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Figure.  Temporal Trend in Percentage Positivity of SARS-CoV-2 Testing Among HCWs
Temporal Trend in Percentage Positivity of SARS-CoV-2 Testing Among HCWs

HCW indicates health care worker; MGB, Mass General Brigham; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2. All dates given are for the year 2020. The size of each data marker is proportional to the total number of SARS-CoV-2 tests performed each day over the time of the study period (x-axis), while the position of each data marker along the y-axis shows the percentage of daily test results that were positive among HCWs. The horizontal bars below the x-axis represent the timing of key interventions implemented in the state of Massachusetts and at MGB. The dotted lines represent the implementation dates of hospital policies. The study period is divided into 3 phases: a preintervention period before implementation of universal masking of HCWs (pink), which includes March 26, the day after implementation of universal masking for HCWs, to account for HCWs who became symptomatic after business hours on March 25 and were tested on March 26; a transition period until implementation of universal masking of patients (purple) plus an additional lag period (yellow); and the intervention period (green). For the preintervention and intervention periods, daily tests were fitted by weighted nonlinear regression (curves). The change in overall slope was compared between the 2 curves to determine any statistically significant changes in trend (as shown by the P value).

1.
Adams  JG, Walls  RM.  Supporting the health care workforce during the COVID-19 global epidemic.   JAMA. 2020;323(15):1439-1440. doi:10.1001/jama.2020.3972PubMedGoogle ScholarCrossref
2.
Hunter  E, Price  DA, Murphy  E,  et al.  First experience of COVID-19 screening of health-care workers in England.   Lancet. 2020;395(10234):e77-e78. doi:10.1016/S0140-6736(20)30970-3PubMedGoogle ScholarCrossref
3.
Black  JRM, Bailey  C, Przewrocka  J, Dijkstra  KK, Swanton  C.  COVID-19: the case for health-care worker screening to prevent hospital transmission.   Lancet. 2020;395(10234):1418-1420. doi:10.1016/S0140-6736(20)30917-XPubMedGoogle ScholarCrossref
4.
Klompas  M, Morris  CA, Sinclair  J, Pearson  M, Shenoy  ES.  Universal masking in hospitals in the Covid-19 era.   N Engl J Med. 2020;382(21):e63. doi:10.1056/NEJMp2006372PubMedGoogle Scholar
5.
Sen  S, Karaca-Mandic  P, Georgiou  A.  Association of stay-at-home orders with COVID-19 hospitalizations in 4 states.   JAMA. 2020;323(24):2522-2524. doi:10.1001/jama.2020.9176PubMedGoogle ScholarCrossref
6.
Massachusetts Department of Public Health COVID-19 dashboard—April 30, 2020. Accessed June 27, 2020. https://www.mass.gov/doc/covid-19-dashboard-april-30-2020/download
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    9 Comments for this article
    EXPAND ALL
    Analysis of Cases in the General Public Should be Provided
    Ben Apgar | Public
    In the discussion, the authors write "the case number continued to increase in Massachusetts throughout the study period." The reference provided (Massachusetts Department of Public Health COVID-19 dashboard—April 30, 2020), however, is only raw data, not an analysis of the data.

    My unweighted linear regression of that data for the period of April 11 to April 30 shows daily cases decreasing. While I didn't do the work to determine the positivity rate, the number of tests performed increased over this period, suggesting positivity rate was also decreasing over this period.

    This appears to contradict their statement in the
    discussion that their results suggest "the decrease in the SARS-CoV-2 positivity rate in MGB HCWs took place before the decrease in the general public." It appears to have happened at the same time, though perhaps masks had an additional effect.

    Additionally, Massachusetts cases were subject to a data error on April 24th, as reported in the Boston Herald. I am not sure if this is reflected or corrected in the data, but it may deserve a mention to avoid confusion.

    Regardless of my simple analysis, I believe, at minimum, it shows that raw data is insufficient to support the statements in the discussion and their analysis of the cases in the general public needs to be included.
    CONFLICT OF INTEREST: None Reported
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    Confounding of Effects of Masking and Hospital Visits on SARS-CoV-2 Infections in HCWs
    Knut Wittkowski, PhD, DSc | ASDERA, LLC
    Wang et al. (2020) report an increase in the proportion of positive PCR tests to 10% on March 24, which coincides with an increase in proportion of hospital visits with influenza-like illnesses in MA to 6.6%, as reported by the CDC's ILInet, which peaked in week 12, March 16 to 22 (1).

