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Oct. 2, 2023 - Jim Fetzer
51:13
'Definite Causal Link’ Between COVID Vaccine Rollouts and Peaks in All-Cause Mortality
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Thank you.
Well, good morning, CHD, and welcome to Doctors and Scientists.
I'm your host, Brian Hooker, and I want to point out a paper that everybody should read.
It's an 180-page paper, but it's called COVID-19 Vaccine-Associated Mortality in the Southern Hemisphere.
And we have today, joining us, the lead author of that paper, Dr. Denis Rancourt.
Dr. Rancourt, welcome to the show.
Thank you so much for agreeing to the interview on such short notice.
We're really, really glad to have you.
It's a privilege to have you on the show.
I'm happy to be here, Dr. Hooker.
It's my pleasure to join you.
Well, before we get started, tell us a little bit about your background.
Just give us some context.
This is, you know, by the way, this is an amazing paper.
It's 180 pages of Very, very compelling information regarding all-cause mortality.
But before we get into that, and thank you so much for doing this work.
I mean, it obviously took quite a long time to be able to compile this.
But give us an idea of your background, you know, what you've done career-wise, and what led to publishing this paper.
Okay.
Well, I'm a physicist.
I'm a PhD physicist.
I did postdoctoral research work in France and then in the Netherlands.
And then I was a Natural Sciences and Engineering Research Council of Canada University Fellow, which is a research position, a national research position, that's held at Canadian universities.
I became a professor at the University of Ottawa.
I was there for 23 years and I attained the highest rank of tenured full professor.
I was the lead scientist of a large internationally recognized laboratory that did interdisciplinary applications of physics, including in environmental science and biochemistry and even biology interactions in the environment and so on.
I've published over a hundred papers in peer-reviewed scientific journals in many different areas of science, everything from planetary science to magnetism to theoretical physics and also environmental science.
My most cited papers are typically in environmental science and also in soil science, but my papers in physics, in fundamental physics, are also very cited, including a paper That explains iron oxide hematite that's very much cited in the Martian literature, and so on.
And so I'm a scientist, and I was the lead of a research team in a laboratory for more than 20 years, and I'm truly interdisciplinary.
So I've often changed fields, and I've often been invited to conferences in entirely different areas of science.
And when this health crisis was declared, I immediately became interested.
And the first thing I saw was this crazy recommendation that masks, that everyone be masked in the general public and school children and so on.
So I immediately did a review of the scientific literature on masking, concentrating on the best studies, which are randomized control trials.
And I summarized that research in a paper where I said that all the randomized controlled trials show that there is no benefit whatsoever that can be detected from wearing a mask.
That was a very popular article that was read, I think, 500,000 times or so.
It was on ResearchGate.
They removed it from ResearchGate and they told me it was because it was being read by too many people from too many different websites and so on.
So that was my first encounter with COVID censorship.
But the next thing that happened was that I thought, well, they're really going wild with this supposed crisis.
So are people really dying?
I'm not seeing anyone.
I don't know anyone who's dying or who has died and I'm not seeing people out there on the streets.
So what is going on?
So I decided to look at mortality data and all cause mortality in particular.
So I immediately dug in and tried to find the best data I could.
And one of the the easiest data to access, and that's very current, is in the USA, which is our immediate neighbor here in Canada.
So I went to that data right away and showed that the spikes in all-cause mortality that were occurring could not possibly be due to a viral respiratory disease because they were occurring only in hotspots, New York, Northern Italy, Madrid, and so on, and synchronously around the world immediately after or coincident with the pandemic being declared.
And I And so this is completely impossible if you believe the theory of spreading viral respiratory diseases because the seeding a new pathogen, the time between that seeding and a surge in mortality is extremely sensitive to the society and to the details.
And to the culture and so on.
And it can vary by months and even years.
So I thought this synchronicity, exactly with this political event of the announcement, exactly with all the medical people getting hyped up because they're getting ready to have an influx of people into the hospitals.
Right.
And being told that it's a completely new disease and therefore they have a free hand to try whatever treatments they want, that's a recipe for disaster.
