Nov 17, 2023·edited Nov 20, 2023Liked by Fabian Spieker
Seeing 14,010 (less than 1% of 1,637,277) VAERS reports with terms like this after clearing sentences containing 'history': cancer, neoplasm, tumor, malignant, metastasize, stage 1, oncologist, chemotherapy, carcinoma, melanoma
This article is about turning off the body's normal alarm system to allow the foreign matter into cells and consequences of doing so. ukcolumn.org/article/stabilising-the-code The scientists were recently honoured for this achievement.
Nov 17, 2023·edited Nov 17, 2023Liked by Fabian Spieker
Hypothesis: "In both the youngest and highest age tier these negative trends were broken when COVID shots started to be administered to the respective age group. The signal in adults 75 or older precedes the signal in children 12 or older by around 5 months. This is suggestive of the vaccines being responsible for the rise in cancer deaths."
Possible alternative hypothesis: "Given the meltdown in screening and early interventions during the harsh lockdowns, it was the lack of early access to healthcare, screening and (etc etc) that caused the reversal of recent gains in cancer survival. The elderly died earlier, because their immune system was more fragile anyway",
As they say in college: "Discuss these alternative explanations, in less than 300 words".. !
Thanks Rob, and it is indeed good to consider alternative hypotheses like lack of early access to healthcare and screening. Looking at the excess deaths time series in this light, there is an all-time low of reported cancers near mid-2020 which is right in the middle of the severe lockdowns, and this certainly could have been driven at least partially by a significant decrease in healthcare engagement. There is then a very high "rebound" value that winter. To then see the graph go way off kilter in late 2021 and 2022 to me seems too extreme to be explained solely by this, but I don't see any reason why both hypotheses could not be true.
The low 2020 mortality you are seeing could be cancer deaths being misattributed to COVID, since deaths are listed by UCOD, but it still seems highly unlikely that skipped screenings didn't impact cancer mortality with some delay.
Good work Fabian. I believe it would make sense to run separate regressions for each of the four regions, as we have seen a geographical Simpson's paradox with all-cause mortality. Mathew Crawford noticed this some time ago, and is talking about it again recently: https://roundingtheearth.substack.com/p/mistakes-were-not-made-by-the-society It looks like at least a couple of the slopes will be positive.
Also wanted to cross-link to the series last year by The Ethical Skeptic, who also looks at malignant neoplasms:
Okay I ran the SLR again for each division. I had actually looked for this via visual inspection, but it didn't seem very promising.
p for vaccination coverage still ranges between 0.27 and 0.60. :/
I am afraid we need more data to gain a better understanding of the underlying mechanisms.
As Rob has pointed out, "the meltdown in screening and early interventions" is probably a big factor here, possibly the main factor.
EDIT: We do see a little something in the West, when I limit the regression to individuals 75 and older, but it's not impressive (p=0.021). Considering what I have seen so far (and as Diane suggested), I don't think it is unlikely that virus prevalence at the time of vaccination is another factor... I could look at cumulative [daily cases p.c. * daily doses p.c.], but then again "cases" are a poor measure of virus prevalence.
The signs of the correlations / slopes (positive or negative) are more important here than p-values. The geographical Simpson's paradox appears to have been largely overlooked or ignored.
I had seen EthicalSceptic's work on Twitter before and was actually gonna mention it, but forgot. He is also correcting for linear trends to isolate the signal.
I've also scrolled through Mathew's CHD interview shortly before he posted that article and was a bit disappointed, that he has been ignoring my findings. A lot of time is spent talking about very weak correlations (r around 0.1-0.4), when we can see the exceptionally strong link between lagging first doses, cases and COVID deaths during this time (r around 0.9).
Keep in mind Crawford's correlations are at a county level by month, and though they are smaller, I believe they are significant and demonstrate clear connections with large well-known effects like household income and education level (healthy user bias). Going to the county-level data and adjusting for these effects seems very reasonable from a causal inference perspective, since there can be large differences between urban and rural counties within the same state.
I still want to integrate his variables into a more comprehensive model. I received no reply from him when you first suggested it, but will hopefully have a zoom with him in the course of the week.
