Associations between specialized dementia care, COVID-19 and central nervous system medication use in assisted living: a population-based repeated cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Assisted living (AL) is an increasingly common residential setting for persons with dementia; yet concerns exist about sub-optimal care of this population in AL given its lower levels of staffing and services. Our objectives were to (i) examine associations between AL setting (dementia care vs. other), COVID-19 pandemic waves, and prevalent antipsychotic, antidepressant, anti-dementia, benzodiazepine, and anticonvulsant drug use among residents with dementia/cognitive impairment, and (ii) explore associations between resident and home characteristics and prevalent medication use. METHODS: We conducted a population-based, repeated cross-sectional study using linked clinical and health administrative databases for all publicly funded AL homes in Alberta, Canada, examined between January 2018 - December 2021. The quarterly proportion of residents dispensed a study medication was examined for each setting and period (pandemic vs. comparable historical [2018/2019 combined]) focusing on four pandemic waves (March-May 2020, September 2020-February 2021, March-May 2021, September-December 2021). Log-binomial GEE models estimated prevalence ratios (PR) for period (pandemic vs. historical periods), setting (dementia care vs. other) and period-setting interactions, adjusting for resident (age, sex) and home (COVID-19 cases, health region, ownership) characteristics. RESULTS: On March 1, 2020, there were 2,779 dementia care and 3,013 other AL residents (mean age 83, 69% female) with dementia/cognitive impairment. Antipsychotic use increased during waves 2-4 in both settings, but this was more pronounced in dementia care than other AL during waves 3 and 4 (e.g., adjusted [adj]PR 1.20, 95% CI 1.14-1.27 vs. adjPR 1.09, 95% CI 1.02-1.17, interaction p = 0.023, wave 3). Both settings showed a statistically significant but modest increase in antidepressant use and decrease in benzodiazepine use. For dementia care AL residents only, there was a statistically significant increase in gabapentinoid use during several waves (e.g., adjPR 1.32, 95% CI 1.10-1.59, wave 3). Other than a modest decrease in prevalent anti-dementia drug use for both settings in wave 2, no other significant pandemic effects were observed. CONCLUSIONS: The persistence of the pandemic-associated increase in antipsychotic and antidepressant use in AL residents coupled with a greater increase in antipsychotic and gabapentinoid use for dementia care settings raises concerns about the attendant risks for residents with cognitive impairment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".