J Clin Epidemiol. 2024 Jul 1:111446. doi: 10.1016/j.jclinepi.2024.111446. Online ahead of print.
ABSTRACT
OBJECTIVE: Understanding how social categories like gender, migration background, LGBT status (lesbian/gay/bisexual/transgender), education and their intersections affect health outcomes is crucial. Challenges include avoiding stereotypes and fairly assessing health outcomes. This paper aims to demonstrate how to analyse these aspects.
STUDY DESIGN AND SETTING: The study used data from N=19,994 respondents from the German Socio-Economic Panel (SOEP) 2021 data collection. Variations between and within intersectional social categories regarding depressive symptoms and self-reported depression diagnosis were analyzed. We employed Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (I-MAIHDA) to assess the impact of gender, LGBT status, migration, education and their interconnectedness. A Configuration-Frequency Analysis (CFA) assessed typicality of intersections. Differential Item Functioning (DIF) analysis was conducted to check for biases in questionnaire items.
RESULTS: I-MAIHDA analysis revealed significant interactions between these categories for depressive symptoms and depression diagnosis. The CFA showed that certain combinations of social categories occurred less frequently compared to their expected distribution. The DIF analysis showed no significant bias in a depression short scale across social categories.
CONCLUSION: Results reveal interconnectedness between the social categories, affecting depressive symptoms and depression probabilities. More privileged groups had significant protective effects while those with less societal privileges showed significant hazardous effects. Although statistical significance was found in interactions between categories, the variance within categories outweighs that between them, cautioning against individual-level conclusions.
PMID:38960291 | DOI:10.1016/j.jclinepi.2024.111446
