A Longitudinal Examination of Post-COVID-19 Mortality in Residents in Long-Term Care Homes
Bibliographic record
Abstract
The most adverse outcomes of the COVID-19 pandemic include high post-infection mortality among long-term care (LTC) home residents. Research about mortality over a longer period after contracting COVID-19 and in different pandemic years is limited. In the current study, we examined outcomes for 1,596 LTC residents from the day of a positive COVID-19 test until January 31, 2023. We reported all-cause mortality 30 days after contracting COVID-19 and monthly throughout the follow-up, up to 35 months after the pandemic start. We also examined mortality among 2,724 residents residing in the same LTC homes, with no history of COVID-19 during the same period. The results underscored a large number of deaths in the first month post-infection, with 30-day mortality substantially decreasing over the years-from 28% (95% CI [24.3, 31.8]) among residents contracting COVID-19 in 2020, to 8.3% (95% CI [7.4, 9.2]) in the 2022 cohort. Observed over longer periods, monthly mortality among residents with a COVID-19 history was similar to mortality in the No-COVID residents, and no evidence was found of increased mortality risk in the COVID group beyond the first post-infection month. We discuss mortality in LTC during the pandemic and a continuing need to reduce mortality in the acute phase of COVID-19.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".