Extended: Analysis of COVID-19 Vaccine Death Reports from the Vaccine Adverse Events Reporting System (VAERS) Database

Abstract

In 2021 we presented an interim analysis of reported deaths associated with Covid-19 using data from the Vaccine Adverse Events Reporting System (VAERS). This work applies the same analytical approach used on the original 250 reports from the December 2020 to March 2021 VAERS dataset, to a larger collection containing 1012 reports from the December 2021 to March 2022 VAERS dataset. Crucial differences between both data sets are that in the original data set the vaccination regime was targeted primarily to the elderly and front-line healthcare workers, while in this more recent dataset the vaccines were broadly available to most of the community. Our analysis indicates that: (i) as the vaccines were rolled out to each younger age group, VAERS reports for that age group also grew; (ii) that the identified disparity between male and female VAERS death reports in the 2021 cohort became less pronounced in the 2022 cohort; (iii) that cardiac and diabetic comorbidities continue to be significantly correlated with a VAERS death report; and (iv) that almost half of all death reports in the 2022 cohort include evidence of the individual having been diagnosed with a breakthrough Covid-19 infection. While there are concerns regarding the accuracy and quality of the data recorded on the VAERS system, there is a precedent for using this database. Even taking these concerns into account, the present analysis is consistent with many of the insights and key determinants of mortality identified in the previous analysis. Compared to adverse events associated with all other vaccines in the VAERS database, there is a significant increase in deaths associated with covid-19 vaccines.

Full text

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Extended: Analysis of COVID-19 Vaccine Death
Reports from the Vaccine Adverse Events
Reporting System (VAERS) Database
Scott McLachlan1,3, Martin Neil2,4, Yvonne Choi3, Clare Craig, Jonathon Engler,
Kudakwashe Dube3, Magda Osman5, Norman Fenton2,4
1 School of Nursing, Midwifery and Palliative Care, King’s College London, London, UK
2 Risk and Information Management Group, Queen Mary University of London, London, UK
3 Health informatics and Knowledge Engineering Research (HiKER) Group
4Agena Ltd, Cambridge, UK
5Centre for Science and Policy, University of Cambridge
In 2021 we presented an interim analysis of reported deaths associated with Covid-19
using data from the Vaccine Adverse Events Reporting System (VAERS). This work
applies the same analytical approach used on the original 250 reports from the
December 2020 to March 2021 VAERS dataset, to a larger collection containing 1012
reports from the December 2021 to March 2022 VAERS dataset. Crucial differences
between both data sets are that in the original data set the vaccination regime was
targeted primarily to the elderly and front-line healthcare workers, while in this more
recent dataset the vaccines were broadly available to most of the community. Our
analysis indicates that: (i) as the vaccines were rolled out to each younger age group,
VAERS reports for that age group also grew; (ii) that the identified disparity between
male and female VAERS death reports in the 2021 cohort became less pronounced in
the 2022 cohort; (iii) that cardiac and diabetic comorbidities continue to be
significantly correlated with a VAERS death report; and (iv) that almost half of all
death reports in the 2022 cohort include evidence of the individual having been
diagnosed with a breakthrough Covid-19 infection. While there are concerns
regarding the accuracy and quality of the data recorded on the VAERS system, there
is a precedent for using this database. Even taking these concerns into account, the
present analysis is consistent with many of the insights and key determinants of
mortality identified in the previous analysis. Compared to adverse events associated
with all other vaccines in the VAERS database, there is a significant increase in deaths
associated with covid-19 vaccines.
1. Introduction
In June 2021 we released our first investigation into deaths reported to the Vaccine Adverse
Events Reporting System (VAERS) (McLachlan et al, 2021). Clinicians, nurses, midwives,
health informaticians, a statistician and a mathematician were involved in analysing 250 early
post-Covid-19 vaccination death reports. These were people who had been vaccinated shortly
after the vaccination programme started. They were primarily the elderly and front-line
healthcare workers. The objective of our first investigation was to detect and quantify the
possible relationships between the Covid-19 vaccines with age, comorbidity and death, and to
identify the number of VAERS reports made by clinicians and health service employees.

