Examining the overlap in lymphatic filariasis prevalence and malaria insecticide-treated net access-use in endemic Africa

Joanna L Whisnant; Mustafa Kamal Sikder; Gizachew Taddesse Akalu; Tsegaye Alemu; Mubarek Yesse Ashemo; Amelia Bertozzi-Villa; Annie J Browne; Ewerton Cousin; Paulina Agnieszka Dzianach; Yalemzewod Assefa Gelaw; Peter W Gething; Taren M Gorman; Simon I Hay; Olayinka Stephen Ilesanmi; Cathleen Keller; Juniper Boroka Kiss; Jailos Lubinda; Michael A McPhail; Olivia D Nesbit; Gideon Olamilekan Oluwatunase; Verner N Orish; Amel Ouyahia; Susan Fred Rumisha; Adam Saddler; Afeez Abolarinwa Salami; Francesca Sanna; Desalegn Shiferaw; Jacques Lukenze Tamuzi; Daniel J Weiss; Naod Gebrekrstos Zeru; Francis Zeukeng; Stephanie R M Zimsen; Jonathan F Mosser

doi:10.1371/journal.pntd.0013165

. 2025 Jun 26;19(6):e0013165. doi: 10.1371/journal.pntd.0013165

Examining the overlap in lymphatic filariasis prevalence and malaria insecticide-treated net access-use in endemic Africa

Joanna L Whisnant ¹, Mustafa Kamal Sikder ², Gizachew Taddesse Akalu ^3,⁴, Tsegaye Alemu ^5,⁶, Mubarek Yesse Ashemo ^7,⁸, Amelia Bertozzi-Villa ⁹, Annie J Browne ¹⁰, Ewerton Cousin ^1,¹¹, Paulina Agnieszka Dzianach ¹², Yalemzewod Assefa Gelaw ^13,¹⁴, Peter W Gething ^15,¹², Taren M Gorman ¹, Simon I Hay ^1,¹¹, Olayinka Stephen Ilesanmi ^16,¹⁷, Cathleen Keller ¹, Juniper Boroka Kiss ¹⁸, Jailos Lubinda ¹⁹, Michael A McPhail ¹⁸, Olivia D Nesbit ¹, Gideon Olamilekan Oluwatunase ^20,²¹, Verner N Orish ^22,²³, Amel Ouyahia ^24,²⁵, Susan Fred Rumisha ^18,²⁶, Adam Saddler ¹⁹, Afeez Abolarinwa Salami ^27,²⁸, Francesca Sanna ¹², Desalegn Shiferaw ^29,³⁰, Jacques Lukenze Tamuzi ^31,³², Daniel J Weiss ^33,¹², Naod Gebrekrstos Zeru ^34,³⁵, Francis Zeukeng ^36,³⁷, Stephanie R M Zimsen ¹, Jonathan F Mosser ^1,^11,^*

Editor: Nigel Beebe³⁸

¹Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America

²Department of International Health, Johns Hopkins University, Baltimore, Maryland, United States of America

³Department of Microbiology, Immunology and Parasitology, St. Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia

⁴Department of Microbial, Cellular, and Molecular Biology, Addis Ababa University, Addis Ababa, Ethiopia

⁵Department of Public Health, Hawassa University, Hawassa, Ethiopia

⁶Department of Public Health, Ministry of Health, Hawassa, Ethiopia

⁷Department of Public Health, Jimma University, Jimma, Ethiopia

⁸Department of Public Health, Wachemo University, Hossana, Ethiopia

⁹Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America

¹⁰Big Data Institute, University of Oxford, Oxford, United Kingdom

¹¹Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, Washington, United States of America

¹²Child Health Analytics Research Program, Telethon Kids Institute, Perth, Western Australia, Australia

¹³Institute of Public Health, University of Gondar, Gondar, Ethiopia

¹⁴Division of Epidemiology and Biostatistics, The University of Queensland, Brisbane, Queensland, Australia

¹⁵School of Population Health, Curtin University, Perth, Western Australia, Australia

¹⁶Department of Community Medicine, University of Ibadan, Ibadan, Nigeria

¹⁷Department of Community Medicine, University College Hospital, Ibadan, Nigeria

¹⁸The Malaria Atlas Project, Telethon Kids Institute, Perth, Western Australia, Australia

¹⁹Geospatial Health and Development Team, Telethon Kids Institute, Perth, Western Australia, Australia

²⁰Department of Anatomy, University of Medical Sciences, Ondo, Ondo, Nigeria

²¹Department of Anatomy, Olabisi Onabanjo University, Sagamu, Nigeria

²²Department of Microbiology and Immunology, Dambi Dollo University, Dembi Dollo, Ghana

²³Sickle Cell Unit, Ho Teaching Hospital, Ho, Ghana

²⁴Faculty of Medicine, University Ferhat Abbas of Setif, Setif, Algeria

²⁵Division of Infectious Diseases, University Hospital of Setif, Setif, Algeria

²⁶Department of Health Statistics, National Institute for Medical Research, Dar es Salaam, Tanzania

²⁷Department of Oral and Maxillofacial Surgery, University College Hospital, Ibadan, Nigeria

²⁸Campaign for Health and Neck Cancer Education (CHANCE) Programme, Cephas Health Research Initiative Inc, Ibadan, Nigeria

²⁹Department of Public Health, Dambi Dollo University, Dembi Dollo, Ethiopia

³⁰Department of Epidemiology, Jimma University, Jimma, Ethiopia

³¹Department of Epidemiology, Stellenbosch University, Cape Town, South Africa

³²Department of Medicine, Northlands Medical Group, Omuthiya, Namibia

³³Curtin School of Population Health, Curtin University, Perth, Western Australia, Australia

³⁴Department of Statistics, Mekelle University, Mekelle, Ethiopia

³⁵Department of Biostatistics, Jimma University, Jimma, Ethiopia

³⁶The Biotechnology Centre, University of Yaoundé I, Yaounde, Cameroon,

³⁷Department of Biochemistry and Molecular Biology, University of Buea, Buea, Cameroon

³⁸University of Queensland & CSIRO Biosecurity Flagship, AUSTRALIA

^✉

* E-mail: jmosser@uw.edu

I have read the journal's policy and the authors of this manuscript have the following competing interests: EC reports payment or honoraria for lectures, presentations, speakers, bureaus, manuscript writing or educational events from the Royal Holloway University of London, and support for attending meetings and/or travel from the Bill and Melinda Gates Foundation; outside the submitted work. JFM reports grant funding from Gavi and support for attending meetings and/or travel from Bill and Melinda Gates Foundation; outside the submitted work.

Roles

Joanna L Whisnant: Conceptualization, Data curation, Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

Mustafa Kamal Sikder: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

Gizachew Taddesse Akalu: Validation, Writing – review & editing

Tsegaye Alemu: Data curation, Methodology, Validation, Writing – review & editing

Mubarek Yesse Ashemo: Methodology, Validation

Amelia Bertozzi-Villa: Data curation, Formal analysis, Validation, Writing – original draft, Writing – review & editing

Annie J Browne: Data curation, Formal analysis, Writing – review & editing

Ewerton Cousin: Validation, Writing – original draft, Writing – review & editing

Paulina Agnieszka Dzianach: Data curation, Formal analysis, Methodology, Validation, Writing – original draft

Yalemzewod Assefa Gelaw: Data curation, Formal analysis

Peter W Gething: Data curation, Methodology, Writing – original draft

Taren M Gorman: Data curation, Methodology

Simon I Hay: Data curation, Methodology, Supervision, Validation, Writing – review & editing

Olayinka Stephen Ilesanmi: Data curation, Validation, Writing – review & editing

Cathleen Keller: Validation, Writing – original draft, Writing – review & editing

Juniper Boroka Kiss: Data curation, Formal analysis

Jailos Lubinda: Data curation, Formal analysis

Michael A McPhail: Data curation, Formal analysis, Methodology, Validation

Olivia D Nesbit: Project administration, Validation, Writing – original draft, Writing – review & editing

Gideon Olamilekan Oluwatunase: Data curation, Writing – review & editing

Verner N Orish: Validation, Writing – review & editing

Amel Ouyahia: Data curation, Validation, Writing – review & editing

Susan Fred Rumisha: Data curation, Formal analysis, Methodology, Validation

Adam Saddler: Data curation, Formal analysis, Methodology

Afeez Abolarinwa Salami: Data curation, Visualization, Writing – review & editing

Francesca Sanna: Data curation, Formal analysis

Desalegn Shiferaw: Validation

Jacques Lukenze Tamuzi: Validation, Writing – review & editing

Daniel J Weiss: Data curation, Formal analysis, Writing – original draft

Naod Gebrekrstos Zeru: Data curation, Validation

Francis Zeukeng: Data curation, Validation, Writing – review & editing

Stephanie R M Zimsen: Data curation, Validation, Writing – original draft, Writing – review & editing

Jonathan F Mosser: Conceptualization, Formal analysis, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

Nigel Beebe: Editor

PMCID: PMC12201664 PMID: 40570066

Abstract

Eradication and elimination strategies for lymphatic filariasis (LF) primarily rely on multiple rounds of annual mass drug administration (MDA), but also may benefit from vector control interventions conducted by malaria vector control programs. We aim to examine the overlap in LF prevalence and malaria vector control to identify potential gaps in program coverage. We used previously published geospatial estimates of LF prevalence from the Institute for Health Metrics and Evaluation, as well as publicly available insecticide-treated net (ITN) access (proportion of the total population with access to ITNs) and use (proportion of the total population that slept under an ITN) estimates among the total population and malaria Plasmodium falciparum parasite rates (PfPR) from the Malaria Atlas Project (MAP). We aggregated the 5x5 km² estimates of LF prevalence estimates and ITN estimates to the implementation unit (IU) level using fractional aggregation, for 33 LF and malaria-endemic locations in Africa, and then overlaid the IU-level aggregates. In this analysis, ITN coverage was low in areas where LF is common, with 51.7% (90/174) of high-LF-prevalence-IUs having both access and use estimates under 40%. Most (67.8%; 61/90) of these low-ITN-coverage, high-LF-prevalence locations were also categorized as high- or highest-prevalence for malaria by PfPR, suggesting suboptimal ITN coverage even in some malaria-co-endemic locations. Even in IUs with high LF prevalence but low malaria prevalence, almost half (48.2%; 39/81) had high levels of access to ITNs. When accounting for population, however, gaps in ITN access in such areas were evident: more individuals lived in high-LF, low-malaria IUs with low ITN access (8.68 million) than lived in high-LF, low-malaria IUs with high ITN access (6.76 million). These results suggest that relying on current malaria vector control programs alone may not provide sufficient ITN coverage for high LF prevalence areas. Opportunities for coordinated vector control programs in places where LF and malaria prevalence are high but ITN coverage is low – or additional ITN distribution in high-LF, low-malaria locations - should be explored to help achieve elimination goals.

Author summary

Lymphatic filariasis is a vector-borne disease that can cause significant disability. There is evidence that insecticide-treated nets used by malaria programs can contribute to lymphatic filariasis elimination, but current lymphatic filariasis programs primarily focus on mass drug administration. As funding for programs has stalled and interventions have become more costly, there is a greater interest and need for vector management to be better integrated across sectors and diseases, with WHO promoting integrated vector management specifically for countries co-endemic with LF and malaria. We sought to review the overlap in lymphatic filariasis prevalence and malaria insecticide-treated nets across endemic African countries to identify areas where net distribution can be enhanced. We used previously published, publicly available lymphatic filariasis prevalence and malaria insecticide-treated net coverage results from the Institute for Health Metrics and Evaluation and the Malaria Atlas Project, respectively. Areas with high lymphatic filariasis prevalence were largely found to have low insecticide-treated net coverage. There is a need for disease programs to work together to maximize effective tools and methods to help achieve elimination goals. The impact of insecticide-treated nets on lymphatic filariasis prevalence will be location-specific and depend on a variety of epidemiological and programmatic factors.

