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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".