Tag Archives: excess mortality

The State of Things Pandemic – Week 25 2025

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It is not the noise of your platform, but the strength of your message and how many choose to listen. As the first analysis to detect and warn of the excess mortality signal in late 2021, this is the culmination in the article series that shifted the world’s perception of the Covid mRNA vaccines.

Marking the 277th week of the SARS-CoV-2 pandemic, this article provides a detailed examination of excess mortality rates in the U.S. through Week 25 of 2025, with a focus on the primary contributing factors. Despite challenges in visibility and varying levels of its censorship across platforms such as Apple, LinkedIn, YouTube, and Facebook, this analysis strives to present an unbiased, evidence-based perspective on the trends and causes behind the rise in ongoing excess mortality rates.

Abstract

While the Covid mRNA vaccines may have provided a therapeutic effect during the fall 2021 B.1.617.2 variant wave, helping to keep some vulnerable individuals out of the hospital, this benefit did not translate into reduced infection rates or into any arguable offset in overall mortality.

In contrast, the Week 14, 2021 inception of the mRNA vaccine has led to a pronounced and pervasive inflection in mortality across multiple-dozens of ICD codes. This shift has resulted in more than 840,000 excess deaths from causes other than Covid or non-natural means (see Charts 3b and 4c). These deaths have come mainly as a result of the vaccine itself (see Charts 3c and 3d) – a mortality total which is 25 times greater than the 33,000 lives the CDC claimed could have been saved by the B.1.617.2-valent vaccine during the Delta Variant wave. Nations with a low rate of Covid and high rate of mRNA vaccination serve to confirm both the timing and cause of this excess mortality.

The mRNA Vaccine Continues to Kill 2,000 to 4,000 Americans per Week Now – (See Charts 3, 4c, 10, 11, 12, and 13)

We Also Have a Cancer Emergency Developing in Its Early Stages – (See Charts 6, 7, and 11)

As of Week 25 of 2025, 5.33 years into the Covid-19 Pandemic and its aftermath, 1,865,928 excess deaths have been recorded to date. Now certainly, the SARS-CoV-2 virus was a deadly pathogen, itself 6.6 times more deadly than the typical annual mortality total for all influenza viruses combined. However, as the reader will infer from the material below, it was the panic-fueled, and in some cases malicious, actions of those few in power which have served to precipitate the larger part of total excess mortality during the pandemic, as well as post-pandemic, periods.

As of June 21st 2025, there have been

843,922 Excess Non-Covid Natural Cause Deaths (primarily from the Covid Vaccine),
253,830 Excess Non-Natural Deaths (including 130,000 sudden cardiac deaths in casual drug users),
384,763 Excess Deaths from Malpractice and Denial of Treatment,1
383,413 Excess Deaths from the SARS-CoV-2 virus (6.6 x annual influenza-pneumonia)2

making for a grand total of 1,482,515 (79.5%) Manmade Excess Deaths of US Citizens, out of a Pandemic Total Excess Mortality of 1,865,928. These statistics are summarized in the chart below:

. . .

Background

A wide diversity of ‘Omicron’ variants were discovered to have been percolating throughout global populations in 2021 (we contend that the mutation rate, the immediate high genetic diversity, as well as the genetics themselves indicate circulation since 2017), featuring a Case Fatality Rate which turned out to be curiously on par with the well-established annual HCoV and flu mortality benchmarks (a metric of human frailty as opposed to necessarily just pathogen virility). This far lower CFR of 1.0 to 6.6 (also corroborated by means of CDC data in Chart 1) versus Wuhan-Alpha-Delta strains is substantiated by the Our World in Data dataset, and can be seen in Exhibit A below.

Exhibit A – Omicron turned out to be half as deadly as was the 2009 H1N1 flu. There were two Covid-19’s. One imparted advance immunity in Asia, and after the fact globally in 2022. This diverse set of strains was falsely labeled ‘Omicron’. Fortunately, that ruse was ended, and they began to name these long-extant, myriad, and diverse background strains independently in mid-2022.

As one may observe, ‘Omicron’ is about half as deadly as was the 2009 H1N1 flu. It arrives amazingly at a CFR of about the level of a normal year’s influenza and pneumonia.

Therefore, something else is behind the non-Covid excess natural cause deaths of our younger citizens which began in mid 2021 (and it is not Covid-19, Long-Covid, lockdowns, nor fentanyl).

Notice that public health authorities never cite this. Notice as well, how they avoid the implication that the diverse set of ‘Omicron’ strains were the most likely candidate to have produced Pacific Asian immunity to Wuhan-Alpha-Delta (and not their ‘lockdowns’, which thereafter showed to be ineffective beginning with 2022 strains in Asia – amazingly right on time for the well established four-year HCoV mutation/infection cycle).

Thus, it became increasingly clear throughout our analysis that most of the globe (having already been exposed to lower-mortality proto-Covid variants) fared Covid-19 relatively well because of the advance immunity imparted in the years prior to the official pandemic. Moreover, that disruption, lockdowns, iatrogenics, denial of treatment, along with our quod fieri final solution, have collectively served to kill the majority (namely 1,482,515 or 79.5%) of the US Citizens who died during the 277 weeks of the pandemic and its aftermath thus far. It is clear, despite the original danger presented by the Wuhan through Delta variants of the SARS-Cov-2 virus, that this circumstance quickly escalated into a man-made tragedy within the United States and other Western nations.

USA Weekly Mortality

– (Real) Excess All Cause Mortality – 6.1% (or 3,424 deaths)
– Nominal Excess All Cause Mortality – 1.4%
– Excess Non-Covid Natural Cause Mortality – 6.5% (or 3,103 deaths)
– Excess Non-Natural Mortality – 4.0% (or 202 deaths)
– Covid UCoD Mortality – 119 deaths (51% lower than same week of 2024)

The beige line in the chart below shows the actual CDC Wonder deaths per week for the two years prior, as well as years-of and years-post the pandemic. The dark orange baseline is normalized from the years 2014-2019, representing a 1.14% annual growth in mortality for the US in terms of all causes. This 6-year baseline reflects a balance between a retrospective lengthy enough to provide statistical significance, but not so long as to be confounded by generational effects or immigration impacts.

Please note that we do not employ the incorrect terms/metrics ‘crude mortality rate’ and ‘age-standardized mortality rate’ (Chart 2 outlines our terms and metrics). These indices are used to compare death rates between nations in a normal circumstance, not for a retrospective impact of a black swan event inside a single nation.3 This is an essential element of professional competence.

Chart 1 – Excess All Cause Mortality (USA) – without Pull-Forward Effect (without PFE, or ‘nominal’) resides at 1.4% excess (796 deaths in top-right calculations). With Pull-Forward Effect taken into account, this excess is at 6.1% or 3,424 persons per week (see Chart 2 below). A portion of this excess is Non-Natural or Covid-19 excess mortality – thus, Non-Covid Natural Cause Excess Mortality stands at 6.5% or 3,103 persons per week. In comparison, Excess Non-Natural Mortality stands at 4.0% or 202 persons per week (see Chart 4 below). Pandemic mortality totals are shown in the beige set of statistics on the left hand side of the chart. Notice that Wuhan-Delta variants were 6.6 times more deadly than are the more-highly-diverse and immune-system-familiar ‘Omicron’ variants. Take this as a hint that Omicron was a series of already highly diverse and population-exposed viruses when it ’emerged.’

Covid is Currently Not As Dangerous as Annual Flu or Even Common Cold

The currently observed excess mortality in Chart 1 above is not caused by Covid or Long Covid.

Chart 1b below, titled “COVID-19 UCOD Mortality,” depicts weekly COVID-19 deaths from 2020 through 2025, showing a dramatic decline in mortality over that time — in particular after Covid-17 (Omicron) reemerged and produced significant Covid-19 (Wuhan-Alpha-Delta) immunity in Western populations in 2022 (see Exhibit A). The early pandemic waves associated with SARS-CoV-2b variants (Wuhan through Delta) produced pronounced peaks, whereas the later Omicron lineage (SARS-CoV-2a) exhibits a sustained and minimal death rate. By Week 37 of 2025, COVID-19 mortality is shown to have fallen 64% year-over-year and 66% over two years, with its current death rate at just 43% that of influenza and pneumonia. These data indicate that COVID-19 has diminished to a level of lethality below that of seasonal influenza—and potentially comparable to or milder than common respiratory infections in the elderly.

Chart 1b – SARS-CoV-2a and 2b UCoD Mortality – The series of COVID variants (2017 SARS-CoV-2a or ‘Omicron’) that both preceded and followed the Wuhan COVID-19 strain (2019 SARS-CoV-2b or ‘Wuhan’) are far less deadly than either the original strain or even seasonal influenza. At present, COVID-19’s overall mortality is roughly 43% that of the annual flu, even accounting for seasonality.

