Spatiotemporal trends and ecological determinants of human brucellosis among 31 provinces in mainland China, 2004–2021: a Bayesian spatiotemporal modeling study

BACKGROUND: Brucellosis shows pronounced spatiotemporal heterogeneity in China, with changing epidemiological patterns as traditionally non-endemic southern regions experience increasing incidence. This study analyzes the spatiotemporal distribution and influencing factors of brucellosis in China to inform differentiated prevention strategies. METHODS: Using human brucellosis data from 31 Chinese provinces (2004-2021), combined with meteorological, socioeconomic, and livestock husbandry indicators, we developed Bayesian spatiotemporal models to analyze provincial-level spatial effects, temporal effects, spatiotemporal interactions, and quantify the impact of multiple factors on brucellosis incidence risk at the provincial scale. Richardson classification was applied for hotspot analysis, and average annual percentage change (AAPC) evaluated incidence trends. RESULTS: China reported 687,529 brucellosis cases (2004-2021). Spatial distribution showed a "high north, low south" pattern, with Inner Mongolia having the highest incidence (45.81/100,000) and Shanghai the lowest (0.01/100,000). Northern regions contained most hotspots (41.94%), while southern areas comprised most coldspots (51.61%). Temporal analysis revealed increasing risk (2004-2016), brief decline (2016-2018), and subsequent increase (post-2018). Spatiotemporal interaction effects indicated provincial-level risk pattern shifts from northern to central-southern regions, with Gansu, Hubei, Yunnan, and Hunan showing highest growth (AAPCs: 64.42%, 53.80%, 53.61%, 50.47%). Ecological regression identified six significant factors: mean temperature, sunshine duration, NDVI, population density, and medical institutions density negatively correlated with risk; dairy production positively associated with risk. CONCLUSIONS: At the provincial level, brucellosis risk patterns in China show shifts from traditional northern high-incidence provinces to central-southern provinces. Environmental, socioeconomic, and livestock factors significantly influence disease risk. Prevention strategies should implement region-specific approaches while strengthening multi-departmental coordination.