The impact of COVID-19 health measures on the utilization of antipsychotics in schizophrenia in Manitoba – a population-based study using administrative data
Bibliographic record
Abstract
Background: During the COVID-19 pandemic, measures were implemented to stabilize access to drugs, yet it is unknown whether those with chronic medical conditions were able to sufficiently access their medications. The purpose of this study was to assess the effects of COVID-19-induced health measures on antipsychotic utility during the pandemic (in 2020 and 2021) compared to the expected trend. This was achieved through three objectives: (1) to describe the overall and individual incidence and prevalence of antipsychotics, (2) to describe the incidence and prevalence of first generation and atypical antipsychotics and (3) to describe the incidence and prevalence of oral and injectable antipsychotics. Methods: In this repeated cross-sectional study, I used dispensed prescription drug data in Manitobans with schizophrenia. The incident and prevalent dispensation of antipsychotics in schizophrenia was assessed at two time periods (1: April-June 2020, 2: April-June 2021) and were compared with the expected trend from the previous 5 years. I stratified the primary objective results by age categories and sex. Results: The population with schizophrenia in the first fiscal quarter (April-June) of the corresponding year ranged over the study from 8,196 (2015) to 9,166 (2021). At both time points studied, the prevalent use of antipsychotics remained stable (2020: estimate: -1.7, standard error (SE): 4.3, p = 0.6 and 2021: estimate: 4, SE: 4.3, p = 0.3). Those 65-79 years old showed a significant drop in prevalent antipsychotics in the early stages of the pandemic. In the early stages of the pandemic, the incident use of antipsychotics was reduced (estimate: -1.3, SE: 0.5, p = 0.01), although this effect was no longer significant upon extending the data to include one year later (p=0.7). We noted a significant rise in the atypical antipsychotics and risperidone incident use in April-June 2021. All other findings were non-significant. Conclusions: The present study highlights the need for further considerations for those newly diagnosed with schizophrenia as well as elderlies during the COVID-19 pandemic and future pandemics regarding access to antipsychotic medications among users of antipsychotics with schizophrenia.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".