Failing our Most Vulnerable: COVID-19 and Long-Term Care Facilities in Ontario
Bibliographic record
Abstract
Abstract Background The COVID-19 epidemic has taken a fearsome toll on individuals residing in long-term care facilities (LTC). As of April 10, 2020 half of Canada’s COVID-19 deaths had occurred in LTC. We sought to better understand trends and risk factors for COVID-19 death in LTC in Ontario. Methods We analyzed a COVID-19 outbreak database created by the Ontario Ministry of Health, for the period March 29-April 7, 2020. Mortality incidence rate ratios for LTC were calculated with community living Ontarians aged > 69 used as the comparator group. Count-based regression methods were used to model temporal trends and identify associations between infection risk in staff and residents, and subsequent LTC resident death. Results Confirmed or suspected cases of COVID-19 were identified in 272/627 LTC by April 7, 2020. The incidence rate ratio for COVID-19 death was 13.1 (9.9-17.3) relative to community living adults over 69. Incidence rate ratio increased over time and was 87.28 (90% CrI 9.98 to 557.08) by April 7, 2020. Lagged infection in staff was a strong predictor of death in residents (e.g., adjusted IRR for death per infected staff member 1.17, 95% CI 1.11 to 1.26 at a 6-day lag). Interpretation Mortality risk in elders in Ontario is currently concentrated in LTC, and this risk has increased sharply over a short period of time. Early identification of risk requires a focus on testing and provision of personal protective equipment to staff, and restructuring the LTC workforce to prevent movement of COVID-19 between LTC. Funding The research was supported by a grant to DNF from the Canadian Institutes for Health Research (2019 COVID-19 rapid researching funding OV4-170360).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".