Characteristics, determinants, and frequency of persistent use among prescription opioid episodes initiated postpartum: a population-based cohort study from British Columbia, Canada
Bibliographic record
Abstract
Objective : To determine the characteristics, determinants, and persistent use for prescription opioid episodes initiated postpartum in British Columbia (BC), Canada. Design : Population-based cohort, 2008-2015. Setting : Linked administrative databases including outpatient and inpatient visits and outpatient prescription dispensations for all individuals in BC tested for hepatitis C or HIV. Population : Opioid-naïve individuals aged 13-49 with a delivery hospitalization record. Methods: We used modified Poisson regression to estimate risk ratios (RR) for pre-pregnancy characteristics and adjusted RR (aRR) for delivery characteristics adjusted for potential confounders. Main Outcome Measures: All and persistent (≥90 days) prescription opioid episodes initiated postpartum. Results: Among 292,684 eligible deliveries, 8.8% (95% CI: 8.7% to 8.9%) initiated a postpartum prescription opioid episode and 0.4% (0.4%- 0.5%) had a persistent episode. Persistent prescription opioid episodes were more frequent among cesarean vs. vaginal deliveries (1.0% vs. 0.2%, aRR 5.1 [4.6-5.8]). Opioid episodes varied regionally and declined from 12.8% in 2008 to 7% in 2012-2015. Persistent prescription opioid episodes were associated with pre-pregnancy: mental illness history (RR 2.4 [1.9-3.1]), psychotropic medication use (RRs 3.6-4.9), chronic pain (RR 2.7 [1.8-3.9]) and alcohol misuse (4.6 [2.2-9.7]), and delivery complications: intensive care unit admission (aRR 5.4 [3.3-8.9]), postpartum hospital readmission (aRR 3.9 [3.2-4.8]), and vaginal deliveries with hysterectomy (aRR 23.7 [6.1-91.8]) or tubal ligation/salpingectomy (aRR 14.6 [5.4-39.4]). Conclusions: Persistent prescription opioid episodes were initiated following 0.4% of deliveries. Postpartum pain management strategies should consider the strong associations between pre-pregnancy and delivery characteristics and persistent prescription opioid episodes initiated postpartum.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".