More missing age data in VAERS COVID19 injection reports for severe than mild adverse events in children and women at peak fertility ages

Abstract

Numerous reports at VAERS for COVID19 injections lack information (sex, age, state, death etc) at adequate spaces in VAERS forms. That information often exists within the form's write-up text describing case specifics (example in Figure 1). VaersAware.com screens report texts for missing information and compares statistics in the original database with the cleansed database after screening for "missing" information (Figure 2). Biases for missing information increase as a function of event severity and decrease with age. Tendencies for increased bias in missing information with event severity are greatest in children, and decrease with age. Pre-and post-screening biases in female/male ratios increase with event severity for ages above 17, the pattern is strongest at peak female fertility ages, 18-29. No form adapted to include data of both pregnant women and their un-or newborn child exists, suggesting a systemic, in addition to systematic, maladministration of the VAERS information database. Could mechanisms lacking intent explain these observations?

Full text

More missing age data in VAERS COVID19 injecon reports for severe than mild adverse events in children and
women at peak ferlity ages
Albert Benavides and Hervé Seligmann
Summary
Numerous reports at VAERS for COVID19 injecons lack informaon (sex, age, state, death etc) at adequate spaces in
VAERS forms. That informaon oen exists within the form's write-up text describing case specics (example in
Figure 1). VaersAware.com screens report texts for missing informaon and compares stascs in the original
database with the cleansed database aer screening for "missing" informaon (Figure 2). Biases for missing
informaon increase as a funcon of event severity and decrease with age. Tendencies for increased bias in missing
informaon with event severity are greatest in children, and decrease with age. Pre- and post-screening biases in
female/male raos increase with event severity for ages above 17, the paern is strongest at peak female ferlity
ages, 18-29. No form adapted to include data of both pregnant women and their un- or new-born child exists,
suggesng a systemic, in addion to systemac, maladministraon of the VAERS informaon database. Could
mechanisms lacking intent explain these observaons?
Introducon
In the VAERS report in Figure 1 the paent age is missing at the form locaon dedicated to this informaon (see
arrow). The informaon exists elsewhere in the form (highlighted within the case descripon). Missing age
informaon occurs in about 28% of reports on adverse events associated with COVID19 injecons. The CDC search
engine includes such reports in a category where age is unknown. Hence these reports are not included in descripve
stascs exploring associaons between event frequencies and age, for example. The underlying assumpon of
analyses of the original reports as in VAERS is that such missing informaon and possible errors distribute randomly
and hence do not alter overall paerns. Here, we test this assumpon for age and sex raos using data cleansed by
VaersAware report screening. Similar bias analyses for other informaon (delays in report publicaon) and other
vaccine types (inuenza, HPV etc) are planned.

Figure 1. Report in VAERS on 2-year old male dead aer COVID19-Janssen injecon with missing age at the form
locaon dedicated to this informaon (indicated by an arrow) but included in the write-up describing case specics
(highlighted).
Materials and Methods
VaersAware.com is updated each Friday, upon VAERS updates. It compares reports with previous downloads and only
adds new reports, aer comparing older versions with the latest one. It also records changes in the informaon in
subsequent downloads for the same report identy. VaersAware screens systemacally reports for COVID19
injecons for missing informaon such as in the Figure 1 example, using adequate key words used in texts in relaon
with the missing informaon, such as year old for age etc. This method might not catch all cases but will improve the
completeness of the data.
VaersAware ranks event severity as follows (from lowest to highest severity rank): oce visit, emergency, hospital,
life threat, birth defect, permanent disability, and death. Analyses here consider also another event severity ranking,

where event severity is proporonal to event rarity, and birth defect gets the same rank as permanent disability
(from lowest to highest severity rank): ): oce visit, hospital, emergency, permanent disability+birth defect, life
threat, and death.
Results
Missing data by event category
Figure 2a-h compares counts for events by age categories according to the original CDC reports and aer screening
for missing age (cleansed). Reports for all events with unknown age drop from 472827 before screening to 132305
aer screening. Considering only deaths, unknown ages drop aer cleansing from 12171 to 3196.

Figure 2. Counts of reports by event type (a-all, b-death, c-life threat, d-permanent disability, e-emergency, f-hospital,
g-oce visit, h-birth defect) from original CDC reports at the form's dedicated locaon and aer screening for
missing age informaon in the form's case descripon write-up by VaersAware (cleansed). VaersAware.com was
accessed July 1st 2023.
Cleansed/original bias in missing age vs age
For deaths (data from Figure 2b), the bias for missing age informaon, esmated by the rao between cleansed and
original death counts, is highest for younger age categories and decreases with age (Pearson coecient correlaon r
= -0.843; aer excluding age category 0-6-years-old r = -0.914 (two-tailed P < 0.01 in both cases), Figure 3). The high
rao for age category 0-6 is probably inated by prenatal deaths for which age might not be indicated in original
reports.

