Assessing intersectional disparities in obesity among Brazilian adults: a MAIHDA approach.

Abstract

Obesity disproportionately affects socially marginalized populations, but traditional analyses often fail to capture the complexity of intersecting social determinants. To address this limitation, we applied the intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to examine how combinations of different systems of power shape obesity prevalence among Brazilian adults. Data from 71,896 individuals from the 2019 Brazilian National Health Survey were analyzed. Obesity was defined as body mass index ≥ 30kg/m2. Individuals were categorized into 162 intersectional strata based on five dimensions (race, gender, age, income, and education). Two multilevel logistic models were estimated to account for additive and multiplicative effects of these dimensions, and predicted obesity prevalence values across different social strata were examined. The prevalence of obesity varied significantly within and across intersectional strata, with the highest prevalence concentrated among Brown and Black women with lower income and education. The explanatory effect of the intersectional strata decreased from 3.08% to 1.67% after including social dimensions in the model. With a proportional change in variance of 46%, the analysis showed that interaction effects are needed to capture the observed inequities between groups. While additive effects account for part of the variance in obesity, persistent intersectional disparities highlight the limitations of traditional models. These findings underscore the importance of intersectional frameworks in revealing how oppression systems are embodied in health outcomes.