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Jan. 21, 2023 - RFK Jr. The Defender
48:45
All Cause Mortality with Denis Rancourt

All cause mortality and suspicious data is analyzed in this episode featuring Denis Rancourt and RFK Jr.

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Hey, everybody.
I have a special guest today, Dr.
Denis Rancourt.
And I wanted to get Dr.
Rancourt onto this program as quickly as possible because he has just released a bombshell study that looks at all-cause mortality.
It's a synthesis of four different studies, the latest one in India, which shows 3.7 million Excess deaths almost certainly related to the vaccine and not related to COVID-19.
Let me introduce you to Dr.
Rancourt.
Dr.
Denny Rancourt obtained his PhD in physics from the University of Toronto in 1984.
He did postdoctoral research in France and the Netherlands.
He became a nationally funded university research fellow and professor at the University of Ottawa.
Welcome to my show.
Nanoparticles, measurement theory, diffraction physics, statistical methods, and exotic topics such as co-discovering a new meteorotic mineral called antirenite.
Did I pronounce that?
No.
Antitanite.
anti-tendite.
He launched a COVID-19 research group early in the pandemic.
He has studied all-cause mortality since June of 2020.
He has now written over 30 articles about science related to COVID-19 and especially detailed studies on all-cause mortality.
His most recent article just a few days ago shows that based upon all-cause mortality, some 3.7 million fragile residents were killed by the vaccine rollout in India between April and July of 2021.
I'll say one other thing.
He is a volunteer researcher and the chair of directors of a new nonprofit called Correlation Research in the Public Interest.
He has been a researcher since 2014 at the Ontario Civil Liberties Association, which has lobbied government against the aggressive COVID measures from the start.
Dr.
Rancourt has his own website, Circumvent the Censorship.
It's a one-stop resource and you can find it at Dennis Rancourt, Denny Rancourt, it's spelled D-E-N-I-S like Dennis, one N. Denny Rancourt, R-A-N-C-O-U-R-T dot C-A, Canadian Canada.
So welcome to the show.
It's a pleasure to be here.
Let's start by kind of giving us the punchline, Dr.
Rancourt, and then, you know, let's explore your arguments.
So what is your ultimate finding?
The ultimate finding is, and I'm sorry, there's no easy way around this.
If you want me to start with the conclusion, I have to tell you That the all-cause mortality, which is very detailed data as a function of time, by age, at the time you die, you have a certain age in a certain jurisdiction.
If you study all that data in great detail, you have to conclude that it is not behaving like a spreading viral respiratory disease, and it cannot be assigned as being due to a spreading respiratory disease.
The deaths occur in response to what is being done jurisdiction by jurisdiction, which is very different.
The deaths are correlated strongly to socioeconomic factors such as poverty and disability.
That's why in the United States, there's a huge number of excess deaths during the COVID period.
1.3 million people died.
Additional excess deaths.
You say the COVID period because...
There was a period, and this is what we focus on, you know, a lot of people focus on that there was a COVID pandemic in 2020, and then there were excess deaths that appeared to be from a different cause in 2021 that came after the rollout.
And the deaths were different.
They were assigned differently on death certificates and described differently by, you know, by doctors, etc., And so when you say during the COVID period, what are you talking about?
Well, when I say the COVID period, I mean from the date that the pandemic was announced on the 11th of March 2020 to as far as you can come.
We're still in the COVID period.
So as much data as we have.
That's what I call the COVID period.
And over that period in the U.S., you can quantify accurately the number of excess deaths compared to the historic trend.
It is 1.3 million.
And you know what ages they had.
You know which jurisdiction they died in.
You know all of that.
When you analyze it, you see, the first thing you have to realize, and this was our very first paper back in June 2020, as soon as the pandemic was announced, there was a large surge of all-cause deaths that went straight up From the 11th of March 2020, you had an increase in all-cause deaths, but only in certain jurisdictions.
And around the world, in those jurisdictions, whether it be New York or Paris or London, around the world, it was happening at exactly the same time.
You know, the surge went up at the same time, but then didn't happen in many other jurisdictions.
So, for example, there are about 30 states in the United States that did not have a peak of excess deaths immediately after the pandemic was announced, even though you had this massive peak in New York and in many other states.
