Association between COVID-19 outcomes and mask mandates, adherence, and attitudes

  1. Dhaval Adjodah
  2. Karthik Dinakar
  3. Matteo Chinazzi
  4. Samuel P. Fraiberger
  5. Alex Pentland
  6. Samantha Bates
  7. Kyle Staller
  8. Alessandro Vespignani
  9. Deepak L. Bhatt

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Abstract

We extend previous studies on the impact of masks on COVID-19 outcomes by investigating an unprecedented breadth and depth of health outcomes, geographical resolutions, types of mask mandates, early versus later waves and controlling for other government interventions, mobility testing rate and weather. We show that mask mandates are associated with a statistically significant decrease in new cases (-3.55 per 100K), deaths (-0.13 per 100K), and the proportion of hospital admissions (-2.38 percentage points) up to 40 days after the introduction of mask mandates both at the state and county level. These effects are large, corresponding to 14% of the highest recorded number of cases, 13% of deaths, and 7% of admission proportion. We also find that mask mandates are linked to a 23.4 percentage point increase in mask adherence in four diverse states. Given the recent lifting of mandates, we estimate that the ending of mask mandates in these states is associated with a decrease of -3.19 percentage points in mask adherence and 12 per 100K (13% of the highest recorded number) of daily new cases with no significant effect on hospitalizations and deaths. Lastly, using a large novel survey dataset of 847 thousand responses in 69 countries, we introduce the novel results that community mask adherence and community attitudes towards masks are associated with a reduction in COVID-19 cases and deaths. Our results have policy implications for reinforcing the need to maintain and encourage mask-wearing by the public, especially in light of some states starting to remove their mask mandates.

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  1. Version published to 10.1371/journal.pone.0252315
  2. Version published to 10.1101/2021.01.19.21250132v3 on medRxiv
  3. Version published to 10.1101/2021.01.19.21250132v2 on medRxiv
  4. ScreenIT

    SciScore for 10.1101/2021.01.19.21250132: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

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

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our results suffer from the usual limitations of econometrics-based studies: although we control for many confounders such as other NPIs (stay-at-home and non-essential business closures), mobility, weather and test rate, it is possible that unexplained exogeneity and endogeneity persists. For example, it is possible that our treatment effects are underestimates due to the peer effects caused by neighboring states’ mask mandates affecting other states [36]. Once outcome data disaggregated by demographics (e.g. age, race, gender) is available at the state or county level, an interesting question for future work is to investigate the differential impact of mask mandates over these demographic groups. In our study of mask mandates on mask adherence, ongoing efforts using computer vision to estimate mask adherence from social media images and videos [38–41] should soon provide more detailed data on mask adherence, hopefully starting from the beginning of the pandemic. Another limitation is that although our survey-based sample estimates of mask adherence and mask attitudes in the 68-country dataset include weights to correct for a number of biases, it is possible that there is some bias remains although this is unlikely due to their small estimated instrument design effect [25]. We leave to future work the important design and estimation around these limitations but expect that our findings will still provide new insights into the long-term effect of mask mandates on mask adheren...

    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.

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  5. Version published to 10.1101/2021.01.19.21250132v1 on medRxiv