Introduction

Lymphatic filariasis (LF) is a vector-borne disease caused by the filarial nematodes Wuchereria bancrofti, Brugia malayi, and Brugia timori, and is primarily transmitted by Anopheles, Aedes, Culex, and Mansonia mosquito species, varying geographically [1]. LF can lead to permanent disability, including that related to lymphedema and hydrocele, and causes significant mental, social, and financial burden to those afflicted. Under the World Health Organization (WHO)–established Global Programme to Eliminate Lymphatic Filariasis (GPELF), many countries have made significant progress: 17 countries have entered post-validation surveillance (ongoing transmission monitoring following GPELF certification recognizing elimination of LF as a public health problem), 11 have reached post–mass drug administration (MDA) surveillance, and all but two remaining countries have delivered MDA in some capacity [1]. To build upon these gains, the neglected tropical disease (NTD) Roadmap 2021–2030, in alignment with the Sustainable Development Goals, aims to eliminate LF as a public health problem in 58 countries by 2030 [2,3]. In 34 of the countries in the WHO Africa Region and Sudan, LF is a threat to approximately 406 million people [1,4]. In 2019, LF was estimated to have a prevalence rate of 1,472.22 cases per 100,000 (1,024.05 - 2,194.37) and contribute 432,679.92 Disability-adjusted life years (255,366.1 – 729,720.21) for the African Union alone [5]. Within this region, the countries with the highest prevalence include Nigeria, Côte d’Ivoire, the Democratic Republic of the Congo, and Mozambique, which made up approximately 57.6% of the region’s prevalent cases in 2019 [5].

Eradication and elimination strategies in endemic African countries primarily rely on multiple rounds of MDA and may additionally benefit from malaria vector control programs, since Anopheles species are one of the vectors of LF [6]. Malaria vector control initiatives, particularly insecticide-treated net (ITN) programs, have increased in recent years and contributed to ongoing success in combating malaria, while increasing evidence suggests secondary impacts on other vector-borne diseases [7]. However, although LF and malaria are largely co-endemic, areas with persistently high LF prevalence may not always coincide with areas where malaria prevalence or vector control is high [8,9]. In 2011, WHO released a statement promoting integrated vector management specifically for countries co-endemic with LF and malaria [10]. This statement was followed by the Global Vector Control Response 2017–2030, which aims to reduce mortality and incidence due to vector-borne diseases by at least 75% and 60% respectively, while also preventing epidemics by increasing capacity, enhancing surveillance, and improving coordination and integrated action across diseases and programs [11]. As global funding stalls and the cost of implementing interventions increases [7], it is more important than ever for vector control to be integrated across sectors and disease programs. To enhance cross-disease vector control management, it is crucial to identify where current vector control programs could be expanded to have the most impact. Here we aim to provide one of the first examinations of the overlap in LF prevalence and malaria vector control across endemic Africa to identify potential gaps in program coverage.

Methods

We used previously published geospatial LF prevalence estimates from the Institute for Health Metrics and Evaluation (IHME) [12] as well as publicly available ITN access and use estimates among the total population, indoor residual spraying (IRS) estimates among the total population, and malaria Plasmodium falciparum parasite rates (PfPR) from the Malaria Atlas Project (MAP) [13]. Briefly, the LF estimates were created using Bayesian model-based geostatistics and time-series methods to generate spatially continuous estimates of global, all-age LF prevalence as measured by immunochromatographic test (ICT) in 2000–2018 [12]. The ITN estimates used a Bayesian mixed modeling framework, and the IRS estimates were generated by collating IRS deployment data from various sources and converting to a standard proportion of households sprayed within the administrative division [13–15]. For PfPR, a cartographic approach was taken for 36 high-burden countries, while a surveillance approach was taken for other Pf-endemic countries [16]. Further details regarding the methodology used to create each set of estimates can be found in their respective publications [12–16]. For the purpose of this analysis, we chose to compare the most recent year available for each dataset at the time of analysis to present the most recent comparisons possible: 2018 for LF prevalence, 2019 for malaria prevalence, and 2020 for the vector control datasets. Unlike ITN access and use, which changes very rapidly year-to-year, LF epidemiology and elimination happen on longer time scales [4]. As such areas with high LF prevalence in 2018 are likely to be the same as in 2020, and therefore the maps presented below give the most up-to-date view of this overlap that is currently possible with available results. For a more direct comparison, figures using data from only 2018 have been provided in S4-S10 Figs.

Using population estimates from WorldPop [17], we aggregated the 5x5 km² estimates of LF prevalence, ITN use, ITN access, malaria prevalence, IRS use, and population to the Expanded Special Project for Elimination of Neglected Tropical Diseases (ESPEN) administrative implementing units (IU) level using fractional aggregation, for 33 LF and malaria-endemic locations in Africa, and then overlaid the IU-level aggregates [18]. IUs represent the administrative units designated by a country to be used for intervention implementation [19]. While these are typically at the district level, there may be variation between countries depending on the structure and objectives of each country’s control program [19]. To account for partial coverage of the 5 km² grid by the IU boundaries and water bodies, we used fractional aggregation, whereby grid cells overlapping multiple IUs were proportionally distributed using the fraction of the cell lying within each IU. Our analyses included a total of 5,195 IUs and 162,868 overlapping 5x5 km² grids.

We use two ITN metrics in this analysis: access, or the proportion of people among the total population who have access to an ITN; and use, or the proportion of the total population that use an ITN. Following Bertozzi-Villa et al., 2021 [14] we refer to specific metrics, like access and use, by name, and use coverage to more generally refer to combinations of metrics. This analysis focused on the proportion of the total population that had access to ITNs and the proportion of the total population that slept under an ITN, as the use of IRS has largely declined since 2010 [7]. However, maps of LF prevalence and IRS use have been included in S1 and S2 Figs [13]. All maps were created using ESPEN IU shapefiles as the base layer, which are made freely available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) for academic use [18].

In this analysis, we defined IUs having LF prevalence ≥5% as high, while those with a prevalence <5% were considered low. In the absence of well-established standard classification thresholds, we categorized ITN access, use, and malaria prevalence using the following definitions based on the IU-level distributions of these metrics: lowest (<20%); low (20- < 40%); high (40%- < 60%); highest (≥60%).

Results

In this analysis, although only 3.4% (174/5,195) of IUs were categorized as having high LF prevalence (Table 1), a total of 38.2 million individuals lived in these locations, primarily located in Nigeria, Côte d’Ivoire, and Liberia. Of those living in these high prevalence areas, 21.9 million (57.3%) lived in IUs with low ITN access, and 1.66 million (4.3%) in IUs with the lowest ITN access, accounting for 51.7% (90/174) and 1.2% (2/174) IUs, respectively (Fig 1). In contrast, there were 10.6 million individuals (27.8%) living in 35.6% (62/174) of IUs with high ITN access and 13.5 million individuals (35.3%) in 42.0% (73/174) of IUs with high ITN use, while only 4.0 million (10.5%) lived in IUs (11.5%; 20/174) with the highest access and 1.8 million people (4.7%) in IUs (6.3%; 11/174) with the highest use (Fig 2).

Table 1. Table of number of IUs by LF prevalence, ITN access, and ITN use stratified by level of malaria PfPR. LF: Lymphatic filariasis; ITN: Insecticide-treated bednets; PfPR: Plasmodium falciparum parasite rate.

		LF prevalence
		Low				High				All
		Lowest malaria PfPR	Low malaria PfPR	High malaria PfPR	Highest malaria PfPR	Lowest malaria PfPR	Low malaria PfPR	High malaria PfPR	Highest malaria PfPR	Total
ITN Access	Lowest	889	115	19	0	0	2	0	0	1,025
	Low	916	188	82	0	4	24	57	5	1,276
	High	890	695	236	30	6	33	17	6	1,913
	Highest	521	323	115	2	1	11	8	0	981
ITN Access Total		3,216	1,321	452	32	11	70	82	11	5,195
ITN Use	Lowest	1,225	130	19	0	2	4	0	0	1,380
	Low	811	294	104	1	2	21	56	5	1,294
	High	836	673	265	31	6	38	23	6	1,878
	Highest	344	224	64	0	1	7	3	0	643
ITN Use Total		3,216	1,321	452	32	11	70	82	11	5,195

Open in a new tab

Fig 1 — The bivariate choropleth map and scatter plot color key in the center show the degree to which LF prevalence (vertical axis, white to red) and ITN access (horizontal axis, white to blue) overlap. Grey indicates areas considered to be non-endemic. LF: lymphatic filariasis; ITN: insecticide-treated net. Map base layer shapefile is from ESPEN, available from: https://espen.afro.who.int/tools-resources/data-query-tools/cartography-database [18].

Fig 2 — The bivariate choropleth map and scatter plot color key indicate the degree to which LF prevalence (vertical axis, white to red) and ITN use (horizontal axis, white to blue) overlap. Grey indicates areas considered to be non-endemic. LF: lymphatic filariasis; ITN: insecticide-treated net. Map base layer shapefile is from ESPEN, available from: https://espen.afro.who.int/tools-resources/data-query-tools/cartography-database [18].

Just under half of the high LF prevalence locations had at least 40% ITN access (47.1%; 82/174 of IUs) and use (48.3%; 84/174) (Figs 3 and 4). For high LF prevalence areas, 51.7% (90/174) had both ITN access and use in the low or lowest categories. Geographically, areas with high LF prevalence and low ITN use were concentrated in Liberia, Zambia, Kenya, Angola, and Nigeria, whereas large parts of the Central African Republic, Democratic Republic of the Congo, Côte d’Ivoire, Mali, and Sierra Leone had high LF prevalence and high ITN use (Fig 4).

Fig 3 — The bivariate choropleth map and scatter plot color key in the center indicate the degree to which LF prevalence (vertical axis, white to red) and ITN access (horizontal axis, white to blue) overlap. Grey indicates areas considered to be non-endemic. LF: lymphatic filariasis; ITN: insecticide-treated net. Map base layer shapefile is from ESPEN, available from: https://espen.afro.who.int/tools-resources/data-query-tools/cartography-database [18].

Fig 4 — The bivariate choropleth map and scatter plot color key in the center indicate the degree to which LF prevalence (vertical axis, white to red) and ITN use (horizontal axis, white to blue) overlap. Grey indicates areas considered to be non-endemic. LF: lymphatic filariasis; ITN: insecticide-treated net. Map base layer shapefile is from ESPEN, available from: https://espen.afro.who.int/tools-resources/data-query-tools/cartography-database [18].

Approximately 53.5% (93/174) of high LF prevalence areas had high or highest prevalence of malaria, representing 18.4 million (48.2%) individuals. Of these areas, 33.3% (31/93) of IUs had both ITN access and use ≥ 40%, of which only 9.7% (3/31) had the highest levels of both access and use. IUs with high prevalence of both LF and malaria most commonly had low access (66.7% [62/93] of IUs, containing 13.3 million [72.3%] people) and low use (65.6% [61/93] of IUs, containing 12.7 million [69.0%] people). None of the areas with high prevalence of both LF and malaria had both access and use in the lowest categories, however.

Of the IUs with high LF prevalence but low malaria prevalence, almost half had high ITN access (48.2%; 39/81) and over half had high ITN use (54.3%; 44/81) representing 6.76 million and 8.08 million individuals respectively. Even though only around one third of these IUs (34.6%; 28/81) had low ITN access, there were more people living in these IUs (8.68 million) than in those with high ITN access (6.76 million). These low access IUs were primarily located in Nigeria.

Of the 903 million individuals living in the remaining 96.7% (5,021/5,195) of IUs with low LF prevalence, 200 million (22.2%) lived in IUs with low ITN access (23.6% of IUs; 1,186/5,021) and 114 million (12.6%) in IUs with the lowest ITN access (20.4%; 1,023/5,021). There were 509 million (56.4%) individuals living in IUs with low LF prevalence but with ITN access ≥40% (56.0% of IUs; 2,812/5,021), compared to 124 million people (13.7%) living in IUs with the highest ITN access (19.1%; 961/5,021 IUs). Over half of these low LF prevalence areas had ITN use < 40% (51.5%; 2,584/5,021). Of these, there were fewer areas with low ITN use (24.1%; 1,210/5,021) than the lowest use (27.4%; 1,374/5,021) (Fig 4).

Among all low-LF-prevalence areas, 9.6% (484/5,021) were high-or-highest-prevalence for malaria, accounting for 84.6 million (9.4%) individuals (S3 Fig). Within these, 18.4 million (21.8%) individuals lived in IUs with low ITN use (21.7%; 105/484 IUs) and 14.0 million individuals in IUs with low ITN access (16.9%; 82/484 IUs). Only 3.9% (19/484) of these low-LF prevalence, but high-or-highest malaria prevalence IUs, containing 1.6 million people, had the lowest ITN access and use. In comparison, 50.0% (242/484) of these IUs had high ITN access and use, and 13.2% (64/484) the highest.

Discussion

This analysis found that most individuals living in high-LF-prevalence areas live in places with low ITN coverage, with the majority falling in the low coverage range. Conversely, the majority of individuals living in areas of low LF prevalence had high ITN coverage. Over half of the areas with high LF prevalence also had high malaria prevalence, but only around one third of these areas had high ITN coverage. For the high LF prevalence areas with low malaria prevalence, despite almost half of IUs having high ITN access, more individuals lived in IUs with low ITN access, suggesting that relying on current malaria vector control programs may not be sufficient for some high LF prevalence areas.

