Support of Barrington-Style Risk-Stratified Vaccination in 2021/2022—in Florida- Depends on Assumptions of Vaccine Efficacy
This is a lesson on how to handle all-cause mortality, indetermined deaths, and age-stratified risk. Society needs policy based \on -VE re: New Cases. Either way, treatments should rule.
We must move from reactive suppression of disease to proactive cultivation of resilience.
EXECUTIVE SUMMARY: Given the age-stratified all-cause mortality data from Florida (2019–2023), a full, unbiased forensic determination of mRNA vaccine efficacy in the elderly is urgently needed. While apparent benefits have been presumed, this assumption remains unproven without accounting for critical confounders—including the Lyons-Weiler/Fenton effect, the widespread RT-PCR false positive misdiagnoses, the Birx Error (confusing “died with” for “died from” COVID-19), and the systemic, likely intentional misattribution of cause of death. Everything hinges on resolving this. Without a rigorous, corrected analysis, public health policies rest on a foundation of error, bias, and possibly fraud.
A Fatal Assumption
In 2021, the dominant public health messaging surrounding COVID-19 vaccination was framed as an all-or-nothing proposition: Get vaccinated to protect yourself and others. Full stop. This approach assumed uniform benefit across all ages, health statuses, and exposure scenarios. The policy architecture that followed—from mandates and passports to school and workplace exclusions—was built atop a single foundational assumption:
That COVID-19 vaccines reduce mortality in everyone.
This article challenges that assumption—using Florida’s all-cause mortality (DFAC) data, stratified by age group, across five consecutive years (2019–2023).
What makes Florida uniquely informative is not only the scale and quality of its state-collected data, but the opportunity it presents to analyze real-world, population-wide vaccine impact in the context of changing variants, vaccine rollout, and policy divergence from federal norms.
But even this rich dataset does not capture the full story. Crucially, this analysis reflects outcomes in the absence of meaningful access to improved therapeutic interventions—notably, early integrative protocols such as Dr. David Brownstein’s iodine/peroxide/ozone/nutrient strategy. These and other early treatments were systematically ignored, if not suppressed. Had they been properly studied, supported, and deployed, it is likely that the baseline mortality rates attributed to COVID-19 in all age groups would have been lower.
Thus, what follows is a best-case scenario for vaccines—because no serious effort was made to offer the public alternatives.
Preview of What We’ll Show
That younger adults (18–49) saw elevated all-cause mortality post-vaccination, even after COVID deaths are subtracted.
That COVID-19 deaths post-vaccine may themselves be partially vaccine-mediated due to negative efficacy—and thus cannot be cleanly subtracted.
That risk-stratified vaccination policy, not universal mandates, is the only scientifically defensible strategy going forward.
Florida All-Cause Mortality Timeline by Age Group (2019–2023)
Note the logarithmic increase in the deaths from all causes in the elderly.
What Is Risk-Stratified Vaccination?
At the height of the pandemic, any deviation from the universal vaccination narrative was met with resistance, if not outright censorship. Yet the principle of risk-stratified intervention is not controversial—it is a foundational tenet of epidemiology, clinical medicine, and public health ethics.
Risk-stratified vaccination means this:
Target those most likely to benefit, and avoid unnecessary exposure to risk in those unlikely to benefit.
This is exactly what the Great Barrington Declaration called for in October 2020—long before vaccines were available. Its authors, including professors from Harvard, Stanford, and Oxford, advocated for focused protection: shielding the vulnerable while allowing low-risk groups to continue normal life with minimized disruption.
Critics dismissed this as unworkable or unethical. Yet what Florida's mortality data now reveals is that failing to follow a risk-stratified model may have cost lives—especially among younger people who had extremely low infection fatality rates (IFRs) but were subjected to mandates, boosters, and even coercion.
Why Risk Stratification Matters
Let’s consider infection fatality rate (IFR) estimates from the pre-vaccine era:
Contrast this with Florida’s observed post-vaccine all-cause mortality (DFAC) for 2021–2022:
The graph comparing age-specific all-cause mortality (DFAC) in the years 2021–2022 to the pre-vaccine COVID-19 infection fatality rate (IFR) is essential because it directly confronts the core assumption behind universal vaccination policy: that the intervention reduces mortality in the populations to whom it is given. Each bar in the chart represents the ratio of total deaths from any cause per 100,000 vaccinated individuals in a given age group, to the number of deaths per 100,000 infections that would have occurred if those same individuals had contracted COVID-19 before vaccines were available. In other words, it compares the actual, observed death rate during the vaccine era to the theoretical risk the vaccine was intended to reduce.
