COVID-19 AND PSYCHOTROPIC DRUG USE IN ASSISTED LIVING RESIDENTS: VARIATION BY WAVE AND SETTING TYPE
Bibliographic record
Abstract
Abstract The onset of COVID-19 was associated with significant, albeit modest, increases in the use of psychotropics and opioids in nursing home residents. Little research exists on whether similar trends occurred among older residents of publicly funded assisted living (AL) homes, a growing and poorly investigated setting. We examined the impact of pandemic wave (1 to 4) and setting type (dementia designated spaces [AL-D] vs other [AL-O]) on prevalent antipsychotic, antidepressant, benzodiazepine, opioid and anticonvulsant use in AL residents from Alberta, Canada. Using linked population-based clinical and health administrative databases, we conducted a repeated cross-sectional study of quarterly medication prevalence from January 2018 to December 2021. Log-binomial GEE models estimated prevalence ratios (PR) for 4 waves (vs 2018-19 historical months) and setting (AL-D vs AL-O) and period-setting interactions. On March 1, 2020, there were 2,874 AL-D and 6,611 AL-O residents in our cohorts (mean age 82.4 vs 79.9 years and 93.5% vs 42.6% with dementia, respectively). Antipsychotic prevalence increased during waves 2-4 for both settings but this increase was significantly greater for AL-D than AL-O in later waves (e.g., AL-D: PR 1.21, 95%CI 1.14-1.27; AL-O: 1.12 (1.07-1.17) for March-May 2021 vs 2018-19). For both settings, there was a significant but modest increase in antidepressants but a decrease in benzodiazepines during several waves. No pandemic effect was observed for opioids in either setting. The AL resident and home characteristics associated with these medication trends, concerns about medication risks (particularly for dementia care settings), and consequent health outcomes for residents require further study.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".