Science, Public Health Policy,
and the Law
Volume 3:100–129
October, 2021
Clinical and Translational
Research

An Institute for Pureand Applied Knowledge (IPAK)
Public Health Policy
Initiative (PHPI)

Critical Appraisal of VAERS Pharmacovigilance: Is
the U.S. Vaccine Adverse Events Reporting System(VAERS) a Functioning Pharmacovigilance System?
Jessica Rose, PhD, MSc, BSc
The Institute for Pure and Applied Knowledge“Patterns of adverse events, or an unusually high number of adverse events reported after a particular
vaccine, are called ‘signals.’ If a signal is identified through VAERS, scientist[s] may conduct further
studies to find out if the signal represents an actual risk.”
CDC on Vaccine Safety
Abstract
Following the initiation of the global rollout and administration of the COVID-19 vaccines1,2 on
December 17, 2020, in the United States, hundreds of thousands of individuals have reported Adverse
Events (AEs) using the Vaccine Adverse Events Reports System (VAERS). To date, approximately 50%of the population of the United States have received 2 doses of the COVID-19 products with 427,831
AEs reported into VAERS as of August 6th, 2021.
Pharmacovigilance (PV) is the process of collecting, monitoring, and evaluating AEs for safety signals
to reduce harm to the public in the context of pharmaceutical and biological agents. Many of the issues
with VAERS are becoming well known – especially with regards to reporting and recording of data – inlight of the extensive use of this system this year, challenging its functionality as a pharmacovigilancesystem.
This appraisal assesses three issues that respond to the question of VAERS pharmacovigilance byanalyzing VAERS data: 1. deleted reports, 2. delayed entry of reports and 3. recoding of Medical
Dictionary for Regulatory Activities (MedDRA) terms from severe to mild. The most recently updated
publicly available VAERS dataset was found to have N=1516 (0.4%) VAERS IDs removed (“missing”).
1

2

The Brand Name: Pfizer-BioNTech COVID-19 Vaccine, the Previous Name: BNT162b2 or the Company Name:
Pfizer Inc. and BioNTech SE. can be used in the case of the Pfizer/BioNTech COVID-19 products. The Brand
Name: mRNA-1273 and/or Company Name: Moderna, Inc. can be used in the case of the Moderna COVID-19
products.
mRNA biologicals are not true vaccines. True vaccines undergo time-dependent testing protocols to ensure safety
and efficacy, typically enduring between 10 and 15 years. True vaccines are a preparation of a weakened or killed
pathogen, such as a bacterium or virus, or of a portion of the pathogen’s structure that, upon administration to anindividual, stimulates antibody production or cellular immunity against the pathogen but is incapable of causing
severe infection. The mRNA biologicals do not satisfy either these requirements and as such are more akin toexperimental treatments than vaccines.
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Of this missing data, 13% represented death, 11% represented COVID-19 and 63% represented Severe
Adverse Events (SAEs). Of these missing death data, only 59% represented redundancies – re-assigned
new VAERS IDs – the remainder were unaccounted for.
A lag time between onset of AEs and entry of AEs into the VAERS public database was discovered,and it appears to depend on the AE type. For example, in the case of COVID-19 breakthrough cases,
approximately mid-May, 4100 (38% of total) reports were retroactively added approximately 8.5 weeks
following the original onset date. SAEs were not found to be downgraded to mild AEs (MAEs) for atested cohort within 10 selected updates.
VAERS is designed to reveal potential early-warning risk signals from data, but if these signals are notdetectable as they are received, then they are not useful as warnings. Considering the relevance of safetyconcerns in the face of the large numbers of AEs being reported into the VAERS system in the context
of COVID-19 products, it is essential that the VAERS system be carefully and meticulously maintained.
Despite the emergence of the Standard Operating Procedures (SOP) for COVID-19, VAERS is lacking
in transparency and efficiency as a PV system, and it requires amendment or replacement.
Copyright © The Author — Published Under the Creative Commons License
Share/Alike (see https://creative commons.org/licenses/)
Correspondence: jrose@ipaknowledge.org
Keywords
COVID-19; Vaccine Adverse Events Reports System (VAERS); Adverse Events (AEs); Severe Adverse Events(SAEs); Mild Adverse Events (MAEs); VAERS Wayback Machine; Standard Operating Procedures (SOP);
Medical Dictionary for Regulatory Activities (MedDRA); Pharmacovigilance (PV)Contents
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1

Background
Methods
Results
Discussion
Conclusion
References
Supplementary Materials
Author statements

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Background

Pharmacovigilance is the process of collecting,monitoring, and evaluating AEs for safety signals
to reduce harm and promote safety to the public in
the context of pharmaceutical and biological agents[1,2]. There are a number of organizations andagencies that exist to ensure pharmacovigilance aspart of regulation of biological products fromconception to administration into humans for use.

The Center for Biologics Evaluation and Research(CBER), as an example, actively participates ininternational pharmacovigilance efforts under theumbrella of the Food and Drug Administration(FDA) and the Department of Human HealthServices (DHHS) [3]. International regulatoryorganizations such as the World HealthOrganization (WHO), the Pan American HealthOrganization (PAHO) and the World Intellectual
Property Organization (WIPO) also function toensure pharmacovigilance in biologicals and serve
as sources of guidance pertaining to pharmacovigilance efforts. In addition, individual countries
have their own regulatory authorities, such as the
Medicines & Healthcare products Regulatory
Agency (MHRA) of the United Kingdom (U.K.),
responsible for rule and regulation enforcement andthe issuance of guidelines to ensure pharmacovigilance in the development and administration of
biological products. The U.K. ‘Coronavirus Yellow
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Card’ reporting site allows collection of AE data
monitored by the MHRA.
The U.S. FDA and Centers for Disease Control
and Prevention (CDC) created and implemented the
Vaccine Adverse Event Reporting System(VAERS) in 1990 to receive reports about AEs that
may be associated with biological products such as
vaccines.3 Most vaccine AE reports in VAERS
concern relatively minor events, such as injection
site pain. Other reports describe serious events,
such as hospitalizations, life-threatening illnesses,
or deaths [4,5,6,7,8]. The reports of serious events
are of greatest concern and are meant to receive themost scrutiny by VAERS staff and healthcare
professionals. The primary purpose of the databaseis as a pharmacovigilance tool – to serve as an early
warning or signaling system for AEs not detectedduring pre-market testing. The National Childhood
Vaccine Injury Act of 1986 (NCVIA) requires
health care providers and vaccine manufacturers toreport AEs to the DHHS following theadministration of vaccines outlined in the Act[4,5,6,7]. Reported AEs, as part of the VAERS
system, represent a fraction of the actual number of
AE incidents, so the numbers reported herein arelikely far lower than actual numbers [6,7,9].
VAERS reports can be made by nurse practitioners,
general practitioners, or family members, which
can result in duplicate reports being made. As partof the VAERS Standard Operating Procedures for
COVID-19 (SOP)4 published on January 29th,
2021, the CDC and the FDA are meant to performroutine VAERS surveillance to identify potential
emergent safety concerns in the context of COVID-

