Your observation that mortality rates across US states were already negatively correlated in 2020 with subsequent vaccination rates is valuable, and complements similar observations (concerning *excess* mortality rates) I and others have made for inter-country comparisons:
Note that the implications of such observations for estimating the net effect of Covid vaccinations remain controversial.
As for the rest of your analysis, focusing on temporal correlations between vaccination and mortality, I am not entirely sure I understand what you are doing, so it would help to have things written down more formally and completely. But, for example, the link to Pantazatos and Seligmann, 2021 seems to be broken: ResearchGate tells me that the DOI has been removed by the author.
Thanks, good catch. The correct DOI is 10.13140/RG.2.2.28257.43366/1 and I corrected it in the main text. Thanks for raising the issues with the Neil et. al. I will try to take a look when I can.
be aware trumps transition team plan to hit the ground running on day one by taking america out of the world health organisation..for confirmation and the details checkout an article in the uk newspaper the financial times dated december 22nd 2024...also checkout and read carefully a petition at citizengo.org deal a fatal blow to the pandemic treaty tell trump to exit the WHO now
Possibly. If you can show/demonstrate a confounding factor please share the link. Just like all things in science, this data should be take into account along with multiple other independent lines of evidence to see if they converge. Also see this post about Bradford-Hill criteria for causation being met: https://jessicar.substack.com/p/the-bradford-hill-criteria
“Correlation is not necessarily causation” reminds me of the movie Dumb and Dumber, “so you’re telling me there’s a chance” line. The truth is that causation will ALWAYS have correlation.
Very interesting. My only thought is that population density (easier spread in more dense areas) may be an important factor in some of this, especially the vaccine doses vs. Covid cases. But even if it is, it would mean that vaccines made little/no difference because the graphs may have looked similar with or without vaccination.
Good point. That is another reason to including prior years deaths and cases for the same month as it would control for differences in population densities across states.
My whole issue with this is we don't and never really will know how many true Covid infections there were due to the flawed testing methods. What we do know for sure is the differences before and after they rolled out the jabs. Here is a quick article with a graph that shows one thing I am talking about. Here is what it says.
Disability data hit a new high in November 2024. The prior high point was in June 2023, but November saw an increase of 787,000 new cases over the previous month of October. Since 2021, there has been an increase of 4.8 million cases.
Not necessarily. When I was first in practice my senior partner referred to a study that asked people one question: Have you ever had a stroke & then had a team of neurologists examine each one (this was before 1979 when I first started practicing). This was before CT Scans , etc. so, crude diagnostic testing.
There was a pretty tight correlation/correspondence.
I agree that PCR testing was abused, but we only or largely used rapid antigen testing & clinical diagnosis (respiratory Sx with dysguesia or anosmia = COVID in our minds—a little sloppy, but in epidemic situations, good enough for government work.
It wasn’t just flawed testing methodology, it was attributing a MVA death to COVID because their test was +, & all admits were tested.
We will never know down to the last digit but we will know order of magnitude or better.
Won’t quiet the unreachable, but then nothing will.
Thanks for sharing. I have not looked at Medicare data. Are you saying the Medicare data is now publicly available after Kirsch published the spreadsheets? I will see if I can take a look when I have more time later this month.
How do you know the increase was in vaccinated people? In the Czech Republic the Delta wave only peaked around November to December 2021. But if you look at ages 40-59, vaccinated people had almost no increase in ASMR during the Delta wave, but the ASMR of unvaccinated people almost doubled between September 2021 and the Delta peak: sars2.net/czech.html#Daily_deaths_and_vaccine_doses_by_age_group.
In the US in August to September 2021, ages 65 and above accounted for about 57% of all excess deaths (when I calculated the expected number of deaths by doing a linear regression of age-specific CMR in August to September of 2010-2019 and I multiplied a projection of the trend by the population estimates of each age):
I have been reading your paper and unfortunately I don't think your linear regression model makes any sense, as a means of correcting for Robinson's paradox. Detailed comments below. I welcome, of course, any feedback.
-------------
You write
"To preclude Robinson's paradox, we need to plot individual-level counts of vaccine doses vs. individual-level counts of COVID (or total) deaths in each state"
Your reference to "individual-level" suggests you have in mind logistic regression, where the dependent variable would be, for example, the indicator function of death for individuals in the population (0 = didn't die, 1 = died), and one of the independent variables would be the indicator for vaccination status (0 = unvaccinated, 1 = vaccinated).
But (*) is not that. To do logistic regression you would need individual record level data which gives the vaccination status for each person, along with any other confounding variables which might be though to affect the probability of death.
Your variables Y_21, Y_20, Vax are all quantitative, not categorical, and you are just replacing the linear model
Y_21 = b_0 + b_1 * Y_20 + b_2 * Vax + e
with a "log-linear" model which replaces each variable with its logarithm. This will in no way resolve the issues with the linear model - it just obscures them behind a model which is more complicated. The model (*) translates to
Y_21 = E* B_o * (Y_20)^B_1 * Vax^B_2, for some constants B_0, B_1, B_2,
which doesn't look like any model of population dynamics that I am familiar with.
The variable Vax is still a *number* of vaccinations in a certain time-frame, rather than a *rate* of vaccination. The variables Y_20 (*number* of deaths in corresponding month of 2020) and Vax are thus both, a priori, proportional to total population size, as is Y_21. As long as Y_21 is greater than Y_20, one would thus expect the best fit to (*) to assign positive values to both b_1 and b_2, and assign negative values when Y_21 is less than Y_20. The values of b_1 and b_2 somehow weight how well Y_20 and Vax predict Y_21, but only in the sense of how well both predict the total population size.
All of this reduces to the essentially trivial observation that total deaths correlate strongly with Vax, since both correlate with total population size - a trivial observation, but couched behind a model made more complicated than necessary.
