When Brian Hooker and Children’s Health Defense team up, you know you're going to get your fact-checking money's worth.
We read the actual 23-page paper behind the CHD headline. Not just the headline. Not somebody’s thread about the headline. Not a screenshot of a chart. The paper.
The dataset may contain signals worth investigating, but the tables, definitions, statistics, and even CHD’s own reporting raise questions that need answers before anyone claims vaccines caused these outcomes.
And it includes findings that deserve a closer look. I’m not interested in dismissing research because of who funded it, who wrote it, or because the conclusions make one side of a vaccine argument uncomfortable.
If the data show something important, investigate it.
But the reverse is also true: being willing to challenge the medical establishment does not mean we stop challenging papers that tell us something we want to hear.
That’s where this study runs into trouble.
Children’s Health Defense reported that children vaccinated before 13 months had substantially higher odds of several conditions, including recurrent respiratory disease, ear infections, rhinitis and “strep throat.” The underlying preprint analyzed records from 6,239 children at eight pediatric practices.[1][2]
Those associations exist in the authors’ analysis. The study does not establish that vaccination caused them.
And once I started checking the actual tables against both the paper and CHD’s summary, I found problems that go considerably beyond the usual “correlation isn’t causation” objection.
First problem: these are unadjusted comparisons
The paper uses basic 2×2 contingency tables and reports odds ratios. There is no multivariable model controlling for major differences between the vaccinated and unvaccinated families.
The authors acknowledge that they had no usable information on race or ethnicity, socioeconomic status, gestational age, birth weight, breastfeeding, mode of delivery, family medical history, environmental exposures, and other potentially important variables. They explicitly describe their reported odds ratios as unadjusted observational associations.
Later, they acknowledge the problem even more plainly: without multivariable modeling, they cannot disentangle vaccination from confounding variables.
That’s not a minor limitation.
Vaccination isn’t randomly assigned in this population. Parents who decline vaccines can differ from parents who vaccinate in education, income, breastfeeding, daycare use, medical-care utilization, lifestyle and many other ways that can also affect childhood diagnoses.
So when two groups show different rates of disease, you first have to ask whether you’re measuring the effect of vaccination—or differences between the families.
This study cannot adequately answer that question.
Then I found something harder to explain: Table 4 doesn’t match the cohort
According to the paper, there were:
2,414 children vaccinated before 13 months
and
3,825 children in the comparison group.
That’s 6,239 children total.
Now look at the respiratory-disease row in Table 4.
It lists:
1,369 vaccinated children with respiratory disease
and
1,324 vaccinated children without respiratory disease.
Add those together.
That’s 2,693 vaccinated children.
But the paper says there were only 2,414 vaccinated children in the cohort.
Where did the additional 279 come from?
This isn’t limited to respiratory disease. Several rows in Table 4 total 2,693 on the vaccinated side rather than the declared 2,414.
And here’s where it gets even more interesting.
When I add the male and female respiratory-disease counts from Tables 5 and 6, they do reconcile with the stated cohort:
523 + 484 = 1,007 vaccinated respiratory cases
720 + 687 = 1,407 vaccinated noncases
Total: 2,414
The unvaccinated sex-specific counts similarly total 3,825.
But using those sex-table numbers produces an odds ratio of about 1.19, not the 1.6 reported in Table 4.
That’s not a philosophical dispute about vaccines.
That’s an internal numerical discrepancy.
There may be an explanation. There may be an error in the table. There may be a coding or labeling issue.
But until the authors explain it, one of the paper’s headline findings cannot simply be accepted at face value.
The healthcare-use problem may be even bigger
Several of the study’s important outcomes weren’t defined as one diagnosis.
To qualify as recurrent ear infection, respiratory disease, skin rash, strep throat or fever, children needed at least three separate diagnoses within 12 months.
Now ask the obvious question:
Did vaccinated and unvaccinated children visit doctors equally often?
The study doesn’t know.
The authors acknowledge that they did not have total healthcare visit information and that vaccinated and unvaccinated families may interact with the medical system differently.
That matters enormously when your outcome literally requires repeated medical diagnoses. A child who visits the pediatrician frequently has more opportunities to receive vaccines. That same child also has more opportunities to have an earache coded, a sore throat swabbed, a cough entered into the medical record, or a respiratory infection diagnosed.