34
5

19 injectable products [5,6,7,10,11,12]. Accordingly,
VAERS reports are received, processed, andmanaged by trained CDC contractors. The VAERS
reports are received online for subsequent review,and symptoms and diagnoses are assigned
MedDRA standard codes. Additional information,
including hospital records and autopsy reports, will
be requested by these trained staff when appropriate,
as outlined in the SOP. Reports are often changed
or deleted. For example, in the case where a person
successfully files a report using the VAERS systemand subsequently dies, they are, in some cases,
assigned a new VAERS ID number, unlinking theirreported AEs and death records. In addition, as theAEs may become more enumerable in an
individual, multiple changes can be made to their
VAERS report under the same VAERS ID number
or, as indicated, under a different VAERS ID
number if they die.
An Adverse Event (AE) is defined as any
untoward or unfavorable medical occurrence in ahuman study participant, including any abnormal
physical exam or laboratory finding, symptom, ordisease temporally associated with the participants’
involvement in the research, whether or notconsidered related to participation in the research.
Based on the Code of Federal Regulations, a
Serious or Severe Adverse Event (SAE)5 is defined
as any adverse event that results in death, is life
threatening, or places the participant at immediaterisk of death from the event as it occurred, requires
or prolongs hospitalization, causes persistent orsignificant disability or incapacity, results incongenital anomalies or birth defects, or is another

VAERS has benefits of the PREP Act – while vaccine manufacturers are shielded from liability, and vaccine
proponents tout VAERS as an example of active PV, VAERS users must acknowledge the data cannot be used toestablish causality.
Vaccine Adverse Event Reporting System (VAERS), Standard Operating Procedures for COVID-19 (as of 29
January 2021), VAERS Team: Immunization Safety Office, Division of Healthcare Quality Promotion National
Center for Emerging and Zoonotic Infectious Diseases and Centers for Disease Control and Prevention.
NIA Adverse Event and Serious Adverse Event Guidelines (2018).
https://www.nia.nih.gov/sites/default/files/2018-09/nia-ae-and-sae-guidelines-2018.pdf
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condition which investigators judge to represent
significant hazards.6 The VAERS handbook statesthat approximately 15% of reported AEs areclassified as severe [4]. Nowhere in the VAERS
handbook or on the website published by the
CDC/FDA is there mention of deleted data ortransparent description of the processes and criteria
used for record deletion. The only reference I could
find to legitimate removal of data, from
WONDER’s ‘Reporting Issues’ section, claims that
‘Duplicate event reports and/or reports determined
to be false are removed from VAERS’.7
A Wayback Machine8 is an initiative of theInternet Archive, a 501(c)(3) non-profit, building a
digital library of Internet sites and other cultural
artifacts in digital form. The VAERS Wayback
Machine9 therefore allows an examination of the
VAERS government data input each week. The
U.S. Government publishes a new version of its
VAERS database weekly and VAERS IDs can bechanged or even deleted without documentation ofedits. The VAERS Wayback Machine provides away to trace and track deleted files based onmatches in field entries between VAERS ID
versions.10

2

Methods

General methodology and descriptive statistics
To analyze the VAERS data sets, R was used. (R: alanguage and environment for statisticalcomputing.) VAERS data are accessed through the
CDC Wide-ranging Online Data for Epidemiologic
Research (WONDER) system. The VAERS data
are available for download11 in three separate
67comma-separated values (csv) files representing (i)general data for each report; (ii) the reported AEs or‘symptoms’; and (iii) vaccine data for each report,
including vaccine manufacturer and lot number.
The VAERS dataset is updated weekly. Upon
individual reporting of vaccine side effects or AEs,a VAERS ID number is provided to the individual
to preserve confidentiality, and a detailed
description of the AEs are transcribed along with
the individual’s age, residence by state, past
medical history, allergies and gender, and manyother details. In addition, the vaccine lot number,place of vaccination and manufacturer details areincluded in the report.
The VAERS ID was used as a linking variable tomerge the three csv files. Data was filteredaccording to vaccine type (reports made only for
COVID-19), and all variables were retained,
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, disabilities, life threatening AEs, birthdefects and onset date of AEs. Deaths are
categorized according to whether or not theindividual had been marked as ‘DIED’. Erroneouslabelling is an issue in VAERS, for example, when
‘Death’ is an AE and yet the ‘DIED’ column ismarked ‘NA’ or ‘not applicable’, thus the dataframe
was checked and corrected for inconsistencies inthe ‘DIED’ column vector. For the purposes of thisanalysis, deaths according to VAERS classification
by ‘DIED’ plus these corrected cases of
misclassification are reported here and used in theanalysis. The grouped AE categories hospitalizations and emergency doctor visits were created byhttps://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/cfrsearch.cfm?
VAERS data can be accessed through the CDC Wide-ranging Online Data for Epidemiologic Research(WONDER) system. https://wonder.cdc.gov/vaers.html
8 https://web.archive.org/
9 https://medalerts.org/vaersdb/wayback/10 https://www.cdc.gov/vaccinesafety/ensuringsafety/monitoring/vaers/index.html
11 https://vaers.hhs.gov/data/datasets
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selecting ‘Y’ in the respective column vectors,
while the cardiovascular, neurological andimmunological groups were created by selecting
keywords indicative of a respective medical issue.
The SAEs were classified according to whether theindividual succumbed to death, was hospitalized,
was admitted to the ER, experienced a disability ora life-threatening AE, or if a birth defect ensued.
It should be noted at this point that anyone using
the VAERS WONDER system will not see thesame counts that are described in this analysis, since
hospitalizations, ER visits and all SAEs counts
were calculated by counting the ‘Y’ entries in therespective fields in the merged file. The difference
between the counts in this analysis and counts froma WONDER query are simply due to the effect oflosing field entries by merging the files. If one uses
the files available for download from the VAERS
website with the aim of comprehensive analysis of
the full range of data, the 3 csv files must be
merged. In order to know what ‘SYMPTOMS’ an
individual succumbed to prior to death, forexample, or to know what injectable product they
were given, it is necessary to merge the DATA filewith the SYMPTOM file and the VAX file. It is also
vital to omit redundancies in VAERS IDs – if notdone, this could lead to excess numbers in absolutecounts. The downside to the merge is loss of data
due to incomplete field entries; however, it isimportant to note that the merge counts are underapproximations, yet still prove the points madeherein.
Deleted data were isolated and aggregated byusing anti-join iterations in R on sequential
dataframes. Anti-join returns the rows of the first
dataframe that are not matched in a second
dataframe. This was done iteratively for all
sequential dataframes, and the unmatched data were
aggregated and put into a new file entitled ‘missing

