RFK Jr. The Defender - All Cause Mortality with Denis Rancourt

00:00:00.000 Hey, everybody.

00:00:00.000 I have a special guest today, Dr.

00:00:03.000 Denis Rancourt.

00:00:04.000 And I wanted to get Dr.

00:00:06.000 Rancourt onto this program as quickly as possible because he has just released a bombshell study that looks at all-cause mortality.

00:00:15.000 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.

00:00:28.000 Let me introduce you to Dr.

00:00:31.000 Rancourt.

00:00:31.000 Dr.

00:00:31.000 Denny Rancourt obtained his PhD in physics from the University of Toronto in 1984.

00:00:37.000 He did postdoctoral research in France and the Netherlands.

00:00:40.000 He became a nationally funded university research fellow and professor at the University of Ottawa.

00:00:47.000 Welcome to my show.

00:01:06.000 Nanoparticles, measurement theory, diffraction physics, statistical methods, and exotic topics such as co-discovering a new meteorotic mineral called antirenite.

00:01:18.000 Did I pronounce that?

00:01:19.000 No.

00:01:20.000 Antitanite.

00:01:21.000 anti-tendite.

00:01:22.000 He launched a COVID-19 research group early in the pandemic.

00:01:27.000 He has studied all-cause mortality since June of 2020.

00:01:31.000 He has now written over 30 articles about science related to COVID-19 and especially detailed studies on all-cause mortality.

00:01:40.000 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.

00:01:57.000 I'll say one other thing.

00:01:59.000 He is a volunteer researcher and the chair of directors of a new nonprofit called Correlation Research in the Public Interest.

00:02:07.000 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.

00:02:19.000 Dr.

00:02:19.000 Rancourt has his own website, Circumvent the Censorship.

00:02:23.000 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.

00:02:39.000 So welcome to the show.

00:02:42.000 It's a pleasure to be here.

00:02:43.000 Let's start by kind of giving us the punchline, Dr.

00:02:47.000 Rancourt, and then, you know, let's explore your arguments.

00:02:50.000 So what is your ultimate finding?

00:02:52.000 The ultimate finding is, and I'm sorry, there's no easy way around this.

00:02:58.000 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.

00:03:11.000 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.

00:03:25.000 The deaths occur in response to what is being done jurisdiction by jurisdiction, which is very different.

00:03:32.000 The deaths are correlated strongly to socioeconomic factors such as poverty and disability.

00:03:39.000 That's why in the United States, there's a huge number of excess deaths during the COVID period.

00:03:44.000 1.3 million people died.

00:03:46.000 Additional excess deaths.

00:03:47.000 You say the COVID period because...

00:03:52.000 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.

00:04:09.000 And the deaths were different.

00:04:11.000 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?

00:04:22.000 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.

00:04:31.000 We're still in the COVID period.

00:04:32.000 So as much data as we have.

00:04:34.000 That's what I call the COVID period.

00:04:36.000 And over that period in the U.S., you can quantify accurately the number of excess deaths compared to the historic trend.

00:04:43.000 It is 1.3 million.

00:04:45.000 And you know what ages they had.

00:04:47.000 You know which jurisdiction they died in.

00:04:49.000 You know all of that.

00:04:50.000 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.

00:05:10.000 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.

00:05:20.000 You know, the surge went up at the same time, but then didn't happen in many other jurisdictions.

00:05:26.000 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.

00:05:40.000 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.

00:05:49.000 And the things that were done were done in hospitals.

00:05:52.000 And what was done was that they were told to get the hospitals ready.

00:05:58.000 So they were clearing out people who needed care and putting them into old folks homes, locking them in. 0.98

00:06:05.000 They were doing all kinds of things like that.

00:06:06.000 They were applying a protocol for this new disease that was, at least initially, quite aggressive and very vicious.

00:06:14.000 So you can actually see an immediate surge of deaths.

00:06:19.000 When a viral respiratory disease spreads, the epidemiology normally tells you that there's a center and then it spreads.

00:06:27.000 You can see it spread, but this is not like that.

00:06:29.000 This is immediately the same thing happening in hotspots where it happens around the world.

00:06:35.000 And so that was the first clue for me to note that this is not consistent with the story that they're telling.

00:06:42.000 And so I would go into those jurisdictions and look what was happening at those times.

00:06:47.000 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.

