Trends in pharmacotherapy for anxiety and depression during COVID-19: A north york area pilot study
Bibliographic record
Abstract
Introduction: During the COVID-19 pandemic, with the implementation of social distancing regulations, there is increased concern around the mental health of the general population, including depression and anxiety. Mental health prescribing trends in Canada during COVID-19, at the time of writing, have not been investigated. Methods: This pilot study collected refill information of 365 patients from an independent community pharmacy in North York, Ontario to compare (1) initiation, (2) dose change, (3) dispensing frequency, and (4) defined daily dose of first-line antidepressants as defined by the Canadian Network for Mood and Anxiety Treatments and other select medications, including Z-drugs and benzodiazepines. Data from January 1 to May 31, 2019 were compared with data from January 1 to May 31, 2020. Results: The number of newly initiated antidepressant and antianxiety medications during the COVID-19 pandemic was not significantly affected compared to the same months in the prior year (Z=-1.149, p=0.251). Upon investigation of logistic regression, age was significantly correlated to antidepressant initiation in the year prior (p=0.038) whereas it was not during COVID-19, which may represent an increase in antidepressants in the younger population. There was a significant difference in the number of dose changes, which occurred between the two years, showing significantly more increases and switches of therapy (p=0.008) during COVID-19. There was significantly more frequent dispensing of benzodiazepine tablets (Z=2.402, p=0.016) in the first five months of 2020 compared to those of 2019. There were no statistically significant changes in the number of defined daily doses. Discussion: There are shifting trends in mental health prescribing. This result is concerning during a time when accessing appropriate mental health care is significantly impacted. This study emphasizes the need for benzodiazepine deprescribing due to the increase in benzodiazepines dispensed and the risk of misuse, tolerance, and dependence with long-term benzodiazepines.
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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) | 0.007 | 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".