Feb 4, 2022Liked by NE - nakedemperor.substack.com
don't know about UK, but in US you are not considered "vaccinated" until 2 weeks after your second jab. Convenient for those "unvaxxed" death stats isn't it?
Feb 4, 2022Liked by NE - nakedemperor.substack.com
Thank you! Very logical and convincing, beyond a reasonable doubt. They miscategorize in order to provide the headline "data" so that the further jabs can be pushed, despite their deadliness and harm. Very effective construct. Works every time. No shame, no morals, no principles, no conscience, no fear of God.
This is the logical devolution of atheist societies. Although the proof of the existence of God is simple and indisputable. We know the devil exists. Thence, God must exist too. Q.E.D.
Feb 5, 2022Liked by NE - nakedemperor.substack.com
It looks to me like it is a form of 'healthy vaccinee' effect.
They're vaccinating pretty much everyone, but there is a group that isn't being vaccinated -- those so very ill and close to death that the vaccine side effects might finish them off.
The important point that many people miss is that most people don't die suddenly -- they become ill with a disease/condition that ends in death, and thus their final fate is telegraphed early.
With this 'healthy vaccinee' hypothesis as each vaccination dose campaign gets underway in each age group you see those closest to death not get vaccinated and thus get concentrated into the 'previous state of vaccination' group. This makes the death rates in the 'previous state of vaccination' soar. Then, over the next few months, these very very ill individuals die and deaths in the 'previous state of vaccination' return to being similar to the group that took the next dose.
The time course of the increase in deaths in the prior-vaccine-state group reflects the longevity of those identified as 'shouldn't have the next dose'. This looked to be about 3 months or so for first doses given in early 2020, but it has become progressively longer as, presumably, they are allowing more very ill people to forgo their next vaccine dose.
This is why we need to have proper matched cohort studies to investigate the vaccines; the current vaccine surveillance methods (in particular test-negative case-control) are not designed to be used in the current situation.
Feb 5, 2022Liked by NE - nakedemperor.substack.com
So just received an email from CVS. Get the booster after testing positive for COVID????? Booster shots are the best protection against severe complications from COVID-19, especially during the ongoing surge of the Omicron variant.
If you’ve recently tested positive for COVID-19, the CDC advises that you get a booster once you’re symptom-free and have completed the recommended isolation period.
Feb 5, 2022Liked by NE - nakedemperor.substack.com
"Why does the unvaccinated mortality rate shoot up during 1st dose vaccinations, they haven’t been vaccinated?"
For older groups, this reflects short-term healthy user bias. People "on their death bed" don't get the first dose. So as people not on their death bed exit the group (by being 1st-dosed) the denominator drops and the death rate goes up.
"Why does the 1st dose mortality rate shoot up after second dose vaccinations, they haven’t had a second dose?"
Same, again. It just repeats, those who can take the 2nd dose leave the denominator and those whose health went south after the first dose are left behind.
"And now, why does 2nd dose mortality rates shoot up, as boosters begin, when they haven’t had a booster?"
Literally same, again.
Once you get into younger groups, this gets a lot more complicated. There's bias toward the vulnerable for pre-spring 1st doses, so the death rate drops as the general population qualifies. Then for the 18-39 group you add another wrinkle, in that the unvaccinated 18-39 "get younger" when the vaccine becomes available because more 30-39s take it, so there's a drop all the way until summer. The summer bump could be normal trends or reflect the younger+healthy leaving the 18-29 pool by getting 1st-dosed. The various dosed groups "get older" at first and then "get younger" as well, all the way to boosters, which hides the healthy user drain visible in the above sets.
Feb 5, 2022Liked by NE - nakedemperor.substack.com
I don't think it's miscategorisation (I used to as you probably know). They have added <21 days for doses 1 and 2 now and have said in numerous FOI requests that they are categorising unvaccinated correctly.
So, if it's not the numerator, it must be the denominator? There is little transparency on their person-years calculation. I'm looking at that...
It sounds like this could be chalked up to (some degree) these deaths, just aren't given the label as a COVID-19 death. I'm not saying they were COVID-19 deaths prior because we know they were mostly just deaths with a previous positive SARS-CoV-2 PCR test. I'm also not saying the vaccine isn't killing people; I'm just saying this data is still inconclusive, and we need to do more research. But indeed the vaccine program should be stopped immediately.
I have a theory about the graphs above and I do not think that they show anything about vaccine safety or that their pattern is even unique to vaccines. After my theory statement I have a longer explanation, a few predictions, and a non-covid thought experiment.
Theory: the mortality rates will always be higher in groups of people that only partially complete a medical intervention relative to those who either finish the intervention or never start it.
Explanation: Controlling for similarities between study groups is really hard. If there is ever selection bias in how the study groups are formed, then that bias difference can easily overwhelm the effect of what intervention you are trying to study. The graphs above look at people at various stages of their vaccination status. However, there is likely selection bias in what causes people to move from one group to another. Many people who start their series of shots, but do not complete it would fail to do so because new or worsening medical problems make future vaccinations medically unnecessary. This effect results in a disproportionate number of healthy people finishing their vaccination series and a disproportionate number of the unhealthy not finishing. As a result, the partially completed groups will have worse mortality data and the fully completed groups will have better than expected mortality data. In fact, when the ‘>21 days after 2nd shot’ group represented a fully complete series it had great mortality data. Once boosters were approved and that same group started to represent people who did not complete their series of shots, then that group started to select for unhealthy people and the group’s mortality rate rose.