So when you look at that, it is consistent with doing something right away, synchronously at the same time, as soon as the pandemic is announced.
And the things that were done were done in hospitals.
And what was done was that they were told to get the hospitals ready.
So they were clearing out people who needed care and putting them into old folks homes, locking them in.
They were doing all kinds of things like that.
They were applying a protocol for this new disease that was, at least initially, quite aggressive and very vicious.
So you can actually see an immediate surge of deaths.
When a viral respiratory disease spreads, the epidemiology normally tells you that there's a center and then it spreads.
You can see it spread, but this is not like that.
This is immediately the same thing happening in hotspots where it happens around the world.
And so that was the first clue for me to note that this is not consistent with the story that they're telling.
And so I would go into those jurisdictions and look what was happening at those times.
So what we found, for example, in France, where the data is very, very good, you go down to the neighborhood level almost with the data, you can actually see the hotspots.
When you make a map, you see a red hotspot on a big hospital.
You actually see that in the French data.
On a county that had a big hospital, right beside it and all around, and places that are just as dense in population, there are no excess deaths.
So we came to believe that the deaths were directly associated with what was being done at the beginning.
And then when you look at the integrated excess deaths over the whole COVID period, there is a correlation in the United States to poverty, which is stunning.
It's unlike anything we've ever seen before.
You have what we call a Pearson correlation coefficient of plus 0.86, which is unheard of.
And it's not just a strong correlation.
It's the proportionality.
It goes through the origin.
When you do a graph of excess all-cause mortality versus...
The percentage of people in the state that are living in poverty, you get perfect proportionality.
So if you double the number of people living in poverty in a state, you would have doubled the excess deaths due to COVID that were attributed to COVID. So this is...
This is a very stunning correlation.
And what we found was that it's because poverty is related to many things, and in particular disabilities.
We found that disabilities were very important, mental disability in particular.
There are 13 million people in the United States who live with a serious mental illness, and they're on medication, they're very fragile.
And that disability correlated with the excess deaths that we were seeing, systematically.
So I couldn't understand the actual mortality, which is something you can't fudge.
You don't have to talk about assigning the cause of death.
You just count the deaths, you know how old the person was, and you look at it as a function of time and jurisdiction by jurisdiction.
And when I look at that, let me make one more point.
One more point.
For example, all the clinical papers that carefully look at people who are infected in hospital and how old they are when they die, they find that death from this infection goes exponentially with age.
Well, when you look for that in the data of the entire population, all-cause mortality, there is no such relationship with age.
What I mean by that is, if I do excess mortality versus the mean age in a state or the number of people in the state that are over 85 or over 75 or over 65, if I look for a correlation, I get a shotgun pattern all the time.
It does not correlate with age.
Which would be impossible for something that goes exponentially with age, like it does in the careful clinical studies.
So I'm not seeing something that can be understood at the large macro epidemiological scale as something that is this virus.
Now, that doesn't mean that viruses don't exist.
It doesn't mean any of that.
It just means that I cannot understand this phenomenon in terms of the deaths Yeah.
Yeah.
with, you know, the hypothesis that I think most people are going to recommend here, which is that the reason that you had these huge spikes and deaths in Paris and New York, and for example, Northern Italy, which you did not mention in and for example, Northern Italy, which you did not mention in Tuscany, at the same That was earlier, actually, than the spikes that I was mentioning.
Well, I'd love to hear your explanation for that, because that seemed to be...
I mean, you know, that's where we all got the idea that this virus was galloping through and killing lots and lots of people.
And they didn't have remdesivir in Italy at that time, so...
Something was killing people.
And couldn't it be that the virus landed in those places?
You know, it lands in big cities, it spreads in big cities much more quickly because there's denser populations.
And of course, there's 35 states that just don't have dense populations where the virus didn't reach or didn't infect large groups of people for a long period of time.
Yeah.
No, it doesn't work that way.
It can't work that way.
Let me explain this.
Epidemiological models, which we work with, which we calculate, we were modelers as well.
You need a seed, as you say.
Someone lands in an airplane, you have a seed, and then they have to start infecting people.
