Trends and Determinants of Prescription Drug Use during Pregnancy and Postpartum in British Columbia, 2002–2011: A Population-Based Cohort Study
Bibliographic record
Abstract
PURPOSE: To describe trends, patterns, and determinants of prescription drug use during pregnancy and postpartum. METHODS: This is a retrospective, population-based study of all women who gave birth between January 2002 and 31 December 2011 in British Columbia, Canada. Study population consisted of 225,973 women who had 322,219 pregnancies. We examined administrative datasets containing person-specific information on filled prescriptions, hospitalizations, and medical services. Main outcome measures were filled prescriptions during pregnancy and postpartum. We used logistic regressions to examine associations between prescription drug use and maternal characteristics. RESULTS: Approximately two thirds of women filled a prescription during pregnancy, increasing from 60% in 2002 to 66% in 2011. The proportion of pregnant women using medicines in all three trimesters of pregnancy increased from 20% in 2002 to 27% in 2011. Use of four or more different types of prescription drug during at least one trimester increased from 8.4% in 2002 to 11.7% in 2011. Higher BMI, smoking during pregnancy, age under 25, carrying multiples, and being diagnosed with a chronic condition all significantly increased the odds of prescription drug use during pregnancy. CONCLUSIONS: The observed increase in the number of prescriptions and number of different drugs being dispensed suggests a trend in prescribing practices with potentially important implications for mothers, their neonates, and caregivers. Monitoring of prescribing practices and further research into the safety of most commonly prescribed medications is crucial in better understanding risks and benefits to the fetus and the mother.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".