIP-seq reads were counted at the TE fraction that was bound by Chr2-cl or Chr4-cl KRAB-ZFPs (overlapping with 200 bp peaks). Count tables were concatenated and analyzed using DESeq2 (Love et al., 2014). The previously published ChIP-seq datasets for KAP1 (Castro-Diaz et al., 2014) and H3K9me3 (Dan et al., 2014) were re-mapped using Bowtie (--best).",
+ "text": "ChIP-seq libraries were constructed and sequenced as indicated in Supplementary file 4. Reads were mapped to the mm9 genome using Bowtie (RRID:SCR_005476; settings: --best) or Bowtie2 (Langmead and Salzberg, 2012) as indicated in Supplementary file 4. Under these settings, reads that map to multiple genomic regions are assigned to the top-scored match and, if a set of equally good choices is encountered, a pseudo-random number is used to choose one location. Peaks were called using MACS14 (RRID:SCR_013291) under high stringency settings (p<1e-10, peak enrichment >20) (Zhang et al., 2008). Peaks were called both over the Input control and a FLAG or HA control ChIP (unless otherwise stated in Supplementary file 4) and only peaks that were called in both settings were kept for further analysis. In cases when the stringency settings did not result in at least 50 peaks, the settings were changed to medium (p<1e-10, peak enrichment >10) or low (p<1e-5, peak enrichment >10) stringency (Supplementary file 4). For further analysis, all peaks were scaled to 200 bp regions centered around the peak summits. The overlap of the scaled peaks to each repeat element in UCSC Genome Browser (RRID:SCR_005780) were calculated by using the bedfisher function (settings: -f 0.25) from BEDTools (RRID:SCR_006646). The right-tailed p-values between pair-wise comparison of each ChIP-seq peak and repeat element were extracted, and then adjusted using the Benjamini-Hochberg approach implemented in the R function p.adjust(). Binding motifs were determined using only nonrepetitive (<10% repeat content) peaks with MEME (Bailey et al., 2009). MEME motifs were compared with in silico predicted motifs (Najafabadi et al., 2015) using Tomtom (Bailey et al., 2009) and considered as significantly overlapping with a False Discovery Rate (FDR) below 0.1. To find MEME and predicted motifs in repetitive peaks, we used FIMO (Bailey et al., 2009). Differential H3K9me3 and KAP1 distribution in WT and Chr2-cl or Chr4-cl KO ES cells at TEs was determined by counting ChIP-seq reads overlapping annotated insertions of each TE group using BEDTools (MultiCovBed). Additionally, ChIP-seq reads were counted at the TE fraction that was bound by Chr2-cl or Chr4-cl KRAB-ZFPs (overlapping with 200 bp peaks). Count tables were concatenated and analyzed using DESeq2 (Love et al., 2014). The previously published ChIP-seq datasets for KAP1 (Castro-Diaz et al., 2014) and H3K9me3 (Dan et al., 2014) were re-mapped using Bowtie (--best)."
+ },
+ {
+ "self_ref": "#/texts/46",
+ "parent": {
+ "$ref": "#/texts/35"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/47"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Luciferase reporter assays",
+ "text": "Luciferase reporter assays",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/47",
+ "parent": {
+ "$ref": "#/texts/46"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "For KRAB-ZFP repression assays, double-stranded DNA oligos containing KRAB-ZFP target sequences (Supplementary file 3) were cloned upstream of the SV40 promoter of the pGL3-Promoter vector (Promega) between the restriction sites for NheI and XhoI. 33 ng of reporter vectors were co-transfected (Lipofectamine 2000, Thermofisher) with 33 ng pRL-SV40 (Promega) for normalization and 33 ng of transient KRAB-ZFP expression vectors (in pcDNA3.1) or empty pcDNA3.1 into 293 T cells seeded one day earlier in 96-well plates. Cells were lysed 48 hr after transfection and luciferase/Renilla luciferase activity was measured using the Dual-Luciferase Reporter Assay System (Promega). To measure the transcriptional activity of the MMETn element upstream of the Cd59a gene, fragments of varying sizes (Supplementary file 3) were cloned into the promoter-less pGL3-basic vector (Promega) using NheI and NcoI sites. 70 ng of reporter vectors were cotransfected with 30 ng pRL-SV40 into feeder-depleted Chr4-cl WT and KO ES cells, seeded into a gelatinized 96-well plate 2 hr before transfection. Luciferase activity was measured 48 hr after transfection as described above.",
+ "text": "For KRAB-ZFP repression assays, double-stranded DNA oligos containing KRAB-ZFP target sequences (Supplementary file 3) were cloned upstream of the SV40 promoter of the pGL3-Promoter vector (Promega) between the restriction sites for NheI and XhoI. 33 ng of reporter vectors were co-transfected (Lipofectamine 2000, Thermofisher) with 33 ng pRL-SV40 (Promega) for normalization and 33 ng of transient KRAB-ZFP expression vectors (in pcDNA3.1) or empty pcDNA3.1 into 293 T cells seeded one day earlier in 96-well plates. Cells were lysed 48 hr after transfection and luciferase/Renilla luciferase activity was measured using the Dual-Luciferase Reporter Assay System (Promega). To measure the transcriptional activity of the MMETn element upstream of the Cd59a gene, fragments of varying sizes (Supplementary file 3) were cloned into the promoter-less pGL3-basic vector (Promega) using NheI and NcoI sites. 70 ng of reporter vectors were cotransfected with 30 ng pRL-SV40 into feeder-depleted Chr4-cl WT and KO ES cells, seeded into a gelatinized 96-well plate 2 hr before transfection. Luciferase activity was measured 48 hr after transfection as described above."
+ },
+ {
+ "self_ref": "#/texts/48",
+ "parent": {
+ "$ref": "#/texts/35"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/49"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "RNA-seq analysis",
+ "text": "RNA-seq analysis",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/49",
+ "parent": {
+ "$ref": "#/texts/48"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Whole RNA was purified using RNeasy columns (Qiagen) with on column DNase treatment or the High Pure RNA Isolation Kit (Roche) (Supplementary file 4). Tissues were first lysed in TRIzol reagent (ThermoFisher) and RNA was purified after the isopropanol precipitation step using RNeasy columns (Qiagen) with on column DNase treatment. Libraries were generated using the SureSelect Strand-Specific RNA Library Prep kit (Agilent) or Illumina’s TruSeq RNA Library Prep Kit (with polyA selection) and sequenced as 50 or 100 bp paired-end reads on an Illumina HiSeq2500 (RRID:SCR_016383) or HiSeq3000 (RRID:SCR_016386) machine (Supplementary file 4). RNA-seq reads were mapped to the mouse genome (mm9) using Tophat (RRID:SCR_013035; settings: --I 200000 g 1) unless otherwise stated. These settings allow each mappable read to be reported once, in case the read maps to multiple locations equally well, one match is randomly chosen. For differential transposon expression, mapped reads that overlap with TEs annotated in Repeatmasker (RRID:SCR_012954) were counted using BEDTools MultiCovBed (setting: -split). Reads mapping to multiple fragments that belong to the same TE insertion (as indicated by the repeat ID) were summed up. Only transposons with a total of at least 20 (for two biological replicates) or 30 (for three biological replicates) mapped reads across WT and KO samples were considered for differential expression analysis. Transposons within the deleted KRAB-ZFP cluster were excluded from the analysis. Read count tables were used for differential expression analysis with DESeq2 (RRID:SCR_015687). For differential gene expression analysis, reads overlapping with gene exons were counted using HTSeq-count and analyzed using DESeq2. To test if KRAB-ZFP peaks are significantly enriched near up- or down-regulated genes, a binomial test was performed. Briefly, the proportion of the peaks that are located within a certain distance up- or downstream to the TSS of genes was determined using the windowBed function of BED tools. The probability p in the binomial distribution was estimated as the fraction of all genes overlapped with KRAB-ZFP peaks. Then, given n which is the number of specific groups of genes, and x which is the number of this group of genes overlapped with peaks, the R function binom.test() was used to estimate the p-value based on right-tailed Binomial test. Finally, the adjusted p-values were determined separately for LTR and LINE retrotransposon groups using the Benjamini-Hochberg approach implemented in the R function p.adjust().",
+ "text": "Whole RNA was purified using RNeasy columns (Qiagen) with on column DNase treatment or the High Pure RNA Isolation Kit (Roche) (Supplementary file 4). Tissues were first lysed in TRIzol reagent (ThermoFisher) and RNA was purified after the isopropanol precipitation step using RNeasy columns (Qiagen) with on column DNase treatment. Libraries were generated using the SureSelect Strand-Specific RNA Library Prep kit (Agilent) or Illumina’s TruSeq RNA Library Prep Kit (with polyA selection) and sequenced as 50 or 100 bp paired-end reads on an Illumina HiSeq2500 (RRID:SCR_016383) or HiSeq3000 (RRID:SCR_016386) machine (Supplementary file 4). RNA-seq reads were mapped to the mouse genome (mm9) using Tophat (RRID:SCR_013035; settings: --I 200000 g 1) unless otherwise stated. These settings allow each mappable read to be reported once, in case the read maps to multiple locations equally well, one match is randomly chosen. For differential transposon expression, mapped reads that overlap with TEs annotated in Repeatmasker (RRID:SCR_012954) were counted using BEDTools MultiCovBed (setting: -split). Reads mapping to multiple fragments that belong to the same TE insertion (as indicated by the repeat ID) were summed up. Only transposons with a total of at least 20 (for two biological replicates) or 30 (for three biological replicates) mapped reads across WT and KO samples were considered for differential expression analysis. Transposons within the deleted KRAB-ZFP cluster were excluded from the analysis. Read count tables were used for differential expression analysis with DESeq2 (RRID:SCR_015687). For differential gene expression analysis, reads overlapping with gene exons were counted using HTSeq-count and analyzed using DESeq2. To test if KRAB-ZFP peaks are significantly enriched near up- or down-regulated genes, a binomial test was performed. Briefly, the proportion of the peaks that are located within a certain distance up- or downstream to the TSS of genes was determined using the windowBed function of BED tools. The probability p in the binomial distribution was estimated as the fraction of all genes overlapped with KRAB-ZFP peaks. Then, given n which is the number of specific groups of genes, and x which is the number of this group of genes overlapped with peaks, the R function binom.test() was used to estimate the p-value based on right-tailed Binomial test. Finally, the adjusted p-values were determined separately for LTR and LINE retrotransposon groups using the Benjamini-Hochberg approach implemented in the R function p.adjust()."
+ },
+ {
+ "self_ref": "#/texts/50",
+ "parent": {
+ "$ref": "#/texts/35"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/51"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Reduced representation bisulfite sequencing (RRBS-seq)",
+ "text": "Reduced representation bisulfite sequencing (RRBS-seq)",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/51",
+ "parent": {
+ "$ref": "#/texts/50"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "For RRBS-seq analysis, Chr4-cl WT and KO ES cells were grown in either standard ES cell media containing FCS or for one week in 2i media containing vitamin C as described previously (Blaschke et al., 2013). Genomic DNA was purified from WT and Chr4-cl KO ES cells using the Quick-gDNA purification kit (Zymo Research) and bisulfite-converted with the NEXTflex Bisulfite-Seq Kit (Bio Scientific) using Msp1 digestion to fragment DNA. Libraries were sequenced as 50 bp paired-end reads on an Illumina HiSeq. The reads were processed using Trim Galore (--illumina --paired –rrbs) to trim poor quality bases and adaptors. Additionally, the first 5 nt of R2 and the last 3 nt of R1 and R2 were trimmed. Reads were then mapped to the reference genome (mm9) using Bismark (Krueger and Andrews, 2011) to extract methylation calling results. The CpG methylation pattern for each covered CpG dyads (two complementary CG dinucleotides) was calculated using a custom script (Source code 1: get_CpG_ML.pl). For comparison of CpG methylation between WT and Chr4-cl KO ES cells (in serum or 2i + Vitamin C conditions) only CpG sites with at least 10-fold coverage in each sample were considered for analysis.",
+ "text": "For RRBS-seq analysis, Chr4-cl WT and KO ES cells were grown in either standard ES cell media containing FCS or for one week in 2i media containing vitamin C as described previously (Blaschke et al., 2013). Genomic DNA was purified from WT and Chr4-cl KO ES cells using the Quick-gDNA purification kit (Zymo Research) and bisulfite-converted with the NEXTflex Bisulfite-Seq Kit (Bio Scientific) using Msp1 digestion to fragment DNA. Libraries were sequenced as 50 bp paired-end reads on an Illumina HiSeq. The reads were processed using Trim Galore (--illumina --paired –rrbs) to trim poor quality bases and adaptors. Additionally, the first 5 nt of R2 and the last 3 nt of R1 and R2 were trimmed. Reads were then mapped to the reference genome (mm9) using Bismark (Krueger and Andrews, 2011) to extract methylation calling results. The CpG methylation pattern for each covered CpG dyads (two complementary CG dinucleotides) was calculated using a custom script (Source code 1: get_CpG_ML.pl). For comparison of CpG methylation between WT and Chr4-cl KO ES cells (in serum or 2i + Vitamin C conditions) only CpG sites with at least 10-fold coverage in each sample were considered for analysis."
+ },
+ {
+ "self_ref": "#/texts/52",
+ "parent": {
+ "$ref": "#/texts/35"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/53"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Retrotransposition assay",
+ "text": "Retrotransposition assay",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/53",
+ "parent": {
+ "$ref": "#/texts/52"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The retrotransposition vectors pCMV-MusD2, pCMV-MusD2-neoTNF and pCMV-ETnI1-neoTNF (Ribet et al., 2004) were a kind gift from Dixie Mager. To partially delete the Gm13051 binding site within pCMV-MusD2-neoTNF, the vector was cut with KpnI and re-ligated using a repair oligo, leaving a 24 bp deletion within the Gm13051 binding site. The Rex2 binding site in pCMV-ETnI1-neoTNF was deleted by cutting the vector with EcoRI and XbaI followed by re-ligation using two overlapping PCR products, leaving a 45 bp deletion while maintaining the rest of the vector unchanged (see Supplementary file 3 for primer sequences). For MusD retrotransposition assays, 5 × 104 HeLa cells (ATCC CCL-2) were transfected in a 24-well dish with 100 ng pCMV-MusD2-neoTNF or pCMV-MusD2-neoTNF (ΔGm13051-m) using Lipofectamine 2000. For ETn retrotransposition assays, 50 ng of pCMV-ETnI1-neoTNF or pCMV-ETnI1-neoTNF (ΔRex2) vectors were cotransfected with 50 ng pCMV-MusD2 to provide gag and pol proteins in trans. G418 (0.6 mg/ml) was added five days after transfection and cells were grown under selection until colonies were readily visible by eye. G418-resistant colonies were stained with Amido Black (Sigma).",
+ "text": "The retrotransposition vectors pCMV-MusD2, pCMV-MusD2-neoTNF and pCMV-ETnI1-neoTNF (Ribet et al., 2004) were a kind gift from Dixie Mager. To partially delete the Gm13051 binding site within pCMV-MusD2-neoTNF, the vector was cut with KpnI and re-ligated using a repair oligo, leaving a 24 bp deletion within the Gm13051 binding site. The Rex2 binding site in pCMV-ETnI1-neoTNF was deleted by cutting the vector with EcoRI and XbaI followed by re-ligation using two overlapping PCR products, leaving a 45 bp deletion while maintaining the rest of the vector unchanged (see Supplementary file 3 for primer sequences). For MusD retrotransposition assays, 5 × 104 HeLa cells (ATCC CCL-2) were transfected in a 24-well dish with 100 ng pCMV-MusD2-neoTNF or pCMV-MusD2-neoTNF (ΔGm13051-m) using Lipofectamine 2000. For ETn retrotransposition assays, 50 ng of pCMV-ETnI1-neoTNF or pCMV-ETnI1-neoTNF (ΔRex2) vectors were cotransfected with 50 ng pCMV-MusD2 to provide gag and pol proteins in trans. G418 (0.6 mg/ml) was added five days after transfection and cells were grown under selection until colonies were readily visible by eye. G418-resistant colonies were stained with Amido Black (Sigma)."
+ },
+ {
+ "self_ref": "#/texts/54",
+ "parent": {
+ "$ref": "#/texts/35"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/55"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Capture-seq screen",
+ "text": "Capture-seq screen",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/55",
+ "parent": {
+ "$ref": "#/texts/54"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "To identify novel retrotransposon insertions, genomic DNA from various tissues (Supplementary file 4) was purified and used for library construction with target enrichment using the SureSelectQXT Target Enrichment kit (Agilent). Custom RNA capture probes were designed to hybridize with the 120 bp 5’ ends of the 5’ LTRs and the 120 bp 3’ ends of the 3’ LTR of about 600 intact (internal region flanked by two LTRs) MMETn/RLTRETN retrotransposons or of 140 RLTR4_MM/RLTR4 retrotransposons that were upregulated in Chr4-cl KO ES cells (Figure 4—source data 2). Enriched libraries were sequenced on an Illumina HiSeq as paired-end 50 bp reads. R1 and R2 reads were mapped to the mm9 genome separately, using settings that only allow non-duplicated, uniquely mappable reads (Bowtie -m 1 --best --strata; samtools rmdup -s) and under settings that allow multimapping and duplicated reads (Bowtie --best). Of the latter, only reads that overlap (min. 50% of read) with RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeats (ETn) or RLTR4, RLTR4_MM-int or MuLV-int repeats (RLTR4) were kept. Only uniquely mappable reads whose paired reads were overlapping with the repeats mentioned above were used for further analysis. All ETn- and RLTR4-paired reads were then clustered (as bed files) using BEDTools (bedtools merge -i -n -d 1000) to receive a list of all potential annotated and non-annotated new ETn or RLTR4 insertion sites and all overlapping ETn- or RLTR4-paired reads were counted for each sample at each locus. Finally, all regions that were located within 1 kb of an annotated RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeat as well as regions overlapping with previously identified polymorphic ETn elements (Nellåker et al., 2012) were removed. Genomic loci with at least 10 reads per million unique ETn- or RLTR4-paired reads were considered as insertion sites. To qualify for a de-novo insertion, we allowed no called insertions in any of the other screened mice at the locus and not a single read at the locus in the ancestors of the mouse. Insertions at the same locus in at least two siblings from the same offspring were considered as germ line insertions, if the insertion was absent in the parents and mice who were not direct descendants from these siblings. Full-length sequencing of new ETn insertions was done by Sanger sequencing of short PCR products in combination with Illumina sequencing of a large PCR product (Supplementary file 3), followed by de-novo assembly using the Unicycler software.",
+ "text": "To identify novel retrotransposon insertions, genomic DNA from various tissues (Supplementary file 4) was purified and used for library construction with target enrichment using the SureSelectQXT Target Enrichment kit (Agilent). Custom RNA capture probes were designed to hybridize with the 120 bp 5’ ends of the 5’ LTRs and the 120 bp 3’ ends of the 3’ LTR of about 600 intact (internal region flanked by two LTRs) MMETn/RLTRETN retrotransposons or of 140 RLTR4_MM/RLTR4 retrotransposons that were upregulated in Chr4-cl KO ES cells (Figure 4—source data 2). Enriched libraries were sequenced on an Illumina HiSeq as paired-end 50 bp reads. R1 and R2 reads were mapped to the mm9 genome separately, using settings that only allow non-duplicated, uniquely mappable reads (Bowtie -m 1 --best --strata; samtools rmdup -s) and under settings that allow multimapping and duplicated reads (Bowtie --best). Of the latter, only reads that overlap (min. 50% of read) with RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeats (ETn) or RLTR4, RLTR4_MM-int or MuLV-int repeats (RLTR4) were kept. Only uniquely mappable reads whose paired reads were overlapping with the repeats mentioned above were used for further analysis. All ETn- and RLTR4-paired reads were then clustered (as bed files) using BEDTools (bedtools merge -i -n -d 1000) to receive a list of all potential annotated and non-annotated new ETn or RLTR4 insertion sites and all overlapping ETn- or RLTR4-paired reads were counted for each sample at each locus. Finally, all regions that were located within 1 kb of an annotated RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeat as well as regions overlapping with previously identified polymorphic ETn elements (Nellåker et al., 2012) were removed. Genomic loci with at least 10 reads per million unique ETn- or RLTR4-paired reads were considered as insertion sites. To qualify for a de-novo insertion, we allowed no called insertions in any of the other screened mice at the locus and not a single read at the locus in the ancestors of the mouse. Insertions at the same locus in at least two siblings from the same offspring were considered as germ line insertions, if the insertion was absent in the parents and mice who were not direct descendants from these siblings. Full-length sequencing of new ETn insertions was done by Sanger sequencing of short PCR products in combination with Illumina sequencing of a large PCR product (Supplementary file 3), followed by de-novo assembly using the Unicycler software."
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+ "orig": "Funding Information",
+ "text": "Funding Information",
+ "level": 1
+ },
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+ "$ref": "#/texts/56"
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+ "orig": "This paper was supported by the following grants:",
+ "text": "This paper was supported by the following grants:"
+ },
+ {
+ "self_ref": "#/texts/58",
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+ "orig": "http://dx.doi.org/10.13039/100009633Eunice Kennedy Shriver National Institute of Child Health and Human Development 1ZIAHD008933 to Todd S Macfarlan.",
+ "text": "http://dx.doi.org/10.13039/100009633Eunice Kennedy Shriver National Institute of Child Health and Human Development 1ZIAHD008933 to Todd S Macfarlan.",
+ "enumerated": false,
+ "marker": ""
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+ "orig": "http://dx.doi.org/10.13039/501100001711Swiss National Science Foundation 310030_152879 to Didier Trono.",
+ "text": "http://dx.doi.org/10.13039/501100001711Swiss National Science Foundation 310030_152879 to Didier Trono.",
+ "enumerated": false,
+ "marker": ""
+ },
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+ "orig": "http://dx.doi.org/10.13039/501100001711Swiss National Science Foundation 310030B_173337 to Didier Trono.",
+ "text": "http://dx.doi.org/10.13039/501100001711Swiss National Science Foundation 310030B_173337 to Didier Trono.",
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+ "orig": "http://dx.doi.org/10.13039/501100000781European Research Council No. 268721 to Didier Trono.",
+ "text": "http://dx.doi.org/10.13039/501100000781European Research Council No. 268721 to Didier Trono.",
+ "enumerated": false,
+ "marker": ""
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+ {
+ "self_ref": "#/texts/62",
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+ "orig": "http://dx.doi.org/10.13039/501100000781European Research Council No 694658 to Didier Trono.",
+ "text": "http://dx.doi.org/10.13039/501100000781European Research Council No 694658 to Didier Trono.",
+ "enumerated": false,
+ "marker": ""
+ },
+ {
+ "self_ref": "#/texts/63",
+ "parent": {
+ "$ref": "#/texts/0"
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+ "children": [
+ {
+ "$ref": "#/texts/64"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Acknowledgements",
+ "text": "Acknowledgements",
+ "level": 1
+ },
+ {
+ "self_ref": "#/texts/64",
+ "parent": {
+ "$ref": "#/texts/63"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "We thank Alex Grinberg, Jeanne Yimdjo and Victoria Carter for generating and maintaining transgenic mice. We also thank members of the Macfarlan and Trono labs for useful discussion, Steven Coon, James Iben, Tianwei Li and Anna Malawska for NGS and computational support. This work was supported by NIH grant 1ZIAHD008933 and the NIH DDIR Innovation Award program (TSM), and by subsidies from the Swiss National Science Foundation (310030_152879 and 310030B_173337) and the European Research Council (KRABnKAP, No. 268721; Transpos-X, No. 694658) (DT).",
+ "text": "We thank Alex Grinberg, Jeanne Yimdjo and Victoria Carter for generating and maintaining transgenic mice. We also thank members of the Macfarlan and Trono labs for useful discussion, Steven Coon, James Iben, Tianwei Li and Anna Malawska for NGS and computational support. This work was supported by NIH grant 1ZIAHD008933 and the NIH DDIR Innovation Award program (TSM), and by subsidies from the Swiss National Science Foundation (310030_152879 and 310030B_173337) and the European Research Council (KRABnKAP, No. 268721; Transpos-X, No. 694658) (DT)."
+ },
+ {
+ "self_ref": "#/texts/65",
+ "parent": {
+ "$ref": "#/texts/0"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Additional information",
+ "text": "Additional information",
+ "level": 1
+ },
+ {
+ "self_ref": "#/texts/66",
+ "parent": {
+ "$ref": "#/texts/0"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Additional files",
+ "text": "Additional files",
+ "level": 1
+ },
+ {
+ "self_ref": "#/texts/67",
+ "parent": {
+ "$ref": "#/texts/0"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/68"
+ },
+ {
+ "$ref": "#/texts/69"
+ },
+ {
+ "$ref": "#/texts/70"
+ },
+ {
+ "$ref": "#/texts/71"
+ },
+ {
+ "$ref": "#/texts/72"
+ },
+ {
+ "$ref": "#/texts/73"
+ },
+ {
+ "$ref": "#/texts/74"
+ },
+ {
+ "$ref": "#/texts/75"
+ },
+ {
+ "$ref": "#/texts/76"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Data availability",
+ "text": "Data availability",
+ "level": 1
+ },
+ {
+ "self_ref": "#/texts/68",
+ "parent": {
+ "$ref": "#/texts/67"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "All NGS data has been deposited in GEO (GSE115291). Sequences of full-length de novo ETn insertions have been deposited in the GenBank database (MH449667- MH449669).",
+ "text": "All NGS data has been deposited in GEO (GSE115291). Sequences of full-length de novo ETn insertions have been deposited in the GenBank database (MH449667- MH449669)."
+ },
+ {
+ "self_ref": "#/texts/69",
+ "parent": {
+ "$ref": "#/texts/67"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The following datasets were generated:",
+ "text": "The following datasets were generated:"
+ },
+ {
+ "self_ref": "#/texts/70",
+ "parent": {
+ "$ref": "#/texts/67"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Wolf G. Retrotransposon reactivation and mobilization upon deletions of megabase scale KRAB zinc finger gene clusters in mice. NCBI Gene Expression Omnibus (2019). NCBI: GSE115291",
+ "text": "Wolf G. Retrotransposon reactivation and mobilization upon deletions of megabase scale KRAB zinc finger gene clusters in mice. NCBI Gene Expression Omnibus (2019). NCBI: GSE115291"
+ },
+ {
+ "self_ref": "#/texts/71",
+ "parent": {
+ "$ref": "#/texts/67"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Wolf G. Mus musculus musculus strain C57BL/6x129X1/SvJ retrotransposon MMETn-int, complete sequence. NCBI GenBank (2019). NCBI: MH449667",
+ "text": "Wolf G. Mus musculus musculus strain C57BL/6x129X1/SvJ retrotransposon MMETn-int, complete sequence. NCBI GenBank (2019). NCBI: MH449667"
+ },
+ {
+ "self_ref": "#/texts/72",
+ "parent": {
+ "$ref": "#/texts/67"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Wolf G. Mus musculus musculus strain C57BL/6x129X1/SvJ retrotransposon MMETn-int, complete sequence. NCBI GenBank (2019). NCBI: MH449668",
+ "text": "Wolf G. Mus musculus musculus strain C57BL/6x129X1/SvJ retrotransposon MMETn-int, complete sequence. NCBI GenBank (2019). NCBI: MH449668"
+ },
+ {
+ "self_ref": "#/texts/73",
+ "parent": {
+ "$ref": "#/texts/67"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Wolf G. Mus musculus musculus strain C57BL/6x129X1/SvJ retrotransposon MMETn-int, complete sequence. NCBI GenBank (2019). NCBI: MH449669",
+ "text": "Wolf G. Mus musculus musculus strain C57BL/6x129X1/SvJ retrotransposon MMETn-int, complete sequence. NCBI GenBank (2019). NCBI: MH449669"
+ },
+ {
+ "self_ref": "#/texts/74",
+ "parent": {
+ "$ref": "#/texts/67"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The following previously published datasets were used:",
+ "text": "The following previously published datasets were used:"
+ },
+ {
+ "self_ref": "#/texts/75",
+ "parent": {
+ "$ref": "#/texts/67"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Castro-Diaz N, Ecco G, Coluccio A, Kapopoulou A, Duc J, Trono D. Evollutionally dynamic L1 regulation in embryonic stem cells. NCBI Gene Expression Omnibus (2014). NCBI: GSM1406445",
+ "text": "Castro-Diaz N, Ecco G, Coluccio A, Kapopoulou A, Duc J, Trono D. Evollutionally dynamic L1 regulation in embryonic stem cells. NCBI Gene Expression Omnibus (2014). NCBI: GSM1406445"
+ },
+ {
+ "self_ref": "#/texts/76",
+ "parent": {
+ "$ref": "#/texts/67"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
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+ "orig": "Andrew ZX. H3K9me3_ChIPSeq (Ctrl). NCBI Gene Expression Omnibus (2014). NCBI: GSM1327148",
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+ "text": "Chr13.2-cl KO gRNA.1",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 25,
+ "end_row_offset_idx": 26,
+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "This paper",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 25,
+ "end_row_offset_idx": 26,
+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "Cas9 gRNA",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 25,
+ "end_row_offset_idx": 26,
+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": "GGGTTTCTGAGAAACGTGTA",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ }
+ ],
+ [
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 26,
+ "end_row_offset_idx": 27,
+ "start_col_offset_idx": 0,
+ "end_col_offset_idx": 1,
+ "text": "Sequenced-based reagent",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 26,
+ "end_row_offset_idx": 27,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "Chr13.2-cl KO gRNA.2",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 26,
+ "end_row_offset_idx": 27,
+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "This paper",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 26,
+ "end_row_offset_idx": 27,
+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "Cas9 gRNA",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 26,
+ "end_row_offset_idx": 27,
+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": "GTGTAATGAGTTCTTATATC",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ }
+ ],
+ [
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 27,
+ "end_row_offset_idx": 28,
+ "start_col_offset_idx": 0,
+ "end_col_offset_idx": 1,
+ "text": "Commercial assay or kit",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 27,
+ "end_row_offset_idx": 28,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "SureSelectQXT Target Enrichment kit",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 27,
+ "end_row_offset_idx": 28,
+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "Agilent",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 27,
+ "end_row_offset_idx": 28,
+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "G9681-90000",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 27,
+ "end_row_offset_idx": 28,
+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": "",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ }
+ ],
+ [
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 28,
+ "end_row_offset_idx": 29,
+ "start_col_offset_idx": 0,
+ "end_col_offset_idx": 1,
+ "text": "Software, algorithm",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 28,
+ "end_row_offset_idx": 29,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "Bowtie",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 28,
+ "end_row_offset_idx": 29,
+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "http://bowtie-bio.sourceforge.net",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 28,
+ "end_row_offset_idx": 29,
+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "RRID:SCR_005476",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 28,
+ "end_row_offset_idx": 29,
+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": "",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ }
+ ],
+ [
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 29,
+ "end_row_offset_idx": 30,
+ "start_col_offset_idx": 0,
+ "end_col_offset_idx": 1,
+ "text": "Software, algorithm",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 29,
+ "end_row_offset_idx": 30,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "MACS14",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 29,
+ "end_row_offset_idx": 30,
+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "https://bio.tools/macs",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 29,
+ "end_row_offset_idx": 30,
+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "RRID:SCR_013291",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 29,
+ "end_row_offset_idx": 30,
+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": "",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ }
+ ],
+ [
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 30,
+ "end_row_offset_idx": 31,
+ "start_col_offset_idx": 0,
+ "end_col_offset_idx": 1,
+ "text": "Software, algorithm",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 30,
+ "end_row_offset_idx": 31,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "Tophat",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 30,
+ "end_row_offset_idx": 31,
+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "https://ccb.jhu.edu",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 30,
+ "end_row_offset_idx": 31,
+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "RRID:SCR_013035",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 30,
+ "end_row_offset_idx": 31,
+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": "",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ }
+ ]
+ ]
+ },
+ "annotations": []
+ }
+ ],
+ "key_value_items": [],
+ "form_items": [],
+ "pages": {}
+}
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/elife-56337.xml.md b/tests/data/groundtruth/docling_v2/elife-56337.nxml.md
similarity index 98%
rename from tests/data/groundtruth/docling_v2/elife-56337.xml.md
rename to tests/data/groundtruth/docling_v2/elife-56337.nxml.md
index e28a28b3..e1e34acd 100644
--- a/tests/data/groundtruth/docling_v2/elife-56337.xml.md
+++ b/tests/data/groundtruth/docling_v2/elife-56337.nxml.md
@@ -99,16 +99,16 @@ Key resources table
| Cell line (Mus musculus) | B6;129‐ Gt(ROSA)26Sortm1(cre/ERT)Nat/J | The Jackson Laboratory | 004847 | ES cells used to generate KO cell lines and mice |
| Cell line (Mus musculus) | R1 ES cells | Andras Nagy lab | R1 | 129 ES cells used to generate KO cell lines and mice |
| Cell line (Mus musculus) | F9 Embryonic carcinoma cells | ATCC | ATCC CRL-1720 | |
-| Antibody | Mouse monoclonal ANTI-FLAG M2 antibody | Sigma-Aldrich | Cat# F1804, RRID:AB\_262044 | ChIP (1 µg/107 cells) |
-| Antibody | Rabbit polyclonal anti-HA | Abcam | Cat# ab9110, RRID:AB\_307019 | ChIP (1 µg/107 cells) |
-| Antibody | Mouse monoclonal anti-HA | Covance | Cat# MMS-101P-200, RRID:AB\_10064068 | |
-| Antibody | Rabbit polyclonal anti-H3K9me3 | Active Motif | Cat# 39161, RRID:AB\_2532132 | ChIP (3 µl/107 cells) |
-| Antibody | Rabbit polyclonal anti-GFP | Thermo Fisher Scientific | Cat# A-11122, RRID:AB\_221569 | ChIP (1 µg/107 cells) |
-| Antibody | Rabbit polyclonal anti- H3K4me3 | Abcam | Cat# ab8580, RRID:AB\_306649 | ChIP (1 µg/107 cells) |
-| Antibody | Rabbit polyclonal anti- H3K4me1 | Abcam | Cat# ab8895, RRID:AB\_306847 | ChIP (1 µg/107 cells) |
-| Antibody | Rabbit polyclonal anti- H3K27ac | Abcam | Cat# ab4729, RRID:AB\_2118291 | ChIP (1 µg/107 cells) |
-| Recombinant DNA reagent | pCW57.1 | Addgene | RRID:Addgene\_41393 | Inducible lentiviral expression vector |
-| Recombinant DNA reagent | pX330-U6-Chimeric\_BB-CBh-hSpCas9 | Addgene | RRID:Addgene\_42230 | CRISPR/Cas9 expression construct |
+| Antibody | Mouse monoclonal ANTI-FLAG M2 antibody | Sigma-Aldrich | Cat# F1804, RRID:AB_262044 | ChIP (1 µg/107 cells) |
+| Antibody | Rabbit polyclonal anti-HA | Abcam | Cat# ab9110, RRID:AB_307019 | ChIP (1 µg/107 cells) |
+| Antibody | Mouse monoclonal anti-HA | Covance | Cat# MMS-101P-200, RRID:AB_10064068 | |
+| Antibody | Rabbit polyclonal anti-H3K9me3 | Active Motif | Cat# 39161, RRID:AB_2532132 | ChIP (3 µl/107 cells) |
+| Antibody | Rabbit polyclonal anti-GFP | Thermo Fisher Scientific | Cat# A-11122, RRID:AB_221569 | ChIP (1 µg/107 cells) |
+| Antibody | Rabbit polyclonal anti- H3K4me3 | Abcam | Cat# ab8580, RRID:AB_306649 | ChIP (1 µg/107 cells) |
+| Antibody | Rabbit polyclonal anti- H3K4me1 | Abcam | Cat# ab8895, RRID:AB_306847 | ChIP (1 µg/107 cells) |
+| Antibody | Rabbit polyclonal anti- H3K27ac | Abcam | Cat# ab4729, RRID:AB_2118291 | ChIP (1 µg/107 cells) |
+| Recombinant DNA reagent | pCW57.1 | Addgene | RRID:Addgene_41393 | Inducible lentiviral expression vector |
+| Recombinant DNA reagent | pX330-U6-Chimeric_BB-CBh-hSpCas9 | Addgene | RRID:Addgene_42230 | CRISPR/Cas9 expression construct |
| Sequence-based reagent | Chr2-cl KO gRNA.1 | This paper | Cas9 gRNA | GCCGTTGCTCAGTCCAAATG |
| Sequenced-based reagent | Chr2-cl KO gRNA.2 | This paper | Cas9 gRNA | GATACCAGAGGTGGCCGCAAG |
| Sequenced-based reagent | Chr4-cl KO gRNA.1 | This paper | Cas9 gRNA | GCAAAGGGGCTCCTCGATGGA |
@@ -120,9 +120,9 @@ Key resources table
| Sequenced-based reagent | Chr13.2-cl KO gRNA.1 | This paper | Cas9 gRNA | GGGTTTCTGAGAAACGTGTA |
| Sequenced-based reagent | Chr13.2-cl KO gRNA.2 | This paper | Cas9 gRNA | GTGTAATGAGTTCTTATATC |
| Commercial assay or kit | SureSelectQXT Target Enrichment kit | Agilent | G9681-90000 | |
-| Software, algorithm | Bowtie | http://bowtie-bio.sourceforge.net | RRID:SCR\_005476 | |
-| Software, algorithm | MACS14 | https://bio.tools/macs | RRID:SCR\_013291 | |
-| Software, algorithm | Tophat | https://ccb.jhu.edu | RRID:SCR\_013035 | |
+| Software, algorithm | Bowtie | http://bowtie-bio.sourceforge.net | RRID:SCR_005476 | |
+| Software, algorithm | MACS14 | https://bio.tools/macs | RRID:SCR_013291 | |
+| Software, algorithm | Tophat | https://ccb.jhu.edu | RRID:SCR_013035 | |
### Cell lines and transgenic mice
diff --git a/tests/data/groundtruth/docling_v2/example_8.html.itxt b/tests/data/groundtruth/docling_v2/example_8.html.itxt
deleted file mode 100644
index 505408e3..00000000
--- a/tests/data/groundtruth/docling_v2/example_8.html.itxt
+++ /dev/null
@@ -1,8 +0,0 @@
-item-0 at level 0: unspecified: group _root_
- item-1 at level 1: section: group header-1
- item-2 at level 2: section_header: Pivot table with with 1 row header
- item-3 at level 3: table with [6x4]
- item-4 at level 2: section_header: Pivot table with 2 row headers
- item-5 at level 3: table with [6x5]
- item-6 at level 2: section_header: Equivalent pivot table
- item-7 at level 3: table with [6x5]
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/example_8.html.json b/tests/data/groundtruth/docling_v2/example_8.html.json
deleted file mode 100644
index e77d5cf4..00000000
--- a/tests/data/groundtruth/docling_v2/example_8.html.json
+++ /dev/null
@@ -1,2008 +0,0 @@
-{
- "schema_name": "DoclingDocument",
- "version": "1.3.0",
- "name": "example_8",
- "origin": {
- "mimetype": "text/html",
- "binary_hash": 12799593797322619937,
- "filename": "example_8.html"
- },
- "furniture": {
- "self_ref": "#/furniture",
- "children": [],
- "content_layer": "furniture",
- "name": "_root_",
- "label": "unspecified"
- },
- "body": {
- "self_ref": "#/body",
- "children": [
- {
- "$ref": "#/groups/0"
- }
- ],
- "content_layer": "body",
- "name": "_root_",
- "label": "unspecified"
- },
- "groups": [
- {
- "self_ref": "#/groups/0",
- "parent": {
- "$ref": "#/body"
- },
- "children": [
- {
- "$ref": "#/texts/0"
- },
- {
- "$ref": "#/texts/1"
- },
- {
- "$ref": "#/texts/2"
- }
- ],
- "content_layer": "body",
- "name": "header-1",
- "label": "section"
- }
- ],
- "texts": [
- {
- "self_ref": "#/texts/0",
- "parent": {
- "$ref": "#/groups/0"
- },
- "children": [
- {
- "$ref": "#/tables/0"
- }
- ],
- "content_layer": "body",
- "label": "section_header",
- "prov": [],
- "orig": "Pivot table with with 1 row header",
- "text": "Pivot table with with 1 row header",
- "level": 1
- },
- {
- "self_ref": "#/texts/1",
- "parent": {
- "$ref": "#/groups/0"
- },
- "children": [
- {
- "$ref": "#/tables/1"
- }
- ],
- "content_layer": "body",
- "label": "section_header",
- "prov": [],
- "orig": "Pivot table with 2 row headers",
- "text": "Pivot table with 2 row headers",
- "level": 1
- },
- {
- "self_ref": "#/texts/2",
- "parent": {
- "$ref": "#/groups/0"
- },
- "children": [
- {
- "$ref": "#/tables/2"
- }
- ],
- "content_layer": "body",
- "label": "section_header",
- "prov": [],
- "orig": "Equivalent pivot table",
- "text": "Equivalent pivot table",
- "level": 1
- }
- ],
- "pictures": [],
- "tables": [
- {
- "self_ref": "#/tables/0",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [],
- "content_layer": "body",
- "label": "table",
- "prov": [],
- "captions": [],
- "references": [],
- "footnotes": [],
- "data": {
- "table_cells": [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 0,
- "end_row_offset_idx": 1,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "Year",
- "column_header": true,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 0,
- "end_row_offset_idx": 1,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Month",
- "column_header": true,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 0,
- "end_row_offset_idx": 1,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "Revenue",
- "column_header": true,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 0,
- "end_row_offset_idx": 1,
- "start_col_offset_idx": 3,
- "end_col_offset_idx": 4,
- "text": "Cost",
- "column_header": true,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 5,
- "col_span": 1,
- "start_row_offset_idx": 1,
- "end_row_offset_idx": 6,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "2025",
- "column_header": true,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 1,
- "end_row_offset_idx": 2,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "January",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 1,
- "end_row_offset_idx": 2,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "$134",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 1,
- "end_row_offset_idx": 2,
- "start_col_offset_idx": 3,
- "end_col_offset_idx": 4,
- "text": "$162",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 2,
- "end_row_offset_idx": 3,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "February",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 2,
- "end_row_offset_idx": 3,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "$150",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 2,
- "end_row_offset_idx": 3,
- "start_col_offset_idx": 3,
- "end_col_offset_idx": 4,
- "text": "$155",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 3,
- "end_row_offset_idx": 4,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "March",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
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- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 4,
- "end_row_offset_idx": 5,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "April",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 4,
- "end_row_offset_idx": 5,
- "start_col_offset_idx": 3,
- "end_col_offset_idx": 4,
- "text": "$210",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 4,
- "end_row_offset_idx": 5,
- "start_col_offset_idx": 4,
- "end_col_offset_idx": 5,
- "text": "$150",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 7,
- "col_span": 1,
- "start_row_offset_idx": 1,
- "end_row_offset_idx": 8,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "2025",
- "column_header": true,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 2,
- "col_span": 1,
- "start_row_offset_idx": 4,
- "end_row_offset_idx": 6,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Q2",
- "column_header": true,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 5,
- "end_row_offset_idx": 6,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "May",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 5,
- "end_row_offset_idx": 6,
- "start_col_offset_idx": 3,
- "end_col_offset_idx": 4,
- "text": "$280",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 5,
- "end_row_offset_idx": 6,
- "start_col_offset_idx": 4,
- "end_col_offset_idx": 5,
- "text": "$120",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ]
- ]
- }
- }
- ],
- "key_value_items": [],
- "form_items": [],
- "pages": {}
-}
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/example_8.html.md b/tests/data/groundtruth/docling_v2/example_8.html.md
deleted file mode 100644
index 462a8101..00000000
--- a/tests/data/groundtruth/docling_v2/example_8.html.md
+++ /dev/null
@@ -1,29 +0,0 @@
-## Pivot table with with 1 row header
-
-| Year | Month | Revenue | Cost |
-|--------|----------|-----------|--------|
-| 2025 | January | $134 | $162 |
-| 2025 | February | $150 | $155 |
-| 2025 | March | $160 | $143 |
-| 2025 | April | $210 | $150 |
-| 2025 | May | $280 | $120 |
-
-## Pivot table with 2 row headers
-
-| Year | Quarter | Month | Revenue | Cost |
-|--------|-----------|----------|-----------|--------|
-| 2025 | Q1 | January | $134 | $162 |
-| 2025 | Q1 | February | $150 | $155 |
-| 2025 | Q1 | March | $160 | $143 |
-| 2025 | Q2 | April | $210 | $150 |
-| 2025 | Q2 | May | $280 | $120 |
-
-## Equivalent pivot table
-
-| Year | Quarter | Month | Revenue | Cost |
-|--------|-----------|----------|-----------|--------|
-| 2025 | Q1 | January | $134 | $162 |
-| 2025 | Q1 | February | $150 | $155 |
-| 2025 | Q1 | March | $160 | $143 |
-| 2025 | Q2 | April | $210 | $150 |
-| 2025 | Q2 | May | $280 | $120 |
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/pnas_sample.xml.itxt b/tests/data/groundtruth/docling_v2/pnas_sample.xml.itxt
deleted file mode 100644
index e330362d..00000000
--- a/tests/data/groundtruth/docling_v2/pnas_sample.xml.itxt
+++ /dev/null
@@ -1,148 +0,0 @@
-item-0 at level 0: unspecified: group _root_
- item-1 at level 1: title: The coreceptor mutation CCR5Δ32 ... V epidemics and is selected for by HIV
- item-2 at level 2: paragraph: Amy D. Sullivan, Janis Wigginton, Denise Kirschner
- item-3 at level 2: paragraph: Department of Microbiology and I ... dical School, Ann Arbor, MI 48109-0620
- item-4 at level 2: section_header: Abstract
- item-5 at level 3: text: We explore the impact of a host ... creasing the frequency of this allele.
- item-6 at level 2: text: Nineteen million people have die ... factors such as host genetics (4, 5).
- item-7 at level 2: text: To exemplify the contribution of ... follow the CCR5Δ32 allelic frequency.
- item-8 at level 2: text: We hypothesize that CCR5Δ32 limi ... g the frequency of this mutant allele.
- item-9 at level 2: text: CCR5 is a host-cell chemokine re ... iral strain (such as X4 or R5X4) (30).
- item-10 at level 2: section_header: The Model
- item-11 at level 3: text: Because we are most concerned wi ... t both economic and social conditions.
- item-12 at level 3: picture
- item-12 at level 4: caption: Figure 1 A schematic representation of the basic compartmental HIV epidemic model. The criss-cross lines indicate the sexual mixing between different compartments. Each of these interactions has a positive probability of taking place; they also incorporate individual rates of transmission indicated as λ, but in full notation is λ î,,→i,j, where i,j,k is the phenotype of the infected partner and î, is the phenotype of the susceptible partner. Also shown are the different rates of disease progression, γ i,j,k , that vary according to genotype, gender, and stage. Thus, the interactions between different genotypes, genders, and stages are associated with a unique probability of HIV infection. M, male; F, female.
- item-13 at level 3: table with [6x5]
- item-13 at level 4: caption: Table 1 Children's genotype
- item-14 at level 3: section_header: Parameter Estimates for the Model.
- item-15 at level 4: text: Estimates for rates that govern ... d in Fig. 1 are summarized as follows:
- item-16 at level 4: formula: \frac{dS_{i,j}(t)}{dt}={\chi}_{ ... ,\hat {k}{\rightarrow}i,j}S_{i,j}(t),
- item-17 at level 4: formula: \hspace{1em}\hspace{1em}\hspace ... j,A}(t)-{\gamma}_{i,j,A}I_{i,j,A}(t),
- item-18 at level 4: formula: \frac{dI_{i,j,B}(t)}{dt}={\gamm ... j,B}(t)-{\gamma}_{i,j,B}I_{i,j,B}(t),
- item-19 at level 4: formula: \frac{dA(t)}{dt}={\gamma}_{i,j, ... \right) -{\mu}_{A}A(t)-{\delta}A(t),
- item-20 at level 4: text: where, in addition to previously ... on of the infected partner, and j ≠ .
- item-21 at level 4: table with [14x5]
- item-21 at level 5: caption: Table 2 Transmission probabilities
- item-22 at level 4: table with [8x3]
- item-22 at level 5: caption: Table 3 Progression rates
- item-23 at level 4: table with [20x3]
- item-23 at level 5: caption: Table 4 Parameter values
- item-24 at level 4: text: The effects of the CCR5 W/Δ32 an ... nting this probability of infection is
- item-25 at level 4: formula: {\lambda}_{\hat {i},\hat {j},\h ... \hat {i},\hat {j},\hat {k}} \right] ,
- item-26 at level 4: text: where j ≠ is either male or fe ... e those with AIDS in the simulations).
- item-27 at level 4: text: The average rate of partner acqu ... owing the male rates to vary (36, 37).
- item-28 at level 4: section_header: Transmission probabilities.
- item-29 at level 5: text: The effect of a genetic factor i ... reported; ref. 42) (ref. 43, Table 2).
- item-30 at level 5: text: Given the assumption of no treat ... ases during the end stage of disease).
- item-31 at level 4: section_header: Disease progression.
- item-32 at level 5: text: We assume three stages of HIV in ... ssion rates are summarized in Table 3.
- item-33 at level 3: section_header: Demographic Setting.
- item-34 at level 4: text: Demographic parameters are based ... [suppressing (t) notation]: χ1,j 1,j =
- item-35 at level 4: formula: B_{r}\hspace{.167em}{ \,\substa ... }+I_{2,M,k})}{N_{M}} \right] + \right
- item-36 at level 4: formula: p_{v} \left \left( \frac{(I_{1, ... ght] \right) \right] ,\hspace{.167em}
- item-37 at level 4: text: where the probability of HIV ver ... heir values are summarized in Table 4.
- item-38 at level 2: section_header: Prevalence of HIV
- item-39 at level 3: section_header: Demographics and Model Validation.
- item-40 at level 4: text: The model was validated by using ... 5% to capture early epidemic behavior.
- item-41 at level 4: text: In deciding on our initial value ... n within given subpopulations (2, 49).
- item-42 at level 4: text: In the absence of HIV infection, ... those predicted by our model (Fig. 2).
- item-43 at level 4: picture
- item-43 at level 5: caption: Figure 2 Model simulation of HIV infection in a population lacking the protective CCR5Δ32 allele compared with national data from Kenya (healthy adults) and Mozambique (blood donors, ref. 17). The simulated population incorporates parameter estimates from sub-Saharan African demographics. Note the two outlier points from the Mozambique data were likely caused by underreporting in the early stages of the epidemic.
- item-44 at level 3: section_header: Effects of the Allele on Prevalence.
- item-45 at level 4: text: After validating the model in th ... among adults for total HIV/AIDS cases.
- item-46 at level 4: text: Although CCR5Δ32/Δ32 homozygosit ... frequency of the mutation as 0.105573.
- item-47 at level 4: text: Fig. 3 shows the prevalence of H ... mic, reaching 18% before leveling off.
- item-48 at level 4: picture
- item-48 at level 5: caption: Figure 3 Prevalence of HIV/AIDS in the adult population as predicted by the model. The top curve (○) indicates prevalence in a population lacking the protective allele. We compare that to a population with 19% heterozygous and 1% homozygous for the allele (implying an allelic frequency of 0.105573. Confidence interval bands (light gray) are shown around the median simulation () providing a range of uncertainty in evaluating parameters for the effect of the mutation on the infectivity and the duration of asymptomatic HIV for heterozygotes.
- item-49 at level 4: text: In contrast, when a proportion o ... gins to decline slowly after 70 years.
- item-50 at level 4: text: In the above simulations we assu ... in the presence of the CCR5 mutation.
- item-51 at level 4: text: Because some parameters (e.g., r ... s a major influence on disease spread.
- item-52 at level 2: section_header: HIV Induces Selective Pressure on Genotype Frequency
- item-53 at level 3: text: To observe changes in the freque ... for ≈1,600 years before leveling off.
- item-54 at level 3: picture
- item-54 at level 4: caption: Figure 4 Effects of HIV-1 on selection of the CCR5Δ32 allele. The Hardy-Weinberg equilibrium level is represented in the no-infection simulation (solid lines) for each population. Divergence from the original Hardy-Weinberg equilibrium is shown to occur in the simulations that include HIV infection (dashed lines). Fraction of the total subpopulations are presented: (A) wild types (W/W), (B) heterozygotes (W/Δ32), and (C) homozygotes (Δ32/Δ32). Note that we initiate this simulation with a much lower allelic frequency (0.00105) than used in the rest of the study to better exemplify the actual selective effect over a 1,000-year time scale. (D) The allelic selection effect over a 2,000-year time scale.
- item-55 at level 2: section_header: Discussion
- item-56 at level 3: text: This study illustrates how popul ... pulations where the allele is present.
- item-57 at level 3: text: We also observed that HIV can pr ... is) have been present for much longer.
- item-58 at level 3: text: Two mathematical models have con ... ce of the pathogen constant over time.
- item-59 at level 3: text: Even within our focus on host pr ... f a protective allele such as CCR5Δ32.
- item-60 at level 3: text: Although our models demonstrate ... f the population to epidemic HIV (16).
- item-61 at level 3: text: In assessing the HIV/AIDS epidem ... for education and prevention programs.
- item-62 at level 2: section_header: Acknowledgments
- item-63 at level 3: text: We thank Mark Krosky, Katia Koel ... ers for extremely insightful comments.
- item-64 at level 2: section_header: References
- item-65 at level 3: list: group list
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- item-132 at level 1: caption: Figure 1 A schematic representat ... of HIV infection. M, male; F, female.
- item-133 at level 1: caption: Table 1 Children's genotype
- item-134 at level 1: caption: Table 2 Transmission probabilities
- item-135 at level 1: caption: Table 3 Progression rates
- item-136 at level 1: caption: Table 4 Parameter values
- item-137 at level 1: caption: Figure 2 Model simulation of HIV ... g in the early stages of the epidemic.
- item-138 at level 1: caption: Figure 3 Prevalence of HIV/AIDS ... of asymptomatic HIV for heterozygotes.
- item-139 at level 1: caption: Figure 4 Effects of HIV-1 on sel ... n effect over a 2,000-year time scale.
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/pnas_sample.xml.json b/tests/data/groundtruth/docling_v2/pnas_sample.xml.json
deleted file mode 100644
index 8e494af4..00000000
--- a/tests/data/groundtruth/docling_v2/pnas_sample.xml.json
+++ /dev/null
@@ -1,6353 +0,0 @@
-{
- "schema_name": "DoclingDocument",
- "version": "1.0.0",
- "name": "pnas_sample",
- "origin": {
- "mimetype": "application/xml",
- "binary_hash": 3457590109795003070,
- "filename": "pnas_sample.xml"
- },
- "furniture": {
- "self_ref": "#/furniture",
- "children": [],
- "name": "_root_",
- "label": "unspecified"
- },
- "body": {
- "self_ref": "#/body",
- "children": [
- {
- "$ref": "#/texts/0"
- },
- {
- "$ref": "#/texts/11"
- },
- {
- "$ref": "#/texts/12"
- },
- {
- "$ref": "#/texts/20"
- },
- {
- "$ref": "#/texts/21"
- },
- {
- "$ref": "#/texts/22"
- },
- {
- "$ref": "#/texts/42"
- },
- {
- "$ref": "#/texts/47"
- },
- {
- "$ref": "#/texts/53"
- }
- ],
- "name": "_root_",
- "label": "unspecified"
- },
- "groups": [
- {
- "self_ref": "#/groups/0",
- "parent": {
- "$ref": "#/texts/63"
- },
- "children": [
- {
- "$ref": "#/texts/64"
- },
- {
- "$ref": "#/texts/65"
- },
- {
- "$ref": "#/texts/66"
- },
- {
- "$ref": "#/texts/67"
- },
- {
- "$ref": "#/texts/68"
- },
- {
- "$ref": "#/texts/69"
- },
- {
- "$ref": "#/texts/70"
- },
- {
- "$ref": "#/texts/71"
- },
- {
- "$ref": "#/texts/72"
- },
- {
- "$ref": "#/texts/73"
- },
- {
- "$ref": "#/texts/74"
- },
- {
- "$ref": "#/texts/75"
- },
- {
- "$ref": "#/texts/76"
- },
- {
- "$ref": "#/texts/77"
- },
- {
- "$ref": "#/texts/78"
- },
- {
- "$ref": "#/texts/79"
- },
- {
- "$ref": "#/texts/80"
- },
- {
- "$ref": "#/texts/81"
- },
- {
- "$ref": "#/texts/82"
- },
- {
- "$ref": "#/texts/83"
- },
- {
- "$ref": "#/texts/84"
- },
- {
- "$ref": "#/texts/85"
- },
- {
- "$ref": "#/texts/86"
- },
- {
- "$ref": "#/texts/87"
- },
- {
- "$ref": "#/texts/88"
- },
- {
- "$ref": "#/texts/89"
- },
- {
- "$ref": "#/texts/90"
- },
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- "$ref": "#/body"
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- "$ref": "#/texts/1"
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- "label": "title",
- "prov": [],
- "orig": "The coreceptor mutation CCR5\u039432 influences the dynamics of HIV epidemics and is selected for by HIV",
- "text": "The coreceptor mutation CCR5\u039432 influences the dynamics of HIV epidemics and is selected for by HIV"
- },
- {
- "self_ref": "#/texts/1",
- "parent": {
- "$ref": "#/texts/0"
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- "label": "paragraph",
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- "orig": "Amy D. Sullivan, Janis Wigginton, Denise Kirschner",
- "text": "Amy D. Sullivan, Janis Wigginton, Denise Kirschner"
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- "self_ref": "#/texts/2",
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- "orig": "Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109-0620",
- "text": "Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109-0620"
- },
- {
- "self_ref": "#/texts/3",
- "parent": {
- "$ref": "#/texts/0"
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- "$ref": "#/texts/4"
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- "label": "section_header",
- "prov": [],
- "orig": "Abstract",
- "text": "Abstract",
- "level": 1
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- "$ref": "#/texts/3"
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- "label": "text",
- "prov": [],
- "orig": "We explore the impact of a host genetic factor on heterosexual HIV epidemics by using a deterministic mathematical model. A protective allele unequally distributed across populations is exemplified in our models by the 32-bp deletion in the host-cell chemokine receptor CCR5, CCR5\u039432. Individuals homozygous for CCR5\u039432 are protected against HIV infection whereas those heterozygous for CCR5\u039432 have lower pre-AIDS viral loads and delayed progression to AIDS. CCR5\u039432 may limit HIV spread by decreasing the probability of both risk of infection and infectiousness. In this work, we characterize epidemic HIV within three dynamic subpopulations: CCR5/CCR5 (homozygous, wild type), CCR5/CCR5\u039432 (heterozygous), and CCR5\u039432/CCR5\u039432 (homozygous, mutant). Our results indicate that prevalence of HIV/AIDS is greater in populations lacking the CCR5\u039432 alleles (homozygous wild types only) as compared with populations that include people heterozygous or homozygous for CCR5\u039432. Also, we show that HIV can provide selective pressure for CCR5\u039432, increasing the frequency of this allele.",
- "text": "We explore the impact of a host genetic factor on heterosexual HIV epidemics by using a deterministic mathematical model. A protective allele unequally distributed across populations is exemplified in our models by the 32-bp deletion in the host-cell chemokine receptor CCR5, CCR5\u039432. Individuals homozygous for CCR5\u039432 are protected against HIV infection whereas those heterozygous for CCR5\u039432 have lower pre-AIDS viral loads and delayed progression to AIDS. CCR5\u039432 may limit HIV spread by decreasing the probability of both risk of infection and infectiousness. In this work, we characterize epidemic HIV within three dynamic subpopulations: CCR5/CCR5 (homozygous, wild type), CCR5/CCR5\u039432 (heterozygous), and CCR5\u039432/CCR5\u039432 (homozygous, mutant). Our results indicate that prevalence of HIV/AIDS is greater in populations lacking the CCR5\u039432 alleles (homozygous wild types only) as compared with populations that include people heterozygous or homozygous for CCR5\u039432. Also, we show that HIV can provide selective pressure for CCR5\u039432, increasing the frequency of this allele."
- },
- {
- "self_ref": "#/texts/5",
- "parent": {
- "$ref": "#/texts/0"
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- "children": [],
- "label": "text",
- "prov": [],
- "orig": "Nineteen million people have died of AIDS since the discovery of HIV in the 1980s. In 1999 alone, 5.4 million people were newly infected with HIV (ref. 1 and http://www.unaids.org/epidemicupdate/report/Epireport.html). (For brevity, HIV-1 is referred to as HIV in this paper.) Sub-Saharan Africa has been hardest hit, with more than 20% of the general population HIV-positive in some countries (2, 3). In comparison, heterosexual epidemics in developed, market-economy countries have not reached such severe levels. Factors contributing to the severity of the epidemic in economically developing countries abound, including economic, health, and social differences such as high levels of sexually transmitted diseases and a lack of prevention programs. However, the staggering rate at which the epidemic has spread in sub-Saharan Africa has not been adequately explained. The rate and severity of this epidemic also could indicate a greater underlying susceptibility to HIV attributable not only to sexually transmitted disease, economics, etc., but also to other more ubiquitous factors such as host genetics (4, 5).",
- "text": "Nineteen million people have died of AIDS since the discovery of HIV in the 1980s. In 1999 alone, 5.4 million people were newly infected with HIV (ref. 1 and http://www.unaids.org/epidemicupdate/report/Epireport.html). (For brevity, HIV-1 is referred to as HIV in this paper.) Sub-Saharan Africa has been hardest hit, with more than 20% of the general population HIV-positive in some countries (2, 3). In comparison, heterosexual epidemics in developed, market-economy countries have not reached such severe levels. Factors contributing to the severity of the epidemic in economically developing countries abound, including economic, health, and social differences such as high levels of sexually transmitted diseases and a lack of prevention programs. However, the staggering rate at which the epidemic has spread in sub-Saharan Africa has not been adequately explained. The rate and severity of this epidemic also could indicate a greater underlying susceptibility to HIV attributable not only to sexually transmitted disease, economics, etc., but also to other more ubiquitous factors such as host genetics (4, 5)."
- },
- {
- "self_ref": "#/texts/6",
- "parent": {
- "$ref": "#/texts/0"
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- "children": [],
- "label": "text",
- "prov": [],
- "orig": "To exemplify the contribution of such a host genetic factor to HIV prevalence trends, we consider a well-characterized 32-bp deletion in the host-cell chemokine receptor CCR5, CCR5\u039432. When HIV binds to host cells, it uses the CD4 receptor on the surface of host immune cells together with a coreceptor, mainly the CCR5 and CXCR4 chemokine receptors (6). Homozygous mutations for this 32-bp deletion offer almost complete protection from HIV infection, and heterozygous mutations are associated with lower pre-AIDS viral loads and delayed progression to AIDS (7\u201314). CCR5\u039432 generally is found in populations of European descent, with allelic frequencies ranging from 0 to 0.29 (13). African and Asian populations studied outside the United States or Europe appear to lack the CCR5\u039432 allele, with an allelic frequency of almost zero (5, 13). Thus, to understand the effects of a protective allele, we use a mathematical model to track prevalence of HIV in populations with or without CCR5\u039432 heterozygous and homozygous people and also to follow the CCR5\u039432 allelic frequency.",
- "text": "To exemplify the contribution of such a host genetic factor to HIV prevalence trends, we consider a well-characterized 32-bp deletion in the host-cell chemokine receptor CCR5, CCR5\u039432. When HIV binds to host cells, it uses the CD4 receptor on the surface of host immune cells together with a coreceptor, mainly the CCR5 and CXCR4 chemokine receptors (6). Homozygous mutations for this 32-bp deletion offer almost complete protection from HIV infection, and heterozygous mutations are associated with lower pre-AIDS viral loads and delayed progression to AIDS (7\u201314). CCR5\u039432 generally is found in populations of European descent, with allelic frequencies ranging from 0 to 0.29 (13). African and Asian populations studied outside the United States or Europe appear to lack the CCR5\u039432 allele, with an allelic frequency of almost zero (5, 13). Thus, to understand the effects of a protective allele, we use a mathematical model to track prevalence of HIV in populations with or without CCR5\u039432 heterozygous and homozygous people and also to follow the CCR5\u039432 allelic frequency."
- },
- {
- "self_ref": "#/texts/7",
- "parent": {
- "$ref": "#/texts/0"
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- "children": [],
- "label": "text",
- "prov": [],
- "orig": "We hypothesize that CCR5\u039432 limits epidemic HIV by decreasing infection rates, and we evaluate the relative contributions to this by the probability of infection and duration of infectivity. To capture HIV infection as a chronic infectious disease together with vertical transmission occurring in untreated mothers, we model a dynamic population (i.e., populations that vary in growth rates because of fluctuations in birth or death rates) based on realistic demographic characteristics (18). This scenario also allows tracking of the allelic frequencies over time. This work considers how a specific host genetic factor affecting HIV infectivity and viremia at the individual level might influence the epidemic in a dynamic population and how HIV exerts selective pressure, altering the frequency of this mutant allele.",
- "text": "We hypothesize that CCR5\u039432 limits epidemic HIV by decreasing infection rates, and we evaluate the relative contributions to this by the probability of infection and duration of infectivity. To capture HIV infection as a chronic infectious disease together with vertical transmission occurring in untreated mothers, we model a dynamic population (i.e., populations that vary in growth rates because of fluctuations in birth or death rates) based on realistic demographic characteristics (18). This scenario also allows tracking of the allelic frequencies over time. This work considers how a specific host genetic factor affecting HIV infectivity and viremia at the individual level might influence the epidemic in a dynamic population and how HIV exerts selective pressure, altering the frequency of this mutant allele."