Unless you have individual record level data and can do logistic regression, I don't see any way to fully correct for the fact that underlying mortality rates may exhibit spurious correlations with something (vax rates) which happens to also negatively correlate with other socio-economic indicators of mortality. The obvious thing to first do is to plot *excess* mortality *rates* versus vaccination *rates*. Here, the most important word is *rates* - if you stick with total number of vaccinations (as in your analysis), you're going to be left with the trivial correlation of this with population size. Plotting EM rates vs vax rates will not fully correct for Robinson's paradox, since even the rate of *excess* mortality can be conjectured to correlate with underlying socio-economic indicators. But it is a first step, and it is the one which I and many other people have looked at. On the other hand, your approach in this paper doesn't seem to me to make any sense.
Thanks for your critical comments. I agree that an analysis of individual record level data indicating vaccination status is ideal, but I’m not aware of any publicly available data that has that information. If you know of any please let me know.
The log transform is so that the assumptions of linear regression are met when using count data. Given the data that is available, I don’t think there is any way to make the model any simpler. From what I gather these log-log regressions are fairly standard/popular in econometrics, psychology, sociology and other fields.
The effects observed here are not trivially explained by population differences as those are removed by including the Y20 term in the model (i.e. the y-axis residuals are uncorrelated with population). If they were trivially explained by population then we would see a strong positive result for every month and age group which we do not.
What have you found using your approach of plotting excess mortality rates versus vaccination rates?
"The log transform is so that the assumptions of linear regression are met when using count data"
I simply don't understand. All the variables in your model, Y_21, Y_20, Vax are "count data", sure, i.e.: positive-integer valued, quantitative variables. Your model proposes (fitting) a linear relationship between logs of these, but i see no motivation for this.
For example take the special case where there is no pandemic and no vaccines. In that case, the term Vax vanishes and your model is
log(Y_21) = b_0 + b_1 * log(Y_20) + e,
which, exponentiated, becomes
Y_21 = E*B_0*(Y_20)^B_1
So Y_21, the state-by-state number of deaths in some month of 2021 (in the special case of no pandemic and no vaccines), behaves like some power of Y_20, the corresponding death count in 2020, times some constant, times some exponentiated normal distribution to account for noise.
This does not sound like any realistic population dynamics model. Much simpler is simply
Y_21/Y_20 = constant + noise
i.e.: that the number of deaths increases year-on-year by some factor, under the simplest assumption that number of deaths is proportional to population size and that the population exhibits "natural growth" - thus, increases in one unit of time by a constant factor.
This is the simplest and obvious way to "include Y_20" in the model and leads to plotting excess mortality rates (Y_21/Y_20 - 1) versus vaccination rates (Vax/pop).
So, on the one hand, I see no reason for introducing logs at all. If there are some specific models from other disciplines that employ log-log linear regression that you're thinking of, please give examples, because I see no relevance here.
The way to make your model "simpler" is therefore just to remove logs. But then, the way I propose, to plot Y_21/Y_20 - 1 versus Vax/pop, is the obvious approach, whereas your model
Y_21 = b_0 + b_1*Y_20 + b_2 * Vax + e
will still have the same issues I've described before - whether or not you take logs doesn't affect this. Both Y_20 and Vax (i.e.: *number* of deaths and vaccinations) function here as predictors of Y_21, simply because both are, a priori, proxies for population size. Like I said, the values assigned to b_1 and b_2 when you fit, in some sense seem to "weight" the accuracy of the two predictors. But I don't see how they tell you anything about the effect of vaccination *rates* on mortality *rates*.
I've written two papers, one comparing European countries (using Eurostat and OWID data) and one on global data (using OWID). Both are available on Research Gate. The most important finding, I think, is a "phase transition" from Spring 2022 onwards. There is little evidence of vaccine benefit after this transition, indeed the data points in the opposite direction (how much so depends on how you define excess mortality). In 2021, waves of excess mortality align very well with Covid waves and vax rates are strongly negatively correlated with excess mortality rates. However, for basically the same reason as you described with the US data, there remains uncertainty as to how much the vaccines contribute. This is where individual record level data would be more useful.
I haven't myself looked closely at such data. You're right, there doesn't seem to be much such high-quality data available. The best publicly available data so far seems to be from the Czech Republic and Kirsch, who you seem to be in touch with, has looked closely at it, as have some others. A guy on X who goes under the handle @henjin, and whom I correspond with, seems to have looked at it closely. I know that some researchers in Holland have also got (private) access to high-quality record level data, but so far I think they have only published results in Dutch, and have not yet translated their findings into English. Henjin's analysis of the Czech data seems consistent with mine of the EM data for 2021 - there is a priori evidence of vaccine benefit, especially during the delta wave in the latter part of 2021. However, there is also clear evidence of a healthy vaccinee effect, both a short-term one (people close to death don't get vaccinated, or people who die soon after vaccination don't have their vaccinations registered) and a longer-term "background" one (people who are generally more healthy are more likely to get vaccinated). It remains unclear how much of the apparent benefit of vaccines in 2021 can be explained by HVE.
On the other hand, I have not yet seen any convincing evidence of net vaccine harm in 2021. It's possible the Dutch data will show evidence of short term harms, but I haven't seen it presented in English yet. Kirsch may have a different view, of course, regarding the Czech data for example.
Thanks for the detailed comments. I hadn't thought of the relation to population dynamics, but the model is not intended to reflect population dynamics but rather use y20 deaths as a baseline to adjust for when estimating effects of vax on Y21 deaths. One motivation of keeping the original units (and log transforming for regression) is so that you can use the beta weights to then estimate # Y21 deaths per dose. The second is reducing the risk of ecological fallacy. But if I understand your point about weighting correctly, is that you are suggesting maybe there are still some "residual" trivial effects of population even though population is not significantly correlated with the residuals of the model without the vax term? Is there any way that you can suggest I interrogate this further? Thanks again for your detailed critical comments.
I think the only correct data is from Florida, hence the Florida data being the outlier. DeSantis took on the corrupt State Health administration state, appointed the great Surgeon General Ladapo, and real outcomes were assessed.
