Risk mitigation of shared room ventilation and filtration on SARS-CoV-2 transmission: a multicenter test-negative study
Bibliographic record
Abstract
BACKGROUND: Admission to shared hospital rooms are a risk factor of healthcare-associated (HA) SARS-CoV-2. Quantifying the impact of engineering controls such as ventilation and filtration is essential to informing resource utilization and infection prevention guidelines. METHODS: Multicenter test-negative study of patients exposed to SARS-CoV-2 in shared rooms across five hospitals between January and October, 2022. Independent variables tested were measured air changes per hour (ACH), presence of any room mechanical ventilation (RMV), or portable high-efficiency particulate air (HEPA) filter. Covariates included facility (number of beds in room, outbreak status of unit), source patient (presence of symptoms, RT-PCR cycle threshold (Ct) value), and exposed patient factors (age, sex, time from last SARS-CoV-2 vaccine, previous SARS-CoV-2 infection, exposure duration). Multilevel logistic mixed models used to estimate the impact of engineering controls on transmission. RESULTS: Among 468 exposed patients, secondary attack rate was 26.3% (range 7.5-33.3% across hospitals). In multivariable analysis, increased ACH was associated with decreased odds of infection (adjusted odds ratio (aOR) 0.88, 95% CI 0.78-1.00; p=.046) as were exposure duration and Ct value of source patient. Presence of RMV was also associated with decreased odds of infection (aOR 0.51, 95% CI 0.27-0.95; p=.034) while use of portable HEPA filter was not significant (aOR 0.58, 95% CI 0.26-1.31; p=.18). CONCLUSIONS: Improved ventilation was independently associated with lower odds of SARS-CoV-2 infection among exposed roommates. Ensuring RMV is present and optimizing ACH may significantly mitigate the risk of HA-SARS-CoV-2. Future prospective studies should assess optimal ACH thresholds and the impact of portable HEPA filters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".