In South Australia 'the authorities' are desperate to keep pushing the 'boosters', as is occurring all around Australia, as there are Covid jab stockpiles to move, and a lucrative mRNA vaccine industry to develop...
Consider for example this article published yesterday on InDaily, with a striking headline:
"Unvaccinated people are five times more likely to die if they contract COVID-19 than those who are fully-vaccinated, an analysis of South Australian cases has found.
The analysis by the South Australian Health and Medical Research Institute (SAHMRI) found unvaccinated people are also eight times more likely to be hospitalised than those with up-to-date vaccinations."
I'm highly suspicious of this research, and contacted SAHMRI today to seek more info, and received a brief summary. However, I was advised the full report belongs to SA Health and is not SAHMRI's to publish. I've written to the South Australian Health Minister seeking more information.
But it seems to me this is 'research to order' to support the ongoing Covid jab program...
I think for many it is "I have a conclusion I think will get me published or in the headlines (or that my funder gave me), now I just need to come up with a method that will return that conclusion."
In the Q&A section of the podcast, Bret and Heather go on to discuss how they haven't/can't control the vaccinated population from benefitting from natural immunity from prior covid infection. https://www.youtube.com/watch?v=aja0sOk-vS0
I have only just started to listen to the Q&A. Brilliant observation. Technically here neither side has been controlled for confounders. In fact, I think the entire methodology creates additional confounders that needn't have ever arisen in the first place - and I believe that is deliberate. It's research artfully constructed to deliver a pre-defined conclusion.
The ONS have just released this quarter's data. It appears already on just first blush (a brief glance over) to be as corrupt as it ever was. Yes, they have corrected for the issue we raised that for the first 14 days after vaccination the person was being counted as unvaccinated - BUT - from September 2022 they seem to have lost males in the dataset (Table 4). Table 2 has no 'ever vaccinated' column - this column is shown instead in Table 3 but the place you would really want it is Table 2 - and there is no data at all for children. Josh pointed out to me as well that their language and grammar is sloppy... instead of saying there are x people with such and such, in places they say there are "some" people with such and such. All around not a very good look. It's like they wanted to pretend hey were doing what we, and the Director General for Statistics UK wanted... but have clearly just sought other ways to obfuscate the truth.
This is very useful but in my opinion in no way understandable for 10-year olds, or grannies. They'd need help with terms like confounder, cohort, control, bias, amongst others. A truly layman version of this article, maybe in the form of a video, would be key to getting more people to understand how "science" has been used against them.
Totally agree. We are working on just such a video. Norman and Martin (Substack: Where are the numbers?) will likely release one using some of the Bayesian graph examples that will also demonstrate the degree of influence each cohort manipulation has on the outcome.
Excellent as always, thank you. Not as many people read studies as compared to the number that access the C 19 stats which their respective governments 'produce', so in my view the biggest issue with cohort fudging is in the widely quoted public statistics that all the Karen screech at we, the filthy, disease-ridden, Covid-incubating unvaccinated. Alberta, Canada for a short while published the case, hospitalization and death stats in the 'unvaccinated' group who were vaccinated 14-21 days earlier. Well, that produced some very troubling charts which quite clearly exposed that the 2-3 weeks post vaccination is a very dangerous time for the unvaccinated vaccinated. Not only is not a neutral or protective period, it turns out that the likelihood of contracting C 19 and being hospitalized as a result and then dying, spikes very dramatically in those troublesome post vaccination weeks while one is unvaccinated..... The fact that they produced those statistics in the form of a colourful heat chart made it all the more undeniable. Fortunately, that inconvenient trend was easily reversed when the very successful early treatment called "Statconcealium" was applied, whereafter only the unvaccinated unvaccinated were once again the most susceptible group to C 19. * note to all unvaccinated unvaccinated: You can't get Statconcealium; it is only prescribed for the vaccinated unvaccinated because RCT fair cohort studies have shown that it is otherwise lethal to the prescriber. It's ok though- you have natural immunity, so you don't need it. (Thanks, Wanzhu)
This is the latest example of such publications but perhaps many people do not realise that a lot of these "academic" papers are professionally ghost-written, commissioned by pharma companies.
The company will ask a professional agency (there are quite a few) to come up with a pretend-research paper that promotes a line of thinking. Professional ghost writers will manipulate and fabricate what is needed and at some later stage, after the company and the agency have agreed the paper, academic "authors" will be brought in to have their names stuck on it.
I read the sordid account of how this happens in "The Illusion of Evidence-Based Medicine" by Jureidini and McHenry.
It is anybody's guess what proportion of published papers have been engineered this way, but from what Jureidini and McHenry report, it could be quite a lot.