I don’t believe that any data regarding vaccinated vs unvaccinated outcomes was collected or recorded by the health department in Central Florida or state for that matter. Anecdotally I interviewed many Covid positive people who got Covid multiple times after vaccination and often within a week of a booster- yet were convinced the vax kept them from certain death and despite having had Covid at least 2 or more times were eager for next jab
Dude, the indoctrinated Do Not Base reason upon graphs, confessions, exposed military laws, pharmaceutical debacle, Polio Exposed as Scam, video (even exposed doctors discussing harvesting baby parts), much less “vaxx” facts.
Fascinating to note the vaccine doses vs. residual deaths in the first graph. The large blue states all lie below the trend line, while almost all the red states lie above. Does this lend credence to the theory that hot batches were assiduously shipped to red states? That theory was raised a couple years ago; now the numbers may bear it out. Time to cross-check with VAERS and get to the bottom of this.
Re: " comorbidities that increase risk of death from COVID-19."
The narrative from the mainstream media, the government, the NIH, CDC etc. was that those WITH comorbidities should be the first in line to be vaxxecuted. Far too many people have lost the ability to proactively take care of themselves. The attitude is that there is no need to eat right, get enough rest, etc. when Big Pharma can be relied upon to supply us with all the pills necessary to cheat on our diet, sleep patterns, etc.
Here's a prime example: Perhaps one of the most addictive substances on this planet is sugar. A couple grams is more than enough to overwhelm the liver and yet the average American consumes over 125 lbs. a year. The morbid obesity and chronic inflammation is a gold mine for Big Harma which along with the AMA, the American Cancer Society, The American Heart Association etc. have all become agnostic when it comes to acknowledging the connection between sugar and chronic disease.
Anyone who has been placed into a CT/PET scan might want to ask how a radioactive dye is able to coalesce around cancerous cells or tumors. The simple fact is that it is mixed with glucose. Cancer cells uptake of blood sugar is significantly higher than normal healthy cells. This shows a clear correlation between sugar and cancer growth, but those who are terminally addicted to sugar will never consider reducing their sugar intake when they can take fenbendazole which severely constricts tubules in cancerous cells preventing them from consuming sugar. Boobus Americanus wants the latest "hack" or whatever short cut they can utilize to allow them to sustain their addictions.
Over and over I see people gorging on sugar while their limbs are amputated one after the other. It's completely insane and suicidal which is why one more shot to put them out of their misery isn't as tragic as it is pathetic. It reminds me of that scene where Jesus is about to raise Lazarus, but begins weeping. Sure, he may be upset about losing his friend, but he's about to raise him from the dead so I think what he's really doing is weeping over the sad state of affairs in this fallen, hopelessly broken world. This is not how things are supposed to be.
We're not supposed to be hooked on highly processed, sugary, greasy by products, 500 channels of cable narcotics, technological gadgets that employ algorithms explicitly designed to addict users as a means to gain higher profits while turning us all into a horde of spineless, mindless zombies. Those who are don't have a chance and they don't care. That's how strong addiction is today, and the powers that should not be know it.
Boobus Americanus. Haha. Can I steal that? Actually you're close but not totally with the sugar assessment. The brain runs on glucose. We need some sugar, and the best is in the natural unprocessed forms of organic cane sugar, unfiltered honey, and maple sugar. The deadly cancer causing heart disease causing diabetes causing is the toxic gmo high fructose corn syrup.
Sugar is energy to the body, but useless to those who spend all their time sitting around playing video games or watching 20 or 30 year old sitcom reruns. They're empty calories and evolution has left us with an innate need to seek calories, but when they can be easily consumed without any real effort the results are catastrophic.
Much of the body requires some sugar, but nowhere near as much as most people are consuming. Moreover, just because it's organic or all natural doesn't negate the fact that the sugar addict is going to consume way too much of the stuff.
The body, but especially the brain requires cholesterol as well, but as soon as one introduces too much sugar to the body, perforations begin to appear in the arteries which the body must patch using cholesterol. The cholesterol then clogs up the arteries. The problem isn't cholesterol, but the overabundance of sugar.
Broccoli has sugar in it, but not so much that it becomes a problem for someone who is addicted to the stuff. Not everyone is an addict and there are plenty of people who can consume sugar without any significant problems, but they're the exception rather than the rule.
Yep. that's about as much as the liver can handle in a day. Anything more than that is immediately turned to fat because the liver simply doesn't know what to do with it. Not only that, but while the liver is being overworked, it can't process things like vitamins, minerals, etc. etc. When that happens, one doesn't just get fat, but the inflammation becomes chronic. People usually think that big belly is just fat, but it's actually a grossly inflamed intestinal tract. Sugar in the amounts that most Americans consume is poison to the liver. 75 years ago the average American consumed around 2.5 pounds of sugar a year. Now they go through that in less than a week.
It depends on a number of factors such as metabolism, one's susceptibility to addiction, and how active one is. In general, it's a bad idea. It's completely unnecessary to begin with. The body needs sugar, but more than a few grams is overkill.
There's a tribe over in Africa called "the Hazda" who live primarily on meat. They eat everything from head to toe except the entrails which they feed to their dogs. When they find something high in calories like honey, they go for it, but the men spend all day everyday tracking game which requires some sprinting as well. When there's no meat, nuts or berries, they eat tubers which are an overabundant source of fiber. Here's the kicker. They spit out all of the fiber. They basically just eat meat and sugar.
Degenerative diseases are unknown to them. They all die of old age and are in incredible shape well into their 90's They're ripped because of their lifestyle which allows them to consume massive amounts of sugar, but even that doesn't come close to what Boobus Americanus consumes while moving at a pace that makes sloths look like jack rabbits.
I cut out sugar including corn, wheat, flour, bananas, fruit juice, raisins, prunes, etc. etc. and lost 150 lbs. in less than seven months. I did no exercise and didn't work out at all. After losing the weight, I started doing crunches and within a week I had a six pack because there was no fat to conceal it. The fact is that the six pack was always there. Everyone has one covered under layers of fat.
