Prescription trends of antiseizure medications before and during the COVID-19 pandemic
Bibliographic record
Abstract
Introduction Given the lack of evidence on how the COVID-19 pandemic impacted antiseizure medication (ASM) use, we examined the trends of ASMs before and during COVID-19. Methods We conducted a population-based study using provincial-level health databases from Manitoba, Canada, between 1 June 2016 and 1 March 2021. We used interrupted time series autoregressive models to examine changes in the prevalence and incidence of ASM prescription rates associated with COVID-19 public health restrictions. Results Among prevalent users, the COVID-19 pandemic led to a significant increase in new-generation ASMs with a percentage change of 0.09% (p = 0.03) and a significant decrease in incidence use of all ASMs with a percentage change of −4.35% (p = 0.04). Significant trend changes were observed in the prevalent use of new-generation ASMs (p = 0.04) and incidence use of all (p = 0.04) and new-generation ASMs (p = 0.02). Gabapentin and clonazepam prescriptions contributed 37% of prevalent and 54% of incident use. Conclusion With the introduction of public health measures during COVID-19, small but significant changes in the incident and prevalent use of ASM prescriptions were observed. Further studies are needed to examine whether barriers to medication access were associated with potential deterioration in seizure control among patients. Conference presentation The results from this study have been presented as an oral presentation at the 38th ICPE, International Society of Pharmacoepidemiology (ISPE) annual conference in Copenhagen.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".