It stands to reason that if it's the spike protein somehow involved in injuring and sickening the jab recipients, then the virus itself must also deliver the same damage. But it's a matter of quantity, isn't it? Wouldn't programming the body's cells in most of the organs to crank out unknown quantities of spike for unknown lengths of time make the potential for injury much worse? At least so I reasoned when pressured to take the jabs. Even in 2020 before the scale of the injuries from the jabs was well known, to me it seemed the irrationality of this approach to protection from the virus was mind-boggling.
Because I've had bad reactions to so many modern meds that were touted as "lifesaving", long ago I realized that drugs are developed to aim at some hypothetical mean in terms of human physiology. We seem to be way, way over-extended in meddling with individual biochemistry.. And your comment supports that conclusion. Even some of the meds that are marketed as tailored to the individual, such as immunotherapy for cancer, are still really aiming one bowling ball at all the pins. If I had to characterize our era in a few words they would be hubris and greed, and vainglory.
"I realized that drugs are developed to aim at some hypothetical mean in terms of human physiology. We seem to be way, way over-extended in meddling with individual biochemistry."
Thank you! This article is very interesting, so interesting I can't believe it appeared in a JAMA journal. Agreed, now a study of the harms of these useless meds is needed. Did you take a look at the conflict of interest statement/affiliations of the authors?
The conclusion I'd come to, based solely on my bad experiences with medicines, is that they are inadequately tested and any actual test data is heavily massaged. I'm told I must be an extraordinarily sensitive individual, to have all these bad reactions. I don't believe that. I do believe hundreds of thousands suffer but never figure out that it is the med they're taking, OR they figure suffering is necessary to reap the benefits. Peter Breggin, in his book "Your Drug May Be Your Problem", says that easily 100,000 Americans die of side effects of prescription meds each year and those are just the ones who die in hospital.
Seeing 14,010 (less than 1% of 1,637,277) VAERS reports with terms like this after clearing sentences containing 'history': cancer, neoplasm, tumor, malignant, metastasize, stage 1, oncologist, chemotherapy, carcinoma, melanoma
len(df_cleaned.loc[ df_cleaned.symptoms.str.contains(r'(?:cancer|neoplasm|tumor|malignant|metast|stage \d|\boncol|chemoth|carcinom|melanom) ] )
(The new symptoms column is SYMPTOM_TEXT with symptoms file entries appended and all lowercase for search)
Those are here: https://univaers.com/download/special/2023_10-27_cancer.xlsx
This article is about turning off the body's normal alarm system to allow the foreign matter into cells and consequences of doing so. ukcolumn.org/article/stabilising-the-code The scientists were recently honoured for this achievement.
Excellent, thank you!
Hypothesis: "In both the youngest and highest age tier these negative trends were broken when COVID shots started to be administered to the respective age group. The signal in adults 75 or older precedes the signal in children 12 or older by around 5 months. This is suggestive of the vaccines being responsible for the rise in cancer deaths."
Possible alternative hypothesis: "Given the meltdown in screening and early interventions during the harsh lockdowns, it was the lack of early access to healthcare, screening and (etc etc) that caused the reversal of recent gains in cancer survival. The elderly died earlier, because their immune system was more fragile anyway",
As they say in college: "Discuss these alternative explanations, in less than 300 words".. !
Thanks Rob, and it is indeed good to consider alternative hypotheses like lack of early access to healthcare and screening. Looking at the excess deaths time series in this light, there is an all-time low of reported cancers near mid-2020 which is right in the middle of the severe lockdowns, and this certainly could have been driven at least partially by a significant decrease in healthcare engagement. There is then a very high "rebound" value that winter. To then see the graph go way off kilter in late 2021 and 2022 to me seems too extreme to be explained solely by this, but I don't see any reason why both hypotheses could not be true.
The low 2020 mortality you are seeing could be cancer deaths being misattributed to COVID, since deaths are listed by UCOD, but it still seems highly unlikely that skipped screenings didn't impact cancer mortality with some delay.
The main reason why I looked into this are the countless anecdotes I've been hearing.
I could not find any signal in German mortality or hospital data.
Skipped screenings are the most plausible explanation, but there might be something else happening.
Yeah the elders' mortality could've just reacted sooner. Possibly
Well, its just a thought.. And thanks for your brevity!