This comparison is valid and highly informative. IFR is conditional—only those who became infected were at risk of dying from COVID-19—while DFAC is unconditional and population-wide, capturing everyone vaccinated whether or not they were infected. That makes the DFAC the superior safety endpoint: it reflects the real-world consequences of population-wide vaccination, regardless of cause. For the vaccine to provide a net mortality benefit, DFAC should be lower than or at least close to the pre-vaccine IFR. If it's not—if vaccinated individuals are dying at a higher rate from all causes than they would have if infected with SARS-CoV-2—then the vaccine has failed in its most fundamental claim.
This refined analysis of Florida data (compared to a simpler one) show that in younger age groups, this ratio is extraordinarily high. Among 18–39-year-olds, the post-vaccine DFAC was about 180 per 100,000, while their pre-vaccine IFR was about 3 per 100,000 infections. That yields a ratio of 60 to 1. In the 40–49 age group, the ratio is over 10 to 1, and in 50–59, it is over 5 to 1. These are not subtle differences. They suggest that a vaccinated individual under age 60 was several times more likely to die from any cause in the following year than they ever were to die from COVID-19 infection before vaccines were introduced.
This conclusion becomes even more significant when we consider that most younger adults had already been infected with SARS-CoV-2 by the time vaccine mandates and booster recommendations were being enforced. Natural immunity was widespread and known to be robust, yet vaccine uptake continued to be promoted without individualized risk assessment. This means that for many people, the added risk from vaccination was layered on top of immunity that had already rendered the vaccine marginal in benefit, if not entirely unnecessary.
The implications of this are not merely academic—they are ethical. The principle of non-maleficence requires that we do no harm. Vaccinating low-risk individuals with a product that increases their total risk of death—when they already have durable protection from natural infection—is a violation of that principle. The DFAC-to-IFR ratio graph illustrates this with clinical precision: the benefit-risk equation flips in the wrong direction for the young, and public health policy failed to adjust accordingly.
This is where the necessity of risk-stratified vaccination becomes undeniable. A rational, ethical vaccination policy would not have treated an 85-year-old nursing home resident and a 22-year-old marathon runner as equally in need of an experimental intervention. The science didn’t support it then. Florida’s mortality data confirms it now.
All-Cause Mortality—The Uncensored Endpoint
When assessing the real-world impact of a public health intervention—especially one deployed on a population-wide scale—the question is not simply whether it reduces the disease it targets. The real question is: does it reduce death? And not just from one cause, but from all causes. That’s where all-cause mortality (DFAC) becomes essential. Unlike case counts, hospitalization numbers, or even death certificates coded for COVID-19, DFAC captures everything. It is the most objective, the least manipulable, and the most meaningful health outcome available.
In the early stages of the vaccine rollout, efficacy was primarily defined in terms of reduction in symptomatic COVID-19 or PCR-confirmed cases. Later, the endpoint shifted to hospitalization prevention and reduction in severe outcomes. What was rarely shown to the public—or even demanded by policymakers—was a hard outcome measure: Did the vaccine reduce the overall number of people dying?
The answer from the Florida data is clear: in several age groups, it did not. And in the youngest adult groups, it may have increased it.
By tracking all-cause mortality across five years—two pre-vaccine (2019–2020) and three vaccine-era years (2021–2023)—we can observe changes in total population-level death risk without making assumptions about cause. This removes the uncertainty introduced by diagnostic bias (e.g., labeling deaths as “with” vs. “from” COVID), shifting variant virulence, or inconsistently applied coding practices on death certificates.
Importantly, DFAC also captures potential harms from the intervention itself. If the vaccine causes fatal adverse reactions—whether cardiovascular, neurological, immunological, or otherwise—it will be reflected in elevated DFAC. If it increases susceptibility to future COVID-19 infection (as in the case of negative efficacy), that too will result in more deaths—and it will show up here. If the vaccine delays or displaces more effective interventions (such as early outpatient therapy), DFAC reflects that cost as well.
Put simply: DFAC is the sum of everything that matters.
This is why the Florida data’s post-vaccine DFAC increases in younger age groups are so concerning. The vaccines were intended to prevent death—but in some groups, they coincided with more of it. Even after subtracting COVID-attributed deaths, excess all-cause mortality persisted. When we allow for the possibility—well-supported in the literature—that some of those COVID deaths may have been vaccine-mediated due to waning immunity or immune imprinting, the picture becomes even more stark.
These patterns would have been hidden if we relied solely on disease-specific endpoints like "COVID deaths" or "hospitalizations." Only by using DFAC as our outcome do we get the full picture.
That’s why DFAC must be the gold standard for future vaccine safety evaluations. If a vaccine increases DFAC, it is harming more than it helps—no matter what it does to viral loads or neutralizing antibodies.
