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feat(xml-jats): parse XML JATS documents (#967)

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* chore(xml-jats): separate authors and affiliations

In XML PubMed (JATS) backend, convert authors and affiliations as they
are typically rendered on PDFs.

Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com>

* fix(xml-jats): replace new line character by a space

Instead of removing new line character from text, replace it by a space character.

Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com>

* feat(xml-jats): improve existing parser and extend features

Partially support lists, respect reading order, parse more sections, support equations, better text formatting.

Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com>

* chore(xml-jats): rename PubMed objects to JATS

Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com>

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# Risk factors associated with failing pre-transmission assessment surveys (pre-TAS) in lymphatic filariasis elimination programs: Results of a multi-country analysis
Burgert-Brucker Clara R.; 1: Global Health Division, RTI International, Washington, DC, United States of America; Zoerhoff Kathryn L.; 1: Global Health Division, RTI International, Washington, DC, United States of America; Headland Maureen; 1: Global Health Division, RTI International, Washington, DC, United States of America, 2: Global Health, Population, and Nutrition, FHI 360, Washington, DC, United States of America; Shoemaker Erica A.; 1: Global Health Division, RTI International, Washington, DC, United States of America; Stelmach Rachel; 1: Global Health Division, RTI International, Washington, DC, United States of America; Karim Mohammad Jahirul; 3: Department of Disease Control, Ministry of Health and Family Welfare, Dhaka, Bangladesh; Batcho Wilfrid; 4: National Control Program of Communicable Diseases, Ministry of Health, Cotonou, Benin; Bougouma Clarisse; 5: Lymphatic Filariasis Elimination Program, Ministère de la Santé, Ouagadougou, Burkina Faso; Bougma Roland; 5: Lymphatic Filariasis Elimination Program, Ministère de la Santé, Ouagadougou, Burkina Faso; Benjamin Didier Biholong; 6: National Onchocerciasis and Lymphatic Filariasis Control Program, Ministry of Health, Yaounde, Cameroon; Georges Nko'Ayissi; 6: National Onchocerciasis and Lymphatic Filariasis Control Program, Ministry of Health, Yaounde, Cameroon; Marfo Benjamin; 7: Neglected Tropical Diseases Programme, Ghana Health Service, Accra, Ghana; Lemoine Jean Frantz; 8: Ministry of Health, Port-au-Prince, Haiti; Pangaribuan Helena Ullyartha; 9: National Institute Health Research &amp; Development, Ministry of Health, Jakarta, Indonesia; Wijayanti Eksi; 9: National Institute Health Research &amp; Development, Ministry of Health, Jakarta, Indonesia; Coulibaly Yaya Ibrahim; 10: Filariasis Unit, International Center of Excellence in Research, Faculty of Medicine and Odontostomatology, Bamako, Mali; Doumbia Salif Seriba; 10: Filariasis Unit, International Center of Excellence in Research, Faculty of Medicine and Odontostomatology, Bamako, Mali; Rimal Pradip; 11: Epidemiology and Disease Control Division, Department of Health Service, Kathmandu, Nepal; Salissou Adamou Bacthiri; 12: Programme Onchocercose et Filariose Lymphatique, Ministère de la Santé, Niamey, Niger; Bah Yukaba; 13: National Neglected Tropical Disease Program, Ministry of Health and Sanitation, Freetown, Sierra Leone; Mwingira Upendo; 14: Neglected Tropical Disease Control Programme, National Institute for Medical Research, Dar es Salaam, Tanzania; Nshala Andreas; 15: IMA World Health/Tanzania NTD Control Programme, Uppsala University, &amp; TIBA Fellow, Dar es Salaam, Tanzania; Muheki Edridah; 16: Programme to Eliminate Lymphatic Filariasis, Ministry of Health, Kampala, Uganda; Shott Joseph; 17: Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC, United States of America; Yevstigneyeva Violetta; 17: Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC, United States of America; Ndayishimye Egide; 2: Global Health, Population, and Nutrition, FHI 360, Washington, DC, United States of America; Baker Margaret; 1: Global Health Division, RTI International, Washington, DC, United States of America; Kraemer John; 1: Global Health Division, RTI International, Washington, DC, United States of America, 18: Georgetown University, Washington, DC, United States of America; Brady Molly; 1: Global Health Division, RTI International, Washington, DC, United States of America
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
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 &amp; 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, &amp; 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
## Abstract
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 summaryAchieving 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.
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.
