03267nas a2200361   4500000000100000008004100001260003700042653002900079653003000108653001200138653002800150653002200178100001400200700001700214700001400231700001600245700001400261700001500275700001400290700001300304700001200317700001700329700001400346700001200360700001800372700001300390245012100403856009900524300000900623490000700632520225200639022001402891       2024                            d  bPublic Library of Science (PLoS)10aGeostatistical modelling10aLymphatic filariasis (LF)10aMapping10aEssential care packages10aNational database1 aBarrett C1 aChiphwanya J1 aMkwanda S1 aMatipula DE1 aNdhlovu P1 aChaponda L1 aTurner JD1 aGiorgi E1 aBetts H1 aMartindale S1 aTaylor MJ1 aRead JM1 aKelly-Hope LA1 aRamos AN00aThe national distribution of lymphatic filariasis cases in Malawi using patient mapping and geostatistical modelling  uhttps://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0012056&type=printable  a1-150 v183 a<p><strong>Background:</strong> In 2020 the World Health Organization (WHO) declared that Malawi had successfully eliminated lymphatic filariasis (LF) as a public health problem. Understanding clinical case distributions at a national and sub-national level is important, so essential care packages can be provided to individuals living with LF symptoms. This study aimed to develop a national database and map of LF clinical cases across Malawi using geostatistical modelling approaches, programme-identified clinical cases, antigenaemia prevalence and climate information.</p>

<p><strong>Methodology:</strong> LF clinical cases identified through programme house-to-house surveys across 90 sub-district administrative boundaries (Traditional Authority (TA)) and antigenaemia prevalence from 57 sampled villages in Malawi were used in a two-step geostatistical modelling process to predict LF clinical cases across all TAs of the country. First, we modelled antigenaemia prevalence in relation to climate covariates to predict nationwide antigenaemia prevalence. Second, we modelled clinical cases for unmapped TAs based on our antigenaemia prevalence spatial estimates.</p>

<p><strong>Principle findings:</strong> The models estimated 20,938 (95% CrI 18,091 to 24,071) clinical cases in unmapped TAs (70.3%) in addition to the 8,856 (29.7%), programme-identified cases in mapped TAs. In total, the overall national number of LF clinical cases was estimated to be 29,794 (95% CrI 26,957 to 32,927). The antigenaemia prevalence and clinical case mapping and modelling found the highest burden of disease in Chikwawa and Nsanje districts in the Southern Region and Karonga district in the Northern Region of the country.</p>

<p><strong>Conclusions:</strong> The models presented in this study have facilitated the development of the first national LF clinical case database and map in Malawi, the first endemic country in sub-Saharan Africa. It highlights the value of using existing LF antigenaemia prevalence and clinical case data together with modelling approaches to produce estimates that may be used for the WHO dossier requirements, to help target limited resources and implement long-term health strategies.</p>

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