00:06:58.000 When you make a map, you see a red hotspot on a big hospital.

00:07:02.000 You actually see that in the French data.

00:07:04.000 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.

00:07:12.000 So we came to believe that the deaths were directly associated with what was being done at the beginning.

00:07:20.000 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.

00:07:30.000 It's unlike anything we've ever seen before.

00:07:32.000 You have what we call a Pearson correlation coefficient of plus 0.86, which is unheard of.

00:07:40.000 And it's not just a strong correlation.

00:07:42.000 It's the proportionality.

00:07:43.000 It goes through the origin.

00:07:44.000 When you do a graph of excess all-cause mortality versus...

00:07:49.000 The percentage of people in the state that are living in poverty, you get perfect proportionality.

00:07:54.000 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...

00:08:04.000 This is a very stunning correlation.

00:08:06.000 And what we found was that it's because poverty is related to many things, and in particular disabilities.

00:08:13.000 We found that disabilities were very important, mental disability in particular.

00:08:18.000 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.

00:08:26.000 And that disability correlated with the excess deaths that we were seeing, systematically.

00:08:31.000 So I couldn't understand the actual mortality, which is something you can't fudge.

00:08:38.000 You don't have to talk about assigning the cause of death.

00:08:41.000 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.

00:08:48.000 And when I look at that, let me make one more point.

00:08:52.000 One more point.

00:08:52.000 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.

00:09:06.000 Well, when you look for that in the data of the entire population, all-cause mortality, there is no such relationship with age.

00:09:14.000 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.

00:09:29.000 It does not correlate with age.

00:09:31.000 Which would be impossible for something that goes exponentially with age, like it does in the careful clinical studies.

00:09:38.000 So I'm not seeing something that can be understood at the large macro epidemiological scale as something that is this virus.

00:09:49.000 Now, that doesn't mean that viruses don't exist.

00:09:53.000 It doesn't mean any of that.

00:09:54.000 It just means that I cannot understand this phenomenon in terms of the deaths Yeah.

00:10:03.000 Yeah.

00:10:05.000 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.

00:10:27.000 Well, I'd love to hear your explanation for that, because that seemed to be...

00:10:32.000 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.

00:10:38.000 And they didn't have remdesivir in Italy at that time, so...

00:10:42.000 Something was killing people.

00:10:45.000 And couldn't it be that the virus landed in those places?

00:10:50.000 You know, it lands in big cities, it spreads in big cities much more quickly because there's denser populations.

00:10:57.000 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.

00:11:10.000 Yeah.

00:11:10.000 No, it doesn't work that way.

00:11:12.000 It can't work that way.

00:11:13.000 Let me explain this.

00:11:15.000 Epidemiological models, which we work with, which we calculate, we were modelers as well.

00:11:20.000 You need a seed, as you say.

00:11:22.000 Someone lands in an airplane, you have a seed, and then they have to start infecting people.

00:11:27.000 And it takes a while before that seed becomes enough people that you actually have a surge and you have the peak in mortality.

00:11:34.000 And then it falls within some months as people recover and so on.

00:11:38.000 So that's well known.

00:11:40.000 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.

00:11:54.000 And that's just impossible.

00:11:56.000 It never happens that way.

00:11:58.000 It can't happen that way.

00:12:00.000 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.

00:12:12.000 It's just impossible in terms of the model that you're thinking of, of spread, doesn't work that way.

00:12:19.000 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.

00:12:41.000 And you have the military, the World Military Games in Wuhan on October 19th.

00:12:47.000 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.

00:12:59.000 And so the seeds land in those cities simultaneously.

00:13:03.000 And they spread in those cities before they spread elsewhere.

00:13:07.000 And so that's where you see the death.

00:13:09.000 So that's my hypothesis.

00:13:11.000 Okay, fine.

00:13:12.000 Yeah, it's a hypothesis.

00:13:15.000 The other thing is that the deaths are not following an exponential as they should with age.

00:13:24.000 Okay, so you can do excess all-cause mortality by age group.

00:13:29.000 And what you find is that it doesn't work.

00:13:32.000 So it doesn't appear to be the thing that would kill you exponentially with age.

00:13:37.000 But you can always make these hypotheses.

00:13:40.000 We can't really know.

00:13:42.000 All I can do is I can say, wow, this is one crazy thing.

