Spatial distribution patterns and risk factors of hookworm disease in China: A study based on successive national surveillance

Background Hookworm infection, a neglected tropical disease (NTD) causing iron-deficiency anaemia and malnutrition in low-income populations with poor sanitation, poses a considerable public health challenge in China and worldwide. Methods National surveillance across 31 provincial-level administrative divisions (PLADs) from 2016 to 2021 assessed regional and population-specific hookworm prevalence. Geospatial methods, such as global and local autocorrelation, hotspot detection, spatiotemporal clustering detection and standard deviation ellipse (SDE) analysis characterized distribution patterns. Machine learning identified key determinants and their associations with infection rates, revealing primary influence factors based on 7,929 township records and 40 environmental, climatic and anthropogenic variables. Results Significant geographic disparities emerged, with the highest infection rates in south-western regions and the lowest in the Northeast. Spatial analyses demonstrated significant clustering, with persistent south-western hotspots and north-eastern coldspots (P  Conclusions Hookworm disease clusters in south-western China, disproportionately affecting women and the elderly. Barefoot farming emerged as the primary risk factor, with infection rates positively associated with temperature, humidity and negatively with sunlight duration. The results support recommendations to target intervention zones in endemic areas, implement population-specific prevention programs and intensify health education to advance transmission control.