Identification of high-risk regions for schistosomiasis in the Guichi region of China: an adaptive kernel density estimation-based approach.

Identification of high-risk regions of schistosomiasis is important for rational resource allocation and effective control strategies. We conducted the first study to apply the newly developed method of adaptive kernel density estimation (KDE)-based spatial relative risk function (sRRF) to detect the high-risk regions of schistosomiasis in the Guichi region of China and compared it with the fixed KDE-based sRRF. We found that the adaptive KDE-based sRRF had a better ability to depict the heterogeneity of risk regions, but was more sensitive to altering the user-defined smoothing parameters. Specifically, the impact of bandwidths on the estimated risk value and risk significance (P value) was higher for the adaptive KDE-based sRRF, but lower on the estimated risk variation standard error (s.e.) compared with the fixed KDE-based sRRF. Based on this application the adaptive and fixed KDE-based sRRF have their respective advantages and disadvantages and the joint application of the two approaches can warrant the best possible identification of high-risk subregions of diseases.