data’. The collective missing data file wassubsequently filtered for duplicates to ensure that
redundancies were omitted.
A missing VAERS ID can be missing due tohaving been removed because it is redundant, or forreasons yet unknown. The former entries are reassigned a new VAERS ID and are traceable bymatching fields in column vectors of dataframes.
The latter are missing due to unknown reasons. To
discern between redundant and deleted VAERS
IDs, deleted data were cross-referenced bymatching fields for relevant selected variables in themost recently updated publicly available dataset.
This was done only for the deleted death data, since
it is a time-consuming exercise. The matchingalgorithm was as follows: match age, state, andgender followed by vaccine lot if available, onset,
vaccine and death dates followed by allergies,
medications, and any other unique identifiers of theindividual. If a match was found, the newly
assigned VAERS ID was recorded alongside the old
VAERS ID in a new file. If a match was not found,then the VAERS ID was deemed to have been
deleted from the database.
Two methods were used to investigate temporallags in data entry. The first method involved using
only the most recently updated publicly available
dataset. Assessment of temporal differences in data
entry was done by calculating the difference in thenumber of days between the onset date
(ONSET_DATE)12 and the date that the AE wasentered into the VAERS database (TODAY’S_
DATE).13 The second method involved comparing
the data from the weekly updates to the mostrecently updated file. Each week, a new set of data
is available for download from the VAERS website,
as mentioned previously. As an example of how thedata sets were compared, consider the first and thelast VAERS datasets available for download in

12 Onset Date (ONSET_DATE): The date of the onset of adverse event symptoms associated with the vaccination
as recorded in the specified field of the form.
13 Today’s date (TODAYS_DATE): Date Form Completed.
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2021. According to a reference variable, such as the
ONSET_DATE, these two datasets should both andequally capture all AEs submitted to VAERS fromJanuary 1st through January 7th, 2021, since thefirst available dataset would comprise the first weekof data. If any two datasets do not equally capture
all AEs, then this discrepancy would warrant
explanation. A feasible explanation for a non-matchin the number of VAERS IDs per ONSET_DATE
entries reported would be retroactive addition ofreports to the system due to a backlog.
The incidence of SAE downgrade to MAE wasassessed by choosing 10 update files, calculating
the SAE and MAEs, and subsequently comparing
them to original counts for SAE and MAE in theoriginal files. This was done using the semi-joinfunction in R.
Statistical TestingStatistical analysis was done using the Student’s tTest to determine statistically significant differences
between AE types in the deleted data file. Skewing
in distribution of data was tested using Pearson’s
Skewness Index, I, which is defined as I = (meanmode)/standard deviation. The data set isconsidered to be significantly skewed if |I|≥1.

3

Results

3.1 Historical pharmacovigilance of VAERS
and other safety monitoring systems
VAERS and other safety monitoring systems havebeen useful for pharmacovigilance in the past. In
2010, rotavirus vaccines licensed in the U.S were
found to contain Porcine circovirus (PCV) type 1
and were subsequently suspended. On 22 March,
2010, the FDA issued a statement recommendingthat clinicians and public health professionals in the

United States temporarily suspend the use ofRotarix [13,14,15]. In 2009, an increased risk ofnarcolepsy was found following vaccination with amonovalent H1N1 influenza vaccine that was usedin several European countries during the H1N1
influenza pandemic [15,16,17]. Between 2005 and2008, a meningococcal vaccine was suspected tocause Guillain-Barré Syndrome (GBS) [15,18]. In
1998, a vaccine designed to prevent rotavirus
gastroenteritis was associated with childhood
intussusception after being vaccinated [15,19–29].
Also in 1998, a hepatitis B vaccine product was
linked to multiple sclerosis (MS) [15,30].
Pharmacovigilance has functioned in the context of
COVID-19 VAERS data with regards tomyocarditis, resulting in a COVID-19 vaccine
safety update by the Advisory Committee on
Immunization Practices (ACIP, June 23rd, 2021) by
Tom Shimabukuro. The report did not result in any
changes to the rollout despite the danger signal
having arisen [31].
To date, 50% of the total US population hasreceived 2 doses of COVID-19 products,14 with
427,831 AEs reported as of August 6th, 2021. These
numbers are off the scale with regards to numbers
associated with vaccine rollouts when compared toprevious years. Even more atypical are the numbersof deaths reported in the context of the COVID-19
products. Figure 1 shows the total VAERS reports
from data and total VAERS-reported death counts
per year for the past 10 years up to and including
the VAERS update on August 6th, 2021. Both theabsolute numbers of total AEs and those of deaths
per year dramatically outnumber the absolute
numbers recorded in previous years. To date, there
are 6639 (1.6% of all AEs) deaths in the VAERS
database. Normalization to fully injected
populations were done and compared with
INFLUENZA vaccine data for past years and it was

14 https://usafacts.org/visualizations/covid-vaccine-tracker-states/
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Figure 1: Bar plots showing the number of VAERS reports (left) and reported deaths (right) per year forthe past decade. (2021 is partial data set.)

Figure 2: Histogram plots showing distributions of the AEs of the total VAERS ID count (left) and for
SAEs (right).

found that the increase in AEs is not due simply due
to an increase in injections [32].
As part of an ongoing analysis [8], VAERS data
are being monitored according to weekly updates.
Figure 2 shows the total AE count (up to andincluding the August 6th, 2021, VAERS update) by
age group alongside the SAE data by age group(according to CDC age group classifications). The
distribution in both cases is symmetric andunimodal, not skewed toward any particular age
group, potentially meaning that there is no
particular age group with lesser chance of
succumbing to an AE or, more importantly, an

SAE. Of the SAEs, there are 6,639 deaths, 26,402
hospitalizations, 59,061 ER visits, 7,423 lifethreatening events, 6,861 disabled and 258 birthdefects reported.
Female reproductive issues (FRIs) and AEs inchildren aged 12–18 years are on the rise. There are
currently 6,398 total FRIs and 18,021 AEs reported
in young children aged 12 through 18. These
children represent 4.2% of the total VAERS data
and 12.9% of all cardiovascular AEs. It should behighlighted that the rollout has only just begun
recently for children in these young demographics.
Figure 3 shows histograms for the FRIs (left) and106

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Figure 3: Histogram plots showing the distributions of female reproductive issue AEs and AEs in children
aged 12–18 years old from the VAERS dataset according to age group (left) and age in years (right).