The Vaccine Attribution Fallacy (Negative Efficacy Problem)
Throughout the pandemic, public health institutions and media messaging promoted the idea that any COVID-19 death was attributable to the virus itself, while vaccination could only ever reduce those deaths. This assumption has gone largely unchallenged in public discourse, but in the context of real-world mortality data and the emerging scientific literature, it no longer holds. In fact, the growing body of evidence points to a serious and underacknowledged phenomenon: negative vaccine efficacy.
Vaccine efficacy (VE) is traditionally defined as:
where ARARAR is the attack rate—i.e., the proportion of people who become infected or develop disease. A VE greater than 0 indicates reduced risk among the vaccinated; a VE of 0 means no effect; and a VE less than 0 means the vaccine increases the risk of infection or severe disease. This is not just a theoretical construct—it is a measurable outcome that has been reported across multiple datasets since mid-2021.
Negative efficacy may arise through several mechanisms. One is immune imprinting, also known as original antigenic sin, where repeated exposure to a single version of the spike protein trains the immune system to ignore later variants. Another is antibody-dependent enhancement (ADE), where antibodies produced by vaccination facilitate viral entry into cells rather than blocking it. Rapid waning of spike-specific immunity after mRNA vaccination—combined with possible suppression of innate immune response pathways—can leave the vaccinated more vulnerable in subsequent waves, especially if they’ve received repeated boosters. Evidence of these mechanisms has been discussed in peer-reviewed studies, observed in population data from the UK, Israel, and the CDC’s own datasets, and further substantiated by laboratory findings showing long-lasting mRNA persistence and modified immune signaling.
If negative efficacy is present, then it fundamentally alters how we interpret COVID-19 deaths in the vaccine era. It means that at least a proportion of those deaths were not just despite vaccination—they were possibly because of it. That reframes COVID-19 deaths from being merely the background against which vaccines must be evaluated to being part of the vaccine’s own safety signal.
This brings us to a critical point: you cannot simply subtract COVID-19 deaths from post-vaccine mortality totals and assume you're measuring vaccine safety. If vaccines increased susceptibility to infection or worsened disease severity, then subtracting those deaths hides the signal. Instead, those deaths must be at least partially attributed to the vaccine itself, particularly in populations with low IFRs, robust natural immunity, or high prior exposure rates.
This is why all-cause mortality (DFAC) is the only valid endpoint for safety analysis. It includes COVID deaths, non-COVID deaths, and deaths caused by potential vaccine mechanisms—without making any assumptions about causality. DFAC is immune to narrative bias. It cannot be gamed.
And when we look at Florida’s DFAC across age groups during the first two years of mass vaccination, we see that younger adults experienced sharp increases, even in the absence of high COVID-specific mortality. Subtracting COVID deaths from those totals not only obscures this signal—it violates the logic of safety analysis under conditions of negative efficacy.
In a world where vaccines can fail, and even reverse their protective effect over time, public health has a duty to count every death—especially those it may have caused.
Caveat on Apparent Vaccine Effectiveness
Any discussion of vaccine efficacy—particularly as derived from officially reported COVID-19 case and death data—must acknowledge the context in which that data was produced. The appearance of vaccine effectiveness, especially in 2021 and early 2022, may have been inflated by systemic biases in diagnosis, attribution, and incentive structures.
During the vaccine rollout period:
Hospitals and providers received financial compensation for diagnosing COVID-19, for classifying hospitalizations as COVID-related, and for administering COVID-specific treatments.
Deaths in vaccinated individuals were often recorded as non-COVID if another comorbidity was present, while deaths in unvaccinated individuals were more readily attributed to COVID, even in the absence of confirmed causality.
Vaccine status reporting was selectively inconsistent: many breakthrough infections and deaths were excluded from official tallies if the individual had not passed a certain number of days post-dose.
Test-seeking behavior changed: vaccinated individuals were less likely to be tested for mild or asymptomatic COVID, lowering apparent case rates, while unvaccinated individuals were more aggressively screened in workplaces, schools, and hospitals.
These factors combined to create a data landscape tilted in favor of the vaccines. In such an environment, estimates of vaccine efficacy—whether against infection, severe disease, or death—are likely to be overstated. Apparent benefits may reflect data collection and coding practices more than underlying biological protection.
Therefore, when interpreting comparisons between COVID-attributed deaths and total all-cause mortality post-vaccination, it is critical to remember that the true burden of vaccine-mediated harm may be higher, and the protective benefit may be lower, than officially reported.
The Math—Attributing COVID Deaths in the Vaccine Era
To fairly evaluate the safety profile of a vaccine, we must ask not only whether it prevented a targeted outcome (like symptomatic COVID-19), but whether it reduced or increased the total number of deaths—including deaths from COVID-19 itself. This requires a clear understanding of how we handle COVID-19 deaths that occur after vaccination, especially in light of growing evidence for negative efficacy.
