A review of the evidence
“Seriously people—STOP BUYING MASKS!” So tweeted
then–surgeon general Jerome Adams on February 29, 2020, adding, “They
are NOT effective in preventing general public from catching
#Coronavirus.” Two days later, Adams said, “Folks who don’t know how to
wear them properly tend to touch their faces a lot and actually can
increase the spread of coronavirus.”
Less than a week earlier, on
February 25, public-health authorities in the United Kingdom had
published guidance that masks were unnecessary even for those providing
community or residential care: “During normal day-to-day activities
facemasks do not provide protection from respiratory viruses, such as
COVID-19 and do not need to be worn by staff.”
About a month later, on
March 30, World Health Organization (WHO) Health Emergencies Program
executive director Mike Ryan said that “there is no specific evidence to
suggest that the wearing of masks by the mass population has any
particular benefit.” He added, “In fact there’s some evidence to suggest
the opposite” because of the possibility of not “wearing a mask
properly or fitting it properly” and of “taking it off and all the other
risks that are otherwise associated with that.”
Surgical masks were designed to keep medical personnel from
inadvertently infecting patients’ wounds, not to prevent the spread of
viruses. Public-health officials’ advice in the early days of Covid-19
was consistent with that understanding. Then, on April 3, 2020, Adams
announced that the CDC was changing its guidance and that the general
public should hereafter wear masks whenever sufficient social distancing
could not be maintained.
Fast-forward 15 months. Rand Paul has been suspended from YouTube for
a week for saying, “Most of the masks you get over the counter don’t
work.” Many cities across the country, following new CDC guidance handed
down amid a spike in cases nationally caused by the Delta variant, are
once again mandating indoor mask-wearing for everyone, regardless of
inoculation status. The CDC further recommends that all schoolchildren
and teachers, even those who have had Covid-19 or have been vaccinated,
should wear masks.
The CDC asserts this even though its own statistics show that
Covid-19 is not much of a threat to schoolchildren. Its numbers show
that more people under the age of 18 died of influenza during the 2018–19 flu season—a season of “moderate severity” that lasted eight months—than have died of Covid-19 across more than 18 months. What’s more, the CDC says
that out of every 1,738 Covid-19-related deaths in the U.S. in 2020 and
2021, just one has involved someone under 18 years of age; and out of
every 150 deaths of someone under 18 years of age, just one has been
Covid-related. Yet the CDC declares that schoolchildren, who learn in
part from communication conveyed through facial expressions, should
nevertheless hide their faces—and so should their teachers.
How did mask guidance change so profoundly? Did the medical research
on the effectiveness of masks change—and in a remarkably short period of
time—or just the guidance on wearing them?
Since we are constantly told that the CDC and other public-health
entities are basing their recommendations on science, it’s crucial to
know what, specifically, has been found in various medical studies.
Significant choices about how our republic should function cannot be
made on the basis of science alone—they require judgment and the
weighing of countless considerations—but they must be informed by
knowledge of it.
In truth, the CDC’s, U.K.’s, and WHO’s earlier guidance was much more
consistent with the best medical research on masks’ effectiveness in
preventing the spread of viruses. That research suggests that Americans’
many months of mask-wearing has likely provided little to no health
benefit and might even have been counterproductive in preventing the
spread of the novel coronavirus.
It’s striking how much the CDC, in
marshalling evidence to justify its revised mask guidance, studiously
avoids mentioning randomized controlled trials. RCTs are uniformly
regarded as the gold standard in medical research, yet the CDC basically
ignores them apart from disparaging certain ones that particularly
contradict the agency’s position.
In a “Science Brief”
highlighting studies that “demonstrate that mask wearing reduces new
infections” and serving as the main public justification for its mask
guidance, the CDC provides a helpful matrix of 15 studies—none RCTs. The
CDC instead focuses strictly on observational studies completed after
Covid-19 began. In general, observational studies are not only of lower
quality than RCTs but also are more likely to be politicized, as they
can inject the researcher’s judgment more prominently into the inquiry
and lend themselves, far more than RCTs, to finding what one wants to
find.
