Brownstone Institute
Fauci Fibbed on the Day Everything Changed

BY
Anthony Fauci is finally gone from his government perch. Let us recall that it was he who set this calamity in motion, squandering his credibility, while taking down public health and much else with it. More than anyone, he bears responsibility, even if he was acting on others’ behalf. That is especially true if he was carrying out a hidden agenda (take your pick of theories).
There was already growing political and societal panic on March 11, 2020, when the House Oversight and Reform Committee convened a hearing on the new virus circulating. Fauci was the key witness. The only question on everyone’s mind came down to the most primal fear: am I going to die from this thing, like in the movies?
This was one day before Trump’s announcement of the travel ban from Europe, the UK, and Australia, essentially sealing the borders of the US to an extent never before attempted, thus separating families and loved ones and trapping billions of people in their nation states. It was five days before the evil declaration by all health authorities to immediately shut down all places where people could congregate.
These few days will remain a case study in irrationality and crowd madness. Fauci, on the day of his testimony, however, seemed like a paragon of stability. He was calm and clear, nearly bloodless in his tone. The substance of what he said, at the same time, was clearly designed to generate panic and create the conditions for a full lockdown.
He had the countenance of a doctor who was telling the family that a beloved father was terminally ill with 30 days to live.
In particular, and in contrast to the testimony prepared by CDC/NIH, Fauci spoke to the severity of the virus. To the average member of Congress, the answer here was crucial because it addressed the only two serious issues: “Am I going to die?” and “Will I be blamed and politically punished if my constituents die?”
To this, he responded with what seemed like science but was actually completely wrong, dreadfully wrong, catastrophically wrong. He claimed that we knew for sure that at best Covid was 10 times deadlier than the flu. In fact, he threw around so much data confetti that a person could have easily believed that he was downplaying the severity to promote calm. His intention was the opposite.
Here is what he said, and please read carefully to catch the implications:
SARS was also a Coronavirus in 2002. It infected 8,000 people and it killed about 775. It had a mortality of about 9 to 10 percent. So, that is only 8,000 people in about a year. In the two-and-a-half months that we have had this Coronavirus, as you know, we now have multiple multiples of that.
So, it clearly is not as lethal, and I will get to the lethality in a moment, but it certainly spreads better. Probably for the practical understanding of the American people, the seasonal flu that we deal with every year has a mortality of 0.1 percent. The stated mortality over all of this when you look at all the data including China is about three percent. It first started off as two and now three.
I think if you count all the cases of minimally symptomatic or asymptomatic infection, that probably brings the mortality rate down to somewhere around one percent, which means it is 10 times more lethal than the seasonal flu. I think that is something that people can get their arms around and understand….
I think the gauge is that this is a really serious problem that we have to take seriously. I mean people always say, well the flu, you know, the flu does this, the does that. The flu has immortality of 0.1 percent. This has mortality of ten times that, and that is the reason why I want to emphasize, we have to stay ahead of the game in preventing this.
Just think through the flim-flam here. He begins with the figure of a 10 percent case fatality rate from a similar virus. The thinking in the room is already stuck on 10. Then he says this virus has killed more in a shorter period of time, which implies more severity. He quickly dials that back but warns that this is more easily spread, which suggests that perhaps it is even higher. Then he dials that back and says that so far the mortality rate is 3 percent.
But then he quickly adds in “minimally symptomatic or asymptomatic infection” and comes to a rough number of 1 percent, thus failing completely here to distinguish between cases and infections, which used to be a core metric that he and so many others completely obliterated.
That’s a side point but an important one. The distinction between cases and infections has been crushed, leaving us utter data chaos.
Fauci spoke this final number with so many other numbers before it that no one could figure out which way was up. The main takeaway anyone would have is that there is going to be vast bloodshed.
It’s best to watch this. You can almost feel the fear in the room as he blinds these political critters with fake science.
So what do we do? Fauci here was quick with the answer:
How much worse it will get will depend on our ability to do two things, to contain the influx in people who are infected coming from the outside and the ability to contain and mitigate within our own country.
In other words: lockdown.
Thus was the stage set. To be sure, there is some mental connection between severity and policy response but there probably should not be. Even if this virus had a 10 percent fatality rate, what does locking down achieve? It was never even clear what the point was. The “spread” could not be stopped forever. The hospitals weren’t really overcrowded, as we’ve seen. There was never a chance for Zero Covid, as the catastrophic experience of China and New Zealand has shown.
