Too Many Dead

An Inquiry into Australia’s
Excess Mortality

Ethical doctors and scientists investigate
what is killing so many Australians
Australians are dying at disturbing rates, beginning in 2021. Political andmedical authorities continue to ignore this. The Australian Senate voted
against holding an inquiry into this excess mortality, but independent,
committed Australian medical people have conducted an investigation. This
book shows that even the inadequate pharmacovigilance systems of global
regulators reveal alarming numbers of adverse events, disability and death.
Australia is in the midst of a health crisis, and this analysis indicates it isiatrogenic. Too many people are dead and harmed. Australians deserve
accountability, transparency and justice.

ISBN 978-0-646-88551-3

Too Many Dead1

This book is dedicated to all Australians
who have been harmed by the government’s
public health response to COVID-19.

Too Many Dead – An Inquiry into Australia’s Excess Mortality
Copyright of written works and illustrations
included in this book remain the property of the respective authors.
All rights reserved. Apart from any use as permitted under the Copyright Act, no part of thispublication may be reproduced, recorded, stored in a retrieval system or transmitted in any form
without prior permission of the copyright owner.
Copyright © Australian Medical Professionals Society
E: hotline@amps.asn.au
T: 61 7 3497 5048
Red Union publishing
41 Campbell Street
Bowen Hills
QLD 4006
Australia

First edition 2023
Canberra, Australia
ISBN 978-0-646-88551-3
Cover photograph: Karolina Grabowska

National Library of Australia http://nla.gov.au.obj-135682653
Cover design: Gary Delahunty
Typeset: Gary Delahunty2

Contents

Introductory remarks

5Part 1 This is real, and it’s happening

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Cause unknown - the epidemic of sudden deathsby Ed Dowd

Vaccination meets genetic modification – what could
possibly go wrong? by Conny Turni and Astrid LefringhausenPart 2 Poorly tested – they should have known
The Novelty of mRNA viral vaccines and potential harms:
a scoping review
by Matthew T.J. Halma, Jessica Rose and Theresa Lawrie

The role and dangers of endotoxin in mRNA injections

by Geoff N. Pain

Sequencing of bivalent Moderna and Pfizer mRNA vaccines
reveals nanogram to microgram quantities of expression
vector dsDNA per doseby Kevin McKernan, Yvonne Helbert, Liam T. Kane, Stephen
McLaughlin

3

13
18
29
33
64

102

Serious adverse events of special interest followingmRNA COVID-19 vaccination in randomized trials in adults
by Joseph Fraiman, Juan Erviti, Mark Jones, Patrick Whelan,
Sander Greenland, Robert M. Kaplan, Peter Doshi

Part 3 Lack of evidence leads to policy failures

128
151

The failings of COVID-19 Policy by Clare Craig

153

by Aseem Malhotra

195

Curing the pandemic of misinformation on COVID-19 mRNA
vaccines through real evidence-based medicine Part 1
Curing the pandemic of misinformation on COVID-19 mRNA
vaccines through real evidence-based medicine Part 2
by Aseem Malhotra

Part 4 Australian data – with poor pharmacovigilance
the safety signals go unrecognized

Why all-cause mortality has become the most important
COVID-19 statistic by Phillip M. Altman

Excess deaths in Australia during the COVID period: thenumbers by Clare Pain

Excess mortality in Australia - when were the warning
signs apparent? By Andrew Madry

Part 5 Global excess death – it’s not just Australia
What the ONS mortality COVID-19 surveillance data cantell us about vaccine safety and efficacy
by Norman Fenton, Martin Neil, Clare Craig, Scott McLachlan

A report on the US Vaccine Adverse Events Reporting
System (VAERS) of the COVID-19 messenger ribonucleic
acid (mRNA) biologicals by Jessica Rose
Excess death anomaly in Finland 2021

by Tore Aarhus Gulbrandsen, Kasper RönningPart 6 From policy failure to iatrogenic crisis
Australian COVID-19 pandemic: A Bradford Hill analysis of
iatrogenic excess mortality by Wilson Sy
Simpson’s Paradox in the correlations between excess
mortality and COVID-19 injections: a case study of iatrogenic
pandemic for elderly Australians by Wilson Sy
Excess mortality and bereavement by Monique O’Connor

Conclusion

215
241
243
250
273
315
317
360
389
403
405
432
459
470

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Too Many Dead

Introductory remarks

Something different is killing Australian people in a most unusual way, andin large numbers. This unsettling fact has been evident to the Australian
Medical Professionals Society and the time has come to confront it.
Authorities in medicine, and the mainstream media as well, seem tobe unwilling to address the matter in detail and with clarity. Given theinherent priory, this is simply disgraceful. There is no possible excuse forit. Medical paternalism, which has been enabled by drastic public healthpolicies, needs to be rebalanced with respect for the patient. Australia’s
current excess death rate should be common knowledge and under careful
examination. If the health authorities are not driving this, the Prime
Minister should be. And if neither house of Parliament will inquire in the
public interest, we will.
In all societies, human beings die at certain rates, which become known,
expected, modelled and understood in detail. Illness, accident, murderand suicide are all components of a nation’s usual rates of death. Thesefigures are compiled, including deaths from all causes, to form all-cause
mortality rates. They are surprisingly predictable. It is only if something
goes radically wrong in a country, perhaps a war, or an extreme naturalor economic disaster, that this can be sufficient to drive up the number ofdeaths to a clear excess, by comparison to a statistical average, by a range of5

Australian Medical Professionals Society
possible methodologies. In Australia, we contend that this statistical excess
has been clear and present for 30 consecutive months.
To put it simply, Australian deaths have risen dramatically and are staying
far above the expected monthly and annual numbers: many more people aredying than should be and it is time to find out why. This book is the result ofa collaboration begun by some committed Australian doctors and scientists.
It was a gathering of people of good standing and high qualifications, with
opinions developed after considerable study, and all of them well used to thepeer-review process. The result in these proceedings is a total of eighteen
published papers.
This introduction might well have started off from a position of curiosity,
adopted before the publishing of the conference proceedings took place. It
is customary for seminar proceedings to begin by adopting a naïve position
and then paper by paper being led to a conclusion to the question theconference was posing. In this case, however, a problem arose to make thatapproach difficult – the studies kept coming in, over a six-month period,
from independent researchers, not bound by the system, and, as it turned out,
people every bit as concerned as we are. The evidence began mounting, andthe conclusion the writers all point to became ever clearer. The reader can
now easily ascertain just how persuasive that evidence is.
It should be said that the health authorities were formally invited to attend
the conference, to produce and to bring a study of their own. Strangely, no
reply was received from them.
If this makes our health authorities seem less than apt on this topic of dying
Australians, they are not alone. Too many people in political power havethe same standpoint. AMPS has only taken this action after the AustralianFederal Senate voted down a motion to hold a hearing into the excess
death our country is experiencing. As an association of concerned healthpractitioners, we considered it our duty to advance knowledge and agitate
for the genuine attention of those who serve at the pleasure of the public,
both elected and non-elected. Why so many people are dying unexpectedlyis the kind of thing that medical systems on every level should routinely be
tracking closely and communicating to politicians; if they fail in this duty
then politicians and public servants alike should be holding them accountable.
Certainly, none should be found helping to smother real inquiry.
On the other hand, it is quite possible that the problem and its solution
are as obvious to many of them, just as it is to us. Party politics will haveits way with the elected, in just the same way that the unelected medical
bureaucracy will seek domination on behalf of the system and at the expense

6

Too Many Deadof the rank-and-file medical practitioners who wish to uphold their oaths ofservice and to stand strong in their ethics. Hence, in order to approach thetruth, we have had to make a detour around the existing system and provide
independent publishing.
What was the government definition of COVID public health success? Is
there a public health measurement more indicative of success or failure thanall-cause mortality? If it is claimed that many Australian lives were saved,
a claim that can only be based on assumption-rich modelling, how is thatcompatible with rising excess death rates, per week and month, evident fromthe first half of 2021, well before COVID-19 disease could be construed to be
making a serious contribution? Even if we accept, as former Prime Minister
Scott Morrison claimed during his ill-fated 2022 election campaign (based
very heavily on modelling assumptions), that early pandemic policies had
saved 30,000 Australian lives, why is this not evident in the bottom lineof all-cause mortality? Were these poor souls saved only to be lost again,
alongside many others, to produce the observed excess death? And if so,
what factors were and are in play?
We do not agree that COVID-19 itself, or its sequel, so-called Long COVID,
is the true driving force of excess mortality. Several of the papers we present
demonstrate this elegantly, in particular with Queensland data, with excess
death evidenced eight months prior to any sizable burden of SARS-CoV-2
in that locked-down state. Hence, the trend is initiated in the absence of theinfection, but squarely in the midst of policies like lockdowns and the vaccine
rollout. These easily verifiable facts should have triggered critical evaluationof such policies by public servants, according to the Precautionary Principle.
To continue to assert that SARS-CoV-2 is the only explanation needed may
constitute wilful blindness; it causes failure to evaluate the possibility thatthe cure was, in fact, worse than the disease (at last by the time it reached
Australian shores).
Much more needs to be said about the role of COVID-19 in the excess death,but we note a paradox that is as yet unexplained by the sanctioned experts.
Why did the official death rates attributable to COVID-19 disease only
become notable after the vast majority of Australians had received allegedly‘safe and effective’ vaccines for the infection?
Furthermore, why did the much milder Omicron variant take such a toll on aheavily vaccinated population, if indeed the much-repeated therapeutic claimof protection from severe illness and death was in effect? The Cambridge
Dictionary defines ‘failure’ as the fact of someone or something not
succeeding. Thus, given the basic issue of excess all-cause mortality, how canmass vaccination and other related policies continue to escape critical review?

7Australian Medical Professionals Society
There is continued appeal to COVID-19 itself as all the explanation necessary
for the phenomenology of excess mortality. This is seen in Australia andconsistently around the heavily vaccinated parts of the developed world. In
September 2023, we were informed of the planned non-royal commission
into Australia’s pandemic management, with extremely limited investigative
scope and presided over by only the most committed government apologists.
Is this likely to further the science? Is it likely to give any answers beyond
the engrained position?
It is noteworthy that until the implementation of the coercive mandatory
lockdowns and vaccination-only strategy, there was no pandemic of death.
Yet we now find ourselves in just that, with consecutive increases in excess
mortality not seen since wartime. In continued secrecy, our authorities
implemented authoritarian pandemic policies, which we and others havenoted were in complete contradiction of their own pandemic preparedness
plans. Has this behaviour, in one way or another, cost a great many lives? The
consequences of the failures are difficult to ignore.
In Australia, we have a serious problem. Government excesses of power
created through emergency legislation have been allowed to violate ourliberties. They were justified by largely unscientific and readily refutableclaims. Fear was wrongly employed by political leaders, using secret
health advice to control the public. Health laws gave chief health officers
unprecedented powers to do almost anything they thought was reasonable
during a pandemic – which can be declared on opinion, and not on evidence,
without having to justify their decision.
When has there been a society that prospers because people have beencancelled or removed from their vital work because they dared to disagreewith some regime’s unquestionable truth? Do our modern medical
authoritarians want to be looked back on with the same disdain with which
we judge historical despots?
There are doctors and scientists across the country – and the world – trying
to raise safety signals, seeking answers to questions, writing letters, seeking
data transparency for analysis, and sending reports as they fight to fulfil
their medical codes and oaths. We are unable to produce the scale bar tomeasure their make-up or the sum of their efforts, since many work alone andcommunicate their findings directly. However, we have no doubt that there
are vast numbers of Australians who believe in the power of truth, honour andintegrity and are willing to pay the price to stand behind them. We need realdebate, open scientific discourse, acknowledgement of injuries, and unhindered
access to unredacted data. The doctor-patient relationship should never havethird parties such as government and bureaucracy interfering.

8

Too Many DeadIn this book, we present the work of a tiny fraction of those willing to sacrificecareers and livelihoods to seek the truth from evidence-based science, adhering
to time-honoured medico-scientific principles. The reader needs to bear inmind during the course reading that the doctors whose work is featured here
have had to face summary suspension and loss of their jobs merely for daring
to speak the facts – not the opinions, but the facts – about the conduct ofthe people in the medical hierarchy. They dared to question the protocol, on
scientific grounds, and have paid a huge price, that of their livelihood and theircapacity to serve. It is a great shame that more people in the mainstream media
have not had the same level of courage and commitment to human welfare.
Trust in public health, according to the Organization for Economic
Cooperation and Development, requires that government actions be open to
public scrutiny and that public institutions involve themselves by ‘proactively
releasing timely information,… enhancing transparent and coherent public
communications… and engaging with the public.’
Fundamental principles of public health, political due diligence, institutional
regulation and legal recourse have all been ignored or subsumed by a single focusof achieving consensus to maximize compliance. It has been compliance-basedon false and misleading information where those who have power appear tohave declined to do the most basic levels of review by failing to read even theirown reports.
Winston Churchill said, ‘Courage is what it takes to stand up and speak;courage is also what it takes to sit down and listen.’ We believe that authorities
in health departments and the medical system as we know it are not goingto enjoy the contents of this book. In any segment of bureaucracy, it can no
doubt be tempting to think that the data, analysis and opinion possessedand expressed within one’s department are superior to that from all otheravailable sources. However, the people in the unelected, executive arm ofgovernment now need to let themselves be informed as a result of this small
but representative sample of science. It is being delivered to them from truly
independent and unconstrained researchers drawing from official data, and
who are free to follow those data where they lead. Our public servants need to
pay attention, because the system they are presiding over appears to be doinga great deal of damage. Australians are observing the continuing silence of ourpolitical leaders and medical authorities as they permit it to continue.

9Australian Medical Professionals Society

10Too Many Dead

Part 1This is real, and it’s happening

A lot of people are dying and we need to know why.
Death does not fall onto human beings by accident from a neighbouring star.
There are always causes. These are categorized in great detail and the numbers
are kept. Somehow, in Australia in early 2021, events came together to raise
the numbers of dying in a way that can only be called alarming. The facts in thisbook from various highly-qualified writers point this out in a way that can nolonger be ignored. It is past time to review and reflect on the potential harmsof this nation’s pandemic policy response. The people running the Australian
health system cannot explain why the excess-death figure has risen so far above
historical averages, and they appear to be making no efforts to change this, oreven to discuss it. In early 2021 there was virtually no COVID-19 in theAustralian community, whilst coincidentally the excess mortality rates began
their notable rise. The observed trend can be linked to the introductory phaseof highly novel pharmaceuticals, combined with some of the world’s harshest
pandemic policy responses. Here is what one of our authors, Australian Dr
Astrid Lefringhausen, has to say:
The public message heard everywhere was ‘the vaccines are safe andeffective,’ although by then almost all that was said about them earlyon had already started to be proved wrong. The vaccines were supposed

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Australian Medical Professionals Society
to stay at the injection site, be taken up by the lymphatic system, andbe quickly degraded afterwards. As it turned out, none of this wascorrect…. Everybody was expected to trust the vaccine to deliver
health and protection, although this was physically improbable, if
not impossible…. Despite all promises, the COVID-19 vaccines
were not safe.
Other countries find themselves in the same conditions. The American author
Ed Dowd identified early trends in the deaths of healthy young people. His
book is titled Cause Unknown: the epidemic of sudden deaths in 2021 and
2022.
Dowd contends that all-cause mortality data are showing truly disturbing
trends in excess deaths, disproportionately experienced by healthy working-age
Australians. Similar trends are being witnessed in other countries such as theUnited States where, he says, ‘Millennials have experienced the equivalent ofa Vietnam war, with more than 60,000 excess deaths’ in a mere 12 months.
Dowd discovered report after report from American life insurance companies
that confirmed an extraordinary 40% increase in deaths among working-age
people, and most of the deaths could not be attributed to COVID.
For example, OneAmerica CEO Scott Davison made comments to acommerce meeting picked up by the media: ‘We are seeing, right now, thehighest death rates we have seen in the history of this business – not just at
OneAmerica. The data is consistent across every player in that business.’ As
has been shown in Australia, the majority of these deaths were not attributed
to COVID. Similar findings can be seen in Australia through various data
sources. Dowd again: ‘Ignoring these deaths is the greatest disrespect we could
ever show’ to these people and their grieving families.

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Too Many Dead

Cause unknown —the epidemic of sudden deaths1
by Ed Dowd

Almost all regulatory agencies, chief medical officers and politicians presented
the COVID-19 vaccines as 100% effective against getting and spreading thevirus. Mass vaccination of the population was the only solution presented forpeople to go back to normal life. The safe and effective claims have been proved
false. According to government reports such claims were never justifiable as
there was never any conclusive evidence these novel therapeutics could stop
infection, stop disease and most important from a public policy point of view,
stop transmission. As it became obvious that the vaccines did not and could
not prevent infection with or transmission of COVID-19, the public discoursechanged. The vaccines were then said to be effective at ‘preventing serious
hospitalisation and death.’ This is a claim that to this day has never been proved,a claim that looks to be more marketing than science.
Interestingly on September 1, 2021, the CDC quietly changed the definition of
vaccine. Rather than a vaccine being an inoculant that ‘produces immunity to a
specific disease,’ the definition became ‘a preparation that is used to stimulate
the body’s immune response against disease.’ The definition change would makethese COVID products more therapeutics than vaccines.
1
This article is based on Ed Dowd’s recent book, Cause Unknown: The Epidemic of Sudden Deathsin 2021 & 2022; it was reviewed and approved by him. The data analysis can be found at www.phinancetechnologies.com in the section listed as the Excess Mortality Project.

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Australian Medical Professionals Society
The data that are slowly being revealed and analysed appear to confirm thatbeing employed in 2021-22 was actually detrimental to health. All the public
policy measures introduced during the pandemic to apparently keep us safe haveresulted in what seems to be an iatrogenic pandemic of excess death. The ratechange in deaths was particularly striking as it coincided with the corporate
mandates – it simply was not statistically possible that suicides, overdoses anddeaths from delayed treatment of rapid-onset fatal cancers all spiked after the
introduction of mandates.
The following quarterly excess death rate analysis for Australia has been
undertaken using as sources the Australia Bureau of Statistics, the Department
of Health and Aged Care, Our World in Data and the UN.2 The data are based
upon weekly deaths data from the Australian Bureau of Statistics spanning 2015
to 2022. The data are only available for certain selected age groups.

Quarterly excess mortality analysis

We obtain quarterly excess deaths estimates by aggregating in quarters data thatare based on our weekly analysis of excess deaths.
In order to estimate weekly excess mortality we perform a two-step approachto estimate the baseline deaths. The first step is by estimating the trend in death
rates using annual data as described in our methodology papers, while using
method 2C.3
The second step is to estimate weekly excess deaths by comparing deaths ordeath rates in a given week with the average death rate, which is computed
using the average weekly frequency of deaths over a period of N-years (typically
5 to 10 years depending on the data availability). By using both methods inconjunction we obtain a trend-adjusted and week-of-year adjusted estimate forexcess mortality.
A quarterly analysis of excess deaths allows us to then use different metrics toestimate seasonally adjusted patterns in excess mortality. For example, we cancompare excess mortality in Q1 of 2022 versus Q1 of 2021 to observe the effectof the vaccination policy on excess mortality.

Quarterly evolution in excess mortality vs vax.

The following chart shows the quarterly (annualised) excess mortality from2020 to 2022, for different age groups. The COVID-19 vaccinations data (righthand scale) refers to the total accumulated doses at the end of each quarter, as apercentage of the respective age group population.
2
The charts in this article are derived from the Australian Bureau of Statistics andcan be found at https://phinancetechnologies.com/HumanityProjects/Quarterly%20Excess%20
Death%20Rate%20Analysis%20-%20AU.htm
3https://phinancetechnologies.com/HumanityProjects/Resources.htm

14Too Many Dead
Quarterly excess mortality, from 2020 to 2022.

Excess Mortality - Comparative Metrics

Quarterly comparative metrics for excess death rates
The chart below shows the evolution of the quarter-to-quarter change inexcess mortality from 2020, 2021 and 2022. The user can select the desired age
group. What one would have been expected to observe is that as the pandemic
evolved, changes in excess deaths should turn negative as a result of the rise innatural immunity and or vaccination.
The chart below shows the quarter-to-quarter change in excess mortality for
all age groups, for a specific period. The individual COVID-19 vaccination
doses as a percentage of the age group population are also shown. The user can
specify the period.

Quarter Comparison of Excess Mortality, by age group15

Australian Medical Professionals Society
The following chart allows the user to perform quarter-to-quarter comparisonsin excess mortality during 2020, 2021 and 2022, for the different age groups.
This chart is particularly interesting to investigate excess mortality in seasonallyequivalent periods in different years. The vaccine penetration rate for the endof 2021 is also shown.
Quarter comparison of excess mortality for different age groups.

According to Australian data excess all-cause mortality for working age
Australians began to surge in 2022 following vaccine mandates when mutatingstrains were already becoming less virulent. Knowing the virus affected mostly
older, vulnerable people with pre-existing comorbidities, how can publichealth officials explain these statistically significant increases in deaths among
working age Australians? And why does it appear no one is interested infinding out?
We live in a world where regulatory institutions are captured by financial
and political interests, either unwilling or unable to get to the truth of theissues they set out to investigate and regulate on behalf of the society. Without
unbiased and comprehensive research, there is a risk of misguided policy
decisions at best, and at worst, negligence and malpractice. Never has thisbeen more apparent than during the COVID-19 pandemic.
In this context, we need independent agents to act as gatekeepers of the public
interest. We intend to be such agents, and to provide high-quality research toother individuals and institutions who seek similar outcomes.
Our data analysis confirms that healthy young people have been dying andbecoming disabled with alarming frequency. The rate of these incidents is new16

Too Many Deadand unusual, and not sufficiently explained by government officials. Glaringly
public health questions are not being asked or answered by those in power.
At this point it appears the response to this pandemic of excess death byauthorities has moved beyond something we could write off as incompetence.
They are allowing (and even forcing) mass use of products they know are
harmful. It does look like powerful people in public health and pharma are in
full cover-up mode. At this point, the negligence could be considered criminal.

Edward Dowd is currently a founding partner with
Phinance Technologies, a global macro alternative
investment firm. He has worked in the financial
industry for most of his career, spanning both credit
markets and equity markets. Some of the firms heworked for include HSBC, Donaldson Lufkin &Jenrette, Independence Investments, and most notably
at Blackrock as a portfolio manager where he managed
a $14 billion growth equity portfolio for ten years.
After BlackRock, he founded OceanSquare AssetManagement with two former BlackRock colleagues.
His new book is Cause Unknown: The Epidemic of
Sudden Deaths in 2021 & 2022.

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Australian Medical Professionals Society

Vaccination meets genetic
modification – what could
possibly go wrong?
by Conny Turni and Astrid Lefringhausen1

Since the vaccines predominantly used in Australia are to this day the mRNA
vaccines by Moderna and Pfizer-BioNTech, they are also the topic of thisessay.
The review is written from an Australian perspective and at the time ofwriting the COVID-19 vaccines were still heavily promoted in Australia. The
government at that point was pushing a fourth injection and injections forsmall children and pregnant women in particular, in spite of the fact that the
Therapeutic Goods Administration (TGA), the Australian equivalent of the
FDA in the US, stated at the same time on their website that they had nofull data package from any vaccine producer and large-scale trials were still
progressing.
However, the public message heard everywhere was ‘the vaccines are safe andeffective,’ although by then almost all that was said about the vaccines early on
had already started to be proved wrong. The vaccines were supposed to stay at
the injection site, be taken up by the lymphatic system and be quickly degradedafterwards. As it turned out, none of this was correct. The naming itself was
already misleading as the injections cannot be correctly called vaccines – avaccine until 2020 was always the injected substance causing the immune
reaction that ultimately led to immunity. That substance, the antigen, usually
1This article is based on a review paper by Conny Turni and Astrid Lefringhausen from September
2022 1, the first publication in Australia looking critically at the COVID-19 vaccines and comparing publicstatements with scientific facts. It, in turn, is based on roughly 1100 case studies and scientific publications
from around the globe looking specifically at side effects of the COVID-19 vaccines.

18

Too Many Deadwas either the pathogen or a part of it (with some exceptions like the Tetanus
vaccine being a toxoid), and was always an extracellular, separate entity andgiven in precise and well-defined concentrations.
The COVID-19 injections are working more like synthetic viruses andshould better be called transient genetic pro-vaccines. They contain genetic
information that is protected by a fatty envelope, and enters into an unknown
number of cells of the injected person. depending on the integrity of the vial
content, dilution method, injection technique and physiological status of thevaccinee. The genetic information forces the host cells to produce the viral
spike protein that is afterwards presented on the cell surface and serves asa vaccine by eliciting an attack by the immune system on the spike protein
producing cells, essentially an autoimmune reaction.
The lipid nanoparticles (LNP) making up the envelope are a mix of cholesterol
and two synthetic lipids: ALC-0315 and ALC-0159. Echelon, the manufacturerof these nanoparticles, specifies that they are ‘for research only and not forhuman use). ALC-0315 is an ionizable cationic amino lipid that has been usedin combination with other lipids in the formation of lipid nanoparticles,[2]while ALC-0159 is a polyethylene glycol (PEG) lipid conjugate, by its
nature a non-ionic surfactant. LNPs are able to cross all biological barriers
as a result of their size and reactivity – examples are the blood brain and theblood-placenta barrier. The Japanese government demanded a biodistribution
study from Pfizer that showed the LNPs accumulating rapidly in liver, ovaries
and adrenal glands, but also in smaller amounts in the brain and other organs.
A similar study was submitted to the TGA in January 2021 and was released
in response to a freedom of information request later that year as part of a Non
Clinical Evaluation report (FOI 2389 document 6 (tga.gov.au)). Table 1 is anexcerpt from that report and shows accumulation of LNPs in various organs
including blood and plasma within minutes to hours. Pfizer summarize theirresults on page 4 of their report and also point out the scope of the evaluation
done. They tested the vaccine in mice, rats and rhesus macaques and found that
BNT162b2, their mRNA COVID-19 vaccine, induced humoral and cellularimmune responses in monkeys and mice.
‘However, antibodies and T cells in monkeys declined quickly after 5 weeks after
the second dose, raising long term immunity concerns.’ The vaccine dose given
to monkeys was 100 ug, more than three times the dose given to humans, andsince ‘rhesus macaques do not show clinical signs and generally develop only
mild lung pathology from SARS-CoV-2 infection’ it might not have been thebest study object. Even more worryingly, ‘there were no studies on protectionof older animals from SARS-CoV-2 infection or duration of protection after
immunization. The animal studies were of short term; long term immunity

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Australian Medical Professionals Society
Table 1 from Non Clinical Evaluation Report BNT162b2 (mRNA) COVID-19 vaccine, page
45: Mean concentration of radioactivity (sexes combined) in tissue and blood following a single
IM dose of 50ug mRNA/rat

was not assessed. The sponsor indicated that long term immunity would be
addressed by human data.’
Since the official clinical trial had been finished and invalidated by vaccinationof the placebo group in late 2020, this means the TGA was informed in
January 2021 that the Australian population would be one of the groups used
for testing long term immunity of these new injections. It also clearly showsthat there was no information on duration of protection, protection for theelderly or long-term side effects, which sounds neither safe nor effective. Even
the transient nature of the pro-vaccine turned out to be only partially true,as the mRNA was nucleoside-modified to reduce potential innate immune
recognition, rendering it vastly more stable than normal cellular mRNA which
has a half-life of minutes only.
The vaccine mRNA could be found in the bodies of vaccinated people 60 days
after injection,[3,4] which makes it impossible to predict how much spike20

Too Many Deadprotein each vaccinated person will produce and for how long. Unfortunately,
the spike protein is toxic to human cells and is responsible for most of thesevere side effects of the respiratory infection in humans. Its S1 subunit isfound to circulate within the blood shortly after vaccination and can induceclotting by binding fibrinogen and ACE2 on platelets,[5] while the S2 subunit
has been shown to interact in silico with BRCA-1 and 2 as well as P53,[6]all of which are connected to cancer. P53 is a gene coding for a protein that
controls cell division and apoptosis and is known as a general tumor suppressor.
BRCA-1 and 2 gene products are involved in repair of damaged DNA andare connected to breast and ovarian cancer in women and prostate cancer inmen. Other possible ways in which the mRNA vaccines can interfere withcellular DNA repair mechanisms are downregulation by spike of housekeeping
genes like RNA polymerase I and its promoters; and miRNA dysregulation.
MiR-148 is one such microRNA which is excreted in exosomes by transfected
cells following spike protein production and has been shown to downregulate
homologous G1 phase recombination as well as hyperactivate human microglia,
damaging the central nervous system (CNS) in the process.[7]A further serious concern was brought up very recently by Kevin McKernan
and his team, who found up to 30% plasmid DNA contamination in themRNA vaccines when they sequenced them. Most concerning are simianvirus promoters (SV40) in the bivalent vaccines which have been suspectedof causing cancers in humans. Additionally, the presence of bacterial plasmid
vectors in the mRNA-LNP complexes must be seen as a warning sign thathigh levels of contaminating endotoxins – parts of bacterial membranes thatstrongly stimulate the human immune system up to anaphylactic reactions –might be present in the vaccines. A study by Doshi et al. from August 2021[8]concluded that the Pfizer vaccine was associated with a 36% higher risk ofserious adverse events (SAE) versus the placebo. For Moderna it was 6%
higher risk of SAE versus placebo. Another study by Shimabukuro[9] followed
pregnant participants in the v-safe pregnancy registry and found that only
21% of women enrolled completed their pregnancy.
Regarding the proclaimed effectivity of the mRNA vaccines, it is highly likely
to be inferior to natural infection which produces much broader and morerobust protection by the fact alone that the person is exposed to the entire virus
with all its proteins – spike, envelope, membrane and nucleocapsid proteins– versus only the spike protein. Studies showed that only natural infection
upregulated genes associated with type I interferon production, cytotoxicity
and increased circulating plasmablasts, while the mRNA injections seem tosuppress interferon responses. Studies from Sweden, California, Finland andIsrael showed that the rate of re-infection after recovery from COVID-19
was lower than in the vaccinated, and that vaccinated health care workers in

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hospitals around the world were infected post-vaccination at high rates. Since
Omicron entered the world in late 2021, it was increasingly vaccinated people
who were infected and severely affected by COVID. One of the few states thatpublished COVID-19 cases, hospitalizations and deaths by vaccination status
until late 2022 was NSW.
Figure 1 shows the data obtained in the two weeks leading up to December 31st
2022 and the worsening effects of the booster injections. For those Australians
whose COVID-19 infection turned serious, the recommendation was only
to contact a health care provider when experiencing difficulty breathing, lossof speech or mobility, confusion or chest pain. The recommended treatments

Figure 1 People with a COVID-19 diagnosis in the previous 14 days who were admitted tohospital, to ICU or reported as having died in the two weeks ending December 31st 2022.
Numbers represented as percentage of the total numbers admitted to hospital, to ICU and ofdeaths of patients whose vaccination status was known. Of the patients with known vaccination
status, a total of 1415 were admitted to hospital; 105 went to ICU and 88 died. (https://www.
health.nsw.gov.au/Infectious/covid-19/Documents/weekly-covid-overview-20221231.pdf )were the provisionally-approved and expensive new drugs like Molnupiravir
and Paxlovid. Disregarded were drugs that had been used successfully for
years in respiratory disease treatments. Most COVID-19 deaths were due tosecondary pneumonia, and while it was standard-of-care to give antibiotics topatients in respiratory distress as a result of influenza and secondary bacterial
infection before 2020, this practice was all but stopped during the COVID-19
pandemic, with disastrous consequences. Everybody was expected to trust
the vaccines to deliver health and protection although this was physically
improbable, if not impossible. The mucosal immune system is the largest
component of the entire immune system, having evolved to provide protection
at the main sites of infectious threat: the mucosae. As SARS-CoV-2 initially22

Too Many Dead
infects the upper respiratory tract, its first interactions with the immune system
must occur predominantly at the respiratory mucosal surfaces, during both
inductive and effector phases of the response,[10] and it is at this stage that it canTable 2: All symptoms reported from 1016 case studies and publications regarding serious
adverse events following the mRNA vaccines by either Pfizer or Moderna.
* signifies a side effect with several subtypes that were individually reported
but are combined in this table

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be neutralized by mucosal immunity, predominantly mediated by T lymphocytes
and mucosal antibodies of the type IgA. The serological IgG antibodies producedby vaccination play only a minor role in combatting respiratory viral infections;only after the virus has replicated for days on the mucosal membranes can itenter the blood and encounter the vaccine-induced antibodies.
When looking at the DAEN database maintained by the TGA, it was impossible
to ignore the spike in reported adverse events starting in 2021 when comparedto the previous 20 years. Large insurance companies in the US and Germany
noted that all-cause death rates went up by 40% in ages 18-64 years, which
could not be attributed to COVID-19 alone. Table 2 above is a concentrated
version of the SAE table in the root paper ‘COVID-19 vaccines – an Australianreview’, and summarizes 1016 case reports and studies published until July
2022.[11] COVID-19 vaccines cause without a doubt more side effects than any
other vaccine, likely because both components, mRNA and LNP as well as the
product – the spike protein – are highly inflammatory, reactive molecules thatinterfere with type I interferon signalling and dysregulate protein synthesis, thus
affecting both formation and apoptosis of immune cells.
The spike protein has been found to be freely circulating after the immune attacks
on presenting cells begin, and can in this form attach to any ACE2-expressing
cells, which unfortunately are in almost every organ system. It causes an atypicalsignal cascade in the cells by binding to ACE2, which usually acts as a type
I integral membrane protein that when bound gets activated and cleaves
antiotensin II to angiotensin, thus regulating blood pressure. In SARS-CoV-1
infection, binding of the spike protein (which is 76-78% identical to that of
SARS-CoV-2) to ACE2 triggered the casein kinase II-dependent activationof activator protein-1 transcription factor and subsequent gene transcriptional
events which induced the production of usually not-transcribed mRNA.[12]Spike protein can also induce syncytia formation in lung tissue, a cell fusionthat ultimately leads to cell death but can during viral infection allow the virus
to spread without breaking out of the human cell and exposing itself to theimmune system.
Aside from the proved serious adverse events listed in Table 2, other possible side
effects can be seen but are so far not connected to the injections and possibly neverwill be conclusively proved to be caused by them, because of the extreme variety
in amount and quality of the vaccines as well as differences in the physiology
and pathology of the vaccine recipients. A study by Lyons-Weiler[13] showedthat more than one third of SARS-CoV-2 proteins show problematic homology
to human key proteins, which could lead to the development of autoimmune
disease. Two autoimmune diseases that are already being seen but played down
as extremely rare are immune thrombocytopenia and Guillain-Barré Syndrome;
there are discussions about the officially rare post-vaccine myocarditis being an

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autoimmune reaction as well. The spike protein has been implicated in causing
or exacerbating neurodegenerative diseases like Dementia, Alzheimer’s andParkinson’s disease by being amyloidogenic – aggregation prone – and initiating
protein aggregation through interaction with heparin-binding amyloid proteins.
[14] Antibody dependent enhancement (ADE) is a very real concern and could
explain the increasing morbidity of vaccinated versus unvaccinated people when
infected with SARS-CoV-2. Because of the very high rate of spike protein
mutation, almost every vaccine will force the body to produce an extinct spike
protein from day one of deployment. If this leads to non-neutralizing antibodies,
these can be used by the new, mutated virus during an infection to more easily
invade immune cells and multiply using the cells’ resources.
Despite all promises, even for the elderly the COVID-19 vaccines were notsafe. A recent study by Wilson Sy[15] looking at Simpson’s Paradox in thecorrelations between excess mortality and COVID-19 injections found ‘Earlier
epidemiological evidence that COVID injections reduce illness and death
is now methodologically invalidated, and the claim that the injections are
beneficial for the vulnerable is refuted. The injections explain the mystery ofsignificant numbers of non-COVID excess deaths. The Australian pandemic isshown to be iatrogenic in nature, particularly for the elderly, who have suffereddisproportionate harm. Deliberately ignoring this clear evidence is tantamountto iatrogenic geronticide.’

References
[1]
[2]

[3]

[4]

[5]

[6]

Turni, C., Lefringhausen, A. (2022) COVID-19 vaccines – An Australian
Review. Journal of Clinical & Experimental Immunology. 7(3):491-508.

Saadati, F., Cammarone, S., Ciufolini, M.A. (2022) A Route to Lipid
ALC-0315: a Key Component of a COVID-19 mRNA Vaccine. Chemistry.
28(48):e202200906. doi: 10.1002/chem.202200906.
Röltgen, K., Nielsen, S.C.A., Silva, O., Younes, S.F. et al. (2022). Immune
imprinting, breadth of variant recognition, and germinal center response in
human SARS-CoV-2 infection and vaccination. Cell. 185, 1025 – 1040.
DOI: https://doi.org/10.1016/j.cell.2022.01.018

Fertig, T.E., Chitoiu, L., Marta, D.S., Ionescu, V.-S., Cismasiu, V.B., Radu,
E., Angheluta, G., Dobre, M., Serbanescu, A., Hinescu, M.E., et al. (2022)
Vaccine mRNA Can Be Detected in Blood at 15 Days Post-Vaccination.
Biomedicines. 10, 1538. https://doi.org/10.3390/biomedicines10071538

Zhang, S., Liu, Y., Wang, X., Yang, L., Li, H., Wang, Y., ... &Hu, L. (2020).
SARS-CoV-2 binds platelet ACE2 to enhance thrombosis in COVID-19.
Journal of Hematology & Oncology. 13(1), 1-22.
Singh, N., Singh, A. B. (2020). S2 subunit of SARS-nCoV-2 interacts withtumor suppressor protein p53 and BRCA: an in silico study. Translational
Oncology. 13(10), 100814.

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[7]
[8]

[9]

Mishra R and Banerjea AC (2021) SARS-CoV-2 Spike Targets USP33-IRF9
Axis via Exosomal miR-148a to Activate Human Microglia. Frontiers in
Immunology. 12:656700. doi: 10.3389/fimmu.2021. 656700

Doshi, P. (2020) COVID-19: Do many people have pre-existing immunity?
17 September 2020 BMJ. 2020; 370 doi: https://doi.org/10.1136/bmj.m3563

Shimabukuro, T. T., Kim, S. Y., Myers, T. R., Moro, P. L., Oduyebo,T., Panagiotakopoulos, L., Meaney-Delman, D. M. (2021)
Preliminary findings of mRNA COVID-19 vaccine safety inpregnant persons. New England Journal of Medicine. 384:2273-2282
DOI: 10.1056/NEJMoa2104983

Russell, M.W., Moldoveanu, Z., Ogra, P.L., Mestecky, J. (2020)
Mucosal Immunity in COVID-19: A Neglected but Critical Aspect of[10]
SARS-CoV-2 Infection. Frontiers in Immunology. 11, 611337. doi: 10.3389/
fimmu.2020.611337.

Seneff, S., Nigh, G., Kyriakopoulos, A. M., McCullough, P. A. (2022). Innate
immune suppression by SARS-CoV-2 mRNA vaccinations: The role of[11]
G-quadruplexes, exosomes, and MicroRNAs. Food and Chemical Toxicology,
164, 113008.
Suzuki, Y.J., Gychka, S.G. (2021) SARS-CoV-2 Spike Protein Elicits Cell
Signaling in Human Host Cells: Implications for Possible Consequences of[12]
COVID-19 Vaccines.Vaccines (Basel). 9(1):36. doi: 10.3390/vaccines9010036.
PMID: 33440640; PMCID: PMC7827936.
[13]

[14]

Lyons-Weiler, J. (2020) Pathogenic priming likely contributes to serious andcritical illness and mortality in COVID-19 via autoimmunity. Journal ofTranslational Autoimmunity, 3, 100051.
Idrees, D., Kumar, V. (2021) SARS-CoV-2 spike protein interactions withamyloidogenic proteins: Potential clues to neurodegeneration. Biochemical
and Biophysical Research Communications, 554, 94-98.

Sy, Wilson. (2023) Simpson’s Paradox in the correlations between excess
mortality and COVID-19 injections: a case study of iatrogenic pandemic forelderly Australians. https://www.researchgate.net/publication/371342838_[15]
Simpson’s_Paradox_in_the_correlations_between_excess_mortality_and_
COVID-19_injections_a_case_study_of_iatrogenic_pandemic_for_elderly_
Australians

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Too Many Dead

Dr Astrid Lefringhausen was born in Duesseldorf,
Germany and first studied molecular biology andimmunology. She worked in parallel as a nurse inradiology, teaching medicinal microbiology to nurse
students at the German Red Cross, and also in thebiotechnology research facility of Bayer. She gained a
PhD in virology in 1996 and in the same year began
working for the biotech industry, first in sales, andlater as export manager and technical trainer. From1999 to 2006 she worked internationally across Asia,
Eastern Europe, Africa and South America as staff
and customer trainer in molecular biology, installing
and training in the use of cell-sorting equipment,
assisting in stem cell treatments in paediatricdepartments worldwide. She moved to Australia in
2006 and since 2015 has worked in the diagnostics
industry across Australia and New Zealand in the areaof cell chemistry, immunohaematology, immunology
and molecular biology.

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Too Many Dead

Part 2
Poorly tested – they should have known

Our next authors point toward the importance of the precautionary
principle in the presentation of pharmacological products to the general
populace. To begin taking a deeper look at this, we need to observe thenovel delivery system – this is the first time mRNA vaccines have ever been
approved for use with humans. They operate by providing a synthetic code.
The m-Ψ-RNA delivery system does not provide a natural form of mRNAto encode the spike protein, but a modified mRNA code manipulated toremain in the human body for longer without rapidly degrading (throughsubstitution of pseudouridine, designated Ψ, for the naturally occurring
nucleotide base, uridine). There are potential harms associated with thelipid nanoparticle delivery system. Long-term consequences remain
entirely unknown. Potentially dangerous experimental, novel gene-based
products were never tested for efficacy in stopping transmission and spreadof COVID-19, nor in the opinion of the Australian Medical Professionals
Society, adequately tested for genotoxicity, carcinogenicity, immunotoxicity
and reproductive toxicity. Such a reduction in evidentiary standard is notjustifiable.
In July, 2021, Australians were being mandated through coercive techniques
to be vaccinated with poorly-tested provisionally-approved gene-based
vaccines that our health bureaucrats and politicians repeatedly told us had

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been proved safe and effective. The Therapeutic Goods Administration(TGA) was amending the Therapeutic Goods Regulation Act to further
reduce the safety and efficacy requirements for any medicine that is for thetreatment or prevention of COVID.
According to court-ordered released documents, the Emergency Use
Authorisation (EUA) of the Pfizer-BioNTech COVID-19 Vaccine-BNT
162b2 was granted on the efficacy data of only 170 patients. The TGA
should have been concerned that major disqualifying protocol deviations
were identified in the 170 patients upon whom the EUA was granted.
These protocol deviations raise serious concerns about the legitimacy ofthe clinical trial and the scientific norms and ethical principles upon whichgood medical practice is founded. Pfizer gained provisional approval for
their COVID-19 injection following a mere two-month trial, claiming 95
per cent efficacy for the prevention of coronavirus disease. However, there
have been important concerns expressed with regard to statistical power aswell as the protocol deviations apparent in these data. Despite the pressure
exerted by international medicine regulators marching in lockstep andignoring these concerns, the TGA should have been concerned to probe
the legitimacy of the clinical trial.
An analysis by Fraiman et al. titled ‘Serious Adverse Events of Special
Interest Following mRNA COVID-19 Vaccination in Randomized Trials
in Adults’ goes on in this section to review the randomized trial data from
Pfizer and Moderna. It finds a risk of serious adverse events stands atapproximately one in 800 vaccine recipients. This ‘number needed to harm’is a much higher figure than the most generous estimate of vaccination
benefits, or ‘number needed to vaccinate’. Did regulators consider such ahigh harm potential against any perceived potential benefits? Regardless,
we are aware that many have drawn the attention of health officials to theenormous value of this paper.
It is often stated by those incredulous of our doubts that the trial samples
for novel vaccines were very large. However, when one halves the sample
sizes to account for the fact that only half were exposed to investigational
mRNA vaccines, the sample sizes become less impressive. Safety formRNA vaccine platforms was reported as of November 14th 2020 for
Pfizer-BioNTech Comirnaty and November 25th for Moderna’s SpikeVax.
By combining the two studies and utilising data submitted to the FDA asof December 2020, the authors conducted a unique analysis comparing
adverse event profiles in 33,986 mRNA vaccine recipients, compared to33,951 placebo recipients. Importantly, even though the duration of thestudies up until that date was still short, this is vastly preferable to the less30

Too Many Deadmature safety datasets used for the inaugural publications for the Pfizer
and Moderna offerings. Although many have tried to refute this paper
and its alarming message, none have refuted its methodology, including
the Health Department and related entities like The Australian TechnicalAdvisory Group on Immunisation (ATAGI); we believe they cannot
possibly be unaware of it.
Now we come to consider the way vaccines are manufactured. This is an
integral part of the regulatory process to ensure safety and efficacy. An
analysis of the Pfizer clinical trial identified that nearly all of the vaccine
doses administered during the trial originated from what is known as
Process One clinical batches. A second method of manufacture, using
E.coli, rapidly replicates the DNA from which RNA can be drawn quickly
and mass-produced. It was decided this was necessary, to meet the demandsof large-scale global production; it came to be known as Process Two.1
The differences between the two are profound, and involve the second
process having lowered mRNA integrity. This poses an inestimable risk topopulation health and the human genome from the possible contamination
with plasmid DNA and endotoxins. Let this be clear. The regulators
approved the Pfizer vaccine endorsing a process carrying monstrous risk,
based on Process One; only 250 people were then tested with the vaccine
using Process Two, and an EUA was granted for mass global injection.
To restate it: the regulators approved the Pfizer gene-based vaccine
utilising a manufacturing process that was then not used for the mass
global production of the Pfizer product. It needs to be understood that
the Process Two manufacturing protocol, which was tested on a mere 250
people, carries risk nothing short of catastrophic. The magnitude of thiserror must not be underestimated. Its implications are frightening becauseof the risk posed by the presence of plasmid DNA and endotoxins. The
word endotoxin is never heard in non-scientific discussion, but it is at thepeak of the most toxic products on the planet. This is discussed by our
authors in this part.
Pfizer gained provisional approval for their COVID-19 injection following
a mere two-month trial, claiming 95% efficacy for the prevention ofcoronavirus disease. At the same time, Pfizer set out to have a 75-yearembargo on release of information about not only their clinical trial data
but also about the contents of their products. On these extremely narrow
trial data, we were told two ‘safe and effective’ injections would stop thevirus and society would return to normal. Soon it was three injections.
Then it became four. Now it is more. Records of The Australian Advisory
Committee on Vaccines tell us that pregnant women were not included in
1

Prof Josh Guetzkow, in the BMJ, May 13th, 2023.

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this or any other studies, and that there were no data on mRNA distribution
and degradation, and also on the toxicity of lipid nanoparticles. This
reduces the safe-and-effective governmental mantra to nothing more thanpropaganda, repeated claims that the Pfizer products were ‘not expected’ topose a risk. Hopeful expectations are not conclusive data and are not theusual considerations upon which the precautionary principle rests.
Not only did manufacturers have six years to provide the government withsafety and efficacy data on these provisionally-approved injections. They
also no longer have to demonstrate the medicines could provide a greater
benefit than other available ones, or that the medicine is likely to provide
a major therapeutic advance.
COVID-19 mRNA vaccine products have a novel delivery system,
being the first mRNA vaccines approved for use in humans, as wellas the first approved coronavirus vaccines in humans. The speed atwhich they were designed, developed, approved and administered isalso unprecedented in pharmaceutical history and defies traditional
timelines for testing of biological products for use in humans.
– Halma, et al.
And:
These results raise concerns that mRNA vaccines are associatedwith more harm than initially estimated at the time of emergency
authorization. In addition, our analysis identified a 36% higher riskof serious adverse events in vaccinated participants in the Pfizer trial.
– Fraiman, et al.

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The novelty of mRNAviral vaccines andpotential harms:
a scoping review
by Matthew T.J. Halma,
Jessica Rose and Theresa Lawrie

Abstract: Pharmacovigilance databases are showing evidence of injury inthe context of the modified COVID-19 mRNA products. According torecent publications, adverse event reports linked to the mRNA COVID-19
injections largely point to the spike protein as an aetiological agent of adverse
events, but we propose that the platform itself may be culpable. To assess thesafety of current and future mRNA vaccines, further analysis is needed on
the risks due to the platform itself, and not specifically the expressed antigen.
If harm can be exclusively and conclusively attributed to the spike protein,then it is possible that future mRNA vaccines expressing other antigens will
be safe. If harms are attributable to the platform itself, then regardless of the
toxicity, or lack thereof, of the antigen to be expressed, the platform may be
inherently unsafe, pending modification. In this work, we examine previous
studies of RNA-based delivery by a lipid nanoparticle (LNP) and break down
the possible aetiological elements of harm.

1.

Introduction

Pharmaceutical drug and medical device approvals are predicated on thecompletion of a structured approval process through various regulatory
agencies. Historically, the approval process has contributed to patient safety
by subjecting all new approvals to a rigorous safety assessment. However, there

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are many examples of over-turnings of approvals of pharmaceuticals post
facto, because of the emergence of oversights of particular safety factors that
occurred during the approval process.[1] These failures of regulatory bodies
to sufficiently assess safety during the approval process are costly in termsof health and economic harms.[2] To put this issue into perspective, of 309
novel cardiovascular, orthopaedic, and neurologic devices approved in the EU
between 2005 and 2010, 73 (24%) were subjected to either a safety alert or
product recall,[3] consistent with reported rates for other medical devices.[4]Importantly, as the complexity of novel products increases, approval successrates decrease;[5] for example, new drug approvals are marred by low phase III
trial success rates (-10%).[6]Given the low success rates of novel and unprecedented drugs,[6,7,8] and thepotential risks to the population, it is important to adopt the precautionary
principle[9] when approving any pharmacological products, especially thosegiven to large populations. COVID-19 mRNA vaccine products have a novel
delivery system, being the first mRNA vaccines approved for use in humans,
as well as the first approved coronavirus vaccines in humans. The speed atwhich they were designed, developed, approved, and administered is also
unprecedented in pharmaceutical history,[10] and defies traditional timelines
for testing of biological products for use in humans.
With the approval of the mRNA platform by health regulators across theglobe, the industry is poised to develop new vaccines using mRNA, as it
is a versatile platform that only requires the genetic sequence of the target
antigen. The administration of billions of doses has resulted in great industry
enthusiasm for the platform, and other mRNA products are being developed
using the same core technology.[11,12]
To assess the novelty of COVID-19 mRNA products, we look at the historyof mRNA vaccines, which begins with experiments on in-vitro-transcribed
RNA, that is, delivering RNA to a cell for expression of a protein of interest.
[13] Synthetic RNA technology has a wide variety of applications, from thedelivery of small interfering RNAs (siRNAs) to reduce gene expression, or
messenger RNAs (mRNAs) to encode for a protein of therapeutic value, or to
encode for an antigen to stimulate an immune response, as in the strategy ofmRNA vaccination (Supplementary Table SI).[1v4]
Early attempts to express proteins from injected mRNA faced several
challenges.[35,36] First, bare RNA produces an inflammatory response, limiting
the expression potential of the RNA, as it is broken down.[37] Secondly, it
is difficult for the bare RNA to enter through a cell membrane.[38] Theseissues were addressed through the processes of pseudouridylation[39] andencapsulation of mRNA in a lipid nanoparticle (LNP), respectively.[40] The34

Too Many Deadformer discovery decreased the lability of RNA, enabling it to remain in the
body for longer periods of time.[41] The latter discovery not only shielded theRNA from the host’s immune response, as well as from RNAses, but it alsoenabled efficient uptake by cells,[40,42] where it could be efficiently translated
by host ribosomes. Pseudouridine was later replaced by Nl-methyl-pseudouridine,[43] owing to its greater translation fidelity, higher expression, and better
evasion of the host immune response.[44]LNP development was improved through two innovations, PEGylation,[45]and the use of cationic lipids.[46] (Figure 1) LNP surface modifications bypolyethylene glycol (PEG) enable lipid nanoparticles to survive for longer
lengths of time,[47] so that their package contents can be delivered to cells
to provoke an immune response when the antigen is expressed.[48] Anotherimportant development for LNPs is the use of cationic lipids, enabling
efficient self-assembly and encapsulation of the mRNA.[49] Cationic lipids
can additionally be modified to deliver drugs to certain cell types, an importantconsideration when delivering mRNA.[50,51] Conventional liposome by lipid
nanoparticles were the Pfizer-BioNTech BNT162b2 vaccine and the ModernamRNA-1273 vaccines.[40]Figure 1. Overview of mRNA–LNP vaccine components.

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Several of the assumptions have been either challenged or overturned byexperimental[53] and clinical evidence.[54] Quoted theoretical safety advantages
were the ease of production without contamination (mRNA vaccines do not
require the use of live viruses),[55] and lower (in theory non-existent) risks ofinfection or host genome integration.[55,56] Beforehand, concerns existed overthe induction of type I interferon responses by mRNA vaccines,[57,58] which
are associated with inflammation and autoimmunity.[59,60]
For example, the dual assumptions that LNPs remain at the injection site,
and that the mRNA degrades quickly, have been demonstrated to be false;
biodistribution and bioaccumulation data indicate that LNPs can enter the
bloodstream,[53] and studies have shown the durability of both mRNA andspike protein in vivo two months after injection.[61] Another study foundcirculating spike protein four months post-injection.[62] Given the novelty ofmRNA vaccines, and the increasing evidence of harm from clinical reports,[54]epidemiology,[63] and laboratory science,[64] there are open safety concerns tobe addressed by future research.
This review summarises known mechanisms of harm to mRNA vaccine
recipients, where we examine historical data on mRNA vaccines to determine
whether safety signals were apparent during production or testing. Prior to thetrials on COVID-19 vaccines involving tens of thousands of people, publicdata exist on only 285 patients administered various mRNA vaccines, withthe earliest trials finishing in 2018 and exhibiting high rates (>10%) of severe
adverse events (Supplementary Table SI). The novelty of mRNA-LNP products must be stressed in guiding their safety assessment, as current approvals
still leave many questions unanswered, and serious risks cannot be definitively
ruled out based on current evidence.
In this review, we summarise what is known about the distinct components ofmRNA vaccines, by reviewing the literature on past therapeutics. Additionally,
we review the known safety effects of mRNA vaccines prior to COVID-19, as
well as other coronavirus vaccines, which, while using a non-mRNA platform,
inform us of safety risks when vaccinating against coronaviruses.

2.

mRNA vaccine elements and potential
for harm

2.1

Harms due to lipid nanoparticle (LNP)

Lipid nanoparticles have been used in the delivery of drugs for decades,
beginning with the 1990 EU approval of the drug AmBisome (LNPencapsulated amphotericin B) for fungal infections.[52] In the US environment,
the first LNP-administered drugs were Doxil (LNP-encapsulated doxorubicin)

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Too Many Deadfor Kaposi’s sarcoma and Abelcet (LNP-encapsulated amphotericin B) for
aspergillosis.[52]The simplest form of LNP is a liposome, which is produced endogenously.
[65] This consists merely of a lipid bilayer that separates the contents from theoutside environment.[66] While simple liposomes are detected and destroyedby the body’s immune system,[67,68] the addition of polyethylene glycol (PEG)
enables the liposome to evade the host’s immune response and last longer inthe body to deliver the encapsulated product.[69] While PEG is often inert in
the body, the injection of PEG does elicit anti-IgM antibodies, and subsequent
injections containing PEG are cleared faster as a result of this immune response.
[70] Additionally, a small proportion of the population has an allergy to PEG,
and injection can trigger anaphylaxis, as did happen for several people receiving
COVID-19 vaccines.[71,72,73,74]
The safety of l,2-Distearoyl-Sn-Glycero-3-Phosphocholine (DSPC), a
component of the LNP used in both the Pfizer and Moderna COVID-19
vaccines, has been studied.[75] Studies in mice ruled that it was likely not toxic tohumans, as no clinical manifestations were present.[75] LNPs have been claimed
as safe for the delivery of therapeutic agents, according to a review.[76] However,pro-inflammatory concerns remain over LNPs, even in isolation.[77,78]
2.2

Harms due to exogenous RNA

Foreign RNA triggers an inflammatory response, as toll-like receptors (TLR)[79] and retinoic acid-inducible gene I (RIG-I)[80] are activated. Extracellular
RNA exists as a pro-coagulant,[81] and increases the permeability ofthe endothelial cells in brain microvasculature.[82] The initial reason formodification of the RNA by pseudouridylation was to bypass activation of
TLR.[83] As pseudouridylated RNA was translated at lower fidelity thanRNA,[84] the nucleosides were modified to Nl-methyl-pseudouridine, whichbrought translation fidelity to near that of RNA.[85]The properties of both YRNA and Nl-mYRNA have been studied in somedepth, though questions still remain. For example, through some applicationof the central dogma of molecular biology, it is assumed that RNA vaccines
cannot be incorporated into the genome. This statement is not supported byexperiments,[86] and is, in fact, contradicted by experiments showing reverse
transcription of the Pfizer BioNTech COVID-19 mRNA vaccine into ahuman liver-cell line.[64]YRNA exists in nature and comprises between 0.2% and 0.6% of uridine
content in human cell lines, and has biologically significant differences from
RNA.[87] While Nl-mYRNA exists in nature, found within archaea,[88] studies

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on its properties go back only as recently as 2015.[44] Additionally, importantbiological differences exist between unmodified and modified RNA.
2.3.

Harms due to in-vitro-transcribed (IVT) RNA

The next step in complexity is moving onto RNA therapeutics that are activelytranscribed by host ribosomes. These applications typically replace a damaged
protein of interest by supplying it exogenously.[89] Using an LNP-mRNA
platform here is better than supplying the protein itself, as a protein expressedfrom IVT RNA is more likely to have the correct post-transcriptional
modifications (and subsequent conformation) for its target cell type thanan exogenously supplied protein.[90] For these applications, it is typicallynecessary for the drug to be administered repetitively over long time-periods.
[90,91] With repetitive dosing, safety is very important, as even a low per-dose
AE rate can compound over the many doses of the treatment.
Most studies of this therapeutic modality so far focus on drug efficacy, andlimited safety data exist. In a 2021 review of non-immunologic applicationof mRNA, all studies using LNP-mRNA as protein replacement therapy
demonstrated liver toxicity or lacked safety data.[90] Several studies also
demonstrate the development of anti-drug antibodies (ADAs),[92,93,94] which
can deactivate the drug and prevent treatment.[95,96,97,98] Immunemediatedtoxicity is also a cause for concern.[99,100]
Another concern is the potential development of cross-reactivity toendogenous proteins, which can occur if the endogenous protein possesses
similar structural motifs to the protein expressed from the administered
mRNA.[101] Thromboembolic events have been observed in ADA reactions.
[96] Typically, ADA reactions are decreased in cases where the encoded protein
is a ‘self ’ protein, as opposed to an exogenous protein.[102]Recent work demonstrated a class switch towards an IgG4 antibody response,
observed after three doses of Pfizer BNT162b2 (COVID-19 vaccine) andnot adenoviral vector COVID-19 vaccines,[103] and this raises concerns overpossible immune tolerance, which is linked to an IgG4-dominated response.
[104,105]

2.4

Harms of RNA vaccination

In addition to the other harms present in IVT RNAs, RNA vaccines also
have the safety challenges of expressing an exogenous protein for the express
purpose of generating an immune response and immune memory.[106] Of the
RNA therapeutic systems introduced so far in this review, the mRNA vaccines
are the most complex drug-like therapeutic biologic.

38

Too Many Dead
Limited safety data exist on RNA vaccines against infection[16-23](Supplementary Table SI). Prior to the trials for COVID-19 vaccines, there
were data on 285 patients, with the earliest trials on a non-HIV vaccine only
completed in 2018. The serious adverse event (SAE) rate of these exploratory
trials was 14 zb 2% (grade 3 or above). As a comparison, a post-marketingsurveillance study of influenza vaccines in the UK found an SAE rate of
0.16%,[107] almost 100 times less than the SAE rate for mRNA vaccines.
Given their novelty, mRNA vaccines have limited long-term safety data.
While the type of vaccination (that is, attenuated live virus, inactivated virus,
mRNA) should not have a significant effect on the IgG antibodies produced,
an important consideration must be mentioned: mRNA vaccines encode
for a single antigen in most cases, which better enables immune escaperather than a broader antibody response including other proteins. Recent
evidence revealed a subclass switch from IgGl to IgG4 in the context of the
Comirnaty mRNA product, which may have consequences with regard tocancer,[108] pregnancy,[109] and IgG4-related diseases.[103,110] COVID-19
mRNA vaccines are commonly used in Europe and North America; theseencode specifically and exclusively for the spike (S) protein.[111,112] Since theintroduction of vaccines, mutations have occurred, lessening the neutralizing
capacity of these vaccines.[113,114]
2.5

Harms of coronavirus vaccination

In addition to the considerations on the novelty of mRNA vaccines, theC19 mRNA vaccines are also unprecedented in terms of another quality,
namely, they are the first coronavirus vaccines approved in humans. Following
the 2002-2003 outbreak of SARS- CoV[115] and the 2012 outbreak of
MERS-CoV,[116] vaccines against coronaviruses infecting humans gained
more attention, and were subsequently tested on animal models as well as on
human subjects.[117]A SARS-CoV candidate vaccine given to ferrets elicited enhanced hepatitis.
[118] Animal trials on four SARS vaccine candidates in ferrets demonstrated
an initial protective period against infection, followed by hypersensitivity torechallenge with SARS-CoV. The ferrets developed histopathological changes
in the lungs induced from virus challenge after all four vaccine candidates,suggesting immune-mediated damage.[119] However, a study of MERS-CoV
vaccines on mice and rhesus macaques[120] demonstrated protection without
visible histopathology.
Mice given an inactivated virus later developed a pro-inflammatory pulmonary
response upon challenge.[121] Anti-spike IgG antibodies are produced by all
mRNA COVID- 19 vaccines,[122] and at significantly lower levels by other39Australian Medical Professionals Society
COVID-19 vaccines.[123] Anti-spike IgG antibodies are demonstrated tocause severe acute lung injury in rhesus macaques on re-exposure to the virus,
suggesting a negative effect of a narrow immune response.[124]Immune-mediated danger from vaccines has been widely acknowledged to
be an extant issue in the development of coronavirus vaccines,[125-131] and is
supported by current evidence.[132] During the rapid development of COVID-19
vaccines, it was an issue of concern that sufficient long-term monitoring for antibody-dependent enhancement (ADE) be established.[133,134] Unfortunately,
as of the time of writing, there are no data available on the long-term effects of
COVID-19 vaccines, including effects resulting from rechallenge with the virus.
Veterinary vaccines for other coronaviruses are available, and are summarised
in a recent review.[135] Evidence of immune-dependent enhancement waspresent for cell culture experiments on vaccination against feline coronaviruses.
[136,137,138] ADE is also a concern for avian infectious bronchitis virus (IBV),a coronavirus.[139,140] In IBV, suboptimal vaccination alters the evolutionary
dynamics of the viruses and can contribute to the production of escape mutants.
[141,142,143] Finding broadly neutralizing IBV vaccines remains a substantial
challenge for the poultry industry.[144-148]
Early canine coronavirus vaccines were withdrawn because of neurologicalsymptoms,[149,150] though current vaccines do not carry the same safety issues.
[151,152] Bovine coronavirus vaccinations often fail to provide immunity against
subsequent reinfections.[153,154,155] Immunizations against transmissible
gastroenteritis virus (TGEV) in swine have historically had issues in inducing
immune protection,[156,157] but are widely used now. Too-frequent exposure tovaccine antigens can lower the immune response against TGEV.[158] Anotherswine coronavirus vaccine, porcine epidemic diarrhoea virus (PEDV), is widelyused.[159] Extant safety concerns for the PEDV vaccine are minor, and mostly
deal with lack of efficacy; these are summarised in a review.[159]
There were several human trials of coronavirus vaccines prior to the approvalof COVID-19 vaccines (Table 1). In addition to the endemic corona viruses
that infect humans, several epidemic strains of coronaviruses have occurred in
the past two decades, namely, the coronaviruses associated with severe acuterespiratory syndrome (SARS-CoV) in 2003[115] and Middle East respiratory
syndrome (MERS-CoV) in 2012.[160] These outbreaks impelled the productionof coronavirus vaccine candidates, summarised in a recent review[117] (Table 1).
In total, before the development of the COVID-19 vaccines, data existed on a
total of 179 human participants given a SARS or MERS vaccine candidate, ofwhom 7 (4 zb 2%) experienced a serious adverse event (Table 1). A human trialof 63 adults for a MERS vaccine candidate showed no severe adverse events, but
infections in 36% of participants.[161,162]40

Too Many Dead
Table 1. Summary of human trials of non-COVID-19 coronavirus vaccines.
Adapted from.[117]Studies of corona virus vaccines have a limited number of human participants
and still represent a novel technique, though the recent implementation oflarge-scale vaccination programs for COVID-19 increases the data available
to assess the safety of human coronavirus vaccines.
2.6

Harms of RNA vaccination with SARS-CoV-2 spike (S)antigen

There is reason to believe that vaccines encoding the spike (S) protein of
SARS-CoV- 2 have additional mechanisms of harm, owing to the biologicalimpacts of S protein specifically. There is some research in the literature,[169172] and it is beyond the scope of this review to cover it in substantial depth.
However, the addition of spike protein adds another factor in assessing the41

Australian Medical Professionals Society
complexity of RNA vaccines. The complexity and the uncertainties about
possible harms are non-trivial and cannot be dismissed based on current data.
This section briefly covers some of the hypothesised mechanisms of harmfrom spike-protein-encoding mRNA vaccines and the evidence for eachfrom a clinical-epidemiological outlook, as well as any mechanistic data fromlaboratory work.
Several observations have been made that contradict fundamental claims of
RNA vaccine safety. For example, it was assumed that the RNA was relatively
labile and transient in the cell. However, several studies identified spike
protein and vaccine mRNA months post-injection.[61,62] Spike protein has
been shown in laboratory settings to cause inflammation[173,174] and vasculardamage,[175] and to act as a seed for amyloid formation.[176]3.

Discussion

There is limited information to make a safety assessment of mRNA vaccines.
In the category of mRNA vaccines, there are patient data for 385 patients.
For mRNA vaccines against an infection, there are data for 285 patients. The
rate of serious adverse events was 64 out of 385 for the broad category of
RNA vaccines (including cancer vaccines), or 17%; restricting the definition tovaccines against infection, the rate of SAEs is 41/285 or 14%. While high levels
can be expected for trials of a novel technology where dosage levels must be
determined (many of these trials are phase I),[177] these findings showcase therelative immaturity of mRNA vaccination as a strategy. Given the low efficacy
and short duration of protection of SARS-CoV-2 mRNA products,[178,179]
and the low risk of many populations from COVID-19 complications,[180] it
may be advisable to suspend mRNA vaccines in certain risk cohorts.
The key to the reactivity of mRNA vaccines is the fact that they express a foreign
antigen, for which the antigen-presenting cells are marked for destruction.
While the lipid nanoparticle exhibits an acute inflammatory response by
itself,[77,78,181] the trials using LNPs, so far, have not found a large safety
signal when using LNPs to deliver small molecules, non-expressing RNAs,
or RNAs for endogenous proteins.[77,78,181] In addition to there being harms
attributable to the general immune response from an LNP-RNA delivery
system, there are also some harms specific to the spike protein. Several of thesemechanisms are supported by laboratory experiments and clinical findings, butneed more investigation. Medicine is replete with cases for which safety was
assumed without adequate evidence at the time, which later regrettably led toloss of health and life. mRNA vaccines are demonstrating great unintended
harms, and these harms demand further investigation into the mechanisms,
which is important for identifying treatment modalities.

42

Too Many Dead
Novel biomedical technologies can bring relief for a wide variety of conditions
and diseases. However, their use must take into consideration their possible
harms. Here, we argue that the mRNA technology is novel enough that safetyconcerns in current and future products cannot be definitively ruled out, andfurther research must be performed to ensure their safety for current andfuture users.
Other vaccine platforms have longer-term data on their mechanisms, andthese have fewer unknown long-term effects. Considering the lack of data on
the platform itself, we recommend a robust, independent, and wide-ranging
safety audit of mRNA-LNP formulations, and we call on regulators tohold manufacturers to high safety standards, especially for products usedprophylactically in the general population.
Supplementary Materials: The following supporting information can be
downloaded at: https:/ /www.mdpi.com/article/10.3390/j6020017/si. Table SI:

Safety profile of previous LNP-mRNA products.

Author Contributions: Conceptualization, M.T.J.H.; writing—original draft

preparation, M.T.J.H.; research, M.T.J.H., J.R. and T.L.; writing—review andediting, M.T.J.H., J.R. and T.L.; supervision, M.T.J.H. All authors have readand agreed to the published version of the manuscript.
Funding: This research received no external funding.
Acknowledgments: We thank Cristof Plothe for his comments.
Conflicts of Interest: M.T.J.H and T.L. are members of The World Council for

Health, a non-profit organisation for holistic health promotion.

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Chau, N.V.V.; Ngoc, N.M.; Nguyet, L.A.; Quang, V.M.; Ny,
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Apostolatos, A.; Ioannidis, J.P.A. Age-Stratified Infection Fatality
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2023, 216,114655. [CrossRef ]Kedmi, R.; Ben-Arie, N.; Peer, D. The Systemic Toxicity of Positively
Charged Lipid Nanoparticles and the Role of Toll-like Receptor 4
in Immune Activation. Biomaterials 2010, 31, 6867-6875. [CrossRef ]Matthew Halma’s research career began in studying
the structural dynamics underlying programmed
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phenomenon across all kingdoms of life, and attracts
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expression. Since then, he has been pursuing a doctoral
degree at Vrije Universiteit Amsterdam, studying theapplications of single molecule tools to molecularmotors involved in DNA replication and repair.

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The role and dangers
of endotoxin in
mRNA injections
by Geoff N. Pain
PhD, BSc Hons

Abstract
Traditional concepts in toxicology have to be abandoned when considering
the deaths and injuries caused by the COVID-19 injections. All known A-Z
documented adverse events, from abortion, anaphylaxis, blindness, and heart
injury to tinnitus and zoster can be anticipated via known biological pathways
and mechanisms. All mRNA vaccine technology using plasmid growth inside
live bacteria is inherently unsafe because the manufacturing techniques cannot
remove the supertoxic fragments of the germ cell walls from the liquids ending
up in all vials and the arms of a largely compliant public.

1.

Essential background and central thesis

This paper examines the potential role of contaminant endotoxin in themRNA vaccines promoted for the prevention of COVID-19 in Australia.
The basis of both batch contamination and batch variation will be set forth,in the light of the E. coli-based manufacturing process known as ‘Process2’, together with a pathophysiological examination of endotoxin. It is theauthor’s view that much of the burden of severe adverse events, including
deaths as covered by other authors in this volume, may be attributable
to contaminant endotoxin; it is necessary to discuss the putative role of64

Too Many Dead
endotoxin in specific pathologies, recognised as complications of vaccine
administration.
All of the immunisations marketed in Australia claimed to generate antibodies
to SARS-CoV-2, either by injecting synthetic spike protein or a length ofsynthetic mRNA or DNA designed to take over the cellular machinery of
the recipients and to turn them into spike protein factories. This would resultin the detection and targeting of these cells expressing foreign antigen, suchthat the body would react by destroying the cells transfected in this way. This
strategy was doomed to fail because it could not generate mucosal antibodies
in sufficient quantity or killer capacity to stop viral replication on infection
through the eyes, nose or mouth. As the public at large now largely knows,
the vaccines have not prevented infection, viral replication or transmission by
exhalation.
The central thesis here is that endotoxinaemia explains the unprecedented
magnitude of off-target effects, especially those noted in the early phase afteradministration. Delayed effects and effects of repeat exposure are also possible
in the unique toxicology of endotoxin; among other considerations, ways
in which contaminant endotoxin may synergise with other elements of the
exposure, such as spike protein, are discussed.
Unfortunately, as of the time of writing, clear quantification of endotoxin
concentration in vials has not been provided, either by national regulatory
authorities, university laboratories or open-source investigations. As this is
crucial to the central thesis, an analysis of the measurement of endotoxin,
including those methods most likely to prove useful, as well as those most
likely to obfuscate and mask the issue, are presented, with a clear and urgent
call for the public disclosure of accurate levels in batches.
1.1

What are Process 1 and Process 2?

For both the Pfizer and Moderna mRNA products, two distinct manufacturing
processes have been utilised, the first in the early clinical trial phase (Process 1)
and the second in the penultimate phase of the clinical study (Process 2), into
the worldwide rollout, until now. It is essential to understand the distinctions,in order to understand the differential endotoxin levels likely to be affecting
Process 2 batches.
1.1.1

Process 1: reverse transcription PCR

Process 1 employed reverse transcription PCR (polymerase chain reaction)in order to manufacture the mRNA strand encoding spike protein. This
offered high sample purity, but little scalability beyond the trial. Pfizer andModerna trial subjects were therefore exposed to far lower endotoxin because

65Australian Medical Professionals Society
their vaccines, made by Process 1, were manufactured without the necessity ofbacterial exposure.
The RT-PCR-generated mRNA immunisations were encapsulated using thesame lipid nanoparticles as the later material injected in the mass rollout, so
it is reasonable to surmise that observed differences likely relate to the drug
substance manufacture.
A very limited number of Process 1 vaccines have reported endotoxin levels of
about 0.5 EU/mL. This relatively low value, which is still capable of causing
such problems as anaphylaxis, is likely because the drug substance (mRNA) was
made by RT-PCR of synthetic mRNA, designed to code for spike protein. So
any endotoxin in Process 1 vaccines used in the trial arose from contaminationof materials used in manufacture of drug product, which is the suspension ofmRNA in lipid nanoparticles placed in the vials. Sugar is a major component
and could be a source of endotoxin.
1.1.2

Process 2: plasmid DNA

The endotoxin in mRNA-based immunisations arises from fragments of theE. coli bacteria being used in production of the plasmid complementary DNA,
which is in turn used to produce the mRNA prodrug, and combined withbuffer and lipid nanoparticles to form the drug product. The crude filtration
processes used in production cannot prevent endotoxin, and especially its
concerning Lipid A component, which breaks off easily and is far more toxic
than the larger chunks of bacterial wall.
Endotoxin in mRNA vaccines can float freely outside the LNP as well as
adhering to the surface or trapped inside. As such, it remains to be seen how
much endotoxin is bound to mRNA or contaminant DNA or bioburden.
Approximately 252 trial subjects are thought to have been treated with Pfizer
Process 2 immunisations supplied in the tail end of the clinical trial: no humantrial results specifically pertaining to this population have been published.
Furthermore, Pfizer has stated it has no intention of publishing clinicalevaluation results of its tiny ‘comparability trial’ of Process 2 versus Process 1.
In the ongoing Phase 3 clinical study C4591001 through the cut-off date ofMarch 13th 2021, there was one anaphylaxis case deemed to be related to the
product. I suspect this person received a Process 2 vaccine dose.
Endotoxin in Pfizer Process 2 vaccines is commonly quantified in EU/mL(endotoxin units / per millilitre). However, as experts in the field point out,
this is somewhat vague; it is perhaps the case that 1 EU/ml equates to 200
picograms per mL. The European Union allows 12.5 EU/mL, while the FDA
allows 5 EU/mL for intramuscular vaccines and immunisations; much lower

66

Too Many Dead
limits apply for the eye or spinal injections. No endotoxin levels of Pfizer
Process 2 vaccines used in mass rollout have been published. The TGA simply
states ‘Pass’ on its Batch Release page, with minimal detail.
The advent of Process 2 vaccines appears to have greatly expanded the numberof anaphylaxis victims, especially starting from the unblinded phases of themain trial. For one example, during the unblinded phase, a 17-year-old female
subject, who had received two placebo doses and then a dose of Process 2
vaccine, suffered anaphylaxis six minutes after injection on December 14th
2020. She rescued herself with a shot of adrenalin. She withdrew from thestudy on January 27th 2021.
Subsequently, a Vaccine Adverse Event Reporting System (VAERS)
notification from January 7th 2021 states that a 53-year-old woman sufferedanaphylaxis after being injected with her first dose of Process 2 Lot EE8403.
By July 2021, Pfizer was more candid and expansive on the anaphylaxis
casualties, including nine deaths in the post-marketing injection campaign. It
will be seen that the differential rate of incident anaphylaxis in recipients ofmRNA vaccine doses manufactured under Process 2 is strong circumstantial
evidence that clinically notable endotoxin contamination is taking place.
1.2

What is the evidence for batch variation
and batch contamination?

There is now abundant evidence for batch variation, including differential
rates of adverse events, raising basic concerns around Good Manufacturing
Practice (GMP).
From disclosed ingredients of the various vaccine platforms, we essentiallyknow what is in the vials. A number of the ingredients are clearly toxic.
In particular, there is no disclosed evidence for the use of graphene or its
derivatives.
1.3

Laboratory methods and the urgent need for quantification

I can find no evidence that the TGA tests for the supertoxin lipid A. The
Laboratory Branch of the TGA use the horseshoe blood extract, limulus
amebocyte lysate assay (LAL assay), suitable for large fragments only. This test
is impaired by the presence of cationic lipids and mRNA – as present in thevaccines – so that the actual concentration of endotoxin is likely to be higher
than indicated by the laboratory test. There are more sophisticated tests (basedon mass spectrometry after separation and volatile derivative formation) thatare not used by regulatory authorities like the TGA, but which arguably shouldbe. Endotoxin is so poisonous that electronic sensors have been made that can
detect femtogram levels, that is, a billionth of a millionth of a gram.

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Australian Medical Professionals Society
1.4

Other essential background

1.4.1

General considerations regarding endotoxin toxicity

This paper concentrates on what may be the most lethal component in
the mRNA vaccines, namely endotoxin, also known as lipopolysaccharide(LPS), and especially the fraction called lipid A, with discussion of plausible
mechanistic arguments which may link endotoxin to the plethora of adverse
manifestations following COVID-19 vaccinations.
The website of the Therapeutic Goods Administration (TGA) in Australia
contains some interesting information tucked away in odd places: a slide froma presentation about endotoxin and how it is measured in their laboratory[2]is provided in Figure 1. Endotoxin is a term often used interchangeably with
LPS, although it can more broadly refer to any toxic substance released whena bacterial cell disintegrates. LPS is the primary endotoxin in gram-negative
bacteria, including E. coli, and is a large molecule found in their outermembranes. LPS plays a crucial role in the structural integrity of the bacterialcell wall and serves as a protective layer against host immune. Free LPS is
usually found in micelles and can aggregate in vivo.

Figure 168Too Many Dead
The 16th-century physician Paracelsus famously stated, sola dosis facit
venenumhi: “All things are poison, and nothing is without poison; the dosage
alone makes it so a thing is not a poison.”[1] In the case of LPS, however, thedemonstrable toxicity of very low-dose exposures challenges this notion. The
fact that LPS at very low exposures can have disproportionate biological effects,
via multiple amplification mechanisms, has made endotoxin contamination oftherapeutic biologics a perennial concern of the pharmaceutical industry andits state regulators, and it is the subject of numerous standards within Good
Manufacturing Practice.
1.4.2

Widespread biodistribution

It is the author’s belief that BioNTech-Pfizer, and the complicit marketersof their COVID-19 vaccine, including government departments around theglobe, were entirely incorrect when they repeatedly said that the LNPs and themRNA inside or outside them would simply enter muscle cells in the arm, takethem over, and force them to become factories for synthetic spike generation
and resultant anti-spike antibodies. It is necessary to point out the widespread
biodistribution of LNPs to understand how contaminant endotoxin may also
be disseminated widely in the body after injection.
In a lecture given by one of the BioNTech founders, it is clear that the actual
target of the injection contents is the draining lymph nodes and the wider
lymphatic system, including the spleen. Once the mass vaccine program began,others associated with Pfizer vaccine design were also quite straight about theintended action of the vaccine on the lymphatic system.
In an interview of September 2021, Professor Drew Weissman, said:
LNPs encapsulate the mRNA, protecting it from extracellulardegradation, and facilitate endosomal release of the mRNA into thecytoplasm. When the LNP-Ψ-mRNA is injected into the muscle, every
cell takes it up, but for muscle cells that is very inefficient, you can barely
measure the protein that they make. The LNPs are 80nm in size, which isabout the size of a virus. What happens is that the LNPs travel throughthe lymphatic drainage to lymph nodes, and in the lymph nodes, DCs(Dendritic Cells) take them up. There is also an infiltrate of lymphoidcells into the muscle that picks up the particles. Once the vaccine getsto a lymph node, the DC translates the mRNA and presents it to B andT cells to activate them, and that is how the immune response is started.
From the Pfizer Clinical Trial, it was known that lymphadenopthy wasa small-scale problem caused by their Process 1 product, but there was an
explosion of cases when they moved to Process 2, resulting from the endotoxin

69Australian Medical Professionals Society
from the E. coli bacteria used in production. As expected, lymphadenopathy isusually detected on the side of the body that received the dose.
These observations serve to corroborate specific documentary evidence obtained
from the TGA by way of a FOIA request, namely the Pfizer Comirnaty
Non-Clinical Evaluation Report, which showed widespread biodistributionof 80nm LNPs. These documents have been discussed around the world,
popularising the concept of dissemination of LNPs and their payload wellbeyond the intramuscular injection site, as was popularly understood to the besite of action, and well beyond the lymphatic drainage system, as elaborated by
Prof Weissman. In fact, the LNPs go to numerous organs and carry not only
mRNA, but contaminants including endotoxin. This is extremely important to
the present discussion of potential endotoxin-related toxicities.
Quoting from a Trial document, obtained from the UK government,‘Lymphadenopathy is identified as an adverse reaction for BNT162b2 vaccine.’
I became aware of the big lymphadenopathy problem in Australia when I
inspected the Database of Adverse Event Notifications. By 23 August 2021
there were already 1,126 cases. Worldwide Pfizer reported the followingcase numbers to April 2022: lymphadenopathy 73,287 (5.44 % of all adverse
reaction reports); lymph node pain 8,395; vaccination site lymphadenopathy
4,540; lymphadenitis 2,115; lymphoedema 75; lymphopenia 274.

2.

The centrality of cytokinopathy inendotoxin toxicity

In 1985, Prof. Peter Hotez, while he worked at New York’s Rockefeller
University, was part of a team able to kill ‘endotoxin-resistant’ micewith molecules secreted by endotoxin-altered macrophages. Among thesymptoms the doomed animals suffered were anorexia and cachexia (muscle
wasting). During subsequent years these experiments laid a foundation forunderstanding cachectin (murine Tumour Necrosis Factor, TNF) and the way
in which cytokines triggered by an exposure such as endotoxin can trigger an
exaggerated systemic inflammatory response. This is classically called a positive
feedback loop. During the pandemic, thanks to media reporting, cytokines andthe notion of a ‘cytokine storm’ have quite likely been assimilated into the
public lexicon.
The scientific reality is that even tiny amounts of endotoxin defeat the Paracelsus
doctrine because interleukin 6 (IL-6) and IL-1β (called the apex cytokine)
and other pro-inflammatory stimuli are amplified by positive feedback loops.

70Too Many Dead
2.1

How does endotoxin produce tissue damage
via cytokinopathy the human body?

Cytokine Storm is not a newly-described concept: as will be seen, endotoxin
in Pfizer and Moderna immunisations may create the perfect cytokine storm.
Figure 2 provides a partial overview of the damage that endotoxin can do(Fajgenbaum), illustrating how cytokine storm can essentially damage every
type of tissue in the human body. Note that effects on the reproductive system
are absent in this diagram.