That's very interesting. I misspoke when I said 7 tablespoons of sugar in 10 ounces of coffee--it's 5 tablespoons. But your comments would still apply. I get a lot of exercise going from the living room to the kitchen and the bathroom every half hour or so. That's a joke but if I were to jump up and down while doing it and run and skip maybe that would help. I'm going to try cutting those things out and see if I can lose weight. When you say you lost 150 pounds in less than seven months I assume you weighed more than 150 pounds to begin with. That's a joke. When I was about 21 I was on a medically supervised 28 day fast--It wasn't for weight loss, it was just another, more benevolent, treatment of a series of treatments stemming from misdiagnosis. The others did a lot of additional damage to my already existing neuromuscular condition, the suspected cause of which I will not get into right now (Hint: Sabin live oral attenuated polio vaccine when I was about 14). Anyway, when I started that 28 day fast, which was just water, I weighed 141.5 pounds, and when I ended it I weigh 107.25 pounds, but I felt fine, and gained most of it back in a month. It definitely reset my taste preferences, as I avoided any salt or sugar and was on a recommended lactovegetarian diet, but I didn't have a desire for much dairy, eating mostly raw vegetables, fruits, and nuts and seeds. Until about half year later when some other unrelated researcher suggested I should eat meat, so I broke the vegetarian diet, the main source of protein for which had been a pound of raw cashews a day.
I was tipping the scales at just over 300 lbs. The fat practically came off in slabs. A fair amount of flabby skin and stretch marks were evident for quite a while. I did a 20 day fast about three years ago as well. It was not intentional. My boat broke down out in the middle of the Gulf of Mexico. I lost a lot of weight and when I finally got home, there was a day there when I thought I might have to go to the hospital and be catheterized. I was having a lot of trouble peeing and pooping.
The 150 lbs. came off due to a "food plan" designed by Kay Sheppard M.D. She has a couple of books out on the subject that you can get used on Amazon for probably a few bucks, or your local library might have a copy as well. She calls it a food plan because there is no dieting involved. The biggest complaint from anyone I know who has adopted it is that it's too much food. I had to split my meals up into six meals a day for a few weeks until my stomach stretched out enough to handle it. It only took about two weeks before it jumped started my metabolism. It also took about three weeks for me to really start tasting and enjoying good healthy food again.
A few years after I'd been on her food plan I started a garden and soon realized that if I was going to subsist on what I grew, calories would become as valuable as gold. It's amazing how much food one must consume in order to maintain one's weight. I have a forest of spinach and probably a few thousand pounds of tubers buried in my back yard.
They are silly. Comorbidities should increase risk of death ...but the average number of conditions was not increased amongst official Covid-19 victims.
It makes sense and spotlights that the spike in deaths could only be due to lethal injection. At eight months into the epidemic (Aug. 13), 749,000 people have reportedly died worldwide "with" COVID-19 (that is 0.000096% or 9.6 deaths per 100,000), but more than 80% had pre-existing co-morbid conditions. So maybe only ~150,000 have died of COVID-19 alone (0.000019% or 19 per 100,000).
It depends on where one gets their information and how it's gathered. Initially, the CDC posted this: ""Comorbidities
Table 3 shows the types of health conditions and contributing causes mentioned in conjunction with deaths involving coronavirus disease 2019 (COVID-19). For 6% of the deaths, COVID-19 was the only cause mentioned. For deaths with conditions or causes in addition to COVID-19, on average, there were 2.9 additional conditions or causes per death. The number of mentions for each condition or cause is shown for all deaths and by age groups."
A little over a year later, they came up with this:
"Table 3 shows the types of health conditions and contributing causes mentioned in conjunction with deaths involving coronavirus disease 2019 (COVID-19). The number of deaths that mention one or more of the conditions indicated is shown for all deaths involving COVID-19 and by age groups. For over 5% of these deaths, COVID-19 was the only cause mentioned on the death certificate. For deaths with conditions or causes in addition to COVID-19, on average, there were 4.0 additional conditions or causes per death. For data on deaths involving COVID-19 by time-period, jurisdiction, and other health conditions,"
I'm looking for an interview (or article) in which (I believe) Harvey Risch says that the doses of hydroxychloroquine used in the trials were much too high, possibly ten times too high.
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You should write this up and publish it, if possible. Quite interesting.
I have recently shown that the specific claims made in the paper of Neil et al regarding misclassification are almost entirely meritless:
https://www.researchgate.net/publication/387220055_A_DETAILED_ANALYSIS_OF_CLAIMS_OF_MISCATEGORIZATION_BIAS_IN_STUDIES_OF_COVID-19_VACCINE_EFFECTIVENESS
Your observation that mortality rates across US states were already negatively correlated in 2020 with subsequent vaccination rates is valuable, and complements similar observations (concerning *excess* mortality rates) I and others have made for inter-country comparisons:
https://www.researchgate.net/publication/379815723_EXCESS_MORTALITY_AND_THE_EFFECT_OF_THE_COVID-19_VACCINES_PART_2_GLOBAL_DATA
Note that the implications of such observations for estimating the net effect of Covid vaccinations remain controversial.
As for the rest of your analysis, focusing on temporal correlations between vaccination and mortality, I am not entirely sure I understand what you are doing, so it would help to have things written down more formally and completely. But, for example, the link to Pantazatos and Seligmann, 2021 seems to be broken: ResearchGate tells me that the DOI has been removed by the author.
Thanks, good catch. The correct DOI is 10.13140/RG.2.2.28257.43366/1 and I corrected it in the main text. Thanks for raising the issues with the Neil et. al. I will try to take a look when I can.
be aware trumps transition team plan to hit the ground running on day one by taking america out of the world health organisation..for confirmation and the details checkout an article in the uk newspaper the financial times dated december 22nd 2024...also checkout and read carefully a petition at citizengo.org deal a fatal blow to the pandemic treaty tell trump to exit the WHO now
Correlation is NOT necessarily causation.
