The age range choices for the CDC datasets have confounded many analyses I've attempted to conduct, mostly revolving around age-adjusted CFR's & IFR's.
It's doubly frustrating because the CDC obviously HAS the data, and chooses not to provide it to the public in a form that we can verify their work with
Small point - is it possible that the lockdowns impacted the all cause mortality in the age you noted? (Less car accidents, less homicide etc because of the decrease in driving and interaction)
"Still, there are some people who refuse to believe in even the need for due diligence with respect to some authorities. This article is primarily for them. I hope some will read it, and at least see the need to dig a little deeper."
With deep respect and appreciation for the mathematical/ statistical analysis it is only faith in your skill and honesty that leads someone like me to trust those figures lead to the conclusion. While it is glaringly obvious to you and others who assimilate and manipulate numbers with ease many like me are overwhelmed by the matrix of figures. Offered in the spirit of the clueless user included in Beta test applications.
What we need are simple representations in bar charts, pie charts or graphs to translate the relationships and really grasp what the numbers clearly demonstrate. Most folks don't like to admit to being confused or unable to understand, which only helps the faction promoting talking points like 98% effective that we can feel confident we understand. If the goal is broad understanding more pictures less math is the target audience who struggle to be informed but cannot. <3
Quarterly reports are just raw numbers. Annual reports get into rates and "adjusted" comparisons to suicide in general population. Annual reports also cover data quality, procedures, programs, etc.
As regards sieves, the Bardenheier study showed the problem with using the same people for the treatment arm and for control. Survivorship bias using a nursing home population. The all cause mortality rr for treatment was 0.34.
Thanks for writing this. The two jab regimen has inflated effectiveness by design. It’s why the Jensen jab lost in uptake and why boosters almost always look almost completely worthless. In which there are less vaccinated infected who count as unvaccinated cases due to only a 14 day window vs. 35 day window.
The infuriating part is that to produce the paper someone prepped the data of first prick to death or no prick to death. By cleverly splitting the deaths into covid and non-covid they never report the most important metric, all cause mortality by shot status (from first jab date). See above for more discussion.
We live in the POST-FACT Information economy; raw data, facts, probabilities, these are all ‘injected’ with biased opinion likely layered in logical fallacies. Truth discovery in todays world requires a time, talent, and willingness to dig, uncover, sort, and reveal.
Isn't this a simple cohort mismatch issue? We know that the choice to get vaccinated or not is not a random choice across society. We know certain political leanings are more likely to get vaccinated. We know that blacks are less likely to get vaccinated, as well as education, income, etc. We also know that all these factors matter in other aspects in life, like overall health, risky jobs, living in safe vs less to plain non-safe neighborhoods, etc.
I actually came across this yesterday, when looking at a J&J vs mRNA article from the CDC, that was based on the same data. You here noticed the same thing ('mRNA rulez' according to the CDC), but the CDC actually has a separate article pointing this out. When I looked at it, I already suspected a cohort mismatch, as two separate studies done elsewhere actually showed a more subtle effect, and we know the mRNA shots less effective than these numbers due to known data and definition issues with the unvaccinated. But this study actually proves my suspicions, that indeed we are looking at a cohort mismatch.
It's not that I think CDC numbers are honest, but I do believe there's a plausible explanation for lower overdose and suicide deaths that you haven't considered. It could be a selection effect. The kinds of young people who are inclined to suicide and drug overdoses and drunk driving are not the kinds of young people who line up to be vaccinated.
Vaccination status might be a predictor of social status and (therefore) general health. However, the race/ethnicity part of the table that you cut off provides a similar picture.
Reporting the numbers of deaths is quite pointless. They should be weighted by length of time period spent in that vaccination status. Unvaccinated people are unvaccinated the whole time whereas time in dose-1 limbo should be around two months (less for most that get a 2nd jab, but some people remain in limbo). Multiply the dose-1 figures by 7/2, and you are in the ballpark of the unvaccinated figures.
