Dr Clare Craig Profile picture
Feb 7, 2023 18 tweets 6 min read Read on X
This graph is a lie.

It is very easy to show you why.

It is claiming to show a cumulative death rate by vaccination status in a population with heart failure.

onlinejcf.com/action/showPdf…
Let's unpick it bit by bit.

They measured deaths from 1st Jan '21 to 24th Jan '22.
A total of 389 days.

Let's check, the last value on the graph.

They give us the size of the population in each group at the end of the study.
Using the deaths in the table, the cumulative incidence curve for mortality should peak at:

unvac = 599/3196 = 0.19
partially = 75/645 = 0.12
fully = 195/2200 = 0.09
boosted = 36/1053 = 0.03

Hmm - that's not what the graph shows.

Let's dig a bit deeper.
Let's ignore the vaccinations for a moment and see how many people died over time.

Here we see two spikes in the death curve.

Why would that be?
It is not a good match for covid deaths in USA at the time.
I have tried to reverese engineer the calculation of the rates. We have the deaths and we have the starting and final population sizes for each group.

The rest is estimated.

There is only a small range of possibilities for the population size of each group each week.
This is what the cumulative incidence chart looks more like in reality:
But cumulative charts can hide a lot of interesting information so I also plotted it as the actual number of deaths ocurring in each period.

e.g. Subtracting the penultimate column from the last column shows deaths in last 49 days of the study gives

32
9
27
12 deaths.
Plotting the deaths that occured in each period as a mortality rate gives this.

The high yellow point was only 2 deaths in a small population - it can be ignored.
What we see is that in the early period the deaths were seen in the unvaccinated population but as time went on deaths started in the vaccinated population.

By the end the death rate was the same in all groups.
This is evidence of what is called a "healthy vaccinee effect."

It is the phenomenon of the dying rejecting a vaccine. They then die unvaccinated while the apparent death rate of the vaccinated population seems low for a while.

Eventually it all evens out as time catches up.
The ONS have described it here:
ons.gov.uk/peoplepopulati…
It is not evidence that the vaccine saved lives.

The claims that vaccine would impact on death outcomes beyond covid has always been a bizarre one.
There were 904 deaths in this population during the study period.

Nowhere do they say how many were deaths with a mention of covid.

Now, isn't that a bit odd for a paper on deaths after covid vaccination?
Note that the data for hospitalisation for the unvaccinated is not shown but is instead merged with the partially vaccinated.

People who didn't complete the initial course may represent those who became very unwell after vaccination, coincidentally or through injury.
Please feel free to try and squeeze a vaccine favourable picture out of the raw data by altering the population sizes that I estimated.

You will struggle to.
One author has responded with the excuse that I did not account for censoring.

I have redone the graphs having removed those who had died from the denominator.

They look almost identical.

The message remains the same.

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More from @ClareCraigPath

Jun 6
The overall excess mortality has reduced to almost zero but this hides too many deaths in the young.

Thread of excess mortality in England based on gov estimates of population and death rate trends from 2014-2019.

0-24 year olds. Image
25-49 year olds Image
Read 8 tweets
Mar 20
One of the most important stories of lockdown has rarely been shared.

It is the story of Seattle.

They broke ranks and started an important experiment. 🧵
One research laboratory decided to break the rules for the sake of public health.

Rather than wait for a CDC approved covid test, they adapted an existing influenza test and started testing.

yahoo.com/news/seattle-l…
The regulator was not impressed.

This was a research laboratory doing clinical diagnostic testing and that was not allowed.

The lab was shut down on 9th March 2020.

nytimes.com/2020/03/10/us/…
Read 9 tweets
Feb 9
Remember the Beta variant in South Africa?

Remember how AstraZeneca said their vaccine was only 10% effective against it?

They blamed the variant but it has nothing to do with that...🧵

bbc.co.uk/news/world-afr…
All the vaccines ever did was make people immune suppressed for two weeks.

The consequence was that those who were susceptible to a particular variant had their infections earlier than they would have.

You can measure after that point and get an illusion of benefit.
This trick only works if you vaccinate during a wave.

For the UK - we were fast getting to those most susceptible to dying. Our death wave looks like a witch's hat on top of Europe's because of the earlier infections.

N.B. the impact was felt by the unvaccinated too. Image
Read 6 tweets
Jan 17
How do we how well the covid vaccines performed?

We can look at this question from multiple angles and see. 🧵
First, there was the "secondary attack rate".

When someone tested positive this measure could be taken of the percentage of household contacts that later tested positive.

If vaccines reduced risk of infection this should have fallen.

It did not. Image
Another measure is how many people developed antibodies before and after vaccine. Image
Read 15 tweets
Jan 17
"Cancer" is a big bucket and some diagnoses are much more consequential than others.

This needs drilling down.
e.g. death rates are much more comparable Image
Colon cancer
5.2 for men in 2017
5.3 in 2021 Image
Read 21 tweets
Dec 15, 2024
The last 4 years has been a period of modelling based on assumptions laundered through the medical literature and called "The Science".

If you thought the "real world" evidence was more reliable think again. 🧵
@Jikkyleaks has exposed a massive fraud at the heart of the covid literature.

Instead of using the difficult, fragmented and hard to collate data from the actual real world, pharma sponsored datasets which contain modelled synthetic data were used.
Like all models this synthetic data will have been based on prior assumptions:

assumptions like vaccines preventing 96% of infections.

The consequent results stand out ludicrous disprovable claims.
Read 4 tweets

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