Tuesday, November 03, 2020

 LETTER

Do face masks help? is not the question

Qin Xiang Ng, Michelle Lee Zhi Qing De Deyn, and Wee Song Yeo
PNAS November 3, 2020 117 (44) 27078-27079; first published October 6, 2020; https://doi.org/10.1073/pnas.2012241117

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We read with great interest the study by Zhang et al., which analyzed the trend in the daily number of new confirmed cases and mitigation measures in three COVID-19 epicenters, namely Wuhan, China, Italy, and New York City (NYC) (1). The researchers concluded that social distancing is insufficient per se and wearing of face masks in public spaces is the most effective preventive measure. We appreciate the conclusions reached, but the analyses and conclusions are problematic for several reasons.

First, it is at best imprecise, and at worst grossly inaccurate, to attribute the slower increase in the numbers of daily new cases between NYC and the United States after 14 April to the implementation of wearing face masks in public. The authors did not consider lags in epidemic dynamics. We cannot conclude that facemasks alone or the extended lockdown alone (2) drove the reduction in the transmission rate. Their combined effect is what is being measured in the study. Due to the variable incubation period of COVID-19, which could take between 1 to 24 d (3), as well as testing and reporting delays, new cases and hospitalizations reflect individuals who were infected 1 to 2 wk prior. Furthermore, although Governor Andrew Cuomo urged and later signed an executive order mandating New Yorkers to cover their faces in public, when not practicing social distancing, the actual implementation and compliance on the ground has been widely questioned (4). There are no fines or penalties for noncompliant individuals, and enforcement is purely at the discretion of local jurisdictions. We know that “socially responsible” behaviors may not be intuitive and, as with health behavior change, take time. It is thought that it takes anywhere between 18 and 254 d to develop a new habit (5).

Second, Zhang et al. (1) oversimplify the day-to-day fluctuations and erroneously applied linear regression to the data between 17 April and 9 May in NYC and between 5 April and 9 May in the United States. Linear regression should be limited to variables with a linear relationship (6). In the case of COVID-19, modeling the disease rate as the virus spreads in the community is complex. The researchers used best-fit lines to support a decreasing rate in the daily new infections; we would advise caution in interpreting these linear regressions. Nonlinear regression analysis, or a more explicitly epidemiological model such as EpiEstim (7), would be more appropriate.

It is too premature to discount the preventive benefits of social distancing in favor of face masks. We do certainly hope that properly worn face masks help stem the spread of the coronavirus, and there is growing evidence to support the recommendation to wear masks in public spaces (8). However, ensuring public compliance is in itself a difficult task. In Singapore, stringent laws were put in place to enforce wearing face masks in public and work spaces; first-time offenders would be fined while egregious cases could be prosecuted in court (9).

Footnotes

  • Author contributions: Q.X.N. designed research; M.L.Z.Q.D.D. and W.S.Y. performed research; Q.X.N., M.L.Z.Q.D.D., and W.S.Y. analyzed data; Q.X.N., M.L.Z.Q.D.D., and W.S.Y. wrote the paper; and W.S.Y. provided supervision.

  • The authors declare no competing interest.

This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

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 LETTER

Protective effect of mandatory face masks in the public—relevant variables with likely impact on outcome were not considered

Günter Kampf
PNAS November 3, 2020 117 (44) 27076-27077; first published October 13, 2020; https://doi.org/10.1073/pnas.2012415117

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Zhang et al. (1) conclude that wearing a face mask in public is the most effective means to prevent transmission. This conclusion is scientifically highly questionable. First, the number of epidemic-curve examples is small; an explanation of how they were chosen is lacking. Second, the evaluation is flawed by not taking into account where the majority of transmissions took place locally (e.g., in the public or by healthcare workers) and if adequate personal protective equipment was available for healthcare workers (2). Third, the authors assumed that face covering was the only effect and did not control for or analyze confounding variables. It is very unlikely that “social distancing” was the same in all selected epicenters. The World Health Organization recommends at least 1-m distance (3), whereas the Centers for Disease Control and Prevention recommends 6 feet (∼2 m). It is obvious that the distance itself is likely to have an impact on transmission. Would physical distancing be as effective as face masks when a distance >2 m would be the global standard? This important variable is not included for Italy, China, or the United States. Fourth, weather conditions or the population density may have an impact on its own (4). Coronavirus infections are usually seasonal infections resulting in a flattened curve toward the summer anyway (5). The different epidemic curves for the United States and New York shown by the authors may be also explained by differences of seasonality for New York alone and the entire United States including southern states where the epidemic arrived later. Fifth, mandatory face masks in the public may have the effect that fewer people leave their homes, resulting in a lower population density in the public followed by lower transmission rates. Face masks have been described to increase physical distancing in front of shops (6). However, Zhang et al. do not provide any observational data to demonstrate that population densities or distances were similar in each epicenter before and after mandatory face masks. Sixth, the authors claim that mandated face covering "significantly reduces the number of infections." This claim may be wrong because all databases count “cases” based on the nasopharyngeal detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA (7). A case is not necessarily a clinical infection because a substantial proportion of SARS-CoV-2 RNA carriers remain asymptomatic (8). Seventh, multiple interventions may have been implemented simultaneously, so that the differences are not necessarily attributable to just masks alone. Finally, data from Germany indicate that mandatory face masks in shops and public transport as a single measure did not accelerate the decline of new cases (9). The effect of any measure should have a suitable control including a stratification regarding the most relevant parameter such as age and health of population, epidemic stage, population density, season, weather, and compliance with the intervention measured by observation. The controls are lacking so that the authors’ assumptions are insufficiently justified, and therefore their analysis does not support their main claim.

Footnotes

  • Author contributions: G.K. wrote the paper.

  • Competing interest statement: G.K. has received personal fees from Dr. Schumacher GmbH, Germany, for presentation and consultation.

This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

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