My son says he hasn't had to wear his mask in over a week and a half. Most teachers aren't saying anything anymore.
Hence the Covid problems with your school system in Florida will continue --- and you will soon be going remote.
Re-opening Schools in the era of COVID
- Thread starter gwb-trading
- Start date
Hence the Covid problems with your school system in Florida will continue --- and you will soon be going remote.
Sure, that's what they say.
Wanna make a wager?
Come on, man. I know you might not be able to afford $100 on your salary (hell, you shouldn't be paid at all considering how much work you don't do) but lets do it. Simple wager. What say you?
Fauci The Greatest Liar in Gov’t in History
In my entire career of dealing with governments around the world, NEVER in my 50 years have I EVER encounter such a bureaucrat that is such a blatant liar who is putting the entire health of humanity up at risk for it is IMPOSSIBLE to believe anything this pretend doctor Anthony Fauci who has not seen a patient since 1968. In fact, after completing his medical residency in 1968, Fauci immediately joined the National Institutes of Health (NIH) as a clinical associate. So he has never had a patient and has NEVER had to look into the eyes of someone.
He is simply a professional liar and that is just amazing. Note, when world leaders and the Queen of England all gathered, there was no social distancing and no masks and they certainly did not quarantine for 2 weeks upon arriving. It is always two sets of rules (1) for the elite and (2) for us the Great Stupid Unwashed. He is now saying schools to mandate vaccines for children is a “good idea“ when the FDA has not approved this for children.
https://www.theburningplatform.com/2021/09/01/fauci-the-greatest-liar-in-govt-in-history/
In my entire career of dealing with governments around the world, NEVER in my 50 years have I EVER encounter such a bureaucrat that is such a blatant liar who is putting the entire health of humanity up at risk for it is IMPOSSIBLE to believe anything this pretend doctor Anthony Fauci who has not seen a patient since 1968. In fact, after completing his medical residency in 1968, Fauci immediately joined the National Institutes of Health (NIH) as a clinical associate. So he has never had a patient and has NEVER had to look into the eyes of someone.
He is simply a professional liar and that is just amazing. Note, when world leaders and the Queen of England all gathered, there was no social distancing and no masks and they certainly did not quarantine for 2 weeks upon arriving. It is always two sets of rules (1) for the elite and (2) for us the Great Stupid Unwashed. He is now saying schools to mandate vaccines for children is a “good idea“ when the FDA has not approved this for children.
https://www.theburningplatform.com/2021/09/01/fauci-the-greatest-liar-in-govt-in-history/
Science regarding masks. Not sCiEnCe. Be warned, requires critical thinking skills, so you might not understand if all you are about is a narrative:
Emily Burns #SmilesMatter DM’s OK
The overall effects of this study are miniscule—0.07% absolute reduction in seroprevalence. But the topline finding is “We decreased seroprevalence by 10%!”
Technically true...
But even this finding is questionable. Let's explore.
poverty-action.org/sites/default/…
What the study ACTUALLY measures is the impact of mask promotion on symptom reporting. Only if a person reports symptoms, are they asked to participate in a serology study—and only 40% of those with symptoms chose to have their blood taken.
Is it possible that that highly moralistic framing and monetary incentives given to village elders for compliance might dissuade a person from reporting symptoms representing individual and collective moral failure—one that could cost the village money? Maybe?
Given that the difference is tiny, if this had even a small impact, it could completely skew the results. Might the same factors be at play when an individual weighed whether or not to be tested? After all, a positive test would indicate that your village was “failing”
The fact that age stratification shows that interventions had no impact on younger people, but did on older people calls this finding into question. There is no physical mechanism by which universal masking would protect the elderly, but not the young.
Such a result could be explained by the mask promotion campaign creating a sense of fear that resulted in elderly sequestering themselves. If that were the case, a more elderly-targeted approach could achieve the same results.
It is also possible that elderly people in intervention groups were less likely to report symptoms. 60+ are 2-3x more likely to be symptomatic than <40. Again, when having symptoms means you failed your village, might you choose not to mention them?
Age and Likelihood of Developing Symptoms and Critical Disease After SARS-CoV-2 Infection in ItalyThis cohort study examines the association of age with the likelihood of experiencing symptoms and progressing to critical illness after infection with severe acute respiratory syndrome coronavirus 2 …https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2777314
Perhaps the most compelling aspect of the study is the difference between surgical and cloth mask villages. The authors claim that this is supported by the different filtration efficiencies of cloth and surgical masks.
However, these filtration efficiencies are taken in a no-gap scenario. And there is ALWAYS a gap. We can see that when considering gaps—the most important element of filtration--fabric & surgical masks perform nearly identically--not at all.
Given physical improbability, it is possible that this effect too, might be an artifact of bias in the control group. Surgical masks are “fancier," & might feel like more of an intervention, and may make those in such a village even more likely to repress symptom reporting.
