归一化差异植被指数
糖尿病
调解
人口学
环境卫生
可能性
医学
逻辑回归
四分位数
优势比
中国
老年学
地理
置信区间
生态学
气候变化
生物
法学
病理
考古
社会学
内分泌学
内科学
政治学
作者
Kejia Hu,Zuhui Zhang,Yuanyuan Li,Shiyi Wang,Tingting Ye,Jinglu Song,Yunquan Zhang,Jing Wei,Jun Cheng,Y. R. Shen,Jiahao Pan,Jianchang Fu,Qing Jin,Y.X. Guo,Yi Zeng,Yao Yao
标识
DOI:10.1016/j.landurbplan.2023.104881
摘要
Neighborhood greenness has been shown to reduce diabetes risk, however, no studies have compared the effects of overall greenness with visible greenness, which is crucial for understanding how greenness influences diabetes risk. Our study aims to explore the associations between greenness matrix and diabetes, as well as the potential effect modifications and mediating factors. We used logistic regressions to examine the cross-sectional associations of the satellite-based Normalized Difference Vegetation Index (NDVI) and street view-based Green View Index (GVI) with diabetes in 3,924 urban older adults enrolled in the 2017–2018 wave of Chinese Longitudinal Healthy Longevity Survey (CLHLS). We conducted the stratified analyses by age, sex, household income and education. Mediation analyses were also performed to see whether physical activity, BMI, air pollution, and social interaction mediate the associations. Significant associations with diabetes were only observed for NDVI but not for GVI. Participants in the highest quartile of NDVI and GVI had 52% (95 % CI: 48%, 63%) and 14% (-10%, 44%) lower odds of reporting having diabetes diagnosed by a doctor. The protective effects of NDVI were more pronounced in the young old (≥75–95 years) and high-education or high-income groups. No difference between males and females were observed. Air pollution (e.g., PM2.5, NO2 and O3) partially mediated the associations, but physical activity, BMI, and social interaction may not mediate the associations. Our findings indicate beneficial associations between overall greenness but not visible greenness surrounding residences with diabetes in older urban residents in China, especially for old adults with higher education or household income levels. Environmental factors (e.g., air pollution) but not individual behavioural characteristics are the potential underlying mechanisms.
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