Happy Thanksgiving! And let's all give thanks that Dr. Jay Bhattacharya is about to run the National Institutes of Health

He's a serious scientist - thoughtful, respectful, open to debate. For too long, the biomedical establishment has seen (and run) itself as an unaccountable priesthood. Jay will lead the defrocking.

It is easy to forget how Dr. Jay Bhattacharya first seriously ran afoul of Covid hysterics.

The moment was not when Bhattacharya, a Stanford University epidemiologist whom President-elect Trump has picked to run the National Institutes of Health, co-wrote the “Great Barrington Declaration” in October 2020 calling for Covid countermeasures to be rolled back.

It was six months before, in April 2020, when Bhattacharya ran a study to see how many people in his northern California county had already been infected with Sars-Cov-2. The findings, based on 3,300 blood tests, suggested the number was far higher than what public health authorities were reporting.

That result mattered tremendously.

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(All the truths you need to drive your cousin Karen (more) nuts at the Thanksgiving table! For less than 20 cents a day!)

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Why?

Because if more people had been infected than we knew, then Sars-Cov-2 was less dangerous than the authorities said.

At the time, health authorities and the media predicted death rates of 1 percent or more for Covid. In early March 2020, the World Health Organization had suggested the rate might be 3.4 percent, translating into 10 million deaths in the United States and 250 million worldwide.

Those figures were shocking. They produced a shocking response: global lockdowns, business and school closures, stay-at-home orders, contact tracing, sealing borders (turned out Western countries could end illegal migration quickly when they wanted, who knew?).

Plus a coordinated worldwide campaign to stifle dissent and frighten the public into supporting these measures, which were collectively the greatest rollback of civil liberties in democratic nations since World War 2.

Now here came unfussy, unfancy Jay Bhattacharya suggesting maybe Covid wasn’t so dangerous. That maybe the real infection fatality rate was not 1 percent but closer to 0.1 percent, 1 death for every 1,000 people who got Covid, 330,000 American deaths.¹

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(He’s no Tony Fauci! Thankfully.)

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The Stanford study wasn’t the only one to suggest relatively low fatality rates. A week after it came out, University of Miami researchers would present similar results. But it got the most attention.

And it suggested that maybe the global response to Covid should be a little more muted. Or a lot more muted.

Or maybe not. Maybe even if Covid killed 0.1 percent of people it infected, or 0.2 percent to 0.3 percent - translating into 660,000 to 1 million American deaths, as turned out ultimately to be the case² - hard lockdown measures would be justified.

The fact those deaths happened mostly in very sick and elderly people might also play a role in deciding the correct response. As I and a few other people argued in spring 2020, those were ultimately political and societal - not medical - questions.

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But in the spring of 2020, the biomedical establishment, most notably Drs. Anthony S. Fauci and Francis Collins at the National Institutes of Health, took the lead in deciding how we as a society should respond to this relatively mundane virus.

Along they way they distorted science badly.

What’s so vital to remember about the firestorm that hit Bhattacharya in April 2020: at the time he was simply presenting fresh data.

His study wasn’t technically perfect. No study is, certainly not one conducted in a few days while the United States had just gone into an unprecedented lockdown. (Though, ultimately, the findings held up well.)

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(If I said you had a good antibody seroprevalence would you hold it against me?)

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But other researchers and scientists didn’t just criticize Bhattacharya’s work on technical grounds.

They attacked him and his motives. Three days after he announced his findings, the Mercury News, a major Northern Californian newspaper, headlined an article about the research:

Feud over Stanford coronavirus study: ‘The authors owe us all an apology’

They attacked his wife for the crime of emailing parents at a local high school to encourage them to join the study. The San Francisco Chronicle headlined that article:

Wife's email may have tainted Stanford coronavirus antibody study

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Yep, the papers really stood up for a local researcher doing groundbreaking work!

Take this to the bank: if Bhattacharya’s study had found lower infection rates and thus a higher death rate from Covid, no one would have challenged it. He got in trouble for finding the virus was less dangerous than the media and public health experts hoped it would be.

Yes, hoped.

Because by April 2020 the global media and scientific and public establishment had put all its weight behind forcing the world into lockdowns - lockdowns that cost the world trillions of dollars, lockdowns whose full consequences on schooling and society we are still sorting out. And it had no room for dissent, however principled or thoughtful or data-driven.

No room for dissent, and no room for dissenters. Including Jay Bhattacharya.

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(They even censored us, when they could. But the truth always wins. With your help.)

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I don’t know the specifics of what Bhattacharya plans when he takes over the NIH and its $50 billion research budget. (Though I have some ideas, if he’s interested.)

Here’s what I do expect, though: assuming he’s confirmed, Bhattacharya will make the NIH into a place that encourages debate, discussion, and dissent of all kinds - and funds research that goes against accepted (liberal) medical and scientific shibboleths, and encourages it to be published no matter what it says.

And I sure hope that his new boss-to-be, Robert F. Kennedy Jr., allows him to let the chips fall where they may once the work is done rigorously and honestly. (I suspect Bhattacharya wouldn’t haven’t taken the job without that promise.)

So on this Thanksgiving 2024, let’s all be grateful that someone who saw up close how the scientific establishment can fall in love with its own power at the expense of truth will now have the chance to shake that establishment up from the inside.

Onward.

1. To calculate the infection fatality rate from a virus (or any pathogen), you need two numbers: how many people it has killed, and how many people it has infected. Deaths are rarely missed, but infections frequently are - particularly when a pathogen is new and tests for it are rare and expensive. As a result, the infection fatality rate often seems to be much higher than it really is early on.

This is a known and broad issue early in all epidemics. For Covid, this problem was worsened by the fact that public (and public health) hysteria was so severe in spring 2020. People were discouraged from going to hospitals unless they were very sick, and man simply rode out the virus without testing themselves. The fact that the PCR (live-virus) testing process was so unpleasant did not help either.

2. My best estimate is that the 1.2 million figure for American Covid deaths is overestimated by 30 to 40 percent. Covid is highly contagious, particularly in institutional settings, and both hospitals and families had large financial incentives to classify incidental (“with”) Covid deaths as being the result of Covid.

This issue is separate from the fact that a huge number of the people who died from Covid - that is, who would not have died at the time they did had they not been infected with Covid - were extremely sick and had life expectancies of months or at most a year or two when they died.

Also, public health authorities weirdly continue to count and report Covid deaths cumulatively since the first death n 2020, something we do not do for the flu or any other disease. We never say “close to four million Americans have died of heart disease since 2020.” Why would we? Current annual deaths are a far more relevant comparison. The reason is obvious, they want the number to be as high and Covid to look as bad as possible to justify lockdowns and mandatory vaccinations.