In the end, the pandemic of a respiratory virus is solved through exposure, upgraded immune systems, and herd immunity, regardless of severity. And again, please recall that biological evolution has made such pandemics self-limiting: there is a trade between severity and prevalence subject to latency. Latency here was never a factor, contrary to the lies in the early weeks. So the more infectious this virus would be, the less severe it would be, nearly by definition.
Fauci could have used his time in Congress to give a basic explanation. He did not. He chose to spread irrational fear instead.
So how can we evaluate Fauci’s murky suggestion that SARS-CoV-2 will have a 1 percent fatality rate? What actually happened? These data are pretty settled by now.
0-19 years: 0.0003%
20-29 years: 0.002%
40-49 years: 0.035%
50-59 years: 0.123% (flu)
60-69 years: 0.506% (bad flu)
Let’s just assume that Fauci is correct about the flu, though there is plenty of controversy about his chosen figure of 0.1 percent. If he is right about, for the most affected demographic from Covid, he was off by two times. For youth, he was off by 3,333 times – an exaggeration of more than 300,000 percent! And he did it with a straight face. The rest of the population falls between there for a total of 0.095 percent. So in general for the whole population he was off by 10 times, meaning that the actual infection fatality rate is just slightly less (if this is right) than the seasonal flu.
Throughout the entire pandemic, from the beginning to now, the average age of the 0.09 percent of infected people who died remained at the median age of death in absence of the pandemic. If this same virus arrived decades early, it would have hardly been noticed at all.
Which is to say: Fauci was correct on February 28, 2020, when he wrote that this is more or less the flu, except with a large age gradient. His change of mind in the course of two weeks prior to this testimony is based on absolutely no evidence. What changed was his tactics but why?
We mapped out many times already that there was plenty of information available, even in the popular press, that this bug would be more-or-less like the flu, except with an extreme age gradient – which we knew already in mid-February. All the misinformation that followed was just that. And they knew it. Certainly Fauci knew it. No doubt about it.
So why? Here we get into interesting theorizing. Brownstone has done a lot of this for the better part of 18 months, and we will continue to do so. We can talk all evening about this. We already do. And we continue to collect evidence too.
The point is that the world is not the same. Fauci pulled the lever on the wall that set this in motion. He never should have been given that deference, that power, that influence. There should have been a check on him. And some people tried but the censors then flew into action.
The entire mess began not just with a bad prediction but an outrageously bad falsehood – spoken in front of deeply ignorant and terrified politicians – one that was followed by an egregious demand that we get rid of normal social and market functioning. The consequences are for the ages. Fauci had his own masters and minions but it is impossible to avoid the reality that he bears primary responsibility as the voice of panic that shut down freedoms hard won over a millennium.
Brownstone Institute
Net Zero: The Mystery of the Falling Fertility

From the Brownstone Institute
By
If you want to argue that a mysterious factor X is responsible for the drop in fertility, you will have to explain (1) why the factor affected only the vaccinated, and (2) why it started affecting them at about the time of vaccination.
In January 2022, the number of children born in the Czech Republic suddenly decreased by about 10%. By the end of 2022, it had become clear that this was a signal: All the monthly numbers of newborns were mysteriously low.
In April 2023, I wrote a piece for a Czech investigative platform InFakta and suggested that this unexpected phenomenon might be connected to the aggressive vaccination campaign that had started approximately 9 months before the drop in natality. Denik N – a Czech equivalent of the New York Times – immediately came forward with a “devastating takedown” of my article, labeled me a liar and claimed that the pattern can be explained by demographics: There were fewer women in the population and they were getting older.
To compare fertility across countries (and time), the so-called Total Fertility Rate (TFR) is used. Roughly speaking, it is the average number of children that are born to a woman over her lifetime. TFR is independent of the number of women and of their age structure. Figure 1 below shows the evolution of TFR in several European countries between 2001 and 2023. I selected countries that experienced a similar drop in TFR in 2022 as the Czech Republic.

So, by the end of 2023, the following two points were clear:
- The drop in natality in the Czech Republic in 2022 could not be explained by demographic factors. Total fertility rate – which is independent of the number of women and their age structure – dropped sharply in 2022 and has been decreasing ever since. The data for 2024 show that the Czech TFR has decreased further to 1.37.
