Basically, the authors seem to assume that the virus behaves according to their formulas, and show that under their assumptions, face masks work, but don't actually prove that their assumptions match reality - or did I miss something?
"The effectiveness of masks, however, is still under debate. Compared to N95/FFP2 respirators which have very low particle penetration rates (around ~5%), surgical and similar masks exhibit higher and more variable penetration rates (around ~30-70%) (2, 3). Given the large number of particles emitted upon respiration and especially upon sneezing or coughing (4), the number of respiratory particles that may penetrate masks is substantial, which is one of the main reasons leading to doubts about their efficacy in preventing infections. Moreover, randomized clinical trials show inconsistent or inconclusive results, with some studies reporting only a marginal benefit or no effect of mask use (5, 6). Thus, surgical and similar masks are often considered to be ineffective. On the other hand, observational data show that regions or facilities with a higher percentage of the population wearing masks have better control of the coronavirus disease 2019 (COVID-19) (7–9). So how to explain these contrasting results and apparent inconsistencies?
Here, we develop a quantitative model of airborne virus exposure that can explain these contrasting results and provide a basis for quantifying the efficacy of face masks. "
So they are left with a conundrum that places which use surgical masks seem to be better off, whereas randomized control studies of surgical masks show little to no benefit. This is what they try to explain with a mathematical model.
My hypothesis regarding surgical mask prevalence and how it correlates with better virus control:
People who wear masks--however effective they may be--also behave in other ways to limit the spread. They minimize time spent in public indoor spaces, they keep distance from other people, they're more likely to self-quarantine if a family member is sick, etc.
The mask-wearing prevalence is a proxy for how serious the wearer takes Covid as a threat.
https://slatestarcodex.com/2020/03/23/face-masks-much-more-t...
At the beginning of the pandemic I was skeptical about the effectiveness of masks and this article is what convinced me that there is some benefit.
People refusing to wear masks rarely question whether or not it works, and more to do with a low estimation of the disease's threat, as well as contempt for both the technocrats who chose to mislead the public about masks early on, and leaders who they believe are overstepping their authority by mandating them.
Our leadership has been doing everything possible to burn through its perceived legitimacy, and this is the result. Childish, spiteful, somewhat understandable behavior.
Yet another component is that trust in the press and other public institutions was at possibly an all-time low in America at the time the COVID pandemic became public knowledge, and shows no signs of pulling out of that nosedive.
> Great logic there. But hey, cutting your nose to spite your face is nice, no?
Stupid people are gonna stupid, but you can't lie to people and then expect them to then automatically trust you when you're telling the truth. Those people are objectively and factually wrong, but they are right to be distrustful. If the issue wasn't one of science that we could otherwise objectively evaluate, their position would be perfectly rational and reasonable.
Still, it created a lot of distrust when the message changed a few months ago, it wasn't a tribalist issue and I can completely understand how people ended up with this distrust. They are still wrong and I completely agree with this comment [1].
"Hey, some government people lied to us saying we don't need masks, hence why we won't use masks now"
Great logic there. But hey, cutting your nose to spite your face feels good, no?
Many of us did learn those lessons.
For me personally when this all began, checking out what the world is doing seemed the natural thing to do.
The US is also very insular, in that a smaller percentage of people travel abroad than is typical for many Western Nations. And a small percentage of people seek news produced outside the nation too.
Both of those stats are better elsewhere in the West, so make of that what you will.
This is the thing that makes me saddest. It just seems like people in the US are unwilling to endure even a minor inconvenience if it will help someone else but not do anything for themselves.
The idea of some effort, cost, minor sacrifice or inconvenience for a common, public good is not strong here.
Couple that being basically as insular on a personal level as we generally are nationally, and we find more of us than we may expect are empathy challenged too.
A lot of people in HN were arguing that the 0.7% fatality rate reported by Chinese authorities in Feb/20 was because Chinese medical care is bad.
Casual racism is kinda pernicious.
Agreed. We should have relocated Britain and NYC to the equator long time ago.
The statistical illiteracy here is astounding from professional scientists.
”0% of ths sick passengers wore masks, compared to 47% of the healthy passengers. Another way to look at that is that 0% of the mask wearers got sick, but 35% of non-wearers did”
And another way to look at it is that 65% of non-wearers didn’t get sick. The group of sick people is also quite small, meaning that the error bars on the effect size are large.
Sloppy, sloppy thinking.