The graphs reflect a sociological, economic, overall poor health EPI-phenomenon
Possibly. If you can show/demonstrate a confounding factor please share the link. Just like all things in science, this data should be take into account along with multiple other independent lines of evidence to see if they converge. Also see this post about Bradford-Hill criteria for causation being met: https://jessicar.substack.com/p/the-bradford-hill-criteria
I was referring not to your own analyses, but rather to the KFF claimed inverse correlation between vaccination and deaths, .
“Correlation is not necessarily causation” reminds me of the movie Dumb and Dumber, “so you’re telling me there’s a chance” line. The truth is that causation will ALWAYS have correlation.
While causation will ALWAYS have correlation, MY comment was that
Correlation is NOT necessarily causation.
Excellent, spiro as usual. Best, Marc
Very interesting. My only thought is that population density (easier spread in more dense areas) may be an important factor in some of this, especially the vaccine doses vs. Covid cases. But even if it is, it would mean that vaccines made little/no difference because the graphs may have looked similar with or without vaccination.
Good point. That is another reason to including prior years deaths and cases for the same month as it would control for differences in population densities across states.
My whole issue with this is we don't and never really will know how many true Covid infections there were due to the flawed testing methods. What we do know for sure is the differences before and after they rolled out the jabs. Here is a quick article with a graph that shows one thing I am talking about. Here is what it says.
Disability data hit a new high in November 2024. The prior high point was in June 2023, but November saw an increase of 787,000 new cases over the previous month of October. Since 2021, there has been an increase of 4.8 million cases.
https://open.substack.com/pub/drtenpenny/p/fast-facts-friday-c33?r=1qdxoh&utm_campaign=post&utm_medium=email
Not necessarily. When I was first in practice my senior partner referred to a study that asked people one question: Have you ever had a stroke & then had a team of neurologists examine each one (this was before 1979 when I first started practicing). This was before CT Scans , etc. so, crude diagnostic testing.
There was a pretty tight correlation/correspondence.
I agree that PCR testing was abused, but we only or largely used rapid antigen testing & clinical diagnosis (respiratory Sx with dysguesia or anosmia = COVID in our minds—a little sloppy, but in epidemic situations, good enough for government work.
It wasn’t just flawed testing methodology, it was attributing a MVA death to COVID because their test was +, & all admits were tested.
We will never know down to the last digit but we will know order of magnitude or better.
Won’t quiet the unreachable, but then nothing will.
Kirsch had a source at HHS who gave him data from Medicare that is not publicly available. Kirsch has published two spreadsheets of the Medicare data which both show that during the Delta wave there was almost no increase in deaths among vaccinated people: sars2.net/rootclaim.html#Post_by_Spiro_Pantazatos_for_mortality_by_state_during_the_Delta_wave.
Thanks for sharing. I have not looked at Medicare data. Are you saying the Medicare data is now publicly available after Kirsch published the spreadsheets? I will see if I can take a look when I have more time later this month.
Medicare is OLD people. The increase in death during delta were vaccinated working age people. so medicare is consistent with previous claims.
How do you know the increase was in vaccinated people? In the Czech Republic the Delta wave only peaked around November to December 2021. But if you look at ages 40-59, vaccinated people had almost no increase in ASMR during the Delta wave, but the ASMR of unvaccinated people almost doubled between September 2021 and the Delta peak: sars2.net/czech.html#Daily_deaths_and_vaccine_doses_by_age_group.
In the US in August to September 2021, ages 65 and above accounted for about 57% of all excess deaths (when I calculated the expected number of deaths by doing a linear regression of age-specific CMR in August to September of 2010-2019 and I multiplied a projection of the trend by the population estimates of each age):
us=fread("http://sars2.net/f/uspopdeadmonthly.csv")
us=us[month%in%8:9,.(dead=sum(dead),pop=sum(pop)),.(age,year)]
base=us[year%in%2010:2019,.(base=predict(lm(dead/pop~year),.(year=2021))),age]
a=merge(us[year==2021],base)[,base:=base*pop]
a[age>=65,sum(dead)-sum(base)]/a[,sum(dead)-sum(base)] # 0.568729
I have been reading your paper and unfortunately I don't think your linear regression model makes any sense, as a means of correcting for Robinson's paradox. Detailed comments below. I welcome, of course, any feedback.
-------------
You write
"To preclude Robinson's paradox, we need to plot individual-level counts of vaccine doses vs. individual-level counts of COVID (or total) deaths in each state"
and then proceed to work with the model
log(Y_21) = b_0 + b_1 * log(Y_20) + b_2 * Vax + e ..... (*)
(I assume e is a noise term, that's fine).
Your reference to "individual-level" suggests you have in mind logistic regression, where the dependent variable would be, for example, the indicator function of death for individuals in the population (0 = didn't die, 1 = died), and one of the independent variables would be the indicator for vaccination status (0 = unvaccinated, 1 = vaccinated).
But (*) is not that. To do logistic regression you would need individual record level data which gives the vaccination status for each person, along with any other confounding variables which might be though to affect the probability of death.
Your variables Y_21, Y_20, Vax are all quantitative, not categorical, and you are just replacing the linear model
Y_21 = b_0 + b_1 * Y_20 + b_2 * Vax + e
with a "log-linear" model which replaces each variable with its logarithm. This will in no way resolve the issues with the linear model - it just obscures them behind a model which is more complicated. The model (*) translates to
Y_21 = E* B_o * (Y_20)^B_1 * Vax^B_2, for some constants B_0, B_1, B_2,
which doesn't look like any model of population dynamics that I am familiar with.
The variable Vax is still a *number* of vaccinations in a certain time-frame, rather than a *rate* of vaccination. The variables Y_20 (*number* of deaths in corresponding month of 2020) and Vax are thus both, a priori, proportional to total population size, as is Y_21. As long as Y_21 is greater than Y_20, one would thus expect the best fit to (*) to assign positive values to both b_1 and b_2, and assign negative values when Y_21 is less than Y_20. The values of b_1 and b_2 somehow weight how well Y_20 and Vax predict Y_21, but only in the sense of how well both predict the total population size.
All of this reduces to the essentially trivial observation that total deaths correlate strongly with Vax, since both correlate with total population size - a trivial observation, but couched behind a model made more complicated than necessary.