The chart below, titled “Chart 1c – Average Age of COVID-19 Death Exceeds Cohort Life Expectancy,” illustrates that individuals currently dying with a COVID-19 underlying cause of death (UCOD) are, on average, living beyond their statistically expected lifespan for their age cohort. Based on provisional CDC/NCHS data for 2025, the chart shows that COVID-19 mortality remains overwhelmingly concentrated among older adults, with deaths increasing sharply after age 65 and peaking in the 85+ group. The median age at death is 79.5 years—exceeding the current U.S. life expectancy for an 80-year-old (78.4 years) and matching that of a 70-year-old in 2015 with three or more comorbidities (79.5 years). This relationship suggests that many COVID-19 UCOD assignments may reflect incidental or end-of-life conditions rather than true primary causes of death.

Chart 1c – Average Age of COVID-19 Death Exceeds Cohort Life Expectancy – The data show that individuals currently dying with a COVID-19 underlying cause of death are, on average, living beyond their statistically expected lifespan for their age cohort. In other words, the median age at COVID-19 death (79.5 years) surpasses the life expectancy of the entailed demographic group, suggesting that many COVID-19 UCOD designations likely represent incidental or end-of-life attributions rather than genuine primary causes of death.

Therefore, there exists no argument basis from which to mandate Covid-19 vaccination for any population or cohort.

There’s no way to sugar-coat this folks: The Covid-19 mRNA shots have more than a dozen documented mechanisms by which they can induce and promote cancer. These genetic-therapy interventions are dangerous and potentially deadly. They must be pulled from the market.

Replications and Corroborations of This Work

This chart of course, reconciles with each of the other charts inside this system reporting summary article. We track this system coherency each week to make sure that the entire set of metrics agree with one another.

Modeling a system is like driving a car or conducting a symphony. One is comparing hundreds of inputs for consilience in development of a dynamic description of reality. Everything must work in concert and/or agree. Cursorily scanning the side-view mirror on GitHub, and quibbling with people who have never driven a car in their life, over whether that was a truck or a smudge on the glass, is irrelevant and unproductive. A mere exercise in wanna-be ego.4

This article presents a dynamic systems analytics/intelligence derivation argument. This is not ‘statistics’ and it is not ‘technical analysis’. The mission therein resides in detecting active signal, not comparing batting averages or political candidate approval ratings on a simple spreadsheet. Anyone who has run a business of a significant size knows that intelligence derivation is foundational to success.

When professional systems engineers replicate this work, they corroborate the answers therein
When honest academicians replicate this work, they corroborate the answers therein
When professional actuarials replicate this work, they corroborate the answers therein
When professional epidemiologists replicate this work, they corroborate the answers therein
When doctors examine and replicate this work, they corroborate the answers therein
When third parties conduct the same analysis, they corroborate the answers therein
When other analysts replicate the cancer analysis, they corroborate the answers therein.
When national scientists replicate this work, they corroborate the answers therein.
When professional demographic analysts replicate this work, they corroborate the conclusions therein
When insurance industry analysts replicate this work, they corroborate the excess mortality.
When non-pharma-affiliated scientists replicate this work, they corroborate the excess cancer mortality.

“Our findings raise serious concerns regarding the potential impact of the vaccination campaign and other causes on mortality. A detailed cause-specific investigation of such a significant excess number of deaths is warranted to explore the potential factors leading to this concerning and unexplained increase in population mortality.” ~ Economidou, et al., “Excess Mortality in Cyprus during the COVID-19 Epidemic”5

I do corroborate or falsify my various model conjectures (you don’t see the hundreds which showed as invalid), in the form of retrospective, derivative, cross section, spanning tree, delta-sensitivity, constraint reference testing, and comparative analyses – and not through wasting precious time in trivial arguments with inexperienced pretenders under extreme agency or bias. After all, this is what a systems professional naturally does – one who is used to having their work be subjected to intense scrutiny by knowledgeable client stakeholders as opposed to angry ad hominen focused pretenders.

Time renders the truest of peer review, surpassing the collective savvy of all experts.

If one uses only raw data to craft these charts they will always get a wonderful-looking trend in death. Back when Covid cases were varying highly by season, in a quarter where the case trend ended below the pandemic line, did that mean that the Pandemic was over? No. The same principle applies here then, one cannot use mere raw data (especially one stand alone metric) to draw inference. In general, there are eight adjustments or exclusions/inclusions one need make (depending upon the data scraped/linked) to raw data obtained from the CDC/NCHS:

  1. Weeks -1 to -12 for state reporting lag (need for this practice ended in Feb 2025)
  2. Weeks -1 to -25 for RXX (abnormal findings) hold code shortfall depletion (depends upon ICD code)
  3. Weeks -1 to -25 for 999 (non-natural suspected) hold code build (before dump to Non-Natural ICD’s)
  4. Weeks -12 to -35 for erosion from ICD reassignment by CDC
  5. Weeks -12 to -35 for erosion from UCoD to MCoD reassignment (see Chart 5) by CDC
  6. Weeks -26 to -35 for reassignment of Natural Cause Deaths into Non-Natural (see Chart 4c)
  7. Pull-Forward Effect adjustment (see Chart 3b) of the baseline deaths anticipated for 2021 through 2027
  8. Correction to data when NCHS/States fell short on reporting. In each case the CDC/NCHS was notified of, and acknowledged, the shortfall: Weeks 20 – 24 of 2022, Weeks 42 – 48 and Week 50 of 2024.
  9. An allocation of excess MCoD Cancer assignment, above and beyond past practice, that changes the context of Cancer Mortality post-Covid vs pre-Covid, is added back into UCoD Cancer Mortality (see Chart 6), per Chart 5. This however does not serve to compensate for all death certificate fraud. Actual rates of cancer are higher and growing faster than those shown in this article.

Please note however, that the provisional mortality figures for 2018-2024 are lower than the figures prior to 2018 because the NVSS suppresses county level data with fewer than nine records. We do not adjust 2018 and later mortality figures upward for this. Therefore, these excess mortality projections are lower than the reality as a result. For this reason, all inflection-DFT charts begin with the 2018 suppressed data, in order to avoid false ‘downtrends’ or inflections in the data.

All of these adjusts are either marked on the charts below, or are outlined as to how they are obtained, in a separate chart. If an analyst does not track these confounders faithfully and weekly over time and compare their relative impacts in terms of a total system, or if they dilute a signal through growth by a population which does not exhibit the mortality in question – mortality inflections and trends will be diluted – suggesting an entirely wrong (rosy) short term result. The bad news will only show up in the data years later. Of course, this is the actual plan.

That being said, let’s now examine the various metrics of excess mortality as of Week 25 of 2025.

Balance Sheet – USA Excess Weekly All Cause Mortality – 6.1% or 3,424 Deaths

Evaluating Asymmetry without Univariate or Gaussian Blindness

When examining excess mortality metrics and considering them in the context of the broader summary of excess mortality, the resulting balance sheet (shown in Chart 2 below) is derived. A crucial distinction between systems analytics and mere ‘statistics’ resides in the requirement for systems dynamics to comprehensively depict an entire schema of interdependent relationships, in terms of both end-to-end agreement and coherence. Such systems engineering expertise and rigor characterizes both my academic foundation as well as my five-decade-long professional career. Such exemplifies as well the distinction between a hack or journeyman technician, versus a real scientist.

Chart 2 – Excess Mortality Weekly Balance Sheet (USA) – a summary of all forms of excess mortality, with Pull-Forward Effect (PFE) taken into account. PFE must be included, because each of the Big 12 ICD codes, ranked in terms of percentage excess in the chart above (beige boxes), contains Pull-Forward Effect. The tapered impact of PFE can be observed as indicated by the green annotation in the chart. The reason that negative numbers still exist in these figures (in green text) is because we have chosen a conservative PFE allocation, which is overcome by the actual PFE in those mortality codes which impact the elderly the hardest. This PFE was confirmed by the Society of Actuaries in their Covid-19 Mortality Survey Report of November 2023. The top ICD codes in terms of excess mortality are highlighted in red.

How Statisticians and Fake Experts Cheat to Dilute and Hide This Signal

How malicious and incompetent analysts cheat retrospective data to create a stage magician’s illusion.

The reader should note that the CDC eliminated reporting of the Big 12 ICD categorizations (their Morbidity and Mortality Weekly Report) at the end of September 2023. This data set was useful in helping spot anomalies (‘Pull-Forward Effect’ for instance) in the rates of death in the US, and in preventing paltering and torfuscation of the baseline in order to make later years appear to have less excess death. To this end, below are the five dishonest tactics employed by fake analysts and pharmaceutical narrative science advocates:

Paltering (boosting the baseline – UK example here) – crafting a baseline inflated by rolling or factored-in 2020-23 excess deaths. Alternately, counting excess Covid mRNA vaccine deaths for 2025 and beyond as ‘expected’ deaths. In some cases, pretend analysts will smooth over recent 5-year legacy trends or even brazenly craft an entirely fictional new uptrend in baseline, departing from a longstanding legacy baseline, in a desperate attempt to make excess mortality disappear.

