One of the lasting side effects of the COVID-19 pandemic and our catastrophically incompetent response to it is the decrease in trust in public health experts and their advice.
The media has repeatedly warned about the dangers of “doing your own research” or not blindly following whatever those experts claimed. Despite the long list of examples of those same scientists either purposefully misleading the public — for instance, Anthony Fauci and his efforts to downplay U.S. government involvement in gain-of-function research under his leadership — or getting major policy decisions completely wrong, the assumption has long been that subject matter health researchers are essentially infallible.
When revisiting some of the most important and influential research on major policies like mask mandates, it’s easy to see why those institutions no longer deserve the reverence they’ve been enthusiastically handed by their media partners.
One such study came within the first few months of the pandemic, and was used to inform and create masking policy for years to come. Even though a closer look at their methodology and outcomes shows how inaccurate and misleading this research proved to be.
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This study, published in June 2020, was quite literally used by the CDC to inform public policy and masking recommendations. It covered the supposed effects of mask mandates on the growth rate of COVID cases in the post-mandate period through the early part of the pandemic. The Association of American Medical Colleges referenced it in their “Consensus Guidance on Face Coverings” document. Several other research papers by pro-mask advocates referred to it as “proof” that masks were effective. And it’s hopelessly wrong.
Essentially, the two authors attempted to arrive at some sort of evidentiary basis for justifying the policy they supported. They, however, failed. Yet the CDC supported it anyway, using the ridiculous and disproven conclusions to then enthusiastically promote masking.
The first flaw in this study? How they chose to calculate the data.
“The reference period for estimating the face cover mandate effects was 1–5 days before signing the order. We examined how effects change over five post-event periods: 1–5, 6–10, 11–15, 16–20, and 21 or more days,” they explain.
This is an absurd way to calculate the impact of a policy on outcomes.
You might recall during the pandemic that we were repeatedly told that the effect of a policy or mandate wouldn’t actually be visible for several weeks afterward. Remember the “wait two weeks” mantra?
So why would the post-event period include 1-5 days, 6-10 days, or even 11-15 days afterward? What possible justification can there be for using those date windows, when there’s no reasonable way to ascribe any of the trends to a policy that has not yet been able to see results, due to the lengthy incubation period? The data coming from the 1-5 or 6-10 day window would reflect people who got sick with COVID before the order was even signed.
This would be the first bit of data malpractice. There’s plenty more.
Sure enough, researchers setting out to prove masks work were able to torture the data to show what they wanted the public to see.
“State policies mandating public or community use of face masks or covers in mitigating the spread of coronavirus disease 2019 (COVID-19) are hotly contested,” they write. “This study provides evidence from a natural experiment on the effects of state government mandates for face mask use in public issued by fifteen states plus Washington, D.C., between April 8 and May 15, 2020. The research design is an event study examining changes in the daily county-level COVID-19 growth rates between March 31 and May 22, 2020.
“Mandating face mask use in public is associated with a decline in the daily COVID-19 growth rate by 0.9, 1.1, 1.4, 1.7, and 2.0 percentage points in 1-5, 6-10, 11-15, 16-20, and 21 or more days after state face mask orders were signed, respectively. Estimates suggest that as a result of the implementation of these mandates, more than 200,000 COVID-19 cases were averted by May 22, 2020. The findings suggest that requiring face mask use in public could help in mitigating the spread of COVID-19.”
What the decline in daily COVID-19 growth rate in that 1-5 or 6-10 day post-signing period actually shows is that it’s likely COVID spread was decreasing before the mandate was signed. They are also hopelessly confounded by other policy changes that occurred in that window. The authors even acknowledge that limitation, accidentally.
“The early declines in the daily growth rate over the course of five days after signing the order are broadly consistent with the timing of the effects of other social distancing measures such as business closures,” they say.
Then there’s another problem. That several large counties within the mask mandate states they examined already had such policies in place before a statewide expansion. They used county-level trends to estimate the effect size, based on statewide mandates. It’s absurd.
So, if other polices were enacted around the same time, it takes two weeks to account for major changes in policy, the data is confounded, there are seasonal effects that were not considered, and they were unable to account for the importances in county-level mandates, what value does this study have? The answer, of course, is none.
But there’s more.
“We were unable to measure face cover use in the community (that is, compliance with the mandate),” they admit. “As such, the estimates represent the intent-to-treat effects of these mandates—that is, their effects as passed and not the individual-level effect of wearing a face mask in public on one’s own COVID-19 risk. Related, we did not measure enforcement of the mandates, which might affect compliance. We also did not have data on county-level mandates for wearing face masks in public.”
Essentially, they acknowledge that virtually all important ancillary factors were spuriously ignored or downplayed. Most of these early mandate states were centered around the Northeast, where viral spread started decreasing as a result of normal season patterns.
For example, in New York state, the mask mandate on April 15th came after COVID cases had already started declining.
A decline in growth rate does not reflect any impact in policy if the decline in growth rate had started before the mandate came into effect.
Not to mention that this supposed collective effect was visible in states that did not have mask mandates. And did not remain consistent over time.
No mandate states, predominantly centered around the South, had lower COVID case rates initially, then saw more spread over the summer, patterns that remained consistent over several years. In spring 2021, for example, northern states with mask mandates performed worse than southern states without mandates. Just like they did in spring 2020, despite having likely higher levels of natural immunity thanks to being hit harder earlier on.
And overall numbers throughout the pandemic were remarkably similar.
While Hawaii is included in the “early mandate” states, in April 2020, being situated on an island made for wildly different COVID outcomes than the rest of the country. Still, those early mandate states averaged roughly 327,000 cases per million over the pandemic period. States that never had mandates averaged around 335,000 cases per million.
Removing Hawaii, a significant outlier, compresses the data even further.
The early mandate states in that set then averaged around 335,000 cases per million. Nearly an identical number to the never mandate states. And wound up with a higher mortality rate.
Even when focusing in on 2020 specifically, while the early mandate states enjoyed the benefits of seasonality throughout the summer, by December, they had once again passed the states that never had mandates in daily population adjusted case rate.
Not to mention that their estimate of 200,000 prevented cases is false, considering COVID is an endemic virus that infects everyone.
To sum up, then, this “study” was woefully misleading. It inaccurately attributed effects to mask mandates without considering other ancillary factors and used cherry-picked dates. There was no supporting data afterward that aligned with their results, and no mandate states performed similarly throughout the pandemic.
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That’s one of the sources of research the CDC used to justify further masking. And that’s why, despite the media’s best efforts, trust in public health is at an all-time low.
Because it deserves to be.
Read the full article here