00:13:46.000 For example, I did one study where we looked at lockdown states.

00:13:51.000 That were right beside non-lockdown states and looked at the all-cause mortality in that first peak.

00:13:56.000 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.

00:14:09.000 And so we wrote an article about that with a collaborator from Harvard University.

00:14:14.000 And we said, well, look, here's, you know, this is stunning.

00:14:18.000 They're side by side.

00:14:20.000 They're not states that necessarily have these big cities where you have hotspots in definite cities like New York and really mega hospitals.

00:14:30.000 But when you look at the statistics, none of the lockdown states have this, and all the lockdown states do.

00:14:36.000 So that's the kind of evidence that leads us to conclude that it was about the measures.

00:14:42.000 It's about what was being done and how treatment was being done or not done, and so on.

00:14:48.000 For example, one of the others...

00:14:50.000 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.

00:15:09.000 Was that your study?

00:15:10.000 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.

00:15:21.000 And looked at them all, and it was systematic.

00:15:24.000 And that was published in the Brownstone Institute, republished that study recently.

00:15:30.000 That was with John Johnson from Harvard University.

00:15:34.000 But, you know, you can do many things.

00:15:38.000 It's hard to summarize...

00:15:40.000 You asked me to start with a conclusion.

00:15:42.000 So I normally don't do that.

00:15:44.000 I normally explain in detail what all-cause mortality is, how it goes, and all that kind of thing.

00:15:49.000 It's hard to swallow my conclusion.

00:15:51.000 I agree with you.

00:15:53.000 And I know that there's going to be a lot of resistance like that.

00:15:56.000 But you see, it's hard to explain these strong correlations with disability and poverty in terms of a virus.

00:16:04.000 Let me give you another example.

00:16:06.000 So in the United States...

00:16:08.000 Why were there more people dying?

00:16:10.000 Just because they're more likely to have disabilities, like obesity or...

00:16:17.000 Let's get back to why people were dying.

00:16:20.000 That's the mechanism of their death.

00:16:22.000 But before we do, let me give you one more example of why this can't possibly be a viral spread.

00:16:27.000 In the United States, the excess deaths was massive, 1.3 million.

00:16:32.000 In Canada, there were virtually no excess deaths.

00:16:36.000 You can look at all-cause mortality as a function of time.

00:16:38.000 You have the seasonal up and down, and nothing changes throughout the whole COVID period.

00:16:43.000 You have integrated increase of mortality.

00:16:46.000 That's it.

00:16:47.000 You cannot even see it on the graph, okay?

00:16:49.000 So here we have two countries that are side by side.

00:16:53.000 They share a 5,000-kilometer border.

00:16:56.000 They're two of the biggest exchange partners in the world.

00:16:58.000 One has 1.3 million deaths, the other virtually none.

00:17:03.000 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.

00:17:23.000 Why?

00:17:24.000 Because the US has huge pools of extremely vulnerable people.

00:17:29.000 That's why.

00:17:30.000 Canada does not.

00:17:32.000 And so how did they die, you ask?

00:17:35.000 Well, here's what I think.

00:17:40.000 Here's what we postulated, and we have some data to show that.

00:17:44.000 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.

00:18:10.000 I've talked to people who are isolated in this way, and it was horrendous for them.

00:18:15.000 They wanted to kill themselves.

00:18:17.000 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. 0.99

00:18:31.000 You're going to cause enormous psychological stress and social isolation.

00:18:36.000 Those are two of the factors that are known to most affect the immune system.

00:18:41.000 Those are devastating factors on the human animal in terms of the immune system.

00:18:47.000 So those Individuals are going to quickly become very vulnerable to whatever infection.

00:18:52.000 Now, what was the infection?

00:18:54.000 It could have been anything.

00:18:55.000 It could have been the circulating viruses that we always have, and it could have been bacterial infection, bacterial pneumonia.

00:19:02.000 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.

00:19:14.000 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.

00:19:25.000 So there was a lot of bacterial pneumonia.

00:19:27.000 And at the same time, they cut prescriptions to antibiotics in half in the United States, as they did in most countries.

00:19:35.000 They were not allowing ivermectin, which is a very powerful antibiotic agent against infections of the lungs.

00:19:44.000 And so mechanistically, I think that's how most people died.

00:19:49.000 Their lives were destroyed.

00:19:52.000 They were psychologically affected.

