背景:构建的环境,作为影响居民心血管健康的关键因素,对心血管疾病(CVDs)的发病率有显著的潜在影响。
方法:以南宁市西乡塘区为例,以中国广西壮族自治区为例,我们利用了CVD患者的地理位置信息,详细的路网数据,和城市兴趣点(POI)数据。具体采用核密度估计(KDE)和空间自相关分析来识别空间分布模式,空间聚类,以及建筑环境要素和疾病的空间相关性。GeoDetector方法(GDM)用于评估环境因素对疾病的影响,采用地理加权回归(GWR)分析揭示环境因素对CVD风险的空间异质性影响。
结果:结果表明,建筑环境要素和CVD样本在空间分布上表现出显著的聚类特征,环境要素分布密度与CVD发生率呈正相关(Moran\sI>0,p<0.01)。进一步的因素检测显示,医疗机构的分布对CVD的影响最显著(q=0.532,p<0.01),其次是购物和消费(q=0.493,p<0.01),餐饮(q=0.433,p<0.01),和交通设施(q=0.423,p<0.01),公园和广场(q=0.174,p<0.01)和道路网络(q=0.159,p<0.01)的影响相对较小。此外,不同建筑环境要素之间的相互作用对CVD表现出双因素增强作用。在局部分析中,CVD上不同建筑环境要素的空间异质性进一步揭示了区域差异和复杂性。
结论:建筑环境要素的空间分布与CVD有不同程度的显著相关,并且影响不同地区。强调建筑环境对心血管健康的重要性。在规划和改善城市环境时,应优先考虑对CVD有更大影响的元素和领域。
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.