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PEER simplified tool: mask use by the general public and by health care workers

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Vol 66: JULY | JUILLET 2020 | Canadian Family Physician | Le Médecin de famille canadien
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ÉDITORIAL
PRAXIS
The purpose of this simplified tool is to share the
findings of the PEER (Patients, Experience, Evidence,
Research) umbrella systematic review on mask use
by Dugré et al.1 The first page of the simplified tool sum-
marizes findings for mask use by the public (Figure 1),
and the second page summarizes findings for mask use
by health care workers (Figure 2). An easy-to-print ver-
sion of the tool is available from CFPlus.*
How was this simplified tool developed?
The content in the simplified tool is derived from the
PEER umbrella systematic review of systematic reviews,
which evaluates and meta-analyzes randomized con-
trolled trials based on clinical similarities.1 It focuses
on results that are clinically meaningful to patients or
health care workers.
Results were evaluated with attention to interpreta-
tion of effect estimates and confidence intervals rather
than strict statistical significance.2,3 To do this, the abso-
lute risk of events was calculated by pooling the con-
trol event rates from the original trials and applying
the cluster-adjusted meta-analyzed risk ratio to obtain
the event rate in the treatment group.1 The absolute risk
difference is reported with the 95% confidence interval
to explain the range of possible effects.
Context and limitations
An important consideration when interpreting the mask
literature is understanding that there are studies that
have not yet been done, and that there are limitations
of studies that have been done. No randomized con-
trolled trials identified widespread use of masks by the
public, as recommended by some countries during
the coronavirus disease 2019 (COVID-19) pandemic. The
closest studies were done on small clusters of university
residence halls during influenza seasons.1 Randomized
controlled trials of mask use by health care workers
were limited to hospital settings, with no trials done in
primary care settings or other outpatient settings. Our
review did not look at mask use during specific high-risk
procedures that warrant modification of mask use (eg,
intubation). No studies evaluated the effect of mask use
on prevention of COVID-19 infections. The trials done to
date are limited due to low event rates, variable mask
compliance, and high risk of bias. Further limitations are
summarized in the simplified tool.
This simplified tool is not a guideline; rather, the infor-
mation is presented to promote application informed by
the best available evidence.
Dr Moe is Clinical Evidence Expert at the College of Family Physicians of Canada in
Mississauga, Ont. Dr Dugré is a pharmacist at the CIUSSS du Nord-de-l’Ile-de-Montréal
in Quebec and Clinical Associate Professor in the Faculty of Pharmacy at the University
of Montreal. Dr Allan is Director of Programs and Practice Support at the College of
Family Physicians of Canada, and Professor in the Department of Family Medicine
at the University of Alberta in Edmonton. Dr Korownyk is Associate Professor in the
Department of Family Medicine at the University of Alberta. Dr Kolber is Professor
in the Department of Family Medicine at the University of Alberta. Dr Lindblad is
Knowledge Translation and Evidence Coordinator at the Alberta College of Family
Physicians and Associate Clinical Professor in the Department of Family Medicine
at the University of Alberta. Dr Garrison is Associate Professor in the Department
of Family Medicine at the University of Alberta. Dr Falk is Assistant Professor in the
College of Pharmacy at the University of Manitoba in Winnipeg. Dr Ton is a pharma-
cist in Edmonton and Clinical Evidence Expert at the College of Family Physicians of
Canada. Ms Perry is Knowledge Translation Expert at the Alberta College of Family
Physicians. Ms Thomas is Knowledge Translation Expert at the Alberta College of
Family Physicians. Dr Train is Assistant Professor in the Department of Family Medicine
at Queen’s University in Kingston, Ont. Dr McCormack is Professor in the Faculty of
Pharmaceutical Sciences at the University of British Columbia in Vancouver.
Competing interests
None declared
References
1. Dugre N, Ton J, Perry D, Garrison S, Falk J, McCormack J, et al. Masks for prevention
of viral respiratory infections among health care workers and the public. PEER
umbrella systematic review. Can Fam Physician 2020;66:509-17.