Torfuscation (hiding the bodies in the bog) – failing to adjust 2021-2027 baseline downward by Pull-Forward Effect (PFE). Exploiting Simpson Effect by blending the Pull-Forward Effect of older age brackets with the excess mortality in younger ones, to derive a false arrival which is approximately close to a projected baseline.

Simpson Per Capita Dilution (diluting signal with non-salient ‘population growth’) – making per capita or per-100K adjustments to a signal in a subpopulation

  1. which has had its candidate population shrink and not grow (over age 75 for example, post pandemic),
  2. for which the population growth bracket-profile does not match the profile/death risk of the group from which the signal has been extracted (eg. immigration-influenced growth applied to ‘heart disease’, ‘cancer’, or ‘Alzheimer’s’),
  3. for which the analysis is crafted to detect a short retrospective inflection/excess and not a generational trend,
  4. where the growth trend line itself already reflects the rate of population growth inside its historic metric,
  5. using population projections made prior to a black swan event (Covid-19), or
  6. employing migrant-fueled population growth rates.

Gaussian Blindness – the warning flags of a data charlatan:

  1. Applying linear regression analysis across an entire non-linear, depleted, or inflected data set.
  2. Applying linear or least-squares regression on all annual data points (rather than the stable and more comparable trough references – see Exhibit 3 for example of this practice’s effectiveness), to include single-event surges in peak data which serve to bias the baseline.
  3. Employing a linear trend line when a dynamic baseline is the professional standard for signal analysis.
  4. Depiction by means of a ‘quashed-y’ or ‘zero-base’ chart combined with a linear regression on a high magnitude data set.
  5. Depiction by ‘quashed-x’ or a cherry picked time axis such that an inflection is concealed behind a conveniently selected regression line and time frame, which both shows the trend desired and/or hides the signal in question.
  6. Employing monthly or annual figures as opposed to using the actual weekly data.

Age-Standardized/Crude Retrospective Analysis – employing PFE-absent and age-standardized ‘crude’ analysis to a retrospective analysis of one nation during a black swan event, when age-standardized analysis is used for comparing metrics between nations during a normal circumstance.6

Errant Census Bureau ASMR Denominator Data – While it is often claimed that the Census Bureau adjusts age cohorts annually to account for categorical deaths, this is demonstrably not the case. During the COVID years and beyond, the Bureau misrepresented the population figures for the 72+ age cohort, relying on algorithmic projections rather than actual death certificate data from the National Center for Health Statistics (NCHS). This discrepancy underscores why age-standardized mortality rate (ASMR) indices are unreliable when applied within a single-nation retrospective analysis.

For example, the entire SARS-CoV-2 Pandemic in Sweden can be made to disappear completely by using all these tricks listed above in order to deceive. A comparative of Sweden’s truth vs statistics-lie can be observed by clicking on this X-article link. By using these tricks, one can make any mortality signal, or even the entire pandemic, falsely appear as if it did not occur at all.

~ The Cheats of Covid Narrative Science

Here we provide a link to a clear example of these techniques in action to make the entire pandemic statistically disappear. If one can make the entire pandemic disappear through these data tricks, they can certainly deceive the public into believing that no rise in cancer exists as well. Such elicits a key principle to grasp here: there is no data available which refutes this comprehensive analysis. One can hide the signal through bad technique or extreme single-use constraint (works with one graph but produces incoherence in others). But they cannot refute it through equivalent systemic corroboration or better data. Trained professionals understand why this litmus is important. A false modus tollens is the signal of a corrupt argument.

These data magic tricks are not merely unethical, but when enacted by public health authorities, are also immoral. Just as in the case of their refusal to release V-Safe data, vaccine cohort data, or spurious VAERS record disappearances, data sets effective in targeting the harm introduced by the Covid-19 mRNA vaccine are all being systematically screened from public access. These are human rights crimes.

Beware he who would deny you access to information, for in his heart he dreams himself your master.

Accordingly, we put together a scrape which assembles the same data from Wonder, as one used to be able to obtain from the CDC Weekly MMWR Report, so that we are able to continue this tracking (that is, until they eliminate Wonder altogether and simply appeal to tyrannical authority in its absence). We also have kept a backup of the old MMWR data to show how the CDC palters the baseline in the years to come.

The following charts all feed systemically into and reconcile inside the summary balance sheet above.

USA Excess Weekly Non-Covid Natural Cause Mortality – 6.5% (3-sigma) or 3,103 Deaths

The beige line in the chart below shows the CDC Wonder Excess Non-Covid Natural Cause deaths per week for the two years prior, as well as years of and years post the pandemic. The dark orange baseline is normalized from the years 2014-2019, representing a 1.12% annual growth in mortality for the US in terms of all non-Covid natural causes. Just as in the case of all this specie of charts, the 6-year baseline reflects a balance between a retrospective substantial enough to provide statistical significance, but not so long as to be confounded by generational effects or immigration impacts.

Understanding the Pull-Forward Effect (demarcated as ‘PFE’ in orange), represented by the reduced orange baseline observed in Chart 3 from 2021 onward, is crucial when evaluating Excess Non-Covid Natural Cause Mortality, as well as in actuarial and epidemiological studies more broadly. This is because the combined effects of excess mortality and temporary decreases in mortality following a pandemic can offset each other, a phenomenon known as the Simpson effect, leading researchers astray. This has been evident in the analysis of the Covid-19 pandemic, where analysts have consistently arrived at incorrect conclusions about the relationship between the vaccine and excess mortality due to a lack of understanding in addressing this aspect of the data.

Understanding the Morbidity and Mortality Weekly Reporting hinging around Week 14 of 2021 is crucial for grasping the dynamics of Excess Non-COVID Natural Cause Mortality. This specific week marks the period of most rapid administration of both doses of the COVID-19 vaccine. Upon analyzing numerous charts illustrating etiological and causal influences, it becomes evident that this particular date consistently emerges as a notable turning point.

Two prime illustrations of this vaccine inflection date impact can be observed in the US Natality Birth Weight Chart and the US Infant Not Alive at Time of Natality Report Chart. The Procedure for development of these charts can be found by clicking here.

Long Vaccine not Long Covid

A recent Yale study examined 42 patients with Post-Vaccination Syndrome (PVS) and 22 healthy controls, stratifying both groups by SARS-CoV-2 infection history. Among PVS participants, over one-third had no serological or self-reported evidence of prior COVID-19 infection, yet still developed persistent symptoms such as fatigue, neuropathy, brain fog, and exercise intolerance following vaccination. These symptoms, along with significantly lower General Health Visual Analogue Scale (GHVAS) scores and impaired PROMIS-29 metrics, closely mirrored the clinical profile of Long Covid. Notably, symptom severity was comparable between vaccinated individuals with and without prior infection, and the majority of PVS cases arose only after the first or second vaccine dose. Those who experienced PVS early (after dose 1 or 2) opted out of subsequent boosters, thereby truncating what might have otherwise been a number-of-doses sensitive escalation in PVS symptoms observable in aggregate data.7

These findings support the hypothesis that in some or even most instances, what is regarded among pateints as Long Covid may instead be a form of vaccine-related chronic immune dysregulation, particularly in patients without confirmed prior infection. In other words, Long Covid, is a socially trained self-diagnosis regarding PVS.

Chart 3 – Excess Non-Covid Natural Cause Mortality (USA) – stands at 6.5% or 3,103 persons per week over the last three weeks on average. A total of 843,900 US Citizens have died primarily due to the major factor which has impacted this metric (primarily the mRNA vaccine). This is far more than the 383,413 persons who died from the SARS-CoV-2 virus itself during the pandemic (excludes deaths from denial of treatment).

Perhaps the only good news to be found within Chart 3 above is the flattening in this Excess Mortality trend for most of the years 2023 and into 2025. However, we have documented (in Charts 3b and 8) that this is simply an impact of the Pull-Forward Effect (because of our conservative choices with regard to its metrics), so we will watch how this excess trends over the outyears in order to discern what is indeed occurring. Morevover, as of mid-2025 the metric has taken on a new slope since late 2023, one which is slightly higher than that of the legacy baseline.

This surge in excess natural-cause mortality post–Covid vaccine is corroborated across roughly half of other Western nations, each showing the same pattern of excess deaths emerging after their vaccination rollouts (a clear example is shown here).

Unfortunately however, this excess mortality replicates in other highly mRNA-vaccinated nations, even in absence of a heavy history of Covid infection or mortality. In addition, in 2024 insurance industry professional analysts corroborated this excess mortality and its persistence. To wit:

Excess mortality was the bane of the [life insurance] industry’s financial results over the last few years, leading to depressed earnings and losses. The term is a polite way to say more people were dying than was expected or predicted, thus the policy claims were higher and the payouts greater than the companies had reserved for.

[That] Death toll remains ‘alarming.’