- },
- {
- "self_ref": "#/texts/8",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "CCR5 is a host-cell chemokine receptor, which is also used as a coreceptor by R5 strains of HIV that are generally acquired during sexual transmission (6, 19\u201325). As infection progresses to AIDS the virus expands its repertoire of potential coreceptors to include other CC-family and CXC-family receptors in roughly 50% of patients (19, 26, 27). CCR5\u039432 was identified in HIV-resistant people (28). Benefits to individuals from the mutation in this allele are as follows. Persons homozygous for the CCR5\u039432 mutation are almost nonexistent in HIV-infected populations (11, 12) (see ref. 13 for review). Persons heterozygous for the mutant allele (CCR5 W/\u039432) tend to have lower pre-AIDS viral loads. Aside from the beneficial effects that lower viral loads may have for individuals, there is also an altruistic effect, as transmission rates are reduced for individuals with low viral loads (as compared with, for example, AZT and other studies; ref. 29). Finally, individuals heterozygous for the mutant allele (CCR5 W/\u039432) also have a slower progression to AIDS than those homozygous for the wild-type allele (CCR5 W/W) (7\u201310), remaining in the population 2 years longer, on average. Interestingly, the dearth of information on HIV disease progression in people homozygous for the CCR5\u039432 allele (CCR5 \u039432/\u039432) stems from the rarity of HIV infection in this group (4, 12, 28). However, in case reports of HIV-infected CCR5 \u039432/\u039432 homozygotes, a rapid decline in CD4+ T cells and a high viremia are observed, likely because of initial infection with a more aggressive viral strain (such as X4 or R5X4) (30).",
- "text": "CCR5 is a host-cell chemokine receptor, which is also used as a coreceptor by R5 strains of HIV that are generally acquired during sexual transmission (6, 19\u201325). As infection progresses to AIDS the virus expands its repertoire of potential coreceptors to include other CC-family and CXC-family receptors in roughly 50% of patients (19, 26, 27). CCR5\u039432 was identified in HIV-resistant people (28). Benefits to individuals from the mutation in this allele are as follows. Persons homozygous for the CCR5\u039432 mutation are almost nonexistent in HIV-infected populations (11, 12) (see ref. 13 for review). Persons heterozygous for the mutant allele (CCR5 W/\u039432) tend to have lower pre-AIDS viral loads. Aside from the beneficial effects that lower viral loads may have for individuals, there is also an altruistic effect, as transmission rates are reduced for individuals with low viral loads (as compared with, for example, AZT and other studies; ref. 29). Finally, individuals heterozygous for the mutant allele (CCR5 W/\u039432) also have a slower progression to AIDS than those homozygous for the wild-type allele (CCR5 W/W) (7\u201310), remaining in the population 2 years longer, on average. Interestingly, the dearth of information on HIV disease progression in people homozygous for the CCR5\u039432 allele (CCR5 \u039432/\u039432) stems from the rarity of HIV infection in this group (4, 12, 28). However, in case reports of HIV-infected CCR5 \u039432/\u039432 homozygotes, a rapid decline in CD4+ T cells and a high viremia are observed, likely because of initial infection with a more aggressive viral strain (such as X4 or R5X4) (30)."
- },
- {
- "self_ref": "#/texts/9",
- "parent": {
- "$ref": "#/texts/0"
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- {
- "$ref": "#/texts/10"
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- "$ref": "#/texts/13"
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- "label": "section_header",
- "prov": [],
- "orig": "The Model",
- "text": "The Model",
- "level": 1
- },
- {
- "self_ref": "#/texts/10",
- "parent": {
- "$ref": "#/texts/9"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "Because we are most concerned with understanding the severity of the epidemic in developing countries where the majority of infection is heterosexual, we consider a purely heterosexual model. To model the effects of the allele in the population, we examine the rate of HIV spread by using an enhanced susceptible-infected-AIDS model of epidemic HIV (for review see ref. 31). Our model compares two population scenarios: a CCR5 wild-type population and one with CCR5\u039432 heterozygotes and homozygotes in addition to the wild type. To model the scenario where there are only wild-type individuals present in the population (i.e., CCR5 W/W), we track the sexually active susceptibles at time t [Si,j (t)], where i = 1 refers to genotype (CCR5 W/W only in this case) and j is either the male or female subpopulation. We also track those who are HIV-positive at time t not yet having AIDS in Ii,j,k (t) where k refers to stage of HIV infection [primary (A) or asymptomatic (B)]. The total number of individuals with AIDS at time t are tracked in A(t). The source population are children, \u03c7 i,j (t), who mature into the sexually active population at time t (Fig. 1, Table 1). We compare the model of a population lacking the CCR5\u039432 allele to a demographically similar population with a high frequency of the allele. When genetic heterogeneity is included, male and female subpopulations are each further divided into three distinct genotypic groups, yielding six susceptible subpopulations, [Si,j (t), where i ranges from 1 to 3, where 1 = CCR5W/W; 2 = CCR5 W/\u039432; 3 = CCR5 \u039432/\u039432]. The infected classes, Ii,j,k (t), also increase in number to account for these new genotype compartments. In both settings we assume there is no treatment available and no knowledge of HIV status by people in the early acute and middle asymptomatic stages (both conditions exist in much of sub-Saharan Africa). In addition, we assume that sexual mixing in the population occurs randomly with respect to genotype and HIV disease status, all HIV-infected people eventually progress to AIDS, and no barrier contraceptives are used. These last assumptions reflect both economic and social conditions.",
- "text": "Because we are most concerned with understanding the severity of the epidemic in developing countries where the majority of infection is heterosexual, we consider a purely heterosexual model. To model the effects of the allele in the population, we examine the rate of HIV spread by using an enhanced susceptible-infected-AIDS model of epidemic HIV (for review see ref. 31). Our model compares two population scenarios: a CCR5 wild-type population and one with CCR5\u039432 heterozygotes and homozygotes in addition to the wild type. To model the scenario where there are only wild-type individuals present in the population (i.e., CCR5 W/W), we track the sexually active susceptibles at time t [Si,j (t)], where i = 1 refers to genotype (CCR5 W/W only in this case) and j is either the male or female subpopulation. We also track those who are HIV-positive at time t not yet having AIDS in Ii,j,k (t) where k refers to stage of HIV infection [primary (A) or asymptomatic (B)]. The total number of individuals with AIDS at time t are tracked in A(t). The source population are children, \u03c7 i,j (t), who mature into the sexually active population at time t (Fig. 1, Table 1). We compare the model of a population lacking the CCR5\u039432 allele to a demographically similar population with a high frequency of the allele. When genetic heterogeneity is included, male and female subpopulations are each further divided into three distinct genotypic groups, yielding six susceptible subpopulations, [Si,j (t), where i ranges from 1 to 3, where 1 = CCR5W/W; 2 = CCR5 W/\u039432; 3 = CCR5 \u039432/\u039432]. The infected classes, Ii,j,k (t), also increase in number to account for these new genotype compartments. In both settings we assume there is no treatment available and no knowledge of HIV status by people in the early acute and middle asymptomatic stages (both conditions exist in much of sub-Saharan Africa). In addition, we assume that sexual mixing in the population occurs randomly with respect to genotype and HIV disease status, all HIV-infected people eventually progress to AIDS, and no barrier contraceptives are used. These last assumptions reflect both economic and social conditions."
- },
- {
- "self_ref": "#/texts/11",
- "parent": {
- "$ref": "#/body"
- },
- "children": [],
- "label": "caption",
- "prov": [],
- "orig": "Figure 1 A schematic representation of the basic compartmental HIV epidemic model. The criss-cross lines indicate the sexual mixing between different compartments. Each of these interactions has a positive probability of taking place; they also incorporate individual rates of transmission indicated as \u03bb, but in full notation is \u03bb \u00ee,\ueb30,\uea50\u2192i,j, where i,j,k is the phenotype of the infected partner and \u00ee,\ueb30 is the phenotype of the susceptible partner. Also shown are the different rates of disease progression, \u03b3 i,j,k , that vary according to genotype, gender, and stage. Thus, the interactions between different genotypes, genders, and stages are associated with a unique probability of HIV infection. M, male; F, female.",
- "text": "Figure 1 A schematic representation of the basic compartmental HIV epidemic model. The criss-cross lines indicate the sexual mixing between different compartments. Each of these interactions has a positive probability of taking place; they also incorporate individual rates of transmission indicated as \u03bb, but in full notation is \u03bb \u00ee,\ueb30,\uea50\u2192i,j, where i,j,k is the phenotype of the infected partner and \u00ee,\ueb30 is the phenotype of the susceptible partner. Also shown are the different rates of disease progression, \u03b3 i,j,k , that vary according to genotype, gender, and stage. Thus, the interactions between different genotypes, genders, and stages are associated with a unique probability of HIV infection. M, male; F, female."
- },
- {
- "self_ref": "#/texts/12",
- "parent": {
- "$ref": "#/body"
- },
- "children": [],
- "label": "caption",
- "prov": [],
- "orig": "Table 1 Children's genotype",
- "text": "Table 1 Children's genotype"
- },
- {
- "self_ref": "#/texts/13",
- "parent": {
- "$ref": "#/texts/9"
- },
- "children": [
- {
- "$ref": "#/texts/14"
- },
- {
- "$ref": "#/texts/15"
- },
- {
- "$ref": "#/texts/16"
- },
- {
- "$ref": "#/texts/17"
- },
- {
- "$ref": "#/texts/18"
- },
- {
- "$ref": "#/texts/19"
- },
- {
- "$ref": "#/tables/1"
- },
- {
- "$ref": "#/tables/2"
- },
- {
- "$ref": "#/tables/3"
- },
- {
- "$ref": "#/texts/23"
- },
- {
- "$ref": "#/texts/24"
- },
- {
- "$ref": "#/texts/25"
- },
- {
- "$ref": "#/texts/26"
- },
- {
- "$ref": "#/texts/27"
- },
- {
- "$ref": "#/texts/30"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Parameter Estimates for the Model.",
- "text": "Parameter Estimates for the Model.",
- "level": 1
- },
- {
- "self_ref": "#/texts/14",
- "parent": {
- "$ref": "#/texts/13"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "Estimates for rates that govern the interactions depicted in Fig. 1 were derived from the extensive literature on HIV. Our parameters and their estimates are summarized in Tables 2\u20134. The general form of the equations describing the rates of transition between population classes as depicted in Fig. 1 are summarized as follows:",
- "text": "Estimates for rates that govern the interactions depicted in Fig. 1 were derived from the extensive literature on HIV. Our parameters and their estimates are summarized in Tables 2\u20134. The general form of the equations describing the rates of transition between population classes as depicted in Fig. 1 are summarized as follows:"
- },
- {
- "self_ref": "#/texts/15",
- "parent": {
- "$ref": "#/texts/13"
- },
- "children": [],
- "label": "formula",
- "prov": [],
- "orig": " \\frac{dS_{i,j}(t)}{dt}={\\chi}_{i,j}(t)-{\\mu}_{j}S_{i,j}(t)-{\\lambda}_{\\hat {\\imath},\\hat {},\\hat {k}{\\rightarrow}i,j}S_{i,j}(t), ",
- "text": " \\frac{dS_{i,j}(t)}{dt}={\\chi}_{i,j}(t)-{\\mu}_{j}S_{i,j}(t)-{\\lambda}_{\\hat {\\imath},\\hat {},\\hat {k}{\\rightarrow}i,j}S_{i,j}(t), "
- },
- {
- "self_ref": "#/texts/16",
- "parent": {
- "$ref": "#/texts/13"
- },
- "children": [],
- "label": "formula",
- "prov": [],
- "orig": " \\hspace{1em}\\hspace{1em}\\hspace{.167em}\\frac{dI_{i,j,A}(t)}{dt}={\\lambda}_{\\hat {\\imath},\\hat {},\\hat {k}{\\rightarrow}i,j}S_{i,j}(t)-{\\mu}_{j}I_{i,j,A}(t)-{\\gamma}_{i,j,A}I_{i,j,A}(t), ",
- "text": " \\hspace{1em}\\hspace{1em}\\hspace{.167em}\\frac{dI_{i,j,A}(t)}{dt}={\\lambda}_{\\hat {\\imath},\\hat {},\\hat {k}{\\rightarrow}i,j}S_{i,j}(t)-{\\mu}_{j}I_{i,j,A}(t)-{\\gamma}_{i,j,A}I_{i,j,A}(t), "
- },
- {
- "self_ref": "#/texts/17",
- "parent": {
- "$ref": "#/texts/13"
- },
- "children": [],
- "label": "formula",
- "prov": [],
- "orig": " \\frac{dI_{i,j,B}(t)}{dt}={\\gamma}_{i,j,A}I_{i,j,A}(t)-{\\mu}_{j}I_{i,j,B}(t)-{\\gamma}_{i,j,B}I_{i,j,B}(t), ",
- "text": " \\frac{dI_{i,j,B}(t)}{dt}={\\gamma}_{i,j,A}I_{i,j,A}(t)-{\\mu}_{j}I_{i,j,B}(t)-{\\gamma}_{i,j,B}I_{i,j,B}(t), "
- },
- {
- "self_ref": "#/texts/18",
- "parent": {
- "$ref": "#/texts/13"
- },
- "children": [],
- "label": "formula",
- "prov": [],
- "orig": " \\frac{dA(t)}{dt}={\\gamma}_{i,j,B} \\left( { \\,\\substack{ ^{3} \\\\ {\\sum} \\\\ _{i=1} }\\, }I_{i,F,B}(t)+I_{i,M,B}(t) \\right) -{\\mu}_{A}A(t)-{\\delta}A(t), ",
- "text": " \\frac{dA(t)}{dt}={\\gamma}_{i,j,B} \\left( { \\,\\substack{ ^{3} \\\\ {\\sum} \\\\ _{i=1} }\\, }I_{i,F,B}(t)+I_{i,M,B}(t) \\right) -{\\mu}_{A}A(t)-{\\delta}A(t), "
- },
- {
- "self_ref": "#/texts/19",
- "parent": {
- "$ref": "#/texts/13"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "where, in addition to previously defined populations and rates (with i equals genotype, j equals gender, and k equals stage of infection, either A or B), \u03bc j , represents the non-AIDS (natural) death rate for males and females respectively, and \u03bcA is estimated by the average (\u03bcF + \u03bcM/2). This approximation allows us to simplify the model (only one AIDS compartment) without compromising the results, as most people with AIDS die of AIDS (\u03b4AIDS) and very few of other causes (\u03bcA). These estimates include values that affect infectivity (\u03bb \u00ee,\ueb30,\uea50\u2192i,j ), transmission (\u03b2 \u00ee,\ueb30,\uea50\u2192i,j ), and disease progression (\u03b3 i , j , k ) where the \u00ee,\ueb30,\uea50 notation represents the genotype, gender, and stage of infection of the infected partner, and j \u2260 \ueb30.",
- "text": "where, in addition to previously defined populations and rates (with i equals genotype, j equals gender, and k equals stage of infection, either A or B), \u03bc j , represents the non-AIDS (natural) death rate for males and females respectively, and \u03bcA is estimated by the average (\u03bcF + \u03bcM/2). This approximation allows us to simplify the model (only one AIDS compartment) without compromising the results, as most people with AIDS die of AIDS (\u03b4AIDS) and very few of other causes (\u03bcA). These estimates include values that affect infectivity (\u03bb \u00ee,\ueb30,\uea50\u2192i,j ), transmission (\u03b2 \u00ee,\ueb30,\uea50\u2192i,j ), and disease progression (\u03b3 i , j , k ) where the \u00ee,\ueb30,\uea50 notation represents the genotype, gender, and stage of infection of the infected partner, and j \u2260 \ueb30."
- },
- {
- "self_ref": "#/texts/20",
- "parent": {
- "$ref": "#/body"
- },
- "children": [],
- "label": "caption",
- "prov": [],
- "orig": "Table 2 Transmission probabilities",
- "text": "Table 2 Transmission probabilities"
- },
- {
- "self_ref": "#/texts/21",
- "parent": {
- "$ref": "#/body"
- },
- "children": [],
- "label": "caption",
- "prov": [],
- "orig": "Table 3 Progression rates",
- "text": "Table 3 Progression rates"
- },
- {
- "self_ref": "#/texts/22",
- "parent": {
- "$ref": "#/body"
- },
- "children": [],
- "label": "caption",
- "prov": [],
- "orig": "Table 4 Parameter values",
- "text": "Table 4 Parameter values"
- },
- {
- "self_ref": "#/texts/23",
- "parent": {
- "$ref": "#/texts/13"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "The effects of the CCR5 W/\u039432 and CCR5 \u039432/\u039432 genotypes are included in our model through both the per-capita probabilities of infection, \u03bb \u00ee,\ueb30,\uea50\u2192i,j , and the progression rates, \u03b3 i , j , k . The infectivity coefficients, \u03bb \u00ee,\ueb30,\uea50\u2192i,j , are calculated for each population subgroup based on the following: likelihood of HIV transmission in a sexual encounter between a susceptible and an infected (\u03b2\u00ee\u0131\u0131^^,j,\uea50k k^^\u2192i,j ) person; formation of new partnerships (c j j); number of contacts in a given partnership (\u03d5 j ); and probability of encountering an infected individual (I \u00ee,\ueb30,\uea50 /N \ueb30 ). The formula representing this probability of infection is",
- "text": "The effects of the CCR5 W/\u039432 and CCR5 \u039432/\u039432 genotypes are included in our model through both the per-capita probabilities of infection, \u03bb \u00ee,\ueb30,\uea50\u2192i,j , and the progression rates, \u03b3 i , j , k . The infectivity coefficients, \u03bb \u00ee,\ueb30,\uea50\u2192i,j , are calculated for each population subgroup based on the following: likelihood of HIV transmission in a sexual encounter between a susceptible and an infected (\u03b2\u00ee\u0131\u0131^^,j,\uea50k k^^\u2192i,j ) person; formation of new partnerships (c j j); number of contacts in a given partnership (\u03d5 j ); and probability of encountering an infected individual (I \u00ee,\ueb30,\uea50 /N \ueb30 ). The formula representing this probability of infection is"
- },
- {
- "self_ref": "#/texts/24",
- "parent": {
- "$ref": "#/texts/13"
- },
- "children": [],
- "label": "formula",
- "prov": [],
- "orig": " {\\lambda}_{\\hat {i},\\hat {j},\\hat {k}{\\rightarrow}i,j}=\\frac{C_{j}{\\cdot}{\\phi}_{j}}{N_{\\hat {j}}}\\hspace{.167em} \\left[ { \\,\\substack{ \\\\ {\\sum} \\\\ _{\\hat {i},\\hat {k}} }\\, }{\\beta}_{\\hat {i},\\hat {j},\\hat {k}{\\rightarrow}i,j}{\\cdot}I_{\\hat {i},\\hat {j},\\hat {k}} \\right] , ",
- "text": " {\\lambda}_{\\hat {i},\\hat {j},\\hat {k}{\\rightarrow}i,j}=\\frac{C_{j}{\\cdot}{\\phi}_{j}}{N_{\\hat {j}}}\\hspace{.167em} \\left[ { \\,\\substack{ \\\\ {\\sum} \\\\ _{\\hat {i},\\hat {k}} }\\, }{\\beta}_{\\hat {i},\\hat {j},\\hat {k}{\\rightarrow}i,j}{\\cdot}I_{\\hat {i},\\hat {j},\\hat {k}} \\right] , "
- },
- {
- "self_ref": "#/texts/25",
- "parent": {
- "$ref": "#/texts/13"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "where j \u2260 \ueb30 is either male or female. N \ueb30 represents the total population of gender \ueb30 (this does not include those with AIDS in the simulations).",
- "text": "where j \u2260 \ueb30 is either male or female. N \ueb30 represents the total population of gender \ueb30 (this does not include those with AIDS in the simulations)."
- },
- {
- "self_ref": "#/texts/26",
- "parent": {
- "$ref": "#/texts/13"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "The average rate of partner acquisition, cj , includes the mean plus the variance to mean ratio of the relevant distribution of partner-change rates to capture the small number of high-risk people: cj = mj + (\u03c2/m j) where the mean (mj ) and variance (\u03c2) are annual figures for new partnerships only (32). These means are estimated from Ugandan data for the number of heterosexual partners in the past year (33) and the number of nonregular heterosexual partners (i.e., spouses or long-term partners) in the past year (34). In these sexual activity surveys, men invariably have more new partnerships; thus, we assumed that they would have fewer average contacts per partnership than women (a higher rate of new partner acquisition means fewer sexual contacts with a given partner; ref. 35). To incorporate this assumption in our model, the male contacts/partnership, \u03d5 M , was reduced by 20%. In a given population, the numbers of heterosexual interactions must equate between males and females. The balancing equation applied here is SA F\u00b7m F\u00b7N F = SA M\u00b7m M\u00b7N M, where SAj are the percent sexually active and Nj are the total in the populations for gender j. To specify changes in partner acquisition, we apply a male flexibility mechanism, holding the female rate of acquisition constant and allowing the male rates to vary (36, 37).",
- "text": "The average rate of partner acquisition, cj , includes the mean plus the variance to mean ratio of the relevant distribution of partner-change rates to capture the small number of high-risk people: cj = mj + (\u03c2/m j) where the mean (mj ) and variance (\u03c2) are annual figures for new partnerships only (32). These means are estimated from Ugandan data for the number of heterosexual partners in the past year (33) and the number of nonregular heterosexual partners (i.e., spouses or long-term partners) in the past year (34). In these sexual activity surveys, men invariably have more new partnerships; thus, we assumed that they would have fewer average contacts per partnership than women (a higher rate of new partner acquisition means fewer sexual contacts with a given partner; ref. 35). To incorporate this assumption in our model, the male contacts/partnership, \u03d5 M , was reduced by 20%. In a given population, the numbers of heterosexual interactions must equate between males and females. The balancing equation applied here is SA F\u00b7m F\u00b7N F = SA M\u00b7m M\u00b7N M, where SAj are the percent sexually active and Nj are the total in the populations for gender j. To specify changes in partner acquisition, we apply a male flexibility mechanism, holding the female rate of acquisition constant and allowing the male rates to vary (36, 37)."
- },
- {
- "self_ref": "#/texts/27",
- "parent": {
- "$ref": "#/texts/13"
- },
- "children": [
- {
- "$ref": "#/texts/28"
- },
- {
- "$ref": "#/texts/29"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Transmission probabilities.",
- "text": "Transmission probabilities.",
- "level": 1
- },
- {
- "self_ref": "#/texts/28",
- "parent": {
- "$ref": "#/texts/27"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "The effect of a genetic factor in a model of HIV transmission can be included by reducing the transmission coefficient. The probabilities of transmission per contact with an infected partner, \u03b2\u00ee\u0131\u0131^^,\ueb30\ue2d4\ue2d4^^,\uea50k k^^\u2192i,j , have been estimated in the literature (see ref. 38 for estimates in minimally treated groups). We want to capture a decreased risk in transmission based on genotype (ref. 39, Table 2). No studies have directly evaluated differences in infectivity between HIV-infected CCR5 W/\u039432 heterozygotes and HIV-infected CCR5 wild types. Thus, we base estimates for reduced transmission on studies of groups with various HIV serum viral loads (40), HTLV-I/II viral loads (41), and a study of the effect of AZT treatment on transmission (29). We decrease transmission probabilities for infecting CCR5\u039432/\u039432 persons by 100-fold to reflect the rarity of infections in these persons. However, we assume that infected CCR5\u039432/\u039432 homozygotes can infect susceptibles at a rate similar to CCR5W/W homozygotes, as the former generally have high viremias (ref. 30, Table 2). We also assume that male-to-female transmission is twice as efficient as female-to-male transmission (up to a 9-fold difference has been reported; ref. 42) (ref. 43, Table 2).",
- "text": "The effect of a genetic factor in a model of HIV transmission can be included by reducing the transmission coefficient. The probabilities of transmission per contact with an infected partner, \u03b2\u00ee\u0131\u0131^^,\ueb30\ue2d4\ue2d4^^,\uea50k k^^\u2192i,j , have been estimated in the literature (see ref. 38 for estimates in minimally treated groups). We want to capture a decreased risk in transmission based on genotype (ref. 39, Table 2). No studies have directly evaluated differences in infectivity between HIV-infected CCR5 W/\u039432 heterozygotes and HIV-infected CCR5 wild types. Thus, we base estimates for reduced transmission on studies of groups with various HIV serum viral loads (40), HTLV-I/II viral loads (41), and a study of the effect of AZT treatment on transmission (29). We decrease transmission probabilities for infecting CCR5\u039432/\u039432 persons by 100-fold to reflect the rarity of infections in these persons. However, we assume that infected CCR5\u039432/\u039432 homozygotes can infect susceptibles at a rate similar to CCR5W/W homozygotes, as the former generally have high viremias (ref. 30, Table 2). We also assume that male-to-female transmission is twice as efficient as female-to-male transmission (up to a 9-fold difference has been reported; ref. 42) (ref. 43, Table 2)."
- },
- {
- "self_ref": "#/texts/29",
- "parent": {
- "$ref": "#/texts/27"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "Given the assumption of no treatment, the high burden of disease in people with AIDS is assumed to greatly limit their sexual activity. Our initial model excludes people with AIDS from the sexually active groups. Subsequently, we allow persons with AIDS to be sexually active, fixing their transmission rates (\u03b2AIDS) to be the same across all CCR5 genotypes, and lower than transmission rates for primary-stage infection (as the viral burden on average is not as high as during the acute phase), and larger than transmission rates for asymptomatic-stage infection (as the viral burden characteristically increases during the end stage of disease).",
- "text": "Given the assumption of no treatment, the high burden of disease in people with AIDS is assumed to greatly limit their sexual activity. Our initial model excludes people with AIDS from the sexually active groups. Subsequently, we allow persons with AIDS to be sexually active, fixing their transmission rates (\u03b2AIDS) to be the same across all CCR5 genotypes, and lower than transmission rates for primary-stage infection (as the viral burden on average is not as high as during the acute phase), and larger than transmission rates for asymptomatic-stage infection (as the viral burden characteristically increases during the end stage of disease)."
- },
- {
- "self_ref": "#/texts/30",
- "parent": {
- "$ref": "#/texts/13"
- },
- "children": [
- {
- "$ref": "#/texts/31"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Disease progression.",
- "text": "Disease progression.",
- "level": 1
- },
- {
- "self_ref": "#/texts/31",
- "parent": {
- "$ref": "#/texts/30"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "We assume three stages of HIV infection: primary (acute, stage A), asymptomatic HIV (stage B), and AIDS. The rates of transition through the first two stages are denoted by \u03b3 i,j,k i,j,k, where i represents genotype, j is male/female, and k represents either stage A or stage B. Transition rates through each of these stages are assumed to be inversely proportional to the duration of that stage; however, other distributions are possible (31, 44, 45). Although viral loads generally peak in the first 2 months of infection, steady-state viral loads are established several months beyond this (46). For group A, the primary HIV-infecteds, duration is assumed to be 3.5 months. Based on results from European cohort studies (7\u201310), the beneficial effects of the CCR5 W/\u039432 genotype are observed mainly in the asymptomatic years of HIV infection; \u22487 years after seroconversion survival rates appear to be quite similar between heterozygous and homozygous individuals. We also assume that CCR5\u039432/\u039432-infected individuals and wild-type individuals progress similarly, and that men and women progress through each disease stage at the same rate. Given these observations, and that survival after infection may be shorter in untreated populations, we choose the duration time in stage B to be 6 years for wild-type individuals and 8 years for heterozygous individuals. Transition through AIDS, \u03b4AIDS, is inversely proportional to the duration of AIDS. We estimate this value to be 1 year for the time from onset of AIDS to death. The progression rates are summarized in Table 3.",
- "text": "We assume three stages of HIV infection: primary (acute, stage A), asymptomatic HIV (stage B), and AIDS. The rates of transition through the first two stages are denoted by \u03b3 i,j,k i,j,k, where i represents genotype, j is male/female, and k represents either stage A or stage B. Transition rates through each of these stages are assumed to be inversely proportional to the duration of that stage; however, other distributions are possible (31, 44, 45). Although viral loads generally peak in the first 2 months of infection, steady-state viral loads are established several months beyond this (46). For group A, the primary HIV-infecteds, duration is assumed to be 3.5 months. Based on results from European cohort studies (7\u201310), the beneficial effects of the CCR5 W/\u039432 genotype are observed mainly in the asymptomatic years of HIV infection; \u22487 years after seroconversion survival rates appear to be quite similar between heterozygous and homozygous individuals. We also assume that CCR5\u039432/\u039432-infected individuals and wild-type individuals progress similarly, and that men and women progress through each disease stage at the same rate. Given these observations, and that survival after infection may be shorter in untreated populations, we choose the duration time in stage B to be 6 years for wild-type individuals and 8 years for heterozygous individuals. Transition through AIDS, \u03b4AIDS, is inversely proportional to the duration of AIDS. We estimate this value to be 1 year for the time from onset of AIDS to death. The progression rates are summarized in Table 3."
- },
- {
- "self_ref": "#/texts/32",
- "parent": {
- "$ref": "#/texts/9"
- },
- "children": [
- {
- "$ref": "#/texts/33"
- },
- {
- "$ref": "#/texts/34"
- },
- {
- "$ref": "#/texts/35"
- },
- {
- "$ref": "#/texts/36"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Demographic Setting.",
- "text": "Demographic Setting.",
- "level": 1
- },
- {
- "self_ref": "#/texts/33",
- "parent": {
- "$ref": "#/texts/32"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "Demographic parameters are based on data from Malawi, Zimbabwe, and Botswana (3, 47). Estimated birth and child mortality rates are used to calculate the annual numbers of children (\u03c7 i,j i,j) maturing into the potentially sexually active, susceptible group at the age of 15 years (3). For example, in the case where the mother is CCR5 wild type and the father is CCR5 wild type or heterozygous, the number of CCR5 W/W children is calculated as follows [suppressing (t) notation]: \u03c71,j 1,j =",
- "text": "Demographic parameters are based on data from Malawi, Zimbabwe, and Botswana (3, 47). Estimated birth and child mortality rates are used to calculate the annual numbers of children (\u03c7 i,j i,j) maturing into the potentially sexually active, susceptible group at the age of 15 years (3). For example, in the case where the mother is CCR5 wild type and the father is CCR5 wild type or heterozygous, the number of CCR5 W/W children is calculated as follows [suppressing (t) notation]: \u03c71,j 1,j ="
- },
- {
- "self_ref": "#/texts/34",
- "parent": {
- "$ref": "#/texts/32"
- },
- "children": [],
- "label": "formula",
- "prov": [],
- "orig": " B_{r}\\hspace{.167em}{ \\,\\substack{ \\\\ {\\sum} \\\\ _{k} }\\, } \\left[ S_{1,F}\\frac{(S_{1,M}+I_{1,M,k})}{N_{M}}+ \\left[ (0.5)S_{1,F}\\frac{(S_{2,M}+I_{2,M,k})}{N_{M}} \\right] + \\right ",
- "text": " B_{r}\\hspace{.167em}{ \\,\\substack{ \\\\ {\\sum} \\\\ _{k} }\\, } \\left[ S_{1,F}\\frac{(S_{1,M}+I_{1,M,k})}{N_{M}}+ \\left[ (0.5)S_{1,F}\\frac{(S_{2,M}+I_{2,M,k})}{N_{M}} \\right] + \\right "
- },
- {
- "self_ref": "#/texts/35",
- "parent": {
- "$ref": "#/texts/32"
- },
- "children": [],
- "label": "formula",
- "prov": [],
- "orig": " p_{v} \\left \\left( \\frac{(I_{1,F,k}(S_{1,M}+I_{1,M,k}))}{N_{M}}+ \\left[ (0.5)I_{1,F,k}\\frac{(S_{2,M}+I_{2,M,k})}{N_{M}} \\right] \\right) \\right] ,\\hspace{.167em} ",
- "text": " p_{v} \\left \\left( \\frac{(I_{1,F,k}(S_{1,M}+I_{1,M,k}))}{N_{M}}+ \\left[ (0.5)I_{1,F,k}\\frac{(S_{2,M}+I_{2,M,k})}{N_{M}} \\right] \\right) \\right] ,\\hspace{.167em} "
- },
- {
- "self_ref": "#/texts/36",
- "parent": {
- "$ref": "#/texts/32"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "where the probability of HIV vertical transmission, 1 \u2212 pv , and the birthrate, Br , are both included in the equations together with the Mendelian inheritance values as presented in Table 1. The generalized version of this equation (i.e., \u03c7 i,j i,j) can account for six categories of children (including gender and genotype). We assume that all children of all genotypes are at risk, although we can relax this condition if data become available to support vertical protection (e.g., ref. 48). All infected children are assumed to die before age 15. Before entering the susceptible group at age 15, there is additional loss because of mortality from all non-AIDS causes occurring less than 15 years of age at a rate of \u03bc\u03c7\u03c7 \u00d7 \u03c7 i,j i,j (where \u03bc\u03c7 is the mortality under 15 years of age). Children then enter the population as susceptibles at an annual rate, \u03c2 j j \u00d7 \u03c7 i,j i,j/15, where \u03c2 j distributes the children 51% females and 49% males. All parameters and their values are summarized in Table 4.",
- "text": "where the probability of HIV vertical transmission, 1 \u2212 pv , and the birthrate, Br , are both included in the equations together with the Mendelian inheritance values as presented in Table 1. The generalized version of this equation (i.e., \u03c7 i,j i,j) can account for six categories of children (including gender and genotype). We assume that all children of all genotypes are at risk, although we can relax this condition if data become available to support vertical protection (e.g., ref. 48). All infected children are assumed to die before age 15. Before entering the susceptible group at age 15, there is additional loss because of mortality from all non-AIDS causes occurring less than 15 years of age at a rate of \u03bc\u03c7\u03c7 \u00d7 \u03c7 i,j i,j (where \u03bc\u03c7 is the mortality under 15 years of age). Children then enter the population as susceptibles at an annual rate, \u03c2 j j \u00d7 \u03c7 i,j i,j/15, where \u03c2 j distributes the children 51% females and 49% males. All parameters and their values are summarized in Table 4."
- },
- {
- "self_ref": "#/texts/37",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [
- {
- "$ref": "#/texts/38"
- },
- {
- "$ref": "#/texts/43"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Prevalence of HIV",
- "text": "Prevalence of HIV",
- "level": 1
- },
- {
- "self_ref": "#/texts/38",
- "parent": {
- "$ref": "#/texts/37"
- },
- "children": [
- {
- "$ref": "#/texts/39"
- },
- {
- "$ref": "#/texts/40"
- },
- {
- "$ref": "#/texts/41"
- },
- {
- "$ref": "#/pictures/1"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Demographics and Model Validation.",
- "text": "Demographics and Model Validation.",
- "level": 1
- },
- {
- "self_ref": "#/texts/39",
- "parent": {
- "$ref": "#/texts/38"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "The model was validated by using parameters estimated from available demographic data. Simulations were run in the absence of HIV infection to compare the model with known population growth rates. Infection was subsequently introduced with an initial low HIV prevalence of 0.5% to capture early epidemic behavior.",
- "text": "The model was validated by using parameters estimated from available demographic data. Simulations were run in the absence of HIV infection to compare the model with known population growth rates. Infection was subsequently introduced with an initial low HIV prevalence of 0.5% to capture early epidemic behavior."
- },
- {
- "self_ref": "#/texts/40",
- "parent": {
- "$ref": "#/texts/38"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "In deciding on our initial values for parameters during infection, we use Joint United Nations Programme on HIV/AIDS national prevalence data for Malawi, Zimbabwe, and Botswana. Nationwide seroprevalence of HIV in these countries varies from \u224811% to over 20% (3), although there may be considerable variation within given subpopulations (2, 49).",
- "text": "In deciding on our initial values for parameters during infection, we use Joint United Nations Programme on HIV/AIDS national prevalence data for Malawi, Zimbabwe, and Botswana. Nationwide seroprevalence of HIV in these countries varies from \u224811% to over 20% (3), although there may be considerable variation within given subpopulations (2, 49)."
- },
- {
- "self_ref": "#/texts/41",
- "parent": {
- "$ref": "#/texts/38"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "In the absence of HIV infection, the annual percent population growth rate in the model is \u22482.5%, predicting the present-day values for an average of sub-Saharan African cities (data not shown). To validate the model with HIV infection, we compare our simulation of the HIV epidemic to existing prevalence data for Kenya and Mozambique (http://www.who.int/emc-hiv/fact-sheets/pdfs/kenya.pdf and ref. 51). Prevalence data collected from these countries follow similar trajectories to those predicted by our model (Fig. 2).",
- "text": "In the absence of HIV infection, the annual percent population growth rate in the model is \u22482.5%, predicting the present-day values for an average of sub-Saharan African cities (data not shown). To validate the model with HIV infection, we compare our simulation of the HIV epidemic to existing prevalence data for Kenya and Mozambique (http://www.who.int/emc-hiv/fact-sheets/pdfs/kenya.pdf and ref. 51). Prevalence data collected from these countries follow similar trajectories to those predicted by our model (Fig. 2)."
- },
- {
- "self_ref": "#/texts/42",
- "parent": {
- "$ref": "#/body"
- },
- "children": [],
- "label": "caption",
- "prov": [],
- "orig": "Figure 2 Model simulation of HIV infection in a population lacking the protective CCR5\u039432 allele compared with national data from Kenya (healthy adults) and Mozambique (blood donors, ref. 17). The simulated population incorporates parameter estimates from sub-Saharan African demographics. Note the two outlier points from the Mozambique data were likely caused by underreporting in the early stages of the epidemic.",
- "text": "Figure 2 Model simulation of HIV infection in a population lacking the protective CCR5\u039432 allele compared with national data from Kenya (healthy adults) and Mozambique (blood donors, ref. 17). The simulated population incorporates parameter estimates from sub-Saharan African demographics. Note the two outlier points from the Mozambique data were likely caused by underreporting in the early stages of the epidemic."
- },
- {
- "self_ref": "#/texts/43",
- "parent": {
- "$ref": "#/texts/37"
- },
- "children": [
- {
- "$ref": "#/texts/44"
- },
- {
- "$ref": "#/texts/45"
- },
- {
- "$ref": "#/texts/46"
- },
- {
- "$ref": "#/pictures/2"
- },
- {
- "$ref": "#/texts/48"
- },
- {
- "$ref": "#/texts/49"
- },
- {
- "$ref": "#/texts/50"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Effects of the Allele on Prevalence.",
- "text": "Effects of the Allele on Prevalence.",
- "level": 1
- },
- {
- "self_ref": "#/texts/44",
- "parent": {
- "$ref": "#/texts/43"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "After validating the model in the wild type-only population, both CCR5\u039432 heterozygous and homozygous people are included. Parameter values for HIV transmission, duration of illness, and numbers of contacts per partner are assumed to be the same within both settings. We then calculate HIV/AIDS prevalence among adults for total HIV/AIDS cases.",
- "text": "After validating the model in the wild type-only population, both CCR5\u039432 heterozygous and homozygous people are included. Parameter values for HIV transmission, duration of illness, and numbers of contacts per partner are assumed to be the same within both settings. We then calculate HIV/AIDS prevalence among adults for total HIV/AIDS cases."
- },
- {
- "self_ref": "#/texts/45",
- "parent": {
- "$ref": "#/texts/43"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "Although CCR5\u039432/\u039432 homozygosity is rarely seen in HIV-positive populations (prevalence ranges between 0 and 0.004%), 1\u201320% of people in HIV-negative populations of European descent are homozygous. Thus, to evaluate the potential impact of CCR5\u039432, we estimate there are 19% CCR5 W/\u039432 heterozygous and 1% CCR5 \u039432/\u039432 homozygous people in our population. These values are in Hardy-Weinberg equilibrium with an allelic frequency of the mutation as 0.105573.",
- "text": "Although CCR5\u039432/\u039432 homozygosity is rarely seen in HIV-positive populations (prevalence ranges between 0 and 0.004%), 1\u201320% of people in HIV-negative populations of European descent are homozygous. Thus, to evaluate the potential impact of CCR5\u039432, we estimate there are 19% CCR5 W/\u039432 heterozygous and 1% CCR5 \u039432/\u039432 homozygous people in our population. These values are in Hardy-Weinberg equilibrium with an allelic frequency of the mutation as 0.105573."
- },
- {
- "self_ref": "#/texts/46",
- "parent": {
- "$ref": "#/texts/43"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "Fig. 3 shows the prevalence of HIV in two populations: one lacking the mutant CCR5 allele and another carrying that allele. In the population lacking the protective mutation, prevalence increases logarithmically for the first 35 years of the epidemic, reaching 18% before leveling off.",
- "text": "Fig. 3 shows the prevalence of HIV in two populations: one lacking the mutant CCR5 allele and another carrying that allele. In the population lacking the protective mutation, prevalence increases logarithmically for the first 35 years of the epidemic, reaching 18% before leveling off."
- },
- {
- "self_ref": "#/texts/47",
- "parent": {
- "$ref": "#/body"
- },
- "children": [],
- "label": "caption",
- "prov": [],
- "orig": "Figure 3 Prevalence of HIV/AIDS in the adult population as predicted by the model. The top curve (\u25cb) indicates prevalence in a population lacking the protective allele. We compare that to a population with 19% heterozygous and 1% homozygous for the allele (implying an allelic frequency of 0.105573. Confidence interval bands (light gray) are shown around the median simulation (\ue80b) providing a range of uncertainty in evaluating parameters for the effect of the mutation on the infectivity and the duration of asymptomatic HIV for heterozygotes.",
- "text": "Figure 3 Prevalence of HIV/AIDS in the adult population as predicted by the model. The top curve (\u25cb) indicates prevalence in a population lacking the protective allele. We compare that to a population with 19% heterozygous and 1% homozygous for the allele (implying an allelic frequency of 0.105573. Confidence interval bands (light gray) are shown around the median simulation (\ue80b) providing a range of uncertainty in evaluating parameters for the effect of the mutation on the infectivity and the duration of asymptomatic HIV for heterozygotes."
- },
- {
- "self_ref": "#/texts/48",
- "parent": {
- "$ref": "#/texts/43"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "In contrast, when a proportion of the population carries the CCR5\u039432 allele, the epidemic increases more slowly, but still logarithmically, for the first 50 years, and HIV/AIDS prevalence reaches \u224812% (Fig. 3). Prevalence begins to decline slowly after 70 years.",
- "text": "In contrast, when a proportion of the population carries the CCR5\u039432 allele, the epidemic increases more slowly, but still logarithmically, for the first 50 years, and HIV/AIDS prevalence reaches \u224812% (Fig. 3). Prevalence begins to decline slowly after 70 years."
- },
- {
- "self_ref": "#/texts/49",
- "parent": {
- "$ref": "#/texts/43"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "In the above simulations we assume that people with AIDS are not sexually active. However, when these individuals are included in the sexually active population the severity of the epidemic increases considerably (data not shown). Consistent with our initial simulations, prevalences are still relatively lower in the presence of the CCR5 mutation.",
- "text": "In the above simulations we assume that people with AIDS are not sexually active. However, when these individuals are included in the sexually active population the severity of the epidemic increases considerably (data not shown). Consistent with our initial simulations, prevalences are still relatively lower in the presence of the CCR5 mutation."
- },
- {
- "self_ref": "#/texts/50",
- "parent": {
- "$ref": "#/texts/43"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "Because some parameters (e.g., rate constants) are difficult to estimate based on available data, we implement an uncertainty analysis to assess the variability in the model outcomes caused by any inaccuracies in estimates of the parameter values with regard to the effect of the allelic mutation. For these analyses we use Latin hypercube sampling, as described in refs. 52\u201356, Our uncertainty and sensitivity analyses focus on infectivity vs. duration of infectiousness. To this end, we assess the effects on the dynamics of the epidemic for a range of values of the parameters governing transmission and progression rates: \u03b2\u00ee\u0131\u0131^^,\ueb30\ue2d4\ue2d4^^,\uea50k k^^\u2192i,j and \u03b3 i,j,k i,j,k. All other parameters are held constant. These results are presented as an interval band about the average simulation for the population carrying the CCR5\u039432 allele (Fig. 3). Although there is variability in the model outcomes, the analysis indicates that the overall model predictions are consistent for a wide range of transmission and progression rates. Further, most of the variation observed in the outcome is because of the transmission rates for both heterosexual males and females in the primary stage of infection (\u03b22,M,A \u2192 i ,F, \u03b22,F,A \u2192 i ,M). As mentioned above, we assume lower viral loads correlate with reduced infectivity; thus, the reduction in viral load in heterozygotes has a major influence on disease spread.",
- "text": "Because some parameters (e.g., rate constants) are difficult to estimate based on available data, we implement an uncertainty analysis to assess the variability in the model outcomes caused by any inaccuracies in estimates of the parameter values with regard to the effect of the allelic mutation. For these analyses we use Latin hypercube sampling, as described in refs. 52\u201356, Our uncertainty and sensitivity analyses focus on infectivity vs. duration of infectiousness. To this end, we assess the effects on the dynamics of the epidemic for a range of values of the parameters governing transmission and progression rates: \u03b2\u00ee\u0131\u0131^^,\ueb30\ue2d4\ue2d4^^,\uea50k k^^\u2192i,j and \u03b3 i,j,k i,j,k. All other parameters are held constant. These results are presented as an interval band about the average simulation for the population carrying the CCR5\u039432 allele (Fig. 3). Although there is variability in the model outcomes, the analysis indicates that the overall model predictions are consistent for a wide range of transmission and progression rates. Further, most of the variation observed in the outcome is because of the transmission rates for both heterosexual males and females in the primary stage of infection (\u03b22,M,A \u2192 i ,F, \u03b22,F,A \u2192 i ,M). As mentioned above, we assume lower viral loads correlate with reduced infectivity; thus, the reduction in viral load in heterozygotes has a major influence on disease spread."
- },
- {
- "self_ref": "#/texts/51",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [
- {
- "$ref": "#/texts/52"
- },
- {
- "$ref": "#/pictures/3"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "HIV Induces Selective Pressure on Genotype Frequency",
- "text": "HIV Induces Selective Pressure on Genotype Frequency",
- "level": 1
- },
- {
- "self_ref": "#/texts/52",
- "parent": {
- "$ref": "#/texts/51"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "To observe changes in the frequency of the CCR5\u039432 allele in a setting with HIV infection as compared with the Hardy-Weinberg equilibrium in the absence of HIV, we follow changes in the total number of CCR5\u039432 heterozygotes and homozygotes over 1,000 years (Fig. 4). We initially perform simulations in the absence of HIV infection as a negative control to show there is not significant selection of the allele in the absence of infection. To determine how long it would take for the allelic frequency to reach present-day levels (e.g., q = 0.105573), we initiate this simulation for 1,000 years with a very small allelic frequency (q = 0.00105). In the absence of HIV, the allelic frequency is maintained in equilibrium as shown by the constant proportions of CCR5\u039432 heterozygotes and homozygotes (Fig. 4, solid lines). The selection for CCR5\u039432 in the presence of HIV is seen in comparison (Fig. 4, dashed lines). We expand the time frame of this simulation to 2,000 years to view the point at which the frequency reaches present levels (where q \u223c0.105573 at year = 1200). Note that the allelic frequency increases for \u22481,600 years before leveling off.",
- "text": "To observe changes in the frequency of the CCR5\u039432 allele in a setting with HIV infection as compared with the Hardy-Weinberg equilibrium in the absence of HIV, we follow changes in the total number of CCR5\u039432 heterozygotes and homozygotes over 1,000 years (Fig. 4). We initially perform simulations in the absence of HIV infection as a negative control to show there is not significant selection of the allele in the absence of infection. To determine how long it would take for the allelic frequency to reach present-day levels (e.g., q = 0.105573), we initiate this simulation for 1,000 years with a very small allelic frequency (q = 0.00105). In the absence of HIV, the allelic frequency is maintained in equilibrium as shown by the constant proportions of CCR5\u039432 heterozygotes and homozygotes (Fig. 4, solid lines). The selection for CCR5\u039432 in the presence of HIV is seen in comparison (Fig. 4, dashed lines). We expand the time frame of this simulation to 2,000 years to view the point at which the frequency reaches present levels (where q \u223c0.105573 at year = 1200). Note that the allelic frequency increases for \u22481,600 years before leveling off."
- },
- {
- "self_ref": "#/texts/53",
- "parent": {
- "$ref": "#/body"
- },
- "children": [],
- "label": "caption",
- "prov": [],
- "orig": "Figure 4 Effects of HIV-1 on selection of the CCR5\u039432 allele. The Hardy-Weinberg equilibrium level is represented in the no-infection simulation (solid lines) for each population. Divergence from the original Hardy-Weinberg equilibrium is shown to occur in the simulations that include HIV infection (dashed lines). Fraction of the total subpopulations are presented: (A) wild types (W/W), (B) heterozygotes (W/\u039432), and (C) homozygotes (\u039432/\u039432). Note that we initiate this simulation with a much lower allelic frequency (0.00105) than used in the rest of the study to better exemplify the actual selective effect over a 1,000-year time scale. (D) The allelic selection effect over a 2,000-year time scale.",
- "text": "Figure 4 Effects of HIV-1 on selection of the CCR5\u039432 allele. The Hardy-Weinberg equilibrium level is represented in the no-infection simulation (solid lines) for each population. Divergence from the original Hardy-Weinberg equilibrium is shown to occur in the simulations that include HIV infection (dashed lines). Fraction of the total subpopulations are presented: (A) wild types (W/W), (B) heterozygotes (W/\u039432), and (C) homozygotes (\u039432/\u039432). Note that we initiate this simulation with a much lower allelic frequency (0.00105) than used in the rest of the study to better exemplify the actual selective effect over a 1,000-year time scale. (D) The allelic selection effect over a 2,000-year time scale."
- },
- {
- "self_ref": "#/texts/54",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [
- {
- "$ref": "#/texts/55"
- },
- {
- "$ref": "#/texts/56"
- },
- {
- "$ref": "#/texts/57"
- },
- {
- "$ref": "#/texts/58"
- },
- {
- "$ref": "#/texts/59"
- },
- {
- "$ref": "#/texts/60"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Discussion",
- "text": "Discussion",
- "level": 1
- },
- {
- "self_ref": "#/texts/55",
- "parent": {
- "$ref": "#/texts/54"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "This study illustrates how populations can differ in susceptibility to epidemic HIV/AIDS depending on a ubiquitous attribute such as a prevailing genotype. We have examined heterosexual HIV epidemics by using mathematical models to assess HIV transmission in dynamic populations either with or without CCR5\u039432 heterozygous and homozygous persons. The most susceptible population lacks the protective mutation in CCR5. In less susceptible populations, the majority of persons carrying the CCR5\u039432 allele are heterozygotes. We explore the hypothesis that lower viral loads (CCR5\u039432 heterozygotes) or resistance to infection (CCR5\u039432 homozygotes) observed in persons with this coreceptor mutation ultimately can influence HIV epidemic trends. Two contrasting influences of the protective CCR5 allele are conceivable: it may limit the epidemic by decreasing the probability of infection because of lower viral loads in infected heterozygotes, or it may exacerbate the epidemic by extending the time that infectious individuals remain in the sexually active population. Our results strongly suggest the former. Thus, the absence of this allele in Africa could explain the severity of HIV disease as compared with populations where the allele is present.",
- "text": "This study illustrates how populations can differ in susceptibility to epidemic HIV/AIDS depending on a ubiquitous attribute such as a prevailing genotype. We have examined heterosexual HIV epidemics by using mathematical models to assess HIV transmission in dynamic populations either with or without CCR5\u039432 heterozygous and homozygous persons. The most susceptible population lacks the protective mutation in CCR5. In less susceptible populations, the majority of persons carrying the CCR5\u039432 allele are heterozygotes. We explore the hypothesis that lower viral loads (CCR5\u039432 heterozygotes) or resistance to infection (CCR5\u039432 homozygotes) observed in persons with this coreceptor mutation ultimately can influence HIV epidemic trends. Two contrasting influences of the protective CCR5 allele are conceivable: it may limit the epidemic by decreasing the probability of infection because of lower viral loads in infected heterozygotes, or it may exacerbate the epidemic by extending the time that infectious individuals remain in the sexually active population. Our results strongly suggest the former. Thus, the absence of this allele in Africa could explain the severity of HIV disease as compared with populations where the allele is present."
- },
- {
- "self_ref": "#/texts/56",
- "parent": {
- "$ref": "#/texts/54"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "We also observed that HIV can provide selective pressure for the CCR5\u039432 allele within a population, increasing the allelic frequency. Other influences may have additionally selected for this allele. Infectious diseases such as plague and small pox have been postulated to select for CCR5\u039432 (57, 58). For plague, relatively high levels of CCR5\u039432 are believed to have arisen within \u22484,000 years, accounting for the prevalence of the mutation only in populations of European descent. Smallpox virus uses the CC-coreceptor, indicating that direct selection for mutations in CCR5 may have offered resistance to smallpox. Given the differences in the epidemic rates of plague (59), smallpox, and HIV, it is difficult to directly compare our results to these findings. However, our model suggests that the CCR5\u039432 mutation could have reached its present allelic frequency in Northern Europe within this time frame if selected for by a disease with virulence patterns similar to HIV. Our results further support the idea that HIV has been only recently introduced as a pathogen into African populations, as the frequency of the protective allele is almost zero, and our model predicts that selection of the mutant allele in this population by HIV alone takes at least 1,000 years. This prediction is distinct from the frequency of the CCR5\u039432 allele in European populations, where pathogens that may have influenced its frequency (e.g., Yersinia pestis) have been present for much longer.",
- "text": "We also observed that HIV can provide selective pressure for the CCR5\u039432 allele within a population, increasing the allelic frequency. Other influences may have additionally selected for this allele. Infectious diseases such as plague and small pox have been postulated to select for CCR5\u039432 (57, 58). For plague, relatively high levels of CCR5\u039432 are believed to have arisen within \u22484,000 years, accounting for the prevalence of the mutation only in populations of European descent. Smallpox virus uses the CC-coreceptor, indicating that direct selection for mutations in CCR5 may have offered resistance to smallpox. Given the differences in the epidemic rates of plague (59), smallpox, and HIV, it is difficult to directly compare our results to these findings. However, our model suggests that the CCR5\u039432 mutation could have reached its present allelic frequency in Northern Europe within this time frame if selected for by a disease with virulence patterns similar to HIV. Our results further support the idea that HIV has been only recently introduced as a pathogen into African populations, as the frequency of the protective allele is almost zero, and our model predicts that selection of the mutant allele in this population by HIV alone takes at least 1,000 years. This prediction is distinct from the frequency of the CCR5\u039432 allele in European populations, where pathogens that may have influenced its frequency (e.g., Yersinia pestis) have been present for much longer."
- },
- {
- "self_ref": "#/texts/57",
- "parent": {
- "$ref": "#/texts/54"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "Two mathematical models have considered the role of parasite and host genetic heterogeneity with regard to susceptibility to another pathogen, namely malaria (60, 61). In each it was determined that heterogeneity of host resistance facilitates the maintenance of diversity in parasite virulence. Given our underlying interest in the coevolution of pathogen and host, we focus on changes in a host protective mutation, holding the virulence of the pathogen constant over time.",
- "text": "Two mathematical models have considered the role of parasite and host genetic heterogeneity with regard to susceptibility to another pathogen, namely malaria (60, 61). In each it was determined that heterogeneity of host resistance facilitates the maintenance of diversity in parasite virulence. Given our underlying interest in the coevolution of pathogen and host, we focus on changes in a host protective mutation, holding the virulence of the pathogen constant over time."
- },
- {
- "self_ref": "#/texts/58",
- "parent": {
- "$ref": "#/texts/54"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "Even within our focus on host protective mutations, numerous genetic factors, beneficial or detrimental, could potentially influence epidemics. Other genetically determined host factors affecting HIV susceptibility and disease progression include a CCR5 A/A to G/G promoter polymorphism (62), a CCR2 point mutation (11, 63), and a mutation in the CXCR4 ligand (64). The CCR2b mutation, CCR264I, is found in linkage with at least one CCR5 promoter polymorphism (65) and is prevalent in populations where CCR5\u039432 is nonexistent, such as sub-Saharan Africa (63). However, as none of these mutations have been consistently shown to be as protective as the CCR5\u039432 allele, we simplified our model to incorporate only the effect of CCR5\u039432. Subsequent models could be constructed from our model to account for the complexity of multiple protective alleles. It is interesting to note that our model predicts that even if CCR264I is present at high frequencies in Africa, its protective effects may not augment the lack of a protective allele such as CCR5\u039432.",
- "text": "Even within our focus on host protective mutations, numerous genetic factors, beneficial or detrimental, could potentially influence epidemics. Other genetically determined host factors affecting HIV susceptibility and disease progression include a CCR5 A/A to G/G promoter polymorphism (62), a CCR2 point mutation (11, 63), and a mutation in the CXCR4 ligand (64). The CCR2b mutation, CCR264I, is found in linkage with at least one CCR5 promoter polymorphism (65) and is prevalent in populations where CCR5\u039432 is nonexistent, such as sub-Saharan Africa (63). However, as none of these mutations have been consistently shown to be as protective as the CCR5\u039432 allele, we simplified our model to incorporate only the effect of CCR5\u039432. Subsequent models could be constructed from our model to account for the complexity of multiple protective alleles. It is interesting to note that our model predicts that even if CCR264I is present at high frequencies in Africa, its protective effects may not augment the lack of a protective allele such as CCR5\u039432."
- },
- {
- "self_ref": "#/texts/59",
- "parent": {
- "$ref": "#/texts/54"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "Although our models demonstrate that genetic factors can contribute to the high prevalence of HIV in sub-Saharan Africa, demographic factors are also clearly important in this region. Our models explicitly incorporated such factors, for example, lack of treatment availability. Additional factors were implicitly controlled for by varying only the presence of the CCR5\u039432 allele. More complex models eventually could include interactions with infectious diseases that serve as cofactors in HIV transmission. The role of high sexually transmitted disease prevalences in HIV infection has long been discussed, especially in relation to core populations (15, 50, 66). Malaria, too, might influence HIV transmission, as it is associated with transient increases in semen HIV viral loads and thus could increase the susceptibility of the population to epidemic HIV (16).",
- "text": "Although our models demonstrate that genetic factors can contribute to the high prevalence of HIV in sub-Saharan Africa, demographic factors are also clearly important in this region. Our models explicitly incorporated such factors, for example, lack of treatment availability. Additional factors were implicitly controlled for by varying only the presence of the CCR5\u039432 allele. More complex models eventually could include interactions with infectious diseases that serve as cofactors in HIV transmission. The role of high sexually transmitted disease prevalences in HIV infection has long been discussed, especially in relation to core populations (15, 50, 66). Malaria, too, might influence HIV transmission, as it is associated with transient increases in semen HIV viral loads and thus could increase the susceptibility of the population to epidemic HIV (16)."
- },
- {
- "self_ref": "#/texts/60",
- "parent": {
- "$ref": "#/texts/54"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "In assessing the HIV/AIDS epidemic, considerable attention has been paid to the influence of core groups in driving sexually transmitted disease epidemics. Our results also highlight how characteristics more uniformly distributed in a population can affect susceptibility. We observed that the genotypic profile of a population affects its susceptibility to epidemic HIV/AIDS. Additional studies are needed to better characterize the influence of these genetic determinants on HIV transmission, as they may be crucial in estimating the severity of the epidemic in some populations. This information can influence the design of treatment strategies as well as point to the urgency for education and prevention programs.",
- "text": "In assessing the HIV/AIDS epidemic, considerable attention has been paid to the influence of core groups in driving sexually transmitted disease epidemics. Our results also highlight how characteristics more uniformly distributed in a population can affect susceptibility. We observed that the genotypic profile of a population affects its susceptibility to epidemic HIV/AIDS. Additional studies are needed to better characterize the influence of these genetic determinants on HIV transmission, as they may be crucial in estimating the severity of the epidemic in some populations. This information can influence the design of treatment strategies as well as point to the urgency for education and prevention programs."
- },
- {
- "self_ref": "#/texts/61",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [
- {
- "$ref": "#/texts/62"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "Acknowledgments",
- "text": "Acknowledgments",
- "level": 1
- },
- {
- "self_ref": "#/texts/62",
- "parent": {
- "$ref": "#/texts/61"
- },
- "children": [],
- "label": "text",
- "prov": [],
- "orig": "We thank Mark Krosky, Katia Koelle, and Kevin Chung for programming and technical assistance. We also thank Drs. V. J. DiRita, P. Kazanjian, and S. M. Blower for helpful comments and discussions. We thank the reviewers for extremely insightful comments.",
- "text": "We thank Mark Krosky, Katia Koelle, and Kevin Chung for programming and technical assistance. We also thank Drs. V. J. DiRita, P. Kazanjian, and S. M. Blower for helpful comments and discussions. We thank the reviewers for extremely insightful comments."
