Antipsychotic Medication Use Among Newly Admitted Long-term Care Residents During the COVID-19 Pandemic in Canada
Bibliographic record
Abstract
Objective: Examination of the impact of the COVID-19 pandemic on rates of antipsychotic medication use, initiation and discontinuation, among newly admitted long-term care residents. Design: Repeated cross-sectional study. Settings and Participants: Long-term care home residents in Alberta, British Columbia and Ontario, Canada assessed with the Minimum Data Set (MDS) 2.0 assessment. The cohort was segmented according to admission during the pandemic (March 2020/2021) and 2 pre-pandemic (March 2018/2019 and March 2019/2020) periods. Methods: Multivariable logistic regression models were fit to characterize the association between long-term care admission during the COVID-19 pandemic and the use of antipsychotic medications. A second set of logistic regression models were fit among residents with follow-up assessments to characterize the association between long-term care admission and antipsychotic initiation/discontinuation at follow-up. All models were adjusted for resident characteristics including sex, age group, Aggressive Behaviour Scale score, Cognitive Performance Scale score, and diagnoses of Alzheimer's disease and related dementias, anxiety disorder, depression, and bipolar disorder. Results: 21 612 residents admitted during the pandemic and over 30 000 in each pre-pandemic period were included. Antipsychotic use increased during the pandemic period among newly admitted residents from both community (adjusted odds ratio [aOR] 1.20-1.29) and hospital settings (aOR 1.21-1.23). Residents admitted during the pandemic period were more likely to have antipsychotic medications initiated (aOR 1.25-1.26) and less likely to have had them discontinued (aOR 0.74-0.76) at the time of follow-up assessment. Conclusion and Implications: Multiple factors contributed to the observed increase in antipsychotic medication use among newly admitted long-term care home residents during the COVID-19 pandemic: increased medication use at the time of admission, increased medication initiation at follow-up, and decreased medication discontinuation at follow-up. A whole-systems approach that extends beyond long-term care into hospital and community settings is necessary to address this prevalent issue.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".