And it takes a while before that seed becomes enough people that you actually have a surge and you have the peak in mortality.
And then it falls within some months as people recover and so on.
So that's well known.
For that to happen, you would have to have the seed occurring in all those hot spots at exactly the same time, and then its development follow exactly the same timing, such that the surge is happening at the same time.
And that's just impossible.
It never happens that way.
It can't happen that way.
So you cannot simultaneously have this maximum rise in deaths and the peak occurring simultaneously Around the world on different continents at the same time.
It's just impossible in terms of the model that you're thinking of, of spread, doesn't work that way.
Well, let me, you know, let me give you a sort of a twist on that hypothesis, that the virus was spreading, and let's assume the virus was spreading in October in Wuhan, and that So you're not seeing, immediately you're not seeing, there's many, many more infected people than there are sick people at that time.
And you have the military, the World Military Games in Wuhan on October 19th.
At the end of those games, around the 25th, I think, people then go home To their cities, to New York, to Tuscany, to Paris.
And so the seeds land in those cities simultaneously.
And they spread in those cities before they spread elsewhere.
And so that's where you see the death.
So that's my hypothesis.
Okay, fine.
Yeah, it's a hypothesis.
The other thing is that the deaths are not following an exponential as they should with age.
Okay, so you can do excess all-cause mortality by age group.
And what you find is that it doesn't work.
So it doesn't appear to be the thing that would kill you exponentially with age.
But you can always make these hypotheses.
We can't really know.
All I can do is I can say, wow, this is one crazy thing.
For example, I did one study where we looked at lockdown states.
That were right beside non-lockdown states and looked at the all-cause mortality in that first peak.
And all the lockdown states have this peak and have it very large, whereas the non-lockdown states that are right beside it, there's seven of them with connecting states, you can look at all the pairs, they don't have this peak.
And so we wrote an article about that with a collaborator from Harvard University.
And we said, well, look, here's, you know, this is stunning.
They're side by side.
They're not states that necessarily have these big cities where you have hotspots in definite cities like New York and really mega hospitals.
But when you look at the statistics, none of the lockdown states have this, and all the lockdown states do.
So that's the kind of evidence that leads us to conclude that it was about the measures.
It's about what was being done and how treatment was being done or not done, and so on.
For example, one of the others...
I saw a study, I'm not sure if it's the same one, but I think it must be because it was a Canadian study, That compared Minnesota and Wisconsin, two states, but it also compared California to Florida.
Was that your study?
No, no, I'm talking about a study that looked at every single connecting pair of states that touched a non-lockdown state, so you could make the comparison.
And looked at them all, and it was systematic.
And that was published in the Brownstone Institute, republished that study recently.
That was with John Johnson from Harvard University.
But, you know, you can do many things.
It's hard to summarize...
You asked me to start with a conclusion.
So I normally don't do that.
I normally explain in detail what all-cause mortality is, how it goes, and all that kind of thing.
It's hard to swallow my conclusion.
I agree with you.
And I know that there's going to be a lot of resistance like that.
But you see, it's hard to explain these strong correlations with disability and poverty in terms of a virus.
Let me give you another example.
So in the United States...
Why were there more people dying?
Just because they're more likely to have disabilities, like obesity or...
Let's get back to why people were dying.
That's the mechanism of their death.
But before we do, let me give you one more example of why this can't possibly be a viral spread.
In the United States, the excess deaths was massive, 1.3 million.
In Canada, there were virtually no excess deaths.
You can look at all-cause mortality as a function of time.
You have the seasonal up and down, and nothing changes throughout the whole COVID period.
You have integrated increase of mortality.
That's it.
You cannot even see it on the graph, okay?
So here we have two countries that are side by side.
They share a 5,000-kilometer border.
They're two of the biggest exchange partners in the world.
One has 1.3 million deaths, the other virtually none.
There was no crossover, and this is supposed to be a pandemic of something that initially no one is immune to and that is supposed to spread like wildfire and is very deadly, yet nothing happened in Canada, and the U.S. had Some of the biggest amounts of excess deaths.
Why?
Because the US has huge pools of extremely vulnerable people.
That's why.
Canada does not.
And so how did they die, you ask?
Well, here's what I think.
Here's what we postulated, and we have some data to show that.