## Introduction
@@ -24,6 +30,28 @@ This is a secondary data analysis using existing data, collected for programmati
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.
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 | &lt;5% | Maximum | Varies | Programmatic data |
| Prevalence | Baseline prevalence | 10% cut off | Maximum reported mapping or baseline sentinel site prevalence | &lt;10% | Maximum | Varies | Programmatic data |
| Agent | Parasite | Parasite | Predominate parasite in district | W. bancrofti &amp; mixed | Binary value | 2018 | Programmatic data |
| Environment | Vector | Vector | Predominate vector in district | Anopheles &amp; Mansonia | Binary value | 2018 | Country expert |
| Environment | Geography | Elevation | Elevation measured in meters | &gt;350 | Mean | 2000 | CGIAR-CSI SRTM [9] |
| Environment | Geography | District area | Area measured in km2 | &gt;2,500 | Maximum sum | Static | Programmatic data |
| Environment | Climate | EVI | Enhanced vegetation index | &gt; 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 | &gt;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 |
### 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).
@@ -74,16 +102,51 @@ Sensitivity analysis was performed for the final log-binomial model to test for
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%.
Fig 1 Number of pre-TAS by country.
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Fig 2 District-level baseline prevalence by country.
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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.
Fig 3 Percent pre-TAS failure by each characteristic (unadjusted).
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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).
Fig 4 Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model.
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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.
Table 2 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 &gt; = 10% &amp; 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 &gt; = 10% &amp; 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 &gt; = 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 &lt; 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).
Fig 5 Analysis of failures by model combinations.
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## 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].
@@ -104,119 +167,62 @@ As this analysis used data across a variety of countries and epidemiological sit
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.
## Tables
## Acknowledgments
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 | &lt;5% | Maximum | Varies | Programmatic data |
| Prevalence | Baseline prevalence | 10% cut off | Maximum reported mapping or baseline sentinel site prevalence | &lt;10% | Maximum | Varies | Programmatic data |
| Agent | Parasite | Parasite | Predominate parasite in district | W. bancrofti &amp; mixed | Binary value | 2018 | Programmatic data |
| Environment | Vector | Vector | Predominate vector in district | Anopheles &amp; Mansonia | Binary value | 2018 | Country expert |
| Environment | Geography | Elevation | Elevation measured in meters | &gt;350 | Mean | 2000 | CGIAR-CSI SRTM [9] |
| Environment | Geography | District area | Area measured in km2 | &gt;2,500 | Maximum sum | Static | Programmatic data |
| Environment | Climate | EVI | Enhanced vegetation index | &gt; 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 | &gt;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 |
Table 2: 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 &gt; = 10% &amp; 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 &gt; = 10% &amp; 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 &gt; = 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 &lt; 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) |
## Figures
Fig 1: Number of pre-TAS by country.
<!-- image -->
Fig 2: District-level baseline prevalence by country.
<!-- image -->
Fig 3: Percent pre-TAS failure by each characteristic (unadjusted).
<!-- image -->
Fig 4: Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model.
<!-- image -->
Fig 5: Analysis of failures by model combinations.
<!-- image -->
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.
## References
- World Health Organization. Lymphatic filariasis: progress report 2000–2009 and strategic plan 2010–2020. Geneva; 2010.
- World Health Organization. Validation of elimination of lymphatic filariasis as a public health problem. Geneva; 2017.
- Global programme to eliminate lymphatic filariasis: progress report, 2018. Wkly Epidemiol Rec (2019)
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- World Health Organization. Strengthening the assessment of lymphatic filariasis transmission and documenting the achievement of elimination—Meeting of the Neglected Tropical Diseases Strategic and Technical Advisory Group’s Monitoring and Evaluation Subgroup on Disease-specific Indicators. 2016; 42.
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