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Masks Don’t Work
A review of science relevant to COVID-19 social policy
Denis
G. Rancourt, PhD
Researcher,
Ontario Civil Liberties Association (ocla.ca)
Working
report, published at Research Gate
(https://www.researchgate.net/profile/D_Rancourt) (CENSORED/REMOVED)
https://denisrancourt.ca -- PDF LINK
COVID Links: https://denisrancourt.ca/categories.php?id=1&name=covid
April 2020
Summary / Abstract
Masks and respirators do not work.
There have been extensive randomized controlled trial (RCT)
studies, and meta-analysis reviews of RCT studies, which all show that masks
and respirators do not work to prevent respiratory influenza-like illnesses, or
respiratory illnesses believed to be transmitted by droplets and aerosol
particles.
Furthermore, the relevant known physics and biology, which
I review, are such that masks and respirators should not work. It would be a
paradox if masks and respirators worked, given what we know about viral respiratory
diseases: The main transmission path is long-residence-time aerosol particles
(< 2.5 μm), which are too fine to be blocked, and the minimum-infective-dose
is smaller than one aerosol particle.
The present paper about masks illustrates the degree to which governments, the mainstream media, and institutional propagandists can decide to operate in a science vacuum, or select only incomplete science that serves their interests. Such recklessness is also certainly the case with the current global lockdown of over 1 billion people, an unprecedented experiment in medical and political history.
Review of the Medical Literature
Here are key anchor points to the extensive scientific
literature that establishes that wearing surgical masks and respirators (e.g.,
“N95”) does not reduce the risk of contracting a verified illness:
Jacobs, J. L. et
al. (2009) “Use of surgical face masks to reduce the incidence of the
common cold among health care workers in Japan: A randomized controlled trial”,
American Journal of Infection Control,
Volume 37, Issue 5, 417 - 419.
https://www.ncbi.nlm.nih.gov/pubmed/19216002
N95-masked health-care workers (HCW) were significantly
more likely to experience headaches. Face mask use in HCW was not demonstrated
to provide benefit in terms of cold symptoms or getting colds.
Cowling, B. et al.
(2010) “Face masks to prevent transmission of influenza virus: A systematic
review”, Epidemiology and Infection,
138(4), 449-456.
doi:10.1017/S0950268809991658
https://www.cambridge.org/core/journals/epidemiology-and-infection/article/facemasks-to-prevent-transmission-of-influenza-virus-a-systematicreview/64D368496EBDE0AFCC6639CCC9D8BC05
None of the studies reviewed showed a benefit from
wearing a mask, in either HCW or community members in households (H). See
summary Tables 1 and 2 therein.
bin-Reza et al.
(2012) “The use of masks and respirators to prevent transmission of
influenza: a systematic review of the scientific evidence”, Influenza and Other Respiratory Viruses
6(4), 257–267.
https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.1750-2659.2011.00307.x “There were 17 eligible studies. … None of the
studies established a conclusive relationship between mask ⁄ respirator use and
protection against influenza infection.”
Smith, J.D. et al.
(2016) “Effectiveness of N95 respirators versus surgical masks in
protecting health care workers from acute respiratory infection: a systematic
review and
meta-analysis”, CMAJ
Mar 2016, cmaj.150835; DOI: 10.1503/cmaj.150835 https://www.cmaj.ca/content/188/8/567
“We identified 6 clinical studies ...
In the
meta-analysis of the clinical studies, we found no significant difference
between N95 respirators
and surgical masks in associated risk of (a) laboratory-confirmed respiratory
infection, (b) influenza-like illness,
or (c) reported
work-place absenteeism.”
Offeddu, V. et al.
