Identifying Prenatal Opioid Exposure in Health Administrative Data for Public Health Surveillance and Epidemiologic Research
Bibliographic record
Abstract
IntroductionAccurate estimation of prenatal opioid exposure (POE) is needed for population-based surveillance & research but can be challenging with health administrative data due to varying definitions & methods. Prior research has relied primarily on infant records with a diagnosis of neonatal abstinence syndrome (NAS).
 Objectives and Approach1) Evaluate the impact of using different definitions of maternal opioid use in the estimation of POE; 2) Investigate whether maternal characteristics vary by the type of definition used. Population-based cross-sectional study of all hospital births (N= 454,746) from 2014-2017 in Ontario, Canada. Multiple linked population-based health administrative databases were used to identify opioid-related pre- & perinatal Emergency Department visits & hospitalizations & opioid prescriptions. We examined how pre-conception & in-pregnancy maternal characteristics varied by using different approaches to ascertain POE.
 ResultsThere were 9624 live/still births with POE. Ascertainment of POE was highest using maternal prescription drug data (79%) & infant hospital records with NAS (45%). Maternal characteristics varied by data source used for POE ascertainment. Opioid-related health care during pregnancy identified a high-risk phenotype, contrasted with those ascertained through prescription data, with respective rates of 64% vs. 54% for social assistance, 37% vs. 12% for polydrug use, 23% vs. 6% for alcohol use, 26% vs. 19% for 3+ live births, 13% vs. 5% for victim of violence, 12% vs. 6% for involvement in criminal justice system & 64% vs. 17% for mental health & addictions hospital care.
 Conclusion / ImplicationsPOE ascertainment differs by health administrative data source & ability to link both across maternal records and with infant. Prescription drug data identified the highest number of opioid-exposed births and, with linked healthcare records, is useful to identify illicit opioid use & additional risk factors. Clinically meaningful differences in maternal characteristics of opioid users exist by POE ascertainment method.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".