Provincial distribution of hypertension among adults aged 45 and older in mainland China: a GeoDetector analysis based on CHARLS 2011–2020

This study aims to explore the provincial distribution of hypertension in mainland China based on China Health and Retirement Longitudinal Study (CHARLS) 2011–2020 data and identify its influencing factors by GeoDetector. This study covered 27 provinces in mainland China, classified into eastern, central, and western regions. Hypertension prevalence data were derived from five waves (2011–2020) of the CHARLS, with province-level prevalence calculated using adjusted sampling weights. Eleven potential influencing factors spanning demographic, climatic, economic, and health-resource dimensions were selected from the China Statistical Yearbook and China Meteorological Yearbook. Spatial autocorrelation was assessed using Global Moran’s I and Local Indicators of Spatial Association (LISA) cluster maps, while the Getis-Ord Gi* index in ArcMap was employed to identify “hot spots” and “cold spots” of hypertension prevalence. The GeoDetector method was applied to quantify the explanatory power, interactions, and sensitivity of factors across different years and regions. From 2011 to 2020, the prevalence of hypertension among adults aged 45 and older across 27 provinces in mainland China exhibited spatiotemporal heterogeneity. LISA cluster maps and Getis-Ord Gi* analysis revealed initially concentrated in southeastern coastal regions (2011–2013), the high-prevalence hot spots completely dissipated by 2015, subsequently shifting westward before ultimately achieving spatial homogenization by 2020. GeoDetector analysis identified Industrial SO₂ emissions (q = 0.277) and the number of primary healthcare institutions (q = 0.191) as the dominant driving factors. The single-factor detector further revealed significant region-year heterogeneity. Interaction detection revealed that the spatial distribution was primarily driven by nonlinear synergistic effects among variables, particularly the interaction between healthcare infrastructure and environmental factors (e.g., primary healthcare institutions (x10) and Industrial SO 2 emissions (x5), interaction q = 0.51). Sensitivity analysis comparing the Optimal Parameters-based Geographical Detector (OPGD) with a fixed 5-quantile benchmark confirmed the robustness of the core factor rankings, with x5 and x10 consistently occupying the top two positions. Hypertension prevalence varies substantially across mainland China’s provinces among adults aged 45 and older, with spatial patterns shaped by interacting medical, environmental pollution, and economic factors. Targeted policies for economically underdeveloped and environmentally polluted provinces are urgently needed to address regional disparities in hypertension detection and management.