Psychotropic Medication Use Before and During COVID-19: A Population-Wide Study
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic and public health measures that took place have led to concerns regarding mental health and receipt of psychotropic medications. We aimed to study the changes in psychotropic medication dispensation rates before and during the COVID-19 pandemic in the general population. Methods: Administrative health data from the Canadian province of Manitoba was used to describe the quarterly incidence and prevalence of antipsychotics, antidepressants, and anxiolytic/sedative-hypnotics from January 1, 2015 to December 31, 2020. Individuals who received at least one prescription within each quarter were considered exposed to the medication. The denominator was the total population within each quarter. Incidence was defined as no receipt of medication in the 3 years prior to the quarter of interest. Autoregression models for time series data plus indicator variables were used to compare each quarter of 2020 after public health measures were implemented in March 2020 in relation to the expected trend. Analyses were stratified by age and sex. Results: There were 1,394,885 individuals in the first quarter of 2020, with a mean (SD) age of 38.9 (23.4) years, 50.3% were female, and 36.1% had a psychiatric diagnosis in the previous 5 years. A significant decrease was observed for incident antidepressant use ( p < 0.05 for both sexes and all age groups except for those 65 years and older) and anxiolytic use ( p < 0.05 for both sexes and all age groups except 80 years and older) in the second quarter (April-June) of 2020 compared to the expected trend. Females and those aged 40 years and older had a significantly higher incidence of antidepressant and antipsychotic use in the final quarter of 2020 compared to the expected trend ( p < 0.05). Conclusion: Our findings indicate a decrease in new prescriptions for antidepressants and anxiolytics in the 3 months after COVID-19 in-person restrictions were first implemented. We then observed an increase in the new use of antidepressants and antipsychotics at the end of 2020, in females and people aged 40 years and older, with the highest rates of use in the population 80 years and older.
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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) | 0.002 | 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 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".