Examining Intersectional Role of Race, Ethnicity, and Sexual Orientation in Health Disparities Among Older Adults
Bibliographic record
Abstract
Abstract Lesbian, gay, and bisexual (LGB) older adults who are racially/ethnically minoritized may face heightened health disadvantages due to their combined impact inclusive of the risks due to each social position and any excess risks beyond (i.e., synergistic effect), yet empirical evidence remains scarce. To address this gap, we used the 2012-2023 National Health Interview Survey to examine intersectional health disparities among Hispanic, Black, Asian, and other individuals of color within LGB adults age 50+ in the United States. Using non-Hispanic White heterosexuals as the referent and stratifying by gender, we estimated joint health disparities by sexual orientation and race/ethnicity and tested synergistic effects while adjusting for age, education, and income. Among older women, we observed joint disparities, predominately driven by synergistic effects, in cardiovascular disease for other LGB individuals of color (risk difference [RD] 15.5 percentage points [pp], 95% CI 5.5-25.6). Among older men, joint disparities, largely driven by synergistic effects, were observed in asthma (6.1 pp, 95% CI 0.0-12.2) and mental distress (16.4 pp, 5.5-27.3) for Hispanic LGB individuals; cognitive (12.7 pp, 3.0-22.4) and vision impairments (3.4 pp, 3.7-23.1) for Black LGB individuals; and limited physical functioning (15.1 pp, 3.2-26.9), anxiety (28.8 pp, 12.8-44.9), depression (24.5 pp, 8.4-40.5), and poor general health (21.9 pp, 8.1-35.7) for other LGB individuals of color. Overall, health disparity patterns in LGB older adults varied across race/ethnicity, lending some support for the synergistic multiple disadvantage hypothesis. There is a need for future research on intersectionality to identify modifiable mechanisms to eliminate health disparities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".