When you destroy people's lives by destroying the local economies and you tell people they have to be isolated, they have to stay at home, they can't have social contact, they're going to be psychologically stressed, especially in institutions where the caregivers are not going to be seeing them as often, they're going to be wearing masks, they have to be isolated in their room, they can only go to a certain washroom at a certain time.
I've talked to people who are isolated in this way, and it was horrendous for them.
They wanted to kill themselves.
And so in institutions where you have these highly disabled people and you treat them that way, whether they're very elderly or mentally ill or whatever, You're going to demolish their lives.
You're going to cause enormous psychological stress and social isolation.
Those are two of the factors that are known to most affect the immune system.
Those are devastating factors on the human animal in terms of the immune system.
So those Individuals are going to quickly become very vulnerable to whatever infection.
Now, what was the infection?
It could have been anything.
It could have been the circulating viruses that we always have, and it could have been bacterial infection, bacterial pneumonia.
Not a lot of people know that in the CDC data, when you look at the COVID deaths in the CDC data, a comorbidity more than half the time is bacterial infection.
Of the lungs, in other words, bacterial pneumonia that was not COVID, that is a comorbidity, that is in addition to what they're ascribing as COVID-19.
So there was a lot of bacterial pneumonia.
And at the same time, they cut prescriptions to antibiotics in half in the United States, as they did in most countries.
They were not allowing ivermectin, which is a very powerful antibiotic agent against infections of the lungs.
And so mechanistically, I think that's how most people died.
Their lives were destroyed.
They were psychologically affected.
Their immune systems were crashed.
And they were not being treated.
And they were susceptible to all the infections they normally have.
And you can do a map of the United States where you look at...
This is stunning.
The places where there was the most deaths per state, when you do a map, are the same places that normally are super, have a lot of antibiotic prescriptions.
Okay?
What people don't generally know, that state to state, the number of antibiotic prescriptions per capita greatly change.
It's not just a few percent.
They can be double or triple what it is in another state.
So, those states that normally have a lot, all of a sudden, they were not getting those prescriptions.
And Constant prescriptions of antibiotics is known to also affect your immune system.
So these are the kinds of factors that we came to believe, especially when we look at maps of the US and how it completely correlates to these things.
That's what we think the mechanisms of death were.
Co-infections of viruses and bacteria that cause respiratory problems in weakened immune systems when you're not treating people, that would have been the mechanism.
But the higher level mechanism is the psychological stress.
You know, you can go back to the work of Professor Sheldon Cohen in the United States.
He spent his career showing he used to infect university students with influenza virus when you were allowed to do that in the 70s.
And he proved through his research that the determinants of whether those students will get influenza and will be very sick from it are, one, the psychological stress they're experiencing in their lives, and two, the degree to which they're socially isolated.
In young students, those were the dominant determinants by far.
You didn't have to do anything special.
You saw it right away.
So he did his whole career on that.
And now we know the molecular mechanisms whereby stress affects the immune system.
We know it more than before.
So that is what I think is going on here.
Here's the thing, Robert, is we tend to think of this problem as we're infecting middle class people.
And so it's a virus that has a certain property that will kill a certain number of us in the middle class.
But in fact, that's not what was happening in terms of the overall deaths.
The people who died were overwhelmingly disabled and extremely poor.
And they were obese, and they had diabetes, and they normally get a lot of antibiotics, and a lot of them are institutionalized, and they were now isolated in their rooms, and no one wanted to touch them, and so on.
These are the people who died, overwhelmingly.
1.3 million in the U.S. Yeah, I mean, it's interesting because we have data that show that, you know, the life expectancy of Americans dropped for the first time in history.
And I think on average, it dropped a year and a half or something.
But among Blacks, it dropped 3.3 and a quarter years.
And among Hispanics, it dropped something like two and a half, something over two, a decimal over two, and I don't remember precisely what it is, but with Blacks, it was 3.25.
You know, the impact on Blacks and Hispanics of the pandemic Was much, much higher.
But there's also a lot of comorbidities that are associated with being Black in this country, including obesity, diabetes, and then...
Well, that's all the southern states is how it was mapping.
So it also correlates to race, of course.
