Trends in Postpartum Opioid Prescribing: A Time Series Analysis
Bibliographic record
Abstract
Opioids are commonly prescribed following childbirth, but data are lacking on trends in postpartum opioid prescribing over time. We examined whether a highly publicized 2006 case report questioning the safety of codeine during lactation was associated with changes in postpartum opioid prescribing. We conducted a cross-sectional time series analysis of all publicly funded prescriptions for opioids to postpartum women in Ontario, Canada, from April 1, 2000, to March 31, 2017. The intervention was the publication of a case report in 2006 attributing the death of a breastfeeding neonate to maternal codeine use. The primary outcome was the rate of opioid prescribing to postpartum women. Among postpartum women eligible for prescription drug coverage, 17.5% filled an opioid prescription in the third quarter of 2006 (immediately prior to the intervention), with codeine representing 89.8% of all prescriptions. By the fourth quarter of 2010, only 12.2% of postpartum women filled an opioid prescription, representing a decline of 30% (P < 0.01), with codeine representing 71.9% of all prescriptions. During this period, we observed sizeable relative increases in the proportion of opioid prescriptions filled for morphine, hydromorphone, and oxycodone. By 2017, among women prescribed opioids post partum, 39.0% filled a prescription for codeine, while the remainder filled prescriptions for oxycodone (18.6%), morphine (25.5%), and hydromorphone (16.9%). A highly publicized case report questioning the safety of maternal codeine use during breastfeeding was associated with significant changes in opioid prescribing to postpartum women, including a decline in overall opioid prescribing and a shift from codeine to stronger opioids.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".