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.

Thank you for your efforts. We do a somewhat similar monitoring process in Israel. You might be interested in our findings. Also we can point each other to issues worth investigating.

Thank you for this work. You led with a disclaimer that you are doing it basically for your own gratification. By vocabulary, terminology, concept and depth it is beyond what even most of the Substack readership will want to grapple with. But it must be so. Others of us will attempt to make your insights accessible to the hoi polloi.

Specifically, VT data came with only text COD's w/o ICD's. I spoke with a VT DoH Vital Statistics Information Manager, who confirmed that they transmit to CDC the text COD's & CDC assigns the ICD's, mostly using a software but a small % that confound their software are adjudicated manually, I was trying to get help matching text COD's w/ ICD's

"I am a skeptic of power, and no eager subscriber to Hanlon’s Razor."

I follow the data. I prefer the incompetence hypothesis until the data forces me to abandon it for the malice hypothesis.

I have found that there were 230,000 more US working age deaths in 2021 than in 2019. The CDC compared 2021 with 2020, which is an error, because 2020 has covid as a major confounder, while 2019 was prepandemic and should be used for comparison. I am certain that the CDC is aware of this. Hence, I conclude that comparing 2021 with 2020 is for the purpose of covering up the massive mortality increase in 2021 relative to 2019.

The key to defeating the coverup, I believe, is to insist that the CDC's comparison of 2021 with 2020 is invalid.

When you generate a 'best fit' line for data as presented in the charts of Exhibit A(for example) please also provide your chi-square, or better yet your R^2 value to give us an idea of how well the trend line represents the aggregated data points and their frequency(weekly I guess). Some of those best fit will have a decent R^2 number, but several of them will have a very poor R^2. I ask this not as a skeptic but for those who would attack your data and summary conclusions as unfulfilled by lacking some measure of trust. Thank you.

Even though this is deaths indexed against time (MMWR week), this is not a true regression fit exercise between two independent variables.

1. This is an arrival distribution over time. I am not evaluating how well a particular time series of data fits a linear regression line. That is irrelevant. The arrival could have an R^2 of .18 and be perfectly coherent and informative as an arrival function. Cancer in particular will have a bad fit - but its inference will be the strongest in the signal group.

2. This is the formulation of an inflection point (as change between two underlying functions), not an assessment of the single relationship between two independent variables.

3. There are varying levels of confidence to the weeks contained in the series. They are not all equal in strength. The weeks the CDC tampered with are low confidence and should not count in the R^2, as well - the final weeks are also of weaker confidence. An R^2 would tell me academic fiction and maybe even signal that I did not know what I was doing, to a systems pro who understands points 1 - 4 here.

4. A reverse chi-squared arrival will have a horrid R^2 fit, but tell a very significant story - so the two are not related much.

You will see the 'why' of all this in the next article.

When statisticians see a trend line curve, superimposed on a set of distributed data points, and no R^2 value they are going to wonder what it is. In fact, not only would I insist on a R^2 value for your post-inflection point, but I would insist on the same for the weeks prior to that inflection point. We can then make a qualitative comparison to the 'best fit' of the data. This is true whether there are two independent variables(there aren't, time is not independent), or six. You have broken them down in Exh A nicely. An R^2 for the pre-inflection is always going to be better than the post-inflection. Due in some major part to the grenade(jab of death/vax) that was tossed into the population.

If you don't have those regression analysis done, someone will do them for you(like me), and use it as a weapon to discount your potential conclusion. Frex: "Looking at the data in Exh A, I have calculated an R^2 value for the pre-inflection of 0.712. However, calculations of the R^2 value for the post-inflection points runs from a miserable 0.416 down to a disastrous value of 0.18. The only thing that can be determined from these trend lines is that anything post-inflection may be attributed to herd of elk migrating, or phase of the moon, or the phrenology of the author." (no offense meant).