A particular favorite of the CDC’s, so much so that the agency put
out a glowing press release on it and continues to give it pride of
placement in its brief, is an observational (specifically, cohort) study
focused on two Covid-positive hairstylists at a beauty salon in
Missouri. The two stylists, who were masked, provided services for 139
people, who were mostly masked, for several days after developing
Covid-19 symptoms. The 67 customers who subsequently chose to get tested
for the coronavirus tested negative, and none of the 72 others reported
symptoms.
This study has major limitations. For starters, any number of the 72
untested customers could have had Covid-19 but been asymptomatic, or
else had symptoms that they chose not to report to the Greene County
Health Department, the entity doing the asking. The apparent lack of
spread of Covid-19 could have been a result of good ventilation, good
hand hygiene, minimal coughing by the stylists, or the fact that
stylists generally, as the researchers note, “cut hair while clients are
facing away from them.” The researchers also observe that “viral
shedding” of the coronavirus “is at its highest during the 2 to 3 days
before symptom onset.” Yet no customers who saw the stylists when they
were at their most contagious were tested for Covid-19 or asked about
symptoms. Most importantly, this study does not have a control group.
Nobody has any idea how many people, if any, would have been infected
had no masks been worn in the salon. Late last year, at a gym in
Virginia in which people apparently did not wear masks most of the time,
a trainer tested positive for the coronavirus. As CNN reported,
the gym contacted everyone whom the trainer had coached before getting
sick—50 members in all—“but not one member developed symptoms.” Clearly,
this doesn’t prove that not wearing masks prevents transmission.
Another CDC-highlighted study,
by Rader et al., invited people across the country to answer a survey.
The low (11 percent) response rate—including about twice as many women
as men—indicated that the mix of respondents was hardly random. The
study found that “a high percentage of self-reported face mask-wearing
is associated with a higher probability of transmission control,” and
“the highest percentage of reported mask wearers” are found,
unsurprisingly, “along the coasts and southern border, and in large
urban areas.” However, as the researchers note, “It is difficult to
disentangle individuals’ engagement in mask-wearing from their adoption
of other preventive hygiene practices, and mask-wearing might serve as a
proxy for other risk avoidance behaviors not queried.” Moreover,
achieving greater “transmission control” is not remotely the same thing
as ensuring fewer deaths. For example,
per capita, Utah is in the top ten in the nation in Covid-19 cases and
the bottom ten in Covid-19 deaths, while Massachusetts is in the bottom
half in cases and the top five in deaths.
An additional observational study,
but one that the CDC does not reference in its brief, is a large,
international Bayesian study by Leech, et al. It finds that mask-wearing
by 100 percent of the population “corresponds to” a 24.6 percent
reduction in transmission of the novel coronavirus. Mask mandates
correspond to no decrease in transmission: “For mandates we see no
reduction: 0.0 percent.” Like all observational studies, however, this
study is ill-equipped to show causation, to separate out the effects of
just one variable from among other, frequently related, ones.
Mask supporters often claim that we have no choice but to rely on
observational studies instead of RCTs, because RCTs cannot tell us
whether masks work or not. But what they really mean is that they don’t
like what the RCTs show.
The randomized controlled trial dates, in a
sense, to 1747, when Royal Navy surgeon James Lind divided seamen
suffering from similar cases of scurvy into six pairs and tried
different methods of treatment on each. Lind writes, “The consequence
was, that the most sudden and visible good effects were perceived from
the use of oranges and lemons.”