     As the number of ILI visits declined, but the number of hospitalized patients is expected to have increased (although those numbers are not reported), the proportion of positive tests increased at a lower rate and declined during the time when patients are expected to seroconvert (seroconversion events
    are also not reported). As the changes in the number and infectiousness of patients and the effects of masking requirements, first by HCWs and then by patients, overlap, further studies are needed before conclusions can be drawn about the effectiveness of masking requirements in health care settings (by the authors) or for the general public (by the CDC in the accompanying Editorial).

    Reference

    1. https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html
    CONFLICT OF INTEREST: None Reported
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    Article Title is Deceptive
    Lew Jacobson, PhD | University of Pittsburgh
    The article title posits an association between universal masking and SARS-CoV-2 positivity, but the results reported in the article show the opposite -- masking reduces positivity. This contradictory titling is a repeating error. It could be fixed by changing the title to "Association between universal masking and reduction of SARS-CoV-2 positivity."
    CONFLICT OF INTEREST: None Reported
    Absolute Event Rates Just as Important as Proportion of Positive Tests
    James Siegler, MD | Cooper University Hospital
    There is little doubt that the use of face masks and other personal protective equipment can slow the rate of new infections with SARS-CoV-2 among healthcare workers (HCWs). But equally important to the proportion of symptomatic HCWs testing positive is the overall number of new positive cases of SARS-CoV-2. How many fewer HCWs are acquiring the infection now, as opposed to before the implementation of universal masking?

    If one were to discount the period during which the rise in new cases were occurring (all dates before March 25th), one may consider the relative plateau of new positive HCW cases
    to take place between March 25th and April 11th. As the authors stated, April 11th is an appropriate threshold at which the effect of mask-wearing could reduce the transmission of SARS-CoV-2. In my crude deconstruction of the figure, where the mean daily testing rate was selected from the available ranges (e.g., 10 for a test rate <20, 60 for 20-100, 150 for 101-200, and 250 arbitrarily selected for days with >200 tests), including all available dates after which the first healthcare worker tested positive, the mean daily rate of new diagnoses fell by 27.5% from a mean of 30.6 ± 9.8 cases/day to 22.2 ± 12.4 between these periods (p=0.03 by independent samples t-test).

    Not knowing the true daily test counts renders this oversimplified analysis limited, so I followed the first comparison with a sensitivity analysis that would intentionally attenuate the effect of mask-wearing. In this analysis, assuming the daily test rate was at the 25th percentile of the reported range before April 11th (e.g., 5 tests on days when the test rate was <20, etc., and 225 for dates with >250 tests)—and the 75th percentile for dates after April 11th (e.g., 15 tests when the test rate was <20, and 275 for dates with >250 tests), the difference was no longer significant (26.8 ± 9.5 cases/day vs. 25.5 ± 13.1, p=0.73). This discrepancy with the former analysis highlights the limitations of the data that are presented. Knowing the daily absolute rate of new infections with SARS-CoV-2 among HCWs would clarify this issue, and a follow-up analysis could (potentially) put to rest the question of whether masks may reduce transmission of SARS-CoV-2 to HCWs.
    CONFLICT OF INTEREST: None Reported
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    Positivity Rate in What Population?
    Carson Kent, PhD Candidate | Stanford University
    From the description of how the positivity rate is calculated, it is unclear what population the positivity rate is being calculated for. If the authors wish to measure the positivity rate within the population of HCWs which are susceptible to SARS-CoV-2 on the i-th day, then the denominator of the positivity rate is the number of HCWs who never tested positive prior to day i. This is not the denominator that is described.
    CONFLICT OF INTEREST: None Reported
    Wearing a Face Mask is a Sine Qua Non to Stop the Pandemic
    Giovanni Ghirga, MD | Pediatrician, Civitavecchia, Italy.
    This is one of the most important articles published on COVID-19. While in many areas of the world the battle against the SARS-CoV-2 rages in favor of the virus scientists are trying in every way to find a vaccine, a drug, or something else to combat the disease. Nevertheless, this research very clearly shows and confirms to all of us that we already have a highly effective weapon against the virus: “The face mask!” The latter retains the ability to block the survival of the virus by preventing the infection of susceptible individuals. In fact, if new individuals are not infected sooner or later the SARS-CoV-2 will be overwhelmed by the immune system and die. Wearing a face mask when you are even in low-risk areas is a sine qua non to stop the pandemic.

