MaskScience Masks Facemasks Science HierarchyofEvidence Modeling LaboratoryStudies ObservationalStudies RandomizedControlledTrials MetaAnalysis CDC Evidence
Philip is a General Dentist (DDS). He graduated from the University of Detroit Mercy School of Dentistry in 2010. He is currently on activy duty in the Army, however he is doing appearances only as a civilian.
Not all scientific evidence is created equal. Public health recommendations regarding masks must be evaluated with regard to the quality of evidence cited to support them. The evidential bar for justifying mandates is even higher. You can often rule out a wrong answer even if you aren’t yet precisely certain of the right one.
Broadly speaking, the weakest types of scientific evidence are conjecture (also known as “modeling”) and expert opinion. Laboratory studies are stronger, but hypotheses developed in controlled conditions need to be tested and verified by real-world observational studies and randomized controlled trials. Meta-analyses or systematic reviews aggregate and assess the results of multiple individual lesser studies.
Different sub-types of studies within each of these categories also vary considerably in methodology and overall strength.
A host of additional factors such as size, duration, observational detail, and the precise question being asked, mean that the strength of individual studies varies greatly within and across categories. For example, a study of all 7 million inhabitants of Massachusetts would be great when answering questions specific to that particular state, but looking at a random sample of just 5,000 people from each of the 50 states would give more accurate answers to questions about the U.S. as a whole. A large, detailed, well-designed, multi-month observational study can provide better scientific evidence than a small, multi-week randomized controlled trial, depending on what question is being asked. Conversely, a randomized controlled trial with just a few thousand participants can be stronger than an observational study involving several hundred thousand.
No evidence is worthless, but these distinctions are important, because if predictions based on a weaker type of study – such as modeling or laboratory experiments – are contradicted by stronger real-world data, then a coercive policy based on the weaker studies cannot be rationally or morally justified.
I have personally read hundreds of studies on masks, including the 68 cited by the CDC in their online publications “The Science of Masking to Control COVID-19” (November 2020) and “Science Brief: Community Use of Cloth Masks to Control the Spread of SARS-CoV-2” (as of this writing, last updated in May 2021). I have charted the CDC’s citations and the studies they fail to mention in a “balance of the evidence” diagram. You can watch a 3-minute explanatory video and browse or download the resulting poster-sized pdf here to browse at your leisure.
In the case of masks, plenty of laboratory experiments (though by no means all) suggest that masks should work based on the current theories of respiratory viral transmission, and the CDC leans heavily on these, but real-world observations throughout 2020-2021 and going back decades, including during the Spanish Flu, have been remarkably consistent in their failure to find a statistically-significant difference in viral (and very often even bacterial) transmission rates when masks are used or not used. Either something is wrong with the hypotheses about how viruses are transmitted or how effectively masks filter particles, or there are real-world confounding independent variables which prevent mask use from being effective in the way some laboratory studies suggest they should. Either way, a coercive policy of mandatory masking cannot be empirically or morally justified on the current evidence base (quite the opposite, in fact).
Showing in detail why an intervention works or does not work is useful but secondary to the fact itself. Scientific progress occurs far more often in small increments, as wrong answers to questions are ruled out one at a time, rather than by successfully latching on to a right answer straight out of the gate. Empirically, universal masking for the control of respiratory viral infections is just this sort of wrong answer.