The RCT eventually became firmly established as the most reliable way
to test medical interventions. The following passage, from Abdelhamid
Attia, an M.D. and professor of obstetrics and gynecology at Cairo
University in Egypt, conveys its dominance:
The importance of RCTs for
clinical practice can be illustrated by its impact on the shift of
practice in hormone replacement therapy (HRT). For decades HRT was
considered the standard care for all postmenopausal, symptomatic and
asymptomatic women. Evidence for the effectiveness of HRT relied always
on observational studies[,] mostly cohort studies. But a single RCT that
was published in 2002 . . . has changed clinical practice all over the
world from the liberal use of HRT to the conservative use in selected
symptomatic cases and for the shortest period of time. In other words,
one well conducted RCT has changed the practice that relied on tens, and
probably hundreds, of observational studies for decades.
A randomized controlled trial divides participants into different
groups on a randomized basis. At least one group receives an
“intervention,” or treatment, that is generally tested against a control
group not receiving the intervention. The twofold strength of an RCT is
that it allows researchers to isolate one variable—to test whether a
given intervention causes an intended effect—while at the same time
making it very hard for researchers to produce their own preferred
outcomes.
This is true at least so long as an RCT’s findings are based on
“intention-to-treat” analysis, whereby all participants are kept in the
treatment group to which they were originally assigned and none are
excluded from the analysis, regardless of whether they actually received
the intended treatment. Eric McCoy, an M.D. at the University of
California, Irvine, explains
that intention-to-treat analysis avoids bias and “preserves the
benefits of randomization, which cannot be assumed when using other
methods of analysis.”
Such other methods of analysis include subgroup, multivariable, and
per-protocol analysis. Subgroup analysis is susceptible to
“cherry-picking”—as researchers hunt for anything showing statistical
significance—or to being swayed by random chance. In one famous example,
aspirin was found to help prevent fatal heart attacks, but not in the
subgroups where patients’ astrological signs were Gemini or Libra.
“Multivariable analysis,” writes
Marlies Wakkee, an M.D. and Ph.D. at Erasmus University Medical Center
in the Netherlands, “only adjusts for measured confounding”—that which a
researcher decides is worth examining. (Confounders are extra variables
that affect the analysis; for example, eating ice cream may be found to
correlate with sunburns, but heat is a confounding variable influencing
both.) She adds, “This is a significant difference compared to
randomized controlled trials, where the randomization process results in
an equal distribution of all potential confounders, known and unknown.”
Per-protocol analysis departs from randomization by basically
allowing participants to self-select into, or out of, an intervention
group. McCoy writes, “Empirical evidence suggests that participants who
adhere [to research protocols] tend to do better than those who do not
adhere, regardless of assignment to active treatment or placebo.” In
other words, per-protocol analysis is more likely to suggest that an
intervention, even a fake one, worked. Of these three departures from
intention-to-treat analysis, per-protocol analysis is perhaps the most
extreme.
With these different methods of analysis in
mind, it becomes easier to evaluate the 14 RCTs, conducted around the
world, that have tested the effectiveness of masks in reducing the
transmission of respiratory viruses. Of these 14, the two that have
directly tested “source control”—the oft-repeated claim that wearing a
mask benefits others—are a good place to start.
A 2016 study
in Beijing by MacIntyre, et al. that claimed to find a possible benefit
of masks did not prove very informative, as only one person in the
control group—and one in the mask group—developed a laboratory-confirmed
infection. Much more illuminating was a 2010 study
in France by Canini, et al., which randomly placed sick people, or
“index patients,” and their household contacts together into either a
mask group or a no-mask control group. The authors “observed a good
adherence to the intervention,” meaning that the index patients
generally wore the furnished three-ply masks as intended. (No one else
was asked to wear them.) Within a week, 15.8 percent of household
contacts in the no-mask control group and 16.2 percent in the mask group
developed an “influenza-like illness” (ILI). So, the two groups were
essentially dead even, with the sliver of an advantage observed in the
control group not being statistically significant. The authors write
that the study “should be interpreted with caution since the lack of
statistical power prevents us to draw formal conclusion regarding
effectiveness of facemasks in the context of a seasonal epidemic.”