2. Excellent article, but Faucist data manipulation is even worse than indicated, e.g. PCR test issues such as excessive amplification cycles. 【https://tekdeeps.com/who-finally-admits-pcr-tests-are-unreliable/】 I also seem to recall that early EUA testing kits also tested positive for influenza A & B, etc.
I'm an interviewer for a child cohort study that follows up a large group of children from birth to adulthood.
The main problem is that the children from poor and/or broken families tend to drop out of the study earlier, usually by their teens, which is very hard to adjust for. So you end up with all the glowing conformist high achievers!
This is really excellent.
Big fan of your work and analysis Jessica - keep going and never stop - you're humiliating the right people and helping humanity
Humiliating the right people! How awesome is that? Thanks :)
Haha exactly! :-)
In South Australia 'the authorities' are desperate to keep pushing the 'boosters', as is occurring all around Australia, as there are Covid jab stockpiles to move, and a lucrative mRNA vaccine industry to develop...
Consider for example this article published yesterday on InDaily, with a striking headline:
'Unvaxxed five times more likely to die of COVID: SA research': https://indaily.com.au/news/2023/02/20/unvaxxed-five-times-more-likely-to-die-of-covid-sa-research/
A quote:
"Unvaccinated people are five times more likely to die if they contract COVID-19 than those who are fully-vaccinated, an analysis of South Australian cases has found.
The analysis by the South Australian Health and Medical Research Institute (SAHMRI) found unvaccinated people are also eight times more likely to be hospitalised than those with up-to-date vaccinations."
I'm highly suspicious of this research, and contacted SAHMRI today to seek more info, and received a brief summary. However, I was advised the full report belongs to SA Health and is not SAHMRI's to publish. I've written to the South Australian Health Minister seeking more information.
But it seems to me this is 'research to order' to support the ongoing Covid jab program...
I think for many it is "I have a conclusion I think will get me published or in the headlines (or that my funder gave me), now I just need to come up with a method that will return that conclusion."
Here's the info in the summary report I received from SAHMRI:
COVID-19 Adverse Outcomes by Age and Vaccination Status
South Australian data, August – October 2022
Prepared by Prof Caroline Miller and Prof Joshua Ross
25 January 2023
COVID-19 cases recorded in South Australia between 1 August and 31 October 2022 were
analysed to ascertain the difference in likelihood of hospitalisation or death between those
who had received different numbers of vaccine doses or no doses.
Summary of Results
The risk of adverse outcomes with COVID-19 increases with age. For all age groups, the
risk of adverse outcomes decreases with vaccination, and boosters provide further
protection.
Among those who had COVID-19, for all age groups:
• people who were not vaccinated were more likely to be hospitalised than those
who had been vaccinated;
• people who had received three or more doses were the least likely to be
hospitalised with COVID-19;
• there were very few deaths (only two) in those under 60 years of age during the
dates data were analysed; and,
• among those aged 80+, those who were not vaccinated were more likely die as a
result of COVID-19 than those who had been vaccinated.
Multivariate analyses allow for more sophisticated analyses of the influence of multiple
variables simultaneously. Bayesian logistic regression (multivariate analyses) showed
that:
• vaccination lowers the probability of hospitalisation and the probability of death,
with increasing vaccination doses lowering the probability of each outcome
further, with very high probability;
• from ages 60-69 upwards, age almost certainly raises the probability of
hospitalisation and death, with increasing age increasing the probability of each
outcome;
• Those with No Vaccination are about 8 times more likely to be hospitalised and
about 5 times more likely to die than those with 3 or 4 doses of vaccine; and,
• Those with 1 or 2 doses of vaccine are about 3 times more likely to be
hospitalised and about 2 times more likely to die than those with 3 or 4 doses of
vaccine.
The way they have purposely muddied and continuously revised the criteria for "jabbed/unjabbed" makes their data meaningless.
In the Q&A section of the podcast, Bret and Heather go on to discuss how they haven't/can't control the vaccinated population from benefitting from natural immunity from prior covid infection. https://www.youtube.com/watch?v=aja0sOk-vS0
I have only just started to listen to the Q&A. Brilliant observation. Technically here neither side has been controlled for confounders. In fact, I think the entire methodology creates additional confounders that needn't have ever arisen in the first place - and I believe that is deliberate. It's research artfully constructed to deliver a pre-defined conclusion.
They start talking about at the 59 minute mark. Heather makes an additional point too, but I don't quite get it...
Thank you for this post. A clear explanation.
Please look at the Q3 2021 Delta wave in the USA.
New vaccinations 18-64 correlate strongly with the change in deaths between Q2 and Q3 in the 25-64 age group.