Previous studies evaluating the intersection of LF prevalence, ITN coverage, and malaria prevalence have primarily been limited to a subset of endemic African countries [20–24]. Several of the findings from this analysis, such as ITN coverage being generally low in areas where LF prevalence was high and partial overlap of high LF and malaria prevalence areas, echo the results of these other studies [21,23]. There are likely several factors contributing to the low coverage of ITNs seen in high-LF areas in this analysis. ITN access has been closely linked to development assistance for health funding (DAH), with global organizations playing an important role in deciding resource and program priorities between and within countries [25,26]. Funding for ITNs from organizations such as the Global Fund to Fight AIDS, Tuberculosis and Malaria focuses primarily on high malaria burden areas rather than on areas with high LF prevalence, which likely contributes to some of the low ITN coverage seen for these locations in this analysis [27–29]. Furthermore, many of the countries that initially benefitted from DAH support, much of which was from the Global Fund, tended to be from the same region, such as Eastern Africa [30,31], including some areas where LF prevalence was considered relatively low or non-endemic due to the historical use of dichlorodiphenyltrichloroethane (DDT) spraying against human African trypanosomiasis (HAT) [32]. Importantly, our analysis identified areas with both high malaria prevalence and high LF prevalence but low ITN coverage, suggesting an opportunity for coordinated vector control activities between programs.

In countries affected by war and civil unrest, disruptions to health services and support for ongoing disease programs are likely to further contribute to the observed ITN coverage patterns [33]. Countries also differ in their utilization of ITNs as well as MDA depending on political commitments, competing priorities, and health system structures. In the past, some malaria ITN programs primarily distributed ITNs via antenatal clinics and Expanded Programme for Immunization (EPI) visits, with pregnant persons and children serving as the main target populations due to high health burdens in these groups [34]. For LF, these strategies may have been suboptimal, given that preventing infection across the lifespan is of particular importance to prevent the disabling sequelae of chronic and repeated parasite exposure [35,36]. Although antenatal and EPI visits still play an important role in some countries for continuous ITN distribution [37], since 2007 the WHO has recommended a shift in distribution strategy towards universal coverage [10,37], and more recently towards the subnational tailoring of interventions [38]. In alignment with these recommendations, collaboration to increase the access and use of nets in the highest priority areas could be considered to extend benefits to LF control programs.

The partial overlap seen in this analysis of areas with high LF prevalence and high malaria prevalence could indicate that LF programs may need to consider alternative ITN distribution methods or special net programs for high-LF but low-malaria locations with low ITN coverage, such as some of those outlined in the WHO’s document on scaling up ITNs [34]. Despite ITN use among those with nets usually being high [14,39], there are known factors contributing to ongoing ITN non-use, including decreased risk perception during dry seasons for areas where malaria is seasonal [30]. Seasonal trends in LF and malaria may be similar in settings where the same Anopheles vectors account for most of the transmission for both diseases [6,8], but due to the chronic nature of LF, these extended periods of ITN non-use could be particularly harmful for those living in LF-endemic areas [4].

While vector control is not currently required for validation of EPHP, there is evidence that it helps to greatly reduce LF prevalence in some settings and could accelerate elimination and eradication programs [8,22,40–46]. A study in Papua New Guinea found that the introduction of ITNs directly led to a decrease in the annual infective biting rate [42], and in The Gambia, LF elimination was reached in the absence of MDA while scaling up ITNs [47]. However, as LF can be transmitted by multiple vector species, the impact of ITNs on LF prevalence is likely to vary depending on the predominant vector species and their habits, such as whether the species tends to feed outdoors (exophagic) or indoors (endophagic) and when peak biting times occur [8,45,48,49]. Encouragingly, a study conducted in an area of Southeastern Nigeria endemic for both malaria and LF that had not undergone MDA due to co-endemicity with Loa loa showed that, even in the presence of multiple LF vectors and high transmission, LF transmission could be halted with the use of ITNs alone – though coverage of 1 net per 2 people in each household was required to do so [46]. Vector control programs also have the potential to drive vector behavior modification which may lead to decreased ITN impact over time [50–52]. Furthermore, location differences in MDA coverage, ITN implementation, IRS use, and other factors will also affect ITN impact.

In Africa, Anopheles are the most widespread species, whereas Culex and Mansonia are more localized in east Africa and west Africa, respectively [53–55]. Elimination of LF by MDA alone may be more likely in areas where Anopheles are the primary vector than for other species [6,48,55], but others have argued that adding vector control to MDA in Anopheles-dominant areas would be advantageous [20,45,56,57], and WHO specifically recommends the use of ITN in areas where Anopheles is the primary vector for LF [4]. This is even more important in urban areas where it is costly and challenging to implement MDA [58], as well as areas that are co-endemic with Loa loa where the combination drug approach of MDA drugs for LF (in Africa, albendazole with either ivermectin or diethylcarbamazine), is not recommended, and instead a combination of albendazole-only MDA and vector control is preferred [1].

Differential insecticide resistance patterns should also be considered in areas where the primary vectors are Anopheles or Culex [59–61] but should not deter the use of ITNs [49,62–65]. Importantly, with the increased concern regarding the spread of the urban-dwelling Anopheles stephensi [66], malaria vector control programs may begin shifting from their historically rural focus to include more urban areas, which could increase the potential for overlap between malaria vector control efforts and LF-prevalent locations. Furthermore, Anopheles stephensi has been found to coexist with others such as Aedes and Culex [66–69], presenting an opportunity for broader vector control collaboration to combat not only LF and malaria, but also other mosquito-borne diseases such as dengue.

This analysis carries some limitations. We chose to compare the most recent available geospatial estimates for LF prevalence, ITN coverage, and malaria prevalence, in order to produce the most up-to-date comparisons possible at the time of analysis. As LF results were only available through the year 2018, however, these results do not fully reflect any recent changes in the current spread and level of LF prevalence. Furthermore, both LF and malaria estimates may be subject to accuracy limitations where data is sparse. As mentioned, this analysis used malaria ITN estimates among the total population and does not account for any ITN distribution that may have happened outside of malaria vector control initiatives. An analysis of ITN coverage and LF prevalence over time was outside the scope of this paper, though has been examined in previous country-specific analyses [20].

To the authors’ knowledge this is one of the first papers looking at the overlap of LF and ITNs across endemic Africa. These results illustrate the degree to which malaria control programs have achieved access to and use of ITNs in LF-endemic areas. Where the predominant vector species distributions and the context of MDA and other control efforts suggest a role for ITNs in LF control and elimination, these results help to identify locations where additional ITN coverage may be of the most benefit for both diseases. In high-LF, low-malaria locations with low ITN coverage, LF-driven programs to enhance ITN coverage may be needed. Spatial analyses like these can be combined with other context-specific knowledge to help inform future elimination and control strategies.

Supporting information

S1 Fig. Overlay map of LF prevalence (%; 2018) and IRS use (%; 2020).

The bivariate choropleth map and scatter plot color key in the center indicate the degree to which LF prevalence (vertical axis, white to red) and IRS use (horizontal axis, white to blue) overlap. Grey indicates areas considered to be non-endemic. LF: lymphatic filariasis; IRS: indoor residual spraying. Map base layer shapefile is from ESPEN, available from: https://espen.afro.who.int/tools-resources/data-query-tools/cartography-database [18].

(PDF)

pntd.0013165.s001.pdf^{(8.2MB, pdf)}

S2 Fig. Overlay map of LF prevalence (counts; 2018) and IRS use (%; 2020).

(PDF)

pntd.0013165.s002.pdf^{(8.2MB, pdf)}

S3 Fig. Overlay map of LF prevalence (%; 2018) and malaria PfPR prevalence (%; 2019).

The bivariate choropleth map and scatter plot color key in the center indicate the degree to which LF prevalence (vertical axis, white to red) and malaria Pf prevalence (horizontal axis, white to blue) overlap. Grey indicates areas considered to be non-endemic. LF: lymphatic filariasis; Pf: Plasmodium falciparum. Map base layer shapefile is from ESPEN, available from: https://espen.afro.who.int/tools-resources/data-query-tools/cartography-database [18].

(PDF)

pntd.0013165.s003.pdf^{(8.2MB, pdf)}

S4 Fig. Overlay map of LF prevalence (%; 2018) and ITN access among the total population (%; 2018).

The bivariate choropleth map and scatter plot color key in the center indicate the degree to which LF prevalence (vertical axis, white to red) and ITN access (horizontal axis, white to blue) overlap. Grey indicates areas considered to be non-endemic. LF: lymphatic filariasis; ITN: insecticide-treated nets. Map base layer shapefile is from ESPEN, available from: https://espen.afro.who.int/tools-resources/data-query-tools/cartography-database [18].

(PDF)

pntd.0013165.s004.pdf^{(8.2MB, pdf)}

S5 Fig. Overlay map of LF prevalence (counts; 2018) and ITN access among the total population (%; 2018).

(PDF)

pntd.0013165.s005.pdf^{(8.2MB, pdf)}

S6 Fig. Overlay map of LF prevalence (%; 2018) and ITN use among the total population (%; 2018).

The bivariate choropleth map and scatter plot color key in the center indicate the degree to which LF prevalence (vertical axis, white to red) and ITN use (horizontal axis, white to blue) overlap. Grey indicates areas considered to be non-endemic. LF: lymphatic filariasis; ITN: insecticide-treated nets. Map base layer shapefile is from ESPEN, available from: https://espen.afro.who.int/tools-resources/data-query-tools/cartography-database [18].

(PDF)

pntd.0013165.s006.pdf^{(8.2MB, pdf)}

S7 Fig. Overlay map of LF prevalence (counts; 2018) and ITN use among the total population (%; 2018).

(PDF)

pntd.0013165.s007.pdf^{(8.2MB, pdf)}

S8 Fig. Overlay map of LF prevalence (%; 2018) and IRS use (%; 2018).

(PDF)

pntd.0013165.s008.pdf^{(8.2MB, pdf)}

S9 Fig. Overlay map of LF prevalence (counts; 2018) and IRS use (%; 2018).

(PDF)

pntd.0013165.s009.pdf^{(8.2MB, pdf)}

S10 Fig. Overlay map of LF prevalence (%; 2018) and malaria PfPR prevalence (%; 2018).

The bivariate choropleth map and scatter plot color key in the center indicate the degree to which LF prevalence (vertical axis, white to red) and malaria Pf prevalence (horizontal axis, white to blue) overlap. Grey indicates areas considered to be non-endemic. LF: lymphatic filariasis; Pf: Plasmodium falciparum. Map base layer shapefile is from ESPEN, available from: https://espen.afro.who.int/tools-resources/data-query-tools/cartography-database [18].

(PDF)

pntd.0013165.s010.pdf^{(8.2MB, pdf)}

S1 Data. Codebook.

(XLSX)

pntd.0013165.s011.xlsx^{(10.4KB, xlsx)}

S2 Data. Results dataset.

(CSV)

pntd.0013165.s012.csv^{(777KB, csv)}

Data Availability

The results dataset has been included in the Supporting Information (S1 Data and S2 Data). The code is publicly available via GIT repository (https://github.com/ihmeuw/lf-malaria-overlap). All input estimates used to produce the results dataset are publicly available as indicted in their respective cited publications and at the following URLs: Lymphatic filariasis prevalence https://doi.org/10.1016/S2214-109X(20)30286-2 https://vizhub.healthdata.org/lbd/lf Insecticide-treated net access https://doi.org/10.1038/s41467-021-23707-7 https://data.malariaatlas.org/maps?layers=Interventions:202106_Africa_Insecticide_Treated_Net_Access Insecticide-treated net use https://doi.org/10.1038/s41467-021-23707-7 https://data.malariaatlas.org/maps?layers=Interventions:202106_Africa_Insecticide_Treated_Net_Use Indoor Residual Spraying https://doi.org/10.1186/s12936-020-03216-6 https://data.malariaatlas.org/maps?layers=Interventions:202106_Africa_IRS_Coverage Malaria PfPR prevalence https://doi.org/10.1016/S0140-6736(19)31097-9 https://data.malariaatlas.org/maps?layers=Malaria:202206_Global_Pf_Parasite_Rate Population estimates https://doi.org/10.1080/20964471.2019.1625151 https://hub.worldpop.org/project/categories?id=3 Shapefile base layer https://espen.afro.who.int/tools-resources/data-query-tools/cartography-database.

