Safety Signal
Craig Paardekooper
December 3, 2023

Abstract
Aim
The primary aim of this study is to detect safety signals for all vaccines in the
VAERS database using the Proportional Reporting Ratio (PRR), and to createa public search engine for vaccine safety signals.
PRR is a metric used by both the European Medical Association and by theCentre for Disease Control for detecting safety signals. However, both the EMA
and the CDC have failed to publish their PRR analyses, even though this information is vital for informed choice. This study seeks to carry out an independent
PRR analysis of all of the VAERS data available. A single dataset is created byconcatenating the VAERS datasets for every year from 1990 to 2023, and the
proportional reporting ratios are calculated for each symptom associated witheach vaccine. The result is a useful look-up tool called ”Safety Signal”, wherea user can look-up all the safety signals for any vaccine in rank order.
The null hypothesis : The ”Safety Signal” dataset is used to investigate if
any vaccines generate a safety signal for the symptom of thrombosis. The null
hypothesis is that all vaccines are equally safe, and so there will be no significant
differences between vaccines in the PRR values for thrombosis. (95 % confidence
interval). Any significant PRR values are confirmed by 5 new criteria for safety
signal detection – MSC (multiple sample consistency), SSC (Same Symptom
Consistency), RSC (Related Symptom Consistency), RBC (Related Biomarker
Consistency), and RTC (Related Treatment Consistency. The conclusion :High PRR values for thrombotic events following COVID-19 vaccination arefound, and these high PRR values are consistent across multiple related symptoms and treatments, so the null hypothesis is rejected.

Resources
Safety signal detection is of critical interest to the public, so the data has been
made accessible through downloadable CSV files and as an online search engine.

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Safety Signal (online) :Summary of Signals (online) :Datasets for easy use (csv | excel) :Datasets for analysts (csv | excel) :Coding (python) :

[1]
[2]
[3]
[4]
[5]

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1

Introduction

1.1

What is the PRR ratio ?

PRR calculates the percentage of reports where a particular symptom is recorded
following administration of a drug A, and sees if this varies significantly from thepercentage of reports where the same symptom is recorded after administrationof drug B.
The PRR is defined as the ratio between the frequency with which a specificadverse event is reported for the drug of interest (relative to all adverse events
reported for the drug) and the frequency with which the same adverse event isreported for all drugs in the comparison group.
For example, suppose that nausea was reported 83 times for a given
drug of interest, out of 1356 adverse events reported for the drug.
Thus the proportion of adverse events of nausea for this drug is
83/1356 = 0.061. Suppose that we wish to compare the drug ofinterest to a class of drugs, for which nausea was reported as anadverse event 1489 times, out of 53789 total adverse events reported
for drugs in the class. Thus, nausea was reported with proportion
1489 / 53789 = 0.028 for the class of drugs. The PRR in this caseis 0.061 / 0.028 = 2.18. This tells us that nausea was reported morethan twice as frequently (among all adverse event reports) for the
drug of interest compared to drugs in the comparison group.
Wikipedia, (2023), ”Proportional Reporting Ratio” [6]Figure 1: PRR formula

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1.2

Who uses PRR ratio for Signal Detection?

PRR is used for the detection of serious drug reactions (SDRs) by “the European Medical Association (EMA) in their EudraVigilance Data Analysis System
Different statistical methods to generate SDRs are in use. In the
EudraVigilance Data Analysis System, the Proportional Reporting
Ratio (PRR) has been implemented in the first release. Other methods will be considered for future implementation.
European Medicines Agency,(2006), ”Guideline on the Use of Statistical Signal Detection Methods in the Eudravigilance Data Analysis
System” [7]This method is also used by the Center for Disease Control (CDC) in the USA.
On January 29th of 2021 the CDC released a document titled ’Vaccine Adverse
Event Reporting System (VAERS) Standard Operating Procedures for COVID19’ (for official use only) which announced the CDC’s intention:
CDC will perform Proportional Reporting Ratio (PRR) analysis [...],
excluding laboratory results, to identify AEs that are disproportionately reported relative to other AEs. [...] To determine if results
need further clinical review, consider if clinically important, unexpected findings, seriousness, specific syndrome or diagnosis rather
than non-specific symptoms
Centers for Disease Control and Prevention, (2021), ”Vaccine Adverse
Event Reporting System (VAERS) Standard Operating Procedures
for COVID-19 (as of 29 January 2021) [8]

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1.3

What Criteria Define a Strong Signal ?

1.3.1

CDC Criteria :The CDC uses the following criteria –1. Symptom events >= 3
2. PRR >= 2
3. Chi-squared >= 4 OR
4. Lower limit of 95% confidence interval of PRR >= 2
Ref : [9] Excel spread sheets released by CDC through Freedom ofInformation request
These are exactly the same criteria that were used by Evans and his team who
introduced the PRR signal detection method in 2001 [10]. In 2002 Puijenbroek[11] found that symptom events >= 10 resulted in greater consistency acrossdifferent methods for detecting safety signals - so in this study I will be using
symptom events >= 10 as my criteria. This will serve to remove ”noise” fromthe results.
1.3.2

PRR >= 2

The higher the value of PRR, the stronger the signal. A PRR greater than 2means that a symptom occurs at more than twice the frequency with the drugof interest compared to the comparator drug/s. This is regarded by the CDC asa strong signal, so PRR >= 2, is the level used by the CDC to detect a safety
signal.
We can calculate the limits of random variation of the PRR. If the lower limitof variation is still > 2, then we can be confident that the PRR exceeds 2 bya significant margin. The lower limit of variation is called the lower confidence
limit, and it is given by the equation – [12]Figure 2: Confidence limits for PRR

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1.4

What Criteria Confirm a Strong Signal ?

1.4.1

Large samples

A signal is regarded as strong if it is based on a large sample of data. CDC
accepts a signal if the number of reports of a symptom (symptom events) isgreater than or equal to 3. The larger the number of symptom reports, thegreater our confidence. As stated above, the criteria used in this study is amore stringent one - the number of reports must be greater than or equal to 10.
1.4.2

Multiple Sample Consistency (MSC) :Sample variation is a possible cause of a high PRR. To rule this out we can takemultiple independent samples of equal size to see if there is consistency in the
PRR across samples. If the PRR remains consistently high across all samples
then we can have greater confidence in the PRR score.
1.4.3

Same Symptom Consistency (SSC) :This is where different forms of the same symptom are consistently reportedwith a high PRR. The table below shows 25 different forms of thrombosis. If
a medication has a high PRR score for causing cerebral thrombosis, then ourconfidence in that score is increased if the medication also has high scores formany other forms of thrombosis. This consistency is strong evidence that theeffect is real.
Same Symptom Consistency may be quantified by the number of symptoms that
it is consistent across. In this example, COVID 19 vaccines produce high PRRscores (> 2) across 43 different symptoms of thrombosis.
In addition to this, COVID 19 vaccines have an INF score across 46 additional
symptoms (shown on the next page). An INF score is where COVID 19 vaccines
are THE ONLY vaccines in the database producing that particular symptom.
We may therefore add this score to the previous one, and the total score comesto 89.
In the database there are only 94 symptoms in total containing the word thrombosis, and COVID 19 has high PRR scores (>2) for 89 of them. Other vaccines
never have more than 4. The consistent occurrence of a high PRR across many
related symptoms supports the conclusion that a symptom is occurring disproportionately.
1.4.4

Related Symptom Consistency (RSC)This is where related symptoms are consistently reported with a high PRR.
Related symptoms would include terms such as clots, infarctions, occlusions,and embolisms.
1.4.5

Related Biomarker Consistency (RBC)

In addition, any particular illness or condition is evidenced by several biomarkers or biological indicators. Consequently, if a high PRR is obtained for6

a particular condition, then we would expect bio-markers and effects for thatcondition to have high PRR scores also. When multiple biomarkers for a condition have high PRR scores, then we can have greater confidence in the high
PRR score for the condition.
1.4.6

Related Treatment Consistency (RTC)

Every condition requires different medical treatments. For example a cardiac
disorder may be treated with chest X-rays, electrocardiogram, cardiac imaging,cardiac operation, cardiac pacemaker, cardiac stress test, cardiac rehabilitation
therapy, cardiac ventriculogram, assays etc. So, when associated treatments also
have high PRR scores, then our confidence in a high PRR score for a particularcondition increases.

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1.5

Previous Studies

Clinical Studies : The possibility of finding serious levels of dis-proportionality
in symptoms for COVID vaccines is suggested by several clinical studies - whichshow that COVID vaccines induce the body to produce a spike protein that acts
as a cardio-vascular toxin. [13] [14] [15]Previous Studies of Dis-proportionality with COVID Vaccines : Inprevious studies significant dis-proportionality has been found when comparing
COVID vaccines with flu vaccines using data from the VAERS database for2021 [16] . The vaccines were compared using cardiovascular symptoms. In
a second study, COVID vaccines were compared with Flu vaccines using datafrom the World Health Organisation. Once again the vaccines were comparedusing cardiovascular symptoms, and significant dis-proportionality was found.
[17]
These findings led to a third study were COVID vaccines have also been compared to flu vaccines using full range of symptom categories. World HealthOrganisation data was used in this study. Significant dis-proportionality was
found for reproductive, cardiac and endocrine symptoms [18].
COVID vaccines have been compared with 7 other vaccines, and with common medications such as paracetamol and aspirin. The drugs were compared
for the full range of symptom categories. Significant dis-proportionality was
found - especially for reproductive and cardiac symptoms. [19]
CDC Analysis of Dis-proportionality with COVID Vaccines : The
CDC itself released results of their own PRR analysis of COVID vaccines (20202022 compared to all non-mRNA vaccines (2009-2022) in the VAERS database.
Their analysis was not published publicly, but was obtained through legal coercion using Freedom of Information. Very high dis-proportionality was found.
Their analyses can be viewed here. [9]. Their spreadsheets can be viewed here[20] and here [?]Prelude to the Current Study : Since COVID vaccine have been found to be
associated with serious symptoms, this suggested that other vaccines might also
have serious side-effects. Consequently, all 98 vaccines in the VAERS databasewere compared using the symptom of mortality (death) for the period 1990 to
2022. Significant differences in mortality were found between them.
Current Study : In the current study, I create a dataset of PRR values forevery symptom of every vaccine recorded in the VAERS database, then demonstrate the dataset by using it to determine if safety signals are generated with
COVID-19 vaccines for the symptom of thrombosis.
1. Safety Signal Definition : A safety signal is defined by - PRR >=2, LCI >= 2, minimum number of symptom records >= 10.
2. Safety Signal Confirmation : A safety signal is confirmed by consistency of PRR across samples, symptoms and treatments - MSC, SSC,
RSC, RBC and RTC.
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Due to the critical nature of the information uncovered, the data for all vaccines
has been made publicly available through downloadable CSVs and an online interface (Safety Signal) enabling users to read off the symptoms for each vaccine,sorted by PRR, and read off the vaccines for each symptom, sorted by PRR.

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2

Data Preparation

2.1

Data Source

Vaers Vax csv files and Vaers Symptoms csv files were downloaded from the
VAERS-AWARE website [21] for all years from 1990 to 2023, and read into a
Jupyter Notebook using Python. The same files can also be downloaded fromthe VAERS website [22]2.2

Concatenation and Data Preprocessing

Vaers Vax files were concatenated into a single data file called “datasetvax”,
with two columns – VAERS ID and VAX TYPE. Rows with duplicate VAERS
IDs were removed entirely, because they represent instances where a person
received two or more different vaccines at the same time. Taking multiple
medicines makes it hard to attribute adverse effects to a particular medicine, sothese records were removed.
Vaers Symptom files were concatenated into a single data file called “datasetsymptoms”, with two columns – VAERS ID and SYMPTOM1. Rows where
SYMPTOM1 was null were removed.

2.3

Merging

The datsetvax table was merged with the datasetsymptoms table on the common
field of VAERS ID, so we end up with 1. 9020372 records
2. 2144512 unique VAERS IDs
3. 16849 unique symptoms
4. 99 unique vaccines
5. averaging 4.2 symptoms per VAERS ID
The resulting dataset lists every symptom and its associated vaccine, and thestrength of the safety signal for that symptom.

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2.4

Converting Raw Data into Safety Signals

1. Counting : A count of each symptom for each vaccine was obtained bycreating a pivot table.
2. Converting Counts to PRR Scores : The symptom frequencies werethen converted into PRR scores. The resulting dataset lists every vaccine
as a separate column, and each row is a different symptom.
3. Calculating standard deviation (S) of the PRR : The standard
deviation was calculated using the formula above, then the 95% lower
confidence limit of the PRR was calculated.
4. Filtering for number of events : The resulting dataset of Lower
Confidence Limit values was then filtered so only those LCI values > 2
remained. In addition, the LCI values were further filtered so only thosevalues corresponding to atleast 10 events remained.
5. Transposing : This dataset was then transposed to generate a dataset
where every symptom is a separate column, and each row is a different
vaccine.
The datasets created above can be downloaded as spreadsheets and CSV
files here [4]Finally, an online interface was created that enables users to enter a vaccine,then view all its symptoms ranked by PRR. They can also enter a symptom,
and see all the vaccines with that symptom ranked by PRR. The interface canbe viewed here [1]A webpage showing the python code used in this study is available online here
[23]

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3

Data Search

3.1

PRR Magnitude (PRR)

The Transposed Dataset was used. The symptom column for ”thrombosis” wasselected and sorted by PRR from high to low to show those vaccines with thehighest PRR for thrombosis. The PRR scores were recorded.

3.2

Multiple Sample Consistency (MSC)

Python code was used to generate 100 random samples of COVID vaccine symptoms (each sample size = 40,000 symptoms), and these were compared to 100
random samples of FLU vaccine symptoms (each sample size = 40,000 symptoms), so they were matched exactly on size. The aim was to see if the high PRR
for thrombosis following COVID19 vaccination was consistent across multiple
samples.

3.3

Same Symptom Consistency (SSC)

The PRR Dataset was used. The symptoms column was filtered for ”thrombosis”.The PRR scores were then read from the COVID19 column and recorded.
Same symptoms included 1. ”Venous thrombosis limb”
2. ”Retinal vascular thrombosis
3. ”Superior sagittal sinus thrombosis
4. ”Cerebral venous sinus thrombosis
5. ”Ophthalmic vein thrombosis
6. ”Pulmonary artery thrombosis
7. ”Peripheral artery thrombosis
8. ”Atrial thrombosis
9. etc.

3.4

Related Symptom Consistency (RSC)The PRR Dataset was used. The symptom column was filtered for terms related
to thrombosis. The PRR scores were then read from the COVID19 column andrecorded. Related terms included 1. ”embolism”
2. ”infarction”
3. ”occlusion”
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4. ”aneurysm”
5. ”haemorrhage”
6. ”bleeding”
7. ”ischaemia”
8. ”haematoma”
9. ”stroke”
10. ”arteriosclerosis”
11. ”phlebitis”
Additional terms that could be used are 1. ”coagulation”
2. disorders with key word ”vascular”
3. disorders with key word ”arterial”
4. disorders with the key word ”alveolar”
5. disorders with the key word ”capillary”
6. ”red blood cell agglutination”
7. ”abnormal clotting factor”

3.5

Related Biomarker Consistency (RBC)

The PRR Dataset was used. The symptom column was filtered for the tests
and indicators used to identify thrombosis. Each element of the clotting cascade
involves specific molecules that can be tested for. The PRR scores were thenread from the COVID19 column and recorded. Indicators included 1. ”d-dimer”
2. ”coagulation test”
Additional terms that could be used are 1. ”fibrin”
2. ”coagulation factor V”
3. ”coagulation factor VII”
4. ”coagulation factor VIII”
5. ”coagulation factor inhibitor assay”
6. ”coagulation time”
7. ”duplex ultrasound”
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8. ”venography”
9. ”vascular imaging”
10. ”vascular resistance”
11. ”vascular insufficiency”

3.6

Related Treatment Consistency (RTC)

The PRR Dataset was used. The symptom column was filtered for treatments
used to treat thrombosis. The PRR scores were then read from the COVID19
column and recorded. Treatments included 1. ”thrombectomy”
2. ”anticoagulant therapy”
3. ”catheters”
4. ”stents”
Additional terms that could be used are 1. ”blood thinners”
2. ”thrombolytics”
3. ”vena cava filter”
4. ”stockings”
5. ”compression”
6. ”graft”
7. ”vascular operation”
8. ”vascular procedure complication”
9. ”shunt”

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4

Results

The VAERS data for COVID 19 monovalent vaccines [3] shows that there are
483 adverse symptoms where PRR >=2, LCI >= 2 and number of events (A)>= 10. These symptoms fall into general categories. Here are the number ofsymptoms for each of the top 3 categories Vascular Disorders
Cardiac Disorders
Infections

164
85
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This shows that cardio-vascular damage defines the nature of the largest groupof safety signals associated with COVID monovalent vaccines– accounting forabout 50% of all the safety signals. (Note that this is not a count of symptoms
per se, rather it is a count of those symptoms that qualify as safety signals.)
In the following pages you will see that COVID 19 monovalent vaccines areassociated with safety signals for every form of vascular disease including thrombosis, infarctions, embolisms, aneurysms, occlusions, strokes, haematomas, ischaemias, bleeding, haemorrhages and arteriosclerosis.
In fact, out of all 99 vaccines in the VAERS database, COVID 19 monovalent vaccines have the highest LCI (lower confidence limit of the PRR) for eachof these symptoms. This means that it has the highest values of Proportional
Reporting Ratio in which we can have confidence.
A consistency of these high values across related symptoms, related biomarkers and related treatments is found, confirming a strong association between
COVID vaccines and vascular disorder.

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4.1

PRR for Thrombosis

Here are the results comparing the COVID 19 vaccine with the other 98 vaccines
for the symptom of ”thrombosis” (as a single word). Covid 19 vaccine has avery high PRR score of 8.76 for Thrombosis. It is the only vaccine where thelower confidence limit (LCI) exceeds 2.

Figure 3: Vaccines sorted by PRR for thrombosis

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4.2

Multiple Sample Consistency (MSC)

Here are the results comparing 100 random samples for COVID vaccine with
100 random samples for FLU vaccine (each sampl of size 40,000 symptoms).
The figure below exhibits the results for the first 25 samples. The PRR > 7 forall 100 samples.

Figure 4: Multiple Sample Consistency (COVID vax vs Flu vax : Counts for
symptom of thrombosis for each random sample of symptoms (n = 40,000)
These samples are drawn randomly from a dataset of 6,452,217 COVID 19
vaccination symptoms and 269,177 Flu vaccination symptoms.

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4.3

Same Symptom Consistency (SSC)

There are 94 ”thrombosis” symptoms listed in the database, and COVID 19
vaccines generate a safety signal for 32 of them, where PRR >= 2 and lower
confidence limit (LCI) >=2.

Figure 5: Same Symptom Consistency (COVID monovalent : thrombosis)
If we apply the same criteria to other vaccine (PRR >=2,LCI >=2, A >=10)
then no other vaccine generates a safety signal for any of these symptoms ofthrombosis.

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4.4

PRR for Infarction

There are 39 ”infarction” symptoms listed in the database, and COVID 19
monovalent vaccines generate safety signals for 14 of these symptoms (LCI >=2). 8 of these infarction symptoms are cerebral - causing significant brain damage, cognitive deficit and ”brain fog”.

Figure 6: Related Symptom Consistency (COVID monovalent : infarctions)
If we apply the same criteria to other vaccine (PRR >=2,LCI >=2, A >=10)
then only COVID bivalent vaccine generates a safety signal - for ”acute myocardial infarction (LCI = 4.058)

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4.5

PRR for Embolisms. There are 32 ”embolism” symptoms listed in the database, and COVID 19
monovalent vaccine generates a safety signal for 7 of them.

Figure 7: Related Symptom Consistency (COVID monovalent : embolisms)
If we apply the same criteria to other vaccine (PRR >=2,LCI >=2, A >=10)
then no other vaccine generates a safety signal for any of these symptoms ofembolism.

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4.6

PRR for Stroke. There are 18 ”stroke” symptoms listed in the database, and COVID 19 monovalent vaccines generate a safety signal for 7 of them (LCI >= 2).

Figure 8: Related Symptom Consistency (COVID monovalent : stroke)If we apply the same criteria to other vaccines (PRR >=2,LCI >=2, A >=10)
then only COVID bivalent vaccine generates safety signals for stroke - for ”embolic stroke” (LCI = 2.49), ”lacunar stroke” (LCI = 4.74). No other vaccine
generates any safety signals for stroke.

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4.7

PRR for Haemorrhage. COVID monovalent vaccines generate safety signals for 18 symptoms of haemorrhage (LCI >= 2).

