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JUN 8, 2021

OUR INVESTIGATIONS


CONSPIRACIES, COMPLEXITY AND CLUSTERING: INVESTIGATING REPORTS OF ADVERSE
COVID-19 VACCINE EFFECTS

ANDY CLARKE

PAUL SUDDON

Modelling data from the Vaccine Adverse Event Reporting System (VAERS) - a US
government-sponsored vaccine reaction monitoring service - our team set out to
investigate reports of adverse health effects related to the seismic rollout of
the COVID-19 vaccination programme in the USA.

‍

By the middle of May, the USA had administered 277 million COVID-19 vaccinations
- almost 18% of worldwide doses. The most recent wave of VAERS data, which runs
up until 7th May 2021, records 182,559 reports of adverse vaccine effects from
the US population. This makes for a ratio of 1 VAERS report of adverse vaccine
effects for every 1,519 doses of a COVID-19 vaccination given to an American
citizen. Considering that adverse effect reports can be as harmless as a
headache or a sore arm, this ratio seems pretty reasonable ... right?

‍


15th May 2021: Comparison of VAERS reports with vaccine doses given in USA.

‍


GIVE ME SOME CONTEXT

But it's impossible - and perhaps irresponsible - to ignore context here. As the
pandemic continues to affect people the world over, there is a significant
minority that remains sceptical about the effects of the vaccine programme.
Unfounded claims are spread quickly on social media without the backing of
scientific evidence. It is easy to persuade someone to be worried but less easy
to reassure them.

So, armed with a dataset of adverse vaccine reactions, the temptation was
clearly to dig out a set of conclusions signifying whether the sceptics have any
foundation with which to hold their beliefs. A dataset of this magnitude would
seemingly reveal 'truths' about how COVID-19 vaccines are affecting people. But
in the world of immunology and vaccine research, things are not as simple as
this.

‍

> "The system (VAERS) is not designed to determine whether a reported adverse
> event was caused by the vaccine, but serves as an early warning system and
> helps CDC and FDA identify areas for further study."
> 
> ‍CDC representative corresponding with Graphext

‍

The key point here is that VAERS data is not scientific evidence. Whilst VAERS
is co-managed by the CDC (Centre for Disease Control and Prevention) and the FDA
(Food and Drugs Administration), both arms of the US government, its exclusive
use of reporting to collect data serves only as an "early warning system" to
flag areas of further research.

‍


VAERS data flow. Image courtesy of Shimabukuro et al. (2015)

‍

A representative from the CDC told Graphext that because VAERS passively rely on
patients and healthcare providers to report adverse vaccine events, no
conclusions can be made using VAERS data alone - despite the fact that
healthcare providers are - in some cases - required by law to report to VAERS.

‍

> Immunologists we spoke to emphasized the need to compare vaccination
> populations with unvaccinated populations in order to determine the
> statistical significance of a theory about adverse vaccine events.

‍

This makes sense. In order to validate a theory regarding the side effects of a
vaccine, laboratory conditions are required to carry out tests that can be used
to determine the statistical significance of variations between vaccinated and
unvaccinated populations. VAERS data must be understood as a record of adverse
vaccine events reported by members of the American public. This is a far cry
from laboratory conditions and in no way offers a comparison to unvaccinated or
asymptomatic people.

‍


WHAT'S IN VAERS DATA

Updated regularly on their data portal, VAERS data is published in annual waves
and details the adverse vaccine event reported by a person, their carer or their
healthcare provider following a vaccination. Established in 1990, VAERS
documents the symptoms felt by a person, whether they died, whether they were
hospitalized or recovered from their symptoms as well as information concerning
their existing health condition.

‍

> VAERS data exclusively records information from people that reported an
> adverse vaccine event.

‍



‍

‍

> We clustered VAERS data based on the similarity of an adverse vaccine event
> report. Factors modelled for similarity included the demographics of the
> reporter, the symptoms they suffered and the severity of the event.

‍


VALUE DISTRIBUTION: VAERS DATA 2021 WAVE

Vaccine adverse event reports range from Nov 2020 - May 7th 2021

‍


Variable Charts: The distribution of values in VAERS 2021 data.