1. Statistical uncertainty is normally ignored. They can and will tell politicians to adopt major policy changes on the back of a single dataset with 20 people in it. In the rare cases when they bother to include error bars at all they are usually so wide as to be useless. In many other fields researchers debate P-hacking and what threshold of certainty should count as a significant finding. Many people observe that the standard of P=0.05 in e.g. psychology is too high because it means 1 in 20 studies will result significant-but-untrue findings by chance alone. Compared to those debates epidemiology is in the stone age: any claim that can be read into any data is considered significant.
2. Rampant confusion between models and reality. The top rated comment on this thread observes that the paper doesn't seem to test its model predictions against reality yet makes factual claims about the world. No surprises there; public health papers do that all the time. No-one except out-of-field skeptics actually judge epidemiological models by their predictive power. Epidemiologists admit this problem exists, but public health has become so corrupt that they argue being able to correctly predict things is not a fair way to judge a public health model[1]. Obviously they insist governments should still implement whatever policies the models say are required. It's hard to get more unscientific than culturally rejecting the idea that science is about predicting the natural world, but multiple published papers in this field have argued exactly that. A common trick is "validating" a model against other models [2].
3. Inability to do maths. Setting up a model with reasonable assumptions is one thing but do they actually solve the equations correctly? The Ferguson model from Imperial College, which we're widely assured is one of the world's top teams of epidemiologists, was written in C and filled with race conditions/out of bounds reads that caused their model to totally change its predictions due to timing differences in thread scheduling, different CPUs/compilers etc. These differences were large, e.g. a difference of 80,000 deaths predicted by May for the UK [3]. Nobody in the academic hierarchy saw any problem with this and worse, some researchers argued that such errors didn't matter because they just ran it a bunch of times and averaged the results. This is confusing the act of predicting the behaviour of the world with the act of measuring it, see point (2).
4. Major logic errors. Assuming correlation implies causation is totally normal. Other fields use sophisticated approaches to try and control for confounding variables, epidemiology doesn't. Circular logic is a lot more common than normal, for some reason.
None of these problems stop papers being published by supposedly reputable institutions in supposedly reputable journals. After reading or scan-reading about 50 epidemiology papers, including some older papers from 10 years ago, I concluded that not a single thing from this field can be trusted. The problems aren't specific to COVID, they're cultural and have been around a long time. Life is too short to examine literally every paper making every claim but if you take a sample and nearly all of them contain basic errors or what is clearly actual fraud, then it seems fair to conclude the field has no real standards.
[1] "few models in healthcare could ever be validated for predictive use. This, however, does not disqualify such models from being used as aids to decision making ... Philips et al state that since a decision-analytic model is an aid to decision making at a particular point in time, there is no empirical test of predictive validity. From a similar premise, Sculpher et al argue that prediction is not an appropriate test of validity for such model" https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3001435/
[2] https://github.com/ptti/ptti/blob/master/README.md
[3] https://github.com/mrc-ide/covid-sim/issues/116 https://github.com/mrc-ide/covid-sim/issues/30 https://github.com/mrc-ide/covid-sim/commit/581ca0d8a12cddbd... https://github.com/mrc-ide/covid-sim/commit/3d4e9a4ee633764c...
You should look at the the observational studies measuring vaccine effectiveness in Israel coming from Balicer and his group.
They report the effect of the vaccine on number of positive cases without even mentioning that the vaccinated individuals are not routinely tested by ministry of health policy, or that the main reason people get tested is to shorten the isolation period after contact with covid-19 cases, which vaccinated individuals are exempt from.
But that’s the issue. Science is about finding the truth, not to map itself to trigger some desired social behaviors.
There’s a (social) scientific explanation for that as well.
I see the possibility, but my own anecdotal experience with mask wearers and non-wearers jives with the other correlation: that non-wearers generally didn’t take the pandemic seriously.
The people that I know who shunned masks also ignored the COVID threat in other ways: Large gatherings, vacations to another state to escape lockdown restrictions, etc.
Whereas my religiously-masked friends were basically isolated last year.
And because I have several such anecdotes, I now have data :)
But feel free to wear a mask as much as you want, at this point it is effectively a security blanket though.
Pre-vaccination, I was an indoor-only wearer.
(I don't know either way, just curious to know if this angle has been investigated.)
After? Normal, but with masks.
Effective for the mask wearer, or effective for others in the vicinity of the mask wearer?
It's well established that cheap cloth masks and procedure masks don't protect the mask wearer very much.