~ Insurance NewsNet: Excess Mortality recedes, but remains above normal, 7 May 20248

No Sympathetic Variance Between Excess Non-Covid Natural Cause and Covid-19 Mortality

The question therefore arises: “Is the arrival of each week’s Excess Non-Covid Natural Cause Mortality simply a case of ‘missed Covid-19 deaths’?” The answer to this question is an unequivocal ‘No’. In Chart 3b below, one can observe the progressive loss in covariance between Covid Mortality and Excess Non-Covid Natural Cause Mortality across the retrospective horizon. Basically 3 phases of covariance progression between the two metrics exist:

Significant Relationship (Mar 2020 – Jul 2020) – the timeframe wherein the two indices behaved with extreme covariance during the period before comprehensive PCR testing was in place. Indeed, during this timeframe many Covid-19 deaths were missed and not counted.

Slight to No Relationship (Aug 2020 – Mar 2022) – during this period, the relationship between the two metrics all but disappears. A slight sympathy develops as Covid begins to shift its mortality to the more traditional Nov – Feb high mortality timeframe for all death ICD codes (the scalloping shown in Chart 1 above). This creates a pseudo-trend in relationship between the two metrics (yellow dotted regression line), which is not real. In other words, this is Covid-19 Mortality becoming more conformant with natural cause death patterns (see Chart 1), than it is Excess Non-Covid Natural Cause Mortality becoming conformant with Covid-19 deaths.

No Relationship Whatsoever (Apr 2022 – now) – since the arrival of the diverse set of ‘Omicron’ variants of Covid, there has existed zero relationship between Excess Non-Covid Natural Cause Mortality and Covid-19 Mortality.

Chart 3b – Progressive Differential-Portion Covariance Test – the test is derived by taking each week’s differential mortality from the previous week (smoothed across a 7 week rolling average) and then dividing that by the overall low-to-high range of the metric across the retrospective horizon (the ‘differential-portion’). A covariance test is then applied across a rolling 24 weeks for each week of the two differential-portion retrospective horizons – which results in a rolling index of true variance relationship between the two metrics (the applied procedure links here: PDPC Test mathematical procedure).

Accordingly, there has existed little to no sympathetic statistical relationship between Excess Non-Covid Natural Cause Mortality and Covid-19 Mortality since the introduction of widespread PCR testing in mid-2020. Excess Non-Covid Natural Cause Mortality is not a case of ‘missed Covid-19 deaths’.

Pull-Forward Effect (PFE)

Please note that no argument or chart in this analysis is ‘completely dependent upon’ Pull-Forward Effect. PFE is an addition to the critical levels of signal and excess mortality indicated in each chart herein, constituting a mere 20 to 35% of any excess in any given MMWR week. We merely contend that, correct levels of excess mortality cannot be estimated (for any nation or ICD code) without this critical and real arrival function being taken into consideration.

Pull-Forward Effect (PFE) – when in a given population, a large number of older citizens (in the case of Covid-19 an average age of 82 years) die to the excess in a given short timeframe, due to an exceptional cause (famine, war, pandemic, terror) – then a given set of successive years of baseline death rate for that population or any particular cause of death must be lowered by a function of that excess death for an actuarial-derived period thereafter.

The function we currently use for PFE is described by 6.6 years (345 weeks – April 2021 – Oct 2027) of Chi-squared arrival, with an anticipated x_mode (function peak) of mid-late 2023 at 6.42% of Excess Non-Covid Natural Cause Mortality. Only 729,791 (61%) of the net 1.2 million older person deaths are claimed inside this entire PFE function. The arrival argument is depicted above in this PFE Reference and Calculation Basis Chart. We will continue to update this arrival function (as we have in the past) based upon the patterns observed inside the eleven key PFE index recitations outlined in the following paragraphs, through and including Chart 3b.

The basis of our Pull-Forward Effect (PFE) calculations can be seen by examining the dip-to points in the DFT Chart (3b) below, which shows clearly the 2021-2023 shortfall trend in All Non-Covid Natural Cause Mortality for ages 75+ (antimode minimum = -15.6% PFE). The older-citizen sensitive Alzheimer (G30) ICD mortality (-18.5% PFE), Bladder Cancer mortality (-10.1% PFE), Lung Cancer mortality (-12% PFE in ‘unhealthy quotient’ timeframe), and Dementia and Related mortality (-7.8% PFE) trends over this same period are suitable for confirming this Pull-Forward Effect deficit (most of these show trough points in April 2023 currently). In addition, the Pull-Forward Effect is corroborated by

Collectively, these ten benchmarks more than substantiate our 6.42% drop (at peak, mid-2023) in baseline expected for Excess Non-Covid Natural Cause Mortality (seen as the baseline adjustment in Chart 3 above, and in Chart 6 below). As a reminder, this constitutes use of ~64% (for conservancy) of the available (true) PFE shown in Chart 3b below.

Therefore, the conservancy we employ is on the order of 35 to 40%. The actual PFE is almost double that used in our models. We presume for all intents and purposes that 470,000 of these 1.2 million older-citizen Covid-19 deaths ‘would have lived forever’. Such highlights the ridiculous levels of conservancy we have gone to in order to appease pretend critics.

Chart 3b – Pull-Forward Effect (PFE) Arrival Distribution – is formed by measuring the arrival function of All Non-Covid Natural Cause Mortality shortfalls by week over the period since April 2021. As one can see, the PFE arrival distribution peaks in mid 2023 at an antimode minimum of -15.6%, (5.9% in the Primary 11 ICD Chart) and then begins its taper-off to an actuarial-derived 345 week arrival horizon (Average death age = 82 years, who were expected to live 6.6 more years or 345 weeks in normal circumstances – however, died early).9

Note that Pull-Forward Effect has turned the corner on its antimode minimum as of July 2024. That means that the peak in PFE has come and gone. Mortality rates will begin to rise now, and this illusion of ‘attaining baseline’ which the fraudsters are selling will dissipate.

The above DFT model indicates that, of the 1.851 million excess deaths to date, around 735 K of those deaths (for our purposes) happened early, or were ‘pulled forward’. This will continue to increase across a total period of 6.6 years of baseline. Our models in Charts 3 above and 6 below have only used 467,205 of these 735 K in PFE deaths as of Week 19, 2025 – so 64% of the actual PFE indexed from Chart 3b above (very conservative). The remaining persons not included in this 345-week PFE total are assumed to ‘live forever’, for purposes of conservancy. Thus, this PFE allocation is very conservative. (Please note that, while we use the arrival form of the shortfall in 75+ Non-Covid Natural Cause Mortality, we do not use the percentage magnitude. Our peak PFE used for April 2023 is 6.42% of Excess Non Covid Natural Cause Mortality (suitably confirmed by the 5.9% Primary ICD measure above), and not the 17.9% shown in Chart 3b).

The Pull-Forward Effect can be seen as well inside cancer mortality within older age groups, as indicated in the 75+ age bracket Deviation from Trend charts.

When Pull-Forward Effect, and trend modeling are employed correctly, they result in the disciplined, reliable, and clear modeling of Excess Non-Covid Natural Cause Mortality (expanded Chart 3 below). There is no need to dilute the figures by population growth, as population growth is already incorporated into this baseline. Inside this chart, one has everything they need to calmly and objectively comprehend, replicate, and confirm my work.

Correctly Defining Baseline vs Huckster Methods

The procedure for assembly of this scalloped baseline can be accessed by clicking here. As one can observe, the fit between actual mortality and projected baseline using this method, is extraordinary. The dotted line at the base of the scalloping, intersecting the baseline each August timeframe, is not a regression line. It is a visual reference used at the base of the scalloping because the base of the annual seasonal fluctuation acts as a superior reference as compared to the mids and the peaks, which bear too much volatility and variation by season and year to act as a linear reference.

These arrivals however, do not always involve a linear regression baseline. The chart in Exhibit 3 illustrates a level of accountability that none of the naysayers have achieved with their hasty linear regression graphs to date.

All-Data-Point Linear Regression is a Fraudster’s Heuristic and Here is Why

Exhibit 3 (expanded Chart 3) – All Cause Mortality run three different methods (with extended 2014 – 2019 baseline) – everything one needs in order to to objectively comprehend, replicate, and confirm my work – and show why it is correct.

Using the disciplined method depicted above, which incorporates the Pull-Forward Effect (PFE) and a more reliable baseline trend, we estimate that 828,500 non-Covid natural cause excess deaths have occurred through Week 21 of 2025. This excess mortality is primarily attributable to the Covid-19 vaccine. The conclusions we derive from this are as follows:

  1. Referencing growth from the least volatile context—specifically, the summer mid-trough months, weighting increasingly more recent years, and avoiding anomalous one year surges or depressions—on a weekly basis offers a superior method for accurate analysis. Academic approaches such as annualized figures, per-capita dilutions, monthly averages, and full-year, monthly, or Gaussian least squares/linear regressions (the fuchsia line in Exhibit 3 above) will serve to palter (distort) the baseline and yield inaccurate progressions in excess mortality. These methods often overweight anomalies in the data, such as remote-in-time depressions (Summer 2014) or surge asymmetry (Winter 2017/18), which are frequently misinterpreted as trend data by less competent analysts. Such anomalies should not be considered part of the background rate, and conflating them with broader trends can lead to erroneous conclusions.