CLUSTERING + NLP: BUILDING THE PROJECT

Moving forward to start analyzing the data, our team built a clustering model,
grouping adverse event reports according to the similarity of the demographic
information about the reporter, the symptoms they suffered and the severity of
the event. Setting two target variables - Died and Recovered - our aim was to
uncover connections between reports that shared a similar outcome.  In line with
the purpose of VAERS as an "early warning" system, our intention in clustering
VAERS data with Graphext was to point to areas of further study.

‍



‍

But clustering alone here was not enough to account for the complexity of the
dataset. VAERS include 6 text columns; Other Medication, Allergies, Medical
History Notes, Symptom Description, Prior Vaccinations and Current Illnesses.
All of these text columns contained potentially useful information but unless
processed and parsed would remain largely useless in our analysis. Mitigating
this problem, the team opened up Graphext's code editor and added NLP steps to
extract keywords and, in some instance adjectives and nouns from these columns.

‍

To understand more about how we built the project including a step-by-step guide
on the methods we used to extract language features from the data - check out
the methodology behind this project.

‍


Graphs: VAERS data colored by significant variables; age, sex and died.

‍


AGE: CONFIRMING EXPECTATIONS

Inspecting the Graph for the first time using color mapping to show the
distribution of values across our network of reports, our team immediately
picked up on the influence of the variable Age. As seen in the first Graph
above, a person's age had a significant bearing on their position in the network
as well as the likelihood of them dying.

The pandemic has driven home the vulnerability of older people. Our intuition
told us that older generations would be more likely to be overrepresented in
communities of people that died or didn't recover from their symptoms. Excluding
the presence of Americans under the age of 20 - who would be less likely to have
been vaccinated at the time of writing - age distribution in the VAERS dataset
follows a similar distribution to age distribution in the US population at
large. However, as we hypothesized, older generations saw greater representation
amongst populations reporting not to have recovered from their symptoms or to
have passed away.

‍

> Over 60's represented 79.7% of deaths recorded in VAERS data but make up just
> 29% of the dataset.

‍

Is this to be expected? Pretty much. Over 60's represented 79.7% of deaths
recorded in VAERS data but make up just 29% of the dataset. No doubt this is an
overrepresentation but it is one that most likely has not been influenced by
vaccination and instead is more likely to represent the known fact that older
people suffer more illness, poor health and in general are more likely to die.

‍


Graph & Variable Charts: Older ages are strongly related to reports of higher
mortality and lower recovery rates.

‍


HYPERTENSION

Turning our attention to more specific features of adverse vaccine reports, we
began inspecting the keywords extracted by our NLP steps from the medical
history notes associated with people in the dataset.

Hypertension - commonly known as high blood pressure - was noted 8731 times,
signifying that approximately 5% of the dataset have a history of hypertension.
Next, we filtered the dataset to display values exclusively for people that were
reported to have died - 4015 out of a total of 182,559 - and again turned our
attention to the relative distribution of keywords in the medical history notes
column.

‍

> People associated with a history of high blood pressure account for 5% of the
> entire dataset but represent 13.5% of the population of people reported to
> have died.

‍

This time the presence of hypertension was more notable. Of the 4015 belonging
to the sample of people that were reported to have died - 13.5% were associated
with hypertension in their medical history notes. The data here seems to flag an
overrepresentation of people with high blood pressure amongst those that have
been suffering the most severe events following vaccination. It should also be
noted that due to the increased vulnerability that people with hypertension have
in regard to COVID-19 - if this overrepresentation was validated by further
research - then the benefit of vaccination would still outweigh the risk.

‍


Variable Charts: Extracting language features from Medical History Notes to
suggest that hypertension is influential when considering mortality rates.

‍


MEN VS WOMEN: THE DISTRIBUTION OF REPORTS

The ratio of women to men in America is as good as a 50 / 50 split. But in the
VAERS dataset, 73% of adverse vaccine event reports were made by women or on
behalf of women. Striking our team as strange, we put this finding to the
immunologists we spoke to. They highlighted several confounding factors which
might be at play here.

