Concerning evidence of batch variability in COVID-19 Vaccines and the connected adverse event-rates.
Batch Inconsistencies
In November 2023, Barry Young, a former database administrator for New Zealand’s Te Whatu Ora (Health New Zealand), leaked anonymized data from the country’s COVID-19 vaccine database, revealing dramatic differences in mortality rates across Pfizer vaccine batches.
Working on payment systems for vaccine providers, Young accessed records for over 4 million doses administered to about 2.2 million people. He anonymized the data by randomizing identifiers while preserving key metrics like vaccination dates, doses, and subsequent deaths. Young’s analysis revealed clusters of excess deaths directly linked to specific batches, with some exhibiting death rates as high as 21% – vastly surpassing New Zealand’s baseline mortality rate of 0.75%.
The top 10 deadliest batches were distributed across all age groups and vaccination sites, not just limited to vulnerable high-risk populations. For instance:
Batch 1 (711 doses) with 152 deaths (21.38%),
Batch 8 (221 doses) with 38 deaths (17.19%)
Batch 3 (310 doses) with 48 deaths (15.48%)
Young calculated the statistical probability of these rates occurring naturally at 100 billion to 1, stating unequivocally: “There is no chance that this vaccine is not a killer.”
Patterns Young found included a massive spike in deaths immediately after the vaccines had been deployed. Some hot lots were given that killed 4.5 – 21,3%. Young also identified certain vaccinators that had unusually high mortality, some with rates as high as 1 in 4. It could mean thousands of excess deaths in New Zealand alone.
Young’s whistleblowing led to swift repercussions: his arrest on December 3, 2023, on charges of dishonest computer access, injunctions despite full anonymization of the data, facing up to seven years in prison. He was denied initial bail and blacklisted from employment, with the government securing injunctions to suppress further data release.
Despite this effort to stop the information, subsequent releases under New Zealand’s Official Information Act have validated dose-linked mortality increases, aligning closely with Young’s revelations. Independent analysts, including Steve Kirsch, have corroborated the findings. When Kirsch analyzed Young’s NZ data, he could confirm the disparities and also noted vaccinator-linked clusters. He challenged critics with $250,000 bets on affirming the data’s validity – this stands unchallenged to date. Kirsch estimate a global rate of 1 death per 1,000 doses, projecting up to 13 million vaccine-related deaths worldwide.
Batch discrepancies not isolated to New Zealand.
Craig Paardekooper‘s platform, How Bad Is My Batch examines VAERS and EudraVigilance data, demonstrating that just 5% of batches account for 90% of adverse events, with lethality varying 120-fold (from 0.05% to 6% death rates per reports). Paardekooper identifies alphabetic patterns in batch codes – such as lower letters, Pfizer’s EN/EP series, correlating with elevated harms – suggesting deliberate labeling to track toxicity levels. Early batches were particularly lethal, often showing delayed effects like a death peak 150-180 days post-vaccination in the elderly, indicative of persistent toxins. Listen to Paardekooper here.
Similar evidence comes from a 2023 Danish study by Max Schmeling, Vibeke Manniche, and Peter Riis Hansen, which analyzed 10,793,766 doses of BNT162b2 batches given to 4,026,575 individuals across 52 batches (December 2020–January 2022). After analyzing 61,847 batch-identifiable serious adverse event, SAEs, (including 14,509 severe SAEs and 579 deaths), they identified three distinct trendlines. The group with highest serious adverse event rates represented 4.22% of all vaccine doses but accounted for 70.78% of all SAEs, 27.49% of serious SAEs, and 47.15% of SAE-related deaths. The medium-harm group accounted for 63.69% of doses, 28.84% of SAEs, 71.50% of serious SAEs, and 51.99% of deaths. The group with lowest rates of harm represented 32.09% of doses, and with only 0.38% of SAEs, 1.01% of serious SAEs, and 0.86% of deaths. The authors described the heterogeneity as unexpected and contrary to expectations of uniform safety across batches. They noted that the most dangerous batches appeared early in the rollouts.
Their 2024 follow-up, together with Swedish research scientist Jonathan Gilthorpe affiliated with Umeå University, compared Denmark and Sweden, confirming batch-dependent SAEs across borders. Shared batches exhibited consistent severity and elevated rates during early campaigns. Even accounting for Sweden’s expected underreporting of up to 40% the patterns persisted, indicating possible manufacturing defects like variations in lipid nanoparticles, contaminants.
It is the opinion of epidemiologist Nicolas Hulscher, (McCullough Foundation) that batch-to-batch differences is a major risk modifier for COVID-19 mRNA vaccine harms, especially myocarditis, fatal cardiac events, and delayed deaths. Batch variability explains why some people escape harm from COVID-19 mRNA vaccines while others face mass injury or death. He describes hot lots contaminated with DNA plasmids, excess mRNA, or heavy metals versus “dud lots” that are degraded and inactive, claiming batch determines fate. He recommends routine lot-number documentation in autopsies and standardized checklists to better assess batch contributions.
