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International Journal of Vaccine Theory, Practice, and Research 2(2), September 26, 2022 Page 603
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Revisiting Excess Diagnoses of Illnesses and
Conditions in Children Whose Parents Provided
Informed Permission to Vaccinate Them
James Lyons-Weiler, PhD1,2 and Russell L. Blaylock, MD3
1Institute for Pure and Applied Knowledge
2IPAK-EDU LLC
3Theoretical Neuroscience Research, LLC
Abstract
Controversy over a reported increase in office visits specifically scheduled for illnesses and conditions in
children has stalled progress in understanding adverse outcomes associated with an increasingly crowded
schedule of pediatric vaccines. Studies finding associations between vaccines and adverse conditions have
been targeted for retraction. Here, we revisit data from one such study, comparing the increase in office visits
for conditions independent of the routine “well-child” visits (hereafter, Health Care Visits; HCVs). The
retraction occurred after >1/4 of a million people had read the peer-reviewed study. It was targeted by one
anonymous reader who complained he did not believe the published results. His complaint hinged on the
supposition — unsupported by any data — that vaccinated children made their scheduled HCVs more
regularly than unvaccinated, implying that those unkept appointments led to fewer diagnoses. We show, here,
new data from the same practice that the opposite is true. When the data for vaccinated versus unvaccinated
children are examined, the critic’s claim is exactly reversed. Relative Risk and Odds Ratios sustain and
augment the original report. Additional office visits, beyond scheduled HCVs, are quantified, controlling for
variation in kept HCVs and age/days of care. Estimates of Health Care Incidence (HCI) show that visits above regular
HCVs increase due to vaccination by 2.56 to 4.98 additional office visits for vaccine-related health issues per unit increase in
vaccination per year. Blocking and multiple linear regression analysis of interactions indicate both that the
unvaccinated are keeping scheduled HCVs more often than the vaccinated, and that vaccination comes with a
net increase in non-routine office visits, i.e., not “well-baby visits” but trips to the doctor for reasons other
than vaccination. Taking account of the complexities of healthcare-seeking with measured covariates and
outcomes, especially adverse health events, suggests that vaccination may be driving the increased need for
non-routine office visits for specific health complaints. Meanwhile, one reader’s unsupported and false
criticism of the former study, reflects a pervasive bias leading to systematic removal of many well-designed
studies attributing adverse outcomes to vaccines. Hiding such well-designed and faithfully reported, not to
mention peer-reviewed and published research, clears the way for marketing programs bought and paid for by
vaccine manufacturers and the Centers for Disease Control and Prevention (CDC).
Keywords: chronic illness, pediatric health, relative incidence of office visits, RIOV, scheduled pediatric vaccines, well-baby visits
Introduction
Long-term vaccine safety studies have been restricted to observational, retrospective studies due to
supposed concern over the alleged unethical nature of randomized clinical trials in which some candidates
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would not receive the claimed benefits of vaccination. This position, however, begs the question of the net
risk-to-benefit ratio of vaccinations and their effects on human health.
Contrary to media reports and biased online, non-peer-reviewed summaries produced by agencies such as
the US CDC and FDA, past studies of vaccination have not universally supported the narrative claiming
that childhood vaccines are safe (and effective). Here our focus is on the purported “safety” of childhood
vaccines in the schedule. Studies conducted and funded by US agencies, such as the Centers for Disease
Control and Prevention, are alleged by media outlets to have shown that vaccines in the CDC’s
recommended vaccine schedule are safe for every child, but that claim is an exaggeration of the conclusions
of those studies. The accuracy of claims published on the CDC website, such as “Vaccines Do Not Cause
Autism”, are impossible to assess because the question of association has not been addressed for most of
the pediatric vaccines in the CDC schedule, nor have the interactions between them been systematically
addressed. Likewise, most such studies base their conclusions on a “lack of a mechanism” to explain such a
link, when in fact well-designed studies conducted independently of vested interests in the vaccine industry
— that is, without influence from government funding or pharmaceutical-supported marketing — tend to
find a surprisingly higher rate of undesirable health outcomes associated with vaccination (Mogensen et al.,
2017; Aaby et al., 2018; Mawson et al., 2017, Hooker & Miller, 2020) than are reported in the mainstream
sanitized literature bought and paid for by a multi-trillion dollar industry (E. P. I. C. Magazine, 2017; Liu et
al., 2017; Wong et al., 2017; Dal-Ré et al., 2019; Niforatos et al., 2020). The massive financial conflicts of
interest in the American Academy of Pediatricians (AAP) and its members were enumerated by Lyons-
Weiler and Thomas (2021).
