The effect of the COVID-19 pandemic on delirium incidence in Ontario long-term care homes: A retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: To describe delirium incidence before and during the COVID-19 pandemic and examine factors associated with delirium incidence in the long-term care setting. METHODS: We conducted a retrospective cohort study of Ontario long-term care residents without severe cognitive impairment or baseline delirium with an assessment between February 1, 2019, and March 31, 2021. Data were collected from the interRAI Minimum Data Set (MDS) 2.0. The outcome of interest was delirium development. Selected independent variables were entered into univariate longitudinal generalized estimating equations, followed by multivariate analysis. Odds ratios (ORs) and 95% confidence intervals (CIs) are reported. RESULTS: A total of 63,913 residents were included within the comparison sample from February 2019 to February 2020. The pandemic sample consisted of 54,867 residents from March 2020 to March 2021. Incidence of delirium in the comparison sample was 3.4% (2158 residents) compared to 3.2% (1746 residents) in the pandemic sample (P = 0.06). Residents who were older, cognitively impaired, and increasingly frail had greater odds of developing delirium. Increased odds were associated with a diagnosis of bipolar disorder (OR 1.27, 95% CI 1.07-1.51) and anxiolytic use (OR 1.12, 95% CI 1.01-1.25). Residents who were newly admitted (OR 0.65, 95% CI 0.60-0.71) and those dependent for activities of daily living (OR 0.46, 95% CI 0.33-0.64) had lower odds of delirium development. CONCLUSIONS AND IMPLICATIONS: The incidence of delirium did not differ between the year prior to and the first year of the COVID-19 pandemic, indicating that preventative interventions employed by long-term care homes may have been effective. Long-term care residents who are older, frail, cognitively impaired, or had unstable health would benefit from targeted interventions to prevent delirium. Newly admitted residents or those dependent in activities of daily living had lower odds of developing delirium, which could indicate under detection in these groups.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".