Prescription Medication Use in Pregnancy in People with Disabilities: A Population-Based Cohort Study
Bibliographic record
Abstract
Background: Individuals with disabilities may require specific medications in pregnancy. The prevalence and patterns of medication use, overall and for medications with known teratogenic risks, are largely unknown. Methods: This population-based cohort study in Ontario, Canada, 2004–2021, comprised all recognized pregnancies among individuals eligible for public drug plan coverage. Included were those with a physical ( n = 44,136), sensory ( n = 13,633), intellectual or developmental ( n = 2,446) disability, or multiple disabilities ( n = 5,064), compared with those without a disability ( n = 299,944). Prescription medication use in pregnancy, overall and by type, was described. Modified Poisson regression generated relative risks (aRR) for the use of medications with known teratogenic risks and use of ≥2 and ≥5 medications concurrently in pregnancy, comparing those with versus without a disability, adjusting for sociodemographic and clinical factors. Results: Medication use in pregnancy was more common in people with intellectual or developmental (82.1%), multiple (80.4%), physical (73.9%), and sensory (71.9%) disabilities, than in those with no known disability (67.4%). Compared with those without a disability (5.7%), teratogenic medication use in pregnancy was especially higher in people with multiple disabilities (14.2%; aRR 2.03, 95% confidence interval [CI]: 1.88–2.20). Furthermore, compared with people without a disability (3.2%), the use of ≥5 medications concurrently was more common in those with multiple disabilities (13.4%; aRR 2.21, 95% CI: 2.02–2.41) and an intellectual or developmental disability (9.3%; aRR 2.13, 95% CI: 1.86–2.45). Interpretation: Among people with disabilities, medication use in pregnancy is prevalent, especially for potentially teratogenic medications and polypharmacy, highlighting the need for preconception counseling/monitoring to reduce medication-related harm in pregnancy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".