(2017) “Effectiveness of Masks and Respirators Against Respiratory
Infections in Healthcare Workers: A Systematic Review and
Meta-Analysis”, Clinical Infectious
Diseases, Volume 65, Issue 11, 1 December 2017, Pages 1934–1942,
https://doi.org/10.1093/cid/cix681
https://academic.oup.com/cid/article/65/11/1934/4068747
“Self-reported assessment of clinical outcomes was prone
to bias. Evidence of a protective effect of masks or respirators against
verified respiratory infection (VRI) was not statistically significant”; as per
Fig. 2c therein:
Radonovich, L.J. et
al. (2019) “N95 Respirators vs Medical Masks for Preventing
Influenza Among Health Care Personnel: A Randomized
Clinical Trial”, JAMA. 2019;
322(9): 824–833. doi:10.1001/jama.2019.11645
https://jamanetwork.com/journals/jama/fullarticle/2749214
“Among 2862 randomized participants, 2371 completed the
study and accounted for 5180 HCW-seasons. … Among outpatient health care
personnel, N95 respirators vs medical masks as worn by participants in this
trial resulted in no significant difference in the incidence of
laboratory-confirmed influenza.”
Long, Y. et al.
(2020) “Effectiveness of N95 respirators versus surgical masks against
influenza: A systematic review and meta-analysis”, J Evid Based Med. 2020; 1- 9. https://doi.org/10.1111/jebm.12381
https://onlinelibrary.wiley.com/doi/epdf/10.1111/jebm.12381
“A total of six RCTs involving 9 171 participants were
included. There were no statistically significant differences in preventing
laboratory-confirmed influenza, laboratory-confirmed respiratory viral
infections, laboratory-confirmed respiratory infection and influenza-like
illness using N95 respirators and
surgical masks. Meta-analysis indicated a protective effect of N95 respirators
against laboratory-confirmed bacterial colonization (RR = 0.58, 95% CI
0.43-0.78). The use of N95 respirators compared with surgical masks is not
associated with a lower risk of laboratory-confirmed influenza.”
Conclusion Regarding that Masks Do Not Work
No RCT study with verified outcome shows a benefit for HCW
or community members in households to wearing a mask or respirator. There is no
such study. There are no exceptions.
Likewise, no study exists that shows a benefit from a broad
policy to wear masks in public (more on this below).
Furthermore, if there were any benefit to wearing a mask,
because of the blocking power against droplets and aerosol particles, then
there should be more benefit from wearing a respirator (N95) compared to a
surgical mask, yet several large meta-analyses, and all the RCT, prove that
there is no such relative benefit.
Masks and respirators do not work.
Precautionary Principle Turned on Its Head with
Masks
In light of the medical research, therefore, it is
difficult to understand why public-health authorities are not consistently
adamant about this established scientific result, since the distributed
psychological, economic and environmental harm from a broad recommendation to
wear masks is significant, not to mention the unknown potential harm from
concentration and distribution of pathogens on and from used masks. In this
case, public authorities would be turning the precautionary principle on its
head (see below).
Physics and Biology of Viral Respiratory Disease
and of Why Masks Do Not Work
In order to understand why masks cannot possibly work, we
must review established knowledge about viral respiratory diseases, the
mechanism of seasonal variation of excess deaths from pneumonia and influenza,
the aerosol mechanism of infectious disease transmission, the physics and
chemistry of aerosols, and the mechanism of the so-called
minimum-infective-dose.
In addition to pandemics that can occur anytime, in the
temperate latitudes there is an extra burden of respiratory-disease mortality
that is seasonal, and that is caused by viruses. For example, see the review of
influenza by Paules and Subbarao (2017).
This has been known for a long time, and the seasonal pattern is
exceedingly regular.
For example, see Figure 1 of Viboud (2010), which has
“Weekly time series of the ratio of deaths from pneumonia and influenza to all
deaths, based on the 122 cities surveillance in the US (blue line). The red
line represents the expected baseline ratio in the absence of influenza
activity,” here:
The seasonality of the phenomenon was largely not
understood until a decade ago. Until recently, it was debated whether the
pattern arose primarily because of seasonal change in virulence of the
pathogens, or because of seasonal change in susceptibility of the host (such as
from dry air causing tissue irritation, or diminished daylight causing vitamin
deficiency or hormonal stress). For example, see Dowell (2001).