Figure 2

Our innate immune system defends against invading pathogens by recognizing
conserved Pathogen–Associated Molecular Patterns (PAMPs). Recognition of
PAMPs relies on Pathogen Recognition Receptors (PRRs), expressed on thecell surface or in intracellular compartments. Important immune cells include
macrophages and monocytes.
There is nothing simple in biological systems and this is best explained with a71Australian Medical Professionals Society
diagram: unfortunately a number of acronyms are commonly used, so the text
has been modified from Mohammad and Thiemermann.[2]Figure 3

In Figure 3, LPS is bound in blood to lipopolysaccharide binding protein (LPB)
and this coordinates to glycerophosphatidylinositol-anchored protein (alsocalled CD14). LBP binds LPS micelles via the N-terminal basic patch andforms transient ternary complexes with secreted (sCD14) or membrane-boundGPI-anchored CD14 (mCD14) on the C terminus. Following the generationof CD14/LBP/LPS micelles, CD14 dissociates from LBP and receives
monomeric LPS.
Toll-like receptor 4 (TLR4, a PRR) signalling occurs via the myeloid
differentiation primary response protein 88 (MyD88)-dependent and
MyD88-independent pathway to activate nuclear factor kappa beta
(NF-κB)-related target genes. TLR4 forms a complex with MD-2, a secreted
glycoprotein with an LPS-binding pocket.
The MyD88-dependent pathway involves the activation of MyD88 which
recruits interleukin IL-1 receptor-associated kinase-4 (IRAK-4). IRAK-4
phosphorylates IRAK-1 and allows tumour necrosis factor receptor associatedfactor 6 (TRAF6) to associate with IRAK1. IRAK1-TRAF6 then activates
TAK1, TAB1, and TAB2. The TRAF6, TAK1, TAB1, and TAB2 form alarger complex with ubiquitin-conjugating enzyme E32 variant 1 isoform A(Ubc13) and Uev1A which activates TAK1. The polyubiquitin chain is then72

Too Many Deadremoved by A20 and conserved cylindromatosis (CYLD). Activated TAK1
phosphorylates the IKK complex (IKKα, IKKβ and IKKγ), ultimately resulting
in the translocation of NF-κB into the nucleus, resulting in the transcriptionof pro-inflammatory cytokines.
The MyD88-independent pathway involves TIR-domain-containing
adapter-inducing interferon-β (TRIF) leading to the activation of TNF receptor-associated factor 3 (TRAF3) and the translocation of interferon
regulatory factor 3 (IRF3) to the nucleus leading to IFNB gene transcription.
2.2

Endotoxin tolerance versus pro-inflammatory priming

Gay men are more at risk of E. coli induced endotoxaemia. A study comparing
two samples of differing plasma LPS levels from each person foundHIV-negative men with subclinical endotoxemia had altered CD4-CD8 T
cell ratio and plasma cytokine levels. Subclinical levels of plasma endotoxin
in vivo alter T cell proliferative capacity, monocyte cytokine release and
HLA-DR expression, and furthermore induce TLR cross-tolerance bydecreased phosphorylation of mitogen-activated protein kinase (MAPK)
pathway components. Monocytes from high pre-existing endotoxin samples
did not significantly increase cytokine production after endotoxin stimulation,
whereas monocytes from the matching low endotoxin samples increased
cytokine production (except for IL-8) 8 to 150-fold. Thus, evidence ofendotoxin tolerance is worthy of discussion.
Repeated exposure to endotoxin leads to chronic non-resolving inflammation
but a study of multiple vaccine doses found counter-intuitive results.[6]According to dogma, a second high dose of endotoxin was thought to be
characterized by less robust induction of pro-inflammatory cytokines andincreased production of anti-inflammatory cytokines – known as endotoxin
tolerance. However, retreatment with a very low dose of endotoxin, in contrast,
has an opposite effect, potentiating or priming the pro-inflammatory response
to subsequent endotoxin challenge, referred to as the Shwartzman-likereaction. The Shwartzman, or Sanarelli-Shwartzman effect, after injections, canperhaps be explained by endotoxin located in at least two compartments, one
being free-floating and immediately available and the other being slow-releaseattached to LNPs, mRNA, DNA and or bioburden.
Mice pre-treated with super-low-dose endotoxin (LPS) exhibit increased
mortality in response to challenge with a higher dose. The scheme by Morris
et al. in Figure 4 demonstrates the complexity of endotoxin poisoning.

73Australian Medical Professionals Society

Figure 4

LPS = Endotoxin; TLR4 = Toll-Like Receptor; MyD88 = Myeloid Differentiation primary
response 88 protein; TRIF = Toll/IL-1R domain-containing adaptor protein inducing
Interferon-β; IRAK = Interleukin-Receptor-Associated Kinase; GSK3 = Glycogen Synthase
Kinase 3; JNK = c-Jun N-terminal Kinase; AKT = Protein kinase B (PKB); ERK = Extracellularsignal-Regulated Kinase; IKK = IκB Kinase; NFκB Nuclear Factor κ of activated B cells.

Shwartzman found that intradermal injection of a priming dose of sterile culture
filtrates from gram-negative strains from bacteria (such as E. coli used to makethe COVID-19 mRNA immunisations) into normal rabbits, followed in 24
hours by a second intravenous challenge (the provocative dose) from the same
culture filtrate, resulted in dermal necrosis at the first injection. Note that 22%of the rabbits failed to respond at all, likely as a result of ‘endotoxin tolerance’
which in humans typically lasts no more than 21 days. Subsequently it has beenshown that the cytokines generated by endotoxin septic shock, including TNF,
IL-1, IL-12, IL-15 and or IFN-γ, can substitute for endotoxin.
In 1952 it was found that rabbits pre-treated with cortisone sufferedmultiple organ damage, particularly in the kidneys after they were given asingle intravenous injection of gram-negative bacterial endotoxin. This was
accompanied by heavy fibrin deposition. In 2006 it was shown that multiple
organ damage due to the endotoxin Shwartzman effect involves disseminatedintravascular coagulation (DIC) resulting in thrombosis in the lung and liver.
Endotoxin in Pfizer and Moderna injections creates the perfect cytokine storm.
The list of adverse events after Pfizer injection amazed people around the world
when a US court ordered release of the company reports that they wanted
hidden for 75 years. One of the most widely read Pfizer documents is about
170 pages long, running to thousands of separate medical descriptions; it canbe downloaded from the TGA Freedom Of Information Log website. The UK
government famously issued a contract for Artificial Intelligence to deal withthe flood of reports to its Yellow Card reporting system. The US government
also had to issue a contract for its VAERS system.

74

Too Many Dead
I have shown that endotoxin can account for all adverse events reported inminutes to days after the injection and beyond. The synthetic spike protein
manufactured by the mRNA injections taking over healthy cells is of course
toxic. In fact, the spike protein works in synergy with endotoxin by binding to
it and helping to disrupt large endotoxin micelles into the more toxic lipid A.
While there is no doubt the synthetic SARS-CoV-2 spike protein transcribedfrom synthetic mRNA in COVID-19 vaccines causes immense long-term
damage to a generation, it cannot be responsible for the adverse and sometimes
fatal reactions experienced in minutes to days after the vaccines.
A most elegant proof of this was published by a team in Italy in early 2022
who obtained samples of viral spike from commercial labs and tested themfor inflammatory action on human macrophages (MΦ). They concluded thata number of earlier studies were negated, or at least possibly confounded, by
failure to take into account endotoxin contamination.
Natural virus spike evades the immune system with a coating of glycans as
determined by crystallography, enzyme digestion and mass spectrometry. The
synthetic spike from the mRNA injections does not have this glycam coating,and this makes it highly inflammatory.

3.

Has endotoxinaemia been overlooked as a 		major driver of vaccine adverse events?

3.1

Plausible roles for endotoxinaemia in lethal events

3.1.1

Plausible mechanistic links to the known
risk of anaphylaxis

As mentioned above, a VAERS report from January 7th 2021 states that a
53-year-old woman suffered anaphylaxis after being injected with her first doseof Process 2 Lot EE8403. By July 2021, Pfizer was more candid and expansiveon the anaphylaxis casualties, including nine deaths in the post-marketing
injection campaign.
Pfizer said ‘The most frequently reported relevant PTs (preferred terms) (≥2%),from the Anaphylactic reaction SMQ (Broad and Narrow) search strategy
were: Anaphylactic reaction (435), Dyspnoea (356), Rash (190), Pruritus (175),
Erythema (159), Urticaria (133), Cough (115), Respiratory distress, Throat
tightness (97 each), Swollen tongue (93), Anaphylactic shock (80), Hypotension(72), Chest discomfort (71), Swelling face (70), Pharyngeal swelling (68), and
Lip swelling (64).’
Despite this clear danger signal Pfizer reported:75

Australian Medical Professionals Society‘Conclusion: Evaluation of BC cases Level 1–4 did not reveal any
significant new safety information. Anaphylaxis is appropriately describedin the product labeling as are non-anaphylactic hypersensitivity events.
Surveillance will continue.’
Pfizer post-marketing data showed to April 2022 the following numbers ofvictims known to them to have suffered:Anaphylactic reaction 7,214
Anaphylactic shock 1,184
Anaphylactoid reaction 197
Anaphylactoid shock 8
Anaphylactoid syndrome of pregnancy 1
Pfizer took keen interest in more lethal lot numbers.
Lot ER7449, used in Sunshine Hospital, Victoria, Australia, in early 2021,
caused >3% anaphylaxis. The TGA let Lot ER7449 be used without endotoxin
testing.
Other lots are listed, including EP9605, ER2659, ER9480, and EY2173, as
leading causes of anaphylaxis, injury and death.
A prospective study at Mass General Brigham Hospital in the US followed
employees who received their first dose of an mRNA COVID-19 vaccine
between December 16th 2020 and February 18th 2021. Follow-up was limited
to three days. 25 929 people (40%) received the Pfizer-BioNTech vaccine and
38 971 (60%) received the Moderna vaccine. Interestingly, 19% of staff did notcomplete any of the survey methods used. Acute allergic reaction symptoms
ensued, including itching, rash, hives, swelling, and or respiratory symptoms.
Note that this does not match Pfizer Preferred Terms.
Anaphylaxis was confirmed in 16 employees, 7 from Pfizer and 9 from Moderna.
94% of the anaphylaxis victims were female. The mean time to anaphylaxis
onset was 17 minutes (and the range was 1 minute to 120 minutes).
3.1.2

Plausible links to the known risk of myocarditis

Can endotoxin contamination be considered a novel mechanism ofvaccine-induced cardiac damage, independent of the mRNA or LNP
constituents of the COVID-19 vaccines? Alternatively, may LPS prime forlater cardiotoxicity or vaccine-associated myopericarditis (VAM)?
The classic profile of those affected by VAM, from its recognition in April
2021, includes younger males, often after their second dose. Having said that,76

Too Many Deadmyocarditis and pericarditis can also occur in males and females of all ages, andcan follow booster doses. The time interval from the last dose can be days toweeks.
VAM appears to involve a CD4-predominant lymphocytic infiltrate of the
myocardium, rather than an eosinophilic or other form of infiltrate, compatible
with a hypersensitivity myocarditis. Current thinking is that VAM may be an
autoimmune phenomenon. Baumeier et al. (2022) examined vaccine-derived
spike protein in cardiac biopsies and postulated that the inflammation was areaction to that foreign antigen. Barmada and colleagues (2023) used unbiased
immune sampling techniques in a case-control study of VAM sufferers,
versus vaccinees without VAM. These investigators showed a pattern, on
peripheral blood markers, of cytokinopathy including IL-1 and IL-15, withaberrant cytotoxic lymphocytes. Although cardiac tissue was not examined,
the absence on peripheral blood samples of cardiac autoantigens or clonal
expansion of B and T lymphocytes led these authors to conclude that immune
and pro-inflammatory mechanisms other than classical autoimmunity were
engaged.
More recently a research article titled ‘Sex-specific differences in myocardial
injury incidence after COVID-19 mRNA-1273 booster vaccination’ publishedin the European Journal of Heart Failure in 2023 (Beurgin et al.) was aprospective study of 777 healthcare workers receiving boosters. Elevated day-3
troponin was detected in 40 cases, but after adjudication, 22 were felt to haveno explanation for myocardial injury other than the Moderna vaccine exposure.
Interestingly, women were overrepresented in this sample. It is important
to delineate this condition, in which cardiac myonecrosis is detected by an
ultrasensitive blood test. This finding represents cardiotoxicity with more study
required to determine long-term implications; it should not be ignored andshould be taken very seriously, but also should not be over-interpreted so as tosay 1 in 35 booster recipients suffer myopericarditis. This is because not all of
the criteria necessary for a diagnosis of myocarditis were examined and fulfilled.
Heart damage following vaccine dosing may also happen rapidly, in a hyperacutetimeframe, which may not be compatible with sufficient transcription of spike
protein from mRNA to produce direct cardiotoxicity, and is certainly earlier
than would be expected for the generation of antibodies to spike protein. In
one case, just one day following Bivalent Pfizer BNT162b2 (wild and BA.4-5)
injection, a man was rushed to hospital with dyspnoea, a heartbeat of 207, andblood pressure of 74 mmHg. This was his fifth. The diagnosis was myocarditis
with ventricular tachycardia.
On the other hand, deleterious cardiac effects can occur via direct endotoxin
damage and via cytokine injury. Endotoxin damage to the heart and its blood77Australian Medical Professionals Society
supply system cells from inflammatory interleukins is well known. A prospective
multinational study, limited by small sample size, showed that measured bloodendotoxin level >50 picogram/ml is a significant risk predictor of cardiovasculardisease. Experimental demonstration of heart damage by endotoxin inductionof IL-1β in mice confirmed amplification by positive feedback loop of the
IL-1β. Importantly, the latter induces expression of miR-155.
Upregulation of miR-155, which is detected in both SARS-CoV-2 infected
people and those exposed to endotoxin, is a predictor of myocardial damage
and inflammation, independent of COVID-19. During myocardial infarction,
miR-155 is sharply upregulated in macrophages in the heart muscle andreleased into the extracellular milieu within exosomes. These exosomes aredelivered to fibroblasts, and miR-155 downregulates proteins in the fibroblasts
that protect from inflammation and promote fibroblast proliferation. The
resulting impairment predisposes to cardiac rupture in infarction. The mostplausible explanation for why death from myocarditis is more prevalent inyounger people may be that older people have less muscle mass, which can bemeasured by the Sarcopenia Index, and therefore generate less miR-155.
Analysed airway biopsy samples showed young people have much higher
miR-155 than the elderly, dramatically increasing risk of myocarditis.
Endotoxin induces the calcium-independent NO synthase known as inducible
nitric oxide synthase (iNOS). Tachycardia with associated fever, hypotension
and lymphopenia is the expected outcome from injection of endotoxin, as has
been demonstrated in numerous carefully-managed clinical experiments where
human response is known to differ from that of mice.
Pfizer tells us that during their trial, ‘one (0.3%) participant in the Original-BA.1
30/30μg group reported a life-threatening (Grade 4) adverse event of atrial
fibrillation on day 1 of study vaccination’. Once Pfizer changed product to theProcess 2 injection, they reported 3,285 cases of atrial fibrillation plus 22,873
cases of tachycardia to April 15th 2022.
Pfizer uses numerous medical terms to dilute the high numbers of hearts
damaged by their COVID-19 products.
3.1.3

Plausible links to life-threatening organ dysfunction

Kidney and liver disease can be caused by endotoxin. It is known that Pfizer
reported excessive numbers of kidney injuries to April 2022, with the warning
signal of the thousands of cases spread over a plethora of specialist medical terms.
Endotoxin produces immediate damage to mesangial cells (MC) which maintainthe architecture and cellular communication and indirectly join in the glomerular
filtration rate for the correct functioning of the glomerulus of our kidneys.

78

Too Many DeadIn 1952 it was found that rabbits pretreated with cortisone suffered multiple
organ damage, particularly in the kidneys after they were given a single
intravenous injection of gram-negative bacterial endotoxin. In 1968, Starzl
demonstrated destruction of human kidneys from suspected endotoxin
priming, reported in three transplant patients. Researchers used immortalizedcommercially available mouse kidney cells to show that some particularinflammatory cytokines, including the Apex Cytokine IL-1β and Interleukin
36β (IL-36β), were upregulated in response to LPS exposure. This leads to
NLRP3 inflammasome formation and the activation of the IL-17/IL-23 axis
in kidney tissue, which in turn induces an increase in the inflammatory andfibrotic factors in tubular epithelial cells that allow the formation of tubulointerstitial lesions (normally associated with chronic kidney disease). A strong
effect was found with a concentration of 100 picogram/ml.
Pfizer also knew from its clinical trial and first 90 days of mass vaccination thatsevere and fatal liver failure was a clear signal of systemic assault by its productwith 70 cases and five deaths. The median onset of the liver damage was justthree days. It is the author’s opinion that many cases of vaccine-induced liver
failure are likely hidden under ‘sepsis’ and ‘multi-organ failure’ and few autopsies
appear to have been conducted. Pfizer uses 122 different, often vague, descriptorsof liver damage under the heading ‘hepatobiliary disorders’ apparently to hidetens of thousands of victims.
MiR-155 upregulation by endotoxin may be a causative factor in rapid liver
failure or dysfunction after Pfizer immunisations. In 2007, endotoxin was shownto cause fatal liver damage in E-miR-155 transgenic mice and their wild-type
littermates. Death was hastened by administration of D-galactosamine; rats
given galactosamine showed elevated activities of serum alkaline phosphatase(ALP), alanine aminotransferase (ALT), levels of triglycerides, total cholesterol,
lipid-peroxidation and reduction in the levels of serum total proteins, albumin
and cellular glutathione S-transferase (GSH). Galactosamine increased thenuclear translocation of NFκB and elevated iNOS protein expression, tumour
necrosis factor (TNF-α), interferon (IFN-γ), inflammatory interleukins IL-1β,
IL-6, IL-12, IL-18 and decreased anti-inflammatory IL-10 mRNA expressions.
The mechanism of endotoxin-galactosamine liver toxicity involves attack of theKupffer cells by endotoxin attachment to Toll-like receptor 4 (TLR4).
It is known that TNF-α enhances the liver toxicity of miR-155, which is thought
to directly target transcript coding for several proteins – Fas-Associated Death
Domain protein (FADD), IκB kinase ε (IKKε), and the receptor (TNFR superfamily)-interacting serine-threonine kinase 1 (Ripk1). Eμ-miR-155 transgenic
mice produced higher levels of TNF-α when exposed to endotoxin, promoting
the positive feedback loop.

79Australian Medical Professionals Society
Do the mRNA vaccines upregulate galactosamine in humans, thereby increasing
the lethality of the endotoxin miR-155 pathway? Do the vaccines alter theuridine pathways in humans? Are there racial differences in liver damage by thevaccines that might relate to galactose pathways? Will women suffer more liver
damage and fatality than men related to galactosamine involvement in follicle-stimulating hormone and luteinizing hormone? It is hoped that answers tothese questions will unfold in the near future.
3.2

Plausible roles for endotoxinaemia relating to
non-lethal events

3.2.1

Lymphadenopathy

Did BioNTech-Pfizer and complicit marketers of their COVID-19 injection,
including government departments around the globe, lie to the public when
they said that the lipid nanoparticles and the mRNA floating inside or outsidethem would simply enter muscle cells in the arm, take them over and force
them to become factories for synthetic spike generation and resultant anti-spike
antibodies? A lecture given by one of the BioNTech founders makes it clearthat the actual target of the vaccine contents is the draining lymph nodes andthe wider lymphatic system, including the spleen.
Once the mass injections began, others associated with the Pfizer vaccine
design were quite straight with the intended action on the lymphatic system.
In an interview of September 2021, Professor Drew Weissman, said:
LNPs encapsulate the mRNA, protecting it from extracellulardegradation, and facilitate endosomal release of the mRNA into thecytoplasm. When the LNP-Ψ-mRNA is injected into the muscle, every
cell takes it up, but for muscle cells that is very inefficient, you can barely
measure the protein that they make. The LNPs are 80nm in size, which isabout the size of a virus. What happens is that the LNPs travel throughthe lymphatic drainage to lymph nodes, and in the lymph nodes, DCs(Dendritic Cells) take them up. There is also an infiltrate of lymphoidcells into the muscle that picks up the particles. Once the vaccine getsto a lymph node, the DC translates the mRNA and presents it to B andT cells to activate them, and that is how the immune response is started.
From the Pfizer Clinical Trial, it was known that lymphadenopthy was asmall-scale problem caused by their mRNA vaccines made with Process 1,
but there was an explosion of cases when they moved to Process 2 because of
the endotoxin from the E.coli bacteria used in production. As expected, thelymphadenopathy is usually detected on the side of the body that received theinjection.

80

Too Many Dead
A trial document obtained from the UK government said, ‘Lymphadenopathy
is identified as an adverse reaction for BNT162b2 vaccine.’ During the blinded
placebo-controlled follow-up period, 9 and 2 participants in the BNT162b2
and placebo groups reported AEs of lymphadenopathy, respectively. That isabout 0.041 % of the roughly 22,000 who received the Process 1 injection.
The numbers increased in a follow-up study to one month after the second doseof Process 1 vaccine.
By the trial interim cut-off date of 14 November 2020, Pfizer reported thefollowing data to regulatory authorities in Japan:‘In the Phase II/III part of foreign Study C4591001, serious adverse
events occurred in 126 of 21,621 subjects (0.6%) in the Comirnaty groupand 111 of 21,631 subjects (0.5%) in the placebo group.’
The incidence of lymphadenopathy was 0.3% (70 of 21,621 subjects) in the
Comirnaty group and 0.0% (7 of 21,631 subjects) in the placebo group. Amongthese events, those in 50 subjects in the Comirnaty group and four subjects in theplacebo group were considered related to the study vaccine. Lymphadenopathy
mostly occurred in the arm or neck. Many of the events occurred within two
to four days after study vaccination, but those in 12 subjects in the Comirnaty
group and three subjects in the placebo group occurred ≥8 days after vaccination(98 days at the latest). One subject in the Comirnaty group experienced
lymphadenopathy within 30 minutes of vaccination. The event in one subject
in the Comirnaty group was serious and considered related to the study vaccine,with the outcome of ‘not recovered’ (data cutoff date: November 14, 2020).
The lymphadenopathy problem in Australia is large, as shown by the Databaseof Adverse Event Notifications. By 23 August 2021 there were 1,126 cases.
Worldwide, Pfizer reported the following case numbers to April 2022:
lymphadenopathy, 73,287 (5.44 % of all adverse reaction reports); lymph-node
pain, 8,395; vaccination-site lymphadenopathy, 4,540; lymphadenitis, 2,115;
lymphoedema, 75; lymphopenia, 274.
Also related are cases of lymphadenopathy that can arise as a result of:
Epstein-Barr virus infection reactivation, 115; Epstein-Barr virus infection,
109; Epstein-Barr virus antibody positive, 26.
In a huge Israeli study with the vaccinated and control groups each including amean of 884,828 persons, lymphadenopathy (Risk Ratio, 2.43; 95% CI, 2.05 to
2.78; Risk Difference, 78.4 events per 100,000 persons; 95% CI, 64.1 to 89.3)
stood out as one of many harms of the Pfizer injection.
Case reports include lymphadenopathy imaged in the right breast of a woman just81Australian Medical Professionals Society
one day after her Pfizer COVID-19 injection. Another Pfizer injection case oflymphadenopathy was reported detected at three days in a 72-year-old woman.
Lymphadenopathy was detected during mammograms and the incidence
increased with subsequent injections. As stated earlier, lymphadenopathy isrecognized as one of many IgG4 diseases caused by multiple injections. Buried
in an EMA document, we see 14% of people reporting adverse reaction to
Pfizer boosters suffered lymphadenopathy.
Endotoxemia researchers in Scotland found that the Odds Ratios (OR [95%CI]) for the occurrence of splenomegaly and cervical lymphadenopathy were1•19 [1•01–1•4] and 1•16 [1•02–1•35] respectively for every 10 picogram/mL
increase in plasma endotoxin concentration.
Reviews of the explosion of lymphadenopathy have found incidences of cancer
reactivation and metastasis and called for extra care in imaging interpretation,
to distinguish reactive lymph nodes from metastatic lymph node enlargement,
especially in patients with underlying malignancy.
Pfizer boosters were associated with rapid progression of angioimmunoblastic
T-cell lymphoma in a detailed case report.

4.

Other mechanistic insights

4.1

Immune tolerance and endotoxinaemia

Multiple vaccine doses have been shown to induce immune tolerance to
SARS-CoV-2 Spike Protein COVID-19 by IgG4 switching. WHO is officiallyworried about IgG4 class switching weakening the human immune system.[4]IgG4-related disease (IgG4-RD) was formerly known as IgG4-related
systemic disease. It is a chronic inflammatory condition characterized bytissue infiltration with lymphocytes and IgG4-secreting plasma cells and,various degrees of fibrosis resulting in tissue scarring. IgG4 diseases include
lymphadenopathy, atopic dermatitis, autoimmune pancreatitis, bronchialasthma, Riedel thyroiditis, interstitial pneumonitis, interstitial nephritis,
prostatitis, retroperitoneal fibrosis, inflammatory aortic aneurysm and manyclotting diseases or other blood disorders.
Gram-negative endotoxin is clearly implicated in IgG4 disease. Galectin-3 isan antigen associated with IgG4 disease that drives expansion of circulatingplasmablasts and CD4+ cytotoxic T cells in patients and is a marker of severe
COVID-19. Galectin enhances the endotoxin inflammatory response. Galectin
was shown to allow low endotoxin (LPS) concentrations (1 µg/mL without
serum, 1 ng/mL with serum) to upregulate CD11b expression and reactive
oxygen species (ROS) generation on human neutrophils in vitro and drastically82

Too Many Deadenhanced the binding efficiency of LPS to the neutrophil surface.[5]During his lecture and in private discussion afterwards at the AMPS event heldin Melbourne on May 31st 2023, Emeritus Professor Robert Clancy mentioned
the dangers of IgG4 disease and melanoma, and other cancer susceptibility.
Other research groups have found that IgG4 suppresses our capacity to fightoff cancers and actually accelerates breast and colorectal cancers and carcinogen-induced skin papilloma.
4.2

Carcinogenesis and endotoxinaemia

Case reports point to both sudden onset of cancer and rapid deterioration ofpatients with dormant cancer after mRNA immunisation. Pfizer used 468
different descriptors under the heading ‘Neoplasms benign, malignant andunspecified (including cysts and polyps)’; these included 35 different typesof lymphoma and 14 different terms for leukaemia. Unfortunately, the term
‘turbocancer’ appears to have been injected into the public lexicon, subsequent
to the vaccine roll-out, to convey the rapidity of cancer progression being
observed. This notion gained strength when Prof Angus Dalgleish, a prominent
UK oncologist and cancer biologist who incidentally worked alongside Dr Fauciin the USA in the 1970s, spoke out in late 2022. He raised the alarm about aprominent pattern seen his practice, of reactivations of previously controlled
cancer after mRNA boosters.
Cancer can be caused by a plethora of environmental exposures, including
some drugs, sometimes in combination with genetic vulnerabilities, according
to the ‘multi-hit hypothesis’. It is difficult to imagine a more disturbing scenario
than a worldwide program of vaccination enhancing carcinogenesis. However,plausible mechanisms exist, in particular the ability of spike protein, which
once trafficked into the nucleus is able to modulate P-53 (suppression) andBRCA (activation) in a deleterious way, shown in a cell culture experiment; thisfinding has thus far not been replicated in vivo but is concerning nonetheless,since any medical student would be able to confirm that P-53 and BRCA are
respectively among the most well-known and important tumour suppressor
genes and oncogenes. As previously noted, miR-155, which is upregulated byendotoxin, is also capable of promoting and exacerbating cancer. To illustrate,
genetically-modified mice that over-express miR-155 are bred for the study ofblood cancers.
In the author’s opinion, in the light of effects on mIR-155, we may expect to seea surge in diffuse large B-cell lymphomas, Hodgkin’s lymphoma and subsets
of Burkitt lymphomas (latency type III Epstein–Barr virus-positive Burkitt
lymphomas) resulting from the COVID-19 mRNA vaccines.

83Australian Medical Professionals SocietyOver-expression of miR-155 in humans has also been demonstrated in solid
tumours such as breast, lung, and colon cancer. In lung cancers in particular,
over-expression of miR-155 is an indicator of a bad prognosis. Contaminant
endotoxin in mRNA vaccines alters the microRNA suite and should beconsidered as a contributor to reactivation of dormant cancer, as well as theinduction of so-called turbocancers.
Thousands of cancer victims were used in experiments for Pfizer vaccines inAustralia as a special subgroup of the AusVaxSafety 3-day surveys. In thecase of solid cancers, they suffered much more than the general population.
Children as young as 12 were used. About 1 in 40 victims reported having tovisit the emergency department of a hospital or a doctor within three days of
the injection. Blood cancer sufferers were treated as a separate group in theexperiments and suffered horrendous side effects. Pfizer used a number ofsubjects with pre-existing leukaemia in its trials and reported 14 extra cases inthose who received the BNT162b2 vaccine injection versus an extra 11 in theplacebo group.
4.3

Reproductive health

4.3.1

Women suffer disproportionately from COVID-19
vaccinations; effects on fertility

In a report from Pfizer, 68.5 % of the 1,348,079 Adverse Reaction cases toApril 15th 2022 were for females. In Australia the AusVaxSafety short-term
survey, which ceased on February 8th 2023, did not supply data differentiated bysex; however, 57% of respondents across all age groups were women.
The number of pregnant women reporting that they had to visit a hospitalemergency department or a doctor within three days of their vaccine dose
increased to 1 in 50 after the second dose and a similar number for subsequent
immunisations. The number of pregnant women reporting gastrointestinal
symptoms, (nausea, vomiting, diarrhoea, abdominal pain) after the second
injection was 14%, higher than the 11% for the general population.
The range of effects which form pharmacovigilance signals in women overlap
with endotoxin effects in women and include irregular menstrual cycles,
thrombosis, endometriosis, pregnancy loss, preeclampsia (which is the major
cause of maternal and prenatal death), autoimmune disease and developmental
damage to surviving children. In particular, LPS is implicated in thepathophysiology of endometriosis, in which LPS is found in menstrual blood.
Endotoxin and nickel both do their damage through attachment to toll-likereceptor 4 and it is very provocative that 93% of VAERS reports after Pfizer
immunisations from people known to have nickel allergy were women.

84Too Many Dead
There are numerous online reports and studies that have shown that live
births have fallen since the COVID-19 pandemic. While some may invoke
chronological links to lockdowns and economic uncertainty, leading tofewer marriages and conceptions, there is circumstantial evidence that massvaccination has contributed to reduction in live births. Pfizer reported 338
different types of ‘Reproductive system and breast disorders’ and this helps
to dilute the warning signals. Thousands of women have reported disrupted
menstrual cycles and even postmenopausal bleeding. Pfizer reported a caseof postmenopausal haemorrhage within seven days of vaccine administration
during the clinical trial.
Official data from authorities in The Netherlands showed a very clear warning
signal was available by December 1st 2021. Postmenopausal women have an
increased sepsis endotoxin mortality rate compared to pre-menopausal women.
Postmenopausal women and women with induced menopause resulting fromsurgical removal of the ovaries have reduced levels of B cells and anti-inflammatory cytokines, IL-4, and IFN-γ, while NK cell activity and levels ofpro-inflammatory cytokines such as TNF-α, IL-1β, IL2, and IL-6 are increased.
Direct effects of endotoxin on the uterus have been studied. Contrary to theprevious discussion of a protective role of oestrogen in blood, hitting uterine
macrophages with bacterial endotoxin induces production of biologically active
proinflammatory IL-1β. Cytokine storm as a potential consequence of endotoxin-contaminated immunisation is by now well understood.
Shedding of the uterine lining during menstruation is primed by reduction inprogesterone expression. The RelA(p65) subunit of NF-κB and the progesterone
receptor interact with each other, exhibiting mutual repression. Activation of
NF-κB by TNF-α also results in repression of the progesterone receptor (PR),while PR is able to repress TNF-α-induced NF-κB activity. Decidual casts may
occur when the cessation of progesterone levels results in loss of support forthe decidualized endometrial lining. Definitive proof that endotoxin reduces
progesterone was performed by measuring the association between systemiclevels of lipopolysaccharide-binding protein (LBP), a marker of endotoxin
exposure, and levels of inflammation in the ovary (follicular fluid IL-6), plus
steroid hormone production in 45 women undergoing IVF treatment.
Endotoxin is known to hit this system so hard that it leads to the permanent
reduction of the primordial follicle pool, commonly known as the egg reserve.
Estradiol attenuates endotoxin poisoning in human peripheral blood monocytes
and macrophages. High doses of estradiol enhance endotoxin-induced IL-1β
expression in an oestrogen receptor-dependent manner. Increased cycle
length and increased menstrual flow may also be expected through effects on
hypothalamus pituitary gland ovaries axis (HPO), follicle stimulating hormone

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Australian Medical Professionals Society(FSH), luteinizing hormone, oestrogen and progesterone balance (repeatedvaccine dosing).
Numerous animals have been used to demonstrate these effects. Looking
at in vivo study of the effects of injected endotoxin in mice, infused intraperitoneally with 100 μg of ultrapure endotoxin (LPS) from E. coli serotype
O111:B4 (invivogen), effects are dramatic and dose-dependent. Endotoxin
results in atretic ovarian follicles, that is, follicles that will be resorbed after
being prevented from maturing. In this work, the attack of the endotoxin on
the toll-like receptor 4 (TLR4) was proved by using knockout mice. Similar invitro effects of endotoxin on cows and rats have been reported. Both motherand foetus are threatened by endotoxin circulating in their systems as shown inthe scheme in Figure 5.[12]The focus now shifts to the way endotoxin and LPS attack the human female
reproductive system and the developing placenta and embryo. There areacknowledged underreported US VAERS data to August 22nd 2022. In an

Figure 5This scheme demonstrates that Interleukin 1-beta, IL-1β is the ‘apex cytokine’ in the
inflammatory cascade of preterm birth and foetal inflammatory injury. As discussed earlier thiscytokine is known to self-amplify, defying Paracelsus dogma. PR-A/PR-B = P4 Receptors A
and B; PGs = Prostaglandins; MMPs = Matrix MetalloProteinases; PGF2α = Prostaglandin F2α;PLV = Periventricular Leukomalacia; BPD = BronchoPulmonary Dysplasia; NEC = Necrotizing
EnteroColitis. Increasing colour intensity represents increasing inflammatory response.

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excellent interactive guide to the effect on pregnancies of the Janssen, Modernaand Pfizer COVID-19 injections, Openvaet1 found 15 maternal deaths and908 foetal deaths after Pfizer by extracting symptoms in the text of VAERS
reports. Also, 27.5% of the reported missed pregnancies happened within one
week of the injection.[13] To April 15th 2022, Pfizer reported that 1.3% of
all adverse-event cases after their injections were from 17,156 pregnant orbreastfeeding women. Pfizer spread these adverse events under 179 different
descriptors under two main headings: ‘Injury, Poisoning and Procedural
Complications’ and ‘Pregnancy, Puerperium and Perinatal conditions’.
Endotoxin exposure to women and their developing foetuses may result inchorioamnionitis with fever, leukocytosis, tachycardia, uterine tenderness
and preterm rupture of membranes. First trimester disruption of placenta
formation can be caused by LPS and involves MAPK signalling pathway
phosphorylation and resultant increase in IL-8 and IL-6. Endotoxin damages
spiral artery remodelling, correlated with monocyte chemokine-1 (MCP-1),
and down-regulates markers related to extravillous trophoblast invasion inplacentas according to the following scheme.[14]MyD88 = Myeloid differentiation 88, IRAK = Interleukin Receptor
5Associated Kinase, TLR4 = Toll-Like Receptor 4, ERK = Extracellular

Figure 61

https://www.openvaet.org

87Australian Medical Professionals Society
signal-Regulated Kinase, TRAF6 = TNF Receptor Associated Factor 6,
MAP3Ks = Mitogen-Activated Protein Kinase 3, MEK = MAPK Extracellularsignal-regulated Kinase, p38 = p38 Mitogen-Activated Protein Kinase, JNK= c-Jun N-terminal Kinase. Note that TLR4 is a target for COVID-19,
endotoxin and nickel, all of which elevate IL-8.
Mouse models show what injected endotoxin does at a dose of 10 µg/kg
Gestational Day (GD) 13, and 40 µg/kg LPS daily until GD16. Endotoxin
substantially elevated the percentage of CD86 +, TNF-α+, IL-1β+ and iNOS+dMφ (M1 subtype) but diminished the percentage of CD206 +, CD 163+,
IL-10+ and Arg-1+ dMφ (M2 subtype) in pregnant mice. Endotoxin damages
spiral artery remodelling, correlated with monocyte chemokine-1 (MCP-1),
and down-regulates markers related to extravillous trophoblast invasionin placentas. Decidual macrophages (dMφ) are the second most abundant
immune cells in pregnancy and are adversely affected by endotoxin, resulting
in miscarriage and foetal growth restriction.
Women suffering preeclampsia, the major cause of maternal and perinatal
death, experience hypertension, proteinuria, headache, vomiting, and kidney
and liver dysfunction. They have increased numbers of circulating leukocytes,
neutrophils, and serum levels of TNF-α, interleukin-6 (IL-6), and C-reactive
protein (CRP).This is found in endotoxin poisoning. Women with preeclampsia
have lower levels of anti-inflammatory interleukin 10 (IL10).
Human placentas from women with and without preeclampsia have been
compared. They showed that reduced α7 nicotinic acetylcholine receptor(α7nAChR) is involved in the endotoxin response of exaggerated inflammation.
The level of choline acetyltransferase (CHAT) was reduced in women withpreeclampsia and endotoxin-treated mice.
Endotoxin is routinely used to induce preterm birth, and sheep are a favoured
test animal where the endotoxin can be administered via various routes,
including injection into the uterus or amniotic fluid. Preterm birth is a major
cause of perinatal death and long-term morbidity.
Pfizer has an ongoing teratology study to see what effects their COVID-19
immunisations have on surviving foetuses of mothers who are injected
before or during pregnancy. Intergenerational infertility is a major concern.
Teratogenic damage by endotoxin to ovaries is well known. Pregnant rats
hit with intraperitoneal endotoxin injections produced intrauterine growth
restriction and substantially lower levels of serum anti-Müllerian hormone in
offspring. To April 2022, Pfizer reported 376 different types of ‘Congenital,familial and genetic disorders’. A case of a woman injected with Pfizer whochose to terminate her pregnancy at 25 weeks’ gestation because heart damage88

Too Many Deadwas detected in her developing child is seen in VAERS.
It will take perhaps 20 years to learn the full extent of what will happen in thesecond-generation descendants of these injections.
4.3.2

Endotoxin reduces male fertility

Male infertility has attracted less attention. So far I have found two casesof hypogonadism listed as adverse events during the Pfizer injection trial.
Pfizer reported numerous cases (numbers in parentheses) of testicularadverse events in a document obtained by FOI demand to the Therapeutic
Goods Administration in Australia. These included testicular pain (385),swelling (90), infertility (60), orchitis (52), haematospermia (43), orchitis
noninfective (20), disorder (22), testis discomfort (16), torsion (10),oedema (4), cyst (3), infarction (3), abscess, haemorrhage, injury, neoplasm,
retraction (two cases each), atrophy, hypertrophy, mass, microlithiasis,
necrosis, oligoasthenoteratozoospermia, orchidectomy, spermatozoa
abnormal (4), sperm count decreased (9), sperm analysis abnormal,
spermatic cord haemorrhage, semen discolouration (1 case each).
GELDING (Gut Endotoxin Leading to a Decline In Gonadal) function
is an appropriate term used for endotoxin reduction of testosterone
production by testes, both by direct inhibition of Leydig cell steroidogenic
pathways and indirectly by reducing pituitary luteinizing hormone drive,
leading to a decline in sperm production.
A formal prospective study of 37 sperm donors found vaccine-induced
damage three months after the Pfizer dose. A fever of 39°–40°C, as routinely
observed in Pfizer trials and mass rollout, causes reduced total sperm count,motility rapidly progressive: grade a and slowly progressive grade b, andvitality for 79 days for a volunteer semen donor. A fever of 38°-39°C is
sufficient to produce reduced sperm head size. Complete absence of sperm,
azoospermia, can result from fever. Data from the AusVaxSafety survey
show that adolescents report more fever than the older vaccine recipients.
Endotoxin in mRNA injections likely attacks male fertility by creatingcytokine storm in the human testis and epididymis, reducing messenger
RNA and Type 1 parathyroid hormone receptor (PTH1R) expression.
Oxidative stress has been identified as a factor in low sperm count.
Pro-inflammatory mediators in inflammatory reactions can induce a
respiratory burst resulting in oxidative stress. The reactive oxygen species(ROS) formed can lead to leukocytospermia. Elevated nitric oxide levels
associated with leukocytes can damage sperm.

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Pfizer reported prostatitis (99), prostatic specific antigen increased (63),
prostate cancer (35), benign prostatic hyperplasia (26), prostatomegaly (18),
prostatic disorder (13), prostatic pain (8) and 17 other prostate disorders.
Pfizer also reported balanoposthitis (22), genital tract inflammation (19),ejaculation disorder (18), ejaculation failure (18), oedema genital (18), organic
erectile dysfunction (17), priapism (17), spontaneous penile erection (15),genital blister (16), genital erythema (16), genital paraesthesia (16), penile
oedema (13), varicocele (13), genital lesion (11), penile haemorrhage (11),genital hypoaesthesia (10), penile erythema (10), penile vein thrombosis (10),scrotal erythema (10), Peyronie’s disease (9), genital discharge (8), painfulerection (8), penile blister (7), penile rash (7), scrotal oedema (7), penile size
reduced (6), painful ejaculation (5), penile discharge (5), penile discomfort(5), epididymal cyst (4), penile exfoliation (4), penile haematoma (4), scrotal
discomfort (4), (2). Oedema, varicocele and testicular torsion listed above
can cause destruction of sperm via antisperm antibody (ASA) attack, whichis permanent. Pfizer reported the instances of blood testosterone abnormal,
blood testosterone decreased, (15), and blood testosterone increased (8).
4.3.3

Lactation issues

There have been shocking accounts of Pfizer injection damage to breastfeeding
mothers and their babies in VAERS reports. There have been mothers drying
up or noting their blue or green milk, or haemorrhaging while collecting,having decided to pump and dump, rather than risk the child. At least thatallows mothers to take painkillers for their own symptoms. One study foundthat 1 in 60 women suffered green breast milk after their mRNA vaccine
injection. Another study found 1 in 88 breastfeeding women suffereddiscoloured milk. Haeme-based enzymes can cause this effect. Haeme
peroxidases lactoperoxidase (LPO) and myeloperoxidase (MPO) are present
in human milk, with LPO dominating a few days after beginning lactation.
4.4

Endotoxin readily enters the brain

It has been demonstrated that NADPH oxidase (NOX) and iNOS act insynergy to destroy brain cells. Endotoxin iNOS damages oligodendrocyte(OL) progenitor cells (OPCs) causing periventricular leukomalacia (PVL),
the most common form of brain damage in premature infants via microglia.
Endotoxin injection of mice sees elevation of iNOS with associated memory
loss and amyloidosis linking it with Alzheimer’s disease.
Endotoxin creates quinolinic acid, in a series of steps from tryptophan,which acts as a neurotoxin, gliotoxin, proinflammatory mediator, pro-oxidant
molecule, and can alter the integrity and cohesion of the blood–brain
barrier. Quinolinic acid is linked to Alzheimer’s disease, HIV-associated90Too Many Dead
neurocognitive disorders, Parkinson’s disease, motor neurone diseases,
Huntington’s disease, multiple sclerosis and major psychiatric disorders.
Further aspects of brain damage are discussed under the heading of
autoimmune diseases caused by endotoxin.
The most common symptom after the injection is headache and this is easily
understood because human volunteers have literally had their heads examinedwith magnetic resonance imaging, showing their brain temperature increases
very quickly,[8] creating fatigue, headache, with muscle pain, fever and chills.

Figure 7
Female volunteers with or without fibromyalgia suffered increased brain temperature when
injected with 300 to 400 picograms/kg of endotoxin.

Other effects of endotoxin on the brain include narcolepsy, also known as
jypersomnia, with a well-defined mechanism shown in the figure.[9] Narcolepsy
is often accompanied by increased feelings of anxiety and fear, and this has
been produced experimentally with endotoxin in animals. Further study is91Australian Medical Professionals Society
needed in relation to other brain dysfunctions, including multiple sclerosis.
Pfizer reported the following case numbers after their injections: tremor
10,748; resting tremor 21; balance disorder 4,497; movement disorder 2,736;
gait inability 1,678; bradykinesia 197; Parkinson’s disease 105; Parkinsonism
44; Parkinsonian gait 6; Parkinsonian rest tremor 4; cogwheel rigidity 4;
Parkinsonian crisis 1; vascular Parkinsonism 1.
Insulin resistance is a pathological hallmark in the brain of Parkinson’s disease
sufferers. Pfizer has thousands of cases of diabetes listed in its adverse-event
reports, spread under 35 different headings. When endotoxin was injected
into the right striatum of male wistar rats, it predictably impaired motor
performance of the animals, and increased the levels of α-synuclein andtoll-like receptor 4 (TLR4). Endotoxin also reduced mRNA levels of IRS1 and
IRS2 and enhanced GSK3β mRNA and protein, indicating the developmentof insulin resistance.

Figure 8

Facial and peripheral paralysis were recognized early as neurological adverse
reactions after Pfizer injection trials. Endotoxins from gram-negative E. coli
bacteria, as used in Pfizer injection production, cause paralysis via molecular
mimicry between bacterial glycoconjugates and peripheral nerve gangliosides.
Guillain-Barré syndrome (GBS), possibly arising from a similar assault on thebrain to narcolepsy, has been associated with gram-negative bacterial infection
with campylobacter jejuni and mycoplasma pneumoniae. The endotoxin does

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Too Many Deadthe damage and focus has been on the polysaccharide with explanations at
molecular mimicry level. Those from campylobacter jejuni are associatedwith Guillain-Barré Syndrome patients’ gangliosides. Influenza vaccine dose
endotoxin levels have been correlated with GBS cases reported to VAERS.
Bell’s palsy after nasal influenza treatment led to cancellation of the productwhich used escherichia coli endotoxin as adjuvant.
Microglia are usually in a resting state (M2), but in Parkinson’s disease canenter M1 state because of the presence of α-synuclein aggregates. The M1
microglia release pro-inflammatory factors, including TNFα, interleukin(IL)-1β and IL-6, which can cause motor neurones to die. This is due to the
known epigenetic effects of endotoxin.
Dying cells can release factors to increase the activation of M1 microglia,
leading to a positive feedback loop which causes continually increasing cell
death.
α-Synuclein aggregation leads to dopaminergic neuronal cell death throughdisruption of mitochondrial function.
The substantia nigra has a high density of endotoxin-targeted microglia andis particularly sensitive to a challenge with endotoxin. Figure 9 is adaptedfrom an article discussing long COVID;[10] it can also be used to appreciate
how endotoxin will cause a wide range of problems from altered taste toheart palpitations.
Altered taste was reported to VAERS, especially in people who have nickel
allergy, after mRNA injections. This might be related to the fact that nickel
also uses the TLR4 pathway in its toxic mechanism.
When endotoxin enters the central nervous system, it damages finely-tuned
feedback loops that are involved in the sympathetic nerve pathways. This
explains the frequent reports of diarrhoea and vomiting after the injection.
The large scale AusVaxSafety survey of millions of injected Australians
found 1 in 9 people reported gastrointestinal symptoms, including
diarrhoea, vomiting, nausea and abdominal pain after the second dose. Thiswas considerably more than reports after their first dose. This indicates theirbodies were made allergic to components.
To April 2022, Pfizer reported 798 different ‘Nervous system disorders’ aswell as numerous serious effects under ‘General disorders and administrationsite conditions’.

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4.4.1

Blindness

Figure 9

Pfizer vaccine products cause 400+ different named eye disorders, according
to their own documents.
When lambs are injected with endotoxin the increase in temperature of
their corneas can be measured with great accuracy as a non-invasive methodof systemic inflammation monitoring. There are probably multiple mechanisms
behind the numerous causes of unilateral or bilateral blindness after mRNA
injection. A study that famously attracted the attention of Elon Musk foundhigher risk of all forms of retinal vascular occlusion in two years after vaccination,with an overall hazard ratio of 2.19 (95% confidence interval 2.00–2.39).
The Moderna and Pfizer vaccines were especially bad, compared to othervaccines. A total of 289 cases of vaccine-associated uveitis were reported
between 1984 and 2014. Pfizer caused 327 cases of uveitis to April 15th 2022.
Eye damage has been reported after mRNA COVID-19 injections, traced tomyeloperoxidase (MPO) anti-neutrophil cytoplasmic antibodies (ANCA).
Cytokine storm leads to increased expression of MPO.
VAERS records a 93-year-old woman in France made blind by her Pfizer
vaccine injection within 48 hours. She lost consciousness and died. The lot
number was EJ6788.
4.4.3

Tinnitus

A survey in Ohio USA in 2022 found 0.038% of all mRNA injection

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recipients suffered tinnitus after their first injection and 0.031% after
their second, making a combined incidence of 0.069% or 690 per million.
Numerous cases of tinnitus were reported during the Pfizer Clinical Trials,
and these should have been communicated according to informed consent.
Pfizer decided tinnitus was among numerous ‘Signals determined not tobe risks’ and The European Medicines Agency appears to have shared thisview, stating: ‘Hearing loss and Tinnitus: In the 3rd MSSR AR (covering
1 February until 28 February 2021), based on a cumulative review of thecases reporting Hearing loss and/or Tinnitus it was concluded that a causal
association with Comirnaty exposure was not suggested and the signal was
closed.’To April 15th 2022, Pfizer reported 14,233 cases of tinnitus, representing
1.06% of all people reporting adverse events after their injection. Pfizer also
reported huge numbers for 76 other ‘Ear and labyrinth disorders’. People
often suffer multiple symptoms. A case report of a previously healthy
Hong Kong man shows that two days after his first Pfizer injection he wasdevastated with fever, pain and vesicles in his right ear and canal, together
with vertigo, tinnitus and loss of hearing. He also suffered facial palsy, tongue
numbness and dysgeusia.
Specialists in the field have traced tinnitus to disruption of the nitric oxide
cycle,[11] a known effect of endotoxin.
4.5

Autoimmunity

Figure 10

Note the familiar cytokine storm that is indicated on the right-hand side of their figure,where acronyms are as follows: eNOS Endothelial Nitric Oxide Synthase; IFN Interferon; ILInterleukin; iNOS Inducible Nitric Oxide Synthase; NF Nuclear Factor; NMDA N-Methyl-D-Aspartate; nNOS Neural Nitric Oxide Synthase; TNF Tumour Necrosis Factor.

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Pfizer listed over 100 different kinds of autoimmune disease as adverse events.
Note that these include medical specialist diagnoses using specific disease names,
which helps to dilute the clear warning signal. Pfizer appears to show no interestin consolidating this list of suffering.
Monokine Induced by IFN-γ, (MIG, the G is for Gamma) and its receptor are
known to cause autoimmunity via a positive feedback loop involving recruitmentof Th1 lymphocytes which in turn stimulates MIG secretion from thyrocytes
and numerous other types of cells, creating an amplification of the autoimmune
process that destroys the organ.
E. coli endotoxin is a stimulator of MIG autoimmune response superior to thatderived from other types of bacteria.
There are numerous published reports of thyroid damage by COVID-19 andthe injections. One of these showed that the Pfizer injection causes damage tothe thyroid gland within two days. Endotoxin as a cause of thyroid diseases via
disruption of the kynurenine pathways has been reviewed.
Autoimmune diseases are caused by development of antiphospholid antibodies.
Endotoxin lipid A, as found in Pfizer injections, has been shown in rabbits to
induce antiphospholid antibodies, specifically systemic lupus erythematosus(SLE) type-aCL (β2GPI-dependent) and lupus anticoagulant.
We can learn from the effects of mass flu treatment. Repeated influenza vaccines
increase risk of autoimmune systemic lupus erythematosus (SLE), multiple
sclerosis and Guillain-Barré syndrome.
To April 15th 2022, Pfizer reported 1,461 cases of Guillain-Barré syndrome(GBS). Myelin-associated glycoprotein (MAG) antibodies attack the system.
It is known that broadly reactive influenza antibodies also bind other proteins
including insulin, endotoxin (lipoolysaccharide, LPS), and double-stranded
DNA (dsDNA), demonstrating a propensity for autoreactivity.
A key paper by Toshio Hirano covers his work in the identification and isolationof interleukin 6 (IL-6) and its role in autoimmune diseases, including arthritis.
[15] Th17 helper cells, an arm of the CD4+ T cell effector response, secrete
several proinflammatory cytokines, such as IL-17, and induce various chronic
inflammatory conditions, including autoimmune diseases. IL-6–mediatedsignalling via STAT3 increases the number of Th17 cells by enhancing RORγ
expression. Hirano demonstrated this IL-6 catalytic positive feedback loop to
non-immune cells in autoimmune disease involving NFκB and STAT3 ( JAK–
Signal transducer and activator of transcription 3).
In the case of IL-1β, which catalyses expression of itself by initiating a positive96

Too Many Deadfeedback loop, direct attack on multiple organs including the heart is observed.
4.6

The case of shingles

In August 2021, Queensland Health decided to tell us all about shingles withpictures. They used the occasion to promote Zostavax® vaccine which ‘is available
on prescription to people aged 50 to 69 years and from 80 years, but it must be
paid for by the patient.’ This followed a surge in shingles cases after COVID-19
injection. They said:
If you’ve had chickenpox in the past you can develop shingles. This isbecause the inactive chickenpox virus stays in your nerve cells near yourspine. When shingles develops it’s because the virus has become active
again. Usually, a person will only get shingles once in their lives, but it cansometimes occur again if you have a weakened immune system.
In 2021 a paper (censored by an editor after two reviewers gave it favourable
reviews) by Kevin McKernan et al. asserted that the reactivation of dormant
viruses, including shingles, by the mRNA injections is a worrying warning signal,and pointed to possible mRNA mechanisms. Several other papers appeared
linking increased numbers of case reports of shingles to Pfizer products.
By July 27th 2021, the European EudraVigilance database had reported 4,103
cases of shingles after receiving Pfizer Tozinameran, accounting for 1.3% of
total reported events after the injection, a much higher incidence than reported
after AstraZeneca, Janssen or Moderna. By January 2023 the European
EudraVigilance database had recorded 15,887 cases of shingles after BNT162b2,
accounting for 1.5% of total reported events.
Damage to the eyes is among the serious consequences of vaccine-induced
shingles. A report from Japan in August 2022 found synthetic spike in thegaping shingles wounds of one victim.
In the phase-I/II trial with BNT162b1 in 2020, a dose-dependent decrease inlymphocytes in the first days following injection was reported. This is before thechange of buffer from phosphate to tromethamine.
Two main signalling pathways have been implicated in shingles reactivation:1] PhosphatidylInositol-3 Kinase (PI3K)-Akt pathway
2] The MAPK pathway.
Depletion of nerve growth factor (NGF) receptor TrkA can lead to thereactivation of shingles in some in vitro experiments. Searching the literaturefor information on mechanisms of shingles is made a little more difficult by thelarge volume of research into the related herpesvirus, Kaposi’s-sarcoma-associ-97Australian Medical Professionals Society
ated herpes virus (KHSV) that causes cancer. This virus can also exhibit latency.
It is interesting that this virus encodes a microRNA known as miR-K12-11
that functions as an orthologue of cellular miR-155, providing a replicativeadvantage to the KHSV through the down regulation of the expression of genes
with known roles in cell growth and apoptosis. Endotoxin is clearly linked toshingles.
In 2014 sepsis patients, that is, those who had no exposure to COVID-19, carriedmuch higher measurable loads of herpes, Epstein-Barr and cytomegalovirus,
indicating the inflammatory cytokine storm caused by endotoxin weakened
their immune suppression of the dormant viruses.
4.7

Epigenetics of endotoxin

The epigenetic effects of endotoxin are widespread, altering expression of
hundreds of proteins in the human body. The complexity involves transformationof mast cells into numerous types of immune response cells triggered in responseto threats. These cells secrete toxins that are responsible for the cytokine storm
which can result in fatal sepsis.

5.

There are questions

Apart from endotoxin, are there other molecules from the E. coli floating aroundin variable amounts in the mRNA injection vials? (An example I have raised is
asparaginase.) Another worthy question: can Pfizer synthetic mRNA fragments
enter human DNA?
Did the change of buffer from phosphate to tromethamine affect the relative
toxicity? It is possible that the focus on apparent differences in lots, especiallythose used in Denmark, provides clues. However, there is a fundamental
problem of not having complete inventory for the number of doses actually used.
I have found that all lots which some people suspected were placebo-produced
symptom sets consistent with endotoxin poisoning.
There is an obvious need for more independent laboratory study.

6.

Root cause analysis and remedy:
some conclusions

The COVID-19 vaccine initiative appears to be suffering from a superficial
treatment of the contamination hypothesis and the biohazard which is endotoxin.
Had there not been evidence of an unparalleled burden of harms – attributable
to COVID-19 vaccines, whatever their apologists in industry and conflicted
approving-regulators may say to distract from the obvious and appalling
signals – and had there not been evidence of contamination and substandard

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Too Many Deadmanufacturing standards as evidenced by batch variability, then there may beless impetus to press on for clarity and scientific transparency with regard toendotoxin contamination of mRNA vaccines.
But the fact is, there is now abundant evidence for batch variation, including
differential rates of adverse events, raising basic concerns around Good
Manufacturing Practice (GMP). With a now worldwide focus on theimplications of Process 2 in unravelling this conundrum, we expect and demand
resolution on the endotoxin issue, by accurate measurement, both within the
Health Department and in independent laboratory settings.
As stated in the introductory section, the Laboratory Branch of the TGA use
the LAL assay (the horseshoe blood extract, limulus amebocyte lysate assay),which is suitable for large fragments only and which is impaired in the presenceof cationic lipids and mRNA. Combined, there is an obvious potential forunderestimation and masking of the actual concentrations of endotoxin in vials.
The remedy urgently required is to use more sophisticated tests based on mass
spectrometry after separation and volatile derivative formation. Unless theintention is that of a cover-up, there is simply no argument against a repeat phaseof testing. Remember, endotoxin is so poisonous that electronic sensors havebeen made that can detect femtogram levels, that is, a billionth of a millionth ofa gram. As stated earlier, I can also find no evidence that the TGA tests for thesupertoxin lipid A, and this must be remedied immediately.
We must remember there is a new context, in the form of the Commonwealth’s
stated intention to develop an onshore manufacturing capacity for new mRNA
vaccines, with partners such as Moderna. The sheer madness of failing to review
these issues fully and transparently in the interests of the people cannot be
overstated, and to proceed without the reckoning specified clearly here would
constitute reckless indifference.

7.

[1]
[2]
[3]

[4]

References

https://en.wikipedia.org/wiki/Paracelsus

Therapeutics Goods Administration Australia.
Mohammad S and Thiemermann C. 2020. Role of Metabolic
Endotoxemia in Systemic Inflammation and Potential Interventions.
https://www.frontiersin.org/articles/10.3389/fimmu.2020.594150/
full
WHO. 18 May 2023. Statement on the antigen composition of
COVID-19 vaccines. https://www.who.int/news/item/18-05-2023statement-on-the-antigen-composition-of-covid-19-vaccines99

Australian Medical Professionals Society
[5]

[6]
[7]
[8]

[9]

[10]

[11]
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[13]

[14]

[15]

[16]

Lopes Fermino M, et al. LPS-Induced Galectin-3 Oligomerization
Results in Enhancement of Neutrophil Activation. 2011. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0026004
Morris MC, et al. 2015. Innate immune programing by endotoxin
and its pathological consequences. https://www.frontiersin.org/
articles/10.3389/fimmu.2014.00680/full
Fajgenbaum DC and June CH. 2020. Cytokine Storm. https://www.
nejm.org/doi/full/10.1056/NEJMra2026131
Sandiego CM, et al. 2015. Imaging robust microglial activation after
lipopolysaccharide administration in humans with PET. https://www.
pnas.org/doi/10.1073/pnas.1511003112
Anderson G, et al. 2019. Multiple Sclerosis: Melatonin, Orexin, andCeramide Interact with Platelet Activation Coagulation Factors and
Gut-Microbiome-Derived Butyrate in the Circadian Dysregulationof Mitochondria in Glia and Immune Cells. https://www.mdpi.
com/1422-0067/20/21/5500
Shin Jie Yong. 2021. Persistent Brainstem Dysfunction inLong-COVID: A Hypothesis. https://www.ncbi.nlm.nih.gov/pmc/
articles/PMC7874499/
Pall ML and Bedient SA. 2007. The NO/ONOO- cycle as the
etiological mechanism of tinnitus. International Tinnitus Journal.
13(2):99-104.
Coler BS, et al. 2021. Landscape of Preterm Birth Therapeutics and a
Path Forward. https://www.mdpi.com/2077-0383/10/13/2912
OpenVaet. VAERS Domestic Data - Analyzing COVID Vaccine
impact on pregnancies, as per USA’s adverse effects watch Study
version 1.1, last VAERS report on 2022-08-26 https://openvaet.org/
studies/vaers_fertility?currentLanguage=en
Anton L, et al. Lipopolysaccharide induces cytokine production anddecreases extravillous trophoblast invasion through a mitogen-activated protein kinase-mediated pathway: possible mechanisms of firsttrimester placental dysfunction. 2012. https://academic.oup.com/
humrep/article/27/1/61/714411
Hirano T. 2010. Interleukin 6 in autoimmune and inflammatory
diseases: a personal memoir. https://www.jstage.jst.go.jp/article/
pvaccine dose/immunisation/injection/86/7/86_7_717/_article
Ju Y, et al. 2022. Anti-PEG Antibodies Boosted in Humans by
SARS-CoV‑2 Lipid Nanoparticle mRNA Vaccine. ACS Nano,
16:11769-11780.

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Dr Geoff Pain is a scientific consultant. In his
undergraduate years he studied biochemistry, chemistry,
physics, pure and applied mathematics and information
science.
With a PhD in Chemistry from Monash University
he worked at universities in Bristol, Cambridge andAdelaide, then returned to Monash. He was recruited
by Telecom Australia (now Telstra) for a special projectin optoelectronic semiconductor crystal growth. At
Telecom he completed a Graduate Diploma in Business
Management majoring in Strategy and Innovation. This
included qualification in the Law of Negligence. He wasthen offered a fellowship at UWA to work on plasma
deposition of materials in the Electronic Engineering
Department. NEC Japan invited him to the Microwave
and Satellite Communications Systems division in
Yokahama for a three-year appointment. He ran amarket research business investigating environmental
pollution and toxicology projects for government andcommunity groups. Widely published, he has been
granted patents.
Geoff Pain’s Substack is:https://geoffpain.substack.com

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Sequencing of bivalent
Moderna and Pfizer mRNA
vaccines reveals nanogram tomicrogram quantities of
expression vector dsDNA
per doseby Kevin McKernan, Yvonne Helbert,
Liam T. Kane, Stephen McLaughlin

Introduction
Scientist Kevin McKernan has had an illustrious career in molecularbiology and biotechnology, including managing Research &Development for the Human Genome Project. His story of incidentally
discovering contamination of a serious nature in vials of bivalent
COVID-19 vaccine has become very well known worldwide and has
begun to prompt laboratory work to confirm the findings and broaden
analysis. Although, as McKernan et al. point out, the initial andsubsequent laboratory steps recorded here are not without limitation,
including the fact that some expired vials were studied. However,
McKernan correctly called for attempts in independent laboratories
to reproduce or falsify his work. As of this writing, Professor Phillip
Buckhaults, a molecular biologist with expertise in cancer biology
from the University of South Carolina, has indeed reproduced thefinding of plasmid contamination and testified in a Senate hearing in
September, 2023. This underscores the importance of the McKernan
contribution.

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Too Many DeadTo give a wider-angle view on the issue of manufacturing contamination,with potential relevance to excess mortality, some additional context
and chronology will help.
A peer-reviewed article in Vaccines, prepared in 2020 and publishedon January 23rd 2021, just as provisional approvals for the firstmRNA therapeutics were being granted by the Therapeutic Goods
Administration in Australia, also sheds light. This paper, entitled‘Development of mRNA Vaccines: Scientific and Regulatory Issues’,
is the first to our knowledge to highlight in print the different
manufacturing to be employed for the public rollout of mRNA vaccines,
namely Process 2. As opposed to Process 1, which is fundamentallycleaner and utilizes a reverse transcription polymerase chain reaction(PCR) in small volumes, Process 2 employs E. coli in giant (~300L)
vats. Knowing the process in detail, these sapient authors mentioned
the possibility of DNA plasmid contamination in mRNA vaccines,
which Dr McKernan would in time demonstrate empirically.
Making matters a little more colourful and fuelling concerns about
manufacturing quality, the European Medicines Agency suffered acyberattack, inconveniently exposing 40 megabytes of emails and othernotable files on the dark web. The BMJ and other academics worldwide
investigated, and reported their findings on March 10th 2021, in anarticle, ‘The EMA COVID-19 data leak, and what it tells us about
the mRNA instability’. There were indeed many concerns expressedby EMA scientists, including ‘truncated and modified mRNA species
in the finished product’, as determined by their laboratory evaluation.
Since the batches were derivative of Process 2, the disclosure ofmRNA instability was an early event, also nominating imperfections
in manufacturing.
For some, recognition of safety signals in the rollout of mRNA,
including the recognized risk of anaphylaxis and an early appreciationof a diverse burden of complications and fatalities not previously
encountered, led them to look at manufacturing processes, batch
variation and contamination as a root cause. This could be said to be
the starting point of another of our authors in this volume, Dr Jessica
Rose, who notes contamination subsequent to Process 2.
Contamination with plasmid DNA has been predictable fromknowledge of the manufacturing process. It has not been disclosed by103

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manufacturers or by authorizing regulatory bodies; it may ultimately
be a source of morbidity and mortality in recipients of mRNA
vaccinations. The possibility of genomic integration of plasmid DNA
encoding for spike proteins into human recipients is here discussed, as
it has also been discussed by Prof Buckhaults.
The demonstration of plasmid DNA contamination almost certainly
guarantees an amount of bacterial product contamination, since bothderive from the fact of E. coli use in manufacture. This has beenfoundational for the investigations of another of our contributors, Dr
Geoff Pain, who elaborates the case for endotoxin contamination inmRNA vaccine vials. In that author’s view, this possibility has notbeen falsified by government laboratories, based especially on an
unhelpfully high laboratory cut-off value, arbitrarily defined to denote
vial contamination, in addition to other potential methodologicalflaws. He also takes care to point out how exceedingly small amountsof endotoxin contamination, disseminated along with mRNA andplasmid DNA in lipid nanoparticles, could contribute to disease
manifestation. These issues are worthy of scrutiny together, in thecontext of the so-far silent crisis of excess mortality.
Regardless, it shows that much more care needed to be taken by ourrelevant authorities, in the public interest; it is inconceivable that our
TGA Laboratory Division could derogate its responsibility to know
about undisclosed DNA species in vials nominated for Australian use.
It is important for this study to be included in the present publication,
despite its heavy scientific content, intended as it is for the eyes ofelected and non-elected public servants. Of course, it is possible thatthese findings could also occupy an important place in the outworkingof various legal and political processes in Australia. We also point out
that advance notice of Dr McKernan’s work was sent on July 4th 2023
to two branches of the Health Department, the TGA and the Officeof Gene Technology Regulator.
The Australian Medical Professionals Society recognizes that thisseminal work employs the most technical language of all the papers inthis volume. However, for the reasons elaborated above and becauseof the likely historical value of this contribution, we have elected topresent it here, asking the indulgence of the non-scientific among ourreadership.

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Abstract
Several methods were deployed to assess the nucleic acid composition offour expired vials of the Moderna and Pfizer bivalent mRNA vaccines.
Two vials from each vendor were evaluated with Illumina sequencing,
qPCR, RT-qPCR, Qubit™ 3 fluorometry and Agilent Tape Station™
electrophoresis. Multiple assays confirm DNA contamination that exceeds
the European Medicines Agency (EMA) 330ng/mg requirement and the
FDAs 10ng/dose requirements.
These data may have a bearing on the surveillance of vaccine mRNA in
breast milk or plasma as RT-qPCR assays targeting the vaccine mRNAcannot discern DNA from RNA without RNase or DNase nucleasetreatments. Studies evaluating the reverse transcriptase activity of LINE-1
and vaccine mRNA will need to account for the high levels of DNA
contamination in the vaccines. The exact ratio of linear fragmented DNA
versus intact circular plasmid DNA is still being investigated. Quantitative
PCR assays used to track the DNA contamination are described.

Introduction

Several studies have made note of prolonged presence of vaccine mRNAin breast milk and plasma (Bansal et al. 2021; Hanna et al. 2022; Castruita
et al. 2023). This could be the result of the stability of N1-methylpseudouridine (m1Ψ) in the mRNA of the vaccine. Nance et al. depict a vaccine
mRNA synthesis method that utilizes a dsDNA plasmid that is first
amplified in E.coli prior to an in-vitro T7 polymerase synthesis of vaccine
mRNA (Nance and Meier 2021).
Failure to remove this DNA could result in the injection of spike-encodednucleic acids more stable than the modified RNA. The EMA has statedlimits at 330ng/mg of DNA to RNA ( Josephson 2020-11-19). The FDA
has issued guidance for under 10ng/dose in vaccines (Sheng-Fowler et al.
2009).
Residual injected DNA can result in type I interferon responses and canincrease the potential for DNA integration(Ulrich-Lewis et al. 2022).

Results

To assess the nucleic acid composition of the vaccines, vaccine DNA wasdeeply sequenced using two different methods. The first method used acommercially available New England Biolabs RNA-seq method thatfavoured the sequencing of the RNA but still presented over 500X coveragefor the unanticipated DNA vectors (Figure 1 and 2). The RNA-seq

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assemblies had truncated poly A tracts compared to the constructs
described by Nance et al.
The second method eliminated the RNA with RNase A treatment andsequenced only the DNA using a Watchmaker Genomics fragment librarykit. The DNA focused assemblies delivered vector assemblies with moreintact poly A tracts (Figure 3).
These assemblies were utilized to design multiplex qPCR and RT-qPCR
assays that target the spike sequence present in both the vaccine mRNAand the DNA vector while also targeting the origin of replication sequence
present only in the DNA vector (Figure 3). The assembly of Pfizer vial 1
contains a 72bp insertion not present in the assembly of Pfizer vial 2. This
indel is known for its enhancement to the SV40 promoter and its nuclear
localization signal (Dean et al. 1999) (Moreau et al. 1981)

Figure 1

A Moderna vector assembly of an RNA-seq library with a spike insert (red),Kanamycin resistance gene (green) driven by an AmpR promoter and a highcopy bacterial origin of replication (yellow).

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Figure 2

Pfizer bivalent vaccine assembly of the RNA-seq library. Annotated with
SEB/FCS, spike insert (red), bacterial origin of replication (yellow), Neo/Kan resistance gene(green), F1 origin (yellow) and an SV40 promoter(yellow and white).

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Figure 3

RNase treated vaccines were shotgun sequenced with Illumina (RNase-Seq
not RNA-seq). Pfizer vectors from vial 1 (left) and vial 2 (right) contain a
72bp difference in the SV40 promoter (green and light blue annotation).
qPCR assays are depicted in pink as spike probe and ori probe. The RNase
sequencing provided better resolution over the Eam1104i linearization site
and the poly adenylation sequence. The vectors differ in the length of thepolyA tail (likely sequencing artifact) and the 72bp indel.

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Figure 4109

Figure 5A

Australian Medical Professionals Society110Too Many Dead
Local alignment of Pfizer vial 1 to Pfizer vial 2 vectors highlights the 72bp
tandem duplication in blue.
Close inspection of the Integrative Genome Viewer (IGV) demonstrates theappearance of a 72bp insertion that is heteroplasmic in Pfizer vial 2. The upper
left IGV view is a zoomed-out view where the coloured marks depict the indel.
The lower left IGV view shows inverted paired reads as the 72bp insertion
is a tandem repeat and paired reads shorter than 72bp can be mapped two
different ways. Upper Right IGV view demonstrates a read coverage pile-upor ‘plateau’. This occurs when the reference has one copy of the 72bp repeat
and the sample has two copies. Note – in the upper right IGV depiction, thesequence in Vial 1 is in the opposite orientation in IGV as Vial 2. Lower right
IGV view is a zoomed view of the upper right IGV screen.
Since the two Pfizer vials share the same lot number, finding a heterozygous
copy number change between the two vials is unexpected. It was hypothesizedthat the appearance of a heteroplasmic copy number change is instead theresult of the megahit assembler collapsing what is actually two copies of the
72bp sequence into a single copy as a result of the insert sizes in the sequencing
libraries being too short (105bp). It is noteworthy that the longer paired-end
reads in the library resolve the 72bp tandem repeat.
When references have a single copy of the 72bp repeat and the sample has twocopies of the repeat, reads should pile up to twice the coverage over the single
copy 72bp loci as seen in Figure 5A. To test this hypothesis, we added a second
72bp sequence to the shorter plasmid assembly and observed that the reads
map without artifact and no evidence of heteroplasmy (Figure 5B).
IGV view of the read coverage over Pbiv2_k141_23 shows a discrete 72bp
plateau in coverage (red rectangle). Editing the Pbiv2_k141_23 reference toinclude two copies of the 72bp sequence, and remapping the sequence data
to this corrected sequence shows that the coverage over both vectors is morenormal with no coverage plateau in Pfizer vial 2.
These data conclude that all Pfizer vectors contain a homoplastic 2 copy
72bp SV40 Enhancer associated with more robust expression and nuclear
localization. The initial heteroplastic indel was an artifact of the Megahit
assembler and short insert libraries.
To estimate the size of the DNA, the purified vaccines were evaluated on an
Agilent Tape Station™ using DNA (genomic DNA screen tapes) and RNA
based (high sensitivity RNA tapes) electrophoresis tapes.
Agilent Tape Station™ electrophoresis reveal 7.5 - 11.3 ng/µl of dsDNA
compared to the 23.7 -55.9ng/µl of mRNA detected in each 300µl sample.

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Qubit™ 3 fluorometry estimated 1-2.8ng/µl of DNA and 21.8ng - 52.8ng/µl of RNA. There is higher fragmentation seen in the DNA electrophoresis.
The total RNA levels are less than the expected 30ug (100ng/µl) and 100ug(200ng/µl) doses, suggesting a loss of yield in DNA and RNA isolation,
manufacturing variance or RNA decay with expired lots.

Figure 6

Agilent Tape Station™ electrophoresis demonstrates 23.7ng/µl – 55.9ng/µl
of RNA (left). 7.5ng-11.3ng/µl are observed on DNA based Tape Station™.
While the DNA electropherogram shows a peak suggestive of a full-length
plasmid, this sample is known to have high amounts of N1-methylpseudouridine RNA present. DNA hybrids with N1-methylpseudouridine mRNA may
provide enough intercalating dye cross talk to produce a peak. The sizing of
the peak on the RNA tape on the left is shorter than expected. This may be
the results of N1 methylpseudouridine changing the secondary structure or
the mass to charge ratio of the DNA.
Quantitative PCR assays were designed using IDTs Primer Quest software
targeting a region in the spike protein that was identical between Modernaand Pfizer spike sequences and a shared sequence in the vectors’ origin of
replication. This allowed the qPCR and RT-qPCR assessment of the vaccines.
qPCR only amplifies DNA while RT-qPCR amplifies both DNA and RNA.
Gradient qPCR was utilized to explore conditions where both targets would
perform under the same cycling conditions for both RT-qPCR and PCR(gradient PCR data not shown).

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Figure 7

qPCR of Pfizer’s bivalent vaccine with and without DNase I (left) and RNase
A (right). Untreated mRNA demonstrates equal CTs for Spike and Vector
assays as expected. Vector is more DNase I sensitive than the spike suggesting
the modRNA may inhibit nuclease activity of DNase I against complementary
DNA targets. RNase A treatment does not alter the qPCR signal.

Figure 8

RT-qPCR amplifies both DNA and RNA. The untreated samples show a large
CT offset with Pfizer Spike and Vector assays (left blue versus green). This isanticipated as the T7 polymerization should create more mRNA over spikethan over the vector. Small 1-2 CT shifts are seen with DNase I treatment.
This is expected if the DNA is less than equal concentration of nucleic acid in
RT-PCR. RNase treatment (right) shows a 10 CT offset but does not alter the
DNA vector CT.

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Figure 91µl of the Pfizer bivalent vaccine placed in 100µl leaf lysis buffer for an
8-minute boil step delivers a CT of 24 for both Vector and Spike targets in
qPCR (left). Assay is responsive to 1,5,10µl of input (right).

Figure 10

1µl of the Pfizer bivalent vaccine placed in 100µl leaf lysis buffer for an
8-minute boil step delivers a CT of 20 and 12 for both Vector and Spiketargets in RT-qPCR (left). Assay is responsive to 1,5,10µl of input (right).

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Figure 11

1µl of the Moderna bivalent vaccine exhibits different CTs values for the spikeand the vector targets (left) with qPCR. This needs to be explored further
as the assays provide equal CT scores on Pfizer’s vaccines and the sequenceof the amplicon is identical between the two vector origins. There are twomismatches in the spike amplicons between Moderna and Pfizer but none of
the mismatches are under a primer or probe. The assay is responsive to 1,5,10µlof direct boil mRNA (right).

Figure 12

1µl of the Moderna bivalent vaccine exhibits different CTs values for the spikeand the vector targets (left) with RT-qPCR. The large 10 CT shift between
Spike and Vector needs to take into consideration that qPCR control shows a
5 CT offset. The boil preps can tolerate 1-10µl of vaccine (middle and right).

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Table 1

Qubit™ 3 Fluorometry estimates 1.04-2.8 ng/µl of dsDNA in the vaccinesand 21.8ng-52.8ng/µl of RNA.
Synthetic templates were synthesized with IDT to build RT-qPCR standard
curves to benchmark CTs to the mass of DNA in the reaction. This method
uses ideal templates and fails to quantitate DNA molecules smaller than theamplicon size. As expected, this method delivers lower DNA concentration
estimates than Qubit™ 3 fluorometry or Agilent Tape Station™. It also
represents an ideal environment which does not capture the inhibition or
primer depletion that can occur when large quantities of mRNA with identicalsequence to the DNA target are co-present in a qPCR assay.

Figure 13

Two gBlocks were synthesized at IDT for Spike and Ori positive control
templates used in a RT-qPCR assay. 10-fold serial dilutions were run in
triplicate to correlate CT scores with picograms of DNA. The threshold is
lowered from 102 for review of the background. CT of ~20 = 500fg/RT-qPCR
reaction. Since 100bp targets only represent 1/80th of the vector DNA present
as a potential contaminant, 500 fg/µl manifests in 40pg/µl of vector DNA. Any
DNA that is DNase I-treated and is smaller than the amplicon size cannot

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amplify or be quantitated with this method. This method will under-quantitate
DNase I-treated samples compared to Qubit™ 3 or Agilent Tape Station™.
This work was further validated by testing eight unopened Pfizer monovalentvaccines with both qPCR and RT-qPCR.
Pfizer Bivalent

Moderna Bivalent

Pfizer Monovalent

Figure 14

Moderna and Pfizer Bivalent vaccines (top). 8 Monovalent Pfizer mRNA
vaccines. These were unopened but past expiration (bottom).

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Figure 15

1µl of vaccine boiled in 100µl of leaf lysis buffer was subjected to qPCR (left)
and RT-qPCR (right) for Vector (red) and Spike (blue). Eight samples were
tested in triplicate.
Table 2.

CT values for Spike and Vector during qPCR (DNA only). Standard deviation for the triplicate
measurements run horizontally in black font. Standard deviation for vial to vial run verticallyin red. Delta CT or (Vector CT minus Spike CT) represents the ratio of Spike to Vector DNA
and should = 1.

Table 3CT values for Spike and Vector during RT-qPCR (RNA+DNA). Ratio of RNA:DNA ranges
from 43:1 To 161:1. EMA allowable limit is 3030:1. This is 18-70 times over the EMA limit.

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Discussion
Multiple methods highlight high levels of DNA contamination in both themonovalent and bivalent vaccines. While the Qubit™ 3 and Agilent Tape
Station™ differ on their absolute quantification, both methods demonstrate
it is orders of magnitude higher than the EMA’s limit of 330ng DNA/ 1mg
RNA. qPCR and RT-qPCR confirms the relative RNA to DNA ratio. An
11-12 CT offset should be seen between Spike and Vector RT-qPCR signals
to represent a 1:3030 contamination limit (2^11.6 = 3100). Instead, we observemuch smaller CT offsets (5-7 CTs) when looking at qPCR and RT-qPCR datawith these vaccines. It should be noted that Qubit™ 3 and Agilent methods
stain all DNA in solution while qPCR measures only amplifiable molecules
without DNase I cut sites between the primers. The further apart the spacingof the qPCR primers, the fewer Qubit™ 3 and Agilent detectable molecules
will amplify. The primers used in this study are 106bp and 114bp apart, thus
any molecules that are DNase I cut below this length will be undercountedwith the qPCR methods relative to more general dsDNA measurements from
Qubit™ 3 or Agilent Tape Station™.
This also implies that qPCR standard curves using 100% intact synthetic DNA
standards will amplify more efficiently and thus undercount the total digested
DNA contamination. For example, standard curves with 106-114bp synthetic
templates provide CTs under 20 in the picogram range (not low nanogram
range) suggesting large portions of the library are smaller than the minimum
amplifiable size. Pure standards also do not contain high concentrations ofmodified mRNA with identical sequence which could serve as a competitive
primer sink or inhibitor to qPCR methods.
Alternatively, the Qubit™ 3 and the Agilent Tape Station™ could be
inflating the DNA quantification as a result of intercalating dye cross-talkwith N1-methylpseudouridine RNA. For this reason, we believe the ratio
we observed when these molecules are more scrupulously interrogated withpolymerases specific for each template type in qPCR and RT-qPCR is a more
relevant metric. The EMA metric is also stated as such a ratio.
This also brings into focus the question of whether these EMA limits took into
consideration the nature of the DNA contaminants. Replication competent
DNA should arguably have a more stringent limit. DNA with mammalianpromoters or antibiotic resistance genes may also be of more concern thanjust random background E.coli genomic DNA from a plasmid preparation(Sheng-Fowler et al. 2009). Background E.coli DNA was measured with
qPCR and had CT over 35.
There has been a healthy debate about the capacity for SARs-CoV-2 to120

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integrate into the human genome (Zhang et al. 2021). This work has inspiredquestions regarding the capacity for the mRNA vaccines also to genomeintegrate. Such an event would require LINE-1 driven reverse transcription of
the mRNA into DNA as described by Alden et al. (Alden et al. 2022). dsDNA
contamination of sequence encoding the spike protein would not require
LINE-1 for Reverse Transcription and the presence of an SV40 nuclear
localization signal in Pfizer’s vaccine vector would further increase the odds
of integration. This work does not present evidence of genome integration
but does underscore that LINE-1 activity is not required given the dsDNA
levels in these vaccines. The nuclear localization of these vectors should also
be verified.
Prior sequencing of the monovalent vaccines from Jeong et al. only publishedthe consensus sequence (Dae-Eun Jeong 2021). The raw reads for this project
are not available and should be scrutinized for the presence of vector sequence.
Given these vaccines exceed the EMA limits (330ng/mg DNA/RNA) with
the Qubit™ 3 and Agilent data and these data also exceed the FDA limit(10ng/dose) with the more conservative qPCR standard curves, we should
revisit the lipopolysaccharide (LPS) levels. Plasmid contamination from
E.coli preps are often co-contaminated with LPS. Endotoxins contamination
can lead to anaphylaxis upon injection (Zheng et al. 2021).
A limitation of this study is the unknown provenance of the vaccine vials
under study. These vials were sent to us anonymously in the mail without
cold packs. RNA is known to degrade faster than DNA and it is possible
poor storage could result in faster degradation of RNA than DNA. RNA as amolecule is very stable but in the presence of metals and heat or backgroundubiquitous RNases, it can degrade very quickly. All of the vaccines in this study
are past the expiration date listed on the vial, suggesting more work is required
to understand the DNA to RNA ratios in fresh lots. The publication of theseqPCR primers may assist in surveying additional lots with more controlled
supply chains. Studies evaluating vaccine longevity in breast milk or plasma
may benefit from vector DNA surveillance as this sequence is unique to thevaccine and may persist longer than mRNA.
While the sequencing delivered full coverage of the plasmid backbones, it iscustomary to assemble plasmids from DNase I fragmented libraries. Thesemethods have not discerned the ratio of linear versus circular DNA in the vials.
While plasmid DNA is more competent and stable, linear DNA may havehigher genome integration risks.
The intercalating dyes used in the Qubit™ 3 and Agilent systems are known to121Australian Medical Professionals Society
have low fluorescent cross talk with DNA and RNA but it is unknown to what
degree N1-methylpseudouridine alters the specificity of these intercalatingdyes. As a result, we have relied on the CT offsets between RT-qPCR andqPCR with the vector and spike sequence as the best relative assessment of
the EMA ratio-metric regulation. These qPCR and RT-qPCR reagents may
be useful in tracking these contaminants in vaccines, blood banks or patient
tissues in the future.
Author contributions
KJM- constructed the sequencing libraries, designed the qPCR assays, ran Qubit™ 3s and Agilent
Tape Station™ and performed the analysis, drafted the manuscript.
YH-Optimized DNA isolations, Tape Station™ and qPCR results.

