Factors associated with transmission in COVID-19 outbreaks in long-term care facilities
Bibliographic record
Abstract
Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a disproportionate impact on residents in long-term care facilities (LTCFs). Through our experience and data from managing COVID-19 exposures and outbreaks in LTCFs in the Fraser Health region in British Columbia, Canada, we identified risk factors associated with outbreak severity to inform current outbreak management strategies and future pandemic preparedness planning efforts. Methods We used a retrospective cohort study design to evaluate the association between non-modifiable factors (facility building, organization level, and resident population characteristics), modifiable factors (assessments for infection prevention and control (IPC) and public health measures), and severity of COVID-19 outbreaks (attack rate) in LTCFs. We modelled the COVID-19 attack rates in LTCF outbreaks using negative binomial regression models. Results From March 1, 2020 to January 10, 2021, a total of 145 exposures to at least one confirmed case of COVID-19 in 82 LTCFs occurred. For every item not met in the assessment tool, a 22% increase in the attack rate was observed (rate ratio 1.2 [95% CI 1.1 – 1.4]) after adjusting for other risk factors such as age of the facility, index case type (resident vs. staff) and proportion of single bed rooms. Conclusion Our findings highlight the importance of assessing IPC and public health measures for outbreak management. They also demonstrate the important modifiable and non-modifiable risk factors associated with COVID-19 outbreaks in our jurisdiction. We hope these findings will inform ongoing outbreak management and future pandemic planning efforts.
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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 | 0.007 |
| 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) | 0.004 | 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".