Trends of antiseizure medication utilization among pregnant people in four Canadian provinces from 1998 to 2023; a study from the Canadian mother-child cohort active surveillance initiative (CAMCCO)
Bibliographic record
Abstract
Background Epilepsy management during pregnancy is crucial for both the mother and fetus. The use of antiseizure medications (ASMs) during pregnancy requires careful consideration due to their potential effects on maternal and fetal health. Methods This study analyzed trends in ASMs use among pregnant people in four Canadian provinces over 20 years (Manitoba, Saskatchewan, Alberta, and Quebec). Descriptive statistics were utilized to examine the characteristics of the population, with the frequency and patterns of ASM use estimated throughout each trimester. Linear regression models were developed to analyze yearly patterns of ASM utilization for the overall study population, as well as for people with and without epilepsy. Results Among 1,317,141 pregnant individuals across four provinces, 0.7% had epilepsy. Of the total pregnancies, 1.7% (n = 22,783) were exposed to ASMs, comprising 4,392 from pregnant people with epilepsy (PPWE) and 18,391 from those without epilepsy (PPWOE). Results demonstrated varying trends in ASM usage between provinces, with an overall increase in usage among people without epilepsy in Manitoba, Saskatchewan, and Alberta. ASM use among PPWOE surged significantly in Manitoba (24.2–149.1 per 10,000 pregnant people), Saskatchewan (29.4–107.0 per 10,000), and Alberta (65.7–241.7 per 10,000) (p < 0.05). In Alberta, PPWE’s ASM exposure also rose, from 23.6 in 2008 to 43.0 per 10,000 pregnant people in 2021, while Quebec witnessed a decrease from 59.2 in 1998 to 45.5 per 10,000 pregnancies in 2015. Analysis of ASM use by trimester illustrated a substantial decline among PPWOE from 365 days pre-pregnancy to the third trimester in all provinces. ASM utilization by drug class showcased significant shifts, with second-generation ASMs experiencing a notable rise. Carbamazepine, once prominent, declined, making way for lamotrigine. Regional variations underscore diverse preferences, such as clonazepam’s sustained popularity in Manitoba and Quebec. Conclusion The study identified increasing trends in ASM use, particularly the increased use of second-generation ASMs, and differences in prescription patterns for pregnant individuals with and without epilepsy. These findings reveal changing ASM use patterns, including increased second-generation ASM use and regional disparities, providing valuable insights into real-world prescription practices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| 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".