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 Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to examine how combinations of different systems of power shape obesity prevalence in Brazilian adults. Data from 71,896 individuals in the 2019 Brazilian National Health Survey (PNS) were analyzed. Obesity was defined as BMI ≥ 30 kg/m2. Individuals were categorized into 162 intersectional strata based on five dimensions (race, gender, age, income, and education). Two logistic multilevel models were estimated to account for additive and multiplicative effects of these dimensions, and predicted obesity prevalences across different social strata were examined. The prevalence of obesity varied significantly within and across intersectional strata, with the highest prevalence concentrated among women from Brown and Black groups with lower income and education. The explanatory effect of the intersectional strata decreased from 3.08% to 1.67% after including the social dimensions into the model. With a proportional change in variance of 46%, the analysis showed that the 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 systems of oppression are embodied in health outcomes.