Associations between Land-Use Patterns and Cardiovascular Disease Mortality in the Beijing—Tianjin–Hebei Megacity Region

Abstract

Megacity regions where human activities are intensive are key areas for CVD prevention and control in China. Optimizing land-use patterns has been widely recognized as an important public health intervention. Ecological space, agricultural space, and construction space are three basic management objects in China’s new land-use management system. Given that most existing studies focused on a single type of land use, this study treats them as a whole and not only explores the impact of each type, but also systematically investigates the effects of the interactions between any two types of land use and the whole land-use pattern. Specifically, this study first constructs a hierarchical index system, then uses spatial error models (SEM) to explore the global associations between each index and age-standardized CVD mortality rates (ASMRs) and uses the multiple geographical weighted regression model (MGWR) to explore the spatial heterogeneity of factor effects. The possible association between land-use patterns and CVD mortality is then explored, and recommendations for policy formulation are provided. The analysis results show that the overall pattern of moderately decentralized and organically combined land use can control CVD mortality to a certain extent, but the specific influence mechanisms show significant differences according to different land-use types, relationships, and location conditions. First, in terms of single-type land-use distribution, the concentration of ecological space has positive health benefits, while a too high concentration of agricultural space has negative effects. Second, the combination of different types of land use has a significant association with CVD, in which the mixed layout of ecological and agricultural space helps to suppress CVD, while ecological and construction space need to be appropriately regularized and should not be too interspersed. Third, the same index may have different effects in different regions, suggesting that policy makers need to tailor their policies to local conditions.