Trend in Prescription Medication Utilization for Opioid Use Disorder and Alcohol Use Disorder From 2015 to 2021: A Population-wide Study in a Canadian Province
Bibliographic record
Abstract
OBJECTIVE: To examine the quarterly incidence and prevalence of medications for opioid use disorder (OUD) and alcohol use disorder (AUD) from 2015 to 2021. METHODS: A retrospective population-wide observational study in Manitoba, Canada, was conducted using administrative claims data from the Manitoba Centre for Health Policy to examine the incidence and prevalence of OUD (methadone, buprenorphine-naloxone, buprenorphine) or AUD medications (naltrexone, acamprosate, disulfiram) per 10,000 individuals in each quarter between January 1, 2015, and December 31, 2021. RESULTS: There were 1179 and 451 individuals who received at least one prescription for OUD and AUD, respectively, in the first quarter of 2020. The prevalence of OUD medications more than doubled from 6.3 to 14.3 per 10,000 from January 1, 2015, to December 31, 2021. Likewise, AUD medication prevalence increased almost 10-fold from 0.68 to 6.5 per 10,000 from January 1, 2015, to December 31, 2021, primarily due to naltrexone. The incidence of AUD prescription use increased 8.6-fold from 0.29 to 2.51 per 10,000 during the study period. In contrast, the incidence of opioid agonist therapy declined from 2.1 per 10,000 in the first quarter of 2015 to 0.53 per 10,000 the first quarter of 2016, primarily due to methadone. Whereas methadone incidence declined, buprenorphine-naloxone incidence increased almost 15-fold during the study period. CONCLUSION: An increase in both AUD medication prevalence and incidence in addition to an increase in buprenorphine-naloxone incidence was observed. These findings reflect an increase in the uptake of medications for treating AUD and OUD following changes to improve coverage and access to these medications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".