The rationale for cheap masks is that they drastically reduce the spread of particles from the wearer to everyone else nearby.
If your goal is to protect yourself, wear an N95 or better.
If your goal is to protect a population, and N95s aren't available, then the cheap mask help a lot when people wear them.
It may feel silly to wear a mask if you belief that this is the only effect. But you cannot deny that it is a causal effect, e.g. it would not happen without wearing the masks.
This MIT study [0] was pretty convincing.
They conclude that "face masks can be an extremely effective indoor safety measure".
For example:
If an infected person was riding on a commercial airline with 100 other people, other passengers would be at risk of infection within 70 minutes. If all of the passengers wore masks, however, that space could be safe for up to 54 hours.
Bacteria are a different matter, and possibly other viruses. But real world data about covid is that masks make very little difference either way, and are possibly harmful.
And we can see who's done better in the last year.
There was already good reason to suspect this was the case over a year ago too. The early outbreaks mostly seemed to be a gradient emanating outwards from Italy, with genetic testing suggesting that even the US had done reasonably well at halting spread from China and its big outbreaks came from Europe; Asia is obviously a long way from Italy and it was suspicious that a bunch of countries that in reality had widely varying policies on things like masks, testing, etc showed such similar results. Including countries like Australia and New Zealand that were more or less western in terms of culture but were geographically close to the rest.
One of the justification for not advising N95s to the public was the fact that most people don't know how to properly wear one. As if they couldn't be trained in a few weeks, or duration of the pandemic.
Also, N95s filter out particles smaller than .3 microns (the 95% part). You can look it up, but it has something to do with the electrostatic media and physics. It definitely does not work like a sieve. (https://www.usatoday.com/story/news/factcheck/2020/06/11/fac...)
The 0.3 micron size is used in the specification because it's the "worst" case... too large for maximum electrostatic attraction, too small for mechanical filtration...it's the worst case.
Covid survives for 30 seconds in sunlight, so wearing a mask outside is bad on balance as you breathe in your own CO2 and reduce oxygen intake which both have negative health effects (especially during exercise).
I’ve had both with and without valves since 2015. Would never buy one without a valve again if I can find one with a valve (in the last year at some times could only find valveless)
Fwiw, in Israel it was illegal to use a valved mask without covering the valve with cloth/paper/surgical mask. The official statement was that the pressure out of the valve disperses virions much farther, and is thus more dangerous to people around you if you are presymptomatic than having no mask at all.
An earlier version of the test saw some particle released into the room and a properly fitted mask wouldn't let it though. If people could smell the chemical, the fit was poor.
Almost nobody does this, and many use cloth masks.
You will have deep signs on your face after wearing a fitted N95 a feww hours.
Previous posts with some more details and link to a US Department of labour video: https://news.ycombinator.com/item?id=27129984 https://news.ycombinator.com/item?id=23957506
The masks aren't expected to stop individual virus particles. They are supposed to catch salivary droplets containing the virus.
These are probably bigger than particles of drywall dust.
Are there any reliable and conclusive studies supporting this?
That's fine, because I am not a surgeon and I don't care.
I do care about fogging up my glasses and having to speak up without any proper evidence that wearing a mask helps significantly against covid.
Anyway, you're going to run into the same problem doctors' advice has, where everything not required is forbidden. If a 100-year problem like a pandemic comes up, try some unproven precautions, it's better than not doing anything until it's mandated.
If so, why does states with the highest mask use (CA, NYC) have much higher covid rates than states with some of the lowest mask use (Florida, Texas)?
They...don’t (leaving out NYC because the other three things are large, diverse states and NYC is a single large city):
California 3.7M cumulative cases & 1,336 case/day current 7-day average vs. 39.5M population
Florida 2.3M cumulative cases & 3,007 case/day 7-day average vs 21.5M population.
Texas 2.9M cumulative case & 2,034 case/day current 7-day average vs. 29M population.
FL and TX are worse on per capita cumulative cases, and much worse on current per capita new cases than California.
(case numbers from usafacts.org)
Death rate is exponential in age, so small differences in age distribution gives big differences in relative deaths.
Again, this has a problem of failing to address case timing and distribution, with CA and NY being hard hit and having COVID into the community before there was much awareness or any policy response (e.g., targeted control measures directed at elder facilities) or personal behavior response or evolution in treatment protocols, which radically changes the dynamic vs. states that weren't hit until after public awareness and health care competence was more advannced.