    Giovanni Ghirga, MD, Pediatrician, Civitavecchia, Italy
    CONFLICT OF INTEREST: None Reported
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    The 11% Who Contracted Covid after Universal Masking
    Natasha Mitchell, BSN, MS | University of Pennsylvania
    Assuming, for the sake of argument, that the study's reported 11% incidence of COVID-19 among health care workers after implementation of universal masking is in fact higher than that of the general population at the time of the study, the statistic is intriguing. To what does the group attribute that persistent incidence?

    Particularly in light of the CDC case report on the apparent total efficacy of universal masking in the prevention of COVID transmission between two COVID+ hair dressers and 139 clients they serviced (1) and the fact that universal masking did appear to dramatically reduce transmission rates
    at the Brigham study hospital, that persistent 11% infection rate could hold keys to efficacious transmission prevention even with continued social interaction. Put another way, what is the difference between 11% transmission and 0% transmission? Did 11% of hospital employees contract COVID during the odd moments they weren't wearing masks eating with other staff, drinking, etc.? Did they contract it from patients who weren't wearing masks? Did they contract COVID because the concentration of aerosolized disease was relatively higher than it would be in, say, a hair salon, and therefore an N95 mask would have been required to drive the in-hospital transmission rate towards 0?

    Within these data, and further study, lies the potential for dramatic insight into how to precisely control spread of the virus while allowing people to continue with much of "normal" life. If we could compare disease incidence, for instance, between staff who were rigidly adherent to masking policy (ate alone and outside, never took their masks off during work day) and staff not so strict (ate in the presence of others, with masks down, intermittently pulled mask down for moments of "fresh" air ) in similar work environments, we would perhaps identify high-risk activities which folks could relatively easily avoid without having to avoid human interaction all together (i.e. go ahead and hang out...but don't even think about pulling the mask down so that you can enjoy a meal together); rates of of staff COVID infection where relatively higher proportions of patients wear masks vs. those where relatively lower proportions of patients wear masks; rates of infection in places where aerosolized viral load is is likely to be higher (e.g. hospital ER or ICU) vs. place where it's likely to be relatively lower (hair salons): we might find, for instance, that it really is safe to go to the salon so long as you're wearing a well-fitting, simple face covering...but everyone in a hospital ought to be wearing an N95.

    I am not in a position to pursue such research but I imagine others here are (and perhaps such study is already under way).

    REFERENCE

    1. https://www.cdc.gov/mmwr/volumes/69/wr/mm6928e2.htm?s_cid=mm6928e2_w&fbclid=IwAR1ID1xVHAYH_mF8qE9SjbwsNidSJufpGqCtmpvOOBLSapyeC6H7xEXbF4o
    CONFLICT OF INTEREST: None Reported
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    Moral Arguments for Masks
    Eta Berner, EdD | University of Alabama at Birmingham
    In their editorial comment on this article, Brooks et al. recommend “appealing messaging that normalizes mask wearing.”(1) However the major thrust of most arguments to persuade the public about masks is that although the recommended cloth mask may not provide virus protection to the individual wearing it, the mask can prevent transmission to others. The assumption is that the general public will be persuaded by this argument of public good, even at the cost of personal inconvenience. However, the pictures and stories of US citizens ignoring the recommendations should make us reconsider that assumption.
    /> Over 50 years ago, the late Lawrence Kohlberg formulated six stages of moral reasoning that are still used in studies of moral reasoning today (2-4). Kohlberg’s ideas may provide some direction to persuade a larger portion of the public about the importance of wearing masks. Data have shown that most people reason at the two stages of what Kohlberg calls the conventional level, where the reasoning focuses on doing what is polite, “nice”, being a good person, conforming to community norms, and, at the higher conventional level, following laws and other rules. Furthermore, individuals are able to comprehend arguments only slightly above their usual stage of reasoning (2). The problem with the argument that individuals should inconvenience themselves for the public good is that the argument reflects the higher stages of moral reasoning (post-conventional), which may not be persuasive to the many individuals who do not normally reason at that level. The higher moral (and scientifically accurate) argument promoting the public good should still be made, since some individuals may respond to it. However, an additional argument that emphasizes that a good, responsible, considerate person will follow the rules may be more effective in reaching a larger number of people in a way that they can understand and may be the best way to persuade the largest number of people to do what is best for the public good.