Figure 4: Histogram plots showing the distributions of the missing data of the total AE counts (left) andfor SAEs (right) from the VAERS dataset according to age group.

for the children (right) with respect to age in years.
Most reports within the children aged 12–18 weremade for 17-year-olds.
3.2 Missing data
To date (August 6th, 2021), 1,516 VAERS IDs are
missing from the most recently updated publicly
available VAERS database. This represents 0.4% of
the total VAERS IDs. For each of the 28 updates,
one anti-join iteration was performed between
sequential updates. For each anti-join iteration, of
which there are currently 27, the extracted missing
data counts are as follows: 10, 13, 20, 20, 4, 12, 30,
18, 41, 14, 25, 24, 45, 72, 89, 77, 69, 102, 53, 115,
89, 167, 95, 63, 62, 87 and 101. That is, between thefirst update and the second, 10 VAERS IDs are
missing; between the second and third, 13 VAERS
IDs are missing, and so on up to the second-last andthe most recent update where 101 VAERS IDs are
missing. Figure 4 shows the distribution of themissing data according to age groups for the entire
missing data set (left) and for the SAEs within theset (right). The missing data are distributed in asymmetric and unimodal way with regards to age
groups and are not skewed toward any group in astatistically significant way (I=-0.2) whencompared to the dataset without removals.
Interestingly, when the data are not filtered byage group, 63% of all missing data reports qualify
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as Severe AEs, and this represents 1.2% of the total
SAEs reported to VAERS. When the data arefiltered by age group, this percentage becomes 81%,as shown in Figure 4. The missing SAE data are
distributed in a symmetric and unimodal way withregards to age groups and are not skewed toward
any group in a statistically significant way (I=-0.4).
Of the total missing VAERS ID data set, 41% of
the missing IDs involved hospitalizations and 37%
involved emergency room visits (data not shown).
Histograms of these two categories do not show any
statistically significant skewing toward any
particular age group (I=0.1 and I=-0.1, respectively;
not shown).
Individuals who succumb to and are diagnosed
with COVID-19 post-injection, also known as
breakthrough events, comprise 11% of the total
missing data (1.4% of total VAERS IDs). It is very
strange to report that 70% of the age data contains
an “NA” entry in the “AGE_YRS” field and thus
age-grouped data analysis is not tenable here. FRIs
comprise 0.8% of the missing VAERS IDs (0.2%of total FRIs reported to VAERS).

Figure 5: A histogram plot showing distributionof missing death data according to age group

Figure 5 shows that the distribution of deleted
death data is asymmetric, unimodal and not skewed
in a statistically significantly way toward any
specific age group in this data set (Figure 7 (I)=0.7).
Of the missing death data, 15% of reports weremade within 24 hours and 28% of reports weremade within 48 hours indicating a clustering ofreports in very close temporal proximity to theinjection.

3.2.1 Death data comprises 13% of missing data

3.3 Redundancy deletions versus deletions forunknown reasons in death reports

Although the absolute number of missing VAERS
IDs may not be high, of this small subset of deleteddata, 13% of total missing AEs are deaths. The total
number of deaths is 199 and in each sequential
iteration of the anti-joining of the datasets, deathremained at the highest or near highest frequency
for missing AEs in each “SYMPTOM” list for the
extracted missing data set, save for SYMPTOM
column 5, which rarely contains the primary or
most prevalent AE reported per individual. For
example, of the 5 SYMPTOM column variables
representative of the reported AEs, SYMPTOM
column 1 primarily contains the most prevalent AE
listed and has ‘COVID-19’ as the #1 most frequently
occurring missing report (22%) with ‘Death’ at #2(15%). This missing death data comprises 3% of thetotal VAERS death reports.

There are 199 deleted death entries to date from the
VAERS database and 214 deleted death entries todate collected from the VAERS Wayback Machine.
The discrepancy of 15 deleted deaths, which
accounts for 3% of all reported deaths, arises fromdeletions of individuals in a ‘foreign location’ thatare not included on the publicly available Domestic
dataset. The deleted death data list can be found in
the Supplementary materials. Deletions of
redundant entries are marked by NA in the ‘True
deletions’ column and the accompanying new
VAERS IDs are listed. Deletions due to unknown
reasons are marked by TRUE value in the ‘True
deletions’ column. Of the total list, 59% were foundto be redundant entries and 41% of the entries weretrue deletions. For the remaining 1317 non-deathrelated AEs, a cross-reference search would need to108

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Figure 6: Bar plots showing the discrepancies in cumulative data by slope of increase at the beginning of
the data versus slope of decrease at the end (current update)

be completed in future work to discover whatpercentage of total missing AEs are true deletions.
3.4 Unexplained lag in data input
An anomaly in the data pertaining to data entry
times when compared to onset of AE dates can beseen when total AE counts reported in the mostrecently updated publicly-available VAERS dataset(updated August 6th, 2021) are compared with total
AE counts as per VAERS weekly updates. To date,there are 28 sets of data, and discrepancies can befound between the files from update to update. This
would not necessarily be perceived by a data
analyst if they were simply looking at the data fromthe most recently uploaded data to the VAERS
system. One would only notice this discrepancy if
simultaneously analyzing the individual sets ascompared with the most recently updated set by
update date. If the VAERS system was functioning
as a pharmacovigilance system and in fact passive,these data sets would be expected to follow thesame trajectory. Evidently, there are two
trajectories, and they are not similar quantitatively
or qualitatively.
Figure 6 (left) shows the number of deaths foreach specific update date per week. For example,

the first row of bars with x-axis marker ‘1’ shows
the number of deaths for each of the updates
according to weeks 1–27 (01/30/21–07/30/21). A
closer look (examining only weeks 1–12 for clarity)
at Figure 6 (right) reveals that the number of deaths
were essentially equal for the first 12 updates forweek 1. By week 12, this number started to changewith respect to week-by-week calculations of deathcounts. If we observe the slope of the difference inabsolute number in the data per update date, it isincreasing quite consistently as the week number
increases. This is precisely what we would expect
to see if data were being retroactively added. The
inconsistency is the increasing slope that emerges.
It should not be increasing – not even remotely. The
only increase we would expect to see is a grouped
increase over a week. Absolute numbers should notchange per week with respect to weekly data
already entered. Thus, if data are being retroactively
added, then we would see changes reflected per
week as shown in the red rectangle on the right inFigure 6 (right).
Another way to visualize this phenomenon isusing a heatmap. Figure 7 is a correlation plot
illustrating the number of deaths per week for deathweek versus the week of entry into the VAERS
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Figure 7: Heatmap showing the delayed deathdata entries where n is the number of deaths perintersection tile