For every 100 injections there was one additional death.
These were probably all counted as unvaccinated.
Good article.
Consider linking to their Odysee channel instead of Youtube - https://odysee.com/@BretWeinstein:f
Thanks for your helpful clarifications! I stumbled on one imprecision: the UK Statistics Regulator acknowledged population bias, however they do not consider that vaccination status misclassification was an issue: https://d7694293-ffb8-4ed0-a014-3581d49070e4.usrfiles.com/ugd/d76942_d5fe1cb1d51b4643b9ce09252f732516.pdf
The ONS have just released this quarter's data. It appears already on just first blush (a brief glance over) to be as corrupt as it ever was. Yes, they have corrected for the issue we raised that for the first 14 days after vaccination the person was being counted as unvaccinated - BUT - from September 2022 they seem to have lost males in the dataset (Table 4). Table 2 has no 'ever vaccinated' column - this column is shown instead in Table 3 but the place you would really want it is Table 2 - and there is no data at all for children. Josh pointed out to me as well that their language and grammar is sloppy... instead of saying there are x people with such and such, in places they say there are "some" people with such and such. All around not a very good look. It's like they wanted to pretend hey were doing what we, and the Director General for Statistics UK wanted... but have clearly just sought other ways to obfuscate the truth.
I see, just today! This looks very interesting, although some important data is hard to extract.
https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/deathsinvolvingcovid19byvaccinationstatusengland/deathsoccurringbetween1april2021and31december2022
This is very useful but in my opinion in no way understandable for 10-year olds, or grannies. They'd need help with terms like confounder, cohort, control, bias, amongst others. A truly layman version of this article, maybe in the form of a video, would be key to getting more people to understand how "science" has been used against them.
Totally agree. We are working on just such a video. Norman and Martin (Substack: Where are the numbers?) will likely release one using some of the Bayesian graph examples that will also demonstrate the degree of influence each cohort manipulation has on the outcome.
Excellent as always, thank you. Not as many people read studies as compared to the number that access the C 19 stats which their respective governments 'produce', so in my view the biggest issue with cohort fudging is in the widely quoted public statistics that all the Karen screech at we, the filthy, disease-ridden, Covid-incubating unvaccinated. Alberta, Canada for a short while published the case, hospitalization and death stats in the 'unvaccinated' group who were vaccinated 14-21 days earlier. Well, that produced some very troubling charts which quite clearly exposed that the 2-3 weeks post vaccination is a very dangerous time for the unvaccinated vaccinated. Not only is not a neutral or protective period, it turns out that the likelihood of contracting C 19 and being hospitalized as a result and then dying, spikes very dramatically in those troublesome post vaccination weeks while one is unvaccinated..... The fact that they produced those statistics in the form of a colourful heat chart made it all the more undeniable. Fortunately, that inconvenient trend was easily reversed when the very successful early treatment called "Statconcealium" was applied, whereafter only the unvaccinated unvaccinated were once again the most susceptible group to C 19. * note to all unvaccinated unvaccinated: You can't get Statconcealium; it is only prescribed for the vaccinated unvaccinated because RCT fair cohort studies have shown that it is otherwise lethal to the prescriber. It's ok though- you have natural immunity, so you don't need it. (Thanks, Wanzhu)
How are these cohort selection biases you describe not fraud?
Excellent piece, thank you!
This is the latest example of such publications but perhaps many people do not realise that a lot of these "academic" papers are professionally ghost-written, commissioned by pharma companies.
The company will ask a professional agency (there are quite a few) to come up with a pretend-research paper that promotes a line of thinking. Professional ghost writers will manipulate and fabricate what is needed and at some later stage, after the company and the agency have agreed the paper, academic "authors" will be brought in to have their names stuck on it.
I read the sordid account of how this happens in "The Illusion of Evidence-Based Medicine" by Jureidini and McHenry.
It is anybody's guess what proportion of published papers have been engineered this way, but from what Jureidini and McHenry report, it could be quite a lot.
1. "Lies, damn lies & statistics!"
2. Excellent article, but Faucist data manipulation is even worse than indicated, e.g. PCR test issues such as excessive amplification cycles. 【https://tekdeeps.com/who-finally-admits-pcr-tests-are-unreliable/】 I also seem to recall that early EUA testing kits also tested positive for influenza A & B, etc.
I'm an interviewer for a child cohort study that follows up a large group of children from birth to adulthood.
The main problem is that the children from poor and/or broken families tend to drop out of the study earlier, usually by their teens, which is very hard to adjust for. So you end up with all the glowing conformist high achievers!
How is this data manipulation not deemed as medical fraud with the resultant ability to allow people to sue for malpractice?