The association between frailty, long-term care home characteristics and COVID-19 mortality before and after SARS-CoV-2 vaccination: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: The relative contributions of long-term care (LTC) resident frailty and home-level characteristics on COVID-19 mortality has not been well studied. We examined the association between resident frailty and home-level characteristics with 30-day COVID-19 mortality before and after the availability of SARS-CoV-2 vaccination in LTC. METHODS: We conducted a population-based retrospective cohort study of LTC residents with confirmed SARS-CoV-2 infection in Ontario, Canada. We used multi-level multivariable logistic regression to examine associations between 30-day COVID-19 mortality, the Hubbard Frailty Index (FI), and resident and home-level characteristics. We compared explanatory models before and after vaccine availability. RESULTS: There were 11,179 and 3,655 COVID-19 cases in the pre- and post-vaccine period, respectively. The 30-day COVID-19 mortality was 25.9 and 20.0% during the same periods. The median odds ratios for 30-day COVID-19 mortality between LTC homes were 1.50 (95% credible interval [CrI]: 1.41-1.65) and 1.62 (95% CrI: 1.46-1.96), respectively. In the pre-vaccine period, 30-day COVID-19 mortality was higher for males and those of greater age. For every 0.1 increase in the Hubbard FI, the odds of death were 1.49 (95% CI: 1.42-1.56) times higher. The association between frailty and mortality remained consistent in the post-vaccine period, but sex and age were partly attenuated. Despite the substantial home-level variation, no home-level characteristic examined was significantly associated with 30-day COVID-19 mortality during either period. INTERPRETATION: Frailty is consistently associated with COVID-19 mortality before and after the availability of SARS-CoV-2 vaccination. Home-level characteristics previously attributed to COVID-19 outcomes do not explain significant home-to-home variation in COVID-19 mortality.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".