FAKE NEWS: Study does NOT show covid gene-vaccines “saved nearly 20,000 lives in NSW alone”

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Science journal ‘investigating the issues’ after media amplified wild claims from Moderna-linked-university study

  • Catastrophic confounds in study promoting mRNA
  • Senior author from Monash – which is building an mRNA plant
  • Industry-linked universities are not independent
  • Fake news written off press release becomes fake fact

A flawed study that made wild claims promoting the covid gene-vaccines is being investigated by science journal PLoS One after it became fake news.

The study, Assessing the impact of Australia’s mass vaccination campaigns over the Delta and Omicron outbreaks by Lin et al, published in April, claimed tens of thousands of lives in people aged over 50 were “saved” by the controversial products in NSW alone.

“In the absence of a vaccination campaign, ~21,250 COVID-19 50+ deaths (conservative estimate) could have been expected in NSW,” the study authors wrote.

They claimed this enormous figure was underestimated due to “indirect herd immunity effects” of the gene-vaccine – a product incapable of providing herd immunity as it has a zero to negative effect on your likelihood of catching and spreading covid.

The study consists of computer modelling from the Royal Melbourne Institute of Technology (RMIT) and Monash University, and was derived from problematic NSW Health data.

Gene-vaccine firm Moderna is building an mRNA factory with Monash University, on its campus, to make 100 million yearly doses of mRNA gene-vaccines. Monash is not independent, it has partnered with the mRNA patent-holder – but no competing interests were mentioned in the PLoS One study.

The authors made the claim of thousands of lives saved despite admitting the data used was limited and subject to age and comorbidity confounds – and therefore modelling parameters were difficult to estimate.

Adjunct Professor of Medicine David Richards said without complete data the study results would be null and void.

“There is no possible way of reliably analysing the result,” he said via email.

University of Sydney covid misinformation researcher Raphael Lataster told Letters From Australia that the study was misleading.

“The statistical biases and confounding variables involved in this study, as well as the complete lack of discussion around the risks and harms, means that it cannot tell us if the benefits of the jabs outweigh the risks.”

Some of the RMIT modelling’s worst problems lie in the assumptions and problematic NSW Health definitions found in the supplementary S1 material, accessed by a link in the study itself.

The S1 assumptions reveal that the predictive modelling in this study cannot say anything meaningful about how many lives the gene-vaccines did or did not save because of faulty raw data and catastrophic confounds (flaws) that invalidate its claims.

Here’s why: “Unvaccinated” includes people given one injection

The study can’t correctly count those in the zero gene-vaccinated group because NSW Health counted people given one gene-vaccine shot as unvaccinated for 21 days.

The NSW Health definition used for its data (used in this study, quoted at S1) says:

“Cases reported as no effective dose received their first dose of a vaccination course less than 21 days prior to known exposure to COVID-19 or have not received any vaccine dose”

If you were frail, elderly and became seriously ill after being injected with your first gene-vaccine, then died within 21 days, you were counted as a “no effective dose”. If you returned a positive PCR test and your death had no other obvious cause such as trauma then you were a “no effective dose covid death”.

That moves a bunch of “covid deaths” from the “one-dose” category to the “zero-dose” category, distorting the figures.

Screenshot from the S1 supplementary section. Highlighter, border added.

This is especially important as the controversial gene-vaccines themselves have mechanisms that interfere with and suppress the innate immune system (Seneff et al).

This one confound alone should invalidate the study claims – but it doesn’t end there.

The study conflates everyone aged over 50

The vast majority of deaths – 82.7 percent – were in the 70+ age group, according to a breakdown of the NSW Health data in the study’s supplementary section.

Source: S1 supplement from the study

But the study merges everyone over 50 together.

This is a logical fallacy that invites the public and media to extend the “lives saved” claim to younger age groups – to whom it does not apply.

Merging everyone over 50 together is useless because putting frail-aged people over 90 in the same category as a 55-year-old cannot tell us anything meaningful.

In October 2020 (before the gene-vaccine) University of Auckland public health lecturer Simon Thornley and colleagues wrote to the BMJ to warn that the covid infection fatality rate was only similar to seasonal influenza for the under-70s.

“The corrected median estimates of IFP (infection fatality proportion) for people aged lower than 70 years is currently 0.05%, which, for the population less vulnerable to deaths, is similar to influenza. However overall estimates for covid-19 are higher, due to the higher fatality rate in elderly people,” they wrote.

Stanford University epidemiologist John Ioannidis has since confirmed these early estimates showing the infection fatality rate of covid (before gene-vaccines) was 0.0003% at 0–19 years, 0.002% at 20–29 years, 0.011% at 30–39 years, 0.035% at 40–49 years, 0.123% at 50–59 years, and 0.506% at 60–69 years with a combined median of 0.063–0.082% for all under-70s.

The median age of death from covid is older than natural life expectancy.