SM, LTK- assisted in demultiplexing and trimming the reads and assembly troubleshooting

Conflicts of interest- Authors of this paper are employees of Medicinal Genomics which
manufactures some of the qPCR and DNA isolation kits used in this study.
Acknowledgements.

We would like to thank Lei Zhang for organizing the sequencing outsourcing. We would also like tothank Jessica Rose, Sabine Hazan, Jikkyleaks, Pathogenetics, Steve Massey, Valentine Bruttel, Lynn
Fynn, Sasha Letypova, David Wiseman and Pharmacoconuts for helpful comments on this topic.
Methods

Purifying the mRNA from the LNPs
LiDs/SPRI purification

100µl of each vial was sampled (1/3rd to 1/5th of a dose)••••

5µl of 2% LiDs was added to 100µl of Vaccine to dissolve LNPs
100µl of 100% Isopropanol
233µl of Ampure (Beckman Genomics)
25µl of 25mM MgCl2 (New England Biolabs)

Samples were tip mixed 10X and incubated for 5 minutes for magnetic bead binding. Magnetic
Beads were separated on a 96-well magnet plate for 10 minutes and washed twice with 200µl of
80% EtOH. The beads were left to air dry for 3 minutes and eluted in 100µl of ddH20. 2µl of eluted
sample was run on an Agilent Tape Station™.
CTAB/Chloroform/SPRI purification of Vaccines

Some variability in qPCR performance was noted with our LiDs/SPRI purification method of thevaccines. This left some samples opaque and may represent residual LNPs in the purification. A
CTAB/Chloroform/SPRI isolation was optimized to address this and used for further qPCR andAgilent electrophoresis. Briefly, 300µl of Vaccine was added to 500µl of CTAB (MGC solution A
in SenSATIVAx MIP purification kit. #420004). The sample was then vortexed and heated for 5
minutes at 37°C. 800µl of chloroform was added, vortexed and spun at 19,000 rpms for 3 minutes.
The top 250µl of aqueous phase was collected and added to 250µl of solution B and 1ml of magnetic
binding buffer. Samples were vortexed and incubated for 5 minutes and magnetically separated. The
supernatant was removed and the beads washed with 70% Ethanol two times. Samples were finally
eluted in 300µl of MGC elution buffer.
Simple boil preparation for evaluating vaccine qPCR.

This boil prep process simply takes 1-10µl of the vaccine and dilutes it into a PCR-compatible leaf
lysis buffer and heats it (Medicinal Genomics part number 420208).

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65°C for 6 minutes
95°C for 2 minutes

Library construction for sequencing
50µl of each 100µl sample was converted into RNA-Seq libraries for Illumina sequencing using the
NEB NEBNext UltraII Directional RNA library Kit for Illumina (NEB#E7760S).
To enrich for longer insert libraries, the fragmentation time was reduced from 15 minutes to 10
minutes and the First strand synthesis time was extended at 42°C to 50 minutes per the long insert
recommendations in the protocol.
No Ribo depletion or PolyA enrichment was performed as to provide the most unbiased assessmentof all fragments in the library. The library was amplified for 16 cycles according to the manufacturer’s
protocol. A directional library construction method was used to evaluate the single stranded natureof the mRNA. This is an important quality metric in the EMA and TGA disclosure documents as
dsRNA (>0.5%) can induce an innate immune response. dsRNA content is often estimated using
an ELISA. Directional DNA sequencing offers a more comprehensive method for its estimation
and was previously measured and 99.99% in Jeong et al. It is unclear how this may vary lot to lot orwithin the new manufacturing process for the newer bivalent vaccines.
RNase A treatment of the Vaccines
RNase A cleaves both uracils and cytosines. N1-methylpseudouridine is known to
be RNAse-L resistant but RNase A will cleave cytosines which still exist in the mRNAs. This leaves
predominantly DNA for sequencing. Vaccine mRNA that was previously sequenced was treated at
37°C for 30 minutes with 10µl of 20 Units/µl Monarch RNase A from NEB. The RNase reaction
was purified using 1.5X of SenSATIVAx (Medicinal Genomics #420001). Samples were eluted in
20µl ddH20 after DNA purification. 15µl was used for DNA sequencing.
DNase treatment of the vaccines
50µl of CTAB purified vaccine was treated at 37°C for 30 minutes with 2µl DNase I and 6µl of
DNase I buffer (Grim reefer MGC#420143). 2.5µl of LiDs Lysis buffer was added to stop the
DNase reaction. Reactions were purified using 60µl 100% Isopropanol, 140µl Ampure, 15µl MgCl2.
Magnetic beads were tip-mixed 10 times, left for 5 minutes to incubate, magnetically separated andthen washed twice with 80% EtOH.
Whole genome shotgun of RNase’d Vaccines.
15µl of the DNA was converted into sequence-ready libraries using Watchmakers Genomics WGS
library construction kit. This kit further fragments the DNA to smaller sizes, making fragment
length in the vaccines difficult to predict.
Qubit™ 3 fluorometry

Qubit™ 3 fluorometry was performed using Biotum AccuBlue RNA Broad Range kit (#31073)
and Biotum AccuGreen High Sensitivity dsDNA Quantitation Kit (#31066) according to the
manufacturer’s instructions.
E.coli qPCR

Medicinal Genomics PathoSEEK™ E.coli Detection assay (#420102) was utilized according to the
manufacturer’s instructions.

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qPCR and RT-qPCR spike assay•

MedGen-Moderna_Pfizer_Janssen_Vax-Spike_Forward•

MedGen-Moderna_Pfizer_Janssen_Vax-Spike_Reverse

•
•
•
•

>AGATGGCCTACCGGTTCA>TCAGGCTGTCCTGGATCTT

MedGen-Moderna_Pfizer_Janssen_Vax-Spike_Probe>/56-FAM/CGAGAACCA/ZEN/GAAGCTGATCGCCAA/3IABkFQ/qPCR and RT-qPCR vector origin assay•

MedGen_Vax-vector_Ori_Forward•

MedGen_Vax-vector_Ori_Reverse

•
•
•
•

>CTACATACCTCGCTCTGCTAATCGCGCCTTATCCGGTAACTATC
MedGen_Vax-vector_Ori_Probe/5HEX/AAGACACGA/ZEN/CTTATCGCCACTGGC/3IABkFQ/

Elute primer to 100uM according to IDT instructions.
Make 50X primer-probe mix.
1.

25µl 100uM Forward Primer

3.

12.5µl 100uM Probe2.
4.

25µl 100uM Reverse Primer
37.5µl nuclease free ddH20.

Use 15µl of this mixture in the qPCR master mix setup seen below. (0.5µl primer/probe per reaction)
Use 10µl of this mixture in the RT-qPCR master mix setup seen below.
Medicinal Genomics Master Mix kits were used1.
2.

https://store.medicinalgenomics.com/qPCR-Master-Kit-v3-200-rxns
https://store.medicinalgenomics.com/pathoseek-rt-qpcr-master-kit

Reaction setup for 30 reactions of qPCR•

114µl Enzyme Mix (green tube)•

246µl nuclease free ddH20•••

24µl Reaction Buffer (blue tube)
15µl of Primer-Probe set Spike
15µl of Primer-Probe set Ori

Use 13.8µl of above MasterMix and 5µl of purified sample (1µl Vax DNA/RNA + 4µl ddH20 if
CT <15)
Reaction setup for 34 reactions of RT-qPCR•

200µl Enzyme mix•

20µl RNase Inhibitor (purple tube)••

96µl nuclease free ddH20
4µl DTT (green tube)

124

Too Many Dead••

10µl Primer-Probe set Spike
10µl Primer-Probe set Ori

10µl of MasterMix and 1µl of Vax DNA/RNA
A Medicinal Genomics MIP DNA Purification Kit was used.
Cycling conditions

These conditions work for both qPCR and RT-qPCR. Note: The 50°C RT step can be skippedwith qPCR. The MGC qPCR MasterMix kits used have a hot start enzyme which are unaffectedby this 50°C step. For the sake of controlling RNA to DNA comparisons, we have put qPCR and
RT-qPCR assays on the same plate and run the below program with the RT step included for all
samples.
Cycling Conditions used for qPCR and RT-qPCR

Sequences of amplicons for gBlock Positive Controls. Ori = 106bp, Spike = 114bp.
Ori target

Spike target

125

Australian Medical Professionals Society

References
Alden M, Olofsson Falla F, Yang D, Barghouth M, Luan C, Rasmussen M,
De Marinis Y. 2022. Intracellular Reverse Transcription of Pfizer BioNTech
COVID-19 mRNA Vaccine BNT162b2 In Vitro in Human Liver Cell Line.
Curr Issues Mol Biol 44: 1115-1126.
Bansal S, Perincheri S, Fleming T, Poulson C, Tiffany B, Bremner RM,
Mohanakumar T. 2021. Cutting Edge: Circulating Exosomes with COVID
Spike Protein Are Induced by BNT162b2 (Pfizer-BioNTech) Vaccination
prior to Development of Antibodies: A Novel Mechanism for Immune
Activation by mRNA Vaccines. J Immunol 207: 2405-2410.
Castruita JAS, Schneider UV, Mollerup S, Leineweber TD, Weis N, Bukh J,
Pedersen MS, Westh H. 2023. SARS-CoV-2 spike mRNA vaccine sequences
circulate in blood up to 28 days after COVID-19 vaccination. APMIS 131:
128-132.
Dae-Eun Jeong MM, Karen Artiles, Orkan Ilbay, Andrew Fire*, Kari Nadeau,
Helen Park, Brooke Betts, Scott Boyd, Ramona Hoh, and Massa Shoura*.
2021. Assemblies of putative SARS-CoV2-spike-encoding mRNA sequencesfor vaccines BNT-162b2 and mRNA-1273. GitHub.
Dean DA, Dean BS, Muller S, Smith LC. 1999. Sequence requirements forplasmid nuclear import. Exp Cell Res 253: 713-722.
Hanna N, Heffes-Doon A, Lin X, Manzano De Mejia C, Botros B, Gurzenda
E, Nayak A. 2022. Detection of Messenger RNA COVID-19 Vaccines in
Human Breast Milk. JAMA Pediatr 176: 1268-1270.
Josephson F. 2020-11-19. Rapporteur’s Rolling Review assessment report.
Committee for Medicinal Products for Human Use EMEA/H/C/005735/RR.
Moreau P, Hen R, Wasylyk B, Everett R, Gaub MP, Chambon P. 1981. The
SV40 72 base repair repeat has a striking effect on gene expression both in
SV40 and other chimeric recombinants. Nucleic acids research 9: 6047-6068.
Nance KD, Meier JL. 2021. Modifications in an Emergency: The Role ofN1-Methylpseudouridine in COVID-19 Vaccines. ACS Cent Sci 7: 748-756.
Sheng-Fowler L, Lewis AM, Jr., Peden K. 2009. Issues associated with residual
cell-substrate DNA in viral vaccines. Biologicals 37: 190-195.
Ulrich-Lewis JT, Draves KE, Roe K, O’Connor MA, Clark EA, Fuller DH.
2022. STING Is Required in Conventional Dendritic Cells for DNA Vaccine
Induction of Type I T Helper Cell- Dependent Antibody Responses. Front
Immunol 13: 861710.

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Too Many Dead
Zhang L, Richards A, Barrasa MI, Hughes SH, Young RA, Jaenisch R. 2021.
Reverse-transcribed SARS-CoV-2 RNA can integrate into the genome ofcultured human cells and can be expressed in patient-derived tissues. Proceedingsof the National Academy of Sciences of the United States of America 118.
Zheng Q, Wang T, Zhu X, Tian X, Zhong C, Chang G, Ran G, Xie Y, Zhao
B, Zhu L et al. 2021. Low endotoxin E. coli strain-derived plasmids reducerAAV vector-mediated immune responses both in vitro and in vivo. Mol Ther
Methods Clin Dev 22: 293-303.

Kevin McKernan is the CSO and Founder ofMedicinal Genomics. He managed the Research& Development for the Human Genome Project at
Whitehead Institute-MIT, resulting in several patents
for nucleic acid purification. He holds a Bachelorof Science in Biology from Emory University witha focus on cloning and expressing Norepinephrine
Transporters. Previously, Kevin was the CSO of
Courtagen Life Sciences, Inc., and was Vice President
and Director of R&D of Life Technologies where hemanaged the development of the Life Technologies
next-generation SOLiD sequencing technology.
Integral to the SOLiD R&D process, he oversaw
more than 100 research collaborations exploring new
biological frontiers with next-generation sequencing.
Kevin initiated an R&D project to investigate
chemFET semiconductor-based DNA sequencing.
His collaborations resulted in hundreds of publications
and seven journal covers from Science Translational
Medicine to Nature.

127

Serious adverse eventsof special interest followingmRNA COVID-19 vaccinationin randomized trials in adults1
by Joseph Fraiman, Juan Erviti, Mark Jones,
Patrick Whelan, Sander Greenland,
Robert M. Kaplan, Peter Doshi

Abstract
Introduction: In 2020, before the COVID-19 vaccine rollout, the Brighton
Collaboration created a priority list, endorsed by the World HealthOrganization, of potential adverse events relevant to COVID-19 vaccines. We
adapted the Brighton Collaboration list to evaluate serious adverse events ofspecial interest observed in mRNA COVID-19 vaccine trials.
Method: This is secondary analysis of serious adverse events reported in theplacebo-controlled, phase III randomized clinical trials of Pfizer and ModernamRNA COVID-19 vaccines in adults (NCT04368728 and NCT04470427),
focusing analysis on Brighton Collaboration adverse events of special interest.
Results: Pfizer and Moderna mRNA COVID-19 vaccines were associatedwith an excess risk of serious adverse events of special interest of 10.1 and 15.1
per 10,000 vaccinated over placebo baselines of 17.6 and 42.2 (95% CI −0.4
to 20.6 and −3.6 to 33.8), respectively. Combined, the mRNA vaccines wereassociated with an excess risk of serious adverse events of special interest of
12.5 per 10,000 vaccinated (95% CI 2.1 to 22.9); risk ratio 1.43 (95% CI 1.07

1This study first appeared in Vaccine, Volume 40, Issue 40, September 22, 2022, pages 5798-5805
and is under a Creative Commons licence. It is republished here with permission from Elsevier, and isavailable from PubMed Central.

128to 1.92). The Pfizer trial exhibited a 36% higher risk of serious adverse eventsin the vaccine group; risk difference 18.0 per 10,000 vaccinated (95% CI 1.2
to 34.9); risk ratio 1.36 (95% CI 1.02 to 1.83). The Moderna trial exhibited a
6% higher risk of serious adverse events in the vaccine group: risk difference
7.1 per 10,000 (95% CI –23.2 to 37.4); risk ratio 1.06 (95% CI 0.84 to 1.33).
Combined, there was a 16% higher risk of serious adverse events in mRNA
vaccine recipients: risk difference 13.2 (95% CI −3.2 to 29.6); risk ratio 1.16
(95% CI 0.97 to 1.39).
Discussion: The excess risk of serious adverse events found in our study
points to the need for formal harm-benefit analyses, particularly those thatare stratified according to risk of serious COVID-19 outcomes. These analyses
will require public release of participant-level datasets.

1. Introduction

In March 2020, the Brighton Collaboration and the Coalition for Epidemic
Preparedness Innovations partnership, Safety Platform for Emergency
vACcines (SPEAC), created and subsequently updated a ‘priority list of
potential adverse events of special interest relevant to COVID-19 vaccine
trials.’[1] The list comprises adverse events of special interest (AESIs) basedon the specific vaccine platform, adverse events associated with prior vaccines
in general, theoretical associations based on animal models, and COVID-19
specific immunopathogenesis. The Brighton Collaboration is a global
authority on the topic of vaccine safety and in May 2020, the World HealthOrganization’s Global Advisory Committee on Vaccine Safety endorsed
and recommended the reporting of AESIs based on this priority list. To ourknowledge, however, the list has not been applied to serious adverse events in
randomized trial data.
We sought to investigate the association between FDA-authorized mRNA
COVID-19 vaccines and serious adverse events identified by the Brighton
Collaboration, using data from the phase III randomized, placebo-controlled
clinical trials on which authorization was based. We consider these trial data
against findings from post-authorization observational safety data. Our study
was not designed to evaluate the overall harm-benefit of vaccination programs
so far. To put our safety results in context, we conducted a simple comparisonof harms with benefits to illustrate the need for formal harm-benefit analysesof the vaccines that are stratified according to risk of serious COVID-19
outcomes. Our analysis is restricted to the randomized trial data, and does
not consider data on post-authorization vaccination program effects. It does
however show the need for public release of participant-level trial datasets.

129

2. Methods
Pfizer and Moderna each submitted the results of one phase III randomized trial
in support of the FDA’s emergency use authorization of their vaccines in adults.
Two reviewers (PD and RK) searched journal publications and trial data on the
FDA’s and Health Canada’s websites to locate serious adverse event results tables
for these trials. The Pfizer and Moderna trials are expected to follow participants
for two years. Within weeks of the emergency authorization, however, the sponsors
began a process of unblinding all participants who elected to be unblinded. In
addition, those who received placebo were offered the vaccine. These self-selection
processes may have introduced nonrandom differences between vaccinated andunvaccinated participants, thus rendering the post-authorization data less reliable.
Consequently, to preserve randomization, we used the interim datasets thatwere the basis for emergency authorization in December 2020, approximately
four months after trials commenced.
The definition of a serious adverse event (SAE) was provided in each trial’s
study protocol and included in the supplemental material of the trial’s
publication.[2,3,4] Pfizer and Moderna used nearly identical definitions, consistent
with regulatory expectations. An SAE was defined as an adverse event that results
in any of the following conditions: death; life-threatening at the time of the event;inpatient hospitalization or prolongation of existing hospitalization; persistent orsignificant disability or incapacity; a congenital anomaly or birth defect; medicallyimportant event, based on medical judgement.
In addition to journal publications, we searched the websites of the FDA (foradvisory committee meeting materials) and Health Canada (for sections of thedossier submitted by sponsors to the regulator).[5] For the FDA website, we
considered presentations by both the FDA and the sponsors.[6] Within each ofthese sources, we searched for SAE results tables that presented information by
specific SAE type; we chose the most recent SAE table corresponding to the
FDA’s requirement for a safety median follow-up time of at least two months
after dose 2.
For each trial, we prepared blinded SAE tables (containing SAE types without
results data). Using these blinded SAE tables, two clinician reviewers ( JF andJE) independently judged whether each SAE type was an AESI. SAE types thatmatched an AESI term verbatim, or were an alternative diagnostic name for an
AESI term, were included as an AESI. For all other SAE types, the reviewers
independently judged whether that SAE type was likely to have been caused
by a vaccine-induced AESI, based on a judgement considering the disease
course, causative mechanism, and likelihood of the AESI to cause the SAE
type. Disagreements were resolved through consensus; if consensus could not bereached, a third clinician reviewer (PW) was used to create a majority opinion.

130For each included SAE, we recorded the corresponding Brighton Collaboration
AESI category and organ system. When multiple AESIs could potentially cause
the same SAE, the reviewers selected the AESI that they judged to be the most
likely cause based on classical clinical presentation of the AESI.
We used an AESI list derived from the work of Brighton Collaboration’s Safety
Platform for Emergency vACcines (SPEAC) Project. This project created an
AESI list which categorizes AESIs into three categories: those included because
they are seen with COVID-19, those with a proved or theoretical association
with vaccines in general, and those with proved or theoretical associations
with specific vaccine platforms. The first version was produced in March 2020
based on experience from China. Following the second update (May 2020), the
WHO Global Advisory Committee on Vaccine Safety (GACVS) adopted thelist, and Brighton commenced a systematic review process ‘to ensure an ongoingunderstanding of the full spectrum of COVID-19 disease and modification of the
AESI list accordingly.’[7] This resulted in three additional AESIs being added to
the list in December 2020. The subsequent (and most recent fourth) update did
not result in any additional AESIs being added to the list.[1]We matched SAEs recorded in the trial against an expanded list of AESIs createdby combining Brighton’s SPEAC COVID-19 AESI list with a list of 29 clinicaldiagnoses Brighton identified as ‘known to have been reported but not in sufficientnumbers to merit inclusion on the AESI list.’[7] Sensitivity analysis was used todetermine whether use of the original versus expanded list altered our results.
Risk ratios and risk differences between vaccine and placebo groups were calculated
for the incidence of AESIs and SAEs. We excluded SAEs that were known
efficacy outcomes (that is, COVID-19), consistent with the approach Pfizer(but not Moderna) used in recording SAE data. The Pfizer study trial protocol
states that COVID-19 illnesses and their sequelae consistent with the clinicalendpoint definition were not to be reported as adverse events, ‘even though theevent may meet the definition of an SAE.’[8] For unspecified reasons, Moderna
included efficacy outcomes in their SAE tables, effectively reporting an all-cause
SAE result. Because we did not have access to individual participant data, toaccount for the occasional multiple SAEs within single participants, we reduced
the effective sample size by multiplying standard errors in the combined SAE
analyses by the square root of the ratio of the number of SAEs to the numberof patients with an SAE. This adjustment increased standard errors by 10%(Pfizer) and 18% (Moderna), thus expanding the interval estimates. We estimatedcombined risk ratios and risk differences for the two mRNA vaccines by averaging
over the risks using logistic regression models which included indicators for trial
and treatment group.
We used a simple harm-benefit framework to place our results in context,131comparing risks of excess serious AESIs against reductions in COVID-19
hospitalization.

3. Results

Serious adverse event tables were located for each of the vaccine trials submitted
for EUA in adults (age 16 + for Pfizer, 18 + for Moderna) in the UnitedStates: Pfizer-BioNTech COVID-19 vaccine BNT162b2 (NCT04368728)[2,9,10] and Moderna COVID-19 vaccine mRNA-1273 (NCT04470427).
[3,11,12] (Table 1).
Table 1. Data sources for phase III trials.
Note: Bold font indicates dataset chosen for analysis;
EUA = Emergency Use Authorization.
Trial

Data
cutoff date

JournalarticlesPfizer trial inages 16 and above(NCT04368728)2

14 Nov 2020
(supported
Dec 2020
EUA)

Moderna trial inages 18 and above(NCT04470427)3

25 Nov 2020
(supported Table S11 in
Dec 2020
publication
EUA)

Aggregate
data only

FDA sources

HealthCanada sources

Table 55 in sponsor
Table 23 in
document C4591001
sponsor briefing
Final Analysis
document
Interim Report Body
Table 27 insponsor briefing
document

Table 14.3.1.13.3 insponsor document
mRNA-1273-P301
Unblinded Safety
Tables Batch 1 (DS2)

3.1. Reporting windows and serious adverse events23
Moderna reported SAEs from dose 1 whereas Pfizer limited reporting fromdose 1 to one month after dose 2. Both studies reported all data at the timeof data cutoff (November 14th 2020 for Pfizer, November 25th 2020 for
Moderna). 17 SAEs that were efficacy endpoints were removed from theModerna trial (16 ‘COVID-19’ SAEs and 1 ‘COVID-19 pneumonia’ SAE).
One such efficacy endpoint meeting the definition of a SAE was removed
from the Pfizer trial (‘SARS-CoV-2 test positive’ SAE).
The Pfizer trial exhibited a 36% higher risk of serious adverse events invaccinated participants in comparison to placebo recipients: 67.5 per 10,000
versus 49.5 per 10,000; risk difference 18.0 per 10,000 vaccinated participants(95% compatibility1 interval 1.2 to 34.9); risk ratio 1.36 (95% CI 1.02 to1.83). The Moderna trial exhibited a 6% higher risk of SAEs in vaccinated2
Study to Describe the Safety, Tolerability, Immunogenicity, and Efficacy of RNA Vaccine
Candidates Against COVID-19 in Healthy Individuals Link https://classic.clinicaltrials.gov/ct2/show/
NCT04368728
3
A Study to Evaluate Efficacy, Safety, and Immunogenicity of mRNA-1273 Vaccine in Adults
Aged 18 Years and Older to Prevent COVID-19. Link https://classic.clinicaltrials.gov/ct2/show/
NCT04470427

132people compared to those receiving placebo: 136 per 10,000 versus 129 per
10,000; risk difference 7.1 per 10,000 (95% CI –23.2 to 37.4); risk ratio 1.06
(95% CI 0.84 to 1.33). Combined, there was a 16% higher risk of SAEs inmRNA vaccine recipients than placebo recipients: 98 per 10,000 versus 85
per 10,000; risk difference 13.2 (95% CI −3.2 to 29.6); risk ratio 1.16 (95%
CI 0.97 to 1.39). (Table 2).
Table 2. Serious adverse events.
Total events (events per
10,000 participants)a
Trial

VaccinePlaceboRisk difference
per 10,000
participants(95 % CI)e

Risk ratio(95 % CI)e

Serious adverse events
Pfizerb

52 (27.7)

33 (17.6)

10.1 (-0.4 to 20.6)

1.57 (0.98 to 2.54)

Modernac,d87 (57.3)

64 (42.2)

15.1 (-3.6 to 33.8)

1.36 (0.93 to 1.99)

Combined

139 (40.9)

97 (28.6)

12.5 (2.1 to 22.9)

1.43 (1.07 to 1.92)

fSerious adverse events of special interest
Pfizer52 (27.7)

33 (17.6)

10.1 (-0.4 to 20.6)

1.57 (0.98 to 2.54)

Moderna87 (57.3)

64 (42.2)

15.1 (-3.6 to 33.8)

1.36 (0.93 to 1.99)

139 (40.9)

97 (28.6)

12.5 (2.1 to 22.9)

1.43 (1.07 to 1.92)

Combinedfa. Denominators for Pfizer were 18,801 in the vaccine group and 18,785 in the placebogroup, and for Moderna were 15,185 in the vaccine group and 15,166 in the placebogroup.
b. Pfizer excluded efficacy outcomes from its SAE table (COVID-19 illnesses and theirsequelae meeting the definition of an SAE). However, at least one SAE appears to havebeen inadvertently included, which we removed from our calculations (‘SARS-CoV-2
test positive’: 0 vaccine group; 1 placebo group).
c. Moderna included efficacy outcomes in its SAE table (COVID-19 illnesses andtheir sequelae meeting the definition of an SAE). We removed efficacy SAEs outcomesthat could be identified: ‘COVID-19’ and ‘COVID-19 pneumonia.’ Lacking access toparticipant level data, SAEs that were sequelae of serious COVID-19 could not be
identified and therefore remain included in this analysis.
d. All SAEs for Moderna was calculated using the ‘Number of serious AEs’ row inModerna’s submission to FDA.[11]e. Standard errors used to estimate 95% CIs were inflated by the factor √[#SAE]/[#patients with SAE] to account for multiple SAE within patients.
f. The combined risk differences and risk ratios were computed from the fitted logistic
regression models and so may not exactly equal comparisons computed from the firsttwo columns.

133

3.2. Serious adverse events of special interest
Regarding whether each SAE type was included on the SPEAC-derived
AESI list, agreement between the two independent clinician reviewers
was 86% (281/325); 40 of the 44 disagreements were resolved throughconsensus, and only four disagreements necessitated a third clinician
reviewer. Supplemental Table 1 includes a full list of included and excluded
SAEs across both trials.
In the Pfizer trial, 52 serious AESI (27.7 per 10,000) were reported in thevaccine group and 33 (17.6 per 10,000) in the placebo group. This difference
corresponds to a 57% higher risk of serious AESI (RR 1.57 95% CI 0.98
to 2.54) and a risk difference of 10.1 serious AESI per 10,000 vaccinatedparticipants (95% CI −0.4 to 20.6). In the Moderna trial, 87 serious AESI
(57.3 per 10,000) were reported in the vaccine group and 64 (42.2 per 10,000)
in the placebo group. This difference corresponds to a 36% higher risk of serious
AESI (RR 1.36 95% CI 0.93 to 1.99) and a risk difference of 15.1 serious
AESI per 10,000 vaccinated participants (95% CI −3.6 to 33.8). Combining
the trials, there was a 43% higher risk of serious AESI (RR 1.43; 95% CI
1.07 to 1.92) and a risk difference of 12.5 serious AESI per 10,000 vaccinatedparticipants (95% CI 2.1 to 22.9). (Table 2).
Of the 236 serious AESIs occurring across the Pfizer and Moderna trials,
97% (230/236) were adverse event types included as AESIs because they areseen with COVID-19. In both Pfizer and Moderna trials, the largest excess
risk occurred amongst the Brighton category of coagulation disorders. Cardiac
disorders have been of central concern for mRNA vaccines; in the Pfizer trial
more cardiovascular AESIs occurred in the vaccine group than in the placebogroup, but in the Moderna trial the groups differed by only 1 case. (Table
3, Table 4).
3.3. Sensitivity analysis
As a sensitivity analysis, we restricted the serious AESI analysis to those
AESIs listed in SPEAC’s COVID-19 AESI list (that is, separating out
Brighton’s list of 29 clinical diagnoses ‘known to have been reported but
not in sufficient numbers to merit inclusion on the AESI list.’) This reduced
the total number of AESIs across the two trials by 48 (35 vaccine group, 13
placebo group). There was still a higher risk of serious AESI when limited to
the SPEAC COVID-19 AESI list, but the magnitude of the excess (in both
relative and absolute terms) was smaller than when using the larger AESI
list. (Supplemental Table 2).

134

Table 3. Serious AESIs, Pfizer trial.
Brightoncategory

VaccinePlacebo

Vaccineeventsper
10,000

Placebo
eventsper
10,000Differencein events
per 10,000

Risk
ratio

0.5

0.5

0.0

1.00

Association with immunization in general
Anaphylaxis

1

1

Association with specific vaccine platform(s)Encephalitis/encephalomyelitis

0

2

0.0

1.1

−1.1

0.00

Acute kidney
injury

2

0

1.1

0.0

1.1

N/A

Acute liver injury

0

1

0.0

0.5

−0.5

0.00

Acute respiratory
distress syndrome

2

1

1.1

0.5

0.5

2.00

Coagulation
disorder

16

10

8.5

5.3

3.2

1.60

Myocarditis/
pericarditis

2

1

1.1

0.5

0.5

2.00

Other forms ofacute cardiac
injury

16

12

8.5

6.4

2.1

1.33

Subtotal

39

28

20.7

14.9

5.8

1.39

Seen with COVID-19

Brighton list of 29 clinical diagnoses seen with COVID-19
Abscess

4

1

2.1

0.5

1.6

4.00

Cholecystitis

4

2

2.1

1.1

1.1

2.00

Colitis/Enteritis

1

1

0.5

0.5

0.0

1.00

Diarrhoea

1

0

0.5

0.0

0.5

N/A

Hyperglycaemia

1

1

0.5

0.5

0.0

1.00

Pancreatitis

1

0

0.5

0.0

0.5

N/A

Psychosis

1

0

0.5

0.0

0.5

N/A

Subtotal

13

5

6.9

2.7

4.3

2.60

Total

52

33

27.7

17.6

10.1

1.57

3.4. Harm-benefit considerations
In the Moderna trial, the excess risk of serious AESIs (15.1 per 10,000
participants) was higher than the risk reduction for COVID-19 hospitalization
relative to the placebo group (6.4 per 10,000 participants).[3] In the Pfizer
trial, the excess risk of serious AESIs (10.1 per 10,000) was higher than therisk reduction for COVID-19 hospitalization relative to the placebo group(2.3 per 10,000 participants).

135

4. Comparison with FDA reviews
In their review of SAEs supporting the authorization of the Pfizer andModerna vaccines, the FDA concluded that SAEs were, for Pfizer, ‘balanced
between treatment groups,’[15] and for Moderna, were ‘without meaningful
imbalances between study arms.’[16] In contrast to the FDA analysis, wefound an excess risk of SAEs in the Pfizer trial. Our analysis of Moderna
was compatible with FDA’s analysis, finding no meaningful SAE imbalance
between groups.
The difference in findings for the Pfizer trial, between our SAE analysis andthe FDA’s, may in part be explained by the fact that the FDA analysed the total
number of participants experiencing any SAE, whereas our analysis was basedon the total number of SAE events. Given that approximately twice as many
persons in the vaccine group than in the placebo group experienced multiple
SAEs (there were 24 more events than participants in the vaccine group,compared to 13 in the placebo group), FDA’s analysis of only the incidenceof participants experiencing any SAE would not reflect the observed excess ofmultiple SAEs in the vaccine group.
Table 4. Serious AESIs, Moderna trial.
Brightoncategory

VaccinePlacebo

Vaccineeventsper
10,000

Placebo
eventsper
10,000Differencein events
per 10,000

Risk
ratio

Association with specific vaccine platform(s)Bell’s Palsy

1

0

0.7

0.0

0.7

N/A

Encephalitis/
encephalomyelitis

1

0

0.7

0.0

0.7

N/A

Acute kidney
injury

1

3

0.7

2.0

−1.3

0.33

Acute liver injury

1

0

0.7

0.0

0.7

N/A

Acute respiratory
distress syndrome

7

4

4.6

2.6

2.0

1.75

Angioedema

0

2

0.0

1.3

−1.3

0.00

Coagulation
disorder

20

13

13.2

8.6

4.6

1.54

Generalized
Convulsions

2

0

1.3

0.0

1.3

N/A

Myelitis

0

1

0.0

0.7

−0.7

0.00

Myocarditis/
pericarditis

4

5

2.6

3.3

−0.7

0.80

Seen with COVID-19

136

Other forms ofacute cardiac
injury

26

26

17.1

17.1

Other rash

1

1

0.7

Rhabdomyolysis

0

1

0.0

Single Organ
Cutaneous
Vasculitis

10Subtotal

65

56

0.0

1.00

0.7

0.0

1.00

0.7

−0.7

0.00

0.7

0.0

0.7

N/A

42.8

36.9

5.9

1.16

Brighton list of 29 clinical diagnoses seen with COVID-19
Abscess

1

0

0.7

0.0

0.7

N/A

Arthritis

3

1

2.0

0.7

1.3

3.00

Cholecystitis

4

0

2.6

0.0

2.6

N/A

Colitis/Enteritis

6

3

4.0

2.0

2.0

2.00

Diarrhoea

2

1

1.3

0.7

0.7

2.00

Hyperglycaemia

1

0

0.7

0.0

0.7

N/A

Hyponatremia

1

1

0.7

0.7

0.0

1.00

Pancreatitis

2

0

1.3

0.0

1.3

N/A

Pneumothorax

0

1

0.0

0.7

−0.7

0.00

Psychosis

1

1

0.7

0.7

0.0

1.00

Thyroiditis

1

0

0.7

0.0

0.7

N/A

Subtotal

22

8

14.5

5.3

9.2

2.75

Total

87

64

57.3

42.2

15.1

1.36

A more important factor, however, may be that FDA’s review of non-fatal
SAEs used a different analysis population with different follow-up
windows. The FDA reported 126 of 21,621 (0.6%) of vaccinated participants
experienced at least one SAE at data cutoff compared to 111 of 21,631(0.5%) of placebo participants. In contrast, our analysis found 127 SAEs
among 18,801 vaccine recipients versus 93 SAEs among 18,785 placebo
recipients.[15] While summary results for the population we analysed wasprovided in a table, FDA did not report an analysis of them. The substantiallylarger denominators in FDA’s analysis (5,666 more participants) reflect
the fact that their analysis included all people receiving at least one dose(minus 196 HIV-positive participants), irrespective of the duration of
post-injection follow-up time. In contrast, our analysis was based on the
study population with median follow-up ≥ two months after dose 2 (minus
120 HIV-positive participants), of whom 98.1% had received both doses.
[2,17] The FDA’s analysis of SAEs thus included thousands of additional
participants with very little follow-up, of whom the large majority had
only received one dose.

1374.1 Comparison with post-authorization studies
Although the randomized trials offer high-level evidence for evaluating
causal effects, the sparsity of their data necessitates that harm-benefit
analyses also consider observational studies. Since their emergency
authorization in December 2020, hundreds of millions of doses of Pfizer
and Moderna COVID-19 vaccines have been administered and post-authorization observational data offer a complementary opportunity to study
AESIs. Post-authorization observational safety studies include cohort
studies (which make use of medical claims or electronic health records) anddisproportionality analyses (which use spontaneous adverse event reporting
systems). In July 2021, the FDA reported detecting four potential adverse
events of interest: pulmonary embolism, acute myocardial infarction, immune
thrombocytopenia, and disseminated intravascular coagulation following
Pfizer’s vaccine based on medical claims data in older Americans.[18] Threeof these four serious adverse event types would be categorized as coagulation
disorders, which is the Brighton AESI category that exhibited the largest
excess risk in the vaccine group in both the Pfizer and Moderna trials.
FDA stated it would further investigate the findings but at the time of
our writing has not issued an update. Similarly, spontaneous-reporting
systems have registered serious adverse reactions including anaphylaxis (all
COVID-19 vaccines), thrombocytopenia syndrome among premenopausal
females ( Janssen vaccine), and myocarditis and pericarditis among younger
males (Pfizer and Moderna vaccines).[19,20]
Using data from three postmarketing safety databases for vaccines (VAERS,
EudraVigilance, and VigiBase), disproportionality studies have reported
excess risks for many of the same SAE types as in the present study.
[21,22,23] For example, a study using VAERS and EudraVigilance comparing
the disproportionality of adverse event reports between the influenza
vaccine versus the mRNA COVID-19 vaccines reported excess risks forthe following Brighton AESIs: cardiovascular events, coagulation events,
haemorrhages, gastrointestinal events, and thromboses.[22] While CDC
published a protocol[24] in early 2021 for using proportional reporting ratios
for signal detection in the VAERS database, results from the study have
not yet been reported.[25] Among self-controlled case series, one reported
a rate ratio of 1.38 (95% CI 1.12–1.71) for haemorrhagic stroke following
Pfizer vaccine,[26] another reported 0.97 (95% CI 0.81–1.15),[27] while acohort study[28] reported 0.84 (95% CI 0.54–1.27).

138

5. Discussion
Using a prespecified list of AESI identified by the Brighton Collaboration,higher risk of serious AESI was observed in the mRNA COVID-19 vaccine
group relative to placebo in both the Pfizer and Moderna adult phase III trials,
with 10.1 (Pfizer) and 15.1 (Moderna) additional events for every 10,000 people
vaccinated. Combined, there was a risk difference of 12.5 serious AESIs per
10,000 people vaccinated (95% CI 2.1 to 22.9). These results raise concernsthat mRNA vaccines are associated with more harm than initially estimated at
the time of emergency authorization. In addition, our analysis identified a 36%
higher risk of serious adverse events in vaccinated participants in the Pfizer trial:
18.0 additional SAEs per 10,000 vaccinated (95% CI 1.2 to 34.9). Consistent
with the FDA evaluation, our analysis found no clear difference in SAEs between
groups in the Moderna trial.
Results between the Pfizer and Moderna trials were similar for the AESI analysis
but exhibited substantial variation in the SAE analysis. Caution is needed ininterpreting this variation as it may be substantially explained by differences in
SAE recording practices in the trials rather than differences in actual vaccine
harm profiles. For reasons that are not documented in the trial protocol, Moderna
included efficacy outcomes in its SAE tabulations, while Pfizer excluded them.
As a result, Moderna’s SAE table did not present a traditional SAE analysis
but rather an all-cause SAE analysis. The FDA analysis of the Moderna trial
presented an all-cause SAE analysis, which estimates total vaccine effects
on SAEs, including effects transmitted via effects on COVID-19. It did not
however present a traditional SAE analysis with efficacy endpoints removed,
which attempts to estimate only the direct effects on SAEs. While our analysis
attempted to perform a traditional SAE analysis by excluding efficacy SAEs
(serious COVID-19 and its sequelae), our effort was hindered because we did nothave access to patient level data. Easily recognizable efficacy SAEs (‘COVID-19’,‘COVID-19 pneumonia’, and ‘SARS-CoV-2 test positive’) could be removed,
but many participants who experienced a COVID-19 SAE likely experienced
multiple other SAEs (for example, pneumonia, hypoxia, and thrombotic events)
which could not be identified and therefore remain included in our analysis.
Of 17 total efficacy SAEs (16 ‘COVID-19’ and 1 ‘COVID-19 pneumonia’)removed from our analysis of the Moderna trial, 16 were in the placebo arm. As
a consequence, the background SAE risk (risk in absence of COVID-19) would
be overestimated by the Moderna placebo group, resulting in underestimation of
the actual risk of SAEs and AESIs attributable to the vaccine in the Moderna
comparisons as well as in the combined analysis. Access to patient-level data
would allow adjustments for this problem.
Rational policy formation should consider potential harms alongside potential

139benefits.[29] To illustrate this need in the present context, we conducted a simple
harm-benefit comparison using the trial data comparing excess risk of serious
AESI against reductions in COVID-19 hospitalization. We found excess riskof serious AESIs to exceed the reduction in COVID-19 hospitalizations in both
Pfizer and Moderna trials.
This analysis has the limitations inherent in most harm-benefit comparisons. First,
benefits and harms are rarely exact equivalents, and there can be great variability
in the degree of severity within both benefit and harm endpoints. For example,
intubation and short hospital stay are not equivalent but both are counted in‘hospitalization’; similarly, serious diarrhoea and serious stroke are not equivalent
but both are counted in ‘SAE’. Second, individuals value different endpoints
differently. Third, without individual participant data, we could only compare thenumber of persons hospitalized for COVID-19 against the number of serious
AESI events, not the number of participants experiencing any serious AESI. Somepeople experienced multiple SAEs whereas hospitalized COVID-19 participants
were likely only hospitalized once, biasing the analysis towards exhibiting net
harm. To gauge the extent of this bias, we considered that there were 20% (Pfizer)
and 34% (Moderna) more SAEs than participants experiencing any SAE. As
a rough sensitivity calculation, if we divide the Pfizer excess serious AESI riskof 10.1 by 1.20 it becomes 8.4 compared to a COVID-19 hospitalization risk
reduction of 2.3; if we divide the Moderna excess serious AESI risk of 15.1 by
1.34 it becomes 11.3 compared to a COVID-19 hospitalization risk reductionof 6.4.
Harm-benefit ratios will be different for populations at different risk for serious
COVID-19 and observation periods that differ from those studied in the trials.
Presumably, larger reductions in COVID-19 hospitalizations would have beenrecorded if trial follow-up were longer, if more SARS-CoV-2 were circulating, or
if participants had been at higher risk of serious COVID-19 outcomes, shifting
harm-benefit ratios toward benefit. Conversely, harm-benefit ratios would
presumably shift towards harm for those with lower risk of serious COVID-19
outcomes – such as those with natural immunity, younger age or no comorbidities.
Similarly, waning vaccine effectiveness, decreased viral virulence, and increasing
degree of immune escape from vaccines might further shift the harm-benefit
ratio toward harm. Large, randomized trials in contemporary populations could
robustly answer these questions. Absent definitive trials, however, synthesis ofmultiple lines of evidence will be essential.[30,48,49]
Adverse events detected in the post-marketing period have led to the withdrawal
of several vaccines. An example is intussusception following one brand of rotavirus
vaccine: around 1 million children were vaccinated before identification of
intussusception, which occurred in around 1 per 10,000 vaccinees.[31] Despite140the unprecedented scale of COVID-19 vaccine administration, the AESI types
identified in our study may still be challenging to detect with observational
methods. Most observational analyses are based on comparing the risks of adverse
events ‘observed’ against a background (or ‘expected’) risk, which inevitably display
great variation, by database, age group, and sex.[32] If the actual risk ratio for theeffect was 1.4 (the risk ratio of the combined AESI analysis), it could be quitedifficult to unambiguously replicate it with observational data given concerns
about systematic as well as random errors. [33,34,35]
In addition, disproportionality analyses following COVID-19 vaccination also
have limitations, particularly with respect to the type of adverse events seen in ourstudy. The majority of SAEs that contributed to our results are relatively common
events, such as ischaemic stroke, acute coronary syndrome, and brain haemorrhage.
This complicates signal detection because clinical suspicion of an adverse vaccine
reaction following an event commonly seen in clinical practice will be lower thanfor SAEs like myocarditis.[50] For this reason, clinical suspicion leading to thefiling of an individual case safety report may be far less common in the post-authorization setting than in the trials. At the same time, heightened awarenessabout COVID-19 vaccine SAEs can result in under- and over-reporting. Publichealth messages assuring vaccine safety may lower clinical suspicion of potential
causal relationships, whereas messages about potential harms can conversely
stimulate reports that otherwise might not have been made. These factors canlead to bias in both directions, further complicating interpretation. In contrast tothese problems, in the randomized trials used in this analysis, all SAEs were to berecorded, irrespective of clinical judgement regarding potential causality.
Although our analysis is secondary, reanalyses of clinical trial data have led
to the detection of adverse events well after the market entry of major drugs
such as rofecoxib and rosiglitazone.[36,37] Our analysis has an advantage overpostmarketing observational studies in that the data are from blinded, placebo-controlled randomized trials vetted by the FDA, which were matched against
a list of adverse events created before the availability of the clinical-trial results
and designed for use in COVID-19 vaccine trials.
Our study has several important limitations. First, Pfizer’s trial did not report
SAEs occurring past one month after dose 2. This reporting threshold may have
led to an undercounting of serious AESIs in the Pfizer trial. Second, for bothstudies, the limited follow-up time prevented an analysis of harm-benefit overa longer period. Third, all SAEs in our analysis met the regulatory definitionof a serious adverse event, but many adverse-event types which patients maythemselves judge as serious may not meet this regulatory threshold. Fourth,there are decisions about which SAEs to include or exclude as AESIs require
subjective, clinical judgements in the absence of detailed clinical information

141

about the actual SAEs. We encourage third party replication of our study, withaccess to complete SAE case narratives, to determine the degree to whichthese decisions affected our findings. For additional sensitivity analyses, such
replication studies could also make use of other AESI lists, such as those preparedby FDA,[38,39,40,41] CDC,[24] Pfizer,[42] or a de novo AESI list derived from alist of COVID-19 complications understood to be induced via SARS-CoV-2’s
spike protein.[43,44]
A fifth important limitation is our lack of access to individual participant data,
which forced us to use a conservative adjustment to the standard errors. The
95% CIs[13,14] calculated are therefore only approximate because we do notknow which patients had multiple events. Finally, as described above, in theModerna analysis, the SAEs that were sequelae of serious COVID-19 could
not be identified and therefore remain included in our calculations. Because thevaccines prevent SAEs from COVID-19 while adding SAE risks of their own,
this inclusion makes it impossible to separately estimate SAEs due to the vaccine
from SAEs due to COVID-19 in the available Moderna data, as must be done
to extrapolate harm-benefit to other populations. These study limitations all stemfrom the fact that the raw data from COVID-19 vaccine clinical trials are notpublicly available.[45,46]
We emphasize that our investigation is preliminary, to point to the need for more
involved analysis. The risks of serious AESIs in the trials represent only groupaverages. SAEs are unlikely to be distributed equally across the demographic
subgroups enrolled in the trial, and the risks may be substantially less in somegroups compared to others. Thus, knowing the actual demographics of those who
experienced an increase in serious AESI in the vaccine group is necessary for aproper harm-benefit analysis. In addition, clinical studies are needed to see if
particular SAEs can be linked to particular vaccine ingredients as opposed tounavoidable consequences of exposure to spike protein, as future vaccines could
then be modified accordingly or sensitivities could be tested for in advance.
In parallel, a systematic review and meta-analysis using individual participantdata should be undertaken to address questions of harm-benefit in various
demographic subgroups, particularly in those at low risk of serious complications
from COVID-19. Finally, there is a pressing need for comparison of SAEs andharm-benefit for different vaccine types; some initial work has already begun inthis direction.[47]Full transparency of the COVID-19 vaccine clinical trial data is needed toproperly evaluate these questions. Unfortunately, as we approach two years after
release of COVID-19 vaccines, participant level data remain inaccessible.[45,46]

142

Author contributions
All authors had full access to all of the data in the study (available at https://doi.
org/10.5281/zenodo.6564402), and take responsibility for the integrity of the data
and the accuracy of the data analysis.
The authors declare that they have no known competing financial interests or
personal relationships that could have appeared to influence the work reported inthis paper.
Acknowledgements

We thank Jean Rees for help identifying sources of data.
Funding

This study had no funding support.
Ethical review statement

This research was confirmed to be Not Human Subjects Research (NHSR) by
University of Maryland, Baltimore (HP-00102561).
Conflicts of interest

JF, JE, MJ, SG, PW, RK: none to declare. PD has received travel funds from theEuropean Respiratory Society (2012) and Uppsala Monitoring Center (2018);
grants from the FDA (through University of Maryland M-CERSI; 2020), Lauraand John Arnold Foundation (2017-22), American Association of Colleges of
Pharmacy (2015), Patient-Centered Outcomes Research Institute (2014-16),
Cochrane Methods Innovations Fund (2016-18), and UK National Institute for
Health Research (2011-14); was an unpaid IMEDS steering committee member
at the Reagan-Udall Foundation for the FDA (2016-2020) and is an editor at The
BMJ. The views expressed here are those of the authors and do not necessarily reflect
those of their employers.

Appendix A. Supplementary data

The following are the Supplementary data to this article:
Supplementary data 1.

Download Word document (16KB) Link https://ars.els-cdn.com/content/image/1-s2.0-S0264410X22010283-mmc1.docx
Data availability

All of the data in the study are available at https://doi.org/10.5281/zenodo.6564402

143

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Available from: https://papers.ssrn.com/abstract=4072489.
M.H. Murad, S. Saadi Evidence-based medicine has already adapted
and is very much alive BMJ Evidence-based Medicine (2022), 10.1136/[48]
bmjebm-2022-112046
https://ebm.bmj.com/content/early/2022/07/19/
bmjebm-2022-112046, Accessed 22nd Aug 2022
[49]

A Munro The Pandemic Evidence Failure https://alasdairmunro.substack.
com/p/the-pandemic-evidence-failure (2022), Accessed 22nd Aug 2022

S Mansanguan, P Charunwatthana, W Piyaphanee, W Dechkhajorn, A Poolcharoen, C Mansanguan Cardiovascular Manifestation of the[50]
BNT162b2 mRNA COVID-19 Vaccine in Adolescents Trop. Med. Infect.
Dis., 7 (8) (2022), p. 196, 10.3390/tropicalmed7080196

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Dr Joseph Fraiman is an Emergency Medicine
physician currently working in New Orleans, Louisiana,
and the surrounding rural region. Dr Fraiman served
as the medical manager of Louisiana’s Urban Searchand Rescue Disaster Task Force 1. As an independent
clinical scientist, he researches harm-benefit analysisof clinical interventions. His publications regarding
the COVID-19 mRNA vaccines include a re-analysisof serious harm from the original clinical trials, and on
the FDA’s safety surveillance system.

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Too Many Dead

Part 3Lack of evidence leads to policy failures

When data do not support the government’s narrative, the apparent correct
response that regulators expect from practitioners is to stay silent while authorities
go on to withhold further investigations. This was demonstrated particularly
by NSW Health early in 2023 in their Respiratory Surveillance Report from
December, 2022: ‘Vaccination status of cases admitted to hospital, admitted to
ICU, and those who die will no longer be reported.’
What has unfolded over the last few years is what Stanford Professor Jay
Bhattacharya called ‘the greatest public health mistake in human history.’ The
officially sanctioned medical misinformation that has characterized the COVID
pandemic is unparalleled. For a disease with a highly stratified median infection
fatality rate of .05% in 2020, similar to the seasonal flu, and with healthy children
having a statistically 0% risk, governments locked down this country (and muchof the world), casting about 150 million people into poverty.
Acting on flawed modelling, governments force-quarantined, separated
families, let people die alone, impaired child development, closed businesses,
amassed generational debt, and made livelihood conditional upon submitting
to participation in what former Health Minister Greg Hunt called ‘the world’s
largest clinical trial’. At the same time they were indemnifying manufacturers insecret contracts, reducing legislative safety and efficacy requirements, silencing

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doctors and banning early treatments that showed considerable promise. All of
this was supported by the Australian Health Practitioner Regulation Agency
and the national boards’ silencing of health professionals, for our alleged safety.
The pandemic preparedness plans released in 2019 specifically presented
the importance of engagement between front-line health professionals andpolicymakers. Instead, our health professionals were censored, suppressed, andthreatened with reprisals if they failed to provide health advice consistent withgovernmental public health messaging. These threats were not empty. In a time
when the need was – and is – obviously there for more medical professionals,
qualified and experienced practitioners were summarily suspended and neatly
removed from the system. The question of the accuracy of the medical advice
they were sacked over never arose. Speak out? – goodbye.
It is time for an honest conversation about the risk and benefit of thesenovel products. The reality is that the evidence does not support thenotion that these injections saved millions of lives, and the harms anddeaths caused by the vaccines cannot be ignored.
– Dr Clare Craig
The fact is that there has been no honesty about risks and benefits because
conversation has not been permitted. Dr Malhotra in this part also has asubstantial reputation for his interest in rebalancing the role of nutrition andlifestyle education as opposed to pharmacological interventions. Could this bebecause he has actually done the work and found the benefit? There has beenvery little room for this in modern Australian orthodox medicine, either.
Several new pharmaceutical agents have been administered to billions ofpeople worldwide, including the young and healthy at little risk from thevirus. Considerable leeway has been afforded in terms of the preclinicaland clinical testing of these agents, despite an entirely novel mechanismof action and concerning biodistribution characteristics…. It cannot be
said that the consent to receive these agents was fully informed as isrequired ethically and legally.
Authorities and sections of the medical profession have supported
unethical, coercive and misinformed policies such as vaccine mandates andvaccine passports, undermining the principles of ethical, evidence-based
medical practice and informed consent. These regrettable actions are asymptom of ‘the medical information mess’: the tip of a morality iceberg
where prescribed medications are estimated to be the third most common
cause of death globally after heart disease and cancer….. COVID vaccine
administration must stop until all the raw data have been subjected to
fully independent scrutiny.
– Dr Aseem Malhotra152Too Many Dead

The failings of COVID-19 policy
by Clare Craig

The global response to COVID-19 has been fraught with controversy andsubject to extensive debate. As we navigate these unprecedented times, it
becomes essential to critically assess the decisions made, the validity of theassumptions they were based on and the consequences to the public and to
ethical norms.
It is important to start with the profound undermining of foundational medical
ethics and human rights. Measures designed to curb the virus’s spread havetransgressed upon individual autonomy, informed consent, and the principleof direct patient benefit. The most important breaches were the creation of anenvironment of fear for psychological manipulation and coercion for medical
interventions.
Several unevidenced and misguided assumptions about COVID substantiallyinfluenced policy decisions. These flawed assumptions, paraded as ‘The Science’,
were seldom contested because of the silencing of critics. To truly gauge theeffect of COVID policies, we must examine, as best we can, what would have
happened in the absence of these policies. Consequently, this paper delves into
several misguided assumptions that shaped our response to the pandemic andshows how they were invalid.

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The evidence is examined, first regarding lockdowns and masks and thenthe pharmaceutical interventions. In particular, I examine the widespreadassumption of close-contact droplet transmission of the virus. Evidence
indicates there is aerosol transmission over longer distances, which challenges
the efficacy of measures designed to restrict close-contact spread. Misguided
beliefs about the ubiquity of asymptomatic spread and the entire population
being susceptible further complicate the picture and have bolstered the belief
that lockdowns would work.
The decisions taken regarding treatments for COVID were irrational andunevidenced and this is briefly discussed, followed by a debunking of theclaims made about ‘vaccination’. This is a global review and includes evidence
from more than one country, but Australia is a particular focus as it can act as acontrol group, showing what effects can and cannot be attributed to COVID.
This is an evidence-based analysis which I believe to be true, but I do notclaim omniscience. No one can. The only way we can learn as humans and as asociety is by listening to every voice and dispassionately testing the arguments.
Successful silencing of minority voices has slowed the learning about COVID,
with disastrous results.

Breaching ethical principles, social norms, and rights

Decisions made in the name of COVID prevention have drastically undermined
medical ethics, principles and human rights, once deemed foundational andparamount. Disregarding them in a crisis undermines their status as principles
and rights. Acknowledgement and rectification of such breaches are necessary
to prevent recurrence.
Medical ethics
Adults should protect children, not vice versa. The Convention of the Rightsof the Child dictates special safeguards and legal protection for children on
account of their physical and mental immaturity. All signatories have an
obligation to uphold this. International law precludes giving experimental
drugs to those without capacity to consent unless there will be a direct benefit.
Proposing children receive COVID ‘vaccines’, despite being statistically atzero risk from the disease and while the products were still in phase three
trials in order to shield adults, contravenes this stipulation.
Many faced coercion to take COVID ‘vaccines’ through psychologicalmanipulation and under threat to their livelihood or mobility. Bodily autonomy,
ensuring individuals have control over their body and their medical decisions,has been threatened. The Universal Declaration on Bioethics and Human
Rights affirms that ‘the interests and welfare of the individual should have154

Too Many Deadpriority over the sole interest of science or society’ (Article 3). The requirement
for full informed consent before administering any medical intervention, as
laid out in international law and guidance from medical regulators, was also
breached.
Finally, a core medical ethical principle is to first do no harm. This principle
was inverted and used to justify interventions rather than to ensure caution
before doing anything at all.
Societal norms
Aside from breaches of ethical norms, the response to COVID has also violated
cultural practices and societal values. Unethical policies included enforcedsocial isolation, the denial of access to dying relatives or support for birthingmothers – a particular affront to our social nature and need for loved ones
during vulnerable times. Even in 2023, many care home residents returningfrom hospital visits face 10-day solitary confinements without visitor rights.
It is crucial to recognize these actions as fundamentally immoral. In addition,
they were also unsupported by evidence.
Rights
The encroachment on fundamental rights, such as the right to work, freedomof movement, and freedom of speech, has eroded trust in institutions
and threatened democracy. Democratic societies uphold free speech as afundamental right and silencing individuals breaches not only their rights but
the rights of the listener. It also acts as an important means of error correction.
Dissenting voices have been silenced by a variety of means on social media,
in the scientific literature and even by pre-print servers. As a result important,
inconvenient truths have not been heard, exposing catastrophic errors in
COVID policy.

Exaggeration of the threat

The term ‘pandemic’ was loosely defined by the WHO until 2009, previously
implying the emergence of a novel virus against which the population had no
immunity, causing widespread epidemics with substantial death and illness.
However, in 2009, prior to the announcement of the swine flu pandemic,
WHO removed the phrase ‘enormous numbers of deaths and illness’ fromtheir website, effectively broadening the definition to include any novel virus.
This shift could technically categorize seasonal influenza as a pandemic, as it
mutates annually, presenting as a new variant.
The updated definition does not align with the catastrophic implications ofsocietal disruption and extensive loss of life associated with pandemic plans

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established after 2009. These plans were ‘substantially higher than even themost severe winter epidemics’ with mortality ‘increased in younger age groups.’
They assumed a substantially higher death toll, 4 to 30 times that of a seasonal
influenza season, and heavily affecting younger demographics. Despite thesegrim expectations, pandemic plans acknowledged the limited effectiveness ofattempts to control the spread of airborne respiratory viruses.
The unethical use of fear
Despite early recognition that the SARS-CoV-2 virus posed no greater risk
than influenza to the large majority of the population, fear levels among thegeneral public were ramped up to disproportionate levels., The collateral
damage of this fear inflation to both physical and mental health has been
considerable. The deployment of behavioural science strategies (commonlyreferred to as ‘nudges’) contributed notably to the increase in fear levels of thegeneral population. Plans to increase levels of fear appear to have been carriedout without any consideration as to how to limit or turn off their effects.
Objectivity can be lost when people are fearful and there seemed to be apositive feedback loop whereby those responsible for the fear generation werealso affected by it, justifying their belief in the need for more fear propaganda.
Surveys from November 2020 revealed that the average person believed themean age of death from COVID was 65. However, the actual mean age ofa COVID death was 80, and the median age was 82, exceeding the averageage of death in pre-COVID years. Dr Colin Foad led a UK study surveying
attitudes to COVID and found, ‘people judged the threat of COVID-19 via
the magnitude of the policy response.’ It is worth noting that the perceived
risk was grossly inflated because of government messaging emphasising the
COVID threat. Mortality rates by age calculated by Cambridge University’s
biostatistics department (see Figure 1) indicate a stark contrast with public
perception.
The risk presented in the table below when extrapolated to the whole population
would work out at a 1% mortality risk from COVID, which is now known tobe far too high. These risks therefore represent what the claimed threat wasin March 2020, and not the actual threat. The Omicron variant presented alower death risk for both ‘vaccinated’ and ‘unvaccinated’ populations evident
from its first wave in South Africa, despite having low ‘vaccination’ rates. The
first wave of Omicron resulted in lower death rates than typical for winter inEurope and USA.
Testing
Test results exist on a spectrum, from strong to weak, but were presented

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Too Many Dead
Chance ofdying if you
catch
COVID
(add a zeroto accountfor the fact
most didnot catch
any onevariant)

Same order of risk as

Under 5 year olds

1 in 270,000

Dying this year from a fire18

5 to 14 yr olds

1 in 77,000

Dying from a general anaesthetic

16

15 to 24 yr olds

1 in 29,000

A clover is three times more
likely to have four leaves andan oyster to have a pearl.

15

25 to 44 yr olds

1 in 4,000

Four times less likely than the
chance of finding a double yolk
when you crack open an egg.

1245 to 64 yt olds

1 in 560 to
1 in 280(at peakdeaths)Picking two aces in a row froma pack. During peak death, it
was as likely as drawing four
cards in a row from a pack andthem all being Kings, Queens or
Jacks.

965 to 74 yr olds

1 in 120 to
1 in 43(at peakdeaths)In summer, you would havebeen more likely to win after
placing money on the horsewith the worst odds in the
grand national than to die if
you caught covid. However, in
December 2021 it was more
likely but still only as likely as
placing your money on zero inroulette and winning.

7

75 yr olds and over

1 in 29
to 1 in 5(at peakdeaths)In summer the risk was offlipping a coin 5 times and itcoming up heads every time.
At peak deaths four in five survive.

5

Number ofsequential heads
tossed in a row
in a coin toss

Figure 1: Age dependent risks of death based on pre-omicron variants and an overall 1%
mortality risk (which is now known to have been too high).

as binary. Determining the cut-off for a positive result involves a trade-off:
identifying all possible cases can mistakenly include healthy persons, while
focusing on definite cases risks overlooking weak genuine cases. The strategy
adopted for COVID aimed to identify every possible case, resulting in

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high-volume, high-speed testing with potentially significant implications on
quality as PCR testing is a risky undertaking at scale. The difficulties withinterpreting test results can be countered using clinical judgement basedon symptoms, and on careful labelling such as in defining suspected cases
differently to definite cases, or using secondary confirmation of weak positive
results. This was not done.
For a test result to be positive in any meaningful sense of the word there
must be a clinical implication – for example, the cause of a symptomaticpatient’s illness or an asymptomatic person’s infectious risk. The quantity ofvirus detected is crucial for meaningful results. Testing protocols, however, did
not reflect this. Bystander virus alone in contaminated air that we all breathe
was sufficient to declare a result positive. A positive test result based on official
criteria could have somewhere between 29 and 58,000 times less viral materialpresent than the lowest level ever identified in an infectious person.
Christian Drosten and co-authors’ paper sets out the protocol for the COVID
PCR test. He had a prominent role in Germany as a high-profile scientist
advising their government on COVID measures. Drosten himself sets out theflaws in using PCR testing for respiratory viruses when commenting in aninterview on MERS testing: ‘The method is so sensitive that it can detect asingle genetic molecule of this virus. For example, if such a pathogen scurries
across the nasal mucosa of a nurse for a day without her becoming ill or
noticing anything else, then she is suddenly a MERS case.’
His description explains how evidence of the presence of tiny amounts ofvirus alone in test result samples should not be used as indicative of disease
especially with no regard to symptoms of disease and without quantifying it tounderstand the risk of infectiousness.
In summary, it is clear an approach that maximises positive results has been a
consistent policy. Identifying all possible positive results may be an appropriate
strategy at the beginning of an epidemic but leads to overdiagnosis longer
term.
Death diagnosis
Death certification is not an exact science but an art, subject to physicians’
judgement based on available evidence. The process is intended to offer closure
for relatives and data for public health research, not to chart the course of an
epidemic. Thus, the same death can receive different classifications dependingon the certifying physician’s perspective.
Policy seemed to maximise COVID death numbers. One study found thepresence or absence of symptoms to be insufficient in accurately determining158

Too Many Dead
COVID as the cause of death. Yet, the WHO suggested a plausibility
check on those certificates where COVID was reported but not selected as
the underlying cause, potentially inflating COVID death numbers. Audits
worldwide have illustrated overdiagnosis of COVID death. In California, twocounties saw a reduction in COVID deaths after reviews of between 22 and25%. In Massachusetts, using a federal definition of COVID death led toreclassification of a third of previous COVID deaths., Similarly, in Sweden,
only one in six deaths were definitively due to COVID. The USA witnessedan algorithmic error that removed over 77,000 deaths from the COVID
tally, reducing child COVID deaths by 24%, after those numbers were used
to promote child ‘vaccination’. Consequently, COVID death data are not as
reliable as might be assumed.
It is important to recognise that a positive test for the presence of virus is more
likely in those dying of other causes. Nearly half the deceased in a Spanish
study tested positive for a respiratory virus post-mortem, despite only 7%
receiving a viral infection diagnosis before death. The presence of a positive
test result was enough to result in a death being registered by government as a
COVID death. Any doctor who then came to certify that death would have tofind evidence that COVID was not a contributory cause to confidently assertthat the government had wrongly labelled the death.
Furthermore, a large proportion of COVID deaths occurred in people alreadyclose to death. Any assessment of years of life lost and the overall outcome
needs to account for this. Neil Ferguson said of the proportion who died fromCOVID many would have died soon anyway: ‘(It) might be as much as half or
two thirds of the deaths we see, because these are people at the end of their lives
or [who] have underlying conditions.’ Indeed, the NHS noted that 95% of
COVID-related deaths occurred in people with pre-existing conditions. Age
was a major determinant in COVID mortality, with most deaths occurring
among the elderly. Furthermore, a substantial portion of COVID deaths
occurred from infections in hospitals and care homes. Including these deaths
in risk calculations for the healthy elderly community can be misleading. For
instance, a sample of 30,000 patients who contracted COVID following alengthy hospital stay before spring 2021 saw a mortality rate exceeding one
in four.

The failures of non-pharmaceutical interventions

Lockdowns and masking were both mandated as interventions to slow the
spread of the virus. Both failed because the belief that they would work wasfounded on false assumptions. In the case of lockdown, the false assumptions
were that transmission occurred only with close contact, that asymptomatictransmission was a significant driver of spread, and that everyone was159

Australian Medical Professionals Society
susceptible. With regard to masking, the belief was that stopping droplets
would hamper transmission. The reasons why those beliefs were unfounded
are set out here.
Close contact transmission
Talking, laughing and breathing result in the emission of droplets and aerosols.
Droplets are the larger parts of mouth and nose spray that fall directly to theground. Smaller parts of the spray remain suspended in the air and are calledaerosols. It was broadly assumed that COVID transmission occurred throughclose contact via droplets. However, this assumption is rooted in myth, notevidence. Because droplets fall to the ground, following the course of a ballistic
trajectory, it is almost impossible for them to make contact with the small
exposed mucosal surfaces of the eyes, nose and mouth, even at close distances.
One analysis of the literature on close contact transmission in March 2020concluded it could ‘….find no direct evidence for large droplets as the route oftransmission of any disease.’
Actual evidence demonstrates there was aerosol transmission including overlong distances. While close contact spread does occur (largely via aerosols), it is
not the exclusive method. Belief in close contact droplet transmission originatedin 1910 from a US public health doctor with a phobia of germs, Dr Charles
Chapin, who adamantly opposed the idea of airborne transmission (excluding
in tuberculosis).
With COVID, tracing transmission chains was often impossible, suggesting
potential long-distance transmission. It is agreed that spread through close
contact would be slow (reaching a peak after 14 weeks),, yet COVID and influenza
spread rapidly throughout the country within a few weeks, implying airborne
transmission was the driver. Aerosol physicists who suggested SARS-CoV-2
could spread in aerosols were censored. Professor of Primary Care at Oxford
University Trish Greenhalgh said aerosol scientists were ‘systematically excluded
from key decision-making networks and committees.’
Evidence for aerosol transmission of viruses is well-established and is alsoavailable for SARS-CoV-2. While aerosols have not been definitively proved
to carry SARS-CoV-2 in the outside air, neither has this been ruled out. One
study, across four laboratories, demonstrated that intact whole virus capable ofcausing infection in cells remained at similar levels for the duration of a 16-hour
experiment in aerosols in the air. A different type of coronavirus, causing infectionin pigs at a farm, was shown to spread through the air over 10 miles away andstill be capable of causing infection. In cattle, foot and mouth disease was shownto spread 190 miles over the sea and 37 miles over land from France to the Isleof Wight in 1981. Sand from the Saharan desert, which is certainly larger andheavier than the aerosols, nevertheless can be blown from Africa, turning the160

Too Many Dead
UK sky red and covering cars in a film of dust. In 2018, biologists sampled air
one to two miles above sea level in the mountains of Spain. They demonstrated
that billions of viruses (and bacteria) could be collected each day. Separately,
viruses collected from the air above the sea, travelling miles in the wind, havebeen shown still to be capable of causing infections.
Further evidence of long-distance spread includes a Cambridge University study
which demonstrated that the full range of genetic variation of the virus seen in
the general community was present in the viruses sampled from the care home
population. Also, when the Delta wave arrived in Australia the index source
was believed to be a man who had returned from Sri Lanka and was kept inisolation. Despite his being in isolation there were 44 genetically-related casesthat emerged in the community over the course of two weeks.
Infected people produce thousands of particles per breath, each potentiallyharbouring single-digit numbers of the virus. For other viruses a single virus has
been shown to be enough to cause infection and other studies have shown only
single digit numbers which could be found in a single aerosol. The actual dose
required will be dependent on the level of immune protection of the person being
infected. According to the UK government, sampling of environments where people
have influenza or Monkeypox show far more viral RNA than for SARS-CoV-2, yet
the outbreak data indicate that both are much less transmissible. This suggests that alower viral dose is needed to initiate a SARS-CoV-2 infection than for these otherdiseases.
The prevalent belief that expelled droplets rapidly fall to the ground within ashort distance is a myth, as nearly all matter invisible to the human eye rapidly
evaporates and can remain airborne almost indefinitely, under the influenceof air currents. Aerosol transmission could explain certain enigmatic COVID
outbreaks including an outbreak in December 2021 on the Belgian Antarctic
base despite extensive isolation prior to arrival; an outbreak of a thousand cases
diagnosed within two days of each other in a garment factory in Sri Lanka,
without an identified super-spreader, at a time when there was minimalcommunity COVID, and an outbreak on an Argentinian fishing vessel after afull five weeks at sea, despite everyone testing negative before setting sail. Insteadof acknowledging that this was due to long distance transmission, asymptomatic
spread was blamed. However, there is negligible evidence to support this (seebelow).
As 2020 progressed, evidence for aerosol transmission mounted. Outbreaks
occurred in restaurants, fitness classes and in a room after the index case had
left. Large outbreaks occurred in hospitals despite precautions against droplet
transmission. People in quarantine hotels caught a virus with the same genetic
fingerprint as people down the corridor whom they had never met. Animal

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studies demonstrated that sharing air through ducts between the cages was
sufficient to spread infection. One paper presenting such evidence was describedas misinformation by the WHO. Despite these findings, it took until December
2021 for the WHO to include ‘airborne’ in their guidance. Aerosol spread means
the virus was virtually omnipresent, making exposure virtually inevitable by theend of each wave.
Because spread occurred at long distances through the air, a person who
was sick (and therefore producing plentiful virus) could be an infectious
source to others even if they were isolated indoors. There was evidence from2004 that the original SARS spread in the air currents between apartment
blocks. Lockdown could only ever reduce close contact spread, and not
long-distance aerosol spread. If close contact spread had been the driverof each wave, lockdowns could have slowed the spread. A group at Johns
Hopkins University carried out a meta-analysis of all papers reporting on theeffect of lockdown. They concluded that the benefits of lockdown were ‘little
to none’ and that ‘lockdowns should be rejected out of hand as a pandemic
policy instrument.’
Asymptomatic transmission
With sick people already being isolated, lockdown policy would only reduce
contacts between asymptomatic people. The idea that asymptomatic spreadoccurs also originated with Dr Charles Chapin in 1910. It allowed him toneatly explain why his close contact spread hypothesis could not explain
many instances of infections where there was no contact with a source.
He used this idea to explain ‘the rapidity with which epidemic influenza
spreads, its sudden contemporaneous appearance at many distant points, andthe difficulty of tracing the route of infection.’ The evidence he quoted tosupport this conjecture was based on the culture of a bacterium which is
now known not to be the cause of influenza. From the 1980s, PCR testing
became increasingly available and enabled the detection of insignificant
quantities of microorganism in the airway which rejuvenated the hypothesisof asymptomatic spread in the absence of evidence of actual spread.
In February 2020, Chinese scientists first classified ‘asymptomatic’ COVID
cases as patients not sick enough to have had investigations that can only be
carried out in hospital. However, the WHO’s Dr Maria Van Kerkhove notedin March 2020, Most of the people who were thought to be asymptomatic aren’t
truly asymptomatic. When we went back and interviewed them, most of themsaid, actually I didn’t feel well but I didn’t think it was an important thing tomention. I had a low-grade temperature, or aches, but I didn’t think that counted.
There is evidence to support pre-symptomatic transmission as opposed tonever symptomatic. One estimate put their contribution to the trajectory of162

Too Many Deadan outbreak at 6.4% of total spread.
The idea that large numbers of perfectly healthy people could spread disease
was based only on the fact that perfectly healthy people can test positive andthe fact that people can become infected with no evidence of contact with aknown source. The huge variation in the proportion of asymptomatic ‘cases’,ranging from 4% to 76%, is largely due to erroneous test results and testing
volume rather than its being a feature of the disease like a cough, which
would have a constant percentage.
Most positive cases in various studies eventually developed symptoms.,
Antibody testing also indicated that the number of people developing
antibodies matched the number of symptomatic positives, suggesting thatasymptomatic positives resulted from oversensitive testing.
By the end of 2020, concrete evidence of spread from never-symptomatic
people was limited to a handful of instances of only minimal symptoms.
Later studies did not conclusively demonstrate transmission from theasymptomatic, despite extensive genetic testing., Thus, despite what the
public have been told, the evidence that people who never develop symptoms
have spread disease is practically non-existent.
Who was susceptible?
The COVID models were based on unjustified assumptions and were

Figure 2: Imperial model assumption was that every release of restrictions would lead to arebound and short release periods would need to be followed by repeated restrictions calibrated
to intensive care bed occupancy.

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presented as being far more accurate than they were. One core assumption
was that everyone was susceptible. Without intervention there would be
an unstoppable tsunami of infection that would spread through the whole
population. This did not occur in any country with any wave.
An early COVID outbreak occurred on the Diamond Princess cruise ship
in February 2020, where 7.7% of the mostly elderly passengers and crew had
a symptomatic infection. Only 8-15% of contacts of both pre-Omicron andOmicron variant cases were susceptible to the virus, as seen in household
transmission rates. The percentage of household contacts who were infected
was slightly higher at the beginning of a wave and decreased at the end, leading
to claims of markedly increased transmissibility of each new variant.
In exceptional distinct superspreader outbreaks, a higher proportion would
be susceptible because of the large doses of virus involved – for instance, half
the sailors on the Charles de Gaulle ship. Occasional superspreaders can emit
extraordinary amounts of virus. For example, 90% of virus emission in one
study of 37 infected people came from just two people.
Every COVID wave globally has peaked independent of any changes in humanbehaviour. The cause of such epidemic rises, as seen with influenza, is thought
to be due to conditions ripe for a surge, similar to tomato blight, termed the‘seasonal trigger’ by Dr Edgar Hope-Simpson in 1981. The specific factors
explaining this trigger are still unknown and likely numerous. Such triggers
can happen up to four times a year (see figure 3). He did point out, however,that even in experiments where temperature and humidity were controlled for,it was still easier to infect animals with influenza in the winter.
In Spring 2020, there were large geographical regions, including Southeast
Asia, Oceania, Eastern Europe, and the central USA, where no seasonal trigger
occurred. There was no country or region within these large geographical areasthat acted as an exception to the rule. The reasons are unclear but could involve
existing levels of immunity as well as some unknown environmental factors.
All these areas have since experienced COVID waves, also with no exception.
Even attempts at very harsh lockdowns in China, risking starving people andkilling pets, did not prevent the Omicron surge.
Masking
The recommendation for mask-wearing was primarily predicated on the belief
that COVID spread via droplets at close range, which would hypotheticallybe reduced with a cover. However, this would not substantially affect aerosol
transmission.
The existing body of evidence, including randomised controlled trials, and164

Too Many Dead

Figure 3: Increased testing in 2009 and 2010 revealed up to one wave in each season in
Netherlands and Germany and Eastern Europe.

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comprehensive evidence reviews,, suggests that community-wide mask use
does not substantially reduce respiratory virus spread. Even surgical masks do
not prevent infectious contamination.,
Prior to mid-2020, public health authorities largely agreed that community
mask usage was unnecessary, with some warning of increased risk., A global
shift from recommending against masking to mandating it was not driven
by any new evidence and came without an accompanying assessment of its
effects. This happened despite the World Health Organisation’s document in
December 2020 stating limited and inconsistent evidence for mask efficacy inthe community.
Studies promoting mask effectiveness have often used biased modelling or
selective observational research. Even mask mandates in hospitals did not
significantly alter COVID transmission rates. A recent Cochrane review
echoed previous findings, stating community mask usage ‘probably makes
little or no difference to the outcome of laboratory-confirmed influenza
SARS-CoV-2 compared to not wearing masks.’
The use of face masks, especially in community settings, is linked to avariety of physical, social, and psychological harms. Recent studies suggest
long-term clinical consequences of masking, particularly for vulnerable
groups.
Overall, mask use can impede the human connection, which is vital tothe healing process, particularly those confused, frightened, or suicidal.
Although guidelines on mask-wearing exemptions exist, discrimination
has occurred, making it hard for those vulnerable to mask harms to be able
to avoid them.
Specific adverse effects of masks in healthcare settings include impaired
communication, as they muffle speech and hide non-verbal cues, particularly
affecting the hearing-impaired and elderly, resulting in sub-optimal careto patients. Additionally, masks can increase fall risk in the elderly by
obstructing the lower peripheral visual field and fogging glasses.
Masks increase the work of breathing and can be associated with anincrease in pulse and respiratory rate, with raised CO2 levels and headaches.
They can also raise the risk of acquiring respiratory diseases, potentially
increasing susceptibility to bacterial infection after as little as four hours
of wearing a mask. Moreover, the risks from inhaling micro-plastics andexposure to textile contaminants are yet to be fully understood.,
Masks may also re-traumatise those with a history of abuse as a result of
the sensation of material covering the face and mouth; also, the sight of166

Too Many Deadmasked people can trigger disturbing memories. Furthermore, masks canintensify emotional difficulties for people with mental health issues such as
panic disorders, autism, obsessive-compulsive disorder, and severe healthanxieties.
But more than any of that they have upset child development. Children learn
how to interact by watching faces and responding to them, yet masks weremandatory in some nursery settings where staff cared for babies. A team ofscientists at Brown University showed that children who were at least 15
months old by March 2020 had normal development. However, children
who were born after them had significantly lower verbal, non-verbal, andoverall cognitive scores, suggesting a policy effect on child development.
In the words of Dr Tom Jefferson (the lead author of the Cochrane
Review): ‘We failed to follow an evidence-based approach during the
pandemic. We are now left with the human, social and economic aftermathof evidence-free policies.’

The failures of pharmaceutical and hospitalinterventions

When a patient is sick and especially when in danger of dying the thresholdfor taking a risk on a treatment should be low. However, when giving a healthy
population a vaccine that threshold must be much higher. This principle
was inverted with potentially helpful medicines banned under the guise ofpoorly-evidenced safety concerns, while at the same time safety concerns aboutthe ‘vaccines’ were ignored even as they were injected into ever-younger arms.
For example, an over 80-year-old had a mortality rate of 5%, and for someone
with severe disease it would be higher; add in co-morbidities and the risk could
be doubled. In such circumstances, there should be no hesitation in prescribing
a drug with an established safety record that might reduce mortality.
There are three ways in which pharmaceutical interventions failed. First,
protocol-driven medicine led to changes to normal prescribing practice, andthese were harmful. Secondly, there was a failure to provide early treatments
to the vulnerable and finally there were the failures of the ‘vaccine’ program.
Changes to the practice of medicine
It is imperative that doctors have the freedom to do what they believe isbest for the patient in front of them. Medicine has for several decades been
increasingly controlled through creating protocol-driven guidelines which
restrict what doctors can offer to patients. Although doctors have the ability
to deviate from guidelines, they must be able to provide evidence to defend
their decision to do so and it is far easier for them to simply follow the167

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guidelines. The guidelines often do not have named authors and are notopen to debate or discussion. The consequence is medicine being practisedby nameless bureaucrats, or politicians including those with no medical
qualifications, who have had no contact with the individual patient.
Trust in medicine requires that people know doctors have at heart the bestinterests of the individual patient in front of them and are free to makethose decisions using their professional and ethical judgement and without
entertaining thoughts of the so-called ‘greater good.’
Antibiotics
New healthcare protocols in Spring 2020 resulted in important alterations tothe standard treatment approach for pneumonia. Previously, antibiotics wereadministered to patients presenting with pneumonia regardless of the exact
cause. These could combat possible secondary or incipient bacterial infections
and may have direct anti-inflammatory effects, too., COVID protocols
resulted in patients being told to self-care unless they needed urgent hospital
admission and a positive test often precluded the use of antibiotics, except invulnerable individuals. Effectively, standard treatment was withdrawn.
Ventilators
A belief had been established that having the patient breathe through an
enclosed system was keeping the staff safe by reducing aerosols. In early June
2020, a WHO report said that of those COVID patients treated in critical care,
88% were placed on mechanical ventilation. Ventilator use was a key part ofthe initial response to severe COVID cases, despite subsequent data revealing
an alarmingly high death rate of 80% among ventilated patients, double thatof other conditions causing respiratory distress. This led some physicians to ask
whether the aggressive use of ventilators was causing more harm than good,,
prompting a move towards alternative strategies such as patient positioning
and high-flow oxygen therapy. It is unclear how many lives were lost fromoverly aggressive use of ventilators early on.
Morphine and Midazolam
The use of end-of-life medications, such as morphine and midazolam, hasalso drawn attention and criticism. Both these drugs reduce the respiratory
drive. During an acute respiratory infection, that respiratory drive can belifesaving, even if it is distressing to the patient and those around. These
drugs do not reduce the time to death in palliative care situations, but theireffects in acute respiratory tract infections have not been looked at separately.
Protocols recommended giving these drugs to those with moderate to severe
breathlessness, regardless of concerns about respiratory depression. It is

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Figure 4: Prescriptions given for Midazolam by region. The blue line at the bottom is London.

speculated that fear-induced messaging during the pandemic, giving a false
impression of the chance of survival in the elderly, might have led to a low
threshold for placing patients on end-of-life care, potentially hastening theirdemise.
The counter argument is that these drugs were prescribed at higher levels
because of increasing demand for end-of-life care, because of COVID.
However, a notable increase in midazolam prescriptions was observed during
the first COVID wave in London, but not during the second, despite a larger
wave. The reason for this disparity is unclear and calls for a closer examinationof drug prescription practices.
Finally, the potential misuse of midazolam and the outcome on patients who
might have had reversible causes of deterioration warrant urgent investigation.
Given the discrepancies in prescription practices and the serious accusations
from multiple relatives and whistleblowers, there is a need to rationallyascertain the facts and see if lessons need to be learnt.
Treatment for COVID
Medical treatments for COVID had to strike a balance between the potential
benefits and known or potential risks. However, the approach was skewed,
disregarding the body of knowledge on treating acute respiratory tract
infections and similar coronavirus infections, like SARS1. Instead of adapting

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standard viral pneumonia treatments, protocols were put in place excluding
usual therapies and demanding proof of benefits in randomised clinical trials.
While this standard ensures safety and efficacy, it disregards the urgency of
emergencies, leading to a delayed response to emerging evidence.
COVID involves an initial viraemic phase and a later inflammatory phase,necessitating different treatment strategies at each stage. However, the official
response fixated on finding a magic bullet to cure all stages of the illness.
This approach overlooked the complexity of the disease and the demonstrated
efficacy of multi-drug therapies, including antivirals and vitamins., Witha disproportionate emphasis on treating advanced, hospitalised cases, theimportance of early intervention was underestimated, causing the adopted
strategy to fall short of its aims. The later acceptance of corticosteroids
for treating the inflammatory stage marked a shift in understanding but
underscored the shortcomings of an oversimplified approach.
Hydroxychloroquine
Hydroxychloroquine is a well-established safe drug used as an antimalarial
and to treat autoimmune conditions. It was proposed for early-stage treatmentof COVID, particularly in combination with azithromycin and zinc. However,its use became heavily politicised, and despite many empirical reports of its
success, the drug’s utility was denied in many Western countries. A notable
controversy involved a Lancet paper claiming that hydroxychloroquine
increased mortality, which was later retracted for data fabrication. Yet, thepolicy shifts it triggered were not reversed. The trial often cited as evidence
that ‘hydroxychloroquine doesn’t work’ was actually flawed, administering
highly toxic doses that no successful practitioners were advocating, initiating
treatment well beyond the viraemic stage, and disregarding previous empirical
reports of effective combinations.,,
Remdesivir
Remdesivir, an antiviral drug, also garnered attention. Most countries added
it to national guidance for hospital treatment, starting with the USA on thesame day that trial results were published showing no effect on hospitalisations
or deaths and despite antiviral drugs having limited ability to alter the
inflammatory stage of the disease., Furthermore, a WHO study concluded
that remdesivir had little to no effect on 28-day mortality or hospital-stay
duration, raising questions about its efficacy. One paper showed a benefit bychanging the outcome to length of stay, ignoring readmission rate and ignoringbiases with older, sicker and more ventilated patients in the placebo group. The
BMJ commented that ‘the early adoption of remdesivir was a triumph of hope,and probably marketing, over data.’ Medical regulators reported serious side170

Too Many Deadeffects. In rats and monkeys given remdesivir ‘severe renal toxicity occurred
after short treatment durations,’ according to the Australian regulator. Becauseof this concern and the evidence from animal studies the EU proposed several
safety trials. A total of only163 patients were given Remdesivir in this safety
evaluation before it was terminated because of lack of enrolment.
Ivermectin
Ivermectin has been safely used, even in pregnant women, for the prevention
and treatment of various parasitic diseases. Its potential for use in COVID was
discovered because of its known anti-viral properties and proved effectivenessagainst SARS-CoV-2 in vitro. Advocates emphasised its efficacy in all stagesof COVID, especially when used in combination therapies., Studies thatswung these reviews against Ivermectin had authors who all had conflicts ofinterests, did not give the drug until very late in the illness, underdosed, andone trial even changed the point at which outcomes were measured from 14days to 28 days after symptom onset, at which point people were all either
dead or well, regardless of which treatment they had had. However, despiterobust empirical evidence and meta-analyses, the WHO and other healthinstitutions have resisted accepting Ivermectin as a viable treatment. The
WHO’s own meta-analysis reported a 75% reduction in mortality risk andyet they failed to recommend the use of a very safe drug that has been on the
WHO Essential Medicines list for decades. Its proved prophylactic efficacy
could potentially challenge the ‘vaccine’ market and policy.
Paxlovid
Paxlovid, a Pfizer treatment, was approved in December 2021 for use inhigh-risk groups. Pfizer claimed rebound symptoms occurred at the samerate as in placebo. However, a fifth of treated people experienced reboundsymptoms in the real world. The proposed solution was to study Paxlovid itself
for effectiveness in treating rebound infections, rather than declaring it a failed
drug that gives a high risk of an extended illness.
Vitamin D
Vitamin D, known to improve immunity and suppress inflammatory cytokine
response, has been associated with a reduced risk of acute respiratory infection.
This is important for COVID, as low vitamin D levels predispose people toan increased risk of respiratory infections and pneumonia. Numerous studies
indicate a correlation between low vitamin D levels and more severe COVID
symptoms, increased hospital stays, and higher rates of death. In order to fairly
test whether Vitamin D treatment has a benefit it is imperative that a sufficient
dose is given early in the course of the illness when an enhanced immune

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response has an opportunity to protect against viral attack. Despite someclaims attributing these observations to coincidental correlations, it is essential
to administer high-dose vitamin D early in the course of the illness. High-dose
oral vitamin D has been associated with significantly lower mortality rates.
The consensus among many experts is that for optimal immunity and severe
COVID prevention, a vitamin D level of 100-150 nmol/l is necessary.
Treatment strategies for COVID, consequently, were often contentious. Manyargue that maintaining previous treatment protocols with or without more
efficient and earlier administration of multi-drug therapies in the vulnerable
could have potentially reduced hospitalisations and deaths, suggesting that therigidity of health protocols may have hindered a more effective response to thepandemic.

