Treatment patterns and healthcare resource use among veterans initiating medication for incident moderate‐to‐severe alcohol use disorder
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Several medications for alcohol use disorder (MAUDs) are recommended to treat alcohol use disorder (AUD) in the Veterans Affairs (VA) guidelines. This study descriptively characterized treatment patterns and healthcare resource utilization (HCRU) among VA patients with AUD treated with VA-recommended MAUDs. METHODS: Veterans Health Administration data (VHA; 08/01/2013-11/30/2019) were used to identify 31,384 adults aged ≥18 years with AUD who initiated disulfiram (n = 2115), acamprosate (n = 3756), oral naltrexone (n = 25,082), or extended-release naltrexone (XR-NTX; n = 431) following AUD diagnosis. Study measures, stratified by medication received, included treatment adherence (proportion of days covered), discontinuation, and HCRU over 1 year. RESULTS: Mean time to treatment discontinuation was high for all MAUDs but longest for XR-NTX (92 vs. 55-59 days; all p < .001). Relative to the year preceding AUD diagnosis, treatment with MAUDs was associated with fewer hospitalizations (XR-NTX: 0.48 vs. 0.42; oral naltrexone: 0.58 vs. 0.47; acamprosate: 0.67 vs. 0.60; disulfiram: 0.63 vs. 0.57) and more outpatient visits per patient (XR-NTX: 20.0 vs. 36.0; oral naltrexone: 19.0 vs. 30.0; acamprosate: 19.0 vs. 31.0; disulfiram: 17.0 vs. 29.0). CONCLUSION: Among veterans with AUD, this descriptive analysis found that MAUD use was associated with reduced hospitalizations, and XR-NTX was associated with a longer treatment duration versus oral MAUDs. SCIENTIFIC SIGNIFICANCE: This real-world study is among the first to describe clinical characteristics, treatment patterns, and HCRU in VHA patients who initiated MAUDs when all MAUDs were included in the VHA formulary.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".