Trends of prenatal opioid utilization and neonatal abstinence syndrome in Manitoba, Canada: A 26-year population-based cohort study
Bibliographic record
Abstract
Background: Opioids are prescribed for pain and as opioid agonist therapy for opioid use disorder. This can lead to neonatal abstinence syndrome (NAS) in newborns when used in pregnancy. Few studies have described trends in prenatal opioid prescriptions and NAS by important determinants of health. Methods: To examine trends in prenatal opioid prescriptions and NAS diagnosis rates we conducted a population-based cohort study of live births in Manitoba, Canada, from January 1995 to March 2021. Live births were considered exposed to opioids prenatally if the pregnant person filled ≥1 opioid prescription during pregnancy. We described trends in NAS diagnosis rates by year, sex, urbanicity, and income quintile. Results: The cohort included 381,826 live births, of which 26,382 (6.7%) were exposed to prescription opioids prenatally. The proportion of live births exposed to opioid prescriptions during pregnancy increased from 3.7% in 1995 to 7.4% in 2017; however, there was a reduction in recent years. We identified a decrease in codeine prescriptions during pregnancy and an increased number of prescriptions for more potent opioids (oxycodone, hydromorphone, morphine, and opioid agonist therapy). During the study period, there were 1318 newborns diagnosed with NAS. The incidence of NAS in Manitoba more than tripled between 1995 and 2021 (2.0 to 7.6 per 1000 live births). Interpretation: The incidence of NAS increased over the study period, in line with other jurisdictions. Further research is needed to study the safety of different opioid agonist therapies and multidisciplinary support needed to support parents to care for newborns with NAS in the postpartum period and beyond.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".