The effect of ‘vaccines’

Overview of the evidence prior to rollout
It is worth remembering how cautious people were about any novel products thatmight claim to prevent COVID. In February 2020, England’s Chief Medical
Officer said: The rate limiting steps are late clinical trials for safety and efficacy, andthen manufacturing. For a disease with a low (for the sake of argument 1%) mortality
a vaccine has to be very safe so the safety studies can’t be shortcut. So important for thelong run. He was right.
Globally, medical regulators decided that these novel products should beclassified as vaccines. This was a critical decision for three reasons. First, the word
vaccine is heavily loaded. The public perception of a vaccine is that it will stop
infection and will be entirely safe. Secondly, anyone who might have a concern
will be smeared as an anti-vaxxer which is a heavily emotionally-laden term thatcauses people to close their ears to the arguments. Thirdly, despite the reputation
for complete safety, the regulatory pathway for a product described as a vaccine
is not as demanding. The belief that vaccines were safe had led to a circular belief
that vaccines required fewer safety checks than other novel therapies.
All medicines cause side effects and so there is a balance to be struck between
risk and benefit. It is imperative that the cure is not worse than the disease but
for COVID ‘vaccines’ the risks have been played down and the benefits havebeen exaggerated. The benefits are also highly age-dependent because of the big
difference in risk from COVID to older people. No account has been made ofthis in the decisions taken around ‘vaccination’.
Regulatory failure
The belief that vaccines were safe had led to a circular belief that vaccinesrequired fewer safety checks than other novel therapies. Novel vaccines take a172

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decade or more to go through safety checks. Influenza vaccines do not because
a well-established technique using eggs to grow influenza virus was used. Thesenovel drugs were treated like influenza vaccines for regulatory purposes despite
their being a totally novel delivery platform. Drug withdrawals are not unusual,
but it can take a decade or more on average before harmful drugs are withdrawn
from the market by regulators.
Regulators permitted pharmaceutical companies to bypass essential tests
for gene and cancer toxicity, and even studies showing the production andlongevity of spike protein in the body. Pfizer said these studies were ‘notconsidered necessary.’ This was in some contrast to what their trial information
sheet said: Due to the urgent need for a vaccine against Covid-19, with agreementfrom the MHRA, some of the tests usually required for a newly manufactured
vaccine have been modified, in order to make the vaccine available more quickly forassessment. Post-emergency approvals, these tests were not demanded either.
No human studies were conducted to investigate the fate of synthetic modified
RNA, leaving continuing uncertainties about its degradation timeline in the
body. Regulators also allowed termination of the placebo arm of the study
after about three months, although it was known from the Pandemrix vaccine
experience that diagnosis of harmful effects such as narcolepsy could take anaverage of eight months.
Additionally, the lipid nanoparticles used for delivery of modified syntheticmRNA are known to have toxicity. This mechanism of delivery was shelvedin 2016 for gene therapy to treat inherited genetic conditions because of themultiple doses needed. The viral vector used for delivering the AstraZeneca
DNA message had been reported in 2007 to cause platelet activation, which
can lead to blood clots. Nevertheless, regulatory bodies failed to act on these
critical issues. The regulators also did not ensure timely investigations into
deaths, patient complaints, or proper manufacturing processes.
‘Vaccine’ Design Choices
In the ‘vaccine’ design, manufacturers chose to use the entire Wuhan spikesequence rather than its parts, or peptides, known to be safer for vaccine design.
This choice was problematic as the spike protein is the most toxic part of thevirus, causing damage to lungs and vessel walls, and promoting clot formation.
Part of the sequence also closely resembles a bacterial sequence that can binddirectly to a certain type of white blood cells, triggering lethal cytokine storms.
This part of the sequence was heavily mutated in the Omicron variant, making
it less lethal. However, even the most recent injections contained the original
Chinese spike sequence with this dangerous sequence.
AstraZeneca did not modify the sequence. From November 2020 it was clear

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Australian Medical Professionals Societythat parts of AZ spike could be shed outside of cells., Some manufacturers
modified the spike so that it could not bind to the receptor and enter a cell.
This might have reduced some harm from receptor binding but not from theaction of spike within cells. The spike was delivered into cells, so spike wasproduced inside the cells in the first place.
The Pfizer and Moderna clinical trial data reveal a higher rate of serious
adverse reactions from the treatment group compared to any reduction inserious events from COVID. Overall 1 in 800 ‘vaccine’ recipients had a serious
adverse event following vaccination in the trials. However, the claim of over
90% efficacy was highlighted, shaping the decisions on approvals, despite avery large number of injections needed to prevent a single COVID death (seebelow).
Unjustified broadening of ‘vaccine’ approval
Initially, the focus was on protecting the old and vulnerable, who accounted
for 98% of COVID deaths. Despite this, regulators went on to approve thesenovel products for younger and younger patients and even pregnant women.
Approvals for children were granted on the basis of raised antibodies, without
supporting evidence of the effect on COVID itself and despite there being no
antibody level which ensures protection from COVID. The government wording
says the product aims ‘to generate neutralising antibodies, which may contributeto protection against COVID-19’ (emphasis added). This is not based on any
scientific evidence, merely hope. Such regulatory failure in allowing the ‘vaccines’

Figure 5: WHO campaign from August 2020: ‘no-one is safe, unless everyone is safe.’174

Too Many Deadto be given to anyone, and not promptly withdrawing them once evidence ofclinically relevant issues emerged, constitutes a major failing.
Overview of evidence post rollout
After rollout it was possible, with difficulty, to measure the true extent ofadverse reactions from the ‘vaccines’. It became evident that there were potential
risks associated with different batches and there was a detrimental effect of therollout on hospital resources and the workforce. However, the WHO started acampaign in August 2020, pushing the mantra that ‘no-one is safe until everyone
is safe.’
This idea penetrated the consciousness of key decision-makers and the injections
started to be aimed at healthy and ever younger arms. The decision to do thiscame long after it was clear these products were not safe.
The adverse reaction alarm system started to blare red from the beginning of
the rollout. It was claimed this was due to increased awareness of the system.
Over the same time period, reports for other drugs did not rise. The US VAERS
reporting system has been forced to release its data which show signals ofharm for 770 conditions. Two thirds of these showed a stronger safety signal
than for myocarditis and pericarditis, which were acknowledged as a genuine
adverse event in mid-2021. Because the mechanism of harm is likely to be a
combination of effects on the immune and clotting systems, manifesting inautoimmune attack and small vessel damage, it is not surprising that almost
every organ system can be affected. Small vessel damage was not rare with atripling in the first three months in the risk of occlusion of the small vessels of
the eye, where such damage is easily measured.
‘Vaccine’ harm icebergs
It is a general rule of thumb that the first indications of a drug safety problem
will underestimate the size of the problem. This is due to poor measuring of theextent of illness. The opposite is true for infectious disease epidemics. The early
indicators will always overestimate the risk of death per infection because ofunderestimating how many were infected in the first place.
Side effects from COVID ‘vaccines’ can be visualized as icebergs, with only thetip visible above water, and an unknown amount hidden beneath the surface.
Within the first year of the ‘vaccine’ rollout, the tips of three such ‘icebergs’ havebeen recognized – the risks of myocarditis, the risk of unusual brain clots and therisk of Guillain-Barré syndrome. However, the full extent of the harm hidden
below the surface is yet to be measured.
The situation is even more challenging with the rise in a condition that is alreadycommon, such as strokes or heart attacks. The additional diagnoses can be lost

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in the background noise. To identify such a problem, one needs to focus on
younger age groups where the underlying risk is much lower. Such work hasbarely begun.
Heart disease
There were claims that the myocarditis issue was limited to mRNA ‘vaccines’
and to young men but neither of these claims has been sustainable over time.,
Attempts to measure the extent of heart damage in working aged people
showed 3% had evidence of dying heart cells after a dose, while in young males
up to 29% had cardiac symptoms. Heart cells which have died are replacedwith scar tissue. The heart requires smooth electrical conduction and even atiny amount of scarring will increase the risk of a potentially fatal cardiac
rhythm disorder. It has also been hypothesised that the underlying pathology
may not be inflammation but abnormal protein deposition causing a condition
called amyloidosis.
Added to this issue there is the evidence of the notable rise in cardiac arreststhat followed the ‘vaccine’ rollout. A proportion of these could have been due
to such scarring and electrical conduction problems. However, there remains
a question about whether there is also inflammation caused by ‘vaccination’which increases the risk of cardiac vessel narrowing (atherosclerosis) that
leads to the most common cause of a heart attack, a myocardial infarction.
‘Vaccinated’ people had a rise in cardiovascular risk factors that would predicta significantly increased risk of heart disease (from 11% to 25% risk of a heart

Figure 6: Public Health Scotland Data showing ambulance calls for cardiovascular issues.

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attack in five years). An Israeli paper showed a 25% increase in acute coronary
syndrome and cardiac arrest calls in 16-39 year olds in Israel associated withthe first and second doses of ‘vaccine’ but not with COVID infection. The
Scottish data also show a clear rise in cardiac problems in the young.
There has been a notable rise in deaths since the ‘vaccine’ rollout. These deaths
have predominantly been attributed to cardiac causes, particularly ischaemic
heart disease and heart failure (see below). One post mortem demonstrated
death due to continuing ‘vaccine’ injury in the vessels of the heart a full fourmonths after the last dose.
Blood
The first admission of any ‘vaccine’ injury was the occurrence of potentiallyfatal rare brain clots. These were caused by ‘vaccine’-induced antibodies against
platelet factor 4 which results in the activation of clotting. The brain clots
were attributed to AstraZeneca which is based on DNA rather than synthetic
mRNA.
Subsequently, many clinicians raised concerns about what they were seeingin their practice. In particular, post operative clotting disorders, odd clotting
conditions like portal vein thrombosis and clotting of the artery of the gut,
both of which are normally incredibly rare, seemed to become more common
after ‘vaccine’ rollout, including in those given mRNA products. Because theseare so rare it should be possible to measure any increase but such studies have
not yet been published.
Regulators have since acknowledged a risk of abnormal menstruation. An
FDA paper showed an increased risk of pulmonary embolism but this finding
was denied because of how the data were analysed. Other conditions show amarkedly low incidence immediately after ‘vaccination’ because ‘the healthyvaccinee effect’ means that people self-select when to be ‘vaccinated’ such thatnew diagnoses are rare afterwards. That means the baseline for comparison
should be the lower rate seen for other conditions after ‘vaccination’, not theoverall higher rates seen in the whole population. However, the regulators
invariably choose a higher threshold and then claim there is no signal present.
Neurological and autoimmune
The third iceberg is the least well defined. Although the government recognised‘vaccination’ caused Guillain Barré syndrome in 2021, they continued to advisethat those affected should receive further doses.
Many patients complained of new conditions. These included tremors,
postural orthostatic tachycardia syndrome, (POTS, a disabling condition

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where standing or sitting up leads to a racing heartbeat as blood flow to theheart and brain fails to be maintained), and various autoimmune conditions.
The medical establishment dismissed all these issues as coincidental. People
do develop new onset symptoms and conditions randomly and the assumption
was that the attribution of these problems to the ‘vaccine’ was just a case of
unfortunate timing and ignorant attribution on the part of those suffering.
However, an important study spanning six neurological departments in the
USA demonstrated that these patients have an underlying mechanisms fortheir neurological symptoms. The study only described the presentation of
23 patients, 92% female, all of whom developed symptoms within days of‘vaccination’ (half within minutes or hours of their dose). Those with priorconditions or risk factors for neurological problems or other causes for small
nerve damage were excluded. None had had symptomatic COVID. They all
had abnormal sensations (especially burning) in face or limbs and 60% had
blood pressure drops on standing, heat intolerance and palpitations. Half of
those tested had damage to the autonomic nervous system preventing normal
sweating or leading to POTS.
These doctors thoroughly investigated these patients and found skin biopsies
demonstrated nerve abnormalities. When there has been an immune reaction,
where antibodies have bound a target leading to the triggering of immune
cascades, a marker is left behind at the site called ‘C4d.’ This marker was
identified at a higher rate in the blood vessel walls of the patients than was thecase with controls. Some of those with normal skin biopsies had demonstrable
abnormalities of the nerves elsewhere, such as those that control bloodpressure and heart rates. Two out of the five tested showed protein within
the cerebrospinal fluid in keeping with raised antibody levels and indicating
inflammation.
These doctors successfully treated their patients with corticosteroids or
immunoglobulins which indicates an underlying autoimmune pathology.
(An immune modulation model of disease has also been described for otherinjuries including myocarditis, Guillain Barré Syndrome and the clotting
disorder Thrombotic Thrombocytopenic Purpura).
There may yet be further studies that attempt to measure the extent of the
injuries, but they will be faced with a problem. Patients with symptoms that
do not neatly fit into categories that doctors commonly see, such as new-onset
tremor, sensory changes or palpitations, are often categorised as having
psychosomatic problems or anxiety. For those with palpitations there may
even be some truth in that as anxiety is a natural consequence of experiencing
a racing heartbeat. This misclassification of ‘vaccine’ injury means that those178

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Figure 7: Cases of myocarditis and pericarditis compared to overall emergency visits in 40
hospitals in USA over time.

trying to quantify the extent of these icebergs may never be able to see into thedepths to get a clear measure.
The known adverse effects of these novel COVID ‘vaccines’, like myocarditis,

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brain clots, and Guillain-Barré syndrome, as well as the rise in common
conditions post-‘vaccination’, deserve serious attention. We must improvemonitoring and understanding of these risks, without discounting patient
experiences as mere coincidences.
How many people were affected?
It has been difficult to measure the adverse reactions from the ‘vaccines’ forthree separate reasons: some were uncommon, some emerged a time after the
injection, and also, the risk appears to be batch-dependent.
Rare side effects such as brain clots and myocarditis are easier to be sure aboutbecause they occur shortly after injection and the effect on the total numbers of
those rare conditions is large. Demonstrating a raised incidence for a common
condition can only occur if there is a marked outcome causing a large numberof additional cases.
There is an added complication that certain batches of ‘vaccine’ have had
a much higher adverse reaction and death rate than others. A Danish study
showed the rates of reports per dose fell into three categories with high,medium or low adverse events. One batch of Pfizer-BioNTech resulted in thehospitalisation of 120 children in Vietnam.
MHRA, the Medicines and Healthcare products Regulatory Agency, said they

Figure 8: NHS England hospital bed occupancy by diagnosis (Pale blue = non-covid, lime green= COVID, dark green = available beds and dark blue = incidental COVID diagnosis). Dotted
red line shows total NHS bed capacity in England in January 2021.

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Too Many Deadwould do a prospective survey of adverse events but have never publishedtheir results. A German survey of 500,000 people found events that led tohospitalisation, life-changing disability or death occurred in 1 in 142 people,
for AstraZeneca and 1 in 500 for Pfizer-BioNTech. Those will include a small
number of genuine coincidences. Reports filed by German doctors put thefigure for serious reactions at 1 in 3,300 by September 2022.
The ‘vaccine’ rollout coincided with a rise in pressures on hospital resources.
Whereas with COVID there was never pressure on the number of empty
hospital beds, once the ‘vaccine’ rolled out there were increasing numbersof inpatients, exceeding the expanded total capacity from January 2021.
At the same time as there were reports of an accident and emergency crisisin the UK, hospitals were overwhelmed in the USA. COVID had neveroverwhelmed total hospital bed capacity anywhere. In late 2021, the ‘vaccinated’
were attending the emergency department five times more frequently than the

Figure 9: Category 1 ambulance calls (for life threatening conditions) over time in England.

‘unvaccinated’. All ambulance calls in England for life-threatening conditions
increased by 25 percent, an extra 500 calls every day from June 2021. It is
worth reiterating the point evidenced above that there was an increase in
cardiac emergencies in this period that correlated with ‘vaccination’.
It was claimed this was all due to the Delta wave or long COVID effects.
But not everywhere had COVID before Omicron; there is a control group.
Australia had had minimal COVID prior to the rollout of the ‘vaccines’ and had

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Figure 10: Total cumulative COVID cases diagnosed in Southern and Western Australia

opposite seasons. Yet, its hospitals were also overwhelmed. Queensland doctors
called the problem a ‘ticking time bomb’ in April 2021 and described a ‘floodof patients.’ By May 2021, there was an ambulance crisis even though there
were fewer than 100 COVID patients in all hospitals in Australia. By October,despite its being springtime in Australia, headlines reported on ambulances
unable to drop off patients in hospitals that were at full capacity. In Oct 2021,
Mark McGowan, Premier of Western Australia, said he could not explain the
overwhelmed hospitals: ‘Our hospitals are under enormous pressure. This has
been something no one has ever seen before. Why it is, is hard to know.’ In April
2022, Yvette D’ath, Queensland Health Minister, said she could not explain
the rise in the most urgent ambulance calls (‘code ones’): ‘I don’t think anyone
can explain why we saw a 40% jump in code ones... We just had a lot of heart
attacks and chest pains and trouble breathing, respiratory issues. Sometimes
you can’t explain why those things happen but unfortunately they do.’Western Australia and South Australia had almost no COVID before Omicron.
Up to mid-December 2021, Western and Southern Australia had had around1000 cases each. The graph shows total cumulative cases by state.
Despite having fewer than 1000 COVID cases prior to December 2021, South
Australia saw 25,800 extra ambulance calls (mostly cardiac) in the year from
July 2020 to June 2021 compared to previous years. There was a year-on-yearincrease from 2018 to 2019 and 2019 to 2020 but the rise in 2021 was about
double the increase seen in the preceding two years. There was a clear rise inattendances for particular conditions which correlated with the ‘vaccine’ rollout.

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A Freedom of Information request showed that South Australia normally seesaround 1,300 cardiac presentations per month for 15-44 year olds. This rose
sharply in August 2021 with ‘vaccine’ rollout, peaking at 2,172 in December,before COVID hit. T﻿his was not due to COVID – the whole state had seen
only 1000 COVID cases by 15th December.

Figure 11: South Australian emergency cardiac presentations in 15-44 year olds in top graph.

A similar control group is Singapore which also had minimal COVID prior to
Omicron but saw an excess of cardiovascular deaths from 2021, although data
have been annualised.
In England, each COVID wave saw a rise in cardiac or respiratory arrest calls
but there was an additional rise seen from the ‘vaccine’ rollout, which led tonumbers far higher than in previous years. Heart attacks can be caused by direct
damage to the electrical circuitry of the heart, for instance, from inflammationor scarring because of myocarditis, or else can be due to slow narrowing of thevessel walls supplying the heart muscle because of inflammation. In either case,a time lag should be expected after an event that contributes to the cause.
As well as sickness needing immediate care, there was a notable rise in people
who were not working because of long-term sickness which was not seen in2020 but began in English Spring 2021 when the ‘vaccine’ was rolled out to the183Australian Medical Professionals Society

Figure 12: UKHSA data for ambulance calls in England for cardiac or respiratory arrests.

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Figure 13a and 13b: The rate of economically inactive working-aged people due to long-term
sickness in England, with females in the bottom graph and males in the top graph.

working aged population. In May 2022 the Governor of the Bank of England

Figure 14: USA data showing rise in people over 16 years of age with a disability.

said there were 320,000 more people not working because they were sick. At
the time it was estimated from self-reporting that only 80,000 had ever had
symptoms of long COVID.

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Figure 15: COVID case rates over time by number of doses given.

Figure 16: Excess mortality in England and Wales and ‘vaccine’ doses given in males 15-19.

The rise was also evident in the USA disability data.
As if that were not bad enough, we now have evidence that the more doses
given the higher the COVID rates. Repeated injections have been shown toswitch the immune response into the same mode used to prevent an immune
response to food such that the spike protein is ignored entirely, increasing therisk of infection.

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The adverse reaction alarm system has been blaring since early 2021, with reportsof serious reactions, hospitalizations, and deaths from COVID ‘vaccines.’
The rollout of the ‘vaccines’ has coincided with a rise in hospitalizations andambulance calls for heart attacks and other serious conditions across the world.
The rise in long-term sickness and disability with the exact same timing is also
concerning. It is clear that these novel products are not without risks, andmore research is needed to fully understand the scope of those risks.
Overview of evidence around deaths
Of the death reported as potentially due to ‘vaccination’ in VAERS in 2021,
there were 60% more males. This suggests these were not random but causedby spike-induced pathology that also caused more males to die of COVID.
If there was a small risk of increased death due to the ‘vaccine’ in the period
shortly after ‘vaccination’ then this would be hard to detect in age groups
where there were high numbers of background deaths. However, in younger
age groups, where there are fewer deaths normally, a signal might be noted,
and this is what we did actually see for 15-19 year old males. In females there

Figure 17: UKHSA data showing deaths mentioning ischaemic heart disease; turquoise bars
show total excess deaths, and pale grey bars above the purple dotted line indicate non-COVID
excess deaths.

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Figure 18: UKHSA data showing deaths mentioning heart failure.

was no signal.
England saw a stepwise rise in cardiac deaths after the ‘vaccine’ rollout,

Figure 19: UKHSA excess deaths for 50-64-year-olds.

separate from COVID. This included deaths attributed to ischaemic heartdisease. Expected levels are harder to predict in the first months of the year
where there is wide annual variation and a mild winter season for viral deaths
and low numbers of remaining frail elderly meant the step-wise increase was

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Too Many Deadnot evident for a short period. Heart failure deaths show a similar pattern.

Figure 20a and 20b: Australian total deaths in red plotted against average and range fromprevious years in 2021 (top graph) and 2022 and 2023 (bottom graph).

A quiet winter for deaths in the elderly (which account for most deaths everyyear) in 2021-22 hides the high numbers of deaths in the young and cardiac
deaths. Here are deaths in the 50-64 year old age group:
Australia also saw a rise in deaths before any significant COVID and it has

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just grown worse since. Note the government chose to plot COVID infections
rather than COVID deaths on this chart. Apart from a quiet winter season in
2021, there was an excess mortality (red line) above the 2015-2019 baseline

Figure 21: Cumulative global covid attributed deaths (in black) compared tomodelled estimate of what would have happened without ‘vaccines.’(orange line) which was more marked from February 2021. Note the marked
increase in the ‘normal’ baseline in the more recent graph.
Finally there have been several studies demonstrating a correlation between

Figure 22: United states COVID hospitalisations per million.

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Too Many Dead‘vaccination’ rates and COVID mortality in 2022 comparing geographicalregions. This is particularly damning given the marked socioeconomic
differences between the ‘vaccinated’ and ‘unvaccinated’ populations whichmeant their pre-‘vaccination’ mortality rate was higher.
Some people have accepted that there were harms from these novel products
but then justify it in their minds saying they saved millions of lives. The
evidence does not support that position. The claims are based on fantasy
modelling which supposes there would have been a huge increase in COVID
deaths in the absence of injections. In reality, the global cumulative deaths(shown in the graph below) increased at a steady trajectory until Omicron
arrived. The less deadly Omicron caused the rate of accumulation of death

Figure 23: United states and European COVID deaths per million.

to slow in a way that ‘vaccines’ had failed to.

It is true that the deaths per case fell with Delta, but we have no influenza
data to compare that to. All we know is that for influenza each wave has asimilar number of hospitalisations and deaths. Here is what happened to
USA hospitalisations despite ‘vaccination’. Omicron had an effect where‘vaccination’ had not.
Here are COVID deaths per million in USA and Europe as a whole. Again,

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Figure 24: Cumulative COVID deaths per million.

Figure 25: Cumulative COVID-attributed deaths in Europe in the first and autumn wave.

it was Omicron that reduced mortality from early 2022.
We can further show this by looking at mortality in the first wave in places
that did not have significant COVID before Omicron. These places reached
500-600 deaths per million by November, 2022.
That was the same order as Europe saw in the first wave despite extensive‘vaccination’ and a less lethal variant.

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What did the trials show? AZ issued a press release claiming 100% efficacy
against hospitalisation and death after only two severe COVID hospitalisations
and one death in the placebo arm. This claim was repeated widely and was
believed but it is clearly not adequate evidence.
Pfizer-BioNTech reported in their six-month follow-up paper that the numberof deaths from any cause was higher in the group given a ‘vaccine’, which had 15
deaths, compared to 14 in the placebo group., Of these deaths there was only one

Figure 26: Pfizer-BioNTech trial results.

COVID pneumonia death which occurred in the ‘vaccine’ group. Two deaths in
the placebo arm were attributed to COVID in the absence of pneumonia. At
best, therefore, injection of nearly 22,000 people prevented one death over thecourse of several months. The trial was global and ran from July to November
2020 including places in the Southern Hemisphere, Brazil, Argentina and South
Africa which had notable COVID at the time, and also including the Autumn
waves in the Northern Hemisphere. Consequently, the real-world ability of
injection to prevent COVID deaths can be seen for the very low effect it could
have.
Of note, there were four cases of cardiac arrest in the group that received the
Pfizer-BioNTech ‘vaccine’, compared to one in the placebo group. These are low
numbers and it is hard to interpret how much of a safety signal they represent,
which in itself illustrates the inadequacy even of a trial including 44,000 people atproving the safety of a product. In addition, Pfizer-BioNTech gave a submission
to the FDA based on the same period of follow-up, but after more time had
elapsed for them to collect more comprehensive data. In this submission theysaid there had been 21 deaths in the ‘vaccinated’ group compared to 17 in theplacebo group. The whole purpose of placebo-controlled trials is to allow thistype of direct comparison to be made. There is a concerning sign here that the‘vaccines’ not only failed to prevent death but may have introduced an increased
risk of death from other causes.
The data suggest that there may be a small risk of increased death immediately

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after ‘vaccination’ evident from the data in younger males. The increase in deathsin the months after injection including the increases in cardiac and respiratoryarrest, as well as the rise in excess mortality in countries with minimal COVID
prior to the ‘vaccine’ rollout, cannot be dismissed as mere coincidences. It is time
for an honest conversation about the risks and benefits of these novel products.
The reality is that the evidence does not support the notion that these injections
saved millions of lives, and the harms and deaths caused by the ‘vaccines’ cannot
be ignored.

Conclusion

The global response to COVID caused more harm than good. As we grapple
with the aftermath of certain policy decisions, it becomes clear that, in the mostgenerous light, these were based on flawed assumptions. The best we can hopefor is that lessons are learnt for the future. Foundational medical ethics including
individual autonomy and human rights must be safeguarded even in the face ofa crisis. An open and vigorous debate that allows for the inclusion of multiple
perspectives is crucial in all circumstances and dissenting views must not besilenced. Interventions must never be forced on populations and attempts at
persuasion need a sound evidence base regardless of the level of fear. Harms havebeen caused in terms of the economy, the effects on children and young people
on physical and mental health; there has been loss of trust in doctors, scientists
and government. The abandonment of ethical principles and rights has been
immense and should not be excused as justified.
Dr Clare Craig BM BCh FRCPath is a qualified
pathologist, who worked in the NHS and reachedconsultant level in 2009. She specialized in cancer
diagnostics including diagnostic testing for cancer
within mass screening programs. She was the
day-to-day pathology lead for the cancer arm of the
100,000 Genomes Project. She was clinical lead forthe data team and led on research and development
projects at Genomics England, writing national
guidance and helping build bespoke software. Dr
Craig advocates evidence-based policy around
COVID issues, and the importance of upholding basic
medical ethical principles including informed consent
and bodily autonomy. She is Co-chair of the Health
Advisory and Recovery Team, a voluntary body of
professionals educating the public on COVID issues.
Her first book is Expired – COVID, the untold story.

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Curing the pandemic of
misinformation on COVID-19
mRNA vaccines through real
evidence-based medicine
Part 1*by Aseem Malhotra

Background: In response to severe acute respiratory syndrome coronavirus 2(SARS-CoV-2), several new pharmaceutical agents have been administered
to billions of people worldwide, including the young and healthy at little
risk from the virus. Considerable leeway has been afforded in terms of the
pre-clinical and clinical testing of these agents, despite an entirely novel
mechanism of action and concerning biodistribution characteristics.
Aim: To gain a better understanding of the true benefits and potential harmsof the messenger ribonucleic acid (mRNA) coronavirus disease (COVID)
vaccines.
Methods: A narrative review of the evidence from randomised trials andreal-world data of the COVID mRNA products with special emphasis on
Pfizer-BioNTech vaccine.
Results: In the non-elderly population the ‘number needed to treat’ toprevent a single death runs into the thousands. Re-analysis of randomised
controlled trials using the messenger ribonucleic acid (mRNA) technology
suggests a greater risk of serious adverse events from the vaccines thanbeing hospitalised from COVID-19. Pharmacovigilance systems and*First Published: Journal of Insulin Resistance ISSN: (Online) 2519-7533, (Print) 2412-2785

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real-world safety data, coupled with plausible mechanisms of harm, aredeeply concerning, especially in relation to cardiovascular safety. Mirroringa potential signal from the Pfizer Phase 3 trial, a significant rise in cardiac
arrest calls to ambulances in England was seen in 2021, with similar data
emerging from Israel in the 16–39-year-old age group.
Conclusion: It cannot be said that the consent to receive these agents was
fully informed, as is required ethically and legally. A pause and reappraisal of
global vaccination policies for COVID-19 is long overdue.

Vaccines save lives

The development of safe and highly effective vaccines during the latter halfof the 20th century has been one of medicine’s greatest achievements. The
prominent scars on my left arm are a constant reminder of the success of ourability to curb some of the deadliest diseases such as smallpox, tuberculosis(TB), measles, mumps and rubella to name but a few. Collectively, traditional
vaccines are estimated to save approximately 4–5 million lives per year.[1] The
greatest success of vaccination was the global eradication of smallpox, which
had a 30% mortality rate.[2]In other words, almost one in three people who contracted it died. The
development of a safe and effective vaccine after much trial and error resulted
in 95 out of 100 people being protected from symptomatic infection fromsmallpox with immunity lasting five years, which by the 1970s resulted incomplete eradication of the virus. Similarly, one dose of the measles vaccine
is said to be ‘95% effective’. What is meant by this? What most people would
assume is that 95 out of 100 who take the inoculation are protected fromsymptomatic infection and transmission, and they also have long-lasting
immunity. Similarly, if exposed to chickenpox, only five out of 100 vaccinatedchildren will catch it.
Vaccines are also some of the safest interventions in the world whencompared to most drugs used in chronic disease management, as indeed weshould expect, given that they are being administered to prevent somethingin healthy people, not treat an illness. It was consequently welcome news thatin the summer of 2020, several drug companies including both Pfizer andModerna announced the results of their two-month randomised controlled
trial in which they had developed a vaccine with more than ‘95% effectiveness’at preventing infection from what at the time was the predominantly
circulating strain of the coronavirus disease 2019 (COVID-19).

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A doctor’s experience
Volunteering in a vaccine centre, I was one of the first to receive two doses of
Pfizer’s messenger ribonucleic acid (mRNA) vaccine, at the end of January
2021. Although I knew my individual risk was small from COVID-19 at age
43 with optimal metabolic health, the main reason I took the injection was
to prevent transmission of the virus to my vulnerable patients. During early
2021, I was both surprised and concerned by a number of my vaccine-hesitantpatients and people in my social network who were asking me to comment
on what I regarded at the time as merely ‘anti-vax’ propaganda.
I was asked to appear on Good Morning Britain after a previously
vaccine-hesitant film director Gurinder Chadha, Order of the British
Empire (OBE), who was also interviewed, explained that I convinced her totake the injection.
But a very unexpected and extremely harrowing personal tragedy was tohappen a few months later that would be the start of my own journey into
what would ultimately prove to be a revelatory and eye-opening experienceso profound that after six months of critically appraising the data myself,
speaking to eminent scientists involved in COVID-19 research, vaccine
safety and development, and two investigative medical journalists, I haveslowly and reluctantly concluded that contrary to my own initial dogmatic
beliefs, Pfizer’s mRNA vaccine is far from being as safe and effective as wefirst thought. This critical appraisal is based upon the analytical frameworkfor practising and teaching evidence-based medicine, specifically utilisingindividual clinical expertise and or experience with use of the best available
evidence and taking into consideration patient preferences and values.

A case study

Case studies are a useful way of conveying complex clinical information andcan elicit useful data that would be lost or not be made apparent in thesummary results of a clinical trial.
On 26 July 2021, my father, Dr Kailash Chand OBE, former deputy
chair of the British Medical Association (BMA) and its honorary vicepresident (who had also taken both doses of the Pfizer mRNA vaccine six
months earlier), suffered a cardiac arrest at home after experiencing chest
pain. A subsequent inquiry revealed that a notable ambulance delay likely
contributed to his death.[3] But his post-mortem findings are what I foundparticularly shocking and inexplicable. Two of his three major arteries had
severe blockages: 90% blockage in his left anterior descending artery and a

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75% blockage in his right coronary. Given that he was an extremely fit andactive 73-year-old man, having walked an average of 10–15,000 steps/day
during the whole of lockdown, this was a shock to everyone who knew him,
but most of all to me. I knew his medical history and lifestyle habits in great
detail. My father, who had been a keen sportsman all his life, was fitter thanthe overwhelming majority of men his age. Since the previous heart scans(a few years earlier, which had revealed no significant problems with perfect
blood flow throughout his arteries and only mild furring), he had quit sugar,
lost belly fat, reduced the dose of his blood pressure pills, started regular
meditation, reversed his prediabetes and even greatly dropped his blood
triglycerides, substantially improving his cholesterol profile.
I could not explain his post-mortem findings, especially as there was no evidenceof an actual heart attack but with severe blockages. This was precisely my own
special area of research. That is, how to delay progression of heart disease andeven potentially reverse it. In fact, in my own clinic, I successfully prescribe
a lifestyle protocol to my patients on the best available evidence on how toachieve this. I’ve even co-authored a high-impact peer-reviewed paper with
two internationally reputed cardiologists (both editors of medical journals) on
shifting the paradigm on how to most effectively prevent heart disease throughlifestyle changes.[4] We emphasised the fact that coronary artery disease is achronic inflammatory condition that is exacerbated by insulin resistance. Then,in November 2021, I was made aware of a peer-reviewed abstract published in
Circulation, with concerning findings. In over 500 middle-aged patients underregular follow-up, using a predictive score model based on inflammatory
markers that are strongly correlated with risk of heart attack, the mRNA
vaccine was associated with substantially increasing the risk of a coronary
event within five years from 11% pre-mRNA vaccine to 25% 2–10 weeks post
mRNA vaccine. An early and relevant criticism of the validity of the findings
was that there was no control group, but nevertheless, even if partially correct,
that would mean that there would be a large acceleration in progression ofcoronary artery disease, and more importantly heart attack risk, within monthsof taking the injection.[5] I wondered whether my father’s Pfizer vaccination,
which he received six months earlier, could have contributed to his unexplainedpremature death and so I began to critically appraise the data.

Questioning the data

I recalled a cardiologist colleague of mine informing me, to my astonishment at
the time, that he had made a decision not to take the vaccine for a number ofreasons, including his personal low background COVID-19 risk (see Table 1)[6]and concerns regarding unknown short-and longer-term harms. One thing thatalarmed him about Pfizer’s pivotal mRNA trial published in The New England198

Too Many Dead
Journal of Medicine was the data in the supplementary appendix, specifically that
there were four cardiac arrests in those who took the vaccine versus only one in
the placebo group.[7] These figures were small in absolute terms and did not reachstatistical significance in the trial, suggesting that it may just be coincidence, but
without further studies it was not possible to rule out this being a genuinely
causal relationship (especially without access to the raw data), in which case it
could have the effect of causing a surge in cardiac arrests once the vaccine wasrolled out to tens of millions of people across the globe.
Table 1: Infection fatality rate of ancestral variants of COVID-19 pre-vaccination by age.
Source: Adapted from Axfors C, Ioannidis JPA. Infection fatality rate of COVID-19 incommunity-dwelling elderly populations. Eur J Epidemiol. In press 2022;37(3):235–249.
https://doi.org/10.1007/s10654-022-00853-w IFR = infection fatality rate.

Table 2: Deaths prevented, and number needed to vaccinate to prevent a death based on death
rates and case fatality rates from UKHSA data for England during Delta wave.
Source: Adapted from HART. How many injections to prevent one covid death? [homepage on
the Internet]. No date. Available from: https://www.hartgroup.org/number-needed-to-vaccinate/
UKHSA, United Kingdom Health Security Agency.

In terms of efficacy, headlines around the world made very bold claims of 95%
effectiveness, the interchangeable use of ‘efficacy’ and ‘effectiveness’ glossing
over the big difference between controlled trial and real-world conditions.[8] It

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would be understandable for the lay public and doctors to interpret this that
if 100 people are vaccinated then 95% of people would be protected fromgetting the infection. Even the Centers for Disease Control (CDC) director
Rochelle Walensky recently admitted in an interview that it was initial news
from CNN that made her optimistic that the vaccine would significantly stoptransmission and infection, but this was later to be proved far from true for
the COVID-19 vaccines.[9] The original trial revealed that a person was 95%‘less likely’ to catch the autumn 2020 variant of COVID-19. This is known
in medical-speak as relative risk reduction, but to know the true value ofany treatment one needs to understand for that person, by how much theirindividual risk is reduced by the intervention – that is, the absolute individual
risk reduction.
Importantly, it turns out the trial results suggest that the vaccine was only
preventing a person from having a symptomatic positive test, and the absoluterisk reduction for this was 0.84% (0.88% reduced to 0.04%). In other words, if
10,000 people had been vaccinated and 10,000 had not, for every 10,000 people
vaccinated in trial 4 would have tested positive with symptoms compared to 88
who were unvaccinated. Even in the unvaccinated group, 9,912 of the 10,000(over 99%) would not have tested positive during the trial period. Anotherway of expressing this is that it would be necessary to vaccinate 119 people
to prevent one such symptomatic positive test (assumed to be indicative of an
infection, which, in itself, is potentially misleading but beyond the scope of
this article).[10]This absolute risk reduction figure (0.84%) is extremely important for doctors
and patients to know, but how many of them were told this when theyreceived the injection? Transparent communication of risk and benefit of any
intervention is a core principle of ethical evidence-based medical practice andinformed consent.[11]The Academy of Medical Royal Colleges made this clear in a paper publishedin the BMJ in 2015.[12] A co-author at the time was also the then chair of
the General Medical Council. In fact, in a 2009 World Health Organization(WHO) bulletin Gerd Gigerenzer, the director of the Max Planck Institute,stated, ‘It’s an ethical imperative that every doctor and patient understandthe difference between relative and absolute risks to protect patients against
unnecessary anxiety and manipulation’.[13]Contrary to popular belief, what the trial did not show was any statisticallysignificant reduction in serious illness or COVID-19 mortality from thevaccine over the 6-month period of the trial, but the actual numbers of deaths(attributed to COVID-19) are still important to note. There were only two
deaths from COVID-19 in the placebo group and one death from COVID-19

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Too Many Deadin the vaccine group. Looking at all-cause mortality over a longer period, there
were actually slightly more deaths[14] in the vaccine group (19 deaths) thanin the placebo group (17 deaths). Also of note was the extremely low rate of
COVID-19 illness classed as severe in the placebo group (nine severe cases outof 21,686 subjects, 0.04%), reflecting a very low risk of severe illness even inregions chosen for the trial because of perceived high prevalence of infection.
Finally, the trials in children did not even show a reduction in symptomatic
infections but instead used the surrogate measure of antibody levels in the
blood to define efficacy, even though the relationship between Wuhan-spike
vaccine-induced antibody levels and protection from infection is tenuous, atbest. The Food and Drug Administration’s (FDA’s) own website states that:[R]esults from currently authorised SARS-COV-2 antibody tests
should not be used to evaluate a person’s level of immunity or protection
from COVID-19 at any time, and especially after the person received a
COVID-19 vaccination.[15]Now that we know what the published trial did and did not show in terms ofthe vaccine efficacy, we can attempt to extrapolate what the effect of the vaccine
would be in reducing mortality or any other adverse outcome from the virus.
If there is a 1 in 119 chance the vaccine protects against getting symptomatic
infection from ancestral variants, then to find the protection against death, thisfigure (n = 119) must be multiplied by the number of infections that lead to asingle death for each age group. This would give (for up to two months after
the inoculation) the absolute risk reduction (for death) from the vaccine. For
example, if my risk at age 44 from dying from Delta (should I get infected withit) is 1 in 3,000, then the absolute risk reduction from the vaccine protecting
me from death is 1 over 3,000 multiplied by 119, that is, 1 per 357,000.
Of course, even for those people who do become infected the vaccination may
provide some protection against death. From observational data it is possible tocalculate the number who would need to be vaccinated to prevent a COVID-19
death. For example, comparing the population death rates[16] during the Delta
wave gives 230 for people over 80 needing to be vaccinated to prevent a single
death in that period with that number rising to 520 for people in their 70s
and 10,000 for people in their 40s (see Table 2 and Figure 1[17]). However,these figures will be distorted by inaccuracies in the measure of the size ofthe unvaccinated population. As also pointed out in a recent editorial by John
Ioannidis in BMJ Evidence-Based Medicine the inferred efficacy of the vaccine
from non-randomised studies may be ‘spurious’, with bias being generated
by ‘pre-existing immunity, vaccination misclassification, exposure differences,
testing, disease risk factor confounding, hospital admission decision, treatment
use differences and death attribution’.[18]201Australian Medical Professionals Society

Figure 1: Calculation of number needed to be vaccinated from COVID-19 death rates invaccinated and unvaccinated from UKHSA data for England during the Delta wave. The
difference between the deaths that occurred in the vaccinated and that would have occurred
if they had the same rate as the unvaccinated was used to calculate the number of people who
would need to be vaccinated to prevent a single death.

Source: Fraiman J, Erviti J, Jones M, et al. Serious adverse events of special interest followingmRNA COVID-19 vaccination in randomized trials in adults. Vaccine. 2022 Aug 30:S0264410X(22)01028-3. https://doi.org/10.1016/j.vaccine.2022.08.036 Note: Difference between
proportion of unvaccinated and vaccinated population dying with COVID-19 from 27 Aug to
16 Dec 2021. UKHSA, United Kingdom Health Security Agency.

These numbers are for the whole population of England and do not necessarily
apply to the healthy; more than 95% of deaths were in people with pre-existing
conditions.[19] It is also important to note that the vaccinated and unvaccinatedpopulations are different in other ways, which could bias the death data. For
example, the unvaccinated are more likely to be from a lower socioeconomic
demographic, which puts them at a greater risk of severe illness or deathshould they be infected.
Professor Carl Heneghan, the director of the Centre of Evidence Based
Medicine in Oxford, has explained his own clinical experience of healthy user
bias. Some of his own patients who ended up in intensive care unit (ICU) with
COVID-19 (classified as unvaccinated) did not take the vaccine because they
were already suffering from terminal illness.
Given these limitations, the above figures are likely an overestimate of theindividual benefit of vaccination; the open and frank discussion of such
uncertainties is an essential component of shared decision-making.
What should be part of the shared decision-making informed consent
discussion when any member of the public is considering taking the injection

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Too Many Deadis something along these lines: Depending on your age, several hundreds or
thousands of people like you would need to be injected in order to prevent
one person from dying from the Delta variant of COVID-19 over a period ofaround three months. For the over 80s, this figure is at least 230, but it rises
the younger you are, reaching at least 2,600 for people in their 50s, 10,000
for those in their 40s, and 93,000 for those between 18 and 29 years. For
Omicron, which has been shown to be 30% to 50% less lethal, it means thatmany more people would need to be vaccinated to prevent one death. Howlong any protection actually lasts is unknown; boosters are currently being
recommended after as short a period as four months in some countries.
But how many people have had a conversation that even approaches an
explanation similar to that? This is before we get into the known, unknown
and as yet to be fully-quantified harms.
Although many have proposed that Omicron is intrinsically less lethal(supported by observed molecular differences between Omicron and the
Wuhan-type virus) immunity built up by prior exposure protecting against
severe illness is likely to be relevant to some extent as well. The critical point tonote is that, whether it is a viral or immune-related phenomenon, the milder
nature of Omicron is evident in the unvaccinated and therefore the reduction
in mortality should not be attributed to vaccines.

What are the harms?

Concerns have already been raised about the under-reporting of adverse eventsin the clinical trials for the COVID-19 vaccines. Investigative medical reporter
Dr Maryanne Demasi analysed the various ways that the pivotal mRNA trials
failed to account for serious harms.[20] Not only were trial participants limited
to the type of adverse event they could report on their digital apps, but someparticipants who were hospitalised after inoculation were withdrawn from thetrial and not reported in the final results. After two months into the pivotal
trials, the FDA allowed vaccine companies to offer the vaccine to subjects inthe placebo group, essentially torpedoing any chance of properly recording
adverse events from that point on, forcing a reliance on pharmacovigilance
data.
Such data have shown that one of the most common mRNA COVID-19
vaccine-induced harms is myocarditis. A study across several Nordic countries
showed an increased risk from mRNA vaccination over background, especiallyin young males.[21] Authorities have repeatedly maintained that myocarditis is
more common after COVID-19 infection than after vaccination.[22] However,trial data demonstrating that vaccination reduces the risk of myocarditis insubsequent infection are elusive, and in fact the risks may be additive. Incidence

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Australian Medical Professionals Societyof myocarditis rocketed from the northern Spring 2021 when vaccines wererolled out to the younger cohorts. It had remained within normal levels for thefull year prior, despite COVID-19.[23] The most up-to-date evidence, a paper
from Israel[24] found that the infection itself, prior to roll-out of the vaccine,conferred no increase in the risks of either myocarditis or pericarditis fromCOVID-19, strongly suggesting that the increases observed in earlier studies
were because of the mRNA vaccines, with or without COVID-19 infections
as an additional risk in the vaccinated.[24]Indeed, this reflects my own clinical experience of advising and managing
several patients in the community who presented with a clear suggestion
from their history of myocarditis post mRNA vaccination but who were notnecessarily unwell enough to require hospital admission. A very fit woman inher 50s developed fatigue and shortness of breath on exertion a few weeks after
her second Pfizer injection. An echocardiogram revealed severe impairmentof her left ventricular function. Another woman in her 30s experienced
similar symptoms with distressing palpitations within a few days of her
second injection; mild left ventricular impairment was also present on echo
and a subsequent cardiac MRI scan revealed several areas of late gadolinium
enhancement, a feature seen on the scan, which is consistent with damaged
heart tissue, and given that heart cells cannot be replaced this is likely to havea long-term effect.
Although vaccine-induced myocarditis is not often fatal in young adults, MRI
scans reveal that, of the ones admitted to hospital, approximately 80% havesome degree of myocardial damage.[25,26] It is like suffering a small heart attackand sustaining some – likely permanent – heart muscle injury. It is uncertain
how this will play out in the longer-term, including if, and to what degree, itwill increase the risk of poor quality of life or potentially more serious heart
rhythm disturbances in the future.
A number of reports have produced concerning rates of myocarditis, dependingon age, ranging from 1 in 6,000 in Israel[27] to 1 in 2,700 in a Hong Kong study
in male children and adolescents aged 12–17 years.[28] Most of the epidemiology
studies that have been carried out have measured myocarditis cases that havebeen diagnosed in a hospital setting, and do not claim to be a comprehensive
measure of more mild cases (from which long-term harm cannot be ruled out).
In addition, under-reporting of adverse events is the scourge of pharmacovigilance data.[29]The United Kingdom relies on the Medicines and Health Regulatory Agency’s(MHRA’s) ‘Yellow Card’ reporting system,[30] which is far from adequate tocope with a rapid roll-out of a brand-new product. It only detected the clotting
problems that resulted in the withdrawal of the AstraZeneca product in April

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2021 for younger people after 9.7 million doses had been given in the United
Kingdom;[31] in contrast, Denmark detected the problem after only 150,000
doses had been administered.[32]In the United Kingdom, since the vaccine roll-out there have been almost
500,000 adverse event reports recorded (via the Yellow Card system) inassociation with the mRNA COVID-19 vaccinations involving over 150,000
people. In terms of the number of reports per person (that is, having received
at least one dose), the MHRA figures show around 1 in 120 suffering a likely
adverse event that is beyond mild.[30] However, the MHRA are unclear
about the rate and furthermore do not separate out the serious adverse events.
Nevertheless, this level of reporting is unprecedented in the modern medical
era and equals the total number of reports received in the first 40 years of the
Yellow Card reporting system (for all medicines – not just vaccines) up to
2020.[33] In comparison, for the measles, mumps and rubella (MMR) vaccine,the number of reports per person vaccinated was around 1 in 4,000, more than
thirty times less frequent than the 1 in 120 Yellow Card reports for COVID-19
vaccine recipients.[34] Norway does separate out the reported serious adverse
reactions and has shown a rate of approximately 1 in 1,000 after two dosesof BioNTech-Pfizer mRNA product that result in hospitalisation or are lifechanging.[35]Another, and more useful, source of information (because of the level of detailfor each report made available to the public) is the United States (US) Vaccine
Adverse Effect Reporting System (VAERS). As with the UK’s system, thelevel of reports – including serious ones – associated with COVID-19 vaccines
is completely unprecedented. For example, over 24,000 deaths have now beenrecorded in VAERS as of March 2nd 2022; 29% of these occurred within 48
hours of injection, and half within two weeks. The average reporting rate prior
to 2020 was fewer than 300 deaths per annum. One explanation often given
for this is that the COVID-19 vaccine roll-out is unprecedented in scope;
however, this is not valid, since (for the last decade at any rate) the UnitedStates has administered 150 million to 200 million vaccinations annually.
Another criticism of VAERS is that ‘anyone can make an entry’, yet, in fact,
an analysis of a sample of 250 early deaths suggested that the vast majority are
hospital or physician entries,[36] and knowingly filing a false VAERS report isa violation of Federal law punishable by fine and imprisonment.[37]Given that VAERS was set up to generate early signals of potential harm for
new vaccines, and was instrumental in doing so for several products, it seems
perverse only now to criticise it as unreliable when there seem to have been nochanges in the way it operates.
It has been estimated serious adverse effects that are officially reported are205

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actually a gross underestimate, and this should be borne in mind when the above
comments in relation to VAERS reports are considered. For example, a paper byDavid Kessler (a former FDA Commissioner) cites data suggesting that as few
as 1% of serious adverse events are reported to the FDA.[38] Similarly in relation
to the Yellow Card scheme in the United Kingdom, it has been estimatedthat only 10% of serious adverse effects are reported.[39,40] A recent pre-print
publication co-authored by some of the most trusted medical scientists in theworld in relation to data transparency adds validity to pharmacovigilance data.
Accessing data from the FDA and Health Canada websites and combining
results from journal articles that published the Pfizer and Moderna trials,
the authors concluded that the absolute risk of a serious adverse event fromthe mRNA vaccines (a rate of one in 800) substantially exceeded the risk of
COVID-19 hospitalisation in randomised controlled trials.[17]What VAERS and other reporting systems (including the yet to be accessed
and independently evaluated raw data from randomised controlled trials) will
miss are potential medium-to longer-term harms that neither patients nor
doctors will automatically attribute to the drug. For example, if the mRNA
vaccine increases the risk of a coronary event within a few months (in what wasa likely contributory factor in my father’s sudden cardiac death), then this would
increase event rates well beyond the first few weeks of the injection yet linking
it back to the vaccine, and thus reporting it is highly unlikely to occur later on.
It is instructive to note that according to ambulance service data, in 2021 (theyear of the vaccine roll-out), there were approximately an extra 20,000 (~20%
increase) out-of-hospital cardiac arrest calls compared to 2019, and approximately
14,000 more than in 2020. Data obtained under Freedom of Information laws
from one of the largest ambulance trusts in England suggest that there was noincrease from November 2020 to March 2021, and thereafter the rise has been
seen disproportionately in the young.[41] This is a huge signal that surely needs
investigating with some urgency.[42]Similarly, a recent paper in Nature revealed a 25% increase in both acute coronary
syndrome and cardiac arrest calls in the 16 to 39-year-old age groups significantly
associated with administration with the first and second doses of the mRNA
vaccines but no association with COVID-19 infection.[43] The authors state:[T]he findings raise concerns regarding vaccine-induced undetected
severe cardiovascular side effects and underscore the already establishedcausal relationship between vaccines and myocarditis, a frequent cause of
unexpected cardiac arrest in young individuals. (p. 1)
The disturbing findings in this paper have resulted in calls for a retraction.
In the past, scientists with a different view of how data should be analysed

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Too Many Deadwould have published a paper with differing assumptions and interpretation
for debate. Now they try to censor discussion.
Many other concerns have been raised about potential harms from thevaccines in the mid-to long-term. Although some of these concerns remain
hypothetical, it may be a grave mistake to focus only on what can be measured
and not on the wider picture, especially for the young.

What could be the mechanism of harm?

For conventional vaccines, an inert part of the bacteria or virus is used to‘educate’ the immune system. The immune stimulus is limited, localised andshort-lived. For the COVID-19 vaccines, spike protein has been shown tobe produced continuously (and in unpredictable amounts) for at least fourmonths after vaccination[44] and is distributed throughout the body after
intramuscular injection.[45] For the severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2) vaccines, the spike protein was chosen, possibly
because it enables cell entry. However, this protein is not inert, but rather itis the source of much of the pathology associated with severe COVID-19,
including endothelial damage,[46] clotting abnormalities[47] and lung damage.
It is instructive to note that prior to roll-out of the mRNA products, theWHO endorsed a priority list of potential serious adverse events of special
interest that may occur as a direct result of COVID-19 vaccines. The list was
based upon the specific vaccine platform, adverse events associated with priorvaccines in general, theoretical associations based upon animal models and
COVID-19-specific immunopathogenesis[40] (see Figure 2).

Is the vaccine doing more harm than good?

The most objective determinant of whether the benefits of the vaccines
outweigh the harms is by analysing its effects on ‘all-cause mortality’. This getsround the thorny issue as to what should be classified as a COVID-19 death,and also takes full account of any negative effects of the vaccine. It would
be surprising – to say the least – if during an apparently deadly pandemic,
an effective vaccine could not clearly and unequivocally be shown to reduce
all-cause mortality.
Pfizer’s pivotal mRNA trial in adults did not show any statistically significant
reduction in all-cause mortality, and in absolute terms there were actuallyslightly more deaths in the treatment arm versus in the placebo.
Work by Fenton et al. showed an unusual spike in mortality in each age groupof the unvaccinated population, which coincides with the vaccine roll-out foreach age group.[48] The rapid shrinking in the size of this population means asmall-time lag could theoretically produce this effect artifactually. Alternative

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Included SAE types (matching AESI list): Abdominal pain, Abdominal pain upper, Abscess, Abscessintestinal, Acute coronary syndrome, Acute kidney injury, Acute left ventricular failure, Acute myocardial
infarction, Acute respiratory failure, Anaemia, Anaphylactic reaction, Anaphylactic shock, Angina pectoris,
Angina unstable, Angioedema, Aortic aneurysm, Aortic valve incompetence, Arrhythmia supraventricular,
Arteriospasm coronary, Arthritis, Atrial fibrillation, Atrial flutter, Axillary vein thrombosis, Basal ganglia
haemorrhage, Bile duct stone, Blood loss anaemia, Bradycardia, Brain abscess, Cardiac failure, Cardiac
failure acute, Cardiac failure congestive, Cardiac stress test abnormal, Cardio-respiratory arrest, Cerebral
infarction, Cerebrovascular accident, Chest pain, Cholecystitis, Cholecystitis acute, Cholelithiasis, Colitis,
Coronary artery disease, Coronary artery dissection, Coronary artery occlusion, Coronary artery thrombosis,
Deep vein thrombosis, Dermatitis bullous, Diabetic ketoacidosis, Diarrhoea, Diplegia, Dyspnoea, Embolic
stroke, Empyema, Facial paralysis, Fluid retention, Gastroenteritis, Gastrointestinal haemorrhage,
Haematoma, Haemorrhagic stroke, Hemiplegic migraine, Hepatic enzyme increased, Hyperglycaemia,
Hyponatraemia, Hypoxia, Ischaemic stroke, Laryngeal oedema, Multiple sclerosis, Myocardial infarction,
Non-cardiac chest pain, Oedema peripheral, Pancreatitis, Pancreatitis acute, Pericarditis, Peripheral arteryaneurysm, Peritoneal abscess, Pleuritic pain, Pneumothorax, Post procedural haematoma, Post procedural
haemorrhage, Postoperative abscess, Procedural haemorrhage, Psychotic disorder, Pulmonary embolism,
Rash, Rash vesicular, Respiratory failure, Retinal artery occlusion, Rhabdomyolysis, Rheumatoid arthritis,
Schizoaffective disorder, Seizure, Subarachnoid haemorrhage, Subcapsular renal haematoma, Subdural
haematoma, Tachyarrhythmia, Tachycardia, Thrombocytopenia, Thyroid disorder, Toxic encephalopathy,
Transaminases increased, Transient ischaemic attack, Traumatic intracranial haemorrhage, Type 2 diabetes
mellitus, Uremic encephalopathy, Uterine haemorrhage, Vascular stent occlusion, Ventricular arrhythmia
Figure 2: The World Health Organization endorsed a list of adverse events of special interest
associated with COVID-19 vaccinations.
Source: Fraiman J, Erviti J, Jones M, et al. Serious adverse events of special interest followingmRNA COVID-19 vaccination in randomized trials in adults. Vaccine. 2022 Aug 30:S0264410X(22)01028-3. https://doi.org/10.1016/j.vaccine.2022.08.036.
SAE, serious adverse events; AESI, adverse events of special interest.

explanations must include the (more likely) possibility that a rise in mortality
after vaccination was misattributed to the unvaccinated population: in otherwords, those counted as ‘unvaccinated deaths’ would in fact be those who had
died within 14 days of being vaccinated (a freedom of information [FOI]
request has now confirmed that authorities in Sweden were indeed categorising
deaths within 14 days of dosing as unvaccinated, creating a misleading pictureof efficacy versus death).
One has to raise the possibility that the excess cardiac arrests and continuingpressures on hospitals in 2021-2022 from non-COVID-19 admissions may
all be signalling a non-COVID-19 health crisis exacerbated by interventions,which would of course also include lockdowns and or vaccines.
Given these observations, and reappraisal of the randomised controlled trial
data of mRNA products, it seems difficult to argue that the vaccine roll-out
has been net beneficial in all age groups. While a case can be made that the208Too Many Dead
vaccines may have saved some lives in the elderly or otherwise vulnerable
groups, that case seems tenuous at best in other sections of the population,
and when the possible short-, medium- and unknown longer-term harms
are considered (especially for multiple injections, robust safety data for whichsimply do not exist), the roll-out into the entire population seems, at best, a
reckless gamble. It is important to acknowledge that the risks of adverse eventsfrom the vaccine remain constant, whereas the benefits reduce over time, as
new variants are (1) less virulent and (2) not targeted by an outdated product.
Having appraised the data, it remains a real possibility that my father’s sudden
cardiac death was related to the vaccine. A pause and reappraisal of vaccination
Policies for COVID-19 is long overdue.

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Special notes
Acknowledgements: The author thanks Dr Clare Craig for edits and data
analysis, and Alex Starling for comments and suggestions. The author is also
grateful for the assistance of a scientist who wishes to remain anonymous on
account of career pressure.
Competing interests: The author declares that he has no financial or personal
relationships that may have inappropriately influenced him in writing thisarticle.
Author’s contribution: Aseem Malhotra is the sole author of this article.
Ethical considerations: This article followed all ethical standards for researchwithout direct contact with human or animal subjects.
Funding information: This research received no specific grant from any
funding agency in the public, commercial or not-for-profit sectors.
Data availability: Data sharing is not applicable to this article, as no new data
were created or analysed in this study.
Disclaimer: The views and opinions expressed in this article are those of
the author and do not necessarily reflect the official policy or position of any
affiliated agency of the authors.

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Too Many Dead

Curing the pandemic of
misinformation on COVID-19
mRNA vaccines through real
evidence-based medicine
Part 2*by Aseem Malhotra

A pandemic of misinformation
What has become clear with regard to the coronavirus disease 2019 (COVID-19)
vaccines is that we have a pandemic of misinformed doctors and a misinformed
and unwittingly harmed public. Coercively mandating these COVID-19
injections (most certainly not an evidence-based policy) has been a particularly
egregious mis-step, especially in the light of clear indicators suggesting that theuse of these pharmaceutical interventions – especially in younger age groups– should have been suspended. Such policies continue to undermine theprinciples of ethical evidence-based medical practice and informed consent, tothe detriment of optimising patient outcomes.
In his 2017 paper, ‘How to survive the medical misinformation mess’, Professor
John Ioannidis and colleagues highlight that:[M]ost clinical trial results may be misleading or not useful for patients.
Most guidelines (which many clinicians rely on to guide treatment
decisions) do not fully acknowledge the poor quality of data on which
they are based. Most medical stories in mass media do not meet criteria
for accuracy, and many stories exaggerate benefit and minimise theharms.[1] (p. 1)

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A senior doctor in regular contact with the United Kingdom’s (UKs) Chief
Medical Officer Professor Chris Whitty recently expressed concerns to me
that he felt most of his colleagues in leadership positions influencing healthpolicy may not be critically appraising the evidence and instead are relying
on media stories on COVID-19 and the vaccine. This is consistent with theadmission of Rochelle Walensky, the former chair of the Centers of Disease
Control (CDC), whose optimism about the efficacy of Pfizer’s COVID-19
vaccine came from reading a CNN news story, which was an almost verbatim
reproduction of Pfizer’s own press release.[2]Has the UK’s Chief Medical Officer Professor Chris Whitty criticallyappraised the evidence? Recently, he publicly shared a letter[3] outlining theimportance of healthcare staff to become vaccinated against COVID-19,
which was neither comprehensive nor consistent with the totality of theevidence: ‘The COVID-19 vaccines are safe and effective’. It would havebeen more accurate to state that ‘the vaccine is not completely safe and not
anywhere close to being as effective as we’d hoped for. Not even in the sameball park when compared to the efficacy and safety of traditional vaccines’.
Professor Chris Whitty stated:
Our professional responsibility is to get the COVID vaccines asrecommended to protect our patients.[3]He should have said as far as Omicron is concerned, that the vaccine offers
little to no protection against infection. Data on the Delta variant also
revealed that once infected there is no significant difference in transmission
rates between the vaccinated and unvaccinated patients.
Professor Whitty’s statements are especially surprising given that the CEOof Pfizer has stated that in relation to Omicron, ‘We know that the two dosesof a vaccine offer very limited protection, if any’.[5]Could it be that Professor Whitty is also a victim of the medical
misinformation mess?
There are four key drivers and seven sins that are at the root of the medical
misinformation mess:• Drivers:•

Much published medical research is not reliable or is of uncertain
reliability, offers no benefit to patients or is not useful for
decision-makers;•

Most healthcare professionals are not aware of this problem;216

Too Many Dead•

Even if they are aware of this problem, most healthcare professionals
lack the skills necessary to evaluate the reliability and usefulness of
medical evidence; and•Patients and families frequently lack relevant, accurate medical evidence
and skilled guidance at the time of medical decision-making.[1]• Sins:•Biased funding of research (research that is funded because it is likely
to be profitable, not beneficial for patients)•

Biased reporting in medical journals•

Biased reporting in the media•

Biased patient pamphlets•

Commercial conflicts of interest•

Defensive medicine•

An inability of doctors to understand and communicate health statistics.
[6]

Ioannidis and colleagues say:
‘Ignorance of this problem, even at the highest levels of academic andclinical leadership, is profound’[1]Compounded over several decades, these upstream and downstream risk
factors for misinformation have had a devastating effect in the healthcare
environment we find ourselves in today. Over-prescription of drugs is
considered such a public health threat that two leading medical journals
in the past 10 years (the BMJ and JAMA Internal Medicine) have launchedcampaigns to reduce the harms of too much medical intervention. According
to the cofounder of the Cochrane Collaboration, Peter Gøtzsche, prescribed
medications are the third most common cause of death globally after heartdisease and cancer.[7] This is not surprising when one understands that mostpublished research is misleading specifically where benefits from drug trials
are exaggerated, and harms played down (Box 1[8]).
If a doctor is making clinical decisions on biased information, it will lead(at best) to suboptimal outcomes and (more concerningly) harm to patients.

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Shortcomings of the medical profession
According to Professor Carl Heneghan, an urgent care General Practitioner
and the director of the University of Oxford’s Centre of Evidence-Based
Medicine: ‘with every intervention you do as a doctor you must ask yourself
two questions: how much difference does it make? How do I know this?’[9]Building on the Academy of Medical Royal College’s Choosing Wisely
campaign,[10] it is instructive to note that the General Medical Council in2020 issued guidance on the duty of doctors to engage in shared decisionmaking with patients, underpinned by informed consent.[11]BOX 1: Major limitations in the interpretation, external validity and usefulness of drug
industry-sponsored clinical trials.
Source: Adapted from Jureidini J, McHenry L. The illusion of evidence-based medicine.
Adelaide: Wakefield Press; 2020

There are six components essential to informed decision-making:(1) description of the nature of the decision; (2) discussion of alternatives;(3) discussion of risks and benefits (in absolute terms); (4) discussion ofrelated uncertainties; (5) assessment of the patient’s understanding; and (6)elicitation of the patient’s preference.

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If the administration of the vaccine did not adhere to these principles(which is likely widespread, consistent with historical evidence,[12]) then it
is also a substantial breach of General Medical Council duties of a doctor
to ‘give patients the information they want or need in a way that they canunderstand’.[13]It is instructive to note that the greater the financial interests in a given field,
the less likely the research findings are to be true.[14] As has been alreadydemonstrated in Part 1[15] of this article, mandating a novel emergency-use
authorisation vaccine to non-vulnerable people has little to no effect on
preventing infection and serious illness, therefore does not have any scientificvalidity, and therefore breaches the principles of informed consent. It does,
however, dramatically increase the profits of the manufacturer. By expanding
the uptake of the mRNA vaccine to the majority of the population who
are very low-risk of serious complications from COVID-19 but are more
likely to suffer serious and or life-threatening adverse events such as
myocarditis or sudden cardiac death, Pfizer has generated tens of billionsof dollars in revenues to date, making it one of the most lucrative products
in history. If policymakers had focused more on protecting the vulnerable– and doctors had been given the opportunity to practise shared decisionmaking with patients using transparent communication of risk and benefit– patient outcomes would likely have been greatly improved,[16] but the
drug companies’ profits would likely have been a tiny fraction of what they
actually generated. As former editor of the New England Journal of Medicine
Dr Marcia Angell has previously pointed out, ‘the real battle in healthcare is
one of truth versus money’.[17]Institutional corruption and erosion of public trust
Institutional corruption is defined as an institution’s deviation from a baselineof integrity.[18] There is a long-documented history (both through studies
and lawsuits) of the strategies in which drug companies hide, ignore or
misrepresent evidence about new drugs. Distortion of medical literature andmisrepresentation of data by companies keen to expand the marketplace for
their product may result in overprescribing with predictable consequences of
millions of patients suffering from avoidable adverse reactions.
Prior to 2020 there already existed gross shortcomings in the medical–
industrial complex – there has been too much pharmaceutical industry
influence on clinical decision- making. This has not gone unnoticed, resulting
in a growing crisis of trust in medical research; a report by the Academy ofMedical Sciences in 2017 revealed that 82% of GPs and 63% of the public
did not believe the results of pharmaceutical industry-sponsored research

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to be unbiased.[19] Similarly, only 37% of the public trust medical researchcompared to 65% who trust the experience of their friends and family.[20]This growing lack of trust – most recently exacerbated by coercion, vaccine
passports and little mainstream media coverage of an unprecedented scale ofreported vaccine harms in the population – has been most recently exemplified
by eight million people in the UK refusing to take the COVID-19 booster.
In addition, all the attention on COVID-19 (which poses almost zero risk tochildren in its current Omicron form) diverts attention away from, and evenworse raises the suspicion about, more efficacious and safe interventions suchas the measles, mumps, rubella (MMR) vaccine. Indeed, in the UK MMR
vaccination rates have hit their lowest for 10 years.
Failure of regulation and research misconduct
Authorities want the public to ‘trust the science’, but vaccine manufacturers
have successfully negotiated deals with several major governments globally thatindemnify them against any financial liability in the event of vaccine-related
harm. Interestingly, India, the world’s largest democracy, refused to grant Pfizer
indemnity from harms for its vaccine. An Indian government source told Reuters:[T]he whole problem with Pfizer is the indemnity bond. Why should
we sign it? If something happens, a patient dies, we will not be able toquestion them [Pfizer]. If somebody challenges in a court of law, the central
government will be responsible for everything, not the company.[21]Pfizer walked away from the Indian market rather than undertake a local safety
and immunogenicity study.[22]It is important to first understand that drug companies have a fiduciary obligation
to deliver profits to their shareholders, not any legal responsibility to provide
patients with the best treatment. At a talk at the Centre of Evidence-Based
Medicine in Oxford in 2014, Peter Wilmshurst said the real scandal is thatmany of those with a responsibility to patients and scientific integrity (doctors,
academic institutions and medical journals) often collude with industry for
financial gain.[23] It is this very industry that has been found guilty of the mostegregious corporate crimes: between 2003 and 2016 the top 11 pharmaceuticalcompanies paid $28.8 billion in fines just within the United States (US),[24] muchof it for criminal activity such as the illegal marketing of drugs, manipulation of
results and hiding data on harms. As pointed out in the BMJ, since then nosystemic changes have been made to mitigate these harms.[9]In an international survey of respondents from higher education institutions,
14% admitted to knowing a colleague who fabricated, falsified and modified
data, and 34% of scientists report questionable research practices that220

Too Many DeadBox 2: Written evidence from Dr Peter Wilmshurst to UK Parliamentary Science andTechnology Research Integrity Committee ( June 2018).
Source: Wilmshurst P. Written evidence [homepage on the Internet]. 2017 [cited 2022
Jun 5]. Available from: http://data.parliament.uk/writtenevidence/committeeevidence.svc/

evidencedocument/science-and-technology-committee/research-integrity/ written/68813.html
GMC, General Medical Council; UKRIO, United Kingdom Research Integrity Office.

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included selective reporting of clinical outcomes in published research, andconcealing conflicts of interest.[25] An egregious documented case of research
misconduct involved a prominent Dutch physician whose work influenced
the European Society of Cardiology guidelines on the use of beta blocker
drugs in non-cardiac surgery. He was dismissed from Erasmus University for‘violations in academic integrity’, including using ‘fictitious data’ in research.
It’s estimated that these guidelines increased patient mortality by 27%
resulting in 800,000 excess deaths across Europe over an 8-year period.[26]In evidence submitted to the UK parliamentary science and technology review
into research integrity committee in 2017 (Chaired by Sir Norman Lamb), Dr
Peter Wilmshurst lists a number of risk factors that drive research misconduct
in British institutions (see Box 227). His solution, which I agree with, would
be to ensure that serious forms of research misconduct are made into criminal
offences with meaningful sanctions and that allegations of such activity should
be investigated by an independent body with legal powers.[27]One researcher at a prestigious UK institution contacted me to inform methat in his cardiology department a group of academics were deliberately
suppressing research revealing that the mRNA vaccine was shown tosignificantly increase coronary risk when determined by cardiac imaging as
compared to the unvaccinated. The chair of the group expressed concerns thatpublishing the data may result in loss of funding from the pharmaceuticalindustry.[28] After I had alluded to this on GB News, the whistle-blower
informed me that non-disclosure agreement letters were sent to all membersof the team involved in this particular area of research.

Evidence-based medicine and COVID-19
vaccine roll-out

Neither the drug regulators nor the vaccine manufacturers have yet to share
all the raw data from the pivotal trials for the COVID-19 vaccines.[29] The
raw data from clinical trials comprise thousands of pages that have yet tobe released for independent scrutiny. This is important because historicallywhen independent researchers have on occasion gained access to these datathen it can completely overturn the conclusions of the published trials. A
case in point is Tamiflu.[30] Getting access to clinical case reports for Tamiflu
ultimately revealed that the drug was no more effective than paracetamol for
influenza and also came with small but significant harms. The UK government
had spent half a billion dollars stockpiling a drug that in effect proved to beuseless despite claims by the manufacturers (Roche, Basil, Switzerland) that it222

Too Many Deadshortened the duration and severity of the illness. The independent researchers
who were able to analyse the data concluded that all industry-sponsoredresearch should be considered marketing until proved otherwise.
It is against this backdrop that transparency advocates sued the Food andDrug Administration (FDA) to gain access to the data upon which the Pfizer(BNT162b2) vaccine was granted emergency use authorisation.[31] The FDA
wanted a US Federal court judge to allow the agency 55 years to release these
data.[32] Why would the FDA – ‘which is responsible for the oversight of morethan $2.7 trillion in consumption of food, medical products, and tobacco’[33]– do this? Secrecy should never surround any public health intervention. The
lawyer acting on behalf of the plaintiff Aaron Siri reported that:[T]he government also sought to delay full release of the data it relied
upon to license this product until almost every American alive today isdead. That form of governance is destructive to liberty and antitheticalto the openness required in a democratic society.[31]Instead, the judge ordered the FDA to release the data over a period of eightmonths after all commercially sensitive information has been redacted.
A major risk factor for failure to protect the public from such harms is lackof independence of the regulator. The FDA’s Center for Drug Evaluation
Research (CDER) receives 65% of its funding from the pharmaceuticalindustry (mainly in the form of user fees).[34] For example, as part of theapproval process for its COVID-19 vaccine, Pfizer made a wire transfer tothe FDA of $2,875,842 in May 2021[35] under the Prescription Drug User
Fee Act of 1992.[36] Full FDA approval for Pfizer’s COVID-19 injection
duly followed in August 2021[37] despite recent evidence emerging that theoriginal RCT data suggested a greater risk of serious adverse events from thevaccine than from hospitalisation because of COVID-19.
Separate analyses have revealed the overwhelming majority of new drugs thathave been approved by the FDA in the past few decades have later been shown
to be just copies of old ones, which is not surprising when one understands
that drug companies spend 19 times more on marketing than they do on
researching new molecular entities, which all contributes to considerable waste.
Between 2000 and 2008, of the 667 drugs approved by the FDA, only 11%
were found to be truly innovative. In the US it is estimated that 30% to 50%
of healthcare activity brings no benefit to patients. Extraordinarily, a survey of
FDA scientists revealed 70% of them did not feel the FDA had the resources

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to perform effectively in its mission in ‘protecting public health … and helping
the public get accurate science-based information to use medicines and foods
to improve their health’.[38]An analysis of every new drug product approved in France between 2002 and2011 revealed only 8% offered some advantages and double that number – at
15.6% – were found to be more harmful than beneficial with the majority ofother new drugs being essentially copies of old ones contributing to a colossal
waste of public money.[18] Similar conclusions have been drawn in Canadaand Holland. In my opinion the evidence is overwhelming that the overall net
effect of the pharmaceutical industry in the last few decades on society andpopulation health has been a hugely negative one.

COVID-19 vaccination in lower risk people

Irrespective of the merits of inoculating higher risk groups where a small but
significant benefit may exist against the original Wuhan strain, vaccinating
lower risk children in the name of preventing asymptomatic transmission has
no strong scientific validity and therefore exposes them to possible harm.
In the UK the Office for National Statistics has revealed an as-yet unexplained
significant increase in deaths over the 5-year average in 15 to 19-year-old
children since May 2021. Given what we now know of potential harms
especially in relation to myocarditis, myocardial infarction and sudden cardiac
death (even in 16 to 39-year-olds), has the COVID-19 vaccine been excluded
as a possible cause?[39]In September 2021, the Joint Committee on Vaccination and Immunisation( JCVI) made a controversial recommendation that the Pfizer-BioNTech
vaccine is marginally beneficial for 12 to 15-year-old children.[40] The
Medicines and Healthcare products Regulatory Agency (MHRA, the UK’s
equivalent of the FDA) had previously stated that:[T]hey have carefully reviewed clinical trial data for Pfizer-BioNtech
vaccine in over 2000 children aged 12–15 years of age and haveconcluded that the benefits of this vaccine outweigh any risk and that itis effective and acceptably safe in this age group … No new side effects
were identified and the safety data in children was comparable to thatseen in young adults. As in the young adult age group, the majority ofadverse events were mild to moderate, relating to reactogenicity (e.g.
sore arm and tiredness).[41]Is this in keeping with the totality of the evidence?

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Too Many DeadAward-winning investigative science journalist Dr Maryanne Demasi
published the harrowing story of one of those trial participants, 12-year-old
Maddie De Garay. After experiencing severe abdominal pain followed by
seizures she was admitted to hospital and is now left permanently disabled,
wheelchair-bound and fed through a nasogastric tube. In Pfizer’s trial theyreported her adverse effect as mild: stomach upset.[42]It is important to emphasise that the risk of death from COVID-19 in a 12
to 15-year-old is close to zero at 1 in 76,000. In keeping with the principlesof ethical evidence-based medical practice through shared decision-making,parents need to be told that there are no high-quality data regarding children to
the effect that the vaccine will prevent infection, transmission, serious illness ordeath, but it may come with serious side effects of myocarditis – particularly inyoung males where it occurs in up to 1 in 2,700[43] – and serious disability as a
general principle of transparent communication of risk and informed consent.
Without understanding the numbers involved the public is vulnerable to theirhopes and anxieties being exploited by political and commercial interests.
Could financial interests be biasing the recommendations?
On its website the MHRA declares that most of its funding comes from thepharmaceutical industry and £3 million (UK pounds) from the Bill and Melinda
Gates Foundation (BMGF). Are policymakers and the public aware that thefoundation’s corporate stock endowment is heavily invested in food (including
McDonald’s and Coca-Cola) and pharmaceutical companies, directly andindirectly? As pointed out in a 2009 Lancet paper, the funders’ priorities areoften driven by personal interests, not the health priority interests of therecipient country.[44] ‘The BMGF’s portfolio of pharmaceutical companies
calls for attention given Mr Gates’ personal belief in the role of patents as
motors for innovation in medicines and medical technology’.[45]Obesity researcher Dr Zoe Harcombe has also investigated the financial ties
that could potentially be biasing the view of the joint committee for vaccines
and immunisation and discovered that the subcommittee members workfor organisations that receive in total $1bn from the BMGF.[46] It is also
worth noting that Professor Wei Shen Lim, chairman of the JCVI vaccine
subcommittee, has direct responsibility for material levels of funding received
by his department from Pfizer.[47] This is not in any way suggesting that the
JCVI have acted in an improper way, but when confidence in an organisationsuch as the JCVI is imperative it is essential that there should be no perceptionsof conflicts of interest. The systems of selection of panellists, the scrutiny ofevidence and the methodology and openness of their recommendations need
to be beyond reproach.

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The most proximate cause of detrimental health outcomes:
corporate power and the commercial determinants of health
The commercial determinants of health are best defined by ‘strategies andapproaches adopted by the private sector to promote products and choicesthat are detrimental to health’.[48] Corporations exert their power by a
combination of factors including intellectual exploitation. This includes theability to define the dominant narrative: set the rules and procedures by whichsociety is governed; determine the rights, living and working conditions ofordinary people; and take ownership of knowledge and ideas[49] (see Figure145). It appears that in the case of the mRNA vaccine, Pfizer has at least
to some degree taken advantage of this corporate framework strategy byshaping the knowledge environment (Pfizer was responsible for the designand conduct of the trial, data collection, data analysis, data interpretationand the writing of the manuscript), the political environment (lobbying),
preference shaping (corporate foundations and philanthropy, spokespersons
and key opinion leaders, capture of the media), the legal environment (limit

Figure 1: Diagram of dimensions, vehicles, practices and outcomes of power.

Source: Madureira Lima J, Galea S. Corporate practices and health: A framework andmechanisms. Global Health. 2018;14(1):21

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Figure 2: Markers of metabolic health.