Unless you have individual record level data and can do logistic regression, I don't see any way to fully correct for the fact that underlying mortality rates may exhibit spurious correlations with something (vax rates) which happens to also negatively correlate with other socio-economic indicators of mortality. The obvious thing to first do is to plot *excess* mortality *rates* versus vaccination *rates*. Here, the most important word is *rates* - if you stick with total number of vaccinations (as in your analysis), you're going to be left with the trivial correlation of this with population size. Plotting EM rates vs vax rates will not fully correct for Robinson's paradox, since even the rate of *excess* mortality can be conjectured to correlate with underlying socio-economic indicators. But it is a first step, and it is the one which I and many other people have looked at. On the other hand, your approach in this paper doesn't seem to me to make any sense.
Thanks for your critical comments. I agree that an analysis of individual record level data indicating vaccination status is ideal, but I’m not aware of any publicly available data that has that information. If you know of any please let me know.
The log transform is so that the assumptions of linear regression are met when using count data. Given the data that is available, I don’t think there is any way to make the model any simpler. From what I gather these log-log regressions are fairly standard/popular in econometrics, psychology, sociology and other fields.
The effects observed here are not trivially explained by population differences as those are removed by including the Y20 term in the model (i.e. the y-axis residuals are uncorrelated with population). If they were trivially explained by population then we would see a strong positive result for every month and age group which we do not.
What have you found using your approach of plotting excess mortality rates versus vaccination rates?
The sentence
"The log transform is so that the assumptions of linear regression are met when using count data"
I simply don't understand. All the variables in your model, Y_21, Y_20, Vax are "count data", sure, i.e.: positive-integer valued, quantitative variables. Your model proposes (fitting) a linear relationship between logs of these, but i see no motivation for this.
For example take the special case where there is no pandemic and no vaccines. In that case, the term Vax vanishes and your model is
log(Y_21) = b_0 + b_1 * log(Y_20) + e,
which, exponentiated, becomes
Y_21 = E*B_0*(Y_20)^B_1
So Y_21, the state-by-state number of deaths in some month of 2021 (in the special case of no pandemic and no vaccines), behaves like some power of Y_20, the corresponding death count in 2020, times some constant, times some exponentiated normal distribution to account for noise.
This does not sound like any realistic population dynamics model. Much simpler is simply
Y_21/Y_20 = constant + noise
i.e.: that the number of deaths increases year-on-year by some factor, under the simplest assumption that number of deaths is proportional to population size and that the population exhibits "natural growth" - thus, increases in one unit of time by a constant factor.
This is the simplest and obvious way to "include Y_20" in the model and leads to plotting excess mortality rates (Y_21/Y_20 - 1) versus vaccination rates (Vax/pop).
So, on the one hand, I see no reason for introducing logs at all. If there are some specific models from other disciplines that employ log-log linear regression that you're thinking of, please give examples, because I see no relevance here.
The way to make your model "simpler" is therefore just to remove logs. But then, the way I propose, to plot Y_21/Y_20 - 1 versus Vax/pop, is the obvious approach, whereas your model
Y_21 = b_0 + b_1*Y_20 + b_2 * Vax + e
will still have the same issues I've described before - whether or not you take logs doesn't affect this. Both Y_20 and Vax (i.e.: *number* of deaths and vaccinations) function here as predictors of Y_21, simply because both are, a priori, proxies for population size. Like I said, the values assigned to b_1 and b_2 when you fit, in some sense seem to "weight" the accuracy of the two predictors. But I don't see how they tell you anything about the effect of vaccination *rates* on mortality *rates*.
I've written two papers, one comparing European countries (using Eurostat and OWID data) and one on global data (using OWID). Both are available on Research Gate. The most important finding, I think, is a "phase transition" from Spring 2022 onwards. There is little evidence of vaccine benefit after this transition, indeed the data points in the opposite direction (how much so depends on how you define excess mortality). In 2021, waves of excess mortality align very well with Covid waves and vax rates are strongly negatively correlated with excess mortality rates. However, for basically the same reason as you described with the US data, there remains uncertainty as to how much the vaccines contribute. This is where individual record level data would be more useful.
I haven't myself looked closely at such data. You're right, there doesn't seem to be much such high-quality data available. The best publicly available data so far seems to be from the Czech Republic and Kirsch, who you seem to be in touch with, has looked closely at it, as have some others. A guy on X who goes under the handle @henjin, and whom I correspond with, seems to have looked at it closely. I know that some researchers in Holland have also got (private) access to high-quality record level data, but so far I think they have only published results in Dutch, and have not yet translated their findings into English. Henjin's analysis of the Czech data seems consistent with mine of the EM data for 2021 - there is a priori evidence of vaccine benefit, especially during the delta wave in the latter part of 2021. However, there is also clear evidence of a healthy vaccinee effect, both a short-term one (people close to death don't get vaccinated, or people who die soon after vaccination don't have their vaccinations registered) and a longer-term "background" one (people who are generally more healthy are more likely to get vaccinated). It remains unclear how much of the apparent benefit of vaccines in 2021 can be explained by HVE.
On the other hand, I have not yet seen any convincing evidence of net vaccine harm in 2021. It's possible the Dutch data will show evidence of short term harms, but I haven't seen it presented in English yet. Kirsch may have a different view, of course, regarding the Czech data for example.
Thanks for the detailed comments. I hadn't thought of the relation to population dynamics, but the model is not intended to reflect population dynamics but rather use y20 deaths as a baseline to adjust for when estimating effects of vax on Y21 deaths. One motivation of keeping the original units (and log transforming for regression) is so that you can use the beta weights to then estimate # Y21 deaths per dose. The second is reducing the risk of ecological fallacy. But if I understand your point about weighting correctly, is that you are suggesting maybe there are still some "residual" trivial effects of population even though population is not significantly correlated with the residuals of the model without the vax term? Is there any way that you can suggest I interrogate this further? Thanks again for your detailed critical comments.
