A population‐based time‐series analysis of opioid agonist treatment dispensed during pregnancy
Bibliographic record
Abstract
BACKGROUND AND AIMS: Identifying effective opioid treatment options during pregnancy is a high priority due to the growing prevalence of opioid use disorder across North America. We assessed the temporal impact of three population-level interventions on the use of opioid agonist treatment (OAT) during pregnancy in Ontario, Canada. DESIGN: This was a population-based time-series analysis to identify trends in the monthly prevalence of pregnant people dispensed methadone and buprenorphine. The impact of adding buprenorphine/naloxone to the public drug formulary, the release of pregnancy-specific guidance and the start of the COVID-19 pandemic were assessed. SETTING AND PARTICIPANTS: The study was conducted in Ontario, Canada between 1 July 2013 and 31 March 2022, comprising people who delivered a live or stillbirth in any Ontario hospital during the study period. MEASUREMENTS: We identified any prescription for methadone or buprenorphine dispensed between the estimated conception date and delivery date and calculated the monthly prevalence of OAT-exposed pregnancies among all pregnant people in Ontario. FINDINGS: Overall, rates of OAT during pregnancy have declined since mid-2018. Methadone-exposed pregnancies decreased from 0.46% of all pregnancies in Ontario in 2015 to a low of 0.16% in 2022. In the primary analysis, none of the interventions had a statistically significant impact on overall OAT rates; however, in the stratified analyses, there was a small increase in buprenorphine after the formulary change [0.006%, 95% confidence interval (CI) = 0.0032-0.0081, P < 0.0001] and a decrease in buprenorphine after the release of the 2017 guidelines (-0.005%, 95% CI = -0.0080 to -0.0020, P = 0.001) and the start of the COVID-19 pandemic (-0.003%, 95% CI = -0.0054 to -0.0006, P = 0.015). CONCLUSION: Despite changes in guidance and funding, opioid agonist treatment during pregnancy has been declining in Ontario, Canada since 2018.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".