Whatever the result of the surgical masks, this shows that the impact from cloth masks is 0. (.02% absolute reduction and a p-value of 0.540 is zero, even if the authors want to pretend otherwise).
Color me skeptical on this one. This study increased mask wearing to 40%. In much of blue America mask compliance approached 100% for almost a year. Even with that kind of incredible compliance, there was no impact on case transmission rates.
Let's see what the scientific community is saying about this study of masks in Bangladesh. Yes, science is important. This large scale study proves conclusively that masks are effective in stopping the spread of Covid.
The study that 'should basically end any scientific debate' about masks
https://theweek.com/coronavirus/100...sically-end-any-scientific-debate-about-masks
A massive randomized trial on how well masks hold up against symptomatic COVID-19 infections may be one of the most crucial studies of the coronavirus pandemic because it was able to solve the tricky issue of examining mask-wearing at a community level rather than an individual one.
The study involved launching pro-mask campaigns in some Bangladeshi villages, but not others, and the authors made two key findings. First, they determined that the public health interventions nearly tripled mask usage from 13 percent to 42 percent. Secondly, they discovered — by conducting sero-surveys backed up by interviews about COVID-19 symptoms and medical history — that masks did their job and reduced symptomatic infections in the communities that were subject to the campaigns by 9.3 percent. Jason Abaluck, an economist at Yale University who helped lead the study, toldThe Washington Post that figure would probably be higher if masking was universal.
There were a few other key notes in the study. Surgical masks were found to be particularly effective, while the jury is still out on cloth masks. And they were more effective in people older than 50, which could be explained by a few factors, including that young people were less likely to be symptomatic either way. They also may have been less compliant when it comes to masks.
Either way, Abaluck is pretty confident about the research, arguing that it "should basically end any scientific debate about whether masks can be effective in combating [COVID-19] at the population level" and calling it "a nail in the coffin" for anti-mask arguments. Read more atThe Washington Post.
This is a very easy read that shows you why the study is such a joke. That is, if you understand how to do a medical RCT (which clearly, you do not).
Let’s start with the basics on how to do a medical RCT.
- establish the starting condition. if you do not know this, you have no idea what any later numbers or changes mean. it also makes step 2 impossible.
- randomize cohorts into even groups in terms of start state and risk. this way, they are truly comparable. randomizing by picking names out of a hat is not good enough. you can wind up doing a trial on a heart med where 20% of active arm and 40% of control have hypertension. you cannot retrospectively risk adjust in a prospective trial. this needs to be done with enrollment and study arm balancing. fail this, and you can never recover.
- isolate the variable you seek to measure. to know if X affects Y, you need to hold all other variables constant. only univariate analysis is relevant to a unitary intervention. if you give each patient 4 drugs, you cannot tell how much effect only drug 1 had. you’ve created a multivariate system. get this wrong, and you have no idea what you measured.
- collect outcomes data in a defined, measurable, and equal fashion. uniformity is paramount. if you measure unevenly and haphazardly, you have no idea what manner of cross confounds and bias you have added. you will never be able to separate signal from measurement artifacts. this means your data is junk and cannot support conclusions.
- set clear outcomes and measures of success. you need to decide these ahead of time and lay them out. data mining for them afterwards is called “p-hacking” and it’s literal cheating. patterns emerge in any random data set. finding them proves nothing. this is how you perform a study that will not replicate. it’s a junk analysis.
and balance cohorts: this was a total fail and doomed them before they even began.
this is their key claim in the finding. but notice what is jarringly absent: any clear idea what prior disease exposure was in each of these villages and village cohorts at study commencement. small current seroprevalence probes cannot tell us this. there is no way to know if one had had a big wave and another had not. so, we literally have no idea what had happened here and on what sort of population. we have no idea if we are studying a naïve population or not.
performing this study without a clear and broad based baseline and sense of past exposure is absurd. this is a tiny signal (7 in 10,000) we need a very high precision in start state. it’s absent. that’s the ballgame right there. we do not know and can never know what happened. even miniscule variance in prior exposure would swamp this.
for cohorts, they paired by past covid cases (so thinly reported as to be near meaningless and a possible injection of bias as testing rates may vary by village) and tried to establish a “cases per person” pair metric to sort cohorts. this modality is invalid on its face without reference to testing levels. you have no idea if high cases are high testing or high disease prevalence. you have no idea how much testing varied.
and testing levels are so low, the authors estimate a 0.55% case detection rate. so a modality invalid even if data were good is multiplied in terms of error because the data is terrible (and likely wildly variable by geography)
nowhere here does the word “testing” or “sample rate” appear.
so this study ended before it even began.
