Towards Causal Interpretation of Sexual Orientation in Regression Analysis: Applications and Challenges

Abstract

This study presents an approach to analyze health disparities in Sexual and Gender Minority (SGM) populations, with a focus on the role of social support levels as an example to allow causal interpretations of regression models. We advocate for precisely defining the exposure variable and incorporating mediators into analyses, to address the limitations of comparing counterfactual outcomes solely between SGM and heterosexual populations. We define sexual orientation into domains (attraction, behavior, and identity), and emphasize a consideration of these elements either separately or together, depending on the research question. We also introduce social support measured before and after the disclosure of sexual orientation to facilitate inference. We illustrate this approach by examining the association between SGM status and depression diagnosis with data from the 2020 and 2021 National Health Interview Survey. We find a direct effect of SGM status on depression (OR: 3.07, 95% CI: 2.64 - 3.58) and no indirect effect through social support (OR: 1.07, 95% CI: 0.87-1.31). Our research emphasizes the necessity of the comprehensive measurement of sexual orientation and a focus on intervenable variables like social support in order to empower SGM communities and address SGM related health inequalities.