And presumbaly, you mean “CA and NY with high mask use”, otherwise this argument is nonsense. OTOH, no one has cited any data supporting the “CA and NY are representative of high mask use states while FL (and TX, as claimed in other posts) are representative of low mask use” argument, which seems to be based on stereotyping based on dominant political parties in each state and leanings of political leaders of each party on mask mandates. What data there is on this doesn’t seem to support that this stereotyping accurately reflects mask use, OTOH; i.e., a study on mask use with self-reported data for May through August of last year had mask use as:
AUG: CA 74.6% > FL 71.6% > NY 69.6% > TX 69.1%
JUL: NY 80.6% > CA 74.3% > FL 66.9% > TX 66%
JUN: NY 56.4% > CA 48.7% > FL 45.1% > TX 42.4%
MAY: NY 46.1% > CA 45.2% > TX 43.8% > FL 43.5%
APR: CA 38.1% > NY 36.1% > FL 35.0% > TX 32.4%
And, notably, all four states were in the top half of the country in mask use in all 5 months with data.
https://journals.plos.org/plosone/article?id=10.1371/journal...
Self reported results are prone to preference falsification and hidden bias in whom answers. (“Yes, mom, I did my homework” ;) )
Self reported results of politically charged questions are effectively polls. To indicate the problem you can observed how inaccurate polls around politically charged choices has become when compared to actual choices.
The normal problems with self reported results are probably aggravated by how academics, major authorities, media, and tech companies have openly without cover used force to promote the “right” answer and suppressed the “wrong”. The use of force to enforce specific conclusions had been an extraordinary and very noticeable break from western traditions.
I don’t, in the absence of strong enforcement of policy (which did not exist in, e g., California COVID policy), especially on issues of strong partisan posturing; even solid red or blue states tend to be pretty closely balanced in the population; majoritarianism, reinforced by gerrymandering and related mechanisms, produces strong and durable political dominance from slight population imbalances, so “state dominant party” stereotypes and state policy vastly exaggerate differences in distribution of individual belief, preference, and, in the absence of effective compulsion, behavior.
> To indicate the problem you can observed how inaccurate polls around politically charged choices has become when compared to actual choices.
They...haven’t, really, become particularly inaccurate. Poll-based predictions of binary outcomes of things that poll very close to even have become somewhat less reliable than they very briefly were before (they were a fairly new practice compared to just reporting polls without predictions, anyway, so its not like there was a well-established baseline) because when polls are near even, very slight changes in accuracy have an outsize effect on binary predictions.
> California 3.7M cumulative cases & 1,336 case/day current 7-day average vs. 39.5M population
Its 3.77M, so about 9.5% of population.
> Texas 2.9M cumulative case & 2,034 case/day current 7-day average vs. 29M population.
That's 10%.
> Florida 2.3M cumulative cases & 3,007 case/day 7-day average vs 21.5M population.
That's about 10.6%.
> FL and TX are worse on per capita cumulative cases, and much worse on current per capita new cases than California.
With the total cases being about the same so this does not support your conclusion that masks "drastically reduce spread".
I didn’t offer any conclusion except that the upthread claim that California had a much higher rate of COVID cases than FL/TX was bunk. To actually tease out the effects of mask use from jurisdictional case statistics, you’d also have to (1) have stats on mask use, which (despite people making claims about it in coordination with blatantly false claims about cases), I haven’t seen, and (2) have good stats for the other things that would reasonably be expected to contribute to differences in infection spread, (3) either have enough a priori knowledge of the contribution of the items in 2 to control for their effect in 1:1 comparisons, or have data from enough different places to do an analysis that determines the contributions of different factors.
Even with good mask stats, a head to head comparison of a couple states case numbers with mask stats wouldn’t otherwise be useful for anything.
Absolutely, 100% with you. And effectiveness of a mitigation should be determined before mandating it, and the positives and negatives of the mitigation should be well understood.
> I didn’t offer any conclusion except that the upthread claim that California had a much higher rate of COVID cases than FL/TX was bunk.
I was responding to the parents claim that masks “ drastically reduce the spread” which I think I’ve showed is an unsupported claim, although I stated my initial claim too strongly. There is no significant observable effect on state covid case count or state death to confirmed case count (which would to some degree control for some differences in testing).
> Even with good mask stats, a head to head comparison of a couple states case numbers with mask stats wouldn’t otherwise be useful for anything.