2. McCormack J, Vandermeer B, Allan GM. How confidence intervals become confusion
intervals. BMC Med Res Methodol 2013;13:134.
3. Allan GM, Finley CR, McCormack J, Kumar V, Kwong S, Braschi E, et al. Are potentially
clinically meaningful benefits misinterpreted in cardiovascular randomized trials? A
systematic examination of statistical significance, clinical significance, and authors’
conclusions. BMC Med 2017;15(1):58.
This article is eligible for Mainpro certified Self-Learning credits. To earn
credits, go to www.cfp.ca and click on the Mainpro link.
This article has been peer reviewed. Can Fam Physician ;:-
La traduction en français de cet article se trouve à www.cfp.ca dans la
table des matières du numéro de juillet  à la page e.
PEER simplified tool: mask use by the general
public and by health care workers
Samantha Moe PharmD Nicolas Dugré PharmD MSc G. Michael Allan MD CCFP Christina S. Korownyk MD CCFP
Michael R. Kolber MD CCFP MSc Adrienne J. Lindblad ACPR PharmD Scott Garrison MD PhD CCFP Jamie Falk PharmD
Joey Ton PharmD Danielle Perry RN Betsy Thomas BScPharm Anthony Train MB ChB MSc CCFP James McCormack PharmD
*An easy-to-print version of the simplified tool is available at
www.cfp.ca. Go to the full text of the article online and click on
the CFPlus tab.
506 Canadian Family Physician | Le Médecin de famille canadien } Vol 66: JULY | JUILLET 2020
PRAXIS PEER simplified tool: mask use by the general public and by health care workers
Can we trust these results?
Some of the limitations include: What we do not know yet:
Masks not worn consistently in studies. Do cloth masks work in the community?
Will use of masks in public prevent others from getting sick?
Will masks prevent COVID-19 infections?
For household studies, people already sick
before starting to wear masks.
Too few people got sick to show a dierence
in outcomes.
Denition of u-like illness inconsistent
between trials.
MASKS FOR THE GENERAL PUBLIC
Based on evidence from randomized controlled trials
Masks are only one part of preventing infection.
(for example: physical distancing, hand washing)
25% versus 21%
The reduction in u-like illness may be
4% (range: 0-8%) over 6 weeks.
2 trials
1683 people
UNIVERSITY
RESIDENCE HALLS
Sick person
wears mask
2 trials, 903 people
Healthy household
members wear masks
1 trial, 290 people
Healthy and sick people
wear masks
4 trials, 2750 people
If I wear a surgical mask while out in public, will it protect me from u-like illness?
What about wearing a surgical mask at home after a household member becomes sick?
But no
dierence in
lab-conrmed
inuenza
In all three scenarios, wearing a mask did NOT reduce the risk
of getting u-like illness or conrmed inuenza.
Figure 1
Vol 66: JULY | JUILLET 2020 | Canadian Family Physician | Le Médecin de famille canadien
507
PEER simplified tool: mask use by the general public and by health care workers PRAXIS
Can we trust these results?
Some of the limitations include: What we do not know yet:
Masks not worn consistently in studies. There is no research in primary care.
Too few people got sick to show a
dierence in outcomes.
This research does not identify high-risk
procedures requiring modication of mask use.
Denition of u-like illness inconsistent
between trials. There is no research yet in COVID-19.
Infection spread outside of work setting
may impact studies.
Interpretation of results sensitive
to the statistics used.
MASKS FOR HEALTHCARE WORKERS
Based on evidence from randomized controlled trials
Risk of u-like
illness
4 trials, 7607 people
N95 mask: 3.6%
versus
Surgical mask: 4.6%
1 trial, 1149 people
Surgical mask: 0.3%
versus
Cloth mask: 2.3%
If there is a dierence between
groups, it may be about 1%
(range: 0-2%) over 4-12 weeks.
No dierence in lab-conrmed
inuenza or lab-conrmed viral
respiratory infections.
