Kim R, et al. - Researchers evaluated the extent to which basic socioeconomic factors contribute to variation in body-mass index (BMI) across different populations. They noted an important inter-relation in the inferential questions that target within and between populations and must be considered simultaneously; As seen on variance decomposition in BMI and explanation by socioeconomic factors at population and individual levels.

Methods

• Authors 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).
• The estimates from multilevel variance component models for BMI were compared before and after adjusting for age and socioeconomic factors (place of residence, education, household wealth, and marital status).
• Three levels were included at the hierarchical structure of the sample with women at level 1, communities at level 2, and countries at level 3.
• BMI was the primary outcome.
• Using the 2002-03 World Health Surveys, a sensitivity analysis was done.

Results

• As per data, out of 1,212,758 women nested within 64,764 communities and 58 countries, they 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).
• A large proportion of between-population variance in BMI (14·8% for countries and 47·1% for communities) was explained by socioeconomic factors, but only about 2% of interindividual variance.
• Findings suggested that substantial variation was found in the country-specific models 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%).
• Results demonstrated consistent apperaence of the disproportionately large unexplained between-individual variance in BMI in additional analyses including more comprehensive set of predictor variables, both men and women, and populations from low-income and high-income countries.