And in certain hotspots, especially large hospitals and so on, there were these peaks.
So I immediately, that was back in June of 2020, I wrote an article about that.
There's no urgent and so on.
So then we just continued.
We just continued.
And then the vaccines were rolled out.
And at first, I thought, from looking at the VARS data, I thought, I'll never see a signal in the all cause mortality when you're counting all the deaths.
That right from the vaccines, it just won't be detectable because it's too small, according to the virus reports.
And we analyze the virus data.
And so I didn't expect to see anything.
And then I started seeing peaks of mortality that would occur in coincidence or synchronous with rollouts of boosters or vaccines.
And the very first one that I saw that really uh turn the light on for me um is that i noticed that there were many authors writing about india now it is a special case because it's it doesn't publicize good public data on by time so by week or by month you get good yearly reports but they're a little bit late and you're not getting the time resolution so
So researchers had to go in and actually go into the various provinces and medical centers and so on and actually do research to figure out what was going on if they wanted current data that was by time.
And so, four independent research groups did this and published in peer-reviewed journals, and they all foresaw the same thing, which is no detectable excess mortality, all-cause mortality, for more than a year after the pandemic was declared, until the vaccines were rolled out, and then a huge surge in all-cause mortality.
And so I thought, my God, that cannot be a coincidence.
There's nothing for a year.
They roll up the vaccine and they get these deaths.
And so we quantified that in India, the vaccine rollout would have killed 3.7 million people.
Incredible.
And we wrote a paper about that, and we said it is unlikely that it could be due to anything else.
And so that convinced me, the Indian data in those papers and that coincidence, which none of the authors mentioned.
One just barely mentioned it, but didn't attribute it as a cause.
And the other three didn't even mention that the vaccines had been rolled out at that time.
That really turned the light on for me and I thought we have to really get in there and quantify this and look at this phenomenon.
And so we looked at data from around the world.
We had seen peaks in the U.S.
and we saw when we were looking for good quality data that has high time resolution, but also that has good discrimination by age group.
Right.
One of the first ones we found was Australia.
And Australia is a very special case.
Well, it turns out it's not that special, but when we first saw it, we thought it was pretty special.
There is, again, no excess all-cause mortality for about a year after the pandemic is announced until they roll out the vaccine.
And then in Australia, you get a sudden rise to a regime of much higher all-cause mortality.
And in addition to that, you get peaks when they roll out extra boosters because the boosters tended to be rolled out very quickly.
Right.
And to a specific age group, they're rolled out very quickly for that age group.
And the peaks occur immediately after they're rolled out.
So we saw this in Australia and we saw this coincidence and we wrote a paper just about Australia because you could see there was a peak in their summer.
This is the Southern Hemisphere now.
So there was a peak in January, February 2022 that coincided with a peak of booster rollout.
And we thought, we're seeing this in five of the eight provinces that have enough people that you have enough statistics that you can see it.
This cannot be an accident.
We wrote a paper just about Australia.
And then we thought, we've got to do more.
We've got to keep looking at data and keep analyzing.
So we did India as well.
Now the Indian data gave us the advantage because we knew the booster rollouts by age group, we could actually look at by age dependence of this risk of dying from being injected, which we were now quantifying.
And we found in India that the risk from dying because you're injected increases exponentially with age.
with a doubling time of about five years.
So this meant that the most elderly group was suffering a very high risk of dying from the injection.
And so we published about that.
And then people said, but you've only got a few countries and you're cherry picking and so on.
So we thought, sure, well, then we'll do the whole world, you know, and we launched into a project to getting all the data we could from around the world.
And as we analyze, we decided to first write about the Southern Hemisphere.
We're going to be putting out a large paper about everything in the world.
Right, right.
Within about a month or so, I would say, maybe two months.
Yes, we will be doing that.
But for now, we just put out this large paper about the Southern Hemisphere and the equatorial regions.
And there we see, and it's very interesting, one of the reasons we started with the Southern Hemisphere is because, you see, vaccines were rolled out pretty much synchronously around the world.
It was like a military-style rollout.
Big pharma was everywhere and they were coming out at the same time.