The Wrong Way: Subtracting All COVID Deaths
The standard public health method has been to take the total number of deaths following vaccination, subtract those attributed to COVID-19, and then analyze what’s left. The implication is that COVID-19 deaths are "not the vaccine’s fault" and therefore should be removed from the equation when evaluating vaccine safety.
Mathematically, this looks like:
The Correct Approach: Attribution Modeling
If we allow for the possibility that some proportion of COVID-19 deaths post-vaccination are actually vaccine-mediated, we need a new model. Let’s define:
Then the adjusted Delta DFAC becomes:
This means:
What the Data Show
We applied this model to Florida mortality data from 2021–2022, comparing all-cause mortality in five age groups (18–39, 40–49, 50–59, 70–79, 80+) to their 2019–2020 baselines. We then recalculated excess mortality assuming that 25%, 50%, or 75% of COVID-19 deaths were vaccine-associated due to negative efficacy.
The result is a sensitivity analysis: it shows how excess mortality changes as our assumptions about vaccine-mediated COVID deaths change.
If vaccines make people more likely to be infected again, the game is over for ANY utility of these jabs. This chart reveals that, depending on assumption, even with moderate attribution (50%), significant excess mortality remains in younger age groups. For example:
In short, younger adults experienced higher total mortality post-vaccination than pre-pandemic, even if a majority of COVID deaths are considered unrelated to the vaccine.
Why This Matters
This is not just a methodological correction—it is an ethical imperative. By blindly subtracting all COVID deaths from post-vaccine DFAC, public health authorities have created a systematic bias in vaccine safety analysis. The assumption of perfect or neutral efficacy, in the face of mounting evidence to the contrary, has led to a mass underestimation of risk—particularly in low-risk populations who never needed the intervention in the first place.
With this math, the case for risk-stratified vaccination is no longer theoretical. It is statistical. It is visual. And it is morally unavoidable.
Figure: Sensitivity analysis of post-vaccine all-cause mortality ratios by age group, comparing 2021–2022 average DFAC to 2019–2020 pre-vaccine baseline. Each bar represents a different assumption about what proportion of COVID-19 deaths post-vaccination were vaccine-mediated due to negative efficacy (25%, 50%, or 75%). A ratio above 1 indicates increased mortality following vaccination. The persistence of elevated ratios—especially in younger age groups—across all attribution levels highlights the robustness of the excess mortality signal and supports the need for risk-stratified vaccination policy.
Florida’s Data—A Clear Pattern
With five years of population-wide mortality data across distinct epidemiological phases—pre-COVID, pandemic pre-vaccine, vaccine rollout, Delta and Omicron waves, and the post-mandate period—Florida offers one of the most robust datasets available for evaluating vaccine-era mortality. And the pattern that emerges is not subtle. It is stark.
Florida's all-cause mortality (DFAC) rose across all age groups between the pre-vaccine period (2019–2020) and the first two years of vaccination (2021–2022), but the relative increases were not evenly distributed. The largest proportional increases occurred in the youngest adult age groups—those with the lowest COVID-19 risk and the least to gain from vaccination in the first place.
For example, in the 18–39 age group, the average DFAC increased from 155.2 per 100,000 (2019–2020) to 180.3 per 100,000 (2021–2022). In 40–49, it rose from 301.8 to 364.5. These increases occurred despite widespread immunity from prior infection, despite a declining threat from SARS-CoV-2 in the Omicron period, and despite falling COVID-attributed death counts in 2022 and 2023.
And when we isolate COVID-specific deaths and adjust for the possibility that some of them were vaccine-mediated—i.e., reflecting negative efficacy—the pattern doesn't flatten, it sharpens. In our sensitivity analysis (see previous figure), younger age groups remained in excess mortality across every tested attribution level, including the assumption that 75% of COVID deaths were unrelated to vaccination. The signal is that strong.
Meanwhile, older age groups (70–79 and 80+) also experienced an increase in DFAC during the vaccine period, but the magnitude of the increase was lower relative to baseline, and in some cases, reversed when COVID deaths were removed. This suggests a benefit-to-risk inflection point in the sixth or seventh decade of life. In other words, the vaccine may have conferred net benefit in the elderly, but imposed net harm in younger adults.
These age-stratified differences are precisely what a rational public health policy would have anticipated and accommodated. Instead, the federal narrative adopted a “no one is safe until everyone is safe” framing, which led to blanket mandates, suppressed early treatment, and the coercion of entire low-risk populations into accepting a medical intervention that statistically increased their overall risk of death.
