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The Deadly Myths About Masks – A Comprehensive Analysis of the Data

Background of blue medical masks in bright light, top view. Flat lay with a pile of protective masks. Illness prevention objects during virus pandemic.

Editor’s note:  Below is an article that was posted on The Heartland Institute blog on September 23, 2021, by James Agresti, the president of Just Facts and a policy advisor to The Heartland Institute.  The article lists an extensive list of studies and data regarding mask effectiveness in controlling the spread of COVID -19 and for that reason, Health Care News is presenting the text in its entirety. 
By James Agresti

In a terse essay titled “Science and Dictatorship,” Albert Einstein warned that “Science can flourish only in an atmosphere of free speech.” And on his deathbed, Einstein cautioned, “Whoever is careless with the truth in small matters cannot be trusted in important affairs.”

With reckless disregard for both of those principles, powerful government officials and big tech executives have corrupted or suppressed the central scientific facts about face masks. The impacts of this extend far beyond the issue of masks and have caused widespread harm and countless deaths.

Despite the fog of contradictory claims and changing government guidelines, dozens of scientific journals have published consistent data that establish these facts:

The leaders of big tech corporations like Facebook, Twitter, and Google/YouTube have empowered government officials who misled the public about every matter above and others. Together, they continue to do so by engaging in actions that resemble common disinformation tactics. These include but are not limited to cherry-picking, censorship, muddying the waterscitation bluffsnon-sequiturs, half-truths, and outright falsehoods.

Opinions are Not Science

very common and naive talking point about masks is that “experts say” they reduce the spread of Covid-19. Such statements are oblivious to the reality that other experts disagree with that opinion, like these for example:

Regardless of what any experts say or how many say it, their opinions do not constitute scientific facts. Yet, journalistscommentators, and “fact-checkers“ often treat the mere opinions of selected experts as “facts” or “science,” and politicians use the phrase, “science says“ as if it magically turns claims into facts.

Such misuse of the word “science” has been a longstanding problem. As the renowned physicist Richard Feynman remarked half a century ago, “When someone says, ‘Science teaches such and such,’ he is using the word incorrectly.” People who are actually discussing science, explained Feynman, don’t “say science has shown”—but rather “this experiment, this effect, has shown.”

This article presents actual science, and there is no substitute for it when lives are on the line. Although greatly condensed from more than 500 hours of research, these thoroughly vetted facts will take more than an hour for most people to read. This is the price of being informed instead of indoctrinated.

Easy vs, Hard Measurements

Because masks have been used in operating rooms for more than a century, many studies have been conducted on them dating back to at least 1935. One might assume that these studies quickly found benefits given that the main purpose of surgical masks is simple: to prevent bacteria from the mouths and noses of surgeons from falling into the open wounds of their patients.

Yet more than half a century later, a 2001 paper in the Journal of Hospital Infection reviewed all known studies about “surgical face masks in the operating theatre” and found that their “effectiveness remains unresolved.” A 2016 paper found the same. Such outcomes commonly occur when the effects of something are very minimal or difficult to measure.

Measuring the impact of masks on the spread of infectious diseases in homes and public places is considerably harder than in operating rooms. This is because such settings are far more diverse and less controlled than operating rooms, which are subject to strict infection control protocols.

These facts suggest that cocksure and simple-minded statements like “masks work” should be treated with skepticism.

Strong vs, Weak Studies

In 2019—the year before Covid-19 pandemic began—the World Health Organization published a lengthy analysis of different strategies to limit the impact of the flu in “community” or “non-healthcare“ settings. The analysis found “there was no evidence that face masks are effective in reducing transmission” of the flu in these situations.

Covid-19 differs from the flu, and one of the main differences is that C-19 is much more transmissible. This raises the question: How can masking reduce the community spread of C-19 when there is “no evidence” that it does so for the flu?

