Factors associated with transmission of COVID-19 in long-term care facility outbreaks
Bibliographic record
Abstract
BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a disproportionate impact on residents in long-term care facilities (LTCFs). AIM: To identify risk factors associated with outbreak severity to inform current outbreak management and future pandemic preparedness planning efforts. METHODS: A retrospective cohort study design was used to evaluate the association between non-modifiable factors (facility building, organization level, and resident population characteristics), modifiable factors (measured through an assessment tool for infection prevention and control (IPC) and pandemic preparedness), and severity of COVID-19 outbreaks (attack rate) in LTCFs. FINDINGS: , 2021, a total of 145 exposures to at least one confirmed case of COVID-19 in 82 LTCFs occurred. Risk factors associated with increased outbreak severity were older facility age, a resident (vs staff) index case, and poorer assessment tool performance. Specifically, for every item not met in the assessment tool, a 22% increase in the adjusted rate ratio was observed (1.2; 95% confidence interval: 1.1-1.4) after controlling for other risk factors. CONCLUSION: Scores from an assessment tool, older building age, and the index case being a resident were associated with severity of COVID-19 outbreaks in our jurisdiction. The findings reinforce the importance of regularly assessing IPC measures and outbreak preparedness in preventing large outbreaks. Regular, systematic assessments incorporating IPC and outbreak preparedness measures may help mitigate impacts of future outbreaks and inform future pandemic preparedness planning.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".