    The calculations and method used to establish a salient All Cause Mortality growth rate (1.14% from Chart 1 and Exhibit 3 above) can be seen by clicking on this extract from our 2014 – 2019 baseline module for Chart 1. This is the same approach I have used to estimate category and product line growth for major clients for decades. I maintain a successful track record and stellar industry reputation in this type of strategic analysis. A similar method is employed as a facet of competitive advantage in my successful ‘category killer’ retail company as well. I am more than happy to work with sincere questions on various industry approaches to analysis, but if you preface your ‘discussion’ with defamation of me or my professional brand, you will get a block or proactive response from my lawyer.
  2. Therefore, the 2014 to 2019 dynamic baseline (represented in dark orange in Exhibit 3), which reflects a precedent growth rate of 1.14%, serves as a sound and valid reference for determining excess mortality.
  3. Non-Covid natural cause excess mortality shows a significant rise without relying on the Pull-Forward Effect (PFE). While PFE is an important complement to this analysis, it primarily serves to enhance the accuracy and validity of the excess mortality assessment. Leaving Pull-Forward Effect out of the above analysis, is another tactic of fraud on the part of bad-actor analysts.
  4. Using the method outlined here, we identified issues in the NCHS data during the summer months of 2024 and alerted both the NCHS and CDC to the discrepancies. The subsequent corrections caused increased volatility in the late 2024 numbers. As a note, despite the optical illusion created by the mid-summer dip, the trendline for those summer months is actually higher than that of 2023. This bears monitoring.

Resulting Covid-19 Vaccine Negative Cost-Benefit Function

In terms of US county-by-county arrival of this Excess Non-Covid Natural Cause Mortality, the heaviest concentrations of this death group has been in the most highly vaccinated counties in the US. In the dynamic analysis shown in Chart 3c below, one can see that this Excess Non-Covid Natural Cause Mortality both begins solely after the rollout of the vaccine nationwide, and as well bears its heaviest impact in those counties which are most heavily vaccinated. One cannot use state level data for this because of Hope-Simpson effect during the Delta variant timeframe (a competence flag in those showing such regressions) hitting south border states most heavily. This serves to impart a misleading Yule-Simpson effect in the state level data. Only county-level data is salient.

Exhibit 3c – Excess Non-Covid Natural Cause Mortality by US County per-100K vs Percent Population Vaccinated – both initiates nationwide solely after the rollout of the vaccine (blue shot icon in Mar 2021), and as well bears its heaviest impact in those counties which are most heavily vaccinated (to the right on x-axis).

Despite this mRNA vaccine associated death quotient by US County (Exhibit 3c), the Covid-19 vaccines had no appreciable impact on Covid Mortality when analyzed by US County (Exhibit 3d below). Please note that US State level data is compromised (Yule-Simpson Effect) by Hope-Simspon Effect (seasonality by latitude for a virus) and the timing of the arrival of the Delta variant. For this reason, do not trust anyone who touts state level data.

Exhibit 3d – Covid Deaths (per 100K) by Percent of Population Vaccinated by US County – Higher vaccinated counties show lower rates of Covid Mortality. The problem is, that this taper in mortality (from denominator compression and possibly some advance immunity – not better Covid outcomes) existed long before the vaccine was ever rolled out. Therefore, the retrospective differential effect of the Covid vaccine was negligible when examining mortality.

A purported vaccine efficacy signal only showed in small sample hospitalization studies sponsored by the CDC. Lots of unvaccinated persons went to the hospital, but curiously they never showed up in the mortality totals. Statistically this is impossible. Moreover, when the CDC small study sample ratios are expanded to the entire population, the result is a severe overage in total mortality. Both of these falsifications bring the CDC Covid-19 Response, Epidemiology Task Force study integrity into question.

USA Excess Weekly Non-Natural Mortality – 4.0% or 202 Deaths

The beige line in the chart below shows the CDC Wonder Excess Non-Natural Cause deaths per week for the two years prior, as well as years-of and years-post the pandemic. The dotted baseline is normalized from the years 2014-2019, and comprises a 1.42% annual growth in mortality for the US in terms non-natural causes. This reflects excess mortality from unsound lockdown and open border practices, in terms of suicide, addiction, assault, accidents, abandonment, and despair (SAAAAD). Disruption (loss of access to medical services or medications) deaths are now included inside iatrogenic and denial of treatment tallies (medical mistakes).

Chart 4 – Excess Non-Natural Mortality (USA) – stands at 4.0% or 202 persons per week over the last three weeks on average. A total of 253,830 persons have died to date as a result of our policies selected and enforced during the early pandemic – and in particular the US Democratic Party’s allowance of the cross-border inflow of fentanyl.

It is our hope that the downward trend in this metric since mid-2021 continues until this mortality recovers its baseline. The jump which occurred in mid-late 2023 is something we are watching – however appears now to be a regular summer effect. However, given that a portion of these deaths each week are due to ‘unknown substance overdose,’ we anticipate that the trend will asymptote to +130 deaths per week in the years 2025 and beyond. Because of efforts to mitigate smuggling of fentanyl across the US border, along with its subsequent introduction into substances preferred by casual drug users, this excess death curve has undergone a steady decline over the 2023 and 2024 time frame.

However, it is also important to note that many of the excess deaths being attributed to Non-Natural Causes above, are actually from vaccine-induced myocarditis. The proof of this can be seen in DFT Chart 4c below. In addition, the NCHS is abusing this category after the fact in order to hide excess natural cause deaths during weeks -26 thru -50 (Weeks -13 thru -26 are the normal adjust period). We watch those adjustments in detail each week to ensure that these re-categorizations do not falsely deplete Excess Non-Covid Natural Cause Mortality (shown in Chart 3).

As can be ascertained from Charts 4b and 4c below, there exists absolutely no doubt concerning the relationship between the vaccine and excess death.

Chart 4b “What about Sweden?” – Cumulative Excess Mortality (Last 12 Months – Relative to Population) vs Percentage Population Vaccinated – (click on image to expand) – once the noise cleared from national mortality data sets, it became abundantly clear that excess death was non-linearly proportional to the rate of vaccination by European country. The dip in excess mortality below the zero-line of the x-axis (Cumulative Excess Mortality) is Pull-Forward Effect (PFE). The reason the curve flattens near the lower end, is that the nations in this section of the chart are smaller in population, and there is a consequential denominator amplification of the cumulative excess death (relative to population) in the data for smaller nations. The excess mortality in each nation is actually higher than these indices indicate, and the relationship to the vaccine is stark.

This effect has been coorobrated and confirmed by a November 2025 Kuhbandner, et al. German Study.

As well, the most impactful influence upon ‘unspecified drug overdose’ mortality in younger persons, has been the introduction of the Covid-19 vaccine. This is also confirmed by the Sudden Cardiac Death Mortality for ages 0-54 shown in Chart 10. The two charts bear concomitance, one of the strongest signals in analytical systems and science.

Chart 4c – Excess Mortality from Overdose from Unspecified Drug – Yes, the combination of lockdowns and Covid-19 resulted in a temporary increase in undetermined drug overdoses. However, this is not ‘trend’ data. The trend begins at the end of 2020 with the introduction of the vaccine (which peaks the very week of vaccine maximum uptake). Vaccine-induced myocarditis rendered this population vulnerable to sudden cardiac death – which was then falsely attributed to drug overdose. This excess attribution, as of Week 49 2023, is in the range of 290 deaths per week, for ‘unspecified drug’. This suggests an alarming increase in young person Sudden Cardiac Death.

Recent policies targeting the closure of the border to illegal drug and human trafficking has saved lives. The Party was killing us.

USA Excess Weekly Cancer Mortality – 8.6% (14-sigma) with Novel 1.5% Nonlinear CAGR

It should be noted that our cancer findings herein have been professionally corroborated as of 12 Mar 2024.

Chasing truth means calling out the smoke and mirrors, even if it ruffles feathers. @EthicalSkeptic’s data on cancer mortality cuts through the BS.

~ @Grok, 8 Jul 2025

The green-to-red line in Chart 5 below shows the CDC Wonder Excess Attributions of underlying cause of death (UCoD) Cancer to multiple cause of death (MCoD) Cancer, per week for the two years prior, as well as years-of and years-post the pandemic. The dotted baseline is normalized from the years 2017-2019, and indexed to the last 7 week average as compared to those same 7 weeks of 2017-2019, so it is not a ‘regression’. Since this is a relative index, it should exhibit no trend or growth rate (outside the context of Covid mortality peak periods of course). The current excess in these deaths reflects cancer deaths which are concealed from the underlying cause of death ICD code, and must be added back into the mix in order to make Excess Cancer Mortality comparable to its past baseline. We reconcile this into Chart 6 below, applying it only after the pandemic period ended.