In a landmark study, Shaman et al. (2010) showed that the
seasonal pattern of extra respiratory-disease mortality can be explained
quantitatively on the sole basis of absolute humidity, and its direct
controlling impact on transmission of airborne pathogens.
Lowen et al. (2007) demonstrated the phenomenon of
humidity-dependent airborne-virus virulence in actual disease transmission
between guinea pigs, and discussed potential underlying mechanisms for the
measured controlling effect of humidity.
The underlying mechanism is that the pathogen-laden aerosol
particles or droplets are neutralized within a half-life that monotonically and
significantly decreases with increasing ambient humidity. This is based on the
seminal work of Harper (1961). Harper experimentally showed that
viral-pathogen-carrying droplets were inactivated within shorter and shorter
times, as ambient humidity was increased.
Harper argued that the viruses themselves were made
inoperative by the humidity (“viable decay”), however, he admitted that the
effect could be from humidity-enhanced physical removal or sedimentation of the
droplets (“physical loss”): “Aerosol viabilities reported in this paper are
based on the ratio of virus titre to radioactive count in suspension and cloud
samples, and can be criticized on the ground that test and tracer materials
were not physically identical.”
The latter (“physical loss”) seems more plausible to me,
since humidity would have a universal physical effect of causing particle /
droplet growth and sedimentation, and all tested viral pathogens have
essentially the same humidity-driven “decay”. Furthermore, it is difficult to
understand how a virion (of all virus types) in a droplet would be molecularly
or structurally attacked or damaged by an increase in ambient humidity. A
“virion” is the complete, infective form of a virus outside a host cell, with a
core of RNA or DNA and a capsid. The actual mechanism of such humidity-driven
intra-droplet “viable decay” of a virion has not been explained or studied.
In any case, the explanation and model of Shaman et al.
(2010) is not dependant on the particular mechanism of the humidity-driven
decay of virions in aerosol / droplets. Shaman’s quantitatively demonstrated
model of seasonal regional viral epidemiology is valid for either mechanism (or
combination of mechanisms), whether “viable decay” or “physical loss”.
The breakthrough achieved by Shaman et al. is not merely
some academic point. Rather, it has profound health-policy implications, which
have been entirely ignored or overlooked in the current coronavirus
pandemic.
In particular, Shaman’s work necessarily implies that,
rather than being a fixed number (dependent solely on the spatial-temporal
structure of social interactions in a completely susceptible population, and on
the viral strain), the epidemic’s basic
reproduction number (R0) is highly or predominantly dependent on ambient
absolute humidity.
For a definition of R0, see HealthKnowlege-UK (2020): R0 is
“the average number of secondary infections produced by a typical case of an
infection in a population where everyone is susceptible.” The average R0 for
influenza is said to be 1.28 (1.19–1.37); see the comprehensive review by
Biggerstaff et al. (2014).
In fact, Shaman et al. showed that R0 must be understood to
seasonally vary between humidsummer values of just larger than “1” and
dry-winter values typically as large as “4” (for example, see their Table 2).
In other words, the seasonal infectious viral respiratory diseases that plague
temperate latitudes every year go from being intrinsically mildly contagious to
virulently contagious, due simply to the bio-physical mode of transmission
controlled by atmospheric humidity, irrespective of any other consideration.
Therefore, all the epidemiological mathematical modelling
of the benefits of mediating policies (such as social distancing), which
assumes humidity-independent R0 values, has a large likelihood of being of
little value, on this basis alone. For studies about modelling and regarding
mediation effects on the effective reproduction number, see Coburn (2009) and
Tracht (2010).
