Houston, We Have a Problem (Part 1 of 3)

Note: This article has aged well. What we detected and sounded the alarm over more than two years ago, is now confirmed as showing in the actuarial data as of Aug 2023. To wit:

More young Americans are dying – and it’s not COVID. Life insurance actuaries are reporting that many more people are dying – still – than in the years before the pandemic. And while deaths during COVID-19 had largely occurred among the old and infirm, this new wave is hitting prime-of-life people hard. There was an extreme and sudden increase in worker mortality in the fall of 2021.

No one knows precisely what is driving the phenomenon, but there is an inexplicable lack of urgency to find out.1

~ USA Today, 11 Aug 2023, Dr. Pierre Kory and Mary Beth Pfeiffer, Opinion contributors

The following work is the result of thousands of hours of dynamic data tracking and research on the part of its author. The reader should anticipate herein, a journey which will take them through the methods and metrics which serve to identify this problem, along with a deductive assessment of the candidate causal mechanisms behind it. Alternatives as to cause which include one mechanism in particular, that is embargoed from being allowed as an explanation, nor even mere mention in some forums.

At the end of this process, we will be left with one inescapable conclusion. One which threatens the very fabric and future of health policy in the US for decades to come.

Seven of the major eleven International Classification of Diseases codes tracked by the US National Center for Health Statistics exhibit stark increase trends beginning in the first week of April 2021 – featuring exceptional growth more robust than during even the Covid-19 pandemic time frame. This date of inception is no coincidence, in that it also happens to coincide with a key inflection point regarding a specific body-system intervention in most of the US population. These seven pronounced increases in mortality alarmingly persist even now.

Storm Warnings

On March 21st 2021, a longtime mentor, friend, and business partner of mine, an otherwise healthy 68 year old male, unexpectedly suffered an organ failure cascade which resulted in a shut-down of his pancreas, liver, kidneys, and finally heart. He had just received his second dose of the Pfizer vaccine on that Thursday prior. Carl quickly descended into a coma, and then died on March 26th.2

On May 29th 2021, a rather odd signal began to develop in my regular Covid-19 tracking models. The change which alerted me resided inside the magnitude of the ‘Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)’ ICD death code group (see chart in Exhibit D and also by clicking here). About this time and as a result of this observation, I began to track R00-R99 deaths, along with eleven other ICD-10 death codes, non-natural cause deaths (suicide, overdose, assault, etc.), and finally a statistic called ‘Excess Non-Covid Natural Cause Deaths’. As the reader reviews the calculated trends featured inside each of these death categorizations, they should note that this was indeed both a prescient and sound decision.

On December 1st of 2021, attending a business meeting at client’s medical complex, passing through the facility I took notice that their large oncology department waiting room was slammed full with patients. This queue of persons awaiting their oncology appointments spilled out into the hallway and finally on into the building atrium.3 While tempted at first blush to pass this off as a result of patients and their physicians ‘catching up on deferred screenings’ and/or ‘Covid-limited office days/hours effect’, my prior observational lessons suggested that I hold-off on such a knee-jerk inference, at least until the CDC – National Center for Health Statistics data (three bullet point sources below) proved out over the coming months. This as well, proved to be a wise decision.

It is not simply the probative and reliable nature of the data one has sourced, but moreover the relative dynamic in how that data changes over a significant or critical period of time, which allows the astute investigator to draw key inference.

The reader should note that there are few fancy academic heuristic tricks employed inside the models presented in this article. Rather, I’ve elected to employ good old-fashioned persistence, curiosity, hard work, logical deduction, and an experienced nose for strategy, systems, and problem-solving. Within my models, I seek to derive this inference through comparing the dynamic (not static statistics) patterns of change across a large set of differentially-compared data points and critical interval in elapsed time, in order to drive this article’s process of deduction. This is what I do professionally inside markets and for corporations and nations after all. I identify, and develop strategy to address exceptional challenges. My motivation in writing this article however is simple. I do not seek income, subscribers, power, office, notoriety, a political victory, book sales, nor a new career. I am simply compelled to stand in the gap for those who have no voice – they who lay victim to the present political hubris and its long shadow of darkness.

That being said, let us outline briefly the data sources employed in these models. All the data used within the analyses presented within this tripartite article series are derived primarily from the following three resources and links. Herein, they are collectively referred to as the MMWR (CDC Morbidity and Mortality Weekly Report) data, because these databases are updated as a part of that CDC weekly reporting process.

  1. US Center for Disease Control and Prevention: Weekly Counts of Deaths by State and Select Causes, 2014-20194
  2. US Center for Disease Control and Prevention: Weekly Provisional Counts of Deaths by State and Select Causes, 2020-20225 (please note that the term ‘provisional’ with regard to this file only impacts the first four to six weeks of this data for the most part. The taper curve can be seen here for the August 17th 2022 drop. Don’t let anyone tell you that 2021 and 2022 data is unreliable because it is provisional – if we have an emergency we must rely upon this data)
  3. US Center for Disease Control and Prevention: Wonder: Provisional Mortality Statistics, 2018 through Last Month – Query by Constraint Engine6

As a part of the process of tracking this MMWR reporting data, by October 2020 it became clear that Excess Non-Covid Natural Cause Mortality (see Exhibit E) was slightly elevated versus its historical trend, yet still conformed to annual seasonal death arrival patterns. A November 2020 chart depicting this can be observed by clicking here. Remember this rather nominal arrival form of non-Covid natural cause deaths for later on – as it is the Holmesian ‘dog that did not bark’.