Journals that seek favor and compensation from agencies and advertisers feel pressure either to not publish
studies that find issues with vaccines, or, if they do publish any such study, the authors and editors may
expect pressure to retract it later on (Shaw, 2020). There has been an increase in the practice of targeted
retractions, often following a non-reviewed letter by an anonymous critic. This form of post-publication
threat to a publication due to its results, with no evidence of fraud, nor hard evidence of the alleged
problems with the published study, has been addressed by a group of Israeli criminology scientists, who
found that authors of such retracted studies felt targeted (Elisha et al., 2020).
The retraction of studies due to variance from the outcomes predicted by the mainstream vaccine marketing
narrative will, of course, bias the resulting literature, preventing meta-analyses that might pick up a systemic
pattern of adverse effects. For vaccination studies, the anonymous readers’ comments leading to a retraction
of the initial publication in the instance at issue here can only be seen as a “ghouling” bias. Anonymous
individuals targeting studies because they do not like or agree with the results of a study produce an
unwelcome double jeopardy of dubious necessity. Such a practice is patently unfair: the reader’s comments
were not themselves subject to formal critical peer-review, and thus a power imbalance exists. Such post-
publication anonymous attacks permitting a single reader’s guesswork to overrule the recommendations of
the original peer-reviewers is not consistent with any reasonable ethical standard for publishers. Journals
should instead publish statements of concern following peer-review, allowing the authors to either defend
their research or withdraw their study if the concerns expressed are true and bring valid issues to light that
have been overlooked or misunderstood. Such rational discourse is part of the staid and honored practice in
genuine science, allowing individual scientists and the community to engage in a transparent manner
consistent with honor and integrity. Many legitimate journals allow readers to comment at the end of the
article, thus encouraging useful scientific debate.
In this study, we examine the likelihood of alleged bias in the publication of the first round of results in the
study “Relative incidence of office visits and cumulative rates of billed diagnoses along the axis of
vaccination” (Lyons-Weiler & Thomas, 2020, retracted). That study reported an increased risk of the need
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for medical attention for many conditions among the more vaccinated cohort compared to that required by
the less vaccinated children in the same practice. For convenience, we will refer to the two groups as
“vaccinated” and “unvaccinated” throughout this paper. The original study in question was retracted after an
anonymous reader communicated the unsubstantiated opinion, evidently a personal suspicion, that the
results were due entirely to variation in the health-seeking behavior of parents who provide informed
permission for their children to be vaccinated compared to those who refuse to provide informed
permission. That reader gave no evidence or explanation as to why such variation would necessarily exist.
The concern might be interpreted as a seemingly reasonable hypothesis. The logic is as follows: if
vaccinated parents go to the “well-child” visits more frequently, physicians have more opportunities to find
undesirable conditions, leading to higher diagnosis rates in the vaccinated. However, the critical reader
provided no data to support such an idea, which had already been addressed during peer-review. An entire
set of results had already been generated during the prior peer-review process showing that the reader’s
supposition was likely to be false.
The study in question had remarkable results. For example, the data analyzed by Lyons-Weiler and Thomas
(2020) found zero cases of ADHD in the unvaccinated group (a condition usually diagnosed outside the
practice). This result could not be due to methodological irregularities; the data are the data. No means of
adjustment for covariates or methodological manipulation could change zero cases of ADHD in the
unvaccinated into a non-zero number of cases. Lyons-Weiler and Thomas (2020) also reported the
distinctive signal of Dr. Thomas’ practice honoring the parent’s decision to refuse to provide informed
permission to vaccinate their child (or children) as required for human subjects research by the United States
Code of Federal Regulations 45 CFR 46). That is the code governing the conduct of post-market,
retrospective vaccine “pharmacovigilance” studies of patient whose data could be used in long-term vaccine
safety studies regarding vaccine safety. Similarly, he abided by parent refusal to consent to vaccination as a
medical procedure for all or any individual vaccinations, or for the cessation of vaccinations, as required by
Oregon State law governing informed consent.
