In medicine you cannot distinguish. It is all about the intervention and not about some theoretical best-case scenario.
The intervention is to ask people to wear masks. People comply as they in real real life and then we measure the results. There was no reliable evidence that this made any noticeable difference.
Now you can change the intervention – instead of asking and mandating masks as we did, we could educate masks wearers more. Unfortunately we have no evidence that it helps.
Perhaps masking could help to an individual wearer? Alas, we didn't collect such evidence either.
Some studies are lab based. In those masks had some effect. But that's not how people use masks in real life, so they don't mean much.
I think saying "Using X is effective, but only if you actually use X" is obvious. The thing people want to know is "do masks stop the virus" which is an entirely different question from "How many people will wear masks", which is a different question from "What is the effectiveness of interventions to promote mask wearing"
Therefore the real question is how effective is the intervention. It will be (or should be) asked by people responsible with public health policies.
P.S. Cochrane group is not for giving scientific answers to individual people. Its main aim is to evaluate the evidence of different treatments and provide guidance to policy makers and healthcare authorities.
We have similar problems getting schizophrenics to take their meds and getting communities with high rates of open defecation to use toilets, but nobody suggests that we give up on antipsychotics or sanitation facilities.
Every other treatment in medicine including schizophrenia is tested how it works in practice. It is incurable disease and the treatments have many side-effects. Thus the question becomes not “does this medicine cure schizophrenia” but “does this treatment works better than placebo or another treatment?”. When studies are completed, we gather evidence by monitoring real life experience with this treatment.
Medicine is tested according to how it works when people actually take it. People participating in research studies who fail to take their medications (or their placebo for that matter) are kicked from the program and their data is typically discarded entirely.
In fact, often clinical trials are statistically analysed by intention-to-treat, including all people who have been randomised even if they later don't receive the treatment.
Per-protocol-analysis (including only people who follow the study protocol) can also be used but it is more prone to bias.
Besides, with masks it is not simply wearing or not wearing a mask. Even a very diligent mask wearers may wear it in a way that makes it less effective without being aware of that.
In short, when the doctor prescribes a medicine it is important to understand the factors why the patient may not take the medicine as prescribed. If the real life situation is that most people take medicine in a way that makes it ineffective and so much that the clinical trial cannot find significant effect, then he shouldn't prescribe it. It is just a waste of resources and giving people false hopes.
Fair, that said, intention-to-treat is more likely to greatly underestimate the efficacy of a treatment when non-adherence is expected to be high/isn't being monitored.
> In short, when the doctor prescribes a medicine it is important to understand the factors why the patient may not take the medicine as prescribed.
I agree, but the solution is to help the patient overcome those barriers not to throw out the medication. It's to give people the information and tools they need to follow the treatment. People who wear masks could be trained on how to properly fit them, take them on/off, store them, replace them, etc. The real life situation around masking included basically none of that. "Wear a mask" was basically all people were told.
It doesn't make sense to fault/dismiss masking if a large part of the population isn't wearing them because they were tricked into believing that masks don't work or that masks will actually make them sick, and another large part of the population wears them, but wasn't shown how to do it correctly.
It's important to be aware that those things are going on within the population, but the next step from there is still "educate the public" and not "abandon all efforts at masking" - at least not until a more accessible alternative which is also as effective as masking becomes available
If we still could not find reliable evidence that masks are effective, then the policy makers should be told that.
There is very little you can do to improve mask wearing technique. We certainly explained these things to doctors, it made no difference in results. If you want to make more controlled studies, you can do that. Don't hold your breath however.
No, we should not continue requiring wearing masks because you are only doing that out of hope. That's not how we do things in medicine. It would be unethical. There are many medicines that show effectiveness in the lab but fail in clinical trials. We don't demand for those medicines to be used until we find more effective alternative. Many unknown factors could cause ineffectiveness in clinical trials, we don't need to understand all of them, just the fact that the drug failed to demonstrate effectiveness and safety in real life settings.
>> a more accessible alternative which is also as effective as masking becomes available
The point is masking was not effective. It has not shown effectiveness anywhere in the real world.
https://jamanetwork.com/journals/jama/fullarticle/2776536
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7191274/
> > "We examined whether promoting abstinence prevents teen pregnancy and the results were inconclusive"
> The first is obviously wrong,
No. They're equivalent. They both mean "we looked, and we didn't find any confirming evidence." You're confusing "we found no reliable evidence of X" with "we found evidence of NOT X", which is different, and essentially never achievable in empirical studies (note: this is not an invitation to get side-tracked in pedantic debates about proving the null; I'm telling you how actual randomized controlled trials work, in real life.)
Proving a negative via statistics is ~impossible, so what you do instead is to look for significant differences in X, attributable solely or partially to the intervention. If you don't find such a difference (as was the case in the mask review), you say "we found no reliable evidence of X".
But when the Cochrane authors wrote "Wearing masks in the community probably makes little or no difference to the outcome of influenza‐like illness", they really did mean exactly what it sounds like -- the effect size in an aggregated pool of randomized controlled trials was statistically indistinguishable from zero. You can debate whether or not they looked for the right thing (X), you can debate whether or not adding another big randomized trial would help find X, and so on. But the plain-text interpretation is correct.
"We found no reliable evidence that abstinence prevents teen pregnancy"
"We examined whether promoting abstinence prevents teen pregnancy and the results were inconclusive"
The first is obviously wrong, and if the the second is true it would mean the government should look for other ways to prevent teen pregnancy, but it wouldn't mean that practicing abstinence as an individual doesn't work to prevent pregnancy.