I think the only correct data is from Florida, hence the Florida data being the outlier. DeSantis took on the corrupt State Health administration state, appointed the great Surgeon General Ladapo, and real outcomes were assessed.
He should have been the presidential winner. He had true character and verifiable intelligence.
I don’t believe that any data regarding vaccinated vs unvaccinated outcomes was collected or recorded by the health department in Central Florida or state for that matter. Anecdotally I interviewed many Covid positive people who got Covid multiple times after vaccination and often within a week of a booster- yet were convinced the vax kept them from certain death and despite having had Covid at least 2 or more times were eager for next jab
Dude, the indoctrinated Do Not Base reason upon graphs, confessions, exposed military laws, pharmaceutical debacle, Polio Exposed as Scam, video (even exposed doctors discussing harvesting baby parts), much less “vaxx” facts.
...up to 47% of American US adults still don’t think there is a link between the COVID vaccine and a significant number of unexplained deaths.
Meaning 53% do. Is that not enough?
Not really. For something like this, you need a plurality, not a simple majority. Hence, 66%, not 51%.
Fascinating to note the vaccine doses vs. residual deaths in the first graph. The large blue states all lie below the trend line, while almost all the red states lie above. Does this lend credence to the theory that hot batches were assiduously shipped to red states? That theory was raised a couple years ago; now the numbers may bear it out. Time to cross-check with VAERS and get to the bottom of this.
Re: " comorbidities that increase risk of death from COVID-19."
The narrative from the mainstream media, the government, the NIH, CDC etc. was that those WITH comorbidities should be the first in line to be vaxxecuted. Far too many people have lost the ability to proactively take care of themselves. The attitude is that there is no need to eat right, get enough rest, etc. when Big Pharma can be relied upon to supply us with all the pills necessary to cheat on our diet, sleep patterns, etc.
Here's a prime example: Perhaps one of the most addictive substances on this planet is sugar. A couple grams is more than enough to overwhelm the liver and yet the average American consumes over 125 lbs. a year. The morbid obesity and chronic inflammation is a gold mine for Big Harma which along with the AMA, the American Cancer Society, The American Heart Association etc. have all become agnostic when it comes to acknowledging the connection between sugar and chronic disease.
Anyone who has been placed into a CT/PET scan might want to ask how a radioactive dye is able to coalesce around cancerous cells or tumors. The simple fact is that it is mixed with glucose. Cancer cells uptake of blood sugar is significantly higher than normal healthy cells. This shows a clear correlation between sugar and cancer growth, but those who are terminally addicted to sugar will never consider reducing their sugar intake when they can take fenbendazole which severely constricts tubules in cancerous cells preventing them from consuming sugar. Boobus Americanus wants the latest "hack" or whatever short cut they can utilize to allow them to sustain their addictions.
Over and over I see people gorging on sugar while their limbs are amputated one after the other. It's completely insane and suicidal which is why one more shot to put them out of their misery isn't as tragic as it is pathetic. It reminds me of that scene where Jesus is about to raise Lazarus, but begins weeping. Sure, he may be upset about losing his friend, but he's about to raise him from the dead so I think what he's really doing is weeping over the sad state of affairs in this fallen, hopelessly broken world. This is not how things are supposed to be.
We're not supposed to be hooked on highly processed, sugary, greasy by products, 500 channels of cable narcotics, technological gadgets that employ algorithms explicitly designed to addict users as a means to gain higher profits while turning us all into a horde of spineless, mindless zombies. Those who are don't have a chance and they don't care. That's how strong addiction is today, and the powers that should not be know it.
Boobus Americanus. Haha. Can I steal that? Actually you're close but not totally with the sugar assessment. The brain runs on glucose. We need some sugar, and the best is in the natural unprocessed forms of organic cane sugar, unfiltered honey, and maple sugar. The deadly cancer causing heart disease causing diabetes causing is the toxic gmo high fructose corn syrup.
Sugar is energy to the body, but useless to those who spend all their time sitting around playing video games or watching 20 or 30 year old sitcom reruns. They're empty calories and evolution has left us with an innate need to seek calories, but when they can be easily consumed without any real effort the results are catastrophic.
Much of the body requires some sugar, but nowhere near as much as most people are consuming. Moreover, just because it's organic or all natural doesn't negate the fact that the sugar addict is going to consume way too much of the stuff.
The body, but especially the brain requires cholesterol as well, but as soon as one introduces too much sugar to the body, perforations begin to appear in the arteries which the body must patch using cholesterol. The cholesterol then clogs up the arteries. The problem isn't cholesterol, but the overabundance of sugar.
Broccoli has sugar in it, but not so much that it becomes a problem for someone who is addicted to the stuff. Not everyone is an addict and there are plenty of people who can consume sugar without any significant problems, but they're the exception rather than the rule.
A couple of grams of sugar are a half a teaspoon. You're saying that's enough to overwhelm the liver?
Yep. that's about as much as the liver can handle in a day. Anything more than that is immediately turned to fat because the liver simply doesn't know what to do with it. Not only that, but while the liver is being overworked, it can't process things like vitamins, minerals, etc. etc. When that happens, one doesn't just get fat, but the inflammation becomes chronic. People usually think that big belly is just fat, but it's actually a grossly inflamed intestinal tract. Sugar in the amounts that most Americans consume is poison to the liver. 75 years ago the average American consumed around 2.5 pounds of sugar a year. Now they go through that in less than a week.
So you think putting 7 tablespoons of organic cane sugar in 10 ounces of coffee is too much?
It depends on a number of factors such as metabolism, one's susceptibility to addiction, and how active one is. In general, it's a bad idea. It's completely unnecessary to begin with. The body needs sugar, but more than a few grams is overkill.
There's a tribe over in Africa called "the Hazda" who live primarily on meat. They eat everything from head to toe except the entrails which they feed to their dogs. When they find something high in calories like honey, they go for it, but the men spend all day everyday tracking game which requires some sprinting as well. When there's no meat, nuts or berries, they eat tubers which are an overabundant source of fiber. Here's the kicker. They spit out all of the fiber. They basically just eat meat and sugar.
