For-profit nursing homes and the risk of COVID-19 outbreaks and resident deaths in Ontario, Canada
Bibliographic record
Abstract
Abstract Background Nursing homes have become the epicentre of the coronavirus disease 2019 (COVID-19) pandemic in Canada. Previous research demonstrates that for-profit nursing homes deliver inferior care across a variety of outcome and process measures, raising the question of whether for-profit homes have had worse COVID-19 outcomes than non-profit homes. Methods We conducted a retrospective cohort study of all nursing homes in Ontario, Canada from March 29-May 20, 2020 using a COVID-19 outbreak database maintained by the Ontario Ministry of Long-Term Care. We used hierarchical logistic and count-based methods to model the associations between nursing home profit status (for-profit, non-profit or municipal) and nursing home COVID-19 outbreaks, COVID-19 outbreak sizes, and COVID-19 resident deaths. Results The analysis included all 623 Ontario nursing homes, of which 360 (57.7%) were for-profit, 162 (26.0%) were non-profit, and 101 (16.2%) were municipal homes. There were 190 (30.5%) COVID-19 nursing home outbreaks involving 5218 residents (mean of 27.5 ± 41.3 residents per home), resulting in 1452 deaths (mean of 7.6 ± 12.7 residents per home) with an overall case fatality rate of 27.8%. The odds of a COVID-19 outbreak was associated with the incidence of COVID-19 in the health region surrounding a nursing home (adjusted odds ratio [aOR], 1.94; 95% confidence interval [CI] 1.23-3.09) and number of beds (aOR, 1.40; 95% CI 1.20-1.63), but not profit status. For-profit status was associated with both the size of a nursing home outbreak (adjusted risk ratio [aRR], 1.96; 95% CI 1.26-3.05) and the number of resident deaths (aRR, 1.78; 95% CI 1.03-3.07), compared to non-profit homes. These associations were mediated by a higher prevalence of older nursing home design standards in for-profit homes. Interpretation: For-profit status is associated with the size of a COVID-19 nursing home outbreak and the number of resident deaths, but not the likelihood of outbreaks. Differences between for profit and non-profit homes are largely explained by older design standards, which should be a focus of infection control efforts and future policy.
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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 | 0.003 |
| 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) | 0.003 | 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".