After one year of mandating this mitigation we should be able to see some observable difference to continue mandating that people cover their faces. This is not zero-cost, especially for the ones living alone, as the masks block facial expressions showing emotion and the masks make it harder to breathe which can on aggregate cause health issues. For instance, a large number of dental issues is caused by mask use [1].
[1] https://fineartsdentistry.com/how-face-masks-are-affecting-o...
There is also precedence for some of these statistics with Asian countries during the SARS pandemic, and in these places rule following is generally much better.
And with a smaller sample size there are some case studies from restaurant transmission. Staff seem to get infected less frequently than other diners, even though they are in the space for longer. There are a few possible explanations, but the fact that staff don't take their masks off is a pretty compelling one.
IMO that’s the least compelling reason. One big one would be the staff having way more contacts and thus higher exposure to the other circulating hCoVs as well as SARS-2 itself.
The evidence for face masks is weak at best. In my opinion they don’t even make sense theoretically unless you pretend that droplet transmission is the dominant transmission mode, which is completely unproven yet widely believed dogma (go figure)
I'm also not sure why you would assume staff have had more contacts. The people that were eating at indoor restaurants in the middle of the pandemic were unlikely the type to be limiting their contacts. Most case studies also checked for antibodies after the fact, not just active COVID testing, so prior immunity would have been detected.
Why didn't they make one with masks? Really can't be that hard to take a known infected person, sit them on a chair, arrange 100 people around them for a few hours with masks applied in a checkerboard pattern, and observe the results.
This is usually out of question for ethical reasons, but they already decided that that wasn't an obstacle, so why not (also) do a study on this?
(Or rather, arrange 10x 10 people, in 5 rounds putting a mask on the infected person.)
Lots of places have R hovering around 1. If it goes to 1.1 everything goes to shit, if it drops to 0.9. Everything will be fine.
So even if masks reduce spread by a tiny amount, even 10% better. That could easily swing you below 1 and save the day.
So what actually happens in reality is that if R is hovering around 1, there are going to be some places where it's actually above 1 and cases are growing exponentially, and some where it's below 1 and they're shrinking exponentially. The end result of this is that places where R is actually below 1 make up an exponentially shrinking proportion of all cases, and as this happens it causes the overall measurement of R to go back above 1.
I think you’re looking to find holes in a perfectly reasonable argument by adding complexity.
I don't think it's just a nitpicky minor thing. Governments have pretty regularly been making decisions and citing the value of "R" (they mean Rt), or "exponential growth", as a justification for new restrictions, apparently without realizing that by itself these things means little and justify nothing. Exponential growth can only be said to be a problem when taking into account the serial interval, the actual exponent, the starting population sizes, total population sizes, fixed capacity limits (e.g. hospital bed counts) and so on. Yet the scientists advising governments routinely ignore all those things.
It would be interesting to have a trial where everyone was wearing N95s, but we're not likely to see that.
Bavaria and Austria had FFP2/KN95 only while the rest of Germany had the choice of surgical masks or FFP2, where almost everybody picked surgical masks (cheaper, not as annoying to wear).
Now the rest of Germany switched to only FFP2 in public transport.
So we do have some population studies. It didn't seem to make much of a difference. I'd say the factor overwhelming almost every other measure is the weather. More people outside means dropping case numbers. More people at home means rising case numbers.
It's been true for influenza and cold, and it's still holding up for Corona.
If any measure would make sense it would be to allow people to meet outside while forbidding it at home. They only forbade at home without allowing outside meetups, which made home parties more likely.
If you forbid inside and outside meetups, people will meet in secret, even closing their Windows so the neighbors don't snitch (this is Germany after all, the nation of snitches, historically :D).
A little bit of basic human psychology could have prevented some of these waves.
The states with the highest mask use (NYC, CA) has some of the highest covid positive rates, so the data does not seem to back up your assertion.
NYC is not a state, so you can't directly compare it.
Deaths to confirmed cases is about 1.6% for all except NY. NY death to cases is 2.6% (52k deaths over 2m cases). However, this way of computing deaths is only indicative as you do not capture untested cases.
> Florida 2.3M cumulative cases & 3,007 case/day 7-day average vs 21.5M population.
That's about 10.6%.
> FL and TX are worse on per capita cumulative cases, and much worse on current per capita new cases than California.
With the total cases being about the same so this does not support your conclusion that masks "drastically reduce spread".