Degenerative diseases are unknown to them. They all die of old age and are in incredible shape well into their 90's They're ripped because of their lifestyle which allows them to consume massive amounts of sugar, but even that doesn't come close to what Boobus Americanus consumes while moving at a pace that makes sloths look like jack rabbits.
I cut out sugar including corn, wheat, flour, bananas, fruit juice, raisins, prunes, etc. etc. and lost 150 lbs. in less than seven months. I did no exercise and didn't work out at all. After losing the weight, I started doing crunches and within a week I had a six pack because there was no fat to conceal it. The fact is that the six pack was always there. Everyone has one covered under layers of fat.
That's very interesting. I misspoke when I said 7 tablespoons of sugar in 10 ounces of coffee--it's 5 tablespoons. But your comments would still apply. I get a lot of exercise going from the living room to the kitchen and the bathroom every half hour or so. That's a joke but if I were to jump up and down while doing it and run and skip maybe that would help. I'm going to try cutting those things out and see if I can lose weight. When you say you lost 150 pounds in less than seven months I assume you weighed more than 150 pounds to begin with. That's a joke. When I was about 21 I was on a medically supervised 28 day fast--It wasn't for weight loss, it was just another, more benevolent, treatment of a series of treatments stemming from misdiagnosis. The others did a lot of additional damage to my already existing neuromuscular condition, the suspected cause of which I will not get into right now (Hint: Sabin live oral attenuated polio vaccine when I was about 14). Anyway, when I started that 28 day fast, which was just water, I weighed 141.5 pounds, and when I ended it I weigh 107.25 pounds, but I felt fine, and gained most of it back in a month. It definitely reset my taste preferences, as I avoided any salt or sugar and was on a recommended lactovegetarian diet, but I didn't have a desire for much dairy, eating mostly raw vegetables, fruits, and nuts and seeds. Until about half year later when some other unrelated researcher suggested I should eat meat, so I broke the vegetarian diet, the main source of protein for which had been a pound of raw cashews a day.
I was tipping the scales at just over 300 lbs. The fat practically came off in slabs. A fair amount of flabby skin and stretch marks were evident for quite a while. I did a 20 day fast about three years ago as well. It was not intentional. My boat broke down out in the middle of the Gulf of Mexico. I lost a lot of weight and when I finally got home, there was a day there when I thought I might have to go to the hospital and be catheterized. I was having a lot of trouble peeing and pooping.
The 150 lbs. came off due to a "food plan" designed by Kay Sheppard M.D. She has a couple of books out on the subject that you can get used on Amazon for probably a few bucks, or your local library might have a copy as well. She calls it a food plan because there is no dieting involved. The biggest complaint from anyone I know who has adopted it is that it's too much food. I had to split my meals up into six meals a day for a few weeks until my stomach stretched out enough to handle it. It only took about two weeks before it jumped started my metabolism. It also took about three weeks for me to really start tasting and enjoying good healthy food again.
A few years after I'd been on her food plan I started a garden and soon realized that if I was going to subsist on what I grew, calories would become as valuable as gold. It's amazing how much food one must consume in order to maintain one's weight. I have a forest of spinach and probably a few thousand pounds of tubers buried in my back yard.
They are silly. Comorbidities should increase risk of death ...but the average number of conditions was not increased amongst official Covid-19 victims.
https://zenodo.org/record/8312871
It makes sense and spotlights that the spike in deaths could only be due to lethal injection. At eight months into the epidemic (Aug. 13), 749,000 people have reportedly died worldwide "with" COVID-19 (that is 0.000096% or 9.6 deaths per 100,000), but more than 80% had pre-existing co-morbid conditions. So maybe only ~150,000 have died of COVID-19 alone (0.000019% or 19 per 100,000).
It depends on where one gets their information and how it's gathered. Initially, the CDC posted this: ""Comorbidities
Table 3 shows the types of health conditions and contributing causes mentioned in conjunction with deaths involving coronavirus disease 2019 (COVID-19). For 6% of the deaths, COVID-19 was the only cause mentioned. For deaths with conditions or causes in addition to COVID-19, on average, there were 2.9 additional conditions or causes per death. The number of mentions for each condition or cause is shown for all deaths and by age groups."
A little over a year later, they came up with this:
"Table 3 shows the types of health conditions and contributing causes mentioned in conjunction with deaths involving coronavirus disease 2019 (COVID-19). The number of deaths that mention one or more of the conditions indicated is shown for all deaths involving COVID-19 and by age groups. For over 5% of these deaths, COVID-19 was the only cause mentioned on the death certificate. For deaths with conditions or causes in addition to COVID-19, on average, there were 4.0 additional conditions or causes per death. For data on deaths involving COVID-19 by time-period, jurisdiction, and other health conditions,"
https://www.cdc.gov/nchs/nvss/vsrr/covid_weekly/index.htm#Comorbidities
I'm looking for an interview (or article) in which (I believe) Harvey Risch says that the doses of hydroxychloroquine used in the trials were much too high, possibly ten times too high.
I'd like a link to it if possible.
I think the doctor was William Makis and it might have even been on a VSRF program, I don't know about that.
There is a really well produced documentary with good citations. Beautiful work.
_Epidemic of Fraud_.
https://www.youtube.com/watch?v=CmvwyuV7Uvk&t=3638s
Captions in 28 languges.
High production value.
28 Language Captions. Donate to support this work.
Paypal: https://EpidemicOfFraud.com/donate
Award Winning Film Epidemic of Fraud explores the bizarre media, medical, and partisan political attacks levied against a class of ancient medications, told from the perspective of a former CNN journalist and Hollywood industry veteran. Why were the people who allowed the opiate disaster to go unchecked so eager to discredit a drug that is safer than tonic water? Director John Davidson takes you into an audio/visual time machine back to 2020 to reveal the forbidden knowledge that our medical, academic, and political officials are desperate to hide from you.
I think it was Paul Marik that was in the interview
You might try Dr. Mercola's archives. Trying to remember where this was documented.