Figure 3. Rao between counts of deaths according to cleansed VAERS reports for missing age and original reports as
a funcon of mean of age range per age category. Numbers near datapoints indicate cleansed/original counts for
each age category, data from Figure 2b.
This analysis is repeated for each event type (birth defects excluded) and produces three addional negave
correlaons, two stascally signicant (hospital, r = -0.681; oce visit, r = -0.763, two-tailed P < 0.01 in both cases;
permanent disability, r = -0.119, P > 0.05). For two events this analysis produces posive correlaons that are not
stascally signicant (life threat, r = 0.307; emergency, r = 0.509, both have P < 0.05). Overall, missing age
informaon biases are largest for the young and decrease with age.
Cleansed/original bias in missing age vs event severity
Analyses such as in Figure 3 consider separately each event type and explore variaon within each event type in the
cleansed/original counts across dierent ages. Analyses below consider separately each age category and explore
variaon within each age category in the cleansed/original counts across dierent events. Results from the former
analysis suggest that posive correlaons are to be expected (the more severe the greater the bias), jusfying the
use of one tailed stascal tests. Figure 4 plots cleansed/original raos for event counts for age category 6-11 years
old as a funcon of event severity ranked according to VaersAware (lled symbols) and their rarity in the cleansed
COVID19-injecon report database (hollow symbols). Missing age data biases overall increase with event severity (r =
0.749, P = 0.043 and r = 0.793, P = 0.0299, respecvely).
54/30 136/79
104/65
669/440
966/627
1392/963
2787/2066
5362/4241
8047/6178
8760/6089
4017/2655
206/153
92/20 ->0-6y-old
0-6years-old excluded
y = 4.6145x-0.288
R² = 0.8347
1
020 40 60 80 100
Ratio between cleansed/original cdc death report counts, log scale
Age

Figure 4. Rao between event counts according to cleansed VAERS reports for missing age and original report counts
as a funcon of event severity for age category 6-11 years old. Filled (interrupted line) and hollow (doed line) circles
are for event severity ranked according to VaersAware and event rarity, respecvely (oce visits and deaths have
lowest and highest severity ranks in both rankings). Data are from Figure 2b-h and were cleansed by VaersAware.
An overall tendency for increasing bias for missing data with event severity exists across all age categories besides
the oldest, 100-119y-old (Table 1). Obtaining posive correlaons in 12 among 13 tests has a one tailed stascal
signicance P = 0.0017 according to a sign test using the binomial distribuon.
Age cat.
r rarity
r VaersAware
0-5
0.78
0.81
6-11
0.79
0.75
12-15
0.17
0.61
16-17
0.55
0.81
18-29
0.40
0.62
30-39
0.48
0.68
40-49
0.35
0.59
50-59
0.31
0.53
60-69
0.36
0.55
70-79
0.49
0.59
80-89
0.51
0.56
90-99
0.45
0.55
100-119
-0.21
-0.40
Table 1. Pearson correlaon coecient r of rao between cleansed and original VAERS event counts and event
severity for 13 age categories, such as in the example in Figure 4. Correlaons were using the VaersAware event
severity ranks, and for events ranked by increasing rarity. Data are from Figure 2b-h.
Age, event severity and cleansed/original bias in missing age
Results in Table 1 show that the increase in biases in missing age data with event severity is highest for the young and
decreases with age (Figure 5). This overall decrease is stascally signicant at P < 0.05 for analyses ranking event
severity as in VaersAware and according to event rarity (r = -0.698 and r = -0.558, respecvely).
R² = 0.5614
R² = 0.6291
1
1.2
1.4
1.6
1.8
2
01234567
Ratio between cleansed/original cdc report counts, age
category 6-11y-old
Event severity ranked by event rarity
low ---------------------------------------------------------------------high severity