Materials and Methods
There are three options for studying variation that might be associated with healthcare-seeking behavior.
The first is blocking groups on healthcare check-up “well-child” visits, referred to here and throughout as
Health Care Visits (HCVs). The second is to match, patient per patient, vaccinated children to unvaccinated
children that are most similar in age and in keeping up with their scheduled HCVs. The third option is to
“adjust” for HCVs in a multivariate setting. The details for each of these analyses are as follows.
BLOCKING STUDY
To define three groups (blocks) of patients based on HCVs, patients were ranked irrespective of vaccination
status and separated into the top, middle and lowest thirds; these define high, intermediate, and low HCV
groups. Health outcomes were then compared between vaccinated and unvaccinated patients within each of
these groups using the Relative Incidence of Odds Ratio (RIOV) — a method described by Lyons-Weiler
and Thomas (2020).
MATCHING STUDY
Matching patients in different exposure groups can reduce variation associated with suspected confounders.
To be effective, matching must be performed without prior knowledge of or reference to health outcomes.
To meet this requirement, a total of 561 vaccinated patients were chosen to match the unvaccinated using a
minimum Euclidean distance computed by considering the variables Days of Care and HCVs. Importantly,
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because all the patients in the study were born into the practice, the independent variable Days of Care is
essentially the same measure as Age but accounts for differences among patients who may have moved out
of the practice. In this process, a patient with the next-smallest Euclidean distance was selected if a
vaccinated patient had already been chosen as a match for a prior patient. To ensure unbiased matching, no
other information was referenced or considered in the otherwise blinded selection of the 561 matched
vaccinated patients.
EFFECTS OF VACCINE CESSATION
In the original study, we noted that because physicians in the practice would review the vaccine information
sheets with parents at each Health Care Visit, and also would be responsive to parents’ concerns over
seizures and developmental delays, the data appeared to show a lower rate of neurodevelopmental disorders
in the vaccinated than in the unvaccinated; i.e., opting out of vaccination would enrich the non-vaccinated
group for family members who might have a risk of developmental delay.
To address this aspect of the data on other health effects, we calculated the relative risk of the vaccinated
(>0 vaccines), and older children (≥1,500 days of age) in two groups: the “Low Vaccine Adoption” group
(<0.015 vaccines per Day of Care [DOC]; N=390) vs. the vaccinated “High Vaccine Adoption” group
(>0.02 vaccines per DOC; N=467). Health outcome differences between these groups would reflect the
effect of vaccine cessation on overall health. It could also supply information on the effects of various
vaccines on specific developmental windows during the vaccination period and the health effects of
avoiding further injections on later developmental windows.
MULTIVARIATE STUDY ADJUSTING FOR SUSPECTED CONFOUNDERS
Multiple linear regression (MLR) is classically used to help those studying the relationships among
independent variables via model comparison, and the study of predictor variable effects, changes in effects
in the presence of other independent variables, and interactions. Therefore, MLR was used to study the
effect of variation that might be attributed to healthcare utilization, age, and “natural” lifestyle choice. To
accomplish this, the compound variable “Health Care Visits per Day of Care” (HCV/DOC) was used as an
independent measure to study, among the vaccinated, the effect of parameter inclusion in a model of the
effects of vaccination exposure (number of vaccines) on requiring an office visit for any condition other
than vaccination. Breastfeeding, a correlate of lifestyle choice, was also included to further study the effects
of adjusting for the organic/natural lifestyle suspected to explain health differences among the more
compliant “vaccinated” group and the group referred to as “unvaccinated” in which one or all vaccines were
refused.
Results
In all our analyses, the remarkable outcome of zero ADHD cases among the unvaccinated, but 168 office
visits for ADHD in the vaccinated were, of course, repeated. The health outcome of ADHD was not
included in the results within each outcome but remains one of the most important findings given the data.
