03174nas a2200301   4500000000100000008004100001260004400042653003500086653001500121653001700136653003100153653002400184653001300208100001400221700001500235700001700250700002100267700001600288700001900304700001800323700001300341245016300354856005900517300000900576490000700585520226600592022001402858       2025                            d  bSpringer Science and Business Media LLC10aSoiltransmitted helminth (STH)10aPrevalence10aRisk factors10asatellite imagery analysis10apredictive modeling10aThailand1 aMuenjak J1 aThongrod J1 aChoodamdee C1 aPongpanitanont P1 aYingklang M1 aThanchomnang T1 aLaymanivong S1 aJanwan P00aPrevalence, associated risk factors and satellite imagery analysis in predicting soil-transmitted helminth infection in Nakhon Si Thammarat Province, Thailand  uhttps://www.nature.com/articles/s41598-025-14221-7.pdf  a1-110 v153 a<p>Soil-transmitted helminth (STH) infections remain a significant public health concern in rural areas, often leading to nutritional and physical impairment, particularly in children. This study aimed to assess the prevalence and associated factors of STH infections among schoolchildren in Thasala District, Nakhon Si Thammarat Province, Thailand, and to develop a predictive model for identifying high-risk areas using satellite imagery data. A cross-sectional study was conducted with 319 primary schoolchildren from six sub-districts in Thasala District. Stool samples were analyzed for STH infections using the formalin ethyl acetate concentration technique (FECT) and agar plate culture (APC), while behavioral data were collected through questionnaires to identify key risk factors. We developed an innovative predictive model by integrating convolutional neural networks (CNNs) for land-use classification of satellite imagery with artificial neural networks (ANNs) following dimensionality reduction through principal component analysis (PCA). The STH infections were detected in 31 samples (9.72%), with higher prevalence in males (11.38%) than females (8.67%). Mono-infections predominated, with Trichuris trichiura (5.02%) and hookworm (3.49%) being the most frequent. Mixed infections accounted for 1.25%, primarily co-infections of hookworm with T. trichiura (0.94%) or Strongyloides stercoralis (0.31%). Not cutting nails was identified as a significant behavioral factor associated with STH infections (p = 0.047), while other behavioral factors showed no statistical significance. From the satellite imagery analysis, specific environmental features, particularly higher proportions of agricultural land and closer proximity to water bodies, were positively associated with elevated STH prevalence. The modelling approach generated spatial risk maps for STH infections, providing a cost-effective tool for identifying high-risk transmission zones. These findings highlight that STH infections persist among rural Thai schoolchildren, with poor hygiene practices as a contributing factor. Strengthening hygiene education, improving sanitation, and implementing targeted environmental interventions are essential for effective control.</p>
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