Perhaps making inferences without supporting statistics is why many skeptics are raked over the coals. I await the next articles and their supporting evidence. Thank you, doc

"Looking at the data in Exh A, I have calculated an R^2 value for the pre-inflection of 0.712. However, calculations of the R^2 value for the post-inflection points runs from a miserable 0.416 down to a disastrous value of 0.18."

A person who contended this would be a complete idiot, and I would never listen to another thing they said. These are 'Variations Against Trend (Baseline)' charts - they have an inherent 'bias to R^2 fit'. Any lower R^2 or departure from fit indicates a SIGNAL, not the lack of one.

I think you are missing this, and the four points I made.

You just used the word Trend in your quotes. The trend line represents the linear interpolation of the data points. This is true no matter what signal you are trying to convey. Point 1 is an excellent example of where a regression is called for. Point 2 is what you are trying to convey, or signal. No problem.

In closing, if you leave the trend line out(post-inflection), you would have no concern about the R^2 value, and the non-statistics among us can make and see their own best fit. Your 'aha!' moment. If/when you are going to put a linear regression trend line(post-inflection) of the data points to prove your signal direction and magnitude, leaving out an R^2 value - even if it is worse than pre-inflection is a mistake. As noted, we expect the regression to get worse. But no matter the delta R^2, from pre and post-inflection, either do or don't for both.

Trend and 'linear fit' are not congruent, despite your insistence that they are. There exists such a thing called Non-Linear optimization (my Major is in this). It uses the Golden Section method to conduct regression/trend analysis.

As you can see below, gravity (no inflection, single function, and indeed a TREND) produces a trend in the motion of a planet, but if you place an R^2 on it the fit is going to be horrible. Does that mean that gravity does not exist!!!!!!!!!!?????

This is what you are saying - that gravity does not exist, because it doesn't fit an R^2 well.

Ethical Skeptic: This is the first article of your that I have read. Well done!

I live in MN and I have been studying death files in MN for 2 years. I don’t have your skills. But, I do have the perspective of studying the details provided in the death records. I have learned a lot from this.

Interestingly, I don’t see a lag in CDC coding in the MN data. I am guessing the CDC just isn’t releasing the data to their sites. But they are coding and reporting back to the states.

In MN, the 5 year death average (2015-2019) was 44,000/year.

Clearly, with cancer, the “dog is not barking” in relation to excess deaths during the first year of covid. On the flip side, it appears to be barking loudly for heart disease. More than one mechanism of toxicity? Spike protein toxicity for heart disease (would be correlated to waves of covid as well as waves of vaccination) and pseudouridine enriched mRNA leading to immune suppression for cancer (correlated only to vaccination)?

Thanks for the reminder. I shared, recommended, and commented but failed to "like." I forwarded ES's piece to a friend who has been appalled by our reliance upon the flawed GAST data used to elevate CO² to a pollutant by the EPA. Crunching numbers is his forte...much like ES. This was offered to me from him about ES's part one:

"The short time period from vaccination until death simplifies the modeling but over longer time frames the complexity of the process being modelling will grow. I worry [that] without an active DETOX program, vaccine - driven deaths will go on far longer than most folks today suspect. So does Dr. Malone as I recall."

This is so helpful - thanks so much!

Thank you for your efforts. We do a somewhat similar monitoring process in Israel. You might be interested in our findings. Also we can point each other to issues worth investigating.

https://israelab.substack.com/archive

Thank you for this work. You led with a disclaimer that you are doing it basically for your own gratification. By vocabulary, terminology, concept and depth it is beyond what even most of the Substack readership will want to grapple with. But it must be so. Others of us will attempt to make your insights accessible to the hoi polloi.

Ashmedia wants to share this data with you... idk if you can see subscriber only post...

