Healthcare-Associated COVID-19 in Ontario, Canada: Relative Mortality and Contribution to Community Epidemic Growth
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic placed immense strain on Canada’s healthcare system and disproportionately affected individuals with poorer baseline health. Healthcare-associated infections (HAIs) increase risk for both patients and healthcare workers and are often more severe due to advanced age and comorbidities. While efforts have aimed to reduce in-hospital transmission, the individual- and community-level consequences of HAIs require further study. We aimed to assess whether healthcare-associated COVID-19 cases had higher odds of death compared to hospitalized community-acquired cases, and to evaluate the directionality of transmission between hospitals and the community. Methods We analyzed COVID-19 surveillance data from Ontario’s Case Contact and Management System and the COVaxON vaccine registry (March 17, 2020, to September 4, 2022). Latent class analysis was used to classify hospitalized cases by likelihood of healthcare-associated infection. Mortality odds by category were estimated using binomial logistic regression. Directionality between hospital outbreaks and community cases was assessed using a modified Granger causality approach. Findings Compared to patients with low likelihood of healthcare-associated infection, those moderately likely to have acquired COVID-19 in hospital had elevated odds of death (OR: 1.26, 95% CI: 1.14–1.40); no significant increase was seen in the high-likelihood group (OR: 1.05, 95% CI: 0.96–1.15). Community cases did not predict hospital outbreaks (p=0.5749), but hospital outbreaks predicted community case growth (p<0.0001). Interpretation Hospital-acquired COVID-19 is associated with excess mortality and may drive community transmission. Preventing in-hospital transmission is critical to protecting patients and controlling broader epidemic spread. Funding Supported by a Canadian Institutes for Health Research project grant, #518192.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".