And when you combine 30% reductions for both the receiver and the emitter, it gets much higher.
As such, it’s likely even a 30% reduction (which is on the lower end of the range) in the viral load inhaled by someone could drastically reduce the chance of symptomatic COVID. is vague enough to not be an over statement, but it probably isn't easy to find much in the way of actual support for it either.
There is at least some evidence for it, e.g. a military base study that found less severe disease cases once masks started being worn.
Which is also not entirely true. There are quite a lot of regions (data from worldometer, statista) that have a better control of the virus despite people not giving a crap about face masks. For instance, Florida has 1600 deaths/M vs 3000 deaths/M in New Jersey. What'd be the explanation?
https://www.worldometers.info/coronavirus/usa/florida/
It looks like New Jersey was hit early on (when it was also rampant in New York) and Florida got hit later. It could be that hospitals do better at treating it now. Obviously that's speculative. But there are like 30 things I could think of that would make it difficult to make direct comparisons between states. Climate is another one -- Florida is nice all year round, so you can have family/friend gatherings outdoors. I also live in a warm state, and we've just done family/friend stuff outside, even Christmas. At this point we do things indoors when everyone there is vaccinated, and outdoors otherwise. In the case of Florida, turns out all those people that ignored restrictions and went to the beach were something like like 20x less likely to spread it than people spending time indoors. Again... speculative. Point being, you'd have to somehow have the data you need to properly control for a multitude of factors if you wanted to make direct comparisons (including some solid reasoning on what to control for).
What has on the other hand is extremely aggressive tracing, tracking, and quarantining of all potential contacts. I've had a few friends who were unlucky enough to get caught up in a cluster, and the ones who caught it got out of the hospital far quicker than their contacts got out of their government quarantine. Rather aggressive tactics, but it's effective.
But it's obvious that masks had nothing to do with it. There are plenty of unmasked activities (dining, drinking etc) that haven't spawned clusters, while masked activities (gym, dance club) spawned our big clusters.
I honestly don't believe that a mask can make such a MASSIVE difference when it comes to Hong Kong vs USA numbers. I don't have the answer, but it's something else, something foundationally different that makes them less prone to catching the virus. Genetics? No idea. I mean, c'mon, I can smell literally everything around me when I'm wearing a mask, but it protects from a virus? I'm not an expert by any means, but something tells me it's a joke.
Compare China to the US on: https://en.wikipedia.org/wiki/List_of_countries_by_obesity_r... . Obesity is the second biggest contributor to covid severity after age.
It's not fair to compare the US/Canada with East Asian countries like Hong Kong because the biggest contributor to covid fatality rates apart from population age is obesity rates, which are way lower in East Asia.
https://jsgist.org/?src=7b456a001284587cb90c7693ac0e6f3b
Also, if age is the #1 factor then Japan should have the worst covid as they have the most old people per capita but they don't. They also haven't locked down but they have worn masks. (not saying masks were why covid is so low here). Yes they are having a spike now. It's still tiny (1/20th) other countries with comparable populations sizes.
It's working okay but not as well as Korea.
Regardless of strategy, every country in the northern hemisphere that had a decent number of cases in spring 2020, got absolutely hammered in winter 2020.
Of course there are examples of regions where the one with more restrictions got a better result than the one with less. But there are simply way too many counter-examples where regions that did more, tried more, had more restrictions, still got a similar or worse outcome than comparable regions that didn't.
Here's a pretty fun quiz that highlights this complete lack of correlations in the actual data: https://www.covidchartsquiz.com/
5 is about influenza and 6 is not even worth the paper it's printed on.
No kidding, 6 is "recommending" people to wear a mask (and not even trying to control for the actual number).
So no, I have no qualms in calling study 6 useless, I've seen science fair projects with more scientific rigor and relevance than that.
---
This seems like a largely helpful characterization.
However:
> So they are left with a conundrum that places which use surgical masks seem to be better off, whereas randomized control studies of surgical masks show little to no benefit. This is what they try to explain with a mathematical model.
There wasn't really a conundrum. When the study writes:
> Moreover, randomized clinical trials show inconsistent or inconclusive results, with some studies reporting only a marginal benefit or no effect of mask use (5, 6).
, the two studies they cite with inconclusive data are ridiculously weak.
