Evaluation of at-home methods for N95 filtering facepiece respirator decontamination

  1. T. X. Chen
  2. A. Pinharanda
  3. N. A. Steinemann
  4. K. Yasuma-Mitobe
  5. E. Lee
  6. J. Hahn
  7. L. Wu
  8. S. Fanourakis
  9. D. S. Peterka
  10. E. M. C. Hillman

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Abstract

N95 filtering facepiece respirators (FFRs) are essential for the protection of healthcare professionals and other high-risk groups against Coronavirus Disease of 2019 ( COVID-19). In response to shortages in FFRs during the ongoing COVID-19 pandemic, the Food and Drug Administration issued an Emergency Use Authorization permitting FFR decontamination and reuse. However, although industrial decontamination services are available at some large institutions, FFR decontamination is not widely accessible. To be effective, FFR decontamination must (1) inactivate the virus; (2) preserve FFR integrity, specifically fit and filtering capability; and (3) be non-toxic and safe. Here we identify and test at-home heat-based methods for FFR decontamination that meet these requirements using common household appliances. Our results identify potential protocols for simple and accessible FFR decontamination, while also highlighting unsuitable methods that may jeopardize FFR integrity.

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  1. Version published to 10.1038/s41598-021-99129-8
  2. Version published to 10.1101/2021.01.05.20248590v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.01.05.20248590: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: The Columbia University Institutional Review Board reviewed this study’s protocol and determined that ethical approval could be waived (IRB reference AAAT5503).
    RandomizationFFRs were randomly assigned to either a treatment group or a control group of untreated masks 15,34.
    BlindingWe obtained a pre-treatment baseline fit factor (blind control) and post-treatment fit tests (also blind) to quantify fit factors and variance.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The decrease of fit factors over repeated treatment was evaluated using linear regressions (stat_smooth function, method=lm, ggplot2 v3.3.0)
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2021.01.05.20248590v1 on medRxiv