Geographical disparities and spatial non-stationarity in stroke prevalence across China: a Bayesian analysis.

BACKGROUND

As stroke remains a major public health challenge in China, numerous studies have characterized the epidemiological features and distribution of stroke prevalence across provinces. However, conventional non-spatial analytical approaches may lack the capability to capture spatial dependency and regional variation in the impact of risk factors. This study aims to estimate province-level stroke prevalence in China and quantify how its association with individual-level risk factors varies across provinces, accounting for spatial dependency.

METHODS

In this study, 19,713 adults were included from the fourth China Health and Retirement Longitudinal Study (CHARLS 2018), spanning 28 provinces, autonomous regions, and municipalities. Within each province, prevalence estimates were standardized to the 7th National Census (2020) distribution of age, sex, and residence type. Stroke prevalence and 95% Bayesian credible intervals (BCIs) were estimated by a Bayesian spatially varying coefficient model. Global and local Moran's I statistics were used to assess spatial autocorrelation and identify clustering patterns. Eight metabolic, lifestyle, and socioeconomic risk factors were considered: lower educational attainment, hypertension, diabetes, heart disease, dyslipidemia, smoking, alcohol consumption, and physical inactivity. The model estimated how the association of each with stroke varied across provinces.

RESULTS

Stroke prevalence at province level in China showed marked geographic disparities, ranging from 1.89% (95% BCI: 0.92%-3.61%) to 8.64% (95% BCI: 6.98%-10.63%). A distinct "North-high, South-low" spatial gradient was observed, with significant positive spatial autocorrelation (I = 0.428, p < 0.001). Local cluster analysis identified high-high clusters in Northeast and North China and low-low clusters in South and East China. Except for low educational attainment, the association between stroke prevalence and smoking, drinking, physical inactivity, hypertension, diabetes, dyslipidemia, and heart disease exhibited significant provincial variation. Hypertension showed the strongest association with stroke, with odds ratios ranging from 2.39 to 3.38 across provinces.

CONCLUSIONS

Stroke burden in China is spatially clustered, and the associations between stroke and its risk factors vary markedly across provinces rather than being uniform nationwide. By integrating spatial non-stationarity with census-based demographic standardization, this study provides spatially refined evidence to support region-specific stroke-prevention strategies and optimize the allocation of healthcare resources.