Importance of Modifiable Factors to Infant Health in the Context of Prenatal Opioid Use Disorder
Bibliographic record
Abstract
OBJECTIVE: The aim of the study is to estimate the contributions of common and modifiable risk factors to birth outcomes of individuals with prenatal opioid use disorder (OUD). METHODS: We conducted an observational cohort study of all Wisconsin Medicaid-covered singleton live births from 2011-2019. Using Blinder-Oaxaca decomposition for continuous, and the Fairlie extension for categorical outcomes, we estimated the contributions of comorbidities, tobacco use, pre-pregnancy body mass index (BMI), and gestational weight gain (GWG) to birthweight for gestational age (BW-GA) percentile associated with prenatal OUD and the risk of small for gestational age (SGA), net of other factors. RESULTS: Among 216,684 births, the 5184 (2.4%) with OUD had greater prevalence of tobacco use, a lower average pre-pregnancy BMI (26.7 kg/m 2 , SD = 0.09 versus 28.4 kg/m 2 , SD = 0.02), and on average 2.0 pounds less GWG, when compared to those without OUD. The predicted mean BW-GA percentile among infants with OUD exposure was 11.2 (95% CI 10.5, 11.9) points lower than those without; 62.3% (95% CI 57.4, 67.1) of this difference could be explained by the variables included in the full model and the largest contribution of the explained portion came from the higher prevalence of tobacco use followed by the contributions of comorbidities, GWG, and pre-pregnancy BMI. CONCLUSIONS: More than half of the difference in BW-GA percentile, and risk of SGA associated with prenatal OUD, could be attributed to modifiable factors and not opioids. Moreover, potentially modifiable factors including tobacco use and measures reflecting nutritional status contributed to a majority of the explained portion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".