BLOCKING STUDY
The blocking design revealed differences between high, medium, and low health care visit (HCV) blocks,
specifically in the average age of patients (Figure 1). The groups also varied concerning the differences in
HCV, with the high HCV group exhibiting a difference in health care visit use (HCU in the figure) between
the “vaccinated” and “unvaccinated” patients (Figures 2a, 2b, and 2c). The difference is attenuated in the
Medium HCV group, and non-existent in the Low HCV group. Given that the unvaccinated participated in
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HCVs with a greater frequency in the High and Medium HCV blocks, higher risks of adverse health
outcomes are not expected in the comparisons of vaccinated and non-vaccinated patients within these
blocks.
Figure 1. Average age in days of groups within HCV blocks.
Concern over confounding can be addressed by setting the baseline via the comparison of HCV itself as an
outcome: the ratio (V/UV) of HCV in the three HCV blocks are as follow: High (0.71), Medium (0.842),
and Low (1.016).
Figure 2a. RIOV-type analysis of Health Check Visit use in the High healthcare use block.
ASSESSMENT OF SUSPECTED CONFOUNDING VARIATION IN HEALTHCARE-SEEKING BEHAVIOR
Many vaccine studies adjust for covariates, whether functional relationships of the covariate, the main effect,
and the health outcomes have been determined to be confounding (in reality) or not. They often consider
and interpret the results as if the covariates themselves are, in fact, confounding without providing evidence
of the causality of the suspected effect. In other words, the level of evidence used to show causality of
alleged confounders falls far below that required for inferring causality for the main effect. This is a serious
flaw in the paradigm of adjusting for alleged, unproven confounders; as covariates, they may, in fact, prove
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to be co-predictors. Further, interaction terms are universally ignored in studies that adjust for alleged
confounders, and the significance of the main effect (vaccines) can be hidden in an unstudied or unreported
interaction term. To check on whether HCVs were, in fact, confounding in the study of health outcomes
associated with variation in vaccination status, we calculated and compared the rate of keeping scheduled
HCVs between the vaccinated and unvaccinated.
Figure 2b. RIOV-type analysis of Health Check use in the Medium healthcare use block.
Figure 2c. RIOV-type analysis of Health Check use in the Low healthcare use block.
Highest Health Care Visit Use
In the class with the highest use of Health Care Visits, there were a total of 16,264 HCVs in the 1,105
vaccinated children. In the same class, there were 213 HCVs in the non-vaccinated group of children. That
accounts for 14.71 HCVs per vaccinated patient, and 21.3 HCVs per non-vaccinated patient in the class with
the highest use of Health Check days.
There was an average of 2,316 days of duration of enrollment in the practice among the vaccinated children
in this class, and an average of 2,133 days of duration of enrollment in the practice among the children
whose parents opted out of vaccinations offered.
There were more HCV days per total days of enrollment in the non-vaccinated children in the class (1% of
days of enrollment were HCV days) compared to the vaccinated children (0.63% of DOC were HCV days).
Mid-Level Health Visit Use
In the class with the mid-level use of Health Care days, there were a total of 11,964 HCVs in the 1,018
vaccinated children. In the same class, there were 1,144 HCVs in the non-vaccinated children. That accounts
for 11.75 HCVs per vaccinated patient, and 13.95 HCV per non-vaccinated patient in the class with the mid-
level use of Health Check days.
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There were slightly more HC days per total day of enrollment in the non-vaccinated children in the mid-
class (0.928% of days of enrollment were HC days) compared to the vaccinated children (0.905% of DOC
were HC days).
Lowest Health Care Visit Use
In the class with the lowest use of Health Check days, there were a total of 4,472 HCs in the 640 vaccinated
children. In the same class, there were 3,632 HCs in the non-vaccinated children.
That leads to 6.99 HCVs per vaccinated patient, and 7.74 HCVs per non-vaccinated patient in the class with
the lowest use of Health Check days.
There was a slightly, but not significantly, higher percentage of HC days per total days of enrollment in the
non-vaccinated children in the lower block (1.35% of days of enrollment were HC days) compared to the
vaccinated children (1.33% of DOC were HC days).
All Patients
Considering all the patients in the study, the vaccinated spent 0.77% of their DOC attending HCVs, and the
Unvaccinated spent 1.07% of DOC attending HCV.
