Abstract Chronic diseases disproportionately affect certain ethnic and gender groups, but the social determinants driving these disparities in Latin America are not fully understood. In this study, we analyzed data from national health surveys in Brazil, Mexico, and Ecuador (2018–2019), representing a total weighted population of 96,726,891 adults. We used random forest models to predict chronic disease diagnoses based on education, occupation, and access to essential services such as sanitation, drinking water, and garbage collection. Our models performed better for indigenous and afro-descendant people, highlighting significant inequalities. While occupation and education were strong predictors for women, decreasing model performance by 8.57% and 7.36% respectively, occupation was the critical variable for men, decreasing model performance by 19.6% when neutralized. This work highlights the need for public policies adapted to the specific needs of different ethnic and gender groups.
Chivardi et al. (Fri,) studied this question.