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Contribution of socioeconomic factors to the variation in body-mass index in 58 low-income and middle-income countries: an econometric analysis of multilevel data
Authors: Kim R, Kawachi I, Coull BA, and Subramanian SV
Source: Lancet Global Health , 6(7):e777-e786; DOI: 10.1016/S2214-109X(18)30232-8
Topic(s): Body Mass Index (BMI)
Nutrition
Country: More than one region
  Multiple Regions
Published: JUL 2018
Abstract: Background Most epidemiological studies have not simultaneously quantified variance in health within and between populations. We aimed to estimate the extent to which basic socioeconomic factors contribute to variation in body-mass index (BMI) across different populations. Methods We pooled data from the cross-sectional Demographic and Health Surveys (2005–16) for 15–49 year old women with complete data for anthropometric measures in 58 low-income and middle-income countries (LMICs). We compared estimates from multilevel variance component models for BMI before and after adjusting for age and socioeconomic factors (place of residence, education, household wealth, and marital status). The hierarchical structure of the sample included three levels with women at level 1, communities at level 2, and countries at level 3. The primary outcome was BMI. We did a sensitivity analysis using the 2002–03 World Health Surveys. Findings Of 1?212?758 women nested within 64?764 communities and 58 countries, we found that most unexplained variation for BMI was attributed to between-individual differences (80%) and the remaining was between-population differences (14% for countries and 6% for communities). Socioeconomic factors explained a large proportion of between-population variance in BMI (14·8% for countries and 47·1% for communities), but only about 2% of interindividual variance. In country-specific models, we found substantial variation in the magnitude of between-individual differences (variance estimates ranging from 7·6 to 31·4, or 86·0–98·6% of the total variation) and the proportion explained by socioeconomic factors (0·1–6·4%). The disproportionately large unexplained between-individual variance in BMI was consistently found in additional analyses including more comprehensive set of predictor variables, both men and women, and populations from low-income and high-income countries. Interpretation Our findings on variance decomposition in BMI and explanation by socioeconomic factors at population and individual levels indicate that inferential questions that target within and between populations are importantly inter-related and should be considered simultaneously. Funding None.
Web: https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(18)30232-8/fulltext?dgcid=raven_jbs_etoc_email