The first one (their "5") is [https://academic.oup.com/jid/article/201/4/491/861190]. This was published in 2010, with data from 2006-to-2007, for "influenza-like illness" (not SARS-CoV-2, nor even influenza (explained below)). Apparently they asked students living in the dorms to do certain behaviors for weeks at a time. They concluded:
> Conclusions: These findings suggest that face masks and hand hygiene may reduce respiratory illnesses in shared living settings and mitigate the impact of the influenza A(H1N1) pandemic.
And if that sounds inconclusive, yeah.. their study was ridiculously under-powered. In fact:
> This study has several limitations. First, influenza incidence was low, so it is likely that most ILI cases were not associated with influenza infection, even though the study was conducted during the influenza season.
So not only were they not writing about COVID, apparently the authors argue that most of the cases probably weren't even influenza. There're other huge problems with this study too.
The second one (their "6") is [https://pubmed.ncbi.nlm.nih.gov/33205991/]. This study assigned subjects a recommendation to wear masks -- presumably some who were recommended to wear masks didn't, while some who weren't recommended to wear masks did.
Even then, their statistical analysis was very noisy:
> Although the difference observed was not statistically significant, the 95% CIs are compatible with a 46% reduction to a 23% increase in infection.
And their stated limitations:
> Limitation: Inconclusive results, missing data, variable adherence, patient-reported findings on home tests, no blinding, and no assessment of whether masks could decrease disease transmission from mask wearers to others.
Point being that there's not really a conundrum related to things not fitting together so much as a simple lack of good data.
No need to be so picky.
The prior studies were neither well-done nor directly relevant. As such, they didn't constitute good evidence on the topic of mask-wearing for the recent pandemic. The proper understanding would then be that the effectiveness of masks was little informed by such studies.
So I think the evidential bar for requiring masks should be pretty low (unless it is a special case, like making people wear masks when they're exercising likely has larger negative side effects). Who cares if it may not do much? The cost of doing it is so low.
Whereas the evidential bar for policies that do more collateral damage ought to be higher.
Would you take security advice from Bruce Schneier after he proposed you to cover your keyboard in snakeoil because it might protect against computer viruses (and if not, there is not much damage)?
Regardless of how well they work in practice, there's a clear theoretical reason why masks could/should work: they catch salivary droplets containing the virus.
Covering your keyboard in oil is not even theoretically going to affect your computer's susceptibility to computer viruses.
In practice the snakeoil may prevent you from using your computer more frequently and therefore result in less infections.
In practice I observe a lot of people fumbling out unwashed pieces of cloth from their pockets and placing them in their face, hoping it would preserve their respiratory health.
"What assumptions are required to make this conclusion valid?"
If the assumptions turn out to be outlandish, then maybe we can't take the conclusion to be true.
They admit RCTs show masks don't matter, so they put together a few differential equations (which most certainly could be chosen differently to support something else entirely) such that in some domains (virus-limited) masks help a lot, in some (virus-rich) not.
Then they implicitly assume that RCTs didn't show anything because they were done in virus-rich environments and somehow conclude from this dubious claim that "Face masks effectively limit the probability of SARS-CoV-2 transmission".
Am I not reading it correctly?
Given what we know about the covid transmission now, it was absolutely the N95s that prevented these doctors from getting infected.
This was the biggest event of the last 50 years, we can throw a few billion at a real trial.
Can't knowingly give humans less protection. It's not a money problem.
Equipoise demands you don't assume one outcome.
Otherwise every single pharmaceutical trial would be impossible since "we can't knowingly give humans a placebo"
If the effect is large and significant we will see it quickly and can halt asap.
Lots pharma trials are against current best standard of care and not placebo. Placebo trials are never done in cases where effective therapy is available.
https://www.cancer.net/research-and-advocacy/clinical-trials...
>A: Placebo-controlled trials are never appropriate when a highly effective or potentially curative therapy is available for a patient. An exception is unless the trial allows the patient to receive the new treatment/placebo in addition to the potentially curative therapy. For example, let’s say that a promising new treatment is in development for advanced testicular cancer, a disease that is curable in many cases with the use of chemotherapy. It would not be appropriate for a clinical trial to randomize patients between the new treatment and placebo because potentially curative chemotherapy already exists. However, it might be appropriate to randomize between standard chemotherapy plus the new drug or standard chemotherapy plus placebo because in both cases, patients will receive the standard, potentially curative treatment.
https://en.wikipedia.org/wiki/Placebo-controlled_study#Decla...
Researchers need to measure concentration of virus in the environment as well as mask usage to show the efficacy of masks in real world situations.