Prediction: If a second booster (4th shot) is approved then the mortality data for the ‘>21 days after 3rd shot’ will start to select for a disproportionately unhealthy group and will show a higher non-COVID mortality than unvaccinated people. Likewise, those with a completed 4th shot will be disproportionately healthy. I further predict that this pattern appears frequently in other forms of serial medical interventions.
Thought experiment: Imagine I take a large number of men, meet with them and schedule half of them for a prostate exam in 3 months. One year later I track them all down and look at the mortality data. If I look at the mortality data for those scheduled for a prostate exam against those who were not scheduled for one, then I expect minimal difference. However, if I take the men schedule for a prostate exam and divide than into no-shows and appointment keepers then what would I expect for mortality data between the three groups? Some of the no-shows were due to men having medical issues that made the prostate exam medically unnecessary. Also, I expect on average the no-shows are less concerned about their health than the appointment keeper group. Those two effects would combine to make the no-show group the one with the highest mortality, and because we removed the highest mortality people from those that were scheduled for a prostate exam, the ones that had completed the exam would look disproportionately healthy compared to those that were never scheduled to have one. The end result is those that completed the medical regimen would have the best mortality, those that were supposed to complete it but didn’t would have the worst, and those that were never supposed to do it would be in the middle. To directly analogize to the vaccine data; the men never scheduled would be the unvaccinated, the men that completed their series would be both boosted groups and the ‘>21 days after second shot’ group before boosters were approved, and the no-shows are the ‘>21 days after first shot’ group and the ‘>21 days after 2nd shot’ group after the boosters were approved.
Summary: The data presented looks scary, but it is actually representative of group selection on mortality in people progressing through a series of medical interventions. People that don’t complete the next step are going to have higher average mortality than those that do complete the next step. This is not unique to vaccines and the effect from this overwhelms our ability to pull anything about vaccine safety from this particular type of analysis. I am thankful for the author for providing a unique analysis of the data, but upon review I do not think it provides evidence either for or against vaccine safety.
don't know about UK, but in US you are not considered "vaccinated" until 2 weeks after your second jab. Convenient for those "unvaxxed" death stats isn't it?
Thank you! Very logical and convincing, beyond a reasonable doubt. They miscategorize in order to provide the headline "data" so that the further jabs can be pushed, despite their deadliness and harm. Very effective construct. Works every time. No shame, no morals, no principles, no conscience, no fear of God.
This is the logical devolution of atheist societies. Although the proof of the existence of God is simple and indisputable. We know the devil exists. Thence, God must exist too. Q.E.D.
It looks to me like it is a form of 'healthy vaccinee' effect.
They're vaccinating pretty much everyone, but there is a group that isn't being vaccinated -- those so very ill and close to death that the vaccine side effects might finish them off.
The important point that many people miss is that most people don't die suddenly -- they become ill with a disease/condition that ends in death, and thus their final fate is telegraphed early.
With this 'healthy vaccinee' hypothesis as each vaccination dose campaign gets underway in each age group you see those closest to death not get vaccinated and thus get concentrated into the 'previous state of vaccination' group. This makes the death rates in the 'previous state of vaccination' soar. Then, over the next few months, these very very ill individuals die and deaths in the 'previous state of vaccination' return to being similar to the group that took the next dose.
The time course of the increase in deaths in the prior-vaccine-state group reflects the longevity of those identified as 'shouldn't have the next dose'. This looked to be about 3 months or so for first doses given in early 2020, but it has become progressively longer as, presumably, they are allowing more very ill people to forgo their next vaccine dose.
This is why we need to have proper matched cohort studies to investigate the vaccines; the current vaccine surveillance methods (in particular test-negative case-control) are not designed to be used in the current situation.
So just received an email from CVS. Get the booster after testing positive for COVID????? Booster shots are the best protection against severe complications from COVID-19, especially during the ongoing surge of the Omicron variant.
If you’ve recently tested positive for COVID-19, the CDC advises that you get a booster once you’re symptom-free and have completed the recommended isolation period.
Are you aware of this paper - written by a team of 9 (inc myself)?
https://www.researchgate.net/publication/357778435_Official_mortality_data_for_England_suggest_systematic_miscategorisation_of_vaccine_status_and_uncertain_effectiveness_of_Covid-19_vaccination
Summarising tweet thread starting here:
https://twitter.com/MartinNeil9/status/1481561698792267779?s=20&t=7iyfu0G4s_kmLgjEUleODQ
Thank you for this information. The obfuscation from governments is quite unbelievable and heartbreaking.
"Why does the unvaccinated mortality rate shoot up during 1st dose vaccinations, they haven’t been vaccinated?"
For older groups, this reflects short-term healthy user bias. People "on their death bed" don't get the first dose. So as people not on their death bed exit the group (by being 1st-dosed) the denominator drops and the death rate goes up.