Figure 5. Pearson correlaon coecients r (Table 1) as a funcon of mean age range (lled circles-event severity
ranking according to VaersAware, hollow circles- ranking according to event rarity).
Results overall indicate that missing data for age in VAERS are most frequent for more severe events, and in the
youngest age groups, and this nonrandom bias in missing data is ubiquitous across events and ages.
Biases in missing informaon aect sex raos in relaon to event severity
These results on biases in missing age data increasing with event severity especially in children suggest that missing
informaon biases might occur specically for women, especially at peak female ferlity ages. Therefore, data in
Figure 2 are used to calculated pre- and post-screening female/male raos for each age and event category, including
for birth defect (Figure 2h). The bias in missing age informaon for sex raos is calculated by dividing the post-
screening sex rao by the pre-screening sex rao (Table 2).
Severity
VaersAware
1
3
2
6
4
7
5
rarity
1
2
3
4
5
6
4
Age
Office visit
Hosp.
Emerg.
Perm. Dis.
Life threat
Death
Birth defect
r-VaersAw.
r-rarity
3
1.004
0.952
0.983
1.097
1.043
0.944
2.577
0.265
0.189
8.5
0.998
0.968
0.992
0.965
1.075
0.977
1.000
-0.176
0.247
13
1.010
0.984
0.973
0.983
0.997
0.868
-0.711
-0.690
16.5
1.007
0.976
0.991
0.994
1.015
0.907
1.071
-0.314
-0.302
23.5
0.972
0.978
0.994
1.012
0.992
1.052
1.005
0.884
0.851
34.5
0.975
0.982
0.989
1.008
0.973
1.036
1.001
0.833
0.665
44.5
0.966
0.980
0.991
1.010
0.961
1.006
1.004
0.714
0.516
55.5
0.963
0.989
0.998
1.006
0.975
0.971
1.006
0.391
0.302
65.5
0.970
0.991
0.990
1.000
0.997
0.983
0.994
0.669
0.662
75.5
0.977
0.998
1.000
1.008
0.995
1.020
0.905
0.105
0.167
85.5
0.979
1.020
1.013
1.013
0.980
1.056
1.050
0.665
0.515
95.5
1.014
1.042
1.050
1.013
1.032
1.065
0.351
0.494
109.5
1.004
1.014
1.037
0.963
1.142
1.077
0.212
0.578
Table 2. Rao between post- and pre-screening female/male rao for each age category and event in Figure 2. The
two last columns indicate Pearson correlaon coecients r between ranked event severity and raos for a given age
R² = 0.4871
R² = 0.3108
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
020 40 60 80 100
Pearson correlation coefficient r between bias in
missing data and event severity
Mean of age range

according to VaersAware severity ranks, and events ranked according to event rarity. Stascally signicant Pearson
correlaon coecients r are underlined in bold.
The bias in missing age informaon for post- and pre-screening sex raos increases with event severity in 10 and 11
among 13 age categories (posive r in the two last columns in Table 2) for event severity ranked according to
VaersAware and event rarity, respecvely (P = 0.0461, and P = 0.0112, respecvely, one tailed sign tests using the
binomial distribuon). Figure 5 plots this bias in missing age informaon for sex raos as a funcon of event severity
for age categories 19-29 and 30-39 years old.
A
R² = 0.7759
R² = 0.6879
0.960
0.970
0.980
0.990
1.000
1.010
1.020
1.030
1.040
1.050
1.060
0 1 2 3 4 5 6 7
Ratio between post- and pre-cleansed female/male ratios
Events ranked according to severity
Low------------------------------------------------------------------------------------->High severity

Figure 6. Rao between post- and pre-cleansed female/male raos for women 18-29 and 30-39 years old (lled and
hollow symbols, and interrupted and doed lines, respecvely) versus event severity rank. A-event severity ranks
according to VaersAware, B- event severity ranks according to event rarity.
General discussion
There is a surprising amount of structure in the distribuon of missing age data in VAERS reports, in relaon to age,
event severity and sexes. Such biases could not be detected if the CDC administraon would not make public the
data, as most other administraons. Nevertheless, the very fact that no form adapted to include data of both
pregnant women and their un- or new-born child exists shows a systemic, in addion to systemac,
maladministraon of the VAERS informaon database. We hope that explanaons implying unintenonal
mechanisms for these nonrandom biases will be proposed. Below, the possibility that this structure results from
simple mathemacal eects is discussed.
Considering age categories, one could suggest that the number of missing data among death events that are
recovered is approximately constant. Considering deaths, lets assume for the sake of the example that one missing
age is found by the cleansing process at VaersAware for all age categories. In that case, the proporon that this single
case represents decreases with the number of deaths in that age category. As deaths typically increase with age, this
would produce negave associaons between bias and age. A similar raonale could also explain the posive
correlaons observed between bias in missing data and event severity, for rankings where event numbers decrease
with their severity. However, the data in Figure 2 clearly show that numbers of reports for which age is recovered are
not constant at all. In addion, paerns are in most analyses stronger when using the VaersAware event severity
ranking, which is not proporonal to event rarity. This invalidates the proposed mathemacal trivial explanaon,
which does not explain the observed paerns.
This mathemacal explanaon is even less relevant in relaon to the rao between post- and pre-cleansed sex raos,
because it is a rao between two raos. Such raos between raos are unaected by the proposed scenario where
the number of recovered missing age data is approximately constant. In other terms, at this point and unl new
explanaons and analyses are proposed, the paerns in the distribuon of missing age data are considered to reect
a real process.
B
R² = 0.7213
R² = 0.441
0.960
0.970
0.980
0.990
1.000
1.010
1.020
1.030
1.040
1.050
1.060
0 1 2 3 4 5 6
Ratio between post- and pre-cleansed female/male ratios
Events ranked according to severity
Low------------------------------------------------------------------------------------->High severity