this was not useful randomization. this was garbage in garbage out especially when you later seek to use “symptomatic seroprevalence” as primary outcome. that’s false equivalence. if you’re going to use seroprevalence as an outcome, you need to measure it as a start state and balance the cohorts using it. period. failure to do so invalidates everything. you cannot run a “balance test” on current IgG and presume you know what happened last year, certainly not to the kind of precision needed to find a 0.0007 signal meaningful.
this was an unknown start state in terms of highly relevant variables and the cohorts were not normalized (or even measured) for it.
they also failed to measure masked vs unmasked seroprev in any given village. that would have been useful control data. it seems like they really just missed all the relevant info here.
it’s pure statistical legerdemain.
strike 1 and 2.
but it gets worse. much worse.
isolate the variable you seek to measure.
to claim that masks caused any given variance in outcome, you need to isolate masks as a variable. they didn’t. this was a whole panoply of interventions, signage, hectoring, nudges, payments, and psychological games. it had hundreds of known effects and who knows how many unknown ones.
we have zero idea what’s being measured and even some of those variables that were measured showed high correlation and thus pose confounds. when you’re upending village life, claiming one aspect made the difference becomes statistically impossible. the system becomes hopelessly multivariate and cross-confounded.
the authors admit it themselves (and oddly do not seem to grasp that this invalidates their own mask claims)
who knows what the effects of the “intervention package” were outside of masking? maybe it also leads to more hand washing or taking of vitamins. it seemed to effect distancing (though i doubt that mattered).
but the biggest hole here is that that which affects one attitude can affect another.
if you’ve been co-opted to “lead by example” and put up signage etc or are being paid to mask you may change your attitude about reporting symptoms.
people want to please researchers and paymasters and this is a classic violation of a double blind system. the subjects should no know if they are control or active arm, but in the presence of widespread positive mask messaging, they do.
so maybe they think “i don’t not want to tell them i’m sick.” especially if “being sick” has been vilified.
or maybe they fail to focus on minor symptoms because they are masked and feel safe or were having more trouble breathing anyhow.
this makes a complete mess and injects all manner of unpredictable bias into the “results” because the results are based on “self reporting” a notoriously inaccurate modality.
look at the wide variance in self reporting on masks and reality cited in this very study.
yet we’re to accept self reporting of symptoms in the face of widespread and persistent moral suasion in one arm and not the other and assume that the same interventions that had a large effect on mask wear affected no other attitudes?
no way. this non-blinded issue combining with self reporting adds one tailed error bars so large to this system that they swamp any signal.
as is so often the case, gatopal @Emily_Burns-V has a great take here:
Emily Burns #SmilesMatter DM’s OK @Emily_Burns_V2/2/ What the study ACTUALLY measures is the impact of mask promotion on symptom reporting. Only if a person reports symptoms, are they asked to participate in a serology study—and only 40% of those with symptoms chose to have their blood taken.
September 1st 2021
11 Retweets58 Likes
Emily Burns #SmilesMatter DM’s OK @Emily_Burns_V3/ Is it possible that that highly moralistic framing and monetary incentives given to village elders for compliance might dissuade a person from reporting symptoms representing individual and collective moral failure—one that could cost the village money? Maybe?
September 1st 2021
8 Retweets44 Likes
this is exactly why good studies are blinded. if they are not, the subjects seek to please the researchers and it wrecks the data. adding self reporting is a multiplier on this problem. this whole methodology is junk and so is the data it produced.
for proof of this, one need only look at the age stratification:
first off, this proves conclusively that “your mask does not protect me.” (though we already knew that) if it did, it would protect everyone, not just old people. but it didn’t. and the idea that it stopped old people from getting sick but not young people is similarly implausible.
the odds on bet here is that old people were more inclined to please the researchers than young people and that they failed to report symptoms as a result.
i establish this as the null hypothesis.
can anyone demonstrate that this data makes a more compelling case for “masks worked on old people but not young people and thus decreased overall disease”?
because i very much doubt it.
and unless you can, you must abandon this study as a possibly interesting piece of sociology, but as having zero validated epidemiological relevance.
so, that’s strikes 3 and 4.
i’m honestly a bit unsure about whether we can go on to call it a perfect 5. they did pre-register the study and describe end states, but they never established start states so we have no idea what actual change was.
so, they called their end results shot beforehand (as they should) but then left us with no way to measure change even if the measurement was good, and as we have seen, the measurement was terrible.
so i’m going to sort of punt on scoring this one and assign it an N/A. establishing an outcome and then providing no meaningful way to measure it is not p-hacking per se, but it is also not in any way useful.
so, all in all, it’s just impossible to take this study seriously, especially as it flies in the face of about 100 other studies that WERE well designed.
read many HERE including RCT’s showing not only a failure as source control, but in higher rates of post op infections from surgeons wearing masks in operating theaters vs those that did not.
the WHO said so in 2019.
and the DANMASK study in denmark was a gold standard study for variable isolation and showed no efficacy.