This is not a matter of subtle misjudgment. The increase in DFAC is clearly visible in Florida’s age-stratified timeline, as previously shown in our figures. COVID deaths fell in 2022 and collapsed in 2023—but all-cause mortality remained elevated in younger groups relative to the pre-pandemic norm. The excess did not come from the virus. The question is: what did it come from?
The weight of the evidence points in one direction: the intervention itself.
The Ignored Variable—Suppressed Therapeutics
Everything presented thus far—Florida’s DFAC data, age-stratified mortality curves, vaccine-era excess deaths, and negative efficacy modeling—has one thing in common: it assumes that vaccination was the only tool available to reduce mortality.
But it wasn’t.
The entire global vaccine-first strategy unfolded under a manufactured constraint: the systematic dismissal and suppression of early outpatient therapies. This suppression not only distorted the policy environment, it likely inflated the vaccine’s perceived benefit by ensuring no competing interventions could be credibly evaluated or deployed.
In a properly functioning public health system, vaccine rollout would have occurred alongside aggressive exploration of all reasonable therapeutic options. But instead, physicians who employed early integrative or nutritional protocols were sanctioned, censored, or silenced. Among the most widely reported and systemically ignored was the Brownstein protocol, which combined iodine, nebulized hydrogen peroxide, vitamin D, zinc, and other safe, affordable, readily available agents. Brownstein’s team reported zero COVID-19 deaths among hundreds of patients treated early with this approach—an outcome worthy of serious investigation if not replication.
Yet no state or federal agency incorporated such protocols into population-level strategies. No NIH-funded randomized trial tested it. No Operation Warp Speed–level support was extended to protocols involving vitamins, minerals, immune modulation, or viral load suppression outside of the pharmaceutical model.
This omission has consequences. When you exclude the lowest-risk, lowest-cost interventions from your toolkit, you raise the threshold at which your high-cost, high-risk intervention appears justifiable. In other words: when the vaccine is the only thing permitted to reduce mortality, it becomes much easier to defend—even if its real-world impact is mixed or even harmful in some groups.
Florida’s DFAC trends must be interpreted in this context. The elevated mortality we see in younger adults is not just a product of vaccination, but of a policy landscape that denied those individuals access to alternatives that may have kept them alive.
Had early therapies been embraced rather than buried, the total mortality burden attributed to the pandemic—and perhaps the vaccine era—might have been substantially lower. As a result, the comparisons in this analysis are inherently generous to the vaccine, because they occur in the vacuum created by therapeutic suppression.
This is a blind spot in nearly all retrospective analyses of COVID-19 interventions. The question isn’t only did the vaccine help or harm? The deeper question is: what might have happened if we hadn’t prevented everything else from helping?
Mechanisms of Vaccine-Associated Harm
If we accept that all-cause mortality rose in certain age groups following mass vaccination, and that some COVID-19 deaths may have been vaccine-mediated due to negative efficacy, then a reasonable question follows: how could this happen? What biological mechanisms could plausibly explain the rise in deaths among people who were ostensibly protected?
To answer this, we must move beyond press-release immunology and into the peer-reviewed and mechanistic science that has emerged since early 2021. What it reveals is that the mRNA platform—while novel and potentially useful—is also capable of disrupting immune homeostasis in ways that are increasingly well-characterized.
Below are the primary pathways by which harm may have occurred:
1. Immune Imprinting (Original Antigenic Sin)
Immune imprinting occurs when the immune system, after encountering one version of a pathogen (or its protein), becomes "locked in" to that memory—responding poorly to new variants. In the case of SARS-CoV-2, repeated mRNA exposure to the ancestral Wuhan spike protein may have trained the immune system to ignore or underreact to later, more relevant variants like Delta, Omicron, or their sublineages. This mechanism is now widely recognized in immunology literature and was likely exacerbated by repeated boosting with outdated spike formulations.
2. Antibody-Dependent Enhancement (ADE)
ADE occurs when antibodies facilitate viral entry into host cells instead of neutralizing it, leading to worse disease. While not definitively proven in humans for COVID-19 vaccines, there is precedent in SARS-CoV-1 animal models, dengue vaccines, and in vitro data showing enhanced infection in the presence of certain spike-targeted antibodies. This risk becomes more plausible when a vaccine fails to sterilize infection but still primes high levels of circulating antibodies.
3. T Cell Exhaustion and Reprogramming
Studies have shown that repeated mRNA vaccination can induce T cell exhaustion—a state where immune cells become less responsive over time. Others have reported skewing toward tolerogenic profiles, in which T cells become less inflammatory, potentially impairing both anti-viral and anti-tumor responses. This immune modulation may leave vaccinated individuals more vulnerable not just to COVID-19 but to opportunistic infections and latent virus reactivation.
