Exclusive: Awaiting Trial, Whistleblower Barry Young Discusses New Zealand’s ‘Sham’ COVID Inquiry

New Zealand whistleblower Barry Young, who faces trial for leaking COVID-19 vaccine data, told The Defender that public trust can be restored only through transparency and accountability. He said the country’s COVID-19 inquiry “danced around the issues,” failing to seriously examine vaccine safety concerns or mandates. “The worst possible lesson from COVID-19 would be that institutions can avoid scrutiny by labeling critics as dangerous or misinformed,” he said.

Barry Young, a former New Zealand Ministry of Health programmer and whistleblower, faces trial and up to seven years in prison after allegedly leaking COVID-19 vaccine data he said revealed significant safety concerns. Young was arrested in December 2023 on charges of “dishonestly accessing a computer,” according to The New Zealand Herald. In September 2024, Young told The Defender he was representing himself in the case.

The following is a Q&A with Michael Nevradakis, senior reporter for The Defender.

Michael Nevradakis: What did New Zealand’s Royal Commission of Inquiry into COVID-19 Lessons Learned do right? What shortcomings can you identify?

Barry Young: The inquiry was a complete sham.

COVID-19 Inquiry 1 was a total farce. COVID-19 Inquiry 2 was another example of how to avoid the elephant in the room. They danced around the issues.

Key people — [New Zealand’s former interim Minister of Health and COVID-19 Response Minister Chris] Hipkins and [New Zealand’s prime minister from 2017-2023 Jacinda] Ardern — were given a free pass not to attend, as advised by their lawyers.

If anyone should have been forced to front up to questioning it should have been Hipkins, the actual COVID-19 minister. … Hipkins, who, as it turns out, was given important information on the risk of myocarditis in young people, yet did nothing and actually promoted young people to get vaccinated.

These are the actions of a sociopath, pathological liar or someone who has no brain.

There is evidence here of a huge cover-up.

These people on the list did not have to testify to the COVID-19 inquiry at all to answer why this advice was ignored.

The biggest shortcoming, from my perspective, is that it did not truly test the official vaccine-safety narrative against the raw data. The data from New Zealand represents a gold-standard clinical trial.

We have approximately 2.2 million people followed after their “last vaccination” until either death or censor date, October 2023.

This gives an almost perfect age-standardised time series cohort analysis.

The average time each person was on the “study” was approximately a year, so it avoids a bunch of issues like immortal time bias. Each age group was well represented, and Stats NZ kept accurate age-standardised mortality statistics.

So from these ages, we can compute within a high degree of probability what the expected mortality would be based on each of those age groups by adding up their person-years and comparing to the baseline.

Even compared to the Crown’s expert epidemiology witness, using his elevated mortality figures (from 2022), the average mortality rate (standardized mortality ratio or SMR) came out at 1.6.

This means the vaccinated cohort on the Health New Zealand database had an approximate 60% higher mortality rate compared to the New Zealand baseline. That’s taking into account all ages.

The forest plot looks like this for each age band.

In a cohort of 2.2 million people followed after their last vaccination, there were 37,315 observed deaths compared with 22,560 expected based on age-standardised background rates, giving an excess of 14,755 deaths (SMR 1.65; 95% CI, 1.64 to 1.67)

This corresponds to an absolute excess risk of ~0.67%, with ~40% of all observed deaths being above expectation.

If the effect were random, SMR should fluctuate around 1. Instead, it rises monotonically with age.

That’s a dose-response relationship, which strengthens causal interpretation.

This demonstrates that most of the absolute excess burden sits in the elderly, even though the SMR rises across all ages. Among people ages 65 and older, there were 31,922 observed deaths compared to 18,442 expected, yielding an SMR of 1.73 and ~13,480 excess deaths.

This accounts for ~91% of all excess mortality in the cohort.

Yet the COVID-19 inquiry chose to ignore all of that.

Even when addressing the “clusters of deaths” and doing Monte Carlo simulations, the maths becomes hard to ignore, which is exactly why they have ignored it.

All we need to do is some basic analysis: At a site on one day (maybe a nursing home or retirement village), 54 people were vaccinated sequentially. Of that elderly group, 25 died within a year, and 19 of these were in vaccination sequence, meaning they were vaccinated one after another.

Even assuming the vastly elevated mortality rate (which is high even for nursing homes), the chance of seeing 19 in a row dying like this is astronomically low.

The only alternative argument to this pure mathematics (permutations and combinations) is that the order of vaccination was somehow “non-random” and they separated out the ones who they “knew” were going to die (i.e., a group of super sick and frail people within a larger group of super sick and frail people) and somehow vaccinated them together for some reason.

That is such a bizarre scenario, yet that is the level that sane, rational people are going to avoid this mathematical reality.

I chose this site because, by analogy, it’s fairly close to a deck of playing cards, and you can try doing this at home (52 cards — 26 black and 26 red). If you have 52 (randomly shuffled) playing cards, there are 52! possible combinations of drawing them one after another (i.e., 52 x 51 x 50 x 49 … etc.) — a huge number.

So out of this number of combinations, how likely is it to draw out 19 black cards in a row, for example? It’s almost impossible.

This is high school maths, nothing to do with comorbidities, ages or Pfizer, just pure maths.