It seems much more likely that someone would get a vaccine out-of-network, than they would be to get medical treatment out-of-network. So in the case of a harmful vaccine, that would mean a lot of injuries would show up in VSD as an injury with no associated vaccination. So I'm wondering how much this may skew the numbers.
First let me state that I am blond, and did not read that slowly. :-) But, wouldn't we need to add up ALL of the deaths, from the mRNA jabs combined, and then compare that number to the unjabbed? In that case, if I am reading the chart correctly, the jabbed are dying more from all cause that is not COVID than the unjabbed. (Apologies, but I cannot call it "vaccinated" or "unvaccinated", since it is not truly a "vaccine".) By splitting out the mRNA injections by brand, it automatically makes the number of deaths in each column smaller. Then by further breaking it down by number of jabs, etc., the same thing happens. I think splitting everything out is another slight of hand, for this data.
Now, looking at the chart the crazy way I am, J & J would still show fewer all cause mortality, yet it is the one that the use has been halted in, in several countries, due to the number of concerning side effects, including death. Again, something isn't adding up.
I'm still trying to finish my coffee after a crazy working weekend, so if my thoughts aren't making sense, feel free to ignore them, and go back and read my first sentence.
Proof of Statistical Sieves in Vaccine Efficacy Data
Looking at various pro-vaccine studies, it was obvious to me that the raw data "pea" was being hidden under the adjusted data "shell."
Raw data isn't being reported.
Bias in the adjusted data is what would be called "systematic error" in chemistry and physics.
Systematic error should be caught and prevented with proper procedure. Systematic error should never make it into the reported data.
The age range choices for the CDC datasets have confounded many analyses I've attempted to conduct, mostly revolving around age-adjusted CFR's & IFR's.
It's doubly frustrating because the CDC obviously HAS the data, and chooses not to provide it to the public in a form that we can verify their work with
Small point - is it possible that the lockdowns impacted the all cause mortality in the age you noted? (Less car accidents, less homicide etc because of the decrease in driving and interaction)
Mathew, great article!
That statistical MMWR report was so ridiculous.
I wrote about it in October also. Let's compare notes.
https://igorchudov.substack.com/p/boring-is-there-an-error-in-cdc-mmwr
I commented that CDC used incorrect person-year denominators and the CDC article is either a giant mistake, or a giant lie.
"Still, there are some people who refuse to believe in even the need for due diligence with respect to some authorities. This article is primarily for them. I hope some will read it, and at least see the need to dig a little deeper."
With deep respect and appreciation for the mathematical/ statistical analysis it is only faith in your skill and honesty that leads someone like me to trust those figures lead to the conclusion. While it is glaringly obvious to you and others who assimilate and manipulate numbers with ease many like me are overwhelmed by the matrix of figures. Offered in the spirit of the clueless user included in Beta test applications.
What we need are simple representations in bar charts, pie charts or graphs to translate the relationships and really grasp what the numbers clearly demonstrate. Most folks don't like to admit to being confused or unable to understand, which only helps the faction promoting talking points like 98% effective that we can feel confident we understand. If the goal is broad understanding more pictures less math is the target audience who struggle to be informed but cannot. <3
Trivia: The Department of Defense has an office dedicated to tracking active duty and reserve suicide. The office issues quarterly and annual reports. FYI, I link to Q4 report for CY 2021. Report depicts numbers by active duty vs reserve, Army, Navy, USMC, Air Force, CY quarter, year going back to 2015. https://www.dspo.mil/Portals/113/Documents/2021QSRs/TAB%20A_20220317_OFR_Rpt_Q4%20CY21%20QSR.pdf?ver=PcN7dgtBKM3RG2uP1rAZ-A%3d%3d
Quarterly reports are just raw numbers. Annual reports get into rates and "adjusted" comparisons to suicide in general population. Annual reports also cover data quality, procedures, programs, etc.