    REFERENCES

    1. Brooks JT, Butler JC, Redfield RR. Universal masking to prevent SARS-CoV-2 transmission—The time is now. JAMA. Published online July 14, 2020. doi:10.1001/jama.2020.13107
    2. Rest J, Turiel E, Kohlberg L. Level of moral development as a determinant of preference and comprehension of moral judgments made by others. J Pers. 1969 Jun;37(2):225-52.
    3. McLeod-Sordjan R. Evaluating moral reasoning in nursing education. Nurs Ethics. 2014;21(4):473-483.
    4. Fang Z, Jung WH, Korczykowski M, et al. Post-conventional moral reasoning is associated with increased ventral striatal activity at rest and during task. Sci Rep. 2017;7(1):7105.
    CONFLICT OF INTEREST: None Reported
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    A Possible Confounder
    Abigail Zuger, MD (1); Kent A. Sepkowitz, MD (2) | (1) None (2) Memorial Sloan Kettering Cancer Center
    Wang et al (1) seek to demonstrate that a hospital mandate for universal masking reduced COVID-19 infection rates among its staff. They report that at baseline 21% of symptomatic staff seeking SARS-CoV-2 testing were confirmed to be infected; this rate declined to 15% after staff masking was mandated, and then to 11% after patient masking was mandated. The authors note that their results may reflect the effects of other hospital and municipal infection control measures enacted during the study period. Still, they conclude the data strongly support the effectiveness of universal masking in health care settings -- and other authors extend this conclusion to other settings. (2)

    We would like to propose an important confounding variable in their observation: the strong possibility that clinical staff at a tertiary care academic medical center in the Northeast US in late March 2020 were already using masks well before any official hospital policy went into effect. The risks of nosocomial COVID-19 may have been small in Boston at that time, but the disease's transmission patterns were clear, and Boston's influenza season was still active (3). We suspect that many or most medical and nursing staff, transporters, housekeepers and others were masked, at least when caring for patients with respiratory infections, by the time masking became compulsory.

    If this is the case, other explanations for the sharp decrease in employee infection rates noted after March 26 deserve consideration. Perhaps internal SARS-CoV-2 testing criteria were informally liberalized during the study period to include some of the worried well. Perhaps a single cluster of workplace or community infection drove the spike in infection rates seen at the end of March. An analysis of the specific duties and home zip codes of infected employees might have clarified some of these questions. A precise quantitation of hospital mask use before and during the study period would also have informed the argument.

    Anyone who has worked in a hospital has learned that -- particularly during emergencies -- nothing influences behavior quite so powerfully as do informal negotiations and the group survival instincts born of long camaraderie. Although we may all wish for some numerical proof of the efficacy of universal masking, official hospital policy is unlikely to be the best place to find it.

    Abigail Zuger MD
    New York City

    Kent A. Sepkowitz, MD
    Deputy Physician in Chief, Quality & Safety
    Memorial Sloan Kettering Cancer Center
    New York City

    REFERENCES

    1. Wang X et al. Association between universal masking in a health care system and SARS CoV 2 positivity among health care workers. Jama online 7/14/2020. https://jamanetwork.com/journals/jama/fullarticle/2768533

    2. Brooks JT et al. Universal masking to prevent sars-CoV-2 transmission -- the Time is Now. Jama online 7/14/2020. https://jamanetwork.com/journals/jama/fullarticle/2768532

    3. Massachusetts Department of Public Health weekly influenza update for March 20, 2020, accessed 8/4/20. https://www.mass.gov/info-details/weekly-flu-report-march-20-2020
    CONFLICT OF INTEREST: None Reported
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    Research Letter
    July 14, 2020