A child whose parents handle minor illnesses at home may experience the same number of illnesses without accumulating the required diagnostic codes.
This isn’t merely theoretical. A large U.S. cohort study involving more than 323,000 children found that children who were undervaccinated—particularly those undervaccinated because of parental choice—had different healthcare-utilization patterns, including fewer outpatient visits than age-appropriately vaccinated children.[3]
That makes healthcare utilization an especially important confounder in this particular study.
And it wasn’t controlled for.
The advertised “dose-response” isn’t nearly as clean as the headline suggests
This is another part of the paper worth reading for yourself.
For the paper’s “strep” endpoint, the odds ratios by dose category were:
1.5 → 2.2 → 3.4 → 2.7
So the association rises through the first three groups and then falls in the highest-dose group.
The paper nevertheless describes this as a “clear, monotonic dose-response gradient.” But a monotonic increase doesn’t decrease in the final category.
Respiratory disease is stranger:
1.2 → 1.6 → 2.0 → 0.50
At 10+ vaccine administrations, the association reverses completely and becomes strongly protective.
Developmental delay goes:
0.82 → 0.66 → 0.74 → 1.4
So the 4–5 and 6–9 groups actually showed statistically significant lower odds of developmental delay before the result reversed in the 10+ group.
Those patterns may have explanations.
But here’s the problem with the interpretation: when vaccination appears associated with lower disease, the discussion proposes healthy-vaccinee effects, selection effects or other bias. When vaccination appears associated with higher disease, the discussion repeatedly explores biological vaccine harm.
Bias doesn’t get to operate only when the data point in the inconvenient direction.
The same skepticism has to apply both ways.
CHD gets the developmental-delay result wrong
This isn’t an interpretation issue. It’s a numerical reporting error.
CHD’s article says the analysis found 40% higher odds of developmental delay among children receiving six to nine vaccine doses.
Vaccinated Kids Have Significantly Higher Odds of Developing Multiple Chronic Health Conditions
That’s not what Table 7 reports.
The 6–9 group had an OR of 0.74, meaning lower - not higher - odds in that analysis.
The 1.4 OR belongs to the 10+ vaccine group.
That’s a pretty significant distinction when you’re writing an article claiming a dose-response relationship.
CHD appears to put the rhinitis result in the wrong group too
CHD says rhinitis increased from 30% higher odds in the 1–3 group to 130% higher odds in the 6–9 group.
Again, that’s not what Table 7 says.
The 6–9 category has an OR of 1.7, or 70% higher odds.
The OR of 2.3, corresponding to 130% higher odds, belongs to the 10+ category.
Two different outcomes. Same type of reporting mistake.
Odds are not the same thing as risk
CHD also quotes Brian Hooker saying children were “60% more likely” to suffer recurrent respiratory disease and had “double the risk” of recurrent ear and strep infections.
But the paper calculated odds ratios, not relative risks.
Those are not interchangeable when an outcome is common.[4] BMJ has specifically cautioned that interpreting odds ratios as relative risks can overstate the magnitude of an association.
Using Table 4 exactly as printed, even though its denominators themselves need explanation, the respiratory figures correspond to roughly 50.8% versus 39.6%. That’s about a 28% relative increase in observed risk, not 60%.
The 60% figure comes from treating the OR of 1.6 as though it means 60% higher probability.
It doesn’t.
Again, this doesn’t make the association disappear. It means the result should be communicated using the statistic that was actually calculated.
There’s also a problem with who counts as “unvaccinated”
CHD tells readers the study contained 3,825 unvaccinated children.
But the actual paper says 469 children received their first vaccine after 13 months, and that this late-vaccinated subgroup was retained in the primary analysis as “unvaccinated before 13 months.”
Then, confusingly, the next section defines “unvaccinated” as receiving zero vaccines at any point during the observation period. Those statements don’t fit cleanly together.
Were the 469 late-vaccinated children in the reference group or not?
If they were, then this isn’t literally a comparison between vaccinated children and children who remained completely unvaccinated.
If they weren’t, the reported group totals and methods need clarification.
Either way, the paper should make this unambiguous.
What exactly is “strep throat” in this paper?
This may be one of the most important details buried in the tables.
