03922nas a2200325   4500000000100000008004100001260003700042653002400079653005700103653002700160653002600187653003000213653001500243100001500258700001300273700001200286700001200298700001100310700001400321700001300335700001400348700001800362700001700380245013500397856009900532300000900631490000700640520293500647022001403582       2023                            d  bPublic Library of Science (PLoS)10aInfectious Diseases10aPublic Health, Environmental and Occupational Health10aSocio-economic factors10aEnvironmental factors10aLymphatic filariasis (LF)10aBangladesh1 aWilliams T1 aKarim MJ1 aUddin S1 aJahan S1 aASM SM1 aForbes SP1 aHooper A1 aTaylor MJ1 aKelly-Hope LA1 aCwiklinski K00aSocio-economic and environmental factors associated with high lymphatic filariasis morbidity prevalence distribution in Bangladesh  uhttps://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0011457&type=printable  a1-160 v173 a<p><strong>Background:</strong> Lymphatic filariasis (LF) is a vector-borne parasitic disease which affects 70 million people worldwide and causes life-long disabilities. In Bangladesh, there are an estimated 44,000 people suffering from clinical conditions such as lymphoedema and hydrocoele, with the greatest burden in the northern Rangpur division. To better understand the factors associated with this distribution, this study examined socio-economic and environmental factors at division, district, and sub-district levels.</p>

<p><strong>Methodology:</strong> A retrospective ecological study was conducted using key socio-economic (nutrition, poverty, employment, education, house infrastructure) and environmental (temperature, precipitation, elevation, waterway) factors. Characteristics at division level were summarised. Bivariate analysis using Spearman’s rank correlation coefficient was conducted at district and sub-district levels, and negative binomial regression analyses were conducted across high endemic sub-districts (n = 132). Maps were produced of high endemic sub-districts to visually illustrate the socio-economic and environmental factors found to be significant.</p>

<p><strong>Results:</strong> The highest proportion of rural population (86.8%), poverty (42.0%), tube well water (85.4%), and primary employment in agriculture (67.7%) was found in Rangpur division. Spearman’s rank correlation coefficient at district and sub-district level show that LF morbidity prevalence was significantly (p&lt;0.05) positively correlated with households without electricity (district rs = 0.818; sub-district rs = 0.559), households with tube well water (sub-district rs = 0.291), households without toilet (district rs = 0.504; sub-district rs = 0.40), mean annual precipitation (district rs = 0.695; sub-district rs = 0.503), mean precipitation of wettest quarter (district rs = 0.707; sub-district rs = 0.528), and significantly negatively correlated with severely stunted children (district rs = -0.723; sub-district rs = -0.370), mean annual temperature (district rs = -0.633.; sub-district rs = 0.353) and mean temperature (wettest quarter) ((district rs = -0.598; sub-district rs = 0.316) Negative binomial regression analyses at sub-district level found severely stunted children (p = &lt;0.001), rural population (p = 0.002), poverty headcount (p = 0.001), primary employment in agriculture (p = 0.018), households without toilet (p = &lt;0.001), households without electricity (p = 0.002) and mean temperature (wettest quarter) (p = 0.045) to be significant.</p>

<p><strong>Conclusions:</strong> This study highlights the value of using available data to identify key drivers associated with high LF morbidity prevalence, which may help national LF programmes better identify populations at risk and implement timely and targeted public health messages and intervention strategies.</p>
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