[Spatial autocorrelation and related factors of stroke mortality in Zhejiang Province based on spatial panel model in...

Objective: To explore the spatial autocorrelation and macro influencing factors of stroke mortality in Zhejiang Province in 2015-2020 and provide a scientific basis for stroke prevention and control strategy. Methods: The data on stroke death were obtained from Zhejiang Chronic Disease Surveillance System. The spatial distribution of stroke mortality was explored by mapping and spatial autocorrelation analysis. The spatial panel model analyzed the correlation between stroke mortality and socioeconomic and healthcare factors. Results: From 2015 to 2020, the average stroke mortality was 68.38/100 thousand. The standard mortality of stroke was high in the areas of east and low in the west, high in the south and low in the north. Moreover, positive spatial autocorrelation was observed (Moran's I=0.274-0.390, P<0.001). Standard mortality of stroke was negatively associated with per capita gross domestic product (GDP) (β=-0.370, P<0.001), per capita health expenditure (β=-0.116, P=0.021), number of beds per thousand population (β=-0.161, P=0.030). Standard mortality of ischemic stroke was negatively associated with per capita GDP (β=-0.310, P=0.002) and standard management rate of hypertension (β=-0.462, P=0.011). Standard mortality of hemorrhagic stroke was negatively associated with per capita GDP (β=-0.481, P<0.001), per capita health expenditure (β=-0.184, P=0.001), number of beds per thousand population (β=-0.288, P=0.001) and standard management rate of hypertension (β=-0.336, P=0.029). Conclusions: A positive spatial correlation existed between stroke mortality in Zhejiang Province in 2015-2020. We must focus more on preventing and controlling strokes in relatively backward economic areas. Moreover, to reduce the mortality of stroke, increasing the investment of government medical and health funds, optimizing the allocation of medical resources, and improving the standard management rate of hypertension are important measures.