4. Cardiovascular and Hematologic Toxicity
Spike protein itself is known to bind ACE2 receptors on endothelial cells, induce pro-inflammatory cascades, and disrupt vascular integrity. The mRNA platform enables in vivo spike production—potentially in uncontrolled locations for extended durations. This has been linked in case reports, autopsies, and registry data to:
Myocarditis and pericarditis
Thrombosis and thrombocytopenia
Sudden cardiac death in young adults
The exact incidence may be underreported due to low autopsy rates and diagnostic bias, but the mechanistic link is increasingly well supported.
5. mRNA Persistence and LNP Distribution
Originally thought to degrade rapidly, synthetic pseudouridine-stabilized mRNA has been shown to persist in lymph nodes and other tissues for weeks or longer. Lipid nanoparticles (LNPs) used to deliver the mRNA are bioactive and may distribute systemically. Animal studies and human biopsy data have found spike protein expressed in unexpected locations including the brain, ovaries, liver, and spleen, long after injection.
This raises the risk of long-term immune dysregulation or autoimmunity, especially with repeated boosting.
6. Suppression of Innate Immunity
Recent research has shown that mRNA vaccination may lead to suppression of key innate antiviral pathways, including interferon signaling. This could impair early immune responses to new pathogens, increase susceptibility to viral reinfection, or allow cancers that are normally surveilled by the immune system to progress undetected.
Why This Matters
These mechanisms, taken together, provide biologically plausible explanations for the observed rises in all-cause mortality in younger adults following vaccination. They offer a mechanistic foundation for the statistical signals seen in Florida’s DFAC data, and they counter the simplistic narrative that any COVID death post-vaccine proves the vaccine "didn’t work" rather than may have contributed.
As with any medical intervention, mechanism matters. And in the case of mRNA COVID-19 vaccination, the mechanisms of benefit and harm are intertwined. The same spike protein intended to produce immunity is also a biologically active molecule capable of systemic damage. The same immune pathways intended to protect can, if overstimulated or misdirected, create vulnerability.
Highlight on Pathogenic Priming (Molecular Mimicry–Driven Autoimmunity)
One of the earliest and most critical warnings about COVID-19 vaccines came in January 2020, when Lyons-Weiler published a peer-reviewed analysis showing that certain SARS-CoV-2 spike protein peptides share sequence homology with human proteins. The concern was clear: if antibodies are generated against these viral epitopes, they could cross-react with human tissues, resulting in autoimmune damage.
This phenomenon—termed Pathogenic Priming—refers to the idea that vaccination (or prior infection) primes the immune system to attack host tissues upon later exposure to the pathogen or the antigen. Unlike classical autoimmunity, which may emerge spontaneously, pathogenic priming is triggered by a subsequent exposure—such as reinfection, re-vaccination, or natural boosting from circulating virus.
Since that early warning, multiple lines of evidence have supported this concern:
Sequence homology studies have identified mimicry between SARS-CoV-2 spike and human proteins expressed in lungs, heart, brain, testes, and mitochondria
Animal models from prior coronavirus vaccine attempts (e.g., SARS-CoV-1) showed fatal immunopathology upon challenge, despite initial tolerance
Autopsy reports have documented immune infiltration and inflammation in multiple organ systems—even when viral presence is minimal or absent
Tissue-specific autoimmune conditions (e.g., myocarditis, Guillain-Barré, multisystem inflammatory syndrome) have been temporally associated with both infection and vaccination
What distinguishes Pathogenic Priming from other forms of harm is that it doesn't necessarily result from the vaccine alone—but from the vaccine-induced immune memory interacting pathologically with a later exposure. This makes it particularly dangerous in populations that are repeatedly boosted or exposed to evolving viral strains with partial overlap.
The failure to account for this mechanism in vaccine design, preclinical testing, and regulatory review is not just a scientific oversight—it is a systemic failure of precaution. And it is especially indefensible given that this warning was issued publicly, early, and with full documentation.
Research Priorities: Integrative Pathways to Health
If there is one unifying lesson from the COVID-19 era, it is this: population-wide outcomes are shaped not only by the properties of a pathogen or vaccine, but by the breadth—or narrowness—of the therapeutic toolkit we choose to deploy. The singular focus on vaccination, to the exclusion of early treatment and integrative care, produced not just epidemiological blind spots but a strategic failure to mitigate death and suffering.
The way forward is not merely to refine vaccination. It is to broaden our framework entirely. We must fund and conduct research aimed at building and validating Integrative Pathways to Health: protocols and clinical systems that use the best of conventional, nutritional, and functional medicine to prevent progression, reduce viral load, mitigate immune injury, and accelerate recovery.