However, they state unequivocally, “In various sensitivity analyses, we
did not identify any trend in the results suggesting effectiveness of
facemasks.”
With the two RCTs that directly tested source control providing
essentially no support for the claim that wearing a mask benefits
others, what about RCTs that test the combination of source control and
wearer protection? By dividing participants into a hand-hygiene group, a
hand-hygiene group that also wore masks, and a control group, three
RCTs allow us to see whether the addition of masks (worn both by the
sick person and others) provided any benefit over hand hygiene alone.
A 2010 study
by Larson, et al. in New York found that those in the hand-hygiene
group were less likely to develop any symptoms of an upper respiratory
infection (42 percent experienced symptoms) than those in the
mask-plus-hand-hygiene group (61 percent). This statistically
significant finding suggests that wearing a mask actually undermines the
benefits of hand hygiene.
A multivariable analysis of this same study found a significant
difference in secondary attack rates (the rate of transmission to
others) between the mask-plus-hands group and the control group. On this
basis, the authors maintain that mask-wearing “should be encouraged
during outbreak situations.” However, this multivariable analysis also
found significantly lower rates in crowded homes—“i.e., more crowded
households had less transmission”—which tested at a higher confidence
level. Thus, to the extent that this multivariable analysis provided any
support for masks, it provided at least as much support for crowding.
Two other studies found no statistically significant differences between their mask-plus-hands and hands-only groups. A 2011 study in Bangkok by Simmerman, et al. observed very similar results for both groups. A CDC-funded 2009 study
in Hong Kong by Cowling, et al. observed that the hands-only group
generally did better than the mask-plus-hands group, but not to a
statistically significant degree. Subgroup analysis by Cowling, et al.,
limited to interventions started within 36 hours of the onset of
symptoms, found that the mask-plus-hands group beat the control group to
a statistically significant degree in one measure, while the hands-only
group beat the control group to a statistically significant degree in
two measures. Summarizing this study, Canini writes that “no additional
benefit was observed when facemask [use] was added to hand hygiene by
comparison with hand hygiene alone.”
So, if masks don’t improve on hand hygiene alone, what about masks versus nothing?
Various RCTs have studied this question, with evidence of masks’ effectiveness proving sparse at best. Aside from a 2009 study
in Japan by Jacobs, et al.—which found that those in the mask group
were significantly more likely to experience headaches and that “face
mask use in health care workers has not been demonstrated to provide
benefit”—only two RCTs have produced statistically significant findings
in intention-to-treat analysis, and one of those studies contradicted
itself.
The previously mentioned 2011 study in Bangkok by Simmerman, et al.
found that the secondary attack rate of ILI was twice as high in the
mask-plus-hand-hygiene group (18 percent) as in the control group (9
percent), a statistically significant difference. (The ILI rate was 17
percent in the hand-hygiene-only group.) Finding essentially the same
thing in multivariable analysis, the researchers wrote that, relative to
the control group, the odds ratios for both the mask-plus-hands group
and the hands-only group “were twofold in the opposite direction from
the hypothesized protective effect.”
Subsequently, a small 2014 study—with
164 participants—by Barasheed, et al. of Australian pilgrims in Saudi
Arabia, staying in close quarters in tents, found that significantly
fewer people in the mask group developed an ILI than in the control
group (31 percent to 53 percent). Unlike the exact fever specifications
utilized in other RCTs, however, this study accepted self-reporting of
“subjective” fever in determining whether someone had an ILI. Lab tests
revealed opposite results, with twice as many participants having
developed respiratory viruses in the mask group as in the control group.
These lab-test findings were not statistically significant; still, the
lab tests’ greater reliability makes it far from clear that the masks in
this study provided any genuine benefit.