2
That initial investigation discovered that: (i) the majority of deaths (73.6%) occurred in the
elderly - those over 70 years of age (unsurprising given that during this period the elderly had
been prioritised for vaccination); (ii) 50% of all reported deaths occurred within 48 hours of
injection and 80% within 7 days, suggestive of a temporal link; (iii) there was a 900% increase
over previous years in vaccine-related VAERS death reporting in early 2021; (iv) the number
of VAERS death reports per million vaccinations had increased by 1677% in just 3 months;
(v) only around 14% of the reported deaths were clearly dismissible as not vaccine caused; and
(v) at least 72% of the reviewed reports were authored by qualified clinicians and other allied
health workers and not predominantly by family members or anti-vaxxers
1
as the fact checkers
2
and mainstream media implied (Jarry, 2021; Wadman, 2021).
The new work presented here reviews 1012 VAERS death reports from January to March 2022,
downloaded from the United States of America’s Health and Human Services (HHS) website
3
.
There are several key differences in the background factors between this new cohort and the
cohort investigated in our previous study. First, while in our earlier analysis Covid-19
injections were only available for prioritised sub-groups including the elderly and front-line
healthcare workers, they are now available to almost every age group
4
. Second, in contrast to
our first analysis that reviewed reports from the first weeks that Covid-19 injections were
available and hence almost all (91.4%) had only received a single dose, this new investigation
covers a period twelve months later when people have had the opportunity to receive two or
more doses. Given we regularly see government claims of significant uptake
5
and compliance
with mandates for second and subsequent doses
6
we expect to see more people in this report
who are considered by the guidelines of our health ministries as fully vaccinated
7
. As a result
of these differences in background factors we considered that the two cohorts were not directly
comparable and hence did not combine them.
2. Background: The definition of vaccination, vaccines and vaccine
adverse event reporting
Scientific disagreements persist concerning almost every aspect of SARS-CoV-2, Covid-19
and the global response. Of all the contested issues surrounding Covid-19 none has become
more contentious than that of the Covid-19 vaccinations. Disagreement exists on whether: they
are effective at preventing transmission and blocking infection (Stokel-Walker, 2022) or not
1
It should be noted that the term anti-vaxxer has been so abused by the fact checkers and mainstream media during 2021-2022
that it ceased to mean people who are fervently against vaccines - and is now used to disparage people who may actually be
fully vaccinated against Covid-19 but are against vaccine mandates or digital vaccine passport apps. This change in meaning
can now be found in some online dictionaries such as the Merriam-Webster - who define an anti-vaxxer as “a person who
opposes the use of vaccines OR regulations mandating vaccines.” See: https://www.merriam-webster.com/dictionary/anti-
vaxxer
2
https://www.reuters.com/article/uk-factcheck-vaers-idUSKBN2AE0QQ
3
https://vaers.hhs.gov/data/datasets.html
4
At the time of writing two paediatric doses of Covid-19 vaccination are available inter alia to American, Australian, New
Zealand, Canadian and UK children from as young as 6 months of age.
5
As of April 15, 2022 the American government (CDC) claims 77% of all US citizens 5 years of age and older have received
a first dose, 60% are fully vaccinated with two doses, and 30% have received a booster dose.
https://www.nytimes.com/interactive/2020/us/covid-19-vaccine-doses.html
6
In the same data reported in footnote 2 the CDC claim that 90% of those over 60 are fully vaccinated and that 61% have gone
on to receive at least one booster.
7
Even though several governments are considering second boosters (i.e. a fourth shot) and at least one government is
considering a third booster (i.e. a fifth shot), at the present time the minimum is at least two primary doses and a single booster
(three doses in total).

3
(Singanayagam et al, 2022); they have saved lives
8
or not
9
; and even whether or not
administration of the mRNA-based injections fits within the blanket term vaccination such that
the CDC is said by some to have altered the definition in order to make them qualify
10
.
It is important to understand the motivation for why we do something. There is a vast difference
between doing something I am told is both low risk and preventative for me catching or dying
from a known deadly disease, versus doing the same thing with even an assumed minor risk to
me but where the disease profile means my own life was never really in danger from the disease
- and the intention has been framed as protecting some other person at some future date. The
definition of vaccination focuses on the intended purpose for the vaccine - in effect, why we
are being given (or consenting to receive) the injection. When we look at the intention of early
vaccines such as Jenner’s cowpox variolation to inoculate against smallpox or Pasteur’s rabies
vaccine, the purpose was demonstrably to prevent illness in the recipient by exposing them to
an attenuated or denatured version of the infectious organism, thus protecting them when they
later become exposed to the wild virus (CDC, 1985; Reidel, 2005). This disease prevention
intention sounds more resolute and makes the why of vaccines an easier sell, and vaccination
by this approach of mild exposure is observed in even recent vaccine studies (Nigrovic &
Thompson, 2007) and the CDC’s pre-2015 definition for the term vaccination shown in Figure
1.
Figure 1: Evolution of the CDC definition for vaccination
As can be seen, the CDC definition for vaccination has changed the purpose from an intention
to prevent, to one which seeks to produce immunity in 2015, and more recently in 2021 at the
height of the global Covid-19 vaccine rollout to one intended to produce protection. While the
CDC suggest that these slight changes in wording are innocuous and haven’t impacted the
overall definition (Camero, 2021), when we consider the terms that have been changed and the
way the CDC definition is couched, there appears a strong inverse relationship with the
tolerability of failure of the vaccine (i.e. it simply failing to do what it should). An intention to
prevent disease is significantly stronger and less tolerant to failure than one that will only
produce immunity, which in turn was stronger than the latest that will only produce protection.
With each change the strength of purpose is weakened - and this can even be seen in how
vaccine failure is now discussed in the media and hence, perceived by the public.
8
https://www.theguardian.com/theobserver/commentisfree/2021/jul/04/covid-vaccines-saved-lives-england-but-why-do-
estimates-differ
9
https://www.normanfenton.com/post/comparing-all-cause-mortality-rate-by-age-group-vaccinated-v-unvaccinated
10
https://twitter.com/FiberCarb/status/1435756292283437060 and
https://twitter.com/Zinnsgh0st/status/1435767014757306372