- Many other European countries experienced the same dramatic and unexpected decrease in fertility that started at the beginning of 2022. I have selected some of them for Figure 1 but there are more: The Netherlands, Norway, Slovakia, Slovenia, and Sweden. On the other hand, there are some countries that do not show a sudden drop in TFR, but rather a steady decline over a longer period (e.g. Belgium, France, UK, Greece, or Italy). Notable exceptions are Bulgaria, Spain, and Portugal where fertility has increased (albeit from very low numbers). The Human Fertility Project database has all the numbers.
This data pattern is so amazing and unexpected that even the mainstream media in Europe cannot avoid the problem completely. From time to time, talking heads with many academic titles appear and push one of the politically correct narratives: It’s Putin! (Spoiler alert: The war started in February 2022; however, children not born in 2022 were not conceived in 2021). It’s the inflation caused by Putin! (Sorry, that was even later). It’s the demographics! (Nope, see above, TFR is independent of the demographics).
Thus, the “v” word keeps creeping back into people’s minds and the Web’s Wild West is ripe with speculation. We decided not to speculate but to wrestle some more data from the Czech government. For many months, we were trying to acquire the number of newborns in each month, broken down by age and vaccination status of the mother. The post-socialist health-care system of our country is a double-edged sword: On one hand, the state collects much more data about citizens than an American would believe. On the other hand, we have an equivalent of the FOIA, and we are not afraid to use it. After many months of fruitless correspondence with the authorities, we turned to Jitka Chalankova – a Czech Ron Johnson in skirts – who finally managed to obtain an invaluable data sheet.
To my knowledge, the datasheet (now publicly available with an English translation here) is the only officially released dataset containing a breakdown of newborns by the Covid-19 vaccination status of the mother. We requested much more detailed data, but this is all we got. The data contains the number of births per month between January 2021 and December 2023 given by women (aged 18-39) who were vaccinated, i.e., had received at least one Covid vaccine dose by the date of delivery, and by women who were unvaccinated, i.e., had not received any dose of any Covid vaccine by the date of delivery.
Furthermore, the numbers of births per month by women vaccinated by one or more doses during pregnancy were provided. This enabled us to estimate the number of women who were vaccinated before conception. Then, we used open data on the Czech population structure by age, and open data on Covid vaccination by day, sex, and age.
Combining these three datasets, we were able to estimate the rates of successful conceptions (i.e., conceptions that led to births nine months later) by preconception vaccination status of the mother. Those interested in the technical details of the procedure may read Methods in the newly released paper. It is worth mentioning that the paper had been rejected without review in six high-ranking scientific journals. In Figure 2, we reprint the main finding of our analysis.

Figure 2 reveals several interesting patterns that I list here in order of importance:
- Vaccinated women conceived about a third fewer children than would be expected from their share of the population. Unvaccinated women conceived at about the same rate as all women before the pandemic. Thus, a strong association between Covid vaccination status and successful conceptions has been established.
- In the second half of 2021, there was a peak in the rate of conceptions of the unvaccinated (and a corresponding trough in the vaccinated). This points to rather intelligent behavior of Czech women, who – contrary to the official advice – probably avoided vaccination if they wanted to get pregnant. This concentrated the pregnancies in the unvaccinated group and produced the peak.
- In the first half of 2021, there was significant uncertainty in the estimates of the conception rates. The lower estimate of the conception rate in the vaccinated was produced by assuming that all women vaccinated (by at least one dose) during pregnancy were unvaccinated before conception. This was almost certainly true in the first half of 2021 because the vaccines were not available prior to 2021. The upper estimate was produced by assuming that all women vaccinated (by at least one dose) during pregnancy also received at least one dose before conception. This was probably closer to the truth in the second part of 2021. Thus, we think that the true conception rates for the vaccinated start close to the lower bound in early 2021 and end close to the upper bound in early 2022. Once again, we would like to be much more precise, but we have to work with what we have got.
Now that the association between Covid-19 vaccination and lower rates of conception has been established, the one important question looms: Is this association causal? In other words, did the Covid-19 vaccines really prevent women from getting pregnant?
The guardians of the official narrative brush off our findings and say that the difference is easily explained by confounding: The vaccinated tend to be older, more educated, city-dwelling, more climate change aware…you name it. That all may well be true, but in early 2022, the TFR of the whole population dropped sharply and has been decreasing ever since.
