What does this research mean for the field?

Intersectionality and area-level indicators significantly influence depression disparities, with specific protective and risk factors identified among diverse populations. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.CHALLENGES_CONSENSUS.

What question did this study set out to answer?

To analyze the impact of intersectional identities and area-level indicators on depression disparities.

March 5, 2026Open Access

Application of intersectionality framework and area-level indicators in machine learning analysis of depression disparities in all of us research program data

Key Points

To analyze the impact of intersectional identities and area-level indicators on depression disparities.
Cross-sectional study using electronic health records from All of Us research program
Cohort consisted of 33,994 individuals with SDOH surveys and inpatient visits
Depression diagnosis as an outcome with various area-level and individual-level variables
Used mixed effects logistic regression model with LASSO method for variable selection
31.7% of the cohort diagnosed with depression from 2020 to 2025
Protective factors identified included male sex at birth and active military service
Inability to work was a strong risk factor for depression
Intersectional risks highlighted for non-binary identity among older adults and women with children

Abstract

Depression is a complex mental health disorder influenced by various social determinants of health (SDOH) at individual and community levels. Area-level indicators and the intersectionality framework, which considers overlapping personal identities, are used in this paper to get a nuanced picture of depression disparities. This cross-sectional study uses electronic health records data from the All of Us research network. Our study cohort includes 33,994 individuals who completed the SDOH surveys in All of Us and had at least one inpatient visit, with 31.7% diagnosed with depression between 2020 and 2025. We used depression diagnosis as an outcome, while independent variables include area-level variables from US Religious Census (religious adherents, %), American Community Survey response (birthrates by living arrangement), and All of Us (median area income and deprivation index); individual-level variables from All of Us surveys: age group, income, gender, sexual orientation, race/ethnicity, and others. We also added the interactions of the individual-level variables with each other and with area-level variables. The association between depression and the variables is reported by fitting the mixed effects logistic regression model on the subset of variables identified by the LASSO method with 3-digit ZIP code as a random effect. Protective factors included male sex at birth among straight (OR 0.71, 95% CI: 0.59–0.86) and non-binary (OR 0.42, 95% CI: 0.24–0.75) individuals, older adults with active military service (OR 0.64, 95% CI: 0.53–0.77) or health insurance (OR 0.80, 95% CI: 0.72–0.88), and younger adults with 1–2 children (OR 0.72, 95% CI: 0.60–0.86). Risk factors were strongest for inability to work (OR 2.01, 95% CI: 1.76–2.31), further impacted by medium per capita income (OR 1.39, 95% CI: 1.13–1.70). Housing concerns (OR 1.24, 95% CI: 1.09–1.41), frequent disrespect (OR 1.41, 95% CI: 1.17–1.69), and high area deprivation in older adults (OR 1.31, 95% CI: 1.12–1.53) increased risk. Other intersectional risks included non-binary identity in older adults (OR 5.14, 95% CI: 1.94–13.62), non-binary individuals born outside the USA (OR 10.23, 95% CI: 2.07–50.48), women with children (OR 1.39, 95% CI: 1.25–1.55), and Hispanic active-duty members (OR 1.90, 95% CI: 1.32–2.74). Our findings, while being limited by 3-digit ZIP code aggregation that might have obscured some area-level effects, suggest that clinical and public health strategies for depression prevention and intervention should account for overlapping identities while incorporating place-aware approaches.

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