Trends in Pregnancy-Associated Opioid Toxicity and Mortality
Bibliographic record
Abstract
ObjectivesTo examine trends in pregnancy-associated non-fatal and fatal opioid toxicity and all-cause mortality, and identify associated factors. ApproachWe conducted a population-based study of 1,555,370 pregnancies in Ontario, Canada, 2013-2022. We analyzed linked administrative datasets, including coroner data, and calculated pregnancy-associated (during pregnancy or within one-year post-pregnancy) non-fatal/fatal opioid toxicity and all-cause mortality ratios per 100,000 livebirths by year and timing (pregnancy, post-pregnancy). Poisson regression models analyzed trends in outcomes and generated adjusted relative risks (aRR) of opioid toxicity by socio-demographic and clinical factors. ResultsPregnancy-associated non-fatal opioid toxicity increased 220% between 2013 and 2020 (45.5-145.4/100,000 livebirths) before declining by 30% in 2021. Over the study period, fatal opioid toxicity increased 150% (6.8-17.5/100,000) and all-cause mortality increased 120% (32.8-71.2/100,000). Our methods did not identify any opioid toxicity deaths in pregnancy, and most non-fatal (66.6%) and fatal (88.9%) opioid toxicity and all-cause mortality (73.9%) occurred 43-365 days post-pregnancy. The percent of deaths attributed to opioids increased from 12.7% in 2015 to 25.0% in 2020. Substance use disorder (aRR 19.52, 95% CI 16.87-22.58), pre-pregnancy opioid toxicity (aRR 4.69, 3.81-5.78), mental illness (aRR 2.01, 1.75-2.29), high neighbourhood deprivation (aRR 1.45, 1.28-1.64), and social disadvantage (aRR 3.21, 2.77-3.71) were associated with elevated risk of opioid toxicity. ConclusionsPregnancy-associated opioid toxicity and mortality have increased substantially. In 2020, 1 in 4 pregnancy-associated deaths involved opioids. ImplicationsFindings highlight the need for comprehensive care and perinatal harm reduction services. System-level improvements to reduce poor outcomes must include complete data capture of all pregnancy-associated deaths.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".