- },
- {
- "self_ref": "#/texts/63",
- "parent": {
- "$ref": "#/texts/0"
- },
- "children": [
- {
- "$ref": "#/groups/0"
- }
- ],
- "label": "section_header",
- "prov": [],
- "orig": "References",
- "text": "References",
- "level": 1
- },
- {
- "self_ref": "#/texts/64",
- "parent": {
- "$ref": "#/groups/0"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Weiss HA, Hawkes S. Leprosy Rev 72:92\u201398 (2001). PMID: 11355525",
- "text": "Weiss HA, Hawkes S. Leprosy Rev 72:92\u201398 (2001). PMID: 11355525",
- "enumerated": false,
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- },
- {
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- "parent": {
- "$ref": "#/groups/0"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "Taha TE, Dallabetta GA, Hoover DR, Chiphangwi JD, Mtimavalye LAR. AIDS 12:197\u2013203 (1998). PMID: 9468369",
- "text": "Taha TE, Dallabetta GA, Hoover DR, Chiphangwi JD, Mtimavalye LAR. AIDS 12:197\u2013203 (1998). PMID: 9468369",
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- {
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- "parent": {
- "$ref": "#/groups/0"
- },
- "children": [],
- "label": "list_item",
- "prov": [],
- "orig": "AIDS Epidemic Update. Geneva: World Health Organization1\u201317 (1998).",
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- "children": [],
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- "$ref": "#/groups/0"
- },
- "children": [],
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- "prov": [],
- "orig": "Garred P, Eugen-Olsen J, Iversen AKN, Benfield TL, Svejgaard A, Hofmann B. Lancet 349:1884 (1997). PMID: 9217763",
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- "$ref": "#/groups/0"
- },
- "children": [],
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- "end_col_offset_idx": 3,
- "text": "Value",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
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- "start_col_offset_idx": 0,
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- "text": "\n\n",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
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- "start_row_offset_idx": 2,
- "end_row_offset_idx": 3,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\u03bc\nF\n\nF, \u03bc\nM\n\nM\n",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
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- "start_row_offset_idx": 2,
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- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "All-cause mortality for adult females (males)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
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- "start_row_offset_idx": 2,
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- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "0.015 (0.016) per year",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 3,
- "end_row_offset_idx": 4,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\u03bc\u03c7\u03c7",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
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- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "All-cause childhood mortality (<15 years of age)",
- "column_header": false,
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- {
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- "text": "0.01 per year",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
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- "end_row_offset_idx": 5,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\nB\n\nr\n\nr\n",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
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- "col_span": 1,
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- "end_row_offset_idx": 5,
- "start_col_offset_idx": 1,
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- "text": "Birthrate",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
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- "text": "0.25 per woman per year",
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- "text": "\nSA\n\nF\n\nF\n",
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- "row_header": false,
- "row_section": false
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- {
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- "text": "Percent females acquiring new partners (sexual activity)",
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- },
- {
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- "text": "\nSA\n\nM\n\nM\n",
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- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 6,
- "end_row_offset_idx": 7,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Percent males acquiring new partners (sexual activity)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
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- "col_span": 1,
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- "row_header": false,
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- },
- {
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- "end_row_offset_idx": 8,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\nm\n\nF\n\nF(\u03c2$$ {\\mathrm{_{{F}}^{{2}}}} $$)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
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- "end_row_offset_idx": 8,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Mean (variance) no. of new partners for females",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
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- "start_col_offset_idx": 2,
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- "text": "1.8 (1.2) per year",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
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- "end_row_offset_idx": 9,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\u03c2$$ {\\mathrm{_{{M}}^{{2}}}} $$\n",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 8,
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- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Variance in no. of new partners for males",
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- "row_header": false,
- "row_section": false
- },
- {
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- "col_span": 1,
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- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "5.5 per year ",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 9,
- "end_row_offset_idx": 10,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "1 \u2212 p\n\nv\n\nv\n",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 9,
- "end_row_offset_idx": 10,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Probability of vertical transmission",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
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- "col_span": 1,
- "start_row_offset_idx": 9,
- "end_row_offset_idx": 10,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "0.30 per birth",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 10,
- "end_row_offset_idx": 11,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\nI\n\ni,j,k\n\ni,j,k(0)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 10,
- "end_row_offset_idx": 11,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Initial total population HIV-positive",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 10,
- "end_row_offset_idx": 11,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "0.50% ",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 11,
- "end_row_offset_idx": 12,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\u03c7\ni,j\n\ni,j(0)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 11,
- "end_row_offset_idx": 12,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Initial total children in population (<15 years of age)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 11,
- "end_row_offset_idx": 12,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "45%",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 12,
- "end_row_offset_idx": 13,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\nW/W (0)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 12,
- "end_row_offset_idx": 13,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Initial total wild types (W/W) in population",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 12,
- "end_row_offset_idx": 13,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "80% ",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 13,
- "end_row_offset_idx": 14,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\nW/\u039432(0)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 13,
- "end_row_offset_idx": 14,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Initial total heterozygotes (W/\u039432) in population",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 13,
- "end_row_offset_idx": 14,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "19%",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
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- "col_span": 1,
- "start_row_offset_idx": 14,
- "end_row_offset_idx": 15,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\u039432/\u039432(0)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 14,
- "end_row_offset_idx": 15,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Initial total homozygotes (\u039432/\u039432) in population",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
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- "start_row_offset_idx": 14,
- "end_row_offset_idx": 15,
- "start_col_offset_idx": 2,
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- "text": "1%",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 15,
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- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\nr\n\nM\n\nM(r\n\nF\n\nF)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 15,
- "end_row_offset_idx": 16,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Initial percent males (females) in total population",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 15,
- "end_row_offset_idx": 16,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "49% (51%)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
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- "col_span": 1,
- "start_row_offset_idx": 16,
- "end_row_offset_idx": 17,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\u03d5\nF\n\nF, \u03d5\nM\n\nM\n",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
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- "start_col_offset_idx": 1,
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- "text": "Number of sexual contacts a female (male) has",
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- "text": "30 (24) per partner",
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- },
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- "start_col_offset_idx": 0,
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- "text": "\u025b\ni,j,k\n\ni,j,k\n",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
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- "text": "% effect of mutation on transmission rates (see Table 2)",
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- "text": "0 < \u025b\ni,j,k\n\ni,j,k < 1",
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- "text": "\u03b4",
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- "text": "Death rate for AIDS population",
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- "text": "1.0 per year ",
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- "text": "0.105573",
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- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\u03bc\nF\n\nF, \u03bc\nM\n\nM\n",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 2,
- "end_row_offset_idx": 3,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "All-cause mortality for adult females (males)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 2,
- "end_row_offset_idx": 3,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "0.015 (0.016) per year",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 3,
- "end_row_offset_idx": 4,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\u03bc\u03c7\u03c7",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 3,
- "end_row_offset_idx": 4,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "All-cause childhood mortality (<15 years of age)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 3,
- "end_row_offset_idx": 4,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "0.01 per year",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 4,
- "end_row_offset_idx": 5,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\nB\n\nr\n\nr\n",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 4,
- "end_row_offset_idx": 5,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Birthrate",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 4,
- "end_row_offset_idx": 5,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "0.25 per woman per year",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 5,
- "end_row_offset_idx": 6,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\nSA\n\nF\n\nF\n",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 5,
- "end_row_offset_idx": 6,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Percent females acquiring new partners (sexual activity)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 5,
- "end_row_offset_idx": 6,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "10%",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 6,
- "end_row_offset_idx": 7,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\nSA\n\nM\n\nM\n",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 6,
- "end_row_offset_idx": 7,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Percent males acquiring new partners (sexual activity)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 6,
- "end_row_offset_idx": 7,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "25%",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 7,
- "end_row_offset_idx": 8,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\nm\n\nF\n\nF(\u03c2$$ {\\mathrm{_{{F}}^{{2}}}} $$)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 7,
- "end_row_offset_idx": 8,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Mean (variance) no. of new partners for females",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 7,
- "end_row_offset_idx": 8,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "1.8 (1.2) per year",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 8,
- "end_row_offset_idx": 9,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\u03c2$$ {\\mathrm{_{{M}}^{{2}}}} $$\n",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 8,
- "end_row_offset_idx": 9,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Variance in no. of new partners for males",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 8,
- "end_row_offset_idx": 9,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "5.5 per year ",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 9,
- "end_row_offset_idx": 10,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "1 \u2212 p\n\nv\n\nv\n",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 9,
- "end_row_offset_idx": 10,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Probability of vertical transmission",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 9,
- "end_row_offset_idx": 10,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "0.30 per birth",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 10,
- "end_row_offset_idx": 11,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\nI\n\ni,j,k\n\ni,j,k(0)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 10,
- "end_row_offset_idx": 11,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Initial total population HIV-positive",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 10,
- "end_row_offset_idx": 11,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "0.50% ",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 11,
- "end_row_offset_idx": 12,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\u03c7\ni,j\n\ni,j(0)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 11,
- "end_row_offset_idx": 12,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Initial total children in population (<15 years of age)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 11,
- "end_row_offset_idx": 12,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "45%",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 12,
- "end_row_offset_idx": 13,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\nW/W (0)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 12,
- "end_row_offset_idx": 13,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Initial total wild types (W/W) in population",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 12,
- "end_row_offset_idx": 13,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "80% ",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 13,
- "end_row_offset_idx": 14,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\nW/\u039432(0)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 13,
- "end_row_offset_idx": 14,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Initial total heterozygotes (W/\u039432) in population",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 13,
- "end_row_offset_idx": 14,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "19%",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 14,
- "end_row_offset_idx": 15,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\u039432/\u039432(0)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 14,
- "end_row_offset_idx": 15,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Initial total homozygotes (\u039432/\u039432) in population",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 14,
- "end_row_offset_idx": 15,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "1%",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 15,
- "end_row_offset_idx": 16,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\nr\n\nM\n\nM(r\n\nF\n\nF)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 15,
- "end_row_offset_idx": 16,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Initial percent males (females) in total population",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 15,
- "end_row_offset_idx": 16,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "49% (51%)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 16,
- "end_row_offset_idx": 17,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\u03d5\nF\n\nF, \u03d5\nM\n\nM\n",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 16,
- "end_row_offset_idx": 17,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Number of sexual contacts a female (male) has",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 16,
- "end_row_offset_idx": 17,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "30 (24) per partner",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 17,
- "end_row_offset_idx": 18,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\u025b\ni,j,k\n\ni,j,k\n",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 17,
- "end_row_offset_idx": 18,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "% effect of mutation on transmission rates (see Table 2)",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 17,
- "end_row_offset_idx": 18,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "0 < \u025b\ni,j,k\n\ni,j,k < 1",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 18,
- "end_row_offset_idx": 19,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\u03b4",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 18,
- "end_row_offset_idx": 19,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Death rate for AIDS population",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 18,
- "end_row_offset_idx": 19,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "1.0 per year ",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ],
- [
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 19,
- "end_row_offset_idx": 20,
- "start_col_offset_idx": 0,
- "end_col_offset_idx": 1,
- "text": "\nq\n",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 19,
- "end_row_offset_idx": 20,
- "start_col_offset_idx": 1,
- "end_col_offset_idx": 2,
- "text": "Allelic frequency of \u039432 allele",
- "column_header": false,
- "row_header": false,
- "row_section": false
- },
- {
- "row_span": 1,
- "col_span": 1,
- "start_row_offset_idx": 19,
- "end_row_offset_idx": 20,
- "start_col_offset_idx": 2,
- "end_col_offset_idx": 3,
- "text": "0.105573",
- "column_header": false,
- "row_header": false,
- "row_section": false
- }
- ]
- ]
- }
- }
- ],
- "key_value_items": [],
- "pages": {}
-}
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/pnas_sample.xml.md b/tests/data/groundtruth/docling_v2/pnas_sample.xml.md
deleted file mode 100644
index 41dfe80d..00000000
--- a/tests/data/groundtruth/docling_v2/pnas_sample.xml.md
+++ /dev/null
@@ -1,258 +0,0 @@
-# The coreceptor mutation CCR5Δ32 influences the dynamics of HIV epidemics and is selected for by HIV
-
-Amy D. Sullivan, Janis Wigginton, Denise Kirschner
-
-Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109-0620
-
-## Abstract
-
-We explore the impact of a host genetic factor on heterosexual HIV epidemics by using a deterministic mathematical model. A protective allele unequally distributed across populations is exemplified in our models by the 32-bp deletion in the host-cell chemokine receptor CCR5, CCR5Δ32. Individuals homozygous for CCR5Δ32 are protected against HIV infection whereas those heterozygous for CCR5Δ32 have lower pre-AIDS viral loads and delayed progression to AIDS. CCR5Δ32 may limit HIV spread by decreasing the probability of both risk of infection and infectiousness. In this work, we characterize epidemic HIV within three dynamic subpopulations: CCR5/CCR5 (homozygous, wild type), CCR5/CCR5Δ32 (heterozygous), and CCR5Δ32/CCR5Δ32 (homozygous, mutant). Our results indicate that prevalence of HIV/AIDS is greater in populations lacking the CCR5Δ32 alleles (homozygous wild types only) as compared with populations that include people heterozygous or homozygous for CCR5Δ32. Also, we show that HIV can provide selective pressure for CCR5Δ32, increasing the frequency of this allele.
-
-Nineteen million people have died of AIDS since the discovery of HIV in the 1980s. In 1999 alone, 5.4 million people were newly infected with HIV (ref. 1 and http://www.unaids.org/epidemicupdate/report/Epireport.html). (For brevity, HIV-1 is referred to as HIV in this paper.) Sub-Saharan Africa has been hardest hit, with more than 20% of the general population HIV-positive in some countries (2, 3). In comparison, heterosexual epidemics in developed, market-economy countries have not reached such severe levels. Factors contributing to the severity of the epidemic in economically developing countries abound, including economic, health, and social differences such as high levels of sexually transmitted diseases and a lack of prevention programs. However, the staggering rate at which the epidemic has spread in sub-Saharan Africa has not been adequately explained. The rate and severity of this epidemic also could indicate a greater underlying susceptibility to HIV attributable not only to sexually transmitted disease, economics, etc., but also to other more ubiquitous factors such as host genetics (4, 5).
-
-To exemplify the contribution of such a host genetic factor to HIV prevalence trends, we consider a well-characterized 32-bp deletion in the host-cell chemokine receptor CCR5, CCR5Δ32. When HIV binds to host cells, it uses the CD4 receptor on the surface of host immune cells together with a coreceptor, mainly the CCR5 and CXCR4 chemokine receptors (6). Homozygous mutations for this 32-bp deletion offer almost complete protection from HIV infection, and heterozygous mutations are associated with lower pre-AIDS viral loads and delayed progression to AIDS (7–14). CCR5Δ32 generally is found in populations of European descent, with allelic frequencies ranging from 0 to 0.29 (13). African and Asian populations studied outside the United States or Europe appear to lack the CCR5Δ32 allele, with an allelic frequency of almost zero (5, 13). Thus, to understand the effects of a protective allele, we use a mathematical model to track prevalence of HIV in populations with or without CCR5Δ32 heterozygous and homozygous people and also to follow the CCR5Δ32 allelic frequency.
-
-We hypothesize that CCR5Δ32 limits epidemic HIV by decreasing infection rates, and we evaluate the relative contributions to this by the probability of infection and duration of infectivity. To capture HIV infection as a chronic infectious disease together with vertical transmission occurring in untreated mothers, we model a dynamic population (i.e., populations that vary in growth rates because of fluctuations in birth or death rates) based on realistic demographic characteristics (18). This scenario also allows tracking of the allelic frequencies over time. This work considers how a specific host genetic factor affecting HIV infectivity and viremia at the individual level might influence the epidemic in a dynamic population and how HIV exerts selective pressure, altering the frequency of this mutant allele.
-
-CCR5 is a host-cell chemokine receptor, which is also used as a coreceptor by R5 strains of HIV that are generally acquired during sexual transmission (6, 19–25). As infection progresses to AIDS the virus expands its repertoire of potential coreceptors to include other CC-family and CXC-family receptors in roughly 50% of patients (19, 26, 27). CCR5Δ32 was identified in HIV-resistant people (28). Benefits to individuals from the mutation in this allele are as follows. Persons homozygous for the CCR5Δ32 mutation are almost nonexistent in HIV-infected populations (11, 12) (see ref. 13 for review). Persons heterozygous for the mutant allele (CCR5 W/Δ32) tend to have lower pre-AIDS viral loads. Aside from the beneficial effects that lower viral loads may have for individuals, there is also an altruistic effect, as transmission rates are reduced for individuals with low viral loads (as compared with, for example, AZT and other studies; ref. 29). Finally, individuals heterozygous for the mutant allele (CCR5 W/Δ32) also have a slower progression to AIDS than those homozygous for the wild-type allele (CCR5 W/W) (7–10), remaining in the population 2 years longer, on average. Interestingly, the dearth of information on HIV disease progression in people homozygous for the CCR5Δ32 allele (CCR5 Δ32/Δ32) stems from the rarity of HIV infection in this group (4, 12, 28). However, in case reports of HIV-infected CCR5 Δ32/Δ32 homozygotes, a rapid decline in CD4+ T cells and a high viremia are observed, likely because of initial infection with a more aggressive viral strain (such as X4 or R5X4) (30).
-
-## The Model
-
-Because we are most concerned with understanding the severity of the epidemic in developing countries where the majority of infection is heterosexual, we consider a purely heterosexual model. To model the effects of the allele in the population, we examine the rate of HIV spread by using an enhanced susceptible-infected-AIDS model of epidemic HIV (for review see ref. 31). Our model compares two population scenarios: a CCR5 wild-type population and one with CCR5Δ32 heterozygotes and homozygotes in addition to the wild type. To model the scenario where there are only wild-type individuals present in the population (i.e., CCR5 W/W), we track the sexually active susceptibles at time t [Si,j (t)], where i = 1 refers to genotype (CCR5 W/W only in this case) and j is either the male or female subpopulation. We also track those who are HIV-positive at time t not yet having AIDS in Ii,j,k (t) where k refers to stage of HIV infection [primary (A) or asymptomatic (B)]. The total number of individuals with AIDS at time t are tracked in A(t). The source population are children, χ i,j (t), who mature into the sexually active population at time t (Fig. 1, Table 1). We compare the model of a population lacking the CCR5Δ32 allele to a demographically similar population with a high frequency of the allele. When genetic heterogeneity is included, male and female subpopulations are each further divided into three distinct genotypic groups, yielding six susceptible subpopulations, [Si,j (t), where i ranges from 1 to 3, where 1 = CCR5W/W; 2 = CCR5 W/Δ32; 3 = CCR5 Δ32/Δ32]. The infected classes, Ii,j,k (t), also increase in number to account for these new genotype compartments. In both settings we assume there is no treatment available and no knowledge of HIV status by people in the early acute and middle asymptomatic stages (both conditions exist in much of sub-Saharan Africa). In addition, we assume that sexual mixing in the population occurs randomly with respect to genotype and HIV disease status, all HIV-infected people eventually progress to AIDS, and no barrier contraceptives are used. These last assumptions reflect both economic and social conditions.
-
-Figure 1 A schematic representation of the basic compartmental HIV epidemic model. The criss-cross lines indicate the sexual mixing between different compartments. Each of these interactions has a positive probability of taking place; they also incorporate individual rates of transmission indicated as λ, but in full notation is λ î,,→i,j, where i,j,k is the phenotype of the infected partner and î, is the phenotype of the susceptible partner. Also shown are the different rates of disease progression, γ i,j,k , that vary according to genotype, gender, and stage. Thus, the interactions between different genotypes, genders, and stages are associated with a unique probability of HIV infection. M, male; F, female.
-
-
-
-Table 1 Children's genotype
-
-| Parents | Mother | Mother | Mother | Mother |
-|-----------|----------|--------------------|------------------------------|--------------------|
-| | | | | |
-| Father | | W/W | W/Δ32 | Δ32/Δ32 |
-| | W/W | χ1,j 1,j | χ1,j 1,j, χ2,j 2,j | χ2,j 2,j |
-| | W/Δ32 | χ1,j 1,j, χ2,j 2,j | χ1,j 1,j, χ2,j 2,j, χ3,j 3,j | χ2,j 2,j, χ3,j 3,j |
-| | Δ32/Δ32 | χ2,j 2,j | χ2,j 2,j, χ3,j 3,j | χ3,j 3,j |
-
-### Parameter Estimates for the Model.
-
-Estimates for rates that govern the interactions depicted in Fig. 1 were derived from the extensive literature on HIV. Our parameters and their estimates are summarized in Tables 2–4. The general form of the equations describing the rates of transition between population classes as depicted in Fig. 1 are summarized as follows:
-
-$$ \frac{dS_{i,j}(t)}{dt}={\chi}_{i,j}(t)-{\mu}_{j}S_{i,j}(t)-{\lambda}_{\hat {\imath},\hat {},\hat {k}{\rightarrow}i,j}S_{i,j}(t), $$
-
-$$ \hspace{1em}\hspace{1em}\hspace{.167em}\frac{dI_{i,j,A}(t)}{dt}={\lambda}_{\hat {\imath},\hat {},\hat {k}{\rightarrow}i,j}S_{i,j}(t)-{\mu}_{j}I_{i,j,A}(t)-{\gamma}_{i,j,A}I_{i,j,A}(t), $$
-
-$$ \frac{dI_{i,j,B}(t)}{dt}={\gamma}_{i,j,A}I_{i,j,A}(t)-{\mu}_{j}I_{i,j,B}(t)-{\gamma}_{i,j,B}I_{i,j,B}(t), $$
-
-$$ \frac{dA(t)}{dt}={\gamma}_{i,j,B} \left( { \,\substack{ ^{3} \\ {\sum} \\ _{i=1} }\, }I_{i,F,B}(t)+I_{i,M,B}(t) \right) -{\mu}_{A}A(t)-{\delta}A(t), $$
-
-where, in addition to previously defined populations and rates (with i equals genotype, j equals gender, and k equals stage of infection, either A or B), μ j , represents the non-AIDS (natural) death rate for males and females respectively, and μA is estimated by the average (μF + μM/2). This approximation allows us to simplify the model (only one AIDS compartment) without compromising the results, as most people with AIDS die of AIDS (δAIDS) and very few of other causes (μA). These estimates include values that affect infectivity (λ î,,→i,j ), transmission (β î,,→i,j ), and disease progression (γ i , j , k ) where the î,, notation represents the genotype, gender, and stage of infection of the infected partner, and j ≠ .
-
-Table 2 Transmission probabilities
-
-| HIV-infected partner (îıı^^, ^^, k k^^) | Susceptible partner (i, j) | Susceptible partner (i, j) | Susceptible partner (i, j) | Susceptible partner (i, j) |
-|-----------------------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
-| HIV-infected partner (îıı^^, ^^, k k^^) | | | | |
-| HIV-infected partner (îıı^^, ^^, k k^^) | (^^ to j) | W/W | W/Δ32 | Δ32/Δ32 |
-| | | | | |
-| Acute/primary | | | | |
-| W/W or Δ32/Δ32 | M to F | 0.040 | 0.040 | 0.00040 |
-| | F to M | 0.020 | 0.020 | 0.00020 |
-| W/Δ32 | M to F | 0.030 | 0.030 | 0.00030 |
-| | F to M | 0.015 | 0.015 | 0.00015 |
-| Asymptomatic | | | | |
-| W/W or Δ32/Δ32 | M to F | 0.0010 | 0.0010 | 10 × 10−6 |
-| | F to M | 0.0005 | 0.0005 | 5 × 10−6 |
-| W/Δ32 | M to F | 0.0005 | 0.0005 | 5 × 10−6 |
-| | F to M | 0.00025 | 0.00025 | 2.5 × 10−6 |
-
-Table 3 Progression rates
-
-| Genotype | Disease stage | Males/females |
-|------------|-----------------|------------------|
-| | | |
-| W/W | A | 3.5 |
-| | B | 0.16667 |
-| W/Δ32 | A | 3.5 |
-| | B | 0.125 |
-| Δ32/Δ32 | A | 3.5 |
-| | B | 0.16667 |
-
-Table 4 Parameter values
-
-| Parameter | Definition | Value |
-|-----------------------------------------|----------------------------------------------------------|-------------------------|
-| | | |
-| μ F F, μ M M | All-cause mortality for adult females (males) | 0.015 (0.016) per year |
-| μχχ | All-cause childhood mortality (<15 years of age) | 0.01 per year |
-| B r r | Birthrate | 0.25 per woman per year |
-| SA F F | Percent females acquiring new partners (sexual activity) | 10% |
-| SA M M | Percent males acquiring new partners (sexual activity) | 25% |
-| m F F(ς$$ {\mathrm{_{{F}}^{{2}}}} $$) | Mean (variance) no. of new partners for females | 1.8 (1.2) per year |
-| ς$$ {\mathrm{_{{M}}^{{2}}}} $$ | Variance in no. of new partners for males | 5.5 per year |
-| 1 − p v v | Probability of vertical transmission | 0.30 per birth |
-| I i,j,k i,j,k(0) | Initial total population HIV-positive | 0.50% |
-| χ i,j i,j(0) | Initial total children in population (<15 years of age) | 45% |
-| W/W (0) | Initial total wild types (W/W) in population | 80% |
-| W/Δ32(0) | Initial total heterozygotes (W/Δ32) in population | 19% |
-| Δ32/Δ32(0) | Initial total homozygotes (Δ32/Δ32) in population | 1% |
-| r M M(r F F) | Initial percent males (females) in total population | 49% (51%) |
-| ϕ F F, ϕ M M | Number of sexual contacts a female (male) has | 30 (24) per partner |
-| ɛ i,j,k i,j,k | % effect of mutation on transmission rates (see Table 2) | 0 < ɛ i,j,k i,j,k < 1 |
-| δ | Death rate for AIDS population | 1.0 per year |
-| q | Allelic frequency of Δ32 allele | 0.105573 |
-
-The effects of the CCR5 W/Δ32 and CCR5 Δ32/Δ32 genotypes are included in our model through both the per-capita probabilities of infection, λ î,,→i,j , and the progression rates, γ i , j , k . The infectivity coefficients, λ î,,→i,j , are calculated for each population subgroup based on the following: likelihood of HIV transmission in a sexual encounter between a susceptible and an infected (βîıı^^,j,k k^^→i,j ) person; formation of new partnerships (c j j); number of contacts in a given partnership (ϕ j ); and probability of encountering an infected individual (I î,, /N ). The formula representing this probability of infection is
-
-$$ {\lambda}_{\hat {i},\hat {j},\hat {k}{\rightarrow}i,j}=\frac{C_{j}{\cdot}{\phi}_{j}}{N_{\hat {j}}}\hspace{.167em} \left[ { \,\substack{ \\ {\sum} \\ _{\hat {i},\hat {k}} }\, }{\beta}_{\hat {i},\hat {j},\hat {k}{\rightarrow}i,j}{\cdot}I_{\hat {i},\hat {j},\hat {k}} \right] , $$
-
-where j ≠ is either male or female. N represents the total population of gender (this does not include those with AIDS in the simulations).
-
-The average rate of partner acquisition, cj , includes the mean plus the variance to mean ratio of the relevant distribution of partner-change rates to capture the small number of high-risk people: cj = mj + (ς/m j) where the mean (mj ) and variance (ς) are annual figures for new partnerships only (32). These means are estimated from Ugandan data for the number of heterosexual partners in the past year (33) and the number of nonregular heterosexual partners (i.e., spouses or long-term partners) in the past year (34). In these sexual activity surveys, men invariably have more new partnerships; thus, we assumed that they would have fewer average contacts per partnership than women (a higher rate of new partner acquisition means fewer sexual contacts with a given partner; ref. 35). To incorporate this assumption in our model, the male contacts/partnership, ϕ M , was reduced by 20%. In a given population, the numbers of heterosexual interactions must equate between males and females. The balancing equation applied here is SA F·m F·N F = SA M·m M·N M, where SAj are the percent sexually active and Nj are the total in the populations for gender j. To specify changes in partner acquisition, we apply a male flexibility mechanism, holding the female rate of acquisition constant and allowing the male rates to vary (36, 37).
-
-#### Transmission probabilities.
-
-The effect of a genetic factor in a model of HIV transmission can be included by reducing the transmission coefficient. The probabilities of transmission per contact with an infected partner, βîıı^^,^^,k k^^→i,j , have been estimated in the literature (see ref. 38 for estimates in minimally treated groups). We want to capture a decreased risk in transmission based on genotype (ref. 39, Table 2). No studies have directly evaluated differences in infectivity between HIV-infected CCR5 W/Δ32 heterozygotes and HIV-infected CCR5 wild types. Thus, we base estimates for reduced transmission on studies of groups with various HIV serum viral loads (40), HTLV-I/II viral loads (41), and a study of the effect of AZT treatment on transmission (29). We decrease transmission probabilities for infecting CCR5Δ32/Δ32 persons by 100-fold to reflect the rarity of infections in these persons. However, we assume that infected CCR5Δ32/Δ32 homozygotes can infect susceptibles at a rate similar to CCR5W/W homozygotes, as the former generally have high viremias (ref. 30, Table 2). We also assume that male-to-female transmission is twice as efficient as female-to-male transmission (up to a 9-fold difference has been reported; ref. 42) (ref. 43, Table 2).
-
-Given the assumption of no treatment, the high burden of disease in people with AIDS is assumed to greatly limit their sexual activity. Our initial model excludes people with AIDS from the sexually active groups. Subsequently, we allow persons with AIDS to be sexually active, fixing their transmission rates (βAIDS) to be the same across all CCR5 genotypes, and lower than transmission rates for primary-stage infection (as the viral burden on average is not as high as during the acute phase), and larger than transmission rates for asymptomatic-stage infection (as the viral burden characteristically increases during the end stage of disease).
-
-#### Disease progression.
-
-We assume three stages of HIV infection: primary (acute, stage A), asymptomatic HIV (stage B), and AIDS. The rates of transition through the first two stages are denoted by γ i,j,k i,j,k, where i represents genotype, j is male/female, and k represents either stage A or stage B. Transition rates through each of these stages are assumed to be inversely proportional to the duration of that stage; however, other distributions are possible (31, 44, 45). Although viral loads generally peak in the first 2 months of infection, steady-state viral loads are established several months beyond this (46). For group A, the primary HIV-infecteds, duration is assumed to be 3.5 months. Based on results from European cohort studies (7–10), the beneficial effects of the CCR5 W/Δ32 genotype are observed mainly in the asymptomatic years of HIV infection; ≈7 years after seroconversion survival rates appear to be quite similar between heterozygous and homozygous individuals. We also assume that CCR5Δ32/Δ32-infected individuals and wild-type individuals progress similarly, and that men and women progress through each disease stage at the same rate. Given these observations, and that survival after infection may be shorter in untreated populations, we choose the duration time in stage B to be 6 years for wild-type individuals and 8 years for heterozygous individuals. Transition through AIDS, δAIDS, is inversely proportional to the duration of AIDS. We estimate this value to be 1 year for the time from onset of AIDS to death. The progression rates are summarized in Table 3.
-
-### Demographic Setting.
-
-Demographic parameters are based on data from Malawi, Zimbabwe, and Botswana (3, 47). Estimated birth and child mortality rates are used to calculate the annual numbers of children (χ i,j i,j) maturing into the potentially sexually active, susceptible group at the age of 15 years (3). For example, in the case where the mother is CCR5 wild type and the father is CCR5 wild type or heterozygous, the number of CCR5 W/W children is calculated as follows [suppressing (t) notation]: χ1,j 1,j =
-
-$$ B_{r}\hspace{.167em}{ \,\substack{ \\ {\sum} \\ _{k} }\, } \left[ S_{1,F}\frac{(S_{1,M}+I_{1,M,k})}{N_{M}}+ \left[ (0.5)S_{1,F}\frac{(S_{2,M}+I_{2,M,k})}{N_{M}} \right] + \right $$
-
-$$ p_{v} \left \left( \frac{(I_{1,F,k}(S_{1,M}+I_{1,M,k}))}{N_{M}}+ \left[ (0.5)I_{1,F,k}\frac{(S_{2,M}+I_{2,M,k})}{N_{M}} \right] \right) \right] ,\hspace{.167em} $$
-
-where the probability of HIV vertical transmission, 1 − pv , and the birthrate, Br , are both included in the equations together with the Mendelian inheritance values as presented in Table 1. The generalized version of this equation (i.e., χ i,j i,j) can account for six categories of children (including gender and genotype). We assume that all children of all genotypes are at risk, although we can relax this condition if data become available to support vertical protection (e.g., ref. 48). All infected children are assumed to die before age 15. Before entering the susceptible group at age 15, there is additional loss because of mortality from all non-AIDS causes occurring less than 15 years of age at a rate of μχχ × χ i,j i,j (where μχ is the mortality under 15 years of age). Children then enter the population as susceptibles at an annual rate, ς j j × χ i,j i,j/15, where ς j distributes the children 51% females and 49% males. All parameters and their values are summarized in Table 4.
-
-## Prevalence of HIV
-
-### Demographics and Model Validation.
-
-The model was validated by using parameters estimated from available demographic data. Simulations were run in the absence of HIV infection to compare the model with known population growth rates. Infection was subsequently introduced with an initial low HIV prevalence of 0.5% to capture early epidemic behavior.
-
-In deciding on our initial values for parameters during infection, we use Joint United Nations Programme on HIV/AIDS national prevalence data for Malawi, Zimbabwe, and Botswana. Nationwide seroprevalence of HIV in these countries varies from ≈11% to over 20% (3), although there may be considerable variation within given subpopulations (2, 49).
-
-In the absence of HIV infection, the annual percent population growth rate in the model is ≈2.5%, predicting the present-day values for an average of sub-Saharan African cities (data not shown). To validate the model with HIV infection, we compare our simulation of the HIV epidemic to existing prevalence data for Kenya and Mozambique (http://www.who.int/emc-hiv/fact-sheets/pdfs/kenya.pdf and ref. 51). Prevalence data collected from these countries follow similar trajectories to those predicted by our model (Fig. 2).
-
-Figure 2 Model simulation of HIV infection in a population lacking the protective CCR5Δ32 allele compared with national data from Kenya (healthy adults) and Mozambique (blood donors, ref. 17). The simulated population incorporates parameter estimates from sub-Saharan African demographics. Note the two outlier points from the Mozambique data were likely caused by underreporting in the early stages of the epidemic.
-
-
-
-### Effects of the Allele on Prevalence.
-
-After validating the model in the wild type-only population, both CCR5Δ32 heterozygous and homozygous people are included. Parameter values for HIV transmission, duration of illness, and numbers of contacts per partner are assumed to be the same within both settings. We then calculate HIV/AIDS prevalence among adults for total HIV/AIDS cases.
-
-Although CCR5Δ32/Δ32 homozygosity is rarely seen in HIV-positive populations (prevalence ranges between 0 and 0.004%), 1–20% of people in HIV-negative populations of European descent are homozygous. Thus, to evaluate the potential impact of CCR5Δ32, we estimate there are 19% CCR5 W/Δ32 heterozygous and 1% CCR5 Δ32/Δ32 homozygous people in our population. These values are in Hardy-Weinberg equilibrium with an allelic frequency of the mutation as 0.105573.
-
-Fig. 3 shows the prevalence of HIV in two populations: one lacking the mutant CCR5 allele and another carrying that allele. In the population lacking the protective mutation, prevalence increases logarithmically for the first 35 years of the epidemic, reaching 18% before leveling off.
-
-Figure 3 Prevalence of HIV/AIDS in the adult population as predicted by the model. The top curve (○) indicates prevalence in a population lacking the protective allele. We compare that to a population with 19% heterozygous and 1% homozygous for the allele (implying an allelic frequency of 0.105573. Confidence interval bands (light gray) are shown around the median simulation () providing a range of uncertainty in evaluating parameters for the effect of the mutation on the infectivity and the duration of asymptomatic HIV for heterozygotes.
-
-
-
-In contrast, when a proportion of the population carries the CCR5Δ32 allele, the epidemic increases more slowly, but still logarithmically, for the first 50 years, and HIV/AIDS prevalence reaches ≈12% (Fig. 3). Prevalence begins to decline slowly after 70 years.
-
-In the above simulations we assume that people with AIDS are not sexually active. However, when these individuals are included in the sexually active population the severity of the epidemic increases considerably (data not shown). Consistent with our initial simulations, prevalences are still relatively lower in the presence of the CCR5 mutation.
-
-Because some parameters (e.g., rate constants) are difficult to estimate based on available data, we implement an uncertainty analysis to assess the variability in the model outcomes caused by any inaccuracies in estimates of the parameter values with regard to the effect of the allelic mutation. For these analyses we use Latin hypercube sampling, as described in refs. 52–56, Our uncertainty and sensitivity analyses focus on infectivity vs. duration of infectiousness. To this end, we assess the effects on the dynamics of the epidemic for a range of values of the parameters governing transmission and progression rates: βîıı^^,^^,k k^^→i,j and γ i,j,k i,j,k. All other parameters are held constant. These results are presented as an interval band about the average simulation for the population carrying the CCR5Δ32 allele (Fig. 3). Although there is variability in the model outcomes, the analysis indicates that the overall model predictions are consistent for a wide range of transmission and progression rates. Further, most of the variation observed in the outcome is because of the transmission rates for both heterosexual males and females in the primary stage of infection (β2,M,A → i ,F, β2,F,A → i ,M). As mentioned above, we assume lower viral loads correlate with reduced infectivity; thus, the reduction in viral load in heterozygotes has a major influence on disease spread.
-
-## HIV Induces Selective Pressure on Genotype Frequency
-
-To observe changes in the frequency of the CCR5Δ32 allele in a setting with HIV infection as compared with the Hardy-Weinberg equilibrium in the absence of HIV, we follow changes in the total number of CCR5Δ32 heterozygotes and homozygotes over 1,000 years (Fig. 4). We initially perform simulations in the absence of HIV infection as a negative control to show there is not significant selection of the allele in the absence of infection. To determine how long it would take for the allelic frequency to reach present-day levels (e.g., q = 0.105573), we initiate this simulation for 1,000 years with a very small allelic frequency (q = 0.00105). In the absence of HIV, the allelic frequency is maintained in equilibrium as shown by the constant proportions of CCR5Δ32 heterozygotes and homozygotes (Fig. 4, solid lines). The selection for CCR5Δ32 in the presence of HIV is seen in comparison (Fig. 4, dashed lines). We expand the time frame of this simulation to 2,000 years to view the point at which the frequency reaches present levels (where q ∼0.105573 at year = 1200). Note that the allelic frequency increases for ≈1,600 years before leveling off.
-
-Figure 4 Effects of HIV-1 on selection of the CCR5Δ32 allele. The Hardy-Weinberg equilibrium level is represented in the no-infection simulation (solid lines) for each population. Divergence from the original Hardy-Weinberg equilibrium is shown to occur in the simulations that include HIV infection (dashed lines). Fraction of the total subpopulations are presented: (A) wild types (W/W), (B) heterozygotes (W/Δ32), and (C) homozygotes (Δ32/Δ32). Note that we initiate this simulation with a much lower allelic frequency (0.00105) than used in the rest of the study to better exemplify the actual selective effect over a 1,000-year time scale. (D) The allelic selection effect over a 2,000-year time scale.
-
-
-
-## Discussion
-
-This study illustrates how populations can differ in susceptibility to epidemic HIV/AIDS depending on a ubiquitous attribute such as a prevailing genotype. We have examined heterosexual HIV epidemics by using mathematical models to assess HIV transmission in dynamic populations either with or without CCR5Δ32 heterozygous and homozygous persons. The most susceptible population lacks the protective mutation in CCR5. In less susceptible populations, the majority of persons carrying the CCR5Δ32 allele are heterozygotes. We explore the hypothesis that lower viral loads (CCR5Δ32 heterozygotes) or resistance to infection (CCR5Δ32 homozygotes) observed in persons with this coreceptor mutation ultimately can influence HIV epidemic trends. Two contrasting influences of the protective CCR5 allele are conceivable: it may limit the epidemic by decreasing the probability of infection because of lower viral loads in infected heterozygotes, or it may exacerbate the epidemic by extending the time that infectious individuals remain in the sexually active population. Our results strongly suggest the former. Thus, the absence of this allele in Africa could explain the severity of HIV disease as compared with populations where the allele is present.
-
-We also observed that HIV can provide selective pressure for the CCR5Δ32 allele within a population, increasing the allelic frequency. Other influences may have additionally selected for this allele. Infectious diseases such as plague and small pox have been postulated to select for CCR5Δ32 (57, 58). For plague, relatively high levels of CCR5Δ32 are believed to have arisen within ≈4,000 years, accounting for the prevalence of the mutation only in populations of European descent. Smallpox virus uses the CC-coreceptor, indicating that direct selection for mutations in CCR5 may have offered resistance to smallpox. Given the differences in the epidemic rates of plague (59), smallpox, and HIV, it is difficult to directly compare our results to these findings. However, our model suggests that the CCR5Δ32 mutation could have reached its present allelic frequency in Northern Europe within this time frame if selected for by a disease with virulence patterns similar to HIV. Our results further support the idea that HIV has been only recently introduced as a pathogen into African populations, as the frequency of the protective allele is almost zero, and our model predicts that selection of the mutant allele in this population by HIV alone takes at least 1,000 years. This prediction is distinct from the frequency of the CCR5Δ32 allele in European populations, where pathogens that may have influenced its frequency (e.g., Yersinia pestis) have been present for much longer.
-
-Two mathematical models have considered the role of parasite and host genetic heterogeneity with regard to susceptibility to another pathogen, namely malaria (60, 61). In each it was determined that heterogeneity of host resistance facilitates the maintenance of diversity in parasite virulence. Given our underlying interest in the coevolution of pathogen and host, we focus on changes in a host protective mutation, holding the virulence of the pathogen constant over time.
-
-Even within our focus on host protective mutations, numerous genetic factors, beneficial or detrimental, could potentially influence epidemics. Other genetically determined host factors affecting HIV susceptibility and disease progression include a CCR5 A/A to G/G promoter polymorphism (62), a CCR2 point mutation (11, 63), and a mutation in the CXCR4 ligand (64). The CCR2b mutation, CCR264I, is found in linkage with at least one CCR5 promoter polymorphism (65) and is prevalent in populations where CCR5Δ32 is nonexistent, such as sub-Saharan Africa (63). However, as none of these mutations have been consistently shown to be as protective as the CCR5Δ32 allele, we simplified our model to incorporate only the effect of CCR5Δ32. Subsequent models could be constructed from our model to account for the complexity of multiple protective alleles. It is interesting to note that our model predicts that even if CCR264I is present at high frequencies in Africa, its protective effects may not augment the lack of a protective allele such as CCR5Δ32.
-
-Although our models demonstrate that genetic factors can contribute to the high prevalence of HIV in sub-Saharan Africa, demographic factors are also clearly important in this region. Our models explicitly incorporated such factors, for example, lack of treatment availability. Additional factors were implicitly controlled for by varying only the presence of the CCR5Δ32 allele. More complex models eventually could include interactions with infectious diseases that serve as cofactors in HIV transmission. The role of high sexually transmitted disease prevalences in HIV infection has long been discussed, especially in relation to core populations (15, 50, 66). Malaria, too, might influence HIV transmission, as it is associated with transient increases in semen HIV viral loads and thus could increase the susceptibility of the population to epidemic HIV (16).
-
-In assessing the HIV/AIDS epidemic, considerable attention has been paid to the influence of core groups in driving sexually transmitted disease epidemics. Our results also highlight how characteristics more uniformly distributed in a population can affect susceptibility. We observed that the genotypic profile of a population affects its susceptibility to epidemic HIV/AIDS. Additional studies are needed to better characterize the influence of these genetic determinants on HIV transmission, as they may be crucial in estimating the severity of the epidemic in some populations. This information can influence the design of treatment strategies as well as point to the urgency for education and prevention programs.
-
-## Acknowledgments
-
-We thank Mark Krosky, Katia Koelle, and Kevin Chung for programming and technical assistance. We also thank Drs. V. J. DiRita, P. Kazanjian, and S. M. Blower for helpful comments and discussions. We thank the reviewers for extremely insightful comments.
-
-## References
-
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\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/pntd.0008301.xml.itxt b/tests/data/groundtruth/docling_v2/pntd.0008301.nxml.itxt
similarity index 100%
rename from tests/data/groundtruth/docling_v2/pntd.0008301.xml.itxt
rename to tests/data/groundtruth/docling_v2/pntd.0008301.nxml.itxt
diff --git a/tests/data/groundtruth/docling_v2/pntd.0008301.nxml.json b/tests/data/groundtruth/docling_v2/pntd.0008301.nxml.json
new file mode 100644
index 00000000..1fae7c12
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+ "text": "Clara R. Burgert-Brucker, Kathryn L. Zoerhoff, Maureen Headland, Erica A. Shoemaker, Rachel Stelmach, Mohammad Jahirul Karim, Wilfrid Batcho, Clarisse Bougouma, Roland Bougma, Biholong Benjamin Didier, Nko'Ayissi Georges, Benjamin Marfo, Jean Frantz Lemoine, Helena Ullyartha Pangaribuan, Eksi Wijayanti, Yaya Ibrahim Coulibaly, Salif Seriba Doumbia, Pradip Rimal, Adamou Bacthiri Salissou, Yukaba Bah, Upendo Mwingira, Andreas Nshala, Edridah Muheki, Joseph Shott, Violetta Yevstigneyeva, Egide Ndayishimye, Margaret Baker, John Kraemer, Molly Brady"
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+ "text": "Global Health Division, RTI International, Washington, DC, United States of America; Global Health, Population, and Nutrition, FHI 360, Washington, DC, United States of America; Department of Disease Control, Ministry of Health and Family Welfare, Dhaka, Bangladesh; National Control Program of Communicable Diseases, Ministry of Health, Cotonou, Benin; Lymphatic Filariasis Elimination Program, Ministère de la Santé, Ouagadougou, Burkina Faso; National Onchocerciasis and Lymphatic Filariasis Control Program, Ministry of Health, Yaounde, Cameroon; Neglected Tropical Diseases Programme, Ghana Health Service, Accra, Ghana; Ministry of Health, Port-au-Prince, Haiti; National Institute Health Research & Development, Ministry of Health, Jakarta, Indonesia; Filariasis Unit, International Center of Excellence in Research, Faculty of Medicine and Odontostomatology, Bamako, Mali; Epidemiology and Disease Control Division, Department of Health Service, Kathmandu, Nepal; Programme Onchocercose et Filariose Lymphatique, Ministère de la Santé, Niamey, Niger; National Neglected Tropical Disease Program, Ministry of Health and Sanitation, Freetown, Sierra Leone; Neglected Tropical Disease Control Programme, National Institute for Medical Research, Dar es Salaam, Tanzania; IMA World Health/Tanzania NTD Control Programme, Uppsala University, & TIBA Fellow, Dar es Salaam, Tanzania; Programme to Eliminate Lymphatic Filariasis, Ministry of Health, Kampala, Uganda; Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC, United States of America; Georgetown University, Washington, DC, United States of America"
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+ "orig": "Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five effective rounds of mass drug administration (MDA) and demonstrating low prevalence in subsequent assessments. The first assessments recommended by the World Health Organization (WHO) are sentinel and spot-check sites—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit after MDA. If pre-TAS shows that prevalence in each site has been lowered to less than 1% microfilaremia or less than 2% antigenemia, the implementation unit conducts a TAS to determine whether MDA can be stopped. Failure to pass pre-TAS means that further rounds of MDA are required. This study aims to understand factors influencing pre-TAS results using existing programmatic data from 554 implementation units, of which 74 (13%) failed, in 13 countries. Secondary data analysis was completed using existing data from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. Additional covariate data were obtained from spatial raster data sets. Bivariate analysis and multilinear regression were performed to establish potential relationships between variables and the pre-TAS result. Higher baseline prevalence and lower elevation were significant in the regression model. Variables statistically significantly associated with failure (p-value ≤0.05) in the bivariate analyses included baseline prevalence at or above 5% or 10%, use of Filariasis Test Strips (FTS), primary vector of Culex, treatment with diethylcarbamazine-albendazole, higher elevation, higher population density, higher enhanced vegetation index (EVI), higher annual rainfall, and 6 or more rounds of MDA. This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. This information can help countries more effectively forecast program activities, such as the potential need for more rounds of MDA, and prioritize resources to ensure adequate coverage of all persons in areas at highest risk of failing pre-TAS.",
+ "text": "Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five effective rounds of mass drug administration (MDA) and demonstrating low prevalence in subsequent assessments. The first assessments recommended by the World Health Organization (WHO) are sentinel and spot-check sites—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit after MDA. If pre-TAS shows that prevalence in each site has been lowered to less than 1% microfilaremia or less than 2% antigenemia, the implementation unit conducts a TAS to determine whether MDA can be stopped. Failure to pass pre-TAS means that further rounds of MDA are required. This study aims to understand factors influencing pre-TAS results using existing programmatic data from 554 implementation units, of which 74 (13%) failed, in 13 countries. Secondary data analysis was completed using existing data from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. Additional covariate data were obtained from spatial raster data sets. Bivariate analysis and multilinear regression were performed to establish potential relationships between variables and the pre-TAS result. Higher baseline prevalence and lower elevation were significant in the regression model. Variables statistically significantly associated with failure (p-value ≤0.05) in the bivariate analyses included baseline prevalence at or above 5% or 10%, use of Filariasis Test Strips (FTS), primary vector of Culex, treatment with diethylcarbamazine-albendazole, higher elevation, higher population density, higher enhanced vegetation index (EVI), higher annual rainfall, and 6 or more rounds of MDA. This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. This information can help countries more effectively forecast program activities, such as the potential need for more rounds of MDA, and prioritize resources to ensure adequate coverage of all persons in areas at highest risk of failing pre-TAS."
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+ "prov": [],
+ "orig": "Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five rounds of mass drug administration (MDA) and being able to demonstrate low prevalence in several subsequent assessments. LF elimination programs implement sentinel and spot-check site assessments, called pre-TAS, to determine whether districts are eligible to implement more rigorous population-based surveys to determine whether MDA can be stopped or if further rounds are required. Reasons for failing pre-TAS are not well understood and have not previously been examined with data compiled from multiple countries. For this analysis, we analyzed data from routine USAID and WHO reports from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. In a model that included multiple variables, high baseline prevalence and lower elevation were significant. In models comparing only one variable to the outcome, the following were statistically significantly associated with failure: higher baseline prevalence at or above 5% or 10%, use of the FTS, primary vector of Culex, treatment with diethylcarbamazine-albendazole, lower elevation, higher population density, higher Enhanced Vegetation Index, higher annual rainfall, and six or more rounds of mass drug administration. These results can help national programs plan MDA more effectively, e.g., by focusing resources on areas with higher baseline prevalence and/or lower elevation.",
+ "text": "Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five rounds of mass drug administration (MDA) and being able to demonstrate low prevalence in several subsequent assessments. LF elimination programs implement sentinel and spot-check site assessments, called pre-TAS, to determine whether districts are eligible to implement more rigorous population-based surveys to determine whether MDA can be stopped or if further rounds are required. Reasons for failing pre-TAS are not well understood and have not previously been examined with data compiled from multiple countries. For this analysis, we analyzed data from routine USAID and WHO reports from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. In a model that included multiple variables, high baseline prevalence and lower elevation were significant. In models comparing only one variable to the outcome, the following were statistically significantly associated with failure: higher baseline prevalence at or above 5% or 10%, use of the FTS, primary vector of Culex, treatment with diethylcarbamazine-albendazole, lower elevation, higher population density, higher Enhanced Vegetation Index, higher annual rainfall, and six or more rounds of mass drug administration. These results can help national programs plan MDA more effectively, e.g., by focusing resources on areas with higher baseline prevalence and/or lower elevation."
+ },
+ {
+ "self_ref": "#/texts/7",
+ "parent": {
+ "$ref": "#/texts/0"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/8"
+ },
+ {
+ "$ref": "#/texts/9"
+ },
+ {
+ "$ref": "#/texts/10"
+ },
+ {
+ "$ref": "#/texts/11"
+ },
+ {
+ "$ref": "#/texts/12"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Introduction",
+ "text": "Introduction",
+ "level": 1
+ },
+ {
+ "self_ref": "#/texts/8",
+ "parent": {
+ "$ref": "#/texts/7"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Lymphatic filariasis (LF), a disease caused by parasitic worms transmitted to humans by mosquito bite, manifests in disabling and stigmatizing chronic conditions including lymphedema and hydrocele. To eliminate LF as a public health problem, the World Health Organization (WHO) recommends two strategies: reducing transmission through annual mass drug administration (MDA) and reducing suffering through ensuring the availability of morbidity management and disability prevention services to all patients [1]. For the first strategy, eliminating LF as a public health problem is defined as a ‘reduction in measurable prevalence in infection in endemic areas below a target threshold at which further transmission is considered unlikely even in the absence of MDA’ [2]. As of 2018, 14 countries have eliminated LF as a public health problem while 58 countries remain endemic for LF [3].",
+ "text": "Lymphatic filariasis (LF), a disease caused by parasitic worms transmitted to humans by mosquito bite, manifests in disabling and stigmatizing chronic conditions including lymphedema and hydrocele. To eliminate LF as a public health problem, the World Health Organization (WHO) recommends two strategies: reducing transmission through annual mass drug administration (MDA) and reducing suffering through ensuring the availability of morbidity management and disability prevention services to all patients [1]. For the first strategy, eliminating LF as a public health problem is defined as a ‘reduction in measurable prevalence in infection in endemic areas below a target threshold at which further transmission is considered unlikely even in the absence of MDA’ [2]. As of 2018, 14 countries have eliminated LF as a public health problem while 58 countries remain endemic for LF [3]."
+ },
+ {
+ "self_ref": "#/texts/9",
+ "parent": {
+ "$ref": "#/texts/7"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The road to elimination as a public health problem has several milestones. First, where LF prevalence at baseline has exceeded 1% as measured either through microfilaremia (Mf) or antigenemia (Ag), MDA is implemented and treatment coverage is measured in all implementation units, which usually correspond to districts. Implementation units must complete at least five rounds of ‘effective’ treatment, i.e. treatment with a minimum coverage of 65% of the total population. Then, WHO recommends sentinel and spot-check site assessments—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit to determine whether prevalence in each site is less than 1% Mf or less than 2% Ag [4]. Next, if these thresholds are met, national programs can progress to the first transmission assessment survey (TAS). The TAS is a population-based cluster or systematic survey of six- and seven-year-old children to assess whether transmission has fallen below the threshold at which infection is believed to persist. TAS is conducted at least three times, with two years between each survey. TAS 1 results determine if it is appropriate to stop MDA or whether further rounds are required. Finally, when TAS 2 and 3 also fall below the set threshold in every endemic implementation unit nationwide and morbidity criteria have been fulfilled, the national program submits a dossier to WHO requesting that elimination be officially validated.",
+ "text": "The road to elimination as a public health problem has several milestones. First, where LF prevalence at baseline has exceeded 1% as measured either through microfilaremia (Mf) or antigenemia (Ag), MDA is implemented and treatment coverage is measured in all implementation units, which usually correspond to districts. Implementation units must complete at least five rounds of ‘effective’ treatment, i.e. treatment with a minimum coverage of 65% of the total population. Then, WHO recommends sentinel and spot-check site assessments—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit to determine whether prevalence in each site is less than 1% Mf or less than 2% Ag [4]. Next, if these thresholds are met, national programs can progress to the first transmission assessment survey (TAS). The TAS is a population-based cluster or systematic survey of six- and seven-year-old children to assess whether transmission has fallen below the threshold at which infection is believed to persist. TAS is conducted at least three times, with two years between each survey. TAS 1 results determine if it is appropriate to stop MDA or whether further rounds are required. Finally, when TAS 2 and 3 also fall below the set threshold in every endemic implementation unit nationwide and morbidity criteria have been fulfilled, the national program submits a dossier to WHO requesting that elimination be officially validated."
+ },
+ {
+ "self_ref": "#/texts/10",
+ "parent": {
+ "$ref": "#/texts/7"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Pre-TAS include at least one sentinel and one spot-check site per one million population. Sentinel sites are established at the start of the program in villages where LF prevalence was believed to be relatively high. Spot-check sites are villages not previously tested but purposively selected as potentially high-risk areas due to original high prevalence, low coverage during MDA, high vector density, or other factors [4]. At least six months after MDA implementation, data are collected from a convenience sample of at least 300 people over five years old in each site. Originally, Mf was recommended as the indicator of choice for pre-TAS, assessed by blood smears taken at the time of peak parasite periodicity [4]. WHO later recommended the use of circulating filarial antigen rapid diagnostic tests, BinaxNow immunochromatographic card tests (ICTs), and after 2016, Alere Filariasis Test Strips (FTS), because they are more sensitive, easier to implement, and more flexible about time of day that blood can be taken [5].",
+ "text": "Pre-TAS include at least one sentinel and one spot-check site per one million population. Sentinel sites are established at the start of the program in villages where LF prevalence was believed to be relatively high. Spot-check sites are villages not previously tested but purposively selected as potentially high-risk areas due to original high prevalence, low coverage during MDA, high vector density, or other factors [4]. At least six months after MDA implementation, data are collected from a convenience sample of at least 300 people over five years old in each site. Originally, Mf was recommended as the indicator of choice for pre-TAS, assessed by blood smears taken at the time of peak parasite periodicity [4]. WHO later recommended the use of circulating filarial antigen rapid diagnostic tests, BinaxNow immunochromatographic card tests (ICTs), and after 2016, Alere Filariasis Test Strips (FTS), because they are more sensitive, easier to implement, and more flexible about time of day that blood can be taken [5]."
+ },
+ {
+ "self_ref": "#/texts/11",
+ "parent": {
+ "$ref": "#/texts/7"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "When a country fails to meet the established thresholds in a pre-TAS, they must implement at least two more rounds of MDA. National programs need to forecast areas that might fail pre-TAS and need repeated MDA, so that they can inform the community and district decision-makers of the implications of pre-TAS failure, including the need for continued MDA to lower prevalence effectively. In addition, financial and human resources must be made available for ordering drugs, distributing drugs, supervision and monitoring to implement the further MDA rounds. Ordering drugs and providing MDA budgets often need to be completed before the pre-TAS are implemented, so contingency planning and funding are important to ensure rounds of MDA are not missed.",
+ "text": "When a country fails to meet the established thresholds in a pre-TAS, they must implement at least two more rounds of MDA. National programs need to forecast areas that might fail pre-TAS and need repeated MDA, so that they can inform the community and district decision-makers of the implications of pre-TAS failure, including the need for continued MDA to lower prevalence effectively. In addition, financial and human resources must be made available for ordering drugs, distributing drugs, supervision and monitoring to implement the further MDA rounds. Ordering drugs and providing MDA budgets often need to be completed before the pre-TAS are implemented, so contingency planning and funding are important to ensure rounds of MDA are not missed."
+ },
+ {
+ "self_ref": "#/texts/12",
+ "parent": {
+ "$ref": "#/texts/7"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "This study aims to understand which factors are associated with the need for additional rounds of MDA as identified by pre-TAS results using programmatic data from 13 countries. The factors associated with failing pre-TAS are not well understood and have not previously been examined at a multi-country scale in the literature. We examine the association between pre-TAS failure and baseline prevalence, parasites, environmental factors, MDA implementation, and pre-TAS implementation. Understanding determinants of pre-TAS failure will help countries identify where elimination may be most difficult and prioritize the use of limited LF elimination resources.",
+ "text": "This study aims to understand which factors are associated with the need for additional rounds of MDA as identified by pre-TAS results using programmatic data from 13 countries. The factors associated with failing pre-TAS are not well understood and have not previously been examined at a multi-country scale in the literature. We examine the association between pre-TAS failure and baseline prevalence, parasites, environmental factors, MDA implementation, and pre-TAS implementation. Understanding determinants of pre-TAS failure will help countries identify where elimination may be most difficult and prioritize the use of limited LF elimination resources."
+ },
+ {
+ "self_ref": "#/texts/13",
+ "parent": {
+ "$ref": "#/texts/0"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/14"
+ },
+ {
+ "$ref": "#/texts/15"
+ },
+ {
+ "$ref": "#/tables/0"
+ },
+ {
+ "$ref": "#/texts/17"
+ },
+ {
+ "$ref": "#/texts/19"
+ },
+ {
+ "$ref": "#/texts/35"
+ },
+ {
+ "$ref": "#/texts/37"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Methods",
+ "text": "Methods",
+ "level": 1
+ },
+ {
+ "self_ref": "#/texts/14",
+ "parent": {
+ "$ref": "#/texts/13"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "This is a secondary data analysis using existing data, collected for programmatic purposes. Data for this analysis come from 568 districts in 13 countries whose LF elimination programs were supported by the United States Agency for International Development (USAID) through the ENVISION project, led by RTI International, and the END in Africa and END in Asia projects, led by FHI 360. These countries are Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. The data represent all pre-TAS funded by USAID from 2012 to 2017 and, in some cases, surveys funded by host government or other non-United States government funders. Because pre-TAS data were collected as part of routine program activities in most countries, in general, ethical clearance was not sought for these surveys. Our secondary analysis only included the aggregated survey results and therefore did not constitute human subjects research; no ethical approval was required.",
+ "text": "This is a secondary data analysis using existing data, collected for programmatic purposes. Data for this analysis come from 568 districts in 13 countries whose LF elimination programs were supported by the United States Agency for International Development (USAID) through the ENVISION project, led by RTI International, and the END in Africa and END in Asia projects, led by FHI 360. These countries are Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. The data represent all pre-TAS funded by USAID from 2012 to 2017 and, in some cases, surveys funded by host government or other non-United States government funders. Because pre-TAS data were collected as part of routine program activities in most countries, in general, ethical clearance was not sought for these surveys. Our secondary analysis only included the aggregated survey results and therefore did not constitute human subjects research; no ethical approval was required."
+ },
+ {
+ "self_ref": "#/texts/15",
+ "parent": {
+ "$ref": "#/texts/13"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Building on previous work, we delineated five domains of variables that could influence pre-TAS outcomes: prevalence, agent, environment, MDA, and pre-TAS implementation (Table 1) [6–8]. We prioritized key concepts that could be measured through our data or captured through publicly available global geospatial data sets.",
+ "text": "Building on previous work, we delineated five domains of variables that could influence pre-TAS outcomes: prevalence, agent, environment, MDA, and pre-TAS implementation (Table 1) [6–8]. We prioritized key concepts that could be measured through our data or captured through publicly available global geospatial data sets."
+ },
+ {
+ "self_ref": "#/texts/16",
+ "parent": {
+ "$ref": "#/body"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Table 1 Categorization of potential factors influencing pre-TAS results.",
+ "text": "Table 1 Categorization of potential factors influencing pre-TAS results."
+ },
+ {
+ "self_ref": "#/texts/17",
+ "parent": {
+ "$ref": "#/texts/13"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/18"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Data sources",
+ "text": "Data sources",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/18",
+ "parent": {
+ "$ref": "#/texts/17"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Information on baseline prevalence, MDA coverage, the number of MDA rounds, and pre-TAS information (month and year of survey, district, site name, and outcome) was gathered through regular reporting for the USAID-funded NTD programs (ENVISION, END in Africa, and END in Asia). These data were augmented by other reporting data such as the country’s dossier data annexes, the WHO Preventive Chemotherapy and Transmission Control Databank, and WHO reporting forms. Data were then reviewed by country experts, including the Ministry of Health program staff and implementing program staff, and updated as necessary. Data on vectors were also obtained from country experts. The district geographic boundaries were matched to geospatial shapefiles from the ENVISION project geospatial data repository, while other geospatial data were obtained through publicly available sources (Table 1).",
+ "text": "Information on baseline prevalence, MDA coverage, the number of MDA rounds, and pre-TAS information (month and year of survey, district, site name, and outcome) was gathered through regular reporting for the USAID-funded NTD programs (ENVISION, END in Africa, and END in Asia). These data were augmented by other reporting data such as the country’s dossier data annexes, the WHO Preventive Chemotherapy and Transmission Control Databank, and WHO reporting forms. Data were then reviewed by country experts, including the Ministry of Health program staff and implementing program staff, and updated as necessary. Data on vectors were also obtained from country experts. The district geographic boundaries were matched to geospatial shapefiles from the ENVISION project geospatial data repository, while other geospatial data were obtained through publicly available sources (Table 1)."
+ },
+ {
+ "self_ref": "#/texts/19",
+ "parent": {
+ "$ref": "#/texts/13"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/20"
+ },
+ {
+ "$ref": "#/texts/21"
+ },
+ {
+ "$ref": "#/texts/22"
+ },
+ {
+ "$ref": "#/texts/24"
+ },
+ {
+ "$ref": "#/texts/26"
+ },
+ {
+ "$ref": "#/texts/30"
+ },
+ {
+ "$ref": "#/texts/33"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Outcome and covariate variables",
+ "text": "Outcome and covariate variables",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/20",
+ "parent": {
+ "$ref": "#/texts/19"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The outcome of interest for this analysis was whether a district passed or failed the pre-TAS. Failure was defined as any district that had at least one sentinel or spot-check site with a prevalence higher than or equal to 1% Mf or 2% Ag [4].",
+ "text": "The outcome of interest for this analysis was whether a district passed or failed the pre-TAS. Failure was defined as any district that had at least one sentinel or spot-check site with a prevalence higher than or equal to 1% Mf or 2% Ag [4]."
+ },
+ {
+ "self_ref": "#/texts/21",
+ "parent": {
+ "$ref": "#/texts/19"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Potential covariates were derived from the available data for each factor in the domain groups listed in Table 1. New dichotomous variables were created for all variables that had multiple categories or were continuous for ease of interpretation in models and use in program decision-making. Cut-points for continuous variables were derived from either a priori knowledge or through exploratory analysis considering the mean or median value of the dataset, looking to create two groups of similar size with logical cut-points (e.g. rounding numbers to whole numbers). All the variables derived from publicly available global spatial raster datasets were summarized to the district level in ArcGIS Pro using the “zonal statistics” tool. The final output used the continuous value measuring the mean pixel value for the district for all variables except geographic area. Categories for each variable were determined by selecting the mean or median dataset value or cut-off used in other relevant literature [7]. The following section describes the variables that were included in the final analysis and the final categorizations used.",
+ "text": "Potential covariates were derived from the available data for each factor in the domain groups listed in Table 1. New dichotomous variables were created for all variables that had multiple categories or were continuous for ease of interpretation in models and use in program decision-making. Cut-points for continuous variables were derived from either a priori knowledge or through exploratory analysis considering the mean or median value of the dataset, looking to create two groups of similar size with logical cut-points (e.g. rounding numbers to whole numbers). All the variables derived from publicly available global spatial raster datasets were summarized to the district level in ArcGIS Pro using the “zonal statistics” tool. The final output used the continuous value measuring the mean pixel value for the district for all variables except geographic area. Categories for each variable were determined by selecting the mean or median dataset value or cut-off used in other relevant literature [7]. The following section describes the variables that were included in the final analysis and the final categorizations used."
+ },
+ {
+ "self_ref": "#/texts/22",
+ "parent": {
+ "$ref": "#/texts/19"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/23"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Baseline prevalence",
+ "text": "Baseline prevalence",
+ "level": 3
+ },
+ {
+ "self_ref": "#/texts/23",
+ "parent": {
+ "$ref": "#/texts/22"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Baseline prevalence can be assumed as a proxy for local transmission conditions [14] and correlates with prevalence after MDA [14–20]. Baseline prevalence for each district was measured by either blood smears to measure Mf or rapid diagnostic tests to measure Ag. Other studies have modeled Mf and Ag prevalence separately, due to lack of a standardized correlation between the two, especially at pre-MDA levels [21,22]. However, because WHO mapping guidance states that MDA is required if either Mf or Ag is ≥1% and there were not enough data to model each separately, we combined baseline prevalence values regardless of diagnostic test used. We created two variables for use in the analysis (1) using the cut-off of <5% or ≥5% (dataset median value of 5%) and (2) using the cut-off of <10% or ≥10%.",
+ "text": "Baseline prevalence can be assumed as a proxy for local transmission conditions [14] and correlates with prevalence after MDA [14–20]. Baseline prevalence for each district was measured by either blood smears to measure Mf or rapid diagnostic tests to measure Ag. Other studies have modeled Mf and Ag prevalence separately, due to lack of a standardized correlation between the two, especially at pre-MDA levels [21,22]. However, because WHO mapping guidance states that MDA is required if either Mf or Ag is ≥1% and there were not enough data to model each separately, we combined baseline prevalence values regardless of diagnostic test used. We created two variables for use in the analysis (1) using the cut-off of <5% or ≥5% (dataset median value of 5%) and (2) using the cut-off of <10% or ≥10%."
+ },
+ {
+ "self_ref": "#/texts/24",
+ "parent": {
+ "$ref": "#/texts/19"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/25"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Agent",
+ "text": "Agent",
+ "level": 3
+ },
+ {
+ "self_ref": "#/texts/25",
+ "parent": {
+ "$ref": "#/texts/24"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "In terms of differences in transmission dynamics by agent, research has shown that Brugia spp. are more susceptible to the anti-filarial drug regimens than Wuchereria bancrofti parasites [23]. Thus, we combined districts reporting B. malayi and B. timori and compared them to areas with W. bancrofti or mixed parasites. Two variables from other domains were identified in exploratory analyses to be highly colinear with the parasite, and thus we considered them in the same group of variables for the final regression models. These were variables delineating vectors (Anopheles or Mansonia compared to Culex) from the environmental domain and drug package [ivermectin-albendazole (IVM-ALB) compared to diethylcarbamazine-albendazole (DEC-ALB)] from the MDA domain.",
+ "text": "In terms of differences in transmission dynamics by agent, research has shown that Brugia spp. are more susceptible to the anti-filarial drug regimens than Wuchereria bancrofti parasites [23]. Thus, we combined districts reporting B. malayi and B. timori and compared them to areas with W. bancrofti or mixed parasites. Two variables from other domains were identified in exploratory analyses to be highly colinear with the parasite, and thus we considered them in the same group of variables for the final regression models. These were variables delineating vectors (Anopheles or Mansonia compared to Culex) from the environmental domain and drug package [ivermectin-albendazole (IVM-ALB) compared to diethylcarbamazine-albendazole (DEC-ALB)] from the MDA domain."