But most importantly, to follow up what you're saying now, the only way you can reduce life expectancy is to kill...
Young people, not old people.
If you kill old people, they're going to die anyway.
That's not going to change the life expectancy.
And the thing about the U.S. is that a lot of young people were killed on a massive scale.
And why?
Because among the severely mentally disabled, remember there's 13 million of them, they're predominantly young people.
The age structure of that group is hugely among young people.
There's almost no one over 50 in that group.
They're all in the 18 to 25 year range.
So you're killing a lot of young people.
And when we look at all-cause mortality by age group, you really see it.
You really see that that's skewing.
And so that's how you affect a life expectancy.
And so tell me about your...
You're India Day.
Well, let me just ask you a follow-up question on that.
The scenario that you gave me about how old people are dying was that they're locked in these senior centers or they're isolated in homes for the elderly.
I tried to say that it wasn't only old people.
It was institutionalized young people who have a serious mental deficiency.
They're disabled people.
A lot of disabled people in institutions.
They're not necessarily old people.
That's my point.
The correlation is to disability and to poverty.
It's not to age.
You cannot find a clear correlation to age.
We weren't able to find it.
But as soon as you consider factors, socioeconomic factors like poverty and obesity and diabetes itself and so on, which is not necessarily old people, then you find these very strong correlations.
So how many people are in social assistance for disability?
We know that from government data.
That correlates very well with the excess mortality.
So that's the point, is that we've got to stop thinking that it was just old people.
Even that initial peak in New York, if you actually look by age group, you see a peak for all the big age groups, not just the most elderly.
So it wasn't just the elderly that were killed at that time.
Institutionalized young people were also killed.
Well, you know, it would be interesting to look at Australia's data because Australia has a much more homogenous population, but it had the toughest lockdowns probably in the world.
And if lockdowns, and they didn't have much COVID there, you know, they were able to keep, they've got COVID now.
During that first year, they were able to keep the COVID out.
But it would be interesting to see whether there was an increased alcoholism.
Our understanding of lockdowns is not necessarily that the lockdown itself kills people, but that the states and jurisdictions that apply strong lockdowns are also the same states that have a more militaristic approach to medicine in the big hospitals and in how they treat institutionalized people.
Because we think that that's how it's connected.
So in other words, we see it as a proxy for what's going on where people actually die, which is in the big hospitals, in the care homes, and in the institutional care facilities.
That's where we think people actually die.
Like in Vancouver, for example, in Canada, there are an enormous number of people who live on the street and who are drug addicts, and they weren't dying at the beginning.
They were, their lives was continuing about as before.
They weren't dying at all of a sudden being infected and dying, which was one of the surprising things.
So it's that kind of a puzzle.
I'll tell you just something interesting that you may or may not know.
We've used flu data in the United States for many years.
The CDC has distorted the flu data in order to get people to take flu vaccines.
And the flu data, when you look at it, is very interesting because there are only about 2,000 to 3,000 during the worst years Flu deaths in the entire US population that are confirmed influenza, positive cause of death.
3,000 people in a population of 350 million, 370 million.
And so it's not a, you know, it's basically in the area, I think lightning strike kills 200 or something, or snake bites probably killed the same amount.
I'm not sure what snake bites kill, but anyway, it's not much.
So the CDC could not get people to take flu shots.
Oh, it took the pneumonia deaths.
Pneumonia kills 30 to 70,000 people, 60,000 people.
Big killer, yeah.
And it conflated the pneumonia after, I think, and they started doing this in the 1990s.
They created the pneumonia deaths with the flu deaths, and then they characterized them all as flu deaths, even though When you test the people with pneumonia who died, fewer than 7% of them had the flu.
They were all characterized as flu deaths.
Yeah, the PI, pneumonia and inferential, is what they were calling it.
Nobody who died of flu during the COVID period.
Right.
So that 30,000 people who died of flu.
Yeah.
This is where I don't like to go there.
You see, cause of death data is political data.
It is always political.
It is very difficult to get proper cause of death and to be able to medically say what the cause of death is in situations like this.
This is why you look at all-cause mortality.
This is why we insisted on just counting deaths By age group, by jurisdiction, as a function of time.
Remember, we get it by day or by week.
