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 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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".