The Australian Bureau of Statistics (ABS) found it was 85.5 years for all those who died from covid in Australia to November 2022 (which includes the study’s period of August 2021 to July 2022).

That is higher than life expectancy at birth for both men (81.2 years) and women (85.3 years), according to the ABS, which is the third-highest life-expectancy in the world.

Frail-aged people will be pushed over by anything because they are at the end of their lifespan. Throw in a positive PCR test, smash them in with the 55-year-olds and you’ve just cooked the books.

The study uses case fatality rate for infection fatality rate, overestimating it

The over-all median infection fatality rate of people under 70, without any gene-vaccine, across multiple countries, was found to be 0.063–0.082% (Ioannidis et al).

But the RMIT study authors plugged a figure more than 50 times higher into their modelling.

“We reviewed the public reports of NSW Health, and calculated that from 16 June to 7 October 2021, the case fatality rate (CFR) of Delta outbreak among the unvaccinated 50population was 4.45%,” they wrote.

You might notice they refer to the case fatality rate not the infection fatality rate.

But the study used the case fatality rate as the infection fatality rate.

“Although the number of infection cases may be under-reported, the crude infection fatality rate (IFR) should be approximated by the CFR. Therefore, we take the CFR of the delta variant as the IFR for 50+ unvaccinated in NSW,” they wrote.

The study set the baseline Omicron IFR at 1.38% and the Delta IFR at 4.45% and plugged those numbers in to the modelling.

Stanford Epidemiology Professor John Ioannidis told Letters From Australia via email that this assumption was too high.

“I think given the structure of the NSW population, the IFR in people above 50 is less than 1%, clearly not 4.45%. It would have been preferable to age-stratify, of course, since the risk is dramatically different between a 50-60-year old (roughly 0.12%) and an 80-100-year old (4.45% may be very well appropriate there), increasing about 4-fold for each 10-year increase,” he said.

Confusing infection fatality rate and case fatality rate has previously lead to serious policy errors due to getting the figures wrong.

Canadian public health expert Ronald Brown from the University of Waterloo warned that it was imperative not to confuse fatality rates or misleading calculations with significant consequences would follow.

“Confusion between CFRs and IFRs may seem trivial, and it is easy to overlook at first, but this confusion may have ultimately led to an unintentional miscalculation in coronavirus mortality estimation. IFRs from samples across the population include undiagnosed, asymptomatic, and mild infections, and are often lower compared with CFRs, which are based exclusively on relatively smaller groups of moderately to severely ill diagnosed cases at the beginning of an outbreak,” he wrote in 2020.

Faulty predictive modelling from Neil Ferguson at Imperial College London (funded by the Gates Foundationconfused IFR with CFR early on in the pandemic.

His wrong and discredited estimates led to shutting down most of the English-speaking world unnecessarily ahead of the gene-vaccines.

University of Auckland senior lecturer Simon Thornley warned in the BMJ back in October 2020 that Imperial College had overestimated UK deaths seven-fold.

“Observed fatalities in the UK now show that these models overestimated deaths by seven times … We now know that these projections were too high, and that lockdowns are of questionable value.”

“With” or “from” covid: comorbidities not accounted for

NSW Health’s definition of “covid death”, in the study’s S1 supplementary attachment, is not meaningful because it is confounded by a lack of information about comorbidities.

“According to NSW Health, a COVID-19 death is defined for surveillance purposes as a death in a confirmed COVID-19 case, unless there is a clear alternative cause of death that cannot be related to COVID-19 (e.g., trauma). There should be no period of complete recovery from COVID-19 between illness and death.”

The data from NSW Health cannot show whether a person they counted as a “covid death” would have otherwise lived if they had not been suffering from other problems.

If an 85-year-old person with late-stage pancreatic cancer caught covid, then died of breathing difficulties, they may be listed as a covid death under this definition. Would they have died if they had not had cancer? Or if they had not been 85?

NSW Health cannot say so, therefore the study cannot say either – they get plugged into the modelling, giving the wrong result.

Many factors increased the likelihood of death from covid that had nothing to do with the gene-vaccines.

US CDC figures showed that after old age, obesity is the biggest predictor of severe covid. The US data showed 78 percent of adults hospitalised with covid were overweight or obese – before gene-vaccines were available.

The study authors acknowledge the comorbidities problem as a confound.

But then they go on to claim the gene-vaccine “saved” thousands of lives, even though this cannot be supported by the evidence.

You can’t tell anything from this data. The study says nothing meaningful about lives saved.

The only way to tell is by obtaining the all-cause mortality data (not the covid death data) stratified by age and gene-vaccine status (as of date of injection, not later).

Letters From Australia repeatedly asked the federal Department of Health and Aged Care for this figure for more than a year, and it has refused to release it.

Study cannot count those killed by the gene-vaccine

It’s not accurate to say the gene-vaccine “saved lives” if it kills more people from heart attacks, strokes, clots and cancer than it “saves” from covid.