+ },
+ {
+ "self_ref": "#/texts/26",
+ "parent": {
+ "$ref": "#/texts/19"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/27"
+ },
+ {
+ "$ref": "#/texts/28"
+ },
+ {
+ "$ref": "#/texts/29"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Environment",
+ "text": "Environment",
+ "level": 3
+ },
+ {
+ "self_ref": "#/texts/27",
+ "parent": {
+ "$ref": "#/texts/26"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "LF transmission intensity is influenced by differing vector transmission dynamics, including vector biting rates and competence, and the number of individuals with microfilaria [21,24,25]. Since vector data are not always available, previous studies have explored whether environmental variables associated with vector density, such as elevation, rainfall, and temperature, can be used to predict LF prevalence [8,21,26–31]. We included the district area and elevation in meters as geographic variables potentially associated with transmission intensity. In addition, within the climate factor, we included Enhanced Vegetation Index (EVI) and rainfall variables. EVI measures vegetation levels, or “greenness,” where a higher index value indicates a higher level of “greenness.”",
+ "text": "LF transmission intensity is influenced by differing vector transmission dynamics, including vector biting rates and competence, and the number of individuals with microfilaria [21,24,25]. Since vector data are not always available, previous studies have explored whether environmental variables associated with vector density, such as elevation, rainfall, and temperature, can be used to predict LF prevalence [8,21,26–31]. We included the district area and elevation in meters as geographic variables potentially associated with transmission intensity. In addition, within the climate factor, we included Enhanced Vegetation Index (EVI) and rainfall variables. EVI measures vegetation levels, or “greenness,” where a higher index value indicates a higher level of “greenness.”"
+ },
+ {
+ "self_ref": "#/texts/28",
+ "parent": {
+ "$ref": "#/texts/26"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "We included the socio-economic variable of population density, as it has been positively associated with LF prevalence in some studies [8,27,29], but no significant association has been found in others [30]. Population density could be correlated with vector, as in eastern African countries LF is mostly transmitted by Culex in urban areas and by Anopheles in rural areas [32]. Additionally, inclusion of the satellite imagery of nighttime lights data is another a proxy for socio-economic status [33].",
+ "text": "We included the socio-economic variable of population density, as it has been positively associated with LF prevalence in some studies [8,27,29], but no significant association has been found in others [30]. Population density could be correlated with vector, as in eastern African countries LF is mostly transmitted by Culex in urban areas and by Anopheles in rural areas [32]. Additionally, inclusion of the satellite imagery of nighttime lights data is another a proxy for socio-economic status [33]."
+ },
+ {
+ "self_ref": "#/texts/29",
+ "parent": {
+ "$ref": "#/texts/26"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Finally, all or parts of districts that are co-endemic with onchocerciasis may have received multiple rounds of MDA with ivermectin before LF MDA started, which may have lowered LF prevalence in an area [34–36]. Thus, we included a categorical variable to distinguish if districts were co-endemic with onchocerciasis.",
+ "text": "Finally, all or parts of districts that are co-endemic with onchocerciasis may have received multiple rounds of MDA with ivermectin before LF MDA started, which may have lowered LF prevalence in an area [34–36]. Thus, we included a categorical variable to distinguish if districts were co-endemic with onchocerciasis."
+ },
+ {
+ "self_ref": "#/texts/30",
+ "parent": {
+ "$ref": "#/texts/19"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/31"
+ },
+ {
+ "$ref": "#/texts/32"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "MDA",
+ "text": "MDA",
+ "level": 3
+ },
+ {
+ "self_ref": "#/texts/31",
+ "parent": {
+ "$ref": "#/texts/30"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Treatment effectiveness depends upon both drug efficacy (ability to kill adult worms, ability to kill Mf, drug resistance, drug quality) and implementation of MDA (coverage, compliance, number of rounds) [14,16]. Ivermectin is less effective against adult worms than DEC, and therefore it is likely that Ag reduction is slower in areas using ivermectin instead of DEC in MDA [37]. Models also have shown that MDA coverage affects prevalence, although coverage has been defined in various ways, such as median coverage, number of rounds, or individual compliance [14–16,20,38–40]. Furthermore, systematic non-compliance, or population sub-groups which consistently refuse to take medicines, has been shown to represent a threat to elimination [41,42].",
+ "text": "Treatment effectiveness depends upon both drug efficacy (ability to kill adult worms, ability to kill Mf, drug resistance, drug quality) and implementation of MDA (coverage, compliance, number of rounds) [14,16]. Ivermectin is less effective against adult worms than DEC, and therefore it is likely that Ag reduction is slower in areas using ivermectin instead of DEC in MDA [37]. Models also have shown that MDA coverage affects prevalence, although coverage has been defined in various ways, such as median coverage, number of rounds, or individual compliance [14–16,20,38–40]. Furthermore, systematic non-compliance, or population sub-groups which consistently refuse to take medicines, has been shown to represent a threat to elimination [41,42]."
+ },
+ {
+ "self_ref": "#/texts/32",
+ "parent": {
+ "$ref": "#/texts/30"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "We considered three approaches when analyzing the MDA data: median MDA coverage in the most recent 5 rounds, number of rounds with sufficient coverage in the most recent 5 rounds, and count of the total number of rounds. MDA coverage is considered sufficient at or above 65% of the total population who were reported to have ingested the drugs; this was used as the cut point for MDA median coverage for the most recent 5 rounds. The ‘rounds of sufficient coverage’ variable was categorized as having 2 or fewer rounds compared to 3 or more sufficient rounds. The ‘total number of MDA rounds’ variable was categorized at 5 or fewer rounds compared to 6 or more rounds ever documented in that district.",
+ "text": "We considered three approaches when analyzing the MDA data: median MDA coverage in the most recent 5 rounds, number of rounds with sufficient coverage in the most recent 5 rounds, and count of the total number of rounds. MDA coverage is considered sufficient at or above 65% of the total population who were reported to have ingested the drugs; this was used as the cut point for MDA median coverage for the most recent 5 rounds. The ‘rounds of sufficient coverage’ variable was categorized as having 2 or fewer rounds compared to 3 or more sufficient rounds. The ‘total number of MDA rounds’ variable was categorized at 5 or fewer rounds compared to 6 or more rounds ever documented in that district."
+ },
+ {
+ "self_ref": "#/texts/33",
+ "parent": {
+ "$ref": "#/texts/19"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/34"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Pre-TAS implementation",
+ "text": "Pre-TAS implementation",
+ "level": 3
+ },
+ {
+ "self_ref": "#/texts/34",
+ "parent": {
+ "$ref": "#/texts/33"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Pre-TAS results can be influenced by the implementation of the survey itself, including the use of a particular diagnostic test, the selection of sites, the timing of survey, and the appropriate application of methods for population recruitment and diagnostic test adminstration. We included two variables in the pre-TAS implementation domain: `type of diagnostic method used’ and `diagnostic test used.’ The ‘type of diagnostic method used’ variable categorized districts by either using Mf or Ag. The ‘diagnostic test used’ variable examined Mf (reference category) compared to ICT and compared to FTS (categorical variable with 3 values). This approach was used to compare each test to each other. Countries switched from ICT to FTS during 2016, while Mf testing continued to be used throughout the time period of study.",
+ "text": "Pre-TAS results can be influenced by the implementation of the survey itself, including the use of a particular diagnostic test, the selection of sites, the timing of survey, and the appropriate application of methods for population recruitment and diagnostic test adminstration. We included two variables in the pre-TAS implementation domain: `type of diagnostic method used’ and `diagnostic test used.’ The ‘type of diagnostic method used’ variable categorized districts by either using Mf or Ag. The ‘diagnostic test used’ variable examined Mf (reference category) compared to ICT and compared to FTS (categorical variable with 3 values). This approach was used to compare each test to each other. Countries switched from ICT to FTS during 2016, while Mf testing continued to be used throughout the time period of study."
+ },
+ {
+ "self_ref": "#/texts/35",
+ "parent": {
+ "$ref": "#/texts/13"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/36"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Data inclusion criteria",
+ "text": "Data inclusion criteria",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/36",
+ "parent": {
+ "$ref": "#/texts/35"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The dataset, summarized at the district level, included information from 568 districts where a pre-TAS was being implemented for the first time. A total of 14 districts were removed from the final analysis due to missing data related to the following points: geospatial boundaries (4), baseline prevalence (4), and MDA coverage (6). The final analysis dataset had 554 districts.",
+ "text": "The dataset, summarized at the district level, included information from 568 districts where a pre-TAS was being implemented for the first time. A total of 14 districts were removed from the final analysis due to missing data related to the following points: geospatial boundaries (4), baseline prevalence (4), and MDA coverage (6). The final analysis dataset had 554 districts."
+ },
+ {
+ "self_ref": "#/texts/37",
+ "parent": {
+ "$ref": "#/texts/13"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/38"
+ },
+ {
+ "$ref": "#/texts/39"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Statistical analysis and modeling",
+ "text": "Statistical analysis and modeling",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/38",
+ "parent": {
+ "$ref": "#/texts/37"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Statistical analysis and modeling were done with Stata MP 15.1 (College Station, TX). Descriptive statistics comparing various variables to the principle outcome were performed. Significant differences were identified using a chi-square test. A generalized linear model (GLM) with a log link and binomial error distribution—which estimates relative risks—was developed using forward stepwise modeling methods (called log-binomial model). Models with higher pseudo-r-squared and lower Akaike information criterion (AIC) were retained at each step. Pseudo-r-squared is a value between 0 and 1 with the higher the value, the better the model is at predicting the outcome of interest. AIC values are used to compare the relative quality of models compared to each other; in general, a lower value indicates a better model. Variables were tested by factor group. Once a variable was selected from the group, no other variable in that same group was eligible to be included in the final model due to issues of collinearity and small sample sizes. Interaction between terms in the model was tested after model selection, and interaction terms that modified the original terms’ significance were included in the final model. Overall, the number of potential variables able to be included in the model remained low due to the relatively small number of failure results (13%) in the dataset. Furthermore, the models with more than 3 variables and one interaction term either were unstable (indicated by very large confidence interval widths) or did not improve the model by being significant predictors or by modifying other parameters already in the model. These models were at heightened risk of non-convergence; we limited the number of variables accordingly.",
+ "text": "Statistical analysis and modeling were done with Stata MP 15.1 (College Station, TX). Descriptive statistics comparing various variables to the principle outcome were performed. Significant differences were identified using a chi-square test. A generalized linear model (GLM) with a log link and binomial error distribution—which estimates relative risks—was developed using forward stepwise modeling methods (called log-binomial model). Models with higher pseudo-r-squared and lower Akaike information criterion (AIC) were retained at each step. Pseudo-r-squared is a value between 0 and 1 with the higher the value, the better the model is at predicting the outcome of interest. AIC values are used to compare the relative quality of models compared to each other; in general, a lower value indicates a better model. Variables were tested by factor group. Once a variable was selected from the group, no other variable in that same group was eligible to be included in the final model due to issues of collinearity and small sample sizes. Interaction between terms in the model was tested after model selection, and interaction terms that modified the original terms’ significance were included in the final model. Overall, the number of potential variables able to be included in the model remained low due to the relatively small number of failure results (13%) in the dataset. Furthermore, the models with more than 3 variables and one interaction term either were unstable (indicated by very large confidence interval widths) or did not improve the model by being significant predictors or by modifying other parameters already in the model. These models were at heightened risk of non-convergence; we limited the number of variables accordingly."
+ },
+ {
+ "self_ref": "#/texts/39",
+ "parent": {
+ "$ref": "#/texts/37"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Sensitivity analysis was performed for the final log-binomial model to test for the validity of results under different parameters by excluding some sub-sets of districts from the dataset and rerunning the model. This analysis was done to understand the robustness of the model when (1) excluding all districts in Cameroon, (2) including only districts in Africa, (3) including only districts with W. bancrofti parasite, and (4) including only districts with Anopheles as the primary vector. The sensitivity analysis excluding Cameroon was done for two reasons. First, Cameroon had the most pre-TAS results included, but no failures. Second, 70% of the Cameroon districts included in the analysis are co-endemic for loiasis. Given that diagnostic tests used in LF mapping have since been shown to cross-react with loiasis, there is some concern that these districts might not have been truly LF-endemic [43,44].",
+ "text": "Sensitivity analysis was performed for the final log-binomial model to test for the validity of results under different parameters by excluding some sub-sets of districts from the dataset and rerunning the model. This analysis was done to understand the robustness of the model when (1) excluding all districts in Cameroon, (2) including only districts in Africa, (3) including only districts with W. bancrofti parasite, and (4) including only districts with Anopheles as the primary vector. The sensitivity analysis excluding Cameroon was done for two reasons. First, Cameroon had the most pre-TAS results included, but no failures. Second, 70% of the Cameroon districts included in the analysis are co-endemic for loiasis. Given that diagnostic tests used in LF mapping have since been shown to cross-react with loiasis, there is some concern that these districts might not have been truly LF-endemic [43,44]."
+ },
+ {
+ "self_ref": "#/texts/40",
+ "parent": {
+ "$ref": "#/texts/0"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/41"
+ },
+ {
+ "$ref": "#/pictures/0"
+ },
+ {
+ "$ref": "#/pictures/1"
+ },
+ {
+ "$ref": "#/texts/44"
+ },
+ {
+ "$ref": "#/pictures/2"
+ },
+ {
+ "$ref": "#/texts/46"
+ },
+ {
+ "$ref": "#/texts/47"
+ },
+ {
+ "$ref": "#/pictures/3"
+ },
+ {
+ "$ref": "#/texts/49"
+ },
+ {
+ "$ref": "#/tables/1"
+ },
+ {
+ "$ref": "#/texts/51"
+ },
+ {
+ "$ref": "#/pictures/4"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Results",
+ "text": "Results",
+ "level": 1
+ },
+ {
+ "self_ref": "#/texts/41",
+ "parent": {
+ "$ref": "#/texts/40"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The overall pre-TAS pass rate for the districts included in this analysis was 87% (74 failures in 554 districts). Nearly 40% of the 554 districts were from Cameroon (134) and Tanzania (87) (Fig 1). No districts in Bangladesh, Cameroon, Mali, or Uganda failed a pre-TAS in this data set; over 25% of districts in Burkina Faso, Ghana, Haiti, Nepal, and Sierra Leone failed pre-TAS in this data set. Baseline prevalence varied widely within and between the 13 countries. Fig 2 shows the highest, lowest, and median baseline prevalence in the study districts by country. Burkina Faso had the highest median baseline prevalence at 52% and Burkina Faso, Tanzania, and Ghana all had at least one district with a very high baseline of over 70%. In Mali, Indonesia, Benin, and Bangladesh, all districts had baseline prevalences below 20%.",
+ "text": "The overall pre-TAS pass rate for the districts included in this analysis was 87% (74 failures in 554 districts). Nearly 40% of the 554 districts were from Cameroon (134) and Tanzania (87) (Fig 1). No districts in Bangladesh, Cameroon, Mali, or Uganda failed a pre-TAS in this data set; over 25% of districts in Burkina Faso, Ghana, Haiti, Nepal, and Sierra Leone failed pre-TAS in this data set. Baseline prevalence varied widely within and between the 13 countries. Fig 2 shows the highest, lowest, and median baseline prevalence in the study districts by country. Burkina Faso had the highest median baseline prevalence at 52% and Burkina Faso, Tanzania, and Ghana all had at least one district with a very high baseline of over 70%. In Mali, Indonesia, Benin, and Bangladesh, all districts had baseline prevalences below 20%."
+ },
+ {
+ "self_ref": "#/texts/42",
+ "parent": {
+ "$ref": "#/body"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Fig 1 Number of pre-TAS by country.",
+ "text": "Fig 1 Number of pre-TAS by country."
+ },
+ {
+ "self_ref": "#/texts/43",
+ "parent": {
+ "$ref": "#/body"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Fig 2 District-level baseline prevalence by country.",
+ "text": "Fig 2 District-level baseline prevalence by country."
+ },
+ {
+ "self_ref": "#/texts/44",
+ "parent": {
+ "$ref": "#/texts/40"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Fig 3 shows the unadjusted analysis for key variables by pre-TAS result. Variables statistically significantly associated with failure (p-value ≤0.05) included higher baseline prevalence at or above 5% or 10%, FTS diagnostic test, primary vector of Culex, treatment with DEC-ALB, higher elevation, higher population density, higher EVI, higher annual rainfall, and six or more rounds of MDA. Variables that were not significantly associated with pre-TAS failure included diagnostic method used (Ag or Mf), parasite, co-endemicity for onchocerciasis, median MDA coverage, and sufficient rounds of MDA.",
+ "text": "Fig 3 shows the unadjusted analysis for key variables by pre-TAS result. Variables statistically significantly associated with failure (p-value ≤0.05) included higher baseline prevalence at or above 5% or 10%, FTS diagnostic test, primary vector of Culex, treatment with DEC-ALB, higher elevation, higher population density, higher EVI, higher annual rainfall, and six or more rounds of MDA. Variables that were not significantly associated with pre-TAS failure included diagnostic method used (Ag or Mf), parasite, co-endemicity for onchocerciasis, median MDA coverage, and sufficient rounds of MDA."
+ },
+ {
+ "self_ref": "#/texts/45",
+ "parent": {
+ "$ref": "#/body"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Fig 3 Percent pre-TAS failure by each characteristic (unadjusted).",
+ "text": "Fig 3 Percent pre-TAS failure by each characteristic (unadjusted)."
+ },
+ {
+ "self_ref": "#/texts/46",
+ "parent": {
+ "$ref": "#/texts/40"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The final log-binomial model included the variables of baseline prevalence ≥10%, the diagnostic test used (FTS and ICT), and elevation. The final model also included a significant interaction term between high baseline and diagnostic test used.",
+ "text": "The final log-binomial model included the variables of baseline prevalence ≥10%, the diagnostic test used (FTS and ICT), and elevation. The final model also included a significant interaction term between high baseline and diagnostic test used."
+ },
+ {
+ "self_ref": "#/texts/47",
+ "parent": {
+ "$ref": "#/texts/40"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Fig 4 shows the risk ratio results with their corresponding confidence intervals. In a model with interaction between baseline and diagnostic test the baseline parameter was significant while diagnostic test and the interaction term were not. Districts with high baseline had a statistically significant (p-value ≤0.05) 2.52 times higher risk of failure (95% CI 1.37–4.64) compared to those with low baseline prevalence. The FTS diagnostic test or ICT diagnostic test alone were not significant nor was the interaction term. Additionally, districts with an elevation below 350 meters had a statistically significant (p-value ≤0.05) 3.07 times higher risk of failing pre-TAS (95% CI 1.95–4.83).",
+ "text": "Fig 4 shows the risk ratio results with their corresponding confidence intervals. In a model with interaction between baseline and diagnostic test the baseline parameter was significant while diagnostic test and the interaction term were not. Districts with high baseline had a statistically significant (p-value ≤0.05) 2.52 times higher risk of failure (95% CI 1.37–4.64) compared to those with low baseline prevalence. The FTS diagnostic test or ICT diagnostic test alone were not significant nor was the interaction term. Additionally, districts with an elevation below 350 meters had a statistically significant (p-value ≤0.05) 3.07 times higher risk of failing pre-TAS (95% CI 1.95–4.83)."
+ },
+ {
+ "self_ref": "#/texts/48",
+ "parent": {
+ "$ref": "#/body"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Fig 4 Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model.",
+ "text": "Fig 4 Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model."
+ },
+ {
+ "self_ref": "#/texts/49",
+ "parent": {
+ "$ref": "#/texts/40"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Sensitivity analyses were conducted using the same model with different subsets of the dataset including (1) all districts except for districts in Cameroon (134 total with no failures), (2) only districts in Africa, (3) only districts with W. bancrofti, and (4) only districts with Anopheles as primary vector. The results of the sensitivity models (Table 2) indicate an overall robust model. High baseline and lower elevation remained significant across all the models. The ICT diagnostic test used remains insignificant across all models. The FTS diagnostic test was positively significant in model 1 and negatively significant in model 4. The interaction term of baseline prevalence and FTS diagnostic test was significant in three models though the estimate was unstable in the W. bancrofti-only and Anopheles-only models (models 3 and 4 respectively), as signified by large confidence intervals.",
+ "text": "Sensitivity analyses were conducted using the same model with different subsets of the dataset including (1) all districts except for districts in Cameroon (134 total with no failures), (2) only districts in Africa, (3) only districts with W. bancrofti, and (4) only districts with Anopheles as primary vector. The results of the sensitivity models (Table 2) indicate an overall robust model. High baseline and lower elevation remained significant across all the models. The ICT diagnostic test used remains insignificant across all models. The FTS diagnostic test was positively significant in model 1 and negatively significant in model 4. The interaction term of baseline prevalence and FTS diagnostic test was significant in three models though the estimate was unstable in the W. bancrofti-only and Anopheles-only models (models 3 and 4 respectively), as signified by large confidence intervals."
+ },
+ {
+ "self_ref": "#/texts/50",
+ "parent": {
+ "$ref": "#/body"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Table 2 Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis.",
+ "text": "Table 2 Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis."
+ },
+ {
+ "self_ref": "#/texts/51",
+ "parent": {
+ "$ref": "#/texts/40"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Overall 74 districts in the dataset failed pre-TAS. Fig 5 summarizes the likelihood of failure by variable combinations identified in the log-binomial model. For those districts with a baseline prevalence ≥10% that used a FTS diagnostic test and have an average elevation below 350 meters (Combination C01), 87% of the 23 districts failed. Of districts with high baseline that used an ICT diagnostic test and have a low average elevation (C02) 45% failed. Overall, combinations with high baseline and low elevation C01, C02, and C04 accounted for 51% of all the failures (38 of 74).",
+ "text": "Overall 74 districts in the dataset failed pre-TAS. Fig 5 summarizes the likelihood of failure by variable combinations identified in the log-binomial model. For those districts with a baseline prevalence ≥10% that used a FTS diagnostic test and have an average elevation below 350 meters (Combination C01), 87% of the 23 districts failed. Of districts with high baseline that used an ICT diagnostic test and have a low average elevation (C02) 45% failed. Overall, combinations with high baseline and low elevation C01, C02, and C04 accounted for 51% of all the failures (38 of 74)."
+ },
+ {
+ "self_ref": "#/texts/52",
+ "parent": {
+ "$ref": "#/body"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Fig 5 Analysis of failures by model combinations.",
+ "text": "Fig 5 Analysis of failures by model combinations."
+ },
+ {
+ "self_ref": "#/texts/53",
+ "parent": {
+ "$ref": "#/texts/0"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/54"
+ },
+ {
+ "$ref": "#/texts/55"
+ },
+ {
+ "$ref": "#/texts/56"
+ },
+ {
+ "$ref": "#/texts/57"
+ },
+ {
+ "$ref": "#/texts/58"
+ },
+ {
+ "$ref": "#/texts/59"
+ },
+ {
+ "$ref": "#/texts/60"
+ },
+ {
+ "$ref": "#/texts/61"
+ },
+ {
+ "$ref": "#/texts/62"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Discussion",
+ "text": "Discussion",
+ "level": 1
+ },
+ {
+ "self_ref": "#/texts/54",
+ "parent": {
+ "$ref": "#/texts/53"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. Variables significantly associated with failure were higher baseline prevalence and lower elevation. Districts with a baseline prevalence of 10% or more were at 2.52 times higher risk to fail pre-TAS in the final log-binomial model. In the bivariate analysis, baseline prevalence above 5% was also significantly more likely to fail compared to lower baselines, which indicates that the threshold for higher baseline prevalence may be as little as 5%, similar to what was found in Goldberg et al., which explored ecological and socioeconomic factors associated with TAS failure [7].",
+ "text": "This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. Variables significantly associated with failure were higher baseline prevalence and lower elevation. Districts with a baseline prevalence of 10% or more were at 2.52 times higher risk to fail pre-TAS in the final log-binomial model. In the bivariate analysis, baseline prevalence above 5% was also significantly more likely to fail compared to lower baselines, which indicates that the threshold for higher baseline prevalence may be as little as 5%, similar to what was found in Goldberg et al., which explored ecological and socioeconomic factors associated with TAS failure [7]."
+ },
+ {
+ "self_ref": "#/texts/55",
+ "parent": {
+ "$ref": "#/texts/53"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Though diagnostic test used was selected for the final log-binomial model, neither category (FTS or ICT) were significant after interaction with high baseline. FTS alone is significant in the bivariate analysis compared to ICT or Mf. This result is not surprising given previous research which found that FTS was more sensitive than ICT [45].",
+ "text": "Though diagnostic test used was selected for the final log-binomial model, neither category (FTS or ICT) were significant after interaction with high baseline. FTS alone is significant in the bivariate analysis compared to ICT or Mf. This result is not surprising given previous research which found that FTS was more sensitive than ICT [45]."
+ },
+ {
+ "self_ref": "#/texts/56",
+ "parent": {
+ "$ref": "#/texts/53"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Elevation was the only environmental domain variable selected for the final log-binomial model during the model selection process, with areas of lower elevation (<350m) found to be at 3.07 times higher risk to fail pre-TAS compared to districts with a higher elevation. Similar results related to elevation were found in previous studies [8,31], including Goldberg et al. [7], who used a cutoff of 200 meters. Elevation likely also encompasses some related environmental concepts, such as vector habitat, greenness (EVI), or rainfall, which impact vector chances of survival.",
+ "text": "Elevation was the only environmental domain variable selected for the final log-binomial model during the model selection process, with areas of lower elevation (<350m) found to be at 3.07 times higher risk to fail pre-TAS compared to districts with a higher elevation. Similar results related to elevation were found in previous studies [8,31], including Goldberg et al. [7], who used a cutoff of 200 meters. Elevation likely also encompasses some related environmental concepts, such as vector habitat, greenness (EVI), or rainfall, which impact vector chances of survival."
+ },
+ {
+ "self_ref": "#/texts/57",
+ "parent": {
+ "$ref": "#/texts/53"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The small number of failures overall prevented the inclusion of a large number of variables in the final log-binomial model. However, other variables that are associated with failure as identified in the bivariate analyses, such as Culex vector, higher population density, higher EVI, higher rainfall and more rounds of MDA, should not be discounted when making programmatic decisions. Other models have shown that Culex as the predominant vector in a district, compared to Anopheles, results in more intense interventions needed to reach elimination [24,41]. Higher population density, which was also found to predict TAS failure [7], could be related to different vector species’ transmission dynamics in urban areas, as well as the fact that MDAs are harder to conduct and to accurately measure in urban areas [46,47]. Both higher enhanced vegetation index (>0.3) and higher rainfall (>700 mm per year) contribute to expansion of vector habitats and population. Additionally, having more than five rounds of MDA before pre-TAS was also statistically significantly associated with higher failure in the bivariate analysis. It is unclear why higher number of rounds is associated with first pre-TAS failure given that other research has shown the opposite [15,16].",
+ "text": "The small number of failures overall prevented the inclusion of a large number of variables in the final log-binomial model. However, other variables that are associated with failure as identified in the bivariate analyses, such as Culex vector, higher population density, higher EVI, higher rainfall and more rounds of MDA, should not be discounted when making programmatic decisions. Other models have shown that Culex as the predominant vector in a district, compared to Anopheles, results in more intense interventions needed to reach elimination [24,41]. Higher population density, which was also found to predict TAS failure [7], could be related to different vector species’ transmission dynamics in urban areas, as well as the fact that MDAs are harder to conduct and to accurately measure in urban areas [46,47]. Both higher enhanced vegetation index (>0.3) and higher rainfall (>700 mm per year) contribute to expansion of vector habitats and population. Additionally, having more than five rounds of MDA before pre-TAS was also statistically significantly associated with higher failure in the bivariate analysis. It is unclear why higher number of rounds is associated with first pre-TAS failure given that other research has shown the opposite [15,16]."
+ },
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+ "orig": "All other variables included in this analysis were not significantly associated with pre-TAS failure in our analysis. Goldberg et al. found Brugia spp. to be significantly associated with failure, but our results did not. This is likely due in part to the small number of districts with Brugia spp. in our dataset (6%) compared to 46% in the Goldberg et al. article [7]. MDA coverage levels were not significantly associated with pre-TAS failure, likely due to the lack of variance in the coverage data since WHO guidance dictates a minimum of five rounds of MDA with ≥65% epidemiological coverage to be eligible to implement pre-TAS. It should not be interpreted as evidence that high MDA coverage levels are not necessary to lower prevalence.",
+ "text": "All other variables included in this analysis were not significantly associated with pre-TAS failure in our analysis. Goldberg et al. found Brugia spp. to be significantly associated with failure, but our results did not. This is likely due in part to the small number of districts with Brugia spp. in our dataset (6%) compared to 46% in the Goldberg et al. article [7]. MDA coverage levels were not significantly associated with pre-TAS failure, likely due to the lack of variance in the coverage data since WHO guidance dictates a minimum of five rounds of MDA with ≥65% epidemiological coverage to be eligible to implement pre-TAS. It should not be interpreted as evidence that high MDA coverage levels are not necessary to lower prevalence."
+ },
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+ "orig": "Limitations to this study include data sources, excluded data, unreported data, misassigned data, and aggregation of results at the district level. The main data sources for this analysis were programmatic data, which may be less accurate than data collected specifically for research purposes. This is particularly true of the MDA coverage data, where some countries report data quality challenges in areas of instability or frequent population migration. Even though risk factors such as age, sex, compliance with MDA, and use of bednets have been shown to influence infection in individuals [40,48–50], we could not include factors from the human host domain in our analysis, as data sets were aggregated at site level and did not include individual information. In addition, vector control data were not universally available across the 13 countries and thus were not included in the analysis, despite studies showing that vector control has an impact on reducing LF prevalence [41,48,51–53].",
+ "text": "Limitations to this study include data sources, excluded data, unreported data, misassigned data, and aggregation of results at the district level. The main data sources for this analysis were programmatic data, which may be less accurate than data collected specifically for research purposes. This is particularly true of the MDA coverage data, where some countries report data quality challenges in areas of instability or frequent population migration. Even though risk factors such as age, sex, compliance with MDA, and use of bednets have been shown to influence infection in individuals [40,48–50], we could not include factors from the human host domain in our analysis, as data sets were aggregated at site level and did not include individual information. In addition, vector control data were not universally available across the 13 countries and thus were not included in the analysis, despite studies showing that vector control has an impact on reducing LF prevalence [41,48,51–53]."
+ },
+ {
+ "self_ref": "#/texts/60",
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+ "orig": "Fourteen districts were excluded from the analysis because we were not able to obtain complete data for baseline prevalence, MDA coverage, or geographic boundaries. One of these districts had failed pre-TAS. It is likely these exclusions had minimal impact on the conclusions, as they represented a small number of districts and were similar to other included districts in terms of key variables. Unreported data could have occurred if a country conducted a pre-TAS that failed and then chose not to report it or reported it as a mid-term survey instead. Anecdotally, we know this has occurred occasionally, but we do not believe the practice to be widespread. Another limitation in the analysis is a potential misassignment of key variable values to a district due to changes in the district over time. Redistricting, changes in district size or composition, was pervasive in many countries during the study period; however, we expect the impact on the study outcome to be minimal, as the historical prevalence and MDA data from the “mother” districts are usually flowed down to these new “daughter” districts. However, it is possible that the split created an area of higher prevalence or lower MDA coverage than would have been found on average in the overall larger original “mother” district. Finally, the aggregation or averaging of results to the district level may mask heterogeneity within districts. Though this impact could be substantial in districts with considerable heterogeneity, the use of median values and binomial variables mitigated the likelihood of skewing the data to extreme outliners in a district.",
+ "text": "Fourteen districts were excluded from the analysis because we were not able to obtain complete data for baseline prevalence, MDA coverage, or geographic boundaries. One of these districts had failed pre-TAS. It is likely these exclusions had minimal impact on the conclusions, as they represented a small number of districts and were similar to other included districts in terms of key variables. Unreported data could have occurred if a country conducted a pre-TAS that failed and then chose not to report it or reported it as a mid-term survey instead. Anecdotally, we know this has occurred occasionally, but we do not believe the practice to be widespread. Another limitation in the analysis is a potential misassignment of key variable values to a district due to changes in the district over time. Redistricting, changes in district size or composition, was pervasive in many countries during the study period; however, we expect the impact on the study outcome to be minimal, as the historical prevalence and MDA data from the “mother” districts are usually flowed down to these new “daughter” districts. However, it is possible that the split created an area of higher prevalence or lower MDA coverage than would have been found on average in the overall larger original “mother” district. Finally, the aggregation or averaging of results to the district level may mask heterogeneity within districts. Though this impact could be substantial in districts with considerable heterogeneity, the use of median values and binomial variables mitigated the likelihood of skewing the data to extreme outliners in a district."
+ },
+ {
+ "self_ref": "#/texts/61",
+ "parent": {
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+ "orig": "As this analysis used data across a variety of countries and epidemiological situations, the results are likely relevant for other districts in the countries examined and in countries with similar epidemiological backgrounds. In general, as more data become available at site level through the increased use of electronic data collection tools, further analysis of geospatial variables and associations will be possible. For example, with the availability of GPS coordinates, it may become possible to analyze outcomes by site and to link the geospatial environmental domain variables at a smaller scale. Future analyses also might seek to include information from coverage surveys or qualitative research studies on vector control interventions such as bed net usage, MDA compliance, population movement, and sub-populations that might be missed during MDA. Future pre-TAS using electronic data collection could include sex and age of individuals included in the survey.",
+ "text": "As this analysis used data across a variety of countries and epidemiological situations, the results are likely relevant for other districts in the countries examined and in countries with similar epidemiological backgrounds. In general, as more data become available at site level through the increased use of electronic data collection tools, further analysis of geospatial variables and associations will be possible. For example, with the availability of GPS coordinates, it may become possible to analyze outcomes by site and to link the geospatial environmental domain variables at a smaller scale. Future analyses also might seek to include information from coverage surveys or qualitative research studies on vector control interventions such as bed net usage, MDA compliance, population movement, and sub-populations that might be missed during MDA. Future pre-TAS using electronic data collection could include sex and age of individuals included in the survey."
+ },
+ {
+ "self_ref": "#/texts/62",
+ "parent": {
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+ "orig": "This paper provides evidence from analysis of 554 districts and 13 countries on the factors associated with pre-TAS results. Baseline prevalence, elevation, vector, population density, EVI, rainfall, and number of MDA rounds were all significant in either bivariate or multivariate analyses. This information along with knowledge of local context can help countries more effectively plan pre-TAS and forecast program activities, such as the potential need for more than five rounds of MDA in areas with high baseline and/or low elevation.",
+ "text": "This paper provides evidence from analysis of 554 districts and 13 countries on the factors associated with pre-TAS results. Baseline prevalence, elevation, vector, population density, EVI, rainfall, and number of MDA rounds were all significant in either bivariate or multivariate analyses. This information along with knowledge of local context can help countries more effectively plan pre-TAS and forecast program activities, such as the potential need for more than five rounds of MDA in areas with high baseline and/or low elevation."
+ },
+ {
+ "self_ref": "#/texts/63",
+ "parent": {
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+ "orig": "Acknowledgments",
+ "text": "Acknowledgments",
+ "level": 1
+ },
+ {
+ "self_ref": "#/texts/64",
+ "parent": {
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+ "orig": "The authors would like to thank all those involved from the Ministries of Health, volunteers and community members in the sentinel and spot-check site surveys for their tireless commitment to ridding the world of LF. In addition, gratitude is given to Joseph Koroma and all the partners, including USAID, RTI International, FHI 360, IMA World Health, and Helen Keller International, who supported the surveys financially and technically.",
+ "text": "The authors would like to thank all those involved from the Ministries of Health, volunteers and community members in the sentinel and spot-check site surveys for their tireless commitment to ridding the world of LF. In addition, gratitude is given to Joseph Koroma and all the partners, including USAID, RTI International, FHI 360, IMA World Health, and Helen Keller International, who supported the surveys financially and technically."
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+ "orig": "References",
+ "text": "References",
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+ "start_row_offset_idx": 6,
+ "end_row_offset_idx": 7,
+ "start_col_offset_idx": 6,
+ "end_col_offset_idx": 7,
+ "text": "Static",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 6,
+ "end_row_offset_idx": 7,
+ "start_col_offset_idx": 7,
+ "end_col_offset_idx": 8,
+ "text": "Programmatic data",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 7,
+ "end_row_offset_idx": 8,
+ "start_col_offset_idx": 0,
+ "end_col_offset_idx": 1,
+ "text": "Environment",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 7,
+ "end_row_offset_idx": 8,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "Climate",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 7,
+ "end_row_offset_idx": 8,
+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "EVI",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 7,
+ "end_row_offset_idx": 8,
+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "Enhanced vegetation index",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 7,
+ "end_row_offset_idx": 8,
+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": "> 0.3",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 7,
+ "end_row_offset_idx": 8,
+ "start_col_offset_idx": 5,
+ "end_col_offset_idx": 6,
+ "text": "Mean",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 7,
+ "end_row_offset_idx": 8,
+ "start_col_offset_idx": 6,
+ "end_col_offset_idx": 7,
+ "text": "2015",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 7,
+ "end_row_offset_idx": 8,
+ "start_col_offset_idx": 7,
+ "end_col_offset_idx": 8,
+ "text": "MODIS [10]",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 8,
+ "end_row_offset_idx": 9,
+ "start_col_offset_idx": 0,
+ "end_col_offset_idx": 1,
+ "text": "Environment",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 8,
+ "end_row_offset_idx": 9,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "Climate",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 8,
+ "end_row_offset_idx": 9,
+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "Rainfall",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 8,
+ "end_row_offset_idx": 9,
+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "Annual rainfall measured in mm",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 8,
+ "end_row_offset_idx": 9,
+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": "≤ 700",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 8,
+ "end_row_offset_idx": 9,
+ "start_col_offset_idx": 5,
+ "end_col_offset_idx": 6,
+ "text": "Mean",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 8,
+ "end_row_offset_idx": 9,
+ "start_col_offset_idx": 6,
+ "end_col_offset_idx": 7,
+ "text": "2015",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 8,
+ "end_row_offset_idx": 9,
+ "start_col_offset_idx": 7,
+ "end_col_offset_idx": 8,
+ "text": "CHIRPS [11]",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 9,
+ "end_row_offset_idx": 10,
+ "start_col_offset_idx": 0,
+ "end_col_offset_idx": 1,
+ "text": "Environment",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 9,
+ "end_row_offset_idx": 10,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "Socio-economic",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 9,
+ "end_row_offset_idx": 10,
+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "Population density",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 9,
+ "end_row_offset_idx": 10,
+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "Number of people per km2",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 9,
+ "end_row_offset_idx": 10,
+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": "≤ 100",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 9,
+ "end_row_offset_idx": 10,
+ "start_col_offset_idx": 5,
+ "end_col_offset_idx": 6,
+ "text": "Mean",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 9,
+ "end_row_offset_idx": 10,
+ "start_col_offset_idx": 6,
+ "end_col_offset_idx": 7,
+ "text": "2015",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 9,
+ "end_row_offset_idx": 10,
+ "start_col_offset_idx": 7,
+ "end_col_offset_idx": 8,
+ "text": "WorldPop [12]",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 10,
+ "end_row_offset_idx": 11,
+ "start_col_offset_idx": 0,
+ "end_col_offset_idx": 1,
+ "text": "Environment",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 10,
+ "end_row_offset_idx": 11,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "Socio-economic",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 10,
+ "end_row_offset_idx": 11,
+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "Nighttime lights",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 10,
+ "end_row_offset_idx": 11,
+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "Nighttime light index from 0 to 63",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 10,
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+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": ">1.5",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 10,
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+ "start_col_offset_idx": 5,
+ "end_col_offset_idx": 6,
+ "text": "Mean",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 10,
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+ "start_col_offset_idx": 6,
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+ "text": "2015",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
+ "start_row_offset_idx": 10,
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+ "start_col_offset_idx": 7,
+ "end_col_offset_idx": 8,
+ "text": "VIIRS [13]",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 11,
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+ "start_col_offset_idx": 0,
+ "end_col_offset_idx": 1,
+ "text": "Environment",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 11,
+ "end_row_offset_idx": 12,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "Co-endemicity",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 11,
+ "end_row_offset_idx": 12,
+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "Co-endemic for onchocerciasis",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 11,
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+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "Part or all of district is also endemic for onchocerciases",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
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+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": "Non-endemic",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 11,
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+ "start_col_offset_idx": 5,
+ "end_col_offset_idx": 6,
+ "text": "Binary value",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 11,
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+ "start_col_offset_idx": 6,
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+ "text": "2018",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "start_col_offset_idx": 7,
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+ "text": "Programmatic data",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 12,
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+ "start_col_offset_idx": 0,
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+ "text": "MDA",
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+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "start_row_offset_idx": 12,
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+ "start_col_offset_idx": 1,
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+ "text": "Drug efficacy",
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+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "start_row_offset_idx": 12,
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+ "start_col_offset_idx": 2,
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+ "text": "Drug package",
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+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "start_row_offset_idx": 12,
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+ "start_col_offset_idx": 3,
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+ "text": "DEC-ALB or IVM-ALB",
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+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
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+ "text": "DEC-ALB",
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+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "start_col_offset_idx": 5,
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+ "text": "Binary value",
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+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
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+ "start_col_offset_idx": 6,
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+ "text": "2018",
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+ "row_header": false,
+ "row_section": false
+ },
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+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "start_row_offset_idx": 13,
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+ "start_col_offset_idx": 0,
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+ "text": "MDA",
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+ "row_header": false,
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+ },
+ {
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+ "start_col_offset_idx": 1,
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+ "text": "Implementation of MDA",
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+ "row_header": false,
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+ },
+ {
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+ "row_header": false,
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+ {
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+ "row_header": false,
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+ "start_col_offset_idx": 6,
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+ "text": "Varies",
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+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "row_header": false,
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+ {
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+ "row_header": false,
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+ "start_row_offset_idx": 14,
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+ {
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+ {
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+ {
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+ },
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+ "text": "Programmatic data",
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+ "start_row_offset_idx": 5,
+ "end_row_offset_idx": 6,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "Geography",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 5,
+ "end_row_offset_idx": 6,
+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "Elevation",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 5,
+ "end_row_offset_idx": 6,
+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "Elevation measured in meters",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 5,
+ "end_row_offset_idx": 6,
+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": ">350",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 5,
+ "end_row_offset_idx": 6,
+ "start_col_offset_idx": 5,
+ "end_col_offset_idx": 6,
+ "text": "Mean",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 5,
+ "end_row_offset_idx": 6,
+ "start_col_offset_idx": 6,
+ "end_col_offset_idx": 7,
+ "text": "2000",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 5,
+ "end_row_offset_idx": 6,
+ "start_col_offset_idx": 7,
+ "end_col_offset_idx": 8,
+ "text": "CGIAR-CSI SRTM [9]",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ }
+ ],
+ [
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 6,
+ "end_row_offset_idx": 7,
+ "start_col_offset_idx": 0,
+ "end_col_offset_idx": 1,
+ "text": "Environment",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 6,
+ "end_row_offset_idx": 7,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "Geography",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 6,
+ "end_row_offset_idx": 7,
+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "District area",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 6,
+ "end_row_offset_idx": 7,
+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "Area measured in km2",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 6,
+ "end_row_offset_idx": 7,
+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": ">2,500",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 6,
+ "end_row_offset_idx": 7,
+ "start_col_offset_idx": 5,
+ "end_col_offset_idx": 6,
+ "text": "Maximum sum",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 6,
+ "end_row_offset_idx": 7,
+ "start_col_offset_idx": 6,
+ "end_col_offset_idx": 7,
+ "text": "Static",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 6,
+ "end_row_offset_idx": 7,
+ "start_col_offset_idx": 7,
+ "end_col_offset_idx": 8,
+ "text": "Programmatic data",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ }
+ ],
+ [
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 7,
+ "end_row_offset_idx": 8,
+ "start_col_offset_idx": 0,
+ "end_col_offset_idx": 1,
+ "text": "Environment",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 7,
+ "end_row_offset_idx": 8,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "Climate",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 7,
+ "end_row_offset_idx": 8,
+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "EVI",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 7,
+ "end_row_offset_idx": 8,
+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "Enhanced vegetation index",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 7,
+ "end_row_offset_idx": 8,
+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": "> 0.3",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 7,
+ "end_row_offset_idx": 8,
+ "start_col_offset_idx": 5,
+ "end_col_offset_idx": 6,
+ "text": "Mean",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 7,
+ "end_row_offset_idx": 8,
+ "start_col_offset_idx": 6,
+ "end_col_offset_idx": 7,
+ "text": "2015",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 7,
+ "end_row_offset_idx": 8,
+ "start_col_offset_idx": 7,
+ "end_col_offset_idx": 8,
+ "text": "MODIS [10]",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ }
+ ],
+ [
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 8,
+ "end_row_offset_idx": 9,
+ "start_col_offset_idx": 0,
+ "end_col_offset_idx": 1,
+ "text": "Environment",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 8,
+ "end_row_offset_idx": 9,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "Climate",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 8,
+ "end_row_offset_idx": 9,
+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "Rainfall",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 8,
+ "end_row_offset_idx": 9,
+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "Annual rainfall measured in mm",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 8,
+ "end_row_offset_idx": 9,
+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": "≤ 700",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 8,
+ "end_row_offset_idx": 9,
+ "start_col_offset_idx": 5,
+ "end_col_offset_idx": 6,
+ "text": "Mean",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 8,
+ "end_row_offset_idx": 9,
+ "start_col_offset_idx": 6,
+ "end_col_offset_idx": 7,
+ "text": "2015",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 8,
+ "end_row_offset_idx": 9,
+ "start_col_offset_idx": 7,
+ "end_col_offset_idx": 8,
+ "text": "CHIRPS [11]",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ }
+ ],
+ [
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+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 9,
+ "end_row_offset_idx": 10,
+ "start_col_offset_idx": 0,
+ "end_col_offset_idx": 1,
+ "text": "Environment",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 9,
+ "end_row_offset_idx": 10,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "Socio-economic",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 9,
+ "end_row_offset_idx": 10,
+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "Population density",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 9,
+ "end_row_offset_idx": 10,
+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "Number of people per km2",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 9,
+ "end_row_offset_idx": 10,
+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": "≤ 100",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 9,
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+ "start_col_offset_idx": 5,
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+ "text": "Mean",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
+ "start_row_offset_idx": 9,
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+ "start_col_offset_idx": 6,
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+ "text": "2015",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
+ "start_row_offset_idx": 9,
+ "end_row_offset_idx": 10,
+ "start_col_offset_idx": 7,
+ "end_col_offset_idx": 8,
+ "text": "WorldPop [12]",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ }
+ ],
+ [
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+ "col_span": 1,
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+ "start_col_offset_idx": 0,
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+ "text": "Environment",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
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+ "col_span": 1,
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+ "start_col_offset_idx": 1,
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+ "text": "Socio-economic",
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+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
+ "start_row_offset_idx": 10,
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+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "Nighttime lights",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
+ "start_row_offset_idx": 10,
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+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "Nighttime light index from 0 to 63",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 10,
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+ "start_col_offset_idx": 4,
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+ "text": ">1.5",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
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+ "start_col_offset_idx": 5,
+ "end_col_offset_idx": 6,
+ "text": "Mean",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
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+ "start_col_offset_idx": 6,
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+ "text": "2015",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
+ "start_row_offset_idx": 10,
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+ "start_col_offset_idx": 7,
+ "end_col_offset_idx": 8,
+ "text": "VIIRS [13]",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ }
+ ],
+ [
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+ "start_col_offset_idx": 0,
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+ "text": "Environment",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
+ "start_row_offset_idx": 11,
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+ "start_col_offset_idx": 1,
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+ "text": "Co-endemicity",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
+ "start_row_offset_idx": 11,
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+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "Co-endemic for onchocerciasis",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
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+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "Part or all of district is also endemic for onchocerciases",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
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+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": "Non-endemic",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 11,
+ "end_row_offset_idx": 12,
+ "start_col_offset_idx": 5,
+ "end_col_offset_idx": 6,
+ "text": "Binary value",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 11,
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+ "start_col_offset_idx": 6,
+ "end_col_offset_idx": 7,
+ "text": "2018",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
+ "start_row_offset_idx": 11,
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+ "start_col_offset_idx": 7,
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+ "text": "Programmatic data",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ }
+ ],
+ [
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+ "start_col_offset_idx": 0,
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+ "text": "MDA",
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+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
+ "start_row_offset_idx": 12,
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+ "start_col_offset_idx": 1,
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+ "text": "Drug efficacy",
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+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "start_row_offset_idx": 12,
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+ "start_col_offset_idx": 2,
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+ "text": "Drug package",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "start_row_offset_idx": 12,
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+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "DEC-ALB or IVM-ALB",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
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+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": "DEC-ALB",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 12,
+ "end_row_offset_idx": 13,
+ "start_col_offset_idx": 5,
+ "end_col_offset_idx": 6,
+ "text": "Binary value",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 12,
+ "end_row_offset_idx": 13,
+ "start_col_offset_idx": 6,
+ "end_col_offset_idx": 7,
+ "text": "2018",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
+ "start_row_offset_idx": 12,
+ "end_row_offset_idx": 13,
+ "start_col_offset_idx": 7,
+ "end_col_offset_idx": 8,
+ "text": "Programmatic data",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ }
+ ],
+ [
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+ "col_span": 1,
+ "start_row_offset_idx": 13,
+ "end_row_offset_idx": 14,
+ "start_col_offset_idx": 0,
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+ "text": "MDA",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "start_row_offset_idx": 13,
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+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "Implementation of MDA",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
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+ "start_col_offset_idx": 2,
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+ "text": "Only W. bancrofti parasite districts",
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+ "text": "Only Anopheles vector districts",
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+ },
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+ "text": "Number of Failures",
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+ },
+ {
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+ "col_span": 1,
+ "start_row_offset_idx": 2,
+ "end_row_offset_idx": 3,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "74",
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+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
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+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "74",
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+ "row_header": false,
+ "row_section": false
+ },
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+ "end_row_offset_idx": 3,
+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "44",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
+ "start_row_offset_idx": 2,
+ "end_row_offset_idx": 3,
+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": "72",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
+ "start_row_offset_idx": 2,
+ "end_row_offset_idx": 3,
+ "start_col_offset_idx": 5,
+ "end_col_offset_idx": 6,
+ "text": "46",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "text": " Number of total districts",
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+ "row_header": false,
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+ },
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+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "(N = 554)",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "start_row_offset_idx": 3,
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+ "start_col_offset_idx": 2,
+ "end_col_offset_idx": 3,
+ "text": "(N = 420)",
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+ "row_header": false,
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+ },
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+ "text": "(N = 407)",
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+ "row_header": false,
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+ "text": "(N = 518)",
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+ },
+ {
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+ "start_row_offset_idx": 3,
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+ "end_col_offset_idx": 6,
+ "text": "(N = 414)",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
+ "start_row_offset_idx": 4,
+ "end_row_offset_idx": 5,
+ "start_col_offset_idx": 0,
+ "end_col_offset_idx": 1,
+ "text": "Covariate",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
+ "start_row_offset_idx": 4,
+ "end_row_offset_idx": 5,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "RR (95% CI)",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
+ "row_span": 1,
+ "col_span": 1,
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+ "row_section": false
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+ "row_section": false
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+ "row_header": false,
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+ }
+ ],
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+ },
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+ "row_header": false,
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+ },
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+ "row_header": false,
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+ }
+ ],
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+ "end_row_offset_idx": 3,
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+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "start_row_offset_idx": 2,
+ "end_row_offset_idx": 3,
+ "start_col_offset_idx": 1,
+ "end_col_offset_idx": 2,
+ "text": "74",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
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+ "end_col_offset_idx": 3,
+ "text": "74",
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+ "row_section": false
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+ {
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+ "start_row_offset_idx": 2,
+ "end_row_offset_idx": 3,
+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "44",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "start_row_offset_idx": 2,
+ "end_row_offset_idx": 3,
+ "start_col_offset_idx": 4,
+ "end_col_offset_idx": 5,
+ "text": "72",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
+ "start_row_offset_idx": 2,
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+ "start_col_offset_idx": 5,
+ "end_col_offset_idx": 6,
+ "text": "46",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ }
+ ],
+ [
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+ "start_row_offset_idx": 3,
+ "end_row_offset_idx": 4,
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+ "end_col_offset_idx": 1,
+ "text": " Number of total districts",
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+ "row_header": false,
+ "row_section": false
+ },
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+ "end_col_offset_idx": 2,
+ "text": "(N = 554)",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "col_span": 1,
+ "start_row_offset_idx": 3,
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+ "end_col_offset_idx": 3,
+ "text": "(N = 420)",
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+ "row_header": false,
+ "row_section": false
+ },
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+ "start_col_offset_idx": 3,
+ "end_col_offset_idx": 4,
+ "text": "(N = 407)",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
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+ "start_row_offset_idx": 3,
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+ "end_col_offset_idx": 5,
+ "text": "(N = 518)",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
+ },
+ {
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+ "start_row_offset_idx": 3,
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+ "end_col_offset_idx": 6,
+ "text": "(N = 414)",
+ "column_header": false,
+ "row_header": false,
+ "row_section": false
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+ "text": "0.13 (0.02–0.96)",
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+ }
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diff --git a/tests/data/groundtruth/docling_v2/pntd.0008301.xml.md b/tests/data/groundtruth/docling_v2/pntd.0008301.nxml.md
similarity index 96%
rename from tests/data/groundtruth/docling_v2/pntd.0008301.xml.md
rename to tests/data/groundtruth/docling_v2/pntd.0008301.nxml.md
index 384edc3f..812f0351 100644
--- a/tests/data/groundtruth/docling_v2/pntd.0008301.xml.md
+++ b/tests/data/groundtruth/docling_v2/pntd.0008301.nxml.md
@@ -34,16 +34,16 @@ Table 1 Categorization of potential factors influencing pre-TAS results.
| Domain | Factor | Covariate | Description | Reference Group | Summary statistic | Temporal Resolution | Source |
|------------------------|-----------------------|-------------------------------|-----------------------------------------------------------------|----------------------|---------------------|-----------------------|--------------------|
-| Prevalence | Baseline prevalence | 5% cut off | Maximum reported mapping or baseline sentinel site prevalence | <5% | Maximum | Varies | Programmatic data |
-| Prevalence | Baseline prevalence | 10% cut off | Maximum reported mapping or baseline sentinel site prevalence | <10% | Maximum | Varies | Programmatic data |
-| Agent | Parasite | Parasite | Predominate parasite in district | W. bancrofti & mixed | Binary value | 2018 | Programmatic data |
-| Environment | Vector | Vector | Predominate vector in district | Anopheles & Mansonia | Binary value | 2018 | Country expert |
-| Environment | Geography | Elevation | Elevation measured in meters | >350 | Mean | 2000 | CGIAR-CSI SRTM [9] |
-| Environment | Geography | District area | Area measured in km2 | >2,500 | Maximum sum | Static | Programmatic data |
-| Environment | Climate | EVI | Enhanced vegetation index | > 0.3 | Mean | 2015 | MODIS [10] |
+| Prevalence | Baseline prevalence | 5% cut off | Maximum reported mapping or baseline sentinel site prevalence | <5% | Maximum | Varies | Programmatic data |
+| Prevalence | Baseline prevalence | 10% cut off | Maximum reported mapping or baseline sentinel site prevalence | <10% | Maximum | Varies | Programmatic data |
+| Agent | Parasite | Parasite | Predominate parasite in district | W. bancrofti & mixed | Binary value | 2018 | Programmatic data |
+| Environment | Vector | Vector | Predominate vector in district | Anopheles & Mansonia | Binary value | 2018 | Country expert |
+| Environment | Geography | Elevation | Elevation measured in meters | >350 | Mean | 2000 | CGIAR-CSI SRTM [9] |
+| Environment | Geography | District area | Area measured in km2 | >2,500 | Maximum sum | Static | Programmatic data |
+| Environment | Climate | EVI | Enhanced vegetation index | > 0.3 | Mean | 2015 | MODIS [10] |
| Environment | Climate | Rainfall | Annual rainfall measured in mm | ≤ 700 | Mean | 2015 | CHIRPS [11] |
| Environment | Socio-economic | Population density | Number of people per km2 | ≤ 100 | Mean | 2015 | WorldPop [12] |
-| Environment | Socio-economic | Nighttime lights | Nighttime light index from 0 to 63 | >1.5 | Mean | 2015 | VIIRS [13] |
+| Environment | Socio-economic | Nighttime lights | Nighttime light index from 0 to 63 | >1.5 | Mean | 2015 | VIIRS [13] |
| Environment | Co-endemicity | Co-endemic for onchocerciasis | Part or all of district is also endemic for onchocerciases | Non-endemic | Binary value | 2018 | Programmatic data |
| MDA | Drug efficacy | Drug package | DEC-ALB or IVM-ALB | DEC-ALB | Binary value | 2018 | Programmatic data |
| MDA | Implementation of MDA | Coverage | Median MDA coverage for last 5 rounds | ≥ 65% | Median | Varies | Programmatic data |
@@ -134,12 +134,12 @@ Table 2 Adjusted risk ratios for pre-TAS failure from log-binomial model sensiti
| Number of Failures | 74 | 74 | 44 | 72 | 46 |
| Number of total districts | (N = 554) | (N = 420) | (N = 407) | (N = 518) | (N = 414) |
| Covariate | RR (95% CI) | RR (95% CI) | RR (95% CI) | RR (95% CI) | RR (95% CI) |
-| Baseline prevalence > = 10% & used FTS test | 2.38 (0.96–5.90) | 1.23 (0.52–2.92) | 14.52 (1.79–117.82) | 2.61 (1.03–6.61) | 15.80 (1.95–127.67) |
-| Baseline prevalence > = 10% & used ICT test | 0.80 (0.20–3.24) | 0.42 (0.11–1.68) | 1.00 (0.00–0.00) | 0.88 (0.21–3.60) | 1.00 (0.00–0.00) |
+| Baseline prevalence > = 10% & used FTS test | 2.38 (0.96–5.90) | 1.23 (0.52–2.92) | 14.52 (1.79–117.82) | 2.61 (1.03–6.61) | 15.80 (1.95–127.67) |
+| Baseline prevalence > = 10% & used ICT test | 0.80 (0.20–3.24) | 0.42 (0.11–1.68) | 1.00 (0.00–0.00) | 0.88 (0.21–3.60) | 1.00 (0.00–0.00) |
| +Used FTS test | 1.16 (0.52–2.59) | 2.40 (1.12–5.11) | 0.15 (0.02–1.11) | 1.03 (0.45–2.36) | 0.13 (0.02–0.96) |
| +Used ICT test | 0.92 (0.32–2.67) | 1.47 (0.51–4.21) | 0.33 (0.04–2.54) | 0.82 (0.28–2.43) | 0.27 (0.03–2.04) |
-| +Baseline prevalence > = 10% | 2.52 (1.37–4.64) | 2.42 (1.31–4.47) | 2.03 (1.06–3.90) | 2.30 (1.21–4.36) | 2.01 (1.07–3.77) |
-| Elevation < 350m | 3.07 (1.95–4.83) | 2.21 (1.42–3.43) | 4.68 (2.22–9.87) | 3.04 (1.93–4.79) | 3.76 (1.92–7.37) |
+| +Baseline prevalence > = 10% | 2.52 (1.37–4.64) | 2.42 (1.31–4.47) | 2.03 (1.06–3.90) | 2.30 (1.21–4.36) | 2.01 (1.07–3.77) |
+| Elevation < 350m | 3.07 (1.95–4.83) | 2.21 (1.42–3.43) | 4.68 (2.22–9.87) | 3.04 (1.93–4.79) | 3.76 (1.92–7.37) |
Overall 74 districts in the dataset failed pre-TAS. Fig 5 summarizes the likelihood of failure by variable combinations identified in the log-binomial model. For those districts with a baseline prevalence ≥10% that used a FTS diagnostic test and have an average elevation below 350 meters (Combination C01), 87% of the 23 districts failed. Of districts with high baseline that used an ICT diagnostic test and have a low average elevation (C02) 45% failed. Overall, combinations with high baseline and low elevation C01, C02, and C04 accounted for 51% of all the failures (38 of 74).
diff --git a/tests/data/groundtruth/docling_v2/pone.0234687.xml.itxt b/tests/data/groundtruth/docling_v2/pone.0234687.nxml.itxt
similarity index 100%
rename from tests/data/groundtruth/docling_v2/pone.0234687.xml.itxt
rename to tests/data/groundtruth/docling_v2/pone.0234687.nxml.itxt
diff --git a/tests/data/groundtruth/docling_v2/pone.0234687.nxml.json b/tests/data/groundtruth/docling_v2/pone.0234687.nxml.json
new file mode 100644
index 00000000..7d4f7ef8
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/pone.0234687.nxml.json
@@ -0,0 +1,14628 @@
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+ "self_ref": "#/body",
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+ "orig": "Potential to reduce greenhouse gas emissions through different dairy cattle systems in subtropical regions",
+ "text": "Potential to reduce greenhouse gas emissions through different dairy cattle systems in subtropical regions"
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+ "orig": "Henrique M. N. Ribeiro-Filho, Maurício Civiero, Ermias Kebreab",
+ "text": "Henrique M. N. Ribeiro-Filho, Maurício Civiero, Ermias Kebreab"
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+ "orig": "Department of Animal Science, University of California, Davis, California, United States of America; Programa de Pós-graduação em Ciência Animal, Universidade do Estado de Santa Catarina, Lages, Santa Catarina, Brazil",
+ "text": "Department of Animal Science, University of California, Davis, California, United States of America; Programa de Pós-graduação em Ciência Animal, Universidade do Estado de Santa Catarina, Lages, Santa Catarina, Brazil"
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+ "orig": "Abstract",
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+ "level": 1
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+ "orig": "Carbon (C) footprint of dairy production, expressed in kg C dioxide (CO2) equivalents (CO2e) (kg energy-corrected milk (ECM))-1, encompasses emissions from feed production, diet management and total product output. The proportion of pasture on diets may affect all these factors, mainly in subtropical climate zones, where cows may access tropical and temperate pastures during warm and cold seasons, respectively. The aim of the study was to assess the C footprint of a dairy system with annual tropical and temperate pastures in a subtropical region. The system boundary included all processes up to the animal farm gate. Feed requirement during the entire life of each cow was based on data recorded from Holstein × Jersey cow herds producing an average of 7,000 kg ECM lactation-1. The milk production response as consequence of feed strategies (scenarios) was based on results from two experiments (warm and cold seasons) using lactating cows from the same herd. Three scenarios were evaluated: total mixed ration (TMR) ad libitum intake, 75, and 50% of ad libitum TMR intake with access to grazing either a tropical or temperate pasture during lactation periods. Considering IPCC and international literature values to estimate emissions from urine/dung, feed production and electricity, the C footprint was similar between scenarios, averaging 1.06 kg CO2e (kg ECM)-1. Considering factors from studies conducted in subtropical conditions and actual inputs for on-farm feed production, the C footprint decreased 0.04 kg CO2e (kg ECM)-1 in scenarios including pastures compared to ad libitum TMR. Regardless of factors considered, emissions from feed production decreased as the proportion of pasture went up. In conclusion, decreasing TMR intake and including pastures in dairy cow diets in subtropical conditions have the potential to maintain or reduce the C footprint to a small extent.",
+ "text": "Carbon (C) footprint of dairy production, expressed in kg C dioxide (CO2) equivalents (CO2e) (kg energy-corrected milk (ECM))-1, encompasses emissions from feed production, diet management and total product output. The proportion of pasture on diets may affect all these factors, mainly in subtropical climate zones, where cows may access tropical and temperate pastures during warm and cold seasons, respectively. The aim of the study was to assess the C footprint of a dairy system with annual tropical and temperate pastures in a subtropical region. The system boundary included all processes up to the animal farm gate. Feed requirement during the entire life of each cow was based on data recorded from Holstein × Jersey cow herds producing an average of 7,000 kg ECM lactation-1. The milk production response as consequence of feed strategies (scenarios) was based on results from two experiments (warm and cold seasons) using lactating cows from the same herd. Three scenarios were evaluated: total mixed ration (TMR) ad libitum intake, 75, and 50% of ad libitum TMR intake with access to grazing either a tropical or temperate pasture during lactation periods. Considering IPCC and international literature values to estimate emissions from urine/dung, feed production and electricity, the C footprint was similar between scenarios, averaging 1.06 kg CO2e (kg ECM)-1. Considering factors from studies conducted in subtropical conditions and actual inputs for on-farm feed production, the C footprint decreased 0.04 kg CO2e (kg ECM)-1 in scenarios including pastures compared to ad libitum TMR. Regardless of factors considered, emissions from feed production decreased as the proportion of pasture went up. In conclusion, decreasing TMR intake and including pastures in dairy cow diets in subtropical conditions have the potential to maintain or reduce the C footprint to a small extent."