"We Got the Vermont Death Certificates for *ALL* Deaths for the Years 2015-2022 & it only took the bureaucracy 4 days, which has got to be a record"

"You know who I'd really love to get? Ethical Skeptic. He's been working with the death certificate ICD's since the beginning (CDC MMWR/Wonder)

Idk how to contact him though, he's like a mystic of sorts revealing himself through the mask of his twitter profile, and doesn't accept DM's."

https://ashmedai.substack.com/

https://ashmedai.substack.com/p/paid-subscribers-only-sneak-preview/comments

Specifically, VT data came with only text COD's w/o ICD's. I spoke with a VT DoH Vital Statistics Information Manager, who confirmed that they transmit to CDC the text COD's & CDC assigns the ICD's, mostly using a software but a small % that confound their software are adjudicated manually, I was trying to get help matching text COD's w/ ICD's

Just came across your website and substack. Wow! What a trove of great information. Thank you!!

"I am a skeptic of power, and no eager subscriber to Hanlon’s Razor."

I follow the data. I prefer the incompetence hypothesis until the data forces me to abandon it for the malice hypothesis.

I have found that there were 230,000 more US working age deaths in 2021 than in 2019. The CDC compared 2021 with 2020, which is an error, because 2020 has covid as a major confounder, while 2019 was prepandemic and should be used for comparison. I am certain that the CDC is aware of this. Hence, I conclude that comparing 2021 with 2020 is for the purpose of covering up the massive mortality increase in 2021 relative to 2019.

The key to defeating the coverup, I believe, is to insist that the CDC's comparison of 2021 with 2020 is invalid.

I am wondering if there are age buckets that go with the data and if there are shifts in age that can be seen that might also tell a story?

Are you a vaccinazi?

If you threaten a person’s livelihood because of their vaccine views, you are a vaccinazi.

If you censor a person’s speech because of their vaccine views, you are a vaccinazi.

If you coerce a person to get vaccinated, you are a vaccinazi.

If you restrict a person’s activities because of their vaccine status, you are a vaccinazi.

If you impede the open study and free discussion of vaccines, you are a vaccinazi.

Thank you very much for sharing your professional analysis with us! Looking for to the next installment in this series.

When you generate a 'best fit' line for data as presented in the charts of Exhibit A(for example) please also provide your chi-square, or better yet your R^2 value to give us an idea of how well the trend line represents the aggregated data points and their frequency(weekly I guess). Some of those best fit will have a decent R^2 number, but several of them will have a very poor R^2. I ask this not as a skeptic but for those who would attack your data and summary conclusions as unfulfilled by lacking some measure of trust. Thank you.

Even though this is deaths indexed against time (MMWR week), this is not a true regression fit exercise between two independent variables.

1. This is an arrival distribution over time. I am not evaluating how well a particular time series of data fits a linear regression line. That is irrelevant. The arrival could have an R^2 of .18 and be perfectly coherent and informative as an arrival function. Cancer in particular will have a bad fit - but its inference will be the strongest in the signal group.

2. This is the formulation of an inflection point (as change between two underlying functions), not an assessment of the single relationship between two independent variables.

3. There are varying levels of confidence to the weeks contained in the series. They are not all equal in strength. The weeks the CDC tampered with are low confidence and should not count in the R^2, as well - the final weeks are also of weaker confidence. An R^2 would tell me academic fiction and maybe even signal that I did not know what I was doing, to a systems pro who understands points 1 - 4 here.

4. A reverse chi-squared arrival will have a horrid R^2 fit, but tell a very significant story - so the two are not related much.

You will see the 'why' of all this in the next article.

TES

When statisticians see a trend line curve, superimposed on a set of distributed data points, and no R^2 value they are going to wonder what it is. In fact, not only would I insist on a R^2 value for your post-inflection point, but I would insist on the same for the weeks prior to that inflection point. We can then make a qualitative comparison to the 'best fit' of the data. This is true whether there are two independent variables(there aren't, time is not independent), or six. You have broken them down in Exh A nicely. An R^2 for the pre-inflection is always going to be better than the post-inflection. Due in some major part to the grenade(jab of death/vax) that was tossed into the population.

If you don't have those regression analysis done, someone will do them for you(like me), and use it as a weapon to discount your potential conclusion. Frex: "Looking at the data in Exh A, I have calculated an R^2 value for the pre-inflection of 0.712. However, calculations of the R^2 value for the post-inflection points runs from a miserable 0.416 down to a disastrous value of 0.18. The only thing that can be determined from these trend lines is that anything post-inflection may be attributed to herd of elk migrating, or phase of the moon, or the phrenology of the author." (no offense meant).