To put it simply, the “second wave” of an epidemic is not a
consequence of human sin regarding mask wearing and hand shaking. Rather, the
“second wave” is an inescapable consequence of an air-dryness-driven many-fold
increase in disease contagiousness, in a population that has not yet attained
immunity.
If my view of the mechanism is correct (i.e., “physical
loss”), then Shaman’s work further necessarily implies that the dryness-driven
high transmissibility (large R0) arises from small aerosol particles fluidly
suspended in the air; as opposed to large droplets that are quickly
gravitationally removed from the air.
Such small aerosol particles fluidly suspended in air, of
biological origin, are of every variety and are everywhere, including down to
virion-sizes (Despres, 2012). It is not entirely unlikely that viruses can
thereby be physically transported over inter-continental distances (e.g.,
Hammond, 1989).
More to the point, indoor airborne virus concentrations
have been shown to exist (in day-care facilities, health centres, and onboard
airplanes) primarily as aerosol particles of diameters smaller than 2.5 μm,
such as in the work of Yang et al. (2011):
“Half of the 16 samples were positive, and their
total virus concentrations ranged from 5800 to 37 000 genome copies m−3.
On average, 64 per cent of the viral genome copies were associated with fine
particles smaller than 2.5 µm, which can remain suspended for hours. Modelling
of virus concentrations indoors suggested a source strength of 1.6 ± 1.2 × 105
genome copies m−3 air h−1 and a deposition flux onto
surfaces of 13 ± 7 genome copies m−2 h−1 by Brownian motion.
Over 1 hour, the inhalation dose was estimated to be 30 ± 18 median tissue
culture infectious dose (TCID50), adequate to induce infection.
These results provide quantitative support for the idea that the aerosol route
could be an important mode of influenza transmission.”
Such small particles (< 2.5 μm) are part of air
fluidity, are not subject to gravitational sedimentation, and would not be
stopped by long-range inertial impact. This means that the slightest (even
momentary) facial misfit of a mask or respirator renders the design filtration
norm of the mask or respirator entirely irrelevant. In any case, the filtration material itself
of N95 (average pore size ~0.3−0.5 μm) does not block virion penetration, not
to mention surgical masks. For example, see Balazy et al. (2006).
Mask stoppage efficiency and host inhalation are only half
of the equation, however, because the minimal infective dose (MID) must also be
considered. For example, if a large number of pathogen-laden particles must be delivered
to the lung within a certain time for the illness to take hold, then partial
blocking by any mask or cloth can be enough to make a significant difference.
On the other hand, if the MID is amply surpassed by the
virions carried in a single aerosol particle able to evade mask-capture, then
the mask is of no practical utility, which is the case.
Yezli and Otter (2011), in their review of the MID, point
out relevant features:
•
most respiratory viruses are as infective in
humans as in tissue culture having optimal laboratory susceptibility
•
it is believed that a single virion can be
enough to induce illness in the host
•
the 50%-probability MID (“TCID50”)
has variably been found to be in the range 100−1000 virions
•
there are typically 103−107
virions per aerolized influenza droplet with diameter 1 μm − 10 μm
•
the 50%-probability MID easily fits into a
single (one) aerolized droplet
For further background:
•
A classic description of dose-response
assessment is provided by Haas (1993).
•
Zwart et al. (2009) provided the first
laboratory proof, in a virus-insect system, that the action of a single virion
can be sufficient to cause disease.
•
Baccam et al. (2006) calculated from empirical
data that, with influenza A in humans, “we estimate that after a delay of ~6 h,
infected cells begin producing influenza virus and continue to do so for ~5 h.
The average lifetime of infected cells is ~11 h, and the half-life of free
infectious virus is ~3 h. We calculated the [in-body] basic reproductive
number, R0, which indicated that a single infected cell could
produce ~22 new productive infections.”
•
Brooke et al. (2013) showed that, contrary to
prior modeling assumptions, although not all influenza-A-infected cells in the
human body produce infectious progeny (virions), nonetheless, 90% of infected
cell are significantly impacted, rather than simply surviving unharmed.
