The effects of COVID‐19 on the dispensing rates of antidepressants and benzodiazepines in Canada
Bibliographic record
Abstract
BACKGROUND: Population studies have shown that rates of depressive and anxious symptoms have increased as a result of COVID-19. We analyzed trends in the dispensing rates of antidepressants and benzodiazepines in Canada to determine whether the pandemic has caused changes in rates of pharmacological treatment for depression and anxiety. METHODS: We conducted a population-based, cross-sectional time-series analysis of antidepressants and benzodiazepines dispensed monthly by Canadian community pharmacies between January 2017 and December 2020. We used March 2020 as the intervention month to determine if there were any significant changes in the national rate of antidepressant and benzodiazepine tablets dispensed as the result of the COVID-19 pandemic. RESULTS: There was a temporary reduction in the dispensing rate of antidepressants in April 2020 (from 489 tablets per 100 in March 2020 to 356 tablets per 100 in April 2020; p ≤ .0001); however, the rate returned to its previous level by August 2020. There were no detectable deviations in benzodiazepine dispensing after the declaration of the state of emergency in Ontario. CONCLUSIONS: Despite the increased reporting of depressive and anxious symptoms during the COVID-19 pandemic, there have been no changes in the dispensing trends of medications used to treat these disorders. As the pandemic continues to evolve, future research is needed to monitor the prevalence of depression and anxiety, and associated medication use, in the Canadian population.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".