And we really look at it as a function of time.
And we see the historic pattern since the Second World War, since the 1900s.
You can see this very regular historic pattern.
And the only things...
We've gone back into that data, and we are not able to see...
Any of the declared pandemics of the CDC in the US data.
There is no excess death happening, but we see excess deaths from the Dust Bowl, from the Great Depression, from the Second World War.
Every time there's a major societal, social economic event like that, you can see the signal in deaths.
But these medical emergencies that have been declared and where they write papers and say that there were hundreds of thousands of deaths that should be seen, therefore, as extra deaths in the all-cause mortality, there is no signal of that whatsoever.
So that was in one of our papers.
What happened in Italy?
Do you have an explanation for it?
We didn't study Italy.
It happened before, and we consider it to be something that happened in a particular place, in a particular hospital.
You know, a lot of things can happen in a hospital when you're in an environment where people are talking about a pandemic and where they're talking about it's a new disease, therefore the protocols have to be new.
As soon as you get into that kind of a mental environment, many things can happen in a hospital.
And so you can have hospitals that have these catastrophes, I believe.
My impression, I don't know much about Italy either, other than the news reports and having been over there.
And people having the impression that wasn't just from the hospital, that people were deaths in people's homes and everything else, that people were dying.
But I don't know.
Oh, well, the scientific articles that I saw that came out after talking about Italy were all saying that these were hospital deaths.
People died in hospital and that they all had between, you know, three, four and more comorbidity conditions.
They were all very elderly.
So it was that kind of a situation where it was people that had a high likelihood of dying anyway.
And so if you are applying a new protocol, you're diagnosing something new and you're applying new protocol to people who are very fragile and have three comorbidity conditions in a hospital setting, it's not too surprising that you're going to kill many of them.
I remember that the average age of death mentally was, I think, 86 years old.
Yes, 86 years old.
And that the CDC data for the first year, that the average COVID death had 3.8 Potentially fatal comorbidities that were not COVID. Maybe we should move on to the vaccine deaths that we're seeing in the all-cause mortality.
And let's hear about your Italy or your India study.
Okay.
So in India, something very dramatic happened.
In April, May, June, and July, the all-cause mortality went through the ceiling.
Okay.
Now, it's hard to get really good Indian data, but there have recently, this year, been four separate research groups who did the best they could to get the best possible data they could, and they looked at all-cause mortality in India.
And they all, all four groups, publishing in top leading medical journals, saw the same thing.
A huge immediate surge right away in May, and all caused mortality that was never before seen in the data that they have.
So from the start of the, when the pandemic was announced, All the way to May, June 21, on the scale of this new surge, virtually nothing was happening.
There was no excess mortality.
And all of a sudden, there was this mountain of excess mortality.
And so they wrote papers about that.
And the goal of their papers was to say that the declared COVID death numbers declared by the government of India were underestimates because these deaths All-cause deaths were higher than that.
So the main point, the main perspective of their four different research articles was to say, look, I think you're undercounting your COVID-19 deaths.
But I looked at it from the perspective, well, these are real all-cause deaths.
This is a major event that happened in India, where the all-cause deaths over those months is...
Three or four times, like 500% more than the baseline total deaths in India.
So this is huge.
And nothing happened until then in the whole COVID period.
So what's going on?
And then I noticed that none of the authors even mentioned that the start of this very steep surge coincided with the rollout of the vaccine in India.
First thing.
So that's what got me interested.
Now, you look at the rollout in India, and they expressly said, we're rolling out this vaccine.
First, they vaccinated the health co-workers, young, healthy people.
Nothing happened in all-cause mortality.
Then they said, from this date on, we're vaccinating everyone over 65 and everyone over, I think it was 45, that has any of these comorbidities.
And they listed 20 comorbidities.
And if you had those and you were over 45, they were going to vaccinate you.
And they did that very quickly.
It was military style.
And that's when the deaths just started shooting up.
And by the time they finished all the people with comorbidities and the most elderly, the deaths came down again.
So we concluded in our study that it was the vaccines that were doing this because we had seen in the United States peaks like that.
When you have the so-called vaccine equity programs that went into institutions and vaccinated people that had not yet been vaccinated were more fragile, we saw a coincidence with that in states like Alabama and Mississippi and so on.
