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Background Living in greener areas may reduce adiposity, but epidemiological evidence on this topic is still inconsistence and limited, especially in rural areas. Methods We performed a cross-sectional study among 4651 Uyghur adults in rural areas in Xinjiang province, northwestern China, from May to September 2016. We measured residential greenness levels using satellite-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) in 100 m, 300 m, 500 m, and 1000 m buffers around each home address. Body height, weight, and waist circumference were assessed according to recommended guidelines. Data on baseline characteristics and confounders were collected using a questionnaire. We used generalized linear mixed models to estimate the associations of residential greenness with overweight/obesity prevalence and obesity-related anthropometric indices. Results Higher residential greenness levels were associated with lower waist circumference and body mass index levels, as well as with a lower odds ratio of peripheral overweight/obesity prevalence. No significant association was found for greenness and central obesity prevalence. The associations persisted in magnitude and direction across several sensitivity analyses we performed. Stratified analysis suggested that the associations were generally stronger in older adults than those in younger adults. Additionally, neither air pollutants nor physical activity significantly mediated the associations between greenness and obesity. Conclusions Our results suggest that higher residential greenness were associated with lower odds of overweight/obesity and lower obesity-related anthropometric indices among rural Uyghur adults in China, especially for older adults.

Background The widely used Air Quality Index (AQI) has been criticized due to its inaccuracy, leading to the development of the air quality health index (AQHI), an improvement on the AQI. However, there is currently no consensus on the most appropriate construction strategy for the AQHI. Objectives In this study, we aimed to evaluate the utility of AQHIs constructed by different models and health outcomes, and determine a better strategy. Methods Based on the daily time-series outpatient visits and hospital admissions from 299 hospitals (January 2016–December 2018), and mortality (January 2017–December 2019) in Guangzhou, China, we utilized cumulative risk index (CRI) method, Bayesian multi-pollutant weighted (BMW) model and standard method to construct AQHIs for different health outcomes. The effectiveness of AQHIs constructed by different strategies was evaluated by a two-stage validation analysis and examined their exposure-response relationships with the cause-specific morbidity and mortality. Results Validation by different models showed that AQHI constructed with the BMW model (BMW-AQHI) had the strongest association with the health outcome either in the total population or subpopulation among air quality indexes, followed by AQHI constructed with the CRI method (CRI-AQHI), then common AQHI and AQI. Further validation by different health outcomes showed that AQHI constructed with the risk of outpatient visits generally exhibited the highest utility in presenting mortality and morbidity, followed by AQHI constructed with the risk of hospitalizations, then mortality-based AQHI and AQI. The contributions of NO2 and O3 to the final AQHI were prominent, while the contribution of SO2 and PM2.5 were relatively small. Conclusions The BMW model is likely to be more effective for AQHI construction than CRI and standard methods. Based on the BMW model, the AQHI constructed with the outpatient data may be more effective in presenting short-term health risks associated with the co-exposure to air pollutants than the mortality-based AQHI and existing AQIs.