Funding Statement

EC, TG, CK, JBK, AS, JFM, and JW report support for the present manuscript from the Bill and Melinda Gates Foundation, worktag GR024212. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.World Health Organization. Global programme to eliminate lymphatic filariasis: progress report, 2021 [Internet]. WHO; 2022. [cited 2023 Sep 14]. Available from: https://www.who.int/publications/i/item/who-wer9741-513-524 [Google Scholar]
2.World Health Organization. Ending the neglect to attain the Sustainable Development Goals: A road map for neglected tropical diseases 2021–2030 [Internet]. WHO; 2021. [cited 2023 Sep 14]. Available from: https://www.who.int/publications/i/item/9789240010352 [Google Scholar]
3.United Nations Department of Economic and Social Affairs. The Sustainable Development Goals Report 2023: Special Edition [Internet]. United Nations; 2023. Jul [cited 2023 Sep 18]. Available from: https://www.un-ilibrary.org/content/books/9789210024914 [Google Scholar]
4.World Health Organization. Lymphatic filariasis. [cited 2023 September 18]. Available from: https://www.who.int/news-room/fact-sheets/detail/lymphatic-filariasis [Google Scholar]
5.Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet. 2020;396(10258):1204–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.de Souza DK, Koudou B, Kelly-Hope LA, Wilson MD, Bockarie MJ, Boakye DA. Diversity and transmission competence in lymphatic filariasis vectors in West Africa, and the implications for accelerated elimination of Anopheles-transmitted filariasis. Parasit Vectors. 2012;5:259. doi: 10.1186/1756-3305-5-259 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.World Health Organization. World malaria report 2022 [Internet]. WHO; 2022. [cited 2023 Sep 14]. Available from: https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2022 [Google Scholar]
8.van den Berg H, Kelly-Hope LA, Lindsay SW. Malaria and lymphatic filariasis: the case for integrated vector management. Lancet Infect Dis. 2013;13(1):89–94. doi: 10.1016/S1473-3099(12)70148-2 [DOI] [PubMed] [Google Scholar]
9.Manguin S, Bangs MJ, Pothikasikorn J, Chareonviriyaphap T. Review on global co-transmission of human Plasmodium species and Wuchereria bancrofti by Anopheles mosquitoes. Infect Genet Evol. 2010;10(2):159–77. doi: 10.1016/j.meegid.2009.11.014 [DOI] [PubMed] [Google Scholar]
10.World Health Organization. WHO position statement on integrated vector management to control malaria and lymphatic filariasis. WHO. 2011. Available from: https://www.who.int/publications-detail-redirect/who-wer8613-121-127 [PubMed] [Google Scholar]
11.World Health Organization. Global vector control response 2017–2030. WHO. 2017. Available from: https://www.who.int/publications/i/item/9789241512978 [Google Scholar]
12.Cromwell EA, Schmidt CA, Kwong KT, Pigott DM, Mupfasoni D, Biswas G. The global distribution of lymphatic filariasis, 2000–18: a geospatial analysis. Lancet Glob Health. 2020;8(9):e1186-94. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Malaria Atlas Project. Malaria Atlas Project | Data. [cited 2023 September 14]. Available from: https://data.malariaatlas.org/maps?layers=Malaria:202406_Global_Pf_Parasite_Rate [Google Scholar]
14.Bertozzi-Villa A, Bever CA, Koenker H, Weiss DJ, Vargas-Ruiz C, Nandi AK, et al. Maps and metrics of insecticide-treated net access, use, and nets-per-capita in Africa from 2000-2020. Nat Commun. 2021;12(1):3589. doi: 10.1038/s41467-021-23707-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Tangena J-AA, Hendriks CMJ, Devine M, Tammaro M, Trett AE, Williams I, et al. Indoor residual spraying for malaria control in sub-Saharan Africa 1997 to 2017: an adjusted retrospective analysis. Malar J. 2020;19(1):150. doi: 10.1186/s12936-020-03216-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Weiss DJ, Lucas TCD, Nguyen M, Nandi AK, Bisanzio D, Battle KE, et al. Mapping the global prevalence, incidence, and mortality of Plasmodium falciparum, 2000–17: a spatial and temporal modelling study. Lancet Lond Engl. 2019;394(10195):322–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Lloyd CT, Chamberlain H, Kerr D, Yetman G, Pistolesi L, Stevens FR, et al. Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets. Big Earth Data. 2019;3(2):108–39. doi: 10.1080/20964471.2019.1625151 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Expanded Special Project for Elimination of Neglected Tropical Diseases. Cartography database | ESPEN [Internet]. [cited 2023 Sep 14]. Available from: https://espen.afro.who.int/tools-resources/data-query-tools/cartography-database [Google Scholar]
19.WHO & GPELF. Monitoring and epidemiological assessment of the programme to eliminate lymphatic filariasis at implementation unit level [Internet]. WHO; 2005. [cited 2025 Jan 27]. Report No.: WHO/CDS/CPE/CEE/2005.50. Available from: https://iris.who.int/handle/10665/69172 [Google Scholar]
20.Nsakashalo-Senkwe M, Mwase E, Chizema-Kawesha E, Mukonka V, Songolo P, Masaninga F. Significant decline in lymphatic filariasis associated with nationwide scale-up of insecticide-treated nets in Zambia. Parasite Epidemiol Control. 2017;2(4):7–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Eneanya OA, Reimer LJ, Fischer PU, Weil GJ. Geospatial modelling of lymphatic filariasis and malaria co-endemicity in Nigeria. Int Health. 2023;15(5):566–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Stanton MC, Mkwanda S, Mzilahowa T, Bockarie MJ, Kelly-Hope LA. Quantifying filariasis and malaria control activities in relation to lymphatic filariasis elimination: a multiple intervention score map (MISM) for Malawi. Trop Med Int Health. 2014;19(2):224–35. doi: 10.1111/tmi.12266 [DOI] [PubMed] [Google Scholar]
23.Okorie PN, Ademowo GO, Saka Y, Davies E, Okoronkwo C, Bockarie MJ, et al. Lymphatic filariasis in Nigeria; micro-stratification overlap mapping (MOM) as a prerequisite for cost-effective resource utilization in control and surveillance. PLoS Negl Trop Dis. 2013;7(9):e2416. doi: 10.1371/journal.pntd.0002416 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Kelly-Hope LA, Thomas BC, Bockarie MJ, Molyneux DH. Lymphatic filariasis in the Democratic Republic of Congo; micro-stratification overlap mapping (MOM) as a prerequisite for control and surveillance. Parasit Vectors. 2011;4:178. doi: 10.1186/1756-3305-4-178 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Flaxman AD, Fullman N, Otten MW Jr, Menon M, Cibulskis RE, Ng M, et al. Rapid scaling up of insecticide-treated bed net coverage in Africa and its relationship with development assistance for health: a systematic synthesis of supply, distribution, and household survey data. PLoS Med. 2010;7(8):e1000328. doi: 10.1371/journal.pmed.1000328 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Snow RW, Guerra CA, Mutheu JJ, Hay SI. International Funding for Malaria Control in Relation to Populations at Risk of Stable Plasmodium falciparum Transmission. PLoS Med. 2008. Jul;5(7):e142. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Huang XX, Toure H, Biswas G. Resource tracking for neglected tropical disease programmes: the first step for developing a sustainable financing strategy. Trans R Soc Trop Med Hyg. 2021;115(2):179–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Institute for Health Metrics and Evaluation (IHME). Financing Global Health 2021: Global Health Priorities in a Time of Change [Internet]. Seattle, WA: IHME; 2023. [cited 2023 Nov 29]. Available from: https://www.healthdata.org/research-analysis/library/financing-global-health-2021-global-health-priorities-time-change [Google Scholar]
29.Neglected Tropical Diseases Program. NTD Donor Landscape 2017 [Internet]. USAID; 2020. [cited 2023 Dec 4]. Available from: https://www.neglecteddiseases.gov/ntd-donor-landscape-2017/. [Google Scholar]
30.Korenromp EL, Hosseini M, Newman RD, Cibulskis RE. Progress towards malaria control targets in relation to national malaria programme funding. Malar J. 2013;12:18. doi: 10.1186/1475-2875-12-18 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.IHME. Institute for Health Metrics and Evaluation. [cited 2023 Dec 5]. Financing Global Health. Available from: http://vizhub.healthdata.org/fgh/. [Google Scholar]
32.Bockarie MJ, Rebollo MP. Reducing the population requiring interventions against lymphatic filariasis in Africa. Lancet Glob Health. 2016;4(3):e154-5. doi: 10.1016/S2214-109X(15)00292-2 [DOI] [PubMed] [Google Scholar]
33.Goniewicz K, Burkle FM, Horne S, Borowska-Stefańska M, Wiśniewski S, Khorram-Manesh A. The Influence of War and Conflict on Infectious Disease: A Rapid Review of Historical Lessons We Have Yet to Learn. Sustainability. 2021;13(19):10783. doi: 10.3390/su131910783 [DOI] [Google Scholar]
34.World Health Organization. Scaling up insecticide-treated netting programmes in Africa [Internet]. WHO; 2005. [cited 2023 Nov 29]. Available from: https://www.who.int/publications/m/item/scaling-up-insecticide-treated-netting-programmes-in-africa [Google Scholar]
35.Chesnais CB, Awaca-Uvon N-P, Vlaminck J, Tambwe J-P, Weil GJ, Pion SD, et al. Risk factors for lymphatic filariasis in two villages of the Democratic Republic of the Congo. Parasit Vectors. 2019;12(1):162. doi: 10.1186/s13071-019-3428-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Chesnais CB, Missamou F, Pion SD, Bopda J, Louya F, Majewski AC, et al. A case study of risk factors for lymphatic filariasis in the Republic of Congo. Parasit Vectors. 2014;7:300. doi: 10.1186/1756-3305-7-300 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.World Health Organization. WHO guidelines for malaria [Internet].2024. Nov [cited 2025 Mar 26]. Available from: https://www.who.int/publications/i/item/guidelines-for-malaria [Google Scholar]
38.World Health Organization. Malaria Policy Advisory Committee meeting report (October 2019) [Internet]. 2019. Oct [cited 2025 Mar 26]. Available from: https://www.who.int/publications/i/item/WHO-CDS-GMP-2019.12 [Google Scholar]
39.Koenker H, Kumoji EK, Erskine M, Opoku R, Sternberg E, Taylor C. Reported reasons for non-use of insecticide-treated nets in large national household surveys, 2009–2021. Malar J. 2023;22:61. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Davis EL, Prada J, Reimer LJ, Hollingsworth TD. Modelling the impact of vector control on lymphatic filariasis programs: current approaches and limitations. Clin Infect Dis Off Publ Infect Dis Soc Am. 2021;72(Suppl 3):S152-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Kelly-Hope LA, Molyneux DH, Bockarie MJ. Can malaria vector control accelerate the interruption of lymphatic filariasis transmission in Africa; capturing a window of opportunity?. Parasite Vectors. 2013;6(1):39. [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Reimer LJ, Thomsen EK, Tisch DJ, Henry-Halldin CN, Zimmerman PA, Baea ME, et al. Insecticidal bed nets and filariasis transmission in Papua New Guinea. N Engl J Med. 2013;369(8):745–53. doi: 10.1056/NEJMoa1207594 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Stevens ER, Aldridge A, Degbey Y, Pignandi A, Dorkenoo MA, Hugelen-Padin J. Evaluation of the 2011 long-lasting, insecticide-treated net distribution for universal coverage in Togo. Malar J. 2013;12:162. doi: 10.1186/1475-2875-12-162 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Moraga P, Cano J, Baggaley RF, Gyapong JO, Njenga SM, Nikolay B, et al. Modelling the distribution and transmission intensity of lymphatic filariasis in sub-Saharan Africa prior to scaling up interventions: integrated use of geostatistical and mathematical modelling. Parasit Vectors. 2015;8:560. doi: 10.1186/s13071-015-1166-x [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Bockarie MJ, Pedersen EM, White GB, Michael E. Role of vector control in the global program to eliminate lymphatic filariasis. Annu Rev Entomol. 2009;54:469–87. doi: 10.1146/annurev.ento.54.110807.090626 [DOI] [PubMed] [Google Scholar]
46.Richards FO, Emukah E, Graves PM, Nkwocha O, Nwankwo L, Rakers L. Community-wide distribution of long-lasting insecticidal nets can halt transmission of lymphatic filariasis in southeastern Nigeria. Am J Trop Med Hyg. 2013;89(3):578–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Rebollo MP, Sambou SM, Thomas B, Biritwum N-K, Jaye MC, Kelly-Hope L, et al. Elimination of lymphatic filariasis in the Gambia. PLoS Negl Trop Dis. 2015;9(3):e0003642. doi: 10.1371/journal.pntd.0003642 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Duerr HP, Dietz K, Eichner M. Determinants of the eradicability of filarial infections: a conceptual approach. Trends Parasitol. 2005. Feb;21(2):88–96. [DOI] [PubMed] [Google Scholar]
49.Lindsay SW, Thomas MB, Kleinschmidt I. Threats to the effectiveness of insecticide-treated bednets for malaria control: thinking beyond insecticide resistance. Lancet Glob Health. 2021;9(9):e1325–31. doi: 10.1016/S2214-109X(21)00216-3 [DOI] [PubMed] [Google Scholar]
50.Kreppel KS, Viana M, Main BJ, Johnson PCD, Govella NJ, Lee Y. Emergence of behavioural avoidance strategies of malaria vectors in areas of high LLIN coverage in Tanzania. Sci Rep. 2020;10(1). [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Sokhna C, Ndiath MO, Rogier C. The changes in mosquito vector behaviour and the emerging resistance to insecticides will challenge the decline of malaria. Clin Microbiol Infect. 2013;19(10):902–7. doi: 10.1111/1469-0691.12314 [DOI] [PubMed] [Google Scholar]
52.Sanou A, Nelli L, Guelbéogo WM, Cissé F, Tapsoba M, Ouédraogo P, et al. Insecticide resistance and behavioural adaptation as a response to long-lasting insecticidal net deployment in malaria vectors in the Cascades region of Burkina Faso. Sci Rep. 2021;11(1):17569. [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Brengues J, Bain O. Passage des microfilaires de l’estomac vers l’hémocèle du vecteur, dans les couples Wuchereria bancrofti - Anopheles gambiae A, W. bancrofti - Aedes aegypti et Setaria labiatopapillosa - A. aegypti. In 1972. [cited 2023 Nov 29]. Available from: https://www.semanticscholar.org/paper/Passage-des-microfilaires-de-l’estomac-vers-du-dans-Brengues-Bain/d80e1f649c998a607ce30081aedbf97898ec96c6 [Google Scholar]
54.Subra R. Biology and control of Culex pipiens quinquefasciatus Say, 1823 (Diptera, Culicidae) with special reference to Africa. Int J Trop Insect Sci. 1981;1(4):319–38. [Google Scholar]
55.Ughasi J, Bekard HE, Coulibaly M, Adabie-Gomez D, Gyapong J, Appawu M, et al. Mansonia africana and Mansonia uniformis are vectors in the transmission of Wuchereria bancrofti lymphatic filariasis in Ghana. Parasit Vectors. 2012;5:89. doi: 10.1186/1756-3305-5-89 [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Amuzu H, Wilson MD, Boakye DA. Studies of Anopheles gambiae s.l (Diptera: Culicidae) exhibiting different vectorial capacities in lymphatic filariasis transmission in the Gomoa district, Ghana. Parasit Vectors. 2010;3:85. doi: 10.1186/1756-3305-3-85 [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Zagaria N, Savioli L. Elimination of lymphatic filariasis: a public-health challenge. Ann Trop Med Parasitol. 2002;96 Suppl 2:S3-13. doi: 10.1179/00034980215002347 [DOI] [PubMed] [Google Scholar]
58.Koudou BG, de Souza DK, Biritwum N-K, Bougma R, Aboulaye M, Elhassan E, et al. Elimination of lymphatic filariasis in west African urban areas: is implementation of mass drug administration necessary?. Lancet Infect Dis. 2018;18(6):e214–20. doi: 10.1016/S1473-3099(18)30069-0 [DOI] [PubMed] [Google Scholar]
59.Matowo NS, Abbasi S, Munhenga G, Tanner M, Mapua SA, Oullo D, et al. Fine-scale spatial and temporal variations in insecticide resistance in Culex pipiens complex mosquitoes in rural south-eastern Tanzania. Parasit Vectors. 2019;12(1):413. doi: 10.1186/s13071-019-3676-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Rai P, Bharati M, Subba A, Saha D. Insecticide resistance mapping in the vector of lymphatic filariasis, Culex quinquefasciatus Say from northern region of West Bengal, India. PLoS One. 2019;14(5):e0217706. doi: 10.1371/journal.pone.0217706 [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Bradley J, Ogouyèmi-Hounto A, Cornélie S, Fassinou J, de Tove YSS, Adéothy AA, et al. Insecticide-treated nets provide protection against malaria to children in an area of insecticide resistance in Southern Benin. Malar J. 2017;16(1):225. doi: 10.1186/s12936-017-1873-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Henry MC, Assi S, Rogier C, Dossou-Yovo J, Chandre F, Guillet P. Protective efficacy of lambda-cyhalothrin treated nets in Anopheles gambiae pyrethroid resistance areas of Côte d’Ivoire. ResearchGate. 2005;73(5):859–64. [PubMed] [Google Scholar]
63.Lindblade KA, Mwandama D, Mzilahowa T, Steinhardt L, Gimnig J, Shah M. A cohort study of the effectiveness of insecticide-treated bed nets to prevent malaria in an area of moderate pyrethroid resistance, Malawi. Malar J. 2015;14:31. [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Okoyo C, Mwandawiro C, Kihara J, Simiyu E, Gitonga CW, Noor AM, et al. Comparing insecticide-treated bed net use to Plasmodium falciparum infection among schoolchildren living near Lake Victoria, Kenya. Malar J. 2015;14:515. [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Uttah E, Wokem GN, Okonofua C. The abundance and biting patterns of Culex quinquefasciatus Say (Culicidae) in the coastal region of Nigeria. ResearchGate. 2013;2013(5):1–7. [Google Scholar]
66.Al-Eryani SM, Irish SR, Carter TE, Lenhart A, Aljasari A, Montoya LF. Public health impact of the spread of Anopheles stephensi in the WHO Eastern Mediterranean Region countries in Horn of Africa and Yemen: need for integrated vector surveillance and control. Malar J. 2023;22(1):187. [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Chakraborty S, Ray S, Tandon N. Seasonal prevalence of Anopheles stephensi larvae and existence of two forms of the species in an urban garden in Calcutta City. Indian J Malariol. 1998;35(1):8–14. [PubMed] [Google Scholar]
68.Mariappan T, Thenmozhi V, Udayakumar P, Bhavaniumadevi V, Tyagi B. An observation on breeding behaviour of three different vector species (Aedes aegypti Linnaeus 1762, Anopheles stephensi Liston 1901 and Culex quinquefasciatus Say 1823) in wells in the coastal region of Ramanathapuram district, Tamil Nadu, India. Int J Mosq Res [Internet]. 2015. Jun 1 [cited 2023 Nov 29]; Available from: https://www.semanticscholar.org/paper/An-observation-on-breeding-behaviour-of-three-(-and-Mariappan-Thenmozhi/a4670bbbf5fd44745425859e313e8a6afb8ead5e [Google Scholar]
69.Weetman D, Kamgang B, Badolo A, Moyes CL, Shearer FM, Coulibaly M, et al. Aedes mosquitoes and Aedes-borne arboviruses in Africa: current and future threats. Int J Environ Res Public Health. 2018;15(2):220. doi: 10.3390/ijerph15020220 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0013165.r001