There were about 15 states that have these large extra peaks in mortality when the vaccine equity rollout was put in place in the middle of the summer, which is unheard of.
And at the time, we ascribed it to vaccine deaths, but we said that this would mean that 1% of the injections would give rise to a death.
And the only way we could explain it was if you injected these very vulnerable people, They had a high chance of dying.
And we had already seen that that was the case in the VARS data, because we also analyzed the VARS data, and we saw that there were very high injection fatality ratios when they started injecting at the beginning, when they were going for the elderly people first.
That's when the deaths per injection were very high at the beginning, when the average age was very high.
So we knew all that, and so we looked at India, and we said, that must be what's going on.
There's no other way to explain this.
It's far beyond anything that has happened in the whole COVID period in India.
It's massive.
It coincides exactly with this rollout.
And this rollout is one where the Prime Minister Moody said, it's not going fast enough.
We're going to have what he called...
In Hindi, but he called it a vaccine festival.
And in four days, we're going to ask everyone to convince everyone, the poor, the people who are not educated enough, and so on, to get vaccinated.
We've got to do it right away.
So that was the peak in the peak.
That was in April, and that accompanies a huge surge in mortality.
So what we concluded was, when governments aggressively apply, intervene politically, and want vaccination results, whether they call it vaccine equity in the United States, or a vaccine festival in India, and they hire lots of extra people, and they go out and vaccinate everyone without the clinical assessment of, is it too dangerous to vaccinate this person?
Then you're going to kill people.
And the calculated...
The death-fatality ratio that we calculated is 1%, which is very large.
It's 100 times more than what you see in the VARS data for the Janssen in the United States for people over 65.
It's 100 times more than that.
What are the raw numbers?
How many people got vaccinated and how many people died?
350 million people were vaccinated in that period in India, and 3.7 million people died.
So it's about 1%.
And a large review article just came out as a preprint.
The fatality rates can be as high as that.
So there's now a review of the clinical trials that is giving numbers that are comparable to that, that approach that number.
That's what we came to believe happened in Some of the arguments go like this.
The surge is everywhere.
Where it's happening, that surge is happening simultaneously in every province or state of India, in all the big centers.
But it's very heterogeneous from one place to another.
It's not the same on a per capita basis kill rate.
So it depends on the local composition of who you're injecting.
So we made a series of 10 arguments of that nature that argue in favor of interpreting this as deaths due to vaccines versus what Some authors would say is the second wave, but there was no first wave in India.
And there was no wave anywhere that was as large as this, as sudden and as massive as this.
So we think that that's how we interpret the deaths in India in that period.
The other puzzler, if you want to accept the official orthodoxy, is that the second wave should have been a more innocuous variant.
Everything we know about COVID and about viruses in general, generally, It's interesting because with the Indian data, there's another paper that looked at not all-cause mortality, but deaths that were ascribed to COVID-19.
And they also see a big peak in Delhi, the capital.
And in their paper, they interpreted that as being due to the Delta variant.
They argue that the Delta variant was coming in.
And that the Delta variant now, they argue that the Delta variant was more transmissible and had more breakthrough relative to the vaccine.
But the way that they deduced that was to fit the properties of the new variant of concern to the epidemiological data.
So in other words, they designed by a model what the new variant, what properties it would need to have to produce this result in Delhi.
And they admitted that they needed a seed that would have happened on this date and given everything that happens in Delhi.
Well, if you need that specialist seed and that special variant in Delhi, how do you explain that the same thing was happening synchronously in every other state in India?
We said, listen, you're fitting your properties of the variant to the data in order to deduce what the variant is.
That's no way to demonstrate that this mortality was due to a variant.
That's just a way of creating the illusion that there was a variant.
What do you think the chances are that this kind of scientific debate, you know, that we've been having here is actually going to ever break through to the mainstream?
That people will actually, scientists will actually be allowed to have this debate, that you could have this conversation with the authors of those four studies in India, somewhere where people would hear you?
Well, I'll give a very negative answer.
Scientists themselves so believe in the dominant paradigm that it's impossible to find scientists that would debate people like myself.