Perhaps making inferences without supporting statistics is why many skeptics are raked over the coals. I await the next articles and their supporting evidence. Thank you, doc

"Looking at the data in Exh A, I have calculated an R^2 value for the pre-inflection of 0.712. However, calculations of the R^2 value for the post-inflection points runs from a miserable 0.416 down to a disastrous value of 0.18."

A person who contended this would be a complete idiot, and I would never listen to another thing they said. These are 'Variations Against Trend (Baseline)' charts - they have an inherent 'bias to R^2 fit'. Any lower R^2 or departure from fit indicates a SIGNAL, not the lack of one.

I think you are missing this, and the four points I made.

You just used the word Trend in your quotes. The trend line represents the linear interpolation of the data points. This is true no matter what signal you are trying to convey. Point 1 is an excellent example of where a regression is called for. Point 2 is what you are trying to convey, or signal. No problem.

In closing, if you leave the trend line out(post-inflection), you would have no concern about the R^2 value, and the non-statistics among us can make and see their own best fit. Your 'aha!' moment. If/when you are going to put a linear regression trend line(post-inflection) of the data points to prove your signal direction and magnitude, leaving out an R^2 value - even if it is worse than pre-inflection is a mistake. As noted, we expect the regression to get worse. But no matter the delta R^2, from pre and post-inflection, either do or don't for both.

Haveaniceday; doc :-)

Trend and 'linear fit' are not congruent, despite your insistence that they are. There exists such a thing called Non-Linear optimization (my Major is in this). It uses the Golden Section method to conduct regression/trend analysis.

As you can see below, gravity (no inflection, single function, and indeed a TREND) produces a trend in the motion of a planet, but if you place an R^2 on it the fit is going to be horrible. Does that mean that gravity does not exist!!!!!!!!!!?????

This is what you are saying - that gravity does not exist, because it doesn't fit an R^2 well.

https://theethicalskeptic.com/wp-content/uploads/2022/08/Gravity-does-not-exist.png

Ethical Skeptic: This is the first article of your that I have read. Well done!

I live in MN and I have been studying death files in MN for 2 years. I don’t have your skills. But, I do have the perspective of studying the details provided in the death records. I have learned a lot from this.

Interestingly, I don’t see a lag in CDC coding in the MN data. I am guessing the CDC just isn’t releasing the data to their sites. But they are coding and reporting back to the states.

In MN, the 5 year death average (2015-2019) was 44,000/year.

2020 deaths: 52,000

2021 deaths: 51,000

2022 deaths thru June 30: 25,000+

Clearly, with cancer, the “dog is not barking” in relation to excess deaths during the first year of covid. On the flip side, it appears to be barking loudly for heart disease. More than one mechanism of toxicity? Spike protein toxicity for heart disease (would be correlated to waves of covid as well as waves of vaccination) and pseudouridine enriched mRNA leading to immune suppression for cancer (correlated only to vaccination)?

Correct - complicating these next analyses immensely, yes.

TES

ES - thank you. This is what you do and you’ve done it well here. Damning indictment and one easily shared with our friends and family.

How are there only 87 likes??? This is one of the seminal articles on perhaps the most consequential issue of the pandemic.

It's not like this needs Hold2 to translate ;)

Thanks for the reminder. I shared, recommended, and commented but failed to "like." I forwarded ES's piece to a friend who has been appalled by our reliance upon the flawed GAST data used to elevate CO² to a pollutant by the EPA. Crunching numbers is his forte...much like ES. This was offered to me from him about ES's part one:

"The short time period from vaccination until death simplifies the modeling but over longer time frames the complexity of the process being modelling will grow. I worry [that] without an active DETOX program, vaccine - driven deaths will go on far longer than most folks today suspect. So does Dr. Malone as I recall."

Mind Blowing..thanks so much for your hard work!!

Thank you for your hard work