Young doctors in Canada are dying at a rate 12X normal after their second booster
We now have all the CMA Canadian doctor death data in a spreadsheet. It shows that doctors 50 and younger are being killed after the second booster a rate that is over 12X normal.
Executive summary
The Canadian Medical Association (CMA) data for 2020 shows doctors in Canada aged 50 and under die at a rate of about 10 to 12 per year.
After the latest booster, 6 Canadian doctors, 50 and under, died within a 15 day period.
This suggests that for the 15 day post-vax period only, doctors are dying a rate that is 12X what is normally expected.
Something is very wrong here, but nobody wants to talk about it or look into it. They’d rather look the other way.
Introduction
Thanks to the brilliant work of Marc Godard and Brent Kievit-Kylar, we now have the CMA death data that I wrote about earlier in Google spreadsheet format where it can be easily analyzed for the first time. Note that the CMA removed entries prior to 2020 because they said it was too resource intensive to maintain this data. That’s hard to believe, but that’s what they said.
The results are stunning if we look at young doctor deaths: it makes the impact of the vaccines much easier to see because there is more signal and less noise since doctors under 50 rarely die.
I found a 12X increase in the rate of all-cause mortality post-vaccine for Canadian doctors aged 50 and under (compared to the young doctor death rates in 2020).
How can they explain that one?
Well, they can’t.
Those Canadian doctors died after the 2nd booster decades earlier than they should have.
I wanted to talk to the CMA about this, but all five of my requests for comment from them were ignored. They just don’t want to talk about it. I’m sure they hope nobody sees this article.
So if you are reading this now, you know what to do:
You can verify my calculations yourself. All the data is now in plain sight.
Here’s how you can verify this yourself if you were skeptical of my work:
Open the spreadsheet. Using Filter View “50 and under,” you can see that 6 doctors died in 2020 before the vax rolled out in Canada on December 14. So 6 young doctors dead in 348 days is our baseline death rate.
From my article on Canadian deaths, we know of at least 6 Canadian doctors aged 50 and under that died in a 15 day period from July 13 to July 28 after the fourth booster was required. Here they are:
Note that the COVID vaccines are notorious for accelerating existing cancers and causing new cancers and killing people who are under physical stress such as swimming or running.
I’m not being judgmental here. I’m just tallying up all-cause mortality rates post-vaccine compared to average mortality rates of the same age group a year earlier.
I’m now using the more complete spreadsheet of deaths in the section below
So we have 11 deaths/348 days in 2020 vs. 6 deaths/15 days after the second booster.
6/15/(11/348)=12.6
The 348 days is because the circumstances for the last doctor are suspect since it is after the vaccines rolled out.
So doctors who are 50 and under living in Canada are dying after the second booster at over 12X times the normally expected rate during the 15 days after the shot. What happens for the rest of the year is unknown, but it’s probably elevated at a much lower rate.
I found similar elevation in death rates for the vaccinated in my surveys
If you recall, I published an article just 20 days ago on August 10 where I used data submitted by over 600 readers to calculate an increased death rate for vaccinated people compared to the unvaccinated. The calculation was if you were vaccinated, your risk of death was 20.4X higher compared to unvaccinated people. That calculation was for all ages, not for those 50 and under. These are large signals.
Now, you might ask, “Wow, if the vaccine is elevating death rates by that much, then the mortuary business must be way up.” It is way up, but not by 12X! This is because we are looking at under 50 deaths which are relatively small on an absolute basis.
I thought you should know what the Canadian doctor death data shows and I’m sure there are plenty of biases and confounders that affect this calculation, but even if I’m wrong by an order of magnitude, this should be an extremely troubling result for health authorities everywhere in the world: we should never be giving anyone a shot that is more likely to increase their risk of death than reduce it.
If this were an isolated anecdote and the only piece of negative data, many people would shrug their shoulders and ignore it.
But it’s not an isolated incident. It’s yet another very troubling data point showing the vaccines should be immediately halted.
See these two articles for more examples troubling data points that should not be ignored:
Caveats in interpreting the data
There are about 92,000 active physicians in Canada and I would estimate about 20,000 retired ones, for a total of about 112,000. Let’s say almost all doctors die between age 30 and age 100, or a 70 year period. 112,000/70=1,600 deaths per year. The CMA reports on only about 400 deaths per year, or about 25% of the actual.
On the other hand, my deaths after vaccination are not complete either. These are just the ones I’ve heard about.
Even if we divide 12 by 4, it’s still very very troubling.
Addendum
I just received this table of deaths of doctors 50 and under in Canada by year:
I updated my earlier numbers based on this.
Do you see a problem?
Full data set for further analysis by others
Here is the full Canadian doctor death spreadsheet and recent obituaries so that others with more time than I have can analyze this data.
Summary
I’ve been doing this for more than 15 months now and I have yet to find any reliable data points showing the vaccines are either safe or effective.
I’m still looking.
This is interesting
https://twitter.com/mcmasterpgme/status/1572294207389433857?s=12&t=4Sg76j3zTzGOcYWxXgPzy
Whoever wrote this has no understanding of how to use statistical analyses / relied on the ignorance of their intended audience not understanding statistical analyses.
Comparing data over years to 15 days because it suits their intended hypothesis then using that to make an assumption of the difference in rate is wrong on so many levels. This is why you standardise your data sets otherwise you end up with this absolute shite.