The Lancet: Children Have 7.3 Times Higher Risk of Hospitalization and Higher Risk of Myocarditis/Pericarditis But Authors Claim the Opposite

Children were more than seven times as likely to experience heart inflammation in the first week after vaccination than if they were unvaccinated.

We know mRNA COVID-19 jabs come with an increased risk of myocarditis, especially in boys.

NB: 7.31 times the risk is for serious cases only (hospital admission or death), excluding Emergency Care Data Set (ECDS) records.

That’s why it was surprising to find a December 2025 article in The Lancet Child & Adolescent Health by Sampri et al. being widely cited as proof that COVID-19 vaccines do not carry long-term risks of heart inflammation (specifically myocarditis or pericarditis) in children and adolescents and that COVID-19 diagnosis is associated with more harm to children then COVID-19 vaccination.

The authors claim that vaccine-related heart inflammation only occurs in the first four weeks after vaccination, and even then, the risks are much lower than after a COVID-19 infection.

However, careful inspection of both the main text and supplemental materials reveals a different story:

• Significant risks do persist beyond the 4-week window, especially for teenage boys.

• In several places, the authors’ own tables report hazard ratios (HRs) above 2.0—meaning more than double the risk—and sometimes higher, yet these findings are buried in appendices or framed in a way that minimizes their importance.

This critique uses clear language to explain where those risks appear, how they were obscured, and why the conclusions do not hold up under scrutiny.

What the Study Claimed

The study examined medical records from nearly 14 million children in England and compared the rates of serious cardiovascular and inflammatory conditions after:

• a COVID-19 diagnosis (positive PCR test or hospital code), and

• a COVID-19 vaccination (first dose of the Pfizer/BioNTech vaccine, also called BNT162b2).

The outcomes included:

• Myocarditis (inflammation of the heart muscle)

• Pericarditis (inflammation of the lining around the heart)

• Blood clots

• Low platelet count (thrombocytopenia)

• General inflammatory conditions

They used a statistical method called a Cox model, which calculates an adjusted hazard ratio (aHR). This is a way of comparing how much more likely an event (like myocarditis) is to happen in one group (e.g., vaccinated children) versus another (e.g., unvaccinated). An aHR above 1.0 means an increased risk. For example, an aHR of 2.0 means twice the risk.

Their key conclusion was:

“No evidence of long-term myocarditis signal after vaccination.”

What the Data Actually Show

Buried but Significant Risk Beyond 4 Weeks

In Table S14 of the supplementary material, teenage boys (12–17) had a statistically significant increased risk of myocarditis/pericarditis between 5 and 26 weeks after vaccination:

• aHR = 1.28 (95% confidence interval: 1.03 to 1.59)

• For all males: aHR = 1.31 (1.04 to 1.65)

This directly contradicts the claim that there was no risk after 4 weeks.

7.3 Times Higher Risks in Hospitalization-Only Data Buried

In Table S15, the authors excluded data from the Emergency Care Data Set (ECDS) to focus only on events that led to hospital admission or death. This filters out more minor cases and emphasizes serious outcomes.

• Week 1 after vaccination: aHR = 7.31 (3.58 to 14.93)

• Weeks 2–4: aHR = 2.28 (1.09 to 4.78)

These numbers mean children were more than seven times as likely to experience heart inflammation in the first week after vaccination than if they were unvaccinated or pre-vaccination.

Yet the abstract and headline figures report a lower risk (aHR = 1.84), combining all data including ECDS records.

Signal dilution works, but in this case, it’s too obvious. Anyone can see what they did there.

Endpoint Bundling Obscures the True Signal

The study combines myocarditis and pericarditis into a single outcome, even though they are clinically distinct.

• Myocarditis is more serious and closely associated with vaccination in other studies.

• Pericarditis can be more loosely diagnosed and is often transient or non-specific.

By combining them, the authors may dilute a clear myocarditis signal.