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+ "orig": "Introduction",
+ "text": "Introduction",
+ "level": 1
+ },
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+ "parent": {
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+ "orig": "Greenhouse gas (GHG) emissions from livestock activities represent 10–12% of global emissions [1], ranging from 5.5–7.5 Gt CO2 equivalents (CO2e) yr-1, with almost 30% coming from dairy cattle production systems [2]. However, the livestock sector supply between 13 and 17% of calories and between 28 and 33% of human edible protein consumption globally [3]. Additionally, livestock produce more human-edible protein per unit area than crops when land is unsuitable for food crop production [4].",
+ "text": "Greenhouse gas (GHG) emissions from livestock activities represent 10–12% of global emissions [1], ranging from 5.5–7.5 Gt CO2 equivalents (CO2e) yr-1, with almost 30% coming from dairy cattle production systems [2]. However, the livestock sector supply between 13 and 17% of calories and between 28 and 33% of human edible protein consumption globally [3]. Additionally, livestock produce more human-edible protein per unit area than crops when land is unsuitable for food crop production [4]."
+ },
+ {
+ "self_ref": "#/texts/7",
+ "parent": {
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+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Considering the key role of livestock systems in global food security, several technical and management interventions have been investigated to mitigate methane (CH4) emissions from enteric fermentation [5], animal management [6] and manure management [7]. CH4 emissions from enteric fermentation represents around 34% of total emissions from livestock sector, which is the largest source [2]. Increasing proportions of concentrate and digestibility of forages in the diet have been proposed as mitigation strategies [1,5]. In contrast, some life cycle assessment (LCA) studies of dairy systems in temperate regions [8–11] have identified that increasing concentrate proportion may increase carbon (C) footprint due to greater resource use and pollutants from the production of feed compared to forage. Thus, increasing pasture proportion on dairy cattle systems may be an alternative management to mitigate the C footprint.",
+ "text": "Considering the key role of livestock systems in global food security, several technical and management interventions have been investigated to mitigate methane (CH4) emissions from enteric fermentation [5], animal management [6] and manure management [7]. CH4 emissions from enteric fermentation represents around 34% of total emissions from livestock sector, which is the largest source [2]. Increasing proportions of concentrate and digestibility of forages in the diet have been proposed as mitigation strategies [1,5]. In contrast, some life cycle assessment (LCA) studies of dairy systems in temperate regions [8–11] have identified that increasing concentrate proportion may increase carbon (C) footprint due to greater resource use and pollutants from the production of feed compared to forage. Thus, increasing pasture proportion on dairy cattle systems may be an alternative management to mitigate the C footprint."
+ },
+ {
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+ "parent": {
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+ "orig": "In subtropical climate zones, cows may graze tropical pastures rather than temperate pastures during the warm season [12]. Some important dairy production areas, such as southern Brazil, central to northern Argentina, Uruguay, South Africa, New Zealand and Australia, are located in these climate zones, having more than 900 million ha in native, permanent or temporary pastures, producing almost 20% of global milk production [13]. However, due to a considerable inter-annual variation in pasture growth rates [14,15], the interest in mixed systems, using total mixed ration (TMR) + pasture has been increasing [16]. Nevertheless, to our best knowledge, studies conducted to evaluate milk production response in dairy cow diets receiving TMR and pastures have only been conducted in temperate pastures and not in tropical pastures (e.g. [17–19]).",
+ "text": "In subtropical climate zones, cows may graze tropical pastures rather than temperate pastures during the warm season [12]. Some important dairy production areas, such as southern Brazil, central to northern Argentina, Uruguay, South Africa, New Zealand and Australia, are located in these climate zones, having more than 900 million ha in native, permanent or temporary pastures, producing almost 20% of global milk production [13]. However, due to a considerable inter-annual variation in pasture growth rates [14,15], the interest in mixed systems, using total mixed ration (TMR) + pasture has been increasing [16]. Nevertheless, to our best knowledge, studies conducted to evaluate milk production response in dairy cow diets receiving TMR and pastures have only been conducted in temperate pastures and not in tropical pastures (e.g. [17–19])."
+ },
+ {
+ "self_ref": "#/texts/9",
+ "parent": {
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+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "It has been shown that dairy cows receiving TMR-based diets may not decrease milk production when supplemented with temperate pastures in a vegetative growth stage [18]. On the other hand, tropical pastures have lower organic matter digestibility and cows experience reduced dry matter (DM) intake and milk yield compared to temperate pastures [20,21]. A lower milk yield increases the C footprint intensity [22], offsetting an expected advantage through lower GHG emissions from crop and reduced DM intake.",
+ "text": "It has been shown that dairy cows receiving TMR-based diets may not decrease milk production when supplemented with temperate pastures in a vegetative growth stage [18]. On the other hand, tropical pastures have lower organic matter digestibility and cows experience reduced dry matter (DM) intake and milk yield compared to temperate pastures [20,21]. A lower milk yield increases the C footprint intensity [22], offsetting an expected advantage through lower GHG emissions from crop and reduced DM intake."
+ },
+ {
+ "self_ref": "#/texts/10",
+ "parent": {
+ "$ref": "#/texts/5"
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+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The aim of this work was to quantify the C footprint and land use of dairy systems using cows with a medium milk production potential in a subtropical region. The effect of replacing total mixed ration (TMR) with pastures during lactation periods was evaluated.",
+ "text": "The aim of this work was to quantify the C footprint and land use of dairy systems using cows with a medium milk production potential in a subtropical region. The effect of replacing total mixed ration (TMR) with pastures during lactation periods was evaluated."
+ },
+ {
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+ "orig": "Materials and methods",
+ "text": "Materials and methods",
+ "level": 1
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+ "parent": {
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+ "orig": "An LCA was developed according to the ISO standards [23,24] and Food and Agriculture Organization of the United Nations (FAO) Livestock Environmental Assessment Protocol guidelines [25]. All procedures were approved by the ‘Comissão de Ética no Uso de Animais’ (CEUA/UDESC) on September 15, 2016—Approval number 4373090816 - https://www.udesc.br/cav/ceua.",
+ "text": "An LCA was developed according to the ISO standards [23,24] and Food and Agriculture Organization of the United Nations (FAO) Livestock Environmental Assessment Protocol guidelines [25]. All procedures were approved by the ‘Comissão de Ética no Uso de Animais’ (CEUA/UDESC) on September 15, 2016—Approval number 4373090816 - https://www.udesc.br/cav/ceua."
+ },
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+ "prov": [],
+ "orig": "System boundary",
+ "text": "System boundary",
+ "level": 2
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+ "children": [],
+ "content_layer": "body",
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+ "prov": [],
+ "orig": "The goal of the study was to assess the C footprint of annual tropical and temperate pastures in lactating dairy cow diets. The production system was divided into four main processes: (i) animal husbandry, (ii) manure management and urine and dung deposited by grazing animals, (iii) production of feed ingredients and (iv) farm management (Fig 1). The study boundary included all processes up to the animal farm gate (cradle to gate), including secondary sources such as GHG emissions during the production of fuel, electricity, machinery, manufacturing of fertilizer, pesticides, seeds and plastic used in silage production. Fuel combustion and machinery (manufacture and repairs) for manure handling and electricity for milking and confinement were accounted as emissions from farm management. Emissions post milk production were assumed to be similar for all scenarios, therefore, activities including milk processing, distribution, retail or consumption were outside of the system boundary.",
+ "text": "The goal of the study was to assess the C footprint of annual tropical and temperate pastures in lactating dairy cow diets. The production system was divided into four main processes: (i) animal husbandry, (ii) manure management and urine and dung deposited by grazing animals, (iii) production of feed ingredients and (iv) farm management (Fig 1). The study boundary included all processes up to the animal farm gate (cradle to gate), including secondary sources such as GHG emissions during the production of fuel, electricity, machinery, manufacturing of fertilizer, pesticides, seeds and plastic used in silage production. Fuel combustion and machinery (manufacture and repairs) for manure handling and electricity for milking and confinement were accounted as emissions from farm management. Emissions post milk production were assumed to be similar for all scenarios, therefore, activities including milk processing, distribution, retail or consumption were outside of the system boundary."
+ },
+ {
+ "self_ref": "#/texts/15",
+ "parent": {
+ "$ref": "#/body"
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+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Fig 1 Overview of the milk production system boundary considered in the study.",
+ "text": "Fig 1 Overview of the milk production system boundary considered in the study."
+ },
+ {
+ "self_ref": "#/texts/16",
+ "parent": {
+ "$ref": "#/texts/11"
+ },
+ "children": [
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+ "$ref": "#/texts/17"
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+ {
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+ {
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+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Functional unit",
+ "text": "Functional unit",
+ "level": 2
+ },
+ {
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+ "parent": {
+ "$ref": "#/texts/16"
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+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The functional unit was one kilogram of energy-corrected milk (ECM) at the farm gate. All processes in the system were calculated based on one kilogram ECM. The ECM was calculated by multiplying milk production by the ratio of the energy content of the milk to the energy content of standard milk with 4% fat and 3.3% true protein according to NRC [20] as follows:",
+ "text": "The functional unit was one kilogram of energy-corrected milk (ECM) at the farm gate. All processes in the system were calculated based on one kilogram ECM. The ECM was calculated by multiplying milk production by the ratio of the energy content of the milk to the energy content of standard milk with 4% fat and 3.3% true protein according to NRC [20] as follows:"
+ },
+ {
+ "self_ref": "#/texts/18",
+ "parent": {
+ "$ref": "#/texts/16"
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+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "ECM = Milk production × (0.0929 × fat% + 0.0588× true protein% + 0.192) / (0.0929 × (4%) + 0.0588 × (3.3%) + 0.192), where fat% and protein% are fat and protein percentages in milk, respectively. The average milk production and composition were recorded from the University of Santa Catarina State (Brazil) herd, considering 165 lactations between 2009 and 2018. The herd is predominantly Holstein × Jersey cows, with key characteristics described in Table 1.",
+ "text": "ECM = Milk production × (0.0929 × fat% + 0.0588× true protein% + 0.192) / (0.0929 × (4%) + 0.0588 × (3.3%) + 0.192), where fat% and protein% are fat and protein percentages in milk, respectively. The average milk production and composition were recorded from the University of Santa Catarina State (Brazil) herd, considering 165 lactations between 2009 and 2018. The herd is predominantly Holstein × Jersey cows, with key characteristics described in Table 1."
+ },
+ {
+ "self_ref": "#/texts/19",
+ "parent": {
+ "$ref": "#/body"
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+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
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+ "orig": "Table 1 Descriptive characteristics of the herd.",
+ "text": "Table 1 Descriptive characteristics of the herd."
+ },
+ {
+ "self_ref": "#/texts/20",
+ "parent": {
+ "$ref": "#/texts/11"
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+ "children": [
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+ "$ref": "#/texts/21"
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+ ],
+ "content_layer": "body",
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+ "prov": [],
+ "orig": "Data sources and livestock system description",
+ "text": "Data sources and livestock system description",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/21",
+ "parent": {
+ "$ref": "#/texts/20"
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+ "children": [],
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+ "prov": [],
+ "orig": "The individual feed requirements, as well as the milk production responses based on feed strategies were based on data recorded from the herd described above and two experiments performed using lactating cows from the same herd. Due to the variation on herbage production throughout the year, feed requirements were estimated taking into consideration that livestock systems have a calving period in April, which represents the beginning of fall season in the southern Hemisphere. The experiments have shown a 10% reduction in ECM production in dairy cows that received both 75 and 50% of ad libitum TMR intake with access to grazing a tropical pasture (pearl-millet, Pennisetum glaucum ‘Campeiro’) compared to cows receiving ad libitum TMR intake. Cows grazing on a temperate pasture (ryegrass, Lolium multiflorum ‘Maximus’) did not need changes to ECM production compared to the ad libitum TMR intake group.",
+ "text": "The individual feed requirements, as well as the milk production responses based on feed strategies were based on data recorded from the herd described above and two experiments performed using lactating cows from the same herd. Due to the variation on herbage production throughout the year, feed requirements were estimated taking into consideration that livestock systems have a calving period in April, which represents the beginning of fall season in the southern Hemisphere. The experiments have shown a 10% reduction in ECM production in dairy cows that received both 75 and 50% of ad libitum TMR intake with access to grazing a tropical pasture (pearl-millet, Pennisetum glaucum ‘Campeiro’) compared to cows receiving ad libitum TMR intake. Cows grazing on a temperate pasture (ryegrass, Lolium multiflorum ‘Maximus’) did not need changes to ECM production compared to the ad libitum TMR intake group."
+ },
+ {
+ "self_ref": "#/texts/22",
+ "parent": {
+ "$ref": "#/texts/20"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Using experimental data, three scenarios were evaluated during the lactation period: ad libitum TMR intake, and 75, and 50% of ad libitum TMR intake with access to grazing either an annual tropical or temperate pasture as a function of month ([26], Civiero et al., in press). From April to October (210 days) cows accessed an annual temperate pasture (ryegrass), and from November to beginning of February (95 days) cows grazed an annual tropical pasture (pearl-millet). The average annual reduction in ECM production in dairy cows with access to pastures is 3%. This value was assumed during an entire lactation period.",
+ "text": "Using experimental data, three scenarios were evaluated during the lactation period: ad libitum TMR intake, and 75, and 50% of ad libitum TMR intake with access to grazing either an annual tropical or temperate pasture as a function of month ([26], Civiero et al., in press). From April to October (210 days) cows accessed an annual temperate pasture (ryegrass), and from November to beginning of February (95 days) cows grazed an annual tropical pasture (pearl-millet). The average annual reduction in ECM production in dairy cows with access to pastures is 3%. This value was assumed during an entire lactation period."
+ },
+ {
+ "self_ref": "#/texts/23",
+ "parent": {
+ "$ref": "#/texts/11"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/24"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Impact assessment",
+ "text": "Impact assessment",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/24",
+ "parent": {
+ "$ref": "#/texts/23"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The CO2e emissions were calculated by multiplying the emissions of CO2, CH4 and N2O by their 100-year global warming potential (GWP100), based on IPCC assessment report 5 (AR5; [27]). The values of GWP100 are 1, 28 and 265 for CO2, CH4 and N2O, respectively.",
+ "text": "The CO2e emissions were calculated by multiplying the emissions of CO2, CH4 and N2O by their 100-year global warming potential (GWP100), based on IPCC assessment report 5 (AR5; [27]). The values of GWP100 are 1, 28 and 265 for CO2, CH4 and N2O, respectively."
+ },
+ {
+ "self_ref": "#/texts/25",
+ "parent": {
+ "$ref": "#/texts/11"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/26"
+ },
+ {
+ "$ref": "#/texts/29"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Feed production",
+ "text": "Feed production",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/26",
+ "parent": {
+ "$ref": "#/texts/25"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/27"
+ },
+ {
+ "$ref": "#/tables/1"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Diets composition",
+ "text": "Diets composition",
+ "level": 3
+ },
+ {
+ "self_ref": "#/texts/27",
+ "parent": {
+ "$ref": "#/texts/26"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The DM intake of each ingredient throughout the entire life of animals during lactation periods was calculated for each scenario: cows receiving only TMR, cows receiving 75% of TMR with annual pastures and cows receiving 50% of TMR with annual pastures (Table 2). In each of other phases of life (calf, heifer, dry cow), animals received the same diet, including a perennial tropical pasture (kikuyu grass, Pennisetum clandestinum). The DM intake of calves, heifers and dry cows was calculated assuming 2.8, 2.5 and 1.9% body weight, respectively [20]. In each case, the actual DM intake of concentrate and corn silage was recorded, and pasture DM intake was estimated by the difference between daily expected DM intake and actual DM intake of concentrate and corn silage. For lactating heifers and cows, TMR was formulated to meet the net energy for lactation (NEL) and metabolizable protein (MP) requirements of experimental animals, according to [28]. The INRA system was used because it is possible to estimate pasture DM intake taking into account the TMR intake, pasture management and the time of access to pasture using the GrazeIn model [29], which was integrated in the software INRAtion 4.07 (https://www.inration.educagri.fr/fr/forum.php). The nutrient intake was calculated as a product of TMR and pasture intake and the nutrient contents of TMR and pasture, respectively, which were determined in feed samples collected throughout the experiments.",
+ "text": "The DM intake of each ingredient throughout the entire life of animals during lactation periods was calculated for each scenario: cows receiving only TMR, cows receiving 75% of TMR with annual pastures and cows receiving 50% of TMR with annual pastures (Table 2). In each of other phases of life (calf, heifer, dry cow), animals received the same diet, including a perennial tropical pasture (kikuyu grass, Pennisetum clandestinum). The DM intake of calves, heifers and dry cows was calculated assuming 2.8, 2.5 and 1.9% body weight, respectively [20]. In each case, the actual DM intake of concentrate and corn silage was recorded, and pasture DM intake was estimated by the difference between daily expected DM intake and actual DM intake of concentrate and corn silage. For lactating heifers and cows, TMR was formulated to meet the net energy for lactation (NEL) and metabolizable protein (MP) requirements of experimental animals, according to [28]. The INRA system was used because it is possible to estimate pasture DM intake taking into account the TMR intake, pasture management and the time of access to pasture using the GrazeIn model [29], which was integrated in the software INRAtion 4.07 (https://www.inration.educagri.fr/fr/forum.php). The nutrient intake was calculated as a product of TMR and pasture intake and the nutrient contents of TMR and pasture, respectively, which were determined in feed samples collected throughout the experiments."
+ },
+ {
+ "self_ref": "#/texts/28",
+ "parent": {
+ "$ref": "#/body"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Table 2 Dairy cows’ diets in different scenariosa.",
+ "text": "Table 2 Dairy cows’ diets in different scenariosa."
+ },
+ {
+ "self_ref": "#/texts/29",
+ "parent": {
+ "$ref": "#/texts/25"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/30"
+ },
+ {
+ "$ref": "#/tables/2"
+ },
+ {
+ "$ref": "#/texts/32"
+ },
+ {
+ "$ref": "#/tables/3"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "GHG emissions from crop and pasture production",
+ "text": "GHG emissions from crop and pasture production",
+ "level": 3
+ },
+ {
+ "self_ref": "#/texts/30",
+ "parent": {
+ "$ref": "#/texts/29"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "GHG emission factors used for off- and on-farm feed production were based on literature values, and are presented in Table 3. The emission factor used for corn grain is the average of emission factors observed in different levels of synthetic N fertilization [30]. The emission factor used for soybean is based on Brazilian soybean production [31]. The emissions used for corn silage, including feed processing (cutting, crushing and mixing), and annual or perennial grass productions were 3300 and 1500 kg CO2e ha-1, respectively [32]. The DM production (kg ha-1) of corn silage and pastures were based on regional and locally recorded data [33–36], assuming that animals are able to consume 70% of pastures during grazing.",
+ "text": "GHG emission factors used for off- and on-farm feed production were based on literature values, and are presented in Table 3. The emission factor used for corn grain is the average of emission factors observed in different levels of synthetic N fertilization [30]. The emission factor used for soybean is based on Brazilian soybean production [31]. The emissions used for corn silage, including feed processing (cutting, crushing and mixing), and annual or perennial grass productions were 3300 and 1500 kg CO2e ha-1, respectively [32]. The DM production (kg ha-1) of corn silage and pastures were based on regional and locally recorded data [33–36], assuming that animals are able to consume 70% of pastures during grazing."
+ },
+ {
+ "self_ref": "#/texts/31",
+ "parent": {
+ "$ref": "#/body"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Table 3 GHG emission factors for Off- and On-farm feed production.",
+ "text": "Table 3 GHG emission factors for Off- and On-farm feed production."
+ },
+ {
+ "self_ref": "#/texts/32",
+ "parent": {
+ "$ref": "#/texts/29"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Emissions from on-farm feed production (corn silage and pasture) were estimated using primary and secondary sources based on the actual amount of each input (Table 4). Primary sources were direct and indirect N2O-N emissions from organic and synthetic fertilizers and crop/pasture residues, CO2-C emissions from lime and urea applications, as well as fuel combustion. The direct N2O-N emission factor (kg (kg N input)-1) is based on a local study performed previously [37]. For indirect N2O-N emissions (kg N2O-N (kg NH3-N + NOx)-1), as well as CO2-C emissions from lime + urea, default values proposed by IPCC [38] were used. For perennial pastures, a C sequestration of 0.57 t ha-1 was used based on a 9-year study conducted in southern Brazil [39]. Due to the use of conventional tillage, no C sequestration was considered for annual pastures. The amount of fuel required was 8.9 (no-tillage) and 14.3 L ha-1 (disking) for annual tropical and temperate pastures, respectively [40]. The CO2 from fuel combustion was 2.7 kg CO2 L-1 [41]. Secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were estimated using emission factors described by Rotz et al. [42].",
+ "text": "Emissions from on-farm feed production (corn silage and pasture) were estimated using primary and secondary sources based on the actual amount of each input (Table 4). Primary sources were direct and indirect N2O-N emissions from organic and synthetic fertilizers and crop/pasture residues, CO2-C emissions from lime and urea applications, as well as fuel combustion. The direct N2O-N emission factor (kg (kg N input)-1) is based on a local study performed previously [37]. For indirect N2O-N emissions (kg N2O-N (kg NH3-N + NOx)-1), as well as CO2-C emissions from lime + urea, default values proposed by IPCC [38] were used. For perennial pastures, a C sequestration of 0.57 t ha-1 was used based on a 9-year study conducted in southern Brazil [39]. Due to the use of conventional tillage, no C sequestration was considered for annual pastures. The amount of fuel required was 8.9 (no-tillage) and 14.3 L ha-1 (disking) for annual tropical and temperate pastures, respectively [40]. The CO2 from fuel combustion was 2.7 kg CO2 L-1 [41]. Secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were estimated using emission factors described by Rotz et al. [42]."
+ },
+ {
+ "self_ref": "#/texts/33",
+ "parent": {
+ "$ref": "#/body"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Table 4 GHG emissions from On-farm feed production.",
+ "text": "Table 4 GHG emissions from On-farm feed production."
+ },
+ {
+ "self_ref": "#/texts/34",
+ "parent": {
+ "$ref": "#/texts/11"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/35"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Animal husbandry",
+ "text": "Animal husbandry",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/35",
+ "parent": {
+ "$ref": "#/texts/34"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The CH4 emissions from enteric fermentation intensity (g (kg ECM)-1) was a function of estimated CH4 yield (g (kg DM intake)-1), actual DM intake and ECM. The enteric CH4 yield was estimated as a function of neutral detergent fiber (NDF) concentration on total DM intake, as proposed by Niu et al. [43], where: CH4 yield (g (kg DM intake)-1) = 13.8 + 0.185 × NDF (% DM intake).",
+ "text": "The CH4 emissions from enteric fermentation intensity (g (kg ECM)-1) was a function of estimated CH4 yield (g (kg DM intake)-1), actual DM intake and ECM. The enteric CH4 yield was estimated as a function of neutral detergent fiber (NDF) concentration on total DM intake, as proposed by Niu et al. [43], where: CH4 yield (g (kg DM intake)-1) = 13.8 + 0.185 × NDF (% DM intake)."
+ },
+ {
+ "self_ref": "#/texts/36",
+ "parent": {
+ "$ref": "#/texts/11"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/37"
+ },
+ {
+ "$ref": "#/texts/38"
+ },
+ {
+ "$ref": "#/texts/39"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Manure from confined cows and urine and dung from grazing animals",
+ "text": "Manure from confined cows and urine and dung from grazing animals",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/37",
+ "parent": {
+ "$ref": "#/texts/36"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The CH4 emission from manure (kg (kg ECM)-1) was a function of daily CH4 emission from manure (kg cow-1) and daily ECM (kg cow-1). The daily CH4 emission from manure was estimated according to IPCC [38], which considered daily volatile solid (VS) excreted (kg DM cow-1) in manure. The daily VS was estimated as proposed by Eugène et al. [44] as: VS = NDOMI + (UE × GE) × (OM/18.45), where: VS = volatile solid excretion on an organic matter (OM) basis (kg day-1), NDOMI = non-digestible OM intake (kg day-1): (1- OM digestibility) × OM intake, UE = urinary energy excretion as a fraction of GE (0.04), GE = gross energy intake (MJ day-1), OM = organic matter (g), 18.45 = conversion factor for dietary GE per kg of DM (MJ kg-1).",
+ "text": "The CH4 emission from manure (kg (kg ECM)-1) was a function of daily CH4 emission from manure (kg cow-1) and daily ECM (kg cow-1). The daily CH4 emission from manure was estimated according to IPCC [38], which considered daily volatile solid (VS) excreted (kg DM cow-1) in manure. The daily VS was estimated as proposed by Eugène et al. [44] as: VS = NDOMI + (UE × GE) × (OM/18.45), where: VS = volatile solid excretion on an organic matter (OM) basis (kg day-1), NDOMI = non-digestible OM intake (kg day-1): (1- OM digestibility) × OM intake, UE = urinary energy excretion as a fraction of GE (0.04), GE = gross energy intake (MJ day-1), OM = organic matter (g), 18.45 = conversion factor for dietary GE per kg of DM (MJ kg-1)."
+ },
+ {
+ "self_ref": "#/texts/38",
+ "parent": {
+ "$ref": "#/texts/36"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The OM digestibility was estimated as a function of chemical composition, using equations published by INRA [21], which takes into account the effects of digestive interactions due to feeding level, the proportion of concentrate and rumen protein balance on OM digestibility. For scenarios where cows had access to grazing, the amount of calculated VS were corrected as a function of the time at pasture. The biodegradability of manure factor (0.13 for dairy cows in Latin America) and methane conversion factor (MCF) values were taken from IPCC [38]. The MCF values for pit storage below animal confinements (> 1 month) were used for the calculation, taking into account the annual average temperature (16.6ºC) or the average temperatures during the growth period of temperate (14.4ºC) or tropical (21ºC) annual pastures, which were 31%, 26% and 46%, respectively.",
+ "text": "The OM digestibility was estimated as a function of chemical composition, using equations published by INRA [21], which takes into account the effects of digestive interactions due to feeding level, the proportion of concentrate and rumen protein balance on OM digestibility. For scenarios where cows had access to grazing, the amount of calculated VS were corrected as a function of the time at pasture. The biodegradability of manure factor (0.13 for dairy cows in Latin America) and methane conversion factor (MCF) values were taken from IPCC [38]. The MCF values for pit storage below animal confinements (> 1 month) were used for the calculation, taking into account the annual average temperature (16.6ºC) or the average temperatures during the growth period of temperate (14.4ºC) or tropical (21ºC) annual pastures, which were 31%, 26% and 46%, respectively."
+ },
+ {
+ "self_ref": "#/texts/39",
+ "parent": {
+ "$ref": "#/texts/36"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The N2O-N emissions from urine and feces were estimated considering the proportion of N excreted as manure and storage or as urine and dung deposited by grazing animals. These proportions were calculated based on the proportion of daily time that animals stayed on pasture (7 h/24 h = 0.29) or confinement (1−0.29 = 0.71). For lactating heifers and cows, the total amount of N excreted was calculated by the difference between N intake and milk N excretion. For heifers and non-lactating cows, urinary and fecal N excretion were estimated as proposed by Reed et al. [45] (Table 3: equations 10 and 12, respectively). The N2O emissions from stored manure as well as urine and dung during grazing were calculated based on the conversion of N2O-N emissions to N2O emissions, where N2O emissions = N2O-N emissions × 44/28. The emission factors were 0.002 kg N2O-N (kg N)-1 stored in a pit below animal confinements, and 0.02 kg N2O-N (kg of urine and dung)-1 deposited on pasture [38]. The indirect N2O emissions from storage manure and urine and dung deposits on pasture were also estimated using the IPCC [38] emission factors.",
+ "text": "The N2O-N emissions from urine and feces were estimated considering the proportion of N excreted as manure and storage or as urine and dung deposited by grazing animals. These proportions were calculated based on the proportion of daily time that animals stayed on pasture (7 h/24 h = 0.29) or confinement (1−0.29 = 0.71). For lactating heifers and cows, the total amount of N excreted was calculated by the difference between N intake and milk N excretion. For heifers and non-lactating cows, urinary and fecal N excretion were estimated as proposed by Reed et al. [45] (Table 3: equations 10 and 12, respectively). The N2O emissions from stored manure as well as urine and dung during grazing were calculated based on the conversion of N2O-N emissions to N2O emissions, where N2O emissions = N2O-N emissions × 44/28. The emission factors were 0.002 kg N2O-N (kg N)-1 stored in a pit below animal confinements, and 0.02 kg N2O-N (kg of urine and dung)-1 deposited on pasture [38]. The indirect N2O emissions from storage manure and urine and dung deposits on pasture were also estimated using the IPCC [38] emission factors."
+ },
+ {
+ "self_ref": "#/texts/40",
+ "parent": {
+ "$ref": "#/texts/11"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/41"
+ },
+ {
+ "$ref": "#/tables/4"
+ },
+ {
+ "$ref": "#/texts/43"
+ },
+ {
+ "$ref": "#/texts/44"
+ },
+ {
+ "$ref": "#/texts/45"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Farm management",
+ "text": "Farm management",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/41",
+ "parent": {
+ "$ref": "#/texts/40"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Emissions due to farm management included those from fuel and machinery for manure handling and electricity for milking and confinement (Table 5). Emissions due to feed processing such as cutting, crushing, mixing and distributing, as well as secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were included in ‘Emissions from crop and pasture production’ section.",
+ "text": "Emissions due to farm management included those from fuel and machinery for manure handling and electricity for milking and confinement (Table 5). Emissions due to feed processing such as cutting, crushing, mixing and distributing, as well as secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were included in ‘Emissions from crop and pasture production’ section."
+ },
+ {
+ "self_ref": "#/texts/42",
+ "parent": {
+ "$ref": "#/body"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Table 5 Factors for major resource inputs in farm management.",
+ "text": "Table 5 Factors for major resource inputs in farm management."
+ },
+ {
+ "self_ref": "#/texts/43",
+ "parent": {
+ "$ref": "#/texts/40"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The amount of fuel use for manure handling were estimated taking into consideration the amount of manure produced per cow and the amounts of fuel required for manure handling (L diesel t-1) [42]. The amount of manure was estimated from OM excretions (kg cow-1), assuming that the manure has 8% ash on DM basis and 60% DM content. The OM excretions were calculated by NDOMI × days in confinement × proportion of daily time that animals stayed on confinement.",
+ "text": "The amount of fuel use for manure handling were estimated taking into consideration the amount of manure produced per cow and the amounts of fuel required for manure handling (L diesel t-1) [42]. The amount of manure was estimated from OM excretions (kg cow-1), assuming that the manure has 8% ash on DM basis and 60% DM content. The OM excretions were calculated by NDOMI × days in confinement × proportion of daily time that animals stayed on confinement."
+ },
+ {
+ "self_ref": "#/texts/44",
+ "parent": {
+ "$ref": "#/texts/40"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The emissions from fuel were estimated considering the primary (emissions from fuel burned) and secondary (emissions for producing and transporting fuel) emissions. The primary emissions were calculated by the amount of fuel required for manure handling (L) × (kg CO2e L-1) [41]. The secondary emissions from fuel were calculated by the amount of fuel required for manure handling × emissions for production and transport of fuel (kg CO2e L-1) [41]. Emissions from manufacture and repair of machinery for manure handling were estimated by manure produced per cow (t) × (kg machinery mass (kg manure)-1 × 10−3) [42] × kg CO2e (kg machinery mass)-1 [42].",
+ "text": "The emissions from fuel were estimated considering the primary (emissions from fuel burned) and secondary (emissions for producing and transporting fuel) emissions. The primary emissions were calculated by the amount of fuel required for manure handling (L) × (kg CO2e L-1) [41]. The secondary emissions from fuel were calculated by the amount of fuel required for manure handling × emissions for production and transport of fuel (kg CO2e L-1) [41]. Emissions from manufacture and repair of machinery for manure handling were estimated by manure produced per cow (t) × (kg machinery mass (kg manure)-1 × 10−3) [42] × kg CO2e (kg machinery mass)-1 [42]."
+ },
+ {
+ "self_ref": "#/texts/45",
+ "parent": {
+ "$ref": "#/texts/40"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Emissions from electricity for milking and confinement were estimated using two emission factors (kg CO2 kWh-1). The first one is based on United States electricity matrix [41], and was used as a reference of an electricity matrix with less hydroelectric power than the region under study. The second is based on the Brazilian electricity matrix [46]. The electricity required for milking activities is 0.06 kWh (kg milk produced)-1 [47]. The annual electricity use for lighting was 75 kWh cow-1, which is the value considered for lactating cows in naturally ventilated barns [47].",
+ "text": "Emissions from electricity for milking and confinement were estimated using two emission factors (kg CO2 kWh-1). The first one is based on United States electricity matrix [41], and was used as a reference of an electricity matrix with less hydroelectric power than the region under study. The second is based on the Brazilian electricity matrix [46]. The electricity required for milking activities is 0.06 kWh (kg milk produced)-1 [47]. The annual electricity use for lighting was 75 kWh cow-1, which is the value considered for lactating cows in naturally ventilated barns [47]."
+ },
+ {
+ "self_ref": "#/texts/46",
+ "parent": {
+ "$ref": "#/texts/11"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/47"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Co-product allocation",
+ "text": "Co-product allocation",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/47",
+ "parent": {
+ "$ref": "#/texts/46"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The C footprint for milk produced in the system was calculated using a biophysical allocation approach, as recommended by the International Dairy Federation [49], and described by Thoma et al. [48]. Briefly, ARmilk = 1–6.04 × BMR, where: ARmilk is the allocation ratio for milk and BMR is cow BW at the time of slaughter (kg) + calf BW sold (kg) divided by the total ECM produced during cow`s entire life (kg). The ARmilk were 0.854 and 0.849 for TMR and TMR with both pasture scenarios, respectively. The ARmilk was applied to the whole emissions, except for the electricity consumed for milking (milking parlor) and refrigerant loss, which was directly assigned to milk production.",
+ "text": "The C footprint for milk produced in the system was calculated using a biophysical allocation approach, as recommended by the International Dairy Federation [49], and described by Thoma et al. [48]. Briefly, ARmilk = 1–6.04 × BMR, where: ARmilk is the allocation ratio for milk and BMR is cow BW at the time of slaughter (kg) + calf BW sold (kg) divided by the total ECM produced during cow`s entire life (kg). The ARmilk were 0.854 and 0.849 for TMR and TMR with both pasture scenarios, respectively. The ARmilk was applied to the whole emissions, except for the electricity consumed for milking (milking parlor) and refrigerant loss, which was directly assigned to milk production."
+ },
+ {
+ "self_ref": "#/texts/48",
+ "parent": {
+ "$ref": "#/texts/11"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/49"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Sensitivity analysis",
+ "text": "Sensitivity analysis",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/49",
+ "parent": {
+ "$ref": "#/texts/48"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "A sensitivity index was calculated as described by Rotz et al. [42]. The sensitivity index was defined for each emission source as the percentage change in the C footprint for a 10% change in the given emission source divided by 10%. Thus, a value near 0 indicates a low sensitivity, whereas an index near or greater than 1 indicates a high sensitivity because a change in this value causes a similar change in the footprint.",
+ "text": "A sensitivity index was calculated as described by Rotz et al. [42]. The sensitivity index was defined for each emission source as the percentage change in the C footprint for a 10% change in the given emission source divided by 10%. Thus, a value near 0 indicates a low sensitivity, whereas an index near or greater than 1 indicates a high sensitivity because a change in this value causes a similar change in the footprint."
+ },
+ {
+ "self_ref": "#/texts/50",
+ "parent": {
+ "$ref": "#/texts/0"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/51"
+ },
+ {
+ "$ref": "#/texts/52"
+ },
+ {
+ "$ref": "#/texts/58"
+ },
+ {
+ "$ref": "#/texts/62"
+ },
+ {
+ "$ref": "#/texts/67"
+ },
+ {
+ "$ref": "#/texts/70"
+ },
+ {
+ "$ref": "#/texts/72"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Results and discussion",
+ "text": "Results and discussion",
+ "level": 1
+ },
+ {
+ "self_ref": "#/texts/51",
+ "parent": {
+ "$ref": "#/texts/50"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The study has assessed the impact of tropical and temperate pastures in dairy cows fed TMR on the C footprint of dairy production in subtropics. Different factors were taken in to consideration to estimate emissions from manure (or urine and dung) of grazing animals, feed production and electricity use.",
+ "text": "The study has assessed the impact of tropical and temperate pastures in dairy cows fed TMR on the C footprint of dairy production in subtropics. Different factors were taken in to consideration to estimate emissions from manure (or urine and dung) of grazing animals, feed production and electricity use."
+ },
+ {
+ "self_ref": "#/texts/52",
+ "parent": {
+ "$ref": "#/texts/50"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/53"
+ },
+ {
+ "$ref": "#/pictures/1"
+ },
+ {
+ "$ref": "#/texts/55"
+ },
+ {
+ "$ref": "#/texts/56"
+ },
+ {
+ "$ref": "#/texts/57"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Greenhouse gas emissions",
+ "text": "Greenhouse gas emissions",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/53",
+ "parent": {
+ "$ref": "#/texts/52"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Depending on emission factors used for calculating emissions from urine and dung (IPCC or local data) and feed production (Tables 3 or 4), the C footprint was similar (Fig 2A and 2B) or decreased by 0.04 kg CO2e (kg ECM)-1 (Fig 2C and 2D) in scenarios that included pastures compared to ad libitum TMR intake. Due to differences in emission factors, the overall GHG emission values ranged from 0.92 to 1.04 kg CO2e (kg ECM)-1 for dairy cows receiving TMR exclusively, and from 0.88 to 1.04 kg CO2e (kg ECM)-1 for cows with access to pasture. Using IPCC emission factors [38], manure emissions increased as TMR intake went down (Fig 2A and 2B). However, using local emission factors for estimating N2O-N emissions [37], manure emissions decreased as TMR intake went down (Fig 2C and 2D). Regardless of emission factors used (Tables 3 or 4), emissions from feed production decreased to a small extent as the proportion of TMR intake decreased. Emissions from farm management did not contribute more than 5% of overall GHG emissions.",
+ "text": "Depending on emission factors used for calculating emissions from urine and dung (IPCC or local data) and feed production (Tables 3 or 4), the C footprint was similar (Fig 2A and 2B) or decreased by 0.04 kg CO2e (kg ECM)-1 (Fig 2C and 2D) in scenarios that included pastures compared to ad libitum TMR intake. Due to differences in emission factors, the overall GHG emission values ranged from 0.92 to 1.04 kg CO2e (kg ECM)-1 for dairy cows receiving TMR exclusively, and from 0.88 to 1.04 kg CO2e (kg ECM)-1 for cows with access to pasture. Using IPCC emission factors [38], manure emissions increased as TMR intake went down (Fig 2A and 2B). However, using local emission factors for estimating N2O-N emissions [37], manure emissions decreased as TMR intake went down (Fig 2C and 2D). Regardless of emission factors used (Tables 3 or 4), emissions from feed production decreased to a small extent as the proportion of TMR intake decreased. Emissions from farm management did not contribute more than 5% of overall GHG emissions."
+ },
+ {
+ "self_ref": "#/texts/54",
+ "parent": {
+ "$ref": "#/body"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Fig 2 Overall greenhouse gas emissions in dairy cattle systems under various scenarios. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].",
+ "text": "Fig 2 Overall greenhouse gas emissions in dairy cattle systems under various scenarios. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]."
+ },
+ {
+ "self_ref": "#/texts/55",
+ "parent": {
+ "$ref": "#/texts/52"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Considering IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, the C footprint ranged from 0.99 to 1.04 kg CO2e (kg ECM)-1, and was close to those reported under confined based systems in California [49], Canada [50], China [8], Ireland [9], different scenarios in Australia [51,52] and Uruguay [11], which ranged from 0.98 to 1.16 kg CO2e (kg ECM)-1. When local emission factors for N2O emissions from urine and dung [37] and those from Table 4 were taking into account, the C footprint for scenarios including pasture, without accounting for sequestered CO2-C from perennial pasture—0.91 kg CO2e (kg ECM)-1—was lower than the range of values described above. However, these values were still greater than high-performance confinement systems in UK and USA [53] or grass based dairy systems in Ireland [9,53] and New Zealand [8,54], which ranged from 0.52 to 0.89 kg CO2e (kg ECM)-1. Regardless of which emission factor was used, we found a lower C footprint in all conditions compared to scenarios with lower milk production per cow or in poor conditions of manure management, which ranged from 1.4 to 2.3 kg CO2e (kg ECM)-1 [8,55]. Thus, even though differences between studies may be partially explained by various assumptions (e.g., emission factors, co-product allocation, methane emissions estimation, sequestered CO2-C, etc.), herd productivity and manure management were systematically associated with the C footprint of the dairy systems.",
+ "text": "Considering IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, the C footprint ranged from 0.99 to 1.04 kg CO2e (kg ECM)-1, and was close to those reported under confined based systems in California [49], Canada [50], China [8], Ireland [9], different scenarios in Australia [51,52] and Uruguay [11], which ranged from 0.98 to 1.16 kg CO2e (kg ECM)-1. When local emission factors for N2O emissions from urine and dung [37] and those from Table 4 were taking into account, the C footprint for scenarios including pasture, without accounting for sequestered CO2-C from perennial pasture—0.91 kg CO2e (kg ECM)-1—was lower than the range of values described above. However, these values were still greater than high-performance confinement systems in UK and USA [53] or grass based dairy systems in Ireland [9,53] and New Zealand [8,54], which ranged from 0.52 to 0.89 kg CO2e (kg ECM)-1. Regardless of which emission factor was used, we found a lower C footprint in all conditions compared to scenarios with lower milk production per cow or in poor conditions of manure management, which ranged from 1.4 to 2.3 kg CO2e (kg ECM)-1 [8,55]. Thus, even though differences between studies may be partially explained by various assumptions (e.g., emission factors, co-product allocation, methane emissions estimation, sequestered CO2-C, etc.), herd productivity and manure management were systematically associated with the C footprint of the dairy systems."
+ },
+ {
+ "self_ref": "#/texts/56",
+ "parent": {
+ "$ref": "#/texts/52"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The similarity of C footprint between different scenarios using IPCC [38] for estimating emissions from manure and for emissions from feed production (Table 3) was a consequence of the trade-off between greater manure emissions and lower emissions to produce feed, as the proportion of pasture in diets increased. Additionally, the small negative effect of pasture on ECM production also contributed to the trade-off. The impact of milk production on the C footprint was reported in a meta-analysis comprising 30 studies from 15 different countries [22]. As observed in this study (Fig 2A and 2B) the authors reported no significant difference between the C footprint of pasture-based vs. confinement systems. However, they observed that an increase of 1000 kg cow-1 (5000 to 6000 kg ECM) reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, which may explain an apparent discrepancy between our study and an LCA performed in south Brazilian conditions [56]. Their study compared a confinement and a grazing-based dairy system with annual average milk production of 7667 and 5535 kg cow, respectively. In this study, the same herd was used in all systems, with an annual average milk production of around 7000 kg cow-1. Experimental data showed a reduction not greater than 3% of ECM when 50% of TMR was replaced by pasture access.",
+ "text": "The similarity of C footprint between different scenarios using IPCC [38] for estimating emissions from manure and for emissions from feed production (Table 3) was a consequence of the trade-off between greater manure emissions and lower emissions to produce feed, as the proportion of pasture in diets increased. Additionally, the small negative effect of pasture on ECM production also contributed to the trade-off. The impact of milk production on the C footprint was reported in a meta-analysis comprising 30 studies from 15 different countries [22]. As observed in this study (Fig 2A and 2B) the authors reported no significant difference between the C footprint of pasture-based vs. confinement systems. However, they observed that an increase of 1000 kg cow-1 (5000 to 6000 kg ECM) reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, which may explain an apparent discrepancy between our study and an LCA performed in south Brazilian conditions [56]. Their study compared a confinement and a grazing-based dairy system with annual average milk production of 7667 and 5535 kg cow, respectively. In this study, the same herd was used in all systems, with an annual average milk production of around 7000 kg cow-1. Experimental data showed a reduction not greater than 3% of ECM when 50% of TMR was replaced by pasture access."
+ },
+ {
+ "self_ref": "#/texts/57",
+ "parent": {
+ "$ref": "#/texts/52"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The lower C footprint in scenarios with access to pasture, when local emission factors [37] were used for N2O emissions from urine and dung and for feed production (Table 4), may also be partially attributed to the small negative effect of pasture on ECM production. Nevertheless, local emission factors for urine and dung had a great impact on scenarios including pastures compared to ad libitum TMR intake. Whereas the IPCC [38] considers an emission of 0.02 kg N2O-N (kg N)-1 for urine and dung from grazing animals, experimental evidence shows that it may be up to five times lower, averaging 0.004 kg N2O-N kg-1 [37].",
+ "text": "The lower C footprint in scenarios with access to pasture, when local emission factors [37] were used for N2O emissions from urine and dung and for feed production (Table 4), may also be partially attributed to the small negative effect of pasture on ECM production. Nevertheless, local emission factors for urine and dung had a great impact on scenarios including pastures compared to ad libitum TMR intake. Whereas the IPCC [38] considers an emission of 0.02 kg N2O-N (kg N)-1 for urine and dung from grazing animals, experimental evidence shows that it may be up to five times lower, averaging 0.004 kg N2O-N kg-1 [37]."
+ },
+ {
+ "self_ref": "#/texts/58",
+ "parent": {
+ "$ref": "#/texts/50"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/59"
+ },
+ {
+ "$ref": "#/pictures/2"
+ },
+ {
+ "$ref": "#/texts/61"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Methane emissions",
+ "text": "Methane emissions",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/59",
+ "parent": {
+ "$ref": "#/texts/58"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The enteric CH4 intensity was similar between different scenarios (Fig 2), showing the greatest sensitivity index, with values ranging from 0.53 to 0.62, which indicate that for a 10% change in this source, the C footprint may change between 5.3 and 6.2% (Fig 3). The large effect of enteric CH4 emissions on the whole C footprint was expected, because the impact of enteric CH4 on GHG emissions of milk production in different dairy systems has been estimated to range from 44 to 60% of the total CO2e [50,52,57,58]. However, emissions in feed production may be the most important source of GHG when emission factors for producing concentrate feeds are greater than 0.7 kg CO2e kg-1 [59], which did not happen in this study.",
+ "text": "The enteric CH4 intensity was similar between different scenarios (Fig 2), showing the greatest sensitivity index, with values ranging from 0.53 to 0.62, which indicate that for a 10% change in this source, the C footprint may change between 5.3 and 6.2% (Fig 3). The large effect of enteric CH4 emissions on the whole C footprint was expected, because the impact of enteric CH4 on GHG emissions of milk production in different dairy systems has been estimated to range from 44 to 60% of the total CO2e [50,52,57,58]. However, emissions in feed production may be the most important source of GHG when emission factors for producing concentrate feeds are greater than 0.7 kg CO2e kg-1 [59], which did not happen in this study."
+ },
+ {
+ "self_ref": "#/texts/60",
+ "parent": {
+ "$ref": "#/body"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Fig 3 Sensitivity of the C footprint. Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].",
+ "text": "Fig 3 Sensitivity of the C footprint. Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]."
+ },
+ {
+ "self_ref": "#/texts/61",
+ "parent": {
+ "$ref": "#/texts/58"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The lack of difference in enteric CH4 emissions in different systems can be explained by the narrow range of NDF content in diets (<4% difference). This non-difference is due to the lower NDF content of annual temperate pastures (495 g (kg DM)-1) compared to corn silage (550 g (kg DM)-1). Hence, an expected, increase NDF content with decreased concentrate was partially offset by an increase in the pasture proportion relatively low in NDF. This is in agreement with studies conducted in southern Brazil, which have shown that the actual enteric CH4 emissions may decrease with inclusion of temperate pastures in cows receiving corn silage and soybean meal [60] or increase enteric CH4 emissions when dairy cows grazing a temperate pasture was supplemented with corn silage [61]. Additionally, enteric CH4 emissions did not differ between dairy cows receiving TMR exclusively or grazing a tropical pasture in the same scenarios as in this study [26].",
+ "text": "The lack of difference in enteric CH4 emissions in different systems can be explained by the narrow range of NDF content in diets (<4% difference). This non-difference is due to the lower NDF content of annual temperate pastures (495 g (kg DM)-1) compared to corn silage (550 g (kg DM)-1). Hence, an expected, increase NDF content with decreased concentrate was partially offset by an increase in the pasture proportion relatively low in NDF. This is in agreement with studies conducted in southern Brazil, which have shown that the actual enteric CH4 emissions may decrease with inclusion of temperate pastures in cows receiving corn silage and soybean meal [60] or increase enteric CH4 emissions when dairy cows grazing a temperate pasture was supplemented with corn silage [61]. Additionally, enteric CH4 emissions did not differ between dairy cows receiving TMR exclusively or grazing a tropical pasture in the same scenarios as in this study [26]."
+ },
+ {
+ "self_ref": "#/texts/62",
+ "parent": {
+ "$ref": "#/texts/50"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/63"
+ },
+ {
+ "$ref": "#/pictures/3"
+ },
+ {
+ "$ref": "#/texts/65"
+ },
+ {
+ "$ref": "#/texts/66"
+ }
+ ],
+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Emissions from excreta and feed production",
+ "text": "Emissions from excreta and feed production",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/63",
+ "parent": {
+ "$ref": "#/texts/62"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Using IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, CH4 emissions from manure decreased 0.07 kg CO2e (kg ECM)-1, but N2O emissions from manure increased 0.09 kg CO2e (kg ECM)-1, as TMR intake was restricted to 50% ad libitum (Fig 4A). Emissions for pastures increased by 0.06 kg CO2e (kg ECM)-1, whereas emissions for producing concentrate feeds and corn silage decreased by 0.09 kg CO2e (kg ECM)-1, as TMR intake decreased (Fig 4B). In this situation, the lack of difference in calculated C footprints of different systems was also due to the greater emissions from manure, and offset by lower emissions from feed production with inclusion of pasture in lactating dairy cow diets. The greater N2O-N emissions from manure with pasture was a consequence of higher N2O-N emissions due to greater CP content and N urine excretion, as pasture intake increased. The effect of CP content on urine N excretion has been shown by several authors in lactating dairy cows [62–64]. For instance, by decreasing CP content from 185 to 152 g (kg DM)-1, N intake decreased by 20% and urine N excretion by 60% [62]. In this study, the CP content for lactating dairy cows ranged from 150 g (kg DM)-1 on TMR system to 198 g (kg DM)-1 on 50% TMR with pasture. Additionally, greater urine N excretion is expected with greater use of pasture. This occurs because protein utilization in pastures is inefficient, as the protein in fresh forages is highly degradable in the rumen and may not be captured by microbes [65].",
+ "text": "Using IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, CH4 emissions from manure decreased 0.07 kg CO2e (kg ECM)-1, but N2O emissions from manure increased 0.09 kg CO2e (kg ECM)-1, as TMR intake was restricted to 50% ad libitum (Fig 4A). Emissions for pastures increased by 0.06 kg CO2e (kg ECM)-1, whereas emissions for producing concentrate feeds and corn silage decreased by 0.09 kg CO2e (kg ECM)-1, as TMR intake decreased (Fig 4B). In this situation, the lack of difference in calculated C footprints of different systems was also due to the greater emissions from manure, and offset by lower emissions from feed production with inclusion of pasture in lactating dairy cow diets. The greater N2O-N emissions from manure with pasture was a consequence of higher N2O-N emissions due to greater CP content and N urine excretion, as pasture intake increased. The effect of CP content on urine N excretion has been shown by several authors in lactating dairy cows [62–64]. For instance, by decreasing CP content from 185 to 152 g (kg DM)-1, N intake decreased by 20% and urine N excretion by 60% [62]. In this study, the CP content for lactating dairy cows ranged from 150 g (kg DM)-1 on TMR system to 198 g (kg DM)-1 on 50% TMR with pasture. Additionally, greater urine N excretion is expected with greater use of pasture. This occurs because protein utilization in pastures is inefficient, as the protein in fresh forages is highly degradable in the rumen and may not be captured by microbes [65]."
+ },
+ {
+ "self_ref": "#/texts/64",
+ "parent": {
+ "$ref": "#/body"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "caption",
+ "prov": [],
+ "orig": "Fig 4 Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture.",
+ "text": "Fig 4 Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture."
+ },
+ {
+ "self_ref": "#/texts/65",
+ "parent": {
+ "$ref": "#/texts/62"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Using local emission factors for N2O emissions from urine and dung [37] and those from Table 4, reductions in CH4 emissions from stocked manure, when pastures were included on diets, did not offset by increases in N2O emissions from excreta (Fig 4C). In this case, total emissions from manure (Fig 4C) and feed production (Fig 4D) decreased with the inclusion of pasture. The impact of greater CP content and N urine excretion with increased pasture intake was offset by the much lower emission factors used for N2O emissions from urine and dung. As suggested by other authors [66,67], these results show that IPCC default value may need to be revised for the subtropical region.",
+ "text": "Using local emission factors for N2O emissions from urine and dung [37] and those from Table 4, reductions in CH4 emissions from stocked manure, when pastures were included on diets, did not offset by increases in N2O emissions from excreta (Fig 4C). In this case, total emissions from manure (Fig 4C) and feed production (Fig 4D) decreased with the inclusion of pasture. The impact of greater CP content and N urine excretion with increased pasture intake was offset by the much lower emission factors used for N2O emissions from urine and dung. As suggested by other authors [66,67], these results show that IPCC default value may need to be revised for the subtropical region."
+ },
+ {
+ "self_ref": "#/texts/66",
+ "parent": {
+ "$ref": "#/texts/62"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Emissions for feed production decreased when pasture was included due to the greater emission factor for corn grain production compared to pastures. Emissions from concentrate and silage had at least twice the sensitivity index compared to emissions from pastures. The amount of grain required per cow in a lifetime decreased from 7,300 kg to 4,000 kg when 50% of TMR was replaced by pasture access. These results are in agreement with other studies which found lower C footprint, as concentrate use is reduced and/or pasture is included [9,68,69]. Moreover, it has been demonstrated that in intensive dairy systems, after enteric fermentation, feed production is the second main contributor to C footprint [50]. There is potential to decrease the environmental impact of dairy systems by reducing the use of concentrate ingredients with high environmental impact, particularly in confinements [9].",
+ "text": "Emissions for feed production decreased when pasture was included due to the greater emission factor for corn grain production compared to pastures. Emissions from concentrate and silage had at least twice the sensitivity index compared to emissions from pastures. The amount of grain required per cow in a lifetime decreased from 7,300 kg to 4,000 kg when 50% of TMR was replaced by pasture access. These results are in agreement with other studies which found lower C footprint, as concentrate use is reduced and/or pasture is included [9,68,69]. Moreover, it has been demonstrated that in intensive dairy systems, after enteric fermentation, feed production is the second main contributor to C footprint [50]. There is potential to decrease the environmental impact of dairy systems by reducing the use of concentrate ingredients with high environmental impact, particularly in confinements [9]."
+ },
+ {
+ "self_ref": "#/texts/67",
+ "parent": {
+ "$ref": "#/texts/50"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/68"
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+ {
+ "$ref": "#/texts/69"
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+ "label": "section_header",
+ "prov": [],
+ "orig": "Farm management",
+ "text": "Farm management",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/68",
+ "parent": {
+ "$ref": "#/texts/67"
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+ "children": [],
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+ "orig": "The lower impact of emissions from farm management is in agreement with other studies conducted in Europe [9, 62] and USA [42, 55], where the authors found that most emissions in dairy production systems are from enteric fermentation, feed production and emissions from excreta. As emissions from fuel for on-farm feed production were accounted into the ‘emissions from crop and pasture production’, total emissions from farm management were not greater than 5% of total C footprint.",
+ "text": "The lower impact of emissions from farm management is in agreement with other studies conducted in Europe [9, 62] and USA [42, 55], where the authors found that most emissions in dairy production systems are from enteric fermentation, feed production and emissions from excreta. As emissions from fuel for on-farm feed production were accounted into the ‘emissions from crop and pasture production’, total emissions from farm management were not greater than 5% of total C footprint."
+ },
+ {
+ "self_ref": "#/texts/69",
+ "parent": {
+ "$ref": "#/texts/67"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Emissions from farm management dropped when the emission factor for electricity generation was based on the Brazilian matrix. In this case, the emission factor for electricity generation (0.205 kg CO2e kWh-1 [46]) is much lower than that in a LCA study conducted in US (0.73 kg CO2e kWh-1 [42]). This apparent discrepancy is explained because in 2016, almost 66% of the electricity generated in Brazil was from hydropower, which has an emission factor of 0.074 kg CO2e kWh-1 against 0.382 and 0.926 kg CO2e kWh-1 produced by natural gas and hard coal, respectively [46].",
+ "text": "Emissions from farm management dropped when the emission factor for electricity generation was based on the Brazilian matrix. In this case, the emission factor for electricity generation (0.205 kg CO2e kWh-1 [46]) is much lower than that in a LCA study conducted in US (0.73 kg CO2e kWh-1 [42]). This apparent discrepancy is explained because in 2016, almost 66% of the electricity generated in Brazil was from hydropower, which has an emission factor of 0.074 kg CO2e kWh-1 against 0.382 and 0.926 kg CO2e kWh-1 produced by natural gas and hard coal, respectively [46]."
+ },
+ {
+ "self_ref": "#/texts/70",
+ "parent": {
+ "$ref": "#/texts/50"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/71"
+ }
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+ "content_layer": "body",
+ "label": "section_header",
+ "prov": [],
+ "orig": "Assumptions and limitations",
+ "text": "Assumptions and limitations",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/71",
+ "parent": {
+ "$ref": "#/texts/70"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The milk production and composition data are the average for a typical herd, which might have great animal-to-animal variability. Likewise, DM yield of crops and pastures were collected from experimental observations, and may change as a function of inter-annual variation, climatic conditions, soil type, fertilization level etc. The emission factors for direct and indirect N2O emissions from urine and dung were alternatively estimated using local data, but more experiments are necessary to reduce the uncertainty. The CO2 emitted from lime and urea application was estimated from IPCC default values, which may not represent emissions in subtropical conditions. This LCA may be improved by reducing the uncertainty of factors for estimating emissions from excreta and feed production, including the C sequestration or emissions as a function of soil management.",
+ "text": "The milk production and composition data are the average for a typical herd, which might have great animal-to-animal variability. Likewise, DM yield of crops and pastures were collected from experimental observations, and may change as a function of inter-annual variation, climatic conditions, soil type, fertilization level etc. The emission factors for direct and indirect N2O emissions from urine and dung were alternatively estimated using local data, but more experiments are necessary to reduce the uncertainty. The CO2 emitted from lime and urea application was estimated from IPCC default values, which may not represent emissions in subtropical conditions. This LCA may be improved by reducing the uncertainty of factors for estimating emissions from excreta and feed production, including the C sequestration or emissions as a function of soil management."
+ },
+ {
+ "self_ref": "#/texts/72",
+ "parent": {
+ "$ref": "#/texts/50"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/73"
+ },
+ {
+ "$ref": "#/texts/74"
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+ "label": "section_header",
+ "prov": [],
+ "orig": "Further considerations",
+ "text": "Further considerations",
+ "level": 2
+ },
+ {
+ "self_ref": "#/texts/73",
+ "parent": {
+ "$ref": "#/texts/72"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "The potential for using pasture can reduce the C footprint because milk production kept pace with animal confinement. However, if milk production is to decrease with lower TMR intake and inclusion of pasture [19], the C footprint would be expected to increase. Lorenz et al. [22] showed that an increase in milk yield from 5,000 to 6,000 kg ECM reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, whereas an increase from 10,000 to 11,000 kg ECM reduced the C footprint by only 0.06 kg CO2e (kg ECM)-1. Hence, the impact of increasing milk production on decreasing C footprint is not linear, and mitigation measures, such as breeding for increased genetic yield potential and increasing concentrate ratio in the diet, are potentially harmful for animal’s health and welfare [70]. For instance, increasing concentrate ratio potentially increases the occurrence of subclinical ketosis and foot lesions, and C footprint may increase by 0.03 kg CO2e (kg ECM)-1 in subclinical ketosis [71] and by 0.02 kg CO2e (kg ECM)-1 in case of foot lesions [72].",
+ "text": "The potential for using pasture can reduce the C footprint because milk production kept pace with animal confinement. However, if milk production is to decrease with lower TMR intake and inclusion of pasture [19], the C footprint would be expected to increase. Lorenz et al. [22] showed that an increase in milk yield from 5,000 to 6,000 kg ECM reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, whereas an increase from 10,000 to 11,000 kg ECM reduced the C footprint by only 0.06 kg CO2e (kg ECM)-1. Hence, the impact of increasing milk production on decreasing C footprint is not linear, and mitigation measures, such as breeding for increased genetic yield potential and increasing concentrate ratio in the diet, are potentially harmful for animal’s health and welfare [70]. For instance, increasing concentrate ratio potentially increases the occurrence of subclinical ketosis and foot lesions, and C footprint may increase by 0.03 kg CO2e (kg ECM)-1 in subclinical ketosis [71] and by 0.02 kg CO2e (kg ECM)-1 in case of foot lesions [72]."
+ },
+ {
+ "self_ref": "#/texts/74",
+ "parent": {
+ "$ref": "#/texts/72"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "Grazing lands may also improve biodiversity [73]. Strategies such as zero tillage may increase stocks of soil C [74]. This study did not consider C sequestration during the growth of annual pastures, because it was assumed these grasses were planted with tillage, having a balance between C sequestration and C emissions [38]. Considering the C sequestration from no-tillage perennial pasture, the amount of C sequestration will more than compensates for C emitted. These results are in agreement with other authors who have shown that a reduction or elimination of soil tillage increases annual soil C sequestration in subtropical areas by 0.5 to 1.5 t ha-1 [75]. If 50% of tilled areas were under perennial grasslands, 1.0 t C ha-1 would be sequestered, further reducing the C footprint by 0.015 and 0.025 kg CO2e (kg ECM)-1 for the scenarios using 75 and 50% TMR, respectively. Eliminating tillage, the reduction on total GHG emissions would be 0.03 and 0.05 kg CO2e (kg ECM)-1 for 75 and 50% TMR, respectively. However, this approach may be controversial because lands which have been consistently managed for decades have approached steady state C storage, so that net exchange of CO2 would be negligible [76].",
+ "text": "Grazing lands may also improve biodiversity [73]. Strategies such as zero tillage may increase stocks of soil C [74]. This study did not consider C sequestration during the growth of annual pastures, because it was assumed these grasses were planted with tillage, having a balance between C sequestration and C emissions [38]. Considering the C sequestration from no-tillage perennial pasture, the amount of C sequestration will more than compensates for C emitted. These results are in agreement with other authors who have shown that a reduction or elimination of soil tillage increases annual soil C sequestration in subtropical areas by 0.5 to 1.5 t ha-1 [75]. If 50% of tilled areas were under perennial grasslands, 1.0 t C ha-1 would be sequestered, further reducing the C footprint by 0.015 and 0.025 kg CO2e (kg ECM)-1 for the scenarios using 75 and 50% TMR, respectively. Eliminating tillage, the reduction on total GHG emissions would be 0.03 and 0.05 kg CO2e (kg ECM)-1 for 75 and 50% TMR, respectively. However, this approach may be controversial because lands which have been consistently managed for decades have approached steady state C storage, so that net exchange of CO2 would be negligible [76]."
+ },
+ {
+ "self_ref": "#/texts/75",
+ "parent": {
+ "$ref": "#/texts/0"
+ },
+ "children": [
+ {
+ "$ref": "#/texts/76"
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+ ],
+ "content_layer": "body",
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+ "prov": [],
+ "orig": "Conclusions",
+ "text": "Conclusions",
+ "level": 1
+ },
+ {
+ "self_ref": "#/texts/76",
+ "parent": {
+ "$ref": "#/texts/75"
+ },
+ "children": [],
+ "content_layer": "body",
+ "label": "text",
+ "prov": [],
+ "orig": "This study assessed the C footprint of dairy cattle systems with or without access to pastures. Including pastures showed potential to maintain or decrease to a small extent the C footprint, which may be attributable to the evidence of low N2O emissions from urine and dung in dairy systems in subtropical areas. Even though the enteric CH4 intensity was the largest source of CO2e emissions, it did not change between different scenarios due to the narrow range of NDF content in diets and maintaining the same milk production with or without access to pastures.",
+ "text": "This study assessed the C footprint of dairy cattle systems with or without access to pastures. Including pastures showed potential to maintain or decrease to a small extent the C footprint, which may be attributable to the evidence of low N2O emissions from urine and dung in dairy systems in subtropical areas. Even though the enteric CH4 intensity was the largest source of CO2e emissions, it did not change between different scenarios due to the narrow range of NDF content in diets and maintaining the same milk production with or without access to pastures."
+ },
+ {
+ "self_ref": "#/texts/77",
+ "parent": {
+ "$ref": "#/texts/0"
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+ "children": [
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+ "orig": "Acknowledgments",
+ "text": "Acknowledgments",
+ "level": 1
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+ "self_ref": "#/texts/78",
+ "parent": {
+ "$ref": "#/texts/77"
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+ "orig": "Thanks to Anna Naranjo for helpful comments throughout the elaboration of this manuscript, and to André Thaler Neto and Roberto Kappes for providing the key characteristics of the herd considered in this study.",
+ "text": "Thanks to Anna Naranjo for helpful comments throughout the elaboration of this manuscript, and to André Thaler Neto and Roberto Kappes for providing the key characteristics of the herd considered in this study."
