Three species of sycophancy relate to the loose Shakespearean adage, ‘Methinks he doth protest too much’. When sycophancy becomes the basis of rationale for action, large scale disasters are the result. The net cost of fervent wokeism and social skepticism during the Covid-19 Pandemic? 580,000 young citizen lives and rising fast.
The future death tally from such maliciously-motivated thinking could stand to be on the order of 20 million lives or more – a divergent function which is very difficult to predict, save for its inordinately large reality.
Most American citizens today perceive that there exists in our society pernicious forms of socialized religion, politics, and science. Indeed the purpose of this site, The Ethical Skeptic, has always been to point out both the flaws in thinking and know-them-by-their-fruits handiwork of such social skeptics. Our mission has been to help prepare minds, and equip them in spotting the darkened philosophies of those who pretend to represent rationality, critical thinking, virtue, and science itself.
Theirs has always been an intolerant club of royalty-wannbe self-appointed elites, seeking to impress their way to membership or status therein. How best to achieve such lofty status? Become the most uber of the uber themselves. As Admiral William F (Bull) Halsey is known to have quipped regarding war, “Hit hard, hit fast, and hit often.” Indeed, such poseurs view their conquest of society as a form of warfare – with their only option to impress, to escalate therein. One of my favorite comedy artists elicits this in her monologues.
You know it’s funny the difference between joining a conservative group and a liberal one. When you approach a group of conservatives and say “I’d like to join, I’m a conservative as well”, their response will be “Cool, welcome.” When you approach a group of liberals and say “I’d like to join the effort. I’m as liberal as they come”, their response will be more along the lines of “We’ll see about that.”
~ Taylor Tomlinson, Comedienne
Three types of sycophancy relate to the adage “The lady doth protest toomuch, methinks.” from William Shakespeare’s play, Hamlet. This familiar phrase often refers to a person who overacts inside very visible matters of virtue, in order to enhance or exonerate their personal position inside that group, or to allay a secret doubt on their or other members’ behalf.
These three species of deceptive behavior serve to contrast as much as anything the distinction between the opposites, ethics and virtue.
Religious Sycophancy – embodied in a principle called Neuhaus’s Law (Richard Neuhaus, Christian cleric and Catholic priest)1 : Where orthodoxy is optional, orthodoxy will sooner or later be proscribed. In other words, setting faithfulness aside, ‘God favors the edgy and self-perceived artiste’. Such thinking devolves into a kind of extremist cult inside a cult, a religion of negative reactance. The club of edgy atheists is an example of such religious sycophancy. One does not have to intellectually believe in God, in order to effect the practice of such philosophy inside their life.
Political Sycophancy – embodied in the brainless one-upsmanship of modern progressive liberalism, wherein each player perceives themself to reside in a contest to see who can out-woke all the others. Their habitual defense often involves accusations of straw man, bucket characterization of opponents, and/or ‘racism’.2 The net result of this is a kind of irrational and irreversible fervency on the part of people who seek social control.
Science Sycophancy – embodied by social skeptics : Social skepticism is kabuki, the activist-minded abuse of science by means of its underlying philosophical vulnerability, skepticism. An imperious agency which has politicized and enslaved science through teaching weaponized fake skepticism to useful idiots. The social skeptic is a ‘communicator’ who seeks to squelch scientific dissent, as well as foment and exploit enmity between the lay public and science.3
Such errant thinking rules our universities, media, and politics – much to the injury of the majority of American citizens.
The Cost of Sycophancy
The cost of sycophancy usually involves suffering on the part of the majority of the population, and the enrichment of a very small membership of Royalty, those who profit from the mantle of the fake virtue they have pushed. One should take note that the symbolic group behind such virtue, never actually benefits.4 As often turns out, the cost of this kind of irrationality exploits or functions critically around a principle of human foible I call quod fieri.
quod fieri – (Latin: (lit.) ‘(the fact) that (now a specific thing is) to be done’) – a form of intervention bias action, in which the action is not taken from sound evidence or a history of effectiveness, but rather simply because something must be done. This type of decision or action usually is executed in a panic situation, in the face of a slow moving disaster, or a theater of cataclysmic mirage. Ironically its feckless or inane basis is compensated for, by a religious, political, or social fanaticism as to its claimed (but usually false) effectiveness. Those who raise questions regarding the action are typically cast as deplorable and anti-virtue.
The tyranny of the sycophant forces decision makers into taking symbolic or virtuous action before all factors are known and before the question is even defined. This bears the greater chance of resulting in disaster.
Even worse than The Peter Principle, when you hire and promote only persons who look correct to sycophancy, you inherit their lack of skill, their shortfalls in integrity and experience, and their propensity to hire only persons who are exactly like themselves.
There are times during which, because of a lack of intelligence on the matter at hand, taking limited, parsimonious, or even no action – is the right course of action. One should stay the hand of righteous swift justice until such time as one fully comprehends that they are not God, and indeed bear not the least idea as to what is righteous in the first place. Ethics eschews righteousness, virtue claims it.
The net cost of such human depravity today? Hundreds of thousands, perhaps millions of lives in the future, at the hands of fake science, political, and social virtue proponents. To wit (click on images to expand them):
As one can see from the two charts in Exhibits A and B, the net cost of such sycophancy as of 17 December 2022, is on the order of 580,000 lives. This death tally is growing by a ratio of 5 to 1 (9,115 versus 1,928 lives) each and every week now, fast overtaking total Covid-19 pandemic deaths at 903,000. The future death tally from such maliciously-motivated thinking could stand to be on the order of 20 million lives or more – a divergent function which is very difficult to predict, save for its inordinately large reality.
As we survey the sizeable array of loose-end and speciously categorized data, it becomes readily apparent that the CDC is exhibiting all the symptoms of an organization which is constrained under the burden of a set of Kuhn-paradigm walking dead theories regarding Covid mortality.
Official data compromised so as to portray disinformation, is the warning sign that an entity, ostensibly one granted a government-authorized monopoly, under joint action to serve the American Public, is no longer serving science nor their fellow citizen – rather only social doctrines and out-of-control politics.
American philosopher Thomas Kuhn is credited with the proposition that science does not evolve gradually towards truth, but rather tends to anchor itself to a paradigm – a construct, notion, or hypothesis which bears the risk of remaining in play long past its shelf life. A theory thus can metastasize into a type of cult, zombie, or walking dead notion, if you will. Kuhn proposed that science therefore advances by what he called a paradigm shift and not merely by gradualism, nor especially through accretion of a set of conforming and convoluted explanatory gimmicks.
Such paradigm shifts occur in the particular circumstance where a current theory cannot sufficiently explain a phenomenon, and a coherent set of counter-observations have begun to accrue. A scientific revolution occurs when: (i) a new construct can be inferred directly from a set of these counter-observations; (ii) this novel paradigm offers superior explanatory power regarding the objective, observed reality; and (iii) the new paradigm runs heterogeneous to established (zombie) theory or narrative.1
A zombie theory is often times one which is sponsored and enforced by a syndicate. It will tend to be flagged for proactive support by its philosophical sycophancy, associated social movement, or allied political party. Such activity of course lends no credence whatsoever to the theory’s actual scientific validity.
When protection of a syndicate-sponsored idea becomes more important than the integrity of science itself, this is a particular form of zombie theory which ethical skepticism calls an Omega Hypothesis.
Within a previous article, we identified a mode and form of inference which we coined as heteroduction.2 Heteroduction is the very process of scientific inference which undertakes step (i) of the scientific revolution cycle identified by Kuhn. Heteroduction becomes of paramount importance in the presence of an enforced Omega Hypothesis.
A corporation or a political movement can become so fixated upon an established zombie theory, that its prevailing elements can rule as a form of pluralistic ignorance inside corporate ranks for years or decades – especially inside entities which do not operate in a market, and lack public scrutiny or competition. The entity or corporation will adopt a form of willful blindness toward its own foibles and fraud in support of its Omega Hypothesis. It will fail to self-check, begin to undertake borderline or even fully unethical activity in order to control what is known, and finally seek to actively suppress any form of dissent inside its ranks.
What the reader will observe below are a series of observations, a heteroduction if you will, signaling the presence of several Omega Hypotheses at play inside the US Centers for Disease Control and Prevention (CDC) – specifically the notion that Covid-19 has itself served as the sole origin of all the observed excess mortality in the US, and that we now face merely the aftermath of Covid-19’s wake, in the form of a pseudo-theory called ‘Long Covid’. A pseudo-theory is a mere notion enforced as science, which explains anything, everything, and nothing, all at the same time. Both of these notions have been falsified in spades. As we begin to examine and pull on the large set of tattered loose threads in the form of the database anomalies exhibited below, it becomes apparent that the CDC is exhibiting all the symptoms of an organization which is operating under the burden of a Kuhn-paradigm walking dead theory; and moreover, a politically-fueled Omega Hypothesis.
concealing excess deaths potentially caused by the mRNA vaccines, and
attempting to make mRNA vaccines falsely appear as uber-effective in saving lives.
Please note that we will not resolve an answer to either of these issues in this article, rather herein we will only outline the efforts in disinformation, misinformation, and deception on the part of the CDC which are foisted in an attempt to achieve both goals.
Accordingly, four key issues are entailed inside this two-sided-coin deception:
The National Vital Statistics System Upgrade (hereinafter referred to as the ‘NVSS System Upgrade’) afforded the CDC a timeframe inside which it could alter 22 weeks of NCHS-MMWR data. During this window of opportunity the CDC surreptitiously removed excess death records from its database, and adjusted the policies and techniques as to how ICD-10 mortality codes were populated with state death certificate data thereafter.
We outline herein that a new policy was enacted during the NVSS System Upgrade break, one which centered around two categorical gaming practices. The CDC is employing categorical gaming techniques to conceal dramatic Excess Non-Covid Natural Cause Mortality. If these excess deaths are not Covid deaths and are not vaccine related, as is commonly claimed through appeals to authority, credential, and ignorance, then there should also be no reason to conceal their associated records. Yet, that is exactly what is occurring.
Excess Cancer Mortality is being concealed through Cancer Multiple Cause of Death (hereinafter referred to as ‘MCoD’) categorical reassignment to Covid-19 Underlying Cause of Death (hereinafter referred to as ‘UCoD’).
Sudden Adult Deaths are being concealed by holding Pericarditis-Myocarditis-Conductive heart related deaths inside the R00-R99 temporary disposition bucket, far longer than per historical practice, thereby falsely depleting the associated ICD-10 mortality trend for these related deaths.
Finally, the CDC is using the exact opposite technique, exploiting Multiple Cause of Death attributions and adding in completely fictitious deaths as well, in order to make its mRNA vaccines appear to be performing better than they are.
The CDC is using Multiple Cause of Death categorical gaming, and is creating novel death counts, in order to counterfeit an appearance that the unvaccinated are dying at a rate twelve times that of the vaccinated.
These four issues are detailed as follows.
1. The NVSS System Upgrade Provided an Opportunity to Short and Reassign Death Records
The upgrade of the National Vital Statistics System (hereinafter, NVSS System Upgrade) was Machiavellian in its timing and opportunistic focus. In fact, as of October 2022 the entire evolution appears to have been a charade, crafted merely to obfuscate the set of warning indicators and activities outlined in this article. The NVSS System Upgrade provided an opportunity for the CDC to develop mechanisms to conceal Sudden Adult Deaths and Cancer Deaths (see Exhibit 1B below), and ironically only served to degrade the externally observable overall function and performance of the NCHS/State to CDC reporting process. Ostensibly, the process of final ICD-10 state death certificate record classification was to become tighter as a result of this upgrade. In the end, such benefit failed to manifest, as only Cancer Mortality reporting classification-lag actually appeared to improve. Yet even this ‘improvement’ in lag time turned out to be nothing more than the result of the CDC working to quickly hide cancer deaths in the first place (as documented in Exhibits 2A through 2D below). Overall, the NVSS System Upgrade was a failure – and only served to provide cover for surreptitious activity on someone’s part.3
As of the last tally we conducted regarding records lost during the System Upgrade, of the 51,910 records which disappeared from the data during the seven week hiatus, 13,245 were reassigned to other ICD-10 death codes, while 9,290 records remained missing from the database altogether. 70% of these missing and reassigned records involved death certificates pertaining to Sudden Adult Deaths and Cancer Deaths. This was not accidental in the least. Those two ridiculously negative-impacted ICD-10 death charts are shown in Exhibit 1C below. Although they are silenced now by the successive weeks confirming that we were correct in our assessment, there were Narrative-driven persons who reviewed my material and insisted that I use these figures as fact in my analyses (falsely touting such condemnation as ‘peer review’). I refused on the basis that I will not succumb to publishing Narrative disinformation.
Once this period cleared, and successive MMWR weeks were entered into the database, it became readily apparent that these sudden dips in mortality during the System Upgrade (the two right hand charts in Exhibit 1C above), were either erroneous or fraudulent in nature. Exhibits 2D and 6 show the corrected charts, as we continue below.
2. Categorical Concealment of Multiple Cause of Death Cancer Mortality
The NVSS System Upgrade afforded the CDC opportunity to both manipulate and excuse its reporting of cancer mortality, in order to obfuscate the 9-sigma excess in this ICD-10 code (C00-C97), a trend which had manifested early in 2022. The first signs of this obfuscation effort manifested immediately after the close of the System Upgrade, through a compression observable in the Cancer mortality death lag curve, per Exhibit 2A below. At first we attributed this lag curve compression to an actual improvement in record service time-to-data, as the CDC had indicated was the entire purpose of the NVSS System Upgrade.
As it turned out, this ‘service time improvement’ presented as nothing more than a ruse. The cancer mortality lag curve compression which had been observed in Exhibit 2A above was merely an artifact of records being removed from the ICD-10 tally for cancer UCoD, occurring from week 2 through 18 of the provisional death lag period. In other words, the reason cancer deaths were hitting their long-term figure levels as soon as week -4 (all other ICD codes took 12 weeks to accomplish this), was because cancer deaths were being reassigned to Covid-19 UCoD deaths (see Exhibit 2D), or were being removed from the data altogether (see Exhibit 2E) after week -2 (per Exhibits 2B though 2E below). No wonder the lag cleared so fast.
In order to test just how ludicrous this reassignment of Multiple Cause of Death data is, in Exhibit 2C below one may observe that this quotient of cancer death reassignment to Covid-19 UCoD was not well thought out by the CDC at all. They left a loose end, an Irish Pennant, hanging about – and we caught it.
Since the NVSS System Upgrade, a full 25% of all Covid-19 mortality each week has just happened to be people also dying of cancer. Such constitutes an impossibility in this important mortality account ledger, one which is analogous to the same species of mistake an embezzler might make.†
(† Please note that I have had more than my share of embezzlers caught and intelligence cases broken during my career. I am well qualified in this professional activity.)
The net effect of this nefarious activity has been a shorting of 350 to 450 Cancer Underlying Cause of Death records from the ICD-10 database each week since the NVSS System Upgrade. When those death records are added back into the data (as they were prior to the System Upgrade) the Cancer Mortality trend resumes its 9-sigma cancer death excess which was observed immediately prior to the System Upgrade, and the cancer provisional lag comes back into alignment with the lag observed inside all the other ICD-10 codes, quod erat demonstrandum.
Finally, as one can see below, not satisfied with a mere re-assignment of cancer deaths over to Covid-19 death tallies, the CDC took this one step further and simply removed another 40 to 75 cancer death records from the MMWR database altogether, each and every week of the last 18 weeks. This broaches the question, is such a record reduction then normal? The answer to this question is an unequivocal ‘no’. Almost 100% of the provisional death records end up rising, not dropping, during the lag and provisional reporting period. This drop in cancer deaths is indicative of an exception activity at play on the part of those managing the MMWR reporting databases (Wonder data in the case cited in Exhibit 2E below). By itself, this might not mean that much. However, in light of the full set of nefarious activity centered on obfuscating both SADS and Cancer mortality, this exception too is indicative of fraud underway.
One can confirm these desperate attempts to obfuscate Cancer Mortality data by observing the ICD sub-sub code for Ill-defined and Secondary Site (C76-C80) cancers as well. This is an ICD-10 sub-sub code which normally makes up around 15% of all Cancer deaths each week. Post MMWR Week 14 2021 this sub-sub code suddenly composes 42% of all novel Excess Cancer Mortality. One can observe, by accessing this Cancer (C76-C80) meta-chart, that the CDC is particularly concerned about obfuscating the data for this category of entropy-indicating Cancer Mortality.
One can also confirm such false Covid-19 UCoD attributions, by observing the stark but false rise in case fatality rates in the US in late 2022, as is depicted in this US Case Fatality Rate chart. Comparatively, the same chart for Europe features no such rise in CFR (converging around half the CFR of H1N1 flu, in line with most human coronavirus CFRs). Of course therefore, the World does not exhibit this CFR rise either. Only the US is stoking fear and hiding panic mistakes by means of false death accounting.
3. Categorical Concealment of Sudden Adult Death (Pericarditis/Myocarditis/Conductive Cardiac) Mortality
A paradox exists with regard to sudden young person and adult cardiac or anomalous deaths observable since mid 2021. Tens of thousands of cases of young persons dying suddenly in their sleep, or after a sporting event, are belied by a purported reduction in sudden cardiac death claimed by the CDC and pharmaceutical industry trolls, since the NVSS System Upgrade (see Exhibit 3B top panel).
One of my kids’ group of friends, a healthy young man who just finished college and was filled with hopes and dreams, suddenly died in his sleep of conductive heart failure several months ago. No drugs or alcohol were involved.
We were asked to believe (and it is only a belief), that these deaths ‘happen all the time’ and we were just not paying attention to them before. Bullshit – this is the same type of farce which was played upon us as parents of a child newly diagnosed with permanent disability encephalitis from vaccine injury years ago. I recognize the shtick. This is gaslighting.
In fact, those who enforce such pseudo-theory (remember, a theory which explains everything, anything, and nothing, all at the same time) hold absolutely no data to support their narrative dogma. It is maliciousness, pretending to be helpful. Below, we demonstrate why such activity constitutes gaslighting.
In Exhibit 3A below, one can observe that a temporary bucket exists which holds abnormal clinical and lab finding deaths (heavily represented by myocarditis, pericarditis, and conductive heart disorder deaths – because these are the deaths which most often serve to baffle doctors and coroners – as was the case with our family friend). Such deaths are not to be conflated with fentanyl or drug abuse deaths – which are easily detected through blood testing and are accounted for separately.
During the period prior to MMWR Week 14 2021, to include the pandemic period, these deaths were resolved 90% to their final ICD-10 disposition across about 3 to 12 weeks. As one may observe in Exhibit 3A below, not only has this bucket of deaths grown by 70% since the introduction of mRNA vaccines into the US population, but as well, the CDC has decided to cease resolving these deaths to their final ICD-10 disposition. This has resulted in an estimated 35,600 abnormal clinical and lab finding pericarditis, myocarditis, and conductive disorder deaths which are not being accounted for in US Cardiac Mortality – thereby artificially depressing those ICD-10 mortality trend curves and allaying the conscience-nightmares of the pharmaceutical executive board members of the CDC.
This too, is no different than embezzlement of expense money or tax fraud inside a corporation or charity accounting ledger. Jim and Tammy Faye Baker would be impressed.
If one assigns a mere 18% of these anomalous and heavily cardiac deaths in younger persons, back to the Wonder data concerning myocarditis, pericarditis, and conductive heart disorder deaths – one gets a 22-sigma increase in this mortality sub-group since MMWR Week 14 of 2021. This process is shown in Exhibit 3B, and the result is indicated in its lower panel. In fact a very stark inflection in this data develops immediately commensurate with the roll-out of mRNA vaccines nationwide in the US.
My fear is that far more than 18% of these bucket-hold and unresolved deaths, involve abnormal and clinical findings related to pericarditis, myocarditis and conductive heart disorders – and the CDC is concealing a tsunami of a problem. This is a human rights crime.
In fact, if we prosecute this very inference which we inductively derived above, and test this deductively by querying the Wonder MCoD data for all Cardiac related deaths, including those RXX Abnormal & Clinical Lab Findings deaths which relate to abnormal heart-related conditions4 – those ostensibly held in the 22 week stasis cited above – one finds an alarming result. In the cases where there is no Covid-19 listed on the death certificate, we are at an all time high in heart-related deaths for the entire pandemic period. In fact a 17-sigma high (20-sigma with pull forward effect taken into account).
These excess Diseases of the Heart (IXX) and Uncertain Related Disorders (RXX) are not a result of Covid itself, despite the 2020 sympathy curves (Exhibit 3C 2020 blue line humps are in reality missed Covid and not a true increase in this ICD-10 code).5 This inflection and post-inflection signal should be lighting off warning alarms in all corridors of public health, … yet it is not. The combination of consilient inductive strength, reduced by convergent deduction – is tantamount to proof. Sorry pundits, your appeals to ignorance have only served to harm people.
4. Categorical Exploitation of Covid-19 MCoD Mortality to Coerce the Public with Case Fatality Rate Disinformation Regarding Vaccine Effectiveness
In Exhibit 4A below as well, one lays witness the accounting wherein suddenly, Multiple Cause of Death Covid-19 deaths are all assumed to be Underlying Cause of Deaths in solely the unvaccinated cohort. For the week analyzed in Exhibit 4A below, the Covid-19 MCoD tally for the week was 2,650 MCoD deaths (in the over-50 age bracket). Thus the CDC had to convert 841 MCoD deaths to UCoD deaths, and assign them only to the unvaccinated, and manufacture an additional 548 deaths more in that same cohort, in order to make the unvaccinated appear to have a 12 x case fatality rate as compared to the vaccinated cohorts. Rather than being executed inside a database however, this sleight of hand was accomplished in a sampling study instead – one where a reverse projection check was never done, in order to make sure that the results of the inductive projection study were sound – or in this instance, even possible.
These results are not mathematically possible.
This is coercion of innocent citizens by means of purposeful disinformation, and constitutes fraud.
Below in Exhibit 4B, we take the set exclusion calculations above in Exhibit 4A and portray them along the timeline of their arrival (23 weeks from 2 Apr to 3 Sep 2022), using the CDC’s very own analysis regarding death rates among 50+ year old vaccinated and unvaccinated cohorts.6 When these model calculations are extrapolated to the entire 50+ US population, suddenly a superfluous 12,656 death-count appears – conveniently in the timing and arrival form necessary to comprise the entire excess unvaccinated cohort death component. The superfluous deaths far exceed the 6,617 deaths necessary in fabricating the difference between the unvaccinated and vaccinated cohorts. The calculation base for Exhibit 4B below, which extends from the CDC model, can be seen by clicking here.
There is no doubt therefore, that the CDC and/or the surveillance hospital networks feeding this analysis, have manufactured superfluous deaths and inserted them in to the unvaccinated cohort death rolls, in order to fabricate a misleadingly high fatality rate among the unvaccinated as compared to the vaccinated. Inside this activity, the 18-day gap (shown in the upper panel of Exhibit 4B) between the vaccinated and unvaccinated cohort deaths is pivotal. The gap suggests that the following protocol was used to fabricate this cohort differential. Condition #1 below conforms with case arrivals and not the actual arrival of Covid deaths (18 days later).
Unvaccinated status + any UCoD + nosocomial Covid/no Covid = ‘Covid’ death
Vaccinated status + Covid + any MCoD = definitely not a Covid death (use the MCoD)
In other words, die of anything yet also be unvaccinated – then you ‘died’ of the suspected Covid you ‘caught’ upon entering hospital or hospice. Hence a peak sympathetic with cases, 18 days early. If vaccinated – then one could not possibly have had Covid, nor especially severe-Covid, therefore one didn’t die of Covid. A self-fulfilling model which functions upon the circular logic of constraints alone – one gets only that for which they constrain.
Moreover, the superfluous deaths quantified above in Exhibit 4B cannot be reconciled in any other fashion when constrained by the CDC model published above. The only place this magnitude of death differential can be accommodated in the CDC model, is by stuffing the superfluous death count for the period inside a skewed comparative – and the unvaccinated cohort death rate is the only one large enough to accommodate this large superfluous death count.
Thus, the unvaccinated death counts are falsely inflated and the cohort differentials are fraud, quod erat demonstrandum.
Addendum: The Yule-Simpson Effect Inside All Cause Mortality
Finally of course, it should be noted that the CDC attempts to define a pandemic and furthermore expresses its pandemic updates, in terms of All Cause Mortality. As epidemiological professionals in this case, they should not be using such a misleading metric. Pandemic risk at this juncture needs to be evaluated in terms of Excess Non-Covid Natural Cause Mortality (see Exhibit 6 below). This metric (the contrast of which can be observed in Exhibits 5 and 6) offers an indication of risk from Non-Covid death causes. As one can see in Exhibit 6 below, Excess Non-Covid Natural Cause Mortality is at an alarming 13.3% excess, while All Cause Mortality Excess (shown in Exhibit 5) stands at a rather nominal 3.3%. All Cause Mortality can therefore be deceptive when used as a stand-alone metric. Reader, be cautious of public health pundits who loosely spread unqualified All Cause Mortality figures.
In the end, it is this last chart depicted in Exhibit 6 which serves to confirm the claims made in Sections 1 through 4 of this article. The level of excess natural cause death which is not Covid itself, is around 13.3% to the excess of where it should be – even given a 1.1% baseline growth inside an aging demographic (see Exhibit 6, dark orange baseline ‘annual growth’).
While no other public health entity appears able to or interested in tracking this critical epidemiological metric, we not only track its anomalous magnitude, but we at The Ethical Skeptic believe we know what is causing these excess deaths as well. In fact, the data by ICD-10 sub-sub-code and by US County is starkly indicative, as well as condemning. No wonder the CDC is attempting to obfuscate it – as it will serve to infuriate those whom the CDC serves. Yes, organizations of this type operate under extreme levels of conflict of interest and agency. In the immortal words of Bob Dylan,
But you’re gonna have to serve somebody, yes You’re gonna have to serve somebody Well, it may be the devil or it may be the Lord But you’re gonna have to serve somebody
We choose to stand in the gap for those who cannot defend themselves. Such unequivocal inference regarding the cause of these 385,000 excess natural cause deaths will be the subject of our third article in this series, ‘Houston, We Realize the Problem (Part 3 of 3)’. A problem which is rising at 7,340 deaths per week as of 8 October 2022 – and more importantly, does not appear to be abating any time soon.
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.
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.
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.1
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.2 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.
US Center for Disease Control and Prevention: Weekly Counts of Deaths by State and Select Causes, 2014-20193
US Center for Disease Control and Prevention: Weekly Provisional Counts of Deaths by State and Select Causes, 2020-20224 (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)
US Center for Disease Control and Prevention: Wonder: Provisional Mortality Statistics, 2018 through Last Month – Query by Constraint Engine5
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 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)
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.
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 peaklevel 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.
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).
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.
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.
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.