Facemasks prevent influenza-like illness: implications for COVID-19

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Abstract

The coronavirus disease 2019 (COVID-19) pandemic is causing a huge toll on individuals, families, communities and societies across the world. Currently, whether wearing facemasks in public should be a measure to prevent transmission of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) remains contraversial. 1 This is largely because there have been no randomized controlled trials (RCTs) for coronavirus to directly support this. However, lessons may be taken from published RCTs examining influenza-like illness (ILI). 2,3 Recent studies suggested that SARS-CoV-2 shares similar transmission route with influenza virus, 4 and the incidence of community transmission of SARS-CoV-2 in individuals with ILI is high. 5 Therefore, we undertook this meta-analysis of RCTs examining the efficacy of wearing facemasks to prevent ILI in community settings, irrespective of confirmatory testing for the causative virus.

We undertook a systematic literature search for RCTs related to facemasks and ILI between 1966 and April 2020 using PUBMED, EMBASE, and Cochrane library. RCTs undertaken in community (not hospital) settings comparing wearing and not wearing facemasks for ILI were included. Incidence of ILI (e.g., fever, cough, headache, sore throat, aches or pains in muscles or joints) was estimated per group. Relative risk (RR) and 95% confidence interval (CI) were calculated.

We screened 899 related abstracts and eventually included 8 RCTs ( Figure S1 ). Basic characteristics and quality of included RCTs are listed in Supplement . Participants wearing facemasks had a significantly lower risk of developing ILI than those not wearing facemasks (pooled RR=0.81, 95% CI: 0.70–0.95) and there was no heterogeneity ( Figure 1 ). The decreased risk of ILI was more pronounced if everyone wore facemask irrespective of whether they were infected or not (RR=0.77, 95% CI: 0.65-0.91), compared to those wearing facemasks when infected (RR=0.95, 95% CI: 0.58-1.56) or uninfected (RR=1.26, 95% CI: 0.69-2.31).

This study shows that wearing facemasks, irrespective of infection status, is effective in preventing ILI spread in the community. This situation mirrors what is happening now in public settings where we do not know who has been infected and who has not. Although there are no RCTs of facemasks for SARS-CoV-2, as with other simple measures such as social distancing and handwashing, these data support the recommendation to wear facemasks in public to further reduce transmission of SARS-CoV-2 and flatten the curve of this pandemic, especially when social distancing is impractical, such as shopping, or travelling with public transport for work that cannot be done from home.

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  1. SciScore for 10.1101/2020.05.07.20094912: (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.