This is not fringe or theoretical—it is integrative, adaptive, and grounded in human biology. It is comprehensive, mechanism-informed, systems-based intervention—and it is overdue.
1. Conduct Controlled Trials of Nutrient-Based Immune Modulation
Several observational studies and clinical experiences during COVID-19 highlighted the potential role of vitamin D, zinc, iodine, selenium, quercetin, N-acetylcysteine (NAC), and other micronutrients in immune optimization and viral defense. Despite widespread deficiency in these nutrients and a well-documented association between low levels and poor outcomes, there was no coordinated research effort to assess their synergistic potential.
Future research should prioritize:
Randomized controlled trials (RCTs) of nutrient protocols tailored by baseline deficiency.
Explore how different patient types (based on age, genetics, or existing conditions) respond to therapies. (Stratified designs that assess both prevention and recovery enhancement)
Integration with virological and inflammatory biomarker tracking to assess mechanism of action.
2. Investigate Adjunctive Antiviral and Anti-Inflammatory Agents
Compounds such as ivermectin, hydroxychloroquine, budesonide, curcumin, and melatonin were widely used off-label based on plausible mechanisms and some promising early data. These agents were often dismissed not on the basis of definitive evidence, but because they threatened the exclusivity of the vaccine-centric approach.
New research should:
Revisit these agents using multi-arm adaptive trial designs that allow for combinatorial optimization.
Explore interactions between these agents and conventional antivirals or immunomodulators.
Include pharmacogenomic and age-stratified risk modeling to identify who benefits most.
3. Prioritize Mechanism-Driven Inquiry over Statistical Guesswork
Much of the clinical research deployed during the pandemic was built around p-values rather than pathophysiology. Trials were rushed to statistical endpoints, while little attention was paid to how interventions worked—or failed.
New investment should prioritize:
Mechanistic trials that include cytokine profiles, immune cell phenotyping, and tissue-level inflammation imaging.
Animal and human studies that examine how viral and host pathways interact over time and how interventions modify them.
A shift from “Does it reduce symptom X?” to “What is this agent doing at the molecular and cellular level?”
4. Design and Test Tiered Risk-Adaptive Protocols
One of the great failures of the COVID response was the imposition of one-size-fits-all protocols. Instead, we need tiered care pathways that adapt to the patient’s age, comorbidity status, and viral load trajectory.
Such systems should include:
Dynamic triage models that escalate care from nutritional support to antiviral to immunomodulator, based on real-time metrics.
Point-of-care algorithms accessible to frontline providers, integrating lab results, clinical history, and genomic susceptibility.
Data collection infrastructure to allow these adaptive protocols to refine themselves over time.
5. Build Ethical, Open-Access Research Collaboratives
To prevent institutional bias, funding capture, and the suppression of outlier findings, new research platforms must be:
Transparent in design, open in data sharing, and willing to publish null results.
Funded independently of centralized government agencies and pharmaceutical companies.
Staffed by cross-disciplinary teams that include integrative practitioners, immunologists, epidemiologists, and clinical trialists.
6. Track Long-Term Outcomes, Not Just Acute Events
If the pandemic taught us anything, it’s that acute phase survival does not guarantee recovery. Long COVID, vaccine injury syndromes, and autoimmunity are all consequences of failing to look beyond the immediate.
Future research must:
Include longitudinal cohorts with standardized follow-up intervals (6, 12, 24, 60 months).
Track not only mortality, but quality-of-life metrics, autoimmune diagnoses, and neurologic function.
Develop case definitions and diagnostic criteria for post-intervention syndromes that are reproducible and transparent.
The goal of Integrative Pathways to Health is not to replace vaccines or pharmaceuticals—it is to reduce dependency on them as singular solutions. It is to diversify our response portfolio, decentralize authority, and empower clinicians and patients with the full spectrum of scientific insight.
Only by doing so can we move from reactive suppression of disease to proactive cultivation of resilience. And only then can we say we learned anything from the last four years.
Limitations
This analysis is based on publicly available mortality data and age-stratified COVID-19 death rates in Florida from 2019 through 2023. While the data are robust and longitudinal, several limitations must be acknowledged.
First, the observational nature of the analysis means that we cannot assign direct causality at the individual level. Increases in all-cause mortality (DFAC) temporally associated with vaccination may result from multiple converging factors, including the lingering impact of the pandemic itself, changes in healthcare access, behavioral shifts, or broader societal stressors. However, the use of age-stratified, five-year comparisons and the inclusion of a sensitivity analysis that adjusts for different levels of COVID death attribution help mitigate these confounding effects and strengthen the association between vaccination and elevated mortality in certain groups.
