Masks are neither effective nor safe:

A summary of the science

Colleen Huber, NMD

July 6, 2020

At this writing, there is a recent surge in widespread use by the public of facemasks when in public places, including for extended periods of time, in the United States as well as in other countries.   The public has been instructed by media and their governments that one’s use of masks, even if not sick, may prevent others from being infected with SARS-CoV-2, the infectious agent of COVID-19.

A review of the peer-reviewed medical literature examines impacts on human health, both immunological, as well as physiological.  The purpose of this paper is to examine data regarding the effectiveness of facemasks, as well as safety data.  The reason that both are examined in one paper is that for the general public as a whole, as well as for every individual, a risk-benefit analysis is necessary to guide decisions on if and when to wear a mask.

Are masks effective at preventing transmission of respiratory pathogens?

In this meta-analysis, face masks were found to have no detectable effect against transmission of viral infections. (1)  It found: “Compared to no masks, there was no reduction of influenza-like illness cases or influenza for masks in the general population, nor in healthcare workers.”

This 2020 meta-analysis found that evidence from randomized controlled trials of face masks did not support a substantial effect on transmission of laboratory-confirmed influenza, either when worn by infected persons (source control) or by persons in the general community to reduce their susceptibility. (2)

Another recent review found that masks had no effect specifically against Covid-19, although facemask use seemed linked to, in 3 of 31 studies, “very slightly reduced” odds of developing influenza-like illness. (3)

This 2019 study of 2862 participants showed that both N95 respirators and surgical masks “resulted in no significant difference in the incidence of laboratory confirmed influenza." (4)

This 2016 meta-analysis found that both randomized controlled trials and observational studies of N95 respirators and surgical masks used by healthcare workers did not show benefit against transmission of acute respiratory infections.  It was also found that acute respiratory infection transmission “may have occurred via contamination of provided respiratory protective equipment during storage and reuse of masks and respirators throughout the workday.” (5)

A 2011 meta-analysis of 17 studies regarding masks and effect on transmission of influenza found that “none of the studies established a conclusive relationship between mask/respirator use and protection against influenza infection.” (6)  However, authors speculated that effectiveness of masks may be linked to early, consistent and correct usage.

Face mask use was likewise found to be not protective against the common cold, compared to controls without face masks among healthcare workers. (7)

Airflow around masks

Masks have been assumed to be effective in obstructing forward travel of viral particles.  Considering those positioned next to or behind a mask wearer, there have been farther transmission of virus-laden fluid particles from masked individuals than from unmasked individuals, by means of “several leakage jets, including intense backward and downwards jets that may present major hazards,” and a “potentially dangerous leakage jet of up to several meters.”  (8) All masks were thought to reduce forward airflow by 90% or more over wearing no mask.  However, Schlieren imaging showed that both surgical masks and cloth masks had farther brow jets (unfiltered upward airflow past eyebrows) than not wearing any mask at all, 182 mm and 203 mm respectively, vs none discernible with no mask.  Backward unfiltered airflow was found to be strong with all masks compared to not masking.

For both N95 and surgical masks, it was found that expelled particles from 0.03 to 1 micron were deflected around the edges of each mask, and that there was measurable penetration of particles through the filter of each mask. (9)

Penetration through masks

A study of 44 mask brands found mean 35.6% penetration (+ 34.7%).  Most medical masks had over 20% penetration, while “general masks and handkerchiefs had no protective function in terms of the aerosol filtration efficiency.”  The study found that “Medical masks, general masks, and handkerchiefs were found to provide little protection against respiratory aerosols.” (10)

It may be helpful to remember that an aerosol is a colloidal suspension of liquid or solid particles in a gas.  In respiration, the relevant aerosol is the suspension of bacterial or viral particles in inhaled or exhaled breath.

In another study, penetration of cloth masks by particles was almost 97% and medical masks 44%. (11)

N95 respirators

Honeywell is a manufacturer of N95 respirators.  These are made with a 0.3 micron filter. (12)  N95 respirators are so named, because 95% of particles having a diameter of 0.3 microns are filtered by the mask forward of the wearer, by use of an electrostatic mechanism. Coronaviruses are approximately 0.125 microns in diameter.

