Chun Lei Guo1, Bing Zhang1, Hui Jun Wang1, Guo Shuang Feng2, Jun Ming Li3, Chang Su1, Ji Guo Zhang1, Zhi Hong Wang1, Wen Wen DU1. 1. National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China. 2. Center for Clinical Epidemiology & Evidence-based Medicine of Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China. 3. School of Statistics, Shanxi University of Finance and Economics, Taiyuan 030006, Shanxi, China.
Abstract
OBJECTIVE: To identify the characteristics of Chinese obesogenic environments at a provincial level, infer a spatial distribution map of obesity prevalence in 31 provinces, and provide a foundation for development of policy to reduce obesity in children and adolescents. METHODS: After scanning obesity data on subjects aged 7-17 years from 12 provinces in the China Health and Nutrition Survey 2011 and environmental data on 31 provinces from the China Statistical Yearbook 2011 and other sources, we selected 12 predictors. We used the 12 surveyed provinces as a training sample to fit an analytical model with partial least squares regression and prioritized the 12 predictors using variable importance in projection. We also fitted a predictive model with Bayesian analysis. RESULTS: We identified characteristics of obesogenic environments. We fitted the predictive model with a deviance information criterion of 61.96 and with statistically significant (P < 0.05) parameter estimates of intercept [95% confidence interval (CI): 329.10, 963.11], log(oil) (CI: 13.11, 20.30), log(GDP) (CI: 3.05, 6.93), log(media) (CI: -234.95, -89.61), and log(washing-machine) (CI: 0.92, 5.07). The total inferred average obesity prevalence among those aged 7-17 was 9.69% in 31 Chinese provinces in 2011. We also found obvious clustering in occurrences of obesity in northern and eastern provinces in the predicted map. CONCLUSION: Given complexity of obesity in children and adolescents, concerted efforts are needed to reduce consumption of edible oils, increase consumption of vegetables, and strengthen nutrition, health, and physical activity education in Chinese schools. The northern and eastern regions are the key areas requiring intervention.
OBJECTIVE: To identify the characteristics of Chinese obesogenic environments at a provincial level, infer a spatial distribution map of obesity prevalence in 31 provinces, and provide a foundation for development of policy to reduce obesity in children and adolescents. METHODS: After scanning obesity data on subjects aged 7-17 years from 12 provinces in the China Health and Nutrition Survey 2011 and environmental data on 31 provinces from the China Statistical Yearbook 2011 and other sources, we selected 12 predictors. We used the 12 surveyed provinces as a training sample to fit an analytical model with partial least squares regression and prioritized the 12 predictors using variable importance in projection. We also fitted a predictive model with Bayesian analysis. RESULTS: We identified characteristics of obesogenic environments. We fitted the predictive model with a deviance information criterion of 61.96 and with statistically significant (P < 0.05) parameter estimates of intercept [95% confidence interval (CI): 329.10, 963.11], log(oil) (CI: 13.11, 20.30), log(GDP) (CI: 3.05, 6.93), log(media) (CI: -234.95, -89.61), and log(washing-machine) (CI: 0.92, 5.07). The total inferred average obesity prevalence among those aged 7-17 was 9.69% in 31 Chinese provinces in 2011. We also found obvious clustering in occurrences of obesity in northern and eastern provinces in the predicted map. CONCLUSION: Given complexity of obesity in children and adolescents, concerted efforts are needed to reduce consumption of edible oils, increase consumption of vegetables, and strengthen nutrition, health, and physical activity education in Chinese schools. The northern and eastern regions are the key areas requiring intervention.
Authors: Susannah Westbury; Iman Ghosh; Helen Margaret Jones; Daniel Mensah; Folake Samuel; Ana Irache; Nida Azhar; Lena Al-Khudairy; Romaina Iqbal; Oyinlola Oyebode Journal: BMJ Glob Health Date: 2021-10