Introduction

I said I’d talk about my story in “what’s coming next”, but have since decided I won’t - not for some time to come anyway. I think it’s important that we share our personal tragedies for people to realize what we had to go through during that time and why it’s so important that we find means to put an end to the propaganda machine. But I am not equipped to relive those memories right now. I need more time.

Hypnosis

Most of you have long known it: You've been hypnotized — or psychologically manipulated, if you prefer a more clinical term. And not just since 2020. Fine examples of the employed hypnosis techniques can be found among the statements made by Bill Gates in the decade preceding the mass murder event of 2020-2023.

Lowering the world population projections

In what must be his most legendary public appearance safe for his congress hearing on the Netscape matter (where his sociopathic tendencies and complete obliviousness to the concepts of right and wrong became crystal clear), Bill Gates delivered the following prognosis in a 2010 TED talk:

The world today has 6.8 billion people. That’s headed up to about 9 billion. Now, if we do a really great job on new vaccines, health care, reproductive health services, we could lower that by perhaps 10 or 15 percent. [1,2]

Many of us interpreted this as a Freudian slip - his subconscious must have been doing the talking for him. A sure sign that Gates intended to reduce the population with vaccines.

It stuck with us for a decade.

And when people began dropping like flies in 2021, the clip resurfaced.

But if what we saw was a Freudian slip, then how do we explain, that Gates delivered that statement with perfect calm?
No nerves. No correction. No stutter — like when he’s asked about sexual encounters with minors (which he just might’ve had a number of back in the hey-days of Lolita Island).
None of that. He just drove it home and moved on.

Consider his statement intentional. Carefully calculated even.
Consider that clip a deliberately planted psychological trigger — a decoy meant to occupy your focus.

I’ve already shown you how Denis Rancourt lied to you. He’s not alone. And by and large they are all telling the same lies.

Ed Dowd. Michael Yeadon. Sasha Latypova. Wolfgang Wodarg. Steve Kirsch. Russ Wolfinger. Children’s Health Defense. RFK Jr. and - in extension - the man behind these last two:
Mark Gorton — a shadowy billionaire whose high-frequency trading firm serves as a liquidity provider to nearly the entire European pharmaceutical industry. And who I admit - not without some pride - happens to have been my own personal handler, too, for the better part of a year.

All of them hammer home the same message:
VACCINES KILLED MILLIONS

But ask yourself:
If every prominent actor in “alternative” discourse pushes the same theory,
and yet their claims remain completely isolated from rigorous scientific discussion,
are you really hearing the truth?

Or are you watching a script unfold?

Because when everyone agrees but nothing changes, you’re likely witnessing controlled opposition — a psy-op designed to drain dissent into a dead-end loop and lull you back to sleep just as you think you’re waking up.

So let’s step outside the psy-op — just for a moment.

Let’s entertain the possibility that vaccines were not the thing killing people,
and that Bill Gates and the pharmaceutical industry are not the entities we should be looking at.

Don’t worry — I still despise the guy and I’m not about to shill for vaccines either.

But I am eventually going to show you something very exciting:

• Why it looks like vaccines saved lives

• Why that illusion holds up under publicly available data (contrary to Rancourt’s lies)

• And how lockdowns along with exclusion of the unvaccinated from the public delayed viral infections - particularly in unvaccinated individuals - just long enough to mask vaccine-associated mortality

I’ve used every opportunity I got to hint at what caused COVID and non-COVID excess deaths alike, but if you haven’t figured it out yourself yet, you’ll need some more patience. In the end - COVID, virology and everything else that didn’t add up will make sense again, so those of you so inclined can proceed towards the experimental phase to see if my hypothesis rings true or not.

“Microbes not missiles”

In a 2015 TED talk, Bill Gates announced:

[...] if anything kills over 10 million people in the next few decades, it’s likely to be a highly infectious virus — rather than a war — not missiles but microbes. [1,2,3,4]

If you follow the links you will find out he is not the first person to use that phrasing. He always knew (and you too will soon know why he and many others always knew) that the events of 2019/20 would be accompanied by the emergence of a novel viral particle. Since viral particles are not microbes the statement is easily identified as a PR-constructed slogan.

If we consider this statement another act of hypnosis and apply the same reasoning we used before, a new working hypothesis emerges:

Missiles caused a pandemic.

What sounds wildly absurd at first becomes as eerily plausible as it is difficult to refute, once you begin to take it literally rather than metaphorically.

I’ll stop here. But before we get to the really exciting part, I’ll leave you with a few tasks — just to keep you moving forward as you work to unravel the COVID puzzle:

• Go back to the 1300s and research what the term “influenza (degli astri)” meant back then

• Go back to the 1600s and research the original meaning of the word “pandemic” - OR go back 2000 years further and focus the term “epidemic” instead

The Chart Part

I am gonna keep this brief. It is merely a teaser.

This image is not proof of any causal link between StarLink and either COVID-19 or excess mortality, so save your breath if you are here to agitate. It is merely an illustration of the timeline. Let it sink in for a week or two.

There is a LOT to be said about the relationship between StarLink, morbidity and mortality patterns - and I promise we will get to that. We are gonna go deep into the pathomechanisms making artificial “non-ionizing radiation” exposure so problematic in terms of the impact it has on both animal and plant life and we are gonna get into the pathophysiology of COVID-19 and what role the novel viral particle SARS-CoV-2 might play in the grand scheme of evolution, which will lead us to the exciting part I mentioned, which is the question:

Are Bill and Elon our friends or our foes?

Are virions our friends or our foes?

I blame you for the moonlit sky
And the dream that died…

To be continued…

The Moth

A few nights ago, I woke up and realized I had left the light on in the living room.

When I walked in, I saw a moth.

It was circling the ceiling light — frantically trying to reach it,
crashing into it again and again and again.

The moth couldn’t tell God’s light from the light made by men.

Don’t be the moth.

Don’t chase what only looks like truth.
Don’t mistake guidance for truth.

And don’t take me for your friend — just because you need someone to trust.

Trust only yourself. And God.

I’m not asking for loyalty. I’m asking for clarity.

Don’t trust me.

Judge me not by my smoking habit.

