Brownstone Institute
A Closer Look at the Covid Mortality Rate

Article from the Brownstone Institute
BY
One of the most consistent efforts made by “experts” during the early stages of the pandemic was to attempt to impress on the public that COVID was an extremely deadly disease.
While it’s clear that for the extremely elderly and severely immunocompromised, COVID does present significant and serious health concerns, the “experts” did their best to convince people of all age groups that they were in danger.
Initially the World Health Organization, in their infinite incompetence, made a substantial contribution to this perception by claiming that the mortality rate from COVID was shockingly high.
In March 2020, with precious little data, the WHO made the alarming claim that 3.4% of people who got COVID had died.
CNBC reported that an early press conference by WHO Director-General Tedros Ghebreyesus compared that expected mortality of COVID-19 to the flu:
“Globally, about 3.4% of reported COVID-19 cases have died,” WHO Director-General Tedros Adhanom Ghebreyesus said during a press briefing at the agency’s headquarters in Geneva. In comparison, seasonal flu generally kills far fewer than 1% of those infected, he said.
This stood in contrast to previous estimates, which were also above 2%:
“Early in the outbreak, scientists had concluded the death rate was around 2.3%.”
While “experts” could be forgiven for being unsure about the death rate of a brand new illness with very little data available, the fear-mongering and world-altering policy enacted based on these estimates has caused incalculable damage.
It’s now widely known and accepted that these estimates were wildly incorrect, off by orders of magnitude.
But a new paper out from one of the world’s leading experts confirms that they were off even more than we previously realized.
John Ioannidis is one of the nation’s leading public health experts, employed at Stanford University as Professor of Medicine in Stanford Prevention Research, of Epidemiology and Population Health,” as well as “of Statistics and Biomedical Data Science.”
You’d think that those impeccable qualifications and a track record of being one of the most published and cited scientists in the modern world would insulate him from criticism, but unfortunately that’s no longer how The Science™ works.
Ioannidis first drew the ire of The Keepers of The Science™ early in the outbreak, when he cautioned that society might be making tremendous decisions based on limited data that was of poor quality.
He also took part in the infamous seroprevalence study conducted in Santa Clara County, led by Dr. Jay Bhattacharya.
That examination, which looked at antibody prevalence in the San Jose area, came to the conclusion that COVID was already significantly more widespread by March and April 2020 than most people realized.
This had wide-ranging implications, but the most important revelation was that the estimates of COVID’s mortality rate used by “scientists” and the WHO were almost certainly much too high.
Those estimates were created under the assumption that COVID cases were overwhelmingly detectable; that cases were captured by testing and thus tracking deaths could be achieved with a “case fatality rate,” instead of “infection fatality rate.”
That was the mistake Tedros and the WHO made two and a half years ago.
Of course, for providing substantial evidence and data that COVID was less deadly than initially feared, Ioannidis (and Bhattacharya) was attacked from within the “expert community.”
In what has now become a familiar insult, those behind the study were vilified as COVID minimizers and dangerous conspiracy theorists who would get people killed by not taking the virus seriously enough.
But Ioannidis remained undeterred, and with several authors, he recently released another review of the infection fatality rate of COVID. Importantly, the paper looks at the pre-vaccination time period and covers the non-elderly age groups; those who were most affected by COVID restrictions and endless mandates.
The Numbers
The review begins with a statement of fact that was almost entirely ignored by lockdown “experts” throughout the pandemic, but especially when restrictions, lockdowns and mandates were at their peak early on.
The infection fatality rate (IFR) of COVID-19 among non-elderly people in the absence of vaccination or prior infection is important to estimate accurately, since 94% of the global population is younger than 70 years and 86% is younger than 60 years.
Emphasis added.
94% of the global population is younger than 70 years old.
6% of is older than 70 years old.
86% is younger than 60 years old.
This is relevant because restrictions overwhelmingly impacted the 86-94% of people who are younger than 60 or 70 years old.
Ioannidis and his co-writers reviewed 40 national seroprevalence studies that covered 38 countries to come to determine their estimates of infection fatality rate for the overwhelming majority of people.
