Opioid Use in Pregnancy in Ontario, Canada – Using Population-based Administrative Data to Evaluate the Health of Pregnant People and Children
Bibliographic record
Abstract
Opioid use in pregnancy has increased dramatically over the past two decades in North America, and often occurs in the context of complex socio-environmental and clinical conditions, which can compound risk of adverse health outcomes. This dissertation presents four studies on the development of methods to identify prenatal opioid exposure (POE), epidemiology of POE and receipt of well-child care by type of POE using linked health administrative databases for all births in Ontario, Canada. The first study evaluates different methods for ascertainment of births with POE. I found that combining linked prenatal prescription opioid records with maternal and newborn hospital codes improved identification of dyads with POE, highlighting the importance of data availability and linkage. Using methods developed in study 1, the second study describes the prevalence and type of POE in Ontario from 2014-2019. I found 5.3% of infants had POE, and rates declined from 6.1% in 2014 to 4.4% in 2019. In the third study, I developed distinct groups of people who use opioids in pregnancy and evaluated their association with postpartum drug overdose or death. Using latent class analysis, I identified 5 groups: (1) short-term analgesia with low comorbidity, (2) analgesia in young people, (3) medication for opioid use disorder or unregulated opioid use, (4) pain management with comorbidity, and (5) mixed opioid use plus high social and medical needs, with varying risk and outcome profiles. Finally, I quantified receipt of well-child care up to age 2 years including recommended developmental screening at the 18-month enhanced well-child visit. Overall, only 1 in 2 infants with POE received recommended well-child care and developmental screening, with significant variation by type of POE. Residing in high deprivation or rural areas, maternal mental illness, <19 years at first birth and other social disadvantage were also negatively associated with recommended well-child care, whereas having a regular primary care provider was positively associated. These studies advance our understanding of POE epidemiology and associated health outcomes. Findings can be used to improve identification of high-risk dyads, inform interventions and strengthen guidelines to ensure children with POE receive preventive care and developmental screening.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".