Association between delayed outbreak identification and SARS-CoV-2 infection and mortality among long-term care home residents, Ontario, Canada, March to November 2020: a cohort study
Bibliographic record
Abstract
BackgroundLate outbreak identification is a common risk factor mentioned in case reports of large respiratory infection outbreaks in long-term care (LTC) homes.AimTo systematically measure the association between late SARS-CoV-2 outbreak identification and secondary SARS-CoV-2 infection and mortality in residents of LTC homes.MethodsWe studied SARS-CoV-2 outbreaks across LTC homes in Ontario, Canada from March to November 2020, before the COVID-19 vaccine rollout. Our exposure (late outbreak identification) was based on cumulative infection pressure (the number of infectious resident-days) on the outbreak identification date (early: ≤ 2 infectious resident-days, late: ≥ 3 infectious resident-days), where the infectious window was -2 to +8 days around onset. Our outcome consisted of 30-day incidence of secondary infection and mortality, based on the proportion of at-risk residents with a laboratory-confirmed SARS-CoV-2 infection with onset within 30 days of the outbreak identification date.ResultsWe identified 632 SARS-CoV-2 outbreaks across 623 LTC homes. Of these, 36.4% (230/632) outbreaks were identified late. Outbreaks identified late had more secondary infections (10.3%; 4,437/42,953) and higher mortality (3.2%; 1,374/42,953) compared with outbreaks identified early (infections: 3.3%; 2,015/61,714; p < 0.001, mortality: 0.9%; 579/61,714; p < 0.001). After adjustment for 12 LTC home covariates, the incidence of secondary infections in outbreaks identified late was 2.90-fold larger than that of outbreaks identified early (OR: 2.90; 95% CI: 2.04-4.13).ConclusionsThe timeliness of outbreak identification could be used to predict the trajectory of an outbreak, plan outbreak measures and retrospectively provide feedback for quality improvement, with the objective of reducing the impacts of respiratory infections in LTC home residents.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".