On the effectivity of masks against Covid-19
Written on 2021-04-29
Background: A friend of mine shared a post that quoted the CDC as saying: “CDC is not aware of any randomized control trials that show that masks or double masks or cloth face coverings are effective against COVID-19.” As my response turned out a bit longer than expected, I'm archiving it here for future reference…
Well, you just sent me down the rabbit hole with that one… The quick summary: the post above is misleading, but not completely wrong; the evidence for mask effectiveness is spotty, mixed, but more positive than not.
I've taken a quick but not detailed look at the studies the author cites, as well as reviewing around a dozen other studies. I also have some general comments about the nature of scientific evidence. (Yes, this is going to be a long answer ;-) )
Let's start with the latter: What the FOIA request was asking about was something very specific: randomised control trials (RCT) of mask-wearing with Covid-19.
RCT are a type of experimental design whereby you take a number of test subjects, randomly assign them to a treatment or non-treatment (i.e. control) group, and then compare your response variable across the groups (here: rate of Covid-19 infection). When done properly and with a sufficiently large number of subjects (the “N”), RCT are the gold standard of medical study design. However, they can pose problems. For a start, they take a lot of effort, time, and money to implement. Especially because you really do need a large N, if possible in the thousands - an RCT with a low N is worse than useless. Also, there may be ethical concerns: if you have a risk-free treatment for which there is already some indication that it works, it would be problematic to refuse to give this treatment to your control group - potentially risking their lives just to get slightly better data.
So, for this combination of practical and ethical reasons, it is understandable - though very unfortunate - that there is not yet a RCT of masks with Covid-19. However, RCTs are far from the only study design available to researchers. Two other common categories, which have been used to address masking with Covid-19, are observational and laboratory studies.
Observational studies are what are known as “quasi-experimental” designs. The idea is to find a situation which is as close as possible to the experiment you'd like to have carried out, and then analyse the participants in this situation. For example, there was a study of Covid-infection on flights, where the passengers on some planes had worn masks while those on other planes hadn't. Or a German city that introduced compulsory masking several weeks before the rest of the country. This has multiple advantages: 1) you don't need to spend time conducting the experiment, the situation has already occurred; 2) it's often easier to get a large N; 3) there are no ethical concerns because you aren't actively intervening in the situation. The major drawback is that you can't exclude confounding factors the way you can in an experiment. Indeed, as these are real-life situations, there are likely to be a lot of factors at play, whatever the outcome. There are statistical techniques for reducing the uncertainty this introduces, but basically you'll never be able to guarantee that there wasn't some other factor to blame for your results.
Laboratory studies, on the other hand, exclude “real-life” as much as possible, only recreating the bare necessities in a completely controlled environment. For example, one study used two dummy heads in a glass cage, with infectious aerosols being sprayed out of the one dummy's mouth and sucked in and measured through the other's mouth, with different combinations of masks in between. The benefits of this are that you need few or no human subjects and can completely exclude any factors you're not interested in. The problem is that those excluded factors may actually be the more important ones, or you may be asking the entirely wrong question. (“Yes, masks can physically stop virus particles if they are worn in public - but what if most infections are happening at home?”)
In summary, there are different study designs, all of which have merits and all of which have flaws. While RCT are deservedly seen as the best, it is not appropriate to just ignore all the others, or claim that they are “bad science”.
Among the studies I looked at, there were observational and laboratory studies on masking and Covid-19, as well as RCT on masking and other viral disease (which also should not be discounted!). The evidence is not as clear as I would have expected, and there are not as many studies as I would have thought. Among the studies I found, some say there is no effect, most say the effect varies or is very slight, while yet others find a clear effect. As always, some studies seemed to have a stronger design and/or analysis than others.
In general, my impression was that overall, masks do help - but they are not a cure-all, and their effectiveness is obviously limited to the occasions when they are actually worn. (I.e., masking rules may reduce infections in public gatherings, but they won't stop infections in private home settings.)
The complete list of studies I used is below. The one I thought the best is Chu et al. (2020). This is a meta-study, so it combines the results of many individual studies.
Having already said all that, I should emphasise that situations like this, with spotty and equivocal data even on important questions, is actually very common in science. I'm planning to do my PhD in conservation biology on the question of how effective protected areas are at protecting biodiversity. The first PAs were established 150 years ago, we've been using them intensely for 50 years, and we're still not sure how much they actually help. Turns out, collecting data is really hard and expensive, and nature is incredibly messy. (I'll be combining data sets from over 20 different organisations and agencies, and the analysis techniques I need to use are partly still being developed.)
So in that sense, I'm not particularly worried by the uncertainty in the masking question. We know PAs help at least somewhat, so we keep lobbying for them. If we didn't do anything until we could put a precise percentage on their effectiveness, there would be no nature left to protect. Likewise, we know there can be positive effects of masks, and the negative effects are minor. So if the situation is urgent (which it is) and every little counts (which it does), I think it would be foolish to ignore them, even if we don't yet know as much as we'd like.
Bibliography
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