So the boosters generally came out often at the same time.
And it turns out that the third and fourth boosters, for example, were typically rolled out in the Northern Hemisphere during the Northern Hemisphere winter, which meant that you were already in the seasonally high mortality of the winter.
So it was hard to see that there was a peak there.
But that same period coincided with the summer in the Southern Hemisphere, where you never have a peak historically, and all of a sudden there was a sharp peak there associated with the boosters.
So that's one of the reasons we decided let's right away publish on the Southern Hemisphere, you know, to start.
Because everywhere where we've got good enough data, we're seeing this.
Every time they roll out a booster, they're getting this maximum and we can quantify it.
And that is how and why we got interested in the Southern Hemisphere, this most recent paper.
Yeah.
Well, the results are absolutely stunning.
And when you look at the way that the graphics are broken down, Uh, you've not only, um, you've done some data smoothing as well, so you can see the trends.
It's not just sawtooth.
But even if you look at the sawtooth results that are, you know, obviously seasonal and depend on other factors, You see such a stunning increase in all-cause mortality.
Let me ask you the question, did you see in any of the countries, did you see an increase in all-cause mortality prior to the vaccine?
Was there any COVID effect in any of the countries?
Or did it just kind of flatline until the vaccine was introduced?
Okay, let me answer in this way, maybe a two-part answer.
The first part would be to say that in every country, every jurisdiction we've looked at, there is an increase in all-cause mortality to a higher regime when you roll out the vaccines.
So, irrespective of whether there's excess mortality before then, you definitely see an increase associated with the vaccines.
And the second part of the answer would be, yes, of course, there are many jurisdictions where there's all kinds of excess mortality before you roll out the vaccines.
And the U.S.
is one of them that has significant excess mortality before you roll out the vaccine.
We in our work have not been attributing that to a viral respiratory disease.
Right.
Because we have argued based on, you know, we've now written over 30 papers about COVID.
And most of them are analyses of all-cause mortality, including in each of the 50 states of the U.S.
and so on, and by time and by age group and so on.
And we have concluded that because of that spike I was telling you about at the beginning, that coincides with these political announcements and with these very harsh treatment protocols and so on.
Every time that we've seen anomalous peaks, we've been able to understand it that way.
But we've seen a phenomenon that is that is inconsistent with the idea of a spreading viral respiratory disease.
OK.
Yeah.
For example, the mortality does not cross the border between the United States and Canada.
You know, thousands of kilometers, big trading partners, lots of exchanges.
And you get in the first year or so of the pandemic, you get almost no detectable all cause mortality in Canada, whereas you have, you know, over a million deaths in the U.S., unfortunately.
of excess mortality.
So, it didn't cross borders.
And when you do a map of Europe, and I've shown this in my various presentations,
There are there just borders that the virus just the presumed virus just would not have crossed for example at the beginning there with that with that harsh peak Germany was completely Absent of all-cause mortality on the map Germany is white and right away starting at the border France is red where they have the big hospitals there in big cities and Paris and so on and you have this spike in all-cause mortality well it turns out that
Germany did not apply special, harsh medical protocols in those first months.
They just were business as usual in terms of how they responded in hospitals and so on.
So every time we look, as we analyze all-cause mortality before the vaccine rollouts, you get many different kinds of phenomena depending on what was done in those jurisdictions.
But then after you roll out the vaccines, everything becomes uniform in terms of the all-cause mortality.
It's not a problem.
It rises when you roll out the vaccines, the first doses, and then you get, it stays high and then you get peaks when you roll out the boosters.
So the phenomenon becomes much more regular and predictable in terms of consistency across the world and so on after you've rolled out the vaccine.
So it's almost as though that became the main cause of death, whereas before it was the aggressive measures.
And it's not an exaggeration to say the measures were aggressive.
I mean, in Peru, which is one of the countries we studied, there is huge peaks of mortality before you roll out the vaccines.