Fortunately, a vital tool to answer that question is provided in WHO’s supplement to the same analysis. In it, WHO correctly notes that the key to sorting out masses of studies is to rank the “quality of evidence” from strongest-to-weakest in the following order:

  1. Randomized controlled trials (RCTs): These are studies in which people are randomly assigned to receive or not receive a certain treatment. Done properly, these are the “gold standard“ for clinical research because they provide “a rigorous tool to examine cause-effect,” which “is not possible with any other study design.” That is why the medical textbook Rutherford’s Vascular Surgery calls RCTs “the pinnacle in clinical design.”
  2. Observational studies: These studies observe the outcomes of people who have not been randomly assigned a certain treatment. Unless their results are mathematically and logically overwhelming, observational studies can “rarely” determine the effects of a treatment because a host of other factors are always at play when it comes to people’s health. For example, measuring the C-19 death rates of nations with and without mask mandates cannot determine the effects of the mandates because many factors impact C-19 death rates. As documented in a 2018 paper in the European Heart Journal, “it is not possible to make reliable therapeutic inferences from” observational studies.
  3. Laboratory and simulation studies. These are experiments conducted under artificial conditions and are typically the weakest form of clinical evidence. As explained by the UK’s Department of Health, such “studies provide only theoretical evidence” because they “are run in controlled environments that may not accurately reflect the behaviours that we observe in real life.” Likewise, a 2020 paper in a German medical journal explains that such studies can “provide important mechanistic insights” about Covid-19 transmission, but they “never approximate real-world conditions,” and thus, they should “not directly inform policy decisions.”

The quality gaps between those types of studies are so great that WHO adopted a “general principle” to “not review simulation studies” if observational studies were available and to “not review observational studies or simulation studies” if RCTs were available.

However, WHO broke that rule for masks while explaining that even though all “ten RCTs” showed “no evidence that face masks are effective in reducing transmission” of the flu, “there is mechanistic plausibility for the potential effectiveness of this measure.” Thus, WHO flouted its own principle and “conditionally recommended” that asymptomatic people wear face masks “in severe epidemics or pandemics, to reduce transmission in the community.”

Relevance

Beyond the general quality of a study, another important factor to consider is how applicable it is to the issue at hand. Studies on trained nurses who wear N95 masks to reduce the spread of tuberculosis during 10-minute interactions in sanitized hospital wards are less relevant to the present issue than studies on 5-year-olds wearing cloth masks for six hours in poorly ventilated schoolrooms during the C-19 pandemic.

Of great import for understanding the coming facts, the three main types of masks from the highest-to-lowest quality are:

  1. N95 masks or respirators, which are mainly intended to prevent wearers from inhaling fine aerosols and microscopic particles. These are supposed to be used only oncemust meet strict filtration standards, and must be moldable to each user’s face to form a tight seal. Because N95s heavily restrict breathing and “may place a burden on an employee’s health,” OSHA requires employers to conduct a medical evaluation of each employee who wears them.

  1. Surgical or medical masks, which are primarily designed to prevent wearers from spraying liquid droplets and large particles on other people. The FDA’s Covid-19 guidance for these masks states that they cannot be labeled “for antimicrobial or antiviral protection” and cannot make “filtration claims” for particles of any size. Per a 2013 paper in the Journal of Occupational and Environmental Hygiene, surgical masks have “poor filtration” and a “poor fit,” and thus, they “cannot be expected to significantly reduce the inhalation of infectious aerosols.” They are supposed to be used only once.

  1. Cloth masks, which are made of common fabrics that tend to be highly permeable. These masks were already worn before the C-19 pandemic in developing countries because they are inexpensive and can be used more than once. The CDC says they should be washed at least once a day, and washing them makes the fabrics even more permeable.

In July 2020, the Journal of the American Medical Association published a commentary titled “Universal Masking to Prevent SARS-CoV-2 Transmission—The Time Is Now.” The authors—all of whom were CDC employees—argued that the benefits of using masks during surgery are relevant to the general public. This leap of logic conflates surgical masks with cloth masks, sterilized operating rooms with subways, and open wounds with people’s faces.

The authors also claimed—without citing any research—that it would be “absurd” to conduct surgery without masks “because it is known that use of face coverings under these circumstances reduces the risk of surgical site infection caused by microbes generated during the surgical team’s conversations or breathing.”

That assertion is at direct odds with the strongest, most relevant research on this issue. This was summarized in a 2016 paper published by “the leading journal and database for systematic reviews in health care.” After conducting an extensive search for all available RCTs on the use of surgical masks to prevent wound infections, the authors located three trials and found “there was no statistically significant difference in infection rates between the masked and unmasked group in any of the trials.”

This straightforward example dramatically illustrates how the claims of “experts” published in one of the world’s leading medical journals can be at odds with documented facts. Yet with callous disregard for the facts—and thus the wellbeing of people—Google/YouTubeFacebook, and Twitter have banned factual statements about masks that conflict with the opinion of their chosen experts.

