As Mask Mandates End, A Final Observation on the Significant Literature Surrounding Effectiveness

As time has gone on during the COVID-19 pandemic, the debate around masks have continued well into the end of COVID-19 around the world. Even still, people are divided on this one topic. My stance is clear, masks do not work to stop the spread of infection or illness. It is clear, it’s undeniable. The reason I can say this is because of the research that we have at our disposal at this time to confirm this claim. The reason for this is solely because we understand COVID more and we confirm that masks work in the same way with COVID as other respiratory viruses.

There is a list of things we have to settle before we dive into this final observation:

1. COVID might be more infectious (replicate faster) than the flu or other respiratory viruses, but all viruses – in the physical domain – travel in the same way. (i.e. nothing is above Newtons Law, not even COVID).
2. Because they travel in the same way we can use studies relating to influenza and other respiratory viruses, solely for the reason that COVID, influenza etc. all have the same viral physics.
3. Given the viral physics with COVID and influenza, evidence pre-2020 apply.

    When we talk about evidence, we need to assess the type of evidence we consider ‘significant literature’. Significant literature relates to a pyramid of evidence – the higher quality the study, the higher up the pyramid in terms of quality research. Furthermore, the higher up the pyramid the less risk of bias; therefore, we know what to look for in significant literature to find an answer. Granted, both sides of the debate have cross-sectional and case studies (more in favor of the null hypothesis) to solidify their claim.

    Speaking of the null hypothesis, this is an important factor to understanding my conclusion on the efficacy of masks. When doing research, you must consider hypothesis testing to determine actuals and decisions. This involves Type I and Type II errors stemming from null and alternative hypothesis.

	Reject H0	Fail to Reject H0
H0 is True	Type I Error	Correct 1-α
H0 is False	Correct (Statistical Power) 1-β	Type II Error

    Ultimately, the null hypothesis with masks reflects a failure to find a statistically significant finding, as you are trying to find a x or y level of efficacy; thus, researchers are testing to find a rejection of the null (Reject H₀) or that masks are effective through statistically significant outcomes. We can only reject the null hypothesis when statistically significant findings are found through higher-order studies or significant literature.

Studies

The studies will always be a contentious debate – especially in discussion of observational studies – but studies have been conducted on the level higher up in the pyramid. As the pyramid comes to a point, this means we only have very few studies to consider for this analysis. Since COVID started, and studies related leading up to COVID, there have been six significant studies that observe the efficacy of masks through higher-order research. They are as follows.

Author	Title	Year	Type of Study	Participants
Abaluck et al.	The impact of community masking on COVID-19: A cluster-randomized trial in Bangladesh.	2021	Cluster RCT.	342,183
Bundgaard et al.	Effectiveness of adding a mask recommendation to other public health measures to prevent SARS-CoV-2 infection in Danish mask wearers.	2021	RCT.	4862
Long et al.	Effectiveness of N95 respirators versus surgical masks against influenza: A systematic review and meta-analysis.	2020	SLR, Meta-analysis	9171
MacIntyre et al.	A cluster randomised trial of cloth masks compared with medical masks in healthcare workers.	2015	Cluster RCT.	1607
Shah et al.	Experimental investigation of indoor aerosol dispersion and accumulation in the context of COVID-19: Effects of masks and ventilation.	2021	Experimental Control Setting.	N/A
Smith et al.	Effectiveness of N95 respirators versus surgical masks in protecting health care workers from acute respiratory infection: a systematic review and meta-analysis.	2016	SLR, Meta-analysis	4108

Findings

    Total participants were over 361,000 and all studies showed that masks comparably and overall were NOT statistically significant in reducing the spread of COVID or respiratory viruses. Through multiple meta-analysis, literature review, randomized control trials, and experimental control settings, all failed to meet a significant reduction in spread of COVID or respiratory viruses. All outcomes met a 95% Confidence Interval (95% CI), and studies with P values failed to meet statistical significance (95% CI [0.39-0.90], P = 0.33). Even studies involving relative risk (RR) failed to meet statistical significance for effectiveness of masks.

    The Bangladesh study did find statistically significant outcomes with members over the age of 60+; however, the authors confirm demographic limitations, and that data is not statistically significant amongst all groups.

“While we confirm that blood consent rates are not significantly different in the treatment and control group and are comparable across all demographic groups, we cannot rule out that the composition of consenters differed between the treatment and control groups.”

    Experimental controls with a variety of masks confirm that (compared to no mask) all masks reflect a level 3-45% filtration (cloth to N95) which fails to meet the threshold significant reduction.

    Overall, the studies fail to offer a significant statistical outcome related to masks. Cloth masks fail to offer any benefit with 97% particle penetration. Surgical masks are obviously better than cloth masks, but are still statistically insignificant to reduce spread as surgical masks render only a 12% filtration. Furthermore, the failure of N95 to meet statistical significance compared to surgical masks for filtration and transmission of respiratory illness.

Conclusion

    It is clear, debate is currently over, masks effectiveness cause a Type I Error as we fail to reject null hypothesis, making the null hypothesis true. This simply means, through higher-order studies, masks are not a statistically significant measure to stop the transmission of COVID-19 or any other respiratory virus. Therefore, policies surrounding masking for COVID-19 are not significant for a public health measure.

    The sad thing is, this has been known for some time now, and policy makers failed to listen. Perhaps with the mask mandates ending, and it not being used as a sociological or political hammer for people, this will end the debate on masking once and for all, and we can say full heartedly: Masks don’t work!