Decision Letter 0

Nigel Beebe

Examining the overlap in lymphatic filariasis prevalence and malaria insecticide-treated net access-use in endemic Africa

PLOS Neglected Tropical Diseases

Dear Dr. Whisnant,

Thank you for submitting your manuscript to PLOS Neglected Tropical Diseases. After careful consideration, we feel that it has merit but does not fully meet PLOS Neglected Tropical Diseases's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript within 60 days Feb 20 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosntds@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pntd/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

* A rebuttal letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to any formatting updates and technical items listed in the 'Journal Requirements' section below.

* A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

* An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Nigel Beebe, PhD

Section Editor

PLOS Neglected Tropical Diseases

Nigel Beebe

Section Editor

PLOS Neglected Tropical Diseases

Shaden Kamhawi

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

orcid.org/0000-0003-4304-636XX

Paul Brindley

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

orcid.org/0000-0003-1765-0002

Additional Editor Comments :

Please respond to reviewers comments and suggestions, especially reviewer 2.

Journal Requirements:

1) Please upload all main figures as separate Figure files in .tif or .eps format. For more information about how to convert and format your figure files please see our guidelines:

https://journals.plos.org/plosntds/s/figures

2) Some material included in your submission may be copyrighted. According to PLOSu2019s copyright policy, authors who use figures or other material (e.g., graphics, clipart, maps) from another author or copyright holder must demonstrate or obtain permission to publish this material under the Creative Commons Attribution 4.0 International (CC BY 4.0) License used by PLOS journals. Please closely review the details of PLOSu2019s copyright requirements here: PLOS Licenses and Copyright. If you need to request permissions from a copyright holder, you may use PLOS's Copyright Content Permission form.

Please respond directly to this email and provide any known details concerning your material's license terms and permissions required for reuse, even if you have not yet obtained copyright permissions or are unsure of your material's copyright compatibility. Once you have responded and addressed all other outstanding technical requirements, you may resubmit your manuscript within Editorial Manager.

Potential Copyright Issues:

i) Figures 1, 2, 3, 4, and S1-S10. Please (a) provide a direct link to the base layer of the map (i.e., the country or region border shape) and ensure this is also included in the figure legend; and (b) provide a link to the terms of use / license information for the base layer image or shapefile. We cannot publish proprietary or copyrighted maps (e.g. Google Maps, Mapquest) and the terms of use for your map base layer must be compatible with our CC BY 4.0 license.

Note: if you created the map in a software program like R or ArcGIS, please locate and indicate the source of the basemap shapefile onto which data has been plotted.

If your map was obtained from a copyrighted source please amend the figure so that the base map used is from an openly available source. Alternatively, please provide explicit written permission from the copyright holder granting you the right to publish the material under our CC BY 4.0 license.

If you are unsure whether you can use a map or not, please do reach out and we will be able to help you. The following websites are good examples of where you can source open access or public domain maps:

* U.S. Geological Survey (USGS) - All maps are in the public domain. (http://www.usgs.gov)

* PlaniGlobe - All maps are published under a Creative Commons license so please cite u201cPlaniGlobe, http://www.planiglobe.com, CC BY 2.0u201d in the image credit after the caption. (http://www.planiglobe.com/?lang=enl)

* Natural Earth - All maps are public domain. (http://www.naturalearthdata.com/about/terms-of-use/).

3) Thank you for stating that "the code is publicly available via GIT repository." Please note that your Data Availability Statement is currently missing the DOI/accession number of each dataset OR a direct link to access each dataset. If your manuscript is accepted for publication, you will be asked to provide these details on a very short timeline. We therefore suggest that you provide this information now, though we will not hold up the peer review process if you are unable.

4) Please amend your detailed Financial Disclosure statement. This is published with the article. It must therefore be completed in full sentences and contain the exact wording you wish to be published.

1) State what role the funders took in the study. If the funders had no role in your study, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

Reviewers' Comments:

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: I am unable to assess the statistical and geospatial analysis in the paper. Other aspects of the methodology are appropriately described.

Reviewer #2: The objectives are clearly stated. The paper aims to overlay published geospatial estimates of ITN access and use, and LF prevalence estimates at 33 LF and malaria endemic locations in Africa. The paper then looks at the ITN coverage in high and low LF prevalence areas to assess how effective malaria control programs have been in providing access and use to ITNs that would potentially contribute to interruption of LF. The study design is appropriate to address this since these spatial datasets are available through the extensive prior published work on the three aspects studied (ITNs access and use, LF prevalence spatial prediction, Malaria prevalence spatial prediction PfPR) that has been done by this group and many others, together with the decades of work of surveyors and national programmes who provided the raw data to the spatial modelers of the three aspects. I appreciated being provided with the prior publications of these parameters as well as IRS coverage. Putting these three predictions together is a valid and useful approach to visualizing and assessing the overlap in ITN access-use and LF/malaria prevalence. The explanation for which year has been chosen for the overlap is clear and reasonable (lines 136 -137). However, there are some gaps in 1) how the results are explained 2) evaluating the implications of the findings for future control. There is no specific hypothesis about what would have been expected as far as I could tell.

Given that the LF prevalence estimates are produced by 5 km2 grid (Local Burden of Disease collaborators, 2019) like the ITN and PfPR, why did you choose to aggregate the estimates into Implementation units (line 147)? What is the usual size of an IU (district or other unit?) and how much does it vary by country?

**********

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: Yes.

Reviewer #2: Presentation of results is sometimes very confusing. Definitions need to be very clear and cutoffs (e.g. low' or high' ITN "coverage") justified.

