Impacts of the COVID-19 pandemic on enrollment in medications for opioid use disorder (MOUD) in Vancouver, Canada: An interrupted time series analysis
Bibliographic record
Abstract
BACKGROUND: In anticipation of COVID-19 related disruptions to opioid use disorder (OUD) care, new provincial and federal guidance for the management of OUD and risk mitigation guidance (RMG) for prescription of pharmaceutical opioids were introduced in British Columbia, Canada, in March 2020. This study evaluated the combined impacts of the COVID-19 pandemic and counteracting OUD policies on enrollment in medications for OUD (MOUD). METHODS: Using data from three cohorts of people with presumed OUD in Vancouver, we conducted an interrupted time series analysis to estimate the combined effects impact of the COVID-19 pandemic and counteracting OUD policies on the prevalence of enrollment in MOUD overall, as well as in individual MOUDs (methadone, buprenorphine/naloxone, slow-release oral morphine) between November 2018 and November 2021, controlling for pre-existing trends. In sub-analysis we considered RMG opioids together with MOUD. RESULTS: We included 760 participants with presumed OUD. In the post-COVID-19 period, MOUD and slow-release oral morphine prevalence rates showed an estimated immediate increase in level (+7.6%, 95% CI: 0.6%, 14.6% and 1.8%, 95% CI: 0.3%, 3.3%, respectively), followed by a decline in the monthly trend (-0.8% per month, 95% CI: -1.4%, -0.2% and -0.2% per month, 95% CI: -0.4, -0.1, respectively). There were no significant changes in the prevalence trends of enrollment in methadone, buprenorphine/naloxone, or when RMG opioids were considered together with MOUD. CONCLUSIONS: Despite immediate improvements in MOUD enrollment in the post-COVID-19 period, this beneficial trend reversed over time. RMG opioids appeared to have provided additional benefits to sustain retention in OUD care.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".