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JUN 8, 2021

OUR INVESTIGATIONS


THE METHOD BEHIND OUR INVESTIGATION OF REPORTS OF ADVERSE COVID-19 VACCINE
EVENTS

ANDY CLARKE

PAUL SUDDON

Taking on an investigation into the adverse reactions associated with the
COVID-19 vaccination rollout in the USA, our team were aware of the increased
need for transparency whilst conducting our analysis. This article documents the
methodology behind our study of Vaccine Adverse Event Reporting System (VAERS)
data.

‍

> Here's the write up of the COVID-19 adverse events investigation we conducted
> using VARES Data.

‍


WORKING WITH VAERS DATA


WHO ARE VAERS

The primary aim of the Vaccine Adverse Event Reporting System (VAERS) is to
monitor and detect the effects of vaccinations programmes in the USA. They
record patient-level data on the vaccination received by a person alongside
adverse symptoms that they report.

Established in 1990, VAERS is co-managed by the Centers for Disease Control and
Prevention (CDC) and the Food and Drug Administration (FDA), both agencies of
the US Department of Health and Human Services.

‍



‍


ADVERSE EVENTS IN CONTEXT

VAERS data records "adverse events and reactions that occur following
vaccination". It's important to note that VAERS data does not represent everyone
that has received a vaccination in the USA. Instead, each row in the data
documents a person suffering an adverse effect following a recent vaccination.

As a result, if we are to assume that each of the 182,559 entries in our dataset
represents one instance of a vaccination given to a unique individual, then the
total entries in the dataset would refer to just 0.07% of the 277 million
vaccine doses given in the USA at the time of writing (20th May, 2021).

‍


THE DATA COLLECTION PROCESS

VAERS data is based on a system of reporting. Reports can be made by individuals
suffering vaccine reactions or by healthcare providers on behalf of their
patients. Anyone can submit a report to VAERS but healthcare providers are
either required by law or strongly encouraged to report vaccine adverse effects.

This does not, however, mean that all adverse vaccine reactions are reported to
VAERS. Research published in "Vaccine" documented a 2005 survey finding that
"37% of healthcare providers had identified an adverse event following
immunization, yet only 17% of those indicated they had ever reported to VAERS".

‍

> VAERS data is based on a system of reporting. Healthcare providers, vaccine
> manufacturers, individuals and carers are encouraged to make reports to VAERS.

‍

Although the context of the pandemic's vaccination rollout is likely to be
significantly different, with VAERS and CDC repeatedly pointing towards the
issue of underreporting it would be fair to assume that not all vaccine adverse
reactions are represented in the dataset.

‍


A SAMPLE OF VAERS DATA | 2021 WAVE

The embedded CSV file below shows a sample of all columns inside the dataset we
used for this study. The sample represents VAERS data after our team had joined
cleaned and pre-processed the original data. Data in this sample accounts for
just 19% of the data used in our study and should not be taken as representative
of the most recent publication of VAERS data.

‍



‍


ACCESSING THE DATA

VAERS regularly update their data on adverse vaccine events reported to them.
The latest date included in the data is well documented on the VAERS data page.
The 2021 wave will typically include data recent to approximately 2 weeks.

The three CSV files provided by VAERS contain specific information about
different aspects of a person's vaccination. The first file, VAERS DATA,
contains information about the person, their medical history, their symptoms
alongside other information included in the report that they submitted to VAERS.
The second file, VAERS Symptoms, contains more precise information about the
symptoms. The third file, VAERS Vaccine, contains information about the
vaccination dose given to the person.

In the data studied in this project and in the sample data shown above, our team
joined the original VAERS files. Check out our notebook to follow or reproduce
how we did this.

‍

> Download the most recent VAERS dataset here.

‍


LIMITATIONS OF VAERS DATA

Alongside the issue of underreporting mentioned above, data collected by
reporting systems have other limitations that VAERS draw attention to.
Importantly the quality of the data is completely dependent on the people
reporting on adverse vaccine events. This not only means that the accuracy and
amount of information can vary significantly between reports but also that
reports can be affected - and in some cases driven - by outside events such as
increased media coverage of medical outcomes.

It is also important to recognise that VAERS data is unable to determine causal
relationships. There are many possible explanations as to why a person suffered
the symptoms described in VAERS data including the influence of any existing
health conditions a person has as well as any medication they take. Simply
because symptoms follow a vaccination does not mean that they occurred because
of it.