    Association Between Universal Masking in a Health Care System and SARS-CoV-2 Positivity Among Health Care Workers

    Author Affiliations
    • 1Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
    • 2Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
    • 3Center for Clinical Investigation, Brigham and Women’s Hospital, Boston, Massachusetts
    • 4Occupational Health Services, Mass General Brigham, Boston, Massachusetts
    JAMA. Published online July 14, 2020. doi:10.1001/jama.2020.12897

    The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has severely affected health care workers (HCWs).1 As a result, hospital systems began testing HCWs2 and implementing infection control measures to mitigate workforce depletion and prevent disease spread.3 Mass General Brigham (MGB) is the largest health care system in Massachusetts, with 12 hospitals and more than 75 000 employees. In March 2020, MGB implemented a multipronged infection reduction strategy involving systematic testing of symptomatic HCWs and universal masking of all HCWs and patients with surgical masks.4 This study assessed the association of hospital masking policies with the SARS-CoV-2 infection rate among HCWs.

    Methods

    The institutional review board of MGB approved the study and waived informed consent. Using electronic medical records, we identified HCWs providing direct and indirect patient care who were tested for SARS-CoV-2 with reverse transcriptase–polymerase chain reaction between March 1 and April 30, 2020. The primary criterion for testing HCWs in our health care system was having symptoms consistent with SARS-CoV-2 infection. Information on the job description of each HCW was obtained by linking their record to the MGB Occupational Health Services and Human Resources databases.

    We identified 3 phases during the study period: a preintervention period before implementation of universal masking of HCWs (March 1-24, 2020); a transition period until implementation of universal masking of patients (March 25–April 5, 2020) plus an additional lag period to allow for manifestations of symptoms (April 6-10, 2020), as previously defined5; and an intervention period (April 11-30, 2020). Positivity rates included the first positive test result for all HCWs in the numerator and HCWs who never tested positive plus those who tested positive that day in the denominator. For each HCW, any tests subsequent to their first positive test result were excluded. Using weighted nonlinear regression, we fit the best curve for the preintervention and intervention periods (based on R2 value). The number of daily tests was used as the weight such that days with more tests had more weight in determining the curve. The overall slope of each period was calculated using linear regression to estimate the mean trend, regardless of curve shape. The change in overall slope between the preintervention and intervention periods was compared to determine any statistically significant change in mean trend, using a 2-sided α = .05. The analysis was conducted using R version 4.0 (R Foundation).

    Results

    Of 9850 tested HCWs, 1271 (12.9%) had positive results for SARS-CoV-2 (median age, 39 years; 73% female; 7.4% physicians or trainees, 26.5% nurses or physician assistants, 17.8% technologists or nursing support, and 48.3% other). During the preintervention period, the SARS-CoV-2 positivity rate increased exponentially from 0% to 21.32%, with a weighted mean increase of 1.16% per day and a case doubling time of 3.6 days (95% CI, 3.0-4.5 days). During the intervention period, the positivity rate decreased linearly from 14.65% to 11.46%, with a weighted mean decline of 0.49% per day and a net slope change of 1.65% (95% CI, 1.13%-2.15%; P < .001) more decline per day compared with the preintervention period (Figure).

    Discussion

    Universal masking at MGB was associated with a significantly lower rate of SARS-CoV-2 positivity among HCWs. This association may be related to a decrease in transmission between patients and HCWs and among HCWs. The decrease in HCW infections could be confounded by other interventions inside and outside of the health care system (Figure), such as restrictions on elective procedures, social distancing measures, and increased masking in public spaces, which are limitations of this study. Despite these local and statewide measures, the case number continued to increase in Massachusetts throughout the study period,6 suggesting that the decrease in the SARS-CoV-2 positivity rate in MGB HCWs took place before the decrease in the general public. Randomized trials of universal masking of HCWs during a pandemic are likely not feasible. Nonetheless, these results support universal masking as part of a multipronged infection reduction strategy in health care settings.

    Section Editor: Jody W. Zylke, MD, Deputy Editor.
    Back to top
    Article Information

    Corresponding Author: Deepak L. Bhatt, MD, MPH, Brigham and Women’s Hospital, 75 Francis St, Boston, MA 02115 (dlbhattmd@post.harvard.edu).