So, something must have happened in the spring of 2021. Had the population of women just spontaneously separated into two groups – rednecks who wanted kids and didn’t want the jab, and city slickers who didn’t want kids and wanted the jab – the fertility rate of the unvaccinated would indeed be much higher than that of the vaccinated. In that respect, such a selection bias could explain the observed pattern. However, had this been true, the total TFR of the whole population would have remained constant.
But this is not what happened. For some reason, the TFR of the whole population jumped down in January 2022 and has been decreasing ever since. And we have just shown that, for some reason, this decrease in fertility affected only the vaccinated. So, if you want to argue that a mysterious factor X is responsible for the drop in fertility, you will have to explain (1) why the factor affected only the vaccinated, and (2) why it started affecting them at about the time of vaccination. That is a tall order. Mr. Occam and I both think that X = the vaccine is the simplest explanation.
What really puzzles me is the continuation of the trend. If the vaccines really prevented conception, shouldn’t the effect have been transient? It’s been more than three years since the mass vaccination event, but fertility rates still keep falling. If this trend continues for another five years, we may as well stop arguing about pensions, defense spending, healthcare reform, and education – because we are done.
We are in the middle of what may be the biggest fertility crisis in the history of mankind. The reason for the collapse in fertility is not known. The governments of many European countries have the data that would unlock the mystery. Yet, it seems that no one wants to know.
Author
Brownstone Institute
FDA Exposed: Hundreds of Drugs Approved without Proof They Work

From the Brownstone Institute
By
The US Food and Drug Administration (FDA) has approved hundreds of drugs without proof that they work—and in some cases, despite evidence that they cause harm.
That’s the finding of a blistering two-year investigation by medical journalists Jeanne Lenzer and Shannon Brownlee, published by The Lever.
Reviewing more than 400 drug approvals between 2013 and 2022, the authors found the agency repeatedly ignored its own scientific standards.
One expert put it bluntly—the FDA’s threshold for evidence “can’t go any lower because it’s already in the dirt.”
A System Built on Weak Evidence
The findings were damning—73% of drugs approved by the FDA during the study period failed to meet all four basic criteria for demonstrating “substantial evidence” of effectiveness.
Those four criteria—presence of a control group, replication in two well-conducted trials, blinding of participants and investigators, and the use of clinical endpoints like symptom relief or extended survival—are supposed to be the bedrock of drug evaluation.
Yet only 28% of drugs met all four criteria—40 drugs met none.
These aren’t obscure technicalities—they are the most basic safeguards to protect patients from ineffective or dangerous treatments.
But under political and industry pressure, the FDA has increasingly abandoned them in favour of speed and so-called “regulatory flexibility.”
Since the early 1990s, the agency has relied heavily on expedited pathways that fast-track drugs to market.
In theory, this balances urgency with scientific rigour. In practice, it has flipped the process. Companies can now get drugs approved before proving that they work, with the promise of follow-up trials later.
But, as Lenzer and Brownlee revealed, “Nearly half of the required follow-up studies are never completed—and those that are often fail to show the drugs work, even while they remain on the market.”
“This represents a seismic shift in FDA regulation that has been quietly accomplished with virtually no awareness by doctors or the public,” they added.
More than half the approvals examined relied on preliminary data—not solid evidence that patients lived longer, felt better, or functioned more effectively.
And even when follow-up studies are conducted, many rely on the same flawed surrogate measures rather than hard clinical outcomes.
The result: a regulatory system where the FDA no longer acts as a gatekeeper—but as a passive observer.
Cancer Drugs: High Stakes, Low Standards
Nowhere is this failure more visible than in oncology.
Only 3 out of 123 cancer drugs approved between 2013 and 2022 met all four of the FDA’s basic scientific standards.
Most—81%—were approved based on surrogate endpoints like tumour shrinkage, without any evidence that they improved survival or quality of life.
Take Copiktra, for example—a drug approved in 2018 for blood cancers. The FDA gave it the green light based on improved “progression-free survival,” a measure of how long a tumour stays stable.
But a review of post-marketing data showed that patients taking Copiktra died 11 months earlier than those on a comparator drug.
It took six years after those studies showed the drug reduced patients’ survival for the FDA to warn the public that Copiktra should not be used as a first- or second-line treatment for certain types of leukaemia and lymphoma, citing “an increased risk of treatment-related mortality.”