Despite the fact that many maladies are not seasonal, the reality is that humans indeed are seasonal beings. We tend to die more commonly in the (northern hemisphere) winter months of December and January of each year. Such mortality trends tend to form familiar patterns across the years. These patterns and trends are therefore useful as a comparative in spotting anomalous conditions, such as pandemics. It was reasonable to assume in October of 2020 however, that this slight elevation in non-Covid mortality was indeed an outcome of the systemic damage which the SARS-CoV-2 infection and virus spike protein can produce in the human body. An erstwhile Covid delayed death if you will.

However, by MMWR Week 3 of 2022, a disruptive-exception pattern began to manifest inside this non-Covid mortality group, one which contrasted highly with the 2020 pandemic period alone (not to mention the 2014 through 2019 timeframe), and finally one which could no longer be denied (see an example chart by clicking here). Within these early charts it became clear to me that the complexion of US mortality, the who, when, and why – had changed substantially from early 2021 to the end of 2021 and on into early 2022. In fact, an inflection-point could even be estimated, establishing when this change had occurred (April 3rd – 10th, MMWR Week 14 of 2021) – a crucial date with regard to this novel mortality arrival pattern. Yes, of course people were dying of Covid-19 and as a nation we needed to continue diligent action addressing its challenge.

Nonetheless, by the end of 2021 it had become abundantly clear that US citizens were not just dying of Covid-19 to the excess, they were also now dying of something else, and at a rate which eventually became higher than that of Covid itself.

Identifying The Problem (Methodology Employed and Results Observed)

In a past article, we outlined for the reader those characteristic elements which render a problem facing a nation or corporation, as exceptional. These are the problems I call ACAN problems, or challenges which feature characteristics of Asymmetry – Complexity – Ambiguity – Novelty. As the reader will note below, the challenge with regard to Excess Non-Covid Natural Cause Mortality bears all the requisite features of an ACAN problem. Asymmetry in terms of which cancers are suddenly rising, which age groups are dying to greater numbers, or disparities between cohort vaccination rates and observed infections. Complexity in terms of the Yule-Simpson vulnerable distribution of excess deaths into their various ICD-10 codes. Ambiguity in terms of the political motivations behind official health data tracking practices and Nelsonian gaps in information. And finally, novelty in that we are facing a challenge for which our epidemiological community did not prepare, and with which mankind has never truly grappled before.

In many ways the challenge before us now, may well be as daunting as Covid-19 and the pandemic response itself. This of course all depends upon how the trends depicted inside this article pan out. In my experience, accelerated growth never continues forever, and there are always mitigating circumstances and unintended consequences which serve to confound the future. The reader should keep this in mind as they view the charts and inferences herein. We should always hold out hope in the face of a storm. This was of course as sound of an advice at the beginning of the pandemic as it is now.

For a detailed data and derivation flow chart, outlining data source, handling, and modification, to the first derivative baseline, its smoothing, series of calculations, and how the entailed risk-points are compensated for – in other words, how the charts in this article are assembled – please click on the flow chart thumbnail icon to the right.

Regardless, data is derived from sources 1 and 2 above, and the basic formula for the derivation of Non-Covid Natural Cause Mortality is straightforward, just as it sounds.

ENCNCM = All Cause Mortality – Non Natural Cause Deaths – COVID-19 (U07.1, UCoD) – Baseline Death Reference (BOY 2014 – EOY 2019)

The Problem

Exhibit A – Ten separate ICD-10 death groupings which sum to overall Excess Non-Covid Natural Cause Deaths (top chart).

The series of charts in Exhibit A to the right constitute a set of quick charts (called ‘Variation Against Trend’ or VAT charts) I maintain in my databases, and monitor each week (along with other factors such as reporting lag, Pull Forward Effect, etc.). I began to notice a potential problem beginning to coalesce with regard to many of these depicted trend lines, in late 2021 and into early in 2022. However, before anything statistically significant could be reported, the data needed sufficient time for the tail of statistical deaths from the deadly Delta variant to clear from the weekly MMWR reporting data (the three sources listed earlier). This process was delayed as well by the CDC’s ‘system upgrade’ which began June 3rd 2022 and still has not been fully completed (see pertinent CDC announcement).

As of the publishing of this article, 9,290 death records posted in the June 2nd MMWR update showed as redacted four weeks later and still remain missing from the data. Another 13,245 deaths were re-categorized by the CDC from primarily cancer and heart death, to other codes such as Alzheimer, kidney, or respiratory deaths, as can be seen in part inside this chart. It is hard to envision a scenario explaining this 52,000-record data tampering across the most at-risk weeks (MMWR Weeks 4 through 20) of 2022, as not constituting malicious obfuscation of US citizen mortality data. As a former intelligence officer and strategist for nations facing some pretty tough corruption challenges, I am a skeptic of power, and no eager subscriber to Hanlon’s Razor.

Keep in mind that the charts in this article do not even reflect addition of the CDC-shorted death records redacted for MMWR Weeks 4 through 20 of 2022.

Despite this death record data shortfall, seven of the ICD-10 VAT charts depicted to the right (click on the image to obtain a separate tab version, and click again to magnify the image) depict trends which should instill enormous concern in the mind of any professional, in terms of US citizen mortality post MMWR Week 14 of 2021. In order to comprehend why this week is of critical importance, please click on Chart 1: Critical Inflection Date in Vaccine Doses and examine Exhibit B: Arrival Comparative Between Doses and Deaths (below) – both of which will be detail outlined in Part 2 of this article series. The alignment of critical dates inside these charts is not only pivotal in our argument, but is prohibitively compelling as well.

