Identifying co-endemic areas for major filarial infections in sub-Saharan Africa: seeking synergies and preventing severe adverse events during mass drug administration campaigns

BACKGROUND: Onchocerciasis and lymphatic filariasis (LF) are major filarial infections targeted for elimination in most endemic sub-Saharan Africa (SSA) countries by 2020/2025. The current control strategies are built upon community-directed mass administration of ivermectin (CDTI) for onchocerciasis, and ivermectin plus albendazole for LF, with evidence pointing towards the potential for novel drug regimens. When distributing microfilaricides however, considerable care is needed to minimise the risk of severe adverse events (SAEs) in areas that are co-endemic for onchocerciasis or LF and loiasis. This work aims to combine previously published predictive risk maps for onchocerciasis, LF and loiasis to (i) explore the scale of spatial heterogeneity in co-distributions, (ii) delineate target populations for different treatment strategies, and (iii) quantify populations at risk of SAEs across the continent. METHODS: Geographical co-endemicity of filarial infections prior to the implementation of large-scale mass treatment interventions was analysed by combining a contemporary LF endemicity map with predictive prevalence maps of onchocerciasis and loiasis. Potential treatment strategies were geographically delineated according to the level of co-endemicity and estimated transmission intensity. RESULTS: In total, an estimated 251 million people live in areas of LF and/or onchocerciasis transmission in SSA, based on 2015 population estimates. Of these, 96 million live in areas co-endemic for both LF and onchocerciasis, providing opportunities for integrated control programmes, and 83 million live in LF-monoendemic areas potentially targetable for the novel ivermectin-diethylcarbamazine-albendazole (IDA) triple therapy. Only 4% of the at-risk population live in areas co-endemic with high loiasis transmission, representing up to 1.2 million individuals at high risk of experiencing SAEs if treated with ivermectin. In these areas, alternative treatment strategies should be explored, including biannual albendazole monotherapy for LF (1.4 million individuals) and 'test-and-treat' strategies (8.7 million individuals) for onchocerciasis. CONCLUSIONS: These maps are intended to initiate discussion around the potential for tailored treatment strategies, and highlight populations at risk of SAEs. Further work is required to test and refine strategies in programmatic settings, providing the empirical evidence needed to guide efforts towards the 2020/2025 goals and beyond

Model-based geostatistical mapping of the prevalence of onchocerca volvulus in West Africa.

Background: The initial endemicity (pre-control prevalence) of onchocerciasis has been shown to be an important determinant of the feasibility of elimination by mass ivermectin distribution. We present the first geostatistical map of microfilarial prevalence in the former Onchocerciasis Control Programme in West Africa (OCP) before commencement of antivectorial and antiparasitic interventions. Methods and Findings: Pre-control microfilarial prevalence data from 737 villages across the 11 constituent countries in the OCP epidemiological database were used as ground-truth data. These 737 data points, plus a set of statistically selected environmental covariates, were used in a Bayesian model-based geostatistical (B-MBG) approach to generate a continuous surface (at pixel resolution of 5 km x 5km) of microfilarial prevalence in West Africa prior to the commencement of the OCP. Uncertainty in model predictions was measured using a suite of validation statistics, performed on bootstrap samples of held-out validation data. The mean Pearson’s correlation between observed and estimated prevalence at validation locations was 0.693; the mean prediction error (average difference between observed and estimated values) was 0.77%, and the mean absolute prediction error (average magnitude of difference between observed and estimated values) was 12.2%. Within OCP boundaries, 17.8 million people were deemed to have been at risk, 7.55 million to have been infected, and mean microfilarial prevalence to have been 45% (range: 2–90%) in 1975. Conclusions and Significance: This is the first map of initial onchocerciasis prevalence in West Africa using B-MBG. Important environmental predictors of infection prevalence were identified and used in a model out-performing those without spatial random effects or environmental covariates. Results may be compared with recent epidemiological mapping efforts to find areas of persisting transmission. These methods may be extended to areas where data are sparse, and may be used to help inform the feasibility of elimination with current and novel tools

Lymphatic filariasis in the Democratic Republic of Congo; micro-stratification overlap mapping (MOM) as a prerequisite for control and surveillance