Source: Araujo J, Cai J, Stevens J. Prevalence of optimal metabolic health in American adults:
National Health and Nutrition Examination Survey 2009–2016. Metab Syndr Relat Disord.
2019;17(1):46–52. https://doi.org/10.1089/met.2018.0105
HDL-C, high density lipoprotein cholesterol.

liability) and the extra-legal environment (opposition fragmentation byde-platforming critics of the current dominant narrative that the vaccine
is safe and effective).[45] Consequently, it has made tens of billions ofdollars in revenue from a product that in comparison with time-tested
traditional vaccines and most other drugs has extremely poor efficacy andunprecedented reports of serious harms.
Biased reporting in the media and censorship oflegitimate scientific debate
Corporations are able to shape preferences and frame the dominant narratives
on the determinants of health, through unchecked invisible power. One pathway
is through the ownership of mass media. The global media market is dominatedby seven corporations and chains that own 80% of the newspapers in the US.[50]The grants paid to global media companies by the BMGF are notable – forexample, The Guardian Media Group has been in receipt of over $12m in grants
from the BMGF over the last 12 years. Control over advertising in print andbroadcast media also has an influence over editorial decisions. Most healthjournalists (including a number I have spoken to) are generally unaware that the
information they obtain for stories has been deliberately shaped by the privateinterests of manufacturers and ‘research’ universities.
The BBC, though seemingly not directly influenced by industry interests,
has traditionally been seen by some as the UK’s most trusted media source.
Its coverage of issues surrounding COVID-19 has in my view (possibly
through additional government pressure) been extremely poor and –specifically on issues surrounding the vaccine – grossly negligent. Duringa recent report on tennis player Novak Djokovic explaining his decision

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not to take the vaccine until he has more information on its benefits
and harms, a reporter asked the question ‘how much more information
does he need?’ The reporter failed to mention the fact that Djokovic has
had COVID-19 and that evidence suggests that natural immunity offers
substantial protection against reinfection and severe disease, and thatsystemic side effects are almost threefold more likely in those with naturalimmunity who subsequently get vaccinated. Furthermore, the BBC falsely
framed a guest of popular podcast host Joe Rogan, Dr Robert Malone, asa ‘known anti-vaxxer, who is against vaccinating kids’, failing to mention
that Dr Malone is a co-inventor of the very technology that led to thevaccine, has spent 20 years in vaccine development at US government
level and was one of the first to actually receive the Moderna injections
twice. The BBC also strangely failed to cover perhaps one of the mostimportant stories of the pandemic published in one of the most respected
and influential medical journals in the world: An investigation by the
BMJ revealed evidence of poor practices at a contract research company
involved in Pfizer’s pivotal COVID-19 vaccine trial. A regional director
employed at one of the trial sites in Texas, US, documented evidence that
Pfizer falsified data, unblinded patients, employed inadequately controlled
vaccinators and was slow to follow up on adverse events. The very same
day that she emailed her complaint to the FDA she was fired from her
position.[51] She subsequently commenced litigation under whistle-blower
legislation for fraud against Pfizer on behalf of the American Government(and the people of the US). Pfizer’s motion to dismiss the case (whichapparently did not sway the judge) was based on the fact that the FDA had
not acted on her (or any other) complaints, hence the allegations were notmaterial to the Government.
In the US, Senator Ron Johnson conducted hearings with healthcare
professionals who were presenting data on clear, substantial and very
common adverse effects from the mRNA injections, and these deserved
widespread public attention. He said ‘the mainstream media are
co-conspirators in this political dirty trick. Will they be held accountable
for their role in this deception?’[52]Social media platforms continue to be guilty of spreading misinformation.
Their business model that focuses on increasing engagement at any costmakes society increasingly lose access to the truth and worsens our capacity
for empathy as individuals, sowing even greater division and hostility.
The so-called ‘fact checkers’ have censored anything that challenges theprevailing mainstream narrative (the establishment is trustworthy, and thevaccines are completely safe). They even labelled the BMJ’s investigation

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into potential fraud in Pfizer’s pivotal trial as misinformation and stopped
users sharing the story on their platform. A letter from the journal’s current
and former editor-in-chief to Mark Zuckerberg calls into question the
integrity of Facebook’s fact checkers:[R]ather than investing a proportion of Meta’s substantial profits
to help ensure the accuracy of medical information shared throughsocial media, you apparently delegated responsibility to people
incompetent in carrying out this crucial task.[53] (p. 1)
It has also come to light that Facebook has partnered with drug company
Merck in deciding what content should be censored on its platform in
relation to COVID-19 and the vaccine.[54] Is Facebook aware that Merck
paid one of the largest fines in US history for being found guilty of fraud
in relation to their pain killer Vioxx?[55] Not only did an investigation
reveal that the drug did not reduce gastric bleeds (their original key selling
point) in comparison with ibuprofen, but it significantly increased the riskof heart attacks and strokes, estimated to have caused excess deaths ofbetween 40,000 and 60,000 Americans over a 5-year period.[56]Improving metabolic health
Failure of public health messaging and policies to help people to improvetheir lifestyles during the pandemic represents a missed opportunity to
mitigate harms from respiratory diseases such as COVID-19. After age,the biggest risk factor for worse COVID-19 outcomes has been obesity andconditions related to excess body fat. More than 90% of the deaths fromCOVID-19 occurred in countries where more than 50% of the population
is overweight or obese. The United Kingdom’s biobank data during thefirst wave revealed a more than fourfold higher risk in hospitalisation fromCOVID-19 depending on lifestyle factors. For example, a non-smoking
adult in the mid-fifties with a normal body mass index (BMI) and obtaining
adequate physical activity levels had a 1 in 1,521 chance of being admitted
to hospital after contracting COVID-19, whereas an obese, smoking,sedentary person’s risk was 1 in 327.[57]Postulated pathophysiological mechanisms of risk and complications from
infection include an array of markers that have insulin resistance andchronic inflammation at the root.
Even a single high blood-glucose reading in non-diabetics (a marker of
insulin resistance) admitted to hospital has been shown to be associatedwith worse outcomes.[58] It has also recently emerged in the UK that of
the 175,256 deaths associated with COVID-19 (2020–2021 inclusive),229

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fewer than 10% (17,371) had COVID-19 as the only cause on the deathcertificate, suggesting that the risk to those with optimal metabolic healthfrom COVID-19 (Figure 2[59]) was notably smaller, as per the results of
the aforementioned UK biobank study.[60]The government and medical authorities should have made it a priority
to emphasise the importance of eliminating ultra-processed foods andlow-quality carbohydrates to reduce risk. They could have made the publicaware that reversal of metabolic syndrome has been shown to occur in up
to 50% of patients – independent of weight loss – within four weeks of
dietary changes alone.[61]The coronavirus disease 2019 was a momentary crisis that exploited a slow
pandemic of poor metabolic health (see Figure 2[59]), which is also thepredominant root cause behind the major chronic diseases that have been
putting healthcare systems around the world under increasing strain for
decades. It is estimated that healthier lifestyles would (in absolute terms)
potentially eliminate 40% of cancers and 75% of cardiovascular disease andtype 2 diabetes.[63]Optimising metabolic health would not just improve immune resiliencebut also reduce the burden of heart disease, type 2 diabetes, cancer anddementia. Learning lessons from tobacco control, policy changes that target
the availability, acceptability and affordability of ultra-processed food anddrink and low-quality carbohydrates would substantially reduce the burdenof obesity and related metabolic diseases, and also likely optimise immune
resilience in populations within a few years (see Box 3[62]).
The solutions
There was never any evidence justifying any COVID-19 vaccine mandates,
passports or any of the other coercive measures adopted by various governments
worldwide. Every patient who was offered any COVID-19 vaccine shouldhave been made aware of what the risk from COVID-19 is according to age
and risk factors. In keeping with ethical medical practice, doctors shouldhave informed patients of their absolute risk reduction for infection fromprevious more lethal variants being approximately 0.84% or 1 in 119 (basedon non-transparent data) and that this level of protection only lasts for a few
months. They should also have provided more precise and robust data on what
the actual absolute individual risk reduction of COVID-19 death from thevaccine is, and also what the true rates are of serious adverse events (such aspermanent disability, hospitalisation or death).
It is only when doctors and patients have all this information that they can

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BOX 3: Policies to curb obesity and lifestyle-related disease.

Source: Malhotra A. The 21-day immunity plan. United Kingdom: Yellow Kite; 2021.

BOX 4: Defining real evidence-based medicine and actions to deliver it.

Source: Adapted from Greenhalgh T, Howick J, Maskrey N. Evidence based medicine
Renaissance Group. Evidence based medicine: A movement in crisis? BMJ. 2014;348:g3725.
https://doi.org/10.1136/bmj.g3725

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then be empowered to have frank decision-making conversations on whether
any treatment – including this vaccine – is right for them.
The profession must explain that optimising metabolic health will give patientsthe best chance for ensuring they are not just resilient to infection but reducing
their risk of chronic disease including heart disease, cancer and dementia.
The time has come to stop misleading evidence flowing downstream into media
reporting and clinical decision-making and resulting in unethical and unscientificpolicy decisions. It is time for real evidence-based medicine (Box 4[64]).
There is also a strong scientific, ethical and moral case to be made that thecurrent mRNA vaccine administration must stop until Pfizer releases all
the raw data for independent scrutiny.[30] This will allow a more accurate
understanding of which groups are more likely to potentially benefit from thevaccine versus those who are more likely to be harmed.
Given all the recent well-documented aforementioned shortcomings inmedical research integrity (including that possibly half the published medical
literature ‘may simply be untrue’), the editor of the Lancet Richard Horton
wrote in 2015 that science has taken a turn towards darkness and asked who
is going to take the first step in cleaning up the system.[65] The unprecedentedroll-out of an emergency use authorisation vaccine without access to the raw
data, with increasing evidence of substantial harms, compounded by mandates
that appear to serve no purpose other than to bolster profits of the drug
industry, have highlighted modern medicine’s worst failings on an epic scale,with additional catastrophic harms to trust in public health.
We must use this as an opportunity to transform the system to produce better
doctors, better decision-making and healthier patients, and to restore trust in
medicine and public health. Until all the raw data on the mRNA COVID-19
vaccines have been independently analysed, any claims purporting that they
confer a net benefit to humankind cannot be considered to be evidence-based.

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Special Notes
Acknowledgements: I thank Dr Clare Craig for edits and data analysis, andAlex Starling for comments and suggestions. The author is also grateful for theassistance of a scientist who wishes to remain anonymous because of careerpressure.
Competing interests: The author declares that he has no financial or personal
relationships that may have inappropriately influenced him in writing this article.
Author’s contribution: Aseem Malhotra is the sole author of this article.
Ethical considerations: This article followed all ethical standards for researchwithout direct contact with human or animal subjects.
Funding information: This research received no specific grant from any funding
agency in the public, commercial or not-for-profit sectors.
Data availability: Data sharing is not applicable to this article as no new data
were created or analysed in this study.
Disclaimer: The views and opinions expressed in this article are those of theauthor and do not necessarily reflect the official policy or position of any affiliatedagency of the author.

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Dr Aseem Malhotra, MBChB, MRCP, CCT
Cardiology, is a consultant cardiologist and aninternational expert in prevention, diagnosis andmanagement of heart disease. He is an honorary council
member to the Metabolic Psychiatry Clinic at Stanford
University School of Medicine, California. He is afounding member of Action on Sugar, highlighting
the harm caused by excess sugar consumption in the
United Kingdom, particularly in type 2 diabetes andobesity. In 2015 he helped co-ordinate the Choosing
Wisely campaign by the Academy of Medical Royal
Colleges as lead author in a BMJ paper to highlightthe risks of overuse of medical treatments. In thesame year he was appointed to the board of trusteesof The King’s Fund advising the UK government on
health policy. A frequent expert commentator in print
and broadcast media, he has written scores of articles
for the BMJ, British Journal of Sports Medicine, BMJ
Open Heart, JAMA Internal Medicine, Prescriber, The
Pharmaceutical Journal, and The European Scientist.
He has won awards for raising awareness of diet-related
illness in the UK and internationally. In 2018 he was
ranked by software company Onalytica as the number
one doctor in the world influencing obesity thinking.
In 2016 he was named in the Sunday Times Debrett’s
list as one of the most influential people in science andmedicine in the UK in a list that included Professor
Stephen Hawking. Award-winning American sciencejournalist Gary Taubes describes Aseem Malhotra
as someone who has ‘probably done more in the
UK to inject sanity into nutrition science and thepharmaceutical industry debate than any humanbeing alive’.

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Part 4Australian data – with poor pharmacovigilance
the safety signals go unrecognizedWe human beings need data, so that we can recognize facts on a larger scale.
Under-reporting is dangerous. The TGA’s reporting system is passive andvoluntary; it risks under-reporting which may conceal serious signals of harm.
Comprehensive data collection and assessment are critical. This part provides acomprehensive analysis of pertinent excess mortality data which are not being
evaluated for potential disturbing safety signals.
The World Health Organization says: ‘a safety signal refers to information ona new or known side effect that may be caused by a medicine and is typicallygenerated from more than a single report of a suspected side effect.’
Our authors in this section raise telling questions.

No therapeutic agents (that is, drugs) are ‘safe’…
The Australian Therapeutic Goods Administration declared the
COVID-19 vaccines (a more apt description is COVID gene-based spike
injections) to be ‘safe’ without any qualification based, as we now know,
on troublesome animal and clinical safety data. Such reckless advice had
no equivalent in the history of the pharmaceutical industry. Furthermore,
the TGA provisionally approved the use of these experimental gene-based
injections for the entire population including healthy people, children,
infants, and in pregnancy knowing full well that important safety data were

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lacking….….Post-marketing surveillance of adverse drug reactions is ofparticular importance when the safety and efficacy data for any drug underresearch are limited… (It) is necessary to have a transparent, efficient anddependable adverse drug reporting system to identify safety signals should
they arise….One cannot have an expedited drug approval system which
depends on very limited evidence of safety and, at the same time, have an
unreliable and non-transparent adverse drug event reporting system whichfails to identify and report important safety signals.
– Dr Philip Altman

Australia, like many other Western countries is currently experiencing
excess mortality at a level not known of outside war times. Even after
COVID deaths are taken into account, there is significant remaining excess
mortality. Health authorities have no explanation for the cause of these
Australian excess deaths above historical averages.
– Dr Andrew Madry
One must ask why death rates are at their highest in 2022 so long after the
population was first exposed to the virus…. Results from three separate
models show that a persistent trend of high excess deaths began in Australia
in Spring, 2021, and continues in 2023.
– Clare Pain

If evidence shows government COVID public health policy is causing an
iatrogenic catastrophe rather than a measurable benefit, doctors and nurses havea moral, legal, and scientific duty to warn people. National and internationallaws, agreements, and regulation from Nuremberg to the Health Practitioners
Regulation National Law reinforce the duty of physicians to serve humanity andto first do no harm. If policy created at the ‘speed of science’ appears to be neither
safe nor effective, the community expects protective action and this involves free
and open communication. For our medical authorities and political leaders tocontinue to enforce censorship in the face of an iatrogenic miscarriage of medical
science causing untold harm shows a reckless indifference for the sufferings ofAustralians.

Doctors must be permitted to use their constitutional right of intellectual freedom,
medical experience, clinical judgement and freedom of expression to save lives
even from state-sanctioned policies.

Dr Philip Altman said, ‘There appears to be a growing body of opinion that the
COVID “vaccines” are doing more harm than good and they should be withdrawn.’
This would seem a sensible move. Perhaps it might give rise to the next logical step,
having serious medical attention being paid to providing proper treatment for theenormous numbers of those who have been damaged and whose suffering the
medical system resolutely refuses to acknowledge.

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Why all-cause mortality
has become the mostimportant COVID-19 statistic
by Phillip M. Altman

BPharm (Hons), MSc, PhD

No drug or vaccine is ‘safe’. All have potentially serious and or fatal effects.
Yet the Australian Therapeutic Goods Administration (TGA) declared the
COVID-19 ‘vaccines’, more appropriately described as COVID gene-based
spike injections, to be ‘safe’ without any qualification based, as we now know,
on troublesome animal and clinical safety data. Such reckless advice had noequivalent in the history of the pharmaceutical industry. Furthermore, theTGA released these experimental gene-based injections for use in the entire
population including healthy people, children, infants and in pregnancy,
knowing full well that important safety data were lacking.
After two and a half years of use, these COVID gene-based spike injections(they are not really vaccines because they do not prevent infection nor do they
prevent transmission of the virus) have been reported to be associated with thehighest incidence of serious adverse events and death of any drug ever released,according to multiple vaccine adverse event reporting systems, including the
Vaccine Adverse Event Reporting System (VAERS) of the US Centers for
Disease Control (CDC).1 The latest VAERS report through August 4th
2023 reports 35,821 associated deaths and 207,715 hospitalisations. The true
incidence of deaths due to serious adverse events in this US reporting system,
following application of the widely acknowledged under-reporting factor of
1

Openvaers.com (last visited 28 June 2023)

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about 50x, is 1.8 million.2,3 These reported vaccine iatrogenic deaths exceed thenumber of declared US COVID deaths.4 Even when compared to conventional
vaccines the COVID so-called vaccines have been reported to cause more than
10 times the reported incidence of death, according to VAERS.
However, as of this time, the reported incidence of death in Australia causedby the COVID gene-based spike injections according to the TGA stands at
14.5 The TGA says: ‘The 14 deaths likely to be related to vaccination occurredin people aged 21–81 years old. There have been no deaths in children oradolescents determined to be linked to COVID-19 vaccination.’
So, why is there such a discrepancy between the overseas adverse event data andour TGA adverse event data?
The answer lies in the design of the voluntary adverse-event reporting systems
and the way these systems are administered. Gross under-reporting may be a
result of a lack of adequate staff to service the system and analyse the data in atimely fashion. However, other factors include: complex or cumbersome design
which discourages reporting and searching, computer coding and or definition
anomalies which make reporting or searching the database difficult. Failure tofollow up important missing data in relation to deaths and other serious adverse
events is another problem.6
Misclassification or deletion of records has been reported in relation to adverse
drug reporting systems and important safety signals from these systems havebeen ignored.7,8 In addition, health professionals are loath to report adverse
drug reactions from the COVID injections because they fear being labelled
as ‘anti-vaxxers’ or being seen as undermining the prevailing vaccine narrative
promoted by the health regulators, and they fear being disciplined or evensuspended for reporting. 9,3

2
UK All Party Parliamentary Group – Pandemic Response and Recovery Group. 17 July 2023.
https://appgpandemic.org/news/yellow-card
3
Kirsch, S., Rose, J. and Crawford, M. Estimating the number of COVID vaccine deaths in
America. https://sunfellow.com/wp-content/uploads/2021/09/VAERS-Deaths-Kirsch-Rose-Crawford.pdf
4
Ourworldindata.org United States confirmed deaths. Last visited 21 Aug. 2023.
5
Australian Government – Dept. of Health and Aged Care. COVID-19 vaccine safety report15-12-2022. https://www.tga.gov.au/news/covid-19-vaccine-safety-reports/covid-19-vaccine-safety-report-15-12-2022.
6
Rose, J.: Critical Appraisal of VAERS Pharmacovigilance: Is the U.S. vaccine Adverse Events
Reproting System (VAERS) a Functioning Pharmacovigilance System”. Science, Public Health Policy, and theLaw. Vol 3:100-129, Oct. 2021. https://www.researchgate.net/publication/370158323_Critical_Appraisal_
of_VAERS_Pharmacovigilance_Is_the_US_Vaccine_Adverse_Events_Reporting_System_VAERS_a_
Functioning_Pharmacovigilance_System
7
Josh Guezknow Substack: CDC Finally Released its VAERS Safety Monitoring Analyses for
COVID Vaccines via FOIA. 5 Jan. 2023. https://open.substack.com/pub/jackanapes/p/cdc-finally-released-its-vaers-safety?r=10pxn5&utm_campaign=post&utm_medium=email
8
Jessica Rose Substack: 19 June 2023. Scrub-a-dub-dub, is Janssen gettin’ thrown off the sub?
https://open.substack.com/pub/jessicar/p/scrub-a-dub-dub-is-janssen-gettin?r=10pxn5&utm_campaign=post&utm_medium=email
9
Josh Guezknow Substack: CDC Finally Released its VAERS Safety Monitoring Analyses for244

Too Many Dead
Post-marketing adverse drug reporting systems have served a very importantrole in the past. This is because during the research and development of any
new drug, depending on circumstances, usually only a few thousand people arestudied in perhaps 10 to 30 clinical trials over 7 to 10 years.
While these clinical studies are highly monitored for adverse effects, the limited
number of studied subjects in these R&D programs means that those adverse
effects which occur, maybe one in a hundred or one in a thousand, will bedifficult to identify as being caused by the drug tested rather than occurringby chance. For this reason, post-marketing surveillance in pharmacovigilancesystems play an important and indispensable role. A total of 462 medicinal
products have been withdrawn from the market between 1950 and 2013 using
post-marketing surveillance.10
Post-marketing surveillance of adverse drug reactions is of particular importancewhen the safety and efficacy data for any drug under research are limited by the
number of clinical trials conducted or limited by the types of patients studied. In
the case of Provisional Approval in Australia or Emergency Use Authorisationin the US or Conditional Approval of the COVID ‘vaccines’ in the European
Union, all these limitations applied.
In order to have a reliable estimate of safety a drug released under conditional
approval where insufficient safety and efficacy data exist for full approval, it
is necessary to have a transparent, efficient and dependable adverse drug
reporting system to identify safety signals should they arise. Given thediscrepancy between the large numbers of adverse events reported in relation to
the COVID ‘vaccines’ overseas compared to Australia, it appears that Australia
does not have a reliable and transparent adverse drug event reporting system
to identify important safety signals and neither does the US Food and Drug
Administration (FDA).11
For example, up to the writing of this paper, a reported total of nine children
have died in relation to the administration of the COVID so-called vaccines12 in
the Australian TGA Drug Adverse Event Notification (DAEN) system. Thereis insufficient transparency to provide confidence to conclude these deaths are
not related to the COVID vaccine. Indeed, there is considerable suspicion that
COVID Vaccines via FOIA. 5 Jan. 2023. https://open.substack.com/pub/jackanapes/p/cdc-finally-released-its-vaers-safety?r=10pxn5&utm_campaign=post&utm_medium=email
10
Onakpoya, I, J. et al: Post-marketing withdrawal of 462 medicinal products because of adverse
drug reactions: a systematic review of the world literature. DOI 10.1186/s12916-016-0553-2
11
Demasi, M.: FDA urged o publish follow-up studies on Covid-19 vaccine safety signals.
https://www.bmj.com/content/379/bmj.o2527
12
Personal communication: Case numbers 616124, 647663, 659048, 719838, 724023, 733723,
734187, 744306 and 762472

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the TGA may be under-reporting and misclassifying deaths.13,14 The full ADR
records are not made publicly available to provide any level of assurance.
Reanalysis of the risk of serious adverse drug events which occurred in the
Pfizer and Moderna COVID ‘vaccine’ clinical trials showed that about 1 in 800
people had a chance of a serious adverse event15 representing a 16% higher riskof serious adverse events compared to placebo and far more than 1-2 for eachmillion reported for vaccines in general.16 The official reported TGA DAEN
incidence for serious adverse events for COVID ‘vaccines’ fails even to comeclose to this statistic.
A practical example of the unreliability of adverse drug-event reporting systems
is provided by the discrepancy between the official TGA-reported incidence of
myocarditis and the incidence of myocarditis reported in clinical practice.
According to the TGA: ‘Myocarditis is a known but very rare side effect’ of the
Pfizer and Moderna so-called vaccines. The TGA goes on to say: ‘It is usually
temporary, with most people getting better within a few days. Myocarditis isreported in around 1-2 in every 100,000 people who receive Comirnaty (Pfizer)
and around 2 in every 100,000 of those who receive Spikevax (Moderna).’17
However, in a rare admission, one prominent Australian cardiologist revealed
that he has seen about a hundred cases of myocarditis since the COVID so-calledvaccines were rolled out.18 Given that there are about 1200 cardiologists inAustralia, this means there might have been 120,000 cases of symptomatic
myocarditis – not around 500 cases as estimated by the TGA. This degree of
discrepancy is unacceptable.
The problem is that insufficient safety data were generated prior to the release of
the COVID so-called vaccines and the population is entirely dependent upon
13
Mercola, J.: Epoch Times 21 May 2022. Thousands of Deaths and Adverse Reactions Deleted
from VAERS. https://www.theepochtimes.com/thousands-of-deaths-and-adverse-reactions-deletedfrom-vaers_4481440.html?utm_source=Health&utm_campaign=health-2022-05-22&utm_medium=email&est=7m17NiFo5EoGT1omDWz1WO3DSAnrvrbqvGJvEw%2BYltfW41BaiwbGVeQQ6zkYgnniTyq%2FL7wEg7MbfdTV3MyrR1w%3D#Print
14
Mohanoor, A: A review of recent Creutzfeldt-Jakob disease VAERS reports. 17 April 2923.
https://open.substack.com/pub/vaccinedatascience/p/a-review-of-recent-creutzfeldt-jakob?r=10pxn5&utm_campaign=post&utm_medium=email
15
Fraiman, J. et al: Serious adverse events of special interest following mRNA COVID-19
vaccination in randomized trials in adults. Vaccine 40 (2022) 5798–5805. https://doi.org/10.1016/j.
vaccine.2022.08.036
16Office of Infectious Disease. HIV/AIDS Policy. Vaccine side effects. 2021. www.hhs.gov/
immunization/basics/safety/side-effects/index.html
17
Australian Government – Dept. of Health and Aged Care. COVID-19 vaccine safety report15-12-2022. https://www.tga.gov.au/news/covid-19-vaccine-safety-reports/covid-19-vaccine-safety-report-15-12-2022.
18
Radio Interview of Dr. Ross Walker 6 June 2023: 2HD 1143AM Newcastle. https://www.2hd.
com.au/2023/06/06/dr-ross-walker-explains-the-importance-of-good-muscle-strength/?fbclid=IwAR2xpd22lfXIuQRcHOYpEPU8o1_qzzPg3QDeOGM2zfGaOYIA_RuIONJkGsM_aem_th_AYsku5AtEgbXgSaCfd6mJHWWNlaJbUNkNPjBn9WQ_Jyn0imbMS6CE-1Ia3bCRYf428E

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Too Many Deadan unreliable adverse drug event reporting system to prove safety. One cannot
have an expedited drug approval system which depends on very limited evidenceof safety, and, at the same time, have an unreliable and non-transparent adverse
drug-event reporting system which fails to identify and report important safety
signals. If safety signals such as cardiac arrest, pulmonary embolism, stroke,sudden death, cancer, diabetes and neurological disease such as dementia occur
significantly above baseline values and are not duly recognised, there is no pointin having an adverse drug-event reporting system.
There are other clues that our adverse drug-event reporting systems areunder-reporting the real incidence of death associated with the so-called
COVID vaccines. Life insurance companies around the world are reporting
record numbers of unexpected deaths. These are not statistical fluctuations.
For example, Lincoln National, the fifth largest insurance company in the US,
reported a 153% increase in life insurance claims in 2021.19
It is becoming increasingly obvious that we cannot rely upon the various adverse
drug-reaction reporting systems in order to assess the safety of the so-called
COVID vaccines.
However, from time to time there is a substantial clue to the true and exceptionallyhigh incidence of adverse events associated with the COVID ‘vaccines.’ One
such clue was provided by Adverse Event Following Immunisation statistics
released by the West Australian government for 2021. These data show inrelative terms the rocketing numbers of adverse events reported for COVID‘vaccines.’ Twice as many COVID ‘vaccines’ were injected as compared to allother vaccines – but forty times the number of adverse drug reactions werereported.20
But dangerous drugs can be identified in another way apart from adverse
drug reporting systems. Most countries accurately measure a statistic termed
the All-Cause Mortality and a statistic called Excess Deaths. The All-Cause
Mortality is the number of deaths each year from all causes and Excess Deaths
are the number of deaths from all causes above that normally expected based
usually on recent years. The Australian Government publishes these data on aregular basis as Provisional Mortality Statistics.21
In Australia and around the world these All-Cause Mortality statistics have

19
Menge, M.: Crossroads Report. Fifth largest life insurance company in the US paid out 163%
more for deaths of working people ages 18-64 in 2021 – total claims/benefits up $6 Billion. 16 June 2022.
https://crossroadsreport.substack.com/p/breaking-fifth-largest-life-insurance?utm_source=substack&utm_
medium=email
20
Western Australian Vaccine Safety Surveillance – Annual Report 2021. https://www.health.
wa.gov.au/~/media/Corp/Documents/Health-for/Immunisation/Western-Australia-Vaccine-Safety-Surveillance-Annual-Report-2021.pdf
21
Australian Bureau of Statistics – Provisional Mortlity Statistics. Release 28.6.23. https://www.
abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/latest-release247

Australian Medical Professionals Society
shown a disturbing trend of about 16-20% Excess Deaths since the rollout of
the COVID ‘vaccines’ in 2021 but not in 2020 when there were no COVID‘vaccines’ and the SARS-CoV-2 virus was at its most virulent. The majority ofthese excess deaths in 2021 and 2022 were non-COVID-19 deaths and include
heart attacks, strokes, diabetes, dementia and other neurological conditions.
So, what caused most of the Excess Deaths if it was not COVID-19?
There is now evidence to show that an analysis of COVID vaccine use isstrongly correlated with All-Cause Mortality over 31 European Union member
states and Norway, Iceland, Liechtenstein and Switzerland.22 The report showsthat the more a country engages in COVID vaccination, the higher is theoverall mortality from all causes. A Bradford Hill analysis of Excess Mortality
in relation to the COVID ‘vaccines’ showed mass vaccination was strongly
correlated with excess deaths.23
There appears to be a growing body of opinion that the COVID ‘vaccines’ aredoing more harm than good24 and they should be withdrawn.
Additional evidence that the COVID ‘vaccines’ are responsible for the majorityof the excess deaths comes from a report by Rancourt et al.25 The excess
All-Cause Mortality following the COVID-19 vaccine rollout (31,000 deaths,
mid-April 2021 through August 2022) is more than twice the total number ofAustralian deaths registered as being from or with COVID-19 (14,014 deaths,
January 1st 2020 through August 29th 2022).
The Australian Government is currently attempting to minimise the numberof excess deaths reported by the Australian Bureau of Statistics in their latest
All-Cause Mortality statistics for 2022 first by ignoring the low number ofexcess deaths in 2020 used as a baseline and more recently by adjusting thenumber of excess deaths downwards by 12,000 using a mysterious statisticalmodel26 rather than actual Excess Death numbers. In reality, for 2022, there
were about 10,000 COVID-19 deaths (as determined by PCR testing) and
20,000 unexplained non-COVID excess deaths.
22
Aarstad, J and Kvitastein, O.A.: Is there a Link between the 2021 COVID-19 vaccination uptake
in Europe and 2022 Excess All-Cause Mortality? https://www.preprints.org/manuscript/202302.0350/v1
23
Sy, W.: Australian COVID-19 pandemic: A Bradford Hill Analysis of Iatrogenic Excess
Mortality. J. Clin. Exp. Immunol. 2023, Vol 8, Issue 2, 542-556. 1 April 2023. https://www.opastpublishers.com/peer-review/australian-covid19-pandemic-a-bradford-hill-analysis-of-iatrogenic-excess-mortality-5339.html
24Classen, J.B.: US COVID-19 Vaccines Proven to Cause More Harm than Good Based on
Pivotal Clinical Trial Data Analyzed Using the Proper Scientific Endpoint, “All Cause Severe Morbidity”.
Trends int. Med. 2021, Vol 1, issue 1, pp1-6.
25
Correlation Research in the Public Interest. Rancourt, D.G. et al. 20 Dec. 2022. https://www.
researchgate.net/publication/366445769_Probable_causal_association_between_Australia%27s_new_
regime_of_high_all-cause_mortality_and_its_COVID-19_vaccine_rollout
26
Wilson Sy. Australian excess deaths: Moving the Goalposts. In print – Principia Scientific2023 and phillipaltman.substack.com 24 Aug. 2023.

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Too Many DeadThis is reprehensible. This needs investigation. This needs explaining.
The Australian government continues to insist that the so-called COVID
vaccines are ‘safe and effective’ but evidence to support this claim is lacking. In
addition, there is no credible or supportable explanation for the dramatic rise in
the unexpected non-COVID excess deaths which have occurred only after therollout of the COVID ‘vaccines’.
One would have thought, given the magnitude of the COVID disaster, that
it would be of utmost importance to determine what is causing more than
30,000 Australians to die non-COVID deaths above average since the COVID‘vaccines’ were rolled out. A motion to investigate the possible causes of theunexpected non-COVID deaths was introduced into the Australian Federal
Parliament in March 2023 and was defeated.

Dr Phillip Altman is an Australian authority on
clinical trials and regulatory affairs with more than
40 years of experience in designing, managing andreporting of clinical trials and in working with theAustralian Therapeutic Goods Administration in
gaining new drug approvals.
He established Australia’s first contract researchorganisations and served as a senior industry consultant
for more than half of the pharmaceutical companies
present in Australia. Involved in over a hundred
clinical trials, he has been personally responsible formarket approval of numerous new drugs since joining
the pharmaceutical industry in 1974.
A graduate of Sydney University with an Honours
degree in pharmacy, Master of Science and Doctor ofPhilosophy degrees (focusing on drug development,
pharmacology and pharmaceutical chemistry), he
co-founded and is a life member of the largest
professional body of pharmaceutical industry
scientists involved in clinical research and regulatory
affairs. Most recently Dr Altman has provided expert
reports in relation to both the Australian and NZ
Judicial Review and High Court cases in relation to
the COVID vaccines.

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Australian Medical Professionals Society

Excess deaths in Australia
during the COVID period:
the numbers
by Clare Pain

BSc Hons (Chemistry), MSc (Operational Research)

Abstract
The Australian Bureau of Statistics (ABS) is using two different models that
estimate excess mortality over the COVID period, which are here called
the simple and complex models. For 2022, using these models, the ABS haspublished estimates of 15.3% and 10.9% excess deaths in 2022 respectively
and the corresponding figures to date for 2023 are 12.3% and 9.1%.
Examining data back to 1950, even the lower level of excess deaths in 2022
(10.9%, complex model) is unprecedented. Z-score analysis gives a highly
significant value of 3.4 standard deviations from the mean. A pattern of two
such high years in succession, (10.9% in 2022, 9.1% in 2023 to date, complex
model) has not been seen before.
Similar trends of high excess deaths are evident in many other countries,
which suggests that new factors may be causing substantial effects. Differences
between the excess deaths pattern for Sweden, which had few restrictions, andother countries, suggests that patterns of excess deaths may be related to theways governments intervened during the COVID period. Australia and New
Zealand show very similar patterns. New Zealand and Sweden may represent
two extremes, with an opposite sequence of vaccination and viral spread.

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Too Many Dead
Results from three separate models show that a persistent trend of high excess
deaths began in Australia in the Antipodean Spring of 2021, and continues
in 2023.
An independent investigation is urgently needed, as is complete transparency
about the ABS’s complex model for excess deaths, and the excess death rates
by age band and state that it produces.

1.

Introduction - why measure excess deaths?

As Australia went through the COVID-19 period the population faced anovel virus, for which they were told there was no protection through existing
immunity. Fear about this new virus became high, stoked by the media,
seemingly with government support.
Australian Federal, State and Territory governments took unprecedented
measures. Australia closed out the world, sealing its international borders, andcitizens were told that they could not leave the country without permission.
State and Territory borders were closed, opened, and closed again. Citizens
learned a new word, lockdown, and were confined to their homes for most of
the day for weeks and months. When people did encounter each other, they
were told to keep apart and wear masks.
A completely new class of vaccination – genetic vaccines – was provisionallyapproved by the Therapeutic Goods Administration (TGA) and administered
to almost the entire population. These new vaccinations were mandated bysome States, Territories, and employers.
As we emerge from the COVID period it is important to examine theoutcomes of the way it was managed in Australia. Did the decisions taken
give better results than would have occurred with no interventions, or withdifferent interventions? Did the governments’ actions save thousands of lives,or have they, perhaps, cost thousands of lives? Have they caused economic
effects[1] that have cost lives? One way to get a handle on this is to look at thepattern of deaths in Australia and in other countries, from any cause – known
as ‘all-cause mortality’.
There are strong arguments for looking at all-cause mortality rather thansimply looking at the number of deaths from COVID.[2] Although themeasures taken by governments such as lockdowns, treatment protocols, andvaccinations were intended to reduce COVID deaths, these measures andothers may have affected the number of deaths from other causes too, making
them higher or lower than normal.
Indeed, when the ABS released the first of its Provisional Mortality Statistics[3]251Australian Medical Professionals Society
series on June 26th 2020, designed to report death statistics during the COVID
period, they said: ‘Examining mortality data across a broader range of causes
provides a more comprehensive view of the impact of the pandemic on publichealth. This can give an indication of where “excess” mortality might occur
beyond deaths directly from COVID-19.’
But how do we know whether the all-cause mortality data being reported is ‘inexcess’ of what would be expected? For this we need to define an excess death.
1.1 What is an excess death?
The concept of excess mortality was used long before the COVID period, tomeasure how many extra deaths were caused by exceptional events such as
influenza epidemics[2] and heatwaves. The idea is straightforward: compare
the number of deaths that have occurred during an influenza epidemic or
heatwave, with the number of deaths that would have been expected, had theexceptional event not happened.
Excess deaths are thus, da - deAnd the excess deaths as a percentage is calculated as (da -de)/de*100, where dais the actual number of deaths and de is the expected number of deaths.
An excess mortality rate (deaths per population) can be calculated in an
analogous way. Using mortality rates may be preferable to simply using
numbers of deaths especially in countries with high rates of migration like
Australia. This is because the number of deaths in a country will increase as
the population increases.1
1.2 Types of models used for estimating expected deaths
Many different modelling approaches for estimating the expected mortality
can be used. A simple approach is to predict that mortality will continue
at the same average level as it was for a certain number of years before the
COVID period. The more years included, the less likely the prediction
will be biased by a quirky year, but this must be balanced by the fact thatrecent years are more likely to be good predictors of the immediate futurethan older ones. For example, data for the year 2019 are more likely to be
relevant when predicting 2020 deaths than are data from 2010.
The ABS began its Provisional Mortality Statistics series by choosing sucha simple model to estimate expected deaths during the COVID period. The
expected number of deaths in a particular week of the COVID period year
was chosen to be the average of the number of deaths in the corresponding
1
Similarly, if a country has a declining population, perhaps through emigration, the number ofdeaths will decrease.

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Too Many Deadweek in the five pre-COVID years, 2015-2019.
Other, more complicated approaches fit a trend line to a consecutive series ofyears of past data, often using least squares regression analysis, and modellingis sometimes extended to include higher-order (polynomial) terms in fitting
the trend and the addition of sine and cosine terms to model seasonality.
Many temperate countries, including Australia, have a seasonal pattern ofhigh mortality in the winter months (driven by respiratory infections such as
influenza) and lower mortality in summer.
1.3 The importance of model choice
The calculation of excess mortality is simple enough, but the choice of themodel used to estimate the expected or predicted mortality (called ‘baseline’ inthe ABS Provisional Mortality statistics) during the COVID period is crucial.
As an example, using the Short-term Mortality Fluctuations (STMF) online
tool[4] provided by the Human Mortality Database, one can choose different
models that use either a simple average or a trend line through past data. One
can also select the number of pre-COVID years to include in the model andchoose to model using data for deaths or death rates.
Using these options, predictions for the expected deaths in 2022 in Australia
range from 159,966 to 180,265 (Appendix A). This means that the number ofcalculated excess deaths in 2022 ranges from 9,671 to 29,970.
The choice of whether to model using data for deaths or for death rates canalso make an important difference. For example, the STMF tool gives figuresof 18.4% or 12.4% excess mortality for Australia in 2022, depending onwhether the estimate is based on average deaths or average death rates for theyears 2015-2019.
The concern here is that by careful or deliberate selection of a model, very
different excess mortality statistics can be produced. This raises dangers,
particularly when models are selected to calculate excess deaths after actual
deaths data is known. This is because, should a particular spin be wanted on
numbers of excess deaths, it would be quite possible to find a model that willproduce the results required.2
It is important, then, to establish a model for predicted deaths in a period before
one looks at the actual death numbers, using logic and principled reasoning.
2
Indeed, Professor John Gibson, Professor of Economics at Waikato University in New Zealand,
has published a working paper[5] in which he criticises claims by Sir Ashley Bloomfield, former Director-General of Health in New Zealand, that over the COVID period there had been fewer deaths than
expected (in other words a death deficit). The model on which the claims were based implicitly assumed that
immigration would continue at the rates seen in 2015-2019 (thus inflating predictions of expected deaths)
in a country with locked-down borders. When actual deaths were compared with inflated expected deaths,
excess deaths appeared low.

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Australian Medical Professionals Society
To their credit, the ABS did this when they launched their Provisional
Mortality Statistics series in mid-2020. If models are chosen after the actual
deaths data have come out, (as was the case with the revised complex model
the ABS has developed, see later), great care needs to be taken by the producerof the statistics to ensure that any such model is developed in as logical and as
principled a way as possible.
1.4 How the ABS has measured Australian excess deaths over the
COVID period
1.4.1 Provisional mortality statistics (ABS original and new simple
models)As already mentioned, in June 2020 the ABS took the initiative to provide
monthly updates on provisional death figures during the pandemic, starting
its Provisional Mortality Statistics series.3
In these statistics, the ABS provided measurements of deaths compared tothe expected deaths from their model, terming the expected deaths ‘baseline’.
As mentioned earlier, their predictor was that deaths in each week during the
pandemic would be the average of the deaths in the corresponding week inthe five years before the pandemic (2015-2019). We will call this model for
calculating excess deaths the ABS’s ‘original simple model’.
The simple model as used by the ABS in its Provisional Mortality Statistics in2020 and 2021 can be justified in many ways. It makes sense to compare what
happened during the COVID period with the years immediately preceding
it. Further, using a reasonably long period of five years reduces the risk of theaverage being unduly influenced by years with unusually high or low deaths.
(For example, 2017 and 2019 were years with bad influenza outbreaks.)
Importantly, this model was chosen before the actual death numbers camein. It was a sensible, principled model. One flaw, however, was that because
deaths, rather than death rates, were modelled, it did not adjust for changes inthe size of the Australian population; another was that any trend in the data
was ignored.
However, on March 30th 2022, the ABS announced[6] it would be using a newmodel for the Provisional Mortality Statistics in 2022. For 2022 and 2023,
deaths have been compared to the average of four years: 2017-2019 and 2021.
This ‘new simple model’ is harder to defend than the original model for thefollowing reasons:3
As explained in the methodology section of the first Provisional Mortality Statistics release, thedata differed from that in the existing series, published annually, called ‘Deaths Australia’ and ‘Causes of
Death, Australia’ in several respects. For example, the Provisional Mortality Statistics do not include deaths
certified by a coroner; and they are based on the date of death, not the date of registration of the death.

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Too Many Dead•

Only four years of data were averaged (compared with five)•

Two of the included years were bad influenza years (2017 and 2019)
and thus had high deaths (4.8% and 6.9% above the 2015-2019 average
respectively)

•

•

•

There was a gap in the sequence of years

2020, a year from the COVID period, was excluded – because, the ABS
said: ‘it included periods where numbers of deaths were significantly
lower than expected.’ Deaths in 2020 were -0.1% below the average for2015-2019
2021, a year from the COVID period, in which Australia was not‘normal’– with border closures, lockdowns, a vaccination rollout and
COVID outbreaks – was included. Deaths were 6.2% higher in 2021
than the 2015-2019 average

Of course, if the model uses years in which there were comparatively highrates of death, then it will predict high expected deaths in 2022 and 2023 –which, in turn, has the effect of reducing the calculated figure for excess deathsin those years.

Why did the ABS feel the need to switch from their logically rigorous original
simple model, which used a consecutive series of pre-COVID data years from2015-2019, to this new simple model, with its seemingly arbitrary choice ofdata years, 2017-2019 and 2021? The new simple model led to lower excess
death figures being reported than would have been the case using the originalmodel. It is noteworthy that when the choice of baseline was announced, excess
deaths had been running at a high level for six months from October 2021.
1.4.2 Measuring Australia’s Excess Mortality (ABS initial and revised
complex models)
Since November 25th 2020, the ABS has also published occasional reports[7]entitled ‘Measuring Australia’s excess mortality during the COVID-19
pandemic’ and using another model for excess deaths.
The more sophisticated mathematical model in these publications is anadaptation by the ABS of a model used by NSW Health to detect yearsof unusually high influenza. It uses death rates and has a constant term, alinear trend and sine and cosine terms to model seasonality. It also has 95%
confidence intervals which provide a threshold level, above which an anomaly
is assumed to have occurred (such as a COVID or influenza wave). We willcall this the ‘initial complex4 model’.
4This is unrelated to the concept of complex systems used in mathematics

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Australian Medical Professionals Society
This initial model was extensively revised[8] on July 17th 2023 in a report
entitled ‘Measuring Australia’s excess mortality during the COVID-19
pandemic until the first quarter 2023’. While the initial version modellednational deaths and deaths from specific causes based on data for the five years
before the COVID period, the revised model used age-specific death rates
from 2013-2019. Age-specific death rates are deaths in a particular five-year
age band (for example age 50-54 years) divided by the number of people inthat age-band.
The methodology section of the July ’23 release does not make it completely
clear how the revised complex model has been done, but it appears that it
is more granular – with death rate models for each age-band in each state
to predict expected death rates by age band by state. It appears that thesepredictions have then been multiplied by population estimates for each age
band during the COVID era and pooled to produce expected total numbersof deaths nationally.
Tantalisingly, this revised model presumably produces excess mortality rates
by age-band and state by week, data which would be extremely illuminating
about how COVID itself, and government interventions, have affected eachage band throughout the COVID years. Sadly, the pooling of these data loses
the insights it might have brought. It appears this information is not yet
publicly available.
Good points about this revised complex model are: that a long period of data
has been used (2013-2019); and all these data are from before the COVID
period, when things were ‘normal’. The use of death rates, rather than deaths,
has the advantage that changes in population are accounted for, and using
age-specific death rates allows for population changes in each age band to bemodelled. For example, in the short term, an influx of younger people increases
the population size with people who are unlikely to die in that year.
On the downside, to convert the age-specific death rate predictions into
number of deaths, estimates of the populations of these age bands must be
used, which may introduce inaccuracies. Populations are only known accurately
on census nights.
Logically, this revised complex model has more credibility for establishinglevels of excess deaths than the new simple model currently being used in theProvisional Mortality Statistics. However, one must also bear in mind that therevised complex model was chosen after the actual death numbers were known
for the pandemic period up to at least the end of 2022. In view of this, andthe potential for political pressure to be put on the ABS to reduce reported
numbers of excess deaths, it is important that all aspects of the choice and256

Too Many Deaddevelopment of this model be brought into the public eye.
1.5 Other models
There are other models being used around the world to predict expected
deaths, and thus to calculate excess deaths over the COVID period. Two aredescribed below because their output is used in this analysis. Brief informationon others is given in Appendix B.
1.5.1 Clarity on health models
The author’s business, Clarity on Health (CoH) is engaged in a collaborative
project to compare excess death patterns in different countries. However, themodels used by the ABS are not used in all other countries. As a result, CoH
has created a simple model that can be used across many countries. This model
uses the average of annual death rates for the pre-COVID years 2015-2019 toestimate excess deaths during the COVID period. This model is only suitable
for looking at annual data.
1.5.2 Rönning-Gulbrandsen model
Two researchers in Finland have developed a model[9] with a linear trend anda sine component which was used to analyse Finnish data. One of the authors,
Tore Gulbrandsen, has used this model on ABS data and a graph showing his
findings is presented later.

2.

ABS estimates of excess deaths over the
COVID period

As mentioned above, the choice of model can make a substantial difference tothe excess death story over the COVID period, with the simple and complex
ABS models giving a cumulative total of 42,208 and 19,064 excess deaths over
the COVID period respectively.
The number of deaths in 2022 equates to thousands of Australian lives lost.
Using the simple model, it is equivalent to a plane of the size of an Airbus
380-300 crashing roughly every four days, with the loss of everyone on board;
with the complex model it is equivalent to such a plane crashing roughly every
week.

3.

What pattern of excess deaths should we
expect after a pandemic?

Normally during an influenza epidemic, in the absence of intervention fromgovernments and public health officials, there is a toll of deaths as the infection
passes through the population, killing the vulnerable (mainly the frail and elderly

257

258

2021
6.0%

2020

Excess death predictions by model (% excess)ABS ‘simple’ - Provisional Mortality Statistics (Theoriginal simple model applies to 2020 and 2021, the -0.1%
new simple model thereafter)
10.9%

15.3%

2022

18634

25235

2022

2023
(part year)

Cumulative
excess to date

Cumulative
excess to end 2022

9.1%

3.5%

8.3%

Cumulative
excess to date2023
(part year)
13.2%

19064

42208

3280

8564

3.1%

7.5%

Cumulative
excess to end 2022

15784

33644

https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/latest-release
https://www.abs.gov.au/articles/measuring-australias-excess-mortality-during-covid-19-pandemic-until-first-quarter-2023

Sources: (Accessed 29 August 2023)

Notes:
Data for 2023 are not consistent as they are reported to different time points. The Provisional Mortality Statistics are to May
1st, and the Measuring Australia’s Excess Mortality is to March 26th.

1.4%

2378

ABS ‘revised complex’ - Measuring Australia’s Excess-5228
Mortality

ABS ‘revised complex’ - Measuring Australia’s Excess-3.1%
Mortality

8517

-108

ABS ‘simple’ - Provisional Mortality Statistics (Theoriginal simple model applies to 2020 and 2021, the
new simple model thereafter)

2021

2020

Excess death predictions by model (numbers)

Table 1: Excess deaths (number and %) as calculated by ABS simple (original and new) and revised complex models

Table 1

Australian Medical Professionals Society

Too Many Deadand others with weakened immunity). However, the next year there is typicallya reduced number of deaths, known as a ‘harvesting’ or ‘pull-forward’ effect, andtermed a mortality displacement[8] by the ABS.5 COVID, we know, is mostdangerous for a similar cohort.6
COVID became endemic in Australia in late 2021 and early 2022 when
the Omicron variant reached the nation: lockdowns were ending and stateand international borders were opened to vaccinated people. However, thecompensatory decline in deaths expected a year later has not been seen in 2023.
Instead, deaths remain in excess at 13.2% or 9.1%, depending on the ABS modelused.
We must question the way the normal mortality displacement has not occurredwith COVID. And we must ask why excess deaths are continuing with so many
deaths assigned to COVID, despite the population being heavily vaccinatedwith vaccines that were billed as preventing death from the disease.

4.

How unusual are the 2022-2023 excess deaths?

4.1 Visual inspection of history
The ABS does not seem to have addressed an important question. How
unusual is it to have a 15.3% or 10.9% spike in deaths in a particular year?
This is something that Clarity on Health has examined, using data from theAustralian Institute of Health and Welfare going back to 1950. The graphshows the percentage of excess deaths in a particular year, where the excess iscalculated in comparison with the average number of deaths in the preceding
five years. In the graph two lines are shown from 2020, namely the excess
deaths calculated by the ABS simple and revised complex models.
Looking back over history we see that the highest spike in deaths between
1955 and 2019 occurred in 1964, with a spike of 10.6%. The graph shows thatthe two-year pattern of excess deaths of 15.3% as reported by the ABS for
2022 using its new simple model, followed by 13.2% for 2023 (to the end ofMay), is unprecedented.
And using the revised complex model, even though the excess deaths are lower,
the graph shows that a pattern of two years of 10.9% and 9.1% excess deaths
has not been seen in the past 67 years.
Thus visual inspections show the annual 2022 and year-to-date 2023 excess

5
Similarly, after the deficit in deaths seen during lockdowns in 2020, one might expect a catch-up
of deaths, particularly among older people, once Australia resumed normal life.
6
The median age of death from COVID was 86.9 years in 2020[10] and 79.1 years in 2021[11](the most recent data available from the ABS). This lower age of deaths from COVID in 2021 than in 2020
has been ascribed to the Delta variant; however, normally variants tend to become not more but less virulent
with time.

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Graph 1.

death numbers give a clear signal that something very extraordinary has
happened, regardless of which model one uses.
4.2 Z-scores
A more objective way to measure how unusual the excess death spikes in 2022
is to use z-scores, which measure how far a data point is from the mean interms of the number of standard deviations. Graph 2 shows the z-scores ofannual percentage changes in the death rate in Australia (year on year). The
blue shaded band shows z-scores between +2 and -2 - and 95.45% of data
points are expected to lie within this range. In 2022, the z-score was 3.39. The

Graph 2. Z-scores of year-on-year percentage changes in death rates, Australia

probability of getting such a z-score or higher by chance is 0.034%.

5.

Excess deaths in other countries

Through a collaboration with Reignite World Freedom, Clarity on Health has
been examining excess death statistics in other countries and posting results on
www.excessdeathstats.com. The project relies on volunteers who are familiar with
events in their own countries and who have been following their own statistical260

Too Many Dead
office publications. For each country, a simple annual model of excess death rates
during the pandemic is produced. The models use a predicted death rate of theaverage of the five pre-pandemic years (2015-2019). To put excess death rates
during the COVID period in perspective, the results are displayed graphically,
with the history going back as far as 1950 where possible. All data are taken fromthe country’s official statistics office.
The project team has examined data from Australia, Philippines, New Zealand,
Denmark, and Sweden so far and graphs for these countries are presented below.
Australia and New Zealand, which had very similar responses to the COVID
period, aiming for zero COVID levels at times, have similar graphs. The virus
only became widespread in 2022 in these countries - in January in Australia

Graph 3.

Graph 4.

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Australian Medical Professionals Society
and March in New Zealand. For both countries 2022 shows the largest spikein death rate in 67 years.
Denmark, with land borders, was not able to keep COVID out in 2020, butlike Australia imposed lockdowns and rolled out COVID vaccines. It too

Graph 5.

shows a pattern of excess deaths, with 2022 having the biggest increase indeath rate in 67 years. One must ask why death rates are at their highest in
2022, so long after the population was first exposed to the virus.
Sweden, which did not institute lockdowns, had a much more normal
epidemic response with the virus spreading in 2020 and a 4.7% spike in death
rate that year. Notably, the Swedish spike in death rates was smaller than thatfor any of the other countries discussed here, and it was not unprecedented

Graph 6.

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Too Many Deadin the post-war period (there was a 5.2% spike in 1976). The death rate had
returned to normal by 2022 with a small 0.3% deficit of deaths. One mightargue that Sweden’s graph shows the spike-followed-by-harvesting effect,
which would be expected after an epidemic.
In the Philippines, the spike was phenomenal: 42.9% in 2021. Final data
have yet to come in for 2022, but provisional data suggest excess deaths
have dropped down to 4.7% (this is still higher than pre-COVID averages).
Of course, after such a large spike in deaths in 2021, a very large harvesting
effect would be expected.

Graph 7.

The Philippines had strict lockdowns over an extended period and rolled
out seven different COVID vaccines between March and August 2021,
including Chinese and Russian vaccines. The reason for this huge spike inthe death rate in 2021 should be investigated.
These graphs tell us that similar patterns to those found in Australia are being
seen elsewhere in countries that responded to the COVID period in similarways, with New Zealand being the most similar. Sweden and the Philippines
show us though that different patterns of excess deaths can occur and it seems
likely that this reflects different approaches to managing the response to
COVID.

It is noteworthy that New Zealand and Sweden may represent opposite
situations with regard to the relative timing of COVID injections and COVID
infections. In New Zealand vaccinations preceded COVID spreading widely
within the country. In Sweden it was probably the reverse.

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6.

In Australia, when did things start to go awry?

6.1 What the ABS’s simple model tells us (Provisional Mortality
Statistics)
The provisional mortality statistics include a graph which shows average deaths inthe years 2015-2019 (the predictor of expected deaths), and actual deaths. The blue
shaded area gives the range of death numbers in those years.
As far as we know, no warning system was in place to alert the government orGraph 8. ABS original simple model output 2020

the public that deaths were reaching unusually high levels. Such a system could
have been set up using excursions above the blue shaded area for a few weeks insuccession.

Graph 9. ABS original simple model output 2021

The three graphs published by the ABS covering the COVID period demonstrate
how often warning signals would have arisen. The first[12] shows 2020, the264Too Many Deadsecond[13] 2021 (it appears that ABS mislabelled the red line 2020-21), and thethird[14] shows 2022 and 2023 on the same graph.

Graph 10. ABS new simple model output 2022-2023

In 2020 there was a brief spike in deaths above the blue shaded area that coincidedwith the first COVID wave. However, during the winter of 2020 there wereperiods where there is a deaths deficit even as a second COVID wave hit.
In 2021, there were extended periods where actual deaths (the red line) wereabove the blue shaded area in autumn and spring. Warnings would have been
given in early October and, except for one week, they would have remained forthe rest of the year.
Rather confusingly, the ABS swapped the colours of the lines in the third graph.
In this graph actual deaths are shown in yellow-orange for 2022 and blue for 2023.
Except for three weeks in late winter and spring 2022, deaths remained

Graph 11. ABS revised complex model output 2020-2023

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consistently above the blue-shaded area, and the curve remained stubbornly
above the shaded region in 2023.
In summary, had excursions above the blue shaded area for a few weeks beentaken as a warning signal, the ABS’s simple model would have signalled thatsomething was wrong from October 2021 to May 2022.
6.2 What the ABS’s complex model tells us (measuring
Australia’s Excess Mortality)
In this model,[8] the blue shaded area shows 95% confidence intervals andthe upper boundary is designed to act as a threshold.
Here is what the ABS said about interpreting the information in their report:‘When actual observations (counts of death) exceed the upper thresholdor drop below the lower threshold this indicates a statistically significant
change in the pattern of mortality. This should be used in conjunction withthe percentage excess mortality.
‘A single week above threshold does not necessarily suggest statisticallysignificant excess mortality. Prolonged periods (2 or more weeks) wherecounts exceed thresholds suggest more strongly that the numbers of deaths
are above or below normal.’Examining the graph, the threshold was breached in the Australian Springof 2021 and remained breached until late winter or early spring of 2022.
Since mid-spring 2022 up to at least March 2023 the line has remained
above the threshold.
6.3 What the Rönning-Gulbrandsen model tells us
Like the ABS complex model, this model[9] uses a mathematical formula
to model the data from 2013-2019 and to predict deaths over the COVID
period. However, the formula (shown on the graph) is not the same as thatused by the ABS.
In the graph below, actual deaths (in blue in the upper panel) can clearly beseen to depart from expected deaths in spring 2021, and except for a brief
period in spring 2022, have remained in excess.
In the lower left panel, excess deaths are plotted in red and deaths due to
COVID are plotted in green. Clearly, COVID death peaks and excess deathpeaks coincide in 2022.
The graph in the right lower panel shows cumulative excess deaths (in red)and cumulative deaths due to COVID (in green, derived from ABS monthly
data). The fact that, from 2022, the slope of the excess deaths cumulative266

Too Many Dead

Graph 12. Rönning-Gulbrandsen model output for 2020-2023

curve is steeper than the curve of deaths due to COVID suggests that
COVID is not the only factor causing excess deaths.
This cumulative graph also raises intriguing questions: what caused thechanges in slope of the cumulative excess deaths line in late 2020 and inearly 2022?
In conclusion, three different models show excess deaths rising to levels thatshould trigger alarm from the Australian Spring of 2021 onwards.

7.

Conclusion

7.1 Transparent publication of the details of the ABS ‘revised
complex model’
The model here called the revised complex model was produced after atrend of excess deaths had been established in Australia for nearly two
years. Ideally such a model should be created using a logical and principledapproach before any actual mortality data become available. The ABS
needs to publish full details of the model, including coefficients and resultsfor excess deaths by age band and state throughout the COVID period, toprovide complete transparency.
7.2 An independent investigation
The Australian data discussed here show that Australians have been dying
at rates approximately 10% greater than expected since the Spring of 2021.
Yet it seems that no investigation has been set up into the causes of theseexcess deaths.
Notably, despite what the ABS tells us is “statistically significant excess
mortality”, Australian senators chose to vote against establishing a

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committee to investigate excess deaths in March, 2023.
Since it is possible that some of these excess deaths may be the resultof actions taken by public health officials and Australian governments
during the COVID period, care needs to be taken in selecting a committee
to investigate them. People who were involved in interventions such as
lockdowns and vaccination rollouts may have a potential conflict of interest.
A truly independent investigation into the causes of these deaths is long
overdue.
Thanks go to Ralph P, Terry Anderson, Tore Gulbrandsen, Jamie C and Colin M
for their ideas and contributions to this paper.

©Clarity on Health 2023

References
[1]

[2]

[3]

[4]

[5]

[6]

[7]

Foster, G. (2022) Do Lockdowns and Border Closures Serve the “greater
Good”? A Cost-benefit Analysis of Australia’s Reaction to COVID-19.

Németh, L., Jdanov, D.A. and Shkolnikov, V.M. (2021) “An open-sourced,
web-based application to analyze weekly excess mortality based on the
Short-term Mortality Fluctuations data series,” PLOS ONE, 16(2), p.
e0246663. Available at: https://doi.org/10.1371/journal.pone.0246663.

Provisional Mortality Statistics, Jan-Mar 2020 (2020). Available at: https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/jan-mar-2020 c) Provisional Mortality Statistics, Jan-Mar 2020 (2020).
Available at: https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/jan-mar-2020
https://mpidr.shinyapps.io/stmortality/

Gibson, John. Cumulative Excess Deaths in New Zealand in the COVID-19
Era: Biases from Ignoring Changes in Population Growth Rates. Working
Paper No. 23/02, Department of Economics, University of Waikato. 2023 e)
Gibson, John. Cumulative Excess Deaths in New Zealand in the COVID-19
Era: Biases from Ignoring Changes in Population Growth Rates. Working
Paper No. 23/02, Department of Economics, University of Waikato. 2023

Provisional mortality Statistics, Jan 2020 - Dec 2021 (2022). Available at:https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/jan-2020-dec-2021.
Measuring excess mortality in Australia during the COVID-19 pandemic(25/11/2020). Available at: https://www.abs.gov.au/articles/measuring-excess-mortality-australia-during-covid-19-pandemic.

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[8]

[9]
[10]
[11]
[12]

[13]

[14]

[15]
[16]
[17]

Measuring Australia’s excess mortality during the COVID-19 pandemic
until the first quarter 2023 (17/07/2023). Available at: https://www.abs.gov.
au/articles/measuring-australias-excess-mortality-during-covid-19-pandemic-until-first-quarter-2023.

Rönning, Kasper and Gulbrandsen, Tore A. (2022) “Excess deathanomaly in Finland 2021,” Available at: https://jarjenaani.fi/wp-content/
uploads/2023/05/Excess_Death_Anomaly_Finland_2021.pdf
Causes of Death, Australia, 2020 (2021). Available at: https://www.abs.gov.
au/statistics/health/causes-death/causes-death-australia/2020.
Causes of Death, Australia, 2021 (2022). Available at: https://www.abs.gov.
au/statistics/health/causes-death/causes-death-australia/2021

Provisional Mortality Statistics, Jan-Dec 2020 (no date). Available at: https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/jan-dec-2020

Provisional mortality Statistics, Jan 2020 - Dec 2021 (2022b). Available at:https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/jan-2020-dec-2021.
Provisional mortality statistics, Jan - May 2023 (2023). Available at: https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/jan-may-2023.

Karlinsky, A. and Kobak, D. (2021) “Tracking excess mortality across countries
during the COVID-19 pandemic with the World Mortality Dataset,” eLife,
10. Available at: https://doi.org/10.7554/elife.69336.
COVID-19 Data Explorer (no date). Available at: https://ourworldindata.
org/explorers/coronavirus-data-explorer.
The Economist (2022) ‘Tracking covid-19 excess deaths across countries,’ The
Economist, 13 May. Available at: https://www.economist.com/graphic-detail/coronavirus-excess-deaths-tracker.

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Appendix A
Estimates of excess deaths for Australia obtained using the
Short-term Mortality Fluctuations Tool (STMF)

Measure/Modelling
approach

Deaths/Week-specificaverages

Deaths/Week-specifictrendsMortality Rate
(Deaths/100,000 population)/Week-specificaverages

Mortality Rate
(Deaths/100,000 population)/Week-specifictrendsBaseline
Years

Referencemortality

Actualmortality

Excessmortality

Excessmortality

2015-2019

160,429

189,936

29,50718.4%

2016-2019

161,301

189,936

28,635

17.8%

2017-2019

162,453

189,936

27,48316.9%

2018-2019

161,720

189,936

28,216

17.4%

2019

164,370

189,936

25,56615.6%

2015-2019

168,470

189,936

21,466

12.7%

2016-2019

167,928

189,936

22,008

13.1%

2017-2019

159,966

189,936

29,97018.7%

2018-2019180,265

189,936

9,671

5.4%

2019

164,370

189,936

25,56615.6%

2015-2019

652.86733.95

81.09

12.4%

2016-2019

651.35733.95

82.60

12.7%

2017-2019

650.98733.95

82.97

12.7%

2018-2019

643.08733.95

90.87

14.1%

2019

649.00733.95

84.95

13.1%

2015-2019635.29733.95

98.66

15.5%

2016-2019

633.38733.95100.57

15.9%

2017-2019615.39733.95118.5619.3%

2018-2019

684.71733.95

49.24

7.2%

2019

649.00733.95

84.95

13.1%

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Appendix B
Other models and data on excess deaths.
The Short-term Mortality Fluctuations Tool (SMFT)
Researchers from the Max Planck Institute in Germany and the University ofBerkley in California developed an online tool[4] for modelling excess deaths.
Users can choose the period of years used to estimate the baseline and whether
to model deaths or death rates.
The World Mortality Dataset
A repository[15] collecting weekly, monthly or quarterly mortality data for 125
countries and territories from 2015 onwards.
Our World in Data
A portal[16] which has access to data on excess mortality that enables graphs
to be plotted by country
The Economist
A webpage with underling model[17] tracking excess deaths by country.

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Clare Pain is a medical journalist and science writerwith over a decade’s experience of writing for doctors
and the general public.
After two years studying medicine, she completed
a chemistry degree, followed by a Master’s inoperational research. She then worked for eight years
in industry in the UK as a statistician. Clare Pain
turned to science writing and medical journalism in2011. She has written for the ABC, New Scientist, theSpringer Nature publishing group, Medical Observer,
Australian Doctor and Spectator Australia.
Since December 2021, she has run projects on
www.ClarityonHealth.org to examine the response
to COVID in Australia. Projects include the ‘The
Australian Survey of Reasons for COVID-19
Vaccination’ and the Mandate Update project, which
tracks which employers are (and are not) mandating
COVID injections.
Clare Pain’s current focus is to bring the phenomenonof worldwide excess deaths to the attention ofordinary people and their elected representatives.
Her substack is clarityonhealth.substack.com.

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Too Many Dead

Excess mortality in Australia— when were thewarning signs apparent?
by Andrew Madry
BSc (Hons) PhD

1.

Introduction

Australia, like many other Western countries, is currently experiencing excess
mortality at a level unknown outside war times. Even after COVID deaths
are taken into account, there is substantial remaining excess mortality. Healthauthorities have no explanation for the cause of these Australian excess deaths
above historical averages.
We know in 2023 that excess mortality is well above estimated baselines, andhas been so for some time: we consequently ask: were there trends apparent inmortality data, unrelated to COVID-19 disease, that should have raised early
warnings to health authorities?
We take a statistical signal processing approach for detection of suchphenomena. As well as classical time-series analysis approaches, we apply
modern machine-learning methods to time series to investigate trends in detail.
The COVID vaccination campaign, in response to the COVID-19 disease,is the first time such a widespread effort has been made to deliver a novel
pharmacological product to the Australian population.
We review the Therapeutic Goods Administration (TGA) reporting system273

Australian Medical Professionals Society
for adverse events related to COVID-19 vaccination. There are approximately
1,000 deaths reported following COVID vaccination. The TGA states that only
14 deaths were directly caused by COVID-19 vaccination with the implication
being that all the other deaths are ‘coincidental’.
This report focuses on data for the State of Queensland. COVID was kept
out of the community in Queensland until January 2022 through hard border
lockdowns. This allows mortality trends to be investigated for a longer period
without the effects of COVID deaths.
Mortality in older ages exhibits a distinct change in trend in early 2021. This may
not be another coincidence. The analysis in this report is based on a customised
data set purchased from the Australian Bureau of Statistics ABS. ABS material
was used as supplied. The analysis is the work of the author alone. The intentionof this report is to demonstrate analytical techniques that can be applied to raw
mortality data to track changes in trends of mortality.

2.

Long-term mortality trends in Australia

The Australian Institute of Health and Welfare (AIHW) publication Deathsin Australia,[1] last updated in June 2022, describes rates of death progressively
falling in Australia for both males and females. From reference[1] on trends indeaths over time:
In Australia, death rates have continued to decline since at least theearly 1900s. Between 1907 and 2020, the crude death rate decreased by 42%(44% for males and 39% for females). When accounting for changes in the
population age structure over this period, the age standardised death rate
fell by 76% (74% for males and 78% for females). This was largely driven by
the decline of infant and child deaths during this period; from 2,412 deaths
per 100,000 children under 5 in 1907 to 71 per 100,000 in 2020 (decreaseof 97%).
As in many other developed nations, Australia has experienced a ‘healthtransition’ during the 20th century. While infectious diseases such as
influenza and tuberculosis caused the most deaths in the early 1900s, fromthe 1930s onwards cardiovascular diseases and cancers were the leading
causes of death.
Figure 1 shows the age-standardised death rate in Australia for males andfemales from 1907 to 2020.

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Figure 1. Source: Australian Institute of Health and Welfare (2022).‘Figure 5.1: Age-standardised
death rates in Australia, by sex, 1907–2020.’ https://www.aihw.gov.au/reports/life-expectancy-death/deaths-in-australia/contents/trends-in-deaths, accessed July 4th, 2023.[1]2.1Life expectancy

The following data are taken from Australian government websites.
From reference[2] (ABS):

Life expectancy at birth was 81.3 years for males and 85.4 years forfemales in 2019-21.

In 2020, Australia recorded a lower than expected death rate as publichealth measures put in place to restrict the spread of COVID-19 also
resulted in a reduction of deaths across a number of other causes. In 2021,
the death rate increased but was still lower than pre-pandemic levels.[2]Further comments shall be made on the last statement above, regarding 2021,
in Section 7.
From reference[3] (AIHW):

In 2020, there were 161,300 deaths registered in Australia (84,588 males;
76,712 females). The majority of deaths in Australia, like other developed
countries, occur among older people (see Figure 2). Sixty-six per cent ofdeaths registered in Australia in 2020 were among people aged 75 or over
(60% for males and 73% for females).
The median age at death was 79 years for males and 85 years for females.

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Figure 2. Source: Australian Institute of Health and Welfare (2022).
‘Figure 2.1: Deaths in Australia by sex and age group, 2020.’ https://www.aihw.
gov.au/reports/life-expectancy-death/deaths-in-australia/contents/age-at-death,accessed July 4th, 2023.[3]As of October 31st, 2022, the median age for those who had died fromCOVID-19 was 85.5 years (83.7 years for males, 87.5 years for females).[4] At
this point there had been 13,201 deaths from or with COVID in Australia.
81.6% of these were from COVID, where it was considered the underlying
cause of death.

3.

Data sources used

Several data sources have been used to perform the analysis in this reportand are listed in Table 1. The primary source is a dataset purchased from theAustralian Bureau of Statistics (ABS) on mortality. This has finer-grained age
categories than those provided on the public website. Fortunately, the ABS isa very professional organisation and provides carefully curated data. The samecannot be said of data provided publicly from health authorities in Australia.
The COVID pandemic has highlighted the lack of rigorous data standards
in government health reporting. The researcher is faced with challenges
including: categorisation that changes over time, data only provided ingraphics on websites, and data that are not updated in retrospect. Relevant
data are sometimes only available through formal Freedom of Information(FOI) requests.
This has been unacceptable, particularly with the health of Australians at stake.

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Table 1. Data sources used for this report.

SourceDescription

ABS

Custom mortality dataset

ABS

Population fromABS Data Explorer

covidbaseAU

3rd party aggregator site

TGA

Database of Adverse Event
Notifications (DAEN)

TGA

Freedom ofInformation Requests

4.

Type of Data
Mortality, Australia andQueensland
Quarterly by State
and Age GroupCOVID deaths,
Vaccination rollout.
Reports of adverse events
following COVID-19
vaccination.
Cases resulting in death,
Batch numbers

Analysis of excess mortality

In this section a brief review is provided on reporting of excess mortality inAustralia.
4.1

Australian Bureau of Statistics

With each monthly release of mortality data, the ABS provides somecommentary. The ABS is clear that its reporting of excess mortality uses very
simple methods and it is not intended for providing health guidance. It makes
a simple estimate of a baseline based on the average of a number of preceding
years. In 2020 and 2021 the baseline was based on years 2015-2019. In 2022,
years 2017-2019 and 2021 are used. There has been conjecture about whether
the choice of years is appropriate, for example 2020 being a lower than normal
year and 2021 being a higher year.
Simple estimates have their purpose. A graph from the ABS monthly report,[5]released at the end of May, 2023, covering the period up to February 2023, isshown in Figure 3.

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Figure 3. Australian Bureau of Statistics (2023): ‘All deaths, Australia, 3 January 2022
– 26 February 2023 vs baseline Benchmarks.’ https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/jan-feb-2023, accessed July 4th 2023.[5]Deaths in 2022 are shown in orange. Deaths January-February 2023 are shown inblue. The estimated baseline is shown in red.

It is important to note that ABS data are marked as provisional. These are
updated over time. The numbers for the last few months of the provisional
reports always revise upwards as death reports are received late. The baseline
shown in red in Figure 3 above is the average of the selected baseline years. The
blue shading indicates the range of the values over those years.
In the report, covering the period up till December 2022, it is stated:
In 2022, there were 190,394 deaths that occurred by 31 December andwere registered by 28 February 2023, which is 25,235 (15.3%) morethan the historical average.
By the February 2023 ABS report[5] that number revises up to 190,775 deaths.
The ABS reports on the number of COVID deaths separately.[4]4.2Actuaries Institute

The Australian Actuaries Institute has a COVID-19 Working Group thatprovides analysis of excess deaths using their own methods. In the recent report,
reference,[6] they estimate excess mortality for 2022 at 12%. They have useddifferent methods for their analysis of mortality over the pandemic period. The
most recent method uses Standardised Death Rates (SDR) that are publishedby the ABS. In the absence of detailed age breakdowns, this is a way to take278

Too Many Deadage differences into account. A simple rate of death can be computed by the
number of deaths divided by the total population. But at different ages there
are different numbers of deaths. A top-level description only of their method
is provided in their reports and it is not possible to reproduce the analysis.
The Actuaries Institute goes to a lot of effort to subtract all COVID deaths(‘from’ and ‘with’) in coming up with an excess, had COVID not occurred.
Unfortunately, this is too simplistic. Many of the COVID deaths that haveoccurred would unfortunately have occurred in the presence of the normal
influenza seasonality, perhaps not the same people but typically in the same
demographic, that is: older people with comorbidities.
They report that over half of the excess mortality in 2022 is due to deaths fromCOVID-19 (approximately 10,000 deaths). Subtracting all COVID deaths is
not taking into account the normal seasonality of mortality. We know also thatthe median age of death due to COVID is greater than population median age
of death. Those who die of COVID typically have several comorbidities. In
this report we address this carefully, in Section 6 onwards.
They also make an attempt in the report to explain what could be causesof the non-COVID excess with a section titled: ‘What could be causing
the non-COVID-19 excess deaths?’ They rate ‘Post COVID-19 sequalae
or interaction with other causes of death’ as High and ‘Delay in emergency
care’ as High with a caveat only during COVID-19 peaks. They rate lowest
and negligible, ‘Vaccine related deaths’. This is apparently based on the TGA
having a ‘very good’ vaccine approval and safety monitoring process. They alsobase it on correlation of periods of excess mortality not being aligned withvaccine rollout, the logic being vaccine numbers were low in 2022 and excess
deaths were high. There appears to have been no detailed analysis behind thisassertion.
A further detailed critique of the methods used is outside the scope of thisreport.
4.3

TGA safety monitoring process

Data collected by the TGA have been used as another source of information.
In an Appendix (see Section 12) we provide a detailed analysis of information
gleaned from the TGA’s safety monitoring system and through Freedom ofInformation requests made by the public. As at the time of writing ( June 25th,2023) 993 deaths have been reported following COVID-19 vaccination. The
TGA attributes 14 only as being caused by COVID-19 vaccination. For abetter understanding of the nature of the deaths reported, a visualisation fromthe work covered in the Appendix is provided in Figure 4.

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Figure 4. 3D histogram of TGA reported deaths following COVID-19 vaccination. Reported
ages are shown in 10-year age bands, against time in months from the start of the rollout(Month one being March 2021).

As a result of FOI requests made by the public (see Section 12.4), it is known
that the TGA was not transparent regarding deaths of children following
vaccination. Deaths of two children under age 10 are represented in theforeground of the graphic (month 13, age 0-10).
The TGA monitoring system can be described as politically influenced or
deficient, or both.

5

Other Australian mortality studies

Analyst Dr Wilson Sy has written insightful reports on Australia’s excess
mortality.[7,8] Useful graphics from reference[7] are reproduced in Figure 5,
Australian monthly all-cause mortality, from reference.[7]The lines drawn in Figure 5, Australian monthly all-cause mortality, fromreference [7] show the approximate time of the mortality ‘regime change’. It isclear that this occurs somewhere in 2021. Wilson Sy’s analysis shows a correlationin excess mortality at a 5-month lag period from the time of vaccinations.
Other recent studies[9] highlight the need to revisit appropriate methods of
estimating the baseline in the context of the pandemic, so as not to underestimate
the excess. For example, bad influenza years should not necessarily be includedin the ‘normal’ baseline estimates.

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Figure 5. Australian monthly all-cause mortality, from reference.[7]Figure 6. Australian annual excess deaths, from reference.[7]6Mortality analysis

In this section the methods used for mortality analysis in this report are described.
Australian mortality data were available in five-year age bands. Narrow age bands
mean we expect consistent behaviour within the group. With wide age bands atrend could be getting worse in one subgroup and better in another and then thetrend can disappear. However, it is not acceptable to say overall the average is thesame. When something has worsened in one subgroup it should be investigated.
It would be like saying more children are dying by drowning but fewer teenagers
are drowning so the average is the same and everything is all right.
We focus on mortality in the State of Queensland, where there was no
locally-acquired COVID leading to death until January 2022.

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We consequently avoid the approximations inherent in age-standardised
approaches used in Section 4.2. We want to understand effects of pandemic
measures on specific age groups. We know adverse outcomes from COVID havea strong age dependency. We know adverse effects from COVID-19 vaccines
also have an age and gender dependency, for instance: myocarditis in young men.
We expect that some age ranges are more sensitive to external factors. Looking
at the age distribution of deaths in Australia, Figure 2, we see the rapid rise as age
increases. It is from around age 65 this increase is most rapid. As age increases,
death obviously becomes more likely. We focus on ages over 60 in this report.
The example in the following sections steps through the methodology used.
6.1

Example: ages 80-84

We take an older age group, ages 80-84, in Queensland. Raw weekly mortality
data from 2015 are shown in Figure 7.

Figure 7. Queensland deaths by week, 2015-2022. Raw data.

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Figure 8. Smoothed 5-point average weekly deaths, in age group 80-84, for Queensland.

There is an inherent random variation embedded in the number of deaths, week
to week. The deaths time series, week to week, is noisy. We find that simple
smoothing cleans these data up, making trends and seasonality more apparent.
See Figure 8. There are typically more deaths than the average in the Winterflu season. This is mid-year in the Southern Hemisphere. We see that in 2017and 2019 there were worse flu seasons than other years. This was also shown
previously in Figure 5. Australian monthly all-cause mortality, from reference.
[7] for Australia all-ages mortality. Mortality following bad seasons sometimes
tends to be lower. This is called a pull-forward effect. The bad flu season has
pulled forward some deaths. Some people unfortunately die earlier in timethan might otherwise have occurred, had they not succumbed to an infection.
We see a much larger than normal number of deaths in 2022, of people inthis age group, when the COVID Omicron wave reached Queensland after
borders were opened. COVID is also expected to have a pull-forward effect.
We will see whether this occurs in years to come.
What we see in these data is a gradual increase in raw numbers of deaths overtime. This is called an increasing ‘trend’. However, the total population in thegroup is changing over time and so, even though there are more deaths as time
progresses, that can be expected because there is a bigger population. The rateof death can actually be falling.

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Quarterly population data are available from the ABS. When weekly mortality
data are used in this report, we interpolate between the available quarterly data
points. The population for this age group is shown in Figure 9.

Figure 9. Population of Queensland residents, age 80-84, 2015 to 2022.

Population quarterly estimates were available up to Q3 2022. Rather thanextrapolate to Q4 a constant value is used. This will have a minimal error given
the typical change from quarter to quarter.
The population of this older age group increases by over 30% over the 8-year
period shown. We can now scale the number of deaths by the population sizeat the time. There are several ways to do the scaling. In this report we do it
by adjusting values so that the number of deaths remains the same at the endof the analysis period and all previous rates of death are appropriate to the
population at the time. We thus show an ‘estimated’ number of deaths, given
the most recent population estimate. These scaled data are shown in Figure 10.
This is now ‘the signal’ on which we perform analysis. We see that numbers ofdeaths in earlier years are larger than the actual numbers from Figure 8. Thereis now a trend that decreases from the start and turns upwards towards theend.
We can split these data and view them year on year in what is called a seasonal
plot (see Figure 11).
This graph initially appears a little confusing with mortality for eight individualyears overlaid. However, we can see that there is a seasonal variation in the284Too Many Dead

Figure 10. Population adjusted mortality for the 80-84 age group in Queensland.

pattern that repeats, within some bounds, year on year. In particular, we seea large number of deaths in Winter 2022 (black line) because of COVID-19(peaking in week 29). We also see a similar size maximum, shifted slightly right(week 34), for the 2017 influenza season (green line). So, while COVID-19
had an adverse effect on mortality in this age group, it was similar in magnitudeat its peak to the bad influenza season of Winter 2017, on a population-ad-

Figure 11. Seasonal mortality plot for Queensland, ages 80-84. Population is adjusted, androlling 5-point average is applied.

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justed basis. The 2022 COVID-19 Omicron peak, early in 2022, is abnormal,
created upon the opening up from strict lockdowns to a community naïve tocirculating virus.
One can ask how then is it that we are now experiencing an excess of mortality,
if at the peak of a COVID wave mortality was similar to a previous influenza
wave? The answer is complex in that the excess mortality calculation shouldtake into account the trend of the data, trending down over time on a population-adjusted basis for this age group. What we should expect in a particular
year is not necessarily the average of previous years. Taking the average ofprevious years, while intuitive, oversimplifies the dynamics. We should also
not necessarily assume that a very bad previous influenza season is the norm.
Of note is that when it comes to medical resources available to deal with
illness, if capacity to treat people has not increased over the intervening years
to take into account population growth, then it can seem like systems are
overflowing, when in fact it is bad health resource management.
2020 appears lower than other years but still with a seasonal variation. This ispossibly partly a result of a pull-forward effect from 2019. It was also different,
as a year of strict lockdowns.
From this data estimates can be made of a ‘baseline’ (see Section 4.1). This is anaverage estimate taken over previous years. When a subsequent year is above
this baseline, we say there is an excess. However, baseline estimation can be too
simplistic. If there is a trend where mortality is decreasing, year on year, and wehave no reason to expect this trend not to continue, then this trend needs to betaken into account before we estimate an excess.
Care must also be taken, because it is easy to be deceived by these plots as aresult of the random component. The overall mortality is the ‘area under the
curve,’ that is, the sum of all the weekly deaths. One line can appear lower
than others but when integrated turns out to be larger than expected. For theexample above the annual mortality is shown in Table 2.
We see from Table 2 that the 2020 number of deaths was larger than the fiveprevious years in raw death numbers, but when population-adjusted it follows
a downwards trend until 2021. We see that 2020 is only 167 deaths fewer thanthe previous year, which was considered a bad flu season.
In Table 2, the annual, population-adjusted deaths decrease year on year (apartfrom a small uptick in 2017) until 2021 when something changed. It was statedby authorities (reference section 2.1) that the death rate in 2021 increased
but was still lower than pre-pandemic years. We can see in Table 2 in the
population adjusted column that we can go back in time to find a pre-pandemic286

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Table 2. Queensland, ages 80-84, annual number of deaths. Years marked * have 53
weeks using the ISO week convention. 52 weeks only are used.