The charts of particular concern, I have highlighted with a yellow background and listed below. These include the charts featuring stark post MMWR Week 14 2021 rises in mortality. Specifically, they are

  • Excess non-Covid natural cause, 8+ sigma
  • Cancer and lymphomas, 9+ sigma
  • Other respiratory conditions, 2 sigma
  • Nephritis/Nephrotic syndrome, 4 sigma
  • Septicemia, 2 sigma
  • Heart diseases and ailments, 2 sigma
  • All other ICD-10 tracked natural cause deaths, 7+ sigma (see Exhibit A2 below)
Exhibit A2 – a significant departure in the balance between the Big 11 ICD underlying causes of death and all other ICD codes occurred in the spring of 2021, just as the vaccine rollout gained its initial tip-in. Around 2,700 excess deaths are squirreled away inside this array of ICD underlying death causes each week. This equates to around 45% of the Excess Non-Covid Natural Cause Mortality shown in Exhibit E later in this article.

With regard to these select ICD-10 codes, I have endeavored to highlight only those which have exhibited a stark difference between their arrival patterns during the 2020 pandemic period, and that period after MMWR Week 14 2021. While there are indeed increases in deaths incumbent inside the other ICD-10 codes, those increases appeared to plausibly conform to their same arrival patterns for 2020 as well. In other words, they appeared to be heavily Covid-related in their dynamics, both before and after the Week 14 2021 inflection.

Of particular concern, are those deaths which relate to body-wide regulatory systems as opposed to specific organs or causes. In other words, cancer and lymphomas, heart, autonomous myocarditis/pericarditis/conductive disorders, injuries to the liver and kidneys, etc. These are not only the canaries in the coal mine in terms of pathology, but may serve to indicate as well that a pervasive systemic disruption is at play inside the average US citizen human physiology, especially over the last 71 weeks. These are the death groups which exhibit the most stark trend of increase post MMWR Week 14 2021. I sincerely wish to be wrong in this, and would be the happiest person on Earth if I found a critical flaw in the underlying data or methodology which served to refute it all. Unfortunately, after months of challenging my own work from every angle I could conceive, and patiently waiting for the CDC/NCHS to fix their MMWR reporting systems and processes, I sadly fear that I am not wrong. Hence the need for this article.

As challenging as the excess mortality and VAT charts are, before we examine three particular sets of excess mortality, let us for a moment also review the compelling rationale behind the MMWR Week 14 2021 inflection date. This date is a critical matter of concern for no small reason. Its derivation is no coincidence. The ‘Doses and Deaths Comparison Chart’, Exhibit B below, outlines why.

Exhibit B – The MMWR Week 14 2021 inflection date also happens to correspond to the fastest velocity in administered vaccine doses inside the US population. The red line is Excess Non-Covid Natural Cause Mortality, extracted from the data behind Exhibit E below.

The Inflection Signal in Three Charts

Three charts in particular compel the greatest concern in terms of their being indicative of population-wide systemic health disruption. They are Excess Malignant neoplasm and lymphoma deaths (C00-C97 – Exhibit C), Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified deaths (R00-R99 – Exhibit D), and finally yet most importantly, Excess Non-Covid Natural Cause Deaths (Exhibit E). Those three charts’ ICD-10 trends against historic baseline, along with three corroborating additional charts for Cancer Mortality (Exhibits C-top, C2, and C3), are depicted below. Please note that we are evaluating the trend in the peak level of the R00-R99 data in Exhibit D, and not the fact that this ICD code acts as a disposition-depleting bucket (hence the normal stark rise in later weeks to the right hand side of the chart). I will leave these three charts here, for your examination and consideration, before venturing into Part 2 of of this article series – wherein we conjecture regarding the potential cause(s) of this undeniable problem in terms of US citizen health and mortality.

Exhibit C – C00-97 Malignant Neoplasms (top – CDC Wonder ‘Deviation from Trend’) (bottom – NCHS ‘Actuals vs 2014-2019 Normalized Baseline’) – Cancers and lymphomas have risen to a 9+ sigma (+4.9%) level since MMWR Week 14 2021. Both the MMWR weekly and Wonder monthly data sets are shown in the two images above – and both data sets agree on a 4.9% current excess death rate. This condition did not exist during the 2020 Covid pandemic period. See PFE Footnote7 Of key note in the lower image, is the commensurate rise in Cancer Treatment subsector PPI Expenditures (constant dollars) of 9.9% for this same time period. That chart and its data sources may be observed by clicking here.

Please note that the pull forward effect (PFE) for malignant neoplasms is conservatively substantiated through a detailed analysis of all 15 cancer ICD-10 sub-sub codes maintained in Source #3 at the beginning of this article. That analysis can be seen by clicking here. An example chart, indicating an alarming cancer signal in one of the 15 ICD-10 sub-sub codes is shown as Exhibit C2 below. As one can plainly see, neither the lag calculation, nor the pull forward effect cause this alarming signal. In fact, the lag calculation actually offsets the rate of trend growth in 10 of the 15 ICD-10 sub-sub codes in the analysis (see the ‘click here‘ chart linked above in this paragraph).

Exhibit C2 – C76-C80 (Ill-defined or Secondary Site) Malignant Neoplasms Mortality indicate signals of both an increase in magnitude as well as entropy (Increase in off-season cancers, as well as a rise in less common and secondary site cancers).
Exhibit C3 – Malignant neoplasms deviation from trend in age 0 – 54 age brackets through Week 7 of 2023. Take the early increases with a grain of salt, as those deaths constituted early capture of persons dying from cancer (dry tinder). That is a discrete and artificial peak, not a trend basis (hence the sudden rise in Feb 2020). What is significant is the robust trend which emerges once this dry tinder effect is no longer in play. This is a 12-sigma run in increase in younger person cancers. No, the stories people are telling across US society are not ‘mere anecdote’.