Judge me not by my lack of formal education.

[SARCASM ON] Judge me not for never having worked for Blackrock or Pfizer or because JFK isn’t my uncle. [SARCASM OFF]

Judge me only by what I show you.

And again:
Don’t trust me.
Test what I say.

I’ve spoken at times about 20 million murders, and other times about 30 million.

That’s not a contradiction.

It reflects both the flexibility in how excess deaths are calculated —
and the reality that the crimes we are uncovering are not limited to these deaths.

The machinery of deception shielding these crimes is older than we are.

And the cost of those lies is best measured not in millions of lives, but in
billions of human life years lost.

In what’s coming next, I will tell you my story.

I’ve been meaning to do so for a very long time. It’s just something I feel I need to do.

I haven’t managed to tell it until now, because it causes me great pain.

I even tried therapy — for over a year. It only amplified that pain.

The only thing that seems to help… is fighting. And I’m hoping that telling you my story will help me heal — and maybe it will give some weight to all those big words I’m using.

I will name many of the entities I believe have betrayed not just me,
but all of humanity.

Some have done so out of greed, others out of pride or fear.
It makes little difference.

But before I go on, I owe you something.

In the course of this work, I’ve let my pain speak for me — and sometimes my anger.
Especially when speaking to those I believe are not to be trusted - like Mark Gorton, Michael Yeadon, Markus Böhnig, Robert Kennedy Jr., Steve Kirsch
and the one who calls himself Ulf Lorré - and also to many of their fans and followers.

I regret my tone. Not my concern.

So let me be just as clear:

I will not stop uncovering what they are hiding.

I will not be seduced by reputations.
I will not be softened by politeness.
No lawsuit will ever silence me.

And neither will another attempt on my life — which I do have every reason to expect — unless it succeeds. That is a price I will gladly pay, because by then, the trail will speak louder than I ever could, and this trail will be followed by others like me.

And once you find yourself on that trail, you might reach conclusions that differ from mine — and that’s perfectly fine.

As a general tool for guidance, I will quote the bible:

“You will know them by their fruits.”
— Matthew 7:16

Once you have figured out the truth - and I will do my best not to spoon-feed it to you - you will know how to apply this tool.

As for me — my judgment of these people, and the harshness of my tone:
I was wounded, and I let my wounds do the speaking for me.
I should have let clarity speak instead.

From now on, it will.

I’m not here to be believed.
I’m just here to show you where to look.

Most of what I say will point to things you can verify for yourself:
Documents. Patterns. Actions. Cold logic connecting them.
Not opinions. Not belief.

The rest you are free to disagree with, but I do not intend to spend a lot of time discussing those parts with you.

If you’re seeking truth — not comfort —
stay with me.

That’s all I ask of you.

And to those who built the ceiling lamp to look like heaven:

People aren’t moths. We see you.

“For there is nothing hidden that will not be disclosed,
and nothing concealed that will not be known or brought out into the open.”
— Luke 8:17

To be continued...

Introduction

I know from experience how painful it is to feel unmoored from reality. Many of you have spent years trying to see through the lies, to map the agendas behind the narrative of our time, and — ultimately — to understand why 25 million people had to die.

About a fifth of those 25 million suffocated in the most inhumane way: In isolation, feeling neglected by those they loved, and by those who were supposed to care for them professionally.

The responsible entities remain untouched by justice. It is up to us to change that.

For the longest time, nothing made sense. The pieces were there, but the picture never formed — just a blur of pain and doubt. I didn’t know what was real. But every lie uncovered lit a small path forward. So I followed it.

I can’t promise everything will make sense by the end of this series — but I’ve done my best to put things back in order. A lot of the questions that have remained unanswered or unasked will have answers that are satisfying to even the most curious and the most critical thinkers among you. And those that I can not tackle, will be open to either experimental research or criminal investigation.

So if you still long for that feeling — that reality is something we can mostly agree on — then you're going to have to let go of some beliefs you might still be clinging to.

You’re gonna need the strength it takes to admit you were wrong.

I was wrong about (almost) everything

By the end of 2024, I thought my journey was over. I had convinced myself I understood what had happened. Fooled myself into thinking I was done with COVID.

Asking questions had already cost me my marriage, my friends, and at times, my sanity. When Steve Kirsch (we will get to him) invited me for an interview about my statistical analysis of the U.S. death wave in the summer of 2021, I felt something like closure. A sense of accomplishment. I had done enough.

I thought I’d cleared the fog. But I had only added to it.

I didn’t mean to mislead. I was trying to help. But I steered us further from the truth — and I own that.

I’m afraid the same can’t be said for everyone.

When I first told Michael Yeadon that Denis Rancourt had deceived us, his reaction was immediate:

“That’s how I know you’re lying.”

To him, doubt itself was betrayal. He didn’t ask me what I meant. Didn’t ask for evidence. Just shut it down. Keep that in mind when it comes to Michael ‘Pfizer’ Yeadon.

If that’s your reaction too, you might want to stop reading now. I’m not here to argue, and I’m not here for accusations. I’m here to tear your heroes down and show you how you were lied to. Take it or leave it.

The Call

“Just when I thought I was out, they pull me back in”

In December 2024, I was invited to yet another conference call hosted by a group of German academics called “7 Argumente”. Just to be clear: I’m not an academic. I’m a bipolar underachiever — a mailman, a dish dog, just another n… nobody really – a nobody obsessed with justice. I don’t belong in that circle. However, since I started writing about vaccine harm, all kinds of people — millionaires, billionaires, trillion-dollar traders, professors and otherwise achieved career-driven types of people — started reaching out. Most offered money, jobs or other kinds of help.

For the record: I don’t take money from anyone – not anymore I don’t. I survive on government support — just enough for food and smokes. Besides, the offers became a lot more sparse as I closed in on the cause for these 30mio. excess deaths. In fact, nobody’s offered me help in a long time. Most of the people who used to offer help don’t even reply to my messages anymore, but they all listen when I have something to say. They listen and observe quietly, because these spineless cowards are a lot more scared of us than we should ever have been scared of them.

And they should be.

Back to the call. Marcel Bartz was presenting his findings on German excess mortality — which, however well-intended, are in a class of their own in terms of self-deception. He was supported by a shady character operating under the pseudonym “Ulf Lorré,” who through his expertise as an electrical engineer seemed to be offering me help in my latest studies, but in retrospect gave me information and advice, that led me further off course. It was only when I cut my ties to him that I managed to connect the dots.
[corrected from: an electrical engineer claiming to be firmly convinced that electromagnetic waves have no impact on health]

Be very skeptical of anything he tells you.

During the call, someone asked whether Denis Rancourt’s work was solid enough to be shared without damaging credibility. Out of respect for Marcel and Ulf, I gave a diplomatic answer:

“I’ll write you an email after the call.”

I already knew something was off with Rancourt’s methods — I had tried desperately to estimate the vaccine’s impact on mortality myself, without success. There was no clean positive correlation to be found. Not across countries and not across jurisdictions of any country that I looked at. The correlations are negative across the board. Any analysis concluding vaccines meant net harm to population health would require complex modelling.

So how was Rancourt producing these bold, clear-cut claims?

I couldn’t just tell these people “Denis Rancourt is full of shit” without doing the work. Due diligence had to be done. Even if my sentiment was likely true.

So painful as it was - I flew over his final paper on vaccine-related mortality – a steaming pile of shit stacked 521 pages high. Surprisingly – thank God! - it took a mere five to ten minutes to spot the fraud. It took another 3000 hours to make sense of it, but we are gonna get to that part later. First, let’s take a closer look at his sciency science paper that is oh-so-very sciency.

Denis Rancourt’s Deception

Let’s get right to it. This chart (Fig 52, page 277) and an array of others like it are at the core of Rancourt’s claim that there is a causal link between vaccinations and excess mortality.

To the untrained eye — and even to many trained ones — the chart looks persuasive.
High correlation. Linear slope. Data points across countries.
The message is clear: the more vaccines a country gave, the more people died.

But it’s a trick.

Rancourt plotted total excess deaths per country on the y-axis, and total vaccine doses administered per country on the x-axis — without adjusting either for population size.

He was using “non-standardized” variables.

Let that sink in.

It’s not excess deaths per capita vs. doses per capita.
It’s number of excess deaths vs. number of doses.

It’s like plotting “annual toilet paper sales” against “annual marriages” and calling it causality — all you’re measuring is population size. Slightly more obvious in this example, because I believe neither the number of defecations a person dedicates time to throughout a day nor the amount of paper used per sitting has an impact on the likelihood for this person to become married. I hope we all agree on this.

It’s no surprise that the U.S. and Brazil are in the top-right — they have huge populations.
It’s no surprise that smaller countries are clustered in the bottom-left — they don’t have tens of millions of people to die, or hundreds of millions of vaccinees to administer doses to.

Bigger countries do more of everything. Eating, defecating, dying, getting vaccinated. The more people there are, the more people will have comitted time to these activities by the end of the year.

This is not science.

It’s narrative engineering through the art of statistical deception.

The Chart Part

I know a lot of you hate this stuff, so I’ll keep it brief. I’m afraid it’s important we do this, so I can show you, that it’s not just a few charts on pages 277ff that are affected, but that his entire publication has been adjusted to make the deception possible.

I thank Christian Meyer for this contribution. If you don’t know who he is, I urge you to look into his Substack. It might not always shine through his brief articles, but he’s probably the most brilliant person on this platform. Whenever I need a second opinion on anything numbers-related, I ask him.

First, we replicate Rancourt’s chart, using numbers from his pdf’s appendices where possible, while filling in what’s missing from World in Data. This is what we get:

And the result looks a lot like the fraudulent chart by Rancourt et al. Russia, Japan, Brazil, Mexico and the USA are in the same spots relative to the chart area and the clustered nations. Our Pearson correlation coefficient is even slightly higher than his. I’m not sure why, but this is what I came up with. You can double-check the figures using the spreadsheet I am making available to you.

So much for the nonsense. Now we divide each country’s excess deaths and administered doses by the respective 2019 population estimate to get closer to a valid representation of the relationship.

Looks different, huh? Inverted even. Trust me when I say: I am not here to make a case for these rotten gene shots. I am not. But we should at least consider this: Some substances can elicit effects that will improve your chance of survival in certain contexts, but can still kill you or be detrimental to your overall health. This is a lot more common than you might think.

But whether or not these ungodly concoctions improved chances for survival upon exposure to some noxious agent and what the nature of this noxious agent might be is not what I am talking about today. We will take this one step at a time. So please be patient.

The Issue with “Large Countries”

Page 14 has a short paragraph on what countries were excluded from analysis.

“Large countries” were excluded. Interesting. Statements like these are tell-tale signs for the type of statistical deception employed by Rancourt, Hickey and Linard – using non-standardized variables. Instead of going through lengthy explanations, let’s just look at what Rancourt’s charts would look like if he had indeed included “large countries”.

The large countries I am adding are China, India, Indonesia, Pakistan, Nigeria, Ethiopia, Democratic Republic of Congo, Tanzania and Kenya, i.e. all the ones explicitly named by Rancourt et crooks, plus the 5 largest African countries by population. None of these data are unavailable or even remotely difficult to acquire. You can grab the three csv files from World in Data (vaccinations_global.csv, population.zip, number-of-deaths-per-year.zip). So the alleged “lack of available data” was just another lie - a necessary one.

Yeah, I know, it looks odd. The USA are relatively small next to India. If these crooks had included India and China, people would have surely been more likely to notice the fraud.

Fraud. That’s what it is. The 3 authors - Denis G. Rancourt, Joseph Hickey and Christian Linard – are clearly guilty of misleading their readers through the extremely bold use of statistical trickery.

I hate to ruin someone’s reputation. I really do. Not only did I keep this information private for half a year, but I also immediately contacted the authors and offered them a way out of their misery, promising them I had no intention to harm their reputation in any way, sincerely so.

A First Hint at Military Involvement

The first person I contacted was Denis Rancourt. His reaction was to immediately go offline on both Telegram and Substack simultaneously. He probably pulled the cable in panic. That’s what it looked like to me anyway. I apparently triggered a “fight or flight” reaction – and when it comes to his type, you know what the choice is gonna be when those are the only two options.

It took a full five hours for him to come back online and block me. I can only imagine what happened in the meantime (e.g. he might’ve asked the money man for support), because shortly thereafter my local network devices found themselves under attack. All three connected devices were compromised. My network security had already improved since the first government-resistance-related attack in late May of 2024 and has improved further since the day I contacted Rancourt, but at the time it didn’t take much to invade my network, especially not with Telegram running on each of my devices. Telegram hands your conversations to the enemy gift-wrapped. It’s free, unlimited, and tailor-made to lure in government critics and more likely than not contains at least one backdoor at any given point in time.

I started tcpdumping all traffic when I realized this was happening, but I’m probably never gonna take the time look into that data, since I doubt it’ll amount to much useful information.

What’s fascinating about this incident is, that I offered Denis a way to get out of this, without me writing about him. My offer was for him to talk to me about it and then cooperate to turn this into something good, something positive. Regardless of what species an animal belongs to, I am firm believer that strength comes from cooperation, not from shooting each other down.

Yet he didn’t really seem to consider me a threat to his reputation. This confused me profoundly. But then again, he wasn’t a teacher anymore. His career is a thing of the past.

His two co-authors Joseph Hickey and Christian Linard however were not done with their careers. They were still teaching at university. And there are few threats that are greater to an academic’s reputation than being rightfully accused of scientific misconduct.

So, I’d probably have more luck addressing these two. “They probably weren’t even aware of how Denis had abused their trust.” I thought in my naïvety.

Yet, I received no reply from them at all. I called their offices in Canada a number of times, but never got a call back. They didn’t seem concerned at all.

Joseph Hickey and Christian Linard either do not care about their careers as academics or they know there is an entity shielding them from harm in exchange for their service – the service being their names under this 521 page long piece of misinformation.

To be continued…

I’ve found yet another unsettling safety signal in my German hospital dataset.

As soon as the vaccine commission recommended pregnant women in the 2nd and 3rd trimester to get vaccinated, the number of newborns suffering U07.1-associated respiratory distress rose steeply.

• Between September 2020 and August 2021, the average hospitalization rate of neonates diagnosed with both U07.1 and J96.00 was 3.17 per month.

• Between September 2021 and August 2022, the average hospitalization rate of neonates diagnosed with both U07.1 and J96.00 was 39.75 per month.

These figures show an increase of more than 1150% from one year to the next. Yet maternal vaccinations had been promoted for protecting infants from falling ill with COVID-19.

COVID burden was almost negligible for infants in 2020 and early 2021, but it was well known that the shots provided little protection against Delta variants which had already gained full predominance over previous strains by September 2021. Here is the “scientific” rationale behind the STIKO’s decision (German PDF).

It gets worse!

As of today (January 20, 2024) the German RKI is still recommending these vaccines to pregnant women, but worst of all:

The STIKO is recommending Biontech/Pfizer products as the ideal candidate for pregnant women - in fact they are explicitely discouraging use of Moderna products during pregnancy!

According to the STIKO, Spikevax products should generally not be used in pregnant women.

(Emphasis not mine)

I copied this from the RKI homepage today, long after it had become common knowledge, that most Pfizer/Biontech batches are contaminated with DNA. Why is that an issue? Well, the younger a human organism is, the more frequently its cells divide (the embryo’s relative growth rate peaks 20 days post-conception).

Cell division is prone to errors. Cells that divide are at a particularly high risk to integrate foreign DNA, which would then end up in the daughter cells. This DNA can in turn be transferred to the next generation of cells the next time the daughter cells divide.

Linear DNAs, including those derived from cleavage of circular DNAs after entering a cell, may undergo non-homologous end joining (NHEJ) to be randomly integrated into the host cell genome, while being prone to exonuclease digestion.

Source: https://www.nature.com/articles/s41598-023-33862-0

Now you are probably wondering what exonucleases are. Exonucleases are enzymes that cut nucleic acids off DNA/RNA and are highly active during DNA replication, which is precisely what needs to happen for a cell to divide.

Timing of vaccination

The details of the recommendation are quite interesting (German Source).

Unvaccinated pregnant women are recommended to build up basic immunity. It is important for basic immunity that the immune system has three contacts with components of the pathogen (vaccination) or the pathogen itself (infection). At least one of these contacts should be through the vaccination.

Furthermore, RKI states the following:

Pregnant women should only be vaccinated from the 2nd trimester onwards.

However…

According to the STIKO [...] the 3rd vaccination to achieve basic immunity should be given at least 6 months after the 2nd vaccination in order to achieve optimum protection.

This can only result in a large proportion of pregnant women receiving a dose shortly before giving birth, which might be the reason we are seeing signs of Vaccine-Mediated Enhanced Disease (VMED) in these newborns.

RESCUE with Michael Capuzzo

Ask Why 429 Moms Died

Read more

2 years ago · 379 likes · 132 comments · Pierre Kory, MD, MPA and Mary Beth Pfeiffer

EDIT

Since posting this article a few individuals have pointed out to me that COVID incidence increased around the same time hospitalizations of newborns with J96.00 and U07.1 increased.

Here you are seeing the monthly proportion of infants (age 28d - <1 year) among all children hospitalized with a diagnosis of U07.1. The signal is still present.

Introduction

Hey readers, I just wanted to let you know what I have been up to, since my substack hasn’t seen a lot of action in the past month.

One thing I was busy with was trying to explain the US situation to a journalist (Mary Beth Pfeiffer) to push the maternal mortality story to the mainstream.

Mary Beth Pfeiffer is an uncommon gem—One of the last true journalists out there—Honest, fearless, and completely unswayed by the political narratives that rule most media these days.

Steven Phillips, 2022

I would second that.

She is trying hard to get the VMED story to the mainstream, after already succeeding to publish an article on excess mortality in The Hill - one that ranged in the Top 3 for 3 days.

Pierre Kory from the FLCCC Alliance (Front Line COVID-19 Critical Care Alliance) is helping her realize this. Alas, it is proving very difficult to get past the editors. It doesn’t look good.

Here is the gist of it:

Truth just doesn’t sell well…

German Hospitalization Data

The other thing that kept me busy is equally exciting.

I am building a German hospital dataset that will provide a lot of new insights. It takes up quite a bit of my time and costs me a bunch of money, but I think it’s worth it.

There are timeseries for around 40k keys (diagnoses, procedures etc.), each available for 16 age groups (plus all ages), both for deaths and cases (hospitalizations). I can also get additional datasets pre-filtered to my liking, e.g. by U07.1 diagnosis or by location (federal state).

Since building the dataset takes a long time, I have to limit myself to monthly resolution, but in theory it could reach daily resolution.

All timeseries are available by date of admission and by date of discharge. Date of admission means we can get accurate monthly CFR’s for all age groups and thousands of keys.

I will give you a few examples of the things I’ve already found just by drawing charts in LibreOffice. Some of the signals were found by Ulf Lorré and Tom Lausen, the latter of whom pointed out the data source to me, for which I am very grateful. If you don’t know him, google him. He is invaluable to the German fight for medical freedom.

Okay, here we go.

1. Vaccine injuries, classified as such

Assuming a baseline rate of 107 hospitalizations per month, that’s a cumulative 8,000 excess hospitalizations due to COVID vaccines.

These are just the ones…

• …officially registered as vaccine injuries

• …severe enough to require hospitalization (strictly speaking, hospitalization could be unrelated, but still…)

The background rate of 107 hospitalizations is also very interesting. These are due to other vaccines.

2. Ages 16-29: Cardiac MRI in resting state

This is for individuals aged 18-29:

And these are teenagers aged 16-17:

I have nothing to add here. You already know what happened.

3. Care and examination of lactating mothers

This is another interesting one.

Cases shot sky-high, reaching 100% excess in 2023. Strangely enough, Germany saw a decline in births around the same time…

So let’s look at the ratio of the two. I divided Z39.1 cases by the 12-month moving average of O80 cases for this chart:

The proportion grew by 70% throughout 2022 and more than doubled by mid-2023. There also is a bump in mid to late 2021 that looks interesting.

Impressive, isn’t it?

4. U07.1 CFR in individuals aged 30-39

These CFR’s are by date of discharge, so they aren’t accurate. Once I have the full dataset, I can do the same by date of admission, which will result in accurate CFR’s for all age groups.

The peak occurs around the time this subpopulation was vaccinated, confirming my previous findings based on US population-level data (VMED).

And we can also display the cumulative CFR, which is just the number of total deaths with a diagnosis of U07.1 divided by the number of total admissions with a diagnosis of U07.1.

It almost looks as if the COVID-19 case fatality rate kept rising all throughout 2021.

This is what the German public health agency Robert-Koch-Institut was trying to sell us (keep in mind these are all cases, not just hospitalized ones):

They intentionally covered the trend up by:

• looking at entire years

• using huge age groups (18-59)

• employing incorrect rounding techniques

  • Rounding to one digit in 2020, but to two digits in 2021 and ending up with 0.1% vs. 0.12% could be anything from a 23.333% reduction to a 149.999% increase in CFR.

  • These devils take themselves for so clever, don’t they?

So yes, the RKI knew VMED was a thing and actively covered it up - just like the CDC did.

5. COVID deaths in children aged 6-9

On December 17th the German corrupted-to-the-bone vaccination commitee STIKO (Ständige Impfkomission) recommended the poison death shots to children aged 5-11. The discussion was all over the media in the preceding two months.

So let’s see what happened after they issued the recommendation…

But in 2022 Omicron took over, so COVID wasn’t the cause of these deaths, right? Well we can find out by limiting deaths to those children who also had a diagnosis for acute respiratory distress (J96.00).

Less deaths overall, but the pattern holds. As soon as children were turned into GMO’s, they started dying of COVID.

But hey - at least they didn’t murder their grandparents with their breathe, right?

Or did they?

6. Healthy individuals aged 80+ dying of COVID

It may sound paradox, but there are healthy 90 year olds out there. PCCL is short for “Patient Clinical Complexity Level”. It is based on an algorithm that uses comorbidities and other factors to assess the likelihood of complications among other things.

Patients with a PCCL of 0 are the healthiest of patients. They have much better chances of survival.

We already know, that only the frail died of COVID, right? Well let’s find out.

Ouch. However, let’s not jump to conclusions and put those figures in relation to all COVID hospital deaths occurring in this age group.

Something happened in January of 2021 that caused healthy people of advanced age to die of COVID.

It’s VMED!

7. The iatrogenesis hypothesis

There’s a very popular narrative which attributes COVID deaths to medical mistreatment.

I haven’t made up my mind on that. There is strong indication that SARS-CoV-2 was circulating in Europe in late 2019, without causing excess mortality.

A former friend of mine was in charge of a COVID unit. He is literally the worst non-psychiatrist doctor that I personally know. He just followed protocols, because he’s very insecure about his skills as a doctor.

With all the experimental pharmacotherapies (compassion trial etc.) and the excessive use of ventilators, it is plausible to assume treatment was suboptimal and caused avoidable deaths.

However, the question we should ask ourselves is this:

• Were people who were misdiagnosed with COVID-19 (false positives) also killed by COVID treatment, e.g. because they didn’t receive proper diagnostics/therapy to identify and treat what really caused their symptoms and/or other signs of disease?

With my dataset we can find out. This is how:

• I calculate the monthly CFR of patients with a diagnosis of U07.1

• I calculate the monthly CFR of patients without a diagnosis of U07.1

• I take the difference of the two

• The proportion of false positives among SC2-positive individuals should rise when the SARS-CoV-2 prevalence drops

• When SARS-CoV-2 prevalence is near zero, most U07.1 cases in hospital should be false positives

So let’s look at how the CFR difference is affected by SARS-CoV-2 prevalence. I will also present the 95% confidence intervals along with the spot estimates.

But first, monthly COVID incidence in hospitalized patients:

Both in the summer of 2020 and in the summer of 2021 SARS-CoV-2 prevalence must’ve been near zero.

And this is how that is reflected in the CFR difference between U07.1 patients and non-U07.1 patients. Instead of looking at all patients, we are only looking at isolated patients to make the two variables comparable.

We can see the CFR difference dropped all the way to insignificance (p>0.05) when SARS-CoV-2 prevalence was low.

So unless patients were actually infected with the virus, their chance of survival was the same as that of patients prophylactically isolated for other reasons.

Also take note of the huge CFR differences at the height of the waves. We are seeing an additional 1 in 7 hospitalized U07.1 patients die compared to non-U07.1 patients who were prophylactically isolated.

Notes

I will supply it to all the analysts I know and everyone else who asks me for it.

EDIT

As per reader request:

If you have any literature references to add or come across any errors or inaccuracies, please do let me know in the comment section.

Summary

I am presenting retrospective safety signals based on proportional reporting ratios among spontaneous reports in this article, utilizing only reports that were processed by March 31st, 2021.

There are four important early signal clusters emerging from spontaneous reports:

• Cardiac signals made up of arrythmias and concepts surrounding heart attacks

• Cerebrovascular signals, both haemorrhagic and ischemic

• With 4 affected nerves, non-vascular neurological signals are almost exclusively related to cranial nerve dysfunction

• Reproductive system disorders centered around menstruation

Population-level data shows that proportions of decedents who had diagnoses for cerebrovascular disease, acute myocardial infarction or lung embolisms were either peaking in 2021 and 2022 or - in the case of acute myocardial infarction - diverging upwards from the previous downward trend.

While cardiac complications of COVID vaccination have been well-described in scientific literature, this is not the case for neuroinflammatory and hypercoagulative disorders. Except for Bell’s palsy cranial nerve dysfunction after COVID vaccination has received little attention from the scientific community.

Introduction

Some of you will be familiar with my safety signal website pervaers.com. It offers a nearly complete list of age-adjusted safety signals for all vaccine types by antigen based on “proportional reporting differences” to proportions among reference report cohorts, giving patients and clinicians an idea of how common certain vaccine adverse events are.

A more common way to identify safety signals are PRR s (proportional reporting ratios). PRRs are a popular method to identify unusual increases in certain medical concepts among spontaneous reports submitted to post-approval pharmacovigilance databases like VAERS.

As of today, there still is no complete listing of PRRs publically available - not anywhere I know of. I decided to go back to my U.S. VAERS signals and present the most important signals to you, starting with past signals for Pfizer from March 2021, using an old version of the database.

These signals are the ones that could have been identified very early on, before the damage was done.

As already mentioned I limited this analysis to Pfizer reports (excluding all SARS-CoV-2 infection reports). Depending on how well this article is received I might write one on early Moderna and Johnson signals as well.

We will also look at population-level data to see if any of the signals are reflected in mortality.

Methods

You can find a detailled explanation of how proportional reporting ratios are calculated in the help section of pervaers.com.

Safety signals

• For each analysis, all reports are first split into 2 groups:

  • A study report cohort. In this case we will be using reports processed as of March 2021, submitted in response to adverse events occurring after administration of COVID vaccines by Pfizer

  • A control report cohort, containing all remaining reports that were submitted in response to adverse events occurring after administration of non-COVID vaccines

• For the age-adjusted analysis, age adjustment of the two cohorts is performed (but we are just looking at crude signals today)

• The occurrences of each medical concept in both cohorts are counted

• The number of occurrences is then divided by the number of reports in that cohort, yielding “report proportions”

• The ratio of report proportions between the two cohorts is the “proportional reporting ratio”. When a concept is significantly (p<0.1) overrepresented, a signal is generated

Population-level data

• I join monthly (2015-2017) and weekly (2018-2022) data by converting each to pseudo-daily data. Think of this as data with monthly precision with a little extra granularity.

• Proportions of one mortality type among another type are calculated

• 2015-2019 serves as reference timeframe for calculation of excess proportions, which are calculated by taking the difference between the expected and actual proportion.

Downloads

• All Signals, 24 files in total:

  • by product (Pfizer, Moderna, Johnson, any COVID vaccine)

  • by snapshot time (March 2021, January 2023)

  • by method (crude, age-adjusted, pseudo-placebo reference)

I will only be presenting the strongest signals and the ones I consider most important, since there are just way too many overall to discuss them all in one article.

We will only be looking at crude signals. Age-adjusted ones and those utilizing pseudo-placebo reference cohorts can be found in the download package and are mostly in the same ballpark, but have wider confidence intervals due to the uncertainty introduced by age-adjustment.

Results

SOC stands for System Organ Class, the highest level of hierarchy in the MedDRA system. To give you an idea how early signals were distributed across these top-level categories, I will present a chart representing the number of lowest hierarchy levels signals - Preferred Terms - in each SOC.

PT Distribution across SOCs

There were 358 unique signals for Preferred Terms as of March 2021 among Pfizer reports. Some of these exist in multiple SOCs. For example, “taste disorder” is present in both nervos system and gastrointestinal disorders.

Keep in mind that these are very early signals. The distribution changed profoundly throughout the course of the vaccination campaign. Especially the number of cancer signals increased massively later on.

Now for a more detailled look at the Pfizer signals, I will group them into SOCs as long as there are enough important signals in a category.

PRRs by SOC

Pulseless electrical activity

• Pulseless electrical activity (PEA), also known as electromechanical dissociation, describes a state where electrical impulses are still being generated and conducted by the specialized cardiac muscle cells responsible for this, but the remaining cardiomyocytes are not responding to this with contractions - resulting in a heart that is not beating.

• Myocarditis-associated case report 1

• Myocarditis-associated case report 2

Coronary artery stenosis

• Coronary artery stenosis/obstruction occurs when the coronary arteries — the blood vessels supplying blood to the heart — narrow. This narrowing can restrict blood flow to the heart, causing oxygen deficiency accompanied by symptoms such as chest pain or shortness of breath.

  With or without treatment, coronary arteries can become completely blocked, a condition called chronic total occlusion. Coronary artery stenosis ultimately leads to myocardial infarction.

• Review of myocardial infarctions after modRNA vaccine administration

Acute myocardial infarction

• Acute myocardial infarction or heart attack is myocardial necrosis resulting from acute obstruction of a coronary artery. Symptoms include radiating chest pain from mild to unbearably painful, dyspnea, nausea and diaphoresis (excessive sweating without physical exertion).

• Review of myocardial infarctions after modRNA vaccine administration

Cardiogenic shock

• Cardiogenic shock is a life-threatening condition in which your heart is unable to pump enough blood to meet your body's needs. The condition is most often caused by a severe heart attack, but not everyone who has a heart attack experiences cardiogenic shock.

• Review of myocardial infarctions after modRNA vaccine administration

Cardiac flutter

• A relatively common and often benign type of arrythmia that starts in the atrial chambers of the heart. When it occurs, it is associated with an elevated pulse, that represents an unstable rythm which often degrades into (less benign) atrial fibrillation.

• A short article on arrythmias occurring after COVID vaccination

Sinus tachycardia

• This is just a heartbeat of over 100 beats per minute. The excitation begins in the sinus node - where it should begin - and spreads regularly. Just a high heart beat with a scary name.

• A short article on arrythmias occurring after COVID vaccination

Palpitations

• Palpitations are perceived abnormalities of the heartbeat characterized by awareness of cardiac muscle contractions in the chest, which is further characterized by hard, fast and/or irregular beating of the heart.

  Palpitations are not always accompanied by actual arrythmias and arrythmias are not always accompanied by palpitations.

• A short article on arrythmias occurring after COVID vaccination

Atrial fibrillation

• Atrial fibrillation (AF, AFib or A-fib) is an abnormal heart rhythm (i.e. arrhythmia) characterized by rapid and irregular beating of the atrial chambers of the heart. It often begins as short periods of abnormal beating, which become longer or continuous over time.

  It can be a consequence of myocarditis and myocardial infarctions and can result in heart failure or stroke among other complications.

• A short article on arrythmias occurring after COVID vaccination

Extrasystoles

• Extrasystoles are another benign form of arrythmia corresponding to a premature contraction of one of the chambers of the heart. They typically have no long-term complications and do not require treatment.

Dyspnoea exertional

• Exertional dyspnea is dyspnea that presents with exercise and improves with rest.

  Dyspnea is - like palpitations - a symptom and describes the perceived difficulty to breathe. It impacts millions of people and is often the main manifestation of respiratory, cardiac, neuromuscular, psychogenic or other illnesses.

• A general review about exertional dyspnea

Chest discomfort

• Chest discomfort often accompanies respiratory and cardiac disorders, like myocardial infarctions, pulmonary embolisms, myopericardits or pneumonia.

• Review of myocardial infarctions after modRNA vaccine administration, warning of chest discomfort

Herpes zoster reactivation

• Herpes zoster, also known as shingles, is caused by reactivation of varicella-zoster virus (VZV), the same virus that causes varicella (chickenpox).

  Primary infection with VZV causes varicella. After a person has varicella, the virus remains latent in the dorsal root ganglia (nerve cells). VZV can reactivate later in a person’s life and cause herpes zoster, a painful maculopapular and then vesicular rash.

• Review of post-vaccination Herpes Zoster reinfection articles

Lymph node pain

• Painful lymph nodes usually occur as a result of vaccination or infection from bacteria or viruses. Less commonly, swollen lymph nodes are caused by cancer.

• Dr. Edmonds of Penn Medicine explains “Lymph node swelling following the COVID-19 vaccine isn’t even technically an adverse response. It’s simply a response.”

• Review of post-vaccination Lymphadenopathy articles

Lymphadenopathy

• Lymphadenopathy or adenopathy is a disease of the lymph nodes, in which they are abnormal in size or consistency.

  Dr. Edmond of Penn Medicine approves of lymphadenopathy after COVID vaccination, because “It means you're responding. We just don't want to confuse it with a bad thing.”

• Review of post-vaccination Lymphadenopathy articles

Menstrual disorder

• Any irregulatity in menstrual cycles

• Review of post-vaccination menstrual disorders

Intermenstrual bleeding

• Intermenstrual bleeding or metrorrhagia is just menstrual bleeding at unexpected points in time without any changes in the timing of periods.

• Review of post-vaccination menstrual disorders

Premenstrual syndrome

• Premenstrual syndrome (PMS) is a disruptive set of emotional and physical symptoms that regularly occur in the one to two weeks before the start of each menstrual period. It is often accompanied by headaches, breast tenderness and bloating.

• Review of post-vaccination menstrual disorders

Polymenorrhoea

• Polymenorrhoea occurs when menstrual cycles are shorter than usual (28 days), leading to more frequent periods.

• Review of post-vaccination menstrual disorders

Uterine spasm

• While often described as contractions of the pelvic floor muscles occurring spontaneously, uterine spasms can also be a complication of giving birth. A look at individual reports could clear up how this signal is to be understood.

Breast swelling

• Penn Medicine tells us “Swelling after the COVID vaccine or booster can actually be considered good.”

Nipple pain / Breast discomfort

• In most cases, sore nipples are caused by hormonal changes from pregnancy or menstruation, allergies or friction from clothing. In rare cases, it can be a sign of a serious disease like breast cancer.

• An article on how COVID vaccination symptoms can mimic breast cancer

Breast discharge

• This can mean both lactation and discharge of other bodily fluids, the latter of which can represent signs of breast cancer.

• An article on how COVID vaccination symptoms can mimic breast cancer

  

Retinal vein occlusion

• The central retinal vein is the most important vessel responsible for draining blood from the retina. RVO is a blockage of the central retinal vein usually manifests secondary to inherited or acquired vascular or metabolic disorders.

• A review on post-vaccination RVO

Retinal artery occlusion

• Retinal artery occlusion is usually caused by blood clots. The retinal artery supplies blood to the retina.

• A case report about retinal artery occlusion occurring post COVID vaccination

Retinal migraine

• Retinal migraines are a type of migraine that affect the eyes. They can cause temporary vision loss in the affected eye and other ocular symptoms.

• Like many other symptoms, this seems to occur both as a COVID-19 sequester and as a vaccine adverse event

Eye haemorrhage

• Any form of bleeding occurring in the eye. Since there are signals for retinal vessel occlusions, this one is probably related to them.