Year
2015*
2016
2017
2018
2019
2020*
2021
2022

Deaths(raw numbers)
4,216
4,159
4,464
4,418
4,561
4,665
5,145
5,724

Population(as at Q2)
80,74282,55985,892
89,681
94,614
99,884
105,049
110,933

Adjusted
Deaths
5,792
5,588
5,765
5,465
5,348
5,181
5,433
5,724

year higher than 2021. However, this ignores the fact that, had there not beenexternal factors (that is, a pandemic and government interventions), we would
have expected a continual downwards trend in mortality in this age group,
albeit with small temporary increases for bad influenza seasons (as seen for2017).
Note that 2015 and 2020 are 53-week years using the ISO Week convention.
In Table 2 only 52 weeks are included for those years in the sum for consistency.
6.2

Time series decomposition

Time series analysis and forecasting is a mature field. There are also newtechniques adapted from machine learning which can be applied.
A time series signal can be decomposed into components of:•••

Seasonal
Trend
Random variation (also called error, remainder)

The decomposed components add together to equate to the actual time series.
The aim of decomposition is to minimise the random component and make it
truly random. From these components we obtain an understanding of a timeseries. We can also use these components to forecast forward in time. This
assumes a trend continues and seasonal variation is similar.
There are also other components that can be embedded in time series. Seasonalmeans a variation on a fixed period. In this context, that is yearly. There is a287

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pattern that tends to repeat each year; for example, there are typically moredeaths in Winter, and fewer in Summer. Another type of variation is called:•

CyclicalCyclical means variation over some other time period that may not be regular;
for example, that could be due to economic variations.
There are also possible external shock components. These shocks are also
referred to as exogenous impacts.
Exogenous describes an external effect, as opposed to endogenous for internal.
These are unexpected events. Shock components make it difficult to predict
data. Unless we know when they occur, they are unforecastable. For example,
New Zealand mortality data need to take into account specific events. The
Christchurch earthquake and mosque murders were both extreme mortality
events. They would have disproportionately affected younger people comparedto natural mortality. Reference[10] from New Zealand, looking at excess
mortality associated with the booster rollout in New Zealand, deals with this.
Health authority-mandated COVID-19 measures, which may have had aneffect on mortality, should be treated as exogenous effects.
So, one approach is to decompose a time series into components for time
periods, where we know behaviour is what can be called ‘typical.’ We assume
known trends continue in the absence of any interventions or new diseases.
We use that to forecast the future and then we compare that forecast withwhat actually happened.
A decomposition of the time series from January 2015 to March 2021 isshown in Figure 12. Time series decomposition of the signal from Figure
10, population adjusted. The method used is known as Seasonal and Trend
decomposition using Loess (stl). Tuning parameters control the allowablevariation of the trend and seasonality. March 1st 2022 is chosen as theend-time for decomposition because this marked the start of the rollout of thenovel COVID-19 vaccination.
With real-world data the decomposition is never perfect. In this decomposition
there are some discrepancies in the seasonal component and the 2020 error
part is larger.
The decomposition can be used to predict what will happen ahead. For thefollowing period, from March 2021 to December 2022, the actual number ofweekly deaths (smoothed and population adjusted) is shown as a solid line andthe forecast of the same data is shown as a dotted line in Figure 13.

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Figure 12. Time series decomposition of the signal from Figure 10 using the stl method.

The forecast (the dotted line) is what we would expect to occur had there
been no external effects. We see that the forecast and the actual (solid line)
mortality data distinctly diverge in level and shape during this period. The
COVID-19 Omicron variant arrived in Australia in late 2021 and the firstwave of deaths in Queensland occurred in January 2022. We see this in theactual deaths data, peaking in the middle of the graph in February 2022. The
Winter 2022 COVID wave peaks in July 2022.

Figure 13. Forecast for period from March 2021 till Dec 2022.

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However, near the start of the prediction period there is a peak that starts
rising from April 2021, with maximum in July 2021. This is not predicted. We
know this is not COVID-19 disease-related, as there was no locally-acquired
COVID leading to death in Queensland until January 2022.
6.3

Alternative methods to determine trend

A trend component can be directly computed from the data. We propose a
simple method for computing a trend based on a one-year running average.
The one-year average runs across the seasonal variation that occurs over a
year. It irons out the seasonal variation that goes up in Winter and downin Summer. For each week we take the average of the 52 weeks from thatweek backwards in time. This measure is potentially usable in a real time
monitoring system as we do not rely on any values in the future, only values
in the past. The result is shown in Figure 14 below for this age group.

Figure 14. One-year running average of the signal from Figure 10. Population adjusted mortality
for the 80-84 age group in Queensland. Linear regression line is fitted through values prior to
March 2021, and is shown as the blue dashed line.

Available values start from one year after the first data point, when we havea full year to compute the average. In Figure 14 a linear regression line (bluedashed line) is fitted to the period prior to March 2021 (that is, the black line).
In the absence of any exogenous effects, we would expect the computed trend tofollow that line, tracking above and below it, slowly decreasing over time. This
fitted line is like a longer-term trend that we expect the mortality to follow.

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Too Many DeadThis running one-year average for computing the trend of the mortality has an
intuitive interpretation. At any time, it is the average weekly number of deaths
for the previous year. When it has increased by one unit it means that the numberof deaths per week over the last year has increased by 1. If there are suddenly 52
extra deaths a year occurring, we will also see the one-unit increase. In Figure
14 above, there is a sharp increase of at least 2 units over a period of 1-2 weeks
around July. Also, refer back to the time-series forecast in Figure 13 where we
saw an increase in the actual value above the forecast around the same time.
At the onset of the 2017 flu season, we see the trend, computed by one-year
running average, pick up, as there were more deaths starting to occur in that
Winter season. Note that the peak in the running average is not necessarily at
the same time as the peak in the raw data, as a result of the way the signal rises
and falls. However, this method is still sensitive to onset of changes, picking up
where mortality starts increasing. There is a very small local peak observed forthe first Omicron wave and a larger one for the 2022 Winter COVID wave. To
understand the change in trend upwards from April 2021 we can subtract thevalues of the regression line from the data and observe the variation from thisline. This is shown in Figure 15.

Figure 15. Data from Figure 14 for Queensland ages 80-84, with regression line through thetrend values subtracted to show the variation.

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The variation of the trend, above the fitted regression line, prior to March 2021
(marked by a vertical dashed line in Figure 15), is a maximum of 4 deaths per
week, occurring in 2017 from the bad influenza season. After March 2021,
and by January 2022 (marked with another vertical dashed line) it has reachedapproximately 5 deaths per week above the regression line. That is one death
per week above the previous maximum value. If that value continued for a year,
it would be 5x52 = 260 deaths per year above the line fitted through the trend.
The first vertical dashed line in Figure 15 shows the time of the start of thevaccination campaign in Australia. The second vertical dashed line shows theonset of the first major wave of COVID deaths in Queensland. There had
only been 19 deaths in Queensland by mid-January 2022. See Section 8.1 fordetails on early COVID-19 deaths in Queensland. The very early COVID
deaths registered for Queensland were of cruise-ship passengers and somedied outside of Queensland. Clearly, in the time period of 10 months boundedby the vertical dashed lines, something has happened to take the trend above
the previous high mark. To the end of 2022 there is no sign of any downturn.
Our interest in this work is in protecting and improving the health ofAustralians. Methods such as this could have been implemented in real time
to detect historically large changes in mortality.
By the second half of 2021 we would have detected mortality in this age group
trending above the worst previous influenza season, at a time when there wasminimal influenza in the community and no COVID-19.

7

Analysis of Queensland mortality trends

We now plot the same mortality trend data for Queensland, as shown in Figure
14 (that is, prior to subtraction of the regression line through the trend), formultiple five-year age bands on the same graph. See Figure 16.
Inspection of Figure 16 shows what appears to be fairly flat lines over theeight-year time period (from 2015 to 2022) when plotted on a broad yscale. As previously discussed, the number of deaths is population-adjusted
so that in earlier years, when population was less, that number is adjusted
up to be relative to the value at the end of the time period. For the four
highest lines, ages from 75-89, a decreasing trend is clear from the startfollowed by an upswing occurring somewhere in 2021. An upswing is not
so obvious in age ranges 60-69.
For age 95+ the downwards trend is small, but the upswing is clear. Notealso that the graph is showing an adjusted actual number of deaths and nota rate of deaths (that is, deaths per 1,000). So, for example, in the older292

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Figure 16. Queensland mortality trend, 5-year age bands from age 60 years up.

age range of 95+ there is a much smaller population (see Figure 2) and so
numbers are smaller even though death is more likely.
To see the trends more clearly, we can adjust the vertical position of thelines so that they are all the same value at a particular date. Then we canlook at what the number of deaths is, relative to what it was on that date.
We choose the date of March 1st 2021, aligning with the vaccine rollout inAustralia. The result is shown in Figure 17.

Figure 17. Queensland mortality trend, 5-year age bands from age 60 years up, referenced tovalue on March 1st 2021.

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In Figure 17 small week-to-week variations are more apparent with thesmaller y scale range. However, the most striking feature of this graph isthat the historic downward trend, prior to March 2021, is turned aroundwithin months of the reference date, to an upward trajectory with no signof plateauing. In the absence of any external input, such as the COVID-19
disease in the community, we would have expected a continuing downwards
trend in each of the age groups, at their respective rates, with small naturalvariations up and down.

Figure 18. Mortality Queensland ages 60 up. one-year running average, difference from fitted
regression line (up to 1 March 2021) and standardised.

The onset of the excess mortality phenomenon currently being experienced inAustralia is identified. This upswing in all-cause mortality, occurring shortly
after the rollout of COVID vaccines to the elderly population, could havebeen detected in real time.
It could be argued that the upswing occurs to compensate for a previous
downswing in 2020. However, the upswing starting in 2021 should havealready come down again to follow the historical decreasing trend prior to
the point in 2022 when COVID-19 creates additional pressure upwards inQueensland.
We could subtract COVID-19 deaths from the start of 2022 onwards tomonitor the increasing non-COVID excess. However, these data are notavailable for the age groups for Queensland.

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The fitted linear regression through the trend prior to March 2021 can be
subtracted for each age group, as was shown in the example in Figure 15. We
assume that the trend should follow this downwards in the absence of any
external influences. Each age group varies by a different amount about thefitted line for that age. We can standardise the values, so we look at standard
deviations from the fitted line for each age group. This is computed and shownin Figure 18.
For the four oldest age groups we see that the standardised deviation fromthe trend, prior to any COVID-19 deaths occurring in the Queensland
community ( January 2022), is higher than the maximum deviation from trend
encompassing the bad flu season of 2017.
Of concern is that for the younger age groups (for example, 60-64), while
the creep upwards is slower, by the end of 2022 it exceeds previous maximum
values. The effect of COVID-19 disease on younger ages is much less than forthe older ages.

8

COVID-19 and vaccinations in Queensland

COVID deaths in Queensland were minimal until mid-January 2022.
Consequently, it appears that interventions in 2021 did something to altermortality trends.
The TGA Database of Adverse Event Notifications records approximately
1,000 deaths following COVID-19 vaccination. All but 14 of these areconsidered to be coincidences by the TGA. We therefore find anothercoincidence, that in a state in Australia, where COVID was not circulating,mortality suddenly increased in older age groups, starting from the time of
introduction of novel pharmacological products to most of this community.
8.1

COVID deaths in Queensland

On November 1st 2021 Australian borders opened to vaccinated Australian
citizens to return to Australia. Australia opened borders to vaccinatedinternational tourists from February 21st, 2022. COVID had escaped quarantine
only in sporadic outbreaks prior to this in Australia.
The state of Queensland maintained a hard border, not only for international
travellers but also for travel from states outside of Queensland, with amotto ‘Keeping Queenslanders Safe’. It is now known that the COVID-19
vaccines do not stop infection or transmission of the virus. Without enforced
quarantine, the prevalent Omicron variant was released in Australia, leading
to a major wave of COVID infections and death of the vulnerable, startingin January-February 2022.

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Information on early COVID deaths in Queensland was found in documents
uncovered during legal proceedings by Queensland doctors in mid-2022 against
the Queensland Chief Health Officer’s imposed vaccine mandates. The first
locally-acquired infection leading to death in Queensland was of a vaccinatedman in his 80s in the last weeks of December 2021. Early reported deaths after
this date included a teenager who died in a car accident. Up to this point there
had only been seven COVID-19 deaths of Queenslanders. These were primarilyof cruise ship passengers (including three from the Ruby Princess). Some diedoutside of Queensland.
From the first locally-acquired case up to January 15th 2022 there were 19
COVID-19 deaths total for Queensland, predominantly in people above age 80.
See Figure 19 below for a graph of the daily deaths in Queensland (all ages) up
till September 2022. From September 2022 deaths were reported weekly. Notethat these are messy data; spikes in bars shown may be due to delayed reportingof numbers, and deaths are not necessarily shown on date of death. However,the data give us an idea of the waves of death peaking in January for the first
Omicron wave and then a Winter wave somewhere in July to August.
As a result of the late arrival of COVID-19 to Queensland, with a small
number of deaths until mid-January (Figure 19. COVID deaths in
Queensland, reported by day, January to September 2022. Data were obtained
from aggregator website covidbaseau.com). We have a wider time-view of theeffect of government-imposed health measures, not confounded by the effectof COVID-19 disease.

Figure 19. COVID deaths in Queensland, reported by day, Jan to Sep 2022. Data were obtained
from aggregator website covidbaseau.com.

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8.2 Vaccination rollout in Queensland
The cumulative vaccine rollout in Queensland is shown in Figure 20, Thevaccine rollout in Queensland. The cumulative number of first, second, thirdand fourth doses is shown.
The doses per day, calculated from the cumulative number, are shown in Figure
21, Vaccine rollout in Queensland, daily number of doses shown. There aredata points where quantities were reported irregularly, presumably because of
backlogs in reporting. Poor data curation has unfortunately been typical of
Health Department data reporting.

Figure 20. The vaccine rollout in Queensland. The cumulative
number of doses is shown.

Features to notice in the daily doses are a push in the second half of 2021
to get first doses out. This was ‘Operation COVID Shield’ starting from
August 2021, which included targeting Culturally And Linguistically Diverse(CALD) communities. First and second dose rollouts peak around the sametime through older age groups receiving their second while younger werestarting to get the first. The third dose was rolled out right in the midst of theonset of the Omicron wave. Demand had died out by mid-2022.
In the cumulative dose count in Figure 20, The vaccine rollout in Queensland,
the cumulative is the fixed remaining difference between first and second doses.

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Figure 21. Vaccine rollout in Queensland. Daily number of Doses shown.

As at the end of December 2022, 4,267,680 people had received a first doseand 4,151,158 the second. This is out of 5,378,277 Queenslanders (ABS Dec
2022 figures). 116,553 persons therefore only took one injection. That means
97% of those receiving a first dose went on to a second. The 3% of people with
only one dose are those who either sadly did not make it to a second dose
because of death, or those who had a severe adverse reaction to the first and did
not risk a second. Because of the Queensland mandates and restrictions placedon the unvaccinated (those who had not received the primary two doses), few
people capable of receiving a second dose declined to do so voluntarily.

9Machine Learning methods
For Time Series forecasting

In this section we look at another specific age group and test other methods
for tracking excess mortality, from TGA reports of death (see Figure 4. 3D
histogram of TGA reported deaths following COVID-19 vaccination).
Reported ages are shown in 10-year age bands, against time in months fromthe start of the rollout (Month one being March 2021), and Figure 31 in the
Appendix. There are many reports of death in the 70s age group following
COVID-19 vaccination.
Methods developed in recent years for Machine Learning have been applied
to time series analysis. Machine Learning algorithms are the basis of Artificial
Intelligence. An algorithm called Prophet, developed by researchers at
Facebook, is used for time series forecasting. This algorithm has a feature of
requiring minimal tuning and handling changes in trend within the data. It
can identify what are known as changepoints.

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In Figure 22 the time series of data for age 75-79 is used. The whole timeseries from 2015 till the end of 2022 is used to develop the model to predict
data. The changepoint identified is shown in the first half of 2021.
The method used to generate data shown in Figure 22 allows us to determine, in
retrospect, where trends changed, but we need future data to do this. The simple
running average method, described in Section 6.3 and evaluated in Figure 17,
detects the change in trend in real time.
We can train the algorithm only on data up to March 1st 2021, where we

Figure 22. Changepoint identified using Prophet algorithm.

expect typical mortality patterns, and then base future predictions on the model
generated. We then compare with the actual data and see if it diverges fromwhat should be expected. This is shown in Figure 23. We can inspect where theactual deaths data go outside the error bounds. The actual values go outside theprediction bounds in the first half of 2021. A local peak observed in April-May
2021 is unusual, as this is prior to the normal seasonal variation peak.

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Figure 23. Algorithm trained on data prior to March 1st 2021. Error bounds of prediction are
shown in shaded blue region. Actual data are the black line. Onset of COVID-19 in Queenslandis shown with a vertical dashed line.

Dementia is typically a disease of the elderly. In Figure 24, ABS monthly
data are shown for dementia as a cause of death for ages 75-79. These data
are for all Australia and therefore are influenced by COVID deaths from thestart of the pandemic. These data have not been population-adjusted. The data
are for doctor-certified deaths only. The reason for this is that for 2022 coroner-certified deaths are not available. For dementia as a cause of death there is
only a small number of coroner-certified deaths relative to the doctor-certified
deaths. For example, in March 2021 there were 140 doctor-certified deaths
and then three extra coroner-certified deaths.
The algorithm XGBoost has been shown to be effective for time series
prediction.[11] Data prior to March 1st 2021 are used to train the algorithm.
With this algorithm we need to first remove trend as it only predicts within
the previously known data space. Figure 25 shows the actual values in black up
to February 2021 and in red for contrast from February 2021. The predictedvalues (in grey), using the XGBoost algorithm, are shown from March 2021
onwards. Of note is a peak in the actual data in March 2021. Note that thevalue for a month is all deaths occurring in that month up to the end of themonth. In Australia there was minimal COVID in 2021 from January up until
July when the Delta wave started. This March 2021 peak is higher than all300

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Figure 24. Deaths by cause Dementia, Australia, ages 75-79.

Figure 25. Deaths by Cause Dementia, ages 75-79, trend adjusted. Prediction shown in greyusing XGBoost algorithm. Actual data shown in black prior to March 2021
and in red from March 2021.

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previous historical peaks (relative to trend) and is at an unusual time of year,
being before the Winter flu season.
There is a peak in August 2021, seen in Figure 25, which could have been
influenced by COVID deaths. The Delta wave was waning at the end of 2021
prior to the onset of the Omicron wave in January 2022. Actual data are well
above predicted values at the end of 2021. This is also traditionally when deaths
are lowest in Australia (that is, Summer time).
People with dementia are a vulnerable cohort. They have difficulty caring forthemselves. We had no prior information on the side effects of vaccinationin this group from clinical trials; indeed, none was available. There were early
warnings received by the TGA regarding deaths in nursing homes in Norway
early in the vaccine rollout there in late 2020 (see section 12.3). It is therefore
not surprising that we see a corresponding increase in deaths in a vulnerable
cohort in Australia early in the rollout here, as seen in Figure 24 and Figure 25.
This brief overview of the application of some modern time series analysis
method indicates that concerning trends in mortality are identified invulnerable groups shortly after the rollout of COVID-19 vaccinations. It seems
astounding that the TGA discounts all but 14 deaths following COVID-19
vaccination, out of one thousand deaths reported, given the historically unusual
mortality patterns identified during times when there were no deaths fromCOVID-19 disease.

10

Summary

In this report we have looked at trends in mortality in Australia. Prior
to the pandemic, in older ages, population-adjusted mortality had beenslowly decreasing year on year, with older people living longer. We posed
the question: when did the non-COVID excess mortality, currently being
experienced in Australia since 2021, start?
In Queensland we have an extended time window to view mortality
without the effects of COVID-19 disease. In the period from the start of
the worldwide pandemic to the onset of COVID-19 disease in Queensland
we can assess the effects of the numerous interventions imposed byhealth authorities. They include pharmacological, social and financial
interventions.
We find an alarming upturn in the trend in mortality, in older age groups,
starting from March 2021. Using data from Queensland means this is notconfounded by any effect of COVID-19 disease. Two years later, at theend of 2022, there is no sign of mortality levelling out, let alone going backto a trend of slow decline in mortality in older age groups. A significant

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proportion of the excess mortality is from ‘unknown cause’, with authorities
suggesting it is COVID-19 disease-related.
Coincidentally the start of the upturn in mortality occurs shortly after therollout of COVID-19 vaccinations to the elderly population. Almost 1,000
deaths have been reported, following COVID-19 vaccination, in the TGA
Database of Adverse Event Notifications. The TGA considers all but 14of these as ‘coincidences.’ It is disturbing that reported deaths of children
following COVID-19 vaccination, with a possible causal link identified,appear to have been dismissed, and not disclosed to the public for fear of
creating ‘vaccine hesitancy.’
It would have been quite feasible to have implemented in real time thealgorithmic methods described in this report. If that had been done thereporting of deaths following COVID-19 vaccination would have been
witnessed, coincident with an upturn in mortality in the same age groups, inreal time. That upturn reached a level above any previous deviation from thetrend. This is in an environment where there was no COVID-19 disease toconfound interpretation.
Clearly the health of Australians has been adversely affected by the panic
promulgated and health measures applied by Australian government
authorities.

11

[1]

[2]

[3]

[4]

[5]

References‘Deaths in Australia, Trends in Death,’ Australian Institute of Health andWelfare, June 2022. [Online]. Available: https://www.aihw.gov.au/reports/
life-expectancy-death/deaths-in-australia/contents/trends-in-deaths.

‘Life tables. Statistics about life tables for Australia, states and territories andlife expectancy at birth estimates for sub-state regions,’ Australian Bureau of
Statistics, 8 Nov 2022. [Online]. Available: https://www.abs.gov.au/statistics/
people/population/life-tables/2019-2021.
‘Deaths in Australia, Age at Death,’ Australian Institute of Health and Welfare, 9 June 2022. [Online]. Available: https://www.aihw.gov.au/reports/
life-expectancy-death/deaths-in-australia/contents/age-at-death.

‘COVID-19 Mortality in Australia: Deaths registered until 31 October
2022,’ Australian Bureau of Statistics, 25 11 2022. [Online]. Available:
https://www.abs.gov.au/articles/covid-19-mortality-australia-deaths-registered-until-31-october-2022.
‘Provisional Mortality Statistics’, Australian Bureau of Statistics, 26 May
2023. [Online]. Available: https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/jan-feb-2023.

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Australian Medical Professionals Society[6]‘COVID-19 Mortality Working Group: Confirmation of 20,000 excess
deaths for 2022 in Australia,’ Actuaries Institute COVID-19 Mortality
Working Group, 6 April 2023. [Online]. Available: https://www.actuaries.
digital/2023/04/06/covid-19-mortality-working-group-conf irmation-of20000-excess-deaths-for-2022-in-australia/.

[7]

W. Sy, ‘Australian COVID-19 pandemic: A Bradford Hill analysis of iatrogenic excess mortality,’ J Clin Exp Immunol, vol. 8, no. 2, pp. 542-556, 2023.

[8]

W. Sy, ‘Simpson’s Paradox in the correlations between excess mortality and
COVID-19 injections: a case study of iatrogenic pandemic for elderly Australians,’ Medical and Clinical Research (in Press).

[9]

V. Shkolnikov, ‘What should be the baseline when calculating excess
mortality? New approaches suggest that we have underestimated the impactof the COVID-19 pandemic and previous winter peaks,’ SSM - Population
Health, vol. 18, 2022.

[10]

[11]

12

J. Gibson, ‘The Rollout of the COVID-19 Booster Vaccines is Associatedwith Rising Excess Mortality in New Zealand,’ Working Paper in Economics,
University of Waikato, no. June, 2022.
A. D. Lainder and R. Wolfinger, ‘Forecasting with gradient boosted trees:augmentation, tuning, and cross validation strategies. Winning solution to
the M5 uncertainty Competition.,’ International Journal of Forecasting, vol.
38, pp. 1426-1433, 2022.

Appendix – TGA Safety Monitoring System

Prior to analysing mortality in Australia, which is currently above historic averages,
we review the reporting of deaths following administration of pharmacologicalproducts.
The TGA Database of Adverse Event Notifications (DAEN) is a system to provide
information about adverse events and incidents related to therapeutic goods usedin Australia. Unfortunately, it is a is a poorly designed, clumsy system to use.
The system was updated during the pandemic (released temporarily as a ‘beta’
version) to allow export of datasets. The data export can be filtered to output
adverse events from COVID vaccines only (Figure 26). Exported files have fields
for:• Case number• Age• Report entry data
• Gender• Medicines reported as being taken• MedDRA reaction terms.

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Too Many Dead
The Medical Dictionary for Regulatory Activities (MedDRA) is aninternationally used set of terms relating to medical conditions, medicines andmedical devices. It was created to assist regulators with sharing information.
Unfortunately, the data in the DAEN do not indicate which cases resulted indeath from a product. Death is not considered a medical reaction term. The
TGA website states the total number of deaths reported following COVID-19
vaccination on a page where search is performed.

Figure 26. Search on web page: https://daen.tga.gov.au/medicines-search/ (25 June 2023).

The public has to make Freedom of Information (FOI) requests to find out
further information regarding the reported deaths.
12.1

Freedom of Information requests

Originally, FOI request 3586 obtained a redacted document with ages of persons
where death was a result (921 deaths at the time). Unfortunately, this did notallow cross referencing with actual DAEN reports as it was just a list of ages left
unredacted. Note also the Age field is sometimes left empty or null. This author
submitted an FOI request, resulting in FOI 3785 (released June 23th 2022),
where the Case number (field TGAICSRIdentifier) was also unredacted. This
then allowed cross referencing with exported DAEN records. A similar request,
FOI 3845, released August 1st 2022, provided the same information. The
number of deaths was updated to 892 Deaths up to June 28th 2022. Analysis in
this report uses data from the later FOI 3845.
A table with Case number and Age of death was joined with the table exported
for all adverse events related to COVID-19 vaccines.
FOI 3545 provides an Excel spreadsheet with Batch number for Adverse events,
on or before January 15th 2022. This table was joined with the table of deaths
obtained. An extract of the first 25 entries of the resulting table is shown in
Table 3. This covers deaths reported in the first month of vaccine rollout up to
March 29th 2021. The COVID-19 vaccine was being delivered primarily to theelderly. However, a death of a person in their 60s occurred following vaccinationin March 2021.

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Table 3. First 25 TGA DAEN reports, by date, where death was an outcome.
Case
numberAge

Reportentry date

GenderVaccine51514146

21/12/2020

FemalePfizer52083792

1/03/2021

Male

Pfizer52115398

3/03/2021

FemalePfizer52130281

4/03/2021

FemalePfizer52131176

4/03/2021

Male

Pfizer52164478

5/03/2021

FemalePfizer52166767

5/03/2021

Male

Pfizer52168381

5/03/2021

FemalePfizer52174390

6/03/2021

Male

Pfizer52178995

6/03/2021

Male

Pfizer52210852239192
92

9/03/2021
11/03/2021

Male
FemalePfizer
Pfizer52241495

11/03/2021

FemalePfizer52271083

12/03/2021

Male

52273193

12/03/2021

Male

PfizerNot
Specified

52273983

12/03/2021

FemaleAstra
Zeneca52338787

16/03/2021

FemalePfizer52408377

18/03/2021

Male

Pfizer52424752447396
91

19/03/2021
19/03/2021

Male
Male

Pfizer
Pfizer526002

77

24/03/2021

Male

Pfizer52735790

26/03/2021

Male

Pfizer52741386

26/03/2021

Male

Pfizer52789478

28/03/2021

Male52834593

29/03/2021

Male

Batch
Number

Dose
In
Series--

EP2163

1

-

1

-

1

NIL

1

-

1

EP2163-

Aspiration; Sepsis; Vomiting

NIL

1

Dyspnoea; Vomiting
Pyrexia;
Unresponsive to stimuli
Dyspnoea
Ischaemic stroke; Mydriasis
Diabetic ketoacidosis;
Urosepsis
Cardiac arrest--

EP2163-

EP2163
EP2163-

EP2163

-

-

1

-

-

PV46672--

1

EP2163

1

EP9602EP9605

1-

unknown

1--

ER7448/9

1--3001577

1

MedDRA reaction terms
Numerous reactions reported
including renal failure, sepsis…
See note.
Adverse event following
immunisation; Loss of
consciousness
Decreased appetite
Decreased appetite; Depressed
level of consciousness;
Dysphagia; Vomiting
Cardiac arrest;
Unresponsive to stimuli
Dysphagia; Injection site
reaction; Somnolence; Weightdecreased
Adverse event following
immunisation

Not Specified
Astra
ZenecaPneumonia Respiratory failure
Multiple organ dysfunction
syndrome; Pancreatitis; acute
Rash macularPulmonary embolism
General physical health deter
ioration
Pneumonia viral
MalaiseConcomitant disease aggravated; Depressed level of consciousness; Lethargy; Pyrexia;
Respiratory arrest
Pneumonia
Cerebral haemorrhage Cerebrovascular accident
General physical health deterioration
Chest pain General physicalhealth deterioration306

Too Many Dead
There is an odd report, case number 515141, of a 46-year-old female. Numerous
reaction terms are reported, not all included in Table 3 for brevity. It did notappear in FOI 3785 but appeared two months later in FOI 3845. Perhaps it wasfrom an Australian citizen overseas. The reported date of December 1st 2020 isprior to the rollout in Australia.
Later FOI 4077 provides a list of batch numbers and dose in series for adverse
event cases where outcome was reported as fatal, from January 10th 2022 andNovember 8th 2022. It is disturbing that the batch number is only recorded fora small number of reported deaths. In many cases it is marked as unknown.
The TGA claims to be diligent in monitoring batches. It is hard to understandhow this could be, given the lack of recording of batch numbers. The recordingof batch numbers appears to have become laxer over time. While the recordingof the batch number is not the TGA’s responsibility at the point of entry, one
would think there would be diligent follow-up particularly in the cases where
death was an outcome.
12.2

COVID-19 vaccines in Australia

On February 15th a Singapore Airlines flight touched down in Sydney carrying
142,000 doses of the Pfizer-BioNTech vaccine. This was presumably Batch
EP2163 (based on information in Table 30). The first doses were administered

Figure 27. From TGA Vaccine Safety Report from September 2022: https://www.tga.gov.au/
news/covid-19-vaccine-safety-reports/covid-19-vaccine-safety-report-23-09-2022.
Accordingto TGA FOI 3845 the last sentence in Figure 27. From TGA Vaccine Safety Report from
September 2022: https://www.tga.gov.au/news/covid-19-vaccine-safety-reports/covid-19-vaccine-safety-report-23-09-2022. According to TGA FOI 3845 the last sentence in Figure 27 isquestionable (see Section 12.4). is questionable (see Section 12.4).

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Australian Medical Professionals Society
on February 21st, including to Prime Minister Scott Morrison. The general
COVID-19 vaccination in Australia program began on February 22nd 2021.
The first death following vaccination was reported on March 1st 2021,
according to Table 3, of a 92-year-old man who lost consciousness following
administration. This was a coincidence according to the TGA.
The TGA Vaccine Safety Report from September 2022 provides sparseinformation on the reported deaths (see Figure 27).
12.3

Early warnings of deaths in the elderly

On January 14th 2021, prior to the vaccine being available in Australia, theTGA received reports from Norway about the deaths of frail elderly people
following vaccination with the Pfizer vaccine. The TGA concluded that there
is no specific risk of vaccination with the Pfizer-BioNTech COVID-19
vaccine in elderly patients. Reference: https://www.tga.gov.au/news/
media-releases/investigation-reveals-no-specific-risk-covid-19-vaccinations-elderly-patients. While noting that ‘the clinical impact of even relatively
mild systematic adverse events in the frail elderly should be carefully assessedon a case-by-case basis’ the TGA assessed that these people were going to die
anyway. TGA correspondence related to this warning is found in TGA FOI
4073 (9 documents).
Table 4. Top 25 by Occurrences (Occ) of MedDRA reaction terms in reported TGA deaths
following COVID vaccination.
MedDRA reaction term

Occ

1

Adverse event following immunisation

176

2

Cardiac arrest

87

3

Dyspnoea

4

Pulmonary embolism

5
6
7
8

MedDRA reaction term

Occ

14

Pneumonia

39

15

Deep vein thrombosis

37

81

16

Myocardial infarction

37

70

17

Pyrexia32

Concomitant disease progression

68

18

Sepsis28

Cerebrovascular accident

66

19

Fatigue27

Malaise53

20

Myocardial ischaemia

27
25

Thrombocytopenia

48

21

Multiple organ dysfunction syndrome

9

Vomiting

47

22

Lethargy

24

10

Fibrin D dimer increased

46

23

Fall

22

11

Chest pain

42

24

Acute myocardial infarction

21

12

Headache41

25

Respiratory failure

21

13

Concomitant disease aggravated

40

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Too Many Dead
Analysis of the reactions, in the cases reported leading to death, is summarisedin Table 4 with the top 25 terms associated with death cases listed in the table.
The most common is the non-specific term ‘Adverse event following
immunisation’. ‘Cardiac arrest’ is the second.
12.4

Deaths of children

Of concern is that in known cases where children died following COVID
vaccination, the process of the TGA has not been transparent.
Follow-up of specific cases by General Practitioner Dr Melissa McCann,using FOI request (FOI 3727), found internal TGA reports regarding deathsof 10 young people, two being children. Internal TGA reports show the text
‘causal’ with some of these reports. However, the TGA disputes that the term‘causal’ uncovered in internal reports means causality was confirmed, but rather
that it is ‘template’ text.
The TGA response to the FOI request was initially not uploaded to the TGA
online disclosure log as would be the normal process. Senator Gerard Rennick
has reported on this: https://gerardrennick.com.au/tga-cover-up-child-deaths/.
From our joined tables, constructed from different FOI data, we find detailsof these child cases:
The cases were reported in March 2022. A young boy and girl both sufferedcardiac arrest following COVID-19 vaccination. It is disturbing that batch
numbers were not recorded. The batches for these cases should have beenfound from FOI 4077. The FOI 4077 file contains one data row for eachdistinct report associated with a suspected interacting COVID-19 vaccine
in an accepted adverse event case created between January 10th 2022 andNovember 8th 2022, where the outcome of the adverse event was reported as
fatal. Those fields were empty for these cases.
Batch reporting clearly became laxer as rollout continued.
Table 5. Reported deaths of young children following COVID-19 vaccination.
Case
numberAge

Reportentry date

GenderVaccine

MedDRA
reaction terms

Batch
Number

Dose
In
Series7198387

11/03/2022

Male

PfizerCardiac arrest Generalised tonic-clonicseizure-1

7240239

25/03/2022

FemalePfizerCardiac Arrest

-

-

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Australian Medical Professionals Society
12.5

DAEN deaths summary

A 3D visual showing the reported deaths as time progressed from rollout, fordifferent age bands, based on our analysis is shown in Figure 28 (also shownin Figure 4 in the body of this report). We can see from this plot the deaths in
the elderly, prioritised for vaccination first, progressing to middle ages as therollout continued. Deaths of young people can be seen reported from month
6 of the rollout. The two sad deaths of children under 10, listed in Table 5, arerepresented in the foreground of the visual.
Slices can be taken through these data to see the effect on particular ages.
See Figure 29, Figure 30 and Figure 31 for 5-year age bands from 95, 85 and75 respectively. Counts in each bin (covering half a month) are separated bygender. Note that the date is the date of report which will be some time after
the actual date of death. We do not have actual date-of-death data.
In the oldest age band age 95 and above (Figure 29), women are the mostaffected. However, the population of women in this age band is significantly
larger than the men still left alive. In the age band 75-79, Figure 31, males

Figure 28. 3D Histogram of numbers of reported deaths for ages of death in 10-year bands,
from the month of start of vaccination in Australia (March 2021-March 2022).

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Too Many Deadappear to be disproportionately affected.
Note these data were only available up to the date of data obtained via the FOI
request from August 2022. There are other deaths, not shown, where age was
not reported.

Figure 29. TGA reported deaths following COVID vaccination: Age band 95-100.

Figure 30. TGA reported deaths following COVID vaccination: Age band 85-89.

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Australian Medical Professionals Society

Figure 31. TGA reported deaths following COVID vaccination: Age band 75-79.

312Too Many Dead

Andrew Madry has a degree in physics, a BSc (Hons)and PhD from the University of Sydney. His PhD
work was in acoustics and signal processing. His early
career was in the defence industry, developing andtesting sonar systems for the Royal Australian Navy.
This involved designing and testing transducers anddeveloping signal detection algorithms.
He had a second career in medical devices, leadinga team at a major Sydney hospital developing
technology for treating abnormal heart rhythms.
That led to a career-long interest in understanding
abnormal electrical signals in the heart and developing
algorithms for understanding mechanisms of atrial
fibrillation.
A technical consultant since 2001, he has provided
services to medical device companies including
Cochlear and Resmed, as well as defence and industrial
companies. Dr Madry developed a software productcalled Cardiac Electrophysiology Analysis System(CEPAS), used by researchers in cardiac electrophysiology, in the USA, Japan and Australia.
In the last five years he has focused on data science,
providing customised training, based on years ofexperience in industry. Currently on the executiveof the Systems Engineering Society of Australia, he
has particular interest in applying Systems Thinking
principles to data projects and healthcare.

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Australian Medical Professionals Society314Too Many Dead

Part 5Global excess death – it’s not just Australia

In the AMPS inquiry into the excess death rates it has become apparent thatweak pharmacovigilance, poor data collection and excess all-cause mortality
are being experienced not only in Australia but globally. This part now presents
samples of reviews from European and American data sources.
Based on analysis of the Vaccine Adverse Event Reporting System (USA)numbers, it may appear that adverse events are not currently imposing
a significant burden on the fully vaccinated population; however, theweekly releases of VAERS data do not include all of the reports made todate – they are all the reports the CDC has processed to date – and thebacklog is likely to be staggering. Thus, as a result of both the problems
of under-reporting and the lag in report processing, this analysis reveals
a strong signal from the VAERS data that the risk of suffering an SAE
following injection is significant and the overall risk signal is high.
– Dr Jessica Rose, USA
Concerning trends are also being noted in Finland:
When the previously stable trend suddenly changes upwards, thisrepresents unexpected increased mortality, that is, excess mortality…
Finnish health authorities have thus far (April, 2022) either played

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down or altogether ignored these alarming data. In the light of theirrefutable evidence presented in this paper, it is high time healthauthorities stopped understating the severity of the situation andcarried out a thorough and independent investigation of the cause forthe dramatically elevated mortality.
– Kasper Rönning, Finland
The Fenton et al. study of The Office for National Statistics (ONS) in the
United Kingdom shows potential bias and misclassification which fail to provide
evidence supporting the hypothesis that the vaccines reduced all-cause mortality.
Overall, the ONS dataset is so compromised with inaccuracies,
anomalies and biases that it cannot be used to reliably determine vaccine
efficacy and safety. We recommend that the ONS adds full caveats to
its future surveillance reports explaining the limitations and biases of
its sample population. Also, any studies of vaccine efficacy or safetycomparing vaccinated and unvaccinated which use whole population
data of COVID cases, hospitalizations and death but which rely on the
ONS estimates of proportion unvaccinated must be retracted.
– Professor Norman Fenton, UK
The high excess death rates are now at more than fifteen per cent above baseline
mortality. Australian and global data show a mass-casualty event, but our politicaland medical authorities seem to think there is nothing worth investigating
It is not as though there is any shortage of opinions of the depth and width of
this silent crisis. It is simply that such opinions have been ignored, suppressed,
diverted from public view and erroneously sent out to pasture, classified
as conspiracy theory. To treat them this way is preposterous, yet the medical
bureaucracy continues in the same way as the death toll rises.
Analysis suggests that the vaccines are likely the cause of reported
deaths, spontaneous abortions, anaphylactic reactions and cardiovascular,
neurological and immunological adverse events. The precautionary
principle promotes transparency and the adoption of preventative
measures to address potential risks to the public in the arena of vaccination
programs, and it is vital that people are informed of these potential risks
before agreeing to participate in any medically involved treatment program.
– Dr Jessica Rose, USA316

Too Many Dead

What the ONS mortality
COVID-19 surveillance data
can tell us about
vaccine safety and efficacy
by Norman Fenton1, Martin Neil1,
Clare Craig2, Scott McLachlan3
26 June 2023

Abstract
The UK has uniquely produced data deaths by vaccination status. These are
critical data that should have been made public in every jurisdiction, buthave not been. Unfortunately, the way the data were handled by the United
Kingdom’s Office for National Statistics (ONS) has meant that it is riddledwith bias including failing to take account of various confounding factors
which can thus be easily misinterpreted. The data presented here cover a reportfor England for the period January 1st 2021 to May 31st 2022 and a further
report for the period April 1st 2021 to December 2022.
Those seeking evidence that the vaccines are unsafe might point to the overall
all-cause mortality rate in the vaccinated (1,367 deaths per 100k person years)
being much higher than in the unvaccinated (671 deaths per 100k person
years). But this fails to take account of age confounding. Those seeking
evidence that the vaccines are safe might point to the overall age-standardizedmortality rate over the whole period being much higher in the unvaccinated(2,338 deaths per 100k person years) than the vaccinated (957 deaths per 100k

1
School of Electronic and Electrical Engineering and Computer Science, Queen Mary,
University of London, UK
2
Independent researcher, UK
3
Faculty of Nursing, Midwifery & Palliative Care, King’s College, London, UK

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Australian Medical Professionals Society
person years). But this fails to take account of major anomalies in the mis-categorization of deaths by vaccination status (especially in the first part of2021), evidence for which can clearly be seen in the implausible differences in
non-COVID mortality rates between different vaccination status categories.
There is also now strong evidence that the ONS underestimates the proportionof unvaccinated, which leads to inflated mortality rates for the unvaccinatedrelative to the vaccinated. This underestimation is a major issue: the ONS
claimed in May 2022 that 8% of adults are unvaccinated whereas the UKHSA
estimated approximately 20% and an extensive and representative ICM survey
estimated 26%.
Because the ONS data are based on a subset of England residents that excludes
all those not registered with a GP and not registered in either the 2011 census(for the earlier dataset) or the 2021 census (for the later one), it is missing
some 8 million adults who are not at all representative of those in the ONS
sample. Hence, whilst it is conceivable that both the ONS 8% figure is correctfor its sample, and the proportion of all adults in England unvaccinated is
at least 20% as per the other sources, this means that at least 69% of adults
missing from the ONS sample are unvaccinated. Hence, either the ONS isunderestimating the proportion of unvaccinated in its sample or the sample isso unrepresentative of the whole population that any inferences made using
the ONS data are worthless. Either way, the ONS estimate of the proportion
unvaccinated must not be used for any comparisons of vaccine efficacy or safetyof the whole England population. We also provide further evidence that the
ONS are grossly underestimating mortality in their dataset, with their 18-39,
40-49 age groups showing approximately half the mortality rates publishedby the ONS in 2016, for both unvaccinated and vaccinated. We show that
there are many missing deaths from their dataset with the eight million people
suffering 30% of deaths despite comprising only 19% of the population, hence
further compromising the accuracy and relevance of their data. Complaints
to the UK Statistics Regulator about these issues were upheld but the ONS
have not addressed these problems. Indeed, the more recent data release shows
larger discrepancies in terms of the difference in mortality rates by vaccination
status, the mortality by age and the proportion of real-world deaths includedin their calculations.

1.

Introduction

Shortly after the early 2021 rollout of the COVID vaccines in the UK, theOffice for National Statistics (ONS) started to produce reports on mortality
by vaccination status. These reports should, in principle, have enabled us toevaluate the overall risk-benefits of the vaccines by simply comparing – foreach age group – the all-cause mortality rate of the vaccinated against the318

Too Many Dead
unvaccinated. However, as we noted in our detailed analysis of the first year’s
data, http://dx.doi.org/10.13140/RG.2.2.28055.09124, the ONS data sufferedfrom many obvious flaws and biases including the misclassification of many
deaths shortly after vaccination as being unvaccinated. All of these flaws
had the effect of underestimating the all-cause mortality of the vaccinatedwhile overestimating the all-cause mortality of the unvaccinated. This wascompounded by the fact that many vaccinated deaths were also simply missing
from the ONS data, http://dx.doi.org/10.13140/RG.2.2.12472.42248. In
November 2022 we produced a second detailed analysis of the most recent
data which at that point was up to May 2022 – http://dx.doi.org/10.13140/
RG.2.2.30898.07362. This study is based largely on that analysis. We
found that many of the flaws and biases continued but that, with extremely
conservative adjustments for these biases, there was strong evidence that inmost age categories all-cause mortality was higher in the vaccinated than theunvaccinated. Nevertheless, we concluded that the data were so unreliable andbiased that no definitive conclusions could be drawn. The ONS data were
simply not fit for purpose. As explained in Section 10 below, we submitted aformal complaint about the data to the Statistics Regulator who agreed with
two of our key recommendations, namely: 1) that the ONS sample was biased
and unrepresentative of the England population (notably because it grossly
underestimated the proportion of unvaccinated); and 2) that no conclusions
about the efficacy or safety of the vaccines could be drawn from the ONS
data because of its flaws and biases. Possibly because of our well-publicised
criticisms, an update to the ONS data after May 2022 – which the ONS
repeatedly promised during the fourth quarter of 2022 would be ‘shortly
forthcoming’ – did not arrive until February, 2023. No data have been released
for 2023. However, as described in Section 10, this latest update failed toaddress the concerns raised by ourselves and the Regulator. Moreover, because
the ONS decided to use the new 2021 census data for the England population(a decision which did not actually address the problem of the biased sample)
it was no longer possible to compare the results to the previous analyses.
Hence, we believe these data were still unfit for purpose. Further problems
surrounding this are highlighted in Section 11.

2.

The ONS data up to May 2022

The Office for National Statistics (ONS) vaccine mortality surveillance
reports including data up until May 2022 (the latest being[1]) are based on asubset of 39 million of the approximately 56 million population of England
and are supposedly an authoritative source of data used by COVID-19 vaccine
advocates and detractors alike. The ONS dataset can be easily misinterpreted
in many ways by failing to take account of various confounding factors in the‘headline’ results. Those seeking evidence that the vaccines are…

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Australian Medical Professionals Society•… unsafe might point to the overall mortality being much higher in thevaccinated than the unvaccinated. But this fails to take account of age
confounding;•… safe might point to the age-standardized mortality rate over the whole
period being much higher in the unvaccinated than the vaccinated. But
this fails to take account of major anomalies in both the mis-categorization of deaths in the first part of 2021 and underestimates of the
proportion of unvaccinated.

The report published in July 2022 in[1] covers the period January 2021 to
May 2022 and claims that, over this full period, the age-standardised all-cause
mortality of people vaccinated against COVID-19 is significantly lower thanthat of their unvaccinated counterparts (957 deaths per 100k person years
compared to 2338). Figure 1 shows a plot of the overall age-standardisedmortality rates by vaccination status for the period of the latest report. However,the ONS dataset has numerous anomalies which might bias its results toward
underestimation of mortality rates of the vaccinated and overestimation ofmortality rates of the unvaccinated. This includes miscategorising many
vaccinated deaths as unvaccinated,[2] and the potential omission of many
vaccinated deaths from the data.[3]Another potential major anomaly in the ONS data that biases the results, theimplications of which have not previously been thoroughly investigated, is anunderestimation of the proportion of unvaccinated.

Figure 1 ONS age standardised mortality rate by vaccination status320

Too Many Dead
There are competing claims about the percentage of adults who remain
unvaccinated; at first glance this appears difficult to reconcile. The ONS
claimed in May 2022 that 8% of adults were unvaccinated and indeed thiswas the headline figure on which the controversial BBC2 television program‘Unvaccinated’ was based.[4] However, the problem with this 8% estimate
is that it was completely at odds with other independent estimates. The
UKHSA report at that time[5] was estimating 20% of those aged at least
18 as unvaccinated. More notably, in an extensive and highly representative
survey undertaken by ICM in May 2022[6] that was used by the BBC for their
documentary program, 26% of those aged at least 18 (664 out of 2570) were
never vaccinated. The detailed survey spreadsheet, noted in reference[5], actuallycompares the survey results with the ONS estimates for all the population data(age, ethnicity, region, socio-economic class); the only attribute where there is
any significant variation is the proportion of unvaccinated where it clearlystates the ONS estimate as 8% compared to the 26% found.4 Moreover, when
the NHS uses ONS denominators to estimate vaccine coverage they report
more than 100% of many older age groups have been vaccinated.[7]This paper analyses the effect of these biases on the latest ONS data. In Section
2 we explain the limitations of the ONS dataset and how they arrived at the
8% adult unvaccinated in May 2022. We show it is conceivable that both the
ONS 8% figure is correct for its sample, while the proportion of all adults in
England unvaccinated is at least 20% as per the other sources. But we show in
Section 3 that this would mean that at least 69% of adults missing from the
ONS sample are unvaccinated. Hence, either the ONS is underestimating the
proportion of unvaccinated in its sample or the sample is so unrepresentativeof the whole population that any inferences made using the ONS data areworthless. Either way, the ONS estimate of proportion unvaccinated must not
be used for any comparisons of vaccine efficacy or safety of the whole England
population.
In Section 4, we review the raw mortality data in the ONS report and highlightsome obvious problems with it. The raw mortality rates (which the ONS do
not report) are higher in the vaccinated than the unvaccinated, but overall,these results are age confounded. Instead of reporting separate comparative
mortality rates for each different age category to avoid age confounding, theONS use overall age-standardised mortality rates (ASMR).
In Section 5 we identify the limitations and anomalies in the ASMR. We
show that, even without adjusting for the previously observed anomalies andbiases in references [2] and [3], the most recent monthly data (in contrast to theespecially flawed data in 2021) provides no evidence that the vaccines reduce4
A detailed analysis of this can be found in https://www.normanfenton.com/post/more-updates-on-bbc2-documentary-unvaccinated, and the linked video.

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all-cause mortality once we make even minor adjustments for the possibility
that the ONS has underestimated the proportion of unvaccinated. This isdemonstrated in detail in Section 6 where we analyse the recent months’
mortality data February to May 2022 for each of the different age categories.
Since all our analyses point toward errors in the claims made in support ofvaccine safety and efficacy, it is unsurprising that vaccine advocates have sought
alternative possible explanations for the anomalies in the ONS data beyond
those already discussed and we address these in Section 7. The most common
alternative explanations are directly contradictory, with some claiming a‘healthy vaccinee effect’ and others claiming an ‘unhealthy vaccinee effect’.
Neither of these alternative explanations is supported either empirically or
theoretically.
Our conclusions are presented in Section 8. We recommend that the ONS
adds full caveats to its future surveillance reports explaining the limitations
and biases of its sample population. Also, any studies of vaccine efficacy orsafety comparing vaccinated and unvaccinated which use whole population
data of COVID cases, hospitalisations, and deaths, but which rely on the ONS
estimate of proportion unvaccinated must be retracted.

3.

The ONS population sample: its limitations
and estimates of number unvaccinated

The ONS vaccine mortality surveillance reports (the latest being[1]) relate to
the population of England (it contains no data at all on Wales, Scotland or
Northern Ireland). However, contrary to what many people assume, the data
do not represent the whole population of England, but rather a biased subset
of it. Specifically, people are in the database only if they were:a) registered in England in the 2011 census, andb) registered with a GP in England in 2019

In addition to all people not registered with a GP, this ONS dataset also
excludes all people who arrived in the country after 2011 (believed to bearound four million) and all children under the age of 10. The total numberin the sample is about 39 million. We refer to Table 3 of the latest report[1]to source these numbers and to help understand how the ONS estimate theproportion unvaccinated. The report covers the 17 months from January 1st
2021 to May 31st 2022 and records the following:••

Total unvaccinated:

Total ever vaccinated:

16,375,484 person-years
38,860,947 person-years

That is a total of 55,236,431 person-years. Since there are 516 days in the period322

Too Many Dead
January 1st 2021 to May 31st 2022, to calculate the number of people in thedataset we must multiply by 365/516 since every person corresponds to 516/365
person-years. Hence, the number of people in the dataset is 39,072,282.
The current estimated England population is approximately 56 million of whom
circa 49 million are aged at least 10. So, the sample excludes roughly 10 million
people aged over 10. This missing 10 million are intrinsically different from thosein the ONS dataset since they are either people who are new immigrants andor have refused, failed or are sufficiently healthy to have not needed to registerwith a GP. This means they are likely to be a much younger sub-population witha much higher proportion of people unvaccinated. In other words, the ONS
dataset is not a representative sample of the population of England, aged at least
10. Indeed, we find very strong evidence of this underrepresentation when we
look at the ONS estimates of the proportion unvaccinated.
First note that the reason the ONS uses person-years rather than number ofpeople in their breakdown of vaccinated versus unvaccinated is that many people
will only have been vaccinated for part of the previous 17-month period. By
using the person-years data above, and by dividing the total unvaccinated by the
total unvaccinated and ever vaccinated, we can calculate that 29.6% of the total
person years were unvaccinated over the 17-month period. This does not meanthat 29.6% of people remain unvaccinated at the end of the period, but rather
that over the whole period, 29.6% of that time (for all people together) was spentunvaccinated. This includes people who spent the entire period unvaccinated aswell as people who spent just a few days unvaccinated.
However, if we focus only on the latest available month, namely May 2022, from
report [1], we can see how the ONS could arrive at their highly disputed estimate
of 8% adult unvaccinated at this time.[8]For any month the report provides (in its Table 1) the number of person years
for the ‘unvaccinated’ and ‘ever vaccinated’. If we multiply the person years‘unvaccinated’ by 365 and divide by the number of days in the month we get
the ONS estimate of the total number of people unvaccinated at that point.5
Similarly, for the ‘ever vaccinated’. So, for May 2022, there are:•448,434 unvaccinated person years, which corresponds to 5,279,949
people (we multiply by 365/31 as there are 31 days in May)•

2,846,174 ever vaccinated years, which corresponds to 33,511,404
people.

Note that, as a consistency check, this totals just under 39 million people

5
Strictly speaking it is only an approximation of the number of people unvaccinated up to theend of the month because the person years contain a small number who were first vaccinated during that
month

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still alive in May 2022 from the original 39,072,282 in the ONS dataset in
January 2021.
This means that, in the ONS dataset, 13.6% of the people were unvaccinated up
to the end of May 2022. However, (as we explain in detail in the next section) if
we remove those aged under 18 and account for the fact that a higher proportionof that age category are unvaccinated, we can see how that would result in a
number close to the 8% adult unvaccinated claimed by the ONS (see below).
In a recent response to an FOI request, the ONS concede that ‘it is difficult
to identify exactly how many people in the population are unvaccinated’.[9] A
plausible explanation for (at least part of ) the difference in unvaccinated rates isthat, compared to the others, the ONS sample is (as we have suspected all along)significantly biased. It does indeed seem reasonable that the England population
who were not registered in the 2011 census and who were not registered witha GP will be a set of people much less likely to be vaccinated than those in the
ONS sample.
While there is no dispute about the number of people (the 39 million) in
the ONS dataset, the number ‘ever vaccinated’ is based on GP records which
may not be accurate for reasons explained in [2]. There is also strong anecdotal
evidence that many unvaccinated people are erroneously recorded as vaccinatedwith records that contain explicit dates and batch numbers.[10] However, such
errors, should they be random, are assumed here not to make a major difference.

4.

Estimating the proportion of unvaccinated inthose missing from the ONS sample.

As explained above we know that in May 2022, in their sample of Englandresidents (all aged 10+), the ONS estimate that there were:••5,279,949 unvaccinated33,511,404 vaccinatedmeaning that 13.6% were unvaccinated in May 2022. If we remove those aged
10-17 and account for the fact that a higher proportion of that age category
are unvaccinated, we can get to the 8% figure estimated by ONS, if we assume
50% of the unvaccinated were aged less than 18 and that 10% of the vaccinatedwere aged less than 18, that is:324

Too Many DeadThis takes us to the 8% adult unvaccinated proportion claimed by the ONS
for their sample.
But we also know that there are approximately 8,000,000 England residents
aged 18+ who are missing from the ONS sample (specifically, the total number
of England residents aged 10+ missing from the ONS sample is approximately
10,000,000 and approximately 80% of these are aged 18+).
Let

In other words, this would mean 99.6% of the adults missing from the ONS
sample are unvaccinated.

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So, if the ONS estimate of 8% adult unvaccinated for their sample is correct then,based on other independent estimates of the adult unvaccinated in the whole
population, this would mean that between 69% and 99.6% of the 8,000,000
adults missing from the ONS sample were unvaccinated.
Is this feasible? It would mean the ONS sample is not at all representative of
the whole England population. It is much more likely that the ONS population
estimate and therefore their estimate of the size of the unvaccinated population
is too low (the fact that NHS data show vaccination rates of greater than 100%
using ONS denominators also confirms this). Yet the ONS are now claiming[11]that, as of end of August 2022 the number of unvaccinated has dropped evenfurther despite extremely weak vaccine take-up during that period. Their latest
report states: ‘of those aged 12 years and over 93.6% had received a first doseof a COVID-19 vaccine’. Hence their latest estimate is that just 6.4% of those
aged 12 and over are unvaccinated and this would mean even less (about 5%) of
those aged 18+ are unvaccinated. It is highly unlikely that many of the 8,000,000
aged 18+ missing from the ONS sample have had the vaccine since May, 2022.
This means the ONS sample is even more unrepresentative of the England
population than originally thought.
If correct, then it means the ONS sample is such a highly biased subset of the
England adult population that it should not be used to make any inferences
about the entire population. Furthermore, any mortality analysis reliant on ONS
estimates for proportion unvaccinated will significantly overestimate mortality
rates for the unvaccinated and underestimate mortality rates for the vaccinated.
This problem extends to the use of whole-population estimates of COVID case,
hospitalisation, and mortality rates for vaccinated and unvaccinated. In otherwords, using the ONS estimate of 8% adult unvaccinated will likely result in a
substantial exaggeration of the efficacy and safety of the vaccines.
So, while the vaccine might appear to support claims of safety and effectivenessfor the ONS population dataset (and we will show in Section 6 that this is not
the case of the ONS most recent data), this would certainly not mean any claims
for safety and efficacy can be extended to the whole population. In fact, becauseof misclassification[2] and missing vaccine deaths,[3] as well as delays caused bypost-mortems, there is even less support for any claims that the vaccine is safeand effective using the ONS’s special population subset.

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Too Many Dead5.

Understanding the ONS mortality dataset
and its limitations

The following detailed analysis of the ONS dataset reveals evidence of further
problems with it.6 Table 3 in the ONS report[1] includes the aggregatedmortality data for England over the 17-month period January 1st 2021 to May
31st 2022 shown in the left-hand side of Table 1. Note that the total number ofdeaths for the 17-month period is 641,009 which equates to an approximate
annual mortality rate for the period of 1,163 deaths per 100k people (basedon the sample size of 39 million and the 17 months equal to 516/365 years).
Note that there is a lower mortality rate for COVID-related deaths in the evervaccinated, but a higher non-COVID mortality rate in the vaccinated, andoverall, the all-cause mortality is substantially higher in the vaccinated.
Table 1: Age-confounded aggregated mortality rates(with lowest death rates in each category italicised)

Superficially this seems to suggest that, over this 17-month period, the risksof the vaccine outweigh the benefits overall. But this is not necessarily the casebecause these aggregated mortality data are age confounded, whereby a muchhigher proportion of young people in this population are unvaccinated and mostdeaths, of course, occur in the older population which has the highest proportionof vaccinated. Indeed, the ONS do not include the death rates shown on theright-hand side in Table 1 to avoid people drawing this inappropriate conclusion.
To determine the actual risk-benefit of vaccination (which may radically differ
between age-groups), we need to look at the all-cause mortality rates withineach age category. Helpfully, the ONS provide an age breakdown in Tables 5
and 6 in their reports. For example, for the 15-19 age category we can computethe mortality rates shown in Table 2.

6This twitter thread also addresses these concerns about the ONS data: https://twitter.com/
os51388957/status/1576204422857703424

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Table 2: Age category 15-19 mortality rates (with lowest death rates in each category italicisedand 95% confidence intervals in brackets)

So, in the 15-19 age category, where there are few deaths overall, there is ahigher mortality rate for COVID-related deaths in the unvaccinated but alower non-COVID mortality rate in the unvaccinated. Overall, the all-cause
mortality is lower in the unvaccinated because there are very few COVID
related deaths in this age category7 meaning that, for this age-group the risksof the vaccine might outweigh the benefits based on the whole 17-month
period.8 However, there may be a bias whereby the sickest 15–19 age groupwith the highest mortality rate might have been more likely to have been
vaccinated.
However, things are very different for example in the 70-74 age category as
shown in Table 3.
Table 3: Age category 70-74 mortality rates (with lowest death rates in each category italicisedand 95% confidence intervals in brackets)

In the 70-74 age category, where there are many deaths overall, the all-cause
mortality is much lower in the vaccinated, meaning that, for this age-group,
the benefits of the vaccine outweigh the risks based on the whole 17-month
period.
However, Table 3 also reveals a major anomaly in the ONS dataset. While wewould expect a lower COVID mortality rate in the vaccinated if the vaccine
is effective, even if there were no serious adverse reactions from vaccination,
7
Moreover, we know that almost all the 31 deaths ‘with COVID’ reported here were not due
to COVID. Based on an FOI request we know that only one person in this age category died up until 31
Dec 2021 with COVID as the only cause: https://www.ons.gov.uk/aboutus/transparencyandgovernance/
freedomofinformationfoi/covid19deathsandautopsiesfeb2020todec2021
8
Because of the low numbers of deaths in this age category the difference is not highly significant
with a 95% confidence Bayesian risk ratio of 0.77 to 1.08 and an 86% probability the rate is higher in thevaccinated.

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Too Many Deadwe should not expect the non-COVID mortality rate in the vaccinated to beless than the unvaccinated. At best, if the vaccine was perfectly safe, these rates
should be approximately equal. Yet the unvaccinated non-COVID mortality
rate is 46% higher than the vaccinated. This is simply not credible.
As this is over the whole 17-month period it is instructive to look at themortality rates for this age category in the latest month only, May 2022.
Unfortunately, this is where we hit another inconsistency in the ONS dataset
because, in contrast to their Tables 5 and 6, they only provide the monthly
age categorised data (ONS Table 1) on a less granular level; we have the age
category 70-79 and not 70-74 or 75-79. These May 2022 mortality data areshown in Table 4, which also distinguishes the different vaccination categories.
Table 4: Age category 70-79 mortality rates for May 2022

Note the following:•

The non-COVID mortality rate is still significantly higher in theunvaccinated compared to the ever vaccinated (2114 compared to1843), meaning there is likely an ongoing mis-categorisation problem,
but the difference has dropped dramatically – from 46% higher down
to 15% higher.

•

In each of the vaccination categories other than ‘Third dose or booster,at least 21 days ago’ the non-COVID mortality of the vaccinated ismuch higher than that of the unvaccinated, with wildly different valuesof 4041, 4801 and 9158. Even with only 273 person years for the ‘Third
dose or booster, less than 21 days ago’ the non-COVID mortality rate is
statistically significantly different from the other rates.

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But, as mentioned above, assuming no significant adverse reactions, thenon-COVID mortality rate for each of the different categories of vaccination
status should be approximately equal, so the fact that they are wildly different
is evidential support for misclassification in the data, as discussed in reference[2], namely that many of those who die shortly after their first dose are wrongly
classified as unvaccinated and those who die shortly after their second dose arewrongly classified as single dose only.

6.

Anomalies in the ONS age-standardisedmortality rate

For risk-benefit analysis we would prefer to consider the separate all-cause
mortality for each of the different age categories. As we already saw, in the
15-19 age category the all-cause mortality of the vaccinated was higher thanthat of the unvaccinated but in the older age categories the all-cause mortalityof the unvaccinated was higher than that of the vaccinated. However, it ispossible to provide an approximate whole population mortality rate that avoids
the age-confounding problem. This is called the age-standardised metric[12]and it is the only mortality metric used by the ONS. The ONS dataset Table 3
provides this metric, and we summarise the results in Table 5 here:
Table 5: Whole period mortality rates with age-standardised metric

The whole point of the ASMR is that it is intended to take full account of
the number of people and deaths in each age category so that age categories

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Too Many Deadwith proportionally more deaths get a heavier weighting. This explains why,
despite the deaths per person years being higher overall in the vaccinated,
it is perfectly feasible for the ASMR to be higher in the unvaccinated. Also,
because most deaths occur in the older age categories the ASMR is much‘closer’ to the mortality rate of the older age-groups (such as those shown in
Table 3) than the younger age-groups (such as those shown in Table 2).
The age-standardized metric is already adjusted to take account of the lengthof the reporting period, so although the time period in Table 5 is 17 months,
the ASMR shown is an estimate of the number of people who die in a year
(not 17 months). Hence, according to the estimate in the table, 1474 out of
every 100k unvaccinated people would die per year from non-COVID causes,
compared to just 893 out of every 100k ever vaccinated people.
But this means that the ASMR exhibit even stranger anomalies than seenin the mortality rates of the older age-groups for the whole period. In Table
5, the non-COVID mortality rate of the unvaccinated is 65% higher thanthe vaccinated. It suggests that such a gross anomaly might be disproportionately due to misclassification errors that occurred early in the 17-month
period, because the latest month’s figures (May 2022) shown in Table 6 arevery different from those in Table 5.
Table 6: Latest month May 2022 age age-standardised mortality (x indicates number too low
to reasonably estimate)

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So, in the May 2022 data there is no longer much difference between theage-standardised non-COVID mortality rate of the vaccinated, 787 per 100k
people, and the unvaccinated, 795 per 100k people. The all-cause ASMR are
also not too far apart (823 versus 873). Moreover, except for the category ‘third
dose or booster at least 21 days ago’ the all-cause ASMR of the unvaccinated ismuch lower than that of each category of vaccinated. In other words, even withall the potential biases and misclassifications in the ONS data, in the latest
available month’s data there is no real evidence to support the hypothesis thatthe vaccine reduced all-cause mortality in May 2022.
To understand the extent of the anomaly with the full period data Table 5 wecan compare them to the historical annual non-COVID mortality rates. The
ONS provide age-standardised rates dating back to 1938 but for England &Table 7: Age-standardised mortality rate (per 100k population)* We are assuming the ASMR for the population of England is similar
to that for England & Wales)

Wales combined[13] as shown in Table 7, whereas Table 5 is for England only.
However, we can estimate the England figures as shown.
As already discussed, there is no logical reason for the ASMR for non-COVID
deaths to be higher in the unvaccinated since the vaccine cannot reduce
non-COVID deaths. So, prior to the COVID year of 2020 the England
ASMR is stable at around 974 deaths per 100k people. This means we should
be seeing a similar yearly figure for both the latest unvaccinated and vaccinated
non-COVID mortality rate. Yet, based on the whole 17-month period of the
ONS dataset we have:••

Vaccinated rate is 893 (an 8% drop from what is expected)
Unvaccinated rate is 1473 (a 51% increase from what is expected)

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Too Many DeadIf we compare historical all-cause mortality, 974 ASMR, with the ONS
dataset values, 2338 ASMR for the unvaccinated and 957 ASMR for thevaccinated, then we would conclude that in a period after the peak of thepandemic while the vaccinated now have a similar mortality rate to historicalrates the unvaccinated are dying at an enormous rate, 240% higher than before.
Hence, whether we focus on non-COVID deaths or all-cause deaths the ONS
dataset cannot be correct. It is also important to compare the recent vaccinatedand unvaccinated data, 823 and 873 respectively from Table 6, with historicalrates, and this shows missing mortality and confirms that the ONS dataset isincorrect.
What was shown in [2] was that, in 2021 when the vaccine rollout began,
the ONS data were showing peaks in non-COVID mortality among theunvaccinated at the very time the vaccine rollouts reached their peak in eachdifferent age category. Figure 2 shows this for the 60-69 age category. Later
smaller peaks in non-COVID mortality were also seen in the unvaccinatedwhen the second dose was rolled out.

Figure 2: Non-COVID mortality rate in age category 60-69

That paper concluded that a possible plausible explanation for such an obvious
anomaly was that people dying shortly after vaccination were being wrongly
classified as unvaccinated. Whether through policy or error this certainly
happens (indeed in Sweden a reply to an FOI9 request confirms that thosedying within 14 days of vaccination are routinely counted as unvaccinated).
Once the ONS data were adjusted for these anomalies there was no evidence
that the vaccines reduced all-cause mortality.
9
https://lakaruppropet.se/public-health-agency-reporting-has-distorted-mortality-rates-for-the-unvaccinated-and-vaccinated/

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Using the data in the latest ONS report[1] Figure 3 shows the weekly non-COVID
mortality rate in the unvaccinated and vaccinated over the whole period fromJanuary 1st 2021 to May 31st 2022. Note how the anomalies seen in the first halfof 2021, when the major vaccine rollouts occurred, subside and the rates for bothvaccinated and unvaccinated converge on the historical non-COVID mortality
rates as they always should have done.

Figure 3: Non-COVID mortality rate January 2021 – May 2022

The fact that the vaccinated and unvaccinated non-COVID mortality rates have
now converged, however, does not necessarily mean that claims of vaccine safety
and efficacy can be supported. On the contrary, there are several reasons to believethat the non-COVID mortality in the vaccinated is being underestimated evenin the ONS dataset:•••

There is a large number of vaccinated deaths missing from the ONS
dataset as explained in [3]It is likely that there is continued misclassification of those dying shortly
after vaccination doses

Even within the highly unrepresentative ONS sample of the England
population the proportion of unvaccinated in the dataset is likely
underestimatedSince we have argued that a much larger proportion of the whole England
population is unvaccinated compared to the proportion in the ONS sample, itfollows that if the whole population proportion is used as the denominator with
the ONS mortality figures, the current non-COVID mortality rate (based on
May 2022) would be significantly higher in the vaccinated.

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7.

Analysis of recent mortality data:‘dead presumed missing’

To best understand the current mortality rate of vaccinated and unvaccinated andto consider the effect of possible underestimation of proportion unvaccinatedin the ONS data, we focus on the four most recent months (February to May2022) and the mortality data in each separate age category. The most recent
months should provide the most stable current estimate of differences inall-cause mortality between vaccinated and unvaccinated especially as there
were no major waves of COVID mortality or vaccination during this period.
The relevant data for this come from Table 2 of the ONS dataset. There are,however, some curious omissions in this table that need to be noted thatslightly compromise the analysis. Specifically:•

Whereas elsewhere in the dataset the ONS provide age categories(10-14, 15-19, 20-24, …, 85-89, 90+) the ONS only provide data forthe age categories 18-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90+ in
Table 2.

•

Whereas elsewhere in the dataset the ONS provide the aggregated‘ever vaccinated’ data in addition to all the individual vaccination status
categories’ data, this is omitted in Table 2. Of course, we can and do
simply calculate the ‘ever vaccinated’ data by aggregating the data for all
the individual vaccination status categories. However, while this enables
us to calculate the mortality rate, it does not allow us to calculate the
ASMR that ONS itself uses. Fortunately, because the data are alreadyage-categorised (albeit quite coarsely) there is minimal age confounding,and these rates are close to the age-standardised rates.

Table 8 shows the aggregated mortality data and mortality rates (expressed as
deaths per 100k person years) for the four most recent months of the ONS
data. The last two columns show the reported percentages of unvaccinated byboth ONS (which in each age category is simply the number of unvaccinatedperson years divided by the total person years in the age category) and the
NIMS estimate for that period and age group.
Note that:•

For each of the younger age categories (18-39 and 40-49), as well as(curiously) the oldest age category 90+, the all-cause mortality rate of
the unvaccinated is lower than that of the ever vaccinated.

•

In each of the other age categories the all-cause mortality rate of theunvaccinated is higher than that of the ever vaccinated.

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Australian Medical Professionals Society•

In every age category the proportion of unvaccinated is significantly
underestimated compared to the NIMS estimate. If we were to
assume that the fatalities were accurately categorised but that theoverall proportion was that of the NIMS estimate and not the ONS
estimate, then the all-cause mortality would be significantly higher
in the vaccinated in every age-group. In each of the age-groups
where the unvaccinated mortality rate is higher than the vaccinatedmortality rate, the NIMS estimate is at least 71% higher than the ONS
estimate. A relative increase of between 20% and 40% over the reported
unvaccinated mortality rate will result in a lower all-cause mortality rate
for the unvaccinated category. However, these kinds of adjustments arequestionable given that the unvaccinated, uncounted by the ONS, haveno mortality represented within the ONS dataset.

However, these latest data provide some of the strongest evidence yet of how
inaccurate the ONS dataset is when we compare the mortality rates with theTable 8: Feb-May 2022 Mortality Rate by Age Category336

Too Many Deadhistorical rates up to 2016[14] as shown in Figure 4 reproduced from [14].
What we find is that the ONS mortality rates are much lower for both thevaccinated and unvaccinated in each age category (while a very small drop
might be expected to account for February to May having slightly lower
annual rates than a full year including January and December this should
be more than compensated for the known increase in deaths in 2022 fromthe various effects of COVID-19 and lockdowns). For example, from Table
9, in the 18-39 age group the ONS data show mortality rates of 21 for theunvaccinated and 25 for the vaccinated, whereas historical rates are around
50. For the 40-49 age group there are ONS rates of 93 for the unvaccinatedand 102 for the vaccinated, whereas historical rates are around 180. As the age
categories increase the reported mortality rates for the unvaccinated and evervaccinated converge.

Figure 4: Historical mortality rates for England by age group

Where are these missing deaths? It might be that deaths reported by the
ONS are only those registered with the authorities but deaths that requirea coroner’s investigation will not be included in the ONS death totals until
the investigation is complete and the death is registered thereafter. Givencoroners’ investigations can take weeks or, in exceptional cases, months, alarger proportion of deaths will be missing in the most recent data from the
ONS.[19] This creates a lag effect where actual mortality lags that which isreported and, over time, this lag effect self-corrects as deaths are registeredand are then retrospectively added into the ONS data with the correct date337

Australian Medical Professionals Societyof occurrence. Clearly this lag effect would be more pronounced for younger
age groups as the proportion of deaths that require investigation is higher inthe young. However, given that the data are from February to May 2022 any
backlog of deaths being investigated by the coroner should have cleared by thetime of writing.
It would be possible to estimate the historical lag effect and, assuming it isrepresentative of current processes, then estimate the total numbers of deaths
missing for which self-correction is expected. However, the ONS have chosen
not to do that and instead reported a misleading low mortality rate which willrise over time as the death data self-correct. Whereas the ONS had publishedupdates to the data every two months or so, there has not been an update
since July 2022 with no explanation as to why or when the next update is to
be expected.
Table 9: February to May 2022 – age-standardized mortality rate compared with approximate
historical mortality rate (deaths per 100k person years)

We have previously encountered this ‘dead presumed missing’ problem in
ONS mortality data. In [3] we found that they had omitted 13,593 deaths
from their dataset and as a result the mortality in the vaccinated was disproportionately low when compared to historical norms and those omitted fromtheir dataset (but which appear in other government statistics). The ONS have338

Too Many Dead
only acknowledged 1,436 deaths post vaccination whose vaccination record
was not entered into the NIMS data system, thus originally categorising themas unvaccinated deaths, whilst stating that 71,318 people with inconsistentvaccination records were simply removed from the analysis.[20]The ONS dataset continues to show grossly unrealistic discrepancies between
the mortality rate of people within the ONS sample and the implied mortality
rate of the remaining population. If we take the claimed ONS mortality rates for
vaccinated and unvaccinated for the sample and extrapolate to the population,
outside of their sample, we find there would have been over 150,000 deaths in
the population that was not sampled (see below). That would mean those eight
million people, while only 19% of the population of England and likely to be
younger overall than those in the sample, accounted for 30% of all deaths in
England and Wales.
In Section 3 we inferred that 69% of the 10+ population not in the ONS
dataset are unvaccinated, and this gives us 5,520,000 unvaccinated people
and 2,480,000 vaccinated people (a total of eight million not in the ONS
dataset). Using the ONS unvaccinated and ever vaccinated all-cause mortality
estimates for the whole period, 2337.5 and 957.4 (from Table 3 in[20]) andapplying them to these ever vaccinated and unvaccinated populations resultsin 129,000 unvaccinated deaths and 23,700 vaccinated deaths (a grand total of
150,000 deaths in a year).
In England there are 42.8 million people aged 10+ and the eight million notin sample are approximately 19% of this population total. However, the total
deaths recorded in England by ONS for 2021 are 496,309 and 150,000 deaths
would be approximately 30% of that figure. Therefore, we can conclude that
19% of the aged 10+ population of England have generated 30% of the deaths.
Rather than 69% if we assumed the upper limit of 99.6%, were unvaccinatedthen the total would be 187,000 deaths in the population that was not sampled,which would be 37% of the total deaths in whole population. How is thisdiscrepancy explained? The obvious explanation is that the ONS dataset not
only misrepresents the true proportion of the unvaccinated but also is selectivein which deaths appear in the dataset and which do not.

8.

Alternative explanations for the anomalous
ONS data

Our analysis demonstrates that, even without any adjustments to take accountof underestimates in the proportion of unvaccinated, the recent months of
ONS data suggest that in the young (less than 50) and very old (90+) the
all-cause mortality is higher among the vaccinated than the unvaccinated. Only
very modest and realistic adjustments to the unvaccinated proportion indicate

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the same may be true of all the other age groups. There is growing evidence
elsewhere that the vaccines may lead to an increase in all-cause mortality acrossall age groups.[15]Because of the potentially devastating effect of these conclusions on thevaccination program, and because it confirms the extent of the anomalies in the
ONS data in the first half of 2021, there has been a concerted effort to invent
alternative explanations for the anomalies in the ONS dataset. One persistentargument has been that the anomalies were the result of especially ill people
being denied the vaccine; so, there was, they claimed, a ‘healthy vaccinee effect’(or equivalently a ‘moribund unvaccinated effect’). Indeed, as shown in [16] oneof the harshest critics of the report [2] repeated that explanation while attacking
the recent paper by Malhotra exposing problems with the COVID vaccines.[15]The ONS even stated the ‘healthy vaccinee effect’ as an explanation in a
subsequent report[17] after the anomalies in their data were identified. But thenotion of the ‘healthy vaccinee’ was contradicted by the NHS guidelines[18](which required the most critically ill people be prioritised for the vaccine, notdenied it) and we know that even terminally ill patients in hospices and carehomes were given the vaccine as a priority. Moreover, in[2] it was shown that
the ONS data could not be explained by a ‘healthy vaccinee’ effect. From themortality pattern across age categories in Table 9 there is no healthy vaccinee
effect in evidence. Only the ever vaccinated in the middle to older age groups(50-59, 60-69, 70-70, 80-89) show lower mortality than the unvaccinated. Yet
we see the opposite result in the very youngest (18-39, 40-49) and very oldest(90+) age groups, where the unvaccinated show lower mortality than the evervaccinated. And in any case as we have already observed there is little evidence of
any of these age groups containing substantial sub-populations of terminally ill
or moribund people, given the mortality rates are less than or equal to historicalfigures across the board.
The most striking feature of Table 9 is that the mortality rates for the younger
age groups in the ONS dataset are significantly less than we would reasonably
expect given historical mortality rates. Given that ONS-reported mortality
rates are adjusted to ensure that differences in population sizes are accounted
for, by age-standardization, differences in population numbers cannot explain
this. Neither can any hypothesis that unhealthy younger people, possibly more
likely to die, are less likely to be in the ONS dataset because, if anything, suchterminally ill young people would almost certainly be registered with a GP andthus be included in the ONS dataset.
Another alternative explanation for this observed reduced mortality effect isthat there are fewer deaths in the ONS dataset than should be reported forthese younger age categories.

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Too Many Dead9.

Conclusions about the data up until May 2022

Previously discussed explanations for the anomalous differences between
non-COVID mortality rates in the vaccinated and unvaccinated include
miscategorising deaths shortly after vaccination as unvaccinated and omitting
completely many vaccinated deaths. This paper considered an additional
major source of bias that has not previously been widely discussed: possible
underestimation of the proportion of unvaccinated people. The ONS estimate
of 8% of adults unvaccinated in May 2022 contrasts starkly with two otherindependent sources which estimate the figure to be 20% and 26% respectively.
Because the ONS data are based on a subset of England residents that excludes
all those not registered with a GP and not registered in the 2011 census, it
is missing some eight million adults who are not at all representative of thosein the ONS sample. Hence, it is conceivable that both the ONS 8% figure iscorrect for its sample, while the proportion of all adults in England unvaccinatedis at least 20% as per the other sources. But we showed this would necessarily
imply that between 69% to 99.6% of adults missing from the ONS sample areunvaccinated. Hence, either the ONS is underestimating the proportion of
unvaccinated in its sample or the sample is so unrepresentative of the whole
population that any inferences made using the ONS data are worthless. Eitherway, the ONS estimate of proportion unvaccinated must not be used for any
comparisons of vaccine efficacy or safety of the whole-England population. We
also showed that even with these anomalies and biases the most recent monthly
data (in contrast to the especially flawed data in 2021) provides no evidence
that the vaccines reduce all-cause mortality. In fact, for each of the younger age
categories (18-39 and 40-49), as well as (curiously) the oldest age category 90+,
the all-cause mortality rate of the unvaccinated is lower than that of the evervaccinated.
The ONS vaccine mortality surveillance reports for England have numerous
anomalies which bias its results strongly toward underestimating mortality rates
for the vaccinated and overestimating mortality rates for the unvaccinated. Clear
evidence of the anomalies can be seen in the latest report by comparing the
ONS reported age standardised mortality rates for non-COVID deaths in thevaccinated and unvaccinated. Assuming the vaccines do not cause many deaths
from serious adverse reactions, there is no reason why these mortality rates forboth vaccinated and unvaccinated should be significantly different from thepre-COVID era steady state figure of approximately 974 deaths per 100k person
years. Yet, based on the whole period of vaccination from Jan 2021 to May 2022,
the ONS data show a completely implausible non-COVID mortality rate of1474 per 100k person years for unvaccinated people, compared to just 893 forvaccinated people. Moreover, when we analyse the recent months’ data by age
categories, we find that in both the vaccinated and unvaccinated the all-cause
mortality rates are much lower than the historical rates. Likewise, the mortality

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Australian Medical Professionals Societyrate outside of the ONS dataset is significantly higher than that within the ONS
dataset. The ONS data therefore suffers from deaths selection bias.
Overall, the ONS dataset is so compromised with inaccuracies, anomalies, andbiases that it cannot be used to reliably determine vaccine efficacy and safety.
We recommend that the ONS adds full caveats to its future surveillance reports
explaining the limitations and biases of its sample population. Also, any studies
of vaccine efficacy or safety comparing vaccinated and unvaccinated which use
whole population data of COVID cases, hospitalisations, and deaths, but which
rely on the ONS estimate of proportion unvaccinated must be retracted.

10.

The follow-up report

We complained to the Statistics Regulator about the July 2022 report and he:(i) agreed with our recommendation to ignore any claims of vaccine safety or
efficacy based on the data; and also (ii) that the ONS underestimates the true
population proportion unvaccinated.
After a seven-month delay since the previous update on February 21st 2023, theONS finally released a new version with data up to December 2022. So, there
was great anticipation that the ONS would in the time available resolve theissues with their dataset and settle open questions about vaccine efficacy andsafety. Unfortunately, this opportunity was missed.
To show how seriously they took their task, and despite all that time they had
to get it right, a corrected version was issued 24 hours later after many people
pointed out errors (more of that below; in fact, they only corrected some errors
on month labels).

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Inevitably, as in the previous report, the ‘headline’ figures are that the all-cause
mortality rate – measured here by the age-standardised mortality rate (ASMR)– has been consistently lower among the vaccinated than the unvaccinated(although the difference is narrowing). If the data were accurate and reliable
then, as we have always argued, the all-cause mortality rate is, indeed, the mostobjective measure of vaccine safety and efficacy. But, as in the previous version,
it is easily shown that there are systemic flaws and biases in this latest version
which make the stated ASMR figures meaningless.
Of course, this did not stop inevitable mainstream media publishing predictablepre-prepared stories ridiculing ‘anti-vaxx myths’10 claiming the data provedthat death rates were lower among the vaccinated. An example of this, of thekind most readers will be used to seeing, can be found in the Mail Online
headline, in the Appendix, on page 358.
A very quick look through the data revealed some obvious concerning changes
from the previous version, namely:•

Data for those under 18 were removed.

•

Data for January to March 2021; the later publication relied on census
data from April 2021. Rather than extrapolate to include an estimate
for January to March 2021 they removed that period altogether fromthe report•

There are many changes to the raw data for April 2021 to May 2022
between latest version and previous versions.

Before summarising some of the main complaints about the new data, it
is important to note that the age-standardised mortality rate (ASMR) is aterrible metric for assessing vaccination safety (also it cannot be reproducedby the data provided in[1]). While it accounts for age-confounding, it obscuresthe information needed to determine risk-benefit for different specific age
groups.
By looking at raw mortality rates within each age category there is no needfor the complex, obfuscated ASMR. The ONS do provide age categorised
breakdown:
18-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90+(The 18-39 category is rather coarse and, unlike the case with previous versions,those aged under 18 are no longer included so we have less information than
before).
10

See the appendix.