Gold Standard Studies

Like the 2019 analysis of RCTs by the World Health Organization, other comprehensive analyses of gold-standard studies have found no evidence that low-quality masks reduce the spread of the flu in community settings. Moreover, such studies have found limited evidence that any type of mask protects against the flu in any setting:

All of those flu RCTs are highly relevant to Covid-19 because:

Broadening the research beyond the flu to other types of infectious respiratory diseases, RCTs have found inconsistent evidence that higher-quality masks may help in healthcare settings but no statistically significant evidence that any type of mask helps in community settings:

The last of those studies are particularly relevant to C-19 mask mandates because:

When the RCT on cloth masks was published in 2015, the lead author of the study, Raina MacIntyre of the University of New South Wales (Australia)—stressed:  “it is important for global disease control that the use of cloth masks be discouraged in high-risk situations.” However, she and some of her coauthors began backpedaling five years later in 2020 when governments began mandating masks for C-19. MacIntyre and company did this by:

The last of those papers—which was coauthored by MacIntyre and published by the CDC—buries the results of the RCT two-thirds of the way into a lengthy paragraph. There, the authors reveal that the “intent-to-treat analysis”—which is the actual RCT—“showed no significant difference” in outcomes between the people who were assigned to wear masks and not wear masks.

While concealing the gold standard results of their own study, MacIntyre’s team focused their analysis on a subset of people who had the highest “adherence to mask use.” This violates the very essence of RCTs, which are supposed to be “randomized control trials.” Randomization is the linchpin that allows these studies to determine cause and effect.

For that reason and others, Dr. Alyson Haslam of the Oklahoma State University Center for Health Sciences Research publicly criticized MacIntyre and her colleague (Dr. Abrar Ahmad Chughtai) for spreading “incorrect/biased summaries of published articles.”

MacIntyre and Chughtai replied without confronting the central fact of this matter: people who were more diligent about wearing masks may have taken other precautions to avoid getting sick, like social distancing or washing their hands more often. In other words, their conclusion that masks “appeared to be effective” is not based on RCTs, even though their paper claims to be an analysis of “randomised controlled trials.”

Furthering that misleading impression, MacIntyre and Chughtai end their reply to Haslam by declaring that “a WHO-commissioned study has shown that masks reduce the risk of infection with beta-coronaviruses by 85%, and are equally protective in community and healthcare settings.” However, that WHO-commissioned study is not an RCT but an analysis of observational studies. Thus, it cannot prove that “masks reduce the risk” of anything. That is precisely why the authors of the study write that their results have “low certainty.”

Compounding the deceit, the CDC published a study in July 2020 that cites MacIntyre and Chughtai’s paper while claiming that they analyzed “randomized trials and concluded that use of face masks and respirators appeared to be protective in both health care and community settings.” Again, none of those findings are actually based on RCTs.

Beyond the CDC’s false portrayal of non-RCTs as RCTs, the same CDC study ignores the actual RCTs, all of which show no statically significant benefit from community masking. To reiterate, these studies are systematically analyzed in papers and reports published by:

The misinformation spread by MacIntyre, Chughtai, and the CDC cannot fool informed people with time to vet it, but it can muddle the issue enough to prevent all but the most tenacious researchers from finding the facts. Combined with the power of big tech corporations who suppress facts that contradict the CDC’s claims about masks, this is more than enough misinformation and censorship to keep the vast bulk of people in the dark.

In summary, a large array of gold-standard studies have found inconsistent benefits from higher-quality masks in healthcare settings and no statistically significant benefits from any type of mask in community settings. Crucially, the only RCT to evaluate cloth masks found that mandating them causes significant disease transmission in high-risk healthcare settings.

Disregarding RCTs

Faced with a mountain of RCTs that undercut their claims, proponents of community masking ignore or deceitfully dismiss them. For example, the CDC’s “Science Brief” on “Community Use of Cloth Masks” does not rely on a single RCT to support its claim that “universal masking” reduces the spread of C-19.

Instead, the CDC ignores all but two of the RCTs, which it brushes aside in a single paragraph. One of these is the lone RCT on cloth masks, and the manner in which the CDC tries to spin it is a textbook case of junk science:

Bottom line: the manner in which certain people disregard and twist gold-standard studies on masks says little about the studies but reveals a great deal about the people.

Observational Studies

To repeat, observational studies cannot determine the effects of medical treatments except in rare cases. This is because many factors influence people’s health, and without an RCT, it is impossible to isolate the effects of any one factor from all of the others. A simple but vivid example that highlights this reality is the number of C-19 deaths in Texas before, during, and after its mask mandate:

 

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