County-level spatial heterogeneity and environmental correlates of human echinococcosis in Xizang, China

Background Human echinococcosis remains a major zoonosis in Xizang, China, where marked climatic, topographic and landscape gradients may be associated with spatial variation in disease prevalence. We investigated county-level spatial patterns of overall human echinococcosis, cystic echinococcosis (CE) and alveolar echinococcosis (AE), and their environmental associations. Methods Surveillance data from 74 counties during 2019–2023 were analysed. Because usable surveillance data were unavailable for 32 counties in 2022, that year was excluded from the spatial and generalized additive mixed model (GAMM) analyses. Global and local Moran’s I assessed spatial autocorrelation and clustering. Tweedie GAMMs evaluated nonlinear environmental associations and pairwise interactions, incorporating a county-level random intercept, year as a categorical fixed effect, and a two-dimensional spatial smooth. Results Among 510,063 individuals screened, 2,449 cases were detected, including 2,214 CE and 71 AE cases. Mean prevalence was 0.48% overall and 0.43% for CE; AE prevalence was 13.92 per 100,000. Overall human echinococcosis and CE were concentrated in western and northern Xizang, with high–high clusters mainly in the west, whereas AE clustered focally in the northeast. Overall prevalence showed an inverse association with mean annual temperature and a significant interaction between mean annual relative humidity and lowest elevation. CE prevalence showed a nonlinear association with mean annual relative humidity and a significant interaction between mean annual temperature and surface water coverage percentage. In the simplified AE model, lowest elevation showed an approximately linear positive association with prevalence; the AE-specific findings were considered exploratory. Conclusions Echinococcosis in Xizang shows pronounced subtype-specific spatial heterogeneity and distinct county-level environmental correlates. These ecological associations should not be interpreted as direct causal effects. Environmental information may complement host, population, behavioral, surveillance, and intervention-related data when identifying areas that warrant enhanced surveillance and further investigation.