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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".