Identification of Prenatal Opioid Exposure Within Health Administrative Databases
Bibliographic record
Abstract
BACKGROUND: Health administrative data offer a vital source of data on maternal prenatal opioid exposure (POE). The impact of different methods to estimate POE, especially combining maternal and newborn records, is not known. METHODS: This population-based cross-sectional study included 454 746 hospital births with linked administrative data in Ontario, Canada, in 2014-2017. POE ascertainment included 3 sources: (1) prenatal opioid prescriptions, (2) maternal opioid-related hospital records, and (3) newborn hospital records with neonatal abstinence syndrome (NAS). Positive percent agreement was calculated comparing cases identified by source, and a comprehensive method was developed combining all 3 sources. We replicated common definitions of POE and NAS from existing literature and compared both number of cases ascertained and maternal socio-demographics and medical history using the comprehensive method. RESULTS: Using all 3 data sources, there were 9624 cases with POE (21.2 per 1000 births). Among these, positive percent agreement (95% confidence interval) was 79.0% (78.2-79.8) for prenatal opioid prescriptions, 19.0% (18.2-19.8) for maternal opioid-related hospital records, and 44.7% (43.7-45.7) for newborn NAS. Compared with other definitions, our comprehensive method identified up to 523% additional cases. Contrasting ascertainment with maternal opioid-related hospital records, newborn NAS, and prenatal opioid prescriptions respective rates of maternal low income were 57%, 48%, and 39%; mental health hospitalization history was 33%, 28%, and 17%; and infant discharge to social services was 8%, 13%, and 5%. CONCLUSIONS: Combining prenatal opioid prescriptions and maternal and newborn opioid-related hospital codes improves identification of a broader population of mothers and infants with POE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".