Elmiron: Ineffective, Dangerous—And Still on the Market
Another striking case is Elmiron, approved in 1996 for interstitial cystitis—a painful bladder condition.
The FDA authorized it based on “close to zero data,” on the condition that the company conduct a follow-up study to determine whether it actually worked.
That study wasn’t completed for 18 years—and when it was, it showed Elmiron was no better than placebo.
In the meantime, hundreds of patients suffered vision loss or blindness. Others were hospitalized with colitis. Some died.
Yet Elmiron is still on the market today. Doctors continue to prescribe it.
“Hundreds of thousands of patients have been exposed to the drug, and the American Urological Association lists it as the only FDA-approved medication for interstitial cystitis,” Lenzer and Brownlee reported.
“Dangling Approvals” and Regulatory Paralysis
The FDA even has a term—”dangling approvals”—for drugs that remain on the market despite failed or missing follow-up trials.
One notorious case is Avastin, approved in 2008 for metastatic breast cancer.
It was fast-tracked, again, based on ‘progression-free survival.’ But after five clinical trials showed no improvement in overall survival—and raised serious safety concerns—the FDA moved to revoke its approval for metastatic breast cancer.
The backlash was intense.
Drug companies and patient advocacy groups launched a campaign to keep Avastin on the market. FDA staff received violent threats. Police were posted outside the agency’s building.
The fallout was so severe that for more than two decades afterwards, the FDA did not initiate another involuntary drug withdrawal in the face of industry opposition.
Billions Wasted, Thousands Harmed
Between 2018 and 2021, US taxpayers—through Medicare and Medicaid—paid $18 billion for drugs approved under the condition that follow-up studies would be conducted. Many never were.
The cost in lives is even higher.
A 2015 study found that 86% of cancer drugs approved between 2008 and 2012 based on surrogate outcomes showed no evidence that they helped patients live longer.
An estimated 128,000 Americans die each year from the effects of properly prescribed medications—excluding opioid overdoses. That’s more than all deaths from illegal drugs combined.
A 2024 analysis by Danish physician Peter Gøtzsche found that adverse effects from prescription medicines now rank among the top three causes of death globally.
Doctors Misled by the Drug Labels
Despite the scale of the problem, most patients—and most doctors—have no idea.
A 2016 survey published in JAMA asked practising physicians a simple question—what does FDA approval actually mean?
Only 6% got it right.
The rest assumed that it meant the drug had shown clear, clinically meaningful benefits—such as helping patients live longer or feel better—and that the data was statistically sound.
But the FDA requires none of that.
Drugs can be approved based on a single small study, a surrogate endpoint, or marginal statistical findings. Labels are often based on limited data, yet many doctors take them at face value.
Harvard researcher Aaron Kesselheim, who led the survey, said the results were “disappointing, but not entirely surprising,” noting that few doctors are taught about how the FDA’s regulatory process actually works.
Instead, physicians often rely on labels, marketing, or assumptions—believing that if the FDA has authorized a drug, it must be both safe and effective.
But as The Lever investigation shows, that is not a safe assumption.
And without that knowledge, even well-meaning physicians may prescribe drugs that do little good—and cause real harm.
Who Is the FDA Working for?
In interviews with more than 100 experts, patients, and former regulators, Lenzer and Brownlee found widespread concern that the FDA has lost its way.
Many pointed to the agency’s dependence on industry money. A BMJ investigation in 2022 found that user fees now fund two-thirds of the FDA’s drug review budget—raising serious questions about independence.

Yale physician and regulatory expert Reshma Ramachandran said the system is in urgent need of reform.
“We need an agency that’s independent from the industry it regulates and that uses high-quality science to assess the safety and efficacy of new drugs,” she told The Lever. “Without that, we might as well go back to the days of snake oil and patent medicines.”
For now, patients remain unwitting participants in a vast, unspoken experiment—taking drugs that may never have been properly tested, trusting a regulator that too often fails to protect them.
And as Lenzer and Brownlee conclude, that trust is increasingly misplaced.
- Investigative report by Jeanne Lenzer and Shannon Brownlee at The Lever [link]
- Searchable public drug approval database [link]
- See my talk: Failure of Drug Regulation: Declining standards and institutional corruption
Republished from the author’s Substack
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