Well, in Peru, they called 10,000 reservists in to force people into the measures that they were imposing very harsh measures you know i mean they were military style aggressions and so when you when you think of the degree to which they disrupted the economy and people's lives the stress that that would have created um and and we know how much stress um affects the immune system regarding any kind of infection right
so these this is how we were able to understand mortality before they rolled out the vaccines because it's so heterogeneous across jurisdictions and so tied to what was being done that we attribute it mostly to to the response and to the measures that were being applied and also to the lack of treatment and so Because we have to know, for example, in the United States that
Do you know that we showed in one of our big papers that in the United States, on a bi-state basis, excess mortality during the COVID period correlates perfectly.
I'm using that word loosely, but correlates perfectly.
The Pearson correlation coefficient of plus 0.86.
Wow.
With the percentage of the population in the state that is living in poverty.
Wow.
So now it's not just a strong correlation.
It's a proportionality because the trend line goes through the origin.
So it's directly proportional.
So in a state that has no poverty, there would have been no excess death and so on.
That's one way to interpret it.
So there's this stunning correlation between excess death and poverty.
Which means that there's a much more excess death, you know, excluding the hotspot of New York initially, but after that, there's much more excess death in the southern poor states.
Let's put it that way, okay?
Now, these southern states are also the states where normally there is a much higher prescription rate of antibiotics, like a factor of two or three more than other states.
And so these are the same people that normally suffer from lung infections, bacterial lung infections, that now during the COVID period, all the Western countries, basically all of them, stopped prescribing antibiotics.
Antibiotics prescription rates dropped by 50% in the United States and elsewhere in the Western world.
So they're not prescribing antibiotics.
to a population that normally gets more of them, where people are poor and stressed out and are being treated in this way.
And so, and then you've got the death certificates of the CDC, more than half of them have as a comorbidity, bacterial pneumonia.
So we believe that mechanistically, bacterial pneumonia was a major killer in the United States.
As was in the Spanish flu pandemic.
You know, it's weird.
It's like bad reruns.
You know, that you're saying that.
I'm sorry, I just had to jump in because that's such an interesting phenomenon.
And why would they stop?
Were they just treating everything as COVID and looking at these respiratory infections?
And then, you know, the standard of care for COVID-19 was basically wait until we put you in the hospital.
Well, when you look into it, you find that there were directives telling MDs not to be irresponsible by prescribing antibiotics for something that is a viral disease.
Okay.
But there was also a scientific article that said exactly that in one of the leading journals that said, you know, we've got to be careful.
We've been prescribing too many antibiotics in the past.
We've got to be responsible.
And this is a viral respiratory disease.
So stop.
And so basically, the medical establishment stopped prescribing.
Now, the other reason they would have stopped prescribing is they weren't seeing patients as much.
They were not actually being able to diagnose them and you can't prescribe antibiotics by phone in most jurisdictions and so on.
So, all of this would have been playing together, I think.
But, you know, Dr. Hooker, your comment about the Spanish flu, I mean, that is so true.
There are several and we talked about this in our paper.
We actually argued in the paper that in the United States, they had basically reproduced the conditions of the Spanish flu.
Because antibiotics had not been discovered yet during the Spanish Flu, and there are several scientific papers that show in detail that people died of bacterial lung infections.
The preserved tissue shows that.
So, we actually argue that, yeah, they reproduced the conditions, the incredible stress, the economic conditions, the living conditions that they created by the measures among poor people.
And then not prescribe antibiotics.
So yeah, yeah, I would agree with that.
That's so stunning.
And then, okay, so the vaccine rolls out.
And, you know, I know we all hate this mantra, correlation does not equal causation.
But The correlation then is stunning.
How did you see that sort of across the boards that the increase in all-cause mortality with vaccination in, you studied 17 countries, correct?
Yes.
In this paper, yes.
But as I said, we've been looking at every country that we can have data for.
Right, right.
Yeah.
And so looking at that correlation, did you see that across the boards in every country?
And to what extent?
I don't know if you can put a percentage on it or some type of value or even a p-value or a correlation coefficient.
To what extent did you see that?
It is a worldwide phenomenon.
The rollout of the vaccine is associated with complexities that depend on the jurisdiction and so on, but it's associated with increases in all-cause mortality, without a doubt.
