Abstract
OBJECTIVES: To investigate whether urban-rural location and socioeconomic factors (income, education and employment) are associated with body mass index (BMI) and waist-hip ratio (W/H-ratio), and to further explore if the associations between urban-rural location and BMI or W/H-ratio could be mediated through variations in socioeconomic factors. DESIGN: Cross-sectional, WHO STEPS survey of non-communicable disease risk factors. SETTING: Urban and rural areas of Myanmar. PARTICIPANTS: A total of 8390 men and women aged 25 to 64 years included during the study period from September to December 2014. Institutionalised people (Buddhist monks and nuns, hospitalised patients) and temporary residents were excluded. RESULTS: The prevalence of overweight and obesity was higher in the urban areas and increased with increasing socioeconomic status (SES) score. Mean BMI was higher among urban residents (ß=2.49 kg/m(2); 95% CI 2.28 to 2.70; p<0.001), individuals living above poverty line, that is, ≥US$1.9/day (ß=0.74 kg/m(2); 95% CI 0.43 to 1.05; p<0.001), and those with high education attainment (ß=1.48 kg/m(2); 95% CI 1.13 to 1.82; p<0.001) when adjusting for potential confounders. Similarly, greater W/H-ratio was observed in participants living in an urban area, among those with earnings above poverty line, and among unemployed individuals. The association between urban-rural location and BMI was found to be partially mediated by a composite SES score (9%), income (17%), education (16%) and employment (16%), while the association between urban-rural location and W/H-ratio was found to be partially mediated by income (12%), education (6%) and employment (6%). CONCLUSION: Residents living in urban locations had higher BMI and greater W/H-ratio, partially explained by differences in socioeconomic indicators, indicating that socioeconomic factors should be emphasised in the management of overweight and obesity in the Myanmar population. Furthermore, new national or subnational STEPS surveys should be conducted in Myanmar to observe the disparity in trends of the urban-rural differential.