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+ "orig": "References",
+ "text": "References",
+ "level": 1
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+ "self_ref": "#/texts/80",
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+ "label": "list_item",
+ "prov": [],
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+ "text": "MontesF, MeinenR, DellC, RotzA, HristovAN, OhJ, et al SPECIAL TOPICS—Mitigation of methane and nitrous oxide emissions from animal operations: II. A review of manure management mitigation options. J Anim Sci. 2013;91: 5070–5094. 10.2527/jas.2013-6584 24045493",
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+ "pages": {}
+}
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/pone.0234687.xml.md b/tests/data/groundtruth/docling_v2/pone.0234687.nxml.md
similarity index 100%
rename from tests/data/groundtruth/docling_v2/pone.0234687.xml.md
rename to tests/data/groundtruth/docling_v2/pone.0234687.nxml.md
diff --git a/tests/data/jats/bmj_sample.xml b/tests/data/jats/bmj_sample.xml
deleted file mode 100644
index 3a6d1365..00000000
--- a/tests/data/jats/bmj_sample.xml
+++ /dev/null
@@ -1,842 +0,0 @@
-
-
-
-
-
-bmj
-BMJ
-BMJ
-0959-8138
-
-BMJ
-
-
-
-jBMJ.v324.i7342.pg880
-11950738
-
-
-Primary care
-
-190
-10
-218
-219
-355
-357
-
-
-
-
-Evolving general practice consultation in Britain: issues of length and
-context
-
-
-
-
-Freeman
-George K
-
-professor of general practice
-
-
-
-
-Horder
-John P
-
-past president
-
-
-
-
-Howie
-John G R
-
-emeritus professor of general practice
-
-
-
-
-Hungin
-A Pali
-
-professor of general practice
-
-
-
-
-Hill
-Alison P
-
-general practitioner
-
-
-
-
-Shah
-Nayan C
-
-general practitioner
-
-
-
-
-Wilson
-Andrew
-
-senior lecturer
-
-
-
-Centre for Primary Care and Social Medicine, Imperial College of Science,
-Technology and Medicine, London W6 8RP
-Royal College of General Practitioners, London SW7 1PU
-Department of General Practice, University of Edinburgh, Edinburgh EH8 9DX
-Centre for Health Studies, University of Durham, Durham DH1 3HN
-Kilburn Park Medical Centre, London NW6
-Department of General Practice and Primary Health Care, University of Leicester,
-Leicester LE5 4PW
-
-
-
Contributors: GKF wrote the paper and revised it after repeated and detailed comments from
-all of the other authors and feedback from the first referee and from the BMJ
-editorial panel. All other authors gave detailed and repeated comments and cristicisms. GKF is
-the guarantor of the paper.
In 1999 Shah1 and others said that the Royal College of
-General Practitioners should advocate longer consultations in general practice as a matter of
-policy. The college set up a working group chaired by A P Hungin, and a systematic review of
-literature on consultation length in general practice was commissioned. The working group agreed
-that the available evidence would be hard to interpret without discussion of the changing context
-within which consultations now take place. For many years general practitioners and those who
-have surveyed patients' opinions in the United Kingdom have complained about short consultation
-time, despite a steady increase in actual mean length. Recently Mechanic pointed out that this is
-also true in the United States.2 Is there any justification
-for a further increase in mean time allocated per consultation in general practice?
-
We report on the outcome of extensive debate among a group of general practitioners with an
-interest in the process of care, with reference to the interim findings of the commissioned
-systematic review and our personal databases. The review identified 14 relevant papers.
-
-Summary points
-
-
-
Longer consultations are associated with a range of better patient outcomes
-
-
-
Modern consultations in general practice deal with patients with more serious and chronic
-conditions
-
-
-
Increasing patient participation means more complex interaction, which demands extra
-time
-
-
-
Difficulties with access and with loss of continuity add to perceived stress and poor
-performance and lead to further pressure on time
-
-
-
Longer consultations should be a professional priority, combined with increased use of
-technology and more flexible practice management to maximise interpersonal continuity
-
-
-
Research on implementation is needed
-
-
-
-
-
-Longer consultations: benefits for patients
-
The systematic review consistently showed that doctors with longer consultation times
-prescribe less and offer more advice on lifestyle and other health promoting activities. Longer
-consultations have been significantly associated with better recognition and handling of
-psychosocial problems3 and with better patient
-enablement.4 Also clinical care for some chronic illnesses
-is better in practices with longer booked intervals between one appointment and the next.5 It is not clear whether time is itself the main influence or
-whether some doctors insist on more time.
-
A national survey in 1998 reported that most (87%) patients were satisfied with the
-length of their most recent consultation.6 Satisfaction
-with any service will be high if expectations are met or exceeded. But expectations are modified
-by previous experience.7 The result is that primary care
-patients are likely to be satisfied with what they are used to unless the context modifies the
-effects of their own experience.
-
-
-Context of modern consultations
-
Shorter consultations were more appropriate when the population was younger, when even a brief
-absence from employment due to sickness required a doctor's note, and when many simple remedies
-were available only on prescription. Recently at least five important influences have increased
-the content and hence the potential length of the consultation.
-
-
-Participatory consultation style
-
The most effective consultations are those in which doctors most directly acknowledge and
-perhaps respond to patients' problems and concerns. In addition, for patients to be committed to
-taking advantage of medical advice they must agree with both the goals and methods proposed. A
-landmark publication in the United Kingdom was Meetings Between Experts, which
-argued that while doctors are the experts about medical problems in general patients are the
-experts on how they themselves experience these problems.8
-New emphasis on teaching consulting skills in general practice advocated specific attention to
-the patient's agenda, beliefs, understanding, and agreement. Currently the General Medical
-Council, aware that communication difficulties underlie many complaints about doctors, has
-further emphasised the importance of involving patients in consultations in its revised guidance
-to medical schools.9 More patient involvement should give
-a better outcome, but this participatory style usually lengthens consultations.
-
-
-Extended professional agenda
-
The traditional consultation in general practice was brief.2 The patient presented symptoms and the doctor prescribed treatment. In 1957 Balint
-gave new insights into the meaning of symptoms.10 By 1979
-an enhanced model of consultation was presented, in which the doctors dealt with ongoing as well
-as presenting problems and added health promotion and education about future appropriate use of
-services.11 Now, with an ageing population and more
-community care of chronic illness, there are more issues to be considered at each consultation.
-Ideas of what constitutes good general practice are more complex.12 Good practice now includes both extended care of chronic medical problems—for
-example, coronary heart disease13—and a public
-health role. At first this model was restricted to those who lead change (“early
-adopters”) and enthusiasts14 but now it is
-embedded in professional and managerial expectations of good practice.
-
Adequate time is essential. It may be difficult for an elderly patient with several active
-problems to undress, be examined, and get adequate professional consideration in under 15
-minutes. Here the doctor is faced with the choice of curtailing the consultation or of reducing
-the time available for the next patient. Having to cope with these situations often contributes
-to professional dissatisfaction.15 This combination of
-more care, more options, and more genuine discussion of those options with informed patient
-choice inevitably leads to pressure on time.
-
-
-Access problems
-
In a service free at the point of access, rising demand will tend to increase rationing by
-delay. But attempts to improve access by offering more consultations at short notice squeeze
-consultation times.
-
While appointment systems can and should reduce queuing time for consultations, they have long
-tended to be used as a brake on total demand.16 This may
-seriously erode patients' confidence in being able to see their doctor or nurse when they need
-to. Patients are offered appointments further ahead but may keep these even if their symptoms
-have remitted “just in case.” Availability of consultations is thus blocked.
-Receptionists are then inappropriately blamed for the inadequate access to doctors.
-
In response to perception of delay, the government has set targets in the NHS plan of
-“guaranteed access to a primary care professional within 24 hours and to a primary care
-doctor within 48 hours.” Implementation is currently being negotiated.
-
Virtually all patients think that they would not consult unless it was absolutely necessary.
-They do not think they are wasting NHS time and do not like being made to feel so. But
-underlying general practitioners' willingness to make patients wait several days is their
-perception that few of the problems are urgent. Patients and general practitioners evidently do
-not agree about the urgency of so called minor problems. To some extent general practice in the
-United Kingdom may have scored an “own goal” by setting up perceived access
-barriers (appointment systems and out of hours cooperatives) in the attempt to increase
-professional standards and control demand in a service that is free at the point of access.
-
A further government initiative has been to bypass general practice with new
-services—notably, walk-in centres (primary care clinics in which no appointment is
-needed) and NHS Direct (a professional telephone helpline giving advice on simple remedies and
-access to services). Introduced widely and rapidly, these services each potentially provide
-significant features of primary care—namely, quick access to skilled health advice and
-first line treatment.
-
-
-Loss of interpersonal continuity
-
If a patient has to consult several different professionals, particularly over a short period
-of time, there is inevitable duplication of stories, risk of naive diagnoses, potential for
-conflicting advice, and perhaps loss of trust. Trust is essential if patients are to accept the
-“wait and see” management policy which is, or should be, an important part of the
-management of self limiting conditions, which are often on the boundary between illness and
-non-illness.17 Such duplication again increases pressure
-for more extra (unscheduled) consultations resulting in late running and professional
-frustration.18
-
Mechanic described how loss of longitudinal (and perhaps personal and relational19) continuity influences the perception and use of time
-through an inability to build on previous consultations.2
-Knowing the doctor well, particularly in smaller practices, is associated with enhanced patient
-enablement in shorter time.4 Though Mechanic pointed out
-that three quarters of UK patients have been registered with their general practitioner five
-years or more, this may be misleading. Practices are growing, with larger teams and more
-registered patients. Being registered with a doctor in a larger practice is usually no guarantee
-that the patient will be able to see the same doctor or the doctor of his or her choice, who may
-be different. Thus the system does not encourage adequate personal continuity. This adds to
-pressure on time and reduces both patient and professional satisfaction.
-
-
-Health service reforms
-
Finally, for the past 15 years the NHS has experienced unprecedented change with a succession
-of major administrative reforms. Recent reforms have focused on an NHS led by primary care,
-including the aim of shifting care from the secondary specialist sector to primary care. One
-consequence is increased demand for primary care of patients with more serious and less stable
-problems. With the limited piloting of reforms we do not know whether such major redirection can
-be achieved without greatly altering the delicate balance between expectations (of both patients
-and staff) and what is delivered.
-
-
-The future
-
We think that the way ahead must embrace both longer mean consultation times and more
-flexibility. More time is needed for high quality consultations with patients with major and
-complex problems of all kinds. But patients also need access to simpler services and advice.
-This should be more appropriate (and cost less) when it is given by professionals who know the
-patient and his or her medical history and social circumstances. For doctors, the higher quality
-associated with longer consultations may lead to greater professional satisfaction and, if these
-longer consultations are combined with more realistic scheduling, to reduced levels of
-stress.20 They will also find it easier to develop
-further the care of chronic disease.
-
The challenge posed to general practice by walk-in centres and NHS Direct is considerable, and
-the diversion of funding from primary care is large. The risk of waste and duplication increases
-as more layers of complexity are added to a primary care service that started out as something
-familiar, simple, and local and which is still envied in other developed countries.21 Access needs to be simple, and the advantages of personal
-knowledge and trust in minimising duplication and overmedicalisation need to be exploited.
-
We must ensure better communication and access so that patients can more easily deal with
-minor issues and queries with someone they know and trust and avoid the formality and
-inconvenience of a full face to face consultation. Too often this has to be with a different
-professional, unfamiliar with the nuances of the case. There should be far more managerial
-emphasis on helping patients to interact with their chosen practitioner22; such a programme has been described.23 Modern information systems make it much easier to record which doctor(s) a patient
-prefers to see and to monitor how often this is achieved. The telephone is hardly modern but is
-underused. Email avoids the problems inherent in arranging simultaneous availability necessary
-for telephone consultations but at the cost of reducing the communication of emotions. There is
-a place for both.2 Access without prior appointment is a
-valued feature of primary care, and we need to know more about the right balance between planned
-and ad hoc consulting.
-
-
-Next steps
-
General practitioners do not behave in a uniform way. They can be categorised as slow, medium,
-and fast and react in different ways to changes in consulting speed.18 They are likely to have differing views about a widespread move to lengthen
-consultation time. We do not need further confirmation that longer consultations are desirable
-and necessary, but research could show us the best way to learn how to introduce them with
-minimal disruption to the way in which patients and practices like primary care to be
-provided.24 We also need to learn how to make the most of
-available time in complex consultations.
-
Devising appropriate incentives and helping practices move beyond just reacting to demand in
-the traditional way by working harder and faster is perhaps our greatest challenge in the United
-Kingdom. The new primary are trusts need to work together with the growing primary care research
-networks to carry out the necessary development work. In particular, research is needed on how a
-primary care team can best provide the right balance of quick access and interpersonal knowledge
-and trust.
-
-
-
-
-
We thank the other members of the working group: Susan Childs, Paul Freeling, Iona Heath,
-Marshall Marinker, and Bonnie Sibbald. We also thank Fenny Green of the Royal College of General
-Practitioners for administrative help.
-
-
-
-
-
-
-Shah
-NC
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-for NHS Service Delivery and Organisation R&D (NCCSDO), Summer 2000
-2001
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-arousal
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-Britain
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-
-
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-
-
Funding: Meetings of the working group in 1999-2000 were funded by the
-Scientific Foundation Board of the RCGP.
-
-
-
Competing interests: None declared.
-
-
-
-
diff --git a/tests/data/jats/pnas_sample.xml b/tests/data/jats/pnas_sample.xml
deleted file mode 100644
index 6c4ed4db..00000000
--- a/tests/data/jats/pnas_sample.xml
+++ /dev/null
@@ -1,3089 +0,0 @@
-
-
-
-
-
-pnas
-Proc Natl Acad Sci U S A
-PNAS
-0027-8424
-
-The National Academy of Sciences
-
-
-
-181325198
-3251
-10.1073/pnas.181325198
-jPNAS.v98.i18.pg10214
-11517319
-
-
-Physical Sciences
-
-Applied Mathematics
-
-
-
-Biological Sciences
-
-Genetics
-
-
-
-
-The coreceptor mutation CCR5Δ32 influences the dynamics of HIV epidemics and is selected for by HIV
-
-
-
-
-Sullivan
-Amy D.
-
-*
-
-
-
-
-Wigginton
-Janis
-
-
-
-
-
-Kirschner
-Denise
-
-†
-
-
-
-Department of Microbiology and Immunology, University
-of Michigan Medical School, Ann Arbor, MI 48109-0620
-
-
-
* Present address: Centers for Disease Control and Prevention Epidemiology Program Office, State Branch Oregon Health Division, 800 NE Oregon Street, Suite 772, Portland, OR 97232.
-
-
-
† To whom reprint requests should be addressed. E-mail: kirschne@umich.edu.
-
-
-
Communicated by Avner Friedman, University of Minnesota, Minneapolis, MN
We explore the impact of a host genetic factor on heterosexual HIV epidemics by using a deterministic mathematical model. A protective allele unequally distributed across populations is exemplified in our models by the 32-bp deletion in the host-cell chemokine receptor CCR5, CCR5Δ32. Individuals homozygous for CCR5Δ32 are protected against HIV infection whereas those heterozygous for CCR5Δ32 have lower pre-AIDS viral loads and delayed progression to AIDS. CCR5Δ32 may limit HIV spread by decreasing the probability of both risk of infection and infectiousness. In this work, we characterize epidemic HIV within three dynamic subpopulations: CCR5/CCR5 (homozygous, wild type), CCR5/CCR5Δ32 (heterozygous), and CCR5Δ32/CCR5Δ32 (homozygous, mutant). Our results indicate that prevalence of HIV/AIDS is greater in populations lacking the CCR5Δ32 alleles (homozygous wild types only) as compared with populations that include people heterozygous or homozygous for CCR5Δ32. Also, we show that HIV can provide selective pressure for CCR5Δ32, increasing the frequency of this allele.
-
-
-
-
-
Nineteen million people have died of AIDS since the discovery of HIV in the 1980s. In 1999 alone, 5.4 million people were newly infected with HIV (ref. 1 and http://www.unaids.org/epidemicupdate/report/Epireport.html). (For brevity, HIV-1 is referred to as HIV in this paper.) Sub-Saharan Africa has been hardest hit, with more than 20% of the general population HIV-positive in some countries (2, 3). In comparison, heterosexual epidemics in developed, market-economy countries have not reached such severe levels. Factors contributing to the severity of the epidemic in economically developing countries abound, including economic, health, and social differences such as high levels of sexually transmitted diseases and a lack of prevention programs. However, the staggering rate at which the epidemic has spread in sub-Saharan Africa has not been adequately explained. The rate and severity of this epidemic also could indicate a greater underlying susceptibility to HIV attributable not only to sexually transmitted disease, economics, etc., but also to other more ubiquitous factors such as host genetics (4, 5).
-
To exemplify the contribution of such a host genetic factor to HIV prevalence trends, we consider a well-characterized 32-bp deletion in the host-cell chemokine receptor CCR5, CCR5Δ32. When HIV binds to host cells, it uses the CD4 receptor on the surface of host immune cells together with a coreceptor, mainly the CCR5 and CXCR4 chemokine receptors (6). Homozygous mutations for this 32-bp deletion offer almost complete protection from HIV infection, and heterozygous mutations are associated with lower pre-AIDS viral loads and delayed progression to AIDS (7–14). CCR5Δ32 generally is found in populations of European descent, with allelic frequencies ranging from 0 to 0.29 (13). African and Asian populations studied outside the United States or Europe appear to lack the CCR5Δ32 allele, with an allelic frequency of almost zero (5, 13). Thus, to understand the effects of a protective allele, we use a mathematical model to track prevalence of HIV in populations with or without CCR5Δ32 heterozygous and homozygous people and also to follow the CCR5Δ32 allelic frequency.
-
We hypothesize that CCR5Δ32 limits epidemic HIV by decreasing infection rates, and we evaluate the relative contributions to this by the probability of infection and duration of infectivity. To capture HIV infection as a chronic infectious disease together with vertical transmission occurring in untreated mothers, we model a dynamic population (i.e., populations that vary in growth rates because of fluctuations in birth or death rates) based on realistic demographic characteristics (18). This scenario also allows tracking of the allelic frequencies over time. This work considers how a specific host genetic factor affecting HIV infectivity and viremia at the individual level might influence the epidemic in a dynamic population and how HIV exerts selective pressure, altering the frequency of this mutant allele.
-
CCR5 is a host-cell chemokine receptor, which is also used as a coreceptor by R5 strains of HIV that are generally acquired during sexual transmission (6, 19–25). As infection progresses to AIDS the virus expands its repertoire of potential coreceptors to include other CC-family and CXC-family receptors in roughly 50% of patients (19, 26, 27). CCR5Δ32 was identified in HIV-resistant people (28). Benefits to individuals from the mutation in this allele are as follows. Persons homozygous for the CCR5Δ32 mutation are almost nonexistent in HIV-infected populations (11, 12) (see ref. 13 for review). Persons heterozygous for the mutant allele (CCR5 W/Δ32) tend to have lower pre-AIDS viral loads. Aside from the beneficial effects that lower viral loads may have for individuals, there is also an altruistic effect, as transmission rates are reduced for individuals with low viral loads (as compared with, for example, AZT and other studies; ref. 29). Finally, individuals heterozygous for the mutant allele (CCR5 W/Δ32) also have a slower progression to AIDS than those homozygous for the wild-type allele (CCR5 W/W) (7–10), remaining in the population 2 years longer, on average. Interestingly, the dearth of information on HIV disease progression in people homozygous for the CCR5Δ32 allele (CCR5 Δ32/Δ32) stems from the rarity of HIV infection in this group (4, 12, 28). However, in case reports of HIV-infected CCR5 Δ32/Δ32 homozygotes, a rapid decline in CD4+ T cells and a high viremia are observed, likely because of initial infection with a more aggressive viral strain (such as X4 or R5X4) (30).
-
-The Model
-
Because we are most concerned with understanding the severity of the epidemic in developing countries where the majority of infection is heterosexual, we consider a purely heterosexual model. To model the effects of the allele in the population, we examine the rate of HIV spread by using an enhanced susceptible-infected-AIDS model of epidemic HIV (for review see ref. 31). Our model compares two population scenarios: a CCR5 wild-type population and one with CCR5Δ32 heterozygotes and homozygotes in addition to the wild type. To model the scenario where there are only wild-type individuals present in the population (i.e., CCR5 W/W), we track the sexually active susceptibles at time t [Si,j
-(t)], where i = 1 refers to genotype (CCR5 W/W only in this case) and j is either the male or female subpopulation. We also track those who are HIV-positive at time t not yet having AIDS in Ii,j,k
-(t) where k refers to stage of HIV infection [primary (A) or asymptomatic (B)]. The total number of individuals with AIDS at time t are tracked in A(t). The source population are children, χ
-i,j
-(t), who mature into the sexually active population at time t (Fig. 1, Table 1). We compare the model of a population lacking the CCR5Δ32 allele to a demographically similar population with a high frequency of the allele. When genetic heterogeneity is included, male and female subpopulations are each further divided into three distinct genotypic groups, yielding six susceptible subpopulations, [Si,j
-(t), where i ranges from 1 to 3, where 1 = CCR5W/W; 2 = CCR5 W/Δ32; 3 = CCR5 Δ32/Δ32]. The infected classes, Ii,j,k
-(t), also increase in number to account for these new genotype compartments. In both settings we assume there is no treatment available and no knowledge of HIV status by people in the early acute and middle asymptomatic stages (both conditions exist in much of sub-Saharan Africa). In addition, we assume that sexual mixing in the population occurs randomly with respect to genotype and HIV disease status, all HIV-infected people eventually progress to AIDS, and no barrier contraceptives are used. These last assumptions reflect both economic and social conditions.
-
-
-
-
A schematic representation of the basic compartmental HIV epidemic model. The criss-cross lines indicate the sexual mixing between different compartments. Each of these interactions has a positive probability of taking place; they also incorporate individual rates of transmission indicated as λ, but in full notation is λ
-î,,→i,j, where i,j,k is the phenotype of the infected partner and î, is the phenotype of the susceptible partner. Also shown are the different rates of disease progression, γ
-i,j,k
-, that vary according to genotype, gender, and stage. Thus, the interactions between different genotypes, genders, and stages are associated with a unique probability of HIV infection. M, male; F, female.
-
-
-
-
-
-
-
-
Children's genotype
-
-
-
-
Parents
-
Mother
-
-
-
-
-
-
-
-
Father
-
-
W/W
-
W/Δ32
-
Δ32/Δ32
-
-
-
-
W/W
-
χ1,j
-1,j
-
-
χ1,j
-1,j, χ2,j
-2,j
-
-
χ2,j
-2,j
-
-
-
-
-
W/Δ32
-
χ1,j
-1,j, χ2,j
-2,j
-
-
χ1,j
-1,j, χ2,j
-2,j, χ3,j
-3,j
-
-
χ2,j
-2,j, χ3,j
-3,j
-
-
-
-
-
Δ32/Δ32
-
χ2,j
-2,j
-
-
χ2,j
-2,j, χ3,j
-3,j
-
-
χ3,j
-3,j
-
-
-
-
-
-
χ1,j
-1,j = wild-type children; (W/W); χ2,j
-2,j = heterozygous children (W/Δ32); χ3,j
-3,j = homozygous children (Δ32/Δ32) of gender j. Children's genotypes are determined by using Mendelian inheritance patterns.
-
-
-
-
-Parameter Estimates for the Model.
-
Estimates for rates that govern the interactions depicted in Fig. 1 were derived from the extensive literature on HIV. Our parameters and their estimates are summarized in Tables 2–4. The general form of the equations describing the rates of transition between population classes as depicted in Fig. 1 are summarized as follows:
-\documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} { <-> linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document} $$ \frac{dS_{i,j}(t)}{dt}={\chi}_{i,j}(t)-{\mu}_{j}S_{i,j}(t)-{\lambda}_{\hat {\imath},\hat {},\hat {k}{\rightarrow}i,j}S_{i,j}(t), $$ \end{document}
-
-
-\documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} { <-> linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document} $$ \hspace{1em}\hspace{1em}\hspace{.167em}\frac{dI_{i,j,A}(t)}{dt}={\lambda}_{\hat {\imath},\hat {},\hat {k}{\rightarrow}i,j}S_{i,j}(t)-{\mu}_{j}I_{i,j,A}(t)-{\gamma}_{i,j,A}I_{i,j,A}(t), $$ \end{document}
-
-
-\documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} { <-> linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document} $$ \frac{dI_{i,j,B}(t)}{dt}={\gamma}_{i,j,A}I_{i,j,A}(t)-{\mu}_{j}I_{i,j,B}(t)-{\gamma}_{i,j,B}I_{i,j,B}(t), $$ \end{document}
-
-
-\documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} { <-> linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document} $$ \frac{dA(t)}{dt}={\gamma}_{i,j,B} \left( { \,\substack{ ^{3} \\ {\sum} \\ _{i=1} }\, }I_{i,F,B}(t)+I_{i,M,B}(t) \right) -{\mu}_{A}A(t)-{\delta}A(t), $$ \end{document}
- where, in addition to previously defined populations and rates (with i equals genotype, j equals gender, and k equals stage of infection, either A or B), μ
-j
-, represents the non-AIDS (natural) death rate for males and females respectively, and μA is estimated by the average (μF + μM/2). This approximation allows us to simplify the model (only one AIDS compartment) without compromising the results, as most people with AIDS die of AIDS (δAIDS) and very few of other causes (μA). These estimates include values that affect infectivity (λ
-î,,→i,j
-), transmission (β
-î,,→i,j
-), and disease progression (γ
-i
-
-,
-
-j
-
-,
-
-k
-) where the î,, notation represents the genotype, gender, and stage of infection of the infected partner, and j ≠ .
-
-
-
-
Transmission probabilities
-
-
-
-
HIV-infected partner (îıı^^, ^^, k
-k^^)
-
Susceptible partner (i, j)
-
-
-
-
-
-
-
-
(^^ to j)
-
W/W
-
W/Δ32
-
Δ32/Δ32
-
-
-
-
-
-
-
-
Acute/primary
-
-
-
W/W or Δ32/Δ32
-
M to F
-
0.040
-
0.040
-
0.00040
-
-
-
-
F to M
-
0.020
-
0.020
-
0.00020
-
-
-
W/Δ32
-
M to F
-
0.030
-
0.030
-
0.00030
-
-
-
-
F to M
-
0.015
-
0.015
-
0.00015
-
-
-
Asymptomatic
-
-
-
W/W or Δ32/Δ32
-
M to F
-
0.0010
-
0.0010
-
10 × 10−6
-
-
-
-
-
F to M
-
0.0005
-
0.0005
-
5 × 10−6
-
-
-
-
W/Δ32
-
M to F
-
0.0005
-
0.0005
-
5 × 10−6
-
-
-
-
-
F to M
-
0.00025
-
0.00025
-
2.5 × 10−6
-
-
-
-
-
-
Listed are the different transmission probabilities (βîıı^^,^^,k
-k^^→i,j
-) for random sexual mixing between persons where i, j, k is the phenotype of the infected partner and i, j is the phenotype of the susceptible partner. M, male; F, female.
-
-
-
-
-
-
-
Progression rates
-
-
-
-
Genotype
-
Disease stage
-
Males/females
-
-
-
-
-
-
-
-
W/W
-
A
-
3.5
-
-
-
-
B
-
0.16667
-
-
-
W/Δ32
-
A
-
3.5
-
-
-
-
B
-
0.125
-
-
-
Δ32/Δ32
-
A
-
3.5
-
-
-
-
B
-
0.16667
-
-
-
-
-
Shown are the rates of progression, γ
-i,j,k
-
-i,j,k reflecting the different rates at which persons progress through different stages of disease by genotype, gender, and disease stage.
-
-
-
-
-
-
-
Parameter values
-
-
-
-
Parameter
-
Definition
-
Value
-
-
-
-
-
-
-
-
μ
-F
-
-F, μ
-M
-
-M
-
-
All-cause mortality for adult females (males)
-
0.015 (0.016) per year
-
-
-
μχχ
-
All-cause childhood mortality (<15 years of age)
-
0.01 per year
-
-
-
-B
-
-r
-
-r
-
-
Birthrate
-
0.25 per woman per year
-
-
-
-SA
-
-F
-
-F
-
-
Percent females acquiring new partners (sexual activity)
-
10%
-
-
-
-SA
-
-M
-
-M
-
-
Percent males acquiring new partners (sexual activity)
Initial total children in population (<15 years of age)
-
45%
-
-
-
-W/W (0)
-
Initial total wild types (W/W) in population
-
80%
-
-
-
-W/Δ32(0)
-
Initial total heterozygotes (W/Δ32) in population
-
19%
-
-
-
Δ32/Δ32(0)
-
Initial total homozygotes (Δ32/Δ32) in population
-
1%
-
-
-
-r
-
-M
-
-M(r
-
-F
-
-F)
-
Initial percent males (females) in total population
-
49% (51%)
-
-
-
ϕ
-F
-
-F, ϕ
-M
-
-M
-
-
Number of sexual contacts a female (male) has
-
30 (24) per partner
-
-
-
ɛ
-i,j,k
-
-i,j,k
-
-
% effect of mutation on transmission rates (see Table 2)
-
0 < ɛ
-i,j,k
-
-i,j,k < 1
-
-
-
δ
-
Death rate for AIDS population
-
1.0 per year
-
-
-
-q
-
-
Allelic frequency of Δ32 allele
-
0.105573
-
-
-
-
-
Shown are the parameter values for parameters other than the transmission probabilities (Table 2) and the progression rates (Table 3). Each were estimated from data as described in text.
-
-
-
-
The effects of the CCR5 W/Δ32 and CCR5 Δ32/Δ32 genotypes are included in our model through both the per-capita probabilities of infection, λ
-î,,→i,j
-, and the progression rates, γ
-i
-
-,
-
-j
-
-,
-
-k
-. The infectivity coefficients, λ
-î,,→i,j
-, are calculated for each population subgroup based on the following: likelihood of HIV transmission in a sexual encounter between a susceptible and an infected (βîıı^^,j,k
-k^^→i,j
-) person; formation of new partnerships (c
-
-j
-
-j); number of contacts in a given partnership (ϕ
-j
-); and probability of encountering an infected individual (I
-
-î,,
-/N
-
-
-). The formula representing this probability of infection is
-\documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} { <-> linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document} $$ {\lambda}_{\hat {i},\hat {j},\hat {k}{\rightarrow}i,j}=\frac{C_{j}{\cdot}{\phi}_{j}}{N_{\hat {j}}}\hspace{.167em} \left[ { \,\substack{ \\ {\sum} \\ _{\hat {i},\hat {k}} }\, }{\beta}_{\hat {i},\hat {j},\hat {k}{\rightarrow}i,j}{\cdot}I_{\hat {i},\hat {j},\hat {k}} \right] , $$ \end{document}
- where j ≠ is either male or female. N
-
-
- represents the total population of gender (this does not include those with AIDS in the simulations).
-
The average rate of partner acquisition, cj
-, includes the mean plus the variance to mean ratio of the relevant distribution of partner-change rates to capture the small number of high-risk people: cj
- = mj
- + (ς
-\documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} { <-> linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document} $$ {\mathrm{_{{\mathit{j}}}^{2}}} $$ \end{document}
-/m
-j) where the mean (mj
-) and variance (ς
-\documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} { <-> linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document} $$ {\mathrm{_{{\mathit{j}}}^{2}}} $$ \end{document}
-) are annual figures for new partnerships only (32). These means are estimated from Ugandan data for the number of heterosexual partners in the past year (33) and the number of nonregular heterosexual partners (i.e., spouses or long-term partners) in the past year (34). In these sexual activity surveys, men invariably have more new partnerships; thus, we assumed that they would have fewer average contacts per partnership than women (a higher rate of new partner acquisition means fewer sexual contacts with a given partner; ref. 35). To incorporate this assumption in our model, the male contacts/partnership, ϕ
-M
-, was reduced by 20%. In a given population, the numbers of heterosexual interactions must equate between males and females. The balancing equation applied here is SA
-F·m
-F·N
-F = SA
-M·m
-M·N
-M, where SAj
- are the percent sexually active and Nj
- are the total in the populations for gender j. To specify changes in partner acquisition, we apply a male flexibility mechanism, holding the female rate of acquisition constant and allowing the male rates to vary (36, 37).
-
-Transmission probabilities.
-
The effect of a genetic factor in a model of HIV transmission can be included by reducing the transmission coefficient. The probabilities of transmission per contact with an infected partner, βîıı^^,^^,k
-k^^→i,j
-, have been estimated in the literature (see ref. 38 for estimates in minimally treated groups). We want to capture a decreased risk in transmission based on genotype (ref. 39, Table 2). No studies have directly evaluated differences in infectivity between HIV-infected CCR5 W/Δ32 heterozygotes and HIV-infected CCR5 wild types. Thus, we base estimates for reduced transmission on studies of groups with various HIV serum viral loads (40), HTLV-I/II viral loads (41), and a study of the effect of AZT treatment on transmission (29). We decrease transmission probabilities for infecting CCR5Δ32/Δ32 persons by 100-fold to reflect the rarity of infections in these persons. However, we assume that infected CCR5Δ32/Δ32 homozygotes can infect susceptibles at a rate similar to CCR5W/W homozygotes, as the former generally have high viremias (ref. 30, Table 2). We also assume that male-to-female transmission is twice as efficient as female-to-male transmission (up to a 9-fold difference has been reported; ref. 42) (ref. 43, Table 2).
-
Given the assumption of no treatment, the high burden of disease in people with AIDS is assumed to greatly limit their sexual activity. Our initial model excludes people with AIDS from the sexually active groups. Subsequently, we allow persons with AIDS to be sexually active, fixing their transmission rates (βAIDS) to be the same across all CCR5 genotypes, and lower than transmission rates for primary-stage infection (as the viral burden on average is not as high as during the acute phase), and larger than transmission rates for asymptomatic-stage infection (as the viral burden characteristically increases during the end stage of disease).
-
-
-Disease progression.
-
We assume three stages of HIV infection: primary (acute, stage A), asymptomatic HIV (stage B), and AIDS. The rates of transition through the first two stages are denoted by γ
-i,j,k
-
-i,j,k, where i represents genotype, j is male/female, and k represents either stage A or stage B. Transition rates through each of these stages are assumed to be inversely proportional to the duration of that stage; however, other distributions are possible (31, 44, 45). Although viral loads generally peak in the first 2 months of infection, steady-state viral loads are established several months beyond this (46). For group A, the primary HIV-infecteds, duration is assumed to be 3.5 months. Based on results from European cohort studies (7–10), the beneficial effects of the CCR5 W/Δ32 genotype are observed mainly in the asymptomatic years of HIV infection; ≈7 years after seroconversion survival rates appear to be quite similar between heterozygous and homozygous individuals. We also assume that CCR5Δ32/Δ32-infected individuals and wild-type individuals progress similarly, and that men and women progress through each disease stage at the same rate. Given these observations, and that survival after infection may be shorter in untreated populations, we choose the duration time in stage B to be 6 years for wild-type individuals and 8 years for heterozygous individuals. Transition through AIDS, δAIDS, is inversely proportional to the duration of AIDS. We estimate this value to be 1 year for the time from onset of AIDS to death. The progression rates are summarized in Table 3.
-
-
-
-Demographic Setting.
-
Demographic parameters are based on data from Malawi, Zimbabwe, and Botswana (3, 47). Estimated birth and child mortality rates are used to calculate the annual numbers of children (χ
-i,j
-
-i,j) maturing into the potentially sexually active, susceptible group at the age of 15 years (3). For example, in the case where the mother is CCR5 wild type and the father is CCR5 wild type or heterozygous, the number of CCR5 W/W children is calculated as follows [suppressing (t) notation]: χ1,j
-1,j =
-\documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} { <-> linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document} $$ B_{r}\hspace{.167em}{ \,\substack{ \\ {\sum} \\ _{k} }\, } \left[ S_{1,F}\frac{(S_{1,M}+I_{1,M,k})}{N_{M}}+ \left[ (0.5)S_{1,F}\frac{(S_{2,M}+I_{2,M,k})}{N_{M}} \right] + \right $$ \end{document}
-
-
-\documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} { <-> linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document} $$ p_{v} \left \left( \frac{(I_{1,F,k}(S_{1,M}+I_{1,M,k}))}{N_{M}}+ \left[ (0.5)I_{1,F,k}\frac{(S_{2,M}+I_{2,M,k})}{N_{M}} \right] \right) \right] ,\hspace{.167em} $$ \end{document}
- where the probability of HIV vertical transmission, 1 − pv
-, and the birthrate, Br
-, are both included in the equations together with the Mendelian inheritance values as presented in Table 1. The generalized version of this equation (i.e., χ
-i,j
-
-i,j) can account for six categories of children (including gender and genotype). We assume that all children of all genotypes are at risk, although we can relax this condition if data become available to support vertical protection (e.g., ref. 48). All infected children are assumed to die before age 15. Before entering the susceptible group at age 15, there is additional loss because of mortality from all non-AIDS causes occurring less than 15 years of age at a rate of μχχ × χ
-i,j
-
-i,j (where μχ is the mortality under 15 years of age). Children then enter the population as susceptibles at an annual rate, ς
-j
-
-j × χ
-i,j
-
-i,j/15, where ς
-j
- distributes the children 51% females and 49% males. All parameters and their values are summarized in Table 4.
-
-
-
-Prevalence of HIV
-
-Demographics and Model Validation.
-
The model was validated by using parameters estimated from available demographic data. Simulations were run in the absence of HIV infection to compare the model with known population growth rates. Infection was subsequently introduced with an initial low HIV prevalence of 0.5% to capture early epidemic behavior.
-
In deciding on our initial values for parameters during infection, we use Joint United Nations Programme on HIV/AIDS national prevalence data for Malawi, Zimbabwe, and Botswana. Nationwide seroprevalence of HIV in these countries varies from ≈11% to over 20% (3), although there may be considerable variation within given subpopulations (2, 49).
-
In the absence of HIV infection, the annual percent population growth rate in the model is ≈2.5%, predicting the present-day values for an average of sub-Saharan African cities (data not shown). To validate the model with HIV infection, we compare our simulation of the HIV epidemic to existing prevalence data for Kenya and Mozambique (http://www.who.int/emc-hiv/fact-sheets/pdfs/kenya.pdf and ref. 51). Prevalence data collected from these countries follow similar trajectories to those predicted by our model (Fig. 2).
-
-
-
-
Model simulation of HIV infection in a population lacking the protective CCR5Δ32 allele compared with national data from Kenya (healthy adults) and Mozambique (blood donors, ref. 17). The simulated population incorporates parameter estimates from sub-Saharan African demographics. Note the two outlier points from the Mozambique data were likely caused by underreporting in the early stages of the epidemic.
-
-
-
-
-
-
-Effects of the Allele on Prevalence.
-
After validating the model in the wild type-only population, both CCR5Δ32 heterozygous and homozygous people are included. Parameter values for HIV transmission, duration of illness, and numbers of contacts per partner are assumed to be the same within both settings. We then calculate HIV/AIDS prevalence among adults for total HIV/AIDS cases.
-
Although CCR5Δ32/Δ32 homozygosity is rarely seen in HIV-positive populations (prevalence ranges between 0 and 0.004%), 1–20% of people in HIV-negative populations of European descent are homozygous. Thus, to evaluate the potential impact of CCR5Δ32, we estimate there are 19% CCR5 W/Δ32 heterozygous and 1% CCR5 Δ32/Δ32 homozygous people in our population. These values are in Hardy-Weinberg equilibrium with an allelic frequency of the mutation as 0.105573.
-
Fig. 3 shows the prevalence of HIV in two populations: one lacking the mutant CCR5 allele and another carrying that allele. In the population lacking the protective mutation, prevalence increases logarithmically for the first 35 years of the epidemic, reaching 18% before leveling off.
-
-
-
-
Prevalence of HIV/AIDS in the adult population as predicted by the model. The top curve (○) indicates prevalence in a population lacking the protective allele. We compare that to a population with 19% heterozygous and 1% homozygous for the allele (implying an allelic frequency of 0.105573. Confidence interval bands (light gray) are shown around the median simulation () providing a range of uncertainty in evaluating parameters for the effect of the mutation on the infectivity and the duration of asymptomatic HIV for heterozygotes.
-
-
-
-
-
In contrast, when a proportion of the population carries the CCR5Δ32 allele, the epidemic increases more slowly, but still logarithmically, for the first 50 years, and HIV/AIDS prevalence reaches ≈12% (Fig. 3). Prevalence begins to decline slowly after 70 years.
-
In the above simulations we assume that people with AIDS are not sexually active. However, when these individuals are included in the sexually active population the severity of the epidemic increases considerably (data not shown). Consistent with our initial simulations, prevalences are still relatively lower in the presence of the CCR5 mutation.
-
Because some parameters (e.g., rate constants) are difficult to estimate based on available data, we implement an uncertainty analysis to assess the variability in the model outcomes caused by any inaccuracies in estimates of the parameter values with regard to the effect of the allelic mutation. For these analyses we use Latin hypercube sampling, as described in refs. 52–56, Our uncertainty and sensitivity analyses focus on infectivity vs. duration of infectiousness. To this end, we assess the effects on the dynamics of the epidemic for a range of values of the parameters governing transmission and progression rates: βîıı^^,^^,k
-k^^→i,j
- and γ
-i,j,k
-
-i,j,k. All other parameters are held constant. These results are presented as an interval band about the average simulation for the population carrying the CCR5Δ32 allele (Fig. 3). Although there is variability in the model outcomes, the analysis indicates that the overall model predictions are consistent for a wide range of transmission and progression rates. Further, most of the variation observed in the outcome is because of the transmission rates for both heterosexual males and females in the primary stage of infection (β2,M,A
-→
-
-i
-
-,F, β2,F,A
-→
-
-i
-
-,M). As mentioned above, we assume lower viral loads correlate with reduced infectivity; thus, the reduction in viral load in heterozygotes has a major influence on disease spread.
-
-
-
-HIV Induces Selective Pressure on Genotype Frequency
-
To observe changes in the frequency of the CCR5Δ32 allele in a setting with HIV infection as compared with the Hardy-Weinberg equilibrium in the absence of HIV, we follow changes in the total number of CCR5Δ32 heterozygotes and homozygotes over 1,000 years (Fig. 4). We initially perform simulations in the absence of HIV infection as a negative control to show there is not significant selection of the allele in the absence of infection. To determine how long it would take for the allelic frequency to reach present-day levels (e.g., q = 0.105573), we initiate this simulation for 1,000 years with a very small allelic frequency (q = 0.00105). In the absence of HIV, the allelic frequency is maintained in equilibrium as shown by the constant proportions of CCR5Δ32 heterozygotes and homozygotes (Fig. 4, solid lines). The selection for CCR5Δ32 in the presence of HIV is seen in comparison (Fig. 4, dashed lines). We expand the time frame of this simulation to 2,000 years to view the point at which the frequency reaches present levels (where q ∼0.105573 at year = 1200). Note that the allelic frequency increases for ≈1,600 years before leveling off.
-
-
-
-
Effects of HIV-1 on selection of the CCR5Δ32 allele. The Hardy-Weinberg equilibrium level is represented in the no-infection simulation (solid lines) for each population. Divergence from the original Hardy-Weinberg equilibrium is shown to occur in the simulations that include HIV infection (dashed lines). Fraction of the total subpopulations are presented: (A) wild types (W/W), (B) heterozygotes (W/Δ32), and (C) homozygotes (Δ32/Δ32). Note that we initiate this simulation with a much lower allelic frequency (0.00105) than used in the rest of the study to better exemplify the actual selective effect over a 1,000-year time scale. (D) The allelic selection effect over a 2,000-year time scale.
-
-
-
-
-
-
-Discussion
-
This study illustrates how populations can differ in susceptibility to epidemic HIV/AIDS depending on a ubiquitous attribute such as a prevailing genotype. We have examined heterosexual HIV epidemics by using mathematical models to assess HIV transmission in dynamic populations either with or without CCR5Δ32 heterozygous and homozygous persons. The most susceptible population lacks the protective mutation in CCR5. In less susceptible populations, the majority of persons carrying the CCR5Δ32 allele are heterozygotes. We explore the hypothesis that lower viral loads (CCR5Δ32 heterozygotes) or resistance to infection (CCR5Δ32 homozygotes) observed in persons with this coreceptor mutation ultimately can influence HIV epidemic trends. Two contrasting influences of the protective CCR5 allele are conceivable: it may limit the epidemic by decreasing the probability of infection because of lower viral loads in infected heterozygotes, or it may exacerbate the epidemic by extending the time that infectious individuals remain in the sexually active population. Our results strongly suggest the former. Thus, the absence of this allele in Africa could explain the severity of HIV disease as compared with populations where the allele is present.
-
We also observed that HIV can provide selective pressure for the CCR5Δ32 allele within a population, increasing the allelic frequency. Other influences may have additionally selected for this allele. Infectious diseases such as plague and small pox have been postulated to select for CCR5Δ32 (57, 58). For plague, relatively high levels of CCR5Δ32 are believed to have arisen within ≈4,000 years, accounting for the prevalence of the mutation only in populations of European descent. Smallpox virus uses the CC-coreceptor, indicating that direct selection for mutations in CCR5 may have offered resistance to smallpox. Given the differences in the epidemic rates of plague (59), smallpox, and HIV, it is difficult to directly compare our results to these findings. However, our model suggests that the CCR5Δ32 mutation could have reached its present allelic frequency in Northern Europe within this time frame if selected for by a disease with virulence patterns similar to HIV. Our results further support the idea that HIV has been only recently introduced as a pathogen into African populations, as the frequency of the protective allele is almost zero, and our model predicts that selection of the mutant allele in this population by HIV alone takes at least 1,000 years. This prediction is distinct from the frequency of the CCR5Δ32 allele in European populations, where pathogens that may have influenced its frequency (e.g., Yersinia pestis) have been present for much longer.
-
Two mathematical models have considered the role of parasite and host genetic heterogeneity with regard to susceptibility to another pathogen, namely malaria (60, 61). In each it was determined that heterogeneity of host resistance facilitates the maintenance of diversity in parasite virulence. Given our underlying interest in the coevolution of pathogen and host, we focus on changes in a host protective mutation, holding the virulence of the pathogen constant over time.
-
Even within our focus on host protective mutations, numerous genetic factors, beneficial or detrimental, could potentially influence epidemics. Other genetically determined host factors affecting HIV susceptibility and disease progression include a CCR5 A/A to G/G promoter polymorphism (62), a CCR2 point mutation (11, 63), and a mutation in the CXCR4 ligand (64). The CCR2b mutation, CCR264I, is found in linkage with at least one CCR5 promoter polymorphism (65) and is prevalent in populations where CCR5Δ32 is nonexistent, such as sub-Saharan Africa (63). However, as none of these mutations have been consistently shown to be as protective as the CCR5Δ32 allele, we simplified our model to incorporate only the effect of CCR5Δ32. Subsequent models could be constructed from our model to account for the complexity of multiple protective alleles. It is interesting to note that our model predicts that even if CCR264I is present at high frequencies in Africa, its protective effects may not augment the lack of a protective allele such as CCR5Δ32.
-
Although our models demonstrate that genetic factors can contribute to the high prevalence of HIV in sub-Saharan Africa, demographic factors are also clearly important in this region. Our models explicitly incorporated such factors, for example, lack of treatment availability. Additional factors were implicitly controlled for by varying only the presence of the CCR5Δ32 allele. More complex models eventually could include interactions with infectious diseases that serve as cofactors in HIV transmission. The role of high sexually transmitted disease prevalences in HIV infection has long been discussed, especially in relation to core populations (15, 50, 66). Malaria, too, might influence HIV transmission, as it is associated with transient increases in semen HIV viral loads and thus could increase the susceptibility of the population to epidemic HIV (16).
-
In assessing the HIV/AIDS epidemic, considerable attention has been paid to the influence of core groups in driving sexually transmitted disease epidemics. Our results also highlight how characteristics more uniformly distributed in a population can affect susceptibility. We observed that the genotypic profile of a population affects its susceptibility to epidemic HIV/AIDS. Additional studies are needed to better characterize the influence of these genetic determinants on HIV transmission, as they may be crucial in estimating the severity of the epidemic in some populations. This information can influence the design of treatment strategies as well as point to the urgency for education and prevention programs.
-
-
-
-
-
We thank Mark Krosky, Katia Koelle, and Kevin Chung for programming and technical assistance. We also thank Drs. V. J. DiRita, P. Kazanjian, and S. M. Blower for helpful comments and discussions. We thank the reviewers for extremely insightful comments.
The authors have declared that no competing interests exist.
* E-mail: cburgert@rti.org16202062020146e000830181120191642020This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five effective rounds of mass drug administration (MDA) and demonstrating low prevalence in subsequent assessments. The first assessments recommended by the World Health Organization (WHO) are sentinel and spot-check sites—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit after MDA. If pre-TAS shows that prevalence in each site has been lowered to less than 1% microfilaremia or less than 2% antigenemia, the implementation unit conducts a TAS to determine whether MDA can be stopped. Failure to pass pre-TAS means that further rounds of MDA are required. This study aims to understand factors influencing pre-TAS results using existing programmatic data from 554 implementation units, of which 74 (13%) failed, in 13 countries. Secondary data analysis was completed using existing data from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. Additional covariate data were obtained from spatial raster data sets. Bivariate analysis and multilinear regression were performed to establish potential relationships between variables and the pre-TAS result. Higher baseline prevalence and lower elevation were significant in the regression model. Variables statistically significantly associated with failure (p-value ≤0.05) in the bivariate analyses included baseline prevalence at or above 5% or 10%, use of Filariasis Test Strips (FTS), primary vector of Culex, treatment with diethylcarbamazine-albendazole, higher elevation, higher population density, higher enhanced vegetation index (EVI), higher annual rainfall, and 6 or more rounds of MDA. This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. This information can help countries more effectively forecast program activities, such as the potential need for more rounds of MDA, and prioritize resources to ensure adequate coverage of all persons in areas at highest risk of failing pre-TAS.
Author summary
Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five rounds of mass drug administration (MDA) and being able to demonstrate low prevalence in several subsequent assessments. LF elimination programs implement sentinel and spot-check site assessments, called pre-TAS, to determine whether districts are eligible to implement more rigorous population-based surveys to determine whether MDA can be stopped or if further rounds are required. Reasons for failing pre-TAS are not well understood and have not previously been examined with data compiled from multiple countries. For this analysis, we analyzed data from routine USAID and WHO reports from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. In a model that included multiple variables, high baseline prevalence and lower elevation were significant. In models comparing only one variable to the outcome, the following were statistically significantly associated with failure: higher baseline prevalence at or above 5% or 10%, use of the FTS, primary vector of Culex, treatment with diethylcarbamazine-albendazole, lower elevation, higher population density, higher Enhanced Vegetation Index, higher annual rainfall, and six or more rounds of mass drug administration. These results can help national programs plan MDA more effectively, e.g., by focusing resources on areas with higher baseline prevalence and/or lower elevation.
http://dx.doi.org/10.13039/100000200United States Agency for International DevelopmentAID-OAA-A-11-00048http://dx.doi.org/10.13039/100000200United States Agency for International DevelopmentAID-OAA-A-10-00050http://dx.doi.org/10.13039/100000200United States Agency for International DevelopmentAID-OAA-A-10-00051This work was made possible thanks to the generous support of the American People through the United States Agency for International Development (https://www.usaid.gov/) and the ENVISION project led by RTI International (AID-OAA-A-11-00048) and the END In Africa and END in Asia project led by FHI 360 (AID-OAA-A-10-00050 and AID-OAA-A-10-00051). The funders had no role in study design, data collection and analysis. The funders via Joseph Schott and Violetta Yevstigneyeva contributed to study aims and review of the manuscript. The authors' views expressed in this publication do not necessarily reflect the views of the United States Agency for International Development or the United States Government.PLOS Publication Stagevor-update-to-uncorrected-proofPublication Update2020-06-11Data AvailabilityThe data that support the findings of this study are available from the corresponding Ministry of Health but restrictions apply to the availability of these data, which were used with permission for the current study, and so are not publicly available. Data are however available from Jaymie Shanahan, jaymieshanahan@rti.org, upon reasonable request and with permission of corresponding Ministry of Health.Data Availability
The data that support the findings of this study are available from the corresponding Ministry of Health but restrictions apply to the availability of these data, which were used with permission for the current study, and so are not publicly available. Data are however available from Jaymie Shanahan, jaymieshanahan@rti.org, upon reasonable request and with permission of corresponding Ministry of Health.
Introduction
Lymphatic filariasis (LF), a disease caused by parasitic worms transmitted to humans by mosquito bite, manifests in disabling and stigmatizing chronic conditions including lymphedema and hydrocele. To eliminate LF as a public health problem, the World Health Organization (WHO) recommends two strategies: reducing transmission through annual mass drug administration (MDA) and reducing suffering through ensuring the availability of morbidity management and disability prevention services to all patients [1]. For the first strategy, eliminating LF as a public health problem is defined as a ‘reduction in measurable prevalence in infection in endemic areas below a target threshold at which further transmission is considered unlikely even in the absence of MDA’ [2]. As of 2018, 14 countries have eliminated LF as a public health problem while 58 countries remain endemic for LF [3].
The road to elimination as a public health problem has several milestones. First, where LF prevalence at baseline has exceeded 1% as measured either through microfilaremia (Mf) or antigenemia (Ag), MDA is implemented and treatment coverage is measured in all implementation units, which usually correspond to districts. Implementation units must complete at least five rounds of ‘effective’ treatment, i.e. treatment with a minimum coverage of 65% of the total population. Then, WHO recommends sentinel and spot-check site assessments—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit to determine whether prevalence in each site is less than 1% Mf or less than 2% Ag [4]. Next, if these thresholds are met, national programs can progress to the first transmission assessment survey (TAS). The TAS is a population-based cluster or systematic survey of six- and seven-year-old children to assess whether transmission has fallen below the threshold at which infection is believed to persist. TAS is conducted at least three times, with two years between each survey. TAS 1 results determine if it is appropriate to stop MDA or whether further rounds are required. Finally, when TAS 2 and 3 also fall below the set threshold in every endemic implementation unit nationwide and morbidity criteria have been fulfilled, the national program submits a dossier to WHO requesting that elimination be officially validated.
Pre-TAS include at least one sentinel and one spot-check site per one million population. Sentinel sites are established at the start of the program in villages where LF prevalence was believed to be relatively high. Spot-check sites are villages not previously tested but purposively selected as potentially high-risk areas due to original high prevalence, low coverage during MDA, high vector density, or other factors [4]. At least six months after MDA implementation, data are collected from a convenience sample of at least 300 people over five years old in each site. Originally, Mf was recommended as the indicator of choice for pre-TAS, assessed by blood smears taken at the time of peak parasite periodicity [4]. WHO later recommended the use of circulating filarial antigen rapid diagnostic tests, BinaxNow immunochromatographic card tests (ICTs), and after 2016, Alere Filariasis Test Strips (FTS), because they are more sensitive, easier to implement, and more flexible about time of day that blood can be taken [5].
When a country fails to meet the established thresholds in a pre-TAS, they must implement at least two more rounds of MDA. National programs need to forecast areas that might fail pre-TAS and need repeated MDA, so that they can inform the community and district decision-makers of the implications of pre-TAS failure, including the need for continued MDA to lower prevalence effectively. In addition, financial and human resources must be made available for ordering drugs, distributing drugs, supervision and monitoring to implement the further MDA rounds. Ordering drugs and providing MDA budgets often need to be completed before the pre-TAS are implemented, so contingency planning and funding are important to ensure rounds of MDA are not missed.
This study aims to understand which factors are associated with the need for additional rounds of MDA as identified by pre-TAS results using programmatic data from 13 countries. The factors associated with failing pre-TAS are not well understood and have not previously been examined at a multi-country scale in the literature. We examine the association between pre-TAS failure and baseline prevalence, parasites, environmental factors, MDA implementation, and pre-TAS implementation. Understanding determinants of pre-TAS failure will help countries identify where elimination may be most difficult and prioritize the use of limited LF elimination resources.
Methods
This is a secondary data analysis using existing data, collected for programmatic purposes. Data for this analysis come from 568 districts in 13 countries whose LF elimination programs were supported by the United States Agency for International Development (USAID) through the ENVISION project, led by RTI International, and the END in Africa and END in Asia projects, led by FHI 360. These countries are Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. The data represent all pre-TAS funded by USAID from 2012 to 2017 and, in some cases, surveys funded by host government or other non-United States government funders. Because pre-TAS data were collected as part of routine program activities in most countries, in general, ethical clearance was not sought for these surveys. Our secondary analysis only included the aggregated survey results and therefore did not constitute human subjects research; no ethical approval was required.
Building on previous work, we delineated five domains of variables that could influence pre-TAS outcomes: prevalence, agent, environment, MDA, and pre-TAS implementation (Table 1) [6–8]. We prioritized key concepts that could be measured through our data or captured through publicly available global geospatial data sets.
10.1371/journal.pntd.0008301.t001
Categorization of potential factors influencing pre-TAS results.
Domain
Factor
Covariate
Description
Reference Group
Summary statistic
Temporal Resolution
Source
Prevalence
Baseline prevalence
5% cut off
Maximum reported mapping or baseline sentinel site prevalence
<5%
Maximum
Varies
Programmatic data
Prevalence
Baseline prevalence
10% cut off
Maximum reported mapping or baseline sentinel site prevalence
<10%
Maximum
Varies
Programmatic data
Agent
Parasite
Parasite
Predominate parasite in district
W. bancrofti & mixed
Binary value
2018
Programmatic data
Environment
Vector
Vector
Predominate vector in district
Anopheles & Mansonia
Binary value
2018
Country expert
Environment
Geography
Elevation
Elevation measured in meters
>350
Mean
2000
CGIAR-CSI SRTM [9]
Environment
Geography
District area
Area measured in km2
>2,500
Maximum sum
Static
Programmatic data
Environment
Climate
EVI
Enhanced vegetation index
> 0.3
Mean
2015
MODIS [10]
Environment
Climate
Rainfall
Annual rainfall measured in mm
≤ 700
Mean
2015
CHIRPS [11]
Environment
Socio-economic
Population density
Number of people per km2
≤ 100
Mean
2015
WorldPop [12]
Environment
Socio-economic
Nighttime lights
Nighttime light index from 0 to 63
>1.5
Mean
2015
VIIRS [13]
Environment
Co-endemicity
Co-endemic for onchocerciasis
Part or all of district is also endemic for onchocerciases
Non-endemic
Binary value
2018
Programmatic data
MDA
Drug efficacy
Drug package
DEC-ALB or IVM-ALB
DEC-ALB
Binary value
2018
Programmatic data
MDA
Implementation of MDA
Coverage
Median MDA coverage for last 5 rounds
≥ 65%
Median
Varies
Programmatic data
MDA
Implementation of MDA
Sufficient rounds
Number of rounds of sufficient (≥ 65% coverage) in last 5 years
≥ 3
Count
Varies
Programmatic data
MDA
Implementation of MDA
Number of rounds
Maximum number of recorded rounds of MDA
≥ 6
Maximum
Varies
Programmatic data
Pre-TAS implementation
Quality of survey
Diagnostic method
Using Mf or Ag
Mf
Binary value
Varies
Programmatic data
Pre-TAS implementation
Quality of survey
Diagnostic test
Using Mf, ICT, or FTS
Mf
Categorical
Varies
Programmatic data
EVI = enhanced vegetation index; MDA = mass drug administration; TAS = transmission assessment survey; DEC = diethylcarbamazine; ALB = albendazole; IVM = ivermectin; Mf = microfilaremia; Ag = antigenemia; FTS = Filariasis Test Strips; ICT = Immunochromatographic Card Test
Data sources
Information on baseline prevalence, MDA coverage, the number of MDA rounds, and pre-TAS information (month and year of survey, district, site name, and outcome) was gathered through regular reporting for the USAID-funded NTD programs (ENVISION, END in Africa, and END in Asia). These data were augmented by other reporting data such as the country’s dossier data annexes, the WHO Preventive Chemotherapy and Transmission Control Databank, and WHO reporting forms. Data were then reviewed by country experts, including the Ministry of Health program staff and implementing program staff, and updated as necessary. Data on vectors were also obtained from country experts. The district geographic boundaries were matched to geospatial shapefiles from the ENVISION project geospatial data repository, while other geospatial data were obtained through publicly available sources (Table 1).
Outcome and covariate variables
The outcome of interest for this analysis was whether a district passed or failed the pre-TAS. Failure was defined as any district that had at least one sentinel or spot-check site with a prevalence higher than or equal to 1% Mf or 2% Ag [4].
Potential covariates were derived from the available data for each factor in the domain groups listed in Table 1. New dichotomous variables were created for all variables that had multiple categories or were continuous for ease of interpretation in models and use in program decision-making. Cut-points for continuous variables were derived from either a priori knowledge or through exploratory analysis considering the mean or median value of the dataset, looking to create two groups of similar size with logical cut-points (e.g. rounding numbers to whole numbers). All the variables derived from publicly available global spatial raster datasets were summarized to the district level in ArcGIS Pro using the “zonal statistics” tool. The final output used the continuous value measuring the mean pixel value for the district for all variables except geographic area. Categories for each variable were determined by selecting the mean or median dataset value or cut-off used in other relevant literature [7]. The following section describes the variables that were included in the final analysis and the final categorizations used.
Baseline prevalence
Baseline prevalence can be assumed as a proxy for local transmission conditions [14] and correlates with prevalence after MDA [14–20]. Baseline prevalence for each district was measured by either blood smears to measure Mf or rapid diagnostic tests to measure Ag. Other studies have modeled Mf and Ag prevalence separately, due to lack of a standardized correlation between the two, especially at pre-MDA levels [21,22]. However, because WHO mapping guidance states that MDA is required if either Mf or Ag is ≥1% and there were not enough data to model each separately, we combined baseline prevalence values regardless of diagnostic test used. We created two variables for use in the analysis (1) using the cut-off of <5% or ≥5% (dataset median value of 5%) and (2) using the cut-off of <10% or ≥10%.
Agent
In terms of differences in transmission dynamics by agent, research has shown that Brugia spp. are more susceptible to the anti-filarial drug regimens than Wuchereria bancrofti parasites [23]. Thus, we combined districts reporting B. malayi and B. timori and compared them to areas with W. bancrofti or mixed parasites. Two variables from other domains were identified in exploratory analyses to be highly colinear with the parasite, and thus we considered them in the same group of variables for the final regression models. These were variables delineating vectors (Anopheles or Mansonia compared to Culex) from the environmental domain and drug package [ivermectin-albendazole (IVM-ALB) compared to diethylcarbamazine-albendazole (DEC-ALB)] from the MDA domain.
Environment
LF transmission intensity is influenced by differing vector transmission dynamics, including vector biting rates and competence, and the number of individuals with microfilaria [21,24,25]. Since vector data are not always available, previous studies have explored whether environmental variables associated with vector density, such as elevation, rainfall, and temperature, can be used to predict LF prevalence [8,21,26–31]. We included the district area and elevation in meters as geographic variables potentially associated with transmission intensity. In addition, within the climate factor, we included Enhanced Vegetation Index (EVI) and rainfall variables. EVI measures vegetation levels, or “greenness,” where a higher index value indicates a higher level of “greenness.”
We included the socio-economic variable of population density, as it has been positively associated with LF prevalence in some studies [8,27,29], but no significant association has been found in others [30]. Population density could be correlated with vector, as in eastern African countries LF is mostly transmitted by Culex in urban areas and by Anopheles in rural areas [32]. Additionally, inclusion of the satellite imagery of nighttime lights data is another a proxy for socio-economic status [33].
Finally, all or parts of districts that are co-endemic with onchocerciasis may have received multiple rounds of MDA with ivermectin before LF MDA started, which may have lowered LF prevalence in an area [34–36]. Thus, we included a categorical variable to distinguish if districts were co-endemic with onchocerciasis.
MDA
Treatment effectiveness depends upon both drug efficacy (ability to kill adult worms, ability to kill Mf, drug resistance, drug quality) and implementation of MDA (coverage, compliance, number of rounds) [14,16]. Ivermectin is less effective against adult worms than DEC, and therefore it is likely that Ag reduction is slower in areas using ivermectin instead of DEC in MDA [37]. Models also have shown that MDA coverage affects prevalence, although coverage has been defined in various ways, such as median coverage, number of rounds, or individual compliance [14–16,20,38–40]. Furthermore, systematic non-compliance, or population sub-groups which consistently refuse to take medicines, has been shown to represent a threat to elimination [41,42].
We considered three approaches when analyzing the MDA data: median MDA coverage in the most recent 5 rounds, number of rounds with sufficient coverage in the most recent 5 rounds, and count of the total number of rounds. MDA coverage is considered sufficient at or above 65% of the total population who were reported to have ingested the drugs; this was used as the cut point for MDA median coverage for the most recent 5 rounds. The ‘rounds of sufficient coverage’ variable was categorized as having 2 or fewer rounds compared to 3 or more sufficient rounds. The ‘total number of MDA rounds’ variable was categorized at 5 or fewer rounds compared to 6 or more rounds ever documented in that district.
