Disparities in alcohol- and substance-related hospitalizations and deaths across sexual orientations in Canada: a longitudinal study
Bibliographic record
Abstract
OBJECTIVES: We aim to quantify disparities in substance-related acute events (i.e. hospitalizations and deaths for substances, including alcohol, cannabis, opioids, narcotics, and/or illicit drugs) across sexual orientations based on health administrative data. STUDY DESIGN: This was a longitudinal analysis from six waves of the Canadian Community Health Survey (2009-2014) linked to hospitalization/mortality data in Ontario, Canada. METHODS: The study sample consisted of survey respondents aged ≥15 (weighted n = 15,406,000) who provided a valid response to the question about sexual orientation. The primary outcome was substance-related acute events, including hospitalizations and deaths due to alcohol, cannabis, opioids, narcotics, and/or illicit drugs. Disparities in substance-related acute events across sexual orientation and gender were examined using flexible parametric survival analysis. RESULTS: Bisexual women had hazard ratios of 2.46 (95% confidence interval: 1.46-4.15) for any substance-related acute event and 2.67 (95% confidence interval: 1.42-5.00) for non-alcohol substance acute events compared to heterosexual women. Lesbian women did not exhibit significant differences in acute event risk compared to heterosexual women. Gay and bisexual men demonstrated elevated but not statistically significant risks compared to heterosexual men. CONCLUSIONS: Bisexual women face higher risks of substance-related acute events, potentially due to self-medication of unique stressors brought on by discrimination and isolation. Enhanced education and training for healthcare professionals are essential to increase awareness and sensitivity toward the heightened substance use risk among lesbian, gay, and bisexual individuals. Targeted interventions aimed at reducing substance use problems among bisexual individuals warrant increased funding and research.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".