Risk factors for retirement home COVID-19 outbreaks in Ontario, Canada: A population-level cohort study
Bibliographic record
Abstract
Abstract Background The epidemiology of COVID-19 in retirement homes (also known as assisted living facilities) is largely unknown. We examined the association between retirement home and community level characteristics and the risk of COVID-19 outbreaks in retirement homes during the first wave of the COVID-19 epidemic. Methods We conducted a population-based retrospective cohort study of licensed retirement homes in Ontario, Canada, from March 1st - September 24th, 2020. Our primary outcome was a COVID-19 outbreak (≥1 resident or staff confirmed case by validated nucleic acid amplification assay). We used time-dependent proportional hazards methods to model the associations between retirement home and community level characteristics and COVID-19 outbreaks. Results Our cohort included all 770 licensed retirement homes in Ontario, which housed 56,491 residents. There were 172 (22.3%) COVID-19 retirement home outbreaks involving 1,045 (1.9%) residents and 548 staff (1.5%). COVID-19 cases were distributed unevenly across retirement homes, with 1,593 (92.2%) resident and staff cases occurring in 77 (10%) of homes. The adjusted hazard of a COVID-19 outbreak in a retirement home was positively associated with homes that had a large resident capacity, homes that were co-located with a long-term care facility, large corporate owned chains, homes that offered many services onsite, increases in regional COVID-19 incidence, and a higher community-level ethnic concentration. Interpretation Readily identifiable retirement home-level characteristics are independently associated with COVID-19 outbreaks and may support risk identification. A higher ethnic concentration of the community surrounding a retirement home is associated COVID-19 outbreaks, with an uncertain mechanism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".