Examining the Junk "Science" Keeping Masks on Kids
It Predictable What One Will Find If They Just Assume Masks Work...
As the politics of the day keep changing the “Science” of Covid, there’s still a large segment of the Public Health Establishment pushing back that it’s “still too soon” to lift the mandates. They point to a constant deluge of junk science claiming how nessecary mask and vaccine mandates are. A good example is a new "study" from Harvard claiming it's "Too Soon to Lift Mask Mandates in Schools." I think it's worth discussing because it's incredibly damning to those still pushing for mask mandates.
There's a reason I use "study" with air quotes, because this latest piece of scientific propaganda coming out of Harvard isn't a study at all, it's a mathematical model. Since the earliest days of the pandemic mathematical models have been used to push policy prescriptions, such as in the earliest days of the pandemic when the Imperial College model predicting over 2 million Covid deaths in the US without mitigation was used to justify lockdowns. The biggest issue with these models is they are incredibly sensitive to their inputs and assumptions. Garbage in, garbage out as the saying goes. And the same is certainly the case with this latest model out of Harvard.
The conclusion of the Harvard model is that in-school masking and student vaccination led to a substantial reduction in transmission thus underscoring the need to keep these measures in place until vaccination rates were much higher or case rates much lower.
How did they come to this conclusion? If you check the inputs and assumptions of the model, one finds it was literally baked into the cake. Seriously!
What did the model assume about masks? It assumed that universal masking in schools would reduce transmission by 60 to 80 percent! This number wasn't derived in some way by analyzing real world data. It is simply an input taken as truth.
How about vaccines? The model assumes that vaccines are 70% effective at preventing transmission. I'm not sure where these modelers have been the last 6 months, as by now we all understand the transmission prevention ability of the vaccines is slim to none.
Go figure that if your model assumes that masks and vaccines prevent transmission, the model will output that masks and vaccines are necessary tools to stop transmission. Except in the real world that simply it's true.
We have multiple RCT's showing that masks do not prevent the spread of Covid. We have observational studies examining schools in the same states, and even the same communities, some with and some without masks, showing they have the same incidence of Covid. Masks don't move the needle!
Why are Public Health Agencies still relying on opaque statistical models with half-baked assumptions to justify these mitigations?
The reason is simple. It's all they have left!
The good data, the hard data, show how ineffective masking kids in schools is, yet our Public Health Betters still need something to justify their two years of policy error.
Another recent example of junk mask science was a phone survey study published by the CDC to justify the effectiveness of masks. The study, conducted between February and December of 2021, called California residents who took a Covid test, asked them about their mask usage and correlated this to whether they tested positive or not. The CDC claims the study shows even cloth face coverings to be over 50% effective, but dig into the study and the multitude of flaws are immediate.
Instead of listing them all myself I'll point to a piece by Vinay Prasad, who calls the study a "New Scientific Low Point", where he details the studies' many flaws including terribly low response rates, small sample sizes, conflicts with similar studies the CDC ran in 2020, and lack of statistical significance.
But by far the biggest flaw in the study was the major difference as to why those that tested positive versus negative were seeking a test in the first place. Among those testing positive 78% were experiencing symptoms versus just 17% in the negative group. On the other hand only 16% of positives got tested as a requirement for work or travel versus 53% of the negative group.
This creates a major confounding variable to the study. The negative control group is supposed to be as similar as possible to the postive group to act as a good control. Getting tested for difference reasons, and for reason that imply the negative group was forced to wear a mask much more often, shatters their abilty to act as a realistic control.
In effect the negative testing group was seeded with a large portion of people required to mask for work/travel who weren't experiencing symptoms. It's pretty easy to show "masks work" if you only check people forced to wear them who don't have Covid...
So there you have it. The CDC et al are clinging to half-baked statistical models and hopelessly confounded phone surveys to justify masking you kids. That's just how “Science” works these days...
Anyway 0 and 2...
Harvard should be ashamed. Mathematical models are my profession and can be extremely useful, however in order to make them useful, the inputs are based on sound estimates that
should be measurable with some precision. Instead, Harvard (Harvard for Godsakes!) uses guesses in their model. As the brilliant Michael Crichton said, guesses are merely expressions of prejudice: https://stephenschneider.stanford.edu/Publications/PDF_Papers/Crichton2003.pdf