Receipt of Medications for Alcohol Use Disorder in the Veterans Health Administration: Comparison of Rates at the Intersections of Racialized and Ethnic Identity With Both Sex and Transgender Status
Bibliographic record
Abstract
OBJECTIVES: Medications for alcohol use disorder (MAUDs) are recommended for patients with alcohol use disorder yet are underprescribed. Consistent with Minority Stress and Intersectionality theories, persons with multiple sociodemographically marginalized identities (eg, Black women) often experience greater barriers to care and have poorer health outcomes. We use data from the Veterans Health Administration to assess disparities in Federal Drug Administration (FDA)-approved MAUDs and all effective MAUDs between the following groups: racialized and ethnic identity, sex, transgender status, and their intersections. METHODS: Among all Veterans Health Administration outpatients between August 1, 2015, and July 31, 2017, with documented alcohol screenings and an International Classification of Diseases diagnosis for alcohol use disorder in the 0-365 days prior (N = 308,238), we estimated the prevalence and 95% confidence intervals of receiving FDA-approved MAUDs and any MAUDs in the following year and compared them using χ2 or Fisher's exact test. Analyses are unadjusted to present true prevalence and group differences. RESULTS: The overall prevalence for MAUDs was low (FDA-MAUDs = 8.7%, any MAUDs = 20.0%). Within sex, Black males had the lowest rate of FDA-MAUDs (7.3%, [7.1-7.5]), whereas American Indian/Alaskan Native females had the highest (18.4%, [13.8-23.0]). Among those identified as transgender, Asian and Black transgender persons had the lowest rates of FDA-MAUDs (0%; 4.3%, [1.8-8.5], respectively), whereas American Indian/Alaskan Native transgender patients had the highest (33.3%, [2.5-64.1]). Similar patterns were observed for any MAUDs, with higher rates overall. CONCLUSIONS: Substantial variation exists in MAUD prescribing, with marginalized veterans disproportionately receiving MAUDs at lower and higher rates than average. Implementation and quality improvement efforts are needed to improve MAUD prescribing practices and reduce disparities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".