This meta-analysis found that N95 respirators did not provide superior protection to facemasks against viral infections or influenza-like infections. (13)  This study did find superior protection by N95 respirators when they were fit-tested compared to surgical masks. (14)

This study found that 624 out of 714 people wearing N95 masks left visible gaps when putting on their own masks. (15)

Surgical masks

This study found that surgical masks offered no protection at all against influenza. (16) Another study found that surgical masks had about 85% penetration ratio of aerosolized inactivated influenza particles and about 90% of Staphylococcus aureus bacteria, although S aureus particles were about 6x the diameter of influenza particles. (17)

Use of masks in surgery were found to slightly increase incidence of infection over not masking in a study of 3,088 surgeries. (18)  The surgeons’ masks were found to give no protective effect to the patients.

Other studies found no difference in wound infection rates with and without surgical masks. (19) (20)

This study found that “there is a lack of substantial evidence to support claims that facemasks protect either patient or surgeon from infectious contamination.” (21)

This study found that medical masks have a wide range of filtration efficiency, with most showing a 30% to 50% efficiency. (22)

Specifically, are surgical masks effective in stopping human transmission of coronaviruses?  Both experimental and control groups, masked and unmasked respectively, were found to “not shed detectable virus in respiratory droplets or aerosols.” (23) In that study, they “did not confirm the infectivity of coronavirus” as found in exhaled breath.

A study of aerosol penetration showed that two of the five surgical masks studied had 51% to 89% penetration of polydisperse aerosols.  (24)

In another study, that observed subjects while coughing, “neither surgical nor cotton masks effectively filtered SARS-CoV-2 during coughs by infected patients.”  And more viral particles were found on the outside than on the inside of masks tested. (25)

Cloth masks

Cloth masks were found to have low efficiency for blocking particles of 0.3 microns and smaller.  Aerosol penetration through the various cloth masks examined in this study were between 74 and 90%.  Likewise, the filtration efficiency of fabric materials was 3% to 33% (26)

Healthcare workers wearing cloth masks were found to have 13 times the risk of influenza-like illness than those wearing medical masks. (27)

This 1920 analysis of cloth mask use during the 1918 pandemic examines the failure of masks to impede or stop flu transmission at that time, and concluded that the number of layers of fabric required to prevent pathogen penetration would have required a suffocating number of layers, and could not be used for that reason, as well as the problem of leakage vents around the edges of cloth masks. (28)

Masks against Covid-19

The New England Journal of Medicine editorial on the topic of mask use versus Covid-19 assesses the matter as follows:

“We know that wearing a mask outside health care facilities offers little, if any, protection from infection.  Public health authorities define a significant exposure to Covid-19 as face-to-face contact within 6 feet with a patient with symptomatic Covid-19 that is sustained for at least a few minutes (and some say more than 10 minutes or even 20 minutes).  The chance of catching Covid-19 from a passing interaction in a public space is therefore minimal.  In many cases, the desire for widespread masking is a reflexive reaction to anxiety over the pandemic.” (29)

Are masks safe?

During walking or other exercise

Surgical mask wearers had significantly increased dyspnea after a 6-minute walk than non-mask wearers. (30)

Researchers are concerned about possible burden of facemasks during physical activity on pulmonary, circulatory and immune systems, due to oxygen reduction and air trapping reducing substantial carbon dioxide exchange.  As a result of hypercapnia, there may be cardiac overload, renal overload, and a shift to metabolic acidosis. (31)

Risks of N95 respirators

Pregnant healthcare workers were found to have a loss in volume of oxygen consumption by 13.8% compared to controls when wearing N95 respirators.  17.7% less carbon dioxide was exhaled. (32)  Patients with end-stage renal disease were studied during use of N95 respirators.  Their partial pressure of oxygen (PaO2) decreased significantly compared to controls and increased respiratory adverse effects. (33)   19% of the patients developed various degrees of hypoxemia while wearing the masks.