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It is also important to understand how issues that arise from doing something will be monitored,
and who will be responsible for managing the monitoring system and making decisions
regarding the data that must be collected. As with our previous analysis (McLachlan et al,
2021), the work presented here is helped by the fact that the United States of America (USA),
in contrast to other countries, provides granular datasets for post-licensure surveillance of
vaccines through their publicly accessible VAERS database. In contrast, while transparency is
promised
11
, the pharmacovigilance approaches of the United Kingdom (UK Yellow Card
System
12
), Australia (AUS Adverse Event Management System
13
), and New Zealand (NZ
Adverse Event Reporting Form
14
) still tend towards releasing only summarised statistics. These
cover patient age, symptoms and in some cases, comorbidities, but do not enable reviewers to
determine whether each symptom or comorbidity occurred alone, or whether there are
underlying relationships between age, symptoms and comorbidities that may be more likely to
produce a severe adverse event. The UK Government’s own advice on what to include in your
yellow card of an adverse event reaction
15
does not stipulate inclusion of vital information such
as current illnesses, other medications being taken, and any diagnosed comorbidities. These
items are considered later, and only as additional information - in language couched to suggest
they are supplementary and thus belying their potential importance. This means that key data
that might lead to detection of a safety issue with a new medicine is not systematically collected
and even when it is, in contrast to the promises of transparency the reporting formats of the
UK, AUS and NZ regulators, this means that it is not being made available to the public in a
form that enables analysis and consideration. It has been known since 2004 (Miller, 2004) that
national adverse event reporting systems are clearly inadequate, or else fallacious, and so are
unfit for the purpose of providing reliable records for analysis of the incidence of vaccine-
related adverse health events.
However, there is also the issue of what a potential source of monitoring data can tell us, and
when a previously acceptable risk may be found intolerable. VAERS was implemented as a
tool for post-market surveillance of vaccines. VAERS collects data about adverse events and
harmful side effects that occur after vaccine administration. VAERS accepts reports from
clinicians, allied healthcare workers and the general public, and it is this last class of reporter
that has been strongly suggested as the weakness that makes Covid-19 vaccine-related VAERS
data meaningless - open to unverified reports by the general public and fake reports by anti-
vaxxers (Jarry, 2021; Motta & Stecula, 2021; Wadman, 2021). However, it is known that the
CDC prunes those reports that their staff consider to be fake (Wadman, 2021), and while this
should alleviate at least some of the claims that ‘anti-vaxxers are polluting the data’ with false
reports, it has also resulted in real reports by healthcare workers also being removed
16
. Some
11
The UK’s MHRA’s Delivery Plan 2021-2023 promises as Objective 5: to make transparency more prominent
(https://www.gov.uk/government/publications/the-medicines-and-healthcare-products-regulatory-agency-delivery-plan-
2021-2023/medicines-and-healthcare-products-regulatory-agency-delivery-plan-2021-2023) and many of their press releases
talk about such things as enshrining transparency requirements into law (https://www.transparimed.org/single-post/mhra-
consultation).
12
United Kingdom Medicines and Healthcare products Regulatory Agency (MHRA) Yellow Card Reporting System:
https://yellowcard.mhra.gov.uk/
13
Australian Therapeutic Goods Administration (TGA) Adverse Event Reporting on the Adverse Event Management System
(AEMS) website: https://aems.tga.gov.au/
14
New Zealand Medsafe Vaccine Event Reporting Form: https://www.medsafe.govt.nz/COVID-19/adverse-event-reporting-
form.asp
15
Found here:
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/404416/What_to_include_
in_your_Yellow_Card_of_an_adverse_drug_reaction.pdf
16
https://twitter.com/kacrn91/status/1519861568036556800?s=20&t=6i0nh_wnYBTi8y-IKfNlsg

5
advocates
17
for vaccine makers have even claimed that vaccine injury claims do not even have
to be scientifically valid to unsettle the pharmaceutical giants who we are deceptively led to
believe are not making very much money from their vaccine endeavours (Jarry, 2021). For
example: (i) Pfizer have reportedly made more than $37bn from sales of the Covid-19
vaccine
18
; (ii) GSK made more than $25mil from LYMErix sales in the year it was on the
market (2000), and more than $842mil from the four vaccines they sold during that year
19
; and
(iii) Moderna - a company that had previously never offered a product for sale, has already
reported an $18.5bil
20
profit on the approximately $1.4bil they received from their government,
taxpayer
21
and philanthropist-funded
22
Covid-19 vaccine. These amounts can hardly be
described as not very much money. Leaving aside profit, we resolved the issue regarding
whether anti-vaxxers were making false reports against vaccines and hence tainting VAERS in
our first study when, on analysis of the textual narrative for each report, we were able to
conclude that at least 72% of the 250 reports we reviewed were authored by clinicians, allied
health workers or the staff of pharmaceutical companies that had manufactured the vaccines
(McLachlan et al, 2021). While the remaining 28% were adjudicated to have been made by lay
persons (family and friends of the deceased), 3% of the 250 total reports we reviewed - all
coming from those we judged to have been written by lay persons, showed evidence that the
VAERS call centre employee (the recorder) had provided their own narrative, interpretation
or commentary on the information that had been provided by the lay person (the reporter). So,
if the reporter is not as significant an issue as journalistic fact checkers would have us believe,
the question becomes what can we learn from careful and cautious consideration of the VAERS
dataset?
VAERS data analysis has previously shown that vaccines may be over-activating the immune
system and several
23
are linked to incidence of a condition called Polymyalgia Rheumatica that
is currently the subject of several class action lawsuits in the United States of America
24
(Bassendine & Bridge, 2020; Falsetti et al, 2020; Liozen et al, 2021; Manzo et al, 2021).
VAERS data regarding more than 64,000 cases of adverse events, including 547 deaths, has
been shown to demonstrate a link between HPV/Gardasil vaccination and the incidence of AEs
at different rates in different ethnic groups (Huang et al, 2018), and has been presented as
evidence in the complaints of as many as seven lawsuits filed against Merck and Kaiser and
their HPV/Gardasil vaccines (including Brunkner v Merck anors in the Superior Court of the
State of California
25
, Sullivan v Merck & Co in the Superior Court of New Jersey
26
, and Dalton
v Merck & Co in the United States District Court for the Eastern District of Michigan
27
). The
complaints in these cases also allege that Merck, rather than anti-vaxxers, has submitted
fraudulent reports to VAERS to conceal Gardasil’s link to the deaths of teenagers
28
. Finally,
17
Who also happen to be journalist fact checkers. McGill University’s Office for Science and Society, funded by private
philanthropists like Matrox’s Lorne Trottier and Microsoft’s Bill Gates, has a strong and possibly singular focus not on the
evidence-based science they claim to champion, but on targeting and browbeating anyone who questions vaccines at all, and
who they accordingly classify as anti-vaxxers: a vexatiousness they demonstrate to striking effect in their half-page article in
the Summer 2019 McGill News Alumni Magazine.
18
https://www.theguardian.com/business/2022/feb/08/pfizer-covid-vaccine-pill-profits-sales
19
https://www.gsk.com/media/4698/annual-report-2000.pdf
20
https://www.pharmaceutical-technology.com/news/moderna-reports-revenue-2021/
21
https://www.forbes.com/sites/judystone/2020/12/03/the-peoples-vaccine-modernas-coronavirus-vaccine-was-largely-
funded-by-taxpayer-dollars/
22
https://www.genengnews.com/topics/omics/moderna-wins-initial-20m-grant-from-gates-foundation/
23
Specifically vaccines for: Hepatitis A, Hepatitis B, Influenza, MMR, PPV, DTaP and Varicella.
24
https://www.mctlaw.com/vaccine-injury/polymyalgia-rheumatica/
25
https://www.baumhedlundlaw.com/documents/Brunker-Gardasil-Complaint-conformed.pdf
26
https://www.baumhedlundlaw.com/documents/Gardasil-complaint-lawsuit-Sullivan-Emma.pdf
27
https://www.baumhedlundlaw.com/images/COMPLAINT-Ashley-Dalton-V-Merck-Co-10-01-2021.pdf
28
For example, see note 30: Dalton v Merck & Co at para 309 of the complaint.

