Here is just one of the most ridiculous graphs I have ever seen in a paper.
The "Dose 2 > 180 days" group had the exact same mortality rate. So the "vaccine efficacy" in this group was 34%. With tight confidence intervals.
Not a chance.
But it gets worse!
The "unvaccinated" death rate drops by half in the second half of the year....
Whilst the "Dose 2 8-90 days" quadruples in the same time frame, yet designated as a "13.9% efficacy"
This paper could go down in history.
The figures are all over the place. For "COVID-specific mortality" there is no significant difference between "Dose 2 > 180 days" and "Unvaccinated" as their confidence intervals cross. The "Vaccine efficacy" should include negative in the range. But of course it doesn't
And the "Dose 3 > 180 days" has a death rate of 4.068 compared to 9.704, a 58% apparent drop. Except it is quoted at 71.9.
Who did these stats?
The "all cause mortality reduction" is ridiculous. In Australia COVID peaked at 3.2% of deaths during the pandemic.
So how can any reduction in 3.2% of deaths create a 70% reduction in all-cause mortality?
Give me a break.
This is just healthy user bias.
@profnfenton
This is just a skim of the paper. There is no way this has passed an adequate peer review. There are red flags everywhere.
For instance this is just the declared interests. There are more undeclared interests...
...such as the involvement of the supervising author with the NCIRS which literally curates this data.
This is also strange.
The Quentin registry study shows a big jump in vaccination rate by age group but the Bernard study doesn't show the same.
This is more like what a synthetic data set might show based on assumed characteristics of the underlying data.
There are possible explanations for all of these anomalies, but this is the problem with secret registry data:
It's not credible when it conveniently matches a narrative and nobody is allowed to see it.
I'm going to explain why this chart is so important and why @jsm2334 is being disingenuous by ignoring it - whilst making points that undermine the "real world vaccine data" industry.
It's a Kaplan-Meier curve and it obliterates Jeffrey's argument.
Just to go over it... the lines show what proportion of subjects (children) ended up without chronic disease up to 10 years after being studied.
It's called a survival analysis because it's used for cancer survival.
If the red line was a cancer drug it would be a blockbuster
It shows that by the end of the 10 year follow-up, of those that they could still follow up (who stayed in the study) 57% (100-43%) of vaccinated kids had chronic disease (e.g. asthma) and 17% (100-83%) of unvaccinated kids did.
Janet Diaz was the person that led the #MAGICApp guideline committees that stopped your grandma getting antibiotics for her post-viral pneumonia, leading to her death.
But she did this with the help of @pervandvik who deleted his account
Diaz here tells you that COVID kills you by an overreacting immune response, but that was never true.
She was an intensivist recruited by the WHO in 2018.
None of this was true, but it sold a LOT of drugs and killed a LOT of people
Which US govt organisation blew a hole in the ozone layer in 1958 by sending atomic bombs to the troposphere over the Antarctic in operation Argus - then blaming the resulting destruction of ozone on CFC's?
It wasn't just Pfizer that hid the fact that the mRNA-LNP complex went to the ovaries (where it could not possibly provide its declared function in the lung).
The AMH drop (ovarian reserve) after vaccination was later shown by the Manniche paper after being denied by the Kate Clancy and Viki Males of the world.
But this time the Arnold foundation's @RetractionWatch have not only revealed with their "exclusive" that they were directly involved in trying to get this important paper retracted...