‍

> "VAERS reports alone cannot be used to determine if a vaccine caused or
> contributed to an adverse event or illness"
> 
> VAERS, Data Disclaimer

‍

Bearing in mind the limitations of VAERS data, the service claims to be most
relevant in the case of "newly licensed vaccines" where it can "generate signals
that trigger further investigations". Since the vaccination efforts against
COVID-19 are happening at a rapid pace with a number of newly licensed vaccines,
there are a number of ways that VAERS data can be used to particularly good
effect.

‍


AN IMPORTANT DISCLAIMER ON MORTALITY AND RECOVERY RATES

In recognition of the inability of VAERS data to determine adverse vaccine event
causality, it is important to note that the mortality and recovery rates
presented in the data and inside our study do not claim to represent people that
have died as a result of being given a vaccine.

Instead these figures show that people have died or failed to recover from their
symptoms following their vaccination. Although the findings put forward by our
team have been carefully considered, laboratory tests should be conducted in
order to determine whether vaccinations actually caused a death or set of
symptoms.

Since there is a lack of an unvaccinated group to compare VAERS data with, the
findings presented in our study can only represent hypothesis for further
investigation. These hypothesis can be examined using studies conducted with
vaccinated and unvaccinated subjects.

‍

‍


PREPARING THE PROJECT


JOINING THE DATA

After downloading the 3 data files from the most recent publication of VAERS
data - including data on vaccinations given up until the 5th May 2021 - our team
set about joining the files so that we could work with just one dataset that
gathered together all information about the person, their symptoms and their
vaccine dose.

First, using the VAERS Symptoms dataset we extracted a list of symptoms for each
VAERS_ID. Next, we extracted information on the manufacturer, route and dose
number of every dose of the vaccine given in the data using the VAERS Vaccine
file. This gave us all of the additional information about each VAERS_ID entry
that wanted to study.

To join our the data, we used the VAERS_ID column to merge each data frame
containing additional information to the central dataset containing all
information in the VAERS Data dataset.

‍


CLEANING THE DATASET

Next, we dropped columns from the data that we felt were extraneous to our
study. These included variables on the date a patient recovered, whether they
have a birth defect and whether they visited a hospital's emergency room or not.
More often than not, the columns we removed were lacking a substantial number of
values and were, therefore, less useful to our analysis.

After consulting with the VAERS Data Use Guide, we renamed the columns in the
data frame so that they more clearly represented the actual values they referred
to.

Inspecting the values in columns 'Disabled', 'Died', 'Life-Threatening Illness'
and 'Hospitalized', our team perceived that these columns should contain
booleans representing true, false and perhaps even unknown values. However,
there were only values in these columns when the outcome was true - or coded as
'Y' in the data. In order to work with these columns in Graphext, we preferred
to have values in these columns.

As a result, we decided to enter a 'Not Reported' value for each row in the data
belonging to one of the above columns and where a 'Y' - or true - value had not
already been reported. Finally, we exported the transformed data to a CSV file
and uploaded it to Graphext.

‍



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view raw 💉 VAERS 2021 | Cleaning & Joining.ipynb hosted with ❤ by GitHub



‍


VARIABLE TYPES

After uploading the dataset to our Graphext workspace, the team inspected the
variable types automatically recognised by Graphext. Most types were correctly
inferred but we had an issue with 5 categorical columns; Other Medication,
Allergies, Prior Vaccinations, Current Illnesses and Medical History Notes.
These columns contained notes from the original VAERS report submitted either by
an individual or by a healthcare provider.

The notes provided in these columns have varying degrees of structure but are
generally untidy and difficult to work with. Instead of keeping them as
categorical variables that we could use inside our model, the team decided to
set these as Text variables and use some NLP to extract the features of values
in these columns.

‍



‍


SETTING INTENTIONS

With the dataset ready to work with, we removed all sampling so that we could
work with the full dataset and turned our attention to considering the kind of
analysis we wanted to conduct.

Our instincts told us that the two columns most crucial to gain a deeper
understanding of were Recovered and Died - both indicators of the severity of
the symptoms suffered. With values like Age, Sex, Symptoms, Life-Threatening
Illness and Vaccine Manufacturer in the dataset alongside information on a
person's location, our team started to feel that an appropriate analysis would
be to cluster rows based on the similarity of their values for each of these key
variables.

The idea in clustering the data was to draw out patterns in the data using the
Graph - or network visualization. If there were any relationships between our
key variables and either recovery or mortality - then our Graph would represent
these relationships visually.

