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Publications

Journal Article Research Support, Non-U.S. Gov't Epublish 2 citations

PubMed: 39217314 DOI: 10.1186/s12889-024-19884-x

Spatial analysis of the impact of urban built environment on cardiovascular diseases: a case study in Xixiangtang, China.

BMC Public Health 24(1):2368

Authors: Shuguang Deng, Jinlong Liang, Ying Peng, Wei Liu, Jinhong Su, Shuyan Zhu

Affiliations

  • School of Geographical and Planning, Nanning Normal University, Nanning, 530100, Guangxi, China.
  • School of Geographical and Planning, Nanning Normal University, Nanning, 530100, Guangxi, China.
  • School of Architecture, Guangxi Arts University, Nanning, 530009, Guangxi, China.
  • Fatulty of Innovation and Design, City University of Macau, Macau, 999078, China.

Abstract

BACKGROUND

The built environment, as a critical factor influencing residents' cardiovascular health, has a significant potential impact on the incidence of cardiovascular diseases (CVDs).

METHODS

Taking Xixiangtang District in Nanning City, Guangxi Zhuang Autonomous Region of China as a case study, we utilized the geographic location information of CVD patients, detailed road network data, and urban points of interest (POI) data. Kernel density estimation (KDE) and spatial autocorrelation analysis were specifically employed to identify the spatial distribution patterns, spatial clustering, and spatial correlations of built environment elements and diseases. The GeoDetector method (GDM) was used to assess the impact of environmental factors on diseases, and geographically weighted regression (GWR) analysis was adopted to reveal the spatial heterogeneity effect of environmental factors on CVD risk.

RESULTS

The results indicate that the built environment elements and CVDs samples exhibit significant clustering characteristics in their spatial distribution, with a positive correlation between the distribution density of environmental elements and the incidence of CVDs (Moran's I > 0, p < 0.01). Further factor detection revealed that the distribution of healthcare facilities had the most significant impact on CVDs (q = 0.532, p < 0.01), followed by shopping and consumption (q = 0.493, p < 0.01), dining (q = 0.433, p < 0.01), and transportation facilities (q = 0.423, p < 0.01), while the impact of parks and squares (q = 0.174, p < 0.01) and road networks (q = 0.159, p < 0.01) was relatively smaller. Additionally, the interaction between different built environment elements exhibited a bi-factor enhancement effect on CVDs. In the local analysis, the spatial heterogeneity of different built environment elements on CVDs further revealed the regional differences and complexities.

CONCLUSIONS

The spatial distribution of built environment elements is significantly correlated with CVDs to varying degrees and impacts differently across regions, underscoring the importance of the built environment on cardiovascular health. When planning and improving urban environments, elements and areas that have a more significant impact on CVDs should be given priority consideration.

MeSH

Built Environment, Incidence, Cardiovascular Diseases, Public Health, Environment Design, Cluster Analysis, Environment, Spatial Analysis

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