Not only have more women been vaccinated in the USA but women also have a longer
life expectancy in America compared with their male peers. An overrepresentation
of women in older age groups could be contributing towards a bigger population
of vulnerable women more likely to suffer adverse vaccine reactions. Other
factors could include the possibility that men are less likely to report
symptoms.

‍

> 73% of VAERS reports came from women.

‍


Graph & Variable Charts: The distribution of VAERS data between men and women.

‍


MALE MORTALITY RATES

‍

> Despite only representing 24% of the dataset, males make up for 54% of deaths.

‍

Despite only representing 24% of the dataset, men make up 54% of the 4015 deaths
reported in the data. This is quite a substantial overrepresentation. Looking
into the influential factors at play here, it is likely that some of the
following points could be affecting this distribution.

‍

 * Men are overrepresented amongst older age groups reporting to VAERS.
 * Men are overrepresented amongst the population of people with
   life-threatening illnesses in VAERS data.
 * Males are also overrepresented amongst VAERS communities associated with
   hypertension and/or diabetes in their medical history notes.
 * Men could be less likely to report less severe symptoms to VAERS.

‍

> "Vaccine safety monitoring of COVID-19 (and other) vaccines continues, and any
> safety concerns pertinent to particular geographic locations or risk
> populations are investigated appropriately."
> 
> ‍CDC representative speaking with Graphext

‍

The academics we spoke to emphasized the need to validate this finding before
any definitive conclusions could be made about male mortality rates following a
COVID-19 vaccination. It would seem counterintuitive that reports for the
population of men reported to have died are spread fairly evenly across
different vaccine manufacturers.

Further investigation of this statistic is probably required to explain it. Our
team understands that this involves comparing results from an unvaccinated
population with results from the vaccinated population - something that isn't
possible using VAERS data alone.

But when we approached the CDC to understand if they were aware of this finding
or investigating it - they avoided specific comments but instead said that
"vaccine safety monitoring of COVID-19 (and other) vaccines continues, and any
safety concerns pertinent to particular geographic locations or risk populations
are investigated appropriately."

‍


Variable Charts: The factors influencing male mortality rates.

‍


KENTUCKY & PUERTO RICO MORTALITY RATES

Looking through the geographical distribution of people reported to have died,
we discovered a crucial variation between the mortality rates across all states
compared with those in Kentucky.

Recovery rates were lower and death rates were higher in Kentucky when compared
with other states. The data suggested that although entries for Kentucky account
for only 1% of the dataset - deaths in Kentucky account for 3% of all reported
deaths. This statistic is 154.3% higher than the average mortality rate across
all states. Similarly, the rate of people recorded as not having recovered from
their symptoms is 22.4% higher in Kentucky compared with the all-state average
for the same statistic.

‍

> Recovery rates were lower and death rates were higher in Kentucky and Puerto
> Rico when compared with other states.

‍

In Puerto Rico, the same statistic was 375% higher than the all-state average.
Although in our opinion, these qualify as findings that warrant further
research, it is important to note the absolute figures here. Relative to the
182,559 adverse vaccine event reports in the VAERS dataset and the 277 million
doses of vaccine given in the USA at the time of writing, the 52 deaths reported
in Kentucky and the 32 in Puerto Rico seem less influential than they would
initially appear.

‍

‍

To read more about how we conducted our analysis of VAERS data, check out the
methodology we wrote alongside this investigation. Although analysis of VAERS
data can offer no conclusions about the effects that COVID-19 vaccination has on
reported symptoms among specific communities, we hope that you'll get in touch
if you have any questions or would like to continue working with the project
that we've built.

‍


PROJECT OVERVIEW

AIM

To investigate relationships and similarity between reports of adverse vaccine
events.

THE DATA

VAERS 2021 Wave - May 7th Export

KEY VARIABLES

Age - Symptoms - Died

TYPE OF ANALYSIS

Models - Cluster

RELEVANT INDUSTRIES

Health - Pharma - Biology

EXPLORE YOURSELF

💉 VAERS Data | COVID Vaccine Adverse Events Study

‍


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