This is especially important as the controversial products were only tested for two-and-a-half months before the control group was injected leaving no comparison group for long-term safety assessments.

Even those rushed trials show a comparatively high rate of deaths and injuries.

Pfizer’s gene-vaccine trial had one more death in the treatment arm than the placebo arm from all causes.

Independent analysis of both the Pfizer and Moderna trial results by Fraiman et al in Vaccine showed a 1-in-800 risk of serious adverse events per “fully vaccinated” dose regimen.

Australia’s drug regulator, the Therapeutic Goods Administration, doesn’t even know how many people have died from these provisionally registered products because of a lack of public awareness about the voluntary reporting it relies on as detailed here.

This is yet another confound that shows the claimed information can only be found through the all-cause-mortality figures stratified by gene-vaccine status and age.

Gene-vaccines cannot provide herd immunity

The study authors claim that widespread gene-vaccination reduced deaths in the uninjected population because it conferred “indirect herd immunity effects”, making their wild predictions “conservative” and “under-estimated”.

“Indirect herd immunity effects of vaccinations also affect the calculation but are not accounted for in the data-driven model. Thus the death rates rk(t) could well be under-estimated for the no vaccination scenario (see Limitations and challenges section below), creating further under-estimation of the total deaths,” the study authors wrote.

This is simply wrong. The gene-vaccines did not provide any herd immunity as they produced blood-borne antibodies that could not prevent or reduce transmission.

It’s not just wrong but obviously untrue. More than 90 percent of Australia’s adult population received multiple doses of the gene-vaccines yet almost everyone had covid multiple times.

This is not due to “waning” but to “negative efficacy” as shown in the 2023 Cleveland Clinic study of more than 48,000 health workers from the USA which found that the risk of covid infection went up with the number of prior doses.

It was also shown in the UK Health Security Agency figures in early 2022, when week after week their tables showed that the more covid shots you got, the more you caught covid – until they were so embarrassed they simply stopped printing the table (see below).

Negative efficacy. Source: UK HSA

This is why Pfizer never tested for transmission. They already knew it was impossible because blood-borne antibodies from an injected gene-vaccine cannot confer mucosal immunity. Covid is spread through the air, entering through your mucosa. Blood-borne antibodies cannot stop it replicating in your mucosa.

For an explanation of this, see emeritus immunology professor Robert Clancy’s interview above.

Good data can be found – but not this way

The confounds listed are just some of the obvious ones. More can be found at Raphael Lataster’s takedown here.

The only way to tell whether or not the controversial products saved lives is to get the raw all-cause mortality data stratified by gene-vaccine status (measured as of day of injection) and age.

It is possible to get these figures because accurate data was collected during the pandemic and is held by a variety of federal government agencies.

The federal Department of Health and Aged Care has, however, refused to collate and release this figure despite being asked repeatedly for more than a year.

Conflicts of interest

The major funder of both RMIT and Monash is the Australian Government, which paid RMIT $627 million in 2022. The government pushed the covid-gene vaccines and is liable for the injuries from them, and therefore has a vested interest in promoting good publicity about them.

This conflict was not noted on the PLoS One study, which stated the authors had declared “no competing interets”.

Associate Professor James Trauer, of Monash University’s School of Public Health and Preventive Medicine, was a supervising co-author of the RMIT study.

Monash University is promoting the mRNA gene-vaccines because Moderna set up its mRNA factory on Monash’s campus.

Monash and Moderna are a public-private partnership creating new training programs for Monash in the mRNA product pipeline including clinical trials and manufacturing.

This conflict was not noted on the study.

Associate Professor Trauer’s profile reveals that since 2020, he has worked with the World Health Organization (WHO) developing a platform to support epidemiological modelling for pandemic control.

The WHO and its second-largest donor the Gates Foundation are major promoters of the covid gene-vaccines

For a break-down of how the WHO works with Cepi, the Wellcome Trust and the Gates Foundation to push Big Pharma products see this piece here.

Associate Professor Trauer is also a chief investigator in the PROPHECY study which is funded by the gene-vaccine promoting Burnet Institute and the Murdoch Children’s Research Institute, both of which are funded by the Gates Foundation. Burnet Institute director Brendan Crabb is on the Victorian Government’s mRNA advisory group promoting the gene-vaccine technology.

This conflict was not noted on the study.

While not a conflict, it is worth noting that the study’s corresponding author, editor and reviewer Lewi Stone has previously used mathematical modelling to make similar claims that more than 1.5 million deaths “were averted” in 12 countries by the gene-vaccine products, backed by similar assumptions.

Read the full article on Alison’s Substack here.

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Author

  • Alison Bevege

    Alison Bevege is a journalist who has variously worked for NewsLtd, Daily Mail and Reuters. She now writes Letters From Australia on Substack and is currently working on a book about the covid gene-vaccine scandal.

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