Effects of universal masking on Massachusetts healthcare workers’ COVID-19 incidence

  1. F-Y Lan
  2. C A Christophi
  3. J Buley
  4. E Iliaki
  5. L A Bruno-Murtha
  6. A J Sayah
  7. S N Kales

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Abstract

Background

Healthcare workers (HCWs) and other essential workers are at risk of occupational infection during the COVID-19 pandemic. Several infection control strategies have been implemented. Evidence shows that universal masking can mitigate COVID-19 infection, though existing research is limited by secular trend bias.

Aims

To investigate the effect of hospital universal masking on COVID-19 incidence among HCWs compared to the general population.

Methods

We compared the 7-day average incidence rates between a Massachusetts (USA) healthcare system and Massachusetts residents statewide. The study period was from 17 March (the date of first incident case in the healthcare system) to 6 May (the date Massachusetts implemented public masking). The healthcare system implemented universal masking on 26 March, we allotted a 5-day lag for effect onset and peak COVID-19 incidence in Massachusetts was 20 April. Thus, we categorized 17–31 March as the pre-intervention phase, 1–20 April the intervention phase and 21 April to 6 May the epidemic decline phase. Temporal incidence trends (i.e. 7-day average slopes) were compared using standardized coefficients from linear regression models.

Results

The standardized coefficients were similar between the healthcare system and the state in both the pre-intervention and epidemic decline phases. During the intervention phase, the healthcare system’s epidemic slope became negative (standardized β: −0.68, 95% CI: −1.06 to −0.31), while Massachusetts’ slope remained positive (standardized β: 0.99, 95% CI: 0.94 to 1.05).

Conclusions

Universal masking was associated with a decreasing COVID-19 incidence trend among HCWs, while the infection rate continued to rise in the surrounding community.

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  1. Version published to 10.1093/occmed/kqaa179
  2. ScreenIT

    SciScore for 10.1101/2020.08.09.20171173: (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
    The analyses were performed using R software (version 3.6.3) and SAS software (version 9.4, SAS Institute).
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    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: We detected the following sentences addressing limitations in the study:
    Nonetheless, there are also some limitations. First, the current study is limited by small sample size of HCWs, the short intervention period and individual HCW compliance with the masking policy was not measured. Larger-scale studies are warranted. Second, the five-day interval assumed for the policy to take effect was somewhat arbitrary, but reasonable since most people develop symptoms within five days of infection. Finally, there could be unmeasured confounding, such as improved practices regarding social distancing, hand hygiene, and isolation of COVID-19 patients. However, our results show significant differences between the estimates, with a drastic change from positive to negative slope shortly after the masking policy’s implementation, which are unlikely to be entirely biased. In conclusion, our results suggest a significant mitigation effect of universal masking on COVID-19 incidence when implemented in the healthcare setting, which may be applicable to other essential workers and indoor businesses. Key points:

    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.

  3. Version published to 10.1101/2020.08.09.20171173v1 on medRxiv