The first issue I had is with the definition of ITN 'coverage'. It is unfortunate that this word has been used to refer to a composite of access and use "where use among the total population and access among the total population were both separately above or below a specified threshold" line 160. ITN coverage was traditionally defined at the household level by WHO and in DHS surveys ("Proportion of HH with at least 1 net"). I understand that this is likely now out of date, as is the household rather than individual definition of access, and better measures such as nets per capita are becoming more common (as defined clearly in the Bertozzi-Villa 2021 paper). Nevertheless it might have been better to coin another term rather than 'coverage' to avoid confusion. Also, a composite measure of two parameters seems less than optimal when one (use) depends on the other (access). I understand the desire for one measure, but this one seems overcomplicated to me.

I find the following sentences in the abstract very obscure and contradictory:

"in the analysis, almost half of the locations (47.1%; 82/174 of IUs) with high LF prevalence (>5%) had at least 40% coverage with ITN access and use. Among high LF prevalence areas, both access and use were low, with 51.7% (90/174) having both access and use estimates under 40%. Additionally when classified using malaria PfPR, most (67.8%; 61/90) of these low ITN coverage, high LF prevalence locations were also considered high prevalence for malaria, Among areas with low LF prevalence (<5%), over half had ITN access >=40% (56.0%, 2812/5021) while only 48.5% (2437/5021) had use >=40%"

I have read this several times, but still cannot grasp what you are trying to say. The problems include

1. sometimes reporting combined access and use, and sometimes the two separately

2. including the % cutoffs for the levels of "coverage" or prevalence (not really necessary once defined in text), next to the % in each category

3. some comparisons using IUs and some using 5km2 units.. why?

So what do we conclude? The malaria programs have successfully covered the LF high prevalence areas, or not? Do we need special net programmes in low malaria/high LF areas?

It might help to provide some tables on the distribution of the geographical units into different categories of LF prevalence and ITN access/use, stratified by malaria PfPR.

I don't see why the LF prevalence count maps are useful (figs 1 and 2) in addition to the LF prevalence maps, if the counts are not adjusted for population. What do they add?

In the Discussion, the first paragraph is very important, and should be captured better in abstract somehow. But I am puzzled by the last sentence (and lines 248 to 250 and lines 259 to 261 which make the same point). If half of the areas had high malaria prevalence, then wouldn't they be targeted for ITN distribution anyway?. If they have low ITN access and use, doesn't that represent a failure of the malaria programme, not need for integration or extra programmes? The areas of most concern would seem to me to be those with low malaria but high LF, where special effort to provide nets for LF might be needed.

**********

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: Yes

Reviewer #2: The last three sentences of the abstract are very general, and could have been written before the work was done. They do not summarize the findings of the study. What can we conclude based on the data presented here? What is the degree to which malaria control programmes have achieved access and use to ITNs in malaria endemic areas? What more should programmes be doing? In which areas should net distribution be enhanced (line 79?).

If net access/use is low in high malaria and LF areas, isn't that a failure of net distribution for malaria, not a need for 'integration' whatever that means.

**********

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: I am not sure if the addition of published manuscripts as part of the supplementary materials is important. The reference to these manuscripts should suffice.

Reviewer #2: Lines 94 to 96 are not quite correct. Post-validation surveillance (PVS) is not the period of transmission assessment after MDA - that is post MDA surveillance, which is first. PVS comes after validation (several years after MDA stops and multiple TAS surveys have been done)

A couple of places with missing refs line 195 and 225

Results text 163-171, 190-195 and 201 onwards, consider putting into Tables to make clearer. It's quite hard to read at the moment.

The Discussion seems overlong, while the Results section could be expanded to be clearer. The limitations of the data analysis are well described, but a bit belaboured since the work is quite comprehensive and impressive.

**********

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: The paper describes the overlap of LF, malaria, and ITN (access and use) and is important in understanding the role of vector control for LF. Although I am unable to assess the statistical approaches, the results are informative. Overall, the paper is well written. There are just some few comments that I believe the authors should address.

Line 55: Italicise Plasmodium falciparum

Line 121: delete the word "endemic" before Africa.

Lines 157 - 158: "For ITN coverage (access and use) and malaria prevalence we used the following definitions: lowest <20%; low 20-39.9%; high 40%-59.9%; highest ≥60%" Kindly clarify. Are these thresholds for ITN coverage or malaria prevalence?

Line 167: Check the reference error

Line 195: Chech the reference error

Line 225: Check the reference error

- An important question to be addressed in this work is the ITN coverage level that results in significant impact on LF prevalence and control. ITN access and use >40% does not really explain the level at which ITNs start having an impact. In Figures 1 and 2, for instance, areas with high LF and high ITN access are shown. However, if high ITN access and use was effective, one would expect low LF prevalence in these high ITN access and use areas. How do the authors explain these?

Lines 273 - 283: In parts of Africa, especially along the East Africa coast, Culex species play a role in the transmission of LF. The authors should explain the overlap of high LF and high malaria in these areas and possibly the low impact of ITNs.

- It is also important to note that malaria vector interventions result in vector behavior modifications, with vectors biting more outdoors than indoors. This could possibly explain the lack of correlation between high malaria and high ITN use and high LF prevalence and should be discussed.

Reviewer #2: Overall, this is a good piece of work combining these datasets but it needs more clarity in the results and more interpretation of what the findings mean for programmes.

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

Figure resubmission:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. If there are other versions of figure files still present in your submission file inventory at resubmission, please replace them with the PACE-processed versions.

Reproducibility:

PLoS Negl Trop Dis. 2025 Jun 26;19(6):e0013165. doi: 10.1371/journal.pntd.0013165.r002

Author response to Decision Letter 1

13 Feb 2025

Attachment

Submitted filename: Response to Reviewers_final.docx

pntd.0013165.s014.docx^{(53.1KB, docx)}

PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0013165.r003

Decision Letter 1

Nigel Beebe

Response to Reviewers Revised Manuscript with Track Changes Manuscript

Shaden Kamhawi

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

orcid.org/0000-0003-4304-636XX

Paul Brindley

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

orcid.org/0000-0003-1765-0002

Additional Editor Comments: Journal Requirements:

- State the initials, alongside each funding source, of each author to receive each grant. For example: "This work was supported by the National Institutes of Health (####### to AM; ###### to CJ) and the National Science Foundation (###### to AM)."

- State what role the funders took in the study. If the funders had no role in your study, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.".

If you did not receive any funding for this study, please simply state: u201cThe authors received no specific funding for this work.u201d

Reviewers' comments:

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: Yes

Reviewer #2: Thanks for the diligence in responding to the comments. I am happy with the responses relevant to this section.

**********

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: Yes

Reviewer #2: Thanks for providing the new Table classifying IUs as suggested. It is very useful to be able to conceptualize the scale of the problem and should help program managers. I strongly suggest that it be put into the main text as most people don't look at Supplementary files and this table is very important and useful.

**********

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: Yes

Reviewer #2: Revised text is much better and clearer.

**********

Editorial and Data Presentation Modifications?

Reviewer #1: (No Response)

Reviewer #2: Ref 19 is a bit garbled (Organization, WH Filariasis GP to EL?). Can be solved in EndNote etc by where you put the comma. WHO should probably be listed as author in several other refs e.g. 1, 2,7, 10, 11, 34, 57.

Something missing ref 53? Biology and control of....

**********

Summary and General Comments

Reviewer #1: My comments are appropriately addressed.

Reviewer #2: Overall the authors have been very responsive to the comments and the paper seems much improved as a result.

I have two remaining comments, in addition to suggesting that the new Supp Table be put in main text:

Lines 271-281 about net distribution policies, alluded to a couple of times in the responses: It is true that in the early days of LLIN, nets were prioritized to children under 5 and pregnant women through antenatal clinics. This happened quite a long time ago (>10 yrs) and I am not aware of anywhere that uses this as the sole method now as it is known not to achieve high enough access (it is used a supplementary continuous distribution method in some countries). So I would change this text to refer to past distribution policies, which may indeed have resulted in low access, but policies have already moved on in WHO and other guidelines towards universal (all ages in population) coverage (could be cited https://www.who.int/publications/i/item/guidelines-for-malaria) and followed by Global Fund etc . Gaps now are probably more about lack of sub-national prioritization of universal coverage in highest risk areas rather than blanket national policies. You could say "PAST sub-optimal ITN coverage" and "IN THE PAST, some malaria ITN programs primarily distributed ITNs via antenatal clinics". "Extend the scope of ITN programmes" is a bit vague and might be better as "increase the access and use of nets in the highest priority areas" or similar.

In the paragraph lines 287 to 297 you do not currently cite the paper by Richards et al 2013 Community-Wide Distribution of Long-Lasting Insecticidal Nets Can Halt Transmission of Lymphatic Filariasis in Southeastern Nigeria

https://doi.org/10.4269/ajtmh.12-0775. This is a very relevant paper you could consider citing that shows the impact of LLIN on LF transmission in an area in Africa, with multiple LF vectors, that had not had MDA for LF, comparing full access of nets with targeted (under five/pregnant women) distribution. An important finding apart from the impact on LF transmission was how many nets had to be given out to get full access (1 net per 2 people) in many large households. Disclosure: I co-managed this project and analysis, and am an author on the paper .

The other responses are comprehensive and satisfactory, thanks.

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: No

Reviewer #2: Yes: Patricia M Graves

Figure resubmission:Reproducibility:--> -->-->To enhance the reproducibility of your results, we recommend that authors of applicable studies deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols-->?>

PLoS Negl Trop Dis. 2025 Jun 26;19(6):e0013165. doi: 10.1371/journal.pntd.0013165.r004

Author response to Decision Letter 2

31 Mar 2025

Attachment

Submitted filename: Second round Response to Reviewers.docx

pntd.0013165.s015.docx^{(23.4KB, docx)}

PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0013165.r005

Decision Letter 2

Nigel Beebe

Dear Ms. Whisnant,

We are pleased to inform you that your manuscript 'Examining the overlap in lymphatic filariasis prevalence and malaria insecticide-treated net access-use in endemic Africa' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Nigel Beebe, PhD

Section Editor

PLOS Neglected Tropical Diseases

Nigel Beebe

Section Editor

PLOS Neglected Tropical Diseases

Shaden Kamhawi

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

orcid.org/0000-0003-4304-636XX

Paul Brindley

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

orcid.org/0000-0003-1765-0002

***********************************************************

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; line-height: 16.0px; font: 14.0px Arial; color: #323333; -webkit-text-stroke: #323333}span.s1 {font-kerning: none

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #2: Yes

**********

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #2: Yes

**********

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #2: Yes

**********

Editorial and Data Presentation Modifications?

Reviewer #2: I suggest spelling out EPHP (presumably Elimination as a Public Health Problem) in line 294. It's only used once, and some readers may not be familiar with it.

**********

Summary and General Comments

Reviewer #2: Thanks for making the changes suggested. All looks good now.

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #2: Yes: Patricia Graves

PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0013165.r006

Acceptance letter

Nigel Beebe

Dear Ms. Whisnant,

We are delighted to inform you that your manuscript, "Examining the overlap in lymphatic filariasis prevalence and malaria insecticide-treated net access-use in endemic Africa," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

We have now passed your article onto the PLOS Production Department who will complete the rest of the publication process. All authors will receive a confirmation email upon publication.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any scientific or type-setting errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Note: Proofs for Front Matter articles (Editorial, Viewpoint, Symposium, Review, etc...) are generated on a different schedule and may not be made available as quickly.

Soon after your final files are uploaded, the early version of your manuscript will be published online unless you opted out of this process. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Shaden Kamhawi

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

Paul Brindley

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Overlay map of LF prevalence (%; 2018) and IRS use (%; 2020).

(PDF)

pntd.0013165.s001.pdf^{(8.2MB, pdf)}

S2 Fig. Overlay map of LF prevalence (counts; 2018) and IRS use (%; 2020).

(PDF)

pntd.0013165.s002.pdf^{(8.2MB, pdf)}

S3 Fig. Overlay map of LF prevalence (%; 2018) and malaria PfPR prevalence (%; 2019).

(PDF)

pntd.0013165.s003.pdf^{(8.2MB, pdf)}

S4 Fig. Overlay map of LF prevalence (%; 2018) and ITN access among the total population (%; 2018).

(PDF)

pntd.0013165.s004.pdf^{(8.2MB, pdf)}

S5 Fig. Overlay map of LF prevalence (counts; 2018) and ITN access among the total population (%; 2018).

(PDF)

pntd.0013165.s005.pdf^{(8.2MB, pdf)}

S6 Fig. Overlay map of LF prevalence (%; 2018) and ITN use among the total population (%; 2018).

