Application of Intersectionality Framework and Area-level Indicators in Machine Learning Analysis of Depression Disparities in All of Us Research Program Data

Application of Intersectionality Framework and Area-level Indicators in Machine Learning Analysis of Depression Disparities in All of Us Research Program Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Application of Intersectionality Framework and Area-level Indicators in Machine Learning Analysis of Depression Disparities in All of Us Research Program Data Dmitry Scherbakov, Michael T. Marrone, Leslie A. Lenert, Alexander V. Alekseyenko This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5536130/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Background/objective: Depression is a complex mental health disorder influenced by various social determinants of health (SDOH) at individual and community levels. Area-level factors and the intersectionality framework, which considers overlapping personal identities, are used in this paper to get a nuanced picture of depression disparities. Methods This cross-sectional study uses electronic health records data from the All of Us research network. Our study cohort includes 36,868 individuals who completed the SDOH surveys in All of Us and had at least one inpatient visit, with 31.9% diagnosed with depression between 2020 and 2025. We used depression diagnosis as an outcome, while independent variables include US Religious Census and American Community Survey responses, area-level variables, sociodemographic characteristics: age group, income, gender, sexual orientation, immigration status, marital status, and race/ethnicity – and the interactions of the latter with each other and with other variables. The association between depression diagnosis and the variables is reported by fitting the logistic regression model on the subset of variables identified by the LASSO method. Results Key findings include reduced depression odds for non-heterosexual individuals living in less religious areas (OR 0.65, 95% CI 0.52–0.80), while high religious adherence in the area was protective for Hispanics (OR 0.24, 95% CI 0.14–0.40). Identity interaction effects revealed heightened risks, such as never-married MENA individuals (OR 2.83, 95% CI 1.38–5.77) and Hispanic active-duty members (OR 1.76, 95% CI 1.25–2.46). Food insecurity (OR 1.44, 95% CI 1.21–1.71) and discrimination (OR 1.43, 95% CI 1.26–1.62) also increased odds. Discussion/Conclusion: The findings underscore the importance of considering all types of factors: individual, area-level, and intersectional in depression research and public health interventions. Psychiatry Statistical Epidemiology Psychology Health Economics & Outcomes Research Depression Mental health disparities Intersectionality Social Determinants of Health LASSO All of Us Research Program Area-Level Indicators Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5536130","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":385746738,"identity":"89493ca6-fedc-4ab0-a1fe-76b9292f0b5c","order_by":0,"name":"Dmitry Scherbakov","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYFACHgYGxgYGA34oF8gmVotkA8laDA4Qq0W+/ezBx5U77IyNr50x/vCDwUZ2wwECWgzO5CUbnj2TbGZ2O8dMsochzZiwFgkeM8nGNmYbkBZmBobDiQS1yM/gMf/Z2FZvYzw7x/gzA8N/wloYbvCYMTa2HTYzkM4xkGZgOEBYi8GZHGPJxjPHjSVup5VJ9hgkG88k6LD2M4YfG3dUG/bPTt784UeFnWwfQYehWUqa8lEwCkbBKBgFOAAA6ONBnBwhq1oAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0005-7274-0934","institution":"Medical University of South Carolina","correspondingAuthor":true,"prefix":"","firstName":"Dmitry","middleName":"","lastName":"Scherbakov","suffix":""},{"id":385746739,"identity":"cf2836ec-75b5-4bdf-b55a-f0624956b095","order_by":1,"name":"Michael T. 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Area-level factors and the intersectionality framework, which considers overlapping personal identities, are used in this paper to get a nuanced picture of depression disparities.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis cross-sectional study uses electronic health records data from the All of Us research network. Our study cohort includes 36,868 individuals who completed the SDOH surveys in All of Us and had at least one inpatient visit, with 31.9% diagnosed with depression between 2020 and 2025. We used depression diagnosis as an outcome, while independent variables include US Religious Census and American Community Survey responses, area-level variables, sociodemographic characteristics: age group, income, gender, sexual orientation, immigration status, marital status, and race/ethnicity \u0026ndash; and the interactions of the latter with each other and with other variables. The association between depression diagnosis and the variables is reported by fitting the logistic regression model on the subset of variables identified by the LASSO method.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eKey findings include reduced depression odds for non-heterosexual individuals living in less religious areas (OR 0.65, 95% CI 0.52\u0026ndash;0.80), while high religious adherence in the area was protective for Hispanics (OR 0.24, 95% CI 0.14\u0026ndash;0.40). Identity interaction effects revealed heightened risks, such as never-married MENA individuals (OR 2.83, 95% CI 1.38\u0026ndash;5.77) and Hispanic active-duty members (OR 1.76, 95% CI 1.25\u0026ndash;2.46). 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