Opioid Use Disorder and Perinatal Outcomes
Bibliographic record
Abstract
OBJECTIVES: Evidence on the perinatal health of mother-infant dyads affected by opioids is limited. Elevated risks of opioid-related harms for people with opioid use disorder (OUD) increase the urgency to identify protective factors for mothers and infants. Our objectives were to determine perinatal outcomes after an OUD diagnosis and associations between opioid agonist treatment and birth outcomes. METHODS: We conducted a population-based retrospective study among all women with diagnosed OUD before delivery and within the puerperium period in British Columbia, Canada, between 2000 and 2019 from provincial health administrative data. Controlling for demographic and clinical characteristics, we determined associations of opioid agonist treatment on birth weight, gestational age, infant disorders related to gestational age and birth weight, and neonatal abstinence syndrome via logistic regression. RESULTS: The population included 4574 women and 6720 live births. Incidence of perinatal OUD increased from 166 in 2000 to 513 in 2019. Compared with discontinuing opioid agonist treatment during pregnancy, continuous opioid agonist treatment reduced odds of preterm birth (adjusted odds ratio: 0.6; 95% confidence interval: 0.4-0.8) and low birth weight (adjusted odds ratio: 0.4; 95% confidence interval: 0.2-0.7). Treatment with buprenorphine-naloxone (compared with methadone) reduced odds of each outcome including neonatal abstinence syndrome (adjusted odds ratio: 0.6; 95% confidence interval: 0.4-0.9). CONCLUSIONS: Perinatal OUD in British Columbia tripled in incidence over a 20-year period. Sustained opioid agonist treatment during pregnancy reduced the risk of adverse birth outcomes, highlighting the need for expanded services, including opioid agonist treatment to support mothers and infants.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".