(PDF)

pntd.0013165.s006.pdf^{(8.2MB, pdf)}

S7 Fig. Overlay map of LF prevalence (counts; 2018) and ITN use among the total population (%; 2018).

(PDF)

pntd.0013165.s007.pdf^{(8.2MB, pdf)}

S8 Fig. Overlay map of LF prevalence (%; 2018) and IRS use (%; 2018).

(PDF)

pntd.0013165.s008.pdf^{(8.2MB, pdf)}

S9 Fig. Overlay map of LF prevalence (counts; 2018) and IRS use (%; 2018).

(PDF)

pntd.0013165.s009.pdf^{(8.2MB, pdf)}

S10 Fig. Overlay map of LF prevalence (%; 2018) and malaria PfPR prevalence (%; 2018).

The bivariate choropleth map and scatter plot color key in the center indicate the degree to which LF prevalence (vertical axis, white to red) and malaria Pf prevalence (horizontal axis, white to blue) overlap. Grey indicates areas considered to be non-endemic. LF: lymphatic filariasis; Pf: Plasmodium falciparum. Map base layer shapefile is from ESPEN, available from: https://espen.afro.who.int/tools-resources/data-query-tools/cartography-database [18].

(PDF)

pntd.0013165.s010.pdf^{(8.2MB, pdf)}

S1 Data. Codebook.

(XLSX)

pntd.0013165.s011.xlsx^{(10.4KB, xlsx)}

S2 Data. Results dataset.

(CSV)

pntd.0013165.s012.csv^{(777KB, csv)}

Attachment

Submitted filename: Response to Reviewers_final.docx

pntd.0013165.s014.docx^{(53.1KB, docx)}

Attachment

Submitted filename: Second round Response to Reviewers.docx

pntd.0013165.s015.docx^{(23.4KB, docx)}

Data Availability Statement

[pntd.0013165.ref001] 1.World Health Organization. Global programme to eliminate lymphatic filariasis: progress report, 2021 [Internet]. WHO; 2022. [cited 2023 Sep 14]. Available from: https://www.who.int/publications/i/item/who-wer9741-513-524 [Google Scholar]

[pntd.0013165.ref002] 2.World Health Organization. Ending the neglect to attain the Sustainable Development Goals: A road map for neglected tropical diseases 2021–2030 [Internet]. WHO; 2021. [cited 2023 Sep 14]. Available from: https://www.who.int/publications/i/item/9789240010352 [Google Scholar]

[pntd.0013165.ref003] 3.United Nations Department of Economic and Social Affairs. The Sustainable Development Goals Report 2023: Special Edition [Internet]. United Nations; 2023. Jul [cited 2023 Sep 18]. Available from: https://www.un-ilibrary.org/content/books/9789210024914 [Google Scholar]

[pntd.0013165.ref004] 4.World Health Organization. Lymphatic filariasis. [cited 2023 September 18]. Available from: https://www.who.int/news-room/fact-sheets/detail/lymphatic-filariasis [Google Scholar]

[pntd.0013165.ref005] 5.Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet. 2020;396(10258):1204–22. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref006] 6.de Souza DK, Koudou B, Kelly-Hope LA, Wilson MD, Bockarie MJ, Boakye DA. Diversity and transmission competence in lymphatic filariasis vectors in West Africa, and the implications for accelerated elimination of Anopheles-transmitted filariasis. Parasit Vectors. 2012;5:259. doi: 10.1186/1756-3305-5-259 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref007] 7.World Health Organization. World malaria report 2022 [Internet]. WHO; 2022. [cited 2023 Sep 14]. Available from: https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2022 [Google Scholar]

[pntd.0013165.ref008] 8.van den Berg H, Kelly-Hope LA, Lindsay SW. Malaria and lymphatic filariasis: the case for integrated vector management. Lancet Infect Dis. 2013;13(1):89–94. doi: 10.1016/S1473-3099(12)70148-2 [DOI] [PubMed] [Google Scholar]

[pntd.0013165.ref009] 9.Manguin S, Bangs MJ, Pothikasikorn J, Chareonviriyaphap T. Review on global co-transmission of human Plasmodium species and Wuchereria bancrofti by Anopheles mosquitoes. Infect Genet Evol. 2010;10(2):159–77. doi: 10.1016/j.meegid.2009.11.014 [DOI] [PubMed] [Google Scholar]

[pntd.0013165.ref010] 10.World Health Organization. WHO position statement on integrated vector management to control malaria and lymphatic filariasis. WHO. 2011. Available from: https://www.who.int/publications-detail-redirect/who-wer8613-121-127 [PubMed] [Google Scholar]

[pntd.0013165.ref011] 11.World Health Organization. Global vector control response 2017–2030. WHO. 2017. Available from: https://www.who.int/publications/i/item/9789241512978 [Google Scholar]

[pntd.0013165.ref012] 12.Cromwell EA, Schmidt CA, Kwong KT, Pigott DM, Mupfasoni D, Biswas G. The global distribution of lymphatic filariasis, 2000–18: a geospatial analysis. Lancet Glob Health. 2020;8(9):e1186-94. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref013] 13.Malaria Atlas Project. Malaria Atlas Project | Data. [cited 2023 September 14]. Available from: https://data.malariaatlas.org/maps?layers=Malaria:202406_Global_Pf_Parasite_Rate [Google Scholar]

[pntd.0013165.ref014] 14.Bertozzi-Villa A, Bever CA, Koenker H, Weiss DJ, Vargas-Ruiz C, Nandi AK, et al. Maps and metrics of insecticide-treated net access, use, and nets-per-capita in Africa from 2000-2020. Nat Commun. 2021;12(1):3589. doi: 10.1038/s41467-021-23707-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref015] 15.Tangena J-AA, Hendriks CMJ, Devine M, Tammaro M, Trett AE, Williams I, et al. Indoor residual spraying for malaria control in sub-Saharan Africa 1997 to 2017: an adjusted retrospective analysis. Malar J. 2020;19(1):150. doi: 10.1186/s12936-020-03216-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref016] 16.Weiss DJ, Lucas TCD, Nguyen M, Nandi AK, Bisanzio D, Battle KE, et al. Mapping the global prevalence, incidence, and mortality of Plasmodium falciparum, 2000–17: a spatial and temporal modelling study. Lancet Lond Engl. 2019;394(10195):322–31. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref017] 17.Lloyd CT, Chamberlain H, Kerr D, Yetman G, Pistolesi L, Stevens FR, et al. Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets. Big Earth Data. 2019;3(2):108–39. doi: 10.1080/20964471.2019.1625151 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref018] 18.Expanded Special Project for Elimination of Neglected Tropical Diseases. Cartography database | ESPEN [Internet]. [cited 2023 Sep 14]. Available from: https://espen.afro.who.int/tools-resources/data-query-tools/cartography-database [Google Scholar]

[pntd.0013165.ref019] 19.WHO & GPELF. Monitoring and epidemiological assessment of the programme to eliminate lymphatic filariasis at implementation unit level [Internet]. WHO; 2005. [cited 2025 Jan 27]. Report No.: WHO/CDS/CPE/CEE/2005.50. Available from: https://iris.who.int/handle/10665/69172 [Google Scholar]

[pntd.0013165.ref020] 20.Nsakashalo-Senkwe M, Mwase E, Chizema-Kawesha E, Mukonka V, Songolo P, Masaninga F. Significant decline in lymphatic filariasis associated with nationwide scale-up of insecticide-treated nets in Zambia. Parasite Epidemiol Control. 2017;2(4):7–14. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref021] 21.Eneanya OA, Reimer LJ, Fischer PU, Weil GJ. Geospatial modelling of lymphatic filariasis and malaria co-endemicity in Nigeria. Int Health. 2023;15(5):566–72. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref022] 22.Stanton MC, Mkwanda S, Mzilahowa T, Bockarie MJ, Kelly-Hope LA. Quantifying filariasis and malaria control activities in relation to lymphatic filariasis elimination: a multiple intervention score map (MISM) for Malawi. Trop Med Int Health. 2014;19(2):224–35. doi: 10.1111/tmi.12266 [DOI] [PubMed] [Google Scholar]

[pntd.0013165.ref023] 23.Okorie PN, Ademowo GO, Saka Y, Davies E, Okoronkwo C, Bockarie MJ, et al. Lymphatic filariasis in Nigeria; micro-stratification overlap mapping (MOM) as a prerequisite for cost-effective resource utilization in control and surveillance. PLoS Negl Trop Dis. 2013;7(9):e2416. doi: 10.1371/journal.pntd.0002416 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref024] 24.Kelly-Hope LA, Thomas BC, Bockarie MJ, Molyneux DH. Lymphatic filariasis in the Democratic Republic of Congo; micro-stratification overlap mapping (MOM) as a prerequisite for control and surveillance. Parasit Vectors. 2011;4:178. doi: 10.1186/1756-3305-4-178 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref025] 25.Flaxman AD, Fullman N, Otten MW Jr, Menon M, Cibulskis RE, Ng M, et al. Rapid scaling up of insecticide-treated bed net coverage in Africa and its relationship with development assistance for health: a systematic synthesis of supply, distribution, and household survey data. PLoS Med. 2010;7(8):e1000328. doi: 10.1371/journal.pmed.1000328 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref026] 26.Snow RW, Guerra CA, Mutheu JJ, Hay SI. International Funding for Malaria Control in Relation to Populations at Risk of Stable Plasmodium falciparum Transmission. PLoS Med. 2008. Jul;5(7):e142. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref027] 27.Huang XX, Toure H, Biswas G. Resource tracking for neglected tropical disease programmes: the first step for developing a sustainable financing strategy. Trans R Soc Trop Med Hyg. 2021;115(2):179–81. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref028] 28.Institute for Health Metrics and Evaluation (IHME). Financing Global Health 2021: Global Health Priorities in a Time of Change [Internet]. Seattle, WA: IHME; 2023. [cited 2023 Nov 29]. Available from: https://www.healthdata.org/research-analysis/library/financing-global-health-2021-global-health-priorities-time-change [Google Scholar]

[pntd.0013165.ref029] 29.Neglected Tropical Diseases Program. NTD Donor Landscape 2017 [Internet]. USAID; 2020. [cited 2023 Dec 4]. Available from: https://www.neglecteddiseases.gov/ntd-donor-landscape-2017/. [Google Scholar]

[pntd.0013165.ref030] 30.Korenromp EL, Hosseini M, Newman RD, Cibulskis RE. Progress towards malaria control targets in relation to national malaria programme funding. Malar J. 2013;12:18. doi: 10.1186/1475-2875-12-18 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref031] 31.IHME. Institute for Health Metrics and Evaluation. [cited 2023 Dec 5]. Financing Global Health. Available from: http://vizhub.healthdata.org/fgh/. [Google Scholar]

[pntd.0013165.ref032] 32.Bockarie MJ, Rebollo MP. Reducing the population requiring interventions against lymphatic filariasis in Africa. Lancet Glob Health. 2016;4(3):e154-5. doi: 10.1016/S2214-109X(15)00292-2 [DOI] [PubMed] [Google Scholar]

[pntd.0013165.ref033] 33.Goniewicz K, Burkle FM, Horne S, Borowska-Stefańska M, Wiśniewski S, Khorram-Manesh A. The Influence of War and Conflict on Infectious Disease: A Rapid Review of Historical Lessons We Have Yet to Learn. Sustainability. 2021;13(19):10783. doi: 10.3390/su131910783 [DOI] [Google Scholar]

[pntd.0013165.ref034] 34.World Health Organization. Scaling up insecticide-treated netting programmes in Africa [Internet]. WHO; 2005. [cited 2023 Nov 29]. Available from: https://www.who.int/publications/m/item/scaling-up-insecticide-treated-netting-programmes-in-africa [Google Scholar]

[pntd.0013165.ref035] 35.Chesnais CB, Awaca-Uvon N-P, Vlaminck J, Tambwe J-P, Weil GJ, Pion SD, et al. Risk factors for lymphatic filariasis in two villages of the Democratic Republic of the Congo. Parasit Vectors. 2019;12(1):162. doi: 10.1186/s13071-019-3428-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref036] 36.Chesnais CB, Missamou F, Pion SD, Bopda J, Louya F, Majewski AC, et al. A case study of risk factors for lymphatic filariasis in the Republic of Congo. Parasit Vectors. 2014;7:300. doi: 10.1186/1756-3305-7-300 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref037] 37.World Health Organization. WHO guidelines for malaria [Internet].2024. Nov [cited 2025 Mar 26]. Available from: https://www.who.int/publications/i/item/guidelines-for-malaria [Google Scholar]