All of this to say that: if anything gets through (and it
always does, irrespective of the mask), then you are going to be infected.
Masks cannot possibly work. It is not surprising, therefore, that no bias-free
study has ever found a benefit from wearing a mask or respirator in this
application.
Therefore, the studies that show partial stopping power of
masks, or that show that masks can capture many large droplets produced by a
sneezing or coughing mask-wearer, in light of the above-described features of
the problem, are irrelevant. For example, such studies as these: Leung (2020),
Davies (2013), Lai (2012), and Sande (2008).
Why There Can Never Be an Empirical Test of a
Nation-Wide Mask-Wearing Policy
As mentioned above, no study exists that shows a benefit
from a broad policy to wear masks in public. There is good reason for this. It
would be impossible to obtain unambiguous and biasfree results:
•
Any benefit from mask-wearing would have to be a
small effect, since undetected in controlled experiments, which would be
swamped by the larger effects, notably the large effect from changing
atmospheric humidity.
•
Mask compliance and mask adjustment habits would
be unknown.
•
Mask-wearing is associated (correlated) with
several other health behaviours; see Wada (2012).
•
The results would not be transferable, because
of differing cultural habits.
•
Compliance is achieved by fear, and individuals
can habituate to fear-based propaganda, and can have disparate basic responses.
•
Monitoring and compliance measurement are
near-impossible, and subject to large errors.
•
Self-reporting (such as in surveys) is
notoriously biased, because individuals have the self-interested belief that
their efforts are useful.
•
Progression of the epidemic is not verified with
reliable tests on large population samples, and generally relies on
non-representative hospital visits or admissions.
•
Several different pathogens (viruses and strains
of viruses) causing respiratory illness generally act together, in the same
population and/or in individuals, and are not resolved, while having different
epidemiological characteristics.
Unknown Aspects of Mask Wearing
Many potential harms may arise from broad public policies
to wear masks, and the following unanswered questions arise:
•
Do used and loaded masks become sources of
enhanced transmission, for the wearer and others?
•
Do masks become collectors and retainers of
pathogens that the mask wearer would otherwise avoid when breathing without a
mask?
•
Are large droplets captured by a mask atomized
or aerolized into breathable components? Can virions escape an evaporating
droplet stuck to a mask fiber?
•
What are the dangers of bacterial growth on a
used and loaded mask?
•
How do pathogen-laden droplets interact with
environmental dust and aerosols captured on the mask?
•
What are long-term health effects on HCW, such
as headaches, arising from impeded breathing?
•
Are there negative social consequences to a
masked society?
•
Are there negative psychological consequences to
wearing a mask, as a fear-based behavioural modification?
•
What are the environmental consequences of mask
manufacturing and disposal?
•
Do the masks shed fibres or substances that are
harmful when inhaled?
Conclusion
By making mask-wearing recommendations and policies for the
general public, or by expressly condoning the practice, governments have both
ignored the scientific evidence and done the opposite of following the
precautionary principle.
In an absence of knowledge, governments should not make
policies that have a hypothetical potential to cause harm. The government has
an onus barrier before it instigates a broad socialengineering intervention, or
allows corporations to exploit fear-based sentiments.
Furthermore, individuals should know that there is no known
benefit arising from wearing a mask in a viral respiratory illness epidemic,
and that scientific studies have shown that any benefit must be residually
small, compared to other and determinative factors.
Otherwise, what is the point of publicly funded science?
The present paper about masks illustrates the degree to
which governments, the mainstream media, and institutional propagandists can
decide to operate in a science vacuum, or select only incomplete science that
serves their interests. Such
recklessness is also certainly the case with the current global lockdown of
over 1 billion people, an unprecedented experiment in medical and political
history.
Endnotes:
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protection level against airborne viruses, and how adequate are surgical
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