CHD repeatedly describes the strongest association as recurrent strep throat.
But look at the paper’s actual ICD definition.
The “strep throat” outcome includes J02.0, which really is streptococcal pharyngitis.
But it also includes broad wildcard categories such as J03, J04, J05, J06, J36, J39, B27, A54 and A56 - along with a long list of ICD-9 categories.
That’s a problem if the result is being communicated specifically as recurrent streptococcal infection.
Official ICD-10-CM documentation identifies J06 as acute upper-respiratory infections of multiple or unspecified sites—and specifically distinguishes it from streptococcal pharyngitis, J02.0. J05 includes croup and epiglottitis. A54 is the gonococcal-infection family, while A56 covers chlamydial infections.[5]
In other words, as the paper’s coding table is written, this isn’t a clean laboratory-confirmed “recurrent strep throat” endpoint.
It’s a much broader diagnostic composite.
If the strongest headline result is “2.3 times the odds of recurrent strep,” then I want the strep outcome to actually measure strep.
There’s another curious coding choice in “respiratory disease”
The paper defines respiratory disease using J*, R09*, and R19* codes.
J codes include respiratory diseases. R09 includes symptoms involving the circulatory and respiratory systems.
But R19 is not respiratory.
Official ICD documentation classifies R19 as “other symptoms and signs involving the digestive system and abdomen”—including such things as abdominal swelling, bowel sounds, changes in bowel habits and diarrhea.[6]
Maybe R19 is a typographical error in the manuscript. Maybe it was actually included in the analysis. Those possibilities have very different implications. The authors need to clarify which one it is.
Even the list of nine conditions changes
The Methods section and Table 1 include nausea and vomiting among the nine outcomes.
But Table 4 reports sinusitis instead.
There is no sinusitis definition in Table 1.
Again, that may be a table-construction error rather than a problem with the underlying data.
But by this point we’re accumulating enough table and definition discrepancies that asking for a corrected version isn’t nitpicking. It’s basic quality control.
Multiple comparisons matter too
The authors analyzed nine conditions, then broke results down by sex, then analyzed four vaccine-dose categories.
They explicitly state that no correction for multiple comparisons was applied.
The more statistical tests you run, the greater the chance that some will cross a conventional p<0.05 threshold purely by chance.
That doesn’t erase very small p-values or prove the results are false.
It does mean isolated subgroup findings—particularly ones emphasized after looking across dozens of comparisons—need more caution than CHD’s article gives them.
Then there’s the most basic causal question: which came first?
The paper counts vaccine exposure through 13 months of age.
But the authors explicitly say they did not assess temporal relationships between particular vaccinations and diagnoses.
Some first diagnoses occurred very early. The paper reports respiratory diagnoses beginning as early as four days of age and its “strep” diagnoses as early as seven days.
That creates a basic causal problem.
If illness leads to more pediatric visits, and pediatric visits lead to more opportunities both for diagnoses and vaccinations, you can generate an association between vaccine count and illness without the vaccination being the cause of the illness.
A proper causal analysis needs to establish that exposure occurred before the outcome being attributed to it.
This study doesn’t consistently do that.
One CHD claim goes well beyond what this study tested
CHD also quotes Hooker saying the current vaccine schedule exposes infants to “toxic levels of aluminum.”
Whatever one thinks about aluminum-adjuvant research, this particular study did not measure aluminum exposure, blood or tissue aluminum, specific vaccines, or aluminum-related outcomes.
So that claim cannot be derived from this dataset.
The FDA’s current description is different: it states that aluminum-containing vaccine adjuvants have a demonstrated safety profile extending over many decades, with severe local reactions uncommon.[7]
That doesn’t make aluminum research off-limits.
It means a claim of “toxic levels” requires evidence specific to dose, exposure and clinical outcome—not an observational vaccinated-versus-unvaccinated table that never measured aluminum.
What does this study actually tell us?
What I think is fair to say: this dataset contains some sizable associations between vaccination status and several coded diagnoses. They deserve independent analysis and replication. The large number of children with limited or no early vaccination makes the dataset potentially useful.
What I do not think is fair to say is that this paper has demonstrated that vaccines caused vaccinated children to become less healthy. The analysis is unadjusted, healthcare utilization isn’t controlled, temporality isn’t adequately established, some outcome definitions are unusually broad, and several tables or descriptions do not reconcile internally.