[pntd.0013165.ref038] 38.World Health Organization. Malaria Policy Advisory Committee meeting report (October 2019) [Internet]. 2019. Oct [cited 2025 Mar 26]. Available from: https://www.who.int/publications/i/item/WHO-CDS-GMP-2019.12 [Google Scholar]

[pntd.0013165.ref039] 39.Koenker H, Kumoji EK, Erskine M, Opoku R, Sternberg E, Taylor C. Reported reasons for non-use of insecticide-treated nets in large national household surveys, 2009–2021. Malar J. 2023;22:61. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref040] 40.Davis EL, Prada J, Reimer LJ, Hollingsworth TD. Modelling the impact of vector control on lymphatic filariasis programs: current approaches and limitations. Clin Infect Dis Off Publ Infect Dis Soc Am. 2021;72(Suppl 3):S152-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref041] 41.Kelly-Hope LA, Molyneux DH, Bockarie MJ. Can malaria vector control accelerate the interruption of lymphatic filariasis transmission in Africa; capturing a window of opportunity?. Parasite Vectors. 2013;6(1):39. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref042] 42.Reimer LJ, Thomsen EK, Tisch DJ, Henry-Halldin CN, Zimmerman PA, Baea ME, et al. Insecticidal bed nets and filariasis transmission in Papua New Guinea. N Engl J Med. 2013;369(8):745–53. doi: 10.1056/NEJMoa1207594 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref043] 43.Stevens ER, Aldridge A, Degbey Y, Pignandi A, Dorkenoo MA, Hugelen-Padin J. Evaluation of the 2011 long-lasting, insecticide-treated net distribution for universal coverage in Togo. Malar J. 2013;12:162. doi: 10.1186/1475-2875-12-162 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref044] 44.Moraga P, Cano J, Baggaley RF, Gyapong JO, Njenga SM, Nikolay B, et al. Modelling the distribution and transmission intensity of lymphatic filariasis in sub-Saharan Africa prior to scaling up interventions: integrated use of geostatistical and mathematical modelling. Parasit Vectors. 2015;8:560. doi: 10.1186/s13071-015-1166-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref045] 45.Bockarie MJ, Pedersen EM, White GB, Michael E. Role of vector control in the global program to eliminate lymphatic filariasis. Annu Rev Entomol. 2009;54:469–87. doi: 10.1146/annurev.ento.54.110807.090626 [DOI] [PubMed] [Google Scholar]

[pntd.0013165.ref046] 46.Richards FO, Emukah E, Graves PM, Nkwocha O, Nwankwo L, Rakers L. Community-wide distribution of long-lasting insecticidal nets can halt transmission of lymphatic filariasis in southeastern Nigeria. Am J Trop Med Hyg. 2013;89(3):578–87. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref047] 47.Rebollo MP, Sambou SM, Thomas B, Biritwum N-K, Jaye MC, Kelly-Hope L, et al. Elimination of lymphatic filariasis in the Gambia. PLoS Negl Trop Dis. 2015;9(3):e0003642. doi: 10.1371/journal.pntd.0003642 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref048] 48.Duerr HP, Dietz K, Eichner M. Determinants of the eradicability of filarial infections: a conceptual approach. Trends Parasitol. 2005. Feb;21(2):88–96. [DOI] [PubMed] [Google Scholar]

[pntd.0013165.ref049] 49.Lindsay SW, Thomas MB, Kleinschmidt I. Threats to the effectiveness of insecticide-treated bednets for malaria control: thinking beyond insecticide resistance. Lancet Glob Health. 2021;9(9):e1325–31. doi: 10.1016/S2214-109X(21)00216-3 [DOI] [PubMed] [Google Scholar]

[pntd.0013165.ref050] 50.Kreppel KS, Viana M, Main BJ, Johnson PCD, Govella NJ, Lee Y. Emergence of behavioural avoidance strategies of malaria vectors in areas of high LLIN coverage in Tanzania. Sci Rep. 2020;10(1). [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref051] 51.Sokhna C, Ndiath MO, Rogier C. The changes in mosquito vector behaviour and the emerging resistance to insecticides will challenge the decline of malaria. Clin Microbiol Infect. 2013;19(10):902–7. doi: 10.1111/1469-0691.12314 [DOI] [PubMed] [Google Scholar]

[pntd.0013165.ref052] 52.Sanou A, Nelli L, Guelbéogo WM, Cissé F, Tapsoba M, Ouédraogo P, et al. Insecticide resistance and behavioural adaptation as a response to long-lasting insecticidal net deployment in malaria vectors in the Cascades region of Burkina Faso. Sci Rep. 2021;11(1):17569. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref053] 53.Brengues J, Bain O. Passage des microfilaires de l’estomac vers l’hémocèle du vecteur, dans les couples Wuchereria bancrofti - Anopheles gambiae A, W. bancrofti - Aedes aegypti et Setaria labiatopapillosa - A. aegypti. In 1972. [cited 2023 Nov 29]. Available from: https://www.semanticscholar.org/paper/Passage-des-microfilaires-de-l’estomac-vers-du-dans-Brengues-Bain/d80e1f649c998a607ce30081aedbf97898ec96c6 [Google Scholar]

[pntd.0013165.ref054] 54.Subra R. Biology and control of Culex pipiens quinquefasciatus Say, 1823 (Diptera, Culicidae) with special reference to Africa. Int J Trop Insect Sci. 1981;1(4):319–38. [Google Scholar]

[pntd.0013165.ref055] 55.Ughasi J, Bekard HE, Coulibaly M, Adabie-Gomez D, Gyapong J, Appawu M, et al. Mansonia africana and Mansonia uniformis are vectors in the transmission of Wuchereria bancrofti lymphatic filariasis in Ghana. Parasit Vectors. 2012;5:89. doi: 10.1186/1756-3305-5-89 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref056] 56.Amuzu H, Wilson MD, Boakye DA. Studies of Anopheles gambiae s.l (Diptera: Culicidae) exhibiting different vectorial capacities in lymphatic filariasis transmission in the Gomoa district, Ghana. Parasit Vectors. 2010;3:85. doi: 10.1186/1756-3305-3-85 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref057] 57.Zagaria N, Savioli L. Elimination of lymphatic filariasis: a public-health challenge. Ann Trop Med Parasitol. 2002;96 Suppl 2:S3-13. doi: 10.1179/00034980215002347 [DOI] [PubMed] [Google Scholar]

[pntd.0013165.ref058] 58.Koudou BG, de Souza DK, Biritwum N-K, Bougma R, Aboulaye M, Elhassan E, et al. Elimination of lymphatic filariasis in west African urban areas: is implementation of mass drug administration necessary?. Lancet Infect Dis. 2018;18(6):e214–20. doi: 10.1016/S1473-3099(18)30069-0 [DOI] [PubMed] [Google Scholar]

[pntd.0013165.ref059] 59.Matowo NS, Abbasi S, Munhenga G, Tanner M, Mapua SA, Oullo D, et al. Fine-scale spatial and temporal variations in insecticide resistance in Culex pipiens complex mosquitoes in rural south-eastern Tanzania. Parasit Vectors. 2019;12(1):413. doi: 10.1186/s13071-019-3676-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref060] 60.Rai P, Bharati M, Subba A, Saha D. Insecticide resistance mapping in the vector of lymphatic filariasis, Culex quinquefasciatus Say from northern region of West Bengal, India. PLoS One. 2019;14(5):e0217706. doi: 10.1371/journal.pone.0217706 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref061] 61.Bradley J, Ogouyèmi-Hounto A, Cornélie S, Fassinou J, de Tove YSS, Adéothy AA, et al. Insecticide-treated nets provide protection against malaria to children in an area of insecticide resistance in Southern Benin. Malar J. 2017;16(1):225. doi: 10.1186/s12936-017-1873-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref062] 62.Henry MC, Assi S, Rogier C, Dossou-Yovo J, Chandre F, Guillet P. Protective efficacy of lambda-cyhalothrin treated nets in Anopheles gambiae pyrethroid resistance areas of Côte d’Ivoire. ResearchGate. 2005;73(5):859–64. [PubMed] [Google Scholar]

[pntd.0013165.ref063] 63.Lindblade KA, Mwandama D, Mzilahowa T, Steinhardt L, Gimnig J, Shah M. A cohort study of the effectiveness of insecticide-treated bed nets to prevent malaria in an area of moderate pyrethroid resistance, Malawi. Malar J. 2015;14:31. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref064] 64.Okoyo C, Mwandawiro C, Kihara J, Simiyu E, Gitonga CW, Noor AM, et al. Comparing insecticide-treated bed net use to Plasmodium falciparum infection among schoolchildren living near Lake Victoria, Kenya. Malar J. 2015;14:515. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref065] 65.Uttah E, Wokem GN, Okonofua C. The abundance and biting patterns of Culex quinquefasciatus Say (Culicidae) in the coastal region of Nigeria. ResearchGate. 2013;2013(5):1–7. [Google Scholar]

[pntd.0013165.ref066] 66.Al-Eryani SM, Irish SR, Carter TE, Lenhart A, Aljasari A, Montoya LF. Public health impact of the spread of Anopheles stephensi in the WHO Eastern Mediterranean Region countries in Horn of Africa and Yemen: need for integrated vector surveillance and control. Malar J. 2023;22(1):187. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0013165.ref067] 67.Chakraborty S, Ray S, Tandon N. Seasonal prevalence of Anopheles stephensi larvae and existence of two forms of the species in an urban garden in Calcutta City. Indian J Malariol. 1998;35(1):8–14. [PubMed] [Google Scholar]

[pntd.0013165.ref068] 68.Mariappan T, Thenmozhi V, Udayakumar P, Bhavaniumadevi V, Tyagi B. An observation on breeding behaviour of three different vector species (Aedes aegypti Linnaeus 1762, Anopheles stephensi Liston 1901 and Culex quinquefasciatus Say 1823) in wells in the coastal region of Ramanathapuram district, Tamil Nadu, India. Int J Mosq Res [Internet]. 2015. Jun 1 [cited 2023 Nov 29]; Available from: https://www.semanticscholar.org/paper/An-observation-on-breeding-behaviour-of-three-(-and-Mariappan-Thenmozhi/a4670bbbf5fd44745425859e313e8a6afb8ead5e [Google Scholar]

[pntd.0013165.ref069] 69.Weetman D, Kamgang B, Badolo A, Moyes CL, Shearer FM, Coulibaly M, et al. Aedes mosquitoes and Aedes-borne arboviruses in Africa: current and future threats. Int J Environ Res Public Health. 2018;15(2):220. doi: 10.3390/ijerph15020220 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Examining the overlap in lymphatic filariasis prevalence and malaria insecticide-treated net access-use in endemic Africa

Joanna L Whisnant

Mustafa Kamal Sikder

Gizachew Taddesse Akalu

Tsegaye Alemu

Mubarek Yesse Ashemo

Amelia Bertozzi-Villa

Annie J Browne

Ewerton Cousin

Paulina Agnieszka Dzianach

Yalemzewod Assefa Gelaw

Peter W Gething

Taren M Gorman

Simon I Hay

Olayinka Stephen Ilesanmi

Cathleen Keller

Juniper Boroka Kiss

Jailos Lubinda

Michael A McPhail

Olivia D Nesbit

Gideon Olamilekan Oluwatunase

Verner N Orish

Amel Ouyahia

Susan Fred Rumisha

Adam Saddler

Afeez Abolarinwa Salami

Francesca Sanna

Desalegn Shiferaw

Jacques Lukenze Tamuzi

Daniel J Weiss

Naod Gebrekrstos Zeru

Francis Zeukeng

Stephanie R M Zimsen

Jonathan F Mosser

Roles

Abstract

Author summary

Introduction

Methods

Results

Table 1. Table of number of IUs by LF prevalence, ITN access, and ITN use stratified by level of malaria PfPR. LF: Lymphatic filariasis; ITN: Insecticide-treated bednets; PfPR: Plasmodium falciparum parasite rate.

Fig 1. Overlay map of LF prevalence (counts; 2018) and ITN access among the total population (%; 2020).

Fig 2. Overlay map of LF prevalence (counts; 2018) and ITN use among the total population (%; 2020).

Fig 3. Overlay map and scatter plot of LF prevalence by (%; 2018) and ITN access among the total population (%; 2020).

Fig 4. Overlay map of LF prevalence (%; 2018) and ITN use among the total population (%; 2020).

Discussion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Nigel Beebe

Roles

Author response to Decision Letter 1

Decision Letter 1

Nigel Beebe

Roles

Author response to Decision Letter 2

Decision Letter 2

Nigel Beebe

Roles

Acceptance letter

Nigel Beebe

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases