They had no Covid in the community and minimal vaccination until around June, when the rollout picked up speed. Record spikes in Covid infection rates have followed in July and August.
Skilled contact tracers, previously with a perfect record of being able to identify and stop trains of transmission, are suddenly unable to identify the source of infection for the majority of cases. It implies they are overlooking something fundamental.
"Since the vaccination programs targeted those at highest risk of COVID first (and VAERS and similar databases show similar age demographic skews in mortality reports), it makes sense that we would see a substantial and measurable proportion of vaccine-induced deaths early on during vaccination programs."
Were you able to analyze any of the data broken down by age group? So for example it would be very interesting to see a chart showing both the deaths among the elderly age bracket and doses given to that same age bracket, for each time period (say, month). If the vaccines were causing excess deaths then a chart like that would make that much clearer.
We desperately need more statisticians working on these tasks. There are a handful of us (three or four of experience), but there is enough work for 20, just sitting in my inbox.
I don’t believe you need more statisticians. You need more clinicians and medical researchers developing mechanistic model identifying death root cause. Without underlying, mechanistic understanding, any data is open to misrepresentation, more specifically correlation bias as illustrated in this article.
Root cause has been assessed for U.K. Yellow Card vaccine deaths by Dr Tess Lawrie (who also drafted a superb meta-analysis of ivermectin efficacy in covid19).
She found, IIRC, that 70% of the vaccine associated deaths had a thromboembolic underpinning event.
As a biological scientist & one time toxicologist, I find that unsurprising. We’ve known for years that spike proteins on the surface of many coronaviruses which infect humans or animals can trigger blood coagulation.
The most likely explanation for substantial numbers of deaths (1:3000 people) post vaccination is exactly this: some of the agents land in the circulation & the huge variability of where taken up, how effectively taken up, how well transcribed, for how long & the balance of clotting v bleeding in that individual could conceivably yield outcomes where one dies, ten are seriously injured and 2989 get minor to no persistent side effects.
I think it’s possible there’s an additional root cause pathology beyond thromboembolic but I’m not sure it’s necessary to posit it.
This comment may be misconstrued as misleading, please add a link to the accepted manuscript or preprint. I hope the paper adheres to FAIR principals and publishes the data.
Your comment raises some questions pertaining to conclusions and root cause:
1) Are these vaccine deaths or deaths "WITH" a vaccine ? For example, the shift to sedentary lifestyles have created the conditions for increased thromboembolic events.
2) Have these deaths been associated with preexisting thromboembolic comorbidities ?
3) Has anyone really looked for increased risk of venous thromboembolism due lifestyle changes ?
As you've said, corona viruses have been long known to trigger coagulation events. Specifically, SARS corona proteins show interactions with human Poly(A) Binding Protein Cytoplasmic 4 (PABPC4), Serine/Cysteine Proteinase Inhibitor Clade G Member 1 (SERPING1) and Vitamin K epOxide Reductase Complex subunit 1 (VKORC1).
Trigger mechanisms are still unknown and conditions including amylase buildup at coagulation sites are a root cause or part of (normal) coagulation events. Without precise understanding of trigger events, there is no way to attribute vaccines proteins as a cause venous thromboembolic events.
If sufficient funding is made available, it should be figured out in the next 18 months.
What is the group "us"? Humans/Westerners, via vaccines?
If this is your prediction, would you be open to figuring out how to make a bet from this? There is probably some way to trade "resources Yeadon cares about, assuming mass death within 18 months" for "resources Human cares about, assuming no mass death within 18 months", even if we don't trust each other much.
That's pretty a pessimistic outlook. The rate of mutation is worrisome, but not at the rate of panic... yet. There's not been a trustworthy study looking at broad vaccine effectiveness or populations with natural immunity.
A while ago, I guestimated 28% natural immunity due to genetic disposition, so that would account for 1.3B survivors.
I truly believe delta variant should be reclassified as another strain due to pathological differences from earlier versions. We shouldn't not be calling "Delta" variant SARS/CoV2, but something more like SARS/CoV3 due to break though events.
You are correct stating we're at war, however the political frameworks and radical media outlets are insistent on calling invasions an "incursions". The Delta variant is more like an invasion of an axis power player, a battle from a different invading force. This type of repositioning would reignite interest in combating the disease instead providing "the knuckle heads" an excuse to make false claims interventions are a failure and we all should give up and die. A very european attitude.
If people continue to die from SARS/CoV2 at 2020 rates (5.17M/yr) and there are no births, the human race will be wiped out in about 1500 years (lol). Fortunately, humans are reproducing at over 105M/yr. That's 20X the SARS/CoV2 mortality rate.
Maybe we'll end up in a dystopian future, like a star trek episode, where people die off as they become adults and the world is run by children (politicians).
Were Dr Lawrie's assessments in the time of adenovirus AZ jabs? Adenoviruses were an already known as a cause of clotting when accidentally administered IV.
with over 170 epitomes crossing numerous cell type eg pneumonocytes and endothelium, traditional pathological assessment methods will be seriously challenged classifying underpinning events
1. I am concerned only 'near' time of inoculation deaths are available for analysis. Given the cytotoxic spike protein and possible other components (eg NLPs) these
'near' time events may prove only a partial VFR. Identifying this broader cohort may take years to get a handle on.
2. Might it be possible to enumerate vaccine deaths from second shots and future 'booster' shots using dates?
CDC's ineptitude regarding data collection leans on being criminal.
Looking at the event ID's mentioned in McLachlan et al, they are strongly clustered towards January, when relatively few 2nd doses had been administered outside of clinical trials. This appears to be a result of his opaque method for choosing the 250 events.
Awesome work. Amazing the amount of work required to get an actionable dataset. Basically, you had to build a better database then existed. Thank you!! Great effort
This is exactly what I was thinking; amazing sleuthing/analysis/reporting- some of the most comprehensive and disturbing reporting of injection injuries I’ve seen yet.
"For example: 933739 - a lady with CP who coded in the ambulance hours after receiving the COVID-19 vaccine. She was tested for COVID-19 at hospital after having been resuscitated and was negative, yet COVID-19 is written as her primary symptom at death not an hour or two later"
As far as I can tell this is false. At least this claim isn't reflected in the VEARS data as of today (2021-08-21).
I'm not sure that it ultimately matters. The VAERS data is a bit of red-herring anyway, it's too inconsistently coded and has a biased incentive structure. It's an interesting data-point, but I don't think it should be used directly in numerical analysis. The fact VAERS only captures around 1% of incidents by some well-informed estimates and that COVID symptoms are so vaguely defined and potentially overlapping with vaccine side-effects means that one can do the analysis without worrying about VAERS at all.
Since VAERS is potentially such a tiny percentage, even if no reports in VAERS are labelled as COVID deaths it wouldn't make any difference to the final interpretation. What matters is if the much larger number of people who are put down as COVID deaths and who are NOT captured in VAERS really should have been.
making sure your claims are correct matters a great deal. making any false claims can undermine any other true thing you say, because you are now a less reliable source.
Absolutely. Couldn't agree more. Good science pushes boundaries by its nature, and pseudoscience often walls itself in with unassailable truths, but even then I concur that it is better to drop demonstrably false claims, or even dubious peripheral claims. You and I may be enlightened enough to no fall victim to the fallacy fallacy, but others are not. My point is just that it *is* a peripheral issue even if you are most likely correct about the coding: It doesn't make a difference to the ultimate analysis.
This is such important work, and I would like to show it to others. One request to nail down regarding the apparent ‘primary symptom at death’ mislabelings:
“For example: 933739 - a lady with CP who coded in the ambulance hours after receiving the COVID-19 vaccine. She was tested for COVID-19 at hospital after having been resuscitated and was negative, yet COVID-19 is written as her primary symptom at death not an hour or two later"
Can you post as many images or other documentation as possible of the internal VAERS classification that the primary symptom was COVID for 933739? This will help us all visualize and validate these major concerns.
McLachlan claims elsewhere that this is verifiable by anyone who downloads the VAERS data. I and several others did not see this when looking at the downloaded data for multiple VAERS entries listed in the paper.
I went and emailed the VAERS officials and they contradicted the claim that there is an internal classification coding all COVID-19 vaccine deaths as COVID-19 deaths. This was several weeks ago, but I can dig up the address and their quote if you're interested.
As of October 18, I was doing my damnedest to see if there was any evidence that the covid categorization claim was true, other than the say-so of the author. I resent the implication that I was not.
Now, given that he replies to your communications, he may have given you information that I do not have. Or given that I later decided the chance of resolution was no longer worth my time, there may be publicly available info about it now. If the latter, I’d appreciate you pointing me to it.
Or you might mean that he said in the linked thread that the the database may have changed. This leaves me at square one. Personally I was not able to download past database copies from standard archive websites. Perhaps there’s a way. But unless someone out there has trustworthy archived database copies, we’re relying on this person’s word for a fairly radical claim.
Yes I believe that most peoples' perception of the excess deaths due to these vaxxines is obscured by their naturally low level of experience with regular causes of death. For people that only encounter death among friends and family relatively rarely (the norm) it isn't apparent when an increase occurs even when this may be statistically significant.
There is an undeniable increase in excess mortality which can't be hidden but will be explained away by other causes. One device is to count deaths within 21 days post jab as unvaccinated deaths often from covid-19. Nice trick if you can get away with it, and they did.
Yet the scale of non-fatal adverse health effects from these vaxxines is far greater but much easier to disguise as being due too other factors. Lockdowns are now the scapegoat for sudden adult death syndrome.
Yes I believe that most peoples' perception of the excess deaths due to these vaxxines is obscured by their naturally low level of experience with regular causes of death. For people that only encounter death among friends and family relatively rarely (the norm) it isn't apparent when an increase occurs even when this may be statistically significant.
There is an undeniable increase in excess mortality which can't be hidden but will be explained away by other causes. One device is to count deaths within 21 days post jab as unvaccinated deaths often from covid-19. Nice trick if you can get away with it, and they did.
Yet the scale of non-fatal adverse health effects from these vaxxines is far greater but much easier to disguise as being due too other factors. Lockdowns are now the scapegoat for sudden adult death syndrome.
I came across a video by Stew Peters interviewing someone in which the claim is made that only 5% of vaccine lots are tied to 100% of VAERS reports. If true. that has far-reaching implications. We would expect a roughly even spread across vaccine lots, so are 5% of the vaccines different in some way?
I also came across data (lost the original tweet but saved the pic) showing that since the vaccine rollouts there have been 10x more GOP voting district 'CovID' deaths then Dems'. If both the above are true, the implications are horrific: those 5% vaccine lots are being steered toward GOP districts.
There was also a Slovenian nurse who claimed that the bottles were numbered 01, 02 or 03, and that 01 contains saline, 02 mRNA, and 03 mRNA for the spike + an oncogene. Wild and fantastic as this claim is, I have seen reports by different people of a surge in cancer amongst vaccinated people, especially those who were in remission.
So, if you look at TOTAL U.S. mortality on this graph here (https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm) and compare it to the weekly rate of vaccinations, in this chart here (https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html) what you see is that total mortality in the U.S. plummeted at precisely the same time as the vaccination campaign peaked. So, the number of weekly vaccines peaks about early April (with over 4 million doses delivered in a single day) with total mortality in the U.S. dropping like a stone from late January, and reaching a low (before the next wave of Covd) in early May. If there were tens of thousands of deaths from the vaccines, you would expect to see the exact opposite. How do you explain this?
If X causes Y, then more X will cause more Y. Between Jan 15th and the middle of April, the vaccinations per day went from (7-day moving average) just under 100k to just under 3.3 million. However, during that time the deaths per week went from a high of around 87k around Jan 15th to around 60k in the middle of April. So, we have increasing vaccinations but decreasing deaths. This is not consistent with a "vaccines cause death" hypothesis...unless you maintain vaccines are saving more than they are killing. Given the near 30k fall in deaths per week, you have to maintain if the vaccine is killing R people, it is saving at least R+30k people.
We can go further. Between the middle of April and the end of July, the vaccination rate went from a high of around that 3.3 million per day to a low of around 500k per day in June, then made a slow climb to around 900k per day at the end of July. However, the deaths per week through that entire time was fluctuating around, but mostly just below, 60k. So, 3.3 million vaccinations per day, 500k vaccinations per day, and 900k vaccinations per day are all paired with around 60k deaths per week. That is not what you expect from an X (vaccines) causes Y (deaths) relationship.
Your response to this is that the mortality curve for vaccination is nearly as steep as it is for covid. If that does not mean "as more people are vaccinated, we should see more deaths", I cannot imagine what it could mean. Does it mean as the vaccination rate drops off, the deaths from vaccines should fall precipitously? Because we do not see that. Does it mean that...I cannot think of anything else. The smoking gun is that deaths fall as vaccine rates rise. This is prima facie inconsistent with the claim that vaccines are causing significant numbers of deaths.
I am calling BS on your "the global data is overwhelmingly pointing to my hypothesis being correct". I have downloaded the data you cite, the github repository (https://github.com/owid/covid-19-data/tree/master/public/data). For each of cases, deaths, and vaccinations, there is multiple different variables that you could use. You did not tell us which one you used. Poor form. Also, why did you only calculate the correlation for 8 points? I did it for all of the dates and got results systematically different from what you got.
Having explored almost all of the correlations between cases variables and vaccination variables (my R script is available upon request), I can say a few things. Any variables that are looking at total cases vs total vaccinations has a correlation near 1. This for the obvious reason that these two are both growing over time. New cases vs New vaccinations, uncorrected for population size, has a correlation near 1. This is dominated by larger and richer countries that will have high levels for both. When I look at New cases per million vs vaccinations (fully or otherwise) per hundred people, I get the closest thing to what your graph looks like, but systematically lower than your graph. When I look at new cases vs vaccinations (fully or otherwise) per hundred people, I get correlations that are mostly negative, but not statistically distinguishable from 0. If I replace the new cases variables with their smoothed counterparts, I don't get systematically different results. Why did you only report one of these? Why did you only report the one that show positive correlations? Why is case per capita vs vaccinated people per capita the right correlation to look at? Let's discuss the mortality data before I critique your claim that this shows anything.
With regard to mortality, the story is pretty similar to the above. Total deaths vs total vaccinations however either are reported have correlations near 1 because of the time trends in cumulative totals. Likewise, new deaths vs new vaccinations has correlation near 1 because it is dominated by large and rich countries. However, everything else has either correlations that are not statistically different from 0, which we tend to say is correlations within 0.05 of 0, or they are mostly negative. Never do I see them get above 0.15, as your graph does. The is suspicious to say the least.
Short story, my attempt to reproduce your work has failed. I used the exact same data you claim to have used. So, can you please share how you processed your data?
Now, let's ask ourselves if the correlation actually shows anything. I claim no. In the article cited, you are doing nothing to control for any confounders. Are there plausible confounders? YES! average age of a population is an obvious confounder. A country with a high vaccination rate but also a higher average age will have more deaths and cases simply because the population is older. A country that is rich and has a high vaccination rate will have lower cases (the rich country has more room to social distance) and fewer deaths (because of a healthcare system better able to treat fatal cases). That countries age and affluence are highly correlated entails that the confounding of these two will make the pure relation between vaccine and cases/deaths basically unreadable from unconditional correlations. On top of that, you have not in anyway attempted to control for serial correlations. These are stochastic processes that have momentum behind them. How does the time trend of the unconditional correlations control for the serial correlations of the data? It does not. Nor have you attempted to control for prevalence of comorbidities, which it should be obvious will confound any relation between anything and death.
You have taken no systematic, but unobservable, features of any of the countries in the data set into account. There is no way to make the claim that the unadjusted, unconditional correlations between any case or mortality variable and a vaccination variable will tell you anything about the effect of vaccination on either of these two.
You, also, seem to be making the mistake of inferring vaccinations cause cases/deaths because they have a positive correlation over time! This inference is not valid! The causation could very well be going in the opposite direction. The correlation could be spurious. You have done nothing to eliminate those possible explanations for the cherry-picked positive correlations that you have found.
I've run 312 different correlation analysis using different endpoints and the entire dataset. Every correlation was positive. If there was one that was negative, don't you think somebody would have done the work, blogged it, and linked back here?
You don't get to call cherry-picking when I've used all the available data.
There are infinitely many variables, so saying that I have ignored them is to say that no data is ever meaningful. On the other hand, if you make an argument for a confounding variable, and defend it, I might have something to work with. You present a moving target, I do not. I've done the work, you haven't.
Further, anything like large scale efficacy should show not just a negative correlation, but a massive one. Massive.
I'll let you cherry-pick this time...show me one single world geography where its constituents show massive negative efficacy. Just one. There are hundreds. Just show one.
Why are you running correlation analyses? They are not going to make a case for causation. Why did you run 312?!?! That screams of p-hacking. What are you taking the correlation of? You said cases/deaths vs proportion of population vaccinated. Does that mean at every date you took the correlation of cases/deaths with the proportion vaccinated? From the data you got from the github repository?
Likewise, your claim "Of course we do...the younger cohort being vaccinated is far less susceptible to the spike protein" is ad hoc hypothesizing that is not relevant to the claim. Biden did not make the vaccine available for everyone until May 1st. Prior to that high risk populations were given priority access. However, we saw decreases in mortality rates as early as Jan 15th! The decrease in the mortality rate starting Jan 15th, when only old people and healthcare professionals and populations with comorbidities' were getting the vaccine, is in no way rebutted by your claim.
I am not entirely sure what you think a model is, but I do not see anything I would call a model in that paper, aside from the claim that vaccines cause death. I see that you have attempted to use a bunch of aggregate statistics to try to make a case for your claim, but again, you have not accounted for the possibility of any confounders.
There is no where in the article you've linked to that discusses how falling mortality rate despite rising vaccination rate is consistent with a vaccine causes death hypothesis explicitly.
"Now, do I believe that the vaccine kill 1 out of every thousand recipients? No. I suspect that this is a ceiling for the actual impact. It makes sense that the first 30 days of mass vaccination skewed toward high risk groups."
to clarify: is it the case that you do believe ~1 in every 1000 doses in Europe so far has resulted in a death (but that the rate will likely drop as time goes on) as indicated in the first paragraph?
Thank you. There seemed to be something odd about that, so I appreciate your feedback. I have to admit, I missed the connection with Renz. He does have an apparently well deserved negative reputation.
Just two questions:
1 - what about Mike Yeaden?
2 - could you or someone you know see if you can reproduce Paardecooper’s results? Or is it too likely to not be worth the effort?
I looked at cases/deaths in the most highly vaxxed countries (at the time) and saw a spike after vax rollout date in the larger populated countries (excludes China as they didn't use our vax and also countries not listed on World o meter) https://joannaf2.substack.com/p/do-covid-vaccines-work?justPublished=true
Australia makes for an interesting study - look at the charts here (click 'Tap to know more' for Australia): https://graphics.reuters.com/world-coronavirus-tracker-and-maps/vaccination-rollout-and-access/
They had no Covid in the community and minimal vaccination until around June, when the rollout picked up speed. Record spikes in Covid infection rates have followed in July and August.
Skilled contact tracers, previously with a perfect record of being able to identify and stop trains of transmission, are suddenly unable to identify the source of infection for the majority of cases. It implies they are overlooking something fundamental.
That would lead me to the jab is responsible for the higher numbers, not the virus so to speak.
Regarding what you wrote here:
"Since the vaccination programs targeted those at highest risk of COVID first (and VAERS and similar databases show similar age demographic skews in mortality reports), it makes sense that we would see a substantial and measurable proportion of vaccine-induced deaths early on during vaccination programs."
Were you able to analyze any of the data broken down by age group? So for example it would be very interesting to see a chart showing both the deaths among the elderly age bracket and doses given to that same age bracket, for each time period (say, month). If the vaccines were causing excess deaths then a chart like that would make that much clearer.
We desperately need more statisticians working on these tasks. There are a handful of us (three or four of experience), but there is enough work for 20, just sitting in my inbox.
I am not directly working with the VAERS data myself, though. I'm focusing my time elsewhere.
I don’t believe you need more statisticians. You need more clinicians and medical researchers developing mechanistic model identifying death root cause. Without underlying, mechanistic understanding, any data is open to misrepresentation, more specifically correlation bias as illustrated in this article.
Root cause has been assessed for U.K. Yellow Card vaccine deaths by Dr Tess Lawrie (who also drafted a superb meta-analysis of ivermectin efficacy in covid19).
She found, IIRC, that 70% of the vaccine associated deaths had a thromboembolic underpinning event.
As a biological scientist & one time toxicologist, I find that unsurprising. We’ve known for years that spike proteins on the surface of many coronaviruses which infect humans or animals can trigger blood coagulation.
The most likely explanation for substantial numbers of deaths (1:3000 people) post vaccination is exactly this: some of the agents land in the circulation & the huge variability of where taken up, how effectively taken up, how well transcribed, for how long & the balance of clotting v bleeding in that individual could conceivably yield outcomes where one dies, ten are seriously injured and 2989 get minor to no persistent side effects.
I think it’s possible there’s an additional root cause pathology beyond thromboembolic but I’m not sure it’s necessary to posit it.
This comment may be misconstrued as misleading, please add a link to the accepted manuscript or preprint. I hope the paper adheres to FAIR principals and publishes the data.
Your comment raises some questions pertaining to conclusions and root cause:
1) Are these vaccine deaths or deaths "WITH" a vaccine ? For example, the shift to sedentary lifestyles have created the conditions for increased thromboembolic events.
2) Have these deaths been associated with preexisting thromboembolic comorbidities ?
3) Has anyone really looked for increased risk of venous thromboembolism due lifestyle changes ?
As you've said, corona viruses have been long known to trigger coagulation events. Specifically, SARS corona proteins show interactions with human Poly(A) Binding Protein Cytoplasmic 4 (PABPC4), Serine/Cysteine Proteinase Inhibitor Clade G Member 1 (SERPING1) and Vitamin K epOxide Reductase Complex subunit 1 (VKORC1).
Trigger mechanisms are still unknown and conditions including amylase buildup at coagulation sites are a root cause or part of (normal) coagulation events. Without precise understanding of trigger events, there is no way to attribute vaccines proteins as a cause venous thromboembolic events.
If sufficient funding is made available, it should be figured out in the next 18 months.
In 18 months, I expect most of us will be dead.
You’re writing as if we’re in peacetime yet we are unquestionably at war.
What is the group "us"? Humans/Westerners, via vaccines?
If this is your prediction, would you be open to figuring out how to make a bet from this? There is probably some way to trade "resources Yeadon cares about, assuming mass death within 18 months" for "resources Human cares about, assuming no mass death within 18 months", even if we don't trust each other much.
That's pretty a pessimistic outlook. The rate of mutation is worrisome, but not at the rate of panic... yet. There's not been a trustworthy study looking at broad vaccine effectiveness or populations with natural immunity.
A while ago, I guestimated 28% natural immunity due to genetic disposition, so that would account for 1.3B survivors.
I truly believe delta variant should be reclassified as another strain due to pathological differences from earlier versions. We shouldn't not be calling "Delta" variant SARS/CoV2, but something more like SARS/CoV3 due to break though events.
You are correct stating we're at war, however the political frameworks and radical media outlets are insistent on calling invasions an "incursions". The Delta variant is more like an invasion of an axis power player, a battle from a different invading force. This type of repositioning would reignite interest in combating the disease instead providing "the knuckle heads" an excuse to make false claims interventions are a failure and we all should give up and die. A very european attitude.
If people continue to die from SARS/CoV2 at 2020 rates (5.17M/yr) and there are no births, the human race will be wiped out in about 1500 years (lol). Fortunately, humans are reproducing at over 105M/yr. That's 20X the SARS/CoV2 mortality rate.
Maybe we'll end up in a dystopian future, like a star trek episode, where people die off as they become adults and the world is run by children (politicians).
Were Dr Lawrie's assessments in the time of adenovirus AZ jabs? Adenoviruses were an already known as a cause of clotting when accidentally administered IV.
Thank!
Where is the best place to get this injection in order to minimize the risk of spike protein getting into the blood stream?
Thank you.
with over 170 epitomes crossing numerous cell type eg pneumonocytes and endothelium, traditional pathological assessment methods will be seriously challenged classifying underpinning events
"epitome" ...I do not think that this word means what you think it means
Apple autocorrect won't let me change it to e-p-i-t-o-p-e-s
Hi Mathew, try reaching out to William M. Briggs. He may be able to help out (https://wmbriggs.com/)
This paper uses a completely different method to come to a similar conclusion https://www.researchgate.net/publication/355581860_COVID_vaccination_and_age-stratified_all-cause_mortality_risk VFR ~ .004%(young children) to .055% (elderly)
I believe that paper was a project stemming from this analysis, with the goal of measuring from a different angle.
oh ok, good to know. I'm referencing both of these analysis in discussions on a "vaccine talk" site
Take a look at Austin's analysis of excess deaths as well. https://austingwalters.com/changes-in-the-cdc-counts-of-deaths-by-state-and-select-causes/
That curve analysis is consistent with my set of observations.
1. I am concerned only 'near' time of inoculation deaths are available for analysis. Given the cytotoxic spike protein and possible other components (eg NLPs) these
'near' time events may prove only a partial VFR. Identifying this broader cohort may take years to get a handle on.
2. Might it be possible to enumerate vaccine deaths from second shots and future 'booster' shots using dates?
CDC's ineptitude regarding data collection leans on being criminal.
Mortality is most chiefly associated with the first shot. This alone is a potential signal of causation.
Looking at the event ID's mentioned in McLachlan et al, they are strongly clustered towards January, when relatively few 2nd doses had been administered outside of clinical trials. This appears to be a result of his opaque method for choosing the 250 events.
Do you have a reference for first shot mortality?
Awesome work. Amazing the amount of work required to get an actionable dataset. Basically, you had to build a better database then existed. Thank you!! Great effort
This is exactly what I was thinking; amazing sleuthing/analysis/reporting- some of the most comprehensive and disturbing reporting of injection injuries I’ve seen yet.
nit-pick : "poured through the data" should be "pored through the data"
But it's beneath the dog sleuth pic so should be "pawed" through the data
🐾 lol
Cool, I actually had to look that up…I learned something new…I thought it was ‘pour’ as well.
To pore through, ie Study, Contemplate, Deliberate, Mull over, Ponder.
"For example: 933739 - a lady with CP who coded in the ambulance hours after receiving the COVID-19 vaccine. She was tested for COVID-19 at hospital after having been resuscitated and was negative, yet COVID-19 is written as her primary symptom at death not an hour or two later"
As far as I can tell this is false. At least this claim isn't reflected in the VEARS data as of today (2021-08-21).
To check:
1. Download the 2021 "CSV File (VAERS Symptoms)" dataset here: https://vaers.hhs.gov/data/datasets.html?fbclid=IwAR3GZ2Ei3HhvoAKAXPwF2nrNu8PxlbYqpiBZJ4aTV1Y79f5FB2ktGNGeNu4
2. find the row with VAERS_ID 933739
None of the columns mention COVID-19 (some other records in this dataset do)
Screenshot of the row in question:
https://i.imgur.com/Sn2VRTs.png
This casts doubt on the claim that vaccine deaths are incorrectly being labelled with the COVID-19 symptom.
I'm not sure that it ultimately matters. The VAERS data is a bit of red-herring anyway, it's too inconsistently coded and has a biased incentive structure. It's an interesting data-point, but I don't think it should be used directly in numerical analysis. The fact VAERS only captures around 1% of incidents by some well-informed estimates and that COVID symptoms are so vaguely defined and potentially overlapping with vaccine side-effects means that one can do the analysis without worrying about VAERS at all.
Since VAERS is potentially such a tiny percentage, even if no reports in VAERS are labelled as COVID deaths it wouldn't make any difference to the final interpretation. What matters is if the much larger number of people who are put down as COVID deaths and who are NOT captured in VAERS really should have been.
making sure your claims are correct matters a great deal. making any false claims can undermine any other true thing you say, because you are now a less reliable source.
Absolutely. Couldn't agree more. Good science pushes boundaries by its nature, and pseudoscience often walls itself in with unassailable truths, but even then I concur that it is better to drop demonstrably false claims, or even dubious peripheral claims. You and I may be enlightened enough to no fall victim to the fallacy fallacy, but others are not. My point is just that it *is* a peripheral issue even if you are most likely correct about the coding: It doesn't make a difference to the ultimate analysis.
screenshot the wrong row! here's the correct one. COVID-19 not listed as a symptom https://i.imgur.com/8mOSDRJ.png
You're misunderstanding. The COVID classification is internal in the VAERS system.
Matthew,
This is such important work, and I would like to show it to others. One request to nail down regarding the apparent ‘primary symptom at death’ mislabelings:
“For example: 933739 - a lady with CP who coded in the ambulance hours after receiving the COVID-19 vaccine. She was tested for COVID-19 at hospital after having been resuscitated and was negative, yet COVID-19 is written as her primary symptom at death not an hour or two later"
Can you post as many images or other documentation as possible of the internal VAERS classification that the primary symptom was COVID for 933739? This will help us all visualize and validate these major concerns.
Yes, I looked up those records as well and didn't see any indication that covid was considered to be the cause of death.
McLachlan claims elsewhere that this is verifiable by anyone who downloads the VAERS data. I and several others did not see this when looking at the downloaded data for multiple VAERS entries listed in the paper.
https://twitter.com/mormo_music/status/1429334592230678528
I went and emailed the VAERS officials and they contradicted the claim that there is an internal classification coding all COVID-19 vaccine deaths as COVID-19 deaths. This was several weeks ago, but I can dig up the address and their quote if you're interested.
The database was altered. You aren't keeping up with the conversation.
As of October 18, I was doing my damnedest to see if there was any evidence that the covid categorization claim was true, other than the say-so of the author. I resent the implication that I was not.
Now, given that he replies to your communications, he may have given you information that I do not have. Or given that I later decided the chance of resolution was no longer worth my time, there may be publicly available info about it now. If the latter, I’d appreciate you pointing me to it.
Or you might mean that he said in the linked thread that the the database may have changed. This leaves me at square one. Personally I was not able to download past database copies from standard archive websites. Perhaps there’s a way. But unless someone out there has trustworthy archived database copies, we’re relying on this person’s word for a fairly radical claim.
Yes I believe that most peoples' perception of the excess deaths due to these vaxxines is obscured by their naturally low level of experience with regular causes of death. For people that only encounter death among friends and family relatively rarely (the norm) it isn't apparent when an increase occurs even when this may be statistically significant.
There is an undeniable increase in excess mortality which can't be hidden but will be explained away by other causes. One device is to count deaths within 21 days post jab as unvaccinated deaths often from covid-19. Nice trick if you can get away with it, and they did.
Yet the scale of non-fatal adverse health effects from these vaxxines is far greater but much easier to disguise as being due too other factors. Lockdowns are now the scapegoat for sudden adult death syndrome.
Yes I believe that most peoples' perception of the excess deaths due to these vaxxines is obscured by their naturally low level of experience with regular causes of death. For people that only encounter death among friends and family relatively rarely (the norm) it isn't apparent when an increase occurs even when this may be statistically significant.
There is an undeniable increase in excess mortality which can't be hidden but will be explained away by other causes. One device is to count deaths within 21 days post jab as unvaccinated deaths often from covid-19. Nice trick if you can get away with it, and they did.
Yet the scale of non-fatal adverse health effects from these vaxxines is far greater but much easier to disguise as being due too other factors. Lockdowns are now the scapegoat for sudden adult death syndrome.
VAERS 942072 has changed and no longer lists COVID-19 as the first symptom.
https://openvaers.com/covid-data/covid-reports/0942072
I came across a video by Stew Peters interviewing someone in which the claim is made that only 5% of vaccine lots are tied to 100% of VAERS reports. If true. that has far-reaching implications. We would expect a roughly even spread across vaccine lots, so are 5% of the vaccines different in some way?
https://tv.gab.com/channel/realstewpeters/view/vaers-reveals-death-by-lot-number-618043ad79fddabeff768f34
I also came across data (lost the original tweet but saved the pic) showing that since the vaccine rollouts there have been 10x more GOP voting district 'CovID' deaths then Dems'. If both the above are true, the implications are horrific: those 5% vaccine lots are being steered toward GOP districts.
https://twitter.com/Great_Briton_I/status/1454871977370456065
There was also a Slovenian nurse who claimed that the bottles were numbered 01, 02 or 03, and that 01 contains saline, 02 mRNA, and 03 mRNA for the spike + an oncogene. Wild and fantastic as this claim is, I have seen reports by different people of a surge in cancer amongst vaccinated people, especially those who were in remission.
There were several suspicious things going on with the Pfizer vaccine in the US.
- Vaccine vials were not serialized which is required by US regulations. https://hedleyrees.substack.com/p/pfizer-refused-to-serialize-its-sars
- Some vaccine boxes did not have the required insert for side effects and other info.
- Pfizer had to get this out quickly and got the help of third parties where quality control was poor or non-existant.
- When the vaccines rolled out Pfizer documents admit the human trials were not done in Jan 2021.
- Vaccine manufacturers have special liability protection for this vaccine so they cannot be sued for damages. https://www.bitchute.com/video/t9vbQXCnrjxw/
Sorry those are all the links I have.
So, if you look at TOTAL U.S. mortality on this graph here (https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm) and compare it to the weekly rate of vaccinations, in this chart here (https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html) what you see is that total mortality in the U.S. plummeted at precisely the same time as the vaccination campaign peaked. So, the number of weekly vaccines peaks about early April (with over 4 million doses delivered in a single day) with total mortality in the U.S. dropping like a stone from late January, and reaching a low (before the next wave of Covd) in early May. If there were tens of thousands of deaths from the vaccines, you would expect to see the exact opposite. How do you explain this?
Almost all of the elderly were already vaccinated by April. The mortality curve for vaccination is nearly as steep as for COVID.
Nah, you have missed the point.
If X causes Y, then more X will cause more Y. Between Jan 15th and the middle of April, the vaccinations per day went from (7-day moving average) just under 100k to just under 3.3 million. However, during that time the deaths per week went from a high of around 87k around Jan 15th to around 60k in the middle of April. So, we have increasing vaccinations but decreasing deaths. This is not consistent with a "vaccines cause death" hypothesis...unless you maintain vaccines are saving more than they are killing. Given the near 30k fall in deaths per week, you have to maintain if the vaccine is killing R people, it is saving at least R+30k people.
We can go further. Between the middle of April and the end of July, the vaccination rate went from a high of around that 3.3 million per day to a low of around 500k per day in June, then made a slow climb to around 900k per day at the end of July. However, the deaths per week through that entire time was fluctuating around, but mostly just below, 60k. So, 3.3 million vaccinations per day, 500k vaccinations per day, and 900k vaccinations per day are all paired with around 60k deaths per week. That is not what you expect from an X (vaccines) causes Y (deaths) relationship.
Your response to this is that the mortality curve for vaccination is nearly as steep as it is for covid. If that does not mean "as more people are vaccinated, we should see more deaths", I cannot imagine what it could mean. Does it mean as the vaccination rate drops off, the deaths from vaccines should fall precipitously? Because we do not see that. Does it mean that...I cannot think of anything else. The smoking gun is that deaths fall as vaccine rates rise. This is prima facie inconsistent with the claim that vaccines are causing significant numbers of deaths.
"So, we have increasing vaccinations but decreasing deaths."
Of course we do...the younger cohort being vaccinated is far less susceptible to the spike protein.
At this point, the global data is overwhelmingly pointing to my hypothesis being correct:
https://roundingtheearth.substack.com/p/systemic-covid-19-vaccine-efficacy-fe3
I am calling BS on your "the global data is overwhelmingly pointing to my hypothesis being correct". I have downloaded the data you cite, the github repository (https://github.com/owid/covid-19-data/tree/master/public/data). For each of cases, deaths, and vaccinations, there is multiple different variables that you could use. You did not tell us which one you used. Poor form. Also, why did you only calculate the correlation for 8 points? I did it for all of the dates and got results systematically different from what you got.
Having explored almost all of the correlations between cases variables and vaccination variables (my R script is available upon request), I can say a few things. Any variables that are looking at total cases vs total vaccinations has a correlation near 1. This for the obvious reason that these two are both growing over time. New cases vs New vaccinations, uncorrected for population size, has a correlation near 1. This is dominated by larger and richer countries that will have high levels for both. When I look at New cases per million vs vaccinations (fully or otherwise) per hundred people, I get the closest thing to what your graph looks like, but systematically lower than your graph. When I look at new cases vs vaccinations (fully or otherwise) per hundred people, I get correlations that are mostly negative, but not statistically distinguishable from 0. If I replace the new cases variables with their smoothed counterparts, I don't get systematically different results. Why did you only report one of these? Why did you only report the one that show positive correlations? Why is case per capita vs vaccinated people per capita the right correlation to look at? Let's discuss the mortality data before I critique your claim that this shows anything.
With regard to mortality, the story is pretty similar to the above. Total deaths vs total vaccinations however either are reported have correlations near 1 because of the time trends in cumulative totals. Likewise, new deaths vs new vaccinations has correlation near 1 because it is dominated by large and rich countries. However, everything else has either correlations that are not statistically different from 0, which we tend to say is correlations within 0.05 of 0, or they are mostly negative. Never do I see them get above 0.15, as your graph does. The is suspicious to say the least.
Short story, my attempt to reproduce your work has failed. I used the exact same data you claim to have used. So, can you please share how you processed your data?
Now, let's ask ourselves if the correlation actually shows anything. I claim no. In the article cited, you are doing nothing to control for any confounders. Are there plausible confounders? YES! average age of a population is an obvious confounder. A country with a high vaccination rate but also a higher average age will have more deaths and cases simply because the population is older. A country that is rich and has a high vaccination rate will have lower cases (the rich country has more room to social distance) and fewer deaths (because of a healthcare system better able to treat fatal cases). That countries age and affluence are highly correlated entails that the confounding of these two will make the pure relation between vaccine and cases/deaths basically unreadable from unconditional correlations. On top of that, you have not in anyway attempted to control for serial correlations. These are stochastic processes that have momentum behind them. How does the time trend of the unconditional correlations control for the serial correlations of the data? It does not. Nor have you attempted to control for prevalence of comorbidities, which it should be obvious will confound any relation between anything and death.
You have taken no systematic, but unobservable, features of any of the countries in the data set into account. There is no way to make the claim that the unadjusted, unconditional correlations between any case or mortality variable and a vaccination variable will tell you anything about the effect of vaccination on either of these two.
You, also, seem to be making the mistake of inferring vaccinations cause cases/deaths because they have a positive correlation over time! This inference is not valid! The causation could very well be going in the opposite direction. The correlation could be spurious. You have done nothing to eliminate those possible explanations for the cherry-picked positive correlations that you have found.
"I did it for all of the dates and got results systematically different from what you got."
Now I will call bullshit. Blog it. I think you're a liar. Blog it and prove me wrong. Put your credentials as a biostatistician on the line.
So...I did as you asked (https://loren-56681.medium.com/for-mathew-crawford-ccc0c556ab09). You going to publish how you calculated your correlations? Or do we take your silence as you running away with your tail between your legs?
I've run 312 different correlation analysis using different endpoints and the entire dataset. Every correlation was positive. If there was one that was negative, don't you think somebody would have done the work, blogged it, and linked back here?
You don't get to call cherry-picking when I've used all the available data.
There are infinitely many variables, so saying that I have ignored them is to say that no data is ever meaningful. On the other hand, if you make an argument for a confounding variable, and defend it, I might have something to work with. You present a moving target, I do not. I've done the work, you haven't.
Further, anything like large scale efficacy should show not just a negative correlation, but a massive one. Massive.
I'll let you cherry-pick this time...show me one single world geography where its constituents show massive negative efficacy. Just one. There are hundreds. Just show one.
Why are you running correlation analyses? They are not going to make a case for causation. Why did you run 312?!?! That screams of p-hacking. What are you taking the correlation of? You said cases/deaths vs proportion of population vaccinated. Does that mean at every date you took the correlation of cases/deaths with the proportion vaccinated? From the data you got from the github repository?
Likewise, your claim "Of course we do...the younger cohort being vaccinated is far less susceptible to the spike protein" is ad hoc hypothesizing that is not relevant to the claim. Biden did not make the vaccine available for everyone until May 1st. Prior to that high risk populations were given priority access. However, we saw decreases in mortality rates as early as Jan 15th! The decrease in the mortality rate starting Jan 15th, when only old people and healthcare professionals and populations with comorbidities' were getting the vaccine, is in no way rebutted by your claim.
"However, we saw decreases in mortality rates as early as Jan 15th!"
This is entirely consistent with my model:
https://roundingtheearth.substack.com/p/estimating-vaccine-induced-mortality
I am not entirely sure what you think a model is, but I do not see anything I would call a model in that paper, aside from the claim that vaccines cause death. I see that you have attempted to use a bunch of aggregate statistics to try to make a case for your claim, but again, you have not accounted for the possibility of any confounders.
There is no where in the article you've linked to that discusses how falling mortality rate despite rising vaccination rate is consistent with a vaccine causes death hypothesis explicitly.
"Now, do I believe that the vaccine kill 1 out of every thousand recipients? No. I suspect that this is a ceiling for the actual impact. It makes sense that the first 30 days of mass vaccination skewed toward high risk groups."
to clarify: is it the case that you do believe ~1 in every 1000 doses in Europe so far has resulted in a death (but that the rate will likely drop as time goes on) as indicated in the first paragraph?
I suspect that the overall vaccine-induced death rate in Europe is likely closer to 400D/M than the early 1000D/M, but that could change.
Are you aware of these results?
https://hillmd.substack.com/p/vaccine-batches-vary-in-toxicity
Q - How do you get a dose response curve from allegedly random data?
A - You don’t.
Yet there it is.
- UPDATE -
https://m.youtube.com/watch?v=1dPKwYjtcOo
Paardekooper hasn't answered any of my concerns about the analysis, and Renz is the most untrustworthy data analyst I may have ever talked with.
Thank you. There seemed to be something odd about that, so I appreciate your feedback. I have to admit, I missed the connection with Renz. He does have an apparently well deserved negative reputation.
Just two questions:
1 - what about Mike Yeaden?
2 - could you or someone you know see if you can reproduce Paardecooper’s results? Or is it too likely to not be worth the effort?
Thanks again.
I looked at cases/deaths in the most highly vaxxed countries (at the time) and saw a spike after vax rollout date in the larger populated countries (excludes China as they didn't use our vax and also countries not listed on World o meter) https://joannaf2.substack.com/p/do-covid-vaccines-work?justPublished=true