Safety of prenatal opioid analgesics: Do results differ between public health insurance beneficiary and population‐based cohorts?
Bibliographic record
Abstract
BACKGROUND: Pregnant patients with particular types of health insurance may have distinct demographic and medical characteristics that have a biologic effect on associations between opioid analgesics and congenital anomalies (CA). METHODS: We followed 199,884 pregnant prescription beneficiaries in Ontario, Canada (1996-2018). Opioid analgesics dispensed in the first trimester and CA were identified from universal-access administrative health records. We estimated propensity score adjusted risk ratios (RR) between first trimester exposure and CA (any, major, minor, specific). RRs were compared to those published from an Ontario population-based cohort (N = 599,579, 2013-2018). RESULTS: 15,724 (7.9%) were exposed to first trimester opioid analgesics, mainly codeine (58.1%) or oxycodone (21.3%); CA prevalence in exposed was 3.1%. RRs in the beneficiary cohort appeared higher than the population-based cohort for any CA with hydromorphone (RR = 2.34, 95% CI: 1.65, 3.30) and oxycodone (RR = 1.73, 95% CI: 1.46, 2.05) and major CA with hydromorphone (RR = 2.74, 95% CI: 1.91, 3.94) and oxycodone (RR = 1.72, 95% CI: 1.42, 2.08). Other RRs that appeared higher in the beneficiary cohort included cardiovascular (codeine, oxycodone), gastrointestinal (oxycodone), musculoskeletal (any, hydromorphone, oxycodone), CNS (oxycodone), chromosomal (codeine), and neoplasm and tumor (oxycodone) anomalies. The beneficiary cohort had higher opioid doses, was younger, had lower socioeconomic status, and greater comorbidities. CONCLUSIONS: Increased risks of CA after first trimester opioid analgesics were observed in low-income prescription beneficiaries, and some estimates were higher than a population-based cohort from the same setting. Biological differences associated with younger age, lower socioeconomic status and greater comorbidity may affect generalizability of results from pregnant low-income beneficiaries.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".