Pre-TAS implementation
Pre-TAS results can be influenced by the implementation of the survey itself, including the use of a particular diagnostic test, the selection of sites, the timing of survey, and the appropriate application of methods for population recruitment and diagnostic test adminstration. We included two variables in the pre-TAS implementation domain: `type of diagnostic method used’ and `diagnostic test used.’ The ‘type of diagnostic method used’ variable categorized districts by either using Mf or Ag. The ‘diagnostic test used’ variable examined Mf (reference category) compared to ICT and compared to FTS (categorical variable with 3 values). This approach was used to compare each test to each other. Countries switched from ICT to FTS during 2016, while Mf testing continued to be used throughout the time period of study.
Data inclusion criteria
The dataset, summarized at the district level, included information from 568 districts where a pre-TAS was being implemented for the first time. A total of 14 districts were removed from the final analysis due to missing data related to the following points: geospatial boundaries (4), baseline prevalence (4), and MDA coverage (6). The final analysis dataset had 554 districts.
Statistical analysis and modeling
Statistical analysis and modeling were done with Stata MP 15.1 (College Station, TX). Descriptive statistics comparing various variables to the principle outcome were performed. Significant differences were identified using a chi-square test. A generalized linear model (GLM) with a log link and binomial error distribution—which estimates relative risks—was developed using forward stepwise modeling methods (called log-binomial model). Models with higher pseudo-r-squared and lower Akaike information criterion (AIC) were retained at each step. Pseudo-r-squared is a value between 0 and 1 with the higher the value, the better the model is at predicting the outcome of interest. AIC values are used to compare the relative quality of models compared to each other; in general, a lower value indicates a better model. Variables were tested by factor group. Once a variable was selected from the group, no other variable in that same group was eligible to be included in the final model due to issues of collinearity and small sample sizes. Interaction between terms in the model was tested after model selection, and interaction terms that modified the original terms’ significance were included in the final model. Overall, the number of potential variables able to be included in the model remained low due to the relatively small number of failure results (13%) in the dataset. Furthermore, the models with more than 3 variables and one interaction term either were unstable (indicated by very large confidence interval widths) or did not improve the model by being significant predictors or by modifying other parameters already in the model. These models were at heightened risk of non-convergence; we limited the number of variables accordingly.
Sensitivity analysis was performed for the final log-binomial model to test for the validity of results under different parameters by excluding some sub-sets of districts from the dataset and rerunning the model. This analysis was done to understand the robustness of the model when (1) excluding all districts in Cameroon, (2) including only districts in Africa, (3) including only districts with W. bancrofti parasite, and (4) including only districts with Anopheles as the primary vector. The sensitivity analysis excluding Cameroon was done for two reasons. First, Cameroon had the most pre-TAS results included, but no failures. Second, 70% of the Cameroon districts included in the analysis are co-endemic for loiasis. Given that diagnostic tests used in LF mapping have since been shown to cross-react with loiasis, there is some concern that these districts might not have been truly LF-endemic [43,44].
Results
The overall pre-TAS pass rate for the districts included in this analysis was 87% (74 failures in 554 districts). Nearly 40% of the 554 districts were from Cameroon (134) and Tanzania (87) (Fig 1). No districts in Bangladesh, Cameroon, Mali, or Uganda failed a pre-TAS in this data set; over 25% of districts in Burkina Faso, Ghana, Haiti, Nepal, and Sierra Leone failed pre-TAS in this data set. Baseline prevalence varied widely within and between the 13 countries. Fig 2 shows the highest, lowest, and median baseline prevalence in the study districts by country. Burkina Faso had the highest median baseline prevalence at 52% and Burkina Faso, Tanzania, and Ghana all had at least one district with a very high baseline of over 70%. In Mali, Indonesia, Benin, and Bangladesh, all districts had baseline prevalences below 20%.
10.1371/journal.pntd.0008301.g001
Number of pre-TAS by country.
10.1371/journal.pntd.0008301.g002
District-level baseline prevalence by country.
Fig 3 shows the unadjusted analysis for key variables by pre-TAS result. Variables statistically significantly associated with failure (p-value ≤0.05) included higher baseline prevalence at or above 5% or 10%, FTS diagnostic test, primary vector of Culex, treatment with DEC-ALB, higher elevation, higher population density, higher EVI, higher annual rainfall, and six or more rounds of MDA. Variables that were not significantly associated with pre-TAS failure included diagnostic method used (Ag or Mf), parasite, co-endemicity for onchocerciasis, median MDA coverage, and sufficient rounds of MDA.
10.1371/journal.pntd.0008301.g003
Percent pre-TAS failure by each characteristic (unadjusted).
The final log-binomial model included the variables of baseline prevalence ≥10%, the diagnostic test used (FTS and ICT), and elevation. The final model also included a significant interaction term between high baseline and diagnostic test used.
Fig 4 shows the risk ratio results with their corresponding confidence intervals. In a model with interaction between baseline and diagnostic test the baseline parameter was significant while diagnostic test and the interaction term were not. Districts with high baseline had a statistically significant (p-value ≤0.05) 2.52 times higher risk of failure (95% CI 1.37–4.64) compared to those with low baseline prevalence. The FTS diagnostic test or ICT diagnostic test alone were not significant nor was the interaction term. Additionally, districts with an elevation below 350 meters had a statistically significant (p-value ≤0.05) 3.07 times higher risk of failing pre-TAS (95% CI 1.95–4.83).
10.1371/journal.pntd.0008301.g004
Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model.
Sensitivity analyses were conducted using the same model with different subsets of the dataset including (1) all districts except for districts in Cameroon (134 total with no failures), (2) only districts in Africa, (3) only districts with W. bancrofti, and (4) only districts with Anopheles as primary vector. The results of the sensitivity models (Table 2) indicate an overall robust model. High baseline and lower elevation remained significant across all the models. The ICT diagnostic test used remains insignificant across all models. The FTS diagnostic test was positively significant in model 1 and negatively significant in model 4. The interaction term of baseline prevalence and FTS diagnostic test was significant in three models though the estimate was unstable in the W. bancrofti-only and Anopheles-only models (models 3 and 4 respectively), as signified by large confidence intervals.
10.1371/journal.pntd.0008301.t002
Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis.
(1)
(2)
(3)
(4)
Full Model
Without Cameroon districts
Only districts in Africa
Only W. bancrofti parasite districts
Only Anopheles vector districts
Number of Failures
74
74
44
72
46
Number of total districts
(N = 554)
(N = 420)
(N = 407)
(N = 518)
(N = 414)
Covariate
RR (95% CI)
RR (95% CI)
RR (95% CI)
RR (95% CI)
RR (95% CI)
Baseline prevalence > = 10% & used FTS test
2.38 (0.96–5.90)
1.23 (0.52–2.92)
14.52 (1.79–117.82)
2.61 (1.03–6.61)
15.80 (1.95–127.67)
Baseline prevalence > = 10% & used ICT test
0.80 (0.20–3.24)
0.42 (0.11–1.68)
1.00 (0.00–0.00)
0.88 (0.21–3.60)
1.00 (0.00–0.00)
+Used FTS test
1.16 (0.52–2.59)
2.40 (1.12–5.11)
0.15 (0.02–1.11)
1.03 (0.45–2.36)
0.13 (0.02–0.96)
+Used ICT test
0.92 (0.32–2.67)
1.47 (0.51–4.21)
0.33 (0.04–2.54)
0.82 (0.28–2.43)
0.27 (0.03–2.04)
+Baseline prevalence > = 10%
2.52 (1.37–4.64)
2.42 (1.31–4.47)
2.03 (1.06–3.90)
2.30 (1.21–4.36)
2.01 (1.07–3.77)
Elevation < 350m
3.07 (1.95–4.83)
2.21 (1.42–3.43)
4.68 (2.22–9.87)
3.04 (1.93–4.79)
3.76 (1.92–7.37)
NOTE: Table shows adjusted Risk Ratio (RR) of failing pre-TAS.
Gray shading & + denote interaction terms, Bold text denotes statistically significant results with p-values ≤0.05
Overall 74 districts in the dataset failed pre-TAS. Fig 5 summarizes the likelihood of failure by variable combinations identified in the log-binomial model. For those districts with a baseline prevalence ≥10% that used a FTS diagnostic test and have an average elevation below 350 meters (Combination C01), 87% of the 23 districts failed. Of districts with high baseline that used an ICT diagnostic test and have a low average elevation (C02) 45% failed. Overall, combinations with high baseline and low elevation C01, C02, and C04 accounted for 51% of all the failures (38 of 74).
10.1371/journal.pntd.0008301.g005
Analysis of failures by model combinations.
Discussion
This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. Variables significantly associated with failure were higher baseline prevalence and lower elevation. Districts with a baseline prevalence of 10% or more were at 2.52 times higher risk to fail pre-TAS in the final log-binomial model. In the bivariate analysis, baseline prevalence above 5% was also significantly more likely to fail compared to lower baselines, which indicates that the threshold for higher baseline prevalence may be as little as 5%, similar to what was found in Goldberg et al., which explored ecological and socioeconomic factors associated with TAS failure [7].
Though diagnostic test used was selected for the final log-binomial model, neither category (FTS or ICT) were significant after interaction with high baseline. FTS alone is significant in the bivariate analysis compared to ICT or Mf. This result is not surprising given previous research which found that FTS was more sensitive than ICT [45].
Elevation was the only environmental domain variable selected for the final log-binomial model during the model selection process, with areas of lower elevation (<350m) found to be at 3.07 times higher risk to fail pre-TAS compared to districts with a higher elevation. Similar results related to elevation were found in previous studies [8,31], including Goldberg et al. [7], who used a cutoff of 200 meters. Elevation likely also encompasses some related environmental concepts, such as vector habitat, greenness (EVI), or rainfall, which impact vector chances of survival.
The small number of failures overall prevented the inclusion of a large number of variables in the final log-binomial model. However, other variables that are associated with failure as identified in the bivariate analyses, such as Culex vector, higher population density, higher EVI, higher rainfall and more rounds of MDA, should not be discounted when making programmatic decisions. Other models have shown that Culex as the predominant vector in a district, compared to Anopheles, results in more intense interventions needed to reach elimination [24,41]. Higher population density, which was also found to predict TAS failure [7], could be related to different vector species’ transmission dynamics in urban areas, as well as the fact that MDAs are harder to conduct and to accurately measure in urban areas [46,47]. Both higher enhanced vegetation index (>0.3) and higher rainfall (>700 mm per year) contribute to expansion of vector habitats and population. Additionally, having more than five rounds of MDA before pre-TAS was also statistically significantly associated with higher failure in the bivariate analysis. It is unclear why higher number of rounds is associated with first pre-TAS failure given that other research has shown the opposite [15,16].
All other variables included in this analysis were not significantly associated with pre-TAS failure in our analysis. Goldberg et al. found Brugia spp. to be significantly associated with failure, but our results did not. This is likely due in part to the small number of districts with Brugia spp. in our dataset (6%) compared to 46% in the Goldberg et al. article [7]. MDA coverage levels were not significantly associated with pre-TAS failure, likely due to the lack of variance in the coverage data since WHO guidance dictates a minimum of five rounds of MDA with ≥65% epidemiological coverage to be eligible to implement pre-TAS. It should not be interpreted as evidence that high MDA coverage levels are not necessary to lower prevalence.
Limitations to this study include data sources, excluded data, unreported data, misassigned data, and aggregation of results at the district level. The main data sources for this analysis were programmatic data, which may be less accurate than data collected specifically for research purposes. This is particularly true of the MDA coverage data, where some countries report data quality challenges in areas of instability or frequent population migration. Even though risk factors such as age, sex, compliance with MDA, and use of bednets have been shown to influence infection in individuals [40,48–50], we could not include factors from the human host domain in our analysis, as data sets were aggregated at site level and did not include individual information. In addition, vector control data were not universally available across the 13 countries and thus were not included in the analysis, despite studies showing that vector control has an impact on reducing LF prevalence [41,48,51–53].
Fourteen districts were excluded from the analysis because we were not able to obtain complete data for baseline prevalence, MDA coverage, or geographic boundaries. One of these districts had failed pre-TAS. It is likely these exclusions had minimal impact on the conclusions, as they represented a small number of districts and were similar to other included districts in terms of key variables. Unreported data could have occurred if a country conducted a pre-TAS that failed and then chose not to report it or reported it as a mid-term survey instead. Anecdotally, we know this has occurred occasionally, but we do not believe the practice to be widespread. Another limitation in the analysis is a potential misassignment of key variable values to a district due to changes in the district over time. Redistricting, changes in district size or composition, was pervasive in many countries during the study period; however, we expect the impact on the study outcome to be minimal, as the historical prevalence and MDA data from the “mother” districts are usually flowed down to these new “daughter” districts. However, it is possible that the split created an area of higher prevalence or lower MDA coverage than would have been found on average in the overall larger original “mother” district. Finally, the aggregation or averaging of results to the district level may mask heterogeneity within districts. Though this impact could be substantial in districts with considerable heterogeneity, the use of median values and binomial variables mitigated the likelihood of skewing the data to extreme outliners in a district.
As this analysis used data across a variety of countries and epidemiological situations, the results are likely relevant for other districts in the countries examined and in countries with similar epidemiological backgrounds. In general, as more data become available at site level through the increased use of electronic data collection tools, further analysis of geospatial variables and associations will be possible. For example, with the availability of GPS coordinates, it may become possible to analyze outcomes by site and to link the geospatial environmental domain variables at a smaller scale. Future analyses also might seek to include information from coverage surveys or qualitative research studies on vector control interventions such as bed net usage, MDA compliance, population movement, and sub-populations that might be missed during MDA. Future pre-TAS using electronic data collection could include sex and age of individuals included in the survey.
This paper provides evidence from analysis of 554 districts and 13 countries on the factors associated with pre-TAS results. Baseline prevalence, elevation, vector, population density, EVI, rainfall, and number of MDA rounds were all significant in either bivariate or multivariate analyses. This information along with knowledge of local context can help countries more effectively plan pre-TAS and forecast program activities, such as the potential need for more than five rounds of MDA in areas with high baseline and/or low elevation.
The authors would like to thank all those involved from the Ministries of Health, volunteers and community members in the sentinel and spot-check site surveys for their tireless commitment to ridding the world of LF. In addition, gratitude is given to Joseph Koroma and all the partners, including USAID, RTI International, FHI 360, IMA World Health, and Helen Keller International, who supported the surveys financially and technically.
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-PLoS Negl Trop DisPLoS Negl Trop DisplosplosntdsPLoS Neglected Tropical Diseases1935-27271935-2735Public Library of ScienceSan Francisco, CA USA32479495728944410.1371/journal.pntd.0008301PNTD-D-19-01870Research ArticleMedicine and Health SciencesInfectious DiseasesDisease VectorsInsect VectorsMosquitoesBiology and Life SciencesSpecies InteractionsDisease VectorsInsect VectorsMosquitoesBiology and Life SciencesOrganismsEukaryotaAnimalsInvertebratesArthropodaInsectsMosquitoesBiology and Life SciencesPopulation BiologyPopulation MetricsPopulation DensityMedicine and Health SciencesPublic and Occupational HealthMedicine and Health SciencesPharmaceuticsDrug TherapyDrug AdministrationResearch and Analysis MethodsMathematical and Statistical TechniquesStatistical MethodsMultivariate AnalysisBivariate AnalysisPhysical SciencesMathematicsStatisticsStatistical MethodsMultivariate AnalysisBivariate AnalysisBiology and Life SciencesOrganismsEukaryotaAnimalsInvertebratesNematodaWuchereriaWuchereria BancroftiMedicine and Health SciencesParasitic DiseasesHelminth InfectionsFilariasisLymphatic FilariasisMedicine and Health SciencesTropical DiseasesNeglected Tropical DiseasesLymphatic FilariasisPeople and placesGeographical locationsAfricaBurkina FasoRisk factors associated with failing pre-transmission assessment surveys (pre-TAS) in lymphatic filariasis elimination programs: Results of a multi-country analysisRisk factors associated with failing ‘pre-TAS’ in lymphatic filariasis programshttp://orcid.org/0000-0002-6001-4960Burgert-BruckerClara R.ConceptualizationData curationFormal analysisMethodologyVisualizationWriting – original draftWriting – review & editing1*ZoerhoffKathryn L.ConceptualizationData curationWriting – review & editing1HeadlandMaureenConceptualizationData curationWriting – review & editing12ShoemakerErica A.ConceptualizationData curationWriting – review & editing1StelmachRachelMethodologyVisualizationWriting – review & editing1http://orcid.org/0000-0002-3376-5726KarimMohammad JahirulData curationWriting – review & editing3BatchoWilfridData curationWriting – review & editing4BougoumaClarisseData curationWriting – review & editing5BougmaRolandData curationWriting – review & editing5Benjamin DidierBiholongData curationWriting – review & editing6GeorgesNko'AyissiData curationWriting – review & editing6MarfoBenjaminData curationWriting – review & editing7LemoineJean FrantzData curationWriting – review & editing8http://orcid.org/0000-0002-4634-0153PangaribuanHelena UllyarthaData curationWriting – review & editing9WijayantiEksiData curationWriting – review & editing9CoulibalyYaya IbrahimData curationWriting – review & editing10DoumbiaSalif SeribaData curationWriting – review & editing10RimalPradipData curationWriting – review & editing11SalissouAdamou BacthiriData curationWriting – review & editing12BahYukabaData curationWriting – review & editing13MwingiraUpendoData curationWriting – review & editing14NshalaAndreasData curationWriting – review & editing15MuhekiEdridahData curationWriting – review & editing16ShottJosephConceptualizationWriting – review & editing17YevstigneyevaViolettaConceptualizationWriting – review & editing17NdayishimyeEgideData curationWriting – review & editing2BakerMargaretConceptualizationSupervisionWriting – original draftWriting – review & editing1KraemerJohnConceptualizationMethodologyWriting – original draftWriting – review & editing118BradyMollyConceptualizationSupervisionWriting – original draftWriting – review & editing1
-Global Health Division, RTI International, Washington, DC, United States of America
-Global Health, Population, and Nutrition, FHI 360, Washington, DC, United States of America
-Department of Disease Control, Ministry of Health and Family Welfare, Dhaka, Bangladesh
-National Control Program of Communicable Diseases, Ministry of Health, Cotonou, Benin
-Lymphatic Filariasis Elimination Program, Ministère de la Santé, Ouagadougou, Burkina Faso
-National Onchocerciasis and Lymphatic Filariasis Control Program, Ministry of Health, Yaounde, Cameroon
-Neglected Tropical Diseases Programme, Ghana Health Service, Accra, Ghana
-Ministry of Health, Port-au-Prince, Haiti
-National Institute Health Research & Development, Ministry of Health, Jakarta, Indonesia
-Filariasis Unit, International Center of Excellence in Research, Faculty of Medicine and Odontostomatology, Bamako, Mali
-Epidemiology and Disease Control Division, Department of Health Service, Kathmandu, Nepal
-Programme Onchocercose et Filariose Lymphatique, Ministère de la Santé, Niamey, Niger
-National Neglected Tropical Disease Program, Ministry of Health and Sanitation, Freetown, Sierra Leone
-Neglected Tropical Disease Control Programme, National Institute for Medical Research, Dar es Salaam, Tanzania
-IMA World Health/Tanzania NTD Control Programme, Uppsala University, & TIBA Fellow, Dar es Salaam, Tanzania
-Programme to Eliminate Lymphatic Filariasis, Ministry of Health, Kampala, Uganda
-Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC, United States of America
-Georgetown University, Washington, DC, United States of AmericaFischerPeter U.EditorWashington University School of Medicine, UNITED STATES
The authors have declared that no competing interests exist.
* E-mail: cburgert@rti.org16202062020146e000830181120191642020This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five effective rounds of mass drug administration (MDA) and demonstrating low prevalence in subsequent assessments. The first assessments recommended by the World Health Organization (WHO) are sentinel and spot-check sites—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit after MDA. If pre-TAS shows that prevalence in each site has been lowered to less than 1% microfilaremia or less than 2% antigenemia, the implementation unit conducts a TAS to determine whether MDA can be stopped. Failure to pass pre-TAS means that further rounds of MDA are required. This study aims to understand factors influencing pre-TAS results using existing programmatic data from 554 implementation units, of which 74 (13%) failed, in 13 countries. Secondary data analysis was completed using existing data from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. Additional covariate data were obtained from spatial raster data sets. Bivariate analysis and multilinear regression were performed to establish potential relationships between variables and the pre-TAS result. Higher baseline prevalence and lower elevation were significant in the regression model. Variables statistically significantly associated with failure (p-value ≤0.05) in the bivariate analyses included baseline prevalence at or above 5% or 10%, use of Filariasis Test Strips (FTS), primary vector of Culex, treatment with diethylcarbamazine-albendazole, higher elevation, higher population density, higher enhanced vegetation index (EVI), higher annual rainfall, and 6 or more rounds of MDA. This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. This information can help countries more effectively forecast program activities, such as the potential need for more rounds of MDA, and prioritize resources to ensure adequate coverage of all persons in areas at highest risk of failing pre-TAS.
Author summary
Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five rounds of mass drug administration (MDA) and being able to demonstrate low prevalence in several subsequent assessments. LF elimination programs implement sentinel and spot-check site assessments, called pre-TAS, to determine whether districts are eligible to implement more rigorous population-based surveys to determine whether MDA can be stopped or if further rounds are required. Reasons for failing pre-TAS are not well understood and have not previously been examined with data compiled from multiple countries. For this analysis, we analyzed data from routine USAID and WHO reports from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. In a model that included multiple variables, high baseline prevalence and lower elevation were significant. In models comparing only one variable to the outcome, the following were statistically significantly associated with failure: higher baseline prevalence at or above 5% or 10%, use of the FTS, primary vector of Culex, treatment with diethylcarbamazine-albendazole, lower elevation, higher population density, higher Enhanced Vegetation Index, higher annual rainfall, and six or more rounds of mass drug administration. These results can help national programs plan MDA more effectively, e.g., by focusing resources on areas with higher baseline prevalence and/or lower elevation.
http://dx.doi.org/10.13039/100000200United States Agency for International DevelopmentAID-OAA-A-11-00048http://dx.doi.org/10.13039/100000200United States Agency for International DevelopmentAID-OAA-A-10-00050http://dx.doi.org/10.13039/100000200United States Agency for International DevelopmentAID-OAA-A-10-00051This work was made possible thanks to the generous support of the American People through the United States Agency for International Development (https://www.usaid.gov/) and the ENVISION project led by RTI International (AID-OAA-A-11-00048) and the END In Africa and END in Asia project led by FHI 360 (AID-OAA-A-10-00050 and AID-OAA-A-10-00051). The funders had no role in study design, data collection and analysis. The funders via Joseph Schott and Violetta Yevstigneyeva contributed to study aims and review of the manuscript. The authors' views expressed in this publication do not necessarily reflect the views of the United States Agency for International Development or the United States Government.PLOS Publication Stagevor-update-to-uncorrected-proofPublication Update2020-06-11Data AvailabilityThe data that support the findings of this study are available from the corresponding Ministry of Health but restrictions apply to the availability of these data, which were used with permission for the current study, and so are not publicly available. Data are however available from Jaymie Shanahan, jaymieshanahan@rti.org, upon reasonable request and with permission of corresponding Ministry of Health.Data Availability
The data that support the findings of this study are available from the corresponding Ministry of Health but restrictions apply to the availability of these data, which were used with permission for the current study, and so are not publicly available. Data are however available from Jaymie Shanahan, jaymieshanahan@rti.org, upon reasonable request and with permission of corresponding Ministry of Health.
Introduction
Lymphatic filariasis (LF), a disease caused by parasitic worms transmitted to humans by mosquito bite, manifests in disabling and stigmatizing chronic conditions including lymphedema and hydrocele. To eliminate LF as a public health problem, the World Health Organization (WHO) recommends two strategies: reducing transmission through annual mass drug administration (MDA) and reducing suffering through ensuring the availability of morbidity management and disability prevention services to all patients [1]. For the first strategy, eliminating LF as a public health problem is defined as a ‘reduction in measurable prevalence in infection in endemic areas below a target threshold at which further transmission is considered unlikely even in the absence of MDA’ [2]. As of 2018, 14 countries have eliminated LF as a public health problem while 58 countries remain endemic for LF [3].
The road to elimination as a public health problem has several milestones. First, where LF prevalence at baseline has exceeded 1% as measured either through microfilaremia (Mf) or antigenemia (Ag), MDA is implemented and treatment coverage is measured in all implementation units, which usually correspond to districts. Implementation units must complete at least five rounds of ‘effective’ treatment, i.e. treatment with a minimum coverage of 65% of the total population. Then, WHO recommends sentinel and spot-check site assessments—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit to determine whether prevalence in each site is less than 1% Mf or less than 2% Ag [4]. Next, if these thresholds are met, national programs can progress to the first transmission assessment survey (TAS). The TAS is a population-based cluster or systematic survey of six- and seven-year-old children to assess whether transmission has fallen below the threshold at which infection is believed to persist. TAS is conducted at least three times, with two years between each survey. TAS 1 results determine if it is appropriate to stop MDA or whether further rounds are required. Finally, when TAS 2 and 3 also fall below the set threshold in every endemic implementation unit nationwide and morbidity criteria have been fulfilled, the national program submits a dossier to WHO requesting that elimination be officially validated.
Pre-TAS include at least one sentinel and one spot-check site per one million population. Sentinel sites are established at the start of the program in villages where LF prevalence was believed to be relatively high. Spot-check sites are villages not previously tested but purposively selected as potentially high-risk areas due to original high prevalence, low coverage during MDA, high vector density, or other factors [4]. At least six months after MDA implementation, data are collected from a convenience sample of at least 300 people over five years old in each site. Originally, Mf was recommended as the indicator of choice for pre-TAS, assessed by blood smears taken at the time of peak parasite periodicity [4]. WHO later recommended the use of circulating filarial antigen rapid diagnostic tests, BinaxNow immunochromatographic card tests (ICTs), and after 2016, Alere Filariasis Test Strips (FTS), because they are more sensitive, easier to implement, and more flexible about time of day that blood can be taken [5].
When a country fails to meet the established thresholds in a pre-TAS, they must implement at least two more rounds of MDA. National programs need to forecast areas that might fail pre-TAS and need repeated MDA, so that they can inform the community and district decision-makers of the implications of pre-TAS failure, including the need for continued MDA to lower prevalence effectively. In addition, financial and human resources must be made available for ordering drugs, distributing drugs, supervision and monitoring to implement the further MDA rounds. Ordering drugs and providing MDA budgets often need to be completed before the pre-TAS are implemented, so contingency planning and funding are important to ensure rounds of MDA are not missed.
This study aims to understand which factors are associated with the need for additional rounds of MDA as identified by pre-TAS results using programmatic data from 13 countries. The factors associated with failing pre-TAS are not well understood and have not previously been examined at a multi-country scale in the literature. We examine the association between pre-TAS failure and baseline prevalence, parasites, environmental factors, MDA implementation, and pre-TAS implementation. Understanding determinants of pre-TAS failure will help countries identify where elimination may be most difficult and prioritize the use of limited LF elimination resources.
Methods
This is a secondary data analysis using existing data, collected for programmatic purposes. Data for this analysis come from 568 districts in 13 countries whose LF elimination programs were supported by the United States Agency for International Development (USAID) through the ENVISION project, led by RTI International, and the END in Africa and END in Asia projects, led by FHI 360. These countries are Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. The data represent all pre-TAS funded by USAID from 2012 to 2017 and, in some cases, surveys funded by host government or other non-United States government funders. Because pre-TAS data were collected as part of routine program activities in most countries, in general, ethical clearance was not sought for these surveys. Our secondary analysis only included the aggregated survey results and therefore did not constitute human subjects research; no ethical approval was required.
Building on previous work, we delineated five domains of variables that could influence pre-TAS outcomes: prevalence, agent, environment, MDA, and pre-TAS implementation (Table 1) [6–8]. We prioritized key concepts that could be measured through our data or captured through publicly available global geospatial data sets.
10.1371/journal.pntd.0008301.t001
Categorization of potential factors influencing pre-TAS results.
Domain
Factor
Covariate
Description
Reference Group
Summary statistic
Temporal Resolution
Source
Prevalence
Baseline prevalence
5% cut off
Maximum reported mapping or baseline sentinel site prevalence
<5%
Maximum
Varies
Programmatic data
Prevalence
Baseline prevalence
10% cut off
Maximum reported mapping or baseline sentinel site prevalence
<10%
Maximum
Varies
Programmatic data
Agent
Parasite
Parasite
Predominate parasite in district
W. bancrofti & mixed
Binary value
2018
Programmatic data
Environment
Vector
Vector
Predominate vector in district
Anopheles & Mansonia
Binary value
2018
Country expert
Environment
Geography
Elevation
Elevation measured in meters
>350
Mean
2000
CGIAR-CSI SRTM [9]
Environment
Geography
District area
Area measured in km2
>2,500
Maximum sum
Static
Programmatic data
Environment
Climate
EVI
Enhanced vegetation index
> 0.3
Mean
2015
MODIS [10]
Environment
Climate
Rainfall
Annual rainfall measured in mm
≤ 700
Mean
2015
CHIRPS [11]
Environment
Socio-economic
Population density
Number of people per km2
≤ 100
Mean
2015
WorldPop [12]
Environment
Socio-economic
Nighttime lights
Nighttime light index from 0 to 63
>1.5
Mean
2015
VIIRS [13]
Environment
Co-endemicity
Co-endemic for onchocerciasis
Part or all of district is also endemic for onchocerciases
Non-endemic
Binary value
2018
Programmatic data
MDA
Drug efficacy
Drug package
DEC-ALB or IVM-ALB
DEC-ALB
Binary value
2018
Programmatic data
MDA
Implementation of MDA
Coverage
Median MDA coverage for last 5 rounds
≥ 65%
Median
Varies
Programmatic data
MDA
Implementation of MDA
Sufficient rounds
Number of rounds of sufficient (≥ 65% coverage) in last 5 years
≥ 3
Count
Varies
Programmatic data
MDA
Implementation of MDA
Number of rounds
Maximum number of recorded rounds of MDA
≥ 6
Maximum
Varies
Programmatic data
Pre-TAS implementation
Quality of survey
Diagnostic method
Using Mf or Ag
Mf
Binary value
Varies
Programmatic data
Pre-TAS implementation
Quality of survey
Diagnostic test
Using Mf, ICT, or FTS
Mf
Categorical
Varies
Programmatic data
EVI = enhanced vegetation index; MDA = mass drug administration; TAS = transmission assessment survey; DEC = diethylcarbamazine; ALB = albendazole; IVM = ivermectin; Mf = microfilaremia; Ag = antigenemia; FTS = Filariasis Test Strips; ICT = Immunochromatographic Card Test
Data sources
Information on baseline prevalence, MDA coverage, the number of MDA rounds, and pre-TAS information (month and year of survey, district, site name, and outcome) was gathered through regular reporting for the USAID-funded NTD programs (ENVISION, END in Africa, and END in Asia). These data were augmented by other reporting data such as the country’s dossier data annexes, the WHO Preventive Chemotherapy and Transmission Control Databank, and WHO reporting forms. Data were then reviewed by country experts, including the Ministry of Health program staff and implementing program staff, and updated as necessary. Data on vectors were also obtained from country experts. The district geographic boundaries were matched to geospatial shapefiles from the ENVISION project geospatial data repository, while other geospatial data were obtained through publicly available sources (Table 1).
Outcome and covariate variables
The outcome of interest for this analysis was whether a district passed or failed the pre-TAS. Failure was defined as any district that had at least one sentinel or spot-check site with a prevalence higher than or equal to 1% Mf or 2% Ag [4].
Potential covariates were derived from the available data for each factor in the domain groups listed in Table 1. New dichotomous variables were created for all variables that had multiple categories or were continuous for ease of interpretation in models and use in program decision-making. Cut-points for continuous variables were derived from either a priori knowledge or through exploratory analysis considering the mean or median value of the dataset, looking to create two groups of similar size with logical cut-points (e.g. rounding numbers to whole numbers). All the variables derived from publicly available global spatial raster datasets were summarized to the district level in ArcGIS Pro using the “zonal statistics” tool. The final output used the continuous value measuring the mean pixel value for the district for all variables except geographic area. Categories for each variable were determined by selecting the mean or median dataset value or cut-off used in other relevant literature [7]. The following section describes the variables that were included in the final analysis and the final categorizations used.
Baseline prevalence
Baseline prevalence can be assumed as a proxy for local transmission conditions [14] and correlates with prevalence after MDA [14–20]. Baseline prevalence for each district was measured by either blood smears to measure Mf or rapid diagnostic tests to measure Ag. Other studies have modeled Mf and Ag prevalence separately, due to lack of a standardized correlation between the two, especially at pre-MDA levels [21,22]. However, because WHO mapping guidance states that MDA is required if either Mf or Ag is ≥1% and there were not enough data to model each separately, we combined baseline prevalence values regardless of diagnostic test used. We created two variables for use in the analysis (1) using the cut-off of <5% or ≥5% (dataset median value of 5%) and (2) using the cut-off of <10% or ≥10%.
Agent
In terms of differences in transmission dynamics by agent, research has shown that Brugia spp. are more susceptible to the anti-filarial drug regimens than Wuchereria bancrofti parasites [23]. Thus, we combined districts reporting B. malayi and B. timori and compared them to areas with W. bancrofti or mixed parasites. Two variables from other domains were identified in exploratory analyses to be highly colinear with the parasite, and thus we considered them in the same group of variables for the final regression models. These were variables delineating vectors (Anopheles or Mansonia compared to Culex) from the environmental domain and drug package [ivermectin-albendazole (IVM-ALB) compared to diethylcarbamazine-albendazole (DEC-ALB)] from the MDA domain.
Environment
LF transmission intensity is influenced by differing vector transmission dynamics, including vector biting rates and competence, and the number of individuals with microfilaria [21,24,25]. Since vector data are not always available, previous studies have explored whether environmental variables associated with vector density, such as elevation, rainfall, and temperature, can be used to predict LF prevalence [8,21,26–31]. We included the district area and elevation in meters as geographic variables potentially associated with transmission intensity. In addition, within the climate factor, we included Enhanced Vegetation Index (EVI) and rainfall variables. EVI measures vegetation levels, or “greenness,” where a higher index value indicates a higher level of “greenness.”
We included the socio-economic variable of population density, as it has been positively associated with LF prevalence in some studies [8,27,29], but no significant association has been found in others [30]. Population density could be correlated with vector, as in eastern African countries LF is mostly transmitted by Culex in urban areas and by Anopheles in rural areas [32]. Additionally, inclusion of the satellite imagery of nighttime lights data is another a proxy for socio-economic status [33].
Finally, all or parts of districts that are co-endemic with onchocerciasis may have received multiple rounds of MDA with ivermectin before LF MDA started, which may have lowered LF prevalence in an area [34–36]. Thus, we included a categorical variable to distinguish if districts were co-endemic with onchocerciasis.
MDA
Treatment effectiveness depends upon both drug efficacy (ability to kill adult worms, ability to kill Mf, drug resistance, drug quality) and implementation of MDA (coverage, compliance, number of rounds) [14,16]. Ivermectin is less effective against adult worms than DEC, and therefore it is likely that Ag reduction is slower in areas using ivermectin instead of DEC in MDA [37]. Models also have shown that MDA coverage affects prevalence, although coverage has been defined in various ways, such as median coverage, number of rounds, or individual compliance [14–16,20,38–40]. Furthermore, systematic non-compliance, or population sub-groups which consistently refuse to take medicines, has been shown to represent a threat to elimination [41,42].
We considered three approaches when analyzing the MDA data: median MDA coverage in the most recent 5 rounds, number of rounds with sufficient coverage in the most recent 5 rounds, and count of the total number of rounds. MDA coverage is considered sufficient at or above 65% of the total population who were reported to have ingested the drugs; this was used as the cut point for MDA median coverage for the most recent 5 rounds. The ‘rounds of sufficient coverage’ variable was categorized as having 2 or fewer rounds compared to 3 or more sufficient rounds. The ‘total number of MDA rounds’ variable was categorized at 5 or fewer rounds compared to 6 or more rounds ever documented in that district.
Pre-TAS implementation
Pre-TAS results can be influenced by the implementation of the survey itself, including the use of a particular diagnostic test, the selection of sites, the timing of survey, and the appropriate application of methods for population recruitment and diagnostic test adminstration. We included two variables in the pre-TAS implementation domain: `type of diagnostic method used’ and `diagnostic test used.’ The ‘type of diagnostic method used’ variable categorized districts by either using Mf or Ag. The ‘diagnostic test used’ variable examined Mf (reference category) compared to ICT and compared to FTS (categorical variable with 3 values). This approach was used to compare each test to each other. Countries switched from ICT to FTS during 2016, while Mf testing continued to be used throughout the time period of study.
Data inclusion criteria
The dataset, summarized at the district level, included information from 568 districts where a pre-TAS was being implemented for the first time. A total of 14 districts were removed from the final analysis due to missing data related to the following points: geospatial boundaries (4), baseline prevalence (4), and MDA coverage (6). The final analysis dataset had 554 districts.
Statistical analysis and modeling
Statistical analysis and modeling were done with Stata MP 15.1 (College Station, TX). Descriptive statistics comparing various variables to the principle outcome were performed. Significant differences were identified using a chi-square test. A generalized linear model (GLM) with a log link and binomial error distribution—which estimates relative risks—was developed using forward stepwise modeling methods (called log-binomial model). Models with higher pseudo-r-squared and lower Akaike information criterion (AIC) were retained at each step. Pseudo-r-squared is a value between 0 and 1 with the higher the value, the better the model is at predicting the outcome of interest. AIC values are used to compare the relative quality of models compared to each other; in general, a lower value indicates a better model. Variables were tested by factor group. Once a variable was selected from the group, no other variable in that same group was eligible to be included in the final model due to issues of collinearity and small sample sizes. Interaction between terms in the model was tested after model selection, and interaction terms that modified the original terms’ significance were included in the final model. Overall, the number of potential variables able to be included in the model remained low due to the relatively small number of failure results (13%) in the dataset. Furthermore, the models with more than 3 variables and one interaction term either were unstable (indicated by very large confidence interval widths) or did not improve the model by being significant predictors or by modifying other parameters already in the model. These models were at heightened risk of non-convergence; we limited the number of variables accordingly.
Sensitivity analysis was performed for the final log-binomial model to test for the validity of results under different parameters by excluding some sub-sets of districts from the dataset and rerunning the model. This analysis was done to understand the robustness of the model when (1) excluding all districts in Cameroon, (2) including only districts in Africa, (3) including only districts with W. bancrofti parasite, and (4) including only districts with Anopheles as the primary vector. The sensitivity analysis excluding Cameroon was done for two reasons. First, Cameroon had the most pre-TAS results included, but no failures. Second, 70% of the Cameroon districts included in the analysis are co-endemic for loiasis. Given that diagnostic tests used in LF mapping have since been shown to cross-react with loiasis, there is some concern that these districts might not have been truly LF-endemic [43,44].
Results
The overall pre-TAS pass rate for the districts included in this analysis was 87% (74 failures in 554 districts). Nearly 40% of the 554 districts were from Cameroon (134) and Tanzania (87) (Fig 1). No districts in Bangladesh, Cameroon, Mali, or Uganda failed a pre-TAS in this data set; over 25% of districts in Burkina Faso, Ghana, Haiti, Nepal, and Sierra Leone failed pre-TAS in this data set. Baseline prevalence varied widely within and between the 13 countries. Fig 2 shows the highest, lowest, and median baseline prevalence in the study districts by country. Burkina Faso had the highest median baseline prevalence at 52% and Burkina Faso, Tanzania, and Ghana all had at least one district with a very high baseline of over 70%. In Mali, Indonesia, Benin, and Bangladesh, all districts had baseline prevalences below 20%.
10.1371/journal.pntd.0008301.g001
Number of pre-TAS by country.
10.1371/journal.pntd.0008301.g002
District-level baseline prevalence by country.
Fig 3 shows the unadjusted analysis for key variables by pre-TAS result. Variables statistically significantly associated with failure (p-value ≤0.05) included higher baseline prevalence at or above 5% or 10%, FTS diagnostic test, primary vector of Culex, treatment with DEC-ALB, higher elevation, higher population density, higher EVI, higher annual rainfall, and six or more rounds of MDA. Variables that were not significantly associated with pre-TAS failure included diagnostic method used (Ag or Mf), parasite, co-endemicity for onchocerciasis, median MDA coverage, and sufficient rounds of MDA.
10.1371/journal.pntd.0008301.g003
Percent pre-TAS failure by each characteristic (unadjusted).
The final log-binomial model included the variables of baseline prevalence ≥10%, the diagnostic test used (FTS and ICT), and elevation. The final model also included a significant interaction term between high baseline and diagnostic test used.
Fig 4 shows the risk ratio results with their corresponding confidence intervals. In a model with interaction between baseline and diagnostic test the baseline parameter was significant while diagnostic test and the interaction term were not. Districts with high baseline had a statistically significant (p-value ≤0.05) 2.52 times higher risk of failure (95% CI 1.37–4.64) compared to those with low baseline prevalence. The FTS diagnostic test or ICT diagnostic test alone were not significant nor was the interaction term. Additionally, districts with an elevation below 350 meters had a statistically significant (p-value ≤0.05) 3.07 times higher risk of failing pre-TAS (95% CI 1.95–4.83).
10.1371/journal.pntd.0008301.g004
Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model.
Sensitivity analyses were conducted using the same model with different subsets of the dataset including (1) all districts except for districts in Cameroon (134 total with no failures), (2) only districts in Africa, (3) only districts with W. bancrofti, and (4) only districts with Anopheles as primary vector. The results of the sensitivity models (Table 2) indicate an overall robust model. High baseline and lower elevation remained significant across all the models. The ICT diagnostic test used remains insignificant across all models. The FTS diagnostic test was positively significant in model 1 and negatively significant in model 4. The interaction term of baseline prevalence and FTS diagnostic test was significant in three models though the estimate was unstable in the W. bancrofti-only and Anopheles-only models (models 3 and 4 respectively), as signified by large confidence intervals.
10.1371/journal.pntd.0008301.t002
Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis.
(1)
(2)
(3)
(4)
Full Model
Without Cameroon districts
Only districts in Africa
Only W. bancrofti parasite districts
Only Anopheles vector districts
Number of Failures
74
74
44
72
46
Number of total districts
(N = 554)
(N = 420)
(N = 407)
(N = 518)
(N = 414)
Covariate
RR (95% CI)
RR (95% CI)
RR (95% CI)
RR (95% CI)
RR (95% CI)
Baseline prevalence > = 10% & used FTS test
2.38 (0.96–5.90)
1.23 (0.52–2.92)
14.52 (1.79–117.82)
2.61 (1.03–6.61)
15.80 (1.95–127.67)
Baseline prevalence > = 10% & used ICT test
0.80 (0.20–3.24)
0.42 (0.11–1.68)
1.00 (0.00–0.00)
0.88 (0.21–3.60)
1.00 (0.00–0.00)
+Used FTS test
1.16 (0.52–2.59)
2.40 (1.12–5.11)
0.15 (0.02–1.11)
1.03 (0.45–2.36)
0.13 (0.02–0.96)
+Used ICT test
0.92 (0.32–2.67)
1.47 (0.51–4.21)
0.33 (0.04–2.54)
0.82 (0.28–2.43)
0.27 (0.03–2.04)
+Baseline prevalence > = 10%
2.52 (1.37–4.64)
2.42 (1.31–4.47)
2.03 (1.06–3.90)
2.30 (1.21–4.36)
2.01 (1.07–3.77)
Elevation < 350m
3.07 (1.95–4.83)
2.21 (1.42–3.43)
4.68 (2.22–9.87)
3.04 (1.93–4.79)
3.76 (1.92–7.37)
NOTE: Table shows adjusted Risk Ratio (RR) of failing pre-TAS.
Gray shading & + denote interaction terms, Bold text denotes statistically significant results with p-values ≤0.05
Overall 74 districts in the dataset failed pre-TAS. Fig 5 summarizes the likelihood of failure by variable combinations identified in the log-binomial model. For those districts with a baseline prevalence ≥10% that used a FTS diagnostic test and have an average elevation below 350 meters (Combination C01), 87% of the 23 districts failed. Of districts with high baseline that used an ICT diagnostic test and have a low average elevation (C02) 45% failed. Overall, combinations with high baseline and low elevation C01, C02, and C04 accounted for 51% of all the failures (38 of 74).
10.1371/journal.pntd.0008301.g005
Analysis of failures by model combinations.
Discussion
This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. Variables significantly associated with failure were higher baseline prevalence and lower elevation. Districts with a baseline prevalence of 10% or more were at 2.52 times higher risk to fail pre-TAS in the final log-binomial model. In the bivariate analysis, baseline prevalence above 5% was also significantly more likely to fail compared to lower baselines, which indicates that the threshold for higher baseline prevalence may be as little as 5%, similar to what was found in Goldberg et al., which explored ecological and socioeconomic factors associated with TAS failure [7].
Though diagnostic test used was selected for the final log-binomial model, neither category (FTS or ICT) were significant after interaction with high baseline. FTS alone is significant in the bivariate analysis compared to ICT or Mf. This result is not surprising given previous research which found that FTS was more sensitive than ICT [45].
Elevation was the only environmental domain variable selected for the final log-binomial model during the model selection process, with areas of lower elevation (<350m) found to be at 3.07 times higher risk to fail pre-TAS compared to districts with a higher elevation. Similar results related to elevation were found in previous studies [8,31], including Goldberg et al. [7], who used a cutoff of 200 meters. Elevation likely also encompasses some related environmental concepts, such as vector habitat, greenness (EVI), or rainfall, which impact vector chances of survival.
The small number of failures overall prevented the inclusion of a large number of variables in the final log-binomial model. However, other variables that are associated with failure as identified in the bivariate analyses, such as Culex vector, higher population density, higher EVI, higher rainfall and more rounds of MDA, should not be discounted when making programmatic decisions. Other models have shown that Culex as the predominant vector in a district, compared to Anopheles, results in more intense interventions needed to reach elimination [24,41]. Higher population density, which was also found to predict TAS failure [7], could be related to different vector species’ transmission dynamics in urban areas, as well as the fact that MDAs are harder to conduct and to accurately measure in urban areas [46,47]. Both higher enhanced vegetation index (>0.3) and higher rainfall (>700 mm per year) contribute to expansion of vector habitats and population. Additionally, having more than five rounds of MDA before pre-TAS was also statistically significantly associated with higher failure in the bivariate analysis. It is unclear why higher number of rounds is associated with first pre-TAS failure given that other research has shown the opposite [15,16].
All other variables included in this analysis were not significantly associated with pre-TAS failure in our analysis. Goldberg et al. found Brugia spp. to be significantly associated with failure, but our results did not. This is likely due in part to the small number of districts with Brugia spp. in our dataset (6%) compared to 46% in the Goldberg et al. article [7]. MDA coverage levels were not significantly associated with pre-TAS failure, likely due to the lack of variance in the coverage data since WHO guidance dictates a minimum of five rounds of MDA with ≥65% epidemiological coverage to be eligible to implement pre-TAS. It should not be interpreted as evidence that high MDA coverage levels are not necessary to lower prevalence.
Limitations to this study include data sources, excluded data, unreported data, misassigned data, and aggregation of results at the district level. The main data sources for this analysis were programmatic data, which may be less accurate than data collected specifically for research purposes. This is particularly true of the MDA coverage data, where some countries report data quality challenges in areas of instability or frequent population migration. Even though risk factors such as age, sex, compliance with MDA, and use of bednets have been shown to influence infection in individuals [40,48–50], we could not include factors from the human host domain in our analysis, as data sets were aggregated at site level and did not include individual information. In addition, vector control data were not universally available across the 13 countries and thus were not included in the analysis, despite studies showing that vector control has an impact on reducing LF prevalence [41,48,51–53].
Fourteen districts were excluded from the analysis because we were not able to obtain complete data for baseline prevalence, MDA coverage, or geographic boundaries. One of these districts had failed pre-TAS. It is likely these exclusions had minimal impact on the conclusions, as they represented a small number of districts and were similar to other included districts in terms of key variables. Unreported data could have occurred if a country conducted a pre-TAS that failed and then chose not to report it or reported it as a mid-term survey instead. Anecdotally, we know this has occurred occasionally, but we do not believe the practice to be widespread. Another limitation in the analysis is a potential misassignment of key variable values to a district due to changes in the district over time. Redistricting, changes in district size or composition, was pervasive in many countries during the study period; however, we expect the impact on the study outcome to be minimal, as the historical prevalence and MDA data from the “mother” districts are usually flowed down to these new “daughter” districts. However, it is possible that the split created an area of higher prevalence or lower MDA coverage than would have been found on average in the overall larger original “mother” district. Finally, the aggregation or averaging of results to the district level may mask heterogeneity within districts. Though this impact could be substantial in districts with considerable heterogeneity, the use of median values and binomial variables mitigated the likelihood of skewing the data to extreme outliners in a district.
As this analysis used data across a variety of countries and epidemiological situations, the results are likely relevant for other districts in the countries examined and in countries with similar epidemiological backgrounds. In general, as more data become available at site level through the increased use of electronic data collection tools, further analysis of geospatial variables and associations will be possible. For example, with the availability of GPS coordinates, it may become possible to analyze outcomes by site and to link the geospatial environmental domain variables at a smaller scale. Future analyses also might seek to include information from coverage surveys or qualitative research studies on vector control interventions such as bed net usage, MDA compliance, population movement, and sub-populations that might be missed during MDA. Future pre-TAS using electronic data collection could include sex and age of individuals included in the survey.
This paper provides evidence from analysis of 554 districts and 13 countries on the factors associated with pre-TAS results. Baseline prevalence, elevation, vector, population density, EVI, rainfall, and number of MDA rounds were all significant in either bivariate or multivariate analyses. This information along with knowledge of local context can help countries more effectively plan pre-TAS and forecast program activities, such as the potential need for more than five rounds of MDA in areas with high baseline and/or low elevation.
The authors would like to thank all those involved from the Ministries of Health, volunteers and community members in the sentinel and spot-check site surveys for their tireless commitment to ridding the world of LF. In addition, gratitude is given to Joseph Koroma and all the partners, including USAID, RTI International, FHI 360, IMA World Health, and Helen Keller International, who supported the surveys financially and technically.
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-PLoS OnePLoS ONEplosplosonePLoS ONE1932-6203Public Library of ScienceSan Francisco, CA USA32555654730250410.1371/journal.pone.0234687PONE-D-20-07831Research ArticleBiology and Life SciencesNutritionDietBeveragesMilkMedicine and Health SciencesNutritionDietBeveragesMilkBiology and Life SciencesAnatomyBody FluidsMilkMedicine and Health SciencesAnatomyBody FluidsMilkBiology and Life SciencesPhysiologyBody FluidsMilkMedicine and Health SciencesPhysiologyBody FluidsMilkBiology and Life SciencesAnatomyBody FluidsUrineMedicine and Health SciencesAnatomyBody FluidsUrineBiology and Life SciencesPhysiologyBody FluidsUrineMedicine and Health SciencesPhysiologyBody FluidsUrinePhysical SciencesPhysicsElectricityEngineering and TechnologyEnergy and PowerFuelsPhysical SciencesMaterials ScienceMaterialsFuelsResearch and Analysis MethodsAnimal StudiesExperimental Organism SystemsModel OrganismsMaizeResearch and Analysis MethodsModel OrganismsMaizeBiology and Life SciencesOrganismsEukaryotaPlantsGrassesMaizeResearch and Analysis MethodsAnimal StudiesExperimental Organism SystemsPlant and Algal ModelsMaizeBiology and Life SciencesNutritionDietMedicine and Health SciencesNutritionDietBiology and Life SciencesPsychologyBehaviorAnimal BehaviorGrazingSocial SciencesPsychologyBehaviorAnimal BehaviorGrazingBiology and Life SciencesZoologyAnimal BehaviorGrazingEarth SciencesAtmospheric ScienceAtmospheric ChemistryGreenhouse GasesCarbon DioxidePhysical SciencesChemistryEnvironmental ChemistryAtmospheric ChemistryGreenhouse GasesCarbon DioxideEcology and Environmental SciencesEnvironmental ChemistryAtmospheric ChemistryGreenhouse GasesCarbon DioxidePhysical SciencesChemistryChemical CompoundsCarbon DioxidePotential to reduce greenhouse gas emissions through different dairy cattle systems in subtropical regionsGreenhouse gas emissions through dairy cattle systems in subtropicshttp://orcid.org/0000-0002-4455-6866Ribeiro-FilhoHenrique M. N.ConceptualizationFormal analysisMethodologyWriting – original draft12*CivieroMaurícioInvestigation2KebreabErmiasMethodologyResourcesSupervisionWriting – review & editing1
-Department of Animal Science, University of California, Davis, California, United States of America
-Programa de Pós-graduação em Ciência Animal, Universidade do Estado de Santa Catarina, Lages, Santa Catarina, BrazilSainjuUpendra M.EditorUSDA Agricultural Research Service, UNITED STATES
Competing Interests: No authors have competing interests.
* E-mail: henrique.ribeiro@udesc.br18620202020156e02346871832020162020This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Carbon (C) footprint of dairy production, expressed in kg C dioxide (CO2) equivalents (CO2e) (kg energy-corrected milk (ECM))-1, encompasses emissions from feed production, diet management and total product output. The proportion of pasture on diets may affect all these factors, mainly in subtropical climate zones, where cows may access tropical and temperate pastures during warm and cold seasons, respectively. The aim of the study was to assess the C footprint of a dairy system with annual tropical and temperate pastures in a subtropical region. The system boundary included all processes up to the animal farm gate. Feed requirement during the entire life of each cow was based on data recorded from Holstein × Jersey cow herds producing an average of 7,000 kg ECM lactation-1. The milk production response as consequence of feed strategies (scenarios) was based on results from two experiments (warm and cold seasons) using lactating cows from the same herd. Three scenarios were evaluated: total mixed ration (TMR) ad libitum intake, 75, and 50% of ad libitum TMR intake with access to grazing either a tropical or temperate pasture during lactation periods. Considering IPCC and international literature values to estimate emissions from urine/dung, feed production and electricity, the C footprint was similar between scenarios, averaging 1.06 kg CO2e (kg ECM)-1. Considering factors from studies conducted in subtropical conditions and actual inputs for on-farm feed production, the C footprint decreased 0.04 kg CO2e (kg ECM)-1 in scenarios including pastures compared to ad libitum TMR. Regardless of factors considered, emissions from feed production decreased as the proportion of pasture went up. In conclusion, decreasing TMR intake and including pastures in dairy cow diets in subtropical conditions have the potential to maintain or reduce the C footprint to a small extent.
http://dx.doi.org/10.13039/501100002322Coordenação de Aperfeiçoamento de Pessoal de Nível Superior001http://orcid.org/0000-0002-4455-6866Ribeiro-FilhoHenrique M. N.http://dx.doi.org/10.13039/501100003593Conselho Nacional de Desenvolvimento Científico e Tecnológico403754/2016-0http://orcid.org/0000-0002-4455-6866Ribeiro-FilhoHenrique M. N.http://dx.doi.org/10.13039/501100005667Fundação de Amparo à Pesquisa e Inovação do Estado de Santa CatarinaTR 584 2019http://orcid.org/0000-0002-4455-6866Ribeiro-FilhoHenrique M. N.HRF Grant number (Finance code) 001, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) https://www.capes.gov.br; Grant number 403754/2016-0, Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brasil (CNPq) http://www.cnpq.br; Grant number TR 584 2019, Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina (FAPESC) http://www.fapesc.sc.gov.br. The funders had no role in study design, ata collection and analysis, decision to publish, or preparation of the manuscript.Data AvailabilityAll relevant data are within the paper.Data Availability
All relevant data are within the paper.
Introduction
Greenhouse gas (GHG) emissions from livestock activities represent 10–12% of global emissions [1], ranging from 5.5–7.5 Gt CO2 equivalents (CO2e) yr-1, with almost 30% coming from dairy cattle production systems [2]. However, the livestock sector supply between 13 and 17% of calories and between 28 and 33% of human edible protein consumption globally [3]. Additionally, livestock produce more human-edible protein per unit area than crops when land is unsuitable for food crop production [4].
Considering the key role of livestock systems in global food security, several technical and management interventions have been investigated to mitigate methane (CH4) emissions from enteric fermentation [5], animal management [6] and manure management [7]. CH4 emissions from enteric fermentation represents around 34% of total emissions from livestock sector, which is the largest source [2]. Increasing proportions of concentrate and digestibility of forages in the diet have been proposed as mitigation strategies [1,5]. In contrast, some life cycle assessment (LCA) studies of dairy systems in temperate regions [8–11] have identified that increasing concentrate proportion may increase carbon (C) footprint due to greater resource use and pollutants from the production of feed compared to forage. Thus, increasing pasture proportion on dairy cattle systems may be an alternative management to mitigate the C footprint.
In subtropical climate zones, cows may graze tropical pastures rather than temperate pastures during the warm season [12]. Some important dairy production areas, such as southern Brazil, central to northern Argentina, Uruguay, South Africa, New Zealand and Australia, are located in these climate zones, having more than 900 million ha in native, permanent or temporary pastures, producing almost 20% of global milk production [13]. However, due to a considerable inter-annual variation in pasture growth rates [14,15], the interest in mixed systems, using total mixed ration (TMR) + pasture has been increasing [16]. Nevertheless, to our best knowledge, studies conducted to evaluate milk production response in dairy cow diets receiving TMR and pastures have only been conducted in temperate pastures and not in tropical pastures (e.g. [17–19]).
It has been shown that dairy cows receiving TMR-based diets may not decrease milk production when supplemented with temperate pastures in a vegetative growth stage [18]. On the other hand, tropical pastures have lower organic matter digestibility and cows experience reduced dry matter (DM) intake and milk yield compared to temperate pastures [20,21]. A lower milk yield increases the C footprint intensity [22], offsetting an expected advantage through lower GHG emissions from crop and reduced DM intake.
The aim of this work was to quantify the C footprint and land use of dairy systems using cows with a medium milk production potential in a subtropical region. The effect of replacing total mixed ration (TMR) with pastures during lactation periods was evaluated.
Materials and methods
An LCA was developed according to the ISO standards [23,24] and Food and Agriculture Organization of the United Nations (FAO) Livestock Environmental Assessment Protocol guidelines [25]. All procedures were approved by the ‘Comissão de Ética no Uso de Animais’ (CEUA/UDESC) on September 15, 2016—Approval number 4373090816 - https://www.udesc.br/cav/ceua.
System boundary
The goal of the study was to assess the C footprint of annual tropical and temperate pastures in lactating dairy cow diets. The production system was divided into four main processes: (i) animal husbandry, (ii) manure management and urine and dung deposited by grazing animals, (iii) production of feed ingredients and (iv) farm management (Fig 1). The study boundary included all processes up to the animal farm gate (cradle to gate), including secondary sources such as GHG emissions during the production of fuel, electricity, machinery, manufacturing of fertilizer, pesticides, seeds and plastic used in silage production. Fuel combustion and machinery (manufacture and repairs) for manure handling and electricity for milking and confinement were accounted as emissions from farm management. Emissions post milk production were assumed to be similar for all scenarios, therefore, activities including milk processing, distribution, retail or consumption were outside of the system boundary.
10.1371/journal.pone.0234687.g001
Overview of the milk production system boundary considered in the study.
Functional unit
The functional unit was one kilogram of energy-corrected milk (ECM) at the farm gate. All processes in the system were calculated based on one kilogram ECM. The ECM was calculated by multiplying milk production by the ratio of the energy content of the milk to the energy content of standard milk with 4% fat and 3.3% true protein according to NRC [20] as follows:
ECM = Milk production × (0.0929 × fat% + 0.0588× true protein% + 0.192) / (0.0929 × (4%) + 0.0588 × (3.3%) + 0.192), where fat% and protein% are fat and protein percentages in milk, respectively. The average milk production and composition were recorded from the University of Santa Catarina State (Brazil) herd, considering 165 lactations between 2009 and 2018. The herd is predominantly Holstein × Jersey cows, with key characteristics described in Table 1.
10.1371/journal.pone.0234687.t001
Descriptive characteristics of the herd.
Item
Unit
Average
Milking cows
#
165
Milk production
kg year-1
7,015
Milk fat
%
4.0
Milk protein
%
3.3
Length of lactation
days
305
Body weight
kg
553
Lactations per cow
#
4
Replacement rate
%
25
Cull rate
%
25
First artificial insemination
months
16
Weaned
days
60
Mortality
%
3.0
Data sources and livestock system description
The individual feed requirements, as well as the milk production responses based on feed strategies were based on data recorded from the herd described above and two experiments performed using lactating cows from the same herd. Due to the variation on herbage production throughout the year, feed requirements were estimated taking into consideration that livestock systems have a calving period in April, which represents the beginning of fall season in the southern Hemisphere. The experiments have shown a 10% reduction in ECM production in dairy cows that received both 75 and 50% of ad libitum TMR intake with access to grazing a tropical pasture (pearl-millet, Pennisetum glaucum ‘Campeiro’) compared to cows receiving ad libitum TMR intake. Cows grazing on a temperate pasture (ryegrass, Lolium multiflorum ‘Maximus’) did not need changes to ECM production compared to the ad libitum TMR intake group.
Using experimental data, three scenarios were evaluated during the lactation period: ad libitum TMR intake, and 75, and 50% of ad libitum TMR intake with access to grazing either an annual tropical or temperate pasture as a function of month ([26], Civiero et al., in press). From April to October (210 days) cows accessed an annual temperate pasture (ryegrass), and from November to beginning of February (95 days) cows grazed an annual tropical pasture (pearl-millet). The average annual reduction in ECM production in dairy cows with access to pastures is 3%. This value was assumed during an entire lactation period.
Impact assessment
The CO2e emissions were calculated by multiplying the emissions of CO2, CH4 and N2O by their 100-year global warming potential (GWP100), based on IPCC assessment report 5 (AR5; [27]). The values of GWP100 are 1, 28 and 265 for CO2, CH4 and N2O, respectively.
Feed productionDiets composition
The DM intake of each ingredient throughout the entire life of animals during lactation periods was calculated for each scenario: cows receiving only TMR, cows receiving 75% of TMR with annual pastures and cows receiving 50% of TMR with annual pastures (Table 2). In each of other phases of life (calf, heifer, dry cow), animals received the same diet, including a perennial tropical pasture (kikuyu grass, Pennisetum clandestinum). The DM intake of calves, heifers and dry cows was calculated assuming 2.8, 2.5 and 1.9% body weight, respectively [20]. In each case, the actual DM intake of concentrate and corn silage was recorded, and pasture DM intake was estimated by the difference between daily expected DM intake and actual DM intake of concentrate and corn silage. For lactating heifers and cows, TMR was formulated to meet the net energy for lactation (NEL) and metabolizable protein (MP) requirements of experimental animals, according to [28]. The INRA system was used because it is possible to estimate pasture DM intake taking into account the TMR intake, pasture management and the time of access to pasture using the GrazeIn model [29], which was integrated in the software INRAtion 4.07 (https://www.inration.educagri.fr/fr/forum.php). The nutrient intake was calculated as a product of TMR and pasture intake and the nutrient contents of TMR and pasture, respectively, which were determined in feed samples collected throughout the experiments.
10.1371/journal.pone.0234687.t002
Dairy cows’ diets in different scenarios<xref ref-type="table-fn" rid="t002fn001"><sup>a</sup></xref>.
Calf
Pregnant/dry
Lactation
Weighted average
0–12 mo
12-AI mo
Heifer
Cow
TMR
TMR75
TMR50
TMR
TMR75
TMR50
Days
360
120
270
180
1220
1220
1220
DM intake, kg d-1
3.35
6.90
10.4
11.0
18.7
17.2
17.0
13.8
12.9
12.8
Ingredients, g (kg DM)-1
Ground corn
309
145
96.3
-
257
195
142
218
183
153
Soybean meal
138
22
26.7
-
143
105
76.1
109
88.0
71.0
Corn silage
149
290
85.6
-
601
451
326
393
308
237
Ann temperate pasture
184
326
257
-
-
185
337
81.3
186
273
Ann tropical pasture
-
-
107
-
-
63
119
13.4
49.1
81.0
Perenn tropical pasture
219
217
428
1000
-
-
-
186
186
186
Chemical composition, g (kg DM)-1
Organic matter
935
924
913
916
958
939
924
943
932
924
Crude protein
216
183
213
200
150
170
198
175
186
202
Neutral detergent fibre
299
479
518
625
382
418
449
411
431
449
Acid detergent fibre
127
203
234
306
152
171
187
174
185
194
Ether extract
46.5
30.4
28.6
25.0
31.8
31.1
30.4
33.2
32.8
32.4
Nutritive value
OM digestibility, %
82.1
77.9
77.1
71.9
72.4
75.0
77.2
74.8
76.3
77.6
NEL, Mcal (kg DM)-1
1.96
1.69
1.63
1.44
1.81
1.78
1.74
1.8
1.8
1.7
MP, g (kg DM)-1
111
93.6
97.6
90.0
95.0
102
102
97.5
102
101
aAI, artificial insemination; TMR, cows receiving exclusively total mixed ration; TMR75, cows receiving 75% of total mixed ration with pasture; TMR50, cows receiving 50% of total mixed ration with pasture; NEL, net energy for lactation; MP, metabolizable protein.