Second, the exact contribution of COVID-19 to post-vaccine DFAC remains difficult to isolate with certainty, given variability in coding practices and testing rates. Some COVID deaths may have been overcounted; others undercounted. This analysis attempts to account for this by presenting models that assume partial vaccine-mediated COVID mortality due to negative efficacy. While the attribution levels used (25%, 50%, 75%) are conservative and grounded in immunological evidence, they are ultimately estimates and should be interpreted accordingly.
Third, we cannot determine from this dataset which specific vaccine batches or manufacturing lots may have contributed to observed excess deaths. Nor can we identify the full range of comorbidities or socio-economic factors that may have modulated individual risk. These are important areas for follow-up investigation, and further stratified analysis is urgently needed.
Fourth, this analysis reflects outcomes in the context of a public health strategy that largely excluded early outpatient treatment protocols. As such, the comparison between vaccinated and unvaccinated or pre-vaccine baselines is inherently skewed in favor of the vaccine. Had integrative therapies been widely used, the baseline mortality risk may have been lower across all groups, and the benefit-risk calculation for vaccination correspondingly different.
Finally, while the all-cause mortality signal is strong and persistent, this analysis does not account for the full psychosocial toll of the pandemic, including lockdowns, delayed care, substance use, and economic distress—all of which may have influenced mortality patterns. However, those pressures were present in both the pre-vaccine and vaccine eras, and their uniformity across age groups does not explain the observed stratification in DFAC increases.
These limitations do not negate the findings—they contextualize them. Taken together, the mortality patterns observed in Florida warrant urgent investigation, deeper stratification, and a reevaluation of universal vaccination as a public health strategy.
Acknowledgements
I thank Steve Kirsch and other readers for comments and communications that led to this substack article.
Citations
Levi, R., Mansuri, F., Jordan, M., & Ladapo, J. A. (2025). Twelve-month all-cause mortality after initial COVID-19 vaccination with Pfizer-BioNTech or mRNA-1273 among adults living in Florida. medRxiv. https://doi.org/10.1101/2025.04.25.25326460
Florida Department of Health – Vital Statistics. All-Cause Mortality and COVID-19 Death Data (2019–2023). Available via Florida Health Charts:
https://www.flhealthcharts.gov/Charts/Charts.aspxLyons-Weiler J. Pathogenic priming likely contributes to serious and critical illness and mortality in COVID-19 via autoimmunity. J Transl Autoimmun. 2020 Apr 9;3:100051. doi: 10.1016/j.jtauto.2020.100051.
Vojdani, A., Kharrazian, D. (2020). Potential antigenic cross-reactivity between SARS-CoV-2 and human tissue with a possible link to an increase in autoimmune diseases. Clinical Immunology, 217, 108480. https://doi.org/10.1016/j.clim.2020.108480
Patone, M., Mei, X. W., Handunnetthi, L., Dixon, S., Zaccardi, F., et al. (2021). Risk of myocarditis following sequential COVID-19 vaccinations by age and sex. AHA Journal https://www.ahajournals.org/doi/full/10.1161/CIRCULATIONAHA.122.059970
Yehuda Shoenfeld (Editor). Vaccines and Autoimmunity. Wiley-Blackwell, 2015.
Doshi, P. (2021). Will covid-19 vaccines save lives? Current trials aren't designed to tell us. BMJ, 371, m4037. https://doi.org/10.1136/bmj.m4037
Florida COVID Action Dashboard (archived) – Dataset snapshots from community-assembled public reporting (2020–2021):
https://web.archive.org/web/20210101000000*/https://floridacovidaction.com/McCullough, P. A., et al. (2020). Pathophysiologic basis and rationale for early outpatient treatment of SARS-CoV-2 (COVID-19) infection. American Journal of Medicine, 134(1), 16–22. https://doi.org/10.1016/j.amjmed.2020.07.003
Brownstein D, Ng R, Rowen R, Drummond J, Eason T, Brownstein H et al. A Novel Approach to Treating COVID-19 Using Nutritional and Oxidative Therapies. Science, Public Health Policy and the Law. 2020 Jul 01; v2.2019-2024 https://publichealthpolicyjournal.com/a-novel-approach-to-treating-covid-19-using-nutritional-and-oxidative-therapies/
Between your omission of the 60-69 age group and the chart showing the IFR per infection as exactly the same as a separate column called “IFR per 100,000”, this article plainly lacks rigor. You need a medical copy editor.
The Freedom to Chose should be the RULE.
ex - if I'm 85 years old and don't want to recieve an "intervention" I should NOT be FORCED/MANDATED/COERSED to take one. F risk stratification "intervention policy mandates" bs. I told Jay the same thing.