In their 2024 U.S study “Batch-dependent Safety of the BNT162b2 mRNA COVID-19 Vaccine in the United States”, Karl Jablonowski and Brian Hooker analyzed VAERS data combined with Pfizer-BioNTech lot allocation records. They identified three distinct clusters of SAE rates, mirroring the Danish pattern findings by Schmeling et al. High-outlier lots for deaths, serious SAEs, and all SAEs were predominantly from early distribution periods (December 2020–early 2021). Geospatial analysis linked these to mass vaccination sites, hospitals, and universities, concluding heterogeneity indicates manufacturing or distribution inconsistencies, calling for deeper batch-quality investigation and potential underreporting in passive surveillance systems like VAERS.
In their 2024 study “Batch-dependent safety of COVID-19 vaccines in the Czech Republic and comparison with data from Denmark”, Thomas Fürst and colleagues examined national pharmacovigilance data for Comirnaty (Pfizer-BioNTech) up to May 2023. Their main findings were a significant heterogeneity in adverse event (AE) rates per batch, with three trendlines (high, intermediate, low) similar to the Danish pattern of batch-dependent variability. Early batches showed noticeably higher AE rates, potentially, according to the authors, linked to the transition from trial-scale to commercial-scale manufacturing.
Expert Commentary
Former Pfizer Vice President and Chief Scientist Dr. Mike Yeadon analyzed early VAERS data showing extreme batch-to-batch differences. He states that ~5–10% of batches were linked to ~90–100% of deaths and serious adverse events, with many lots appearing inert while others were highly toxic – variations “completely without precedent” in pharmaceutical history. Yeadon argues normal manufacturing inconsistencies cannot explain such disparities (e.g., 50x–1000x differences in reported harms), pointing to alphabetic/sequential batch coding correlating with toxicity as evidence of deliberate labeling. It is his opinion that it has been a matter of a “calibration of a killing weapon” or dose-range finding for lethal outcomes, insisting batches were materially different, as in not the same stuff in each vial, and indicative of intentional harm rather than accident. He states that the injections are “toxic by design,” not safe vaccines, with batch differences as proof of criminal activity or agenda-driven experimentation.
Yeadon’s 2022 presentation to the Corona Investigative Committee
Implications
The accumulated evidence of batch-dependent heterogeneity in COVID-19 mRNA vaccine safety profiles from all over the world raises profound and deeply troubling questions, not the least about production, distribution and oversight.
Manufacturing and Product Quality Failures
The non-uniform safety signals point to fundamental inconsistencies in vaccine production. Possible causes include rushed scaling from trial to commercial manufacturing, unstable mRNA integrity, variable lipid nanoparticle composition, residual DNA contaminants, or other quality deviations.
The extreme mortality observed in certain lots suggests the presence of lethal elements – whether spike protein overload, amyloid clot formation, immune dysregulation, heavy metals, or other contaminants—capable of triggering things like sudden cardiac arrest, cancers, multi-organ failure, and delayed fatalities.
Regulatory and Institutional Failures
Governments, health agencies, and pharmacovigilance bodies have had access to the same underlying data. Yet they consistently have failed – and continue to – to investigate or act on clear warning signals. Instead, priority has been to maintain and expand mass vaccination campaigns. The aggressive suppression of whistleblowers reflects a broader pattern of stifling dissent and re-framing legitimate concerns as “disinformation” or threats to public confidence. This world-wide systemic response suggests institutional priorities were aligned more with narrative control and program continuation than with rigorous safety monitoring or harm mitigation.
Global Distribution as a Dilution Mechanism
The worldwide logistics network ensured that individual batches were dispersed across dozens or hundreds of countries and millions of recipients. No single batch dominated any one population long enough to create an unmistakable localized signal. This geographic fragmentation inherently diluted adverse event clusters, making patterns harder to detect in real time and allowing harms to blend into baseline mortality statistics.
Plausible Deniability and Long-Term Risks
Variable lethality, spread thinly across diverse demographics and reporting systems, enables plausible deniability: harms appear sporadic rather than systematic, and can be dismissed as coincidental or underreported artifacts. Paardekooper warns of delayed manifestations, “mortality bombs”, potentially accelerating aging, infertility, or chronic disease – echoing independent concerns about heritable DNA alterations via LINE-1 reverse transcription and gonadal targeting. Japan’s 2023–2025 excess mortality surges, linked to booster campaigns and rising cancers, exemplify how such effects may unfold over years.
Broader and More Disturbing Possibilities
While negligence, haste, and regulatory capture provide one explanation, the orderly patterns – alphabetic coding, early high-toxicity deployment, vaccinator-specific clusters, and consistent cross-border findings – raise the uncomfortable possibility of deliberate design. The existence of “hot lots” versus “dud lots,” combined with global dispersal that minimizes localized scrutiny, could easily be interpreted as intentional variability for dose-range testing, population impact calibration, or something even more sinister. The fact that some batches appear engineered to cause mass injury while others remain inert supports claims that the products were not uniformly safe by accident.
Ultimately, these findings erode public trust to an unprecedented degree. They portray what increasingly appears to be a catastrophic, population-wide experiment in which harms were unevenly distributed and systematically concealed within a “batch lottery.” The mounting calls for a complete moratorium on mRNA technology, full batch-level transparency, independent lot audits, and accountability for decision-makers reflect a growing recognition that the current framework has failed the most basic obligations of medical and public health ethics.
Autopsy findings in cases of fatal COVID-19 vaccine-induced myocarditis