The dierence in u-like
illness may be 2% over
4 weeks (range: 0-2.3%).
N95 mask
N95
Surgical mask Cloth mask
HOSPITAL
SETTING
N95
Masks are only one part of preventing infection. Additional personal protective
equipment and precautions should be used based on the clinical setting.
For healthcare workers, is there a dierence between
masks in protecting against u-like illness?
Figure 2
... You endorsed as valid, an "investigation" that was known by you to be a fraud, a vaccine promotional stunt, that was so corrupt that it had to be retracted within a few days of global publication. 16 But let us focus on Beta-coronavirus, specifically its history versus SARS-CoV-2 . . . as you are aware, the former SARS outbreak dates to 2003. ...
... 15 Please see section above, "Censorship-of and Outright Threats Against Those Associated with Hydroxychloroquine," Pages 4 -8. 16 Please see footnote 15. Please see Question 3, Page 9 above. ...
... • skin irritation and infections, 16 • impaired self-expression, ...
... You endorsed as valid, an "investigation" that was known by you to be a fraud, a vaccine promotional stunt, that was so corrupt that it had to be retracted within a few days of global publication. 16 But let us focus on Beta-coronavirus, specifically its history versus SARS-CoV-2 . . . as you are aware, the former SARS outbreak dates to 2003. ...
... 15 Please see section above, "Censorship-of and Outright Threats Against Those Associated with Hydroxychloroquine," Pages 4 -8. 16 Please see footnote 15. Please see Question 3, Page 9 above. ...
... • skin irritation and infections, 16 • impaired self-expression, ...
Technical Report
Full-text available
Alleged "COVID-19 Pandemic" mandated government enforced lockdowns of citizens, leading to massive but ignored K -12 suicide deaths of our children is connectable to Dr. Anthony Fauci.
Technical Report
A vile new mantra is on the lips of every public health official and politician in the global campaign to force universal masking on the general public: “there is a growing body of evidence”. This propagandistic phrase is a vector designed to achieve five main goals: - Give the false impression that a balance of evidence now proves that masks reduce the transmission of COVID-19 - Falsely assimilate commentary made in scientific venues with “evidence” - Hide the fact that a decade’s worth of policy-grade evidence proves the opposite: that masks are ineffective with viral respiratory diseases - Hide the fact that there is now direct observational proof that cloth masks do not prevent exhalation of clouds of suspended aerosol particles; above, below and through the masks - Deter attention away from the considerable known harms and risks due to face masks, applied to entire populations The said harms and risks include that a cloth mask becomes a culture medium for a large variety of bacterial pathogens, and a collector of viral pathogens; given the hot and humid environment and the constant source, where home fabrics are hydrophilic whereas medical masks are hydrophobic. In short, I argue: op-eds are not “evidence”, irrelevance does not help, and more bias does not remove bias. Their mantra of “a growing body of evidence” is a self-serving contrivance that impedes good science and threatens public safety. I prove that there is no policy-grade evidence to support forced masking on the general population, and that all the latest-decade’s policy-grade evidence points to the opposite: NOT recommending forced masking of the general population. Therefore, the politicians and health authorities are acting without legitimacy and recklessly.
