Excess Deaths in Assisted Living and Nursing Homes during the COVID-19 Pandemic in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVES: Assisted living (AL) is a significant and growing congregate care option for vulnerable older adults designed to reduce the use of nursing homes (NHs). However, work on excess mortality in congregate care during the COVID-19 pandemic has primarily focused on NHs with only a few US studies examining AL. The objective of this study was to assess excess mortality among AL and NH residents with and without dementia or significant cognitive impairment in Alberta, Canada, during the first 2 years of the COVID-19 pandemic, relative to the 3 years before. DESIGN: Population-based, retrospective cohort study. SETTING AND PARTICIPANTS: Residents who lived in an AL or NH facility operated or contracted by the Provincial health care system to provide publicly funded care in Alberta between January 1, 2017, and December 31, 2021. METHODS: We used administrative health care data, including Resident Assessment Instrument - Home Care (RAI-HC, AL) and Minimum Data Set 2.0 (RAI-MDS 2.0, NHs) records, linked with data on residents' vital statistics, COVID-19 testing, emergency room registrations, and hospital stays. The outcome was excess deaths during COVID-19 (ie, the number of deaths beyond that expected based on pre-pandemic data), estimated, using overdispersed Poisson generalized linear models. RESULTS: Overall, the risk of excess mortality [adjusted incidence rate ratio (95% confidence interval)] was higher in ALs than in NHs [1.20 (1.14-1.26) vs 1.10 (1.07-1.13)]. Weekly peaks in excess deaths coincided with COVID-19 pandemic waves and were higher among those with diagnosed dementia or significant cognitive impairment in both, AL and NHs. CONCLUSIONS AND IMPLICATIONS: Finding excess mortality within both AL and NH facilities should lead to greater focus on infection prevention and control measures across all forms of congregate housing for vulnerable older adults. The specific needs of residents with dementia in particular will have to be addressed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".