Chart 5 – Cancer Concealment by Excess MCOD Attribution – stands at 439 concealed cancer deaths per week. In this practice, cancers are moved from the underlying cause of death line in Part I of the death certificate, to one of the multiple cause of death lines above the UCoD line, in excess of past practice. Once Covid has abated, this balance should trend back to zero. It has not. Therefore, in order to capture the true cancer mortality increase, these cancer deaths falsely attributed to Covid and other underlying causes must be added back into the total in the Excess Cancer Mortality chart below. These missing deaths are phased back in from Week 10 of 2022 (point of large decline in Covid mortality) onward.

Note that the year 2017 in Chart 5 uses the correct total deaths for the year from the 1999-2017 Wonder data, but applies an average of the 2018 and 19 weekly arrival forms because Wonder does not offer weekly data in that linked data set. This method is still valid for differential calculations week by week.

Failure to normalize for MCoD Cancer using the above method constitutes an act of publishing fraud.

When done correctly and ethically, cancer shows a clear inflection in growth rate at Week 14 of 2021 – the week of fastest uptake in the mRNA vaccine within the US population (as can be seen in Chart 6 below). In fact, every single chart we have run which depicts an excess mortality currently underway (not all of them do this), indicate this same inflection point of Week 14 of 2021.

The discouraging news is that the 45-54 and 55-64 age bracket Deviation from Trend charts indicate a weighted average of 11% excess cancer mortality as of Week 10 2024. This is the real excess cancer rate, which is partly hidden (reducing it to ~6-8%) by the Pull-Forward Effect inside older age groups, as indicated in the 75+ age bracket Deviation from Trend charts.

The inflection point in the first set of charts for ages 45 – 64 bears a clear demarcation at MMWR Week 14 of 2021. The older age Pull-Forward Effect will not last long – whereupon this excess cancer mortality will begin to become undeniable.

This is unequivocal – the vaccine is causing excess death, and likely 95% of all of our Excess Non-Covid Natural Cause Mortality, the 828,500 deaths shown in Chart 3 above.

Moreover, cancer is a hard ship to turn; but once turned, will not come back to normal for perhaps decades. I contend that the outyear numbers will show that we have made, very possibly, a horrible mistake. Time will tell, but will also only whisper to those who bother to watch. I guarantee you that the smarter-than-thou among us, will not watch at all. Take this as a hint as to their agency and integrity. Nothing they proffer is honest – everything a rhetorical deflection and nothing more.

Take note in Exhibit 6 below, that TES was the first analyst to catch and publish an alert as to the rising cancer mortality signal as of 7 Aug 2021. Fortunately, millions listened.

Chart 6 – Excess Cancer Mortality (USA) – stands at 8.6% or 934 persons per week over the last three weeks on average. More alarmingly however, is the novel compound annualized growth rate (CAGR) of 1.5%, a significant departure from the old growth rate of 0.23%, normalized from the years 2014-2019 (dark orange line). As well, this growth has taken on a nonlinear increase – meaning that cancer mortality has now adopted a slight acceleration, post Covid vaccine.

This chart is expertly adjusted for Pull-Forward Effect (345 week PFE method is depicted in Charts 3b and 6) and excess MCoD attributions (critical data mining method depicted in Chart 5 above). In other words it is showing the true cancer UCoD rate (shorted by pressure on physicians to not report cancers unless ‘proven’ by multiple tests). Despite all these shorts in the data collection, the raw (and misleading) UCoD data nonetheless shows a rise – which can be seen by clicking here.

A bottom-up derivation analysis (which is essential for this critical rollup, yet few perform) of this excess cancer mortality shown in Chart 6, along with the observed Pull-Forward Effect, derived from the 16 leading causes of cancer deaths, can be accessed by clicking upon this image. From this, three things become clear:

  • A tally of each ICD death cause sums up to the overall Excess UCoD Cancer Mortality I have been publishing = ~700-1050 per week in Chart 6 – both in terms of UCoD as well as MCoD.
  • Every single sub-ICD chart for cancer inflects with the rollout of the mRNA Vaccine.
  • Out of convervancy, we have only employed 64% of the true Pull-Forward Effect thus far. In addition, state medical boards are forcing many cancers to be misattributed to other ICD codes. This means that cancer mortality is worse than Chart 6 is even indicating.

These cancer outcomes have been corroborated by a South Korean retrospective study involving 8 million persons.10

The suggestion that the Covid vaccine may be linked to elevated cancer incidence is supported by a July 2025 Cox hazard cohort study by Martellucci et al., which compared cancer hospitalization rates in vaccinated versus unvaccinated populations.11

Both our Pull-Forward Effect (PFE) and the sharp 2021 emergence of the cancer signal we identified in July 2021 were later corroborated by a May 2025 McLoed, et al. peer reviewed study analyzing cancer diagnoses within the Ontario Health Insurance Plan (OHIP) dataset. Unfortunately, the authors attributed this rise in diagnoses to “deferred cancer screenings”—a claim made without presenting any supporting evidence whatsoever. Such an assertion, absent evidentiary foundation, constitutes a serious breach of research ethics.

This cancer inflection and novel nonlinear growth rate is corroborated as well by both the Producer Price Index for growth in the Surgical Equipment Manufacturing Industry, cancer diagnoses in England, as well as Google searches related to new cancer patients—all three of which inflected at Week 14 of 2021. It has been corroborated as well by Voya Financial Services as of December 2024, along with z-score confirmation by Alegria, et al that same year.

We show a rise in excess mortality from neoplasms reported as underlying cause of death, which started in 2020 (1.7%) and accelerated substantially in 2021 (5.6%) and 2022 (7.9%). The increase in excess mortality in both 2021 (Z-score of 11.8) and 2022 (Z-score of 16.5) are highly statistically significant (extreme events).

~ Alegria, et al.; US – Death Trends for Neoplasms ICD codes: C00-D48, Ages 15-4412

Our most recent rate increase is due to “Increased claim frequency for 2024 policy year observed across most categories, most notably in cancer diagnoses across younger ages.”

~Voya Financial raises premiums, 11 Dec 2024

Confirmation of Cancer UCoD Shorting = 24.5% Excess Incidence

In corroboration of this alarming set of indices with regard to Cancer, is the constant dollar rise in expenditures for cancer treatment within the United States as of end-of-month August 2025. The rise in Producer Price Index-Neoplasm Treatment highlighted in Chart 7 below is adjusted for both inflation in Medical Care Services (MCS) and group price escalations (commensurate with CARES Act). The actual raw BLS figures show an even more aggressive increase than the 11.7% 25.7% indicated on top right hand side of Chart 7 below.

With the currently measured rate of Cancer Pull-Forward Effect, the true excess Real Dollar Cancer Rate is 24.5%. This represents an additional $42.7 billion (without PFE included) in real dollars that consumers are spending each year to treat these new cancers.

As well, this has been corroborated by other systems professionals replicating this same work.13

The results indicate that from 2020 a novel phenomenon leading to increased neoplasm deaths appears to be present.

~ Alegria & Nunes, Trends in deaths from neoplasms, US (2024)14

Medicare Part B spending on antineoplastics rose from ~$15.6B in 2019 to $18.5B in 2022. Part D antineoplastics spending increased from ~$23B in 2019 to $32.1B in 2022. Doses/claims in particular were the impetus behind this: e.g., Keytruda users grew from ~30K (2019 est) to 67K in 2022. 2024 forecasts show a novel ~10% annual growth.

~ Sources: MedPAC, CMS Drug Spending Dashboard.
Chart 7 – Excess PPI-Neoplasms Treatment Expenditures (USA) – US Citizens have non-linearly increased their real-dollar expenditures for all forms of cancer treatment since 14 months post the Week 14 2021 vaccine uptake inflection week. This lag was corroborated by the same lag effect which showed for cancer in the VAERS reports regarding reported adverse event tumor appearances (6 months to a year).

This stark rise in cancer treatment expenditures is corroborated by both the skyrocketing 2022/23 sales in cancer treatment drugs all across the board (save for sunsetting-lifecycle names), as well as the American Cancer Society’s Annual ‘Cancer Facts & Figures’ Report, which tracks cancer incidence by means of cases. The latter report (2024 is projected at the link provided) shows a clear 12.7% novel excess in cancer cases for both 2023 and projected 2024 (9.2% without Pull-Forward Effect), along with an increase of the cancer case growth rate from 1% to 2.7% CAGR (not influenced by PFE). These are depicted in Chart 7b below.

Note that Charts 7 and 7b agree in both magnitude and arrival form. The rise in cancer due to the vaccine is unequivocal.

Chart 7b – American Cancer Society Cancer Facts & Figures by Year (with Cancer estimates) – clearly shows a 12.7% excess cancer diagnosis rate, along with a novel 2.7% compounded annualized growth rate. The 2.5-sigma jump in cases immediately following the vaccine rollout is significant. The word ‘estimate’ here is analytical in context, in that these are not projections, forecasts, nor guesses. They are done objectively and based upon observations in actual inflection and trend.

Little of this excess cancer is attributable to the ad hoc rescue of ‘a deferral in cancer screening appointments’, as cancer rates have soared in younger ages (see Chart 11), the biggest cancers dropped in incidence (by Pull-Forward Effect), while at the same time the diversity of cancer mix, as well as a 2022 spike in incidence of rare and secondary cancers, collectively serve to belie this notion.