Good work Fabian. I believe it would make sense to run separate regressions for each of the four regions, as we have seen a geographical Simpson's paradox with all-cause mortality. Mathew Crawford noticed this some time ago, and is talking about it again recently: https://roundingtheearth.substack.com/p/mistakes-were-not-made-by-the-society It looks like at least a couple of the slopes will be positive.
Also wanted to cross-link to the series last year by The Ethical Skeptic, who also looks at malignant neoplasms:
https://theethicalskeptic.com/2022/08/20/houston-we-have-a-problem-part-1-of-3/
Okay I ran the SLR again for each division. I had actually looked for this via visual inspection, but it didn't seem very promising.
p for vaccination coverage still ranges between 0.27 and 0.60. :/
I am afraid we need more data to gain a better understanding of the underlying mechanisms.
As Rob has pointed out, "the meltdown in screening and early interventions" is probably a big factor here, possibly the main factor.
EDIT: We do see a little something in the West, when I limit the regression to individuals 75 and older, but it's not impressive (p=0.021). Considering what I have seen so far (and as Diane suggested), I don't think it is unlikely that virus prevalence at the time of vaccination is another factor... I could look at cumulative [daily cases p.c. * daily doses p.c.], but then again "cases" are a poor measure of virus prevalence.
The signs of the correlations / slopes (positive or negative) are more important here than p-values. The geographical Simpson's paradox appears to have been largely overlooked or ignored.
Thank you.
I had seen EthicalSceptic's work on Twitter before and was actually gonna mention it, but forgot. He is also correcting for linear trends to isolate the signal.
I've also scrolled through Mathew's CHD interview shortly before he posted that article and was a bit disappointed, that he has been ignoring my findings. A lot of time is spent talking about very weak correlations (r around 0.1-0.4), when we can see the exceptionally strong link between lagging first doses, cases and COVID deaths during this time (r around 0.9).
Keep in mind Crawford's correlations are at a county level by month, and though they are smaller, I believe they are significant and demonstrate clear connections with large well-known effects like household income and education level (healthy user bias). Going to the county-level data and adjusting for these effects seems very reasonable from a causal inference perspective, since there can be large differences between urban and rural counties within the same state.
I still want to integrate his variables into a more comprehensive model. I received no reply from him when you first suggested it, but will hopefully have a zoom with him in the course of the week.
Sure, send it over. What most people don't realize is that vaccine mortality and COVID mortality are linked.
You should have my email. It ends with "pervaers.com".
It stands to reason that if it's the spike protein somehow involved in injuring and sickening the jab recipients, then the virus itself must also deliver the same damage. But it's a matter of quantity, isn't it? Wouldn't programming the body's cells in most of the organs to crank out unknown quantities of spike for unknown lengths of time make the potential for injury much worse? At least so I reasoned when pressured to take the jabs. Even in 2020 before the scale of the injuries from the jabs was well known, to me it seemed the irrationality of this approach to protection from the virus was mind-boggling.
Because I've had bad reactions to so many modern meds that were touted as "lifesaving", long ago I realized that drugs are developed to aim at some hypothetical mean in terms of human physiology. We seem to be way, way over-extended in meddling with individual biochemistry.. And your comment supports that conclusion. Even some of the meds that are marketed as tailored to the individual, such as immunotherapy for cancer, are still really aiming one bowling ball at all the pins. If I had to characterize our era in a few words they would be hubris and greed, and vainglory.
"I realized that drugs are developed to aim at some hypothetical mean in terms of human physiology. We seem to be way, way over-extended in meddling with individual biochemistry."
100%, very well said.
Thank you! This article is very interesting, so interesting I can't believe it appeared in a JAMA journal. Agreed, now a study of the harms of these useless meds is needed. Did you take a look at the conflict of interest statement/affiliations of the authors?
The conclusion I'd come to, based solely on my bad experiences with medicines, is that they are inadequately tested and any actual test data is heavily massaged. I'm told I must be an extraordinarily sensitive individual, to have all these bad reactions. I don't believe that. I do believe hundreds of thousands suffer but never figure out that it is the med they're taking, OR they figure suffering is necessary to reap the benefits. Peter Breggin, in his book "Your Drug May Be Your Problem", says that easily 100,000 Americans die of side effects of prescription meds each year and those are just the ones who die in hospital.
We have a long walk back.