database. Any entry that is not on the diagonal is anentry that was not entered on the week that theperson died. 21 tiles (42%) representing n>1 deaths
indicates that many entries were entered well after
the death date. In one case, the AE was entered 77
days post death. This is clear evidence of death data
being retroactively added. Considering that deathcertificates can take time to be processed, it is to be
expected that some death entries to VAERS would
occur quite temporally distal to the date of death,but this is a phenomenon that was observed for any
AE checked.
3.4.1 Why does this matter?
This corroborates the hypothesis that there is a lagphase between reporting and recording of data. The
duration between reporting following onset of an
AE reaction and recording into the VAERS publicly
available data varies from a few days to many
months. Figure 8 shows the difference in data withrespect to the data as per weekly update and to the
updated data as of August 6th, 2021, for all SAEs.
The black shaded area represents data that is inexcess with regards to the data originally presented
to the public. The data under the blue line is themost recently update data and the data under the redline is the weekly updated data. The most alarming
observation from this figure, however, is theamount of data that was present early on that simply
was not publicly available at the time that they weregenerated. For example, the ∆ cumulative AEs
between the individual updated data for week 10 is
19,536. The ∆ time in weeks is 7.6. This means that
almost 20,000 SAEs that should be observable in
the publicly available VAERS Domestic dataset
were not present at the time they occurred and wereoriginally reported. This means that only 7,065(red)/26601 (blue) = ~20% of the actual SAEs as ofthat date (week 1) were entered into the database.
Only after a lag time of almost 2 months did thisdata become visible. If week 5 is examined, this lagtime becomes 10 weeks (Figure 8 - right). It is only
recently that these data were made visible and thisis most likely due to a huge backlog being tended
to. The fact that the data sets have converged is dueto the backlog being sufficiently dealt with. Thisphenomenon was found to exist to varying degrees
in all AEs checked. Figure 9 shows 3 representative
plots for Chills, Death and Breakthrough COVID19 AEs. It is fortunate (in a way) that the death data
does not seem to have been a victim of the lag likesome others. This phenomenon was also notdependent on an AE being mild or severe but thedegree to which the phenomenon occurred in each
AE is yet to be ascertained. This can be checked.
Another way to assess temporal differences indata entry is to calculate the number of days
between the onset date (ONSET_DATE)15 and thedate that the AE was input into the VAERS
database (TODAY’S_DATE)16 using only the mostrecent updated file. For example, the difference
between the completed form entry date and theonset of the AE date should be the same for any two

15 Onset Date: The date of the onset of adverse event symptoms associated with the vaccination as recorded in thespecified field of the form.
16 Today’s date: The date the form was completed.
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Figure 8: Shaded plots showing the SAE data as they were input per respective update (grey shaded
region) compared with these data as they are reported in each individual updated file (black)

Figure 9: Shaded plots showing the Chills, Death and Breakthrough COVID AE data as they were
input per respective update (grey shaded region) compared with these data as they are reported in eachindividual updated file (black)randomly selected AEs. If there was a difference
between the percentages of reports made for any
two AEs, based on the difference between entry
date and onset of AE date, then this would require
explanation, especially if the difference wasstatistically significant. The most frequently
reported AE in the VAERS system in the context of
COVID-19 products is “Chills”. I chose this AE as
a positive control against deaths in the context ofwhether or not these two AE types were being
added to the publicly available VAERS database in
the same way, temporally.

Figure 10: Time series plot showing percentagesof Chills (green/yellow) and Death (green/red) of
the total VAERS dataset (as of update July 30th,2021) against the number of days calculated in
between the entry date of the report into the
database and the onset date of AE for up to 15
days’ difference

Figure 10 shows the percentages of reported
Deaths and Chills as a starting point for thecomparison. The T-test confirms a statisticallysignificant difference between the respective meansof the Death and Chills AEs with regards to differences
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in reporting times following onset of AE with a pvalue = 0.005. The figures show areas under the
curves generated to demonstrate how many more
entries were made in the case of Chills than for
Death within the first 5 days following onset of AE.
3.4.2 Lag time dependency on AE type?
Figure 11 shows the percentages of reported
Deaths, Bell’s palsy, Heavy menstrual bleeding,
Myocarditis, Injection site pruritis, Chills,
Headache, and Fatigue data against the differencesin days between their onset dates and the entry dates
into the Domestic front-end VAERS system that isFigure 11: Time series plot showing percentagesof reported Headache (H), Chills (Ch), Injection
site pruritis (ISP), Fatigue (F), Dizziness (D)(blue), Bell’s palsy (BP), Death (D), Heavy
menstrual bleeding (HMB), Foetal death (FD),COVID-19 (C19) (red) of the total VAERS dataset(as of update July 30th, 2021) against the numberof days calculated in between the entry date of thereport and the onset date of AE

available for download. These 10 were selected
since 5 are classified as severe and 5 are classified
as mild.
There is a clear difference in the percentages ofreports made between the mild AEs: Headache (H),
Chills (Ch), Injection site pruritis (ISP), Fatigue (F)and Dizziness (D) and severe AEs: Bell’s palsy(BP), Death (D), Heavy menstrual bleeding (HMB),
Foetal death (FD), COVID-19 (C19). In the case of
the mild AEs listed, the area under the curves(AUCs) are greater than the AUCs in the first few
days following the onset of the AE. In the cases ofthe more severe AEs, <10% of reports were entered
within the first few days. It is yet unclear whether
or not this is a coincidence.
3.5 Are SAEs being downgraded to MAEs eachweek?
The rate of SAE occurrence according to VAERS
data is 19% (nSAE/N reports to VAERS (%)). If weuse only Pfizer data, this rate increases to 21%. If
we normalize to dose number, we get 0.02% rate ofSAE (nSAE/N doses) so this translates to ~1/5000
individuals succumbing to a SAE. There isvariation between the criteria that the CDC uses todetermine SAEs in VAERS and the medical
definition of SAEs [4,5,6,7]. This raises thequestion of whether specific SAE reports in
VAERS are downgraded over time to MAEs. The
short answer is no. To determine whether or not
SAEs were being downgraded to mild AEs, I
semi-joined the datasets for a selected update date

Table 1: Calculated SAE and MAE differences between reference file and original file for 10 sample
update files downloaded from VAERS∆ (date-RU)∆total AE count∆SAE count∆MAE count∆% SAE∆% MAE

03_05_21
86
14
72
0
0

03_12_21
118
38
80
0
0

03_19_21
105
35
70
0
0

03_26_21
124
49
75
0
0

04_02_21
94
23
71
0
0

04_09_21
94
28
66
0
0

04_16_21
93
28
65
0
0

04_23_21
120
53
67
0
0

04_30_21
162
107
55
0
0

05_07_21
149
98
51
0
0

Reference Update (RU)
05_14_21
21_05_21
77
0
49
0
28
0
0
0
0
0

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(03/05/21) with 10 sequential updates to maintainthe same smaller cohort within the data frames. This
allowed the comparison of the original SAE andMAE counts to the original counts for theindividual dataframes to check if the counts werechanging as updates were being added. None of the
SAE counts were different when compared to semijoined dataframes which means that SAEs are notbeing downgraded to mild AEs as the updates comein (Table 1). The discrepancies in deltas seen inadverse events (and thus both SAEs and MAEs) aremost likely due to variations in data reporting andrecording that are known.