perhaps most hilariously, the very kansas counties data the CDC tried to cherry pick to claim masks worked went on to utterly refute them when the covid surge came.
the evidence that masks fail to stop covid spread is strong, deep, wide, and has a lot of high quality studies. (many more here from the swiss)
to refute them would take very high quality data from well performed studies and counter to the current breathless histrionics of masqueraders desperate for a study to wave around to confirm their priors, this is not that.
to claim “masks worked” with ~40% compliance in the light of total fails with 80-95% is so implausible as to require profound and solid evidence that is nowhere provided here.
this is a junk output from a junk methodology imposed upon an invalid randomization without reference to a meaningful start state for data.
this is closer to apples to orangutans than even apples and oranges.
thus, this and many more like is an absurd and impossible take from this study.
it does not show efficacy. it does not show ANYTHING outside of how an object lesson in poor study design and data collection can be weaponized into a political talking point.
this study is an outright embarrassment and a huge black eye for the NBER et al.
this is not even wrong.
it’s just an epic concatenation of bad techniques and worse data handling used to provide pretext for an idea the researchers clearly favored. there is no way to separate bias from fact or data from artifact.
calling this proof of anything is simply proof of either incompetence or malfeasance.
which one makes you want to listen to the folks pushing it?
From the very liberal Atlantic. Good to know the dialogue is changing. First the WSJ this morning, now the Atlantic.
The Harms of Masking Young Students Are Real
The educational cost of face coverings is far better established than the benefits of mandates.
By Vinay Prasad
Matthew Hatcher / Getty
6:30 AM ET
About the author: Vinay Prasad, a hematologist and oncologist, is an associate professor of epidemiology and biostatistics at UC San Francisco.
Scientists have an obligation to strive for honesty. And on the question of whether kids should wear masks in schools—particularly preschools and elementary schools—here is what I conclude: The potential educational harms of mandatory-masking policies are much more firmly established, at least at this point, than their possible benefits in stopping the spread of COVID-19 in schools. To justify continued masking of schoolkids—with no end date in sight—we have to prove that masks benefit kids, and at what ages. States and communities that are considering masking policies just to be safe should recognize that being overly cautious has a cost, while the benefits are uncertain.
Lucy McBride: Fear of COVID-19 in kids is getting ahead of the data
For most able-bodied adults, masks in public indoor settings pose only minor inconveniences. But children—who even amid the worrisome Delta-variant surge are experiencing serious outcomes from COVID-19 at far lower rates than people in older age groups are—have different needs and vulnerabilities than adults. Early childhood is a crucial period when humans develop cultural, language, and social skills, including the ability to detect emotion on other people’s faces. Social interactions with friends, parents, and caregivers are integral to fostering children’s growth and well-being.
Cloth masks do filter some aerosols, albeit not the majority, so they might catch some exhaled viral particles. Newly released results from a large trial in rural Bangladesh found that the widespread use of surgical masks by adults yields a significant reduction in the spread of symptomatic COVID-19. (The effect of cloth masks was more ambiguous, and the study did not include children.) But the issue facing educators and parents is whether a policy of mandatory masking makes school safer than a policy of optional masking—and whether the difference is enough to justify the imposition on kids.
No scientific consensus exists about the wisdom of mandatory-masking rules for schoolchildren. The World Health Organization, which recommends that children 12 and older wear masks under the same circumstances that adults do, specifically advises against masking kids age 5 and younger. Many European nations have been taking the agency’s advice. The United Kingdom has emphasized rapid testing instead of masking and has not required elementary-school students or their teachers to wear a face covering.
In the United States, though, current CDC and American Academy of Pediatrics guidelines call for kids age 2 and up to wear a mask in indoor school or day-care settings; the CDC specifically makes exceptions for napping and eating. (Masking very young children during sleep is inadvisable because of the risk of suffocation.) In other words, the prevailing wisdom in the U.S. calls for 2-to-4-year-olds to wear masks in day care for six or more hours while they are awake, but go unmasked while sleeping side by side in the same room. Shielding children from all coronavirus exposure is difficult for another practical reason: Little kids fidget with their masks.
A health recommendation that takes little account of how human beings act and what they need is unlikely to be successful. For instance, a diet that told you to eat just two carrots a day would theoretically result in dramatic weight loss. In practice, such a regimen could starve you of nutrients that your body requires. Moreover, overly strict diets often result in no weight loss at all, because nobody can stick to them. Similarly, mask mandates can be challenging for little children to follow and deprive them of stimuli they need.
In addition to recommending masks for young kids, CDC guidelines also urge masks for most vaccinated caregivers who work in infant day-care centers. This advice also deviates from standard practice in other nations, including the U.K. Many studies support the importance of babies seeing caregivers’ faces, and prior to the arrival of COVID-19, many American professional organizations, including the AAP, strongly agreed.
