A Cohort Study of Psychotropic Prescription Drug Use in Pregnancy in British Columbia, Canada from 1997 to 2010
Bibliographic record
Abstract
Background: Psychiatric conditions are relatively common during pregnancy, and many of these conditions are treated with psychotropic medications. In this article, we aim to quantify the rate of pregnancy-related exposures and describe how psychotropic medications are being used in pregnancy. Materials and Methods: We conducted a retrospective cohort study of all pregnancies ending in a live birth in the Canadian province of British Columbia between January 1, 1997 and December 31, 2010. We examined antipsychotic, anxiolytic, antidepressant, and stimulants use during pregnancy. We describe use of these medications across the pregnancy period, in terms of incident and prevalent use in pregnancy and whether women had corresponding diagnoses for mental health conditions. Results: We included 424,307 pregnancies, of whom 7.1% were dispensed a psychotropic medication. The most commonly used psychotropic medications were antidepressants (4.2%) followed by anxiolytics (3.4%). Among psychotropic medication users, the most commonly associated psychiatric diagnosis was major depressive disorder (43.2%) followed by anxiety (15.8%) and adjustment reaction and/or acute stress (15.8%). The majority of antidepressant use was prevalent (continued from preconception period), whereas most anxiolytic use was incident (no prescriptions in the 6 months before conception). Conclusions: The relatively high rate of use of psychotropic drugs in this cohort, and the existence of effective alternative treatments for the commonly treated conditions suggests a need to improve access to nondrug options before and during pregnancy. The finding that fewer women are discontinuing their antidepressants during pregnancy should be further investigated.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".