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Background: Ageing is becoming a considerable public health burden in China, which produces great societal development challenges. Healthy and active longevity could ease the ageing burden on families and communities. To date, most studies of the oldest-old distribution are focused on a simple scale from spatial perspective, and the multi-scale spatio-temporal clusters trend in the oldest-old population has not yet been determined. Thus, the objective in present study is to use a new method to evaluate the spatio-temporal pattern and detect the risk clusters in the oldest-old population from three scales. Methods: Individuals aged 65 years or older and individuals aged 80 years or older on three scales in China from 2000 to 2010 were used. The exploratory spatial data analysis was performed using Moran’s I statistic, and the pattern of the oldest-old clusters among humans was examined by using the spatial scan statistical method. Then, spatial stratified heterogeneity was used to explore the factors affecting the spatial heterogeneity of the oldest-old population. Results: The oldest-old index in the southeast coastal areas is higher than that in the northwest inland areas in China. A three-ladder terrain distribution of the oldest-old index from west to east is obvious. The overall pattern of the oldest-old index evolves from a “concave” shape to an “east-west uplift, and northern collapse” shape. Space-time analysis revealed that high-risk areas were concentrated in five regions: the Yangtze River Delta, the Pearl River Delta, the Southeast Coast, Sichuan and Chongqing, and the Central Plains. The oldest-old cluster at different scales shows a similar pattern, but local differences exist. The risk at the prefecture scale and county scale is greater than at the interprovincial scale; the sublevel can identify clusters that have not been identified at the previous level, especially the bordering areas of prefectures and counties; and more risk units and greater relative risk are found in urban areas than in rural areas. Conclusions: The results emphasized that spatial scan statistics can be used to estimate the spatial clusters of the oldest-old people. The detection of these clusters might be highly useful in the surveillance of the ageing phenomenon, thus helping local public health authorities measure the population burden at all locations, identifying geographical areas that require more attention, and evaluating the impacts of intervention programs. [ABSTRACT FROM AUTHOR]

Background: Residential density was found to be associated with excess body weight among adults in Western societies but it remains unclear in China. We aimed to explore the relationship between residential density and excess body weight among adults in China. Methods: This cross-sectional study was conducted in 2017 in urban areas of Nanjing, China. A multi-stage sampling method was used to randomly select participants aged 35–74 years from 8 urban neighborhoods in Nanjing. Status of excess body weight was the outcome variable which was categorized as "yes (BMI≥24)" or "no (BMI<24)" according to specific recommendations for Chinese adults. Residential density was the main explanatory variable which was grouped into tertiles. Odds ratios (OR) and 95% confidence interval (CIs) were calculated to assess the relationship between residential density and excess body weight using mixed-effects regression models after adjusting for age, sex, education, occupation, marital status, redmeat, smoking, physical activity, diabetic status and potential neighborhood-level clustering effect. Results: A total of 1551 participants were recruited with a response rate of 98.9% (1551/1568). The mean age (standard deviation) of participants was 54.7 (11.1) years, and 46% of them were men. With adjustment for potential influential factors, participants in neighborhoods with lower (OR = 1.38, 95% CI = 1.06–1.81) and middle (OR = 1. 29, 95% CI = 1. 01, 1. 64) tertile of residential density were at significantly higher risk of gaining excess body weight relative to their counterparts in communities with upper tertile of residential density. Such a negative association between residential density and excess body weight was observed for men only after stratified analysis by gender. Conclusions: A negative association between residential density and excess body weight was identified in overall and male urban Chinese adults, and the association was independent of physical activity. Results of our study have important implications in guiding public health policy making regarding prevention of excess body weight at community level via establishment of health-friendly neighborhood environment in China. [ABSTRACT FROM AUTHOR]