Impact of the COVID-19 pandemic on the prevalence of opioid agonist therapy discontinuation in Ontario, Canada: A population-based time series analysis
Bibliographic record
Abstract
BACKGROUND: We assessed the impact of COVID-19, which includes the declaration of a state of emergency and subsequent release of pandemic-specific OAT guidance (March 17, 2020 to March 23, 2020) on the prevalence of OAT discontinuation. METHODS: We conducted a population-based time series analysis using interventional autoregressive integrated moving average models among Ontario residents who were stable (>60 days of continuous use) and not yet stable on OAT. Specifically, we examined whether COVID-19 impacted the weekly percentage of individuals who discontinued OAT, overall and stratified by treatment type (methadone vs. buprenorphine/naloxone). Additionally, we compared demographic characteristics and patient outcomes among people stable on OAT who discontinued treatment during (March 17, 2020 to November 30, 2020) and prior (July 3, 2019 to March 16, 2020) to the pandemic. RESULTS: The weekly prevalence of OAT discontinuation across the study period ranged between 0.6% and 1.1%, among those stable on treatment compared to 7.3% and 16.6%, among those not stable on treatment. Following COVID-19, there was no significant change in the percentage of Ontarians who discontinued OAT, regardless of whether they were stabilized on treatment. Among those stable on OAT, a similar proportion of patients restarted therapy and experienced opioid-related harm following an OAT discontinuation. However, mortality following OAT discontinuation must be noted, as approximately 1.4% and 0.8% of people who discontinued methadone and buprenorphine/naloxone respectively, died within 30 days of discontinuation. CONCLUSIONS: Trends in the prevalence of OAT discontinuation did not significantly change during the first eight months of the COVID-19 pandemic.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".