00:19:54.000 Their immune systems were crashed.

00:19:57.000 And they were not being treated.

00:19:59.000 And they were susceptible to all the infections they normally have.

00:20:03.000 And you can do a map of the United States where you look at...

00:20:07.000 This is stunning.

00:20:09.000 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.

00:20:23.000 Okay?

00:20:24.000 What people don't generally know, that state to state, the number of antibiotic prescriptions per capita greatly change.

00:20:32.000 It's not just a few percent.

00:20:33.000 They can be double or triple what it is in another state.

00:20:36.000 So, those states that normally have a lot, all of a sudden, they were not getting those prescriptions.

00:20:42.000 And Constant prescriptions of antibiotics is known to also affect your immune system.

00:20:48.000 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.

00:20:58.000 That's what we think the mechanisms of death were.

00:21:02.000 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.

00:21:12.000 But the higher level mechanism is the psychological stress.

00:21:16.000 You know, you can go back to the work of Professor Sheldon Cohen in the United States.

00:21:22.000 He spent his career showing he used to infect university students with influenza virus when you were allowed to do that in the 70s.

00:21:31.000 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.

00:21:49.000 In young students, those were the dominant determinants by far.

00:21:53.000 You didn't have to do anything special.

00:21:55.000 You saw it right away.

00:21:56.000 So he did his whole career on that.

00:21:58.000 And now we know the molecular mechanisms whereby stress affects the immune system.

00:22:03.000 We know it more than before.

00:22:04.000 So that is what I think is going on here.

00:22:09.000 Here's the thing, Robert, is we tend to think of this problem as we're infecting middle class people.

00:22:18.000 And so it's a virus that has a certain property that will kill a certain number of us in the middle class.

00:22:23.000 But in fact, that's not what was happening in terms of the overall deaths.

00:22:27.000 The people who died were overwhelmingly disabled and extremely poor.

00:22:33.000 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.

00:22:46.000 These are the people who died, overwhelmingly.

00:22:50.000 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.

00:23:03.000 And I think on average, it dropped a year and a half or something.

00:23:08.000 But among Blacks, it dropped 3.3 and a quarter years.

00:23:14.000 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.

00:23:25.000 You know, the impact on Blacks and Hispanics of the pandemic Was much, much higher.

00:23:34.000 But there's also a lot of comorbidities that are associated with being Black in this country, including obesity, diabetes, and then...

00:23:43.000 Well, that's all the southern states is how it was mapping.

00:23:47.000 So it also correlates to race, of course. 0.95

00:23:49.000 But most importantly, to follow up what you're saying now, the only way you can reduce life expectancy is to kill... 0.77

00:23:59.000 Young people, not old people. 0.99

00:24:01.000 If you kill old people, they're going to die anyway. 1.00

00:24:02.000 That's not going to change the life expectancy. 0.96

00:24:04.000 And the thing about the U.S. is that a lot of young people were killed on a massive scale.

00:24:10.000 And why?

00:24:12.000 Because among the severely mentally disabled, remember there's 13 million of them, they're predominantly young people.

00:24:22.000 The age structure of that group is hugely among young people.

00:24:26.000 There's almost no one over 50 in that group.

00:24:28.000 They're all in the 18 to 25 year range.

00:24:32.000 So you're killing a lot of young people. 0.54

00:24:33.000 And when we look at all-cause mortality by age group, you really see it.

00:24:38.000 You really see that that's skewing.

00:24:40.000 And so that's how you affect a life expectancy.

00:24:46.000 And so tell me about your...

00:24:49.000 You're India Day. 0.62

00:24:50.000 Well, let me just ask you a follow-up question on that.

00:24:54.000 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.

00:25:07.000 I tried to say that it wasn't only old people.

00:25:10.000 It was institutionalized young people who have a serious mental deficiency.

00:25:16.000 They're disabled people. 0.99

00:25:17.000 A lot of disabled people in institutions.

00:25:19.000 They're not necessarily old people.

00:25:21.000 That's my point.

00:25:22.000 The correlation is to disability and to poverty.

00:25:28.000 It's not to age.

00:25:29.000 You cannot find a clear correlation to age.

00:25:32.000 We weren't able to find it.

00:25:34.000 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.

00:25:50.000 So how many people are in social assistance for disability?

00:25:53.000 We know that from government data.