At least some fears about masking are exaggerated. Despite the claims of some critics, kids who wear a face covering are unlikely to suffer any meaningful problems exhaling carbon dioxide or inhaling oxygen. However, some mask wearers who exert themselves may subjectively feel short of breath. Unfortunately, some school districts are brushing aside that concern too. K–8 schools in affluent and highly educated Palo Alto, California, require kids to mask even outdoors at recess. San Diego schools recently announced an outdoor mask mandate as well. Yet scientists have known for some time that outdoor transmission is exceedingly rare, and many experts believe that outdoor masking is misguided. When masks are required in outdoor settings, kids may experience limitations in play, exercise tolerance, and socialization. And for what gain?
The benefits of mask requirements in schools might seem self-evident—they have to help contain the coronavirus, right?—but that may not be so. In Spain, masks are used in kids ages 6 and older. The authors of one study there examined the risk of viral spread at all ages. If masks provided a large benefit, then the transmission rate among 5-year-olds would be far higher than the rate among 6-year-olds.
The results don’t show that. Instead, they show that transmission rates, which were low among the youngest kids, steadily increased with age—rather than dropping sharply for older children subject to the face-covering requirement. This suggests that masking kids in school does not provide a major benefit and might provide none at all. And yet many officials prefer to double down on masking mandates, as if the fundamental policy were sound and only the people have failed.
Céline R. Gounder: Americans are losing sight of the pandemic endgame
Before limiting the amount of face-to-face human contact that children experience during many of their waking hours, policy makers should be acutely aware of what children could lose. Unfortunately, the downside of school mask requirements for children has been difficult to assess systematically because, until this pandemic, face-covering policies were never previously imposed on so many children for such a long period of time. Longitudinal studies cannot be performed on long-term outcomes, because there are no children in prior generations to study.
In the absence of systematic research on the costs and benefits of mask requirements for kids, the issue has been transformed into a right-left political battle. In addition to masking 2-year-olds, the CDC recommends the vaccination of people ages 12 and older. Because both recommendations come from a respected federal agency, supporters of both are likely to say they are “following the science.” But the evidence that supports vaccination is indisputable, in the form of multiple randomized studies, whereas the evidence to support school mask mandates for young kids is fragmentary at best. The problem with overselling unproven recommendations is that it risks turning people away from well-grounded ones.
Unfortunately, scientists have failed to conduct the kind of randomized trials that can provide more reliable answers. Here schools, counties, or districts would be assigned a mandatory or optional masking policy, and researchers could simply track their experience to determine which schools had more coronavirus spread. Kids wouldn’t be banned or prohibited from wearing masks, but rather the policy of making all kids wear masks would be rigorously tested.
In mid-March 2020, few could argue against erring on the side of caution. But nearly 18 months later, we owe it to children and their parents to answer the question properly: Do the benefits of masking kids in school outweigh the downsides? The honest answer in 2021 remains that we don’t know for sure.
Vinay Prasad, a hematologist and oncologist, is an associate professor of epidemiology and biostatistics at UC San Francisco.
The Harms of Masking Young Students Are Real
The educational cost of face coverings is far better established than the benefits of mandates.
By Vinay Prasad
Matthew Hatcher / Getty
6:30 AM ET
About the author: Vinay Prasad, a hematologist and oncologist, is an associate professor of epidemiology and biostatistics at UC San Francisco.
Scientists have an obligation to strive for honesty. And on the question of whether kids should wear masks in schools—particularly preschools and elementary schools—here is what I conclude: The potential educational harms of mandatory-masking policies are much more firmly established, at least at this point, than their possible benefits in stopping the spread of COVID-19 in schools. To justify continued masking of schoolkids—with no end date in sight—we have to prove that masks benefit kids, and at what ages. States and communities that are considering masking policies just to be safe should recognize that being overly cautious has a cost, while the benefits are uncertain.
Lucy McBride: Fear of COVID-19 in kids is getting ahead of the data
For most able-bodied adults, masks in public indoor settings pose only minor inconveniences. But children—who even amid the worrisome Delta-variant surge are experiencing serious outcomes from COVID-19 at far lower rates than people in older age groups are—have different needs and vulnerabilities than adults. Early childhood is a crucial period when humans develop cultural, language, and social skills, including the ability to detect emotion on other people’s faces. Social interactions with friends, parents, and caregivers are integral to fostering children’s growth and well-being.
Cloth masks do filter some aerosols, albeit not the majority, so they might catch some exhaled viral particles. Newly released results from a large trial in rural Bangladesh found that the widespread use of surgical masks by adults yields a significant reduction in the spread of symptomatic COVID-19. (The effect of cloth masks was more ambiguous, and the study did not include children.) But the issue facing educators and parents is whether a policy of mandatory masking makes school safer than a policy of optional masking—and whether the difference is enough to justify the imposition on kids.
