Sunday, June 16, 2024
HomeCOVID-19We were right! The ONS lied about covid vaccine safety

We were right! The ONS lied about covid vaccine safety


IN 2021 when the Office for National Statistics (ONS) started releasing its vaccine by mortality status reports we revealed that there were large spikes in the non-covid death rates in the ‘unvaccinated’. These spikes in mortality coincided with the first main vaccine rollout and did so for each age group (see this report, for example).

Here is the chart for non-covid mortality rates in weeks 1-38 of 2021 for the 60-69 age groups:

The charts for the other age groups looked much the same.

We asserted that these obvious anomalies were a result of the standard ONS procedure of categorising anyone within 20 days of their first dose as ‘unvaccinated’. However, in our own discussions with the ONS they maintained that, although that method was used for their efficacy calculations, it was not used when it came to mortality. They clearly said that a person dying any time after vaccination was correctly categorised as a vaccinated death in the mortality data they regularly released to the public and which formed the basis of a massive public communication campaign encouraging vaccination.

To ‘explain’ the spikes the ONS pushed the implicit assumption that there was a phenomenon called the ‘healthy vaccinee’ effect, whereby they claimed that people ‘close to death’ were not vaccinated. And they made this bold claim without any data to support it whatsoever.

Apart from the fact that this would have contradicted the NHS policy at the time we showed that, while a healthy vaccinee effect might have partly explained the longer term lower non-covid mortality rates in the vaccinated, it could not possibly have explained those spikes in mortality rates.

They could only be explained by categorising deaths shortly after vaccination as unvaccinated. Yet the ONS, along with many of the staunchest covid vaccine disciples, doubled down on their insistence that such miscategorisation did not occur. To them all the anomalies in the ONS data could only be explained by the hallowed ‘healthy vaccinee effect’.

Later, the ONS did actually claim that there was indeed an ‘unhealthy vaccinee effect’ but did so to explain other anomalies in the data. Clearly the ONS was so self-serving they did not see the contradictions between these claims and simply wanted to have their cake and eat it.

As a result of a subject access request that Clare Craig submitted to the ONS we have now found out that we were correct after all!

Clare has posted on this Twitter/X thread an internal ONS email confirming that the NIMS database of vaccinated people, that the ONS relied upon, had excluded those people who had died before vaccine records had been sent back to the central system:

When we pointed out to the ONS exactly this possibility for miscategorisation in 2021 they continued to deny that it had happened (see Table 8 of our report here).

Why is this so important? Because the ONS data – possibly more so than any other source of data in the world – was used to bolster the claim that the vaccines were highly effective and safe.

And, as we have always argued, and which is now certain, any claims of efficacy and safety based on their data were completely illusionary and subject to the cheap trick of miscategorisation whereby even a placebo – or something even worse – could be ‘shown’ to be safe and effective.

They therefore lied and intentionally created and spread misinformation. We were accused of conspiracy thinking and our reputations were tarnished as a result.

But we were right!

Update: Daniel Jupp has also written about this:

This article was co-authored by Martin Neil, Clare Craig, Mr Law, Health and Technology, Jonathan Engler, Dan and Joel Smalley. It appeared in Where are the numbers? on May 23, 2024, and is republished by kind permission. 

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Norman Fenton
Norman Fenton
Norman Fenton is a British mathematician and computer scientist. He is Professor of Risk Information Management in the School of Electronic Engineering and Computer Science at Queen Mary University of London.

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