Bacillary dysentery has long been a considerable health problem in southwest China, however, the quantitative relationship between anthropogenic and physical environmental factors and the disease is not fully understand. It is also not clear where exactly the bacillary dysentery risk is potentially high. Based on the result of hotspot analysis, we generated training samples to build a spatial distribution model. Univariate analyses, autocorrelation and multi-collinearity examinations and stepwise selection were then applied to screen the potential causative factors. Multiple logistic regressions were finally applied to quantify the effects of key factors. A bootstrapping strategy was adopted while fitting models. The model was evaluated by area under the receiver operating characteristic curve (AUC), Kappa and independent validation samples. Hotspot counties were mainly mountainous lands in southwest China. Higher risk of bacillary dysentery was found associated with underdeveloped socio-economy, proximity to farmland or water bodies, higher environmental temperature, medium relative humidity and the distribution of the Tibeto-Burman ethnicity. A predictive risk map with high accuracy (88.19%) was generated. The high-risk areas are mainly located in the mountainous lands where the Tibeto-Burman people live, especially in the basins, river valleys or other flat places in the mountains with relatively lower elevation and a warmer climate. In the high-risk areas predicted by this study, improving the economic development, investment in health care and the construction of infrastructures for safe water supply, waste treatment and sewage disposal, and improving health related education could reduce the disease risk.
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