Healthcare workers’ N95 respirators were measured by personal bioaerosol samplers to harbor influenza virus. (34)  And 25% of healthcare workers’ facepiece respirators were found to contain influenza in an emergency department during the 2015 flu season. (35)

Risks of surgical masks

Healthcare workers’ surgical masks also were measured by personal bioaerosol samplers to harbor for influenza virus. (36)

Various respiratory pathogens were found on the outer surface of used medical masks, which could result in self-contamination.  The risk was found to be higher with longer duration of mask use. (37)

Surgical masks were also found to be a repository of bacterial contamination.  The source of the bacteria was determined to be the body surface of the surgeons, rather than the operating room environment. (38)  Given that surgeons are gowned from head to foot for surgery, this finding should be especially concerning for laypeople who wear masks.  Without the protective garb of surgeons, laypeople generally have even more exposed body surface to serve as a source for bacteria to collect on their masks.

Risks of cloth masks

Healthcare workers wearing cloth masks had significantly higher rates of influenza-like illness after four weeks of continuous on-the-job use, when compared to controls. (39)

The increased rate of infection in mask-wearers may be due to a weakening of immune function during mask use.  Surgeons have been found to have lower oxygen saturation after surgeries even as short as 30 minutes. (40)  Low oxygen induces hypoxia-inducible factor 1 alpha (HIF-1). (41)  This in turn down-regulates CD4+ T-cells.  CD4+ T-cells, in turn, are necessary for viral immunity. (42)

Weighing risks versus benefits of mask use

In the summer of 2020 the United States is experiencing a surge of popular mask use, which is frequently promoted by the media, political leaders and celebrities.  Homemade and store-bought cloth masks and surgical masks or N95 masks are being used by the public especially when entering stores and other publicly accessible buildings.  Sometimes bandanas or scarves are used.  The use of face masks, whether cloth, surgical or N95, creates a poor obstacle to aerosolized pathogens as we can see from the meta-analyses and other studies in this paper, allowing both transmission of aerosolized pathogens to others in various directions, as well as self-contamination.

It must also be considered that masks impede the necessary volume of air intake required for adequate oxygen exchange, which results in observed physiological effects that may be undesirable.  Even 6- minute walks, let alone more strenuous activity, resulted in dyspnea.  The volume of unobstructed oxygen in a typical breath is about 100 ml, used for normal physiological processes.  100 ml O2 greatly exceeds the volume of a pathogen required for transmission.

The foregoing data show that masks serve more as instruments of obstruction of normal breathing, rather than as effective barriers to pathogens. Therefore, masks should not be used by the general public, either by adults or children, and their limitations as prophylaxis against pathogens should also be considered in medical settings.

Proof: Lockdowns Did Not Reduce Deaths

Nature and politics very rarely give us a control group and an experimental group, from which we can gather scientific data.  However, in the Covid19 era, there are US states that did not lock down.

From current CDC data, we can see whether US state lockdowns achieved reduction in deaths.

by Colleen Huber, NMD

June 16, 2020

Abstract

Five weeks of mortality data during the gradual easing of lockdown in most US states during the spring of 2020 show a consistent history among those weeks with regard to the following: States without lockdown, herein “free states,” have had a lower percentage than states with lockdown, herein “locked states,” of total deaths from all causes in these weeks in 2020, compared to the same weeks for each of the states in the years 2017 to 2019.

Each free state had fewer deaths in comparison to its own record of recent years. Locked states averaged more deaths compared to their own records of recent years.

This difference holds for both of the following comparisons: free vs locked states that are immediately surrounding free states, as well as free states compared to the average results of all locked states in the US.

Introduction

US Centers for Disease Control and Prevention (CDC) data from weeks ending May 15, 2020 through June 12, 2020 show consistency over each of those five weeks in the following data.

Five US states: Arkansas, Iowa, Nebraska, North Dakota and South Dakota, did not lock down, and submitted mortality data to the CDC.  These states are the control group, herein “free states” in the mass human experiment of society-wide lockdown in the spring of 2020.