The average age of those vaccinated was 751 days with an average of 8.87 HCVs per patient; the average
age of non-vaccinated patients was 750 days with an average of 8.86 HCV per patient.
Similarly, the average DOC of the vaccinated was 746, and the average DOC in the non-vaccinated patients
was 741.
These would rule out HCV use bias as an explanation for the increased numbers of diagnoses; HCV is not a
confounder in a manner that could explain the results.
COMPARISON OF HEALTH OUTCOMES, VACCINATED VS. UNVACCINATED WITHIN BLOCKS
Blocking necessarily reduces sample size and can reduce statistical power. Comparisons between vaccinated
and non-vaccinated patients in each of the three HCV blocks led to variation in which healthcare outcomes
were increased in the vaccinated population (Table 1). The health conditions where the vaccinated
individuals received more health care visits than the unvaccinated in that block are the ones in which the
ratio of Non-Routine Office Visits are bolded in the table. These are the conditions where the children
whose parents chose to go along with the CDC vaccine schedule in the particular block named at the top
row of the table, required a higher number of Non-Routine Office Visits. Evidently, in the bolded entries,
the more heavily vaccinated individuals had health complaints more frequently than the individuals who
received fewer of the shots in the CDC vaccine schedule.
It is noteworthy that certain health conditions, such as “Edema”, for instance, had zero cases in the non-
vaccinated patients in any Block. Other conditions, such as “Digestive Tract Issues”, and the four conditions
following that one, had no unvaccinated individuals in the High Block.
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Table 1
Ratio of the Incidence of Office Visits for Health Outcomes in by Health Care Use Blocks
Comparing Vaccinated (V) to Unvaccinated (U)
Block
N = total in block,
nV and nU = subtotal for each
group in the named block)
High
N = 1,115
nV = 1,105 & nU = 10
Mid
N = 1,099
nV = 1,018 & nU = 82
Low
N = 1,108
nV = 640 & nU = 469
Routine Health Care Visits,
Baseline
Value for Each Block*
0.71
0.842
1.016
Condition
Ratio of Non-Routine Office Visits,
Vaccinated to Unvaccinated by Block
Fever
1.37
1.47
1.77
Gastroenteritis
2.18
1.147
1.517
Allergic Rhinitis
0.769
1.288
0.183
Edema
Infinite†
Infinite†
Infinite†
Anemia
3.081
1
2.335
Otitis media
2.029
0.493
1.465
Eczema
1.197
0.501
2.564
Digestive Tract Issues
Infinite†
0.241
0.977
Nausea/Vomiting
Infinite†
0.278
0.732
Allergy - Food
Infinite†
0.4833
0.732
Pain
Infinite†
0.467
0.814
Seizure
Infinite†
0.201
N/A
Diarrhea
1.511
0.461
0.879
Breathing Issues
0.771
0.724
1.116
Urticaria
0.434
Inf
0
Ear Pain
0.366
1.02
0.366
Asthma
0.898
0.322
0.977
Dermatitis
0.326
0.38
0.56
Conjunctivitis
0.552
0.511
0.792
*Bolded entries are those in which the ratio of vaccinated individuals in the “Block” exceeds the baseline value for office
visits of the non-routine kind, whereas the unbolded entries are ones in which the ratio is less than or equal to the
baseline.
†In these instances the ratio would be infinitely great because there are no unvaccinated individuals being seen for the
named condition in any non-routine office visit. The value, of course, is incalculable because a ratio cannot contain a zero
as its denominator.
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MATCHING STUDY
Both the RIOV and classical odds ratio analyses showed that many of the health conditions were elevated.
Examples are shown in Figure 3. The least controversial of these, Fever, is a well-established side effect of
vaccination. Odds ratios for office visits scheduled for each condition shown in Figures 3a to 3f are all
significant (p<0.01).
Figure 3. RIOV diagram for Fever following matching for Health Care Visits and Days of Care (age).
Odds ratios for all conditions (except for ADHD) are shown in Figure 4. For reference, the odds ratio of
Health Care Visits (0.99) is shown. Some of the rarer conditions were difficult to study due to small sample
sizes (e.g., no cases of autism were found among the matched sample of 561 vaccinated persons due to its
overall rarity).