No scientific consensus exists about the wisdom of mandatory-masking rules for schoolchildren. The World Health Organization, which recommends that children 12 and older wear masks under the same circumstances that adults do, specifically advises against masking kids age 5 and younger. Many European nations have been taking the agency’s advice. The United Kingdom has emphasized rapid testing instead of masking and has not required elementary-school students or their teachers to wear a face covering.
In the United States, though, current CDC and American Academy of Pediatrics guidelines call for kids age 2 and up to wear a mask in indoor school or day-care settings; the CDC specifically makes exceptions for napping and eating. (Masking very young children during sleep is inadvisable because of the risk of suffocation.) In other words, the prevailing wisdom in the U.S. calls for 2-to-4-year-olds to wear masks in day care for six or more hours while they are awake, but go unmasked while sleeping side by side in the same room. Shielding children from all coronavirus exposure is difficult for another practical reason: Little kids fidget with their masks.
A health recommendation that takes little account of how human beings act and what they need is unlikely to be successful. For instance, a diet that told you to eat just two carrots a day would theoretically result in dramatic weight loss. In practice, such a regimen could starve you of nutrients that your body requires. Moreover, overly strict diets often result in no weight loss at all, because nobody can stick to them. Similarly, mask mandates can be challenging for little children to follow and deprive them of stimuli they need.
In addition to recommending masks for young kids, CDC guidelines also urge masks for most vaccinated caregivers who work in infant day-care centers. This advice also deviates from standard practice in other nations, including the U.K. Many studies support the importance of babies seeing caregivers’ faces, and prior to the arrival of COVID-19, many American professional organizations, including the AAP, strongly agreed.
At least some fears about masking are exaggerated. Despite the claims of some critics, kids who wear a face covering are unlikely to suffer any meaningful problems exhaling carbon dioxide or inhaling oxygen. However, some mask wearers who exert themselves may subjectively feel short of breath. Unfortunately, some school districts are brushing aside that concern too. K–8 schools in affluent and highly educated Palo Alto, California, require kids to mask even outdoors at recess. San Diego schools recently announced an outdoor mask mandate as well. Yet scientists have known for some time that outdoor transmission is exceedingly rare, and many experts believe that outdoor masking is misguided. When masks are required in outdoor settings, kids may experience limitations in play, exercise tolerance, and socialization. And for what gain?
The benefits of mask requirements in schools might seem self-evident—they have to help contain the coronavirus, right?—but that may not be so. In Spain, masks are used in kids ages 6 and older. The authors of one study there examined the risk of viral spread at all ages. If masks provided a large benefit, then the transmission rate among 5-year-olds would be far higher than the rate among 6-year-olds.
The results don’t show that. Instead, they show that transmission rates, which were low among the youngest kids, steadily increased with age—rather than dropping sharply for older children subject to the face-covering requirement. This suggests that masking kids in school does not provide a major benefit and might provide none at all. And yet many officials prefer to double down on masking mandates, as if the fundamental policy were sound and only the people have failed.
Céline R. Gounder: Americans are losing sight of the pandemic endgame
Before limiting the amount of face-to-face human contact that children experience during many of their waking hours, policy makers should be acutely aware of what children could lose. Unfortunately, the downside of school mask requirements for children has been difficult to assess systematically because, until this pandemic, face-covering policies were never previously imposed on so many children for such a long period of time. Longitudinal studies cannot be performed on long-term outcomes, because there are no children in prior generations to study.
In the absence of systematic research on the costs and benefits of mask requirements for kids, the issue has been transformed into a right-left political battle. In addition to masking 2-year-olds, the CDC recommends the vaccination of people ages 12 and older. Because both recommendations come from a respected federal agency, supporters of both are likely to say they are “following the science.” But the evidence that supports vaccination is indisputable, in the form of multiple randomized studies, whereas the evidence to support school mask mandates for young kids is fragmentary at best. The problem with overselling unproven recommendations is that it risks turning people away from well-grounded ones.
Unfortunately, scientists have failed to conduct the kind of randomized trials that can provide more reliable answers. Here schools, counties, or districts would be assigned a mandatory or optional masking policy, and researchers could simply track their experience to determine which schools had more coronavirus spread. Kids wouldn’t be banned or prohibited from wearing masks, but rather the policy of making all kids wear masks would be rigorously tested.
In mid-March 2020, few could argue against erring on the side of caution. But nearly 18 months later, we owe it to children and their parents to answer the question properly: Do the benefits of masking kids in school outweigh the downsides? The honest answer in 2021 remains that we don’t know for sure.
Vinay Prasad, a hematologist and oncologist, is an associate professor of epidemiology and biostatistics at UC San Francisco.
This is a very easy read that shows you why the study is such a joke. That is, if you understand how to do a medical RCT (which clearly, you do not).
