Risk factors associated with severe acute respiratory coronavirus virus 2 (SARS-CoV-2) transmission, outbreak duration, and mortality in acute-care settings
Bibliographic record
Abstract
BACKGROUND: Transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in acute-care settings affects patients, healthcare workers, and the healthcare system. We conducted an analysis of risk factors associated with outbreak severity to inform prevention strategies. METHODS: This cross-sectional analysis of COVID-19 outbreaks was conducted at Fraser Health acute-care sites between March 2020 and March 2021. Outbreak severity measures included COVID-19 attack rate, outbreak duration, and 30-day case mortality. Generalized linear models with generalized estimating equations were used for all outcome measures. A P value of 0.05 indicated statistical significance. Analyses were performed using SAS version 3.8 software, R version 4.1.0 software, and Stata version 16.0 software. RESULTS: Between March 2020 and March 2021, 54 COVID-19 outbreaks were declared in Fraser Health acute-care sites. Overall, a 10% increase in the hand hygiene rate during the outbreak resulted in an 18% decrease in the attack rate (P < .01), 1 fewer death (P = .03), and shorter outbreaks (P < .01). A 10-year increase in unit age was associated with 2.2 additional days of outbreak (P < 0.01) and increases in the attack rate (P < .05) and the number of deaths (P < .01). DISCUSSION: We observed an inverse relationship between increased hand hygiene compliance during outbreaks and all 3 severity measures. Increased unit age was also associated with increases in each of the severity measures. CONCLUSION: This study highlights the importance of hand hygiene practices during an outbreak and the difficulties faced by older facilities, many of which have infrastructural challenges. The latter reinforces the need to incorporate infection control standards into healthcare planning and construction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".