ArticlePsychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors2025
Estimating substance use disparities across intersectional social positions using machine learning: An application of group-lasso interaction network.
Article in Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Intersectional analysis for science and technology.Nature · 2025Review
- Minority stress and the development of problem alcohol use among sexual minority youth: A scoping review synthesizing multidisciplinary mechanisms of risk.Journal of child & adolescent substance abuse · 2025Article
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Authors and funding
6 authors.
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Abstract
objectiveAn aim of quantitative intersectional research is to model the joint impact of multiple social positions on health risk behaviors. Although moderated multiple regression is frequently used to pursue intersectional research hypotheses, such parametric approaches may produce unreliable effect estimates due to data sparsity and high dimensionality. Machine learning provides viable alternatives, offering greater flexibility in evaluating many candidate interactions amid sparse data conditions, yet remains rarely employed. This study introduces group-lasso interaction network (glinternet), a novel machine learning approach involving hierarchical regularization, to assess intersectional differences in substance use prevalence.
methodUtilizing variable selection and parameter stabilization functionality for main and interaction effects, glinternet was employed to examine two-way interactions between three primary social positions (gender, sexual orientation, and race) predicting heavy episodic drinking, cannabis use, and cigarette use prevalence. Analyses were conducted using the All of Us Research Program (
resultsGlinternet prevalence estimates were more stable across discovery and replication samples relative to logistic regression, particularly among sparsely represented groups. Prevalence estimates for cigarette and cannabis use were elevated among sexual minority and White cisgender women compared to heterosexual and non-White women, respectively.
conclusionsGlinternet may improve upon traditional moderated multiple regression methods for pursuing intersectional hypotheses by improving model parsimony and parameter stability, providing novel means for quantifying health disparities among intersectional social positions. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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