00:25:55.000 That correlates very well with the excess mortality.

00:25:59.000 So that's the point, is that we've got to stop thinking that it was just old people.

00:26:03.000 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.

00:26:13.000 So it wasn't just the elderly that were killed at that time.

00:26:16.000 Institutionalized young people were also killed.

00:26:19.000 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.

00:26:29.000 And if lockdowns, and they didn't have much COVID there, you know, they were able to keep, they've got COVID now.

00:26:37.000 During that first year, they were able to keep the COVID out.

00:26:42.000 But it would be interesting to see whether there was an increased alcoholism.

00:26:47.000 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.

00:27:07.000 Because we think that that's how it's connected.

00:27:10.000 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.

00:27:22.000 That's where we think people actually die.

00:27:26.000 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.

00:27:37.000 They were, their lives was continuing about as before.

00:27:40.000 They weren't dying at all of a sudden being infected and dying, which was one of the surprising things.

00:27:46.000 So it's that kind of a puzzle.

00:27:52.000 I'll tell you just something interesting that you may or may not know.

00:27:56.000 We've used flu data in the United States for many years.

00:28:00.000 The CDC has distorted the flu data in order to get people to take flu vaccines.

00:28:08.000 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.

00:28:27.000 3,000 people in a population of 350 million, 370 million.

00:28:36.000 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.

00:28:47.000 I'm not sure what snake bites kill, but anyway, it's not much.

00:28:51.000 So the CDC could not get people to take flu shots.

00:28:56.000 Oh, it took the pneumonia deaths.

00:28:59.000 Pneumonia kills 30 to 70,000 people, 60,000 people.

00:29:03.000 Big killer, yeah.

00:29:05.000 And it conflated the pneumonia after, I think, and they started doing this in the 1990s.

00:29:11.000 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.

00:29:25.000 They were all characterized as flu deaths.

00:29:30.000 Yeah, the PI, pneumonia and inferential, is what they were calling it.

00:29:34.000 Nobody who died of flu during the COVID period.

00:29:38.000 Right.

00:29:39.000 So that 30,000 people who died of flu.

00:29:43.000 Yeah.

00:29:44.000 This is where I don't like to go there.

00:29:49.000 You see, cause of death data is political data.

00:29:54.000 It is always political.

00:29:56.000 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.

00:30:05.000 This is why you look at all-cause mortality.

00:30:08.000 This is why we insisted on just counting deaths By age group, by jurisdiction, as a function of time.

00:30:15.000 Remember, we get it by day or by week.

00:30:19.000 And we really look at it as a function of time.

00:30:21.000 And we see the historic pattern since the Second World War, since the 1900s.

00:30:26.000 You can see this very regular historic pattern.

00:30:30.000 And the only things...

00:30:32.000 We've gone back into that data, and we are not able to see...

00:30:36.000 Any of the declared pandemics of the CDC in the US data.

00:30:41.000 There is no excess death happening, but we see excess deaths from the Dust Bowl, from the Great Depression, from the Second World War.

00:30:51.000 Every time there's a major societal, social economic event like that, you can see the signal in deaths.

00:30:58.000 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.

00:31:12.000 So that was in one of our papers.

00:31:14.000 What happened in Italy?

00:31:16.000 Do you have an explanation for it?

00:31:19.000 We didn't study Italy.

00:31:21.000 It happened before, and we consider it to be something that happened in a particular place, in a particular hospital.

00:31:27.000 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.

00:31:41.000 As soon as you get into that kind of a mental environment, many things can happen in a hospital.

00:31:47.000 And so you can have hospitals that have these catastrophes, I believe.

00:31:53.000 My impression, I don't know much about Italy either, other than the news reports and having been over there.

00:32:01.000 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.

00:32:10.000 But I don't know.

00:32:12.000 Oh, well, the scientific articles that I saw that came out after talking about Italy were all saying that these were hospital deaths.

00:32:21.000 People died in hospital and that they all had between, you know, three, four and more comorbidity conditions.

00:32:28.000 They were all very elderly.

00:32:30.000 So it was that kind of a situation where it was people that had a high likelihood of dying anyway.

00:32:35.000 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.

00:32:51.000 I remember that the average age of death mentally was, I think, 86 years old.

00:32:57.000 Yes, 86 years old.

00:32:59.000 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.

00:33:18.000 And let's hear about your Italy or your India study.