There are other states that have special situations. Wyoming also did not lock down, but the CDC had not posted complete mortality data for Wyoming until June 10, 2020, so I exclude Wyoming in most of the following weeks; however, June 12, 2020 data for Wyoming is included in the June 12, 2020 table (Table 5). Also, Utah and Oklahoma did not impose lockdown at the state level; however, lockdown was imposed in their most populous jurisdictions, so I group Utah and Oklahoma with the locked states. USA Today lists states that locked down, opened up and the dates for each. (1) That article shows that almost all states locked down during the last 10 days of March, 2020. Most states began re-opening during the first three weeks of May, 2020. The CDC shows peak COVID-19 deaths as mid-April in this table. (2)

For comparison with the five free states, I also look at CDC mortality data of the immediately neighboring states, with which the free states share long borders.  These are respectively, Mississippi, Louisiana, Missouri, Oklahoma, Minnesota, Wisconsin, Illinois, Kansas, Colorado and Montana. These are the states in the immediately surrounding experimental group, herein “locked states.”

This paper will examine CDC data to determine whether reduction in deaths happened in US states that locked down.

Lockdowns were imposed by many jurisdictions for the stated purpose of limiting movement, activities and commerce of individuals and businesses, for the goal of limiting COVID-19 incidence and mortality. It was widely hoped this would work.  However, outside of the US, it was found that mortality actually increased steeply closely following lockdowns.(3)  Also, it was found that in Europe, “no lives were saved” by lockdown.(4) In an early analysis in the US also, it was not found that lives were saved by shutdown.(5)  Those last two analyses were relatively early, 4/24 and 4/26/20 respectively, before it was clear that COVID-19 incidence, hospitalizations and deaths had peaked.

This study is likewise of a limited time frame, the five weeks of the decline of lockdown, the American perestroika, one might say, of re-opening.  Through the five weeks of this study there is stark and consistent contrast of mortality in free vs. locked states.

Methods

In this study, I examine whether lockdowns succeeded in reducing total deaths, and whether that data is consistent over the five weeks immediately following when most lockdowns through the US began to ease. In order to answer the question of whether lockdowns “worked” to reduce mortality, it is most helpful to look at all deaths, because total deaths are more precisely enumerated than deaths from any specific cause, due to common multiple co-morbidities.

I chose not to look at COVID-19 deaths in this study for a number of additional reasons, including the following:

1. The very questionable applicability of the manufacturing technique, the reverse-transcriptase / polymerase chain reaction technique, now used throughout the world as a test for presence of an infectious agent; and

2. The 80% false positive rate of this “test” in the diagnosis of COVID-19; (6) and

3. The arbitrary number of iterations of this “test” that have been selected to produce a positive result; (7) and

4. Instructions given to physicians by the CDC to code cases as COVID-19 deaths including presumptively; (8) and

5. Controversy regarding higher Medicare reimbursement for COVID-19 patients ($13,000) (9) than for flu patients ($5,000), which may have skewed reported cause of death on death certificates; and

6. The possibility that there may be emergency aid incentives and/or political influences in altering the true number of deaths from COVID-19; and

7. If COVID-19 is genuinely the deadly pandemic that it is widely thought to be, then total deaths in any jurisdiction would be greater during the period of its peak incidence and closely following weeks.It is not possible to have a deadly pandemic rage through a population without increasing the total number of all-cause deaths over the weeks of its peak incidence.Therefore, if deaths are not significantly increased above previous years for a given region, then there has been no pandemic, nor even an epidemic there.

Therefore, it is most useful and most accurate to look at total deaths in each state, both in free states, the control group, as well as in locked states, the experimental group.

The CDC shows a percentage of deaths in each state compared with the same week in previous years.  This percentage for each is described by the CDC as follows:

“Percent of expected deaths is the number of deaths for all causes for this week in 2020 compared to the average number across the same week in 2017-2019.” (10)

The CDC compares each of the states, free and locked, to their own mortality history from 2017 through 2019. Let’s then compare those two groups to each other.

The CDC tables from which the numbers in this study were derived are screen-printed in this endnote.(11)   These tables are from Friday, May 15, 2020, Friday, May 22, 2020, Friday, May 29, 2020, Friday, June 5, 2020 and Friday, June 12, 2020.