Finally, corroboration of the vaccine’s role in excess cancer mortality can be seen by clicking on this Top 17 vs Bottom 14 Vaccinated States Cancer Mortality chart. This chart compares the most and least vaccinated states in the U.S. and their respective excess cancer mortality rates.

In this chart, one may observe that the more heavily vaccinated states exhibit a 2:1 higher rate of excess cancer mortality compared to the lesser vaccinated states.

Corroboration by Fifteen BLS Medical Sector Economic Indices = 7 to 50% Elevated

While the data we have presented up to this point is certainly consistent, consilient, and overwhelming in its own regard—corroborated by over a dozen agencies and other analysts—the Bureau of Labor Statistics updates fifteen indices in particular that relate to expenditures within the Medical Services sub-vertical.

These fifteen Bureau of Labor Statistics Indices are all elevated, inflect with the introduction of the mRNA vaccine, and serve to corroborate our findings in spades.

The video sequence of charts shown below in Exhibit 7c are all (save for one Civilian Labor Force index) adjusted by Medical Services Inflation as applicable, as published by the Bureau of Labor Statistics, to ensure that they reflect constant dollar comparatives. The elevation in these indices falls anywhere from 7 to 50%—and all inflect with the vaccine uptake periods shown in red on each individual chart.15

Exhibit 7c – Bureau of Labor Statistics Economic Activity Corroboration – all of the observed elevations in mortality we have cited within this article are corroborated by the primary fifteen Medical Services Indices published by the Bureau of Labor Statistics and updated quarterly or monthly. The data behind this chart progression can be found at this link.

Collectively, this is far more robust and significant analysis than is provided in a garden variety epidemiological study.

Accordingly, we now have both the mechanistic basis16 17 and the fingerprint signal (see Charts 6 and 7, a, b, and c)—evidence of the Covid vaccine cancer connection which, in any rational or ethically sound context, would constitute sufficient grounds for the withdrawal of the mRNA vaccine from the market out of precaution alone (eg. Rofecoxib (Vioxx), Merck’s COX-2 inhibitor, was withdrawn in 2004 after only about five years of post-market statistical-association). Versus the efforts linking smoking and lung cancer, we are only missing the case control and cohort studies – studies that will never be conducted inside the current political climate. This, in turn, serves as a powerful indictment of the entire vaccine industrial complex.

USA Weekly Abnormal Clinical and Lab Findings Excess Mortality – 49%

The beige line in the chart below shows the CDC Wonder Abnormal Clinical and Lab Findings deaths per week for the four years prior, as well as latest year of the pandemic. The dotted baseline is normalized from the years 2014-2022, and ends with a pronounced (normal condition) hockey stick formation in the last 33 weeks of the timeframe depicted. The excess above this dotted line reflects excess mortality of uncertain cause (ICD code R99 in particular). As depicted in Chart 8 below, to date this comprises 16,750 concealed death certificates.

As the astute analyst will notice, the peak weekly figure for Week 4 of 2024 had exceeded the old peak for Week 2 of 2023. Since this metric is also an excellent predictor of Excess Non-Covid Natural Cause Mortality (Chart 3 above), we are able to infer that the flattening from 2022 into 2023 observed in Chart 3 is due primarily to Pull-Forward Effect (PFE) and does not originate from a genuine reduction in vaccine-related deaths.

Chart 8 – Abnormal Clinical and Lab Findings Excess Mortality (USA) – stands at 48.7% elevated versus historical standard. For most of 2021 – 2024 this represented the large single component (25 to 35%) of the Excess Non-Covid Natural Cause Mortality weekly total (shown in Charts 2 and 3 above) – deaths of unknown cause or abnormal findings in younger persons. However, since the Biden Administration was held to account for the fentanyl crisis, this figure fell dramatically prior to the 2024 presidential election. The average age of this death certificate hold group is 49 years old.

Before we broach the topic of our Deviation from Trend charts, it is helpful to remember that each of these charts feeds into the Chart 2 ‘Balance Sheet’ above – some charts show increases and inflections, other charts show nothing and/or decreases. Regardless, they do not constitute stand-alone conjecture. If one adjusts the parameters of these charts, then they lose agreement with the overall Balance Sheet depicted in Chart 2 above – and suddenly we have Covid or its mRNA vaccine fictitiously curing a whole host of diseases. One cannot tweak the analytics by means of stand-alone, trivial, or rhetorical critique – because they miss this type of inconsistency.

Every disagreement one brings to the table must be accountable to fit coherently inside the entire model – or it is merely an attempt to deflect, deceive, or push an uninformed opinion, and nothing more.

Deviation from Trend (DFT) – Inflection Charts

The following Deviation from Trend (DFT) charts—shown in Charts 10, 10b, 11, 12, and 13 below (and 4c above)—are developed using the linked Deviation from Trend Plot Procedure. These are not conventional ‘trend charts.’ Instead, they are short-term analytical tools designed to detect changes in direction within the data (inflections) and to measure the strength of those signals.

The ‘trends’ shown here must be interpreted in context:

  1. The entailed trends cannot be manipulated to remove an inflection signal—a practice sometimes seen with conventional trend charts.
  2. Their directional vectors are only salient in the short to medium term. They cannot be projected a decade into the future, nor should they be regarded as forecasts.

In short, these charts reveal dynamics in the data rather than long-term statistical patterns. Over time, they will need to be retired and the data transitioned into classic trend charting methods.

What, therefore, is an inflection in professional strategic analytics (not ‘statistics’)?

A distinct, statistically significant, and sustained change in the underlying dynamics of a data series — such as its direction, rate, or variability — temporally associated with a salient date, mechanism, or event.

This principle carries far more weight than a p-value or confidence interval. Such heuristics are fragile—overly sensitive to sample definition, model choice, noise, and the researcher’s framing. The epistemic strength of a recurring inflection lies in its cross-validation across contexts. Markets, pandemics, and national systems are inherently noisy, riddled with transient fluctuations. P-values and confidence intervals may test against randomness in a single channel, but they cannot capture systemic consilience across multiple channels. A 95% confidence interval in one metric can be rendered meaningless by model misfit, autocorrelation, or flawed screening criteria. Yet when the same inflection surfaces simultaneously in trade volumes, logistics delays, consumer sentiment, and pricing indices, the probability of a genuine structural shift rises dramatically—without the crutch of brittle frequentist scaffolding.

Strategy, in turn, demands anticipation and decisive action under live conditions, not retrospective inference that waits upon the slow machinery of peer review or the safety of armchair debates. Inflection detection is therefore an operational signal—and pandemics are operational crises, not academic exercises.

Analytical basis of an inflection chart, shown in classic trend charting format.

For this reason, when conducting strategy for a nation, market, or corporation, the essential skill is the ability to recognize inflections that consistently align with underlying conditions and emerge clearly through market, seasonal, or operational noise. This discipline is far more rigorous than reliance on academic heuristics alone. Without a reliable method to detect such inflections—whether in the sell-through of an import or the performance of an entire product family—strategic decision-making is left blind and reactionary.

And this is not something taught in universities. It can only be mastered through applied accountability and hard-won experience—by crafting strategies across diverse domains and directly managing medium to large enterprises. By the end of this article, one conclusion should be unmistakable: those who approach this domain armed from the outset with mildly inductive Bayesian constructs, cohorts, p-values, or confidence intervals demonstrate little comprehension of actual pandemic or market strategy, and fail to grasp the gravity and urgency of what they are analyzing. They cloak themselves in the affectations of science while sidestepping the real work that authentic strategic practice demands.

USA Sudden Cardiac Death/Myocarditis Excess Mortality Ages 5-54 – 61.1% (14-sigma) – 110,952 Related Deaths

The blue line in the Deviation from Trend (DFT) chart below (Chart 10) shows the CDC Wonder Sudden Cardiac Death Mortality per week for the two years prior, as well as four years of the pandemic, for the age 5 to 54 bracket. The solid blue (flat line) baseline is normalized from the years 2018-2019. As do many of the charts of this type (not in unaffected ICD sub-groups however), an inflection occurs at Week 51 (13 – 19 Dec) of 2020 through Week 14 of 2021. This is the impact of the mRNA vaccine, including the early administration to medical professionals. Note that we have redacted the final 29 weeks of this chart because that timeframe contains RXX ICD codes which artificially escalate in the final weeks (see Chart 8). So for conservancy’s sake we exclude these weeks from the analysis.

One should note that the mortality measured within this chart is not mortality related to aging-related heart disease. That grouping of ICD code mortality is actually down significantly (-28.3%). Do not let anyone equivocate between aging-related heart disease and sudden cardiac deaths in younger persons. In the latter we have a pronounced problem, which can be concealed by blending it with the PFE-impacted former. We caught the CDC using this trick in November 2023.

Note that this chart does not include 290 X44 sudden cardiac deaths from casual drug use per week (cited in Chart 4c). As one can discern from Chart 4 above, this component of the deaths in Chart 10 below has reduced significantly since 2022. Therefore, across the horizon the vast majority of this mortality has come as a result of the Covid-19 mRNA vaccine.

Chart 10 – Sudden Cardiac Death/Myocarditis Mortality Ages 5-54 (USA) – as of week 19 of 2025 (must avoid the R99 and 999 periods as they are misleadingly high) stands at 61.1% elevated versus historic trend. This is an alarming 14-sigma excess. This is mitigated (concealed) by the severe Pull-Forward Effect in ICD codes for traditional heart disease (which is currently 28.3% lower than baseline because of a strong Pull-Forward Effect). When weight-averaged together, this results in the 2% to 9% elevated ‘Diseases of Heart’ ICD code group in Chart 2 above. The PFE is in essence, serving to conceal through Simpson effect the alarming excess cardiac death (shown above) in our younger age (5-54) cohorts.

It should be noted that an initial mid-2020 surge in excess deaths due to overdoses (X42-44) during the lockdown period exists inside this data (annotated for 2020). This is a death-surge which is non-Natural/non-Vaccine in its basis and can be viewed as to its arrival shape and magnitude in Chart 4c above. This 2020 surge is not trend data, hence its disregard in favor of the true arrival trend attributable to the mRNA vaccine. There exists a slight elevation in these deaths which remains on the chart, attributable to Covid-19, but the permanent surge in mortality arrived as a result of the Covid vaccine beginning in December (Week 51 – 5 days after the vaccine start) 2020. To conflate that pre-vaccine surge with the vaccine arrival data, would constitute obfuscation. Thus, by the end of the chart horizon, essentially 100% of these deaths can be attributed to the Covid vaccine, and no longer to the early arrival factors of Lockdown, fentanyl smuggling, and Covid-19 itself (as show by the 3 deescalation lines on the chart for each).

It is important to note as well that the Sudden Cardiac Death Mortality (without the excess ‘unspecified drug (X44) arrival curve depicted in Chart 10 above, was confirmed as to inflection, excess, and trend, by the Society of Actuaries in their Covid-19 Mortality Survey Report of November 2023. The full report can be accessed by clicking here.

One malady set potentially associated with the above Sudden Cardiac Death Mortality can be observed in the Thrombocytopenia chart below in Exhibit 10b. A clear inflection may be observed, which bears an inception of Week 50 of 2020, but shares this common Week 14 of 2021 inflection dynamic with hundreds of other ICD cause of death codes.

Exhibit 10b – Thrombocytopenia Mortlity (without Covid) – indicates a clear relationship between thrombocytopenia events (a reduced platelet count in the blood) and the Covid-19 Vaccine. Covid-19 itself was clearly was not the cause of this circulatory disruption signal. Like cancer, this portends ill for coming health-years of those vaccinated.

USA Cancer Excess Mortality Ages 0-54 – 44.2% (17-sigma)

The blue line in the Deviation from Trend (DFT) chart below (Chart 11) shows the CDC Wonder Excess Cancer Mortality per week for the two years prior, as well as four years of the pandemic, for the age 0 to 54 bracket. The blue baseline is normalized from the years 2014-2019, eroded by Pull-Forward Effect (PFE = dotted red baseline). As in many of the charts of this type (not in unaffected ICD sub-groups however), an inflection occurs at Week 14 of 2021. This is the impact of the mRNA vaccine. The soft increase which occurs in 2020 is dry tinder. These are individuals who died a couple weeks or months early, and as such, this data does not constitute actual trend data – unlike the data from 2021 and onward, which exhibits a strong and unqualified trend in this cancer mortality metric.

This deviation from trend has been corroborated by other financial and systems professionals replicating this same work.18 19 and through the Voya Financial Report quoted in the chart itself.

Chart 11 – Cancer Excess Mortality Ages 0-54 (USA) – stands at 44.2% elevated versus historic trend. This is a 17-sigma event. This is obfuscated (concealed) by the 2020 dry tinder surge in cancers for this age bracket, as well as the subsequent years heavily-depleted by early Covid-19 deaths (Pull-Forward Effect) in older age brackets (65+). When weight-averaged together, this results in the blended Malignant Neoplasms Excess Mortality cited in Chart 2 above. The PFE in older age brackets is in essence, serving to conceal the alarming excess cancer death rate (shown here in Chart 11) in our younger age (0-54) cohorts.

A note is added to Chart 11 above citing that Voya Financial spotted this cancer signal in terms of diagnoses in their 3rd Quarter Report of fiscal year 2024. That presentation excerpt can be found by clicking on this image. The above inflection was replicated by X-user O.S. using 2010 – 2019 data. That chart can be found by clicking here.

USA All Non-Covid Natural Causes Excess Mortality Ages 0-24 – 39.8% (9-sigma)

The blue line in the Deviation from Trend (DFT) chart below (Chart 12) shows the CDC Wonder All Non-Covid Excess Natural Cause Mortality per week for the two years prior, as well as four years of the pandemic, for the age 0 to 24 bracket. The dotted baseline is normalized from the years 2018-2019, mildly eroded by Pull-Forward Effect (PFE – does not impact younger ages as hard). As in many of the charts of this type, an inflection clearly occurs during Weeks 9 thru 28 of 2021. Just as in Chart 11 above, this too is the impact of the mRNA vaccine.

Chart 12 – All Non-Covid Natural Causes Excess Mortality Ages 0-24 (USA) – stands at 39.8% elevated versus historic trend. This is a 9-sigma event which hinges around inflection in a single week (the start of mRNA vaccines in this age bracket as highlighted in red). This is obfuscated (concealed) by the Covid-19 deaths during 2021 in older age groups; however, becomes clear when this age bracket is broken out separately. This metric is both alarming and condemning as regards the Covid-19 mRNA vaccine.

In addition, as evident from the All Natural Cause Mortality Ages 2-17 chart below, we have made a grave mistake in administering the vaccine to children. For every single life lost to Covid in the 2 to 17-year-old age group over the past five years (280 total, 270 of which occurred after the vaccine rollout, as shown by the orange curve adders), we have lost 20 lives from that same cohort to the vaccine itself—5,478 young lives in just three years.

Chart 13 – All Natural Cause Mortality Ages 2-17 – this definitively proves that the vaccine is the cause of US Excess Non-Covid Natural Cause Mortality, post Week 14 of 2021. 1 life over 5 years versus 20 lives over 3 results in a 33 to 1 death rate comparing vaccine deaths to Covid deaths in this age bracket.

About 20% of my young patients are showing evidence of increased clotting as part of the baseline lab panels. Never used to see this.

~ Steven Phillips, MD – Yale MD, Wharton Biotech Researcher, 22 Nov 2024

In similar fashion to Chart 10, we have chosen to exclude the final 25 weeks of the charts in Exhibits 12 and 13 as well. These weeks include contribution from R99 mortality, which causes an artificial inflation of deaths during this period. As we had initially observed in Chart 11 above, much of 2023 has been characterized by a return to the pre-2020 trend (red flat dotted line). We hope that this continues, but will monitor as we proceed forward.

Nonetheless, it is clear that we have a problem, and that problem is the Covid-19 mRNA vaccine.

This serves to broach the question, just how deadly have the vaccine, along with all our other panic-fueled mistakes, indeed been? We answer that question in Chart 13 below.

USA Full Covid-19 Mortality Accountability – 1,475,100 Manmade US Deaths

The chart below shows the total impact of our poor decision making as a society, in terms of total mortality, and compares that mortality to the various wars and conflicts our nation has suffered. Of key note inside this death tally are the 836,020 deaths inside the Vaccine/Sudden/Long Covid tally. 95% of this metric resulted from the impact of the mRNA vaccine itself, with the remaining 5% attributable to possibly Long Covid.

Speaking with an actuary (40 years experience) at one of the largest insurance companies in the world saying the Mortality Statistics are being dramatically underreported and the actual numbers could be 10 times higher. The number of deaths by drug overdoses, suicides, homicides, traffic accidents, aggressive cancers (including colon cancers as young as 15), blood clots, myocarditis, enlarged hearts, strokes have dramatically increased since 2022.

~ Tony Seruga, Boardmember at Greenlaw Capital
Chart 13 – Full Covid-19 Mortality Accountability – Manmade US Deaths (USA) – stands at 1,482,515 excess deaths. This is obfuscated (concealed) by the Covid-19 deaths during the whole pandemic, as well as the Pull-Forward Effect inside older age brackets. This metric is also both alarming and condemning as regards the impact of lockdowns, denial of treatment (prior to hospitalization), and finally of course, the Covid-19 mRNA vaccine.

Such is the state of things pandemic, week 25 of 2025, our 277th week of the SARS-CoV-2 Pandemic in the United States.

The Ethical Skeptic, “The State of Things Pandemic”; The Ethical Skeptic, WordPress, 25 Sep 2025; Web, https://theethicalskeptic.com/2025/06/20/the-state-of-things-pandemic/