4

Discussion

Functioning pharmacovigilance in VAERS was
examined in this study. It appears from this shortappraisal that although VAERS could be afunctioning pharmacovigilance system, it is not
being used as such. The only reference to legitimate
deletion of data from the VAERS system was in the
VAERS/WONDER ‘Reporting Issues’ section,
which claims that ‘Duplicate event reports and/or
reports determined to be false are removed from
VAERS’. Despite this ‘disclaimer’, there is no way
to check or validate ‘falseness’ of data that may
have been removed. This means that, in the case ofdeleted deaths, which represent 3% of all deathdata, their removal needs to be explained. These
deaths were reported to VAERS and recorded byhired CDC contractors. They represent people whodied in temporal proximity to having been given an
as-yet non-FDA-approved, experimental transfective
biological product by intramuscular injection. They
cannot simply be deleted. Something worth noting
was the commonality in deleted entries where acausality relationship between the injections andthe AE was not only implied but also suggested by
the sender, which is typically the physician or
emergency-room physician who attended to theindividual’s case. Refer to Supplementary Table 1

for deleted death entries in the VAERS Waybackmachine.
Trained contractor staff are required to enter
each VAERS report into the database, and if itshould be deemed necessary to delete a VAERS ID
from this database once entered, then it must be
documented with a valid reason for the deletion. In
addition, when a VAERS ID number is changed toa new number, this should also be documented bycontractor staff. It has been suggested that vaccineinduced deaths have been classified as COVID-19
deaths. If this is the case, then deaths are being
skewed away from the elusive vaccine-induced
death count toward the COVID-19 death count[33,34]. It is unscientific to deny any possibility thatthe injections are the possible cause of the injuries,
particularly in some cases where the clear temporalproximity makes this possibility a high probability[8,35]. If this denial was implemented into a systemof denial, it would most likely manifest in this way.
VAERS was designed to reveal potential risk
signals from data, but if these signals are notdetectable as they are received, then they are notuseful as timely warnings. There is evidence that
the VAERS data are being entered into the publicly
available dataset much later than one would expect,considering that this is a passive system. It isconceivable that death AEs have extended
processing times for the issuance of deathcertificates, but there would be no reason for otherAEs, severe or mild, to have delays with regards todata entry, especially not delays greater than 4
weeks. Public health policy decisions on expanding
the vaccination program might have been madedifferently if the true rates of reported SAEs anddeaths had been known in real time. Similarly, if
individuals knew of SAEs and deaths occurring soearly on in the rollout, and also that the percentageof SAEs is atypically high, then perhaps they would
have exercised their rights to informed consent,
declined these injections or simply waited for safety
data to come in. This is precisely what the VAERS
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system is designed for in its pharmacovigilancetask: to warn policy makers and individuals ofpotential risks not detected during clinical trials. If
there is a large backlog of data, then more trained
staff need to be hired to expedite data entry toensure that the VAERS system is able to deliversafety signals as they are reported. In the case wherelate entry of data occurs due to another reason, thenthis needs to be acknowledged, investigated andremedied. The evidence provided herein lends tothe hypothesis that data is being entered according
to AE severity. This alone requires investigation.
As a point of concern with regards to CDC safety
signal metrics, as defined in section 2.3.1 in the
SOP, the proportional reporting ratio (PRR) is used
to define safety signals originating from VAERS.
The PRR is a metric that compares the ratio of
specific AEs to total AEs for vaccine products. It isdefined as:

where a = specific AE for specific vaccine; b = allother AEs for specific vaccine; c = specific AE for
all other vaccines; d = all other AEs for all othervaccines [36,37]. However, this technique isinherently flawed in that the PRR does not change
when the specific vaccine-related AE event counts
are very large or very small [34,36,37,38].
Therefore, the scaling factor that arises due to theexcess of specific AEs is normalized to the total
number of AEs, and this ratio is then again
normalized to the total for all other vaccines. Thisis a problem in the context of the COVID-19
injectable products since both the specific AEs andthe total number of AEs are atypically high. Thismeans that no matter how many times higher thedeath rate, for example, the PRR will be the sameas it would be for a product that was not killing
people at all. The PRR, therefore, on its own, cannot

be used as reliable a safety signal detection metric– it does not work.
To be clear, the absolute number of AEs reported
in the context of the COVID-19 products isapproximately 11x higher than for all the reported
AEs for 2020 combined. The absolute number ofdeaths reported is approximately 42x higher thanfor all deaths reported for 2020. However, the PRR
does not emit a safety signal even though thenumber of deaths is 266 times higher in the context
of the COVID-19 products when compared to
INFLUENZA products [32]. In spite of peerreviewed studies noting significant association of
COVID-19 injectable products with Bell’s palsy,
thrombocytopenia and myocarditis [39,40,41,42],
the CDC maintains the position that no specificsafety concerns have been identified with regards to
SAEs [8,31,43,44,45]. In a recent CDC report titled‘Local Reactions, Systemic Reactions, Adverse
Events, and Serious Adverse Events: PfizerBioNTech COVID-19 Vaccine’ [44], only theseverity of the most frequently reported AEs in the
VAERS database are reported in tabular form andnot the SAEs themselves. They report that
occurrence of SAEs involving system organ classes
and specific preferred terms were balanced between
vaccine and placebo groups and presented at a mere
0.5%, and although SAEs (grade ≥3, defined asinterfering with daily activity) occurred morecommonly in vaccine recipients than in placebo
recipients, their claim is that no specific safetyconcerns were identified with regards to SAEs,
which is false [43,44,45].
One more discussion point that is worth its own
publication but will be added as a point of interestin this study is the Under-Reporting Factor (URF)
of AEs. Under-reporting is a problem inpharmacovigilance systems, VAERS included.
VAERS is a passive reporting system, and it has
been suggested as part of a Harvard study that amere 1% of AEs are reported to VAERS [46].
However, this is not necessarily the case, nor is it
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universally applicable for all products; certainly notfor distinct AEs. For example, under-reporting of
mild AEs such as rashes or low-grade fever would
most likely be far greater than for SAEs, such as
death. To calculate the URF, the expected numberof SAEs (ESAE) is divided by the observed numberof SAEs (OSAE). The ESAE is calculated bymultiplying the total number of doses administeredin the U.S. (assuming a single dose can result in anAE) by the number of SAEs recorded in COVID19 product safety trials. According to the FDA
Safety Overview of the Pfizer/BioNTech COVID19 product (Study C4591001 – refer to section 5.2.6
page 33) [47,48]. 0.7% of Pfizer/BioNTech
COVID-19 product recipients suffered SAEs. As of
August 10th, 2021, 197,399,471 million Pfizer/
BioNTech COVID-19 product doses had been
administered in the U.S. [49,50] and therefore thenumber of expected SAE occurrences in the U.S.
volunteer recipients of the Pfizer/BioNTech
products should be ~1.4 million SAEs, if we use
this reported rate. Thus, the ratio of ESAE to OSAE is
31 to 1, suggesting a URF of 31(NSAE_Pfizer_trial/NSAE_Pfizer_VAERS = ~1.4M/43,948).
Using this URF for all VAERS-classified SAEs,
estimates to date are as follows: 205,809 dead,818,462 hospitalizations, 1,830,891 ER visits,
230,113 life-threatening events, 212,691 disabled
and 7,998 birth defects to date [38]. Since the URF
for MAEs is very likely larger than for SAEs, it issatisfactory to assume that 31 is a humble estimate
URF for all AEs (refer to Supplementary Table 2).
Relative reporting rates are also shown in
Supplementary Table 2 to demonstrate that that AE
reports associated with COVID-19 products aremuch higher than for previous years. For all
symptoms listed in red, we limited the search to 20–
60-year-olds since these people are less noisy withrespect to symptoms and younger people aren’t yet
vaccinated. All fields color-coded yellow containobserved/expected incidence rates >100, and these
only occur in the non-control AEs, such as reported
AEs that are presumably unrelated to the vaccines,

like ‘Lyme disease’, seen in blue and green in
Supplementary Table 2.

5

Conclusion

It cannot be stressed enough when referring to
VAERS data collected in the context of the
COVID-19 injectable products that effective
antiviral responses against the nCoV-2019 virus in
the form of both cellular and humoral immune
responses have been reported in peer-reviewed
studies [51–56]. Because of the low Infection
Fatality Rate, indicating effective and robust
immune responses, it remains unclear why multiple
experimental mRNA vaccines have been fasttracked through conventional testing protocols andare also being fast-tracked through production andadministration into the public. With repurposed
drugs like hydroxychloroquine and Ivermectin
showing extremely positive results in patients [57–68], it is also unclear why these drugs are not being
more extensively promoted as effective tools in thefight against this virus. What is clear is that the
injectable products are proving unsafe for many
individuals and inefficacious in others (see Israeli
data in Supplementary Material). As part of theWHO’s own minimum requirements for afunctioning pharmacovigilance system, substandard products need to be removed fromcirculation to ensure patient safety. Since VAERS
is capable as a functioning pharmacovigilancesystem as it reveals safety issues with the COVID19 biologicals, it should be used as such, but it isnot.
Despite the low frequency of missing VAERS
IDs, data have been deleted from the VAERS
database, and this requires explanation, not only
ethically but also because it lends to the possibilityof inexact measurements of death counts andtherefore can potentially lead to missed signals.
Statistical power is primarily influenced by sample
size (also effect size and significance level), and the115

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bigger the sample size, the higher the statisticalpower. The deleted data from the total VAERS ID
count are individuals enrolled in post-market
surveillance human-subject studies: the whereabouts of their VAERS reports of death need to beaccounted for. There is absolutely no reason forthese data to be missing, from what can beascertained. If the data were false, as was suggested
as the only reason to delete an entry, then there
needs to be a record of this edited data madeavailable with the publicly available VAERS data.

approximately 1 in every 400 individuals
experiencing an adverse event (~1 in every 25,000
for death) in the context of the COVID-19 fully
vaccinated population in the United States, it istherefore unclear why these injections arecontinuing to be used in the human population,
especially since no long-term effects are known andno long-term data exists, to date. It was important
to contextualize death counts since a disproportionate number of all the missing data AEs
are deaths.

Data are being retroactively added to the
VAERS database far later than would be expected
for the system to be considered a timely,
functioning pharmacovigilance system. This could
be explained by manual curation of a large backlogof data. However, if AEs are being entereddifferentially, with respect to time, based on
severity, then we all must ask the difficult question:“Why?” Again, VAERS was designed to reveal
potential risk signals from data, but if these signals
are not detectable as they are received, then they are
not useful as warnings and pharmacovigilance
becomes moot. The duration between reporting
following onset of an adverse event reaction andrecording into the VAERS publicly available data
varies from a few days to many months. If earlier
information was available to public health policymakers and to the public, including the off-thecharts prevalence of SAEs (19%) and deaths, thenperhaps the decision to volunteer to have theseproducts injected would have been moreprevalently declined or simply put on hold until
more safety data had accumulated. This, again, ispart of pharmacovigilance that has failed withregards to assessment of risk/benefit management.

It may appear that the number of missing
VAERS IDs is nothing to be concerned about froman analytical point of view, but I remind the reader
that these are not just data: they are people. This
report addressed three issues that respond to thequestion of VAERS pharmacovigilance byanalyzing VAERS data in relation to: 1. deleted
reports, 2. delayed entry of reports, and 3. recodingof MedDRA terms from severe to mild.

According to this analysis, VAERS IDs are notbeing downgraded from SAEs to mild AEs. In fact,
the percentage of SAEs continue to increase frommonth to month. Even without considering the
URF, the ratio of fully vaccinated individuals
succumbing to an adverse event is high. With

6

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7

Supplementary Materials

Supplementary Figure 1: Injection rates in eachage group in the general population compared tothe total AE VAERS reports (above) and total
SAE VAERS reports (below)

88. Centers for Disease Control and Prevention.
2021. COVID-19 vaccines for people withallergies.
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estimates of coronavirus disease 2019 (COVID19)–like illness, COVID-19 illness, and rates ofcase ascertainment, hospitalizations, anddeaths—Noninstitutionalized New York City
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ClinicalInfectious Diseases. 2021;ciab038.
https://doi.org/10.1093/cid/ciab038

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Supplementary Table 1: The true deletions shown in the context of all missing data. The new VAERS
IDs assigned to the redundant entries are also shown.
Count
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50

VAERS ID missing
9187239231499303869304189349639379859409509409549442739443859446599460979479749495479519609558789573219604379647299649569662369700449701399701619715619718009788729827789834829998181000669
10009101004651
10115881017127
1020144102410310247311045540
10486871051447
1064933
1074247107691411020771108447
1108969111396311221711131199

New VAERS ID
N
N
N
N
N
N
N
930466NN944641935767940955945253985715NN
N1329449962940Dead in 30 mins
950533950441ITP?
962325921768971969935815978959N986901
977186
N985527989006994544
N
1024592939050N
Litigation request
NN
1090801
1145662? (JJ?P?)
109649710840361084419/1126060
1037207

True deletions
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
NA
TRUE
TRUE
TRUE
NA
NA
NA
NA
TRUE
TRUE
TRUE
NA
NA
TRUE
NA
NA
TRUE
NA
NA
NA
NA
NA
TRUE
NA
NA
TRUE
NA
NA
NA
TRUE
NA
NA
TRUE
TRUE
TRUE
TRUE
TRUE
NA
NA
NA
NA
NA
NA

