Dispensing patterns of mental health medications before and during the COVID-19 pandemic in Alberta, Canada: An interrupted time series analysis
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has negatively impacted the general population in all aspects of life. Estimates of mental health medication dispensing in Alberta were investigated to elucidate areas of need within mental health and pharmacy practice during the pandemic. METHODS: We employed an interrupted time series analysis using linear regression models to estimate community and outpatient medication dispensing trends of 46 medications used to treat mental health disorders. Three parameters were examined. The first was the medication dispensing slope before COVID-19. The second was the immediate effect of COVID-19 on dispensing (i.e., the difference in dispensing rate between the month before and after the first case of COVID-19) and the third was the medication dispensing slope during COVID-19. RESULTS: = 34) of the examined medications remained similar before and during the COVID-19 pandemic. However, eight medications (i.e., amitriptyline, escitalopram, fluoxetine, paroxetine, bupropion, desvenlafaxine, venlafaxine, and oxazepam) showed an immediate and significant increase in dispensing rate following the onset of the pandemic that was sustained over the first 13-months of the pandemic. CONCLUSION: Initial increases in dispensing patterns of antidepressants may be attributed to a "stockpiling phenomenon" but the sustained higher levels of dispensing suggest an unfavorable shift in the population's mental health. Monitoring of medication dispensing patterns during COVID-19 may serve as a useful indicator of the population's mental health during the current pandemic and better prepare community pharmacists in future pandemic planning, medication dispensing strategies, and care of chronic medical conditions.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".