Face mask wearing rate predicts COVID-19 death rates across countries

  1. D Miyazawa
  2. G Kaneko

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Abstract

Identifying biomedical and socioeconomic predictors of the number of deaths caused by COVID-19 can help the development of effective interventions. In this study, we used the hypothesis-driven regression approach to test the hypothesis that the mask wearing rate, along with age and obesity, can largely predict the cumulative number of deaths across countries. Our regression models explained 69% of the variation in the cumulative number of deaths per million (March to June 2020) among 22 countries, identifying the face mask wearing rate in March as an important predictor. The number of deaths per million predicted by our elastic net regression model showed high correlation (r = 0.86) with observed numbers. These findings emphasize the importance of face masks in preventing the ongoing pandemic of COVID-19.

One Sentence Summary

Face mask wearing rate in March is a strong predictor of the cumulative number of deaths per million caused by COVID-19 among 22 countries.

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    SciScore for 10.1101/2020.06.22.20137745: (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

    Software and Algorithms
    SentencesResources
    Population percent by age data was obtained from https://data.worldbank.org on June 15, 2020.
    https://data.worldbank.org
    suggested: (Data World Bank, RRID:SCR_012767)
    The R packages used in this study include ggplot2, car, ggcorrplot, lemon, ggpubr, Hmisc, rstatix, and lubridate.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    Results from OddPub: Thank you for sharing your code and data.

    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 10.1101/2020.06.22.20137745v5 on medRxiv
  3. Version published to 10.1101/2020.06.22.20137745v4 on medRxiv
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  5. Version published to 10.1101/2020.06.22.20137745v2 on medRxiv
  6. Version published to 10.1101/2020.06.22.20137745v1 on medRxiv