Other RCTs found no statistically significant benefit from masks in intention-to-treat analysis. A 2008 pilot study
by Cowling et al. in Hong Kong observed that secondary attack rates,
using the CDC’s definition of ILI, were twice as high in the mask group
(8 percent) as in the hand hygiene (4 percent) or control (4 percent)
groups, but these observed differences were not statistically
significant.
Other methods of analysis, deviating from intention-to-treat analysis, found the following.
A per-protocol analysis
of a 2009 study in Sydney by MacIntyre, et al. found a significant
effect when combining the surgical-mask group with a group wearing N95
hospital respirators. However, the authors write, a “causal link cannot
be demonstrated because adherence was not randomized.”
In subgroup analysis of 2010 and 2012
studies in Michigan by Aiello, et al., limited to the final several
weeks of the respective studies, each study’s mask-plus-hands group had
significantly lower rates of ILI than its control group, while its
mask-only group did not. In 2010, the results for the mask-only group
also hinted at a slight benefit, reducing ILI by an observed (but not
statistically significant) 8 percent to 10 percent. In 2012, the authors
concluded, “Masks alone did not provide a benefit.” They nevertheless
recommended the combination of mask use and hand hygiene, despite not
having tested whether that combination works better than hand hygiene
alone.
A multivariable analysis of a smallish (218 participants) 2012 study
in Germany by Suess, et al. found that combining the mask group and
mask-plus-hands group, while limiting analysis to interventions begun
within 48 hours, produced a finding of significantly lower levels of
lab-confirmed influenza (but not of ILI) in that combined group (but not
in either group separately). The authors, from Berlin, recommended
masking and hand hygiene, while opining, “Concerns about acceptability
and tolerability of the interventions should not be a reason against
their recommendation.”
The only RCT to test mask-wearing’s specific effectiveness against Covid-19 was a 2020 study
by Bundgaard, et al. in Denmark. This large (4,862 participants) RCT
divided people between a mask-wearing group (providing “high-quality”
three-layer surgical masks) and a control group. It took place at a time
(spring 2020) when Denmark was encouraging social distancing but not
mask use, and 93 percent of those in the mask group wore the masks at
least “predominately as recommended.” The study found that 1.8 percent
of those in the mask group and 2.1 percent of those in the control group
became infected with Covid-19 within a month, with this 0.3-point
difference not being statistically significant.
This study—the first RCT on Covid-19 transmission—apparently had
difficulty getting published. After the study’s eventual publication,
Vinay Prasad, an M.D. at the University of California, San Francisco,
described it as “thoughtful,” “useful,” and “well done,” but noted (with
criticism), “Some have turned to social media to ask why a trial that
may diminish enthusiasm for masks and may be misinterpreted was
published in a top medical journal.”
Meanwhile, the CDC website portrays the Danish RCT (with its 4,800
participants) as being far less relevant or important than the
observational study of Missouri hairdressers with no control group,
dismissing the former as “inconclusive” and “too small” while praising
the latter, amazingly, as “showing that wearing a mask prevented the
spread of infection”—when it showed nothing of the sort.
Each of the RCTs discussed so far, 13 in all, examined the effectiveness of surgical
masks, finding little to no evidence of their effectiveness and some
evidence that they might actually increase viral transmission. None of
these 13 RCTs examined the effectiveness of cloth masks. “Cloth
face coverings,” according to former CDC director Robert Redfield, “are
one of the most powerful weapons we have.”
One RCT tested these masks that so many high-profile public-health
officials have touted. This “first RCT of cloth masks,” in the trial’s
own words (it is apparently still the only one), was a 2015 study
by MacIntyre, et al. in Hanoi, Vietnam. A relatively large study, with
over 1,100 participants, it tested cloth masks against surgical masks
and did not feature a no-mask control group. The trial tested the
protection of health-care workers, instructing them to wear a two-layer
cloth mask at all times on every shift (“except in the toilet or during
tea or lunch breaks”) across four weeks.