Let’s start with the basics on how to do a medical RCT.
Establish the Starting Condition/Randomize Cohorts into even groups:
- establish the starting condition. if you do not know this, you have no idea what any later numbers or changes mean. it also makes step 2 impossible.
- randomize cohorts into even groups in terms of start state and risk. this way, they are truly comparable. randomizing by picking names out of a hat is not good enough. you can wind up doing a trial on a heart med where 20% of active arm and 40% of control have hypertension. you cannot retrospectively risk adjust in a prospective trial. this needs to be done with enrollment and study arm balancing. fail this, and you can never recover.
- isolate the variable you seek to measure. to know if X affects Y, you need to hold all other variables constant. only univariate analysis is relevant to a unitary intervention. if you give each patient 4 drugs, you cannot tell how much effect only drug 1 had. you’ve created a multivariate system. get this wrong, and you have no idea what you measured.
- collect outcomes data in a defined, measurable, and equal fashion. uniformity is paramount. if you measure unevenly and haphazardly, you have no idea what manner of cross confounds and bias you have added. you will never be able to separate signal from measurement artifacts. this means your data is junk and cannot support conclusions.
- set clear outcomes and measures of success. you need to decide these ahead of time and lay them out. data mining for them afterwards is called “p-hacking” and it’s literal cheating. patterns emerge in any random data set. finding them proves nothing. this is how you perform a study that will not replicate. it’s a junk analysis.
and balance cohorts: this was a total fail and doomed them before they even began.
this is their key claim in the finding. but notice what is jarringly absent: any clear idea what prior disease exposure was in each of these villages and village cohorts at study commencement. small current seroprevalence probes cannot tell us this. there is no way to know if one had had a big wave and another had not. so, we literally have no idea what had happened here and on what sort of population. we have no idea if we are studying a naïve population or not.
performing this study without a clear and broad based baseline and sense of past exposure is absurd. this is a tiny signal (7 in 10,000) we need a very high precision in start state. it’s absent. that’s the ballgame right there. we do not know and can never know what happened. even miniscule variance in prior exposure would swamp this.
for cohorts, they paired by past covid cases (so thinly reported as to be near meaningless and a possible injection of bias as testing rates may vary by village) and tried to establish a “cases per person” pair metric to sort cohorts. this modality is invalid on its face without reference to testing levels. you have no idea if high cases are high testing or high disease prevalence. you have no idea how much testing varied.
and testing levels are so low, the authors estimate a 0.55% case detection rate. so a modality invalid even if data were good is multiplied in terms of error because the data is terrible (and likely wildly variable by geography)
nowhere here does the word “testing” or “sample rate” appear.
so this study ended before it even began.
this was not useful randomization. this was garbage in garbage out especially when you later seek to use “symptomatic seroprevalence” as primary outcome. that’s false equivalence. if you’re going to use seroprevalence as an outcome, you need to measure it as a start state and balance the cohorts using it. period. failure to do so invalidates everything. you cannot run a “balance test” on current IgG and presume you know what happened last year, certainly not to the kind of precision needed to find a 0.0007 signal meaningful.
this was an unknown start state in terms of highly relevant variables and the cohorts were not normalized (or even measured) for it.
they also failed to measure masked vs unmasked seroprev in any given village. that would have been useful control data. it seems like they really just missed all the relevant info here.
it’s pure statistical legerdemain.
strike 1 and 2.
but it gets worse. much worse.
isolate the variable you seek to measure.
to claim that masks caused any given variance in outcome, you need to isolate masks as a variable. they didn’t. this was a whole panoply of interventions, signage, hectoring, nudges, payments, and psychological games. it had hundreds of known effects and who knows how many unknown ones.
we have zero idea what’s being measured and even some of those variables that were measured showed high correlation and thus pose confounds. when you’re upending village life, claiming one aspect made the difference becomes statistically impossible. the system becomes hopelessly multivariate and cross-confounded.
the authors admit it themselves (and oddly do not seem to grasp that this invalidates their own mask claims)
who knows what the effects of the “intervention package” were outside of masking? maybe it also leads to more hand washing or taking of vitamins. it seemed to effect distancing (though i doubt that mattered).
but the biggest hole here is that that which affects one attitude can affect another.
if you’ve been co-opted to “lead by example” and put up signage etc or are being paid to mask you may change your attitude about reporting symptoms.
people want to please researchers and paymasters and this is a classic violation of a double blind system. the subjects should no know if they are control or active arm, but in the presence of widespread positive mask messaging, they do.
so maybe they think “i don’t not want to tell them i’m sick.” especially if “being sick” has been vilified.
or maybe they fail to focus on minor symptoms because they are masked and feel safe or were having more trouble breathing anyhow.
this makes a complete mess and injects all manner of unpredictable bias into the “results” because the results are based on “self reporting” a notoriously inaccurate modality.
look at the wide variance in self reporting on masks and reality cited in this very study.
yet we’re to accept self reporting of symptoms in the face of widespread and persistent moral suasion in one arm and not the other and assume that the same interventions that had a large effect on mask wear affected no other attitudes?
no way. this non-blinded issue combining with self reporting adds one tailed error bars so large to this system that they swamp any signal.
as is so often the case, gatopal @Emily_Burns-V has a great take here:
2/2/ What the study ACTUALLY measures is the impact of mask promotion on symptom reporting. Only if a person reports symptoms, are they asked to participate in a serology study—and only 40% of those with symptoms chose to have their blood taken.