00:33:22.000 Okay.

00:33:23.000 So in India, something very dramatic happened.

00:33:26.000 In April, May, June, and July, the all-cause mortality went through the ceiling.

00:33:34.000 Okay.

00:33:35.000 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.

00:33:52.000 And they all, all four groups, publishing in top leading medical journals, saw the same thing.

00:33:59.000 A huge immediate surge right away in May, and all caused mortality that was never before seen in the data that they have.

00:34:09.000 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.

00:34:20.000 There was no excess mortality.

00:34:22.000 And all of a sudden, there was this mountain of excess mortality.

00:34:25.000 And so they wrote papers about that.

00:34:29.000 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.

00:34:41.000 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.

00:34:50.000 But I looked at it from the perspective, well, these are real all-cause deaths.

00:34:55.000 This is a major event that happened in India, where the all-cause deaths over those months is...

00:35:03.000 Three or four times, like 500% more than the baseline total deaths in India.

00:35:09.000 So this is huge.

00:35:11.000 And nothing happened until then in the whole COVID period.

00:35:15.000 So what's going on?

00:35:16.000 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.

00:35:28.000 First thing.

00:35:29.000 So that's what got me interested.

00:35:31.000 Now, you look at the rollout in India, and they expressly said, we're rolling out this vaccine.

00:35:38.000 First, they vaccinated the health co-workers, young, healthy people.

00:35:42.000 Nothing happened in all-cause mortality.

00:35:44.000 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.

00:35:56.000 And they listed 20 comorbidities.

00:35:58.000 And if you had those and you were over 45, they were going to vaccinate you. 0.99

00:36:01.000 And they did that very quickly.

00:36:03.000 It was military style.

00:36:04.000 And that's when the deaths just started shooting up.

00:36:08.000 And by the time they finished all the people with comorbidities and the most elderly, the deaths came down again.

00:36:16.000 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.

00:36:26.000 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.

00:36:41.000 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.

00:36:51.000 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.

00:37:00.000 And the only way we could explain it was if you injected these very vulnerable people, They had a high chance of dying.

00:37:08.000 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.

00:37:24.000 That's when the deaths per injection were very high at the beginning, when the average age was very high.

00:37:30.000 So we knew all that, and so we looked at India, and we said, that must be what's going on. 0.84

00:37:36.000 There's no other way to explain this.

00:37:38.000 It's far beyond anything that has happened in the whole COVID period in India.

00:37:43.000 It's massive.

00:37:45.000 It coincides exactly with this rollout.

00:37:47.000 And this rollout is one where the Prime Minister Moody said, it's not going fast enough.

00:37:54.000 We're going to have what he called...

00:37:56.000 In Hindi, but he called it a vaccine festival.

00:37:59.000 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.

00:38:09.000 We've got to do it right away.

00:38:10.000 So that was the peak in the peak.

00:38:13.000 That was in April, and that accompanies a huge surge in mortality.

00:38:19.000 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?

00:38:43.000 Then you're going to kill people.

00:38:45.000 And the calculated... 0.98

00:38:48.000 The death-fatality ratio that we calculated is 1%, which is very large.

00:38:53.000 It's 100 times more than what you see in the VARS data for the Janssen in the United States for people over 65.

00:39:01.000 It's 100 times more than that.

00:39:03.000 What are the raw numbers?

00:39:05.000 How many people got vaccinated and how many people died?

00:39:08.000 350 million people were vaccinated in that period in India, and 3.7 million people died.

00:39:15.000 So it's about 1%.

00:39:17.000 And a large review article just came out as a preprint.

00:39:21.000 The fatality rates can be as high as that.

00:39:24.000 So there's now a review of the clinical trials that is giving numbers that are comparable to that, that approach that number.

00:39:33.000 That's what we came to believe happened in Some of the arguments go like this.

00:39:40.000 The surge is everywhere.

00:39:42.000 Where it's happening, that surge is happening simultaneously in every province or state of India, in all the big centers.

00:39:50.000 But it's very heterogeneous from one place to another.

00:39:53.000 It's not the same on a per capita basis kill rate.

00:39:57.000 So it depends on the local composition of who you're injecting.

00:40:01.000 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.

00:40:17.000 And there was no wave anywhere that was as large as this, as sudden and as massive as this.

00:40:24.000 So we think that that's how we interpret the deaths in India in that period.