The study found that those in the cloth-mask group were 13 times more
likely (2.28 percent to 0.17 percent) to develop an influenza-like
illness than those in the surgical-mask group—a statistically
significant difference. The trial also lab-tested penetration rates and
found that while surgical masks were “poor” at preventing the
penetration of particles—letting 44 percent through—cloth masks were
“extremely poor,” letting 97 percent through. (N95 hospital respirators
let 0.1 percent through.)
The authors write that wearing a cloth mask “may potentially increase
the infection risk” for health-care workers. “The virus may survive on
the surface of the facemasks,” they explain, while “a contaminated cloth
mask may transfer pathogen from the mask to the bare hands of the
wearer,” which could lead to hand hygiene being “compromised.” As for
double-masking, the authors write, “Observations during SARS suggested
double-masking . . . increased the risk of infection because of
moisture, liquid diffusion and pathogen retention.” Absent further
research, they conclude, “cloth masks should not be recommended.”
MacIntyre and several other authors of this study, perhaps under
pressure from the CDC or other entities with similar agendas, released
what the CDC calls a “follow up study,” in September 2020. This
follow-up isn’t really a study at all, certainly not a new RCT, yet the
CDC cites it favorably while disparaging the original study, which, the
CDC asserts, “had a number of limitations.” This 2020 follow-up pretty
much amounts to publishing the finding that when hospitals washed the
cloth masks, health-care workers were only about half as likely to get
infected as when they washed the cloth masks themselves. Still, the 2020
publication says, “We do not recommend cloth masks for health workers,”
much as the 2015 one said.
Other reviews of the evidence have been mixed but generally have come to similar conclusions. Certain masking advocates admit that the RCT evidence is “inconclusive” but cite other forms of evidence that have held up poorly. A study for Cochrane Reviews
by Jefferson, et al. that examines 13 of the 14 RCTs discussed herein
(all but the Denmark Covid-19 study) notes “uncertainty about the
effects of face masks” and writes that “the pooled results of randomised
trials did not show a clear reduction in respiratory viral infection
with the use of medical/surgical masks during seasonal influenza.”
Meantime, a study by Perski, et al.,
which performed a Bayesian analysis on 11 of the 14 RCTs discussed
herein, concluded that when it comes to “the benefits or harms of
wearing face masks . . . the scientific evidence should be considered
equivocal.” They write, “Available evidence from RCTs is equivocal as to
whether or not wearing face masks in community settings results in a
reduction in clinically- or laboratory-confirmed viral respiratory
infections.”
In sum, of the 14 RCTs that have tested the effectiveness of masks in
preventing the transmission of respiratory viruses, three suggest, but
do not provide any statistically significant evidence in
intention-to-treat analysis, that masks might be useful. The other
eleven suggest that masks are either useless—whether compared with no
masks or because they appear not to add to good hand hygiene alone—or
actually counterproductive. Of the three studies that provided
statistically significant evidence in intention-to-treat analysis that
was not contradicted within the same study, one found that the
combination of surgical masks and hand hygiene was less effective than
hand hygiene alone, one found that the combination of surgical masks and
hand hygiene was less effective than nothing, and one found that cloth
masks were less effective than surgical masks.
Hiram Powers, the nineteenth-century
neoclassical sculptor, keenly observed, “The eye is the window to the
soul, the mouth the door. The intellect, the will, are seen in the eye;
the emotions, sensibilities, and affections, in the mouth.” The best
available scientific evidence suggests that the American people,
credulously trusting
their public-health officials, have been blocking the door to the soul
without blocking the transmission of the novel coronavirus.
Jeffrey H. Anderson
served as director of the Bureau of Justice Statistics from 2017 to
2021, and is co-creator of the Anderson & Hester Rankings, part of
college football’s Bowl Championship Series formula from 1998 to 2014.
Photo by Michael M. Santiago/Getty Images
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