That saying something like that is just too far-fetched that they would even want to debate it.
So never mind the mainstream media, never mind the general population.
In fact, I find that when I try to have this debate it's a lot easier to have it on Twitter with serious people who think independently and who read articles and point them out to you on Twitter and so on.
so on.
It's easier to have it with regular people who are thinking for themselves than it is in the groups of scientists that I'm a part of, that I regularly talk with and debate with.
It's harder to get them to understand that this could be due to the measures that we applied.
There wasn't necessarily, just the idea that there wasn't necessarily a super virulent pathogen, and that if you declare a pandemic, declare a new protocol, and have tests that are not specific, and you start applying and have tests that are not specific, and you start applying them in general everywhere, and everyone's screaming cases, cases, cases,
And in an environment like that, you're going to create a disaster because you're going to be not treating people like you should be treating them.
Over-treating others, stressing people out in institutions and care homes and in hospitals to the point where you kill them.
It's not an exaggeration to say that they were, I think, scared to death is not the right way to put it, but demolished to death.
Their lives were dissolved.
They could have no social contact all of a sudden.
They lost their caregivers.
They were locked in.
I think that many, many people were killed this way.
And it's hard to have that discussion with scientists because they cannot let go of their theoretical immunology and everything they want to believe about how viruses spread and so on.
I'm sure you're aware of the scientific corollary that was first advanced by Upton Sinclair, where he said that it's impossible to persuade a man of a fact if the existence of that fact will diminish his salary.
Exactly.
I encounter that every day.
I encounter that every day.
It's incredible.
I mean, 63% of the biomedical research funding on Earth is coming from NIH, from Anthony Fauci and Francis Collins, from Jeremy Farrar at Welkentrust, and from Bill Gates.
And a lot of the remaining 47% comes from pharma and from China.
If you want to be a working scientist on this planet, it's pretty hard to do the kind of studies that you're doing.
Yes.
I need to give you a story from Canada because this is really striking.
We have a public health officer, a chief public health officer, Theresa Tan, who wrote a scientific paper with scientific co-authors that was published in a journal that's funded by the Government of Canada, in which they said, the authors of that paper said that if they had not applied all the measures, the lockdowns, the masks, the distancing, and the vaccines, that in Canada there would have been a million extra deaths.
That's what they said in their article based on modeling.
And so we showed a graph of what the all-cause mortality in Canada actually was, the fact that it didn't change at all.
And then on that graph, we showed what a million extra deaths would look like.
It was way up here.
You'd have to like more than double the mortality of all causes from everything for that whole period if you wanted to have a million extra deaths.
And we explained that.
They're saying that they brought it down by coincidence to exactly the same thing that it would have been historically.
Now that coincidence, scientifically, is impossible.
You cannot have all these varied measures, start vaccinating half the way through, do all this crazy stuff, and bring everything down to exactly what it would have been historically.
There's only one universe in a million where that could happen, you see?
And so we tried to point out with this graph how ridiculous what they were proposing was.
This is what they want us to believe.
They want us to believe that there was this virulent pathogen, and it would have caused a million extra deaths in Canada if they hadn't forced us to do everything they did to us and vaccinated us, then we would have had this terrible extra million deaths.
That's what they want us to believe.
It's complete nonsense.
Yeah, well, in my area, which is pharmaceutical litigation and environmental litigation, we have another corollary that says statistics don't lie, but statisticians do.
And the vehicle for those lies is oftentimes modeling.
There's another saying that says that statistics are like prisoners of war.
If you torture them enough, you can make them say anything that you want.
And the government has become the interrogator of...
Well, at least what I did in my research was we went for statistics that themselves...
Had to be valid, in the sense that if you count a death, you know that person died.
Don't ask me what they died of, but I know that on this day this person died, they were this old, and this is where they died.
And if I deal with just that kind of statistic, at least I'm not folding in all of this craziness, which is always very political, about the cause of death.
So that's why we went after that kind of data and tried to interpret and analyze that kind of data.
That was our approach.
Professor Denny Rancourt, thank you so much for your integrity, for your courage, and for joining us on the show.
Thanks for this study.
It was my pleasure, and thank you for challenging me.
Thank you very much, Dr.
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