September 1st 2021
11 Retweets58 Likes
3/ Is it possible that that highly moralistic framing and monetary incentives given to village elders for compliance might dissuade a person from reporting symptoms representing individual and collective moral failure—one that could cost the village money? Maybe?
September 1st 2021
8 Retweets44 Likes
this is exactly why good studies are blinded. if they are not, the subjects seek to please the researchers and it wrecks the data. adding self reporting is a multiplier on this problem. this whole methodology is junk and so is the data it produced.
for proof of this, one need only look at the age stratification:
first off, this proves conclusively that “your mask does not protect me.” (though we already knew that) if it did, it would protect everyone, not just old people. but it didn’t. and the idea that it stopped old people from getting sick but not young people is similarly implausible.
the odds on bet here is that old people were more inclined to please the researchers than young people and that they failed to report symptoms as a result.
i establish this as the null hypothesis.
can anyone demonstrate that this data makes a more compelling case for “masks worked on old people but not young people and thus decreased overall disease”?
because i very much doubt it.
and unless you can, you must abandon this study as a possibly interesting piece of sociology, but as having zero validated epidemiological relevance.
so, that’s strikes 3 and 4.
i’m honestly a bit unsure about whether we can go on to call it a perfect 5. they did pre-register the study and describe end states, but they never established start states so we have no idea what actual change was.
so, they called their end results shot beforehand (as they should) but then left us with no way to measure change even if the measurement was good, and as we have seen, the measurement was terrible.
so i’m going to sort of punt on scoring this one and assign it an N/A. establishing an outcome and then providing no meaningful way to measure it is not p-hacking per se, but it is also not in any way useful.
so, all in all, it’s just impossible to take this study seriously, especially as it flies in the face of about 100 other studies that WERE well designed.
read many HERE including RCT’s showing not only a failure as source control, but in higher rates of post op infections from surgeons wearing masks in operating theaters vs those that did not.
the WHO said so in 2019.
and the DANMASK study in denmark was a gold standard study for variable isolation and showed no efficacy.
perhaps most hilariously, the very kansas counties data the CDC tried to cherry pick to claim masks worked went on to utterly refute them when the covid surge came.
the evidence that masks fail to stop covid spread is strong, deep, wide, and has a lot of high quality studies. (many more here from the swiss)
to refute them would take very high quality data from well performed studies and counter to the current breathless histrionics of masqueraders desperate for a study to wave around to confirm their priors, this is not that.
to claim “masks worked” with ~40% compliance in the light of total fails with 80-95% is so implausible as to require profound and solid evidence that is nowhere provided here.
this is a junk output from a junk methodology imposed upon an invalid randomization without reference to a meaningful start state for data.
this is closer to apples to orangutans than even apples and oranges.
thus, this and many more like is an absurd and impossible take from this study.
it does not show efficacy. it does not show ANYTHING outside of how an object lesson in poor study design and data collection can be weaponized into a political talking point.
this study is an outright embarrassment and a huge black eye for the NBER et al.
this is not even wrong.
it’s just an epic concatenation of bad techniques and worse data handling used to provide pretext for an idea the researchers clearly favored. there is no way to separate bias from fact or data from artifact.
calling this proof of anything is simply proof of either incompetence or malfeasance.
which one makes you want to listen to the folks pushing it?
I will stick with what the scientists are stating. Rather than the misinformation (actually it is classic FUD stuff) above which you copied/pasted from anti-mask COVID-deniers.
Right. As usual, you are unable to pick out a single item to dispute.
We're on to your game, fraud-ling.
Let's see what your GOP hero, Trump, is doing...
Trump sends son Barron to school which wants kids to wear masks in class despite opposing masking children in schools
https://www.the-sun.com/news/3587258/barron-trump-florida-school-oxbridge-academy-mask-mandate/
DONALD Trump is sending his son Barron to a private academy where students reportedly must wear masks despite him opposing masking children in schools.
Barron, 15, began attending the $34,800-a-year Oxbridge Academy in West Palm Beach, Florida, late last month.
It is among the county's private schools choosing to mask up this semester, telling parents before the school year began that a mandate would be in place, The Palm Beach Post reports.
(More at above url)