A Note on the Shoddy State of US Academia Ethics

Accordingly, any doubt that we have a problem with cancer, has been dispelled in spades. Cancer is a hard ship to turn; however once turned, does not recover to baseline for a decade or more. The charts in Exhibits C2 and C3 above do not even include pull forward effect, thus one cannot foist the claim that baseline adjustments are causing this increase. Nonetheless, failure to utilize pull forward effect inside this type of broader analysis (as is the habit of PhD statisticians who have never really done consumer products demand erosion analysis) is an indicator of their incompetence and/or maliciousness.

To wit, an academic who conducts debunking work for the pharmaceutical industry tried to coerce me (via a couple confrontational emails) into handing over all my thousands of hours of work to him, under the threat that otherwise he would publish a hit piece on this article. A hack job which he had already prepared, and which indeed he released the very next day after I refused to be threatened (obviously, there was no objective intent in his method). This ‘statistics’ (no complex systems, ACAN problem investigation, nor real world experience) professor insisted that I publish a flawed version of Exhibit C according to the raw data the CDC released after the June 3rd ‘system upgrade’ regarding malignant neoplasms and lymphomas. An approach which made cancer appear as if it was abating, not rising (because of the easily documented 18 weeks of delayed state reporting). I refused to do that as well, incurring the ire of both him and his malicious trolls.

The ridiculous chart they insisted that I publish, what I call the ‘Everything is Awesome’ graph, can be seen by clicking here. It is not that I do not possess the statistics skill to produce a graph like this, rather that my ethics prevent me from performing such shoddy and malinformative work. I have employed hundreds of scientists and engineers in my firms over the decades and fired more than a couple dozen. I know how the bad ones work – and they end up back in academia, where their cronies can create a club of correctness for them. This type of exclusion bias graph and the related malicious activity constitute what this cabal calls ‘Covid Science’. Such human rights criminal activity exemplifies why the public is very angry right now. That club failed, and a lot of Americans died because of academic spoiled brattitude, incompetence, and critical-decision-criteria obfuscation just like this.

Please note as well, that time has shown my analysis of Malignant Neoplasms to be correct, and his to constitute malinformation. The current excess cancer mortality as of 18 March 2023 is undeniable (see Exhibit C). When academics meet real world practitioners, who have done this kind of work for nations, for decades, they often get their butts handed to them. What should happen, is they be fired. Unfortunately, academia rarely features such a tool of accountability. Instead, they offer a pretend accountability called ‘peer review’, which is in reality merely a form of club gatekeeping – a practice less employed in the real world (precisely to elicit challenging ideas and avoid costly groupthink).

Next, we survey the dilation and abuse of the abnormal clinical and lab findings mortality code, which is being used as a repository to conceal cancer and sudden adult deaths. This beige curve’s increase in height indicates a problem, and as well its fatness indicates that the CDC is not assigning its records to their final disposition. Thus, cancer deaths are likely higher than even my chart shows.

Exhibit D – The temporary holding bucket for this category of hard-to-determine, abnormal clinical findings, and odd deaths exhibits a stark rise in its weekly peak (65% rise from Dec 2020 through 23 April 2022). Notice the date of commencement in this rise. It coincides with a critical start date depicted in our next article. The reader should note that 39,000 deaths in this ICD hold-code have not been allocated to their final ICD-10 disposition (the fatness of the beige curve as compared to the orange index reference) – very likely resulting in depressed myocarditis, pericardits, and conductive death counts for 2022. The fattening and rounding of this curve in the latter weeks on this chart indicate that the bucket is not being maintained by the CDC/NCHS as it has been in the past. This constitutes obfuscation of critical data during a period of extreme risk, a period which demands clear health and mortality intelligence.

The average age of these deaths? 49 years old for 2022, as opposed to the historical average of 82 for both Covid-19 and R00-R99 deaths, for 2019 and earlier. This clump of younger person deaths concealed by the CDC can be seen by clicking this 2022 to 2019 comparative chart.

This defacto concealment of 39,000 death records (inside the R00-R99 code group), is independent of the 22,535 records which were removed from the June 2nd 2022 death data and have either yet to be placed back into the database or were reassigned to non-threatening ICD codes.

That makes for a total of 61,500 potential myocarditis, cancer, pericarditis, conductive, nephrosis, liver, and/or lymphoma deaths which still have not even yet posted into the data over which this article is sounding the alarm.

That is 8.2% of the total deaths for the period in question, and possibly 15 to 25% of these highly concerning death ICD-10 groups’ trend data – missing. Even absent this data however, the entailed trends are alarming.

Finally, we end with the most important chart of all – the chart which indicates deaths which are not from accidents, suicide, addiction, assault, abuse, despair, disruption, nor Covid-19. The Excess Non-Covid Natural Cause Mortality chart which we began monitoring on May 29th 2021. What I called then, the ‘What the hell is this?’ chart. As one can see, we have lost 574,600 younger (average age of 49) Americans to something besides Covid and non-natural death, during the period from 3 April 2021 to 18 March 2023. The current rate of mortality in this ICD categorization, is around 5,000 – 6,700 per week (the database shows a most recent five-week, weekly average of 6,700 deaths – subject to lag of course) – which exceeds most weeks of the Covid pandemic itself (save for the absolute peak periods).

By now, if all these mortality excesses were indeed a holdover from Covid-19 itself, they should have already begun to tail off. Unfortunately they are not only not tailing off, in many cases they are still increasing.

Exhibit E – Excess Non-Covid Natural Cause Deaths are at an all time high as of MMWR Week 11 of 2023. 574,600 US citizens have died of some additional factor since MMWR Week 14 of 2021. The current rate of excess mortality represents a five week average of 8 sigma in excess (hedging conservatively for lag). See PFE Footnote8

Accordingly, and without a shadow of a doubt, we have established that right now there exists a problem in terms of US citizen health and mortality. One which is differentiated from Covid-19 itself, and began in earnest MMWR Week 14 of 2021. Our next task, and what will be outlaid in Parts 2 and 3 of this article series, is to employ these and other observed arrival distributions to winnow out the causal mechanism(s) behind this concerning trend in US mortality.

Having made significant progress on the second and third article already, we very much look forward to publishing for the reader, our next article in the series, ‘Houston, The CDC Has a Problem (Part 2 of 3)‘.

The Ethical Skeptic, “Houston, We Have a Problem (Part 1 of 3)”; The Ethical Skeptic, WordPress, 20 Aug 2022; Web, https://theethicalskeptic.com/?p=67865

  1. Dr. Pierre Kory and Mary Beth Pfeiffer, USA Today: Opinion contributors, 11 Aug 2023; https://www.usatoday.com/story/opinion/2023/08/11/more-americans-dying-than-before-pandemic-covid-deaths/70542423007/
  2. Twitter: @ethicalskeptic; https://twitter.com/EthicalSkeptic/status/1375586484372303876?s=20&t=SVUOHi_jCHYLNDMTVcJEGw
  3. Twitter: @ethicalskeptic; https://twitter.com/EthicalSkeptic/status/1466257480275701763?s=20&t=SVUOHi_jCHYLNDMTVcJEGw
  4. US Center for Disease Control and Prevention: Weekly Counts of Deaths by State and Select Causes, 2014-2019; https://data.cdc.gov/NCHS/Weekly-Counts-of-Deaths-by-State-and-Select-Causes/3yf8-kanr
  5. US Center for Disease Control and Prevention: Weekly Provisional Counts of Deaths by State and Select Causes, 2020-2022; https://data.cdc.gov/NCHS/Weekly-Provisional-Counts-of-Deaths-by-State-and-S/muzy-jte6
  6. US Center for Disease Control and Prevention: Wonder: Provisional Mortality Statistics, 2018 through Last Month; https://wonder.cdc.gov/controller/datarequest/D176;jsessionid=5E04864E989106BB6376CAC90A74
  7. Please note that the Pull Forward Effect (PFE) is initiated at a single point in time (Week 14 2021), in order to make it clear what is happening in the calculations. This does not affect the eventual number of excess deaths, nor the argument being made. One can observe the Pull Forward Effect manifesting in the beige line immediately preceding this date – and observe as well that the PFE assumed inside our model is far less than its reality at that time. The PFE tapers to zero across the succeeding 130 weeks after its date of inception – so as time winds on, it becomes less and less of the overall excess death calculation. Its principle is contained in the following thought experiment. If you take 100 people of all ages, and in one year, kill off the 20 oldest persons in that group, the intrinsic rate of death for that group will be lower for the next decade, versus the previous one, because the available population of those who are more likely to die, has been depleted for the short term. This risk in conjecture is cited and evaluated for conservancy in the flowchart earlier in the first section of this article. As well, please note that PFE is a minority of the argument by the middle of 2022.
  8. Please note that the Pull Forward Effect (PFE) is initiated at a single point in time (Week 14 2021), in order to make it clear what is happening in the calculations. This does not affect the eventual number of excess deaths, nor the argument being made. One can observe the Pull Forward Effect manifesting in the beige line immediately preceding this date – and observe as well that the PFE assumed inside our model is far less than its reality at that time. The PFE tapers to zero across the succeeding 130 weeks after its date of inception – so as time winds on, it becomes less and less of the overall excess death calculation. Its principle is contained in the following thought experiment. If you take 100 people of all ages, and in one year, kill off the 20 oldest persons in that group, the intrinsic rate of death for that group will be lower for the next decade, versus the previous one, because the available population of those who are more likely to die, has been depleted for the short term. This risk in conjecture is cited and evaluated for conservancy in the flowchart earlier in the first section of this article. As well, please note that PFE is a minority of the argument by the middle of 2022.
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New gun owners were up 5 million from 2020-2022; so were homicides and suicides from 20-45. Fentanyl deaths are through the roof, also targeting this exact age group. And traffic accidents also went up in that time period, although they leveled off a bit in 2022, but the jump was significant in 2020 and 2021. These are where your “missing” deaths are.


Concerned Citizen Your logic, common sense and reasoning explain the past 3 years and you’ve delved into the future based on your investigations. Agreed by October ’23 we will have increased morbidity due to the vaccine. Overlooked is what comes next is yet to be put forth by the virus. To date, the virus has outcompeted the host will infectiousness. With ‘immune imprinting’ or sometimes referred as original antigenic sin increasing, yes, we will see more deaths. This ‘triggers’ all causes. Only ‘hurdle’ left for the virus now is Virulence. Biologically, the equilibrium between host and virus is ‘out of… Read more »

Just Me

In exhibit C2 your data shows excess deaths spiking upward in early 2019 and remains higher up to the detection of Covid-19. In Italy there was a study for something else, where they collected blood from September 2019 – March 2020 across Italy. They retrospectively went back and looked for antibodies for Covid. The earliest detection was September 3 2019 and in September 11.5% of people had antibodies to Covid-19. So in September of 2019 it was geographically dispersed and fairly well building up across Italy. Makes all those early to summer unconfirmed flu cases look suspicious. The difference between… Read more »

Tom Welsh

A propos Hanlon’s Razor, I agree that it is attractive but often can be misleading. Accordingly I suggest “Nolnah’s Rozar”: “Never attribute to stupidity that which can be adequately explained by malice”.

A person with the handle Pig Hogger suggested on Slashdot some time ago that “Any sufficiently advanced incompetence is indistinguishable from malice”.

To which Charles T. Rubin has added: “Any sufficiently advanced benevolence may be indistinguishable from malevolence”.

David AuBuchon

ANALYSIS REQUEST: So I saw your chart on vax rate versus covid death rate, by county, for both 2020 and 2021. Could you do some more county by county analysis, but instead of covid death rates, look at excess death? (and don’t use 2020 or beyond in any baselines) I say this because if a positive association between vax rate and excess deaths shows up there, then that would be very hard to dismiss, given the negative association you already showed for covid death rate One would expect healthier counties (higher vax rate) to have lower excess death, unless vaccines… Read more »

David AuBuchon

It seems excess death calculations may be fairly sensitive to how baselines are defined:
Things to think about when defining and adjusting baselines:
Is the baseline period long enough? Population size, gender, or age distributions change over time? Were any unusual events present in a particular year (covid)? How similar were the flu seasons?, etc?

David AuBuchon

Much gratitude!

David AuBuchon

Don’t forget to do the same for 2020 excess death curve (i.e. if negative association shows up in 2020 then there is no denying 2021 was the year of the problem intervention.)

David AuBuchon

Apologies if you already did that and just didn’t put labels. Not sure what denominator compression refers to. No need to explain here, as I am sure you will in an article.


Hey TES, check out this post, anecdotal but relevant https://unglossed.substack.com/p/would-die-for-pseudo-u


Anti-vaxxers knew before the rollout that this gene therapy shot would decimate humanity, but we didn’t expect the shedding phenomenon. Unfortunately, I personally know of 5 unvaccinated women who had full-term stillborns this spring/summer in Northeast Ohio. My son was spared but his placenta had two abnormalities. (7th baby, first time having a problem). My midwife told me that it’s rare to see a normal placenta in 2022.

Thanks for your work. You add a lot of credibility to the “conspiracy theorist”.


Is it possible the ‘saline’ effect is shots that ‘missed’ the arm but recorded anyway

[…] the Ethical Skeptic, cited by many already. His work is available on twitter and Substack.  The Ethical Skeptic August 20, 2022 Part 1 His anonymity means his work cannot be […]

Frank Truth Warrior

Excellent work. A consideration people tend to overlook is that anything that causes health issues can also contribute to mental issues that can contribute to suicides, criminal activities, misadventure, and very ill-advised actions that feed back into such issues.

Indeed, the cumulative excess deaths from a defined beginning point are most important, because, starkly stated, people cannot die twice. I would look to a point ahead of COVID at which the prec3eding death rates had been near the longer-term averages.


Can you post the list of non R0-R99 codes you used? Some of us have access to massive medicare and private claims datasets.


Thanks for the great work. In my view your methology of data processing is 100% valid although I cannot judge how conservative the resulting numbers are. Its always difficult to work with differential data but looking at the graphs prior to 2020 verifies your approach. One thought that is driving me crazy for a year now: if there is a systematic problem with the vaccines looking at the death data (which is the only data we have in germany) might only show the tip of the iceberg. Most people might just be injured to some degree which will not show… Read more »


I believe I saw one German health plan that put out numbers based on their claims for health care encounters that showed a major increase in visits that they were able to relate to vaccine complications. The extrapolation they did raised the specter of a very large number of vaccine injuries across the country since they were only using their own members.

T-Bone SteakEnjoyer

Holy crap. It’s worse than I thought already. Some of the early studies (now largely scrubbed from the Internet) on mRNA “vaccines” showed that the animals did not start dying en masse until two years or so after the injections. January, 2023 will be two years since the first Facebook boomers started lining up for the Fauxi miracle science injections; convenient that he’s ducking out in December. I’m hoping against hope that a lot of people received placebos, but anecdotally, it’s not looking that way; lots of people are dead or dying before my very eyes. We need to steel… Read more »


I’m from India and live near heart hospital and i have never ever seen the parking lot this full, its overflowing. In Asia, its happening a lot faster but no one to track as being third world, and pleasure centric society we just move onto next news, also we are physically more weaker and medical facilities are very bad. Remember, India is 1.3 billion or entire population of North America and Europe COMBINED. The PTB are smart to make sure people don’t pay attention and are diverting the central agenda to some other BIG POLITICAL NEWS. Its over here…As people… Read more »


Could you please look into the data regarding what happens after multiple unit blood transfusions are given, especially since the vaccinated can donate blood? Given the fact that the LNP’s like to hunker down in the bone marrow, and that is where our red and white blood cells are made, I think you might find some alarming/interesting data just by pulling how many units a person received and then seeing what happens to them. We know the S protein hinders the hemoglobin from carrying oxygen to the organs. I think you will find the more units a person receives, the… Read more »


Is there any way to pull up data on units transfused? This data is good, but just shows an increase in circulatory/bleeding disorders and not necessarily if the units transfused are part of the problem. So yes, the jabs could be a cause or contributor, but not the ONLY one……the units could exacerbate things.

I don’t think the CDC tracks units given……and you would need that to see what happens and if those units are a problem.


Hi, I would like to join this discussion to acquire more information regarding blood transfusions. I’m aware of the fact that vaccinated can donate blood. Now I’m on the other end as I receive donated blood every month, due to an immune system condition. This means that I am not able to produce enough white blood cells. Is there a correlation between people that receive white blood cell transfusions from people that have been vaccinated and people dying, the more units they receive? I’ve been reading your graph but I don’t quite get it. Does it mean that there is… Read more »


Have you looked at amputation statistics for 2021? Lower-limb ischemia can result in amputations of the feet. Now let’s look at US mortality for 2021. There’s a key puzzle piece overlooked by most statisticians. 2021 US mortality for 85+ y.o. declined 14%. Declined! And the 75-84 y.o. group also showed a mortality decline of about 2.8%. Despite those groups showing a declining mortality, deaths among the Big Three–heart disease, cancer, and stroke–were level from 2020. So most of the Big Three deaths had to come from those below 75 y.o. Which is very odd, since the Big Three are elderly… Read more »


Perhaps death rates for the elderly declined due to lock downs, and thus not getting deadly medical care. Our medical system is the 3rd largest cause of death in this country: https://www.hopkinsmedicine.org/news/media/releases/study_suggests_medical_errors_now_third_leading_cause_of_death_in_the_us


Seen this?

Exhaustive study of German mortality data finds excess deaths tightly correlated with mass vaccination (eugyppius.com)

Two points. 1) You’ll notice the dates; enough said. 2) 6% drop in age 0-14 stat for 2020. Lockdown as knockdown for accidents, understandable. Curious if SIDS saw any fluctuation. Do you know?


First part above was coincident to your posting yesterday, so OBE here.

However, large favorable delta in 0-14. Did SIDS change at all?


We were looking at the SIDS numbers earlier this year, the data showed that during the height of the scamdemic, when people were not able to go to the hospital for other than emergencies, targeted childhood vax children missed those jabs and the SIDS rate plummeted.


What do you all think of how Pfizer bought out two different companies that specialize in autoimmune conditions and cancer treatment. They purchased these companies six months apart. One in mid 2021 and one at the end of 2021. Unbelievable. They know what the hell they’ve done. And unfortunately these people that got the shot signed away ANY right they had if something horrible does happen to them. Unfortunately 2 of my coworkers got breast cancer after their shots. One had lymph node surgery and the other is having her breasts removed and redone. She had no genetic precursors to… Read more »

K. Mitchell

HEY! You should make it easier to subscribe. The button is all the way at bottom and was a bit fubared, too.

Good analysis – can’t wait for the next installment.


Great work. I did a small quick model using a Gaussian Distribution over 9 years to see what the numbers would be each year for “events” given 80% of the total population vaccinated had active ingredients and would develop events similar to past other mRNA failed trials. 7,887 deaths per week is about 179% above my model for 2022. If the past is indeed playing out here, this excess deaths per week will increase by a factor of 9 over the next year. Most adverse events in past similar trials did not take place until year three. By year six,… Read more »


You may be interested in Mathew Crawford’s theory of wealth as a confounder to excess mortality analyses. https://roundingtheearth.substack.com/p/reanalysis-of-the-society-of-actuaries


I sent you an email asking for some info about this….I am an actuary working within the life insurance industry….wondering if the audience could be expanded or more info on how to join that meeting.


Thank you for the work you do! Just read a blog post from Jeffrey Morris on your analysis, and I hope you will address his counter arguments – hard for the less statistically literate of us to know what to believe… :)


Skimmed his presentation and summary, plus comments. Interesting comment that he made based upon his experience that cancer is a slow phenomenon. The news of late (this past year) is sporadic reports of insanely aggressive cancer due in theory to suppressed immune function. Granted, this is anecdotal, but life IS anecdotal while the data is being collected for later publication years later to possibly validate one’s senses. Individual action cannot wait for institutional consensus. Life doesn’t happen that way, except it is expected to be the new norm with covid+. If immune suppression is indeed in play, and if such… Read more »


I just learned that my brother got jabbed and then got a blood clot in his brain. He survived, but I am not sure he is capable of seeing the link. Oh well…

John Day

Muchos Gracias, Senor Skeptic…
I’m sorry to be so late commenting and “liking” this extremely useful post.
You were featured on The Automatic Earth when you released this, and I’ve posted your work there before, having discovered you through another commenter there.
You have the count, I’m sure, but you are reaching some people, people who are paying attention.

Mark L

I very much appreciated your Twitter posts when you were documenting the slow-motion trainwreck while it was happening. I got kicked off of Twitter and haven’t followed you as closely for a while, but the Bad Cat linked to this post. Your work has been and will continue to be of incalculable benefit to a world that has been subjected to an unprecedented coordinated assault. Your explanation here is clear and concise, and I look forward to future posts in the series.

Peter Gilbert

An overall increase in deaths from “natural” causes was predicted by numerous researchers; some see this as an inevitable result of the free-floating spike proteins introduced into the body by the so-called mRNA “vaccines.” One of these researchers is Dr. Shankara Chetty. In an interview last November, he said the following: [I]n Covid illness the pathogen is spike protein. And spike protein is what the vaccine is meant to make in your body. So, if I had to give you my opinion, as to what is happening on a global scale… spike protein is one of the most contrived toxins… Read more »


Do you have historical files for the R00-R99 data? Or just data that you downloaded at different times.

Jennifer Depew

Synchronicity – I just finished a post I was working on titled “Houston we have a problem”. It is about a possible solution for ME/CFS which is seen in LongCovid and is likely similarly based in retinoid dysfunction due to viral/immune challenge. It effects liver enzymes permanently is the gist of the theory which overactivate vitamin A and carotenoids to Retinoic Acid for ever after. The fix is to stop eating those food sources fairly strictly, not easy, but it works. Or makes the big needed difference. My post – Houston, we have a problem. – by Jennifer Depew, R.D.… Read more »



Very interesting analysis, perhaps you could look into this and see if you can tease a confirmation out of the CDC data?


Thank you for your great work and for saving lives by informing people. Reading this with a broken heart and unbearable sadness as we lost our beloved brother to aggressive t-cell lymphoma. He went through five months of nightmarish treatments before his death. He took the Moderna shots last year and had facial numbness and swollen lymph nodes after the 1st shot. I begged him not to get the 2nd shot, but his doctor gave him steroids and told him to get the 2nd! The same doctors misdiagnosed many symptoms he had during 2021 until he was finally diagnosed by… Read more »


Thank you. You may find this interesting. This is a conversation between oncologist Vinay Prada (@VPrasadMDMPH) and Dr. Bita Fakhri of UCSF. She explains how she wanted to join the lymphoma group at the hospital, but they did not have enough patients to justify another doctor. But now, they are getting so many referrals that they need more doctors. They both wonder what could have been the cause for the rise in patients, agent orange or something; they speculate laughingly! @ 42:56:


Anecdotally, my father has a tumor in his lung. It was diagnosed a few years ago and has been monitored with annual scans, never exhibiting any growth or changes. Dad got vaxxed and boosted with mRNA. Last month, his last scan showed that the tumor has grown. Theoretically, it might be unrelated to his vaccination, simply a coincidence.


We got my daughter her first \/ in anticipation of her visiting family (including immune compromised grandparents) in the summer of 2021. The very next day I started seeing the word “myocarditis” in relation to the \/. She never got her second, and will not if I have anything to say about it. Thank you for all you have done to bring these figures to the light.


How about surveying the non injected for comparison?


Thank you so much for doing this work.


TES – Do you have any thoughts about the persistent spikes across the various causes in 2018?


It appears that the CDC has not redacted the 2017-2018 Flu report. The 2017-2018 influenza season was a high severity season with high levels of outpatient clinic and emergency department visits for influenza-like illness (ILI), high influenza-related hospitalization rates, and elevated and geographically widespread influenza activity for an extended period. and Flu vaccine is produced by private manufacturers, so supply depends on manufacturers. For the 2017-2018 season, manufacturers originally projected they would provide between 151 million and 166 million doses of injectable vaccine for the U.S. market. As of February 23, 2018, manufacturers reported having shipped approximately 155.3 million doses of flu… Read more »


I’ve been waiting for this post since you started posting the MMWR data many months ago. I’m holding myself back from refreshing every 5 mins until part 2 is posted. ; ) I’m afraid that similarly to the data pointing to 2018 covid seeding parts of the world pre-Wuhan, this too will be conveniently ignored.

I’ll just reread your Raiders article in the meantime to get myself ready for more horrible NE PI calls prolonging game changing drives.


How does “about 5,000 younger Americans” dying per week from natural causes, excluding C-19, compare to pre-COVID? Or are you saying the average has increased by 5,000?


Jeazhus fucking christ. I have had a gut feeling this is going on and keep finding bits and pieces of info here and there but your article really puts it all together. Glad I didn’t get the shots.

Robert Dyson

Worse than I thought. Thanks for this analysis.

The Watchman

Good article will be linking today @https://nothingnewunderthesun2016.com/


Very thought provoking. I am an actuary with 40+ years of experience, including some on mortality assumption setting. I am somewhat versed with WONDER and other CDC data. I consider myself “skeptical” — for example I have not submitted to the mRNA gene therapy being called “vaccines”. It is hard to ignore or disagree with a couple things of anomalies that you have found: the neoplasm cause of death in the past year does seem very high, and the CDC system upgrade data issues, in my view can’t be explained by Hanson’s Razor. However, let me poke at your article… Read more »


Hello. Fascinating work. I have a question : chart exhibit C “malignant neoplasms”.

Data :

Only 2 weeks (W2 2022, and W49 2021) have a total over 12 000.

But on the chart, it’s going over 12 000 after week 26 2022.

What am I reading wrong ?


OK, I apologize. I didn’t understand that you were applying a model (“distribued lag model”, right ?) on top of the “provisional” data for the last 14 weeks. So if I understand, because you follow those weekly reports since long time, you have noticed that the National Center for Health Statistics “revise” (upward) its data, systematically. Hence, your model in order to quantify what the outcome of those revisions will be. Am i correct ? I am not a all a “math” person, so I need time to understand. ;-) Perhaps it would be a good idea to highlight this… Read more »

Doug Thorburn

Anyone who isn’t saying, “Oh, shit,” is not paying attention.


As a former data crunching fed who witnessed firsthand the catastrophic lies of the 2000s, I thank you very much for all your analysis. Beginning in May 2020, I suspected that what we are experiencing is far worse than anything the 2000s gave us, and might be on par with 20th century totalitarians. Yikes…


Appreciate your sustained determination. Willing to help with data science, statistical methods, automation pipeline, strategizing next steps, etc. Hit me up if you’d like to chat. Thanks