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Tore Aarhus Gulbrandsen has produced the relevant all-cause mortality
graphs for each of the age groups11 (note the ONS uses person years rather
than number of people). Note that, as in previous ONS releases, there are wild
fluctuations in mortality rates for the different categories of vaccinated. But inno age group is there any strong evidence of reduced all-cause mortality forthe vaccinated. The following chart summarises these (and the changes fromthe previous version):

11

https://twitter.com/saunasauen/status/1628138191642365958

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Too Many Dead

Why would the ASMRs change so dramatically for the unvaccinated between
releases? The ONS summary data was trumpeted by the mainstream media
and those continuing to push the ‘safe and effective’ vaccine narrative, since itshows the ASMR for the unvaccinated higher than that of the ever vaccinated.
But a deeper look into the data reveals the key problems with this.
Note that the January to March 2021 data have been removed (the ONS says
this is because they are using the new 2021 census which only includes people
alive after March 2021), so it is important to remind people of the following
key graph of non-COVID mortality from the previous report’s data:

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Since this is non-COVID mortality the plots for unvaccinated and vaccinatedshould be similar. The sharp peak in unvaccinated non-COVID mortality in
January to March 2021 (which was when the vaccine was rolled out) musthave been the result of misclassifying those dying shortly after vaccinationas unvaccinated. But the fact that there was a continued (albeit decreasing)difference proves there was systemic confounding of the data through one
or more of:•

misclassification of vaccinated deaths as unvaccinated•the ‘healthy vaccinee effect’•

underestimating the population proportion of unvaccinatedThis bias influenced all of the mortality rates, but the ONS did not adjust for
it at the time. They were however keen to assume a healthy vaccinee bias but
presented no evidence to support this lazy assumption.
Similarly, in our latest report we pointed out that the ASMRs reported for
February to May 2022 were significantly lower than historical rates from2016, for younger age groups (59 and below), giving rise to what we dubbed
the ‘dead presumed missing’ phenomenon. We suggested that this can be
explained by deaths that are missing from their dataset. Comparing the
ASMRs in the latest ONS release reveals this same problem, as shown in
the table below. So, the mortality risk of people in the ONS dataset remains
substantially different from that of the general population and we can see
there are huge changes in the reported mortality rates between this ONS
report and the last one, with percentage changes in mortality ranging from-31% to +38%. Why these dramatic changes?

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Table 9a Feb-May 2022 Age-standardized Mortality Rate compared with approximate
Historical Mortality Rate (deaths per 100k person years) with the addition of% Difference in reported Mortality

How did mortality change in the latest release? USMortality provides the
updated plot,12 below. Clearly the systemic problems have not been resolvedin the latest version. Comparing the latter months of the earlier report showsthey have in fact become worse.

12

https://usmortality.substack.com/p/englands-all-cause-mortality-data

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What about the critical problem with underestimation of the proportion of
unvaccinated? Unfortunately, again, this problem has not been resolved evenwith the new census data. Clare Craig and Igor Chudov13 have both analysed
this. Here is the table provided by Clare Craig with the ONS estimates of
proportions of unvaccinated in each group:

As Igor Chodov pointed out as an example, the March 2022 ONS estimate
for the 50-59 age group is that there were just 6.19% in this age group
unvaccinated, but according to the UKHSA Week 13 Vaccine Surveillance
Report, (Page 17) 87% of the 50-59 age group were vaccinated in March 2022.
So UKHSA say 13% were unvaccinated (a figure we know to be much moreaccurate). The ONS claim this is because their report is only from a subsetof the population but it is clearly not a representative subset with this large
discrepancy.
We also know that miscategorisation is still happening. Indeed, Note 17 of the
ONS spreadsheet asserts:
There were some people who were vaccinated but not included in the
NIMS data as they died soon after vaccination. Of these, 1,029 linked
to our 2021 Census linked dataset. We included the latest vaccination
13

https://www.igor-chudov.com/p/ons-data-25-excess-mortality-among348

Too Many Dead
records for these people in our dataset. This data is provisional andextends up to the 1 November 2022. This will be updated in future
releases.
USMortality also raised the concerns expressed in Screenshot 7, pointing out
a series of important errors and omissions. Also produced were the following
useful charts of absolute death counts in each age group:

While obviously most people in the older age groups are vaccinated, thefollowing plots do not exactly support the much-repeated mantra that thevaccinations ‘stop people dying from COVID’.

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A cursory analysis of the death counts in the ONS dataset shows that thedeath counts from Table 5 and Table 2 differ. @ExcessBurden has produced
the following chart showing the dramatic differences in death counts reported
in different tables of the same data release. The ONS said that table 5 contained
all deaths regardless of a link to the census but any that could not be linked
were removed for table 2.

Clare Craig asked the ONS about this discrepancy in death counts andexplains how deaths can occur amongst the population ‘ghosted’ by the ONS(https://drclarecraig.substack.com/p/deaths-among-the-ghost-population).
Others reached the same conclusion about the data and highlighted further
concerns.

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11.

Postmodern science delivers immortality
benefits

The ONS data show that the vaccines are delivering the prospect of immortality
whether we take them or not. Consensus opinion across those who have looked
in detail at the ONS deaths by vaccine status data14 is that it is full of errors
and biases and, as such, it is near useless for any inference we might wish to
make about vaccine efficacy or safety.
In all of our deep-dive reports we have compared ONS data against historicalactuarial mortality rates. We believe that only by comparing with expectations
can we determine if there is a pandemic and whether the vaccines are putting
a dent in it, or indeed adding to the mortality burden.
However, comparing mortality rates within the ONS data set does not clarify
whether the data as a whole stand up to scrutiny. For that we need to test
against historical data and previous reports. Let us turn again to this tablefrom the previous section:
Table 9b Feb-May 2022 Age-standardized Mortality Rate compared with approximate
Historical Mortality Rate (deaths per 100k person years) with the addition of% Difference in reported Mortality

14

https://wherearethenumbers.substack.com/p/the-latest-ons-data-on-deaths-by

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This table shows that between the old and new ONS reports the mortality rates
changed substantially, with the mortality of unvaccinated and vaccinated older
groups now exhibiting lower mortality figures, dropping by between -6% to-31%. How can this be? In contrast, for the unvaccinated younger age groups
the mortality increased by between 23% and 38% between ONS reports. Thisis an extraordinary change that cannot be explained. It amounts to garbage.
So, comparing across ONS reports shows dramatic changes that cannot be
accounted for. But much more important than this is the fact that the mortality
rates are crazily different from those in 2016. How crazy? One would thinkin this age of data availability we would have the 2016 mortality rates at our
fingertips. All we could find was this grainy image on an ONS web site:

Figure 4a Historical mortality rates for England by age group

From this we can produce rough estimates of the mortality burden by age-groupand compare them against the latest ONS mortality figures. The green line isthe all-cause mortality rate per 100K people for that age group from 2016 andthe red and blue lines are the all-cause mortality rates for the ever vaccinatedand unvaccinated from the latest ONS report.

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Plainly something is very wrong with these data. The most obvious thingis that, across all age groups, the all-cause mortality rate is significantly lessfrom February 2022 than one would expect, compared to 2016, for both thevaccinated and unvaccinated. And this occurs at a time when excess deaths in
the UK are starting to increase and the COVID case rate has plummeted, as
can be seen from week eight in the chart below (the COVID case scale on theright-hand side is cases per 100,000 people):353

Australian Medical Professionals Society

So, more people are dying but in the ONS data set people are living longer.
There are a few things we can conclude from this. Either deaths are not being
recorded in the ONS dataset, and they do not bother to check (perhaps they
are with the coroners’ courts or in the post), or, if the data are correct then thevaccines are so good that they are improving mortality for everyone, whether
we take them or not and the extra real-world total deaths came from elsewhere.
Perhaps all of that vaccine shedding is a good thing after all, and with more of
the vaccines we will achieve immortality.
It seems that detailed blow-by-blow takedowns of ONS are laboured, and thisis the simplest and most obvious way of showing how useless the ONS data is.
Nothing more than this is necessary to win the argument. These data are notrepresentative of anything.

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12.

Final words

To date we have produced three exhaustive analyses of the ONS data, each ofwhich has consumed very considerable time and effort. Recall that these data
were said to be the gold standard produced by the best, most reliable official
statistics department in the world.
Each time we looked at their data we, and many others, have discovered alitany of errors, oddities, missing data, inconsistencies, contradictions andunder- or over-estimates. The ONS have either ignored our analyses or madelazy offhand assumptions that they believe explain these issues. None of theissues we have identified have been addressed and none of their assumptions
have been justified.
We had, perhaps naïvely, hoped that the ONS would have responded to thecriticism from the UK statistics regulator and improved their game, but it seems
they have treated us, and the regulator, with contempt. Far from resolving allof the issues this report was replete with the same errors, was obviously hastily
put together and even had to be amended and updated within hours of release.
More importantly all of the original issues and biases remain unaddressed.
These events raise many legitimate questions:•

If the regulator does not require the ONS to produce accurate or useful
information about vaccine safety and efficacy, what is the ONS for?
What are we paying our taxes for?

•If the regulator can be ignored, where the official statistics produced by
the system are as bent before and after regulatory intervention, why do
we need a regulator at all? Why are we paying for this?

•Why should we accept anything the UK government has historicallyclaimed about the COVID vaccines based on these data? Why should
we accept any of their public health claims in the future?

Acknowledgements
Thanks to Charlotte Bermingham of the ONS for clarifying some points
about the ONS dataset.

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13
[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

References
Office for National Statistics. Deaths by vaccination status, England - Officefor National Statistics [Internet]. 2022 [cited 2022 Oct 6]. Available from:https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/deathsbyvaccinationstatusengland

Neil M, Fenton N, Smalley J, Craig C, Guetzkow J, McLachlan S, et al.
Official mortality data for England suggest systematic miscategorisationof vaccine status and uncertain effectiveness of COVID-19 vaccination[Internet]. 2022. Available from: https://doi.org/http://dx.doi.org/10.13140/
RG.2.2.12472.42248

Craig C, Neil M, Fenton NE, McLachlan S, Smalley J, Guetzkow J, et al.
Official mortality data for England reveal systematic undercounting of deaths
occurring within first two weeks of COVID-19 vaccination [Internet]. 2022.
Available from: http://dx.doi.org/10.13140/RG.2.2.12472.42248

BBC Media Centre. BBC announces new documentary, Unvaccinated,with Professor Hannah Fry - Media Centre [Internet]. 2022 [cited 2022
Oct 6]. Available from: https://www.bbc.co.uk/mediacentre/2022/unvaccinated-professor-hannah-fry
UK Health Security Agency. Weekly national Influenza and
COVID-19 surveillance report [Internet]. 2022 [cited 2022 Oct 6].
Available from: https://assets.publishing.service.gov.uk/government/
uploads/system/uploads/attachment_data/file/1088929/Weekly_Flu_
and_COVID-19_report_w27.pdf
ICM. Exploring attitudes towards COVID-19 vaccinations (for STV)
- icmunlimited [Internet]. 2022 [cited 2022 Oct 6]. Available from:https://www.icmunlimited.com/our-work/exploring-attitudes-towards-covid-19-vaccinations-for-stv/
NHS England. COVID-19 vaccinations [Internet]. 2022 [cited 2022
Oct 27]. Available from: https://www.england.nhs.uk/statistics/statistical-work-areas/covid-19-vaccinations/Fenton NE. BBC wants to understand why 8% of the population
remains unvaccinated against COVID [Internet]. 2022 [cited 2022
Oct 6]. Available from: https://www.normanfenton.com/post/bbcwants-to-understand-why-8-of-the-population-remains-unvaccinated-against-covid
Office for National Statistics. Unvaccinated population of Englandand the UK - Office for National Statistics [Internet]. 2022 [cited
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Smalley J. Evidence of Incorrect Vaccination Records [Internet]. 2022
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evidence-of-incorrect-vaccination
Office for National Statistics. Coronavirus (COVID-19) latest insights- Office for National Statistics [Internet]. 2022 [cited 2022 Oct 6].
Available from: https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/articles/coronaviruscovid19latestinsights/vaccines
Cochrane Org. The standardised mortality ratio and how to calculate
it - Students 4 Best Evidence [Internet]. 2020 [cited 2022 Oct 6].
Available from: https://s4be.cochrane.org/blog/2020/08/26/the-standardised-mortality-ratio-and-how-to-calculate-it/
Office for National Statistics. Annual deaths and mortality rates, 1838
to 2020 (provisional) - Office for National Statistics [Internet]. 2021
[cited 2022 Oct 6]. Available from: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/adhocs/12735annualdeathsandmortalityrates1938to2020provisional
Public Health England. Chapter 2: trends in mortality [Internet].
2018. Available from: https://www.gov.uk/government/publications/health-profile-for-england-2018/c%0Ahapter-2-trends-in-mortality%0A
Malhotra A. Journal of insulin resistance. [Internet]. Vol. 5, Journal of
Insulin Resistance. AOSOS; 2016 [cited 2022 Oct 6]. 10 p. Available
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Norman E Fenton. Can our detractors decide if vaccinees areespecially healthy or especially unhealthy? [Internet]. 2022. Available
from: https://www.normanfenton.com/post/can-our-detractors-decide-if-vaccinees-are-especially-healthy-or-especially-unhealthy
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Deaths involving COVID-19 by vaccination status, England: deaths
occurring between 1 January 2021 and 31 May. [Online]. Available:
https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/deathsinvolvingcovid19byvaccinationstatusengland/latest Data: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/
deathsbyvaccinationstatusengland

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Norman Fenton is Professor Emeritus of Risk at
Queen Mary University of London (retired as Full
Professor Dec 2022) and a Director of Agena, acompany that specialises in artificial intelligence andBayesian probabilistic reasoning. He is a mathematician
by training, with current focus on quantifying risk anduncertainty using causal, probabilistic models thatcombine data and knowledge (Bayesian networks). He
has published seven books and over 350 peer-reviewed
articles. His work covers multiple application domains
including especially health, law and forensics (he has
been an expert witness in major criminal and civil
cases). Since 2020 he has been active in analysing data
related to COVID risk.

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A report on the US Vaccine*
Adverse Events Reporting
System (VAERS) of the COVID-19
messenger ribonucleic acid (mRNA)biologicals
by Jessica Rose
PhD, MSc, BSc

Abstract
Following the global roll-out and administration of the Pfizer-BioNTech(BNT162b2) and Moderna (mRNA-1273) COVID-19 vaccines1 on
December 17, 2020 in the United States, and of the Janssen COVID-19
Vaccine PF (produced by Johnson & Johnson) on April 1st, 2021, tensof thousands of people have reported adverse events (AEs) using theAmerican Vaccine Adverse Events Reports System (VAERS). This worksummarizes these data to date and serves as information for the public,
and as a reminder of the relevance of any adverse events, including deaths,
that occur as a direct result of biologicals as prophylactic treatments. Thisis especially relevant in the context of technologically novel treatmentsin the experimental phase of development. Analysis suggests that thevaccines are likely the cause of reported deaths, spontaneous abortions,anaphylactic reactions and cardiovascular, neurological and immunologicalAEs. The precautionary principle promotes transparency and the adoption
1mRNA biologicals are not true vaccines. True vaccines are a preparation of a weakened
or killedpathogen, such as a bacterium or virus, or of a portion of the pathogen’s structure that, uponadministration to a person, stimulates antibody production or cellular immunity against the pathogen but
is incapable of causing severe infection. Vaccines undergo an extremely rigorous time-dependent testing
protocol to ensure safety and efficacy, typically enduring between 10 and 15 years. The mRNA biologicals
do not satisfy either of these requirements and are thus more akin to experimental treatments.

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Too Many Deadof preventative measures to address potential risks to the public in thearena of vaccination programs, and it is vital that people are informedof these potential risks before agreeing to participate in any medicallyinvolved treatment program. VAERS reporting and recording is essential
to the proper functioning of this system. It cannot be over-emphasizedthat the public should know how to use this system such that they actually
do use it, and that once reports are made, responsible authorities enter eachreport into the database accordingly.

1.

Background

The Vaccine Adverse Event Reporting System (VAERS) was created andimplemented in 1990 in the United States by the Food & Drug Administration(FDA) and Centers for Disease Control and Prevention (CDC) to receive
reports about adverse events that may be associated with vaccines. Most vaccine
adverse event reports concern relatively minor events, such as injection site pain.
Other reports describe serious events, such as hospitalizations, life-threatening
illnesses, or deaths.[1] The reports of serious events are of greatest concern andare meant to receive the most careful scrutiny by VAERS staff and healthcare
professionals. The primary purpose of maintaining the database is to serve as
an early warning or signalling system for adverse events not detected during
pre-market testing. In addition, the National Childhood Vaccine Injury Act
of 1986 (NCVIA) requires healthcare providers and vaccine manufacturers toreport to the DHHS specific adverse events following the administration of
those vaccines outlined in the Act.[l] It must be noted that the adverse events
reported to VAERS represent a fraction of the actual number of incidents.
Studies have shown that the percentage of incidents reported can be quite
low (1-10%) but, for the purposes of this report, in order to do the necessary
calculations, VAERS numbers were used and the results should be considered
to reveal trends.[1,2]An Adverse Event (AE) is defined as any untoward or unfavourable
medical occurrence in a human study participant, including any abnormal
physical examination or laboratory finding, symptom, or disease, temporally
associated with the participants’ involvement in the research, whether or not
it is considered related to participation in the research. A Serious or Severe
Adverse Event (SAE) is defined as any adverse event that results in death,is life threatening, or places the participant at immediate risk of death fromthe event as it occurred, requires or prolongs hospitalization, causes persistentor significant disability or incapacity, results in congenital anomalies or birthdefects, or is another condition which investigators judge to represent significant

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hazards.2, 3 The VAERS handbook states that approximately 15% of reported
AEs are classified as severe.[1]Ongoing collection of data in systems such as VAERS in the United States,
the Coronavirus Yellow Card reporting site for the United Kingdom, as well as
independent reports of AEs, merits further examination into both the safety
and efficacy of the mRNA vaccines currently being rolled out globally inresponse to COVID-19, in particular those designed by Pfizer-BioNTech(BNT162b2, now known as the Pfizer-BioNTech COVID-19 Vaccine) andModerna (mRNA-1273), which have been the most widely administered.4
mRNA platforms are new in medical microbiology and have never before
been implemented for use in human subjects on a global scale in the context
of viruses. Safety is always a point of relevance with regard to new biologicalagents. As stated, the primary purpose for maintaining the VAERS databaseis to serve as an early warning system and one should be cautious in drawing
conclusions regarding safety in its context. But since the number and rangeof side effects is vast and no long-term data of potential damaging effects
such as autoimmune reactions exist, AE collection systems such as these areof utmost importance, not only to flag potential severe AEs not detectedduring premarket testing but also for weighing in on the potential safety ofthe biologicals themselves. The efficacy of a conventional vaccine is measured
via explicit demonstration of broad-spectrum potent immune responses in theforms of both cellular and humoral responses as well as the establishment ofenduring immunity.[3,4,5,6,7]
Although there are some studies claiming efficacy for these mRNA biologicals
in humans,[4,5] that efficacy is not based on immunological assessment but
rather on clinical assessment based on primary and secondary endpoints
including confirmed or severe COVID-19. In these same studies, safety isassessed based on a maximum observation period of six months. This is notadequate to assess long-term safety outcomes. In this context, it is worth notingthat the Pfizer-BioNTech, Moderna, and Janssen COVID-19 vaccines havenot been approved or licensed by the FDA, having been authorized instead for2
National Institute on Aging, Adverse Event and Serious Adverse Event Guidelines. https://www.nia.nih.gov/sites/default/files/2018-09/nia-ae-and-sae-guidelines-2018.pdf[40]3FDA. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/cfrsearch.cfm7frexternal%20icon
4
Messenger ribonucleic acid, first discovered in 1961 at Caltech, has been called the ‘software oflife.’ Conventional vaccine types primarily use live-attenuated whole viruses or killed viruses as a means toelicit potent immune responses in the forms of both cellular and humoral responses and life-long immunity.
Periodically, boosts are required in order to maintain longevity of immune responses, especially in the formof neutralizing antibodies.[42] mRNA treatment types use specific mRNA that encodes a particular
protein, which is meant to be mass-produced by host cells as a means to trigger an appropriate immune
response, primarily in the form of neutralizing antibodies, in order to provide a degree of protection uponchallenge with the wild-type coronavirus. Although studies show cellular and humoral responses upon
injection, it is not known how long immunity might last, and thus it has been suggested that many boosts
will be required. It has also been detailed that these particular vaccines do not prevent transmission, andthus the effectiveness of these vaccines is very questionable.[8,9,10] Perhaps even more important is that it
is unknown what the effect of non-neutralizing antibodies will be in the long term.

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Too Many Deademergency use by the FDA under an Emergency Use Authorization (EUA)
to prevent Coronavirus Disease 2019 (COVID-19), for use in people 16 yearsof age and older.[8,9,10] Ultimately, the roll-out of COVID-19 vaccines is
actively being monitored, but all of the risks are not yet known.[9,10] In spiteof this, real-world trials and administration of these biologicals into pregnant
women and children are being pursued in countries around the world, suchas Israel.[ll] The VAERS dataset is currently the best (if not the only, albeit
imperfect) way the public can monitor and be informed of the risks associatedwith administration of the COVID-19 injectables.
It is vital for the public to be aware of this reporting system and the valuable
information therein so that informed decisions can be made and a risk-benefit
analysis done. One of the ways that risk is assessed using findings from thisstudy is by comparing the death rate reported in VAERS with the Infection
Fatality Rate (IFR), which is a measure of the chance of dying fromthe SARS-CoV-2 pathogen. All infected people, both symptomatic andasymptomatic, are accounted for in the IFR calculation, and data therein are
based on serology. It is important for anyone analysing or comparing death
statistics to use the IFR and not the Case Fatality Rate (CFR) — the ratio
between confirmed deaths and confirmed cases[12,13] — because the CFR isbased on potentially unreliable death and confirmed case accounts.5 There isalso a lag in time between when people are infected and when they die, and,most importantly, it does not capture the population with innate immunity.
The major difference in the numbers, 1.8% (CFR) versus 0.15% (IFR), is due
to a significantly larger denominator whereby infected persons with an effective
innate immune response represent asymptomatic cases. The latter metrichighlights the true risk of succumbing to the virus in the general population.
It is more compelling to use the IFR as a metric for comparison for this andfuture studies.[12]2.

Methods

1.

General methodology and descriptive statistics

To analyse the VAERS data set, R was used (a language and environment
for statistical computing). The VAERS data set is available for download(https://vaers.hhs.gov/data/datasets) in three separate comma-separated
values (csv) files representing i) general data for each report; ii) the reported
AEs or ‘symptoms’; and iii) vaccine data including vaccine manufacturer
and lot number, as per report. The VAERS dataset is updated approximately
once a week and the uploaded set is approximately one week behind the5
The CFR is the fraction of reported deaths from SARS-CoV-2 to the reported confirmed casesof SARS-CoV-2. This gives an unreliable metric in that both the numerator and denominator may not be
accurate (and have been reported not to be).[12,13]363

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reports. Upon individual reporting of vaccine side effects or adverse events,
a VAERS ID number is provided to the person to preserve confidentiality,
and a detailed description of the side effects is transcribed along with theperson’s age, residence by state, past medical history, allergies and gender andmany other details. In addition, the vaccine lot number, place of vaccinationand manufacturer details are included in the report. In order to maximize
the input variables for my analysis, the three files were merged by VAERS
ID that is included as a linking variable in all three files. The merged data set
comprises data collected pertaining to all reported AEs associated with the
Pfizer-BioNtech and the Moderna COVID-19 products. Data were sorted
according to vaccine type (data reported for COVID-19) and relevant variables
were sorted including VAERS ID, AEs, age, gender, state, vaccination date,
date of death, incident of death, dose series, treatment lot number, treatment
manufacturer, hospitalizations, emergency department visits and onset dateof AEs. To determine the total number of AEs, multiple individually-reported AEs were aggregated into a single column vector. An additional column
vector called AGE GROUPS was created to group the people who madereports according to age by decade. The grouped AE categories were createdby selecting ‘Y’ in the case of the death, hospitalizations and emergency doctor
visits while the cardiovascular, neurological and immunological groups werecreated by selecting key words indicative of an immunological medical issue
such as ‘lymphadenopathy’, in the case of the immunological AE group, forexample.
There are two primary vaccine manufacturers responsible for SARS-CoV-2
vaccines currently being administered, Pfizer-BioNTech and Moderna.
Recently, a third, the Janssen COVID-19 Vaccine PF (produced by Johnson &Johnson), has begun to be administered. All three are included in this analysis,
except where discrepancies were found by comparison between manufacturers.
Descriptive statistics on the incidence rates of relevant AEs were calculated asa percentage of the number of unique VAERS IDs and the fully vaccinatedpopulation in the United States.6 Also calculated are the death rates by
SARS- CoV-2 for each respective VAERS update date as reported by the Our
World in Data collection.[14] It should be noted the death rate was reported
from the SARS-CoV-2 virus report CFR, not IFR. Although a confirmed
positive serological test should be conducted, according to the World HealthOrganization (WHO), the US Department of Health and Human Services,
the CDC, the National Center for Health Statistics, and the National Vital
Statistics System, it is acceptable to report COVID-19 on a death certificate
without this confirmation.7
6
Our World in Data. https://ourworldindata.org/covid-vaccinations[14]7CDC. Guidance for Certifying Deaths Due to Coronavirus Disease 2019 (COVID–19).
Vital Statistics Reporting Guidance, Report No. 3, April 2020. https://www.cdc.gov/nchs/data/nvss/vsrg/

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Too Many Dead2.

Statistical testing and causation

Statistical analysis was done using the Student’s t-Test to determine statisticallysignificant differences between age groups in the context of grouped data, suchas people who died versus people who did not die, for example. Causation
implies that a change in one variable necessarily leads to a change in anothervariable. The three criteria for establishing causation are association, timeordering and non-spuriousness. Association is shown in the incidence-rate
data and using heatmaps and is corroborated using Chi-Square Tests. Timeordering is presented in temporal relationship between vaccination date andthe following onset of AE date or the date of death.
Non-spuriousness is more difficult to prove in real-world settings since it is
not truly possible to rule out external influences as contributing factors for the
associations. For example, it is possible that the people who died within 24
hours of being vaccinated did so not on account of the vaccine but because ofunderlying conditions such as heart defects. This challenge is met by looking
at data available on potential third-variable causes such as medications
taken at the time of vaccination, and existing medical conditions. Skewing
in distribution of data is tested using Pearson’s Skewness Index, I, which isdefined as I = (mean-mode)/standard deviation. The data set is considered tobe significantly skewed if III>1.

3

Results

1.

General information

To date, approximately 15% of the total US population has been ‘fully
vaccinated’ against COVID-19, with 183,467,709 million doses administered
as of April 10, 2021; -0.5% of the total US population have been vaccinatedagainst the flu, with 1,300,000 million doses administered as of March 26,
2021.8 Based on the fact that the ratio of COVID-19 to flu vaccinations at
the end of March was -100:1, then it is not surprising that 380 times more
reports have been made in the context of the COVID-19 injections. 99% of
all AEs reported in 2021 have been in the context of COVID-19 reports,
while only 0.3% of all AEs reported to date have been in the context of theinfluenza vaccines. Of all vaccines administered in 2021, 0.7% have been
influenza vaccines,9 meaning that the higher percentage of reports made in thecontext of COVID-19 are due to more frequent reporting subsequent to more
frequent administration of the COVID-19 products.
Figure 1.1 illustrates the total number of adverse events reported and uploaded

vsrg03-508.pdf
8CDC. Seasonal Influenza Vaccine Supply & Distribution. https://www.cdc.gov/flu/prevent/vaccine-supplydistribution.htm
9
Ibid.

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to the VAERS database per year. There is an increase in the number of reports
being made each year over the past 30 years (possibly from increasing awarenessand adoption of the reporting system). 20% of all reports were COVID-19
vaccinerelated in 2020 and this was due to only 14 days of the year sinceadministration began on December 17th, 2020. Figure 1.2 shows reports for2021 by week. The most recent updated data files have almost surpassed thesum total reports for the entire year in 2020 (and this includes reports for all
vaccines, not just ones related to COVID-19). This is because reports relating
to COVID-19 vaccines in 2021 to date are in the order of all vaccines in 2020.

Figure 1.1 Time series plot – VAERS reporting rate normalized to US population by year

Figure 1.2 Time series plot – Absolute number of VAERS reports for the
COVID-19 products for 2021

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SAEs comprise 26% of all AEs, which is almost twice the estimate of SAEs
documented in the VAERS handbook. Of all persons having received the firstdose who reported an SAE, 74% did so after receiving the first dose. Similarly,of those who reported mild AEs, 81% did so after receiving the first dose. Of thetotal population of VAERS reports, 79% were made after receiving the first dose.
1.1

Incidence rates of AE groups by VAERS ID

As of mid-April 2021, a total of 4507 types of AEs have been reported and
46163 VAERS IDs have been assigned. Interestingly, of the reports, 74% camefrom females. This is likely due to a higher proportion of females reportingAEs but could stem from females succumbing to AEs more often than males.
5%, 12% and 16% of all AE reports involved death, hospitalization or an
emergency doctor visit, respectively, as shown in Table 1. 18%, 12% and 35% of
all AE reports involved cardiovascular, neurological or immunological events,
respectively, also shown in Table 1.
Table 1. Summary table showing percentages of categories and
COVID-19 cases by VAERS ID10

Table 2. Summary table showing percentages of categories and COVID-19 cases
according to the fully vaccinated population in the US

1.2
Incidence rates of AE groups per fully vaccinated population
As shown in Table 2, presuming that the deaths are related to the injections, theincidence rate of VAERS-reported deaths with respect to the fully vaccinatedpopulation is quite low with 34 people dying per million. The fully-vaccinatedpopulation comprises 20.5% of people as reported by Our World in Datastatistical group as of April 11, 2021.11 This is comparable to the incidence rate

10
The SAEs total represents all emergency room visits, hospitalizations and deaths.
11
This is the death rate calculated by dividing the number of people who were reported to havedied in the US

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Australian Medical Professionals Societyof SARS-CoV-2-reported deaths, which is 730 out of every million people asof April 11th, 2021. 74% of all who reported death using VAERS did so before
receiving the second dose.

Figure 2.1 Time series plot — Increase in VAERS deaths, ER visits, hospitalizations,cardiovascular, neurological and immunological reports

Figure 2.2 Time series plot — Relative change in deaths, ER visits,
hospitalizations, cardiovascular, neurological and immunological reports withrespect to the fully vaccinated population

In the context of the fully-vaccinated population, hospitalization and ER visit
reports are at 70 and 110 per million, respectively, but as shown in Figures
2.1 and 2.2, the numbers of these reports are steadily increasing as the weeks
pass. 68% of all people who reported being hospitalized and 77% of those whoreported visiting an emergency room physician did so after the first dose.

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Figure 3. Distribution of age groups across all AEs

Figure 4.1 Distribution by VAERS ID according to age in individuals who diedWith regard to AEs such as cardiovascular, neurological and immunologicalevents, the number of reports when compared to the fully-vaccinatedpopulation are currently at 120, 80 and 250 people per million, respectively.
It is important to remember that these reports likely under-estimate the true
values by 10-100 times. Of those who reported cardiovascular AEs, 81% did
so after the first dose. Similarly, 79% of those suffering neurological AEs did
so after receiving the first dose, and 80% of the people who reported suffering
an immunological AE did so after receiving the first dose as well.
Relative to the total number of reports, most of the trajectories of the AEs
remain stable relative to the total number of IDs reported (Figure 2.2), with369

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the exception of the immunological AE trajectory, which continues to rise(relative to other AE categories). Interestingly, immunological AEs appear
to dominate the AE cases, and this warrants investigation from the scientificcommunity. Again, it is important to recall that we are very early on in theanalysis: only four months’ worth of data have been collected to date.
2.

Distribution of data: age association withvaccine-associated AEs

The distribution of all VAERS reports according to age group is symmetric,
unimodal and bell-shaped across all age groups with no significant skewing
whereby 111=0.34. (Figure 3).
The highest absolute number of events reported are for people between 30 and
40 years of age (which account for 18% of all IDs), followed closely by people
between the ages of 40 and 60 years of age (accounting for 17% in each age
group, respectively). In general, the spread of data is normal and symmetricwith low absolute numbers of persons between the ages of 0 and 10 and 100
and 110.
2.1

Deaths, hospitalizations and ER visits

Higher absolute numbers of VAERS deaths and hospitalization reports
are associated with the elderly where the cut-off for the elderly is 65
years of age, and this is not surprising (Student’s T-Test: p<0.05; p<0.05,
respectively). However, emergency doctor visits are not associated with age(Student’s T-Test: p>0.05).

Figure 4.2 Distribution according to age in people who were hospitalized370

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Figure 4.3 Distribution according to age in people who visited an emergency doctor

Absolute numbers of VAERS-reported deaths grouped according to age groupreveal that 84% of those who were the subject of death reports were 70-90
years of age, as is shown in Figure 4.1. The death data are in fact left- skewed
toward the elderly in a statistically significant way whereby the absolute valueof I is 1.15 (abs(I)=1.15). The hospitalization spread is uniform over the age
range, with 50% of reports made by people between the ages of 20 and 70. 43%
of hospitalization reports were made by people between the ages of 70 and 90.
Emergency doctor visit reports are even more uniform across middle-aged age
groups, with more than half of the reports (61%) made by those aged 20 to 60
years. Neither are the distributions for the hospitalizations nor the ER visitsskewed by age in a statistically significant way (abs(I)=0.59 and abs(I)=0.27,
respectively).
2.2

Cardiovascular, neurological and immunological events

A substantial proportion of people reported having cardiovascular, neurologicaland or immunological events at 18%, 11% and 37%, respectively, of the total
number of reports. In spite of the fact that people between the ages of 30
and 40 years comprise the largest subset of reports overall in the context ofage grouping by decade, the highest frequency of cardiovascular reports weremade by those between the ages of 20 and 30.
The highest frequencies of events occur in young and middle-aged people inall three categories, and this might be because they are the most vaccinated inabsolute number. Neurological events were reported at the highest frequency
in people between the ages of 40 and 50 years old. All histograms are
unimodal and bell-shaped, with cardiovascular data appearing more uniform
and neurological and immunological data being more symmetric. None of371

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Figure 5.1 Distribution by VAERS ID according to age in people whoreported cardiovascular adverse event

Figure 5.2 Distribution by VAERS ID according to age in people who reported
neurological adverse events

the cardiovascular, neurological or immunological data are skewed toward a
specific age group in a statistically significant way (abs(I)=0.34, abs(I)= 0.36,
abs(I)=0.40, respectively).
2.3

Anaphylactic reactions

Anaphylactic reactions are reported in the VAERS database at a rate of 1%.
Anaphylaxis was reported in people primarily between 30 and 60 years of age,yet distribution of the data is symmetric, unimodal and bell-shaped over the age

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Figure 5.3 Distribution by VAERS ID according to age in people who reported
immunological adverse events

Figure 6. Distribution according to age in people who reported
anaphylactic reactions

range, as shown in Figure 6. This particular AE is interesting to examine from acausation point of view since most reactions of this nature are known to be causedby specific triggers. It has been reported that one such trigger, poly-ethylene
glycol (PEG), is an ingredient in the Moderna and Pfizer-BioNTech products.
[15] It is also documented that polysorbate is an ingredient in the Janssen
COVID-19 Vaccine PF product, and people are advised against using it if aknown allergy exists for polysorbate. In many cases, they are unaware of thepotential for an acute allergic response. It becomes clearer from time-series plots
and heatmaps that causation is not only likely but probable.

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Anaphylactic events are reported with highest frequency in people between
the ages of 40 and 50. The distribution of data is not skewed toward a specificage group in a statistically significant way (abs(I)=0.29). Of those who reported
an anaphylactic reaction, 76% did so after receiving the first dose.
2.4

Spontaneous abortions

Spontaneous abortions are not technically included as deaths as part of the
VAERS data, but miscarriages involve foetal death. Since the number of thesereports is increasing on average by six per week, it is included in this analysis
as a stand-alone AE and classified as a severe adverse event. Spontaneousabortions were reported in females between the ages of 20 and 45 and weremore frequent in women in their early 30s. This is likely due to more womenin their early 30s being pregnant more frequently.

Figure 7. Distribution by age in individuals who reported spontaneous abortions

The distribution of data is not skewed toward a specific age in a statisticallysignificant way (abs(I)=0.1). Of the women who reported having a spontaneousabortion, 65% did so after receiving the first dose. In the following section,
the likelihood of causation is investigated since it is absolutely necessary to
elucidate the conditions that induced miscarriage in these women, since plans
for large-scale roll-out of these products into pregnant women are looming or
currently active.
3.

Evidence to support causation

A causal effect means that a change in one variable leads to change in anothervariable. In the context of all the AEs, 70% of all people had onset of symptoms

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Table 3. Percentages of individuals reporting AEs following 24- and 48-hour periods

within 48 hours following first or second doses. Table 3 shows the percentagesof those succumbing to particular AEs following a 24-hour or 48-hour period.
Figures 8.1-3 shows the number of days following injection as a percentageof the reported AEs with regard to deaths, hospitalizations and emergency
doctor visits. The percentages of reported deaths, hospitalizations andemergency doctor visits are highest in the first two days post-injection.

Figure 8.1 Time series plot — Percentage of reported deaths by time elapsed between theinjection date and the reported adverse event

If deaths, for example, following COVID-19 injections are not causally
linked, then the reported percentages of deaths should be equally distributed
across days following injection: there should not be an excess of reports
on days 0, 1 and 2, yet there are. Chi-square tests confirm association foreach AE group with p-values less than 0.001 in each case. If risk is notaccentuated by some immediate factor temporally, then that risk shouldnecessarily plateau or diminish each day. This logic applies to each of thegrouped AEs and each follows the same pattern: the percentages of Day
0 and 1 (time periods representing 0-24 hours and 24-48 hours) are much

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higher than the percentages of other time periods post-injection.
This same reasoning applies to the grouped AEs representing cardiovascular,

Figure 9.1 Time series plot — Percentage of reported cardiovascular AEs by time elapsed
between injection date and adverse event

Figure 9.2 Time series plot — Percentage of reported neurological AEs by time elapsed
between injection date and adverse event

neurological and immunological events as shown in Figures 9.1-3. The
percentages of cardiovascular, neurological and immunological events arehighest in the first two days post-injection. Again, if causation was absent,
there should not be an excess of reports on days 0, 1 and 2. Chi-square tests
confirm association for each AE group with p-values less than 0.001 in eachcase. Table 4 shows the percentages of people succumbing to particular AEs

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Figure 9.3 Time series plot — Percentage of reported immunological AEs by time elapsed
between injection date and adverse event

following a 24-hour or 48-hour period.
There is a higher percentage (7%) of people who reported immunologicalevents on the seventh day following injection, as shown in Figure 9.3. From
an immunological point of view, this could be worth investigating in further
studies.
Figures 10.1 and 10.2 show the same trend toward the highest percentagesof anaphylactic reactions and spontaneous abortions occurring in the firsttwo days post-injection. A staggering 87% of all anaphylactic reactions werereported within 48 hours and 76% were reported within 24 hours. This is not
Table 4. Percentages of individuals experiencing AEs following 24- and 48-hour periods

surprising, considering the nature of this stand-alone AE. One would expect
an anaphylactic reaction to occur quite immediately. More than half (61%) of
all spontaneous abortions were reported within 48 hours of injection, and 42%
within 24 hours.

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These descriptive statistics give merit to association and time ordering
post-injection in the contexts of these categorized AEs. In order to rule out
spuriousness, there was examination of the potential contribution of additionalvariables, including pre-existing conditions and medications, that could have

Figure 10.1 Time series plot — Percentage of reported anaphylaxis with respect to time
elapsed between date of injection and AE

Figure 10.2 Time series plot — Percentage of reported spontaneous abortions by time
elapsed between date of injection and AE

contributed to death. Of the medications, the most frequently reported occurredin ~6% of those people , and on the facet of prior conditions which may have
led to death, only 8.5% of them had some heart-related incident reported intheir prior history. This was the highest percentage of conditions reported in the378

Too Many Dead
medical histories. It should be acknowledged that the VAERS-reported medical
history is bound to be incomplete, and therefore it is possible that the AEs inquestion could be due to conditions not reported in VAERS data. Based on thedata available, the three conditions of causation are satisfied, in general, but I
leave it up to the reader to extrapolate beyond the data.

Figure 11. Time series plot — Increase in absolute number of COVID-19 confirmed
cases from VAERS data

4.

Confirmed COVID-19 cases post-vaccination

A total of 1267 COVID-19 cases have been reported to date with the
Pfizer-BioNTech product representing a 3% rate and the Moderna productrepresenting a 0.5% rate. Since the Janssen product first appeared in the
VAERS system so recently, a low 0.007% rate is not surprising. The latter,
included in Table 2, is data on the change in COVID-19 confirmed cases,
which relative to the fully-vaccinated population is decreasing, but increasing
absolutely. Figure 11 illustrates this increase over time, which appears to be a
linear trajectory.
Distribution of COVID-19 cases in vaccinated people across age groups isuniform across the age groups between 30 and 90 years, as shown in Figure
12. No skewing was found relating a specific age group to COVID-19 casesthat was statistically significant. The skewness should be compared versus thevaccinated and reporting population and not within the data subset itself, but
this is for a future study.
When the COVID-19 data are examined by manufacturer, it stands
out that 81% of all confirmed COVID-19 cases are associated with the
Pfizer-BioNTech product. Without knowing the distribution proportions of379

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Figure 12. Distribution of COVID-19 confirmed cases from VAERS data by age

Figure 13. Distribution of COVID-19 cases according to vaccine manufacturer

the manufacturers in the population base, it is not possible to make claims
about this sample with regard to any potential higher probability of getting
COVID-19 in the context of the Pfizer-BioNTech product. If these data canbe acquired, this question can be answered. This is reserved for a future study.
Figure 13 shows the distribution of confirmed COVID-19 cases in VAERS
reports across vaccine manufacturer.

4.

Discussion

Safety and efficacy are the two requirements of any true vaccine. Based on
this study, the risk of suffering an SAE following injection is minimal, withan average of 200 people succumbing to an SAE per million. By comparison,
1,500 people in every million die from to the virus. Of the SAEs in the data380

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reported so far, while taking the reported numbers at face value, the mostundesirable reported is death. According to current VAERS data, 34 people
per million will succumb to death. The rates are slightly higher at 120, 80 and
250 per million pertaining to specific AEs involving cardiovascular, neurological or immunological events. The risk overall, according to analysis of
this data set, appears to be quite low. However, again, these data are very
early and, in the context of a rushed, non-FDA-approved, continuingexperimental roll-out, conclusions about long-term outcomes cannot bemade yet. The VAERS data are very dynamic and new patterns may emerge
at any time, depending on new reports.
The infection fatality rate (IFR), which is the number who died fromCOVID-19 among all those infected (both symptomatic and asymptomatic)
is estimated to be 0.15% or 1500 people per million.[12,13] Thus, if comparedto the death rate reported in the VAERS database in the context of the
COVID-19 injections, which is 0.0034% or 34 people per million, thechance of dying from SARS-CoV-2 is greater than from the injections,based on data collected from the past four months. It is vital to remember
here that the actual number of adverse events ongoing are likely being
under-reported, and there are likely to be thousands more backlogged becauseof under-recording. If the estimated death rate is two orders of magnitude
greater in reality, which it very well could be, this puts the death rate closer
to 3,400 per million, which is higher than the IFR estimate. Despite the factthat 20.5% of the US population is fully vaccinated, the death count is stillrising at a constant rate according to Our World in Data statistics. If one
looks to Israel, the country with the most fully vaccinated people at 57.26%,
it is clear to see that the death count remains on a steady upward trajectory.
[14,16]

In a recent CDC report titled ‘Local Reactions, Systemic Reactions,
Adverse Events, and Serious Adverse Events: Pfizer-BioNTech COVID-19
Vaccine’,[18,19] only the severity of the most frequently reported AEs in the
VAERS database are reported in tabular form and not the SAEs themselves.
They report that occurrence of severe adverse events involving systemorgan classes and specific preferred terms were balanced between vaccine
and placebo groups and presented at a mere 0.5%. Although SAEs (grade>3, defined as interfering with daily activity) occurred more commonly invaccine recipients than in placebo recipients, their claim is that no specificsafety concerns were identified with regard to SAEs.[18,19]
Effective antiviral responses against the SARS-CoV-2 virus in the formof both cellular and humoral immune responses have been reported inpeer-reviewed studies.[20,21,22,23,24, 25] Because of the combination of a381

Australian Medical Professionals Society
low IFR indicating effective and robust immune responses, it remains
unclear why multiple experimental mRNA vaccines have been fast-tracked
through conventional testing protocols and are also being fast-tracked
through production and administration into the public. With repurposed
drugs like Chloroquine and Ivermectin showing extremely positive resultsin patients,[26,27,28,29,30,31,32, 33,34,35,36] it is also unclear why these drugs
are not being more extensively promoted as effective tools in the fightagainst this virus. One looming possibility is that EUA is not permissible if
FDA-recognized, effective treatments exist.

5.

Conclusion

This work summarizes VAERS data to date (April 9th, 2021) and serves as
information for the public and a reminder of the relevance of any adverse
events, including deaths, that likely occurred as a direct result of vaccine
administration. Based on analysis of the VAERS numbers, it may appear thatAEs are not currently imposing a significant burden on the fully vaccinatedpopulation; however, the weekly releases of VAERS data do not include all of
the reports made to date — they are all the reports the CDC has processedto date — and the backlog is likely to be staggering. Thus, as a result of boththe problems of under-reporting and the lag in report processing, this analysis
reveals a strong signal from the VAERS data that the risk of suffering an SAE
following injection is significant and that the overall risk signal is high.
Analysis suggests that the vaccines are likely the cause of reported deaths,
spontaneous abortions and anaphylactic reactions in addition to cardiovascular,
neurological and immunological AEs. Based on the precautionary principle,
since there is currently no precedent for predictability with regard to long-term
effects from mRNA injections, extreme care should be taken when making
a decision to participate in this experiment. mRNA platforms are new tohumans in terms of mass injection programs in the context of viruses. Thereis currently no way to predict potential detrimental outcomes with regard to
SAE occurrences in the long-term. Also, concerning short-term analysis, thesedata are limited, based on reporting that likely substantially underestimates
actual events.
It cannot be emphasized enough that these are very early data and that, basedon the dynamic nature of the data, these conclusions may not be the samein a month’s time. The efficacy of these products needs to be assessed byimmunological assays, and long-term studies are required, while safety needs
to be evaluated by rigorous clinical, laboratory and imaging assessments ofsevere reported adverse events. Autopsies should be done in cases of deaths
temporally associated with COVID-19 injectables.

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Too Many Dead
Overall, it is vital not to be hasty, and to make a proper risk assessment by
being informed prior to making a decision as to whether or not to participate
in experimental trials.
Treatments against SARS-CoV-2 and subsequent COVID-19 symptom
formation are meant to minimize harm from the latter. It appears from thisanalysis that these treatments are, in fact, doing more harm than good when
considering the points made herein, especially in the context of specific risk
groups which are the very people we are claiming to want to protect.
Future work may include an investigation into potential correlations between
SAE occurrences and frequencies and vaccine lot number, and of course
updates should be made in accordance with the VAERS weekly update. In
addition, investigation and focus on immunological issues certainly must bea priority in future studies with regard to adverse events reports related to
COVID-19 biologicals.
Funding and Conflict of Interest information
This article received no specific funding.
No conflicts of interest are declared.

6.

[1]
[2]

[3]

[4]

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Jaafar R, Aherfi S, Wurtz N, Grimaldier C, Van Hoang T, Colson P, Raoult
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Jessica Rose, PhD, MSc, BSc, is a Canadian researcher
with a bachelor’s degree in applied mathematics anda Master’s degree in immunology from Memorial
University of Newfoundland. She also holds a PhD in
Computational Biology from Bar Ilan University andtwo post-doctoral degrees, one in molecular biology
from the Hebrew University of Jerusalem, and thesecond in biochemistry from the Technion Instituteof Technology. Her more recent research efforts are
aimed at descriptive analyses of the Vaccine Adverse
Event Reporting System (VAERS) data in efforts tomake these data accessible to the public.

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Excess death anomaly
in Finland 2021
by Tore Aarhus Gulbrandsen, Kasper Rönning

Abstract
Total excess mortality statistics are a reliable metric for detecting changes
in death rates at the population level. We present a model based on official
statistics for death counts in Finland from the years 2008-2019 to evaluate
the mortality in 2020 and 2021. Observed mortality for 2020 and early 2021
was at the expected level based on our model. However, our calculations
show a substantial increase in mortality starting in early summer 2021 andcontinuing until present, totalling over 3000 excess deaths from May 2021
to February 2022, over 5% of yearly total deaths. We verify our finding ofexcess mortality by multiple independent metrics. Shorter periods of excess
mortality are not uncommon, but the extended duration of the present
observation represents a clear anomaly. In the period 1990-2019 mortality
has consistently been at its lowest during the summer months and peaked
during winter. During the weeks 25-42 of 2021 a total of 1752 excess deaths
were observed, whereas only 231 COVID deaths were recorded during thesame period. It is the duty of health and government officials to recognize
this anomaly and initiate investigations to understand its cause.

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Introduction
Population mortality statistics constitute a reliable metric for detecting significant
events causing changes in death rate at the population level. The number of deaths
is usually reported in weekly, monthly or yearly intervals, without attribution of
causes of death. Excess mortality or mortality deficit means that, based on an
inference from previous mortality, the currently observed mortality is higher orlower than expected.
The Finnish Institute for Health and Welfare (THL) in press releases andinterviews has claimed that no notable changes have occurred in the mortality
rates in Finland during the pandemic. The official Finnish institute for statistics,
Statistics Finland, asserted in an interview in the major Finnish newspaper
Helsingin Sanomat that the number of recorded deaths in 2021 was not
exceptional, comparing it to the year 2020 and noting that there were a mere
500 deaths more.
The statements of THL and Statistics Finland are in conflict with both
EuroMOMO and the most recent Population Prognosis published by Statistics
Finland itself. In this paper we present a model for the expected number ofdeaths from 2020, relying on official statistics on deaths from the preceding 12
years. We demonstrate that the first year of the COVID pandemic 2020 showscompletely normal mortality, whereas the year 2021 displays an extended periodof excess mortality in the second half of the year. We also verify our model’scorrectness by comparing it to several independent data sources.

Materials and methods
Mathematical model

We present a mathematical function to predict the number of deaths per week,with no stratification by age or gender, and then use this function to compare
pandemic death rates to those of previous years. Mortality is highly seasonal,
peaking during the flu season in winter and bottoming out in summer. In
Finland harsh heat waves are rare, but some years have seen excess mortality
in summer from extended hot periods. On the other hand, some winters havebrought quite large peaks of excess mortality from heavy flu seasons. Such
winters are usually followed by periods of mortality deficit once the flu has
subsided.
To model these patterns we apply a function consisting of a linear component
for the long term trend in mortality and an oscillating cosine component
to accommodate the seasonality (Figure 1). Similar mathematical functions
underlie the models of EuroMOMO, although its focus is on short term
excess mortality caused by epidemics.

390

Figure 2. Weekly excess mortality in red, mortality deficit in blue.

Figure 1. Weekly deaths are modelled by a linear trendline (green) modulated by a seasonal sine curve (orange). The blue line represents a 7 week
moving average of the actual recorded deaths.

Too Many Dead

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The long-term trend has been highly linear during the entire reporting periodof Statistics Finland, up until the summer of 2021. In the first half of theperiod, 1990-2006, it was decreasing mildly, and then gently increasing during
2007-2020. We applied our model to the period after 2008, where the trend
has been consistently gently increasing.
The seasonal cosine component peaks in winter and bottoms out in summer.
The amplitude of the cosine component is proportional to the total mortality,
so that the seasonal variability would increase when total deaths increase. The
mathematical function of our mortality model is thus:

Data sources
The source for total excess mortality was the weekly publication ‘Kuolleet
viikoittain pikaennakko’ by Statistics Finland,[1] which contains the weekly
number of deaths without cause of death or sex and gender information,
available since 1990. In addition to this we used the monthly number of deaths
from Statistics Finland[2] and EuroMOMO’s Z-values[3] which represent
excess mortality. The source for deaths ascribed to COVID-19 was THL’s
report ‘Tartuntatautirekisterin COVID-19-tapaukset’,[4] containing weekly
COVID deaths.
Source code
The source code for the model and graphics was made with Python
programming language, using SciPy and Matplotlib libraries. All source code392

Too Many Dead
including PDF-graphics is available online at:https ://github. com/k-ronning/acm_analyzer.

Results and discussion

Calculating the difference between weekly estimated deaths from our model andactual recorded numbers, we arrive at excess mortality numbers per week, illustrated
in Figure 2. Nearly every year the flu season generates mortality numbers thatexceed the normal range, followed usually by some months of mortality deficit.
Yearly cumulative excess mortality
The annual flu season varies in timing and may thus greatly skew the yearlymortality numbers depending on whether flu mortality peaks in December orJanuary. To accommodate this variability, yearly cumulative deaths can be analysedby shifting the calculation starting point to after the flu season. We chose week
16 as the starting point of our cumulative values, as this is used by EuroMOMO
as the week when the flu season is generally considered to have ended[5]. Figure
3 shows the shifted cumulative excess mortality curves for 2008-2021. Mortality
patterns from spring, summer and autumn are seen on the left half of the graph,
while the right side of the graph is dominated by the flu season and the highvariability it brings. 2020 and 2021 are drawn with bold lines, and for these years
an additional dashed line shows the excess mortality minus COVID deaths, bywhich one can evaluate excess deaths unrelated to COVID.

Figure 3. Yearly cumulative excess mortality 1990-2021.
Cumulation starts from week 16.

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It is evident from the figure that 2020 does not stand out from previous years,
regardless of whether we consider the total excess or the excess minus COVID
deaths. In contrast, the year 2021 shows a dramatic departure from the otheryears; it starts out normally but then there is a steady increase from week 25
and it continues until the present (April 2022), totalling 35 weeks. Particularly
noteworthy is the period between week 25 and week 42, during which weestimate 1752 excess deaths, while the same period saw only 231 COVID
deaths.
Cumulative excess mortality of the entire time period
Figure 4 shows the cumulated excess mortality of the entire period used forthe model. One can deduce from the figure that the linear model component
represents the mortality reasonably well, considering that no extended periodof excess or deficit mortality is seen, as is expected. If the linear component
was a poor fit, the cumulative excess would deviate upwards or downwards forlonger periods. While the above is true for the first years of the graph, suchdeviation does indeed happen in the summer of 2021. What could explain
this abrupt change in the mortality pattern in summer 2021, given thatthe mortality has followed a stable downward trend since 2008? When thepreviously stable trend suddenly changes upwards, this represents unexpected
increased mortality, that is, excess mortality. Our model estimates that fromMay 2021 until February 2022 there has been a total of over 3000 deaths morethan expected in Finland, and only a part of these are COVID deaths.

Figure 4. Excess mortality for the entire period 1 Jan 2008 - 20 Feb 2022.

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Model verification
All models are based on assumptions, which may be wrong. To verify ourmodel for correctness, we compared the outcome to three different data sources:
EuroMOMO, Statistics Finland’s monthly reported deaths and Statistics Finland’s
population forecast.
EuroMOMOEuroMOMO is a mortality surveillance tool maintained by the Danish Statens
Serum Institut. The underlying model for the excess death calculations is not in
the public domain, but its Z-values refer to the standard normal distribution. Z =1 refers to one standard deviation difference from the mean, and the mean in thiscase is the EuroMOMO expected number of deaths for the week, as given by theunderlying model. EuroMOMO’s Z-values serve as a process control chart for
mortality, with an upper control limit of 4Z. This control limit is often exceeded
during harsh influenza seasons, indicating a sudden excess mortality as is often thecase during influenza waves. However, in the domain of process control charts also
extended periods of less pronounced deviations must be considered as possible
anomalous events. Nelson’s rules are often referred to when interpreting anomalies
in control charts, and the EuroMoMo Z-values for 2021 trigger at least three ofthese rules:
A)
B)
C)

Nine or more consecutive observations are on the same side of themean line (Z = 0).
Four (or five) of five consecutive observations are farther than 1
standard deviation (= 1Z) in the same direction from the from themean line.
Two (or three) of three consecutive observations are farther than
2 standard deviations (= 2Z) in the same direction from the meanline.

EuroMOMO’s weekly Z-values since 2017 from Finland are shown in Figure 5.
During the second half of 2021 the above-mentioned rules are violated numerous
times:•••

From Week 24, 2021 there are 20 consecutive weeks where Z > 0, and
33 of the subsequent 35 weeks Z > 0, so rule A is violated dozens of times.
From Week 35, 2021 until Week 7, 2022, there are 9 occasions wherefour or more out of five consecutive weeks have Z > 1, so rule B isviolated 9 times.
From Week 27, 2021 until week 6, 2022 there are four occasions where
two or more out of three consecutive weeks have Z > 2, so rule C isviolated four times.

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Figure 5. EuroMOMO’s Z-values, where the Z-values in
the red area violates Nelson’s rules.

Together, these observations constitute a strong anomaly during the entire
second half of 2021, fully in line with the excess deaths from our model.
Further validating the agreement between our model and EuroMOMO’s
Z-values, figure 6 shows a strong correlation between the two, with a correlation
coefficient of r > 0.9. This demonstrates that the two models are well aligned
and describe the same phenomenon.

Figure 6. Correlation between EuroMOMO’s Z-values andthe excess deaths from our model.

Monthly distribution of deaths

As a second verification approach, we used monthly deaths statistics from
Statistics Finland to calculate the relative distribution of deaths among themonths of each year for the period 1990-2021. Figure 7 shows the mortality
patterns for 1990-2020 as a blue area, with both 80% and 100% of the range
per month indicated. This distribution is very repeatable from year to year,
depending mainly on seasonal factors such as winter influenza and summer

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Too Many Deadheat waves. The roughly 10% drop for February is due to the fewer number ofdays in this month.
This figure shows clearly that already from May, the mortality pattern for2021 was at the extremes of the range for 1990-2020, and that never during
1990-2020 did the months from July to November contain this high a
proportion of total deaths of the year. The value for December 2021 was
the third highest, with only 1993 and 2003 being higher, as a result of theinfluenza wave of these two years striking in December.

Figure 7. Monthly mortality 1990 – 2020. The second half of 2021 significantly exceeds therange of the previous 30 years. Covid explains only a small part of this pattern.

Distribution between first and second half of the year

In the methodology of control charts, another approach used is to compare
the proportion of events per segment of time. Figure 8 shows the proportionof all deaths per year that happened during the second half of the year plotted
as a function of the years. From 1990 to 2020, this proportion has meanderedbetween 47.6% and 50.2%, with a standard deviation of 0.66%pt. Also inthis comparison, 2021 deviates profoundly from the other years, with the

Figure 8. Proportion of deaths in second half of the year. 2021 clearly deviatesfrom the entire previous 30 years.

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proportion of deaths during the second half coming in at 51.7%, an astounding
4.4 standard deviations from the mean of 1990-2020. The probability that suchan event would occur by pure chance without a special cause is p - 0.00001, orin other words once every 100,000 years.
Population forecasts
Every 1-3 years, Statistics Finland publishes a Population Forecast, which
includes various metrics on the population, including expected deaths. The
projections from the most recent publications of 2012, 2015, 2018 and 2019are shown in Figure 9, and comparing to the actual number of deaths (blueline), one can see that these projections have been mostly slight underestimates.
Since our model is fitted to the actual deaths of 2008-2019, it is expected tofollow the actual number of deaths closely to the end of 2019. Of particularinterest in this graph, however, is the forecast published on September 30th
2021, which estimates the deaths for 2021 at 1679 higher than the 2019 forecast
predicted for the same year. In spite of being from the end of September andadjusting the 2019 clearly upwards, it still falls 1356 deaths short of the total
number of deaths for the year, which came in only three months later. The total
deaths in 2021 was 3035 more than the Statistics Finland 2019 prediction – anextreme deviation from a prediction only two years old.

Figure 9. Yearly observed deaths in blue, our model’s prediction in green,
remaining colors are population forecasts.

Possible causes for the excess mortality
Healthcare backlog
The policies of the COVID period led to increases in healthcare backlogs,
which could in part explain the excess mortality, but the effects of such backlogs
would hardly lead to sudden changes in deaths, but would rather cause a398

Too Many Dead
gradual increase. In other words, why would missing healthcare follow-up leadto no increased deaths from 2020 until May 2021 only to cause a sudden
and persistent increase from Week 25,2021? The effect of healthcare backlogs
must be must more gradual to be a feasible explanation.
COVID
If many people died of SARS-CoV2 without a diagnosed infection, this could
explain excess deaths. The COVID deaths of 2021 were, however, at theirlowest in summer – totalling a mere 31 deaths in June and July – when thenumber of excess deaths during the same period was +367. Correspondingly,
the total COVID deaths in Weeks 25 through 42 were 231, but excess deaths
during the same period were at +1752. If underdiagnosing of COVID was the
explanation, COVID deaths and excess deaths should correlate. Furthermore,
the tentative numbers of COVID deaths reported by the Finnish Institute for
Health and Welfare (THL) from 2020 – 598 deaths – were adjusted down
to 558 when the final cause of death statistics from Statistics Finland arrived.
Thus, it is expected that THL’s current estimate of 1136 COVID deaths for2021 will be adjusted down. In other words, COVID deaths could at maximum
explain one third of the excess deaths from our model, and mainly only thedeaths from November and December. Also, considering that the roughly
500 COVID deaths in 2020 led to no observable excess mortality, this leaves
nearly 2500 unexplained deaths for 2021.

Figure 10. Deaths from covid per week 2021 (Month numbers on x-axis).

Ageing population
The older age cohorts in Finland have steadily been increasing during recent
times, so it is expected that the total number of deaths will increase accordingly.
This explains why the number of deaths per year in Finland has steadily
increased over the last 10 years or more. If our model had underestimated thisgradual increase, it could explain the observed excess deaths.
However, ageing is a slow process, the effects of which are also slow as aconsequence. Our model takes into account such gradual effects, happening
over decades. Our data show that the mortality patterns of 2021 are normal

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until May, when the numbers suddenly rise and remain high for the entire
second half of the year, and remained high when this paper was first published
in Finnish (April, 2022).

Conclusions

In the present work, we have shown with multiple different approaches a highly
anomalous pattern of excess deaths in Finland starting around June of 2021,
currently totalling over 3,000 excess lives lost, and only partially explainedby COVID infections. In particular, during the Weeks 24 through 42, 2021
there were 1,752 excess deaths, but only 231 COVID deaths, leaving 1521
non-COVID excess deaths.
While age stratification was not in the scope of this study, EuroMOMO
indicates that the majority of the observed excess deaths appears to occur inthe age cohorts above 80 years. Neither underdiagnosing of COVID infection,
healthcare backlog nor ageing population explains this sudden and persistentexcess mortality.
The excess mortality calculated from our model was cross-checked by threedifferent methods: 1) Comparison with EuroMOMO; 2) Deviations inmonthly distribution of each year’s deaths; 3) Deviation from 2019 population
forecast. All three approaches verified the observed anomaly. Given thestrength of the statistical significance as established by EuroMoMo data, theanomaly cannot have arisen from random variation.
Finnish health authorities have thus far (April 2022) either played down or
altogether ignored these alarming data. In the light of the irrefutable evidence
presented in this paper, it is high time health authorities stop understating
the severity of the situation and carry out a thorough and independent
investigation of the cause for the dramatically elevated mortality.

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References
[1]

[2]
[3]

[4]

[5]

Tilastokeskus: Kuolleet viikoittain sukupuolen, ian ja maakunnan
mukaan (Pikaennakko). Link: https://pxdata.stat.fi/PXWeb/pxweb/£i/
Kokeelliset_tilastot/Kokeellise t_tilastot vamuu_koke/koeti_vamuu_
pxt_12ng.px/.
Tilastokeskus: Vaestonmuutokset kuukausittain. Link: https://pxweb2.
stat.fi/PXWeb/pxweb/fi/StatFin/StatFin
vamuu/statfin_vamuu_pxt_
llll.px.
EuroMoMo Graphs and Maps. Link: https://www. eurom omo.eu/
graphs-and-maps/.
THL: COVID-19 cases in the infectious diseases registry. Link:
https://sampo.thl.£i/pivot/prod/en/epirapo/covidl9case/fact_epirapo_
covidl9case?row=dateweek20200101-509030&column=measure-444833.445356.492118.&fo=l.
Bernadette Gergonne. EuroMoMo Work Package 7 Report - A European
algorithm for a common monitoring of mortality across Europe. Link:
https: //www. euromomo .eu/uploads/pdf/wp7_report.pdf. 2011.

Tore A. Gulbrandsen is a data scientist with over
10 years of experience in process and productdevelopment, and in supervising statistical analyses
for regulatory submission. Having a passion for data
and a slight suspicion for medical authorities basedon personal experiences of failed treatment, he has
followed key statistics with a critical eye from the startof the pandemic. One of his specialties is recognizing
anomalous data patterns, which the last three years
have seen in abundance; another is simulations andmodelling. His focus has been on evaluating, modellingand predicting mortality statistics in the Nordics, but
recently also other countries.

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Too Many Dead

Part 6
From policy failure to iatrogenic crisis

Our authors have pointed consistently to the need for investigation. There arethose who have argued that we are in actual policy-induced health crisis, andthat it shows signs of continuing. ‘Moreover, the injections appeared to have acumulative effect on the immune system with the first booster campaign earlyin January 2022 having a devastating effect in Australia.’ Thus, Dr Wilson
Sy. And again: ‘For the whole Australian population it has been shown that
COVID-19 injections increase, not decrease as claimed, severe illness anddeath.’
Australians might be angered by being denied the basic level of objective
scientific information. The government’s reports demonstrate they knew orought to have known using due skill, care, and diligence that these injections
have not been proved safe or effective.
A preprint review of Australia’s all-cause mortality data by Dr Wilson Sy,
using the Bradford-Hill criteria, demonstrates a causal link with the COVID
vaccination roll-out.
Australian official mortality data show no clear evidence of significant
excess deaths in 2020, implying from an older WHO definition that
there was no COVID-19 pandemic. A seasonality analysis suggests

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Australian Medical Professionals Societythat COVID-19 deaths in 2020 were likely misclassifications ofinfluenza and pneumonia deaths. Australian excess mortality became
significant only since 2021 when the level was high enough to justify
calling a pandemic. Significant excess mortality was strongly correlated(+74%) with COVID-19 mass injections five months earlier. Strengthof correlation, consistency, specificity, temporality, and dose-response
relationship are foremost Bradford Hill criteria which are satisfied
by the data to suggest the iatrogenesis of the Australian pandemic,
where excess deaths were largely caused by COVID-19 injections.
Therefore, a strong case has been presented for the iatrogenic originsof the Australian COVID-19 pandemic and, therefore, the associatedmortality risk-benefit ratio for COVID injections is very high.
– Dr Wilson Sy.
If these findings are accurate then there must be accountability and justice forthis deceit that has gone beyond incompetence into the realm of malfeasance.
Bureaucrats, politicians, directors of departments who oversaw the mandatingof the largest vaccination trial ever, all played their part in undermining humanrights and the Constitution. Offers to allow independent medical and scientificprofessionals to review and debate policy and data have been denied at every
step of the process. Once a normal part of the participatory democratic modelof governance, objectivity and transparency were squashed using censorship,fear and enforcement techniques.

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Australian COVID-19 pandemic:
A Bradford Hill analysis
of iatrogenic excess mortality
by Wilson Sy1

1

Abstract

Australian official mortality data show no clear evidence of significant

excess deaths in 2020, implying from an older WHO definition that there was
no COVID-19 pandemic. A seasonality analysis suggests that COVID-19
deaths in 2020 were likely misclassifications of influenza and pneumonia
deaths. Australian excess mortality became significant only since 2021 when
the level was high enough to justify calling a pandemic. Significant excess
mortality was strongly correlated (+74%) with COVID-19 mass injections fivemonths earlier. Strength of correlation, consistency, specificity, temporality,and dose-response relationship are foremost Bradford Hill criteria which
are satisfied by the data to suggest the iatrogenesis of the Australian
pandemic, where excess deaths were largely caused by COVID-19 injections.
Consequently, a strong case has been presented for the iatrogenic origins of
the Australian COVID-19 pandemic and therefore, the associated mortality
risk-benefit ratio for COVID injections is very high.
1
Revised 27 March 2023, PhD, Director, Biotechnology Unit, Investment Analytics Research.
Lex Stewart and Jeremy Beck are thanked for useful comments. The author has no financial or politicalconflicts of interest and is not funded by external sources. This paper has appeared in the Journal of Clinicaland Experimental Immunology. Sy, W. Australian COVID-19 pandemic: A Bradford Hill analysis of
iatrogenic excess mortality, J Clin Exp Immunol, 8(2), 542-556.

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2Introduction

On 11 March 2020, the World Health Organization (WHO) declared[1] the
COVID-19 pandemic based on 4,291 deaths, by 118,000 cases in 114 countries,
with an average of about 1,000 cases in each country. Based on this very small
sample, the WHO assumed that the COVID-19 disease is highly infectious
and has an infection fatality rate (IFR) of at least 0.4 percent. Therefore, the
COVID-19 pandemic was declared based on expectation and not on fact, as
the WHO had previously defined for an influenza pandemic:[2]An influenza pandemic occurs when a new influenza virus appears
against which the human population has no immunity, resulting inseveral, simultaneous epidemics worldwide with enormous numbers ofdeaths and illness.
Emphasis added. A pandemic should be justifiably declared only if there are‘enormous numbers of deaths’, for otherwise seasonal influenza or even thecommon cold of the Rhinovirus could be declared as pandemics, that is, justbased on numbers of cases of infection. By now, it is abundantly clear that the
number of cases defined by the PCR tests may be grossly inflated (see section 2).
By assuming ‘cases’ would lead to ‘enormous deaths’, the WHO declareda pandemic based on supposition, not on scientific fact. The presumption ofsound science by governments has allowed them to justify harsh public healthmeasures which may have been counter-productive, ultimately causing moredeaths. Based on objective data, this paper assesses whether there were enoughexcess deaths to warrant declaring a pandemic in Australia. By investigating
those excess deaths, the probable cause of the Australian pandemic is deducedin this study.
In section 2, it is discussed that assessment of the pandemic based solely andquantitatively on COVID infection cases and deaths is questionable, because
cases of COVID infection and deaths attributed to the SARS-CoV-2 virus havenot been adequately proved. That is, the pandemic cannot be accurately assessedfrom COVID-19 data which are scientifically flawed (see discussion below).
This paper assesses the COVID-19 pandemic in Australia based on all-cause
mortality data, consistent with the earlier WHO definition of pandemics.
Since accurate and reliable data are critically important as inputs to the data
analysis to draw valid conclusions, data methodology is discussed in section 3.
In 2020, when many Victorian deaths were attributed to COVID-19, the effecton total mortality was insufficient to declare a pandemic in Australia. Details
and possible explanations are discussed in section 4, to justify describing 2020as the ‘pre-pandemic’ phase.

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Australian excess deaths began to rise to a statistically significant level in 2021
to warrant the appellation of a ‘pandemic.’ Early increases in excess deaths
accompanied the early rollout of mass COVID-19 injections. The injections
were called ‘vaccines,’ but they do not prevent infections, nor were they tested
to inoculate against infections, as admitted recently by Pfizer to the European
Parliament.[3]This paper rejects calling the COVID-19 injections ‘vaccines’; they were nevertested to be such. The public has been misinformed and misled to accept
COVID-19 injections as ‘vaccines.’ When the injections clearly failed to reducetransmissions, the rhetoric of ‘vaccine’ benefit changed to reducing serious
illnesses and deaths. This claim is also proved false in this paper, where thepandemic phase defined by elevated excess deaths is shown to be correlatedwith mass COVID-19 injections in section 5.
In section 5, the strong correlation between doses of injections administered andincreased levels of excess deaths five months later suggest iatrogenic causality.
This possibility is further strengthened by aspects of consistency and specificity
in section 6 where the evidence of causality is seen by consistency across timeand geography. Also, specificity is evident from the fact that the ‘vaccinated’
are more likely to die than the ‘unvaccinated,’ who are simply defined as those
without any injections, rather than official definitions where the ‘unvaccinated’
may have had injections.
The main contributions of this paper, addressed in sections 5 and 6, are what we
consider the five foremost criteria of Bradford Hill[4] causality for an iatrogenic
pandemic. The remaining four aspects of Bradford Hill analysis are briefly
reviewed from existing literature in section 7 on coherence and plausibility andin section 8 on experiment and analogy.
Essentially, iatrogenesis of the pandemic is coherent with, and does not violate,existing knowledge of pathology and epidemiology; the biological mechanisms
are highly plausible, with some clinical experiments to validate them. In many
ways, the current pandemic is analogous to the previous swine flu pandemic
in 2009, except that the 2009 episode was not a pandemic, and it was without
mass vaccination.
Section 9 contains a summary of preceding sections, with a tabulated synopsis
of all nine Bradford Hill criteria discussed. The final section concludes thata strong case has been presented for the iatrogenic origins of the Australian
COVID-19 pandemic.

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2

COVID-19 data

This section explains why the Australian COVID-19 pandemic cannot be
accurately assessed from COVID-19 data, because COVID-19 cases andinfection were poorly defined. Therefore, COVID-19 data are scientificallyflawed, but nevertheless they drove and continue to drive erroneous healthpolicies.
A COVID infection has no definitive set of symptoms and was not detectedby the presence of the SARS-CoV-2 virus, but was defined by a positive
PCR test. However, a positive PCR test does not detect the presence of
the SARS-CoV-2 virus which is the definitive pathogen of the COVID-19
disease. The CDC has explicitly made clear the following disclaimer:[5]Since no quantified virus isolates of the 2019-nCoV were available for
CDC use at the time the test was developed and this study conducted,
assays designed for detection of the 2019-nCoV RNA were tested withcharacterized stocks of in vitro transcribed full-length RNA (N gene;
GenBank accession: MN908947.2) of known titer (RNA copies/μL)
spiked into a diluent consisting of a suspension of human A549 cells
and viral transport medium (VTM) to mimic clinical specimen.
Emphasis added. Consequently, COVID-19 cases may be cases of
respiratory infections caused by other RNA viruses, which also implies that
COVID cases and deaths may be wrongly attributed to the SARS-CoV-2
virus, wherever its controversial origin.
Deficiency of the PCR test has been acknowledged by the CDC in mid
2021 when it issued a Lab Alert[6] to plan a withdrawal of the test:
After December 31, 2021, CDC will withdraw the request to
the U.S. Food and Drug Administration (FDA) for Emergency
Use Authorization (EUA) of the CDC 2019-Novel
Coronavirus (2019-nCoV ) Real-Time RT-PCR Diagnostic
Panel, the assay first introduced in February 2020 for detectionof SARS-CoV-2 only.
CDC encourages laboratories to consider adoption ofa multiplexed method that can facilitate detection anddifferentiation of SARS-CoV-2 and influenza viruses.
Emphasis added. From 2022, instead of the PCR test which cannot
differentiate between SARS-CoV-2 and influenza viruses, the CDC hassuggested the use of a multiplexed method. A quadraplex method[7] was
not discovered until early 2021, when the researchers claimed to have408

Too Many Deadsimultaneously detected from clinical specimens two SARS-CoV-2 genes,
as well as influenza A and influenza B viruses:
To the authors’ knowledge, this is the first study to report a quadruplex
rRT-PCR assay for the detection of two SARS-CoV-2 genes, hIAV
and hIBV with perfect clinical performance.
Emphasis added. It is unclear whether the research has been independently
verified or whether commercial quantities of the quadraplex method for
detecting SARS-CoV-2 have been produced or widely used since 2022.
It is quite clear that COVID-19 data are scientifically flawed before 2022
everywhere and very likely since. Australian data continue to be flawed
because PCR tests are still being used. The inability to distinguish between
the detection of the SARS-CoV-2 and influenza viruses is a fundamental
scientific uncertainty, which renders COVID-19 data scientifically flawed.
Adding to this uncertainty about what is identified in COVID infections
and cases, there is also a substantial uncertainty about the titer (genetic
fragments per unit volume) needed to define presence of the infection.
Through a sufficient number of cycles of titer amplification, which is variable
and not scientifically determined, the PCR test can nearly always return apositive result. Consequently, whether someone has a COVID infection at
all is not clear from a PCR test.
For the first time in medical history, people who are perfectly healthy withno symptoms have been declared COVID cases, based solely on unreliable
positive PCR tests. A person could have minute amounts of dead influenza
viruses and be declared a COVID threat to public health.
On top of those fundamental uncertainties, there is a question of whether
a particular COVID death is a death with COVID or from COVID ina typical case of the deceased having other comorbidities. Subjective
judgement, distorted at times by financial incentives, creates uncertainties
which can be removed objectively by autopsies, but they have been rarely
performed.
Therefore, COVID cases and deaths cannot be used to characterize
the pandemic, because the division of excess deaths into COVID andnon-COVID causes appears arbitrary and inaccurate. Australian healthpolicy has been based on misinformation from flawed COVID-19 data
which are scientifically unsound.[9]This paper focuses on all-cause mortality and excess deaths rather than
COVID deaths as indicators of the severity of the Australian pandemic.

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3

Data methodology

Even as unreliable as the COVID raw data are, Australian official COVID-19
data seen by the public are not even the raw data which are collated by state healthauthorities. They control and publish selected data in weekly and monthly reports
without making available the raw data which are needed to independently verify
the official data. These reports from health authorities may be misleading because
of selection and classification biases, which have rendered invisible adverse eventsand deaths related to ‘vaccines.’
For example, official reports allowed the national broadcaster ABC to claim
falsely on prime-time television in July 2022 that the ‘unvaccinated’ are 16 to
37 times more likely to die than the doubly ‘vaccinated’.[8] This misinformation
was based on a key official data reporting flaw which came from classifying somedeaths as ‘unvaccinated’ even though they had had COVID-19 injections andoften multiple times.[9]This paper avoids the processed data of health authority reports to eliminate
their selection and classification biases. The main reliance is on data[10] fromthe national collector, the Australian Bureau of Statistics (ABS), which has the
fewest conflicts of interest, but its data and reports are not accepted uncriticallyeither, as will be illustrated below.
In scientific research the raw data and their sources should be publicly accessible
or available and the methods of data analysis should be clearly disclosed so that

Figure 1410

Too Many Deadthe conclusions of this or any other study can be reproduced precisely.
This study depends principally on the all-cause mortality data published by theABS, from January 2015 to September 2022, the latest month of full reporting
data. The raw data are shown in Figure 1, where the horizontal green line and thesloping red line have been added heuristically to suggest a ‘regime change.’
The horizontal green line (for guidance) suggests that 2020 appears to be
merely a continuation of the previous trend of relatively steady fluctuations
in all-cause mortality. On a definition of pandemic based on excess mortality,
there was no evidence of a pandemic in Australia in 2020, which could be called
the pre-pandemic phase, followed by the pandemic phase starting in 2021 (thesloping red line).
The above raw data are used to calculate excess mortality in this paper, insteadof simply accepting the official excess mortality data published by the ABS.
The ABS has changed its baseline definitions (in other words, moved the goal
posts) for calculating 2022 excess mortality in an inconsistent manner, without
providing adequate justification.
Normally, the baseline for calculating excess mortality is the average of theprevious five years, but the baseline for 2022 has been defined by the ABS as theaverage of four years, 2017-2019 and 2021, without adequate reasons :10
Throughout this report, counts of deaths are compared to an average numberof deaths for previous years. In this report, data for 2021 is compared toan average number of deaths recorded over the 5 years from 2015-2019
as was the case in previous publications. Data for 2022 is compared to abaseline comprising the years 2017-2019 and 2021. 2020 is not included in
the baseline for 2022 data because it included periods where numbers of deaths
were significantly lower than expected.
Emphasis added. Note that the arbitrary exclusion of 2020, a year where ‘numbersof deaths were significantly lower than expected’, raises the baseline and therefore
lowers excess mortality statistics for 2021 and 2022, creating a misleading
impression of a less serious pandemic.
The five-year averages of 2015 to 2019 are used uniformly as the baseline
throughout this study to assess the effect of COVID-19 on Australian mortality.
Consequently, our excess mortality statistics for 2022 are different from official
ABS statistics. Even though the differences are not great, a consistent baseline isused throughout in this paper for sake of scientific clarity.
The annual excess mortality for Australia from 2015 to the present is shown in
Figure 2.

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Figure 2

The annual excess mortality for 2020 was well within the range of normal
statistical fluctuations and therefore validates the proposition that there was no
pandemic in Australia, even though there were about 900 COVID-19 deaths(usually revised lower by the ABS over time) in 2020.
Clearly, dramatic rises in excess deaths have occurred since 2021, with the lastbar (in Figure 2) being an annual estimate based on nine months of actual data.

Figure 3412

Too Many Dead
Relative to excess mortality in 2020, 2021 was nearly 7-fold and 2022 is alreadyover 14-fold and potentially more than 19-fold. The data on excess mortality also
validate that the Australian pandemic phase started in 2021, with the 2021 and2022 total excess death toll likely to reach over 41,000, or 26 times that of 2020.
Obviously, the demarcation between the pre-pandemic phase in 2020 and the
pandemic phase since 2021 is the elephant in the room – mass COVID-19
injections for most of the Australian population. To study their relationshipto excess mortality, raw data on total national doses of COVID-injections
administered over time have been obtained from a third-party data aggregator
CovidBaseAU,[11] which also supplies data to international data providers suchas Our World in Data. The data are shown in Figure 3.
Over 64 million doses have been administered to a population of 25.8 million.
The two peaks of mass COVID injections occurred in September 2021 for the
initial drive and in January 2022 for the first booster drive. These drives will beseen below to be correlated to peaks in excess deaths about five months later.
The above raw data in Figure 1-3, which are largely free from data manipulation,
are the main sources from which data analysis is performed transparently inthe rest of this paper to investigate the iatrogenesis of the Australian COVID
pandemic.

4

The pre-pandemic phase

The iatrogenic hypothesis of the Australian pandemic depends necessarilyon objective evidence that there was no significant excess mortality before
government intervention with mass COVID-19 injections. The evidence isalready apparent in Figure 2 above, where all-cause mortality in 2020 was wellwithin normal expectations.
While there was no pandemic in 2020, could the 900 COVID-19 deaths
recorded in 2020 presage a pandemic to develop from the novel coronavirus?
A seasonality analysis with Australian mortality data raises serious doubt about
just how ‘novel’ the SARS-CoV-2 virus is in Australia. Its closest relative, the2003 SARS (now called SARS-CoV-1) was declared an ‘outbreak,’ not even a
pandemic. Respiratory viruses mutate relatively frequently, so when is a mutation‘novel’? COVID-19 viruses had many variants; why are they not ‘novel’ viruses?
Respiratory diseases are seasonal, with most death occurring in late winter,the months of August and September in the Southern Hemisphere, when
respiratory diseases commonly strike near the end stages of life. The typical
pattern of seasonality is shown by the blue bars in Figure 4, based on five-year
averages from 2015 to 2019.