Figure 9: Related Symptom Consistency (COVID monovalent : haemorrhage)
In contract
1. RV vaccines generate safety signals for gastrointestinal and rectal haemorrhages;
2. DF vaccines have a safety signal for cerebral haemorrhages and internal
haemorrhage;
3. HIBV, IPV and PNC13 have safety signals for subcutaneous haemorrhage.
4. HPV4 has a safety signal for genital haemorrhage
So whilst other vaccines have safety signals for only 2 or 3 symptoms of haemorrhage, COVID vaccines have safety signals for 18 symptoms of haemorrhage.
This indicates that with COVID vaccines haemorrhage is taking place in manyorgans distributed throughout the body rather than in a few localised areas.

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4.8

PRR for Bleeding. COVID monovalent vaccines generates safety signals for 8 symptoms of menstrual bleeding (LCI >= 2).

Figure 10: Related Symptom Consistency (COVID monovalent : menstrual
bleeding)
The only other vaccine with safety signals for menstrual disorder is HPV - withsafety signals for 3 symptoms. Perhaps the reason for the menstrual bleeding with COVID vaccines can be found by looking at what COVID and HPV
vaccines have in common.

Figure 11: Related Symptom Consistency (HPV vaccine : menstrual bleeding)

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4.9

PRR for Aneurysm. COVID 19 monovalent vaccines generate a safety signal for 4 symptoms ofaneurysm (LCI >= 2).
If we apply the same criteria to other vaccine (PRR >=2,LCI >=2, A >=10)

Figure 12: Related Symptom Consistency (COVID monovalent : aneurysm)
then no other vaccine generates a safety signal for any of these symptoms ofaneurysm.

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4.10

PRR for Arteriosclerosis. There are 10 ”arteriosclerosis” symptoms listed in the database, and COVID
19 monovalent vaccines generate a safety signal for 4 of them (LCI >= 2).

Figure 13: Related Symptom Consistency (COVID monovalent : arteriosclerosis)
If we apply the same criteria to other vaccine (PRR >=2,LCI >=2, A >=10)
then no other vaccine generates a safety signal for any of these symptoms ofarteriosclerosis.

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4.11

PRR for Ischaemia.

There are 40 ”ischaemic” symptoms listed in the database, and COVID 19
monovalent vaccines generate a safety signal for 8 of them (LCI >= 2).

Figure 14: Related Symptom Consistency (COVID vax : ischaemia)
If we apply the same criteria to other vaccine (PRR >=2,LCI >=2, A >=10)
then only COVID bivalent generates safety signals for Ischaemia - for ”transientischaemic attack” and for ”myocardial ischaemia”. No other vaccines generate
any safety signals for any symptoms of ischaemia.

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4.12

PRR for Haematoma. COVID monovalent vaccines generate safety signals for 4 symptoms of haematoma(LCI >= 2). These include subdural, cerebral and vaccination site haematoma.

Figure 15: Related Symptom Consistency (COVID monovalent : haematoma)
If we apply the same criteria to other vaccine (PRR >=2,LCI >=2, A >=10)
then there are 8 other vaccines that have a safety single for ”injection sitehaematoma”. It seems that for COVID monovalent vaccines haematomas effectregions beyond the injection site, particularly in the brain - since both subduraland cerebral haematomas are located there.

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4.13

PRR for Phlebitis. COVID monovalent vaccines generate safety signals for 4 symptoms of phlebitis(LCI >= 2).

Figure 16: Related Symptom Consistency (COVID monovalent : phlebitis)
If we apply the same criteria to other vaccine (PRR >=2,LCI >=2, A >=10)
then no other vaccine generates a safety signal for any symptoms of phlebitis.

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4.14

PRR for Fibrin D Dimer

Biomarkers for thrombosis include the D-dimer test.

Figure 17: Related Biomarker Consistency (All vaccines : D-dimer)
If we apply the same criteria to other vaccine (PRR >=2,LCI >=2, A >=10)
then only COVID bivalent has a safety signal for D-dimer (LCI = 4.4). No
other vaccine has a signal for D-dimer.

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4.15

PRR for Anticoagulant Therapy

Figure 18: Related Treatment Consistency (All vaccines : anticoagulant therapy)
COVID 19 monovalent vaccines have safety signal for ”anticoagulant therapy”.
Anticoagulant therapy indicates clotting that requires this therapy.
If we apply the same criteria to other vaccine (PRR >=2,LCI >=2, A >=10)
then the only other vaccine with a safety signal for ”anticoagulant therapy” is
COVID bivalent (LCI = 4.4)

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4.16

PRR for Thrombectomy.