As regards sieves, the Bardenheier study showed the problem with using the same people for the treatment arm and for control. Survivorship bias using a nursing home population. The all cause mortality rr for treatment was 0.34.
That study was totally worthless.
Thanks for writing this. The two jab regimen has inflated effectiveness by design. It’s why the Jensen jab lost in uptake and why boosters almost always look almost completely worthless. In which there are less vaccinated infected who count as unvaccinated cases due to only a 14 day window vs. 35 day window.
related note: under-reporting of AE's in VSD:
https://youtu.be/af4BH55CVio
I wrote about this last fall, see here: https://paulkraken.substack.com/p/cdc-vaccine-protective-effect-expected?s=w
The infuriating part is that to produce the paper someone prepped the data of first prick to death or no prick to death. By cleverly splitting the deaths into covid and non-covid they never report the most important metric, all cause mortality by shot status (from first jab date). See above for more discussion.
We live in the POST-FACT Information economy; raw data, facts, probabilities, these are all ‘injected’ with biased opinion likely layered in logical fallacies. Truth discovery in todays world requires a time, talent, and willingness to dig, uncover, sort, and reveal.
(Thanks for doing what you do!)
Isn't this a simple cohort mismatch issue? We know that the choice to get vaccinated or not is not a random choice across society. We know certain political leanings are more likely to get vaccinated. We know that blacks are less likely to get vaccinated, as well as education, income, etc. We also know that all these factors matter in other aspects in life, like overall health, risky jobs, living in safe vs less to plain non-safe neighborhoods, etc.
I actually came across this yesterday, when looking at a J&J vs mRNA article from the CDC, that was based on the same data. You here noticed the same thing ('mRNA rulez' according to the CDC), but the CDC actually has a separate article pointing this out. When I looked at it, I already suspected a cohort mismatch, as two separate studies done elsewhere actually showed a more subtle effect, and we know the mRNA shots less effective than these numbers due to known data and definition issues with the unvaccinated. But this study actually proves my suspicions, that indeed we are looking at a cohort mismatch.
It's not that I think CDC numbers are honest, but I do believe there's a plausible explanation for lower overdose and suicide deaths that you haven't considered. It could be a selection effect. The kinds of young people who are inclined to suicide and drug overdoses and drunk driving are not the kinds of young people who line up to be vaccinated.
Vaccination status might be a predictor of social status and (therefore) general health. However, the race/ethnicity part of the table that you cut off provides a similar picture.
Reporting the numbers of deaths is quite pointless. They should be weighted by length of time period spent in that vaccination status. Unvaccinated people are unvaccinated the whole time whereas time in dose-1 limbo should be around two months (less for most that get a 2nd jab, but some people remain in limbo). Multiply the dose-1 figures by 7/2, and you are in the ballpark of the unvaccinated figures.
It seems much more likely that someone would get a vaccine out-of-network, than they would be to get medical treatment out-of-network. So in the case of a harmful vaccine, that would mean a lot of injuries would show up in VSD as an injury with no associated vaccination. So I'm wondering how much this may skew the numbers.
First let me state that I am blond, and did not read that slowly. :-) But, wouldn't we need to add up ALL of the deaths, from the mRNA jabs combined, and then compare that number to the unjabbed? In that case, if I am reading the chart correctly, the jabbed are dying more from all cause that is not COVID than the unjabbed. (Apologies, but I cannot call it "vaccinated" or "unvaccinated", since it is not truly a "vaccine".) By splitting out the mRNA injections by brand, it automatically makes the number of deaths in each column smaller. Then by further breaking it down by number of jabs, etc., the same thing happens. I think splitting everything out is another slight of hand, for this data.
Now, looking at the chart the crazy way I am, J & J would still show fewer all cause mortality, yet it is the one that the use has been halted in, in several countries, due to the number of concerning side effects, including death. Again, something isn't adding up.
I'm still trying to finish my coffee after a crazy working weekend, so if my thoughts aren't making sense, feel free to ignore them, and go back and read my first sentence.