    Accepted for Publication: July 1, 2020.

    Published Online: July 14, 2020. doi:10.1001/jama.2020.12897

    Author Contributions: Dr Bhatt had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Wang and Ferro contributed equally to this article.

    Concept and design: Wang, Ferro, Hashimoto, Bhatt.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Wang, Ferro.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Wang, Zhou.

    Administrative, technical, or material support: Wang, Ferro, Hashimoto.

    Supervision: Hashimoto, Bhatt.

    Conflict of Interest Disclosures: Dr Bhatt discloses the following relationships: advisory board: Cardax, CellProthera, Cereno Scientific, Elsevier Practice Update Cardiology, Level Ex, Medscape Cardiology, PhaseBio, PLx Pharma, Regado Biosciences; board of directors: Boston VA Research Institute, Society of Cardiovascular Patient Care, TobeSoft; chair: American Heart Association Quality Oversight Committee, NCDR-ACTION Registry Steering Committee, VA CART Research and Publications Committee; data monitoring committees: Baim Institute for Clinical Research, Cleveland Clinic, Contego Medical, Duke Clinical Research Institute, Mayo Clinic, Mount Sinai School of Medicine, Population Health Research Institute; honoraria: American College of Cardiology, Baim Institute for Clinical Research, Belvoir Publications, Duke Clinical Research Institute, HMP Global, Journal of the American College of Cardiology, K2P, Level Ex, Medtelligence/ReachMD, MJH Life Sciences, Population Health Research Institute, Slack Publications, Society of Cardiovascular Patient Care, WebMD; deputy editorship: Clinical Cardiology; research funding: Abbott, Afimmune, Amarin, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Cardax, Chiesi, CSL Behring, Eisai, Ethicon, Ferring Pharmaceuticals, Forest Laboratories, Fractyl, Idorsia, Ironwood, Ischemix, Lexicon, Lilly, Medtronic, Pfizer, PhaseBio, PLx Pharma, Regeneron, Roche, Sanofi Aventis, Synaptic, The Medicines Company; royalties: Elsevier; site coinvestigator: Biotronik, Boston Scientific, CSI, St Jude Medical, Svelte; trustee: American College of Cardiology; unfunded research: FlowCo, Merck, Novo Nordisk, Takeda. No other disclosures were reported.

    Additional Contributions: We thank Stacey A. Duey, MT(ASCP), MCHP, Mass General Brigham, for assistance in extracting data from the Research Patient Data Registry, and Karen Hopcia, ScD, ANP-BC, Mass General Brigham, for assistance in extracting data from Occupational Health Services. No compensation was received for their roles.

    References
    1.
    Adams  JG, Walls  RM.  Supporting the health care workforce during the COVID-19 global epidemic.   JAMA. 2020;323(15):1439-1440. doi:10.1001/jama.2020.3972PubMedGoogle ScholarCrossref
    2.
    Hunter  E, Price  DA, Murphy  E,  et al.  First experience of COVID-19 screening of health-care workers in England.   Lancet. 2020;395(10234):e77-e78. doi:10.1016/S0140-6736(20)30970-3PubMedGoogle ScholarCrossref
    3.
    Black  JRM, Bailey  C, Przewrocka  J, Dijkstra  KK, Swanton  C.  COVID-19: the case for health-care worker screening to prevent hospital transmission.   Lancet. 2020;395(10234):1418-1420. doi:10.1016/S0140-6736(20)30917-XPubMedGoogle ScholarCrossref
    4.
    Klompas  M, Morris  CA, Sinclair  J, Pearson  M, Shenoy  ES.  Universal masking in hospitals in the Covid-19 era.   N Engl J Med. 2020;382(21):e63. doi:10.1056/NEJMp2006372PubMedGoogle Scholar
    5.
    Sen  S, Karaca-Mandic  P, Georgiou  A.  Association of stay-at-home orders with COVID-19 hospitalizations in 4 states.   JAMA. 2020;323(24):2522-2524. doi:10.1001/jama.2020.9176PubMedGoogle ScholarCrossref
    6.
    Massachusetts Department of Public Health COVID-19 dashboard—April 30, 2020. Accessed June 27, 2020. https://www.mass.gov/doc/covid-19-dashboard-april-30-2020/download
    ×