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Figure 4. Odds ratio of office visits for specific health issues in the comparison of 561
unvaccinated and 561 vaccinated patients in the matched analysis.
Multivariate Study
Multiple regression analysis allows the study of relevant variables in the context of the question, “Does
overall vaccine uptake correlate with number of office visits for poor health outcomes?” Thus, the analysis
involved the study of the number of vaccines per year (1, main effect) vs. the number of non-vaccine-
related office visits (Y, the dependent variable).
For this study, the specified fixed intercept model then becomes
Y = 1 + e
where 1 = number of vaccines, and 2 = HCV.
The effect of each variable is studied by its slope and the regression coefficient, R2.
Since the data were already arranged into High, Medium, and Low HCV, these parameters can be studied for
the full data and within block in a fixed intercept model (Table 2).
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Table 2
Linear Regression Model Parameter Values Within HCV Blocks
Any Visit
Any Visit Excluding
Vaccinations
HC Use
Slope
𝑹𝟐
Slope
𝑹𝟐
High
7.96
0.914
6.96
0.890
Mid
6.45
0.924
5.45
0.897
Low
6.47
0.805
5.47
0.747
From this analysis, we can see that the number of office visits for any condition excluding vaccination is
weaker than the relationship for any visit, but each model still has a substantial R2 value showing a strong
and robust shared variance. From this analysis, the following estimates result:
High Between 6.03 and 7.96 health care issues (HCIs) resulting per unit increase in vaccination
Mid Between 4.38 and 6.45 HCIs resulting from vaccination
Low Between 3.79 and 6.47 HCIs resulting from vaccination.
In a multiple regression context, the variation attributed to Health Care Visits and Age can be combined
appropriately in the compound variable HCV/Age, and the independent variable Breastfeeding can be
studied via the following model
Y = 1 + 2 + 3 + e
where 1 = number of vaccines, 2 = HCV, and 3 = breastfeeding (binary, not duration)
Figure 5a. Model one: Number of Vaccines, Health Care Visits, and Breastfeeding
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Figure 5b. Model one: Analysis of Variance table
The effect of each variable is studied by its slope and the regression coefficient, R2, and p-value. Adding the
compound variable HCV/Age and Breastfeeding results in a far less impressive model (Figures 5a and 5b),
and the specific parameter values reverse sign.
However, the model is not fully specified, given that the interactions among terms have not been studied.
The lack of consideration of interaction terms in vaccine studies has been previously noted (Kulldorf,
2013). Adding the appropriate interaction terms (Figures 6a and 6b) leads to a positive and significant slope
for Vaccines even after variation associated with HCV, Age, and Breastfeeding, and interactions among
vaccines and HCV/Age are considered. The slopes of the other terms are also provided.
Figure 6a. Model two: Study of interaction between “Number of Vaccines” and “Health
Care Visits Per Days of Age”
Figure 6b. Model one: Analysis of Variance table
In the parlance of vaccine epidemiologic studies, after “adjusting” for HCV/age, vaccine exposure was still
significant. This result highlights the logical flaw in considering the mere suggestion of hypothetical
alternative factors as definitive positive evidence of the effect of vaccines on health outcomes. Per the
interaction analysis, “Number of Vaccines” increase the number of office visits required for health issues in
a manner that is independent of any effect of the covariates HCV/Age and Breastfeeding and also interacts
significantly with HCV/Age.
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EFFECTS OF VACCINE CESSATION
The comparison of the High- and Low-vaccinated patients aged 1,500 days or more shows that vaccine
cessation leads to a reduction in many conditions (thus the increased relative risk in the vaccinated patients;
Figure 7). The odds ratio of Health Check in this unmatched analysis (1.2) is shown to provide a baseline
for comparison.
Discussion
These data provide a valuable resource for the study of the impact of variation in parents granting informed
permission to pediatricians for vaccinations recommended per the CDC schedule. Nevertheless, five days
after the Lyons-Weiler and Thomas study was published, the Oregon medical board suspended Dr. Thomas’
license under an “emergency” action, without due process. As a condition of temporary reinstatement of
his license, Dr. Thomas was prevented from doing further studies. Were it not for this consideration, Dr.