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Figure 4

By comparison, 2020 was a very odd year, when deaths from influenza
and pneumonia (red bars in Figure 4) substantially disappeared for several
months around their normal peaks in late winter. The correlation between
normal fluctuations and 2020 fluctuations was negative, at -20%, indicating asubstantial seasonal anomaly.
However, COVID-19 is a respiratory disease, with similar symptoms toinfluenza and pneumonia (I&P) and there were surges in supposedly COVID

Figure 4414

Too Many Deaddeaths around August in 2020, particularly in Victoria. If the deaths of I&P
and COVID-19 are added together, then the comparison to normally-expected seasonality is shown in Figure 5.
In the I&P comparison in Figure 5, the green bars including COVID deaths
are now consistent with the blue bars representing the expected seasonality
pattern of previous years, with a positive correlation of +70%.
In view of the poorly-defined characteristics of COVID-19 infection andthe subjective attribution of COVID-19 deaths as discussed in section 2,
there is a strong possibility that COVID deaths may have been substantially
misclassified from I&P deaths.
This likelihood of misclassification is very high because I&P deaths arethemselves not clinically well-defined,[12] as evident in Table 1.
Table 1

Table 1 is a very small and partial extract from an extensive ABS data table
listing detailed causes of doctor-certified deaths for 2019 and 2020 in Australia.
[12] Note the codes in the brackets indicate categories and sub-categories(indented). In 2019, there were 3,855 deaths from influenza and pneumoniaof which 2,970 deaths (77%) had no pathogen identified.
Note that this paper makes no assertion about whether the COVID-19
virus or disease exists or otherwise. The evidence suggests that COVID-19
symptoms and diagnosis are so imprecise and so much like cases of I&P thatthey may have been easily misclassified, as discussed in section 2.
Importantly, there are strong financial incentives for hospitals to reclassify
I&P patients as COVID-19 patients, because the Australian Government
had provided $4.8 billion for COVID-19 pandemic response, stating:[13] ‘The
full resources of our world-class health system – a blend of public and private
systems – are needed to focus on treating COVID-19 patients’, indicating
more COVID-19 patients would mean more funding to hospitals.
Finally, the narrative that Australian public health measures such as masking
and lockdowns were responsible for reducing excess deaths in 2020 has little
credibility, for several reasons. First, it was against the recommendations of the
global pandemic preparedness exercise conducted in 2019 Event 201, which415

Australian Medical Professionals Society
not only did not recommend lockdowns, but instead recommended open
borders:[14]Countries, international organizations, and global transportationcompanies should work together to maintain travel and trade during
severe pandemics. Travel and trade are essential to the global economy
as well as to national and even local economies, and they should bemaintained even in the face of a pandemic.
Emphasis added. Also, tens of thousands of highly credentialled medical
researchers and doctors have signed The Great Barrington Declaration[15]recommending against masking and lockdowns, in favour of ‘focusedprotection’. Overall, large amounts of research[16] have shown that there is noclear evidence that masking and lockdowns are effective, with countries suchas Sweden ignoring such measures, performing overall none the worse whencompared with other countries. If those public health measures were so good,
why do governments even need ‘vaccines’?
In summary, on statistics alone, there was no clear evidence of a new deadly
coronavirus in Australia in 2020. Regardless of the precise nature or cause of
COVID deaths, their effect on excess mortality in 2020 was insufficient tocharacterize that year as a pandemic.

5

The pandemic phase

The pandemic phase in Australia began in 2021 with rising all-cause mortality
and excess mortality (see Figure 1 and Figure 2). Also, beginning in 2021 was
the start of mass COVID-19 injections, which governments called ‘safe andeffective vaccines’, for a pandemic just shown non-existent in 2020.
The coincidental increases in excess mortality and doses of injections
administered (see Figure 3) are investigated here for possible iatrogenic
causality. Essentially, the raw data shown in Figures 1-3 are reassembled into
a new dataset to reveal the relationship between excess deaths and COVID
injections as seen in Figure 6.
Overall, there was a negative correlation of -17% between monthly doses of
injections and monthly excess mortality, with best evidence of correlation
occurring in January 2022 and some evidence of correlation in the first halfof 2021, when mass injections started. Contemporaneous correlation should
not be expected because there is normally a time-lag between medication (thecause) and its effects, as will be shown below.
However, the close correlations observed in some periods suggest the existenceof immediate effect of the injections on mortality probably due to anaphylaxis416

Too Many Dead

Figure 6

or other pre-conditions as reported in OpenVAERS in the USA.[17] Theremay be more than just a concurrent correlation between mass injection drives
and deaths, which have been discussed in a previous paper.[9] The small peakin excess deaths in the first half of 2021, when COVID deaths were largely
absent per ABS data,[10] has been attributed to non-COVID deaths, as seenas the first peak in Figure 7.

As mass injections were rolled out in 2021, there was a surge in deaths of the

Figure 7417

Australian Medical Professionals Societyelderly, particularly those in the 85+ age group. Those who were already frailwith chronic inflammation and numerous comorbidities easily succumbed tothe extra challenge presented by the injections. Whether they had COVID
infections or not, it was probably not unreasonable to assume they died frompre-conditions, even though an attribution to COVID deaths would havebeen inconvenient to the narrative of ‘vaccine protection’.
The excess death peak in January 2022 may be due to the combined effects
of both the initial doses of injection in September 2021 and the subsequent
boosters in January 2022 owing to the phenomenon of ‘pathogenic priming.’[18]There may be a combination of both a concurrent effect of fatal inflammation
and a lagged effect of immune suppression, to be discussed below.
That is, the initial doses of injection may have weakened the immune systemof the recipients to make them more vulnerable to subsequent challenges
introduced for example by the boosters, a phenomenon also known as ‘antibody
dependent enhancement’ of disease.[19,20] Indeed, if the data for total monthly
doses were time shifted forward by five months, the two datasets (as Figure 6)
now overlap well in Figure 8.
The rapid rise in excess mortality in January 2022, which coincided with

Figure 8

the first booster campaign, was correlated with the peak rate of COVID-19
injection, which occurred in September 2021. A secondary injection peakfrom the first booster campaign was correlated another five months later witha secondary peak in excess mortality in July 2022.

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The maximum correlation between COVID-19 injections and excess deaths
at +74% occurs for a five-month lag. From analysis, the correlation for a
four-month lag is 61%, while for a six-month lag it is 64%. As a result, theevidence suggests that the five-month lagged effect on excess mortality isstronger than the concurrent effect or other lagged effects due to the COVID
injections.
The five-month lag has been observed briefly in US and UK datasets, but has
not been supported by more detailed investigations, as is being done here.
Metaphorically, the high correlation in January 2022 between the booster
injections and deaths is likely to be the result of the second of a one-two
knockout punch, where the first punch did the most damage five months
earlier by immune suppression (see discussion below) and then by the second
punch of the boosters which quickly delivered the coup de grace to their victims.
As an example, New South Wales data show[8] that the two-dose population
was dying at a rapid rate of several hundred per week during the first booster
campaign in January 2022, while very few deaths were recorded from theboosters. The boosters were lethal to some of the immune-suppressed
two-dose population, but those deaths were wrongly registered as two-dose
deaths through a flawed data reporting convention,[9] where injections wererecognized only after weeks of delay.
Those who survived the first boosters would have had their immune systemfurther weakened making them susceptible to viral infections and harm of thesecond boosters, which contributed later to the second peak in excess mortality
in July 2022. The more injections anyone takes the more likely they will sustain
iatrogenic injuries and death. Many Australians have learned from their actual
experience, ignored official advice, and have become more hesitant of repeatedinjections.
Fortunately, because of falling rates of COVID-19 injections since July 2022,
the empirical evidence may be predicting good news for lower rates of excess
mortality (with data to be released) for the rest of 2022 per the injection data.
Except for a blip in January 2023, excess mortality should continue to fall, as
presaged by the tail-end of the green curve in Figure 8. (This prediction has
been confirmed by the release of data[10] for October and November 2022,
after the completion of the research for this paper.)The data also suggest that the naïve proportional estimate of excess deaths
for the whole of 2022 in Figure 2 is likely to be an over-estimate because of
rapidly falling rates of injection five months earlier. The trend of falling excess
mortality should continue, unless official advice succeeds in persuading the419

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public to accept more boosters, which would be the fifth dose for many.
The stronger correlation and temporality with the five-month lag satisfy twoof the main criteria of Bradford Hill causality,[4] which are the ‘strength’ ofhigh correlation and ‘temporality’ satisfied by a regular five-month lag of theexcess mortality effect following the COVID-19 injection cause.
Another important Bradford Hill criterion is ‘biological gradient’ in medicine,which is the existence of an expected, monotonic dose-response relationship;that is, higher doses should lead to stronger responses. This criterion is met
statistically in Figure 8, where excess mortality rises and falls with doses
administered. The dose-response relationship can be made mathematicallymore precise by an ordinary-least-squares (OLS) regression which isstatistically significant with a p-value of 0.0015 as shown in Figure 9.

Figure 9

On average, the above dose-response relationship suggests, for example, thatfive million doses administered in a month nationally would lead on average to2,221 excess deaths five months later, with a standard deviation of 705 excess
deaths or a likely range between 1,516 and 2,926.
In summary, in meeting three main Bradford Hill criteria for causality a strong
case, based on statistical data alone, has been made for the iatrogenesis ofexcess mortality in the Australian COVID-19 pandemic.

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6

Consistency and specificity

As shown above, Australian data have displayed consistency in causal associations
over time. Are similar associations observed in other places under similar
conditions? Consistency is another criterion which Bradford Hill[4] thought
was important to consider.
International comparisons of the relationship between COVID-19 injections
and excess mortality are made difficult by heterogeneity of the data. Somecountries, such as those in Africa, have largely avoided mass injections, whileother countries, such as those in the Pacific Islands as well as Africa, haveirregular excess mortality statistics. Even among those countries which have data
both on doses of injection and on excess mortality, some report weekly, whileothers report monthly and their reporting dates and periods of available data are
typically different.
From Our World in Data,[22] there are about two dozen countries, including
most of the large developed countries, which have comparable abundance ofdata to perform a cross-sectional analysis. The level of COVID injection for any
country is taken to be the latest reported total doses administered per hundredof the population. The average monthly excess mortality is calculated from theincrease in cumulative excess mortality per million between the earliest injection
start date and the latest report date, which vary between countries.
While the international dataset is far from complete and the data of selected

Figure 10421

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countries with sufficient quantity are likely inconsistent in quality, a positive
dose-response relationship appears discernible across the selected countries asshown in Figure 10.
Country colours refer to their continents. So far in 2022 the Australian excess
mortality per million population is about double that of the United Kingdom,
but Australians are more highly ‘vaccinated’. Australia with its higher dosage also
leads its US, UK and Canadian partners in excess deaths. A clear dose-response
relationship appears mildly consistent at +31% correlation across 23 countries.
Another useful criterion of Bradford Hill causality is ‘specificity’, which is related
to the question of whether there are competing causes for the excess deaths,
with similar strengths of association. Note that specificity is not a necessary
criterion, but one which, if satisfied, helps to draw conclusions for the mostprobable cause. Is iatrogenesis the strongest and most specific explanation forthe observed excess mortality?
Bradford Hill[4] gave the example of smoking causing lung cancer, which has
potentially many possible causes, but smokers have statistically significant
higher incidences of lung cancer than non-smokers. Therefore, smoking is an
important specific cause of lung cancer. The close association between COVID
injections and excess deaths shown above suggests a similar argument prevails
for iatrogenesis.
Classification bias has resulted in flawed data reported by the health authorities,

Figure 11422

Too Many Deadwhich have misled the public to believe most excess deaths are from the‘unvaccinated’.[8,9] As mentioned above, the national broadcaster ABC wrongly
stated on prime-time television that the ‘unvaccinated’ are 16-37 times more
likely to die than the ‘doubly vaccinated’.
Australian adults (age 16+) nearly all (97.5%) have had at least one dose of theinjection.[11] Is it likely that the remaining 2.5% of the adult population are
responsible for most of the excess deaths?
New South Wales Health has COVID death data segregated by ‘vaccination
status’ which is defined by the number of doses.[8,9] The data permit the‘unvaccinated’ to be properly defined as those without any injections. The
data show that, by mid 2022, the ‘vaccinated’ had about double the COVID
mortality risk compared to the ‘unvaccinated’, as seen in Figure 11.
This COVID injection enhancement of COVID deaths extends to excess
mortality and as the fifth dose or the third booster rolls out across Australia
from March 2023, excess mortality is expected to remain elevated. As Bradford
Hill noted,[4] this is a ‘specificity in the magnitude of association.’

7

Coherence and plausibility

On Bradford Hill’s coherence and plausibility, the suggestion of iatrogenic
origin of excess deaths following five months after COVID injections does notcontradict any research on ‘vaccine safety’. The clinical trials conducted weremuch shorter than five months. For example, the Pfizer BNT162b2 trial[23] wasbetween July 27th 2020 and November 14th 2020, with a data cut-off date ofOctober 9th 2020.
That is, the Pfizer trial data analysed were conducted over eleven weeks or
77 days, about half of the time necessary for fatalities to occur per the above
empirical findings, so the suggested iatrogenesis is coherent and not in conflictwith any known facts.
Are there any plausible biological mechanisms which could explain the causal
effect of COVID-19 injections on the excess mortality of the young andhealthy? In the past three years, there has been a deluge of research published on
how the spike protein, either from the assumed SARS-CoV-2 virus infection
or generated from the mRNA injections, could lead to inflammation in variousorgans causing death.
Most of the proposed mechanisms are evidential, plausible and coherent withexisting knowledge on the cutting edge of medical research. However, the ‘speedof science’ requires many more years of replication and validation of the research
to sort out the best explanations for the ever-accumulating evidence. It is beyond

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our knowledge or the scope of this paper to comment on the vast literature,
except to mention some research findings which may be relevant to the statisticalobservations presented in this paper.
Most theories of the iatrogenesis of the COVID injections revolve aroundmechanisms for how the spike protein can cause a suppression of the immune
system called ‘pathogenic priming’[18] or ‘antibody dependent enhancement’.19,
20
Essentially, after repeated infections or mRNA injections, the body adjusts tothe pathogen, or similar ones, by down-regulating the immune system.
In a recently published clinical study[21] of the mRNA injections, production ofneutralizing IgG3 antibodies against the spike protein was observed to switch
over time to the production of non-neutralizing IgG4 antibodies. Thus, the classswitching may reduce the rate of clearance of the toxic spike protein which may
accumulate sufficient titers to cause pathogenesis and mortality.
The five-month lag between injections and mortality found in this paper may
be related to the switching time between the classes of antibodies; this was not
the focus of the cited clinical study, but it provides some useful indications. The
levels of IgG antibodies were measured 10 days and 210 days after the second
mRNA dose.
Class switching did not occur at 10 days, but was observed at 210 days,
which suggests that it is a relatively slow process.[21] However, some cases ofbreakthrough infection 70 days after the second dose suggest the immuno-suppression effect may already occur meaningfully much earlier.
The recommended interval between the first and second dose of mRNA injections
in Australia is between 8 to 12 weeks. If the antibody class switching mechanism
were responsible for the excess deaths five months later, then the mechanism
would suppress immunity significantly after about 100 days. In summary, theclass switching to IgG4 antibodies is a plausible, but not a proven, mechanism
to explain the observed immune suppression of COVID-19 injections, a topic
worthy of further research.

8

Experiment and analogy

By ‘experiment’, Bradford Hill 4 refers to any laboratory (in vitro) or clinical (invivo) evidence to support the epidemiological association between cause andeffect. In the current context of causes of excess mortality, ‘experiment’ should
be taken to mean post-mortems and autopsies to show the connection between
COVID injections and deaths.
Australian governments have deliberately discouraged such ‘experiments’because they may lead to findings which cause ‘vaccine hesitancy’. For example,424

Too Many Dead
Australian doctors have been threatened with fines of up to $20,000 for using
serological tests to verify the results of the PCR tests for COVID-19 diagnosis.
[24] Nevertheless, the scientific imperative is strong enough to have led to several
post-mortem studies[26,29] to discover the smoking-gun evidence of spike
proteins from COVID injections.
The SARS-Cov-2 virus is defined by a full genome sequence published by the
Wuhan Institute of Virology.[25] Without any claim having been independently
validated, no virus has ever been isolated from COVID-19 patients whichmatches exactly the genome sequence, nor has the spike protein from infections
been exactly matched to that of the SARS-CoV-2 virus. The messenger RNA
which is synthesized and manufactured to go inside the lipid nanoparticles(LNP) of the mRNA injections is presumably conformal to the relevant part ofthe published sequence.
The spike proteins found in tissues from autopsies may originate, a priori, from
infections and or from injections. In view of the way the PCR test was developed,
as discussed in the introduction of this paper, without genetic analysis, the spike
proteins from a COVID-infected person may have come from an influenza
virus, which differs from a coronavirus mainly in having a segmented, rather
than continuous, genome.
If COVID injections suppress the immune system and hinder the clearance ofthe pathogenic spike proteins, and, indeed, manufacture even more spike proteinsby the body’s own cells, then post-mortems and autopsies should provide theevidence from significant quantities of spike proteins.
Indeed, from autopsies, the absence of the nucleocapsid IgG/IgM and theircharacteristic morphological features of COVID-19 is the indicator of mRNA
injection origin of the spike proteins.[26-29] The observed time lags after
injections of deaths occurring within days to several months are consistent withthe combination of a short-term causality and a long-term causality discussed
above.
The autopsy experiments, where COVID morphologies are absent, without
viral nucleocapsid protein and the antibodies associated with them, have largely
deprecated the explanation that the COVID disease or ‘long COVID’ is thecause of those deaths. The young have often died suddenly from myocarditis
and pericarditis, on the sporting fields or in their sleep, after mRNA injections,but without any signs of infections.[29] An analogy to the current COVID-19
pandemic is the 2009 Swine flu pandemic from the H1N1 influenza virus. Then,as now, the pandemic was called, based not on fact, but on expectations of ahighly infectious and very deadly disease projected by the Oxford computermodels. The main difference is that the 2009 ‘pandemic’ was never allowed to be

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transformed to an iatrogenic pandemic and it quickly died out on its own accord,
amounting ultimately to a weaker form of the seasonal influenza. The episodehad more cases worldwide, but fewer deaths (about 18,000) and a much lower
case-fatality rate than a seasonal flu.[30] On an excess mortality definition, the2009 Swine flu season was not a pandemic.
The main difference between then and now is that mass ‘vaccination’ did notplay a significant role in 2009, thus avoiding an iatrogenic pandemic, as now. In
2009, production of ‘vaccines’ and their injections into the population were notfast enough or widespread enough before the Swine flu infections died out on
their own accord.
Between 2009 and 2020, governments were ‘educated’ for ‘pandemic
preparedness’, which meant preparation for legally-declared emergency
measures, unimpeded by the ‘speed of science’. For example, lockdowns were
enforced everywhere without scientific justification,16 and this also had the effectof preventing the development of herd immunity from isolation, thus prolonging
the period of infection. In the extended time available, ‘vaccines’ were developed
under ‘Operation Warp Speed’ and rushed to the market, side-stepping standard
procedures of longer-term testing to ensure safety.
The analogy to the 2009 swine flu is that the COVID-19 pandemic might
not have continued or even existed (in such as the 2003 SARS outbreak), had
there not been mass mRNA injections to cause and perpetuate the COVID-19
pandemic.

9

Bradford Hill analysis

Austin Bradford Hill suggested[4] his nine ‘viewpoints’ to be aspects considered
for causality. He did not call them ‘criteria’, a term which has been used in thispaper for simplicity and convenience. Bradford Hill refrained from calling themnine criteria, because they are neither necessary nor sufficient conditions to makehard and fast decisions on causality. They are aspects to address when examining
alternative causal hypotheses.
In science, the set of available facts at any time determines what is the best
explanation and Bradford Hill has suggested some objective aspects to help
deciding on alternative explanations. This paper has reported some highly
significant facts which may not have been recognized yet. These facts have comefrom epidemiological data when they have been presented without obfuscation
by manipulation and classification, as in official health authority reports.
Previous sections of this paper have been devoted to addressing Bradford Hill
‘criteria’ for assessing the iatrogenic hypothesis for Australian excess mortality
since 2021. The analysis in previous sections is summarized in Table 2.

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The main contributions to existing knowledge of the Australian COVID-19
pandemic are contained in sections 5 and 6, where the first five Bradford
Hill criteria are addressed. These criteria are probably foremost because
they apply equally to hard sciences such as physics. Criteria 6 to 9 arereviewed in Sections 7 and 8 through existing literature, which can be seen
to support generally the iatrogenic hypothesis advanced in this paper.

On the basis that the Australian pandemic is iatrogenic, which caused
the observed excess mortality, then it follows also that harm, or risk ofharm, outweighs any benefit of the COVID injections. This can be shownformally by the equation for mortality risk and benefit which is expressed
as follows:
Lives lost (L) from side effects of injection- Lives saved (S) from disease mitigation= Excess Deaths (X)or L-S = X. Excess deaths X are known to be large, but L and S are unknown
from the data. Since X >> 0, it follows that L-S >> 0 or L >> S, hence L/S >>1. A mortality risk-benefit ratio which may be defined by L/S is very high.
Therefore, because of the very large excess deaths following Australia’s
policy of mass COVID injections, lives lost far exceed lives saved; themortality risk-benefit ratio is very high. Further research is needed toquantify this ratio for health authorities.

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10Conclusion

Australian health policy has been based on misinformation from flawed
COVID-19 data which are scientifically unsound. Based on sound mortality
data, the Australian COVID-19 pandemic did not begin until the adventof mass mRNA injections in 2021. It is ironic that mass injections whichwere introduced to mitigate a non-existent pandemic in fact created a realiatrogenic pandemic. This study, backed by a Bradford Hill analysis, has shownthat more injections administered to reduce the pandemic had the opposite
effect of causing more excess deaths to increase the pandemic.
The very large excess deaths observed from the data imply that the mortality
risk-benefit ratio from COVID injections is very high. That is, the harm or
risk realized has far outweighed any benefit from COVID injections.
This study has introduced a very simple, but robust, methodology, whichshould be used by other countries, particularly those in Figure 10 which appear
to have adequate data, to replicate and investigate the likely iatrogenic originsof their own pandemics. Billions of lives in the world are at stake from thepotential findings of the research.

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Klemm C, Das E and Hartmann T. Swine flu and hype: a systematic review
of media dramatization of the H1N1 influenza pandemic, Journal of Risk
[30] Research, 19:1, 1-20. Retrieved from: https://www.researchgate.net/
publication/272591281_Swine_flu_and_hype_A_systematic_review_of_
media_dramatization_of_the_H1N1_influenza_pandemic

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Simpson’s Paradox in thecorrelations between excess
mortality and COVID-19 injections:a case study of iatrogenic
pandemic for elderly Australians
by Wilson Sy1

BPharm (Hons), MSc, PhD

Abstract
Background: Conflicting findings in correlation studies between COVID-19
injections and excess deaths have been published. Negative correlations with
2021 data appear to justify the official claim that COVID-19 injections
reduce illness and death and therefore should be prioritized for vulnerable
elderly (over-75s) Australians. This claim needs to be reviewed including
2022 data.
Method: Simpson’s Paradox is illustrated to explain how the negative
correlations, supporting injection effectiveness, can come from 2021 data,
while positive correlations, suggesting injection ineffectiveness, have comefrom inclusion of 2022 data. Excess deaths of Australian elderly in the
COVID pandemic are analysed in detail for their statistical significance.
Results: Negative correlations from 2021 data are refuted in this paper
as false causality, because the results have insufficient temporal separation
between cause and effect. Strong positive correlation (69 to 74 percent) in
Australian data is confirmed when the effects of excess mortality are lagged

1
18 June 2023. PhD, Director, Biotechnology Unit, Investment Analytics Research. Lex Stewart,
Julian Gillespie and Jeremy Beck are thanked for useful comments. The author has no financial or politicalconflicts of interest and is not funded by external sources.

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Too Many Dead
optimally by 21 weeks after COVID-19 injections.[1] A strong statisticalsignal (2.5 standard deviations) is shown in this paper in the mortalityof elderly Australians, who suffered the greatest relative harm from theinjections, even when adjusted for age-dependent high expected mortality.
Conclusions: Earlier epidemiological evidence that COVID injections
reduce illness and death is now methodologically invalidated, and the claimthat the injections are beneficial for the vulnerable is refuted. The injections
explain the mystery of substantial numbers of non-COVID excess deaths.
The Australian pandemic is shown to be iatrogenic particularly for theelderly, who have suffered disproportionate harm. Deliberately ignoring thisclear evidence is tantamount to iatrogenic geronticide.

1.

Introduction

Early in the COVID pandemic, the stated purpose of a vaccine was toimmunize against, or protect from, the infectious disease. Medicines which
do not sufficiently prevent infection and transmission should not be labelled‘vaccines,’ because only those with safe and high preventative properties
should be widely used or mandated for the collective social benefit ofstopping a pandemic by blocking spread.
With the pandemic continuing after more than two years of mass ‘vaccination,’a ‘vaccine’ has now been demoted to require merely to stimulate an immune
response, rather than actually to provide immunity. A ‘vaccine’ has been
redefined as ‘a preparation that is used to stimulate an immune response
against diseases’[2] or redefined functionally as ‘the most effective way to
reduce deaths and severe illness from infection. The protective benefits ofvaccination far outweigh the potential risks.’[3]This mislabelling of COVID-19 injections as ‘vaccines’ has continued tomislead most of the public to accept coercive injections for expected immunity
in order to travel, keep employment, protect Grandma and do other public
good. Even though Pfizer and Moderna have still been mislabelling theirs‘preventative vaccines’, the FDA has not adequately clarified[4] to the publicthat infection prevention is officially not needed for authorized use of theinjections:
It is important to note that FDA’s authorization and licensure standards
for vaccines do not require demonstration of the prevention of infection ortransmission. A vaccine can meet the licensure standard if the vaccine’s
benefits of protecting against disease outweigh the vaccine’s risks for the
licensed use.

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Australian Medical Professionals Society
Emphasis added. Vaccines now do not have to meet the immunity definitionof vaccines that most of the public and the media still misguidedly assume.
The COVID-19 injections, whatever their commercial-in-confidence
content, are now defined by health authorities to be a therapy to mitigate
the effects of infection. However, even this therapeutic benefit has not beentested or demonstrated before they were authorized for use. The COVID-19
injections were assumed axiomatically to be so beneficial that they wereprioritized for the most vulnerable, the elderly Australians, as Australianpolicy endorses a recent CDC and WHO recommendation:[5]COVID-19 vaccines are safe and reduce COVID-19 mortality. The World
Health Organization (WHO) recommends that countries prioritize
populations at increased risk, e.g., older adults, for COVID-19 vaccination
with a goal of 100% coverage with a completed primary series for
populations at-risk.”
Emphasis added. What are the facts? It is the purpose of this paper to establish
simple, robust, and verifiable facts to assess whether COVID-19 injections
provide the said therapeutic benefit, especially for elderly Australians.
Three main sources of a priori evidence for safety and therapeutic effectivenessof COVID-19 injections in reducing severe illness and death are briefly
discussed in the next section, where real-world epidemiological data willbe established as the most valid source of evidence, being most free fromdata flaws and official conflicts of interest.[6,7] The main epidemiologicalevidence indicating therapeutic effectiveness consists of a large number ofstudies with 2021 data, finding negative correlation between COVID-19
injections and excess deaths,[8] thus apparently supporting the effectivenessof the injections in reducing deaths.
It is a typical fallacy in medical research as seen in footnote 8 that ameta-analysis of a large number of papers, shown to be invalid here, appears
to have determined the consensus. Whilst there are far fewer publicationsof positive correlation between COVID-19 injections and excess deaths, theconflict of evidence is explained in section 3 by Simpson’s Paradox,[9] whichis resolved in section 4 in favour of the minority view of positive correlation
between COVID-19 injections and Australian excess deaths. The resolution
is based on the important requirement of temporality of correlation to imply
valid causality – that is, the cause must precede the effect by a reasonableamount of time.
If COVID-19 injections actually caused excess deaths, then why should they
be prioritized for vulnerable elderly Australians? This policy could be rational
only if the positive correlation found for the whole Australian population does434

Too Many Deadnot somehow apply to the elderly subpopulation, as an exceptional case of
Simpson’s Paradox.[9] That is, could the elderly subpopulation exhibit negative
correlations between injections and deaths, through unknown confounding
factors, even though the whole population exhibits positive correlation? Thispossibility is refuted in section 5.
While sudden deaths among the young and healthy have attracted worldwide
attention, less recognized is the plight of the elderly who have borne the bruntof most Australian excess mortality. Section 6 provides an analysis of thestatistical significance of excess mortality by age-group and shows that elderly
Australians have suffered disproportionate harm from COVID-19 injections,suggesting geronticide.
Section 7 summarizes the strong evidence for the iatrogenesis of the
COVID-19 pandemic for elderly Australians, thus contra-indicating theofficial assumption that the COVID-19 injections are beneficial for thevulnerable. The concluding section indicates the need to investigate thepossibility of iatrogenic geronticide.

2.

Safety and therapeutic effectivenessA priori evidence for COVID-19 safety and effectiveness in reducing severe
illness and death may come potentially from three data sources: (1) clinical
trials (2) surveillance reports of health authorities and (3) epidemiological dataof statistical agencies.
The double-blind clinical trials, on which emergency use authorization(EUA) was granted, were unblinded within weeks after EUA and full safety
investigation of the COVID-19 injections was never possible. Moreover,recently the interim datasets accompanying the EUA process wereindependently re-analysed[10] for serious adverse events of special interest(AESI). From the analysis,[10] Pfizer and Moderna injections were found tohave excess risk of serious AESI compared to placebo.
Similarly, the Australian TGA recently released, under Freedom of Information(FOI) requests, a nonclinical evaluation report[11] submitted by Pfizer Australia
as a part of its application for approval. The report admitted no human studies
were done on most types of toxicity and that in animal models the toxic lipid
nanoparticles were not localized at the site of injection, but were slowly andimportantly distributed to major organs, particularly to the liver.
Therefore, to date, clinical studies and laboratory experiments have only
raised serious safety concerns and have only provided worrying evidence ofincreased safety risk of the COVID-19 injections, casting doubt on their
therapeutic benefit.

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After rollout of the ‘vaccines,’ safety and effectiveness have been monitored
through weekly and monthly surveillance reports of health authorities which
provided numbers on COVID cases, hospitalizations, ICU admissions anddeaths, and selective comparisons based on ‘vaccination status’. Unfortunately,
these reports are misleading because they are based on flawed COVID data,
which were not collected for scientific accuracy, but for managing public
perception.[12]Flaws in official COVID data originate from two main fundamental defects.
First, PCR test does not detect the presence of the SARS-CoV-2 virus, theattributed pathogen of the COVID disease. It is unclear whether a COVID
infection or other infection is even detected by a positive PCR result, whichitself depends on arbitrary numbers of amplification cycles. A PCR-defined
COVID infection merely indicates the fragmentary presence of any numberof unknown RNA strands,[13,14] but not necessarily presence of any virus of
the COVID disease or of any actual infection or disease. Even whole genome
sequencing of the SARS-CoV-2 virus in faecal samples of a few positive PCR
subjects[15] has not established an association with the disease.
It is clear that certification of deaths during COVID is not an exact science,as the guidance for reporting and financial incentives leave room for bias andsubjective judgement in the raw data, as discussed in.[1] For example, a person
without COVID symptoms could go to a hospital with a heart attack, while
there receive a false positive PCR test result and when having died a day laterthen be declared a COVID death. In some cases, to declare a COVID death, apositive PCR test is not even necessary for registration by CDC:[16]Ideally, testing for COVID-19 should be conducted, but it is acceptable toreport COVID-19 on a death certificate without this confirmation if thecircumstances are compelling within a reasonable degree of certainty.
Emphasis added. Consequently, the distinction between COVID andnon-COVID deaths would be inaccurate and COVID data on the numbers ofcases and deaths are likely inaccurate measurements of the COVID pandemic.
Secondly, on data defects, attributions of COVID deaths according to‘vaccination status’ are also likely to be inaccurate, because ‘vaccination status’
is not a precise record of the number of injections someone had at a particulardate. The recorded status depends on the number of days since last injection.
[17] For example, if someone had their first injection less than 14 days ago,
they are recorded as ‘unvaccinated.’ Should the person die in less than 14days, it is counted as the death of an ‘unvaccinated’ person. Generally, death
numbers of ‘vaccinated’ and ‘unvaccinated’ are confused.

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Too Many Dead
Consequently, reports of lower COVID death rates among the ‘vaccinated’
than ‘unvaccinated,’ using official COVID data, have been shown[7] to be
misleading evidence of the therapeutic effectiveness of COVID injections.
Independent replication of results of surveillance reports to discover theexact sources of errors in the Australian databases has been made extremely
difficult because the raw data have not been collected accurately in databases,
as discussed in the Appendix. Australian COVID data being flawed donot, and cannot, show correctly the therapeutic effectiveness of COVID
injections.
In summary, based on official admissions, neither clinical trials nor surveillance
reports can be relied upon to provide accurate raw data to support thetherapeutic effectiveness of the COVID-19 injections. There remain only
epidemiological data which might provide the needed real-world evidence.
The mortality data collected by national statistical agencies are data which
are more difficult to manipulate to justify government policies and their
public pronouncements. Hence, epidemiological data are the most legitimate
source to assess the therapeutic effectiveness of COVID-19 injections.

3.

Simpson’s Paradox in epidemiology

Epidemiological data used for Australian all-cause mortality are publishedby the Australian Bureau of Statistics (ABS) since 2015.[18] Obviously
excess mortality data depend on how the baseline is calculated. The ABS has
arbitrarily excluded 2020 as a low mortality year for calculating 2022 excess
mortality, thus including only 2017-2019 and 2021 in its baseline.
Other methods of calculating the baseline include that of Actuaries
Institute Australia[19] which used extrapolation of linear regression models
fitted to standardized death rates. The main adjustments of this baseline
are demographic changes in ageing and population. The need to adjust for
two years of demographic changes is unclear and the method renders thereplication of the calculated results unnecessarily complicated and difficult touse for a variety of analytical purposes.
Computer models of excess mortality are not about statistical facts, but aretheoretical models hypothesized to estimate or predict excess mortality basedon assumed causes of mortality;[20] their usefulness depends on the assumptions
they make.[21,22] In contrast, our excess mortality is a calculated statistic toquantify deviations from expectation to indicate anomalous statistical signals.
To investigate the COVID era, excess mortality is calculated in this paper
from the average of five years from 2015 to 2019, as the baseline of the
pre-COVID era, which is used throughout our analysis. Therefore, our excess

437

Australian Medical Professionals Societymortality data are slightly different from those published by the ABS which
has an ad hoc baseline stated above.[18]The main methodological strength of the current study is its economical useof data of highest quality and integrity; essentially only two variables are used.
The new insightful contributions, which are largely statistical, will come froma more rigorous and thorough analysis of limited data. Conclusions will be
fewer, but will be more robust and trustworthy. No direct contribution is madeabout the underlying science of the COVID virology or vaccinology.
Apart from all-cause mortality data, the only other variable used is the total
numbers of doses of COVID-19 injections over time in Australia.[23] The twovariables have data collected independently by two separate agencies which arelargely free from any known conflicts of interests. The relationships between
these two variables have not been investigated together or reported by thehealth authorities, thus allowing new and unbiased findings to be discovered.
Currently, the number of research publications finding negative correlations
between COVID-19 injections and COVID deaths have far exceeded thenumber finding positive correlations, which have only started to appear since2023. The reason for this imbalance will be explained below. Our recent paper[1]found strong positive correlations in Australian data, which imply probablecausality based on Bradford Hill analysis. This is only one peer-reviewed
paper with positive correlation against a large number of other peer-reviewed
papers with negative correlations. Health authorities would conclude that thenumerical consensus sides with negative correlations and therefore the overall
evidence supports therapeutic effectiveness of COVID-19 injections. This
fallacy is explained by Simpson’s Paradox in this section.
Simpson first discovered[9] a paradox in the interpretation of (2 x 2 x 2)
contingency tables for the association between two variables. Generally,
the paradox is a statistical phenomenon where an association between two
variables in a population may be different from, and possibly contradictory to,
those of its subpopulations. The implication is: statistical associations cannot be
generalized from one data sample to others without a proper understanding andinterpretation of the results. This is illustrated in our current epidemiologicalcontext. It is the duty of science to falsify formally any contradictory evidenceor at least reconcile with it to establish true scientific consensus.
Our previous paper[1] found strong positive correlation for the whole dataset
only when the COVID injection cause leads the excess mortality effect by
five months. Virtually all journal-published papers[8] have ignored this
temporality, making causal inference likely invalid. If temporality is ignored,
then the weekly data (rather than monthly data) are shown in Figure 1.

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Visually, it is easy to see (green: doses; red: excess deaths) that there werealternating periods of positive and negative correlation in the Australiandata. For the first few months of mass injection till the end of May 2021,
there was positive correlation and excess deaths which were largely attributed
to ‘unvaccinated’ deaths, because the sick elderly in nursing homes died toosoon after injection to be considered ‘vaccinated. The sudden rise in elderly
deaths was considered coincidental, unrelated to ‘vaccination’.

Figure 1

For the three months to the end of August 2021, strong negative correlations
were attributed to the ‘vaccines reducing illness and death’, but excess deaths
started to rise again from September 2021, the concept of ‘waning’ wasinvented. By the end of 2021, more than 50 studies from different countries
were published showing that the injections[8] ‘were associated with afavourable effectiveness against SARS-CoV2 incidence rate, hospitalization,and mortality rate in the first and second doses in different populations’.
However, the correlation turned strongly positive from December 2021
to March 2022, with the advent of the first boosters, but by then, about
five months after the initial ‘vaccination’ drive, ‘vaccine effectiveness’ wasconsidered established beyond doubt and the new data emerging wereconsidered with suspicion, as misinformation, not ‘peer-reviewed research’.
After March 2022, ‘vaccine hesitancy’ increased understandably and therates of injections declined while excess deaths continued to rise. In thisperiod, the correlation turned from positive to negative again.
A scatter plot of Figure 1 is shown in Figure 2, where first and last periods of439

Australian Medical Professionals Society
anti-correlation are shown as red points and fuchsia points respectively,
while positive correlation periods are shown as green points.
Thus, depending on which data period is selected to obtain the correlation
between COVID-19 injections and excess deaths, it is possible to obtain

Figure 2

statistically significant positive or negative correlation, as shown in Table
1, in an illustration of Simpson’s Paradox.
Colours refer to the data points for each period in Figure 2 and Table 1. The
Table 1: Correlation and Regression of Selected Periods

Period Start

Period End

Sample(Weeks)Correlation
(%)

Slope
(Deaths/Doses)

p-value

23 May 2021

5 Dec 2021

29

-60.5

-0.1430.001

24 Jul 2022

21

-31.7

-0.3170.161

5 Dec 2021

6 Mar 2022

28 Feb 2021

30 Sep 2022

6 Mar 2022

14

84.6

83

-20.0

0.415

-0.0830.000
0.07

total data sample of 83 points shows (bottom row) a negative correlation of-20 percent with moderate statistical significance (p-value 0.07). However,subsamples (top row, red) have higher negative correlation of -60.5 percent
with higher statistical significance (p-value 0.001) with 29 data points inthe first period and have highest positive correlation of +84.6 percent(second row, green) with highest significance (p-value 0.000), but with440

Too Many Dead
only 14 data points in the second period.
Most research for the first period was published in ‘peer-reviewed’journals,[8,24,25] which favoured the official narrative that ‘vaccines’ weretherapeutically effective. This evidence was assumed universally valid. Later
research[26,27] including 2022 data from the second period (see Figure 1)
effectively exposed some of the lagged effects of the injections. Such findings
contradict those of the earlier published papers and would likely be rejected
for journal publication because they do not suit the official narrative. This hascreated a bias in the literature. Who is right? Should the result of the total
Australian data sample be taken as the correct result for the population? Or,should it be the common practice in medical research of reaching a conclusionfrom a meta-analysis, averaging all results?
Existence of Simpson’s Paradox suggests there may be one or more confounding
factors important in interpreting and validating the results. Conflicting results
have been shown, which require science rather than authorities (or the law
courts) to resolve. This paper resolves this Simpson’s Paradox by showing thatcausality requires temporality of correlation on account of inherent time delays
for medical treatments to cause observable therapeutic effects.

4. Temporality of correlation

If a healthy youth without medical conditions dies immediately after a
COVID-19 injection, it could be caused by an anaphylactic reaction. Or, if the
youth dies one or two days later, the injection would still be the likely cause, andcan be confirmed by finding spike proteins in affected organs from an autopsy. In
these cases, the immediate adverse events are most likely reported and recordedin databases, but others are unlikely to be reported and therefore appear rare,
because CDC data reporting convention[17] assumes the injection does not takeeffect until after 14 days.
On the other hand, for the sick elderly who are close to death, the additional
challenge of a synthetic infection from the injections could immediately pushthem over the edge. The cause of death would be attributed to one or more of
their existing comorbidities. From March to May 2021 (see Figure 1), a positive
correlation was seen between injections and non-COVID deaths.
Situations with pre-conditions are where COVID injections could have an
immediate effect on mortality. Except for the elderly, existence of pre-conditions
is relatively uncommon, where, for most people of average health, the COVID
injection takes time to affect metabolic processes of pathogenesis.
The lipid nanoparticles (LNP) of the mRNA injections, observed from studies
with animal models,[11] take 48 hours to spread to most parts of the body,441

Australian Medical Professionals Society
particularly to the major organs. From those sites, the LNPs have to transfectinto body cells to deliver the genetic material into the cytoplasm, which theninitiates processes to manufacture the SARS-CoV-2 spike proteins; the antigens
when expressed from the cell provoke the production of antibodies.
Pathologies originate from the way the spike proteins, manufactured or acquired
from infection, normally interact with antibodies and body tissues over weeks
and months. If the COVID injection were to cause severe illness and deaththrough acquired immune dysfunction, then it would also normally take weeks
and months, potentially through down-regulation,[31,32] to see pathology
manifested. Even the convention of 14-day delay in reporting ‘vaccination status’
tacitly acknowledges the requirement of temporality.
Consequently, many studies reporting negative correlations between COVID
injections and deaths from 2021 data have misleadingly inferred immediate
therapeutic effectiveness in preventing death (for example, see the first period in
Table 1). The inferred causality violates temporality with insufficient lag between
cause and effect, and needs 2022 data to be included. That is, those research
publications should not be used by the health authorities to infer therapeutic
effectiveness of the injections in reducing severe illness and death. The opposite
conclusion is the case, by correct statistical analysis with more data, as shown in
the previous paper.[1]Complementing monthly data analysis,[1] weekly data of Figure 1 are shifted
optimally with doses of injection temporally leading excess deaths by 21 weeks,
and are shown in Figure 3.
From the whole dataset, the overlap period with temporal shift was from August2021 to November 2022. The peaks in excess deaths coincided approximately
with two booster peaks which were five to six months apart. Over this period
the correlations between COVID injections and Australian excess deaths are
consistently high, as shown in Figure 4.
The linear regression is statistically significant with p-value essentially zero and apositive correlation of +69 percent. On average, one million doses administeredin a week would lead to 460 weekly excess deaths 21 weeks later. The correlationof the whole dataset is largely free from Simpson’s Paradox, because only verysmall subpopulations show any sign of negative correlation, as indicated by data
points in red and blue.
The temporal separation between COVID injections and observed deaths, fivemonths or 21 weeks later, as distribution peaks, has been suggested from simple
observations and anecdotes from US and European data.[28]442Too Many Dead

Figure 3

5.

Figure 4

Age-group therapeutic effect

While Australian data show the COVID injections have a negative
therapeutic effect on the whole population, increasing excess deaths, it may bepossible a priori that the COVID injections have a positive therapeutic effecton subpopulations, such as the elderly, as another example of the Simpson’s
Paradox.
For the whole Australian population, it has been shown that COVID-19
injections increase, not decrease as claimed, severe illness and death. Yet,COVID-19 injections have continued to be recommended by health authorities

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for the elderly and the vulnerable. Could Simpson’s Paradox provide a rational
explanation to justify the counter-factual claim that COVID-19 injections
reduce severe illness and death specifically for the vulnerable elderly?
Theoretically, one way the elderly Australians could statistically escapethe conclusion of iatrogenic excess deaths observed in the total Australian
population is for that subpopulation to exhibit Simpson’s Paradox by having
a negative correlation between doses of injection and excess deaths. Thispossibility is examined here.
The ABS monthly all-cause mortality data stratified by age-groups since 2015
are shown in Figure 5.

Figure 5

As expected, mortality increases strongly and monotonically with age. Visually,
from Figure 5, it is evident that notable increases in all-cause deaths above
expectation in the older age-groups have occurred since 2021, when mass
injections started. Is this statistically significant?
Age-standardized mortality statistics used in most published studies mask
information on different effects of COVID-19 injections on different
age-groups, because the data are standardized to fixed age distributions.
Consistent with our aim of clearly exposing the outcomes of the COVID era,
excess deaths for each age-group are calculated from their own baselines using
their own respective averages of the years 2015-2019, of the pre-COVID era.
To simplify discussion, the elderly are defined by an over-75 or 75+ age-groupby aggregating the 75-84 age-group and the 85+ age-group. The rest of theAustralian population is referred to as the under-75 or 75- age-group.

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Too Many Dead
Whether Simpson’s Paradox occurs with the 75+ age-group depends empiricallyon the correlation between the doses injected into that age-group and theresulting excess deaths. Accurate dose-statistics for different age-groups are notavailable in Australia, as explained in the Appendix. Therefore, total nationaldose-statistics are used as proxy, since their variations are expected to closelyreflect the variations of the 75+ and 75- age-groups.
The relationship between monthly total doses and monthly excess mortality forthe 75+ age-group is shown in Figure 6, which closely resembles Figure 8 of theprevious paper[1] and is consistent with the weekly version in Figure 3 above.
The first peak in excess deaths in January 2022 is particularly anomalous because
it was during the height of the Australian Antipodean summer, when fewer
elderly normally die from respiratory diseases. The second peak in July andAugust may appear seasonally more normal, but it too is anomalous because
registered deaths from influenza and pneumonia were unusually low relative
to pre-pandemic averages. Eliminating natural causes at those peaks suggeststhat the excess deaths in the elderly were likely caused by COVID-19 injections
administered five months earlier, given the Bradford Hill analysis in the previous
paper.[1]Figure 6

A similar conclusion is reached for the under-75 age-group with the same
Bradford Hill analysis. Hence, in the current dataset, Simpson’s Paradox has
been eliminated for the elderly and it has been verified that COVID injections
do not reduce, but increase, excess deaths for the elderly, as well as for the whole
population.

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Australian Medical Professionals Society
The remaining question is: are the elderly excess deaths caused by the injections
statistically significant? What is their relative harm compared to otherage-groups?

6.

Age-group comparison

In the COVID era, the annual excess deaths as percentages of baseline
expectations for various age-groups are shown in Figure 7, where it is evident
that the Australian pandemic as defined by excess deaths only started in 2021,
with the advent of mass injections.
In the next three months after the data analysed in this paper, monthly total
Australian deaths have been 15,300, 14,500 and 12,700 to February 2023, withbaseline expected mortality of 12,800, 12,600 and 11,500 giving respectively
excess death tolls of 2,540, 1,950 and 1,270 or 19.9, 15.5, and 10.4 percent
above expectation. These statistics (rounded for ease of reading) have similar
magnitudes to those to November 2022 in Figure 7 (last group), suggesting
still significant excess deaths.
The previous paper[1] showed that 2020 was pre-pandemic in Australia,
because there was no evidence of significant excess deaths to warrant callinga pandemic, according to traditional WHO definitions. This applies to all
age-groups.

Figure 7

As shown in Table 2 below, the percentages of excess deaths for all age-groups
compared to baseline expectations are all less than four percent (column 5),
substantially less than historical fluctuations, resulting in low sigmas (units of446

Too Many Dead
standard deviation). That is, there were no statistically significant signals for apandemic for any age-group in Australia in 2020.
The second last column shows that annualized volatility of percentage excess
deaths and the last column shows sigmas (standard deviations) less than 0.3
percent, indicating statistical insignificance.
For those over 65 years, the 65+ age-group excess deaths (1,980) accountfor more than 100 percent of excess deaths (1,690) in 2020 (column 4,
shaded), because the youngest age-groups had lower deaths than expectation.
This resulted in higher COVID-19 deaths attributed to the elderly, giving
the misleading impression that the elderly were particularly vulnerable to
COVID-19 mortality, and this was false because of statistical insignificance.
Table 2: Pre-Pandemic 2020 Age-Group Excess Deaths

All-Cause
Baseline

All-cause
2020

45-64

21,20021,10075-8441,20042,3001,070

158,000

1,690

Group
0-44

65-74
85+
All

8,000

25,40065,100161,000

7,770

26,40060,100

Excess
2020

Excess %
2020

-60

-0.28

-230
937
-30

-2.88

Excess %Volatility

Sigma

8.4

-0.03

12.5

-0.23

3.69

13.0

0.28

-0.05

12.2

2.60
1.05

9.7
6.9

0.27
0.00
0.15

The situation changed markedly after 2021 with mass COVID injections.
Excess mortality climbed substantially, particularly in the elderly. Australian
COVID injection drives generally lagged the rest of the world by a few months,
partly as a result of global health directives and partly through ordering andsupplying issues, as seen in Figure 8.
The temporal separation between cause and effect meant that the effect of
COVID-19 injections was not fully felt until 2022 in Australia, at least a few
months after completing mass injections of the primary series in the Australian
population. Moreover, the injections appeared to have a cumulative effect on
the immune system with the first booster campaign early in January 2022
having a devastating effect in Australia.[1]On account of Australian injections lagging other countries such as the US
and UK, Australian deaths would lag the rest of the world, making Australianrelative 2022-2021 death tolls higher than those of most other countries,

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Australian Medical Professionals Society

Figure 8

which had more injections and deaths earlier. The Australian excess deaths to
November 2022 by age-groups is shown in Table 3.
All numbers have been rounded to three significant figures, for ease of reading
and adjusted for 11 months of data for 2022. Before the excess mortality of theelderly is discussed, and this is a main focus of this paper, the surprisingly low
2022 excess mortality (first row) of the youngest group needs to be discussed
to allay any fears about the quality and integrity of the data.
Table 3: Age-Group Excess Deaths to November 2022
Group

Population
(m)

Pop
(%)

All-Cause All-cause Excess
Baseline
2022
2022

Excess% 2022

Excess %Volatility

Sigma

0-44

14.8

58.2

7,300

7,270

-29

-0.4

12.5

0.0

45-64

6.26

24.6

19,40020,700

1,290

6.6

8.4

0.8

65-74

2.46

9.7

23,40026,900

3,570

15.3

13.0

1.2

75-84

1.38

5.4

37,90047,200

9,240

24.4

9.7

2.5

85+

0.54

2.1

60,10072,70012,500

20.8

12.2

1.7

All

25.4

100

148,000

175,000

26,600

18.0

6.9

2.6

The current focus of most research has been generally on the young who
normally have very low rates of mortality, but are now appearing to die at
higher rates (see Figure 7). With dramatic statistical signals and with many
years of lifespan at risk for every young person, it is reasonable to urgently
investigate diagnosis, causes and treatments.
Young and fit athletes collapsing and dying suddenly during training or in448

Too Many Deadsports events in front of crowds of spectators provide visually powerful evidenceof unexpected deaths. Most of these deaths have been shrugged off as rare,
perhaps due to asymptomatic COVID infections, but statistically insignificant,as seen above. The contradiction between anecdotes and mortality data needs
explanation.
In the youngest age-group (0-44), deaths from medical causes are normallyrare (about 0.014 percent per annum – see Table 4). This makes highly
conspicuous any sudden rise in medically-caused deaths from a very small
to a larger number. That is, sudden rises in cardiac arrests and strokes relative
to their virtual absence normally have raised statistical alarms. However, inabsolute terms, those deaths may not have overall significance on total excess
mortality in Australia’s youngest age-group, because of large numbers of
non-medical deaths.
For the youngest age-group (0-44), broad categories of causes of deaths
are shown for different age subgroups in Table 4. Top-10 medical causes
mainly include neonatal deaths, malignant cancers and cerebral palsy, withheart disease and strokes only starting to occur after 25 years. The top-10
non-medical causes exceed medical causes, leading by intentional self-harm,followed by accidents which involve misadventure, car, motorcycle and othertransport.
Table 4: Causes of Death in Youngest Age-Groups

Cause/Age (Years)

Population (million)
Top-10 Medical

Top-10 Non-medical<1

1 to 14 15 to 24 25 to 34 35 to 44

Total

801

113

944

2,140

567

1,580

Intentional Self-harm
Accidental Harm

Top-10 Total

All-cause Total

4.64

3.04

3.62

3.49

108

739

1,050

971

76

337

473

32

801

1,010

221
425

69

402
808

1,170

216
581

1,270
1,950

404

1,920
3,250

14.8

2,870
1,290
5,020
7,740

That is, statistical signals in the rise of medically-caused deaths in the youngest
age-group may be masked by a significantly larger number of non-medical
deaths, as seen in the above table (see rows 2 and 3). For example, lockdowns
during the pandemic, particularly before 2022, might have had an unintended
consequence of reducing traffic and other accidents in the young (forexample, ages 15 to 34). Reduced traffic accidents alone may have more thancompensated for any rise in medically-caused deaths.
Over the short term, the aggregate data have not shown significant increased

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Australian Medical Professionals Societymortality in the youngest age-group, though 2022 has faintly hinted at an
emerging upward trend. The long-term effect of COVID injections on future
mortality on the mostly healthy young age-group is as yet unpredictable frompublished data.
The main concern of this paper is the bulk of excess deaths in the over-75 age
group. Table 3 shows Australian elderly (75+ years) are nearly two million,
or 7.5 percent of the population (third column, shaded). Normally they
are responsible for about 66 percent of baseline all-cause mortality of theAustralian population (fourth column). Yet they represented 82 percent excess
deaths in the year to November 2022 (sixth column, shaded).
The excess deaths for 75-84 age group and the 85+ group were 24.4 percent
and 20.8 percent above expectation (seventh column). Such percentages ofincrease in excess deaths are statistically significant, because when measured
against historic volatilities of percentages of excess deaths (eighth column), theirsigmas (or z-scores) were 2.5 and 1.7 (last column) or p-values of 0.006 and0.045 respectively, indicating chance is improbable. Volatilities are calculated
based on scaling of percentage monthly excess deaths over 2015-2019.
Note that the statistical significance of excess deaths is even higher with asigma of 2.6 (p-value of 0.005) for the whole Australian population, because
the overall volatility of percentage excess deaths is lower for a larger sample.
The statistical significance is greatest for the nation as a whole, which isanother example of Simpson’s Paradox. Each age group may have confounding
factors adding ‘noise’ to affect their dose-response relationship. With different
idiosyncrasies of each age group having been washed out, the main factor
affecting all groups becomes clearer statistically.
The high statistical significance of the 2022 excess deaths in the Australianelderly is very clear, even without population adjustments. The Australianeconomy grows by around one percent per annum simply from immigration.
It may appear that demographic changes could affect our interpretation of
the data on excess mortality of the elderly. However, Australian immigrationis heavily biased in favour of the young because of skill shortages in various
sectors and the need to fill high levels of job vacancies. Immigration would
have little numerical effect on the elderly population.
The fact that COVID-19 injections have substantially accelerated the mortality
rates of the Australian elderly can be shown clearly and precisely in monthly
percentage excess mortality data since 2015 in Figure 9, which eliminates
seasonal fluctuations (not usually done).
Between 2015-2019 of the baseline, the monthly excess mortality of the 75-84

450

Too Many Deadage-group rarely exceeded plus or minus five percent of the baseline, withan annualized volatility of 9.7 percent, shown by the horizontal green line
in Figure 9. In 2022, the monthly excess deaths were well in excess of 15
percent (as high as 36 percent), about three times (as high as seven times) thebaseline. On an annual-average basis, 2022 was 24.4 percent above baselinecompared to 9.7 percent annualized volatility of the baseline. The statisticalsignal for high excess deaths in the Australian elderly is very strong, suggesting
the presence of a potent cause.

Figure 9

Obviously, excess deaths have potentially many different a priori causes and areunlikely to be caused entirely by a single factor such as COVID-19 injections,which may statistically provide 70 percent of the causality. For example, it
is difficult to dismiss the direct evidence from an autopsy of a 76-year-oldman[29] who had an unnatural death from improbable simultaneous multiple
organ failures linked to injection-associated spike proteins.
Unless there is another explanation which has better causal credentials, thenfrom the mortality data of this paper, the strong association between excess
deaths and COVID-19 injections would suggest probable causality, given that
other aspects of Bradford Hill analysis[1] are also supportive. Consequently,
this probable causality warrants serious attention and further investigation tojustify continued COVID injections.

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Australian Medical Professionals Society

7.

Summary discussion

This paper has examined the Australian official claim that COVID ‘vaccines’
reduce severe illness and death. After eliminating the empirical evidence fromother methods, which suffer from poor-quality source data,[3,6,7] this paper has
used more reliable real-world epidemiological data to analyse in detail whether
the official claim could be justified. Many published papers supporting this claim
were also based on epidemiological data, but have been shown to be invalid in
their interpretation mainly because of Simpson’s Paradox. Their supposed causal
associations would wrongly fluctuate randomly between positive and negative
effects over different time periods. The correlations of those studies are therefore
temporally inconsistent, because of incorrect temporal separation between
cause and effect to attribute consistent medical causality – they are examples ofcorrelation not being causation.
A temporal separation of 21 weeks or five months between the COVID injections
to cause the mortality effect has been proposed[1] as necessary to explain
consistently the Australian data. This suggests clinical trials needed, but have notbeen provided with, at least five months to observe serious adverse effects. As the
temporal separation from Australian data is consistent with those observed inother datasets from many countries,[28] it may reflect a genuine scientific fact:
the time required for as-yet-unknown processes of pathogenesis to cause most of
the deaths. Consequently, the observed temporality warrants further pathologicalinvestigation.[31,32]
Of course, there may also be other processes and temporalities which result indeath, but these have yet to be observed as empirical facts. Indeed, the lethality of
the injections could have a long tail, judging anecdotally by reported instances ofheart disease, cancer, neuropathy and more, many months following the COVID
injections, particularly in the younger age-groups. Data need to be accurately andsystematically collected for future research.
This paper has provided further proof to confirm the previous hypothesis[1] that
the COVID injections are the main cause of excess deaths reaching pandemic
levels in Australia. The injections explain the mystery of substantial numbers of
non-COVID deaths. This finding falsifies and contradicts the sole rationale ofcurrent official recommendation for the injections which are purported to reducesevere illness and death. On the contrary, this paper has shown clearly that
empirically the COVID injections substantially increase deaths, particularly in theelderly. Thus, COVID-19 injections do more harm than good for the vulnerable.
While these serious findings may not be surprising to those who read widely inthe available research, it is important to have established formally and scientificallythe occurrence of statistically significant iatrogenic excess mortality, which should
not be dismissed as misinformation.

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Too Many Dead8.

Conclusion

Earlier epidemiological evidence that COVID injections reduce illness anddeath has been refuted as an example of Simpson’s Paradox; instead, theevidence has shown increased iatrogenic deaths. Without taking the precautionof investigating the abnormally high excess deaths, Australia has continued toprioritize the elderly for COVID injections which the elderly cannot usuallyrefuse if they are in residential aged-care facilities.
The longer the authorities delay stopping widespread injections to conduct
a thorough investigation into the causes of excess deaths in Australia, thestronger is the implication that the excess deaths in the elderly are deliberate
policy, which is in effect iatrogenic geronticide. Geronticide is a serious
violation of human rights, because it is a morally reprehensible criminal act totarget intentionally older adults based on their age.

References
[1]

[2]
[3]

[4]

[5]

[6]

Sy, W. Australian COVID-19 pandemic: A Bradford Hill analysis of
iatrogenic excess mortality, J Clin Exp Immunol, 8(2), 542-556. https://www.opastpublishers.com/open-access-articles/australian-covid19-pandemic-a-bradford-hill-analysis-of-iatrogenic-excess-mortality.pdf
Centers for Disease Control and Prevention. Definition of Terms, Vaccines &Immunizations. https://www.cdc.gov/vaccines/vac-gen/imz-basics.htm

Therapeutic Goods Administration. COVID-19 vaccine safety report –12-01-2023. https://www.tga.gov.au/news/covid-19-vaccine-safety-reports/
covid-19-vaccine-safety-report-12-01-23

U.S. Food and Drug Administration, Reply to Citizen Petition (Docket
Number: FDA-2023-P-0360) from Coalition Advocating for Adequately
Labeled Medicines (CAALM) https://www.documentcloud.org/documents/23786932-fda-letter-on-covid-19-vaccine-labelingCenter for Disease Control and Prevention, Morbidity and Mortality Weekly
Report, February 3, 2023, COVID-19 Mortality and Progress Toward
Vaccinating Older Adults —World Health Organization, Worldwide,
2020–2022. https://www.cdc.gov/mmwr/volumes/72/wr/pdfs/mm7205-H.
pdf
Sy W, Mortality risk of COVID-19 injections: evidence from New South
Wales and England, Academic.edu. https://www.academia.edu/83924771/
Mortality_risk_of_COVID_19_injections_evidence_from_New_South_
Wales_and_England (accessed 30 July 2022).

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[7]

[8]

[9]

Sy W. Data reporting flaw in plain sight distorting COVID-19 mortality
statistics, Academia, 25 August 2022. https://www.academia.edu/85597731/
Data_reporting_flaw_in_plain_sight_distorting_COVID_19_mortality_
statistics

Rahmani K, Shavaleh R, Forouhi M et al. The effectiveness of COVID-19
vaccines in reducing the incidence, hospitalization, and mortality fromCOVID-19: A systematic review and meta-analysis, Front. Public Health, 26
August 2022, Sec. Infectious Diseases: Epidemiology and Prevention Volume
10 - 2022. https://doi.org/10.3389/fpubh.2022.873596

Simpson E. The Interpretation of Interaction in Contingency Tables, Journalof the Royal Statistical Society: Series B (Methodological), 13(2): 238–241.
doi:10.1111/j.2517-6161.1951.tb00088.x.
http://math.bme.hu/~marib/
bsmeur/simpson.pdf

Fraiman J, Erviti J, Jones M et al. Serious adverse events of special interest
following mRNA COVID-19 vaccination in randomized trials in adults.
[10]
Vaccine, 40(40), 5798-5805, 22 September 2022. https://www.sciencedirect.
com/science/article/pii/S0264410X22010283
Therapeutic Goods Administration. Nonclinical evaluation report, Document[11] 6, Submission No. PM-2020-05461-1-2, Pfizer Australia Pty Ltd. https://www.tga.gov.au/sites/default/files/foi-2389-06.pdf

Igoe M. Deborah Birx: US COVID-19 data was ‘worse than what I foundoverseas’, Devex 15 July 2022, (See video, quotes at 11.30 and 8.20 min).
[12]
https://www.devex.com/news/deborah-birx-us-covid-19-data-was-worsethan-what-i-found-overseas-103640[13]

Centers for Disease Control and Prevention. CDC 2019-Novel Coronavirus(2019-nCoV) Real-Time RT-PCR Diagnostic Panel, FDA News Release.
https://www.fda.gov/media/134922/download

Corman V, Landt O, Kaiser M et al. Detection of 2019 novel coronavirus(2019-nCoV) by real-time RT-PCR, Euro Surveill. 2020;25(3):pii=2000045.
[14]
https://www.ncbi.nlm.nih.gov/pmc/ar tic les/PMC6988269/pdf/
eurosurv-25-3-5.pdf[15]Papoutsis A, Borody T, Dolai S, et al. Detection of SARS-CoV-2 frompatient fecal samples by whole genome sequencing, Gut Pathog (2021) 13:7
https://doi.org/10.1186/s13099-021-00398-5

Center for Disease Control and Prevention, Guidance for Certifying Deaths
Due to Coronavirus Disease 2019 (COVID-19),Vital Statistics Reporting[16]
Guidance Report No. 3, Released April 2020 – Expanded February 2023.
https://www.cdc.gov/nchs/data/nvss/vsrg/vsrg03-508.pdf

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Centers for Disease Control and Prevention, COVID-19 Vaccine
Breakthrough Case Investigation and Reporting (Updated June 23, 2022),[17]
https://www.cdc.gov/coronavirus/2019-ncov/php/hd-breakthrough.
html#report[18]

Australian Bureau of Statistics, Provisional Mortality Statistics, Released
24/02/2023.
https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/jan-nov-2022

Actuaries Institute Australia, Catch up on the Actuaries Institute’s
COVID-19 Mortality Working Group’s latest analysis of excess deaths,[19]
COVID-19 Mortality Working Group, 6 March 2023. https://www.actuaries.
digital/2023/03/06/almost-20000-excess-deaths-for-2022-in-australia/
[20]

[21]

Sy, W. A critique of estimates of excess mortality from COVID-19 pandemic,
March 29, 2022, Principia Scientific International, https://principia-scientific.
com/a-critique-of-estimates-of-excess-mortality-from-covid-19-pandemic/

Watson O, Barnley G, Toor J et al. Global impact of the first year of COVID-19
vaccination: a mathematical modelling study, Lancet Infect Dis, June 23,2022; 22: 1293–302. https://doi.org/10.1016/S1473-3099(22)00320-6

Wang H, Paulson K, Pease S et al. Estimating excess mortality due to
COVID-19 pandemic: a systematic analysis of COVID-19-related mortality,[22]
2020-2021, The Lancet, March 10, 2022. https://www.thelancet.com/action/
showPdf ?pii=S0140-6736%2821%2902796-3[23]

CovidbaseAU, Australia COVID-19 Vaccinations. https://covidbaseau.com/
vaccinations/

Suthar A, Wang J, Seffren V et al. Public health impact of covid-19 vaccines
in the United States: observational study, BMJ 2022;377:e069317. http://[24]
dx.doi.org/10.1136/bmj-2021-069317
https://www.bmj.com/content/
bmj/377/bmj-2021-069317.full.pdf

Haas E, Angulo F, McLaughlin J et al., Impact and effectiveness of mRNA
BNT162b2 vaccine against SARS-CoV-2 infections and COVID-19 cases,
hospitalisations, and deaths following a nationwide vaccination campaign in[25]
Israel: an observational study using national surveillance data, Lancet.2021
May 15;397(10287):1819-1829. doi: 10.1016/S0140-6736(21)00947-8.
Epub 2021 May 5. https://pubmed.ncbi.nlm.nih.gov/33964222/
Redert A. Causal effect of covid vaccination on mortality in Europe,
ResearchGate Preprint, February 2023. https://www.researchgate.net/[26]
publication/368777703_Causal_effect_of_covid_vaccination_on_mortality_
in_Europe[27]

Aarstad J and Kvitastein O. Is there a Link between the 2021 COVID-19
Vaccination Uptake in Europe and 2022 Excess All-Cause Mortality? 18,
February 2023. https://www.preprints.org/manuscript/202302.0350/v1

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[28]

[29]

[30]

Kirsch S. Vaccines are taking an average of 5 months to kill people, 1September 2022. https://stevekirsch.substack.com/p/this-one-graph-tellsyou-everything

Mörz M. A Case Report: Multifocal Necrotizing Encephalitis and Myocarditis
after BNT162b2 mRNA Vaccination against COVID-19. Vaccines 2022, 10,1651. https://doi.org/10.3390/vaccines10101651
Department of Health and Aged Care, COVID-19 vaccination – vaccination
data, Australian Government. https://www.health.gov.au/resources/
collections/covid-19-vaccination-vaccination-data

Irrgang P, Gerling J, et al., Class switch towards non-inflammatory,
spike-specific IgG4 antibodies after repeated SARS-CoV-2 mRNA
[31]
vaccination, Sci. Immunol. 8, eade2798 (2023) 27 January 2023. Retrieved
from: https://www.science.org/doi/pdf/10.1126/sciimmunol.ade2798[32]

Uversky VN, Redwan EM, et al., IgG4 Antibodies Induced by RepeatedVaccination May Generate Immune Tolerance to the SARS-CoV-2 Spike
Protein. Vaccines. 2023; 11(5):991. https://doi.org/10.3390/vaccines11050991

Appendix: official data flaws
Among Australian health authorities, there is an absence of raw-data-download facilities available to the public for COVID research. The published data
in weekly and monthly surveillance reports are based on selections of flawed
COVID data[6,7] which are unprofessionally assembled. Inconsistent reports
which have misled the public cannot be corrected and resolved without accurate
raw data. A few examples of problematic official ‘vaccination’ data are given here.
Official data on COVID-19 ‘vaccination’ are assembled by the Department of
Health and Aged Care[30] collated from the data of eight state and territories
health departments. The data are made available to the public in approximately
170 separate, individually-named weekly EXCEL files.
Those EXCEL files do not satisfy the basic requirements of data tables according
to basic principles of data science because they are two-column or three-column
tables (since April 2023) consisting of arbitrary lists of descriptive items with
their associated numerical values, without data structure.
There are insurmountable problems to extract data reliably and accurately fromthe source. For example, to get the weekly time series of a particular item, say,cumulative total adult doses administered, one would have to open individually
about 170 files and look up the values for the item. Even this theoretically simple
task is impossible because item descriptors are inconsistent, as Table 5 shows.
In Table 5, the first column is the date, which specifies the filename, for456

Too Many Deadexample: “covid-19-vaccination-vaccination-data-31-July-2022.xlsx”, for thedata in the second and third column of the first row. The second and thirdcolumns of Table 5 are the contents from data files specified in the first column.
Table 5: Sample of Official Data on ‘Vaccination’

Date

Measure Name

Value

31/07/2022

National - Number of people 16 and over with 1 dose

20,160,781

31/08/2022

National - Number of people 16 and over who havereceived at least 1 dose

20,203,639

28/02/2022

Age group - 75-79 - Number of people with 1 dose

836,978

28/02/2022

Age group - 75-79 - Number of people fully vaccinated827,31028/02/2022

Age group - 75-79 - Population

773,742

28/02/2023

Age group - 75-79 - Number of people who have received at least 1 dose

890,892

28/02/2023

Age group - 75-79 - Number of people who have received at least 2 doses

885,026

28/02/2023

Age group - 75-79 - Population

807,195

The first two rows of Table 5 are from two data files one month apart; the field
descriptor has changed from adults ‘with 1 dose’ to adults ‘who have received
at least 1 dose’, without any notification or explanation. It is highly probablethey are the same data with their item names arbitrarily changed, since only
one or the other item exists in each data file. Such inconsistencies prevent
meaningful data extraction.
The next three data rows in Table 5 show the numbers of people vaccinated isgreater than the population, which is nonsensical. Moreover, it is unclear what‘fully vaccinated’ means – for instance, do they include the first boosters?
The last three data rows in Table 5 also show the numbers of people vaccinated isgreater than the population, which is also nonsensical. In the 75-79 age-group,
the number ‘with 1 dose’ is 836,978 and the number ‘who have received at least
1 dose’ is 890,892. Do we conclude that 53,914 have two or more doses? Is thisnumber included in those ‘who have received at least 2 doses’? These simple

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Australian Medical Professionals Societyquestions cannot be answered from the data provided. It is possible that theperson entering the data also did not know.
There are many more examples of ambiguity and inconsistency in the data
provided by the national health authority. For example, the data entry
convention of 14-day lag to register injection would imply inconsistencies[7] inthe raw data between persons dosed and total doses administered. From what
has been provided, it is impossible to reconstruct a proper relational databasewhich satisfies professional standards of data consistency and integrity.
Extraction of valid data from what is available would be a very hazardous andtedious exercise, prone to errors and inconsistencies. Having to interpret dodgy
data leads inevitably to making up data, opening the door to data manipulationand fabrication. This may explain the poor quality of official surveillance data
and reports – garbage in, garbage out. Importantly, deliberate data fraud inreports cannot be easily proved or ruled out.[6,7]Suffice to say, government agencies collecting data to support and justify
government policies[12] have inherent conflicts of interests, which can only
be managed if strict measures are in place to ensure data integrity. This has
not been the case for Australian COVID-19 data, which have serious flaws
resulting from inaccurate data collection and which are not organized inprofessional databases, making impossible the extraction of reliable data.

Dr Wilson Sy has held senior positions in academia,
industry and government. He is a quantitative andstatistical analyst with several dozens of journal
publications in physics, finance, economics, biology
and medicine. He is highly experienced in data analysis
in business and government. He has held positions
as Principal Researcher at the Australian Prudential
Regulation Authority (APRA) and as senior adviser tothe Australian Treasury for the Super System Review.
Recently, he has published several journal articles
on epidemiological data analysis of the COVID
pandemic.

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Too Many Dead

Excess mortality
and bereavement
by Monique O’Connor
MBBS FRANZCP

Introduction
Every death has a profound human cost at the individual, family and societal
level, particularly if that death is premature or unexpected. Excess mortality
in Australia and around the world translates to an excess prevalence of people
who are bereaved. This is directly associated with increased personal andsocietal burden of psychosocial, mental health and economic harms and needs.

Normal bereavement

The death of a loved one automatically causes bereavement and a period of acutegrief[1] that is typically associated with functional impairment and profoundemotional pain.
Grief is a natural and expected process. In most bereavements well-being andnormal function resume, albeit with permanent but less troublesome residual
grief, after a period of mourning.
The death of a close loved one is known to be a major life stressor. Bereavement
is recognised to be associated with elevated physical and mental health risks.[2]Premature or unexpected death can be a damagingly transformative life event

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for those left behind (spouses, children or parents). Close bereavement may
bring about enduring detrimental developmental, psychosocial and economicchanges.

Prolonged Grief DisorderIf grief does not attenuate naturally over time (at least a year), the impairment
and intense emotional pain can persist indefinitely. When this occurs, arecognised mental health condition is identifiable, classified as Prolonged
Grief Disorder (PGD) in DSM5-TR and ICD-11.[3,4]Definition
Prolonged Grief Disorder (PGD) is a recognised psychiatric condition
that is impairing; when present, it is associated with serious risks, including
suicide, and requires treatment. The impairment in function can have negative
consequences on the ability to work, parent, carry out responsibilities, study
and so forth. The hallmark of PGD is debilitating grief that persists longer than
expected in social, cultural or religious norms. The grief is preoccupying andcharacterised by longing and yearning for the deceased. It is often associatedwith intense emotional pain, a sense of disbelief, emotional numbness, social
disconnection, profound loneliness and identity confusion.
Risk of Prolonged Grief Disorder
There are many recognised factors that place the bereaved at risk of developing
PGD. These include adversity in childhood, prior losses, trauma, a historyof mental illness and an unexpected or premature death. Also, a very close
relationship or difficult circumstance surrounding the death increase the riskof PGD.[1,2]
It is noteworthy that deaths which occurred during COVID-19 pandemic
restrictions likely have an elevated risk of Prolonged Grief Disorder. The
inability to visit the sick or dying, difficulty or inability to travel to be withfamily, isolation in quarantine whilst bereaved, the inability to attend funerals
and wakes, to follow religious or cultural practices and exposure to traumaticdeaths in which PPE or ventilation of patients was used will have contributed
to this elevated risk.
Prevalence
PGD occurs in at least 7-10% of close bereavements;[5,6,7] it is probably higher
with bereavements complicated by pandemic measures that inhibited normal
grieving.

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Too Many Dead
Comorbidity
High rates of comorbidity occur with PGD, including PTSD, depression,
substance abuse and suicidality.[1,8,9,10]
Management of Prolonged Grief Disorder
The treatment of choice is condition-specific psychotherapy, delivered by atherapist trained in Prolonged Grief Therapy.[11,12,13,14]

Societal costs of Prolonged Grief Disorder

The serious nature of PGD equates to individual and societal costs.[2] The
number of bereaved following each death varies. Those closely bereaved are
at risk of PGD, especially following spousal or child death. The number ofclosely bereaved (that is, those with a close, important and loving relationship)
is roughly five or six for each death. The number of bereaved negatively affectedby a sudden or premature death can be substantially higher. A study by Cerel
et al.[15] found 135 people affected by a single suicide bereavement.
The expected number of cases of PGD can be estimated in broad terms by thefollowing equation:[Number of deaths] x 5 (the closely bereaved) x 10%
The Australian Bureau of Statistics deaths and mortality data[16,17] can beused to estimate expected cases of Prolonged Grief Disorder.
•

Expected new cases of PGD caused by deaths in 2022

There were 190,939 registered deaths in Australia in 2022[16]5 x 190,939 = 954,695
10% of 954,695 (that is, the expected number of cases of PGD following
2022 deaths) = 95,467 cases of PGD in 2022•

Expected new cases of PGD Caused by Excess deaths in 2022
compared to 2021

There were 19,470 more deaths in Australia in 2022 compared to2021[16]5 x 19,470 = 97350 closely bereaved excess bereavements in 2022.
10% x 97350 = 9,735 expected excess cases of PGD resulting fromexcess death in 2022

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A more accurate number of excess deaths in 2022 would be to compare thenumber of deaths in 2022 with a pre-pandemic measure baseline number rather
than the 2021 number, but that unfortunately has not been provided by theABS. However, my calculations are for illustrative purposes. It is noteworthy
that the death rate for males and females aged 25-44 increased by 8.0% and5.1% respectively in 2022, and death rates for those aged 45-64 years were thehighest in the 10 years.[17] These represent premature deaths likely to havebeen unexpected. Sudden cardiac deaths are an example of a premature deathin which the following risk of PGD is high.

Discussion

Grief caused by excess deaths burden the already overloaded mental healthservices and contribute to additional harm and suffering of the Australian
population.
Australia has a severe shortage of mental health services and skilled clinicians.
The management of Prolonged Grief Disorder is a specialised area of mental
health service delivery, which is not widely available in Australia.
It is essential to recognise and prioritise the high human cost of bereavement
and consequent grief, together with the need for specialist clinical services tocare for those bereaved. This is especially so in the context of the present where
there are concerning numbers of excess deaths in Australia.

References
[1]

[2]

[3]

Szuhany KL, Malgaroli M, Miron CD, Simon NM. Prolonged Grief
Disorder: Course, Diagnosis, Assessment, and Treatment. Focus(Am Psychiatr Publ). 2021 Jun;19(2):161-172. doi: 10.1176/appi.
focus.20200052. Epub 2021 Jun 17. PMID: 34690579; PMCID:
PMC8475918.
Shear MK, Simon N, Wall M, Zisook S, Neimeyer R, Duan N,
Reynolds C, Lebowitz B, Sung S, Ghesquiere A, Gorscak B, Clayton
P, Ito M, Nakajima S, Konishi T, Melhem N, Meert K, Schiff M,
O’Connor MF, First M, Sareen J, Bolton J, Skritskaya N, Mancini
AD, Keshaviah A. Complicated grief and related bereavement issues
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org/10.1176/ appi.books.9780890425787

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Nielsen MK, Carlsen AH, Neergaard MA, Bidstrup PE, Guldin
MB. Looking beyond the mean in grief trajectories: A prospective,
population-based cohort study. Soc Sci Med. 2019 Jul;232:460-469.
doi: 10.1016/j.socscimed.2018.10.007. Epub 2018 Oct 19. PMID:
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Lundorff M, Holmgren H, Zachariae R, Farver-Vestergaard I,O’Connor M. Prevalence of prolonged grief disorder in adultbereavement: A systematic review and meta-analysis. J Affect Disord.
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Jan 23. PMID: 28167398.
Keyes KM, Pratt C, Galea S, McLaughlin KA, Koenen KC, Shear MK.
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Cerel J, Brown MM, Maple M, Singleton M, van de Venne J, Moore
M, Flaherty C. How Many People Are Exposed to Suicide? Not Six.
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sltb.12450. Epub 2018 Mar 7. PMID: 29512876.
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Dr Monique O’Connor MBBS FRANZCP graduatedin medicine from Kings College, University of London,
and trained in psychiatry in Western Australia. A
consultant psychiatrist and Australian expert in grief,
she has had broad experience in clinical psychiatry, withspecial interest in Psychiatry of Old Age and Palliative
Care. She was the Director of Advanced Training in
Psychiatry of Old Age in WA for five years. In 2017 sheestablished a private practice specialising in grief andproviding Prolonged Grief Disorder Therapy.
Dr O’Connor shares her expertise through teaching,supervision and academic publication. She is an advocate
of grief education promotion in health sciences. Her
ambition is to improve management of high-riskbereavements, with the aim of reducing the burden ofchronic mental illness and suicide risk associated withprolonged grief.

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Conclusion

This book has been assembled by an aggregation of highly-qualified andexperienced doctors and researchers who take their ethics seriously. Here,information has not been withheld, or, worse, blocked.
In a letter to AMPS, in January of 2023, the then-Australian Secretary of the
Department of Health and Aged Care, Brendan Murphy, wrote: ‘Regarding
excess mortality statistics, there is no credible evidence to suggest that
excess mortality is related to COVID-19 vaccination either in Australia orinternationally. The Actuaries Institute’s COVID-19 Mortality Working
Group are independently publishing regular analysis of excess deaths. The
Working Group have concluded that vaccination impacts on excess mortality
are negligible and do not fit with the timing or shape of excess mortality.’ It isvery telling that Murphy thereby skirts the issue. If the excess mortality rates
are not being grossly raised by the mRNA injections, what are they being
raised by? Would not this be the central concern? Or is denial more important?
‘No credible evidence….’ These are very peculiar words. On the day this bookwas launched, AMPS had access to well over three thousand five hundredstudies casting extreme doubt on the integrity of the COVID-19 injections,and pointing to untold damage from them. All of these studies have been
written by highly-qualified people; only a few of them could be selected for

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this book. They offer vast scientific evidence about the inappropriateness,the damage and the danger found in the Australian government’s slavish
adaptation and succumbing to what can be distilled into the demands ofthe world’s biggest pharmaceutical companies. Drug-company medicine has
overtaken the medical institutions, and rammed its methods home to thepoint where those students trained in them are finally ejected into practicebelieving in pharmaceutical products to the extent of religious evangelism. The
money expended in purchase and disbursement of these products, now shown
never to have worked, has crippled economies worldwide as it has passed tothe drug companies.
‘No credible evidence’?
For three years Australia succumbed to the will of corporate ideologues in big
pharma, big media, big finance, and big government who seem to prioritise
profit over people. This enslavement, and it is nothing less, may help to explainthese high excess death rates being witnessed now at more than 15 per centabove baseline mortality. Put another way, AMPS and all ethical and informed
doctors are horrified at the ten or twenty or thirty thousand excess deaths inthis country in the time since March, 2021. Australian and other Western data
show a mass casualty event; peculiarly, the higher figures are occurring in the
countries that are highly injected, but our political and medical authorities
seem to think there is nothing worth scrutiny. Now, with this book, it has beenprobed. This investigation has had to cut straight across the lockstep media
messaging, the medical misinformation and the censorship.
Big pharma has created a fine business model for itself, but what has been
the cost to humanity? Until Australia’s policymakers become more awareof the influence of big pharma and the technological big food and private
industrial complex, policies will continue to reflect decisions not grounded in
medical thinking. Big pharma ought not to be allowed to continue funding
medical journals and university chairs because the end result of this is highprofit combined with weak medicine. Considering the criminal records andthe level of penalties (in the form of extraordinary fines) on multinational drug
companies, the TGA needs to focus on the integrity of the drug companies.
Their databases accordingly need to be transparent. Is a peak body of pharmacovigilance called for?
In the presence of unprecedented death rates and adverse reactions, and theignoring of safety signals, why has the Therapeutic Goods Administration, thebody meant to stand guard, not withdrawn the COVID vaccines, pending afull investigation? It defies logic. Is this wilful blindness?
Australians have endured relentless psychological manipulation through468

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media and propaganda, misinformation from authorities, and medical
censorship designed to create an illusion of consensus by our own government
and authorities entrusted with our protection. It became obvious quickly that
this was not about ‘following the science’ or ‘keeping people safe’. It was about
the establishment and the enforcement at any cost of a system imposed fromabove by people misusing their authority. The nation has been through darktimes and there now needs to be an accounting.
Australian people were tracked, traced, quarantined, shamed, and finallythreatened into submitting to a vaccination-only strategy. On the streets
to protest they were shot with rubber bullets. Governmental figures haveshown how these gene-based pharmaceuticals were ineffective from the start.
Now the health system continues to advocate perpetual boosters, which inthemselves and by their repetition show how incompetent the novel vaccine
platform is in the protection of patients from contracting COVID. The publichealth system appears to have been used against a trusting population forwhat seems financial benefit in terms of corporate profits, and all with theassistance of misinformed politicians. Australians have a right to feel violated.
If medical regulators believe their job is primarily to protect the government
public health messaging rather than to protect the public then the corruptionof medicine is complete.
The Australian Medical Professionals Society is joining hands with more
and more of the likeminded, people who believe in first doing no harm, in
having informed valid voluntary consent, and protecting bodily autonomy inaccordance with the longest-standing principles of civilized medical practice.
We believe medicine in Australia exists to serve the people, and it should neverbe as it is now, the other way around.
The Australian Health Practitioner Registration Agency (AHPRA) has many
perceived negatives and has demonstrated no adequate ability for oversight, as
this book has amply shown. If it continues to exist then that should be solely
as a registering body. This of course cannot be said by individual practitioners
who see through the holes in the present system, because the system will
immediately punish them and so they will be suspended. Practitioners mustregain the right to speak freely, for this reason, and also to be able to call out
research fraud.
Regulatory agencies should not be in a position to take money from thebig pharmaceutical companies. Australian people need protection frompredatory corporate bodies, those in pharmaceuticals, in food and in
agriculture. In particular, AMPS finds that the regulators’ job is to assess theinformation presented by vested interests. Data have to be released, and need
to be open-sourced in the raw form to independent researchers. Otherwise,469

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censorship is too easily applied, to the detriment of the facts.
This present book is finding its way to medical and political authorities both inAustralia and overseas. The introduction to the book ended with a quotationfrom Winston Churchill. The Australian Medical Professionals Society has
every intention of driving forward this debate to the point of prompting
substantial governmental action to rectify serious problems within our healthsystem, and it may be appropriate to finish with another. ‘Now, this is not theend. It is not even the beginning of the end. But it is, perhaps, the end of the
beginning.’

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