6
research has demonstrated that VAERS data can be used effectively to detect systemic vaccine
adverse events (Wang et al, 2018).
Other things we know about VAERS include that several government investigations have
resolved that vaccine adverse events are considerably underreported in the VAERS dataset,
that concerns regarding instances where clinicians were discouraged from reporting post-
vaccination health events that resulted in lost days of employment or hospitalisation were
founded, and that some administering clinicians did not report to VAERS for fear of potential
legal liability for the adverse event following vaccination (IOM, 1997; Jollenbeck et al, 2002).
And finally, that even the US Congress’ General Accounting Office found that vaccine
manufacturers misleadingly understate the potential for adverse events - with the military
Anthrax vaccine deployment demonstrating a significant adverse event rate almost 200%
higher (85%) than the manufacturer’s claim (30%) (Roos, 2002).
In the late 1990’s and just like we have seen for the Covid-19 vaccines, the GlaxoSmithKline
(GSK) LYMErix vaccine received significant media attention both because it failed to produce
any immunity in more than 20% of recipients, and more significantly because post-marketing
monitoring data linked it to incidence of long term and chronic disease for a small number of
recipients who became known as vaccine victims
29
(Nigrovic & Thompson, 2007; Sheller,
2013). Like many have maintained about the Covid-19 vaccines, LYMErix was rushed to
market without adequate testing and, even more significantly, in spite of strong evidence to the
contrary the media then, as now, claimed there was no indication of any long-term adverse
reactions (Sheller, 2013). Within a year of its release LYMErix was voluntarily withdrawn by
GSK. The public simply could not tolerate a vaccine that not only did not prevent disease in a
large group of recipients, but one which was also found to produce adverse reactions in small
groups of recipients. The CDC’s more recent shift from vaccines for disease prevention to
vaccines to produce protection has allowed pharmaceutical company and media narratives to
adapt as it has become clear that a previous claim about the Covid-19 vaccines was no longer
a fair representation of what was actually happening. This has seen the Covid-19 vaccines go
from being pronounced as 90-95% effective at preventing Covid-19 disease (Herper, 2020) to
their being described as 80% protective against severe disease or death
30
, and more recently,
simply as preventing severe illness (Benmeleh, 2021). As with the CDC definition for
vaccination, with each change in headline the purpose for giving (or receiving) Covid-19
vaccination has weakened.
As with the example of LYMErix, determination of their safety is actually the most pressing
issue - and is the primary focus of this report. In particular, we consider whether the observed
safety signal is significantly stronger than that which has historically triggered regulators to
withdraw or recall medicines or vaccines from the market that were deemed unsafe for
continued use. There may already exist sufficient data to demonstrate that the costs (based not
just on the cost-per-dose for the billions of doses already administered, but also on the negative
impacts to recipient’s health) outweigh the benefits (prevention of an individual becoming
infected with the target disease and transmitting it to others
31
) and suggest we should pause
and reconsider their continued administration.
29
Nigrovic & Thompson discuss the cohort that proceeded to trial as consisting of as few as 121 persons.
30
https://www.who.int/news-room/feature-stories/detail/vaccine-efficacy-effectiveness-and-protection
31
Several studies have identified high number needed to vaccinate (NNV) values for the covid vaccines that mean hundreds
or even thousands must be injected in order to prevent a single case of Covid-19 disease. Cunningham
(https://doi.org/10.1136/bmj.m4347) reports an NNT of 256 wherein, in his words, 255 recipients receive no benefit
whatsoever from receiving the vaccine while being subject to the potential for unpredictable and often undisclosed adverse
reactions. His results are consistent with others, including: Olliaro et al (https://doi.org/10.1016/S2666-5247(21)00069-0) -

