Spatial predictive risk mapping of lymphatic filariasis residual hotspots in American Samoa using demographic and environmental factors

Author summaryThe Global Programme to Eliminate Lymphatic filariasis (LF) aims to interrupt transmission by implementing mass drug administration (MDA) of antifilarial drugs in endemic areas; and to alleviate suffering of those affected through improved morbidity management and disability prevention. Significant progress has been made in the global efforts to eliminate LF. One of the main challenges faced by most LF-endemic countries that have implemented MDA is to effectively undertake post-validation surveillance to identify residual hotspots of ongoing transmission. American Samoa conducted seven rounds of MDA for LF between 2000 and 2006. Subsequently, the territory passed transmission assessment surveys in February 2011 (TAS-1) and April 2015 (TAS-2). However, the territory failed TAS-3 in September 2016, indicating resurgence. We implemented a Bayesian geostatistical analysis to predict LF prevalence estimates for American Samoa and examined the geographical distribution of the infection using sociodemographic and environmental factors. Our observations indicate that there are still areas with high prevalence of LF in the territory, particularly in the north-west of the main island of Tutuila. Bayesian geostatistical approaches have a promising role in guiding programmatic decision making by facilitating more specific targeting of post-MDA surveillance activities and prioritising those areas at higher risk of ongoing transmission. BackgroundAmerican Samoa successfully completed seven rounds of mass drug administration (MDA) for lymphatic filariasis (LF) from 2000-2006. The territory passed the school-based transmission assessment surveys in 2011 and 2015 but failed in 2016. One of the key challenges after the implementation of MDA is the identification of any residual hotspots of transmission. MethodBased on data collected in a 2016 community survey in persons aged & GE;8 years, Bayesian geostatistical models were developed for LF antigen (Ag), and Wb123, Bm14, Bm33 antibodies (Abs) to predict spatial variation in infection markers using demographic and environmental factors (including land cover, elevation, rainfall, distance to the coastline and distance to streams). ResultsIn the Ag model, females had a 26.8% (95% CrI: 11.0-39.8%) lower risk of being Ag-positive than males. There was a 2.4% (95% CrI: 1.8-3.0%) increase in the odds of Ag positivity for every year of age. Also, the odds of Ag-positivity increased by 0.4% (95% CrI: 0.1-0.7%) for each 1% increase in tree cover. The models for Wb123, Bm14 and Bm33 Abs showed similar significant associations as the Ag model for sex, age and tree coverage. After accounting for the effect of covariates, the radii of the clusters were larger for Bm14 and Bm33 Abs compared to Ag and Wb123 Ab. The predictive maps showed that Ab-positivity was more widespread across the territory, while Ag-positivity was more confined to villages in the north-west of the main island. ConclusionThe findings may facilitate more specific targeting of post-MDA surveillance activities by prioritising those areas at higher risk of ongoing transmission.