Excess mortality for care home residents during the first 23 weeks of the COVID-19 pandemic in England: a national cohort study
Bibliographic record
Abstract
Background: To estimate excess mortality for care home residents during the COVID-19 pandemic in England, exploring associations with care home characteristics. Methods: Daily number of deaths in all residential and nursing homes in England notified to the Care Quality Commission (CQC) from 1st January 2017 to 7th August 2020. Care home level data linked with CQC care home register to identify homes characteristics: client type (over 65s/children and adults), ownership status (for-profit/not-for-profit; branded/independent), and size (small/medium/large). Excess deaths computed as the difference between observed and predicted deaths using local authority fixed-effect Poisson regressions on pre-pandemic data. Fixed-effect logistic regressions were used to model odds of experiencing COVID-19 suspected/confirmed deaths. Findings: Up to 7th August 2020 there were 29,542 (95%CI: 25,176 to 33,908) excess deaths in all care homes. Excess deaths represented 6.5% (95%CI: 5.5% to 7.4%) of all care home beds, higher in nursing (8.4%) than residential (4.6%) homes. 64.7% (95%CI: 56.4% to 76.0%) of the excess deaths were confirmed/suspected COVID-19. Almost all excess deaths were recorded in the quarter (27.4%) of homes with any COVID-19 fatalities. The odds of experiencing COVID-19 attributable deaths were higher in homes providing nursing services (OR: 1.8, 95%CI: 1.6 to 2.0); to older people and/or with dementia (OR: 5.5, 95%CI: 4.4 to 6.8); among larger (vs. small) homes (OR: 13.3, 95%CI: 11.5 to 15.4); belonging to a large provider/brand (OR: 1.2, 95%CI: 1.1 to 1.3). There was no significant association with for-profit status of providers. Interpretation: To limit excess mortality, policy should be targeted at care homes to minimise the risk of ingress of disease and limit subsequent transmission. Our findings provide specific characteristic targets for further research on mechanisms and policy priority.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".