Impact of policy changes and drug shortages on acamprosate and naltrexone use in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Acamprosate and naltrexone, evidence-based pharmacotherapies for alcohol use disorder (AUD), are publicly covered by the Ontario Drug Benefit (ODB) programs; however, their availability has changed over time, with expanded formulary access in July 2018, followed by an acamprosate shortage in February 2019 and ending in July 2020. We evaluated the impact of these events on the use of these medications in Ontario, Canada. METHODS: We conducted a time-series analysis among individuals with AUD dispensed acamprosate or naltrexone through the ODB from July 2016 to December 2020. Outcomes included monthly rates of those with AUD on therapy (primary), and rate of initiation (secondary) overall and by treatment type. We used autoregressive moving average models to evaluate the impact of expanded coverage and the acamprosate shortage on rates of use, and reported characteristics at first dispensation. RESULTS: Over the study period, 10,637 individuals (61.0% male) initiated acamprosate or naltrexone. Expanded coverage increased monthly utilization rates of acamprosate (p = 0.0004), naltrexone (p < 0.0001), and either AUD pharmacotherapy (p < 0.0001). The acamprosate shortage led to a 98.1% reduction in acamprosate use (p = 0.0003) but did not impact naltrexone (p = 0.51). Our secondary analysis yielded consistent results with respect to the shortage; however, the expanded formulary listing did not impact the rate of new acamprosate patients (p = 0.3). By December 2020, 5.3% of ODB recipients with AUD were accessing pharmacotherapy. CONCLUSIONS: Although coverage expansion increased access to medications that treat AUD, the shortage of acamprosate led to large reductions in its use, with no responsive increase in naltrexone prescribing.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".