No separate risk estimates are provided for each condition. This lack of clarity is especially problematic when the condition in question could have life-threatening consequences.

Exposure Windows Misattribute Dose 2 Risks

The authors only analyze “time since first dose,” and do not model the timing of the second dose separately.

• This means a heart inflammation event after the second dose gets recorded in the later risk window (e.g., weeks 5–26 after dose 1).

This misclassification weakens the study’s ability to pinpoint which dose caused the event and may dilute or shift the risk signal.

Post-Vaccination Infections Not Censored

Another flaw is that children who were vaccinated and then later infected with COVID-19 are still considered part of the “post-vaccine” group when they get sick.

• So if a child has heart inflammation after a post-vaccine infection, the event may be wrongly attributed to the vaccine.

This contaminates the data and confuses the source of risk.

The infection model includes a sensitivity check that censors by vaccination. The vaccine model does not censor by infection. This asymmetry distorts the comparison.

An objective interpretation would point to, and warn against, pathogenic priming.

Missing Event Counts and Rounding Suppress the Truth

The study follows NHS data suppression policies, which hide numbers when fewer than 10 events occur. It also rounds event counts to the nearest 5.

This makes it impossible to know exactly how many cases of myocarditis occurred in key time windows or groups.

For example, in children aged 5–11, week 1 post-vaccine, the aHR is 11.27 (CI: 3.29 to 38.60), but we don’t know how many actual events this is.

“Small Risk” Framing Ignores Time and Demographics

The authors report the absolute excess risk (AER) of myocarditis/pericarditis as only 0.85 per 100,000 children over 6 months. But this number:

• Combines boys and girls

• Averages across low- and high-risk age groups

• Spreads a brief spike in week 1 over 180 days

This averages away the true short-term, concentrated risk in teenage boys.

The relevant risk window (week 1) shows much higher relative risk, especially in boys aged 12–17. That detail is lost in the AER framing.

Weighted Sampling Blocks Verification

To reduce computational load, the authors analyze all cases but only a 5% sample of non-cases (a 20:1 ratio). They then use inverse probability weighting (IPW) to adjust.

• IPW is a legitimate method, but the paper does not show how the weights were validated.

• Without access to the raw data and code, independent verification is impossible.

This limits transparency and reproducibility.

Financial Conflicts and Data Lock-In

One co-author, Dr. Kamlesh Khunti, discloses funding from Pfizer, Roche, and Sanofi. The data used in the study (NHS Secure Data Environment) are closed to the public.

This combination—industry ties and closed data—raises red flags about selective interpretation.

The burden of clarity and transparency is higher in such a context. The authors do not meet that burden.

Conclusion: The Study Confirms Risk, Then Denies It

The data in Sampri et al. clearly show elevated risk of heart inflammation following COVID-19 vaccination in children, especially in boys. This risk persists beyond 4 weeks and may be even higher than the authors admit when excluding less specific emergency department data.

Yet the authors headline their conclusion with “no long-term risk.”

This is misleading.

A more honest summary would be:

• Short-term risk of myocarditis is high after vaccination, especially after dose 2.

• Risk remains elevated beyond 4 weeks in boys and older children.

• The study does not control for post-vaccine infection, does not isolate dose 2, and suppresses exact event counts.

It is not a debunking of vaccine risk. It is a partially reported signal study that demands independent, dose-specific, transparent reanalysis.

Popular Rationalism calls for:

1. Retraction of the study for failure to interpret their own data correctly.

2. Investigation for repeated rounds of analysis in search of ways to make the known signal of risk disappear.

3. Independent reanalysis with full access to NHS data

4. Dose-specific modeling (not just time since first dose)

5. Separate tracking of myocarditis and pericarditis

6. Stratification by sex and age in all tables and figures

7. Censoring for post-vaccine infections in risk windows

8. Full publication of event counts, not just rounded summaries

Until then, claims of “debunking” are premature, and arguably intentionally deceptive and harmful to public health.