The impact of COVID-19 public health measures on the utilization of antipsychotics in schizophrenia in Manitoba – A population-based study
Bibliographic record
Abstract
PURPOSE: During the COVID-19 pandemic, public health measures were implemented, yet it is unknown whether these measures affected medication access in those with schizophrenia (SCZ). This study aimed to assess whether the antipsychotic utilization in SCZ changed during the pandemic. METHODS: We used dispensed prescription drug data from the Canadian province of Manitoba in individuals with SCZ using linked administrative data from the Manitoba Population Research Data Repository. The quarterly incident and prevalent dispensation of antipsychotics at two periods were compared with the expected trend (April 1, 2015 to April 1, 2020 and 2021) using linear autoregression. We stratified the primary results by age and sex and examined multiple subgroups. RESULTS: There were 9045 individuals with SCZ in the first fiscal quarter of 2020. The prevalent use of the most common antipsychotics were: olanzapine (206.7/1000), risperidone (190.8/1000), quetiapine (174.4/1000), and clozapine (100.9/1000). The overall prevalent use of antipsychotics remained stable during the pandemic compared with the expected trend. A significant decrease in the incident use in April-June 2020 (estimate: -1.3, 95%CI:-2.2,-0.3) was noted compared with the expected. A significantly higher incidence of atypical antipsychotics (estimate: 1.4, 95%CI: 0.2,2.5) and risperidone separately (estimate: 1.8, 95%CI: 0.2,3.3) was noted in 2021 compared with expected. CONCLUSION: This study found a decline in the receipt of antipsychotics for people with SCZ during the initial implementation of COVID-19 public health measures, particularly on the overall incidence. Future work on investigating the impact of these trends on SCZ outcomes is needed to inform future pandemic-related policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".