7
In conclusion: We have seen that the definition for vaccination has been slowly weakened
such that their intended purpose has gone from the more qualified goal to prevent disease in
the recipient, to a less quantifiable goal to produce protection. This has allowed the public
perception of vaccination in the mainstream media to become a moving feast - that during
covid has seen their headline efficacy goal change from being measured as preventing
infections to a less provable preventing severe illness. We have seen that other vaccines have
been withdrawn or recalled when they: (a) have failed to produce immunity, and hence failed
to prevent disease in a large segment of the population; and (b) had been linked to small clusters
of adverse events. We also saw that while fact checkers claimed that VAERS data is plagued
by fraudulent reports by anti-vaxxers and unverified reports by the general public, the majority
of reports in our previous analysis were made by healthcare workers and hence, were more
likely to be credible. Other claims made by fact checkers, including that the pharmaceutical
companies were not making significant profits from vaccines, were also seen to be incorrect.
Finally, we saw that VAERS data: (i) has previously been used to identify clusters of adverse
events and a link between particular vaccines and the incidence of autoimmune inflammatory
disease; (ii) is capable of supporting research to identify systemic adverse events caused by
vaccines; and (iii) that complaints filed in lawsuits against vaccine manufacturers allege that it
has actually been the pharmaceutical companies who may have misused the VAERS system
by entering fraudulent reports.
3. Method
On the 3rd of April, 2022 we downloaded the 2022 Full VAERS Zip File from the HHS VAERS
website
32
. This file contains three comma-separated value (.CSV) files each containing a single
dataset: (i) VAERS data; (ii) VAERS symptoms; and (iii) VAERS vaccine. The VAERS data
contains basic demographics (age, US State), dates (vaccination, symptom onset, death) and a
textual (and often clinical) narrative of the report contained within a column called
SYMPTOM_TEXT. The VAERS symptoms data lists up to five symptoms experienced by the
subject patient and which it is suggested are related to the current presentation that involves or
resulted from the adverse event arising out of injection of a Covid-19 vaccine. Finally, the
VAERS vaccine data provides specific detail regarding the vaccine administered (batch/lot
number, brand name, manufacturer), now including the dose number for that vaccine series
(first, second, third), and administration route (intramuscular, intravenous, subcutaneous). An
individual VAERS_ID identifier number is assigned to each subject patient reported to VAERS
and is used across each of the three data files to identify information relating to that individual.
This enables us to reconstruct the three data files into a single contiguous dataset for use in our
analysis.
In our first analysis we relied on the manual parsing of the symptom text and symptom lists for
each individual VAERS subject by at least two reviewers - of which at least one had to be
clinical. Each clinical narrative was broken down into its qualitative elements, quantitatively
coded and verified. This was a time consuming process that took several weeks to complete.
In this new analysis we began with a larger potential pool of records to review, having
undertaken to review 1012 of the reports involving death that presented with complete or near-
complete responses in every column. We had to work smarter, not harder. Machine Learning
and both leave aside that the NNV varies with disease prevalence, population and location. Some have a significantly higher
NNV (http://dx.doi.org/10.1136/bmjebm-2021-111789) - such as 2,500 (New Mexico) or 10,000 (India).
32
https://vaers.hhs.gov/data/datasets.html

8
Text Classifiers (MLTC) were trained initially on the dataset developed out of our previous
VAERS review, as well as with an initial subset of the first 100 of the 1012 records being
reviewed from the 2022 data. This work was performed by the lead author (a computer scientist
health informatician previously trained in undergraduate pre-registration clinical nursing) and
one currently practicing clinician. The MLTC were used to perform the first pass on the
remaining 912 reports - identifying, classifying, and coding information, and highlighting
elements that the classifier found to be inconsistent or deemed inconclusive for human review.
The human review process was not only tasked with resolving these highlighted items, but also
performed manual validation on a random subset of 200 records. The manual validation process
allowed for identification and correction of any potential inaccuracies of the method, fine
tuning of the classifiers, and ensured the same degree of overall accuracy as the previous
interim report. Some of the areas where this improved both the qualitative and quantitative
accuracy and overall speed include where multiple terms and acronyms are used to describe
the same clinical element. For example, the clinical notes of individuals with hypertension
might mention one or more of the following: (i) hypertension; (ii) high blood pressure; (iii)
HBP; (iv) BPá; (v) HTN; (vi) elevated systolic pressure; or (vii) elevated blood pressure. It is
also possible to include pre-eclampsia toxaemia (PET) or pregnancy induced hypertension
(PIH) in the context of a woman who is pregnant. In this example, and context aside, there may
be seven, nine or possibly even more ways to report what is fundamentally the same measured
symptom. This overall approach allowed for processing the 1012 records while expending
around the same time and effort as had been applied in the previous VAERS data review of
only 250 records.
4. Results
Summary Results: The key differences and new realisations from this second (2022) cohort
include that:
• As the Covid-19 vaccines were rolled out sequentially to the decreasing age groups,
the percentage of deaths reported in those groups were seen to increase.
• Disparity in the likelihood of a reported death based on gender has significantly
reduced (deaths in males in the original 2021 cohort were 1.6 times more likely based
on the reviewed reports).
• All reported deaths in minors reviewed in the 2022 cohort (those below the age of 15
years) were males, and in most age groups males were still more frequently reported.
• The majority of VAERS death reports continue to demonstrate characteristics strongly
supporting that a healthcare provider was the reporting person.
• While 31% fewer people in the 2022 cohort had reported comorbidities, the incidence
of cardiac, hypertensive and diabetic comorbidities was generally consistent with the
previous 2021 cohort.
• For the first time the CDC recognised the occurrence of post-vaccination breakthrough
infections which were described in the reports of almost half (49.1%) of the 2022
cohort.
4.1 Comparative Analysis
Age: Our first analysis reviewed VAERS reports from incidents that occurred the initial
vaccine rollout in late December 2020 and early 2021 when most recipients were the elderly,
with the minor addition of those admitted to hospital at that time for severe infection and front-
line healthcare workers. We see in Figure 2 that half of all post-Covid-19 vaccination deaths