The conditions of the society that you look at affect the phenomenon quite a lot.
So, for example, the phenomenon is stronger in the Eastern Bloc countries, And in Russia, you have more excess mortality and we attribute that to the baby boomers coming of age where you tend to die and having lost their safety net because of the dissolution of the Soviet Union in the early 90s.
OK.
So as a result of that, there is a huge societal difference between Russia and Eastern Bloc countries and the Western world.
And as a result of that, as we understand it, this is a paper that we're now preparing, right?
This is the paper on the world that is not yet published.
Right.
That's the kind of difference that we see.
But generally speaking, let me summarize it this way.
OK.
I have looked at all the data that we can find in relation to the rollouts of the vaccine, and I have not been able to find any example that would suggest a beneficial effect of the vaccine rollouts in terms of people dying, in terms of mortality.
There is no such example of that.
Generally, the mortality goes higher or stays high, and there are extra peaks when you roll out more boosters.
The people who would critique our work, I would say to them, show me a counterexample.
Show me a case based on hard all-cause mortality where the vaccine that was rolled out saved lives visibly in the all-cause mortality.
Show me a counterexample and then we'll examine it in detail.
But I have not been able to find one.
So there's no evidence that this saved lives whatsoever.
And there's all the evidence that it has.
Well, you know what we concluded, and this is important, the rollout of the vaccine is analogous and can be understood and analyzed as though it were exposure to a toxic substance.
So you have, it's like having an environmental spillage or something where you accidentally poison a bunch of people by food poisoning or something like that.
The, the phenomenon is analogous to that.
So your risk of dying from this exposure to the toxic substance is extremely variable from individual to individual and increases dramatically with age.
And this is known from toxicology.
And this is known from overdose studies.
And this has been known from animal studies where they give controlled doses of a dangerous substance to animals.
And the same kind of behavior that I've been reading about in the toxic substance literature is exactly the best model to understand what these vaccines are about.
That's how I would conclude it.
And Dr. Hooker, you asked, well, can you give an overall number?
Yes.
The overall number that we find is that every time we quantify how many excess deaths occur for a given number of injections, we always get the same number.
virtually everywhere within a certain spread, but it's always about the same.
So that strongly suggests it's the same phenomenon.
And that number is always, and we have a pretty good number for the 17 countries, which represents 9% of the world population.
Our number is a risk of 0.1, I forget, 0.126% risk of dying.
we get 0.126% risk of dying.
So, so 0.126% as a fraction per injection, if you like.
So that corresponds to one death per 800 injections on a On an all ages basis, so not discerning by age.
Right, right.
Now, as you increase the age of the individual, what we showed in the countries that we have detailed data for is that, as I said, it increased exponentially, which in Peru and Chile meant that anyone in the age group over 90 years old, there was one chance in dying for every 20 injections.
Five percent.
It was a five percent risk of dying for being injected.
Okay.
Incredible.
So one per 800 is the all ages average over all the countries which we believe is representative of the world because we're on four continents and you know we're doing it across the board and it goes up to five percent so one in twenty for the people over 90.
So that's the kind of number that we get.
Now if you take that number And you apply it to the world because we know how many vaccines were injected into people's arms on the global scale.
It would correspond to 17 million deaths.
17 million deaths.
And that would correspond to over 0.2% of the world population having been killed by vaccines in less than three years.
So 0.2, then that's close to what you said, your 0.12% increase in mortality.
And that number... The 0.1% is the risk per injection.
Per injection, I get it, okay.
And the 0.2% is the percentage of the population that would have suffered an early death as a direct consequence of being injected.
Over the last, since they've been vaccinating.
Oh, I see.
Okay.
Okay.
So that makes sense.
One is a risk per injection, and the other is a summary of the deaths, if you like, on the scale of the world population.
How did that, and first of all, I want to point out that you took, that does not include baseline mortality.
That does not include just the standard.
That is the delta.
That's the increase.
Absolutely.
We're talking excess mortality.
Unambiguous excess mortality compared to the historic trend of mortality in each of the countries and in the world.
So this is absolutely excess mortality.
So even the by age dependence, we're talking about the by age dependence of the excess mortality.
