MASK MANDATES REDUCE COVID-19 MORTALITY: Analysis of 37 States and the District of Columbia, with a further analysis of the impact of demographic and medical factors on efficacy

  1. Michael J. Maloney

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Abstract

As the number of COVID-19 deaths in the US increased, various policies were enacted to slow the spread of the pandemic. While the situation has improved in recent months, determining how best to combat the current pandemic is still essential. Failure to do so invites both further resurgences of the current pandemic, and more pandemics in the years to come. As a result of the widespread failure to contain the spread of COVID-19, enough deaths have occurred that the impact of policy on mortality may be statistically evaluated. This paper uses Optimal Discriminant Analysis (ODA) to evaluate the hypothesized ability of limited mask mandates (MM) to reduce the daily number of COVID-19 deaths in the states analyzed. The mandates were found to reduce mortality in half the states analyzed and did not result in increased mortality in any states. A full range of cofactors were analyzed to determine which, if any, influenced the efficacy of the mandates in the states in which mandates had an effect. Institutional Health Subindex of the Social Capital Index, state health score, population density, portion of the population with nongroup health insurance, state GDP, and the rate of pregnancy related diabetes were all correlated with increased mandate efficacy. In contrast, incarceration rate, overcrowded housing, severely overcrowded housing, portion of the population with military provided insurance, portion of the population uninsured, the portion of the population unable to see a doctor due to cost, and the portion of the population who were American Indian/Native Alaskan were all correlated with reduced mandate efficacy.

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    SciScore for 10.1101/2021.05.09.21256922: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variableNumerical attributes for the analysis of potential cofactors include Social Capital index (State-Level, Using County-Level Methods, and County-Population-Weighted Index, as well as various sub-indexes which are listed and briefly explained at the end of this subsection), pre-pandemic population, pre-pandemic GDP (per capita and total), age distribution (with brackets of 0-18, 18-25, 26-34, 35-54, 55-64, and 65+), pre-pandemic (January 2020) number of unemployed persons and unemployment rate, homelessness, shelter beds, incarceration number and rate, population density (people per square mile in 2015), urban overcrowding (number of houses having >1 person per room), severe urban overcrowding (number of houses having >1.5 people per room), percent population by ethnicity (White, Black, Hispanic, American Indian/Alaska Native, and Native Hawaiian/Other Pacific Islander categories), %population with obesity, health insurance status (divided into categories of Ensured by Employer, Ensured by Non-Group, Ensured by Medicaid, Ensured by Medicare, Ensured by Military, and Uninsured), number of hospital beds per thousand individuals (divided into categories of Government, Non-Profit Hospital, and For-Profit Hospital, as well as total beds per thousand), portion of adults who report not seeing a doctor due to cost (male, female, and all adults), adults who reported asthma (male, female, and all adults), adults told they Have COPD/Emphysema/Chronic Bronchitis, adults who reported having diabetes (with pregnancy related cases being counted separately, as well as those with borderline or pre-diabetes), and adults who reported cardiovascular disease (male, female, and all adults).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  2. Version published to 10.1101/2021.05.09.21256922v1 on medRxiv