title: Obesity in China: the differential impacts of covariates along the BMI distribution.
abstract: Growing prosperity and changing diets have contributed to a surge in obesity prevalence in China. Previous research has investigated the relationships between BMI and several socioeconomic, diet-related, and health-related variables in China. This study proposes that such relationships are likely to differ along the conditional BMI distribution, and seeks to investigate such quantile-dependent variation in effects. Special attention is paid to how variables affect the upper tail of the conditional BMI distribution where overweight and obesity concerns are more acute. Quantile regressions (QRs) and ordinary least squares (OLS) regressions are estimated. The sample consists of 3,407 adult individuals aged 20-45 who participated in the China Health and Nutrition Survey (CHNS), 2006. Substantial cross quantile variation is observed in the relationships between several key variables and BMI. The QR shows that the relationship between energy intake and BMI is largely insignificant in the lower and middle quantiles, whereas the upper quantiles show a positive and significant effect substantially larger than predicted by the least squares regression and by previous studies. This implies that a food-based strategy aimed at limiting energy intake can be an effective way to fight obesity in China. The negative association between smoking and BMI, on the other hand, is found largely to hold only in the lower and middle quantiles, with the upper tail relatively unaffected by smoking status. Thus, smoking cessation policies may not exacerbate obesity.
authors.affiliation: Department of Agricultural and Food Economics, University of Reading, Reading, UK. b.shankar@reading.ac.uk
authors.forename: Bhavani
authors.initials: B
authors.lastname: Shankar
mesh_terms: D000328:Adult; D015992:Body Mass Index; D002681:China / Q000453:epidemiology; D002149:Energy Intake; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D008297:Male; D015233:Models, Statistical; D009765:Obesity / Q000453:epidemiology*; D050177:Overweight / Q000453:epidemiology*; D012907:Smoking / Q000009:adverse effects / Q000453:epidemiology; D016540:Smoking Cessation