02665nas a2200205   4500000000100000008004100001260000800042653003000050653003600080100001400116700002300130700001600153700001100169245018900180856007100369300000900440490000600449520199000455022001402445       2024                            d  bBMJ10aLymphatic filariasis (LF)10aspatial epidemiological methods1 aMartin BM1 aCadavid Restrepo A1 aMayfield HJ1 aLau CL00aTowards global elimination of lymphatic filariasis: a systematic review of the application of spatial epidemiological methods to enhance surveillance and support elimination programmes  uhttps://bmjpublichealth.bmj.com/content/bmjph/2/1/e000534.full.pdf  a1-100 v23 a<p><strong>Background:&nbsp;</strong>In recent decades, spatial epidemiology has increasingly been used to study neglected tropical diseases (NTDs). Spatial methods are particularly relevant when transmission is strongly driven by sociodemographic and environmental factors, resulting in heterogeneous disease distribution. We use lymphatic filariasis (LF)—an NTD targeted for global elimination—as a case study to examine how spatial epidemiology has been used to enhance NTD surveillance.</p>

<p><strong>Methods:&nbsp;</strong>We conducted a systematic literature review of spatial analytical studies of LF published in English across PubMed, Embase, Web of Science and Scopus databases, before 15 November 2022. Additional papers were identified from experts’ suggestions. Studies that employed spatial analytical methods were included, but those that applied only visualisation tools were excluded.</p>

<p><strong>Findings:&nbsp;</strong>Sixty-one eligible studies published between 1997 and 2023 were identified. The studies used a wide range of spatial methods. Thirty-one (50.8%) studies used spatial statistical modelling, with model-based geostatistics being the most common method. Spatial autocorrelation and hotspot analysis were applied in 30 studies (49.2%). The most frequent model outputs were prevalence maps (17 studies, 27.9%), followed by risk maps based on environmental suitability (7 studies, 11.5%) and maps of the odds of seroprevalence being above a predetermined threshold (7 studies, 11.5%).</p>

<p><strong>Interpretation:&nbsp;</strong>By demonstrating the applicability of spatial methods for investigating transmission drivers, identifying clusters and predicting hotspots, we highlight innovative ways in which spatial epidemiology has provided valuable evidence to support LF elimination. Spatial analysis is particularly useful in low-prevalence settings for improving hotspot detection and enhancing postelimination surveillance.</p>
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