It's a direct consequence of being injected.
So one of the graphs in our paper shows the excess mortality versus the number of injections given.
So each point is one country and you see proportionality and it goes through the origin.
So again, that's that's that's a feature of toxicology that the that the effect is proportional to the amount of toxic substance you see that's delivered into people, right?
So yeah, so It's that kind of phenomenon and we've quantified it on enough countries now that we have a pretty good handle on what's happening in the world.
Now, when you look at the effect of boosters and you saw spikes, you know, I love the way that you did that.
You were able to look at Southern Hemisphere countries where the boosters were rolled out in their summer.
And then you saw a spike effect.
That was on top of a sort of a baseline, you know, the mortality had already increased above that.
So you were looking at an increase in all-cause mortality on top of an increase in all-cause mortality.
Yes, for the peaks that are well localized in time and for a given age group, the integrated mortality in that peak is in effect with respect to the mortality that's already there from everything else that's happening.
But all our excess mortalities are over time periods that have peaks or entire time periods of the vaccination, Uh, but compared to the historic baseline before the pandemic was announced.
Right.
So generally that's our methodology.
But in effect, when the peak is sharp, it means that you're really just integrating the mortality in that peak.
Okay.
Okay.
That makes sense.
And then, um, could you tell, you know, you look at the United States, the rollout was primarily mRNA vaccines.
And, you know, you look at the UK and that was primarily the AstraZeneca vaccine.
Could you tell differences based on the vaccine rollout?
I don't know if you were able to get into that level of granularity.
Well, it's difficult because many of the countries will typically use three, four, five, six types of manufacturers.
Right.
So it's a mix.
There's that complexity, but generally speaking, I would say that even though there are large differences in which manufacturers are being used from country to country, the overall coarse grain is always the same.
Right.
But when you do start to look at manufacturers, we do find suggestions that some manufacturers produce a result that is more toxic than others.
Yes, we do.
And we talk about that in the paper.
I believe now I have to remember, but I think South Africa was an example of that where they used a more toxic vaccine.
For example, in the United States, the Johnson and Johnson vaccine was more toxic than the others and so on.
So, yeah, we're able to see that a bit, but we didn't we didn't concentrate on that because we're first doing the coarse grain analysis.
And are the difference from jurisdiction to jurisdiction, we believe, tends to be more about the people, the populations, the age structure of the population than about precisely the manufacturer.
But there are exceptions.
For example, Cuba rolled out its own vaccine.
Right.
And had huge deaths associated with it.
And no deaths before.
None.
So, the Cuban scenario in terms of vaccination was a disaster, and they probably don't want to hear that, but that will be in our next paper.
So, yeah, it was terrible.
So there are.
Yeah, I'm sorry.
Go ahead.
No, no.
And India is another example.
The problem, probably that the vaccines that they were using initially in India were, were, I would say more toxic than some of the mRNA vaccines that were used in the United States.
Yeah.
Now, I know there's a time lag in terms of, you know, getting at this information after, you know, these data are collected and then they're posted.
But with the decrease in vaccine uptake, you know, with, you know, this endless line of boosters, No, it's not too early to tell.
I can say some definite things about that.
that the vaccine uptake was somewhere around 17% or less.
Are you seeing recovery in any of the instances, or is it just too early to tell?
Well, I can, no, it's not too early to tell.
I can say some definite things about that.
Sure.
I can tell you that that effect is not going to be so important for the following reason.
It's mostly elderly people who have a high risk of dying from the vaccine.
It's mostly elderly people who have a high risk of dying from the vaccine.
Right.
And the elderly people get targeted for these boosters more than any other group in the population.
And the elderly people get targeted for these boosters more than any other group in the population.
That makes sense.
That makes sense.
And they are, you know, pretty much under the care of caregivers and so on who really push these boosters because they want to protect them, supposedly.
So we don't find that there's less mortality as you advance these boosters because you're still targeting the elderly and they're the ones that mostly contribute to the death.
And the other thing I can say is that there's definitely a trend that the boosters are more deadly than the first doses.
