Association Between Nursing Home Crowding and COVID-19 Infection and Mortality in Ontario, Canada
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Importance Nursing home residents have been disproportionately impacted by the COVID-19 epidemic. Prevention recommendations have emphasized frequent testing of healthcare personnel and residents, but additional strategies are needed to protect nursing home residents. Objective We developed a reproducible index of nursing home crowding and determined whether crowding was associated with incidence of COVID-19 in the first months of the COVID-19 epidemic. Design, Setting, and Participants Population-based retrospective cohort study of over 78,000 residents of 618 distinct nursing homes in Ontario, Canada from March 29 to May 20, 2020. Exposure The nursing home crowding index equalled the average number of residents per bedroom and bathroom. Outcomes Primary outcomes included the cumulative incidence of COVID-19 infection and mortality, per 100 residents; introduction of COVID-19 into a home (≥1 resident case) was a negative tracer. Results Of 623 homes in Ontario, we obtained complete information on 618 homes (99%) housing 78,607 residents. A total of 5,218 residents (6.6%) developed COVID-19 infection, and 1,452 (1.8%) died with COVID-19 infection as of May 20, 2020. COVID-19 infection was distributed unevenly across nursing homes: 4,496 (86%) of infections occurred in just 63 (10%) of homes. The crowding index ranged across homes from 1.3 (mainly single-occupancy rooms) to 4.0 (exclusively quadruple occupancy rooms); 308 (50%) homes had high crowding index (≥2). Incidence in high crowding index homes was 9.7%, versus 4.5% in low crowding index homes (p<0.001), while COVID-19 mortality was 2.7%, versus 1.3%. The likelihood of COVID-19 introduction did not differ (31.3% vs 30.2%, p=0.79). After adjustment for regional, nursing home, and resident covariates, the crowding index remained associated with increased risk of infection (RR=1.72, 95% Confidence Interval [CI]: 1.11-2.65) and mortality (RR=1.72, 95%CI: 1.03-2.86). Propensity score analysis yielded similar conclusions for infection (RR=2.06, 95%CI: 1.34-3.17) and mortality (RR=2.09, 95%CI: 1.30-3.38). Simulations suggested that converting all 4-bed rooms to 2-bed rooms would have averted 988 (18.9%) infections of COVID-19 and 271 (18.7%) deaths. Conclusions and Relevance Crowding was associated with higher incidence of COVID-19 infection and mortality. Reducing crowding in nursing homes could prevent future COVID-19 mortality.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it