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Objective: To determine the effect of mask use on viral respiratory infection risk. Data sources: MEDLINE and the Cochrane Library. Study selection: Randomized controlled trials (RCTs) included in at least 1 published systematic review comparing the use of masks with a control group, either in community or health care settings, on the risk of viral respiratory infections. Synthesis: In total, 11 systematic reviews were included and 18 RCTs of 26 444 participants were found, 12 in the community and 6 in health care workers. Included studies had limitations and were deemed at high risk of bias. Overall, the use of masks in the community did not reduce the risk of influenza, confirmed viral respiratory infection, influenzalike illness, or any clinical respiratory infection. However, in the 2 trials that most closely aligned with mask use in real-life community settings, there was a significant risk reduction in influenzalike illness (risk ratio [RR] = 0.83; 95% CI 0.69 to 0.99). The use of masks in households with a sick contact was not associated with a significant infection risk reduction in any analysis, no matter if masks were used by the sick individual, the healthy family members, or both. In health care workers, surgical masks were superior to cloth masks for preventing influenzalike illness (RR = 0.12; 95% CI 0.02 to 0.98), and N95 masks were likely superior to surgical masks for preventing influenzalike illness (RR = 0.78; 95% CI 0.61 to 1.00) and any clinical respiratory infections (RR = 0.95; 95% CI 0.90 to 1.00). Conclusion: This systematic review found limited evidence that the use of masks might reduce the risk of viral respiratory infections. In the community setting, a possible reduced risk of influenzalike illness was found among mask users. In health care workers, the results show no difference between N95 masks and surgical masks on the risk of confirmed influenza or other confirmed viral respiratory infections, although possible benefits from N95 masks were found for preventing influenzalike illness or other clinical respiratory infections. Surgical masks might be superior to cloth masks but data are limited to 1 trial.
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Background While journals and reporting guidelines recommend the presentation of confidence intervals, many authors adhere strictly to statistically significant testing. Our objective was to determine what proportions of not statistically significant (NSS) cardiovascular trials include potentially clinically meaningful effects in primary outcomes and if these are associated with authors’ conclusions. Methods Cardiovascular studies published in six high-impact journals between 1 January 2010 and 31 December 2014 were identified via PubMed. Two independent reviewers selected trials with major adverse cardiovascular events (stroke, myocardial infarction, or cardiovascular death) as primary outcomes and extracted data on trial characteristics, quality, and primary outcome. Potentially clinically meaningful effects were defined broadly as a relative risk point estimate ≤0.94 (based on the effects of ezetimibe) and/or a lower confidence interval ≤0.75 (based on the effects of statins). Results We identified 127 randomized trial comparisons from 3200 articles. The primary outcomes were statistically significant (SS) favoring treatment in 21% (27/127), NSS in 72% (92/127), and SS favoring control in 6% (8/127). In 61% of NSS trials (56/92), the point estimate and/or lower confidence interval included potentially meaningful effects. Both point estimate and confidence interval included potentially meaningful effects in 67% of trials (12/18) in which authors’ concluded that treatment was superior, in 28% (16/58) with a neutral conclusion, and in 6% (1/16) in which authors’ concluded that control was superior. In a sensitivity analysis, 26% of NSS trials would include potential meaningful effects with relative risk thresholds of point estimate ≤0.85 and/or a lower confidence interval ≤0.65. Conclusions Point estimates and/or confidence intervals included potentially clinically meaningful effects in up to 61% of NSS cardiovascular trials. Authors’ conclusions often reflect potentially meaningful results of NSS cardiovascular trials. Given the frequency of potentially clinical meaningful effects in NSS trials, authors should be encouraged to continue to look beyond significance testing to a broader interpretation of trial results. Electronic supplementary material The online version of this article (doi:10.1186/s12916-017-0821-9) contains supplementary material, which is available to authorized users.
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Controversies are common in medicine. Some arise when the conclusions of research publications directly contradict each other, creating uncertainty for frontline clinicians. In this paper, we review how researchers can look at very similar data yet have completely different conclusions based purely on an over-reliance of statistical significance and an unclear understanding of confidence intervals. The dogmatic adherence to statistical significant thresholds can lead authors to write dichotomized absolute conclusions while ignoring the broader interpretations of very consistent findings. We describe three examples of controversy around the potential benefit of a medication, a comparison between new medications, and a medication with a potential harm. The examples include the highest levels of evidence, both meta-analyses and randomized controlled trials. We will show how in each case the confidence intervals and point estimates were very similar. The only identifiable differences to account for the contrasting conclusions arise from the serendipitous finding of confidence intervals that either marginally cross or just fail to cross the line of statistical significance. These opposing conclusions are false disagreements that create unnecessary clinical uncertainty. We provide helpful recommendations in approaching conflicting conclusions when they are associated with remarkably similar results.