Universal Masking to Prevent Nosocomial Respiratory Viral Infections in Malignant Hematology Inpatient Units
Bibliographic record
Abstract
Background: Patients with hematologic malignancies are at increased risk for respiratory virus infections (RVIs) and may experience prolonged asymptomatic viral shedding contributing to transmission. In response to 2 extensive RVI outbreaks in our adult cancer center, a universal masking policy was implemented whereby inpatients on malignant hematology units and their visitors were required to wear procedure masks whenever they were walking outside their rooms. Visitors were required to mask when inside patient rooms. Staff were not included in the policy. Here, we describe the impact of universal masking on the incidence of nosocomial RVI in malignant hematology patients. Methods: In this before-and-after study, we examined the effects of universal masking in malignant hematology units of a 170-bed adult cancer hospital in Toronto, Canada, between January 1, 2015, and September 30, 2019. Nosocomial RVI incidence, RVI outbreak descriptions, and hand hygiene compliance rates were collected from hospital infection control databases. Mask utilization was extracted from hospital purchasing records. Staff influenza vaccination rates were obtained from occupational health records. RVI incidence rates before and after the intervention were compared using Wilcoxon rank-sum test. Results: The preimplementation phase ran from January 1, 2015, to February 28, 2017, and the postimplementation phase spanned March 1, 2017, to September 30, 2019. Monthly mask utilization on malignant hematology units increased by 105% after implementing the universal masking policy. Nosocomial RVI incidence decreased significantly after implementing the universal masking policy, and the number of cases involved in RVI outbreaks also decreased (Table 1). There was a 14% increase in nasopharyngeal swab orders after implementation. Staff influenza vaccination rates, hand hygiene compliance and infection control policies remained stable throughout the study. Conclusions: A reduction in the incidence of nosocomial RVI and number of RVI cases in outbreaks was observed after implementing the universal masking policy. Although we were unable to directly measure compliance with the intervention, increased mask utilization after the intervention implied adherence to the policy. Our experience suggests that universal masking in malignant hematology inpatients may be an effective RVI prevention strategy. Further rigorous study is warranted. Funding: None Disclosures: Susy Hota reports contract research for Finch Therapeutics.
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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 | 0.002 |
| 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".