<p>Abstract</p> <p>Background</p> <p>The Democratic Republic of Congo (DRC) has a significant burden of lymphatic filariasis (LF) caused by the parasite <it>Wuchereria bancrofti</it>. A major impediment to the expansion of the LF elimination programme is the risk of serious adverse events (SAEs) associated with the use of ivermectin in areas co-endemic for onchocerciasis and loiasis. It is important to analyse these and other factors, such as soil transmitted helminths (STH) and malaria co-endemicity, which will impact on LF elimination.</p> <p>Results</p> <p>We analysed maps of onchocerciasis community-directed treatment with ivermectin (CDTi) from the African Programme for Onchocerciasis Control (APOC); maps of predicted prevalence of <it>Loa loa</it>; planned STH control maps of albendazole (and mebendazole) from the Global Atlas of Helminth Infections (GAHI); and bed nets and insecticide treated nets (ITNs) distribution from Demographic and Health Surveys (DHS) as well as published historic data which were incorporated into overlay maps. We developed an approach we designate as micro-stratification overlap mapping (MOM) to identify areas that will assist the implementation of LF elimination in the DRC. The historic data on LF was found through an extensive review of the literature as no recently published information was available.</p> <p>Conclusions</p> <p>This paper identifies an approach that takes account of the various factors that will influence not only country strategies, but suggests that country plans will require a finer resolution mapping than usual, before implementation of LF activities can be efficiently deployed. This is because 1) distribution of ivermectin through APOC projects will already have had an impact of LF intensity and prevalence 2) DRC has been up scaling bed net distribution which will impact over time on transmission of <it>W. bancrofti </it>and 3) recently available predictive maps of <it>L. loa </it>allow higher risk areas to be identified, which allow LF implementation to be initiated with reduced risk where <it>L. loa </it>is considered non-endemic. We believe that using the proposed MOM approach is essential for planning the expanded distribution of drugs for LF programmes in countries co-endemic for filarial infections.</p

Designing antifilarial drug trials using clinical trial simulators

Lymphatic filariasis and onchocerciasis are neglected tropical diseases (NTDs) targeted for elimination by mass (antifilarial) drug administration. These drugs are predominantly active against the microfilarial progeny of adult worms. New drugs or combinations are needed to improve patient therapy and to enhance the effectiveness of interventions in persistent hotspots of transmission. Several therapies and regimens are currently in (pre-)clinical testing. Clinical trial simulators (CTSs) project patient outcomes to inform the design of clinical trials but have not been widely applied to NTDs, where their resource-saving payoffs could be highly beneficial. We demonstrate the utility of CTSs using our individual-based onchocerciasis transmission model (EPIONCHO-IBM) that projects trial outcomes of a hypothetical macrofilaricidal drug. We identify key design decisions that influence the power of clinical trials, including participant eligibility criteria and post-treatment follow-up times for measuring infection indicators. We discuss how CTSs help to inform target product profiles

Lymphatic Filariasis in Nigeria; Micro-stratification Overlap Mapping (MOM) as a Prerequisite for Cost-Effective Resource Utilization in Control and Surveillance

Background Nigeria has a significant burden of lymphatic filariasis (LF) caused by the parasite Wuchereria bancrofti. A major concern to the expansion of the LF elimination programme is the risk of serious adverse events (SAEs) associated with the use of ivermectin in areas co-endemic with Loa filariasis. To better understand this, as well as other factors that may impact on LF elimination, we used Micro-stratification Overlap Mapping (MOM) to highlight the distribution and potential impact of multiple disease interventions that geographically coincide in LF endemic areas and which will impact on LF and vice versa. Methodology/Principal findings LF data from the literature and Federal Ministry of Health (FMoH) were collated into a database. LF prevalence distributions; predicted prevalence of loiasis; ongoing onchocerciasis community-directed treatment with ivermectin (CDTi); and long-lasting insecticidal mosquito net (LLIN) distributions for malaria were incorporated into overlay maps using geographical information system (GIS) software. LF was prevalent across most regions of the country. The mean prevalence determined by circulating filarial antigen (CFA) was 14.0% (n = 134 locations), and by microfilaria (Mf) was 8.2% (n = 162 locations). Overall, LF endemic areas geographically coincided with CDTi priority areas, however, LLIN coverage was generally low (<50%) in areas where LF prevalence was high or co-endemic with L. loa. Conclusions/Significance The extensive database and series of maps produced in this study provide an important overview for the LF Programme and will assist to maximize existing interventions, ensuring cost effective use of resources as the programme scales up. Such information is a prerequisite for the LF programme, and will allow for other factors to be included into planning, as well as monitoring and evaluation activities given the broad spectrum impact of the drugs used