Disparities in Documented Drug Use Disorders Between Transgender and Cisgender U.S. Veterans Health Administration Patients
Bibliographic record
Abstract
OBJECTIVES: Transgender people-those whose gender identity differs from their sex assigned at birth-are at risk for health disparities resulting from stressors such as discrimination and violence. Transgender people report more drug use than cisgender people; however, it is unclear whether they have higher likelihood of drug use disorders. We examined whether transgender patients have increased likelihood of documented drug use disorders relative to cisgender patients in the national Veterans Health Administration (VA). METHODS: Electronic health record data were extracted for VA outpatients from 10/1/09 to 7/31/17. Transgender status and past-year documentation of drug use disorders (any, opioid, amphetamine, cocaine, cannabis, sedative, hallucinogen) were measured using diagnostic codes. Logistic regression models estimated odds ratios for drug use disorders among transgender compared to cisgender patients, adjusted for age, race/ethnicity and year. Effect modification by presence of ≥1 mental health condition was tested using multiplicative interaction. RESULTS: Among 8,872,793 patients, 8619 (0.1%) were transgender. Transgender patients were more likely than cisgender patients to have any drug use disorder (Adjusted Odds Ratio [aOR] 1.67, 95% confidence interval [CI] 1.53-1.83), amphetamine (aOR 2.22, 95% CI 1.82-2.70), cocaine (aOR 1.59, 95% CI 1.29-1.95), and cannabis (aOR 1.82, 95% CI 1.62-2.05) use disorders. There was no significant interaction by presence of ≥1 mental health condition. CONCLUSIONS: Transgender VA patients may have higher likelihood of certain drug use disorders than cisgender VA patients, particularly amphetamine use disorder. Future research should explore mechanisms underlying disparities and potential barriers to accessing treatment and harm reduction services faced by transgender people.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".