And in fact, we see quite clearly in this paper that the third booster compared to the fourth booster, the fourth booster is even more deadly than the third booster.
Wow.
You really see it.
And in that figure 17 in our paper, you can see these exponential rises and we do it by dose.
for Peru and Chile, which has really good discriminated data by age.
And you really see that the dose three has these two curves of the two countries.
And then the dose four is clearly above that.
Incredible.
Yes.
So there is an increase with more exposure to that toxic substance.
Which again ties in with toxicology because if you have repeated doses of a toxic substance and your body hasn't had time to repair the damage from the previous dose, then it makes it more lethal potentially.
Wow.
What about, you know, I get concerned about suppression of science and you know that, you know, obviously publishing so many papers in the COVID-19 era looking at this ecological data that this is a contact sport.
So, what about the suppression of science?
Are you seeing some of this information going away?
I mean, you know, the CDC recently shut down the v-safe program, just sort of suddenly, but is this information getting more difficult to actually obtain?
It's getting more difficult to publish in venues that could reach more people.
I have been permanently barred from ResearchGate.
At first, they were only taking down a few papers here and there, but they have now.
I wrote a paper that reviewed the dangers related to vaccines in general.
In the middle, you know, this was, I think, they hadn't quite started the rollouts yet, and it was based on a review of the scientific literature, and they barred They barred me from ever being on Research Kate from that point on.
So that's unprecedented.
I've never, you know, Dr. Rancourt, I've never heard of somebody being barred.
I mean, you know, that's that's a huge entity.
That's not, you know, that's not just like being barred from one journal.
That is that is ResearchGate itself.
That's incredible.
And I'm also barred.
I have tried almost every time we write a paper, I try to put it on MedArchive.
Right.
And they have never accepted to put one of my papers on MedArchive.
They always say that, you know, we have the freedom to say no to certain papers and we're not taking this one.
Now, I write with a co-author, Dr. Joseph Hickey, and we've written some papers together that have been put on MedArchive, but they are not all-cause mortality papers.
They are papers about theoretical epidemiology.
And even those papers have been a real fight to get through to be reviewed and to be published.
And one of them is now that the reviewers were very positive on it and the editors invited us to resubmit.
And so we are hopeful that it will be published in a leading journal soon enough.
We're waiting for the news on that.
And it is a paper that shows that from a theoretical epidemiology perspective, the worst thing you can do during a pandemic is lock elderly people into care homes.
and isolate them from the general population.
And we demonstrate this on the theoretical, basic theoretical epidemiology.
Right.
And that was hard, very difficult to get that.
We had the first journal just threw it out, you know, and so on.
So what is, you know, I have a few other questions regarding the paper, but, you know, this is so perplexing that you can't get good science published.
What is going to be the new frontier in terms of getting this information out?
I mean, you know, this this 180 page paper, this this was essentially self-published, right?
Yes.
Yes, it was published by this new nonprofit corporation that I'm a co-founder of.
Right.
Correlation Research.
in the public interest.
And, uh, that's, that's our venue now, you know, and we put it, we try to get it out on as many websites as we can.
I was just telling one of my coauthors the other day that to not worry that eventually this work will be recognized that if, you know, we, that I, I used to have a friend who would say, you can't lose if you don't give up.
I agree with that sentiment.
If we just keep perfecting our methods and analyzing and honestly reporting this stuff over and over again and correct our errors as we go, if we do this honestly, eventually this work will be recognized.
Maybe it'll have to go and do a book or something like that, but eventually it'll be recognized.
That is my hope.
That's kind of an optimistic view of things.
Well, you know, and I appreciate the effort that goes into, you know, this type of publication.
And I would imagine that, you know, you dot your I's and you cross your T's regardless when you're publishing this type of research.
It's in no, you know, it's in no reflection of the research quality.
You get external peer review, you go through You jump through all the same hoops.
It's just that the powers that be will not allow, you know, because of direct censorship, they will not allow this type of information to get out on, you know, on their domain.
Yes.
I mean, there are four PhD scientists who are co-authors on this paper.
I didn't name them today, unfortunately, but I should have right away.
