Acute Kidney Injury After Vaccination: New Zealand Government Scientists Changed Their Data Without Explanation
By STEVE KIRSCH
The data showed the vaccines were causing kidney injury. So the data magically changed when the paper was submitted for publication. The paper also showed >25X higher risk of myocarditis post vax.
A paper published on a preprint server on Jan 20, 2023, by scientists at Health New Zealand and the NZ Ministry of Health showed conclusively that the COVID vaccine causes kidney injury.
The paper “disappeared” from the preprint server and reappeared 8 months later in a peer-reviewed journal on Aug 9, 2023, but with key numbers changed to make the vaccine look safe with respect to kidney injury.
The incidents in the paper couldn’t happen by chance. The p-value calculated from the data they observed is 1.28e-115 which is ridiculously small. I’ve never seen a p-value that small. It means that it is a certainty that the effect wasn’t just due to random chance: it was causal.
I verified this in VAERS in 60 seconds. Here a graph of acute kidney injury for all vaccines in the 30 year history of VAERS over all time. Only one vaccine has a signal for acute kidney injury: the COVID vaccine. So the signal is real.
How could the New Zealand scientists miss a signal that is this big?
Acute kidney injury reports from VAERS over the ENTIRE HISTORY of VAERS for all vaccines. Just one vaccine causes this adverse event: the COVID vaccine. No doubt about it. This is not a coincidence.
Why did the data change in the published paper
I’ve emailed the authors of the paper to ask them why there was a HUGE signal in VAERS, why there was a signal in their earlier paper, and why the signal simply “went away” in their published paper.
Did they use a different set of ICD-10 codes in the original paper? What ICD-10 codes did they use? And why did they change them?
The Epoch Times article by Colleen Huber with the preprint paper
Here’s the key part:
The published paper from the New Zealand scientists
Here’s the key part:
The observed events decreased dramatically. How can that happen?
I reached out to the authors who had media relations get back to me with the response:
While addressing reviewer comments during the peer-review stage at Drug Safety, VSSR received new information on how the background rates were calculated which impacted the original calculations. To account for this new information, VSSR revised the statistical method and recalculated the statistical associations between the AESIs and vaccination. This is not an unusual occurrence in the process of submitting for publication and highlights the importance of scientific peer-review.
I responded that this doesn’t explain the significant reduction in observed events. I will update this article with their response.
Data availability statement
From the published paper:
The data that support the findings of this study are not able to be made publicly available due to privacy and ethical restrictions outlined by New Zealand Legislation.
Whew! That’s a relief! Wouldn’t want to have data transparency, would we?
I personally proved that such data can be obfuscated so that nobody’s privacy was breached.
I published 4M vaccination records from New Zealand and I haven’t had a single request to remove someone’s personal information (since none of the records matches any person).
My p-value calculation for acute kidney injury
Based on the numbers in the original paper.
def cum(n,m): # observed, expected
The HUGE myocarditis/pericarditis signal ages 5-19
The myocarditis/pericarditis numbers were huge. A SIR of over 25 (this is a ratio of observed/expected). That’s huge. It’s 25X higher than normal. That is a train wreck.
And it’s dose dependent too. That’s called “causality.” No doubt about it. It is not a “potential link.” It is causing these cases.
Why didn’t the New Zealand health authority tell the public they confirmed that the COVID vaccine causes myocarditis and increases the risk by ~25X?
Myocarditis is DOSE DEPENDENT. That means it is CAUSED by the vaccine. A SIR value of 2 or more is an indicator of causality. A SIR value of 25 combined with dose dependence is a 100% indicator of causality. I’ve never heard of a counterexample when the numbers are this big. Have you?
Patient rights in New Zealand were suspended after the COVID vaccines rolled out
The lack of medical data transparency by all world governments should be very troubling.
This is especially true in New Zealand where the government has revoked patient rights for the COVID vax. So coercion is now OK to get you to take a medical intervention. What the government giveth, the government can take away.
This makes the lack of transparency especially troubling in New Zealand.
Acute kidney injuries caused by the COVID vaccines are real; the VAERS data makes that crystal clear. It is astonishing how the New Zealand scientists confirmed this and then the signal just magically completely went away.
But the fact that the New Zealand health authority knows that the vaccines cause myocarditis and still have never acknowledged that to the public and admitted they made an error is stunning as well.
I will let you know if I hear back from the New Zealand scientists.
Don’t hold your breath.
Original source: https://kirschsubstack.com/p/acute-kidney-injury-after-vaccination