Thomas would have been invited to participate in this analysis. Instead, he was specifically left out of the
design of the analyses, the interpretation of the results, and the writing of the manuscript. One must ask
why would anyone consider such an action to be linked with suspension of his medical license based on
“clinical suspicious of malfeasance”? Where is the link to scientific studies based on presented and openly
analyzed data?
These actions represent maneuvers to discredit Dr. Thomas and the original study, to discourage other
physicians from participating in independent (non-government-funded) research on the health effects of
vaccines on the pediatric population, and to keep the information about the impacts of variation in vaccine
acceptance on the total health of the pediatric population.
Months following the suspension of Dr. Thomas’ license, the journal retracted the study, after >250,000
people had read it. The original publishing journal had received one letter of complaint that alleged (without
providing any data or evidence) that the results of the published study must have resulted due to differences
between the vaccinated and unvaccinated populations with respect to adherence to “well-child” visits.
The facts are as follows:
(1) The unvaccinated visit: Dr. Thomas’ unvaccinated pediatric patients kept their Health Check visits
with a higher regularity and higher frequency than the vaccinated, overall, in HCVs thirds, and
regardless of age. Variation in healthcare-seeking behavior cannot explain the increased need for
office visits for health conditions outside of HCVs.
(2) The sole reader’s imagined concerns were unfounded, and the Lyons-Weiler and Thomas study
should not have been retracted.
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https://doi.org/10.56098/ijvtpr.v2i2 https://doi.org/10.56098/ijvtpr.v2i2.59
Figure 7. The age-matched effects of vaccine cessation. High Relative Risk values denote
increased risk of a given health outcome in patients receiving more vaccines in the older age
group (>1,500 days of age). The black bar shows the Relative Risk of HCV between these
groups as a baseline.
(3) There is no way to interpret the data as showing that, overall, the unvaccinated are less healthy than
the vaccinated. This addresses the medical board’s request to answer this question.
(4) The analysis of vaccinated vs. unvaccinated within HCV blocks shows that vaccinated patients have
a higher disease burden overall in the high HCV block for many conditions.
(5) For most of the conditions, the vaccinated have a higher disease burden even when patients are
matched for age, days of care, and healthcare utilization behavioral differences.
International Journal of Vaccine Theory, Practice, and Research 2(2), September 26, 2022 Page 617
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(6) Multivariate analyses show that consideration of interaction terms is necessary for the useful
retrospective study of the impacts of vaccination on human health.
The method developed by Lyons-Weiler and Thomas (RIOV; 2020) was new and was shown to have more
intrinsic statistical power than odds ratios or relative risk estimates. This is because the use of rates of a
given diagnosis, which is how the data are usually presented to odds and relative risk ratio analysis, are lossy
transforms of the rates of office visits required to address health issues related to the diagnoses. RIOV has a
higher dynamic range and represents an analytic advance toward a more sensitive measure of the degree of
illness related to a given diagnosis than OR or RR compared to methods limited to the presence of absence
of a diagnosis.
We have shown, using a variety of exhaustive methods, that the anonymous reader’s concerns that led to the
retraction of Lyons-Weiler and Thomas (2020) were unfounded. Given the insensitivity of the evidence to
methodological differences, we conclude that the paper was wrongfully retracted and for other reasons than
the alleged problem originating from a single reader using unscientific reasoning. At this point, unless the
journal in question reinstates the study, the journal’s reputation as an objective publication outlet will remain
forever suspect.
The medically important findings in these data should not be ignored. These include the possibility of
developmental effects of vaccine aluminum-induced anemia, gastrointestinal disorders, and increased risks
of these medical conditions secondary to vaccine-induced dysfunctions of various elements of the immune
system. Such mechanisms adverse effects and their probable, and in some cases certain, causes have been
well demonstrated in the scientific literature as cited in multiple articles in this journal (e.g., see the
immediately preceding entry by Blaylock and references there).
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Editor’s Comment
At the request of the authors of this paper the peer-review process, contrary to our published approach at this link
under “Peer Review Process for the IJVTPR ”, was double-blinded. Reviewers did not know who the authors were
(except for the Editor in Chief) nor were disclosed reviewer identities disclosed to the authors.
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