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GDP, Big Hospitals, and Lockdowns Drove Canadian Excess Deaths

Day 1 · 7:28:04 · Denis G. Rancourt, PhD · Shawn Buckley

  • 💰 Among 80+ socioeconomic factors, higher provincial GDP per capita strongly correlated with more COVID-period excess deaths.
  • 🏥 Provinces with more large hospitals had higher P-score excess mortality; Quebec’s opening peak was almost entirely Montreal/Laval.
  • 🔒 Oxford elderly-protection severity spikes were followed by 85+ excess-death peaks then dry-tinder troughs—lockdowns killed then left fewer to die.

Part two: non-vaccine assaults and structural correlates of excess mortality.

← Turbo Cancer: Hyper-Progressive Disease in Mortality Data | No Geotemporal Viral Spread: Mortality Was Geostatic Assaults →

Transcript

Denis G. Rancourt, PhD · 7:28:04

And Part 2 is general harm, other kinds of assaults than vaccination that definitely caused death to the general population. And so these are the sections that I will be covering in this Part 2. And the first section is Canada's mortality context. Just to, just to give kind of a mortality crash course here, this is what happens in Canada. If you're looking at the 15 to 44-year-olds, so age-bearing for women, um, you see that the highest specific cause of death is opioids and fentanyl-related accidental poisoning deaths from those substances. The next one is suicides. After that, it's car accidents. And then what is— has been assigned as COVID is, is far below for this age group.

Shawn Buckley · 7:28:54

Age group.

Denis G. Rancourt, PhD · 7:28:56

For the next age group, 45 to 74-year-olds, all cancer types is definitely the, the leading cause of death. The second one is cardiovascular diseases, a particular group of them, and then COVID-19 is down here. In the 75-plus-year-olds, cardiac and, and cancer switch places. So the first killer is cardiovascular disease, then all cancers together, and then the third one is dementia, which is rising for this age group. And then down at the bottom there, you've got various kinds of respiratory diseases that are all related and happen at the same time and so on. So you've got chronic respiratory diseases, pneumonia, flu-like infections, and COVID-19 all happening down there. And you have to know that respiratory diseases are the ones, are the diseases that are are most obviously affected by stress. And so you're going to get increases in those when you have stressful events.

Denis G. Rancourt, PhD · 7:29:56

Okay, the, the next, uh, section of the second part here is that we looked at factors in Canada that correlate to excess mortality during the COVID period. This is years of work that I'm trying to condense into a short presentation here, you have to understand. So we looked at more than 80 province- specific socioeconomic factors that might correlate to excess death in Canada. So these factors include poverty, disability, the age structure, the population, obesity, and so on and so on. We looked at 80 of them and we did detailed statistical analysis of them. We did cluster analysis and various things looking for correlations and so on. And then we can make graphs of the correlation coefficients by age group and by— as a function of time and look for the factors that really correlate strongly with excess mortality in Canada. And the one, one of the factors that really popped out that correlates very strongly is GDP per capita.

Denis G. Rancourt, PhD · 7:31:00

The more wealth generation you have in the province, the more excess deaths you have. Very strong correlation. So you can see on the there, I'm, I'm listing British Columbia. I'm going west to east, and you can see the relationship. So our understanding of this is that when you shut the economy down in provinces that have a very vibrant economy, lots of people working, it has a devastating effect on the families of those workers, their elderly, and so on. And you get a lot of excess deaths during this period. That's how we understand that very strong correlation with GDP. Another strong correlation that we found— we found many, many, but this is just a few examples— opioid deaths on the y-axis there versus GDP real estate per capita. In other words, what we found generally throughout the world is where you have poor people living near very wealthy people in the same urban environment, closely mixed, the poor people do very badly.

Denis G. Rancourt, PhD · 7:32:07

Their death rates are much higher from all kinds of causes, including opioids, but also many other causes. And, and another interesting, uh, correlation that we found is this one.

Shawn Buckley · 7:32:20

Look at this.

Denis G. Rancourt, PhD · 7:32:20

Province to province, the excess mortality corrected for the baseline health of the population— it's called P-score excess mortality— all ages or 85 plus, they're both shown there on graph, and it's compared to the percent of the hospitals in the province that are considered large hospitals. So where you have large hospitals, a lot of people died. Where most of the hospitals are large, in just as a percentage, a lot of people died. So this is very clear, uh, for Ontario and Quebec. And this is what it looks like for Quebec. So at the start of the pandemic, that vertical The blue line indicates the date of the start of the pandemic. You had a huge peak in excess mortality in Quebec, but it was completely centered in the metropolitan area, right in Laval, right in the center of Montreal, and there was virtually no excess mortality in the rest of the population in the entire province. So that gives you an idea of what was happening in Canada.

Denis G. Rancourt, PhD · 7:33:22

Now, um, I want to show some graphs about the impact of COVID period measures, because this is devastating. Here's an important graph. This is excess mortality by week for ages 85+ year olds, and that's the, that's the blue line there. And then the red line is an index that was developed at Oxford that shows the severity Severity with which you protect your elderly. So in other words, the intensity of the protection to the elderly, which means lockdowns, isolations, that kind of thing. So when that severity goes up, when that red line shoots up, like at the— when the pandemic was announced, and then there were two other times there where I put grey lines where that severity of lockdowns and so-called protection of the elderly went up, then immediately following that rise, you have a large peak in excess deaths of the 85+ year olds.

Denis G. Rancourt, PhD · 7:34:25

And then following that large peak of excess deaths, you have a trough where you go subnormal. In other words, there's less deaths than you would normally have expected in normal period. And that's because you've killed so many people in that first peak that there's less people of that age to die in the weeks that follow. That's called the dry tinder effect. And so this is a demonstration. When you see that kind of an S-shaped curve, it's a demonstration that you really actively were killing people in that peak, and then there was less of them to die than normal after that. So this is the result of the devastating measures that we imposed on the elderly in Canada, and this was done in many, many countries. Most countries, most Western countries did this, and it had this kind of an impact. And this is to show the impact of cutting the financial aid in Canada. So there were major programs of financial aid. They're listed up there in the legend. And when you cut them, you immediately following that, you have increases in excess mortality.

Denis G. Rancourt, PhD · 7:35:28

This is of all ages. So you can see that even, even the plateau of mortality is higher after that last large cut in financial funding. So what we concluded by looking at many jurisdictions predictions is that people— it's worse if you give money, money, if you give support and you cut it, it's worse than if you had never given it and you'd gotten people to adapt to the difficult circumstances. And this is to show how important that financial support was. Actual poverty levels significantly decreased in Canada overall in 2020 and 2021 because of this massive support that was being given. In the US, it was more than $1 trillion that was given. In, in Canada, it was a proportionate amount. So very generous because they knew they were shutting down the economy, um, but there were consequences to when you turn it off after you've turned it on.