How well do face masks protect the wearer compared to public perceptions?

  1. Eugenia O’Kelly
  2. Anmol Arora
  3. James Ward
  4. P John Clarkson

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Abstract

Introduction

There is a growing body of evidence to support the wearing of face masks to reduce spread of infectious respiratory pathogens, including SARS-CoV-2. However, the literature exploring the effectiveness of homemade fabric face masks is still in its infancy. Developing an evidence base is an important step to ensure that public policy is evidence based and truly effective.

Methods

Two methodologies were used in this study: quantitative fit testing of various face masks to indicate their effectiveness and a survey of 710 US residents about their perceptions of face mask effectiveness. N95, surgical and two fabric face masks were tested on an individual twenty five times each using a TSI 8038+ machine. Our survey was distributed by Qualtrics XM, asking participants to estimate the effectiveness of N95, surgical and fabric face masks.

Results and Discussion

Our results indicate that fabric face masks blocked between 62.6% and 87.1% of fine particles, whereas surgical masks protected against an average of 78.2% of fine particles. N95 masks blocked 99.6% of fine particles. Survey respondents tended to underestimate the effectiveness of masks, especially fabric masks. Together these results suggest that fabric masks may be a useful tool in the battle against the COVID-19 pandemic and that increasing public awareness of the effectiveness of fabric masks may help in this endeavour.

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  1. ScreenIT

    SciScore for 10.1101/2021.01.27.21250645: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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.

  2. Version published to 10.1101/2021.01.27.21250645v1 on medRxiv