9
in that 2021 cohort occurred in the 80+ age group. This is perhaps unsurprising because they
had been prioritised to receive the first doses of the vaccine. In contrast, for the same period in
2022 deaths in the 80+ age group dropped 17.6% because, and as a result of vaccine rollout to
each decreasing age group, we see incremental increases in all of the younger cohorts.
Figure 2: Reports by Age (percentage of total reports)
Gender: Table 1 shows that for the 2021 cohort more than 50% more males than females were
reported as dying post-Covid-19 vaccination, this resulted in a ratio of male to female deaths
of 1.60:1. It was possible although perhaps premature to conclude from that data that you were
significantly more likely to die post-vaccination if you were male. However, for the 2022
cohort the overall ratio had closed to 1.19:1, meaning the likelihood of death between the
genders for all age groups had become increasingly closer to equal, and indeed for the 80+ age
group had become equal. However, it should be noted that for most of the younger age groups
males deaths were still more frequently reported, and all deaths below the age of 15 were in
males.

10
Table 1: Gender (by age)
Reporter: Identification of the reporting person (the reporter) was an important finding in our
first VAERS analysis given that many in the mainstream media (Wadman, 2021), journalists
self-titled as fact checkers (Jaramillo, 2021; Kertscher, 2021), and a number of prominent
scientists
33
supporting the Covid-19 vaccines continue to claim that aggrieved family members
and anti-vaxxers abuse the VAERS system to make spurious false reports about the Covid-19
vaccines. In our first VAERS analysis we reported that it was possible to identify that health
service employees accounted for at least 67% of the reports we reviewed, and that
pharmaceutical company staff were directly identified as the reporter in a further 5%. This
meant at least 72% of reports at that time were being made by people in the health services
who could be considered qualified to make the report - and that the majority of VAERS reports
were not being made, as has been claimed, by anti-vaxxers with an axe to grind. In the current
analysis we opted to provide a more granular result and, as shown in Table 2, we additionally
identifying instances where primary or secondary signals pointed to a classification for the
reporter type.
Table 2: Reporter
Clinician: Primary signal
351
35%
63%
Clinician: Secondary signal
282
28%
Family/Lay Person: Secondary signal
22
2%
6%
Family/Lay Person: Primary Signal
42
4%
Indeterminate
315
31%
1012
100%
It should be noted that the difference between a primary or secondary signal was more often a
matter of the direct language or context used in the report, and not evidence of uncertainty in
the overall classification of the reporter. For example, a report that directly referenced the
33
https://twitter.com/erictopol/status/1397693291454365696?lang=en-GB and
https://twitter.com/BadAstronomer/status/1477714211740520457

11
clinical reporter
34
or that used terms or a narrative of an undeniably clinical nature
35
was
classified as Clinician: Primary signal. Even though reports providing what was clearly a cut-
and-paste style extract from a clinical health records system
36
or describing the requests or care
orders discussed with the patient’s family members
37
most likely had a clinical reporter, these
were classified as Clinician: Secondary signal. While in some countries (for example, the
United Kingdom) only healthcare providers would have access to this clinical narrative
information, on review we elected to keep this secondary signal classification as a very small
number of US-based healthcare providers now make the complete electronic health record
(EHR) available to the subject patient via web portals. In this way it is more likely, rather than
less, that a clinician or other healthcare professional was the reporter for both Clinician types
but we are recognising the very small possibility that this is not always the case. Next, reports
that directly referenced a family member or other lay person as the reporter
38
were classified
as Family/Lay Person: Primary Signal, while those that used familiar rather than clinical
language to describe the subject patient and their clinical course
39
were classified as
Family/Lay Person: Secondary signal. Finally, reports that were classified as Indeterminate
were those that either contained poor quality information
40
or insufficient information to
classify the reporter
41
.
Comorbidities: In our previous VAERS analysis we reported on the frequency of pre-existing
or comorbid conditions. We found that hypertensive, cardiac and diabetic disorders were those
most often diagnosed for the subjects of VAERS death reports. As shown at the bottom of
Figure 3, the first thing we observed was that unlike in our 2021 cohort where 96% had at least
one pre-existing or comorbid condition, the number of individuals with comorbidities dropped
to slightly less than 65% of the 2022 cohort. Otherwise, the ratios for hypertensive, cardiac and
diabetic disorders were found to be generally consistent with our first study.
34
For example: VAERS ID 1996873 states that the report was received from a pharmacist; VAERS ID 1996876 states the
report was received from a healthcare professional (HCP); and VAERS ID 1998432 states that the report was made by a nurse.
35
For example: VAERS ID 2091897 describes a situation where the patient arrived from an outside hospital, on arrival
immediately appeared to be hypoxic and goes on to say that a rapid response team was called and describes the treatment they
provided; and VAERS ID 2157829 states that the patient presented to the ED in cardiac arrest and repeats what the reporting
nurse was told by family members prior to the doctor arriving and unsuccessfully performing CPR.
36
For example: VAERS ID 2028007 provides an extract of the type that might be entered when closing a medical record at
the end of a hospital episode regarding a patient who presented to ED and the unsuccessful course of treatment that followed;
VAERS ID 2165391 provides clearly copied clinical notes regarding the physical examination performed on a woman who
was delivered to hospital by ambulance.
37
For example: VAERS ID 2058965 describes how discussion between staff and family continued throughout the day
regarding goals of care; and VAERS ID 2168079 describes how family discussed aggressive invasive care with medications
only to alleviate suffering; and VAERS ID 2116374 that states family was approached to discuss goals of care including
comfort measures to which eventually family agreed to withdraw life support.
38
For example: VAERS ID 2069199 says the reporter stated her mom was in the hospital; and VAERS ID 2102549 describes
a long-time friend as the reporter.
39
For example: VAERS ID 2076200 simply states that he started to have strokes after his 2nd Pfizer vaccination; and VAERS
ID 2157783 says that the man reported feeling unwell to his housekeeper but we aren’t told whether it is the housekeeper or
someone else making the report.
40
For example: VAERS ID 2048088 simply reports death; VAERS ID 2092424 says Covid-19 related death, Breakthrough
case; and VAERS ID 2001207 says none stated.
41
For example: VAERS ID 2110214, 2119278, 2024748 and many similar provide a short narrative stating that the person
was fully vaccinated with a particular brand of Covid-19 injection, and that they tested positive or negative for covid, attended
hospital and expired on particular dates.