DIED classification (B&A)
Y(Location: foreign)Y(Location: foreign)Y
Y
Y
Y(Location: foreign)
Y
Y/Y
Y
Y
Y/Y
Y/Y
Y/Y
Y/Y
Y/Y
Y
Y(Location: foreign)
Y
Y/NA
Y/Y
Y
Y/NA
Y/Y
Y
Y/Y
Y/Y
Y/Y
Y/Y
Y
Y(Location: foreign)
Y/Y
Error: Wrong Patient (documentation in EMR)
Y
Y/NA
Y/Y
Y/Y
Y
Y/Y
Y/Y
Y
Y
Y(Location: foreign)
Y
Y
Y/Y
Y/Y
Y/Y
Y/Y
Y/Y
Y/Y

Adverse Event
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Death
Unevaluable
Death
Death
Death
Death
No death
Death
Death
Cerebrovascular Accident
Death
Death
Death (2 y/o)
Death
Death
Death
Death
SARS-CoV-2
Death
Death

Deleted from date
1/7/21
1/7/21
1/15/21
1/15/21
1/15/21
1/15/21
1/15/21
1/15/21
1/15/21
1/22/21
1/15/21
1/15/21
1/22/21
1/22/21
1/29/21
1/22/21
6/11/21
1/22/21
1/29/21
1/29/21
1/29/21
1/29/21
1/29/21
1/29/21
1/29/21
1/29/21
2/4/21
1/29/21
2/4/21
2/12/21
2/4/21
2/4/21
2/18/21
2/18/21
2/12/21
2/12/21
2/12/21
2/12/21
4/1/21
3/5/21
3/11/21
8/6/21
4/1/21
3/19/21
3/19/21
4/1/21
3/19/21
3/19/21
4/1/21
4/1/21124

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Supplementary Table 1 continued

125

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Supplementary Table 1 continued

126

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Supplementary Table 1 continued

127

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Supplementary Table 2: Table using Under-Reporting Factor (URF) conversion (30x) to demonstratesuggested actual numbers of AEs rather than simply reported values in VAERS.
Data source: VAERS/Analysis: Steve Kirsch, Dr. Jessica Rose
Adverse Event (AE)

Observed AE 2021 (N)Number AE (2015-2019)

Metal poisoning
Otitis media
Hepatitis
Bursitis
Conjunctivitis
Caesarean section
Wart
Rotator cuff syndrome
Breech delivery
CancerDiabetes
Obesity
Lyme disease
Abortion Spontaneous

2.0
48.0
331.0
189.0
172.0
38.0
1.0
55.0
0.0
31.0
155.0
14.0
42.0
707.0

47.0
255.0
1,457.0
395.0
278.0
97.0
7.0
148.0
3.0
132.0
284.0
9.0
53.0
90.0

Expected(Average/year)
9.4
51.0
291.4
79.0
55.6
19.4
1.4
29.6
0.6
26.4
56.8
1.8
10.6
18.0

Incidence Rate (AE) (N/Average per year)

URF adjusted (OBS*31)

0.2
0.9
1.1
2.4
3.1
2.0
0.7
1.9
0.0
1.2
2.7
7.8
4.0
39.3

62.0
1,488.0
10,261.0
5,859.0
5,332.0
1,178.0
31.0
1,705.00.0
961.0
4,805.0434.0
1,302.0
21,917.0

Anaphylactic Reaction

1,503.0

204.0

40.8

36.8

46,593.0

Aphasia (inability to talk)

1,184.0

55.0

11.0

107.6

36,704.0

Appendicitis
Bell’s Palsy

433.0
2,637.0

11.0
133.0

2.2
26.6

196.8
99.1

13,423.0
81,747.0

Blindness

723.0

86.0

17.2

42.0

22,413.0

Cardiac arrest
ChillsCough

719.0
61,972.0
9,637.0

14.0
4,725.0
1,002.0

2.8
945.0
200.4

256.8
65.6
48.1

22,289.0
1,921,132.0
298,747.0

Deafness

1,022.0

117.0

23.4

43.7

31,682.0

Death

6,639.0

90.0

18.0

368.8

205,809.0

Deep vein thrombosis
Depression

1,473.0
503.0

14.0
488.0

2.8
97.6

526.1
5.2

45,663.0
15,593.0

Diarrhoea

13,495.0

6,262.0

1,252.4

10.8

418,345.0

Dyspnoea (difficulty breathing)
Dysstasia (difficulty standing)
Fatigue
Guillain-Barre syndrome (GBS)Headache
Herpes zoster
Insulin resistanceMultiple organ dysfunction syndrome
Myalgia
Myocarditis
Neuropathy
Paraesthesia
Paralysis
Parkinson’s disease
Pericarditis

20,674.0
1,349.0
61,900.0
448.0
73,565.0
4,807.0
6.0
26.0
17,047.0
671.0
133.0
9,860.0
179.0
26.0
447.0

194.0
133.0
4,575.0378.0
6,231.0
700.0
6.0
37.0
3,208.0
73.0
195.0
2,440.0
411.0
5.0
49.0

38.8
26.6
915.0
75.6
1,246.2
140.0
1.2
7.4
641.6
14.6
39.0
488.0
82.2
1.0
9.8

532.8
50.7
67.7
5.9
59.0
34.3
5.0
3.5
26.6
46.0
3.4
20.2
2.2
26.0
45.6

640,894.0
41,819.0
1,918,900.0
13,888.0
2,280,515.0
149,017.0
186.0
806.0
528,457.0
20,801.0
4,123.0
305,660.0
5,549.0806.0
13,857.0

Pruritus

18,103.0

11,250.0

2,250.0

8.0

561,193.0

Pulmonary embolism
Seizure1,191.0
2,362.0

10.0
431.0

2.0
86.2

595.5
27.4

36,921.0
73,222.0

Completed suicide

19.0

3.0

0.6

31.7

589.0

Thrombosis

1,588.0

45.0

9.0

176.4

49,228.0

Tinnitus
Total

6,523.0

282.0

56.4

115.7

202,213.0

324,649.0

47,112.0

9,422.4

3,758.3

10,064,119.0

Unrelated events (blue): The goal for symptoms like metal poisoning, hepatitis, and otitis media (shownin blue) is to look for the propensity to over-report this year. If this was just over reporting we’d see a rateincrease for these symptoms that are unrelated to the vaccines and are not comorbidities.
Pre-existing comorbidities (green): These conditions like diabetes and cancer in the table above increase
simply because of the increased number of people filing reports in 2021.
Symptoms: For all symptoms (Deaths and others), we limited the search to 20-60-year-olds since thesepeople are less noisy with respect to symptoms and younger people aren’t yet vaccinated [21].

128Sci, Pub Health Pol, & Law

Critical appraisal of VAERS Pharmacovigilance — Oct. 2021

Supplementary Table 3: Table showing injected versus un-injected individuals in the context of
hospitalizations in Israel. Chart courtesy of Dr. Rafael Zioni. Data source: Israel Ministry of Health.

8Author statement

Funding
This project was funded by donations from thepublic to the Joshua Kuntz IPAK Research
Fellowship at the Institute for Pure and Applied
Knowledge(https://ipaknowledge.org/joshuakuntz-research-fellowship.php).

129