Figure 19: Related Treatment Consistency (All vaccines : thrombectomy)

31

COVID 19 monovalent vaccines generate a safety signal for ”thrombectomy”
If we apply the same criteria to other vaccine (PRR >=2,LCI >=2, A >=10)
then only COVID bivalent generates a safety signal (with an LCI of 1.998)

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4.17

PRR for Catheters. COVID monovalent generates 4 safety signals for use of catheters. Catheters
are an indicator of blockage of blood vessels. (LCI >= 2).

Figure 20: Related Treatment Consistency (COVID monovalent : catheters)
If we apply the same criteria to other vaccine (PRR >=2,LCI >=2, A >=10)
then no other vaccine generates a safety signal for catheter.

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4.18

PRR for Stents. COVID monovalent generates two safety signals for use of stents, and two
further safety signals for stenosis. Use of stents is an indicator of blockage ofblood vessels.

Figure 21: Related Treatment Consistency (COVID monovalent : stents)
If we apply the same criteria to other vaccine (PRR >=2,LCI >=2, A >=10)
then only COVID bivalent has a safety signal - for ”coronary artery stent insertion” (LCI = 2.12)

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5

SummaryThis pilot study provides a publicly accessible dataset where anyone can checkthe safety signals for any vaccine. Safety signals are defined by the magnitudeof the PRR (PRR > 2), where the lower confidence interval of the PRR is also
greater than or equal to 2 (LCI >= 2), and where there are atleast 10 reported
instances of the symptom. High PRR scores are confirmed by consistency of the
PRR across multiple samples, related symptoms, indicators and treatments. In
the demonstration example, I find that COVID 19 vaccines show the highest
disproportionality for thrombosis, and this is confirmed by elevated PRR scoresfor related symptoms and treatments.
COVID 19 vaccines are strongly associated with severe vascular disease characterised by occlusion of blood vessels, and weakening and rupture of blood vessel
walls
Occlusion takes the form of thombosis, embolism, infarction. Occlusion leads toischaemia and localised haematoma. Weakeneing and rupture of blood vessels
leads to haemorrhages, bleeding and strokes.
COVID bivalent vaccines also show safety signals for vascular disease, but to alesser extent than COVID monovalent vaccines.
HPV vaccines are the only vaccines besides COVID monovalent vaccines tohave safety signals for menstrual disorder.

6

Summary of Signals

Please find here brief summary of the safety signals found for other vaccines in
VAERS [3]7

CDC Finally Release their PRR Analysis

Please find here information regarding the CDC’s own findings from their PRR
analysis of VAERS data. [24] [25]8

Numbers for PRR and LCI Calculations

Please find here the spreadsheets for a selection of symptoms related to thrombosis showing the numbers upon which PRR and LCI calculations are based[26]
35

References[1] Paardekooper-Knoll-Frank, “Safety signal,” 2023. Available at link.
[2] Paardekooper, “Summary of signals,” 2023. Available at link.
[3] Paardekooper, “Datasets for easy lookup,” 2023. Available at link.
[4] Paardekooper, “Downloadable datasets for prr safety signals for all vaccines
in vaers,” 2023. Available at link.
[5] Paardekooper, “Detailed methodology for dataset creation : Prr safety
signals for all vaccines in vaers,” 2023. Available at link.
[6] Wikipedia, “Proportional reporting ratio,” 2023. Available at link.
[7] EMA, “Guideline on the use of statistical signal detection methods in theeudravigilance data analysis system,” 2006. Available at link.
[8] CDC, “Vaccine adverse event reporting system (vaers) standard operatingprocedures for covid-19 (as of 29 january 2021),” 2021. Available at link.
[9] EpochTimes, “Cdc finds hundreds of safety signals for pfizer and modernacovid-19 vaccines,” 2022. Available at link.
[10] Evans, “Use of proportional reporting ratios (prrs) for signal generationfrom spontaneous drug reaction reports,” Pharmacoepidemiology and Drug
Safety, 2001.
[11] Puijenbroek, “A comparison of measures of disproportionality for signal
detection in spontaneous reporting systems for adverse drug reactions,”
pharmacoepidemiology and drug safety, 2002.
[12] RxMD, “Proportional reporting ratio,” 2023. Available at link.
[13] React19, “Database of studies,” 2023. Available at link.
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