What would move this forward: publish a corrected explanation of the Table 4 denominators; clearly identify what happened to the 469 late-vaccinated children; provide visit counts; analyze outcomes only after exposure; stratify by practice; use validated disease definitions; control for major confounders; identify actual vaccine types rather than treating vaccination codes as interchangeable “dose”; correct the outcome tables; and provide enough de-identified data or analytic code for independent reproduction.
What would change my assessment: if the authors produce that reanalysis and the associations remain roughly the same after those corrections, I would consider the findings substantially stronger.
That’s how science is supposed to work. You don’t protect a conclusion from scrutiny because you agree with it. You try to break it. And if it survives, then you’ve learned something.
The bottom line
I’m not dismissing this study. I’m asking for it to be treated seriously enough that its inconsistencies are actually addressed.
There may be something worth investigating in these records. But “we found an association” and “we demonstrated causation” are not the same statement.
And CHD’s article makes the problem worse by misreporting at least two dose categories, repeatedly sliding between odds and risk, calling an internally ambiguous comparison group simply “unvaccinated,” and describing a remarkably broad ICD composite as recurrent strep throat.
If this paper is going to be cited as evidence that vaccinated children are chronically less healthy, the burden isn’t on skeptics to explain away every association.
The burden is on the analysis to survive basic questions about its denominators, definitions, confounders, and chronology.
This study deserves criticism, not deference. Its own tables don’t fully reconcile, key outcomes are poorly defined, major confounders weren’t controlled, and CHD’s write-up misstates some of the results while repeatedly turning odds into “risk.” That isn’t a minor presentation problem. It’s exactly how weak evidence gets dressed up to look stronger than it is.
There may be a useful dataset buried underneath this paper, but the analysis presented here is not strong enough to support the sweeping claims being made from it. When a paper has internal numerical inconsistencies, ambiguous exposure groups, broad diagnostic buckets, uncontrolled confounding, and no clean exposure-before-outcome analysis, the answer is not to promote it as proof. The answer is to fix the analysis.
And when CHD then misreports key quartiles and blurs odds with risk, that stops being a simple scientific disagreement and becomes a credibility problem.
If you want people to trust the conclusion, start by getting the numbers right.
Notes & Resources
Yengst T, Ray H, Jablonowski K, Hooker BS. Comparative Health Outcomes of Vaccinated versus Unvaccinated Children: A Retrospective Cohort Analysis. September 26, 2026. The uploaded preprint contains the cohort definitions, statistical methods, ICD definitions and Tables 1–7 discussed above.
Michael Nevradakis, “Vaccinated Kids Have Significantly Higher Odds of Developing Multiple Chronic Health Conditions,” Children’s Health Defense, Sept. 29, 2026. Read the CHD article
Glanz JM et al. “A population-based cohort study of undervaccination in 8 managed care organizations across the United States.” JAMA Pediatrics. 2013;167(3):274–281. The study reported different healthcare-utilization patterns among undervaccinated and age-appropriately vaccinated children. PubMed record
Davies HTO, Crombie IK, Tavakoli M. “When can odds ratios mislead?” BMJ. 1998;316:989. Also see Grant RL, “Converting an odds ratio to a range of plausible relative risks for better communication of research findings,” BMJ 2014. Both discuss why odds ratios should not automatically be communicated as relative risks, particularly for common outcomes. BMJ: When can odds ratios mislead? BMJ: Converting odds ratios to plausible relative risks
Centers for Medicare & Medicaid Services, ICD-10-CM Tabular List. The official coding material identifies J02.0 as streptococcal pharyngitis, J06 as acute upper-respiratory infection of multiple or unspecified sites, and includes separate diagnostic families for gonococcal and chlamydial infection. CMS ICD-10-CM tabular list
CMS/CDC ICD-10 documentation for R19. R19 is classified under symptoms and signs involving the digestive system and abdomen, not respiratory disease. CMS ICD-10-CM reference
U.S. Food and Drug Administration, “Common Ingredients in FDA-Approved Vaccines.” FDA describes why aluminum salts are used as adjuvants and states that aluminum-adjuvanted vaccines have a demonstrated safety profile extending over decades. FDA vaccine-ingredient information