12
Figure 3: Comorbidities in the 2022 cohort
New in this study is that we performed an analysis on those people with only one or two
comorbidities to identify whether certain conditions were more frequent in those decedents.
While our previous analysis reported heart attacks (MI, STEMI), congestive heart failure
(CHF) and other cardiogenic disease separately, in this analysis we grouped them together
under the blanket term cardiac disorders. As shown in Table 3, 66 reports (7%) describe only
a single comorbid disease - with hypertensive and cardiac disorders more frequently recorded.
For our analysis of those individuals with two comorbidities we charted the first mentioned
comorbid against the vertical axis, and the second on the horizontal, such that the totals down
the right-most column represent the total incidence of their first mention in the clinical record
and the totals across the bottom represent their total incidence as a second or subsequently
managed disease. As with the overall incidence in Figure 3, we see that for the 90 individuals
with only two reported comorbidities, hypertensive disorders were identified first in the records
of 34 (38%) and second in 13 (14%). While cardiac disorders were only the fourth-highest first
comorbid (n=9), they were the highest reported second comorbid (n=29). Hypertensive and
cardiac disorders were also co-reported in one-third of all instances of either disorder (n=11).

13
Table 3: Analysis of Reports describing only one or two comorbidities
The results of both analyses have strongly suggested that administration of the vaccine to those
with, at the very least, hypertensive and cardiac orders - even those who are very early in their
disease progression, should be re-evaluated. Also, given the high number of diabetics in both
cohorts, administration to that group should also be given due consideration.
4.2 Extended Analysis
Breakthrough Infection: Use of the term breakthrough infection was only identified in the
complete 2022 VAERS dataset in relation to Covid-19 and the Covid-19 vaccine reports.
Similarly, the CDC website defines a breakthrough infection in the context of no other vaccine
product. For the Covid-19 vaccines they describe a breakthrough infection as a case of illness
in which a vaccinated individual becomes infected with the illness because the vaccine has
failed to provide complete immunity against the virus
42
. In contradiction to what we see in this
data, the CDC still maintain that the Covid-19 vaccines are effective at preventing most
infections
43
. As shown in Table 4, the SYMPTOM_TEXT narrative field of almost half of the
1012 reviewed VAERS reports identified a breakthrough infection after vaccination, through
description of a positive Covid-19 test during the clinical event leading to the subject’s death.
42
https://www.cdc.gov/coronavirus/2019-ncov/vaccines/effectiveness/why-measure-effectiveness/breakthrough-cases.html
43
Ibid. under the heading: What we know about vaccine breakthrough infections.