The above-mentioned CDC tables are the entire source from which all calculated data in this paper is derived. No other source is used, and all derived data may be verified by the reader with a simple calculator.

We see that over the last five weeks of easing of lockdowns, the average factor by which the percentage of all expected deaths are higher in locked states as a group than in free states as a group has stayed fairly consistent, between 1.08 and 1.11.

The locked states as a group averaged an 8% to 11% higher percentage of deaths over their own previous years’ records than the free states did. This is expressed in the following graph showing locked vs free states vs all states, from tables in Endnote 11.

As lockdowns ease, and conditions in formerly locked vs free states begin to resemble their own previous years’ conditions, these different percentages would be expected to gradually converge toward 100% for each state, and Graph 1 suggests that this has begun to happen, although it may be already too late in 2020 for those percentages to converge completely by the end of the year.

Finally, let’s compare the 6 free states with 44 locked states from today’s (the day of this writing) CDC data (now including Wyoming, because June 10, 2020, is the first date of mortality data for Wyoming in the CDC tables.) That comparison is in Tables 6a and 6b.

We now see that not only comparing neighboring states, free vs locked, but now looking at the entire United States, there is a consistent pattern:  Free states show fewer than expected deaths during this week than previous years at this time, but also free states had a distinct survival advantage, and significantly lower mortality than locked states, when each state is compared with its own previous record. The factor by which locked states’ mortality change (as percent of expected) exceeded free states’ mortality change (as percent of expected) was consistently positive and by a factor of 1.08 to 1.11.

Conclusion

Because the free states did not have increased deaths from their data in previous years, but their neighboring locked states did average increased deaths over the free states from their data in previous years, by a factor that stayed within the narrow range of 1.08 to 1.11 through the five weeks including the end of, and the easing of lockdown, we can therefore conclude with certainty that lockdown did not reduce deaths in the US.

In fact, free states had decreased deaths from their data in previous years, but locked states on average did not have decreased deaths from their data in previous years.  Therefore, we can conclude with certainty that lockdown did not reduce deaths.  How is this conclusion certain?  Because if a popular hypothesis is that A caused B (lockdowns caused reduced deaths), but we then learn that B never happened, then we can confidently surmise that A definitely did not cause B.  Causation is very hard to prove, but lack of causation is very easy to prove, particularly when the effect never happened. We can be certain that A did not cause B, if we see that B never happened at all.  Lockdown did happen in most US states, including the states surrounding the free states, which I examined.  However, deaths were not reduced in those locked states, neither in comparison to their own historical mortality data on average, nor in comparison to their free neighbors.  Total deaths from all causes were not reduced in the locked states, as we see from the above data.

This paper examined CDC data to determine whether reduction in deaths happened in lockdown states. That did not happen; therefore, there is nothing, including lockdowns, that has caused it to happen.

The conclusion and its supporting data impact future assessments of whether lockdown was an optimal strategy of state governments. A failure of lockdowns to reduce deaths must in the future be considered when weighed against the considerable damage, including political, economic, humanitarian, social and psychological damage, caused directly by lockdowns. Society’s response to the phenomenon of COVID-19 led to the loss of 30 million to 40 million jobs in the US alone. (12) The US unemployment rate rose to 14.7%. (13) Unemployment’s adverse effects are known to reverberate through families and communities and business sectors, and must be considered in the future, if lockdown is ever proposed again. The consistently worse (by 8 to 11%) mortality results that I showed in locked states over free states likely reflect the life-threatening consequences of mass unemployment. Civil liberties concerns are also paramount to those who value those liberties perhaps as highly as their own lives, aware of wars throughout American history and world history that were fought in defense of or to establish the same. Those liberties were challenged, curtailed and violated to various degrees throughout the US, as a consequence of lockdowns.  Therefore, lockdowns had historic and far-reaching social, political and economic effects, but they did not reduce deaths, and therefore cannot be justified now or in the future. The timeframe of this study is limited, however, and a more thorough assessment of lockdown impact on mortality would be obtained by a study of more weeks than the five examined herein.