Marine Baudin, Joseph Hickey, and Jérémie Mercier, and myself.
So, we go through this with a fine-tooth comb.
We argue among ourselves.
We try to decide how best to make the arguments.
Is everything correct?
As you say, we dot the I's.
And every time the scientists read it and review it, they give us feedback as well.
Right.
And so, we're getting reviewed in that way.
But the journals will not publish this material.
Incredible.
It's so important.
I want to focus on one other issue really quickly.
Obviously, I work for Children's Health Defense.
COVID mortality was non-existent in children.
You know, the virus, you know, because of various biochemical factors and physiological differences, were you able to drown or to drill down in anything regarding childhood mortality or were you more reserved to adults?
Well, we're talking about some 30 articles now, so there are many cases where we have looked at age groups all the way down to infants, and we do see dramatic increases to a higher regime of mortality when the pandemic is declared.
Okay.
Again, I think it's politically motivated.
It's because of the circumstances that this doesn't just happen all of a sudden, synchronously, you know, in many places, just because you declare the pandemic.
But we do see young adults.
I'll give you an example.
In Canada, the industry that was hit the hardest was the energy industry in Alberta, because Canada decided to follow geopolitical instructions and stop producing that kind of energy and they took advantage.
And also politically, it's a province that is not liberal, let's say.
And so they really harmed Alberta's sector, economic sector.
And as a result, young males had a very high death rate right away, right away.
And you could see, and it correlated with what we know about suicides and homicides and so on.
But you can really see it in the all-cause mortality.
And the same is true of young infants.
I mean if families are suffering, infants are suffering as well.
And so, the mortalities of infants that we see in all-cause mortality is much higher than anything you could ever ascribe to COVID, but it's measurable and it's there.
You know, this is the thing is that you have to ask, who died under what circumstances and why did they die?
And the answer often is it's usually not the simplest explanation to ascribe it to COVID.
In fact, it's contrary to that.
Like in the US, for example, there are 13 million people who are disabled from a serious mental illness, 13 million.
These people are completely dependent on caregivers.
Their entire world had a meltdown when they decided to close down, that they wanted to protect them from this infection.
I could imagine that many of these people would have died.
And there is a higher rate of young male adults, which is that group in particular that died in the US.
So those are the kinds of correlations that we see when we look at mortality data doing an epidemiological analysis.
Not just saying, okay, did they die of COVID?
If that's your only, you know, if you're only wearing those glasses, I don't think you can understand the phenomenon.
That makes perfect sense.
Yeah.
I love the way that you broke that down and explained it.
When, uh, so you have this next paper is coming out, uh, within a month.
Will it be published in the same, uh, in the same venue as before or?
Well, we might make a version of it that has more chance of being submitted to a regular journal.
And the way we're going to do it strategically, we're thinking, Yes, we're going to.
We're developing fairly new methods to analyze what happened in terms of mortality.
We're using sophisticated statistical analysis techniques.
There's a method called cluster analysis, which allows us to show that different countries in different parts of the world had very different behaviors.
And you can show this mathematically.
And so by taking that kind of a focus, we think we can avoid the You're crafty.
You've been doing this for a long time and you've been publishing research that goes against the mainstream message for a long time and I so appreciate that.
Your website, if you get to the current paper, it looks like denierancourt.ca.
Is that the best way to get this information?
Yes, that's my personal website that has all the articles about COVID.
There's a COVID section in there.
My presentations are there.
All my research areas are there.
It's a wealth of information.
And the main institutional website is the Correlation-Canada.org.
Yes, and that has a research section that has the 12 most recent articles about COVID and all in one place and commented upon and so on.
Wonderful.
Dr. Rancourt, thank you so much for joining us today.
Thank you for your information.
I hope that after this new paper comes out, we can have you back on.
We can continue these discussions.
Your insight regarding the correlation between the ecological data and, you know, events that were happening on the ground, so insightful.
I appreciate that so much.
Thank you so much, and Godspeed for your future work.
It was my pleasure.
I enjoyed it very much.
Thank you.
And thank you, everybody, for joining us for this important episode of Doctors and Scientists.
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