00:40:30.000 The other puzzler, if you want to accept the official orthodoxy, is that the second wave should have been a more innocuous variant.

00:40:41.000 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.

00:40:57.000 And they also see a big peak in Delhi, the capital.

00:41:00.000 And in their paper, they interpreted that as being due to the Delta variant.

00:41:05.000 They argue that the Delta variant was coming in.

00:41:08.000 And that the Delta variant now, they argue that the Delta variant was more transmissible and had more breakthrough relative to the vaccine.

00:41:18.000 But the way that they deduced that was to fit the properties of the new variant of concern to the epidemiological data.

00:41:26.000 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.

00:41:36.000 And they admitted that they needed a seed that would have happened on this date and given everything that happens in Delhi.

00:41:43.000 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?

00:41:54.000 We said, listen, you're fitting your properties of the variant to the data in order to deduce what the variant is.

00:42:02.000 That's no way to demonstrate that this mortality was due to a variant.

00:42:08.000 That's just a way of creating the illusion that there was a variant.

00:42:12.000 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?

00:42:23.000 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?

00:42:37.000 Well, I'll give a very negative answer.

00:42:40.000 Scientists themselves so believe in the dominant paradigm that it's impossible to find scientists that would debate people like myself.

00:42:53.000 That saying something like that is just too far-fetched that they would even want to debate it.

00:42:59.000 So never mind the mainstream media, never mind the general population.

00:43:02.000 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.

00:43:13.000 so on.

00:43:13.000 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.

00:43:24.000 It's harder to get them to understand that this could be due to the measures that we applied.

00:43:31.000 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,

00:43:54.000 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.

00:44:03.000 Over-treating others, stressing people out in institutions and care homes and in hospitals to the point where you kill them.

00:44:10.000 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.

00:44:18.000 Their lives were dissolved.

00:44:20.000 They could have no social contact all of a sudden.

00:44:23.000 They lost their caregivers.

00:44:25.000 They were locked in.

00:44:26.000 I think that many, many people were killed this way.

00:44:30.000 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.

00:44:42.000 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.

00:44:57.000 Exactly.

00:44:58.000 I encounter that every day.

00:45:00.000 I encounter that every day.

00:45:02.000 It's incredible.

00:45:03.000 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.

00:45:19.000 And a lot of the remaining 47% comes from pharma and from China.

00:45:24.000 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.

00:45:32.000 Yes.

00:45:33.000 I need to give you a story from Canada because this is really striking.

00:45:38.000 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.

00:46:05.000 That's what they said in their article based on modeling.

00:46:08.000 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.

00:46:15.000 And then on that graph, we showed what a million extra deaths would look like.

00:46:20.000 It was way up here.

00:46:21.000 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.

00:46:31.000 And we explained that.

00:46:33.000 They're saying that they brought it down by coincidence to exactly the same thing that it would have been historically.

00:46:40.000 Now that coincidence, scientifically, is impossible.

00:46:43.000 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.

00:46:54.000 There's only one universe in a million where that could happen, you see?

00:46:57.000 And so we tried to point out with this graph how ridiculous what they were proposing was.

00:47:04.000 This is what they want us to believe.

00:47:05.000 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.

00:47:20.000 That's what they want us to believe.

00:47:23.000 It's complete nonsense.

00:47:25.000 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.

00:47:38.000 And the vehicle for those lies is oftentimes modeling.

00:47:43.000 There's another saying that says that statistics are like prisoners of war.

00:47:47.000 If you torture them enough, you can make them say anything that you want.

00:47:51.000 And the government has become the interrogator of...

00:47:56.000 Well, at least what I did in my research was we went for statistics that themselves...

00:48:03.000 Had to be valid, in the sense that if you count a death, you know that person died.

00:48:09.000 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.

00:48:15.000 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.

00:48:25.000 So that's why we went after that kind of data and tried to interpret and analyze that kind of data.

00:48:31.000 That was our approach.

00:48:33.000 Professor Denny Rancourt, thank you so much for your integrity, for your courage, and for joining us on the show.

00:48:39.000 Thanks for this study.

00:48:40.000 It was my pleasure, and thank you for challenging me.

00:48:44.000 Thank you very much, Dr.

RFK Jr. The Defender - January 21, 2023

All Cause Mortality with Denis Rancourt

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