Importantly, those seroprevalence studies were conducted before the vaccines were released, meaning the IFR’s were calculated before whatever impact vaccines had on younger age groups.
So what did they find?
The median infection fatality rate for those aged 0-59 was 0.035%.
This represents 86% of the global population and the survival rate for those who were infected with COVID pre-vaccination was 99.965%.
For those aged 0-69, which covers 94% of the global population, the fatality rate was 0.095%, meaning the survival rate for nearly 7.3 billion people was 99.905%.
Those survival rates are obviously staggeringly high, which already creates frustration that restrictions were imposed on all age groups, when focused protection for those over 70 or at significantly elevated risk would have been a much more preferable course of action.
But it gets worse.
The researchers broke down the demographics into smaller buckets, showing the increase in risk amongst older populations, and conversely, how infinitesimal the risk was amongst younger age groups.
- Ages 60-69, fatality rate 0.501%, survival rate 99.499%
- Ages 50-59, fatality rate 0.129%, survival rate 99.871%
- Ages 40-49, fatality rate 0.035% survival rate 99.965%
- Ages 30-39, fatality rate 0.011%, survival rate 99.989%
- Ages 20-29, fatality rate 0.003%, survival rate 99.997%
- Ages 0-19, fatality rate 0.0003%, survival rate 99.9997%
They added that “Including data from another 9 countries with imputed age distribution of COVID-19 deaths yielded median IFR of 0.025-0.032% for 0-59 years and 0.063-0.082% for 0-69 years.”
These numbers are astounding and reassuringly low, across the board.
But they’re almost nonexistent for children.
Yet as late as fall 2021, Fauci was still fear-mongering about the risks of COVID to children in order to increase vaccination uptake, saying in an interview that it was not a “benign situation:”
“We certainly want to get as many children vaccinated within this age group as we possibly can because as you heard and reported, that this is not, you know, a benign situation.”
It’s nearly impossible for any illness to be less of a risk, or more “benign” than a 0.0003% risk of death.
Even in October 2021, during that same interview with NPR, Fauci said that masks should continue on children as an “extra step” to protect them, even after vaccination:
And when you have that type of viral dynamic, even when you have kids vaccinated, you certainly – when you are in an indoor setting, you want to make sure you go the extra step to protect them. So I can’t give you an exact number of what that would be in the dynamics of virus in the community, but hopefully we will get there within a reasonable period of time. You know, masks often now – as we say, they’re not forever. And hopefully we’ll get to a point where we can remove the masks in schools and in other places. But I don’t believe that that time is right now.
Nothing better highlights the incompetence and misinformation from Dr. Fauci than ignoring that pre-vaccination, children were at vanishingly small risks from COVID, that vaccination uptake amongst kids was entirely irrelevant since they do not prevent infection or transmission, and that mask usage is completely ineffective at protecting anyone. Especially for those who didn’t need protection in the first place.
The CDC, “expert” community, World Health Organization, media figures — all endlessly spread terror that the virus was a mass killer while conflating detected case fatality rates with infection fatality rates.
Yet now we have another piece of evidence suggesting that the initial WHO estimates were off by 99% for 94% of the world’s population.
Just for some perspective, here’s the difference visually portrayed between what the WHO claimed and what Ioannidis found:

Even if the lockdowns, mask mandates, capacity limits and shuttered playgrounds worked, the dangers of the virus were so minuscule that the collateral damage instantly and immediately outweighed any potential benefit.
Economic destruction, increased suicide attempts due to seemingly indefinite isolation, horrifying levels of learning loss, increasing obesity amongst kids, plummeting test scores, increased poverty and hunger, supply chain problems, rampant inflation; all of it is a direct result of policies imposed by terrified, incompetent “experts.”
Their estimates were hopelessly, catastrophically wrong, yet they maintained their unchallenged sense of authority for multiple years, and still receive awards, praise, increased funding and a sense of infallibility amongst politicians and decision-makers.
If sanity and intellectual honesty still existed, these estimates would be front page news for every major media outlet in the world.
Instead, because the media and their allies in the tech, corporate, and political classes promoted and encouraged lockdowns and restrictions while censoring dissent, it’s ignored.
Nothing could be more perfectly COVID than that.
Republished from the author’s Substack
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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