‍


BUILDING THE PROJECT


CLUSTERING

Clustering involves grouping data according to the similarity of features. In
the context of this project, we wanted our clustering model to group reports of
adverse vaccine events according to the similarity of the symptoms shown, the
demographics of the person suffering the adverse event and the details of the
vaccination itself.

The intention of doing so was to understand why recovery or mortality rates
might vary between clusters. Do the defining features of a cluster ultimately
impact mortality or recovery rates following a COVID-19 vaccination?

The team started building our clustering project with Graphext's setup wizard
using the Models → Cluster flow.

‍


We chose Models -> Cluster as our analysis type.

‍


TARGETS AND FACTORS

Clustering models are a powerful technique used to represent relationships in
data. To understand how to define these relationships, we have to tell models
which features of a dataset to use in order to interpret these relationships. We
do this using targets and factors.

‍



‍

Targets | The variables we wanted to gain a deeper understanding of.

Died, Recovered

‍

Factors | The variables our model used to cluster the data.

Age, Sex, Life-Threatening Illness, Hospitalized, Disabled, Facility Type,
Symptoms, Dose Number, Manufacturer, Route

‍


EXTRACTING LANGUAGE FEATURES

With our clustering model setup to group VAERS data points according to the
similarity of values for each of our factors, our team started to consider what
to do about the Text variables in the data.

Symptom Description, Other Medication, Allergies, Prior Vaccinations, Current
Illnesses and Medical History Notes are all columns containing valuable
information that could help to contextualize why a person suffered symptoms. But
with these columns recorded in such an unstructured manner, it would be
difficult to ask our model to consider their values when clustering the data.
Since there are so many different and messy values in these columns, setting
them as factors would disrupt the calculation of effective clusters and skew the
relationships that were calculated.

Nonetheless, we wanted to analyze these values as part of the study and decided
that we needed to find a way of extracting key terms from each of these columns
- despite having built our project with the visual editor using a Models →
Cluster flow. Not examining these values could result in our team leaving key
findings undiscovered or missing obvious relationships in the data.

In order to process text in our clustering flow, we needed to make use of some
additional steps that Graphext uses to analyze text. Although Graphext will
automatically parse text variables presenting key terms in a filterable list -
we wanted to find the significant terms - or ngrams - for all text columns as
well as extracting nouns and adjectives from the Symptom Description column.

We opened up the code editor and added the following steps to the top of our
project script in order to extract features from the text in these columns.

‍



# Configure English as language of text make_constant(ds["Symptom Description"],
{ "value": "en", "out_type": "category" }) -> (ds.lang) # Parse and extract
ADJECTIVES from SYMPTOM DESCRIPTION column. extract_keywords(ds["Symptom
Description"], ds.lang, { "keywords": { "pos_tags": [ "ADJ" ], "entities":
false, "noun_phrases": false }, "extended_language_support": false }) ->
(ds["Symptom Description - Adjectives"]) # Parse and extract NOUNS from SYMPTOM
DESCRIPTION column. extract_keywords(ds["Symptom Description"], ds.lang, {
"keywords": { "pos_tags": [ "NOUN" ], "entities": false, "noun_phrases": false
}, "extended_language_support": false }) -> (ds["Symptom Description - Nouns"])
# Parse all text columns and extract their ngrams. extract_ngrams(ds["Symptom
Description"], ds.lang) -> (ds["Symptom Description - Significant terms"])
extract_ngrams(ds.Allergies, ds.lang) -> (ds["Allergies - Significant terms"])
extract_ngrams(ds["Prior Vaccinations"], ds.lang) -> (ds["Prior Vaccinations -
Significant terms"]) extract_ngrams(ds["Medical History Notes"], ds.lang) ->
(ds["Medical History Notes - Significant terms"]) extract_ngrams(ds["Other
Medication"], ds.lang) -> (ds["Other Medication - Significant terms"])
extract_ngrams(ds["Current Illnesses"], ds.lang) -> (ds["Current Illnesses -
Significant terms"])

view raw graphext-vaccines-nlp-steps.txt hosted with ❤ by GitHub



‍

‍

If you haven't already, check out the article we wrote to find out what we
learned from the project.


PROJECT OVERVIEW

AIM

To explicate the methods that we used to study COVID-19 adverse vaccine events
with Graphext.

THE DATA

VAERS 2021 Data - 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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