14
Table 2: Breakthrough infections in the reviewed VAERS reports
% of This Report
(1012)
Male
Female
Unknown
Mentions breakthrough infection44
82
8.1%
38
43
1
Describes a breakthrough infection45
232
22.9%
131
98
3
Breakthrough infection can be induced46
183
18.1%
100
78
5
497
49.1%
269
219
9
Given such a significant result, we wanted to understand whether breakthrough infections
positively correlated with dose - that is, whether subsequent doses increased efficacy and
therefore reduced the incidence of infection as we have been told by health advisors like
Anthony Fauci
47
(Steenhuysen, 2021; Zimmer & Stolberg, 2021), lesser qualified scientists
48
and the mainstream media (McIlroy, 2021; Patal et al, 2021). To do this we recorded the
number of doses received by each subject in our VAERS dataset and separately totalled the
last dose number for each person diagnosed with a breakthrough infection. It should be noted
that it was not always clear how many doses an individual had received and so it is possible
that the numbers may underestimate later doses. We can see in Table 3 that in spite of
government claims of high uptake of second and subsequent booster doses (often described as
being around 70% for the USA
49
), only 463 (46%) have received a second dose, and of those,
156 (15%) have received a booster dose and would be considered as fully vaccinated under
existing 2+1 guidelines. The final step in the process was to compute the percentage of
breakthrough infections observed at each dose level. As shown by the greyed cells in Table 3,
the distribution of breakthrough infections identified in the 2022 cohort broadly matches the
distribution of doses received such that it can be resolved that subsequent doses appear to have
offered these recipients no additional protection against Covid-19.
Table 3: Doses received per person
Indi
vid
uals
per
dos
es
rece
ive
d
%
of
tota
l
indi
vid
uals
Bre
akt
hro
ugh
Infe
ctio
ns
%
of
Bre
akt
hro
ugh
Infe
ctio
ns
44
For example: VAERS IDs 2192347, 2119476, 2192384, 2196567 and 2189912.
45
For example: VAERS IDs 2024748, 2157877, 2110214, and 2157881. These reports describe in the SYMPTOM_TEXT
narrative that the subject person was fully vaccinated, often including the brand and sometimes batch numbers, and in each
identified case that a positive Covid-19 test was returned during the clinical event that led to their death.
46
For example: VAERS IDs 2068749, 2066045, 2104812 and 2043022. These reports describe a positive covid test during
the clinical event leading to the subject person’s death in the SYMPTOM_TEXT narrative field, and provide details of Covid-
19 vaccination in other fields in their report.
47
Fauci has been variously quoted as saying that getting a booster ‘markedly increased the antibody titers against a wide
range of variants’, and that ‘booster doses of both the Moderna and Pfizer-BioNTech coronavirus vaccines were likely to offer
a substantial increase in protection’.
48
https://twitter.com/dgurdasani1/status/1462542040198168579?s=20&t=njh5O72niajfeHSQzsm3vg,
https://twitter.com/chrischirp/status/1470094661725437954?s=20&t=pFB8DoKAGyfiDoKD8gsMDw and
https://twitter.com/devisridhar/status/1429530261503549453?s=20&t=oob865UfCbPUfpJaOG0MYA
49
https://usafacts.org/visualizations/covid-vaccine-tracker-states

15
Dose
1
443
43.8%
244
49.1%
2
307
30.3%
178
35.8%
3
154
15.2%
75
15.1%
4
2
0.2%
-
-
Unk
106
10.5%
-
-
Total
Individuals
1012
100%
497
100%
5. Discussion and Conclusion
The results of analysis from both the 2021 and 2022 cohorts strongly suggest that
administration of the vaccine to those with, at the very least, hypertensive and cardiac orders,
even those who are very early in their disease progression, should be re-evaluated. Further,
given the high number of diabetics in both cohorts administration to this group of people should
also be reconsidered.
Prior to their emergency use authorisation being revoked, AstraZeneca proclaimed their Covid-
19 vaccine 100% effective at protecting against severe disease, hospitalisation and death
50
. It
is doubtful that these claims would provide any comfort to those whose family members died
of Guillain-Barre Syndrome (Tsirtsakis, 2021), blood clots (Blackwood, 2021; Gallagher,
2021; MacIntyre et al, 2021) and strokes (Fonseca & Brito, 2021) linked to receiving the
AstraZeneca Covid-19 vaccine. This does, however, raise the question of why other Covid-19
vaccines have not seen similar recalls and emergency use authorisation revocation when they
too have been linked to an equal and rapidly burgeoning number of deaths – from: (i)
myocarditis and heart failure (NZMoH, 2022; Tsirtsakis, 2021b; Wolpin, 2021); (ii) as a result
of contamination during manufacture (Blair, 2021; Reuters, 2021); and (iii) acceptance by
regulators that they hasten death in the elderly (Goodall, 2021; Torjensen, 2021).
This report has presented information derived from analysis of a cohort of 1012 deaths reported
during early 2022 to the Vaccine Adverse Event Reporting System (VAERS). Through
comparison with the findings of the 2021 cohort presented in our previous interim report this
work has updated our understanding of those earlier findings and allowed us to explore the
important changes in reporting as the Covid-19 vaccines were made available to younger age
groups. While the 2021 cohort was significantly biased towards those of advanced age (80+),
we now see elevated numbers of reports for those in the 40-79 age range, and disturbingly, for
minors under 15. We have seen that the elevated correlation between post-vaccination death
reports identified in the 2021 cohort remains 12 months later and even as the rollout to younger
people continued. Conversely, the significant disparity between male and female death reports
in the 2021 cohort has become far less pronounced - while males are still more likely to be the
subject of a death report and are being reported at much younger ages, little of the previous
disparity between the sexes, especially in the oldest age groups, remains. A new finding was
that both the CDC, and those making VAERS reports, became aware of the potential for post-
vaccination disease, as almost half of all reports mentioned or implied the existence of
50
https://www.astrazeneca.com/media-centre/press-releases/2021/covid-19-vaccine-astrazeneca-confirms-protection-
against-severe-disease-hospitalisation-and-death-in-the-primary-analysis-of-phase-iii-trials.html

16
breakthrough infection. We intend to provide a second comparative review that includes a third
cohort during 2023. We conclude that the quality and quantity of data provided in the VAERS
dataset continues to be capable of supporting meaningful post-market vaccine research and
challenge other countries to begin providing a similar level of granularity in the publicly
accessible data provided by their yellow-card or post-vaccination surveillance systems.

17
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Appendix A: