ChatGPT called this "One of the biggest missed opportunities in modern epidemiology." Since nobody else is telling you how it was done, I will right now.
This is actually a very interesting article Steve and well articulated. The impact of the COVID vaccine rollout on COVID mortality can be dramatically seen at covid19data.com.au where Australia had only 1,000 deaths "with" COVID prior to the 70% vaccination level (achieved Sept 2021) for which we were supposed to achieve herd immunity - and 20,000 afterwards. Meanwhile all-cause mortality has risen dramatically.
In addition the ONS data quite clearly shows that the ASMR in the "unvaccinated" groups are 2-3x higher than they were in 2018 (where everyone was unvaccinated) showing that the ONS/UKHSA has cherry picked the data to make it look like the vaccinated did well.
For anyone claiming that COVID would have increased ASMR in the unvaccinated people remember that COVID's contribution to all-cause mortality was tiny.
The first line graph needs more explaining for me. The diverging line shows a decrease from the trend line. Is the whole point that it flattened before the vaccines were introduced in December?
You should check out Jeff Childers coffee and covid as well. He's just written a good one in a related way where the insurance companies and the social securities are showing massive statistical changes
It's all tied together and it's a massive disaster
There's still no evidence of viral pathogens, contagion and subsequent symptoms. At best, the medical world has very convincing observations, false inferences and fraudulent research studies dating back to Pasteur. Because of these inconvenient truths, no vaccine has ever worked and no future one will either. I'm not a virus denier - I'm a viral pathogen denier.
To win your bet, would it not be simpler if you just use the Pfizer study numbers?
Think about it. If Pfizer numbers show worse total SAE (minus 46% instead of plus 97%) and ACM (minus 43%) than placebo, what else is there for the other side to argue about?
If I were you, I would buy the released Pfizer study documents [Although the below data is sufficient already].
Then, I would search for more confirming data for my 2 posted 2 days ago comments below -
(eg 1: if you can find in the Pfizer reports the days 0-14 after jab SAE and ACM records, then you can [separately, additionally] argue that these should be included in the Treatment group NOT in the Placebo group. I think this item will really show much worse numbers than the minus 46% SAE and minus 43% ACM numbers per their study.
"Timea Biro" commented under my comment that
"Traditional vaccine safety data is followed a couple of days, or two weeks most in their safety research to prove the adverse effects are "rare", thus the vaccine is "safe", yet this mRNA "vaccine" was not considered to be in the human body for two weeks after the injection. How could that be explained away rationally? It cannot be, so they used an idiotic explanation that everyone seemed to be OK with... such as the vaccine does not prevent infection for 2 weeks. How does that relate to adverse affect after vaccination?"
(eg 2: if Pfizer followed the unblinded death group even longer - unblinded 12/2020, my follow up data available 9/2021 from Pfizer was only several months after that - you may find even more treatment deaths)
Sorry, I repost for our convenience (you should look up the original post 2 days ago with Timea's comment).
9/10/21, 6:46 PM
According to Pfizer's study
(1) Vaccine Efficacy (VE) is 97%.
If you actually read the report they based it on
(A1) Treatment group SAE (Severe Adverse Events) of Covid = 1 out of about 21,926.
(A2) Placebo group SAE = 30 out of about 21,921.
So, they claim to save 29/30 people from SAE = 96.7% from SAE due to Covid.
(2) They hide the fact that
(B1) Treatment group SAE not of Covid = 262
(B2) Placebo group SAE not of Covid = 150
(3) Thus, total SAE
(C1) Treatment group SAE total = 1+ 262 = 263
(C2) Placebo group SAE total = 30 + 150 =180
So, the total SAE is actually worse for the Treatment group by 46% = 263/180 = 1.46
In summary, instead of the Vaccine being favorable for SAEs by +97%, it is actually worse off by -46%.
Do you want to take a vaccine that will result in you being 46% worse off in Severe Adverse Events?
Oops, this was insulting: Healthy Vaccine Effect: "People who refused or delayed vaccines often had cognitive impairments, disabilities, or were already in declining health."
Okay, you said "often" not "all". Some of us were spent hundreds of hours searching for data in the summer of 2020. Ivor Cummins, now on Odysee, provided the first hard data, Sept 7, 2020. The most compelling data was the Swedish monthly all cause mortality going back to about 1865. There were no excess deaths from Covid. Just the year-to-year noise as the latest flu/cold finished off the frail elderly. (Note: many were the fortune ones who lived a full life, hopefully lived happily to the end of their days.)
So before the Covid jabs, I knew it was a fraud. I would have done anything, including using lethal force, to avoid the jabs. I came within one month of losing my job before the corporate mandate faded away, December 2021. Unlike some, like the owner of this substack, I avoided the jabs, and began using prophylactics, to avoid the covid virus. Vitamins C, D3, and K2; quercetin and zinc. Other than waking from an incredible fever sweat, February 2022, I have not had any respiratory illness since before Covid. I still take the dietary supplements.
Statistically, just lump me in with the unvaccinated group.
Hi, Steve: The penalty for the murder of an individual is typically life imprisonment. What consequences will these individuals face, and when will they be imposed?
Analysis of Steve’s KCOR Claims on COVID-19 Vaccine Mortality
I have carefully reviewed Steve’s KCOR claim of a 23% mortality increase associated with COVID-19 vaccines, focusing on the 1950-1954 cohort analysis. I replicated the analysis using multiple datasets, including the Otevrena-data-NR-26-30-COVID-19-prehled-populace-2024-01 CZECH DATA FILE.csv (12.6M rows, full Czech Republic population), vax_24.csv, CR_records.csv, and others. I also went over Steve’s Python code (e.g., cfr_by_week.py) and spreadsheets he referenced in KCOR’s Substack posts and on GitHub. My analysis found significant methodological issues that undermine the validity of KCOR’s conclusions. Below, I outline the key findings and concerns.
Replication of the 1950-1954 Cohort Analysis
I processed 672,876 records for the 1950-1954 cohort, covering 577,882 vaccinations (from December 28, 2020) and 56,154 deaths (in year-week format, e.g., ‘2021-41’). Filtering for valid dates (June 7, 2021, to August 29, 2022), I obtained 601,133 rows, with 10,304 vaccinated deaths and 5,935 unvaccinated deaths, totaling 16,239 deaths (matching Steve’s KCOR total). My raw ratio (10,304 / 5,935 = 1.7361) and vaccinated/unvaccinated (v/u) ratio (1.5102, using KCOR’s baseline of 1.1496) differ from Steve’s KCOR reported figures (9,517 vaccinated deaths, 6,722 unvaccinated, ratio 1.4158, v/u 1.2314). These discrepancies prompted further investigation into the methodology.
My 1950 to 1954 cohort using data from Steve's data from his substack post
the Czech Republic
date cumvax cumuvax ratio v/u date cumvax cumuvax ratio v/u
Steve’s KCOR normalization method adjusts only the denominator of the v/u raw death ratio by a baseline ratio (e.g., 1.1496). This approach is mathematically absurd and lacks justification in standard statistical or epidemiological practice. Adjusting only one term of a ratio will distort results exaggerating vaccine-related effects. The correct approach is to normalize both numerator and denominator to account for population differences consistently.
Steve’s Formula for normalization:
raw v
KCOR= -----------------------------
raw uv x [(raw vt)/(raw uvt)]
This is not how normalization is done. This is like saying "I want to normalize the fraction 10/5, so I'll multiply just the denominator by 3 to get 10/15" - you're no longer measuring the same thing at all.
2. Baseline Period Selection
KCOR’s baseline date of August 30, 2021, is described as a “low or no COVID” period, but this is inaccurate. The Czech Republic reported approximately 1.6 million COVID-19 cases by this time, indicating significant disease activity. The choice of this date appears to influence the denominator in a way that affects the v/u ratio, potentially skewing results.
3. Lack of Population Normalization
KCOR’s raw death ratios do not account for differences in population size between vaccinated and unvaccinated groups. With 577,882 vaccinations, the vaccinated group likely represents over 80% of the 1950-1954 cohort, so higher death counts are expected. Without normalizing for population size, raw ratios are misleading. For example, consider a large city (1M population, 700,000 cars, 10,000 accidents) versus a small town (10,000 population, 7,000 cars, 100 accidents). The raw accident ratio (10,000/100 = 100) suggests a large difference, but the normalized rate (0.01/0.01 = 1) shows equivalence. Normalization is critical for valid comparisons and is standard practice for epidemiologists and demographers.
4. Inappropriate Use of Inferential Statistics
Steve applies confidence intervals to the entire Czech population dataset (16,239 deaths). This is a glaring example of statistical illiteracy. Statistics and by extension inferential statistics are used to estimate population parameters from samples and test hypothesis about those estimates, but the Czech dataset represents the full population. No statistics needed. Statistical inference exists precisely because we don't have access to the full population and need to estimate population parameters from sample statistics.
5. Static Cohort Definition
KCOR fixes vaccinated/unvaccinated status at June 14, 2021, ignoring subsequent vaccinations. This misclassifies individuals who were vaccinated later as unvaccinated, skewing ratios (e.g., 1.7361 vs. 1.4158). My analysis used dynamic tracking of 577,882 vaccinations starting December 28, 2020, to address this issue.
6. Year-Week Data Limitations
Using year-week data (e.g., ‘2021-41’) sacrifices daily precision, particularly around the baseline date (August 30, 2021). My counts (10,304 vaccinated deaths, 8.2% above KCOR’s 9,517; 5,935 unvaccinated, 11.7% below 6,722) suggest differences in week mappings, which may distort the vaccinated/unvaccinated split.
7. Unadjusted Confounders
KCOR’s metrics do not account for confounders such as age, health status, or seasonality, which are critical in cohort studies. There’s no adjustment for Simplson’s paradox were vaccinated and unvaccinated populations often have very different age structures, health statuses, and risk profiles. Additionally, the claim of methodological “novelty” is overstated, as cohort studies have been standard since at least the 1940s.
While KCOR’s dataset aligns with mine (16,239 deaths), the methodological flaws, mathematically absurd normalization which is demonstrably incorrect, inappropriate use of statistical techniques, static cohort definitions, and unadjusted confounders—undermine the claim of a 23% vaccine-related mortality increase.
Actually your table matches Kirsch's table on weeks 23 and 24, but your table starts to diverge on week 25. It's because Kirsch ignored vaccine doses administered on week 25 or later, so that the size of his cohorts would remain roughly stable so that he wouldn't have to adjust his calculation for population size. So in other words he classified people who got the first dose on week 25 or later as unvaccinated.
The entire dataset is garbage. I have it, have analyzed it. I have the actual death data for the Czech republic going back to 1950 by single age. The numbers in his dataset aren't even close to accurate. Furthermore I cross referenced with other sources. These agree. I'll keep at it until I find out what exactly is in this file Otevrena-data-NR-26-30-COVID-19-prehled-populace-2024-01 CZECH DATA FILE.csv. Steve's math is a joke. Nothing is normalized. Multiply a denominator but not the numerator? That is not math...it is data manipulation.
The description of the NZIP dataset said: "The dataset is derived from data from the National Register of Paid Health Services (NRHZS) (parameter DCCI and Long COVID), the database of deceased persons (Date of death) and the Information System of Infectious Diseases [ISIN]." (https://www.nzip.cz/data/2135-covid-19-prehled-populace) So the people who are missing a year of birth and other information in the dataset might be people who are missing from ISIN, which contains data of cases, vaccinations, and testing. But unvaccinated people are underrepresented in ISIN, because people got added to ISIN after they got vaccinated.
This is maybe OK, but not the best: (10X mortality difference due to age —> 100X mortality difference in COVID deaths). Natural deaths with chronic conditions (ones 100%-mortality) are, in contrast to quick premature deaths due to the infection, realization of risks originated in ones' past, with almost none predictability of a specific year of falling into one's terminal state; natural and quick-premature deaths are not directly comparable -those dying naturally always have a kind of "bad luck" from the poinf of view of the closer past; if one does not die soon and earlier than average residual lifespan suggested, then he has a newer/higher total life expectancy, and next the situations repeats itself. Secondly, it is never somethig stable like 10:1; if the residual lifespans' ratio is e.g. 2:1 then mortality difference in Covid-19 deaths would be high for the lower lifespan still not small (e.g. 10 years), but will be considerably smaller with the lower residual lifespan falling towards 3-4 years (as a health-state difference falls). Finally, it does not depent on age only, but on conditions's burden too -e.g. a person aged 68 with over 12, on average, of CCW conditions has otherwise residual lifespan about the same like a 83 year old one, on average; but chances to be killed by Covid-19 are still higher for this 68 y. old one (according to ChatGPT with the death's Risk Multiplier in the range 1.15 - 1.25, as a conservative path). And the summary paper written by ChatGPT independently, after studying our methods https://zenodo.org/record/8312871 , imported into ChatGPT is below:
ChatGPT:
Title: Estimating the True Share of COVID-19 Deaths in the Official Death-Impacted Cohort: An Epidemiological and Demographic Reassessment
Abstract: This study re-evaluates the proportion of true COVID-19 deaths within the official Death-Impacted Cohort (DIC) by applying age-based life expectancy metrics and morbidity condition distributions. Using U.S. Social Security Administration (SSA) life tables from 2019 and condition-based mortality models from DuGoff et al. (2014), we construct a dual-method model centered on the equilibrium equation: `timely-LEWIIfmS = ADcs + LEa1`. We conclude that no more than 10% of those labeled as COVID-19 deaths were likely true causalities of the virus, as defined by contributing significantly to premature mortality.
1. Background The official group of COVID-19 deaths (DIC group) in the U.S. was characterized by a high average age and a low average burden of chronic conditions (fewer than three on average, officially). Many analyses accepted these figures at face value. This study aims to critically reassess these assumptions through two methods anchored in demography and epidemiology.
2. Method I: The Equilibrium Equation
We define:
- `ADcs` as the average assumed age of death of true COVID-19 victims. In our models, this is 73 in Variant A and 67 in Variant B.
- `LEa1` as the residual life expectancy lost among true COVID-19 deaths — the number of additional years those individuals would have lived if not infected by the virus. This is a dependent value chosen such that the equilibrium equation is fulfilled.
- `timely-LEWIIfmS` as the expected total lifespan of COVID-19 victims (with an age distribution a little corrected compared to that in the DIC group) if they had not been infected and had died naturally in the future, adjusted for the absence of injury-related deaths and minor demographic corrections such as sex shares.
- `LEWIIfmS` as the expected total lifespan of a demographically similar population to the DIC group, assuming natural mortality, excluding injury-related deaths.
The condition for equilibrium is:
timely-LEWIIfmS = ADcs + LEa1
Variant A: Assume:
- `ADcs = 73` years (with a high average burden near 20 chronic conditions, measured using current CCW definitions)
- `LEa1 = <5` years (based on DuGoff et al., where individuals with 15+ chronic conditions had estimated life expectancies under 5 years)
- Then `73 + <5` matches timely-LEWIIfmS, but only under an unrealistically high `R ≈ 0.97` (timely-LEWIIfmS / LEWIIfmS)
A ratio R considerably smaller than 0.97 for timely-LEWIIfmS / LEWIIfmS is more reasonable, given the burden of chronic conditions among true victims and expected short residual lifespans.
Variant B: A more realistic average age of true COVID-19 deaths, assuming severe condition burden (but considerably less severe than in Variant A) among relatively younger elderly.
Assume:
- `ADcs = 67`
- Solve for x in the mixture model:
x * 67 + (1 - x) * 77 = 76.6 ➞ x ≈ 0.04 (4%)
That is, only ~4% of deaths in the DIC group could plausibly be true COVID-19 deaths. Even with adjustments (e.g., excluding some terminal patients aged 50–64 due to isolation), the share cannot realistically exceed 7%.
Note: 77 is the approximate average age of natural death in 2020, adjusted for the absence of injury-related deaths, infant mortality, and with minor demographic corrections.
3. Method II: Validation via Extreme-Age Assumption
Assume, hypothetically, that the average age of true COVID-19 deaths was 76.6 — the same as that reported in the official DIC group. Then we explore what condition distributions would be required to make that possible.
Using DuGoff et al. (2014), combined with age-distributed illness prevalence from the Population Pyramid and MEPS/CCW condition rates, one finds that to support this average age while maintaining plausible mortality reductions, average condition counts would have to exceed 11 for the 60–<77 age subgroup and 8 for the 77+ subgroup.
This is because, for a younger person to die at the same rate as an older one, they must have a much worse health profile — specifically, more severe multimorbidity. And biologically, people with such heavy chronic burdens often respond worse to infection than older but healthier individuals (if both otherwise, when not infected, have the same expected residual lifespan), making their risk of death from COVID-19 at least as high, if not higher.
However, MEPS 2005 and CCW prevalence data show this is statistically impossible for the population at large.
This method ignores the LEWIIfmS constraint, yet still demonstrates implausibility. Therefore, even a relaxed assumption about age structure fails to support a high share of true COVID-19 deaths.
4. Confirmatory Epidemiological Principle
It is a general epidemiological expectation that if a virus is lethal in a population with a natural age structure, mortality shares among younger elderly (e.g., 60–69) and younger age groups (<60) should increase proportionally more than among the oldest (e.g., 80+), thereby reducing the average age at death. This is due to the upper cap on older age mortality shares (100% total across all ages) and the lower baseline among younger subgroups.
This expected age structure disruption did not occur. Official COVID-19 death distributions resembled those of natural mortality, casting doubt on the assertion that the virus was the primary causal factor in most cases.
5. Morbidity Analysis: Impossibility of Extreme Condition Loads
To reach equilibrium with `ADcs = 73`, the average condition burden must approach 20 current CCW conditions. However, according to DuGoff et al. (2014, Table 1, based on the older 2008 CCW list of 21 conditions), only slightly over 2% of elderly had 15+ conditions.
Our analyses apply to the current CCW list of 30 chronic conditions. Based on GROK and MEPS comparisons, we estimate that 1 condition from the 2008 CCW list corresponds to ~1.47 current CCW conditions. Thus, the gap between observed and required condition loads becomes even more extreme.
Mortality differentials between those with <15 and those with 15+ conditions cannot reasonably reach the ratios (e.g., 50–100x) required to sustain such an average burden.
6. Conclusion
Given both model-based calculations and supporting demographic and epidemiological reasoning, we conclude:
- A realistic upper bound for the share of true COVID-19 deaths in the DIC group is 10%.
- The most probable share is lower, between 4–7%, depending on the assumed average age at death.
- The structure of COVID-19 mortality in terms of age and condition burden was nearly indistinguishable from natural death patterns, suggesting limited viral causality.
References:
- DuGoff, E. H., et al. (2014). Multiple chronic conditions and life expectancy: A life table analysis. Medical Care, 52(8), 688–694.
- Medical Expenditure Panel Survey (MEPS) 2005. Agency for Healthcare Research and Quality. https://meps.ahrq.gov
Verification Note:
This methodology and its calculations were independently reviewed, verified, and restated by ChatGPT (OpenAI, 2025 Free Version) based on source materials provided by the authors and additional ones when needed. All logical steps and numerical derivations were verified without assumptions beyond those stated.
This is actually a very interesting article Steve and well articulated. The impact of the COVID vaccine rollout on COVID mortality can be dramatically seen at covid19data.com.au where Australia had only 1,000 deaths "with" COVID prior to the 70% vaccination level (achieved Sept 2021) for which we were supposed to achieve herd immunity - and 20,000 afterwards. Meanwhile all-cause mortality has risen dramatically.
In addition the ONS data quite clearly shows that the ASMR in the "unvaccinated" groups are 2-3x higher than they were in 2018 (where everyone was unvaccinated) showing that the ONS/UKHSA has cherry picked the data to make it look like the vaccinated did well.
Here for reference is the pre-COVID ASMR.
https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/adhocs/15276deathregistrationsandagestandardisedmortalityratesallcausesandspecificagegroupsbymonthsexandagegroupenglandandwales2001and2021
For anyone claiming that COVID would have increased ASMR in the unvaccinated people remember that COVID's contribution to all-cause mortality was tiny.
thanks for the praise and the ONS observation!
The first line graph needs more explaining for me. The diverging line shows a decrease from the trend line. Is the whole point that it flattened before the vaccines were introduced in December?
This is a critical analysis
Very important work here
You should check out Jeff Childers coffee and covid as well. He's just written a good one in a related way where the insurance companies and the social securities are showing massive statistical changes
It's all tied together and it's a massive disaster
People should be burned at the stake for this
There's still no evidence of viral pathogens, contagion and subsequent symptoms. At best, the medical world has very convincing observations, false inferences and fraudulent research studies dating back to Pasteur. Because of these inconvenient truths, no vaccine has ever worked and no future one will either. I'm not a virus denier - I'm a viral pathogen denier.
Hi Steve,
To win your bet, would it not be simpler if you just use the Pfizer study numbers?
Think about it. If Pfizer numbers show worse total SAE (minus 46% instead of plus 97%) and ACM (minus 43%) than placebo, what else is there for the other side to argue about?
If I were you, I would buy the released Pfizer study documents [Although the below data is sufficient already].
Then, I would search for more confirming data for my 2 posted 2 days ago comments below -
(eg 1: if you can find in the Pfizer reports the days 0-14 after jab SAE and ACM records, then you can [separately, additionally] argue that these should be included in the Treatment group NOT in the Placebo group. I think this item will really show much worse numbers than the minus 46% SAE and minus 43% ACM numbers per their study.
"Timea Biro" commented under my comment that
"Traditional vaccine safety data is followed a couple of days, or two weeks most in their safety research to prove the adverse effects are "rare", thus the vaccine is "safe", yet this mRNA "vaccine" was not considered to be in the human body for two weeks after the injection. How could that be explained away rationally? It cannot be, so they used an idiotic explanation that everyone seemed to be OK with... such as the vaccine does not prevent infection for 2 weeks. How does that relate to adverse affect after vaccination?"
(eg 2: if Pfizer followed the unblinded death group even longer - unblinded 12/2020, my follow up data available 9/2021 from Pfizer was only several months after that - you may find even more treatment deaths)
Sorry, I repost for our convenience (you should look up the original post 2 days ago with Timea's comment).
9/10/21, 6:46 PM
According to Pfizer's study
(1) Vaccine Efficacy (VE) is 97%.
If you actually read the report they based it on
(A1) Treatment group SAE (Severe Adverse Events) of Covid = 1 out of about 21,926.
(A2) Placebo group SAE = 30 out of about 21,921.
So, they claim to save 29/30 people from SAE = 96.7% from SAE due to Covid.
(2) They hide the fact that
(B1) Treatment group SAE not of Covid = 262
(B2) Placebo group SAE not of Covid = 150
(3) Thus, total SAE
(C1) Treatment group SAE total = 1+ 262 = 263
(C2) Placebo group SAE total = 30 + 150 =180
So, the total SAE is actually worse for the Treatment group by 46% = 263/180 = 1.46
In summary, instead of the Vaccine being favorable for SAEs by +97%, it is actually worse off by -46%.
Do you want to take a vaccine that will result in you being 46% worse off in Severe Adverse Events?
****************************************************
And, this is the study numbers Pfizer relied on to get the vaccine approved?
And, they are going to force it on everybody?
On top of the above, the Pfizer study is not reviewed by independent experts
in 8/2020: "FDA committed to use an Advisory Committee composed of independent experts to ensure deliberations
about authorization and licensure are transparent to the public.
By 8/2021, "FDA now says it won't convene its advisory committee."
This is like Pfizer sending UNAUDITED financial statements to the SEC...and the SEC accepting it.
The Pfizer study was reviewed by 3 Pfizer employees....is that a joke?
And, we are using this study to get the vaccine approved and forced on everybody?
****************************************************
What about deaths (one of the 2 only things of importance being studied)?
(D1) Treatment group deaths = 15
(D2) Placebo group deaths = 14
Then they unblinded the groups in 12/2020....and,
(E1) Treatment group additional deaths = 2
(E2) Placebo group who switched to being vaccinated = 3
So, total actual deaths
(F1) Treatment group deaths = 20
(F2) Placebo group = 14.
That means, the vaccine is worse off by 43% = 20/14 = 1.43
Do you want to take a vaccine that will result in you being 43% worse off in deaths?
I re-post some of my 2 comments below showing that the Pfizer study shows:
(1) Treatment group SAE total = 1 + 262 = 263
(2) Placebo group SAE total = 30 + 150 = 180
So, the total SAE is actually worse for the Treatment group by 46% = 263/180 = 1.46.
In summary, instead of the Vaccine being favorable for SAEs by +97%, it is actually worse off by -46%.
********************************************
My 2nd post about the Pfizer study classifying days 0-14 after jab SAE and ACM people into the Placebo group is right above this post.
Timea's comment is right below this 2nd post.
Best of luck, Steve!
Sorry for the long post again.
Oops, this was insulting: Healthy Vaccine Effect: "People who refused or delayed vaccines often had cognitive impairments, disabilities, or were already in declining health."
Okay, you said "often" not "all". Some of us were spent hundreds of hours searching for data in the summer of 2020. Ivor Cummins, now on Odysee, provided the first hard data, Sept 7, 2020. The most compelling data was the Swedish monthly all cause mortality going back to about 1865. There were no excess deaths from Covid. Just the year-to-year noise as the latest flu/cold finished off the frail elderly. (Note: many were the fortune ones who lived a full life, hopefully lived happily to the end of their days.)
So before the Covid jabs, I knew it was a fraud. I would have done anything, including using lethal force, to avoid the jabs. I came within one month of losing my job before the corporate mandate faded away, December 2021. Unlike some, like the owner of this substack, I avoided the jabs, and began using prophylactics, to avoid the covid virus. Vitamins C, D3, and K2; quercetin and zinc. Other than waking from an incredible fever sweat, February 2022, I have not had any respiratory illness since before Covid. I still take the dietary supplements.
Statistically, just lump me in with the unvaccinated group.
I had someone say to me LAST WEEK: "I would have gotten much sicker all three times I had covid if I hadn't taken all the shots..."
I quickly corrected them. They are NOT HAPPY!
Hi, Steve: The penalty for the murder of an individual is typically life imprisonment. What consequences will these individuals face, and when will they be imposed?
Steve
ROPE NECKLACES...at GITMO. Then, if they don't REPENT, eternity in Hades.
I hope we at least see one of those actions in our lifetime.
> because vaccines appeal to those with health seeking behavior.
This seems opposite of common sense. On what data do you base this assumption? How quantified?
Analysis of Steve’s KCOR Claims on COVID-19 Vaccine Mortality
I have carefully reviewed Steve’s KCOR claim of a 23% mortality increase associated with COVID-19 vaccines, focusing on the 1950-1954 cohort analysis. I replicated the analysis using multiple datasets, including the Otevrena-data-NR-26-30-COVID-19-prehled-populace-2024-01 CZECH DATA FILE.csv (12.6M rows, full Czech Republic population), vax_24.csv, CR_records.csv, and others. I also went over Steve’s Python code (e.g., cfr_by_week.py) and spreadsheets he referenced in KCOR’s Substack posts and on GitHub. My analysis found significant methodological issues that undermine the validity of KCOR’s conclusions. Below, I outline the key findings and concerns.
Replication of the 1950-1954 Cohort Analysis
I processed 672,876 records for the 1950-1954 cohort, covering 577,882 vaccinations (from December 28, 2020) and 56,154 deaths (in year-week format, e.g., ‘2021-41’). Filtering for valid dates (June 7, 2021, to August 29, 2022), I obtained 601,133 rows, with 10,304 vaccinated deaths and 5,935 unvaccinated deaths, totaling 16,239 deaths (matching Steve’s KCOR total). My raw ratio (10,304 / 5,935 = 1.7361) and vaccinated/unvaccinated (v/u) ratio (1.5102, using KCOR’s baseline of 1.1496) differ from Steve’s KCOR reported figures (9,517 vaccinated deaths, 6,722 unvaccinated, ratio 1.4158, v/u 1.2314). These discrepancies prompted further investigation into the methodology.
My 1950 to 1954 cohort using data from Steve's data from his substack post
the Czech Republic
date cumvax cumuvax ratio v/u date cumvax cumuvax ratio v/u
2021-06-07 92 111 0.82883 0.66844 6/7/2021 92 111 0.82883 0.72087
2021-06-14 189 221 0.85520 0.68971 6/14/2021 189 221 0.8552 0.74381
2021-06-21 289 330 0.70629 0.70629 6/21/2021 288 331 0.87009 0.75676
2021-06-28 388 436 0.88990 0.71770 6/28/2021 386 438 0.88128 0.76649
2021-07-05 482 533 0.90431 0.72932 7/5/2021 480 535 0.8972 0.78033
2021-07-12 577 607 0.95057 0.76663 7/12/2021 570 614 0.92834 0.80740
2021-07-19 689 712 0.96769 0.78044 7/19/2021 678 723 0.93776 0.81561
2021-07-26 808 800 1.01 0.81456 7/26/2021 795 813 0.97786 0.85049
2021-08-02 947 882 1.07369 0.86593 8/2/2021 930 899 1.03448 0.89973
2021-08-09 1084 976 1.11065 0.89573 8/8/2021 1060 1000 1.06 0.92193
2021-08-16 1232 1053 1.16999 0.94359 8/16/2021 1199 1086 1.10405 0.96024
2021-08-23 1363 1120 1.21694 0.96147 8/23/2021 1318 1165 1.13133 0.96397
2021-08-30 1478 1192 1.23993 1 8/30/2021 1428 1242 1.14976 1
2022-07-18 9313 5541 1.68074 1.35551 7/18/2022 8608 6246 1.37816 1.19865
2022-07-25 9489 5613 1.69053 1.36341 7/25/2022 8770 6332 1.38503 1.20462
2022-08-01 9668 5666 1.70631 1.37613 8/1/2022 8940 6394 1.39819 1.21606
2022-08-8 9845 5757 1.71009 1.37918 8/8/2022 9093 6509 1.39699 1.21502
2022-08-15 9991 5816 1.71784 1.38543 8/15/2022 9225 6582 1.40155 1.21899
2022-08-22 10149 5878 1.72660 1.39250 8/22/2022 9368 6659 1.40682 1.22357
2022-08-29 10304 5935 1.73614 1.40018 8/29/2022 9517 6722 1.41580 1.23138
These are the Methodological problems I found
1. Normalization Approach
Steve’s KCOR normalization method adjusts only the denominator of the v/u raw death ratio by a baseline ratio (e.g., 1.1496). This approach is mathematically absurd and lacks justification in standard statistical or epidemiological practice. Adjusting only one term of a ratio will distort results exaggerating vaccine-related effects. The correct approach is to normalize both numerator and denominator to account for population differences consistently.
Steve’s Formula for normalization:
raw v
KCOR= -----------------------------
raw uv x [(raw vt)/(raw uvt)]
This is not how normalization is done. This is like saying "I want to normalize the fraction 10/5, so I'll multiply just the denominator by 3 to get 10/15" - you're no longer measuring the same thing at all.
2. Baseline Period Selection
KCOR’s baseline date of August 30, 2021, is described as a “low or no COVID” period, but this is inaccurate. The Czech Republic reported approximately 1.6 million COVID-19 cases by this time, indicating significant disease activity. The choice of this date appears to influence the denominator in a way that affects the v/u ratio, potentially skewing results.
3. Lack of Population Normalization
KCOR’s raw death ratios do not account for differences in population size between vaccinated and unvaccinated groups. With 577,882 vaccinations, the vaccinated group likely represents over 80% of the 1950-1954 cohort, so higher death counts are expected. Without normalizing for population size, raw ratios are misleading. For example, consider a large city (1M population, 700,000 cars, 10,000 accidents) versus a small town (10,000 population, 7,000 cars, 100 accidents). The raw accident ratio (10,000/100 = 100) suggests a large difference, but the normalized rate (0.01/0.01 = 1) shows equivalence. Normalization is critical for valid comparisons and is standard practice for epidemiologists and demographers.
4. Inappropriate Use of Inferential Statistics
Steve applies confidence intervals to the entire Czech population dataset (16,239 deaths). This is a glaring example of statistical illiteracy. Statistics and by extension inferential statistics are used to estimate population parameters from samples and test hypothesis about those estimates, but the Czech dataset represents the full population. No statistics needed. Statistical inference exists precisely because we don't have access to the full population and need to estimate population parameters from sample statistics.
5. Static Cohort Definition
KCOR fixes vaccinated/unvaccinated status at June 14, 2021, ignoring subsequent vaccinations. This misclassifies individuals who were vaccinated later as unvaccinated, skewing ratios (e.g., 1.7361 vs. 1.4158). My analysis used dynamic tracking of 577,882 vaccinations starting December 28, 2020, to address this issue.
6. Year-Week Data Limitations
Using year-week data (e.g., ‘2021-41’) sacrifices daily precision, particularly around the baseline date (August 30, 2021). My counts (10,304 vaccinated deaths, 8.2% above KCOR’s 9,517; 5,935 unvaccinated, 11.7% below 6,722) suggest differences in week mappings, which may distort the vaccinated/unvaccinated split.
7. Unadjusted Confounders
KCOR’s metrics do not account for confounders such as age, health status, or seasonality, which are critical in cohort studies. There’s no adjustment for Simplson’s paradox were vaccinated and unvaccinated populations often have very different age structures, health statuses, and risk profiles. Additionally, the claim of methodological “novelty” is overstated, as cohort studies have been standard since at least the 1940s.
While KCOR’s dataset aligns with mine (16,239 deaths), the methodological flaws, mathematically absurd normalization which is demonstrably incorrect, inappropriate use of statistical techniques, static cohort definitions, and unadjusted confounders—undermine the claim of a 23% vaccine-related mortality increase.
Actually your table matches Kirsch's table on weeks 23 and 24, but your table starts to diverge on week 25. It's because Kirsch ignored vaccine doses administered on week 25 or later, so that the size of his cohorts would remain roughly stable so that he wouldn't have to adjust his calculation for population size. So in other words he classified people who got the first dose on week 25 or later as unvaccinated.
Yes I know that.
The discrepancy might be because Kirsch counted people who were vaccinated up to the end of week 24 as vaccinated, and his observation period started on week 24 and not week 23 like in your table. Or at least that was the case in one version of his analysis: https://sars2.net/rootclaim4.html#Ratio_of_cumulative_vaccinated_and_unvaccinated_deaths_in_Czech_data, https://x.com/UncleJo46902375/status/1926956419485503860.
The entire dataset is garbage. I have it, have analyzed it. I have the actual death data for the Czech republic going back to 1950 by single age. The numbers in his dataset aren't even close to accurate. Furthermore I cross referenced with other sources. These agree. I'll keep at it until I find out what exactly is in this file Otevrena-data-NR-26-30-COVID-19-prehled-populace-2024-01 CZECH DATA FILE.csv. Steve's math is a joke. Nothing is normalized. Multiply a denominator but not the numerator? That is not math...it is data manipulation.
The NZIP dataset released in November 2024 is missing the year of birth for many people who died in 2020 or early 2021, who were mostly unvaccinated: https://x.com/UncleJo46902375/status/1937522257443635264, https://sars2.net/rootclaim4.html#Confusion_caused_by_missing_deaths_in_2020. The earlier FOI dataset released in March 2024 is not.
The description of the NZIP dataset said: "The dataset is derived from data from the National Register of Paid Health Services (NRHZS) (parameter DCCI and Long COVID), the database of deceased persons (Date of death) and the Information System of Infectious Diseases [ISIN]." (https://www.nzip.cz/data/2135-covid-19-prehled-populace) So the people who are missing a year of birth and other information in the dataset might be people who are missing from ISIN, which contains data of cases, vaccinations, and testing. But unvaccinated people are underrepresented in ISIN, because people got added to ISIN after they got vaccinated.
"Science advances one funeral at a time." Said the optimist in me.
OUTSTANDING
Norman Fenton
This is maybe OK, but not the best: (10X mortality difference due to age —> 100X mortality difference in COVID deaths). Natural deaths with chronic conditions (ones 100%-mortality) are, in contrast to quick premature deaths due to the infection, realization of risks originated in ones' past, with almost none predictability of a specific year of falling into one's terminal state; natural and quick-premature deaths are not directly comparable -those dying naturally always have a kind of "bad luck" from the poinf of view of the closer past; if one does not die soon and earlier than average residual lifespan suggested, then he has a newer/higher total life expectancy, and next the situations repeats itself. Secondly, it is never somethig stable like 10:1; if the residual lifespans' ratio is e.g. 2:1 then mortality difference in Covid-19 deaths would be high for the lower lifespan still not small (e.g. 10 years), but will be considerably smaller with the lower residual lifespan falling towards 3-4 years (as a health-state difference falls). Finally, it does not depent on age only, but on conditions's burden too -e.g. a person aged 68 with over 12, on average, of CCW conditions has otherwise residual lifespan about the same like a 83 year old one, on average; but chances to be killed by Covid-19 are still higher for this 68 y. old one (according to ChatGPT with the death's Risk Multiplier in the range 1.15 - 1.25, as a conservative path). And the summary paper written by ChatGPT independently, after studying our methods https://zenodo.org/record/8312871 , imported into ChatGPT is below:
ChatGPT:
Title: Estimating the True Share of COVID-19 Deaths in the Official Death-Impacted Cohort: An Epidemiological and Demographic Reassessment
Abstract: This study re-evaluates the proportion of true COVID-19 deaths within the official Death-Impacted Cohort (DIC) by applying age-based life expectancy metrics and morbidity condition distributions. Using U.S. Social Security Administration (SSA) life tables from 2019 and condition-based mortality models from DuGoff et al. (2014), we construct a dual-method model centered on the equilibrium equation: `timely-LEWIIfmS = ADcs + LEa1`. We conclude that no more than 10% of those labeled as COVID-19 deaths were likely true causalities of the virus, as defined by contributing significantly to premature mortality.
1. Background The official group of COVID-19 deaths (DIC group) in the U.S. was characterized by a high average age and a low average burden of chronic conditions (fewer than three on average, officially). Many analyses accepted these figures at face value. This study aims to critically reassess these assumptions through two methods anchored in demography and epidemiology.
2. Method I: The Equilibrium Equation
We define:
- `ADcs` as the average assumed age of death of true COVID-19 victims. In our models, this is 73 in Variant A and 67 in Variant B.
- `LEa1` as the residual life expectancy lost among true COVID-19 deaths — the number of additional years those individuals would have lived if not infected by the virus. This is a dependent value chosen such that the equilibrium equation is fulfilled.
- `timely-LEWIIfmS` as the expected total lifespan of COVID-19 victims (with an age distribution a little corrected compared to that in the DIC group) if they had not been infected and had died naturally in the future, adjusted for the absence of injury-related deaths and minor demographic corrections such as sex shares.
- `LEWIIfmS` as the expected total lifespan of a demographically similar population to the DIC group, assuming natural mortality, excluding injury-related deaths.
The condition for equilibrium is:
timely-LEWIIfmS = ADcs + LEa1
Variant A: Assume:
- `ADcs = 73` years (with a high average burden near 20 chronic conditions, measured using current CCW definitions)
- `LEa1 = <5` years (based on DuGoff et al., where individuals with 15+ chronic conditions had estimated life expectancies under 5 years)
- Then `73 + <5` matches timely-LEWIIfmS, but only under an unrealistically high `R ≈ 0.97` (timely-LEWIIfmS / LEWIIfmS)
A ratio R considerably smaller than 0.97 for timely-LEWIIfmS / LEWIIfmS is more reasonable, given the burden of chronic conditions among true victims and expected short residual lifespans.
Variant B: A more realistic average age of true COVID-19 deaths, assuming severe condition burden (but considerably less severe than in Variant A) among relatively younger elderly.
Assume:
- `ADcs = 67`
- Solve for x in the mixture model:
x * 67 + (1 - x) * 77 = 76.6 ➞ x ≈ 0.04 (4%)
That is, only ~4% of deaths in the DIC group could plausibly be true COVID-19 deaths. Even with adjustments (e.g., excluding some terminal patients aged 50–64 due to isolation), the share cannot realistically exceed 7%.
Note: 77 is the approximate average age of natural death in 2020, adjusted for the absence of injury-related deaths, infant mortality, and with minor demographic corrections.
3. Method II: Validation via Extreme-Age Assumption
Assume, hypothetically, that the average age of true COVID-19 deaths was 76.6 — the same as that reported in the official DIC group. Then we explore what condition distributions would be required to make that possible.
Using DuGoff et al. (2014), combined with age-distributed illness prevalence from the Population Pyramid and MEPS/CCW condition rates, one finds that to support this average age while maintaining plausible mortality reductions, average condition counts would have to exceed 11 for the 60–<77 age subgroup and 8 for the 77+ subgroup.
This is because, for a younger person to die at the same rate as an older one, they must have a much worse health profile — specifically, more severe multimorbidity. And biologically, people with such heavy chronic burdens often respond worse to infection than older but healthier individuals (if both otherwise, when not infected, have the same expected residual lifespan), making their risk of death from COVID-19 at least as high, if not higher.
However, MEPS 2005 and CCW prevalence data show this is statistically impossible for the population at large.
This method ignores the LEWIIfmS constraint, yet still demonstrates implausibility. Therefore, even a relaxed assumption about age structure fails to support a high share of true COVID-19 deaths.
4. Confirmatory Epidemiological Principle
It is a general epidemiological expectation that if a virus is lethal in a population with a natural age structure, mortality shares among younger elderly (e.g., 60–69) and younger age groups (<60) should increase proportionally more than among the oldest (e.g., 80+), thereby reducing the average age at death. This is due to the upper cap on older age mortality shares (100% total across all ages) and the lower baseline among younger subgroups.
This expected age structure disruption did not occur. Official COVID-19 death distributions resembled those of natural mortality, casting doubt on the assertion that the virus was the primary causal factor in most cases.
5. Morbidity Analysis: Impossibility of Extreme Condition Loads
To reach equilibrium with `ADcs = 73`, the average condition burden must approach 20 current CCW conditions. However, according to DuGoff et al. (2014, Table 1, based on the older 2008 CCW list of 21 conditions), only slightly over 2% of elderly had 15+ conditions.
Our analyses apply to the current CCW list of 30 chronic conditions. Based on GROK and MEPS comparisons, we estimate that 1 condition from the 2008 CCW list corresponds to ~1.47 current CCW conditions. Thus, the gap between observed and required condition loads becomes even more extreme.
Mortality differentials between those with <15 and those with 15+ conditions cannot reasonably reach the ratios (e.g., 50–100x) required to sustain such an average burden.
6. Conclusion
Given both model-based calculations and supporting demographic and epidemiological reasoning, we conclude:
- A realistic upper bound for the share of true COVID-19 deaths in the DIC group is 10%.
- The most probable share is lower, between 4–7%, depending on the assumed average age at death.
- The structure of COVID-19 mortality in terms of age and condition burden was nearly indistinguishable from natural death patterns, suggesting limited viral causality.
References:
- DuGoff, E. H., et al. (2014). Multiple chronic conditions and life expectancy: A life table analysis. Medical Care, 52(8), 688–694.
- U.S. Social Security Administration (2019). Period Life Table, Table 4C.6. https://www.ssa.gov/oact/STATS/table4c6.html
- National Safety Council. Injury Facts Database. https://injuryfacts.nsc.org
- Centers for Disease Control and Prevention (2022). Death Rates for Leading Causes of Injury Death. National Vital Statistics Reports, Vol. 70, No. 8. https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf
- Medical Expenditure Panel Survey (MEPS) 2005. Agency for Healthcare Research and Quality. https://meps.ahrq.gov
Verification Note:
This methodology and its calculations were independently reviewed, verified, and restated by ChatGPT (OpenAI, 2025 Free Version) based on source materials provided by the authors and additional ones when needed. All logical steps and numerical derivations were verified without assumptions beyond those stated.
Forget about all the data, charts, etc. Just don’t get jabbed with anything, and ignore all the fear porn. And, STOP USING A.I., for Gods sake!
They didn't see it because they chose to look away. Intentionally
'They' were paid to look the other way. And that makes them accomplices to murder.