You can run the simulation in RStudio using this code:

longest_run <- function(x) {

r <- rle(x)

max(r$lengths[r$values == 1], na.rm = TRUE)

}

N <- 54

D <- 25

obs_run <- 19

nsim <- 1000000

runs <- replicate(nsim, {

x <- c(rep(1, D), rep(0, N-D))

x <- sample(x)

longest_run(x)

})

p <- mean(runs >= obs_run)

cat(”Observed run:”, obs_run, “\n”)

cat(”Monte Carlo p-value:”, p, “\n”)

# Expected longest run under random arrangement

expected_run <- mean(runs)

# 95% simulation interval

ci <- quantile(runs, c(0.025, 0.975))

cat(”Expected longest run:”, round(expected_run, 2), “\n”)

cat(”95% simulation interval:”, ci[1], “to”, ci[2], “\n”)

cat(”Observed run:”, obs_run, “\n”)

cat(”Monte Carlo p-value:”, p, “\n”)

Monte Carlo P value is zero, meaning that in a million shuffles and draws, it never once got a sequence of 19.

What I have managed to do with this maths and stats evidence is get the Crown (or the prosecution — the Crown represents the state, equivalent to the district attorney in the U.S.) to admit in a memo that EVEN IF there have been excess deaths caused by the COVID-19 vaccine, that is irrelevant to my court case.

You have to read that a couple of times to make it sink in. The Crown are admitting that even if the COVID-19 vaccine can be proven to be killing people, they don’t really care.

An astonishing admission.

My next feat is to get them to either deny or ignore the existence of mathematics. These are the corners I am backing them into.

Science, truth and logic are good like that.

Nevradakis: How did the COVID-19 inquiry deal with vaccine injury and vaccine safety more broadly?

Young: It didn’t. Next ...

Nevradakis: Would you say that the outcome of this inquiry was, in a sense, preordained?

Young: I do think the outcome was heavily shaped by the terms of reference, the institutional assumptions, and the sources of evidence relied upon (absent the huge amount of evidence showing this is a potentially deadly product).

If an inquiry begins from the premise that the central public-health architecture was broadly legitimate, and then mostly asks how it can be improved next time, it is much less likely to ask whether some of the underlying decisions were fundamentally wrong.

The outcome was structurally constrained. They never invited me to speak, even though I asked. The ones that did speak avoided talking about the Health New Zealand data showing serious concerns.

Therefore, it was unlikely to produce a finding that the vaccine programme, mandates or safety monitoring failed in any major way, because the inquiry seemed to rely heavily on the same institutions whose actions were under examination.

Nevradakis: Is this similar to or different from other countries’ COVID-19 inquiries, in your view? Are there any countries you can identify as having performed a “model” inquiry?

Young: I would not say any country has yet produced a truly model inquiry.

A model inquiry would be independent, adversarial where necessary, data-driven and willing to test uncomfortable evidence.

It would hear from vaccine-injured people, clinicians, statisticians, data analysts, mandate-affected workers, public-health officials and dissenting experts.

It would require production of raw data and internal communications.

It would not treat criticism of the response as misinformation by default or prosecute those who are trying to show there are serious red flag concerns with this “vaccine.”

Nevradakis: Is there anything else you would like to add?

Young: Yes. I think the real lesson is that public trust can only be rebuilt by honesty and transparency.

If there were errors, they should be admitted. If people were injured or died, they should be recognised and families supported. If mandates caused harm, that should be acknowledged. If data showed safety signals, those signals should be investigated transparently, not dismissed because they are inconvenient.

The worst possible lesson from COVID-19 would be that institutions can avoid scrutiny by labeling critics as dangerous or misinformed (or conspiracy theorists). That is not how science, democracy or public health should work.

Brief update on my legal case:

My criminal case is ongoing and approaching 900 days since my brutal SWAT team arrest, with eight armed police smashing into my house and putting me in handcuffs.

To put that into perspective, the U.K. July 7 London terrorist bombers received similar treatment. Even in the U.S., only high-profile drug raids are similarly resourced.

Not a usual process for nerdy computer guys who have never been in trouble with the police before and are accused of copying data from a work laptop.

To put it another way, Fred West, Britain’s worst serial killer who gruesomely murdered dozens, was politely “asked” to attend the police station, which he volunteered to do.

That context tells you everything about how they view this.

The current focus is on my claim for protection under the Protected Disclosures (Protection of Whistleblowers) Act 2022. My position is that I was a public-health data worker who saw serious safety signals in official health data and attempted to raise those concerns because I believed there was a serious public-health issue requiring investigation.

The Crown opposes that and strongly retaliated. It says the case is not about a protected disclosure but about alleged dishonest access to computer systems, which is crazy circular logic.

I strongly dispute that framing. They are saying the protected disclosure does not apply to the “crime” of looking at data and seeing serious concerns.

If this is upheld, it will destroy the purpose of the Protected Disclosures Act.

A major issue now is whether the data I saw and relied upon was genuine and whether it was “manipulated,” and whether a reasonable person in my position could have believed it shows serious wrongdoing or serious public-health risk.

The judge has specifically asked the parties to address the relevance and genuineness of the lists of vaccinated persons who died soon after vaccination, and whether comparison with the original data shows wrongdoing or manipulation.

The Crown are claiming the data has been manipulated yet will never — NEVER — provide any actual evidence of this. They are saying that the act of running select queries on a databases is “manipulation.”

They think that by revealing a bunch of dead people in a cluster, you’re somehow creating them out of thin air, and that if the query wasn’t run, they wouldn’t exist.

Classic denial and cognitive dissonance.

For me, the key point remains simple: I did not set out to harm anyone. I raised the alarm because I believed the data showed something profoundly serious that needed proper investigation.

Instead of investigating the concern, Health New Zealand and the Crown have focused on prosecuting the person who raised it.

Two things spring to mind: Don’t shoot the messenger, and no good deed goes unpunished.

I’m not worried. I am shielded and protected by the truth. So even if they lie and ignore the truth, I will have won.

John 8:32: Believe, and the truth shall set you free.