"Why does the 1st dose mortality rate shoot up after second dose vaccinations, they haven’t had a second dose?"
Same, again. It just repeats, those who can take the 2nd dose leave the denominator and those whose health went south after the first dose are left behind.
"And now, why does 2nd dose mortality rates shoot up, as boosters begin, when they haven’t had a booster?"
Literally same, again.
Once you get into younger groups, this gets a lot more complicated. There's bias toward the vulnerable for pre-spring 1st doses, so the death rate drops as the general population qualifies. Then for the 18-39 group you add another wrinkle, in that the unvaccinated 18-39 "get younger" when the vaccine becomes available because more 30-39s take it, so there's a drop all the way until summer. The summer bump could be normal trends or reflect the younger+healthy leaving the 18-29 pool by getting 1st-dosed. The various dosed groups "get older" at first and then "get younger" as well, all the way to boosters, which hides the healthy user drain visible in the above sets.
I assume you have seen this paper? https://www.researchgate.net/publication/356756711_Latest_statistics_on_England_mortality_data_suggest_systematic_mis-categorisation_of_vaccine_status_and_uncertain_effectiveness_of_Covid-19_vaccination
I don't think it's miscategorisation (I used to as you probably know). They have added <21 days for doses 1 and 2 now and have said in numerous FOI requests that they are categorising unvaccinated correctly.
So, if it's not the numerator, it must be the denominator? There is little transparency on their person-years calculation. I'm looking at that...
Nice work. Something so up at was published a few months ago, but the authors and their credentials were identified.
It sounds like this could be chalked up to (some degree) these deaths, just aren't given the label as a COVID-19 death. I'm not saying they were COVID-19 deaths prior because we know they were mostly just deaths with a previous positive SARS-CoV-2 PCR test. I'm also not saying the vaccine isn't killing people; I'm just saying this data is still inconclusive, and we need to do more research. But indeed the vaccine program should be stopped immediately.
I have a theory about the graphs above and I do not think that they show anything about vaccine safety or that their pattern is even unique to vaccines. After my theory statement I have a longer explanation, a few predictions, and a non-covid thought experiment.
Theory: the mortality rates will always be higher in groups of people that only partially complete a medical intervention relative to those who either finish the intervention or never start it.
Explanation: Controlling for similarities between study groups is really hard. If there is ever selection bias in how the study groups are formed, then that bias difference can easily overwhelm the effect of what intervention you are trying to study. The graphs above look at people at various stages of their vaccination status. However, there is likely selection bias in what causes people to move from one group to another. Many people who start their series of shots, but do not complete it would fail to do so because new or worsening medical problems make future vaccinations medically unnecessary. This effect results in a disproportionate number of healthy people finishing their vaccination series and a disproportionate number of the unhealthy not finishing. As a result, the partially completed groups will have worse mortality data and the fully completed groups will have better than expected mortality data. In fact, when the ‘>21 days after 2nd shot’ group represented a fully complete series it had great mortality data. Once boosters were approved and that same group started to represent people who did not complete their series of shots, then that group started to select for unhealthy people and the group’s mortality rate rose.
Prediction: If a second booster (4th shot) is approved then the mortality data for the ‘>21 days after 3rd shot’ will start to select for a disproportionately unhealthy group and will show a higher non-COVID mortality than unvaccinated people. Likewise, those with a completed 4th shot will be disproportionately healthy. I further predict that this pattern appears frequently in other forms of serial medical interventions.
Thought experiment: Imagine I take a large number of men, meet with them and schedule half of them for a prostate exam in 3 months. One year later I track them all down and look at the mortality data. If I look at the mortality data for those scheduled for a prostate exam against those who were not scheduled for one, then I expect minimal difference. However, if I take the men schedule for a prostate exam and divide than into no-shows and appointment keepers then what would I expect for mortality data between the three groups? Some of the no-shows were due to men having medical issues that made the prostate exam medically unnecessary. Also, I expect on average the no-shows are less concerned about their health than the appointment keeper group. Those two effects would combine to make the no-show group the one with the highest mortality, and because we removed the highest mortality people from those that were scheduled for a prostate exam, the ones that had completed the exam would look disproportionately healthy compared to those that were never scheduled to have one. The end result is those that completed the medical regimen would have the best mortality, those that were supposed to complete it but didn’t would have the worst, and those that were never supposed to do it would be in the middle. To directly analogize to the vaccine data; the men never scheduled would be the unvaccinated, the men that completed their series would be both boosted groups and the ‘>21 days after second shot’ group before boosters were approved, and the no-shows are the ‘>21 days after first shot’ group and the ‘>21 days after 2nd shot’ group after the boosters were approved.
Summary: The data presented looks scary, but it is actually representative of group selection on mortality in people progressing through a series of medical interventions. People that don’t complete the next step are going to have higher average mortality than those that do complete the next step. This is not unique to vaccines and the effect from this overwhelms our ability to pull anything about vaccine safety from this particular type of analysis. I am thankful for the author for providing a unique analysis of the data, but upon review I do not think it provides evidence either for or against vaccine safety.