Key points are not available for this paper at this time.
Abstract With the ongoing process of urbanization, the global issue of chronic diseases is becoming increasingly prominent, with rising incidence rates. This study focuses on hypertension, a typical chronic disease, and analyzes the spatial similarity and dependence of hypertension prevalence and its influencing factors through correlation and spatial autocorrelation analyses. A Multiscale Geographically Weighted Regression (MGWR) model is employed to investigate the spatial heterogeneity of environmental factors affecting hypertension prevalence, providing a deeper understanding of the drivers behind changes in hypertension rates. The results indicate that hypertension prevalence in New York City exhibits significant spatial non-stationarity. Income levels, race, and functional mix are primary influencers. Additionally, factors such as building density, vehicle ownership, and the proportion of Black or African American residents also impact hypertension prevalence to varying degrees and directions. The MGWR model effectively assesses the impact of urban environments on hypertension prevalence and ranks the importance of influencing factors. With an R-squared value of 0.785, the MGWR model, outperforms both Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models in identifying the determinants of hypertension prevalence. This study offers valuable decision-making support for the government in formulating intervention policies and urban planning and design to reduce hypertension incidence.
Lu et al. (Thu,) studied this question.