GHG emissions from crop and pasture production
GHG emission factors used for off- and on-farm feed production were based on literature values, and are presented in Table 3. The emission factor used for corn grain is the average of emission factors observed in different levels of synthetic N fertilization [30]. The emission factor used for soybean is based on Brazilian soybean production [31]. The emissions used for corn silage, including feed processing (cutting, crushing and mixing), and annual or perennial grass productions were 3300 and 1500 kg CO2e ha-1, respectively [32]. The DM production (kg ha-1) of corn silage and pastures were based on regional and locally recorded data [33–36], assuming that animals are able to consume 70% of pastures during grazing.
10.1371/journal.pone.0234687.t003
GHG emission factors for Off- and On-farm feed production.
Feed
DM yield (kg ha-1)
Emission factor
Unita
References
Off-farm
Corn grain
7,500
0.316
kg CO2e (kg grain)-1
[30]
Soybean
2,200
0.186
kg CO2e (kg grain)-1
[31]
On-farm
Corn silageb
16,000
0.206
kg CO2e (kg DM)-1
[32,33]
Annual ryegrassc
9,500
0.226
kg CO2e (kg DM)-1
[32,34]
Pearl milletd
11,000
0.195
kg CO2e (kg DM)-1
[32,35]
Kikuyu grasse
9,500
0.226
kg CO2e (kg DM)-1
[32,36]
aCO2e, carbon dioxide equivalent.
bEmission factor estimated as [kg CO2e ha-1: kg DM ha-1].
c,d,eEmission factors estimated as [kg CO2e ha-1: kg DM ha-1 × 0.7], assuming that animals are able to consume 70% of pasture during grazing.
Emissions from on-farm feed production (corn silage and pasture) were estimated using primary and secondary sources based on the actual amount of each input (Table 4). Primary sources were direct and indirect N2O-N emissions from organic and synthetic fertilizers and crop/pasture residues, CO2-C emissions from lime and urea applications, as well as fuel combustion. The direct N2O-N emission factor (kg (kg N input)-1) is based on a local study performed previously [37]. For indirect N2O-N emissions (kg N2O-N (kg NH3-N + NOx)-1), as well as CO2-C emissions from lime + urea, default values proposed by IPCC [38] were used. For perennial pastures, a C sequestration of 0.57 t ha-1 was used based on a 9-year study conducted in southern Brazil [39]. Due to the use of conventional tillage, no C sequestration was considered for annual pastures. The amount of fuel required was 8.9 (no-tillage) and 14.3 L ha-1 (disking) for annual tropical and temperate pastures, respectively [40]. The CO2 from fuel combustion was 2.7 kg CO2 L-1 [41]. Secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were estimated using emission factors described by Rotz et al. [42].
10.1371/journal.pone.0234687.t004
GHG emissions from On-farm feed production.
Item
Corn silage
Annual temperate pasture
Annual tropical pasture
Perennial tropical pasture
DM yield, kg ha-1
16000
9500
11000
9500
Direct N2O emissions to air
N organic fertilizer, kg ha-1a
150
180
225
225
N synthetic fertilizer
-
20
25
25
N from residual DM, kg ha-1b
70
112
129
112
Emission fator, kg N2O-N (kg N)-1c
0.002
0.002
0.002
0.002
kg N2O ha-1 from direct emissions
0.69
0.98
1.19
1.14
Indirect N2O emissions to air
kg NH3-N+NOx-N (kg organic N)-1b
0.2
0.2
0.2
0.2
kg NH3-N+NOx-N (kg synthetic N)-1b
0.1
0.1
0.1
0.1
kg N2O-N (kg NH3-N+NOx-N)-1b
0.01
0.01
0.01
0.01
kg N2O ha-1 from NH3+NOx volatilized
0.47
0.60
0.75
0.75
Indirect N2O emissions to soil
kg N losses by leaching (kg N)-1b
0.3
0.3
0.3
0.3
kg N2O-N (kg N leaching)-1
0.0075
0.0075
0.0075
0.0075
kg N2O ha-1 from N losses by leaching
0.78
1.10
1.34
1.28
kg N2O ha-1 (direct + indirect emissions)
1.94
2.68
3.28
3.16
kg CO2e ha-1 from N20 emissionsd
514
710
869
838
kg CO2 ha-1 from lime+ureab
515
721
882
852
kg CO2 ha-1 from diesel combustione
802
38
23
12
kg CO2e from secondary sourcesf
516
205
225
284
Total CO2e emitted, kg ha-1
1833
964
1130
1148
Emission factor, kg CO2e (kg DM)-1g
0.115
0.145
0.147
0.173
Carbon sequestered, kg ha-1h
-
-
-
570
Sequestered CO2-C, kg ha-1
-
-
-
1393
kg CO2e ha-1 (emitted—sequestered)
1833
964
1130
-245
Emission factor, kg CO2e (kg DM)-1i
0.115
0.145
0.147
-0.037
a100% of N requirements for corn silage and 90% for pastures was supplied by stocked manure.
bFrom IPCC [38].
cFrom a local study [37].
dFrom Assessment report 5 (AR5; [27]).
eFrom [40,41]
fEmissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage. Estimated as described by Rotz et al. [42].
gWithout accounting sequestered CO2-C due to no-tillage for perennial pasture.
hFrom [39].
iAccounting sequestered CO2-C due to no-tillage for perennial pasture.
Animal husbandry
The CH4 emissions from enteric fermentation intensity (g (kg ECM)-1) was a function of estimated CH4 yield (g (kg DM intake)-1), actual DM intake and ECM. The enteric CH4 yield was estimated as a function of neutral detergent fiber (NDF) concentration on total DM intake, as proposed by Niu et al. [43], where: CH4 yield (g (kg DM intake)-1) = 13.8 + 0.185 × NDF (% DM intake).
Manure from confined cows and urine and dung from grazing animals
The CH4 emission from manure (kg (kg ECM)-1) was a function of daily CH4 emission from manure (kg cow-1) and daily ECM (kg cow-1). The daily CH4 emission from manure was estimated according to IPCC [38], which considered daily volatile solid (VS) excreted (kg DM cow-1) in manure. The daily VS was estimated as proposed by Eugène et al. [44] as: VS = NDOMI + (UE × GE) × (OM/18.45), where: VS = volatile solid excretion on an organic matter (OM) basis (kg day-1), NDOMI = non-digestible OM intake (kg day-1): (1- OM digestibility) × OM intake, UE = urinary energy excretion as a fraction of GE (0.04), GE = gross energy intake (MJ day-1), OM = organic matter (g), 18.45 = conversion factor for dietary GE per kg of DM (MJ kg-1).
The OM digestibility was estimated as a function of chemical composition, using equations published by INRA [21], which takes into account the effects of digestive interactions due to feeding level, the proportion of concentrate and rumen protein balance on OM digestibility. For scenarios where cows had access to grazing, the amount of calculated VS were corrected as a function of the time at pasture. The biodegradability of manure factor (0.13 for dairy cows in Latin America) and methane conversion factor (MCF) values were taken from IPCC [38]. The MCF values for pit storage below animal confinements (> 1 month) were used for the calculation, taking into account the annual average temperature (16.6ºC) or the average temperatures during the growth period of temperate (14.4ºC) or tropical (21ºC) annual pastures, which were 31%, 26% and 46%, respectively.
The N2O-N emissions from urine and feces were estimated considering the proportion of N excreted as manure and storage or as urine and dung deposited by grazing animals. These proportions were calculated based on the proportion of daily time that animals stayed on pasture (7 h/24 h = 0.29) or confinement (1−0.29 = 0.71). For lactating heifers and cows, the total amount of N excreted was calculated by the difference between N intake and milk N excretion. For heifers and non-lactating cows, urinary and fecal N excretion were estimated as proposed by Reed et al. [45] (Table 3: equations 10 and 12, respectively). The N2O emissions from stored manure as well as urine and dung during grazing were calculated based on the conversion of N2O-N emissions to N2O emissions, where N2O emissions = N2O-N emissions × 44/28. The emission factors were 0.002 kg N2O-N (kg N)-1 stored in a pit below animal confinements, and 0.02 kg N2O-N (kg of urine and dung)-1 deposited on pasture [38]. The indirect N2O emissions from storage manure and urine and dung deposits on pasture were also estimated using the IPCC [38] emission factors.
Farm management
Emissions due to farm management included those from fuel and machinery for manure handling and electricity for milking and confinement (Table 5). Emissions due to feed processing such as cutting, crushing, mixing and distributing, as well as secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were included in ‘Emissions from crop and pasture production’ section.
10.1371/journal.pone.0234687.t005
Factors for major resource inputs in farm management.
Item
Factor
Unita
References
Production and transport of diesel
0.374
kg CO2e L-1
[41]
Emissions from diesel fuel combustion
2.637
kg CO2e L-1
[41]
Production of electricityb
0.73
kg CO2e kWh-1
[41]
Production of electricity (alternative)c
0.205
kg CO2e kWh-1
[46]
Production of machinery
3.54
kg CO2e (kg mm)-1
[42]
Manure handling
Fuel for manure handling
0.600
L diesel tonne-1
[42]
Machinery for manure handling
0.17
kg mm kg-1
[42]
Milking and confinement
Electricity for milking
0.06
kWh (kg milk)-1
[47]
Electricity for lightingd
75
kWh cow-1
[47]
amm, machinery mass
bBased on United States data.
cBased on the Brazilian electricity matrix.
dNaturally ventilated barns.
The amount of fuel use for manure handling were estimated taking into consideration the amount of manure produced per cow and the amounts of fuel required for manure handling (L diesel t-1) [42]. The amount of manure was estimated from OM excretions (kg cow-1), assuming that the manure has 8% ash on DM basis and 60% DM content. The OM excretions were calculated by NDOMI × days in confinement × proportion of daily time that animals stayed on confinement.
The emissions from fuel were estimated considering the primary (emissions from fuel burned) and secondary (emissions for producing and transporting fuel) emissions. The primary emissions were calculated by the amount of fuel required for manure handling (L) × (kg CO2e L-1) [41]. The secondary emissions from fuel were calculated by the amount of fuel required for manure handling × emissions for production and transport of fuel (kg CO2e L-1) [41]. Emissions from manufacture and repair of machinery for manure handling were estimated by manure produced per cow (t) × (kg machinery mass (kg manure)-1 × 10−3) [42] × kg CO2e (kg machinery mass)-1 [42].
Emissions from electricity for milking and confinement were estimated using two emission factors (kg CO2 kWh-1). The first one is based on United States electricity matrix [41], and was used as a reference of an electricity matrix with less hydroelectric power than the region under study. The second is based on the Brazilian electricity matrix [46]. The electricity required for milking activities is 0.06 kWh (kg milk produced)-1 [47]. The annual electricity use for lighting was 75 kWh cow-1, which is the value considered for lactating cows in naturally ventilated barns [47].
Co-product allocation
The C footprint for milk produced in the system was calculated using a biophysical allocation approach, as recommended by the International Dairy Federation [49], and described by Thoma et al. [48]. Briefly, ARmilk = 1–6.04 × BMR, where: ARmilk is the allocation ratio for milk and BMR is cow BW at the time of slaughter (kg) + calf BW sold (kg) divided by the total ECM produced during cow`s entire life (kg). The ARmilk were 0.854 and 0.849 for TMR and TMR with both pasture scenarios, respectively. The ARmilk was applied to the whole emissions, except for the electricity consumed for milking (milking parlor) and refrigerant loss, which was directly assigned to milk production.
Sensitivity analysis
A sensitivity index was calculated as described by Rotz et al. [42]. The sensitivity index was defined for each emission source as the percentage change in the C footprint for a 10% change in the given emission source divided by 10%. Thus, a value near 0 indicates a low sensitivity, whereas an index near or greater than 1 indicates a high sensitivity because a change in this value causes a similar change in the footprint.
Results and discussion
The study has assessed the impact of tropical and temperate pastures in dairy cows fed TMR on the C footprint of dairy production in subtropics. Different factors were taken in to consideration to estimate emissions from manure (or urine and dung) of grazing animals, feed production and electricity use.
Greenhouse gas emissions
Depending on emission factors used for calculating emissions from urine and dung (IPCC or local data) and feed production (Tables 3 or 4), the C footprint was similar (Fig 2A and 2B) or decreased by 0.04 kg CO2e (kg ECM)-1 (Fig 2C and 2D) in scenarios that included pastures compared to ad libitum TMR intake. Due to differences in emission factors, the overall GHG emission values ranged from 0.92 to 1.04 kg CO2e (kg ECM)-1 for dairy cows receiving TMR exclusively, and from 0.88 to 1.04 kg CO2e (kg ECM)-1 for cows with access to pasture. Using IPCC emission factors [38], manure emissions increased as TMR intake went down (Fig 2A and 2B). However, using local emission factors for estimating N2O-N emissions [37], manure emissions decreased as TMR intake went down (Fig 2C and 2D). Regardless of emission factors used (Tables 3 or 4), emissions from feed production decreased to a small extent as the proportion of TMR intake decreased. Emissions from farm management did not contribute more than 5% of overall GHG emissions.
10.1371/journal.pone.0234687.g002
Overall greenhouse gas emissions in dairy cattle systems under various scenarios.
TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
Considering IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, the C footprint ranged from 0.99 to 1.04 kg CO2e (kg ECM)-1, and was close to those reported under confined based systems in California [49], Canada [50], China [8], Ireland [9], different scenarios in Australia [51,52] and Uruguay [11], which ranged from 0.98 to 1.16 kg CO2e (kg ECM)-1. When local emission factors for N2O emissions from urine and dung [37] and those from Table 4 were taking into account, the C footprint for scenarios including pasture, without accounting for sequestered CO2-C from perennial pasture—0.91 kg CO2e (kg ECM)-1—was lower than the range of values described above. However, these values were still greater than high-performance confinement systems in UK and USA [53] or grass based dairy systems in Ireland [9,53] and New Zealand [8,54], which ranged from 0.52 to 0.89 kg CO2e (kg ECM)-1. Regardless of which emission factor was used, we found a lower C footprint in all conditions compared to scenarios with lower milk production per cow or in poor conditions of manure management, which ranged from 1.4 to 2.3 kg CO2e (kg ECM)-1 [8,55]. Thus, even though differences between studies may be partially explained by various assumptions (e.g., emission factors, co-product allocation, methane emissions estimation, sequestered CO2-C, etc.), herd productivity and manure management were systematically associated with the C footprint of the dairy systems.
The similarity of C footprint between different scenarios using IPCC [38] for estimating emissions from manure and for emissions from feed production (Table 3) was a consequence of the trade-off between greater manure emissions and lower emissions to produce feed, as the proportion of pasture in diets increased. Additionally, the small negative effect of pasture on ECM production also contributed to the trade-off. The impact of milk production on the C footprint was reported in a meta-analysis comprising 30 studies from 15 different countries [22]. As observed in this study (Fig 2A and 2B) the authors reported no significant difference between the C footprint of pasture-based vs. confinement systems. However, they observed that an increase of 1000 kg cow-1 (5000 to 6000 kg ECM) reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, which may explain an apparent discrepancy between our study and an LCA performed in south Brazilian conditions [56]. Their study compared a confinement and a grazing-based dairy system with annual average milk production of 7667 and 5535 kg cow, respectively. In this study, the same herd was used in all systems, with an annual average milk production of around 7000 kg cow-1. Experimental data showed a reduction not greater than 3% of ECM when 50% of TMR was replaced by pasture access.
The lower C footprint in scenarios with access to pasture, when local emission factors [37] were used for N2O emissions from urine and dung and for feed production (Table 4), may also be partially attributed to the small negative effect of pasture on ECM production. Nevertheless, local emission factors for urine and dung had a great impact on scenarios including pastures compared to ad libitum TMR intake. Whereas the IPCC [38] considers an emission of 0.02 kg N2O-N (kg N)-1 for urine and dung from grazing animals, experimental evidence shows that it may be up to five times lower, averaging 0.004 kg N2O-N kg-1 [37].
Methane emissions
The enteric CH4 intensity was similar between different scenarios (Fig 2), showing the greatest sensitivity index, with values ranging from 0.53 to 0.62, which indicate that for a 10% change in this source, the C footprint may change between 5.3 and 6.2% (Fig 3). The large effect of enteric CH4 emissions on the whole C footprint was expected, because the impact of enteric CH4 on GHG emissions of milk production in different dairy systems has been estimated to range from 44 to 60% of the total CO2e [50,52,57,58]. However, emissions in feed production may be the most important source of GHG when emission factors for producing concentrate feeds are greater than 0.7 kg CO2e kg-1 [59], which did not happen in this study.
10.1371/journal.pone.0234687.g003
Sensitivity of the C footprint.
Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
The lack of difference in enteric CH4 emissions in different systems can be explained by the narrow range of NDF content in diets (<4% difference). This non-difference is due to the lower NDF content of annual temperate pastures (495 g (kg DM)-1) compared to corn silage (550 g (kg DM)-1). Hence, an expected, increase NDF content with decreased concentrate was partially offset by an increase in the pasture proportion relatively low in NDF. This is in agreement with studies conducted in southern Brazil, which have shown that the actual enteric CH4 emissions may decrease with inclusion of temperate pastures in cows receiving corn silage and soybean meal [60] or increase enteric CH4 emissions when dairy cows grazing a temperate pasture was supplemented with corn silage [61]. Additionally, enteric CH4 emissions did not differ between dairy cows receiving TMR exclusively or grazing a tropical pasture in the same scenarios as in this study [26].
Emissions from excreta and feed production
Using IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, CH4 emissions from manure decreased 0.07 kg CO2e (kg ECM)-1, but N2O emissions from manure increased 0.09 kg CO2e (kg ECM)-1, as TMR intake was restricted to 50% ad libitum (Fig 4A). Emissions for pastures increased by 0.06 kg CO2e (kg ECM)-1, whereas emissions for producing concentrate feeds and corn silage decreased by 0.09 kg CO2e (kg ECM)-1, as TMR intake decreased (Fig 4B). In this situation, the lack of difference in calculated C footprints of different systems was also due to the greater emissions from manure, and offset by lower emissions from feed production with inclusion of pasture in lactating dairy cow diets. The greater N2O-N emissions from manure with pasture was a consequence of higher N2O-N emissions due to greater CP content and N urine excretion, as pasture intake increased. The effect of CP content on urine N excretion has been shown by several authors in lactating dairy cows [62–64]. For instance, by decreasing CP content from 185 to 152 g (kg DM)-1, N intake decreased by 20% and urine N excretion by 60% [62]. In this study, the CP content for lactating dairy cows ranged from 150 g (kg DM)-1 on TMR system to 198 g (kg DM)-1 on 50% TMR with pasture. Additionally, greater urine N excretion is expected with greater use of pasture. This occurs because protein utilization in pastures is inefficient, as the protein in fresh forages is highly degradable in the rumen and may not be captured by microbes [65].
10.1371/journal.pone.0234687.g004
Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems.
TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture.
Using local emission factors for N2O emissions from urine and dung [37] and those from Table 4, reductions in CH4 emissions from stocked manure, when pastures were included on diets, did not offset by increases in N2O emissions from excreta (Fig 4C). In this case, total emissions from manure (Fig 4C) and feed production (Fig 4D) decreased with the inclusion of pasture. The impact of greater CP content and N urine excretion with increased pasture intake was offset by the much lower emission factors used for N2O emissions from urine and dung. As suggested by other authors [66,67], these results show that IPCC default value may need to be revised for the subtropical region.
Emissions for feed production decreased when pasture was included due to the greater emission factor for corn grain production compared to pastures. Emissions from concentrate and silage had at least twice the sensitivity index compared to emissions from pastures. The amount of grain required per cow in a lifetime decreased from 7,300 kg to 4,000 kg when 50% of TMR was replaced by pasture access. These results are in agreement with other studies which found lower C footprint, as concentrate use is reduced and/or pasture is included [9,68,69]. Moreover, it has been demonstrated that in intensive dairy systems, after enteric fermentation, feed production is the second main contributor to C footprint [50]. There is potential to decrease the environmental impact of dairy systems by reducing the use of concentrate ingredients with high environmental impact, particularly in confinements [9].
Farm management
The lower impact of emissions from farm management is in agreement with other studies conducted in Europe [9, 62] and USA [42, 55], where the authors found that most emissions in dairy production systems are from enteric fermentation, feed production and emissions from excreta. As emissions from fuel for on-farm feed production were accounted into the ‘emissions from crop and pasture production’, total emissions from farm management were not greater than 5% of total C footprint.
Emissions from farm management dropped when the emission factor for electricity generation was based on the Brazilian matrix. In this case, the emission factor for electricity generation (0.205 kg CO2e kWh-1 [46]) is much lower than that in a LCA study conducted in US (0.73 kg CO2e kWh-1 [42]). This apparent discrepancy is explained because in 2016, almost 66% of the electricity generated in Brazil was from hydropower, which has an emission factor of 0.074 kg CO2e kWh-1 against 0.382 and 0.926 kg CO2e kWh-1 produced by natural gas and hard coal, respectively [46].
Assumptions and limitations
The milk production and composition data are the average for a typical herd, which might have great animal-to-animal variability. Likewise, DM yield of crops and pastures were collected from experimental observations, and may change as a function of inter-annual variation, climatic conditions, soil type, fertilization level etc. The emission factors for direct and indirect N2O emissions from urine and dung were alternatively estimated using local data, but more experiments are necessary to reduce the uncertainty. The CO2 emitted from lime and urea application was estimated from IPCC default values, which may not represent emissions in subtropical conditions. This LCA may be improved by reducing the uncertainty of factors for estimating emissions from excreta and feed production, including the C sequestration or emissions as a function of soil management.
Further considerations
The potential for using pasture can reduce the C footprint because milk production kept pace with animal confinement. However, if milk production is to decrease with lower TMR intake and inclusion of pasture [19], the C footprint would be expected to increase. Lorenz et al. [22] showed that an increase in milk yield from 5,000 to 6,000 kg ECM reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, whereas an increase from 10,000 to 11,000 kg ECM reduced the C footprint by only 0.06 kg CO2e (kg ECM)-1. Hence, the impact of increasing milk production on decreasing C footprint is not linear, and mitigation measures, such as breeding for increased genetic yield potential and increasing concentrate ratio in the diet, are potentially harmful for animal’s health and welfare [70]. For instance, increasing concentrate ratio potentially increases the occurrence of subclinical ketosis and foot lesions, and C footprint may increase by 0.03 kg CO2e (kg ECM)-1 in subclinical ketosis [71] and by 0.02 kg CO2e (kg ECM)-1 in case of foot lesions [72].
Grazing lands may also improve biodiversity [73]. Strategies such as zero tillage may increase stocks of soil C [74]. This study did not consider C sequestration during the growth of annual pastures, because it was assumed these grasses were planted with tillage, having a balance between C sequestration and C emissions [38]. Considering the C sequestration from no-tillage perennial pasture, the amount of C sequestration will more than compensates for C emitted. These results are in agreement with other authors who have shown that a reduction or elimination of soil tillage increases annual soil C sequestration in subtropical areas by 0.5 to 1.5 t ha-1 [75]. If 50% of tilled areas were under perennial grasslands, 1.0 t C ha-1 would be sequestered, further reducing the C footprint by 0.015 and 0.025 kg CO2e (kg ECM)-1 for the scenarios using 75 and 50% TMR, respectively. Eliminating tillage, the reduction on total GHG emissions would be 0.03 and 0.05 kg CO2e (kg ECM)-1 for 75 and 50% TMR, respectively. However, this approach may be controversial because lands which have been consistently managed for decades have approached steady state C storage, so that net exchange of CO2 would be negligible [76].
Conclusions
This study assessed the C footprint of dairy cattle systems with or without access to pastures. Including pastures showed potential to maintain or decrease to a small extent the C footprint, which may be attributable to the evidence of low N2O emissions from urine and dung in dairy systems in subtropical areas. Even though the enteric CH4 intensity was the largest source of CO2e emissions, it did not change between different scenarios due to the narrow range of NDF content in diets and maintaining the same milk production with or without access to pastures.
Thanks to Anna Naranjo for helpful comments throughout the elaboration of this manuscript, and to André Thaler Neto and Roberto Kappes for providing the key characteristics of the herd considered in this study.
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-PLoS OnePLoS ONEplosplosonePLoS ONE1932-6203Public Library of ScienceSan Francisco, CA USA32555654730250410.1371/journal.pone.0234687PONE-D-20-07831Research ArticleBiology and Life SciencesNutritionDietBeveragesMilkMedicine and Health SciencesNutritionDietBeveragesMilkBiology and Life SciencesAnatomyBody FluidsMilkMedicine and Health SciencesAnatomyBody FluidsMilkBiology and Life SciencesPhysiologyBody FluidsMilkMedicine and Health SciencesPhysiologyBody FluidsMilkBiology and Life SciencesAnatomyBody FluidsUrineMedicine and Health SciencesAnatomyBody FluidsUrineBiology and Life SciencesPhysiologyBody FluidsUrineMedicine and Health SciencesPhysiologyBody FluidsUrinePhysical SciencesPhysicsElectricityEngineering and TechnologyEnergy and PowerFuelsPhysical SciencesMaterials ScienceMaterialsFuelsResearch and Analysis MethodsAnimal StudiesExperimental Organism SystemsModel OrganismsMaizeResearch and Analysis MethodsModel OrganismsMaizeBiology and Life SciencesOrganismsEukaryotaPlantsGrassesMaizeResearch and Analysis MethodsAnimal StudiesExperimental Organism SystemsPlant and Algal ModelsMaizeBiology and Life SciencesNutritionDietMedicine and Health SciencesNutritionDietBiology and Life SciencesPsychologyBehaviorAnimal BehaviorGrazingSocial SciencesPsychologyBehaviorAnimal BehaviorGrazingBiology and Life SciencesZoologyAnimal BehaviorGrazingEarth SciencesAtmospheric ScienceAtmospheric ChemistryGreenhouse GasesCarbon DioxidePhysical SciencesChemistryEnvironmental ChemistryAtmospheric ChemistryGreenhouse GasesCarbon DioxideEcology and Environmental SciencesEnvironmental ChemistryAtmospheric ChemistryGreenhouse GasesCarbon DioxidePhysical SciencesChemistryChemical CompoundsCarbon DioxidePotential to reduce greenhouse gas emissions through different dairy cattle systems in subtropical regionsGreenhouse gas emissions through dairy cattle systems in subtropicshttp://orcid.org/0000-0002-4455-6866Ribeiro-FilhoHenrique M. N.ConceptualizationFormal analysisMethodologyWriting – original draft12*CivieroMaurícioInvestigation2KebreabErmiasMethodologyResourcesSupervisionWriting – review & editing1
-Department of Animal Science, University of California, Davis, California, United States of America
-Programa de Pós-graduação em Ciência Animal, Universidade do Estado de Santa Catarina, Lages, Santa Catarina, BrazilSainjuUpendra M.EditorUSDA Agricultural Research Service, UNITED STATES
Competing Interests: No authors have competing interests.
* E-mail: henrique.ribeiro@udesc.br18620202020156e02346871832020162020This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Carbon (C) footprint of dairy production, expressed in kg C dioxide (CO2) equivalents (CO2e) (kg energy-corrected milk (ECM))-1, encompasses emissions from feed production, diet management and total product output. The proportion of pasture on diets may affect all these factors, mainly in subtropical climate zones, where cows may access tropical and temperate pastures during warm and cold seasons, respectively. The aim of the study was to assess the C footprint of a dairy system with annual tropical and temperate pastures in a subtropical region. The system boundary included all processes up to the animal farm gate. Feed requirement during the entire life of each cow was based on data recorded from Holstein × Jersey cow herds producing an average of 7,000 kg ECM lactation-1. The milk production response as consequence of feed strategies (scenarios) was based on results from two experiments (warm and cold seasons) using lactating cows from the same herd. Three scenarios were evaluated: total mixed ration (TMR) ad libitum intake, 75, and 50% of ad libitum TMR intake with access to grazing either a tropical or temperate pasture during lactation periods. Considering IPCC and international literature values to estimate emissions from urine/dung, feed production and electricity, the C footprint was similar between scenarios, averaging 1.06 kg CO2e (kg ECM)-1. Considering factors from studies conducted in subtropical conditions and actual inputs for on-farm feed production, the C footprint decreased 0.04 kg CO2e (kg ECM)-1 in scenarios including pastures compared to ad libitum TMR. Regardless of factors considered, emissions from feed production decreased as the proportion of pasture went up. In conclusion, decreasing TMR intake and including pastures in dairy cow diets in subtropical conditions have the potential to maintain or reduce the C footprint to a small extent.
http://dx.doi.org/10.13039/501100002322Coordenação de Aperfeiçoamento de Pessoal de Nível Superior001http://orcid.org/0000-0002-4455-6866Ribeiro-FilhoHenrique M. N.http://dx.doi.org/10.13039/501100003593Conselho Nacional de Desenvolvimento Científico e Tecnológico403754/2016-0http://orcid.org/0000-0002-4455-6866Ribeiro-FilhoHenrique M. N.http://dx.doi.org/10.13039/501100005667Fundação de Amparo à Pesquisa e Inovação do Estado de Santa CatarinaTR 584 2019http://orcid.org/0000-0002-4455-6866Ribeiro-FilhoHenrique M. N.HRF Grant number (Finance code) 001, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) https://www.capes.gov.br; Grant number 403754/2016-0, Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brasil (CNPq) http://www.cnpq.br; Grant number TR 584 2019, Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina (FAPESC) http://www.fapesc.sc.gov.br. The funders had no role in study design, ata collection and analysis, decision to publish, or preparation of the manuscript.Data AvailabilityAll relevant data are within the paper.Data Availability
All relevant data are within the paper.
Introduction
Greenhouse gas (GHG) emissions from livestock activities represent 10–12% of global emissions [1], ranging from 5.5–7.5 Gt CO2 equivalents (CO2e) yr-1, with almost 30% coming from dairy cattle production systems [2]. However, the livestock sector supply between 13 and 17% of calories and between 28 and 33% of human edible protein consumption globally [3]. Additionally, livestock produce more human-edible protein per unit area than crops when land is unsuitable for food crop production [4].
Considering the key role of livestock systems in global food security, several technical and management interventions have been investigated to mitigate methane (CH4) emissions from enteric fermentation [5], animal management [6] and manure management [7]. CH4 emissions from enteric fermentation represents around 34% of total emissions from livestock sector, which is the largest source [2]. Increasing proportions of concentrate and digestibility of forages in the diet have been proposed as mitigation strategies [1,5]. In contrast, some life cycle assessment (LCA) studies of dairy systems in temperate regions [8–11] have identified that increasing concentrate proportion may increase carbon (C) footprint due to greater resource use and pollutants from the production of feed compared to forage. Thus, increasing pasture proportion on dairy cattle systems may be an alternative management to mitigate the C footprint.
In subtropical climate zones, cows may graze tropical pastures rather than temperate pastures during the warm season [12]. Some important dairy production areas, such as southern Brazil, central to northern Argentina, Uruguay, South Africa, New Zealand and Australia, are located in these climate zones, having more than 900 million ha in native, permanent or temporary pastures, producing almost 20% of global milk production [13]. However, due to a considerable inter-annual variation in pasture growth rates [14,15], the interest in mixed systems, using total mixed ration (TMR) + pasture has been increasing [16]. Nevertheless, to our best knowledge, studies conducted to evaluate milk production response in dairy cow diets receiving TMR and pastures have only been conducted in temperate pastures and not in tropical pastures (e.g. [17–19]).
It has been shown that dairy cows receiving TMR-based diets may not decrease milk production when supplemented with temperate pastures in a vegetative growth stage [18]. On the other hand, tropical pastures have lower organic matter digestibility and cows experience reduced dry matter (DM) intake and milk yield compared to temperate pastures [20,21]. A lower milk yield increases the C footprint intensity [22], offsetting an expected advantage through lower GHG emissions from crop and reduced DM intake.
The aim of this work was to quantify the C footprint and land use of dairy systems using cows with a medium milk production potential in a subtropical region. The effect of replacing total mixed ration (TMR) with pastures during lactation periods was evaluated.
Materials and methods
An LCA was developed according to the ISO standards [23,24] and Food and Agriculture Organization of the United Nations (FAO) Livestock Environmental Assessment Protocol guidelines [25]. All procedures were approved by the ‘Comissão de Ética no Uso de Animais’ (CEUA/UDESC) on September 15, 2016—Approval number 4373090816 - https://www.udesc.br/cav/ceua.
System boundary
The goal of the study was to assess the C footprint of annual tropical and temperate pastures in lactating dairy cow diets. The production system was divided into four main processes: (i) animal husbandry, (ii) manure management and urine and dung deposited by grazing animals, (iii) production of feed ingredients and (iv) farm management (Fig 1). The study boundary included all processes up to the animal farm gate (cradle to gate), including secondary sources such as GHG emissions during the production of fuel, electricity, machinery, manufacturing of fertilizer, pesticides, seeds and plastic used in silage production. Fuel combustion and machinery (manufacture and repairs) for manure handling and electricity for milking and confinement were accounted as emissions from farm management. Emissions post milk production were assumed to be similar for all scenarios, therefore, activities including milk processing, distribution, retail or consumption were outside of the system boundary.
10.1371/journal.pone.0234687.g001
Overview of the milk production system boundary considered in the study.
Functional unit
The functional unit was one kilogram of energy-corrected milk (ECM) at the farm gate. All processes in the system were calculated based on one kilogram ECM. The ECM was calculated by multiplying milk production by the ratio of the energy content of the milk to the energy content of standard milk with 4% fat and 3.3% true protein according to NRC [20] as follows:
ECM = Milk production × (0.0929 × fat% + 0.0588× true protein% + 0.192) / (0.0929 × (4%) + 0.0588 × (3.3%) + 0.192), where fat% and protein% are fat and protein percentages in milk, respectively. The average milk production and composition were recorded from the University of Santa Catarina State (Brazil) herd, considering 165 lactations between 2009 and 2018. The herd is predominantly Holstein × Jersey cows, with key characteristics described in Table 1.
10.1371/journal.pone.0234687.t001
Descriptive characteristics of the herd.
Item
Unit
Average
Milking cows
#
165
Milk production
kg year-1
7,015
Milk fat
%
4.0
Milk protein
%
3.3
Length of lactation
days
305
Body weight
kg
553
Lactations per cow
#
4
Replacement rate
%
25
Cull rate
%
25
First artificial insemination
months
16
Weaned
days
60
Mortality
%
3.0
Data sources and livestock system description
The individual feed requirements, as well as the milk production responses based on feed strategies were based on data recorded from the herd described above and two experiments performed using lactating cows from the same herd. Due to the variation on herbage production throughout the year, feed requirements were estimated taking into consideration that livestock systems have a calving period in April, which represents the beginning of fall season in the southern Hemisphere. The experiments have shown a 10% reduction in ECM production in dairy cows that received both 75 and 50% of ad libitum TMR intake with access to grazing a tropical pasture (pearl-millet, Pennisetum glaucum ‘Campeiro’) compared to cows receiving ad libitum TMR intake. Cows grazing on a temperate pasture (ryegrass, Lolium multiflorum ‘Maximus’) did not need changes to ECM production compared to the ad libitum TMR intake group.
Using experimental data, three scenarios were evaluated during the lactation period: ad libitum TMR intake, and 75, and 50% of ad libitum TMR intake with access to grazing either an annual tropical or temperate pasture as a function of month ([26], Civiero et al., in press). From April to October (210 days) cows accessed an annual temperate pasture (ryegrass), and from November to beginning of February (95 days) cows grazed an annual tropical pasture (pearl-millet). The average annual reduction in ECM production in dairy cows with access to pastures is 3%. This value was assumed during an entire lactation period.
Impact assessment
The CO2e emissions were calculated by multiplying the emissions of CO2, CH4 and N2O by their 100-year global warming potential (GWP100), based on IPCC assessment report 5 (AR5; [27]). The values of GWP100 are 1, 28 and 265 for CO2, CH4 and N2O, respectively.
Feed productionDiets composition
The DM intake of each ingredient throughout the entire life of animals during lactation periods was calculated for each scenario: cows receiving only TMR, cows receiving 75% of TMR with annual pastures and cows receiving 50% of TMR with annual pastures (Table 2). In each of other phases of life (calf, heifer, dry cow), animals received the same diet, including a perennial tropical pasture (kikuyu grass, Pennisetum clandestinum). The DM intake of calves, heifers and dry cows was calculated assuming 2.8, 2.5 and 1.9% body weight, respectively [20]. In each case, the actual DM intake of concentrate and corn silage was recorded, and pasture DM intake was estimated by the difference between daily expected DM intake and actual DM intake of concentrate and corn silage. For lactating heifers and cows, TMR was formulated to meet the net energy for lactation (NEL) and metabolizable protein (MP) requirements of experimental animals, according to [28]. The INRA system was used because it is possible to estimate pasture DM intake taking into account the TMR intake, pasture management and the time of access to pasture using the GrazeIn model [29], which was integrated in the software INRAtion 4.07 (https://www.inration.educagri.fr/fr/forum.php). The nutrient intake was calculated as a product of TMR and pasture intake and the nutrient contents of TMR and pasture, respectively, which were determined in feed samples collected throughout the experiments.
10.1371/journal.pone.0234687.t002
Dairy cows’ diets in different scenarios<xref ref-type="table-fn" rid="t002fn001"><sup>a</sup></xref>.
Calf
Pregnant/dry
Lactation
Weighted average
0–12 mo
12-AI mo
Heifer
Cow
TMR
TMR75
TMR50
TMR
TMR75
TMR50
Days
360
120
270
180
1220
1220
1220
DM intake, kg d-1
3.35
6.90
10.4
11.0
18.7
17.2
17.0
13.8
12.9
12.8
Ingredients, g (kg DM)-1
Ground corn
309
145
96.3
-
257
195
142
218
183
153
Soybean meal
138
22
26.7
-
143
105
76.1
109
88.0
71.0
Corn silage
149
290
85.6
-
601
451
326
393
308
237
Ann temperate pasture
184
326
257
-
-
185
337
81.3
186
273
Ann tropical pasture
-
-
107
-
-
63
119
13.4
49.1
81.0
Perenn tropical pasture
219
217
428
1000
-
-
-
186
186
186
Chemical composition, g (kg DM)-1
Organic matter
935
924
913
916
958
939
924
943
932
924
Crude protein
216
183
213
200
150
170
198
175
186
202
Neutral detergent fibre
299
479
518
625
382
418
449
411
431
449
Acid detergent fibre
127
203
234
306
152
171
187
174
185
194
Ether extract
46.5
30.4
28.6
25.0
31.8
31.1
30.4
33.2
32.8
32.4
Nutritive value
OM digestibility, %
82.1
77.9
77.1
71.9
72.4
75.0
77.2
74.8
76.3
77.6
NEL, Mcal (kg DM)-1
1.96
1.69
1.63
1.44
1.81
1.78
1.74
1.8
1.8
1.7
MP, g (kg DM)-1
111
93.6
97.6
90.0
95.0
102
102
97.5
102
101
aAI, artificial insemination; TMR, cows receiving exclusively total mixed ration; TMR75, cows receiving 75% of total mixed ration with pasture; TMR50, cows receiving 50% of total mixed ration with pasture; NEL, net energy for lactation; MP, metabolizable protein.
GHG emissions from crop and pasture production
GHG emission factors used for off- and on-farm feed production were based on literature values, and are presented in Table 3. The emission factor used for corn grain is the average of emission factors observed in different levels of synthetic N fertilization [30]. The emission factor used for soybean is based on Brazilian soybean production [31]. The emissions used for corn silage, including feed processing (cutting, crushing and mixing), and annual or perennial grass productions were 3300 and 1500 kg CO2e ha-1, respectively [32]. The DM production (kg ha-1) of corn silage and pastures were based on regional and locally recorded data [33–36], assuming that animals are able to consume 70% of pastures during grazing.
10.1371/journal.pone.0234687.t003
GHG emission factors for Off- and On-farm feed production.
Feed
DM yield (kg ha-1)
Emission factor
Unita
References
Off-farm
Corn grain
7,500
0.316
kg CO2e (kg grain)-1
[30]
Soybean
2,200
0.186
kg CO2e (kg grain)-1
[31]
On-farm
Corn silageb
16,000
0.206
kg CO2e (kg DM)-1
[32,33]
Annual ryegrassc
9,500
0.226
kg CO2e (kg DM)-1
[32,34]
Pearl milletd
11,000
0.195
kg CO2e (kg DM)-1
[32,35]
Kikuyu grasse
9,500
0.226
kg CO2e (kg DM)-1
[32,36]
aCO2e, carbon dioxide equivalent.
bEmission factor estimated as [kg CO2e ha-1: kg DM ha-1].
c,d,eEmission factors estimated as [kg CO2e ha-1: kg DM ha-1 × 0.7], assuming that animals are able to consume 70% of pasture during grazing.
Emissions from on-farm feed production (corn silage and pasture) were estimated using primary and secondary sources based on the actual amount of each input (Table 4). Primary sources were direct and indirect N2O-N emissions from organic and synthetic fertilizers and crop/pasture residues, CO2-C emissions from lime and urea applications, as well as fuel combustion. The direct N2O-N emission factor (kg (kg N input)-1) is based on a local study performed previously [37]. For indirect N2O-N emissions (kg N2O-N (kg NH3-N + NOx)-1), as well as CO2-C emissions from lime + urea, default values proposed by IPCC [38] were used. For perennial pastures, a C sequestration of 0.57 t ha-1 was used based on a 9-year study conducted in southern Brazil [39]. Due to the use of conventional tillage, no C sequestration was considered for annual pastures. The amount of fuel required was 8.9 (no-tillage) and 14.3 L ha-1 (disking) for annual tropical and temperate pastures, respectively [40]. The CO2 from fuel combustion was 2.7 kg CO2 L-1 [41]. Secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were estimated using emission factors described by Rotz et al. [42].
10.1371/journal.pone.0234687.t004
GHG emissions from On-farm feed production.
Item
Corn silage
Annual temperate pasture
Annual tropical pasture
Perennial tropical pasture
DM yield, kg ha-1
16000
9500
11000
9500
Direct N2O emissions to air
N organic fertilizer, kg ha-1a
150
180
225
225
N synthetic fertilizer
-
20
25
25
N from residual DM, kg ha-1b
70
112
129
112
Emission fator, kg N2O-N (kg N)-1c
0.002
0.002
0.002
0.002
kg N2O ha-1 from direct emissions
0.69
0.98
1.19
1.14
Indirect N2O emissions to air
kg NH3-N+NOx-N (kg organic N)-1b
0.2
0.2
0.2
0.2
kg NH3-N+NOx-N (kg synthetic N)-1b
0.1
0.1
0.1
0.1
kg N2O-N (kg NH3-N+NOx-N)-1b
0.01
0.01
0.01
0.01
kg N2O ha-1 from NH3+NOx volatilized
0.47
0.60
0.75
0.75
Indirect N2O emissions to soil
kg N losses by leaching (kg N)-1b
0.3
0.3
0.3
0.3
kg N2O-N (kg N leaching)-1
0.0075
0.0075
0.0075
0.0075
kg N2O ha-1 from N losses by leaching
0.78
1.10
1.34
1.28
kg N2O ha-1 (direct + indirect emissions)
1.94
2.68
3.28
3.16
kg CO2e ha-1 from N20 emissionsd
514
710
869
838
kg CO2 ha-1 from lime+ureab
515
721
882
852
kg CO2 ha-1 from diesel combustione
802
38
23
12
kg CO2e from secondary sourcesf
516
205
225
284
Total CO2e emitted, kg ha-1
1833
964
1130
1148
Emission factor, kg CO2e (kg DM)-1g
0.115
0.145
0.147
0.173
Carbon sequestered, kg ha-1h
-
-
-
570
Sequestered CO2-C, kg ha-1
-
-
-
1393
kg CO2e ha-1 (emitted—sequestered)
1833
964
1130
-245
Emission factor, kg CO2e (kg DM)-1i
0.115
0.145
0.147
-0.037
a100% of N requirements for corn silage and 90% for pastures was supplied by stocked manure.
bFrom IPCC [38].
cFrom a local study [37].
dFrom Assessment report 5 (AR5; [27]).
eFrom [40,41]
fEmissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage. Estimated as described by Rotz et al. [42].
gWithout accounting sequestered CO2-C due to no-tillage for perennial pasture.
hFrom [39].
iAccounting sequestered CO2-C due to no-tillage for perennial pasture.
Animal husbandry
The CH4 emissions from enteric fermentation intensity (g (kg ECM)-1) was a function of estimated CH4 yield (g (kg DM intake)-1), actual DM intake and ECM. The enteric CH4 yield was estimated as a function of neutral detergent fiber (NDF) concentration on total DM intake, as proposed by Niu et al. [43], where: CH4 yield (g (kg DM intake)-1) = 13.8 + 0.185 × NDF (% DM intake).
Manure from confined cows and urine and dung from grazing animals
The CH4 emission from manure (kg (kg ECM)-1) was a function of daily CH4 emission from manure (kg cow-1) and daily ECM (kg cow-1). The daily CH4 emission from manure was estimated according to IPCC [38], which considered daily volatile solid (VS) excreted (kg DM cow-1) in manure. The daily VS was estimated as proposed by Eugène et al. [44] as: VS = NDOMI + (UE × GE) × (OM/18.45), where: VS = volatile solid excretion on an organic matter (OM) basis (kg day-1), NDOMI = non-digestible OM intake (kg day-1): (1- OM digestibility) × OM intake, UE = urinary energy excretion as a fraction of GE (0.04), GE = gross energy intake (MJ day-1), OM = organic matter (g), 18.45 = conversion factor for dietary GE per kg of DM (MJ kg-1).
The OM digestibility was estimated as a function of chemical composition, using equations published by INRA [21], which takes into account the effects of digestive interactions due to feeding level, the proportion of concentrate and rumen protein balance on OM digestibility. For scenarios where cows had access to grazing, the amount of calculated VS were corrected as a function of the time at pasture. The biodegradability of manure factor (0.13 for dairy cows in Latin America) and methane conversion factor (MCF) values were taken from IPCC [38]. The MCF values for pit storage below animal confinements (> 1 month) were used for the calculation, taking into account the annual average temperature (16.6ºC) or the average temperatures during the growth period of temperate (14.4ºC) or tropical (21ºC) annual pastures, which were 31%, 26% and 46%, respectively.
The N2O-N emissions from urine and feces were estimated considering the proportion of N excreted as manure and storage or as urine and dung deposited by grazing animals. These proportions were calculated based on the proportion of daily time that animals stayed on pasture (7 h/24 h = 0.29) or confinement (1−0.29 = 0.71). For lactating heifers and cows, the total amount of N excreted was calculated by the difference between N intake and milk N excretion. For heifers and non-lactating cows, urinary and fecal N excretion were estimated as proposed by Reed et al. [45] (Table 3: equations 10 and 12, respectively). The N2O emissions from stored manure as well as urine and dung during grazing were calculated based on the conversion of N2O-N emissions to N2O emissions, where N2O emissions = N2O-N emissions × 44/28. The emission factors were 0.002 kg N2O-N (kg N)-1 stored in a pit below animal confinements, and 0.02 kg N2O-N (kg of urine and dung)-1 deposited on pasture [38]. The indirect N2O emissions from storage manure and urine and dung deposits on pasture were also estimated using the IPCC [38] emission factors.
Farm management
Emissions due to farm management included those from fuel and machinery for manure handling and electricity for milking and confinement (Table 5). Emissions due to feed processing such as cutting, crushing, mixing and distributing, as well as secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were included in ‘Emissions from crop and pasture production’ section.
10.1371/journal.pone.0234687.t005
Factors for major resource inputs in farm management.
Item
Factor
Unita
References
Production and transport of diesel
0.374
kg CO2e L-1
[41]
Emissions from diesel fuel combustion
2.637
kg CO2e L-1
[41]
Production of electricityb
0.73
kg CO2e kWh-1
[41]
Production of electricity (alternative)c
0.205
kg CO2e kWh-1
[46]
Production of machinery
3.54
kg CO2e (kg mm)-1
[42]
Manure handling
Fuel for manure handling
0.600
L diesel tonne-1
[42]
Machinery for manure handling
0.17
kg mm kg-1
[42]
Milking and confinement
Electricity for milking
0.06
kWh (kg milk)-1
[47]
Electricity for lightingd
75
kWh cow-1
[47]
amm, machinery mass
bBased on United States data.
cBased on the Brazilian electricity matrix.
dNaturally ventilated barns.
The amount of fuel use for manure handling were estimated taking into consideration the amount of manure produced per cow and the amounts of fuel required for manure handling (L diesel t-1) [42]. The amount of manure was estimated from OM excretions (kg cow-1), assuming that the manure has 8% ash on DM basis and 60% DM content. The OM excretions were calculated by NDOMI × days in confinement × proportion of daily time that animals stayed on confinement.
The emissions from fuel were estimated considering the primary (emissions from fuel burned) and secondary (emissions for producing and transporting fuel) emissions. The primary emissions were calculated by the amount of fuel required for manure handling (L) × (kg CO2e L-1) [41]. The secondary emissions from fuel were calculated by the amount of fuel required for manure handling × emissions for production and transport of fuel (kg CO2e L-1) [41]. Emissions from manufacture and repair of machinery for manure handling were estimated by manure produced per cow (t) × (kg machinery mass (kg manure)-1 × 10−3) [42] × kg CO2e (kg machinery mass)-1 [42].
Emissions from electricity for milking and confinement were estimated using two emission factors (kg CO2 kWh-1). The first one is based on United States electricity matrix [41], and was used as a reference of an electricity matrix with less hydroelectric power than the region under study. The second is based on the Brazilian electricity matrix [46]. The electricity required for milking activities is 0.06 kWh (kg milk produced)-1 [47]. The annual electricity use for lighting was 75 kWh cow-1, which is the value considered for lactating cows in naturally ventilated barns [47].
Co-product allocation
The C footprint for milk produced in the system was calculated using a biophysical allocation approach, as recommended by the International Dairy Federation [49], and described by Thoma et al. [48]. Briefly, ARmilk = 1–6.04 × BMR, where: ARmilk is the allocation ratio for milk and BMR is cow BW at the time of slaughter (kg) + calf BW sold (kg) divided by the total ECM produced during cow`s entire life (kg). The ARmilk were 0.854 and 0.849 for TMR and TMR with both pasture scenarios, respectively. The ARmilk was applied to the whole emissions, except for the electricity consumed for milking (milking parlor) and refrigerant loss, which was directly assigned to milk production.
Sensitivity analysis
A sensitivity index was calculated as described by Rotz et al. [42]. The sensitivity index was defined for each emission source as the percentage change in the C footprint for a 10% change in the given emission source divided by 10%. Thus, a value near 0 indicates a low sensitivity, whereas an index near or greater than 1 indicates a high sensitivity because a change in this value causes a similar change in the footprint.
Results and discussion
The study has assessed the impact of tropical and temperate pastures in dairy cows fed TMR on the C footprint of dairy production in subtropics. Different factors were taken in to consideration to estimate emissions from manure (or urine and dung) of grazing animals, feed production and electricity use.
Greenhouse gas emissions
Depending on emission factors used for calculating emissions from urine and dung (IPCC or local data) and feed production (Tables 3 or 4), the C footprint was similar (Fig 2A and 2B) or decreased by 0.04 kg CO2e (kg ECM)-1 (Fig 2C and 2D) in scenarios that included pastures compared to ad libitum TMR intake. Due to differences in emission factors, the overall GHG emission values ranged from 0.92 to 1.04 kg CO2e (kg ECM)-1 for dairy cows receiving TMR exclusively, and from 0.88 to 1.04 kg CO2e (kg ECM)-1 for cows with access to pasture. Using IPCC emission factors [38], manure emissions increased as TMR intake went down (Fig 2A and 2B). However, using local emission factors for estimating N2O-N emissions [37], manure emissions decreased as TMR intake went down (Fig 2C and 2D). Regardless of emission factors used (Tables 3 or 4), emissions from feed production decreased to a small extent as the proportion of TMR intake decreased. Emissions from farm management did not contribute more than 5% of overall GHG emissions.
10.1371/journal.pone.0234687.g002
Overall greenhouse gas emissions in dairy cattle systems under various scenarios.
TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
Considering IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, the C footprint ranged from 0.99 to 1.04 kg CO2e (kg ECM)-1, and was close to those reported under confined based systems in California [49], Canada [50], China [8], Ireland [9], different scenarios in Australia [51,52] and Uruguay [11], which ranged from 0.98 to 1.16 kg CO2e (kg ECM)-1. When local emission factors for N2O emissions from urine and dung [37] and those from Table 4 were taking into account, the C footprint for scenarios including pasture, without accounting for sequestered CO2-C from perennial pasture—0.91 kg CO2e (kg ECM)-1—was lower than the range of values described above. However, these values were still greater than high-performance confinement systems in UK and USA [53] or grass based dairy systems in Ireland [9,53] and New Zealand [8,54], which ranged from 0.52 to 0.89 kg CO2e (kg ECM)-1. Regardless of which emission factor was used, we found a lower C footprint in all conditions compared to scenarios with lower milk production per cow or in poor conditions of manure management, which ranged from 1.4 to 2.3 kg CO2e (kg ECM)-1 [8,55]. Thus, even though differences between studies may be partially explained by various assumptions (e.g., emission factors, co-product allocation, methane emissions estimation, sequestered CO2-C, etc.), herd productivity and manure management were systematically associated with the C footprint of the dairy systems.
The similarity of C footprint between different scenarios using IPCC [38] for estimating emissions from manure and for emissions from feed production (Table 3) was a consequence of the trade-off between greater manure emissions and lower emissions to produce feed, as the proportion of pasture in diets increased. Additionally, the small negative effect of pasture on ECM production also contributed to the trade-off. The impact of milk production on the C footprint was reported in a meta-analysis comprising 30 studies from 15 different countries [22]. As observed in this study (Fig 2A and 2B) the authors reported no significant difference between the C footprint of pasture-based vs. confinement systems. However, they observed that an increase of 1000 kg cow-1 (5000 to 6000 kg ECM) reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, which may explain an apparent discrepancy between our study and an LCA performed in south Brazilian conditions [56]. Their study compared a confinement and a grazing-based dairy system with annual average milk production of 7667 and 5535 kg cow, respectively. In this study, the same herd was used in all systems, with an annual average milk production of around 7000 kg cow-1. Experimental data showed a reduction not greater than 3% of ECM when 50% of TMR was replaced by pasture access.
The lower C footprint in scenarios with access to pasture, when local emission factors [37] were used for N2O emissions from urine and dung and for feed production (Table 4), may also be partially attributed to the small negative effect of pasture on ECM production. Nevertheless, local emission factors for urine and dung had a great impact on scenarios including pastures compared to ad libitum TMR intake. Whereas the IPCC [38] considers an emission of 0.02 kg N2O-N (kg N)-1 for urine and dung from grazing animals, experimental evidence shows that it may be up to five times lower, averaging 0.004 kg N2O-N kg-1 [37].
Methane emissions
The enteric CH4 intensity was similar between different scenarios (Fig 2), showing the greatest sensitivity index, with values ranging from 0.53 to 0.62, which indicate that for a 10% change in this source, the C footprint may change between 5.3 and 6.2% (Fig 3). The large effect of enteric CH4 emissions on the whole C footprint was expected, because the impact of enteric CH4 on GHG emissions of milk production in different dairy systems has been estimated to range from 44 to 60% of the total CO2e [50,52,57,58]. However, emissions in feed production may be the most important source of GHG when emission factors for producing concentrate feeds are greater than 0.7 kg CO2e kg-1 [59], which did not happen in this study.
10.1371/journal.pone.0234687.g003
Sensitivity of the C footprint.
Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
The lack of difference in enteric CH4 emissions in different systems can be explained by the narrow range of NDF content in diets (<4% difference). This non-difference is due to the lower NDF content of annual temperate pastures (495 g (kg DM)-1) compared to corn silage (550 g (kg DM)-1). Hence, an expected, increase NDF content with decreased concentrate was partially offset by an increase in the pasture proportion relatively low in NDF. This is in agreement with studies conducted in southern Brazil, which have shown that the actual enteric CH4 emissions may decrease with inclusion of temperate pastures in cows receiving corn silage and soybean meal [60] or increase enteric CH4 emissions when dairy cows grazing a temperate pasture was supplemented with corn silage [61]. Additionally, enteric CH4 emissions did not differ between dairy cows receiving TMR exclusively or grazing a tropical pasture in the same scenarios as in this study [26].
Emissions from excreta and feed production
Using IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, CH4 emissions from manure decreased 0.07 kg CO2e (kg ECM)-1, but N2O emissions from manure increased 0.09 kg CO2e (kg ECM)-1, as TMR intake was restricted to 50% ad libitum (Fig 4A). Emissions for pastures increased by 0.06 kg CO2e (kg ECM)-1, whereas emissions for producing concentrate feeds and corn silage decreased by 0.09 kg CO2e (kg ECM)-1, as TMR intake decreased (Fig 4B). In this situation, the lack of difference in calculated C footprints of different systems was also due to the greater emissions from manure, and offset by lower emissions from feed production with inclusion of pasture in lactating dairy cow diets. The greater N2O-N emissions from manure with pasture was a consequence of higher N2O-N emissions due to greater CP content and N urine excretion, as pasture intake increased. The effect of CP content on urine N excretion has been shown by several authors in lactating dairy cows [62–64]. For instance, by decreasing CP content from 185 to 152 g (kg DM)-1, N intake decreased by 20% and urine N excretion by 60% [62]. In this study, the CP content for lactating dairy cows ranged from 150 g (kg DM)-1 on TMR system to 198 g (kg DM)-1 on 50% TMR with pasture. Additionally, greater urine N excretion is expected with greater use of pasture. This occurs because protein utilization in pastures is inefficient, as the protein in fresh forages is highly degradable in the rumen and may not be captured by microbes [65].
10.1371/journal.pone.0234687.g004
Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems.
TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture.
Using local emission factors for N2O emissions from urine and dung [37] and those from Table 4, reductions in CH4 emissions from stocked manure, when pastures were included on diets, did not offset by increases in N2O emissions from excreta (Fig 4C). In this case, total emissions from manure (Fig 4C) and feed production (Fig 4D) decreased with the inclusion of pasture. The impact of greater CP content and N urine excretion with increased pasture intake was offset by the much lower emission factors used for N2O emissions from urine and dung. As suggested by other authors [66,67], these results show that IPCC default value may need to be revised for the subtropical region.
Emissions for feed production decreased when pasture was included due to the greater emission factor for corn grain production compared to pastures. Emissions from concentrate and silage had at least twice the sensitivity index compared to emissions from pastures. The amount of grain required per cow in a lifetime decreased from 7,300 kg to 4,000 kg when 50% of TMR was replaced by pasture access. These results are in agreement with other studies which found lower C footprint, as concentrate use is reduced and/or pasture is included [9,68,69]. Moreover, it has been demonstrated that in intensive dairy systems, after enteric fermentation, feed production is the second main contributor to C footprint [50]. There is potential to decrease the environmental impact of dairy systems by reducing the use of concentrate ingredients with high environmental impact, particularly in confinements [9].
Farm management
The lower impact of emissions from farm management is in agreement with other studies conducted in Europe [9, 62] and USA [42, 55], where the authors found that most emissions in dairy production systems are from enteric fermentation, feed production and emissions from excreta. As emissions from fuel for on-farm feed production were accounted into the ‘emissions from crop and pasture production’, total emissions from farm management were not greater than 5% of total C footprint.
Emissions from farm management dropped when the emission factor for electricity generation was based on the Brazilian matrix. In this case, the emission factor for electricity generation (0.205 kg CO2e kWh-1 [46]) is much lower than that in a LCA study conducted in US (0.73 kg CO2e kWh-1 [42]). This apparent discrepancy is explained because in 2016, almost 66% of the electricity generated in Brazil was from hydropower, which has an emission factor of 0.074 kg CO2e kWh-1 against 0.382 and 0.926 kg CO2e kWh-1 produced by natural gas and hard coal, respectively [46].
Assumptions and limitations
The milk production and composition data are the average for a typical herd, which might have great animal-to-animal variability. Likewise, DM yield of crops and pastures were collected from experimental observations, and may change as a function of inter-annual variation, climatic conditions, soil type, fertilization level etc. The emission factors for direct and indirect N2O emissions from urine and dung were alternatively estimated using local data, but more experiments are necessary to reduce the uncertainty. The CO2 emitted from lime and urea application was estimated from IPCC default values, which may not represent emissions in subtropical conditions. This LCA may be improved by reducing the uncertainty of factors for estimating emissions from excreta and feed production, including the C sequestration or emissions as a function of soil management.
Further considerations
The potential for using pasture can reduce the C footprint because milk production kept pace with animal confinement. However, if milk production is to decrease with lower TMR intake and inclusion of pasture [19], the C footprint would be expected to increase. Lorenz et al. [22] showed that an increase in milk yield from 5,000 to 6,000 kg ECM reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, whereas an increase from 10,000 to 11,000 kg ECM reduced the C footprint by only 0.06 kg CO2e (kg ECM)-1. Hence, the impact of increasing milk production on decreasing C footprint is not linear, and mitigation measures, such as breeding for increased genetic yield potential and increasing concentrate ratio in the diet, are potentially harmful for animal’s health and welfare [70]. For instance, increasing concentrate ratio potentially increases the occurrence of subclinical ketosis and foot lesions, and C footprint may increase by 0.03 kg CO2e (kg ECM)-1 in subclinical ketosis [71] and by 0.02 kg CO2e (kg ECM)-1 in case of foot lesions [72].
Grazing lands may also improve biodiversity [73]. Strategies such as zero tillage may increase stocks of soil C [74]. This study did not consider C sequestration during the growth of annual pastures, because it was assumed these grasses were planted with tillage, having a balance between C sequestration and C emissions [38]. Considering the C sequestration from no-tillage perennial pasture, the amount of C sequestration will more than compensates for C emitted. These results are in agreement with other authors who have shown that a reduction or elimination of soil tillage increases annual soil C sequestration in subtropical areas by 0.5 to 1.5 t ha-1 [75]. If 50% of tilled areas were under perennial grasslands, 1.0 t C ha-1 would be sequestered, further reducing the C footprint by 0.015 and 0.025 kg CO2e (kg ECM)-1 for the scenarios using 75 and 50% TMR, respectively. Eliminating tillage, the reduction on total GHG emissions would be 0.03 and 0.05 kg CO2e (kg ECM)-1 for 75 and 50% TMR, respectively. However, this approach may be controversial because lands which have been consistently managed for decades have approached steady state C storage, so that net exchange of CO2 would be negligible [76].
Conclusions
This study assessed the C footprint of dairy cattle systems with or without access to pastures. Including pastures showed potential to maintain or decrease to a small extent the C footprint, which may be attributable to the evidence of low N2O emissions from urine and dung in dairy systems in subtropical areas. Even though the enteric CH4 intensity was the largest source of CO2e emissions, it did not change between different scenarios due to the narrow range of NDF content in diets and maintaining the same milk production with or without access to pastures.
Thanks to Anna Naranjo for helpful comments throughout the elaboration of this manuscript, and to André Thaler Neto and Roberto Kappes for providing the key characteristics of the herd considered in this study.
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\ No newline at end of file
diff --git a/tests/test_backend_jats.py b/tests/test_backend_jats.py
index a4373be4..79791b12 100644
--- a/tests/test_backend_jats.py
+++ b/tests/test_backend_jats.py
@@ -14,9 +14,9 @@ from .verify_utils import verify_document, verify_export
GENERATE = GEN_TEST_DATA
-def get_pubmed_paths():
- directory = Path(os.path.dirname(__file__) + "/data/pubmed/")
- xml_files = sorted(directory.rglob("*.xml"))
+def get_jats_paths():
+ directory = Path(os.path.dirname(__file__) + "/data/jats/")
+ xml_files = sorted(directory.rglob("*.nxml"))
return xml_files
@@ -25,20 +25,20 @@ def get_converter():
return converter
-def test_e2e_pubmed_conversions(use_stream=False):
- pubmed_paths = get_pubmed_paths()
+def test_e2e_jats_conversions(use_stream=False):
+ jats_paths = get_jats_paths()
converter = get_converter()
- for pubmed_path in pubmed_paths:
+ for jats_path in jats_paths:
gt_path = (
- pubmed_path.parent.parent / "groundtruth" / "docling_v2" / pubmed_path.name
+ jats_path.parent.parent / "groundtruth" / "docling_v2" / jats_path.name
)
if use_stream:
- buf = BytesIO(pubmed_path.open("rb").read())
- stream = DocumentStream(name=pubmed_path.name, stream=buf)
+ buf = BytesIO(jats_path.open("rb").read())
+ stream = DocumentStream(name=jats_path.name, stream=buf)
conv_result: ConversionResult = converter.convert(stream)
else:
- conv_result: ConversionResult = converter.convert(pubmed_path)
+ conv_result: ConversionResult = converter.convert(jats_path)
doc: DoclingDocument = conv_result.document
pred_md: str = doc.export_to_markdown()
@@ -54,9 +54,9 @@ def test_e2e_pubmed_conversions(use_stream=False):
assert verify_document(doc, str(gt_path) + ".json", GENERATE), "export to json"
-def test_e2e_pubmed_conversions_stream():
- test_e2e_pubmed_conversions(use_stream=True)
+def test_e2e_jats_conversions_stream():
+ test_e2e_jats_conversions(use_stream=True)
-def test_e2e_pubmed_conversions_no_stream():
- test_e2e_pubmed_conversions(use_stream=False)
+def test_e2e_jats_conversions_no_stream():
+ test_e2e_jats_conversions(use_stream=False)