• A review of ocular adverse events associated with COVID vaccination

Vestibular migraine

• A type of migraine that involves the vestibular organ and can occur with or without headaches, causing dizziness, motion sickness and loss of balance.

• An article on vestibular adverse events associated with COVID vaccination

Tinnitus

• Tinnitus is a variety of sound that is heard when no corresponding external sound is present.

• An article on COVID vaccine induced tinnitus

  

Hypertensive emergency

• A hypertensive emergency is an acute, marked elevation in blood pressure that is associated with signs of target-organ damage. These can include pulmonary edema, cardiac ischemia, neurological deficits, acute renal failure, aortic dissection, and eclampsia. The incidence after COVID vaccination is estimated to be 0.6%

• This article proposes pathomechanisms of post-vaccination hypertensive emergencies

Thrombophlebitis superficial

• A superficial thrombophlebis is an inflammation of one of the veins located underneith your skin. In most cases this happens due to blood clots being present in the affected vein.

• It seems there are no scientific articles describing the occurrence of thrombophlebitides after COVID vaccination, possibly because it is usually a benign, albeit potentially long and painful process.

Internal haemorrhage

• Internal bleeding or internal hemorrhage is a loss of blood from a vessel inside the body. The blood is usually not visible from outside of the body. It is caused by traumatic or non-traumatic rupturing of blood vessels, medication, thrombocytopenia and other disorders.

• An article on immune thrombocytpenia after modRNA vaccination

Pulmonary thrombosis

• Blood clots originating in the lung and presenting similarly to pulmonary embolisms. It has been proposed that these are a manifestation of local inflammation. Happens in COVID. Happens after vaccination

• Here is an article focussed on this oddity

Pulmonary embolism

• Pulmonary embolisms are clots arising from other parts of the body - usually the legs where blood flow is slower - before becoming stuck in an arterial vessel inside the lung, blocking that vessel and causing anything from no symptoms at all over shortness breath, rapid breathing and coughing blood to sudden death, depending almost solely on the size of the clot.

• Unfortunately there is very little to be found in scientific literature about thrombotic events and hypercoagulability after modRNA vaccination. This is a case series.

Deep vein thrombosis

• There are superficial and deep veins. Superficial veins run under the skin, outside of the body fascia, while deep veins run inside the body fascia. As the name suggests, a deep vein thrombosis is a clot that forms inside the deep veins, most commonly in the legs where the blood flow is slower.

• Again, all I can offer is this case series.

Thrombosis

• Any blood clot forming inside a blood vessel, arterial or venous.

Just a case series

Bell’s palsy

• Bell’s palsy is a malfunction of the facial nerve, which is the seventh cranial nerve.

  There are 12 pairs of cranial nerves, which are nerves emerging directly from the brain, innervating tissue in the head and torso.
  Cranial nerve VII controls the facial muscles, supplies taste fibers and provides parasympathic innervation to a number of glands.

  Bells’s palsy is characterized by a one-sided facial droop - better known as the “Pfizer Smile”.

• A review on post-vaccination Bell’s palsy

IVth nerve paralysis

• The 4th cranial nerve is the trochlear nerve which only function is to move the eye, abducting it, depressing it and rotating it inwards. Like any other nerve, it can become paralyzed, resulting in loss of control over eye movement.

• A review on post-vaccination oculomotor disorders

Taste disorder

• Bell’s palsy can come with taste disorder, but only in the anterior 2/3 of the tongue (the outer 2/3) - other taste disorders are probably caused by spike-mediated effects.

• No biggie. Not spike-related say Lechien et al.

Pharyngeal paresthesia

• The glossopharyngeal nerve is the 9th cranial nerve and the one that supplies sensory fibers to the pharynx among many functions. Paresthesiae are abnormal sensations - usually superficial - like tingling, burning or numbness.

• There’s no publication on this, but here is one on hypoglossal nerve palsy - the 12th cranial nerve. Starting to see a pattern, yet?

Anosmia

• Anosmia is loss of smell. The olfactory nerve is the 1st cranial nerve.

• No biggie. Not spike-related say Lechien et al.

Mental fatigue

• Seeing all these cranial nerve dysfunctions, could mental fatigue be immune-mediated?

Chillblains

• Chil(l)blains aka pernio or “COVID toes” describes inflammation of the capillaries in the hands and feet, particularly upon exposure to cold or humidity. It is not just a symptom of Chilblain Lupus erythematodes (CHBL1), but also of COVID disease and vaccination.

  I suppose that’s just what happens when these peripheral capillaries express viral protein.

• This is a publication on post-vaccination Chilblains which also mentions infection-induced Chilblains.

Acute pulmonary edema

• The term edema describes water accumulating where it shouldn’t. Pulmonary edema occurs in the lungs and is a life-threatening condition, beacause it limits gas exchange.

  The signal could be related to pulmonary embolisms which increase the pressure in pulmonary arteries that transport blood from the heart to the lung.

  It could also have a cardiac cause. When the left ventricle of the heart fails, blood accumulates in the vessels behind it which are carrying oxygenated blood from the lung to the heart. This increases the pressure in those veins and leads to water accumulating in the lung.

• Or it could be pneumonitis. Further analysis of co-occurrences could answer these questions.

Ischaemic hepatitis

• Ischemia is a lack of oxygen due to reduced blood flow. The liver can be affected due to shock, but considering these patients took modRNA vaccines, blood clots in the hepatic artery being causal here is certainly not that unlikely.

• There is a report of hepatic artery occlusion after AstraZeneca vaccination

Appendicitis

• Inflammation of the appendix, a dead end in the first section of your colon.
  I found no sign of this in German hospital data.

• The association between COVID vaccines has been investigated numerous times, despite VAERS signal being much weaker than for thrombotic events (limited hangout?), possibly because there were calls for investigation, but no increased incidence was found.

Diverticulitis

• Inflammation in acquired pouches of the colon, so-called diverticles. Diverticulitis can cause rupture of the colon, most often resulting in death, just like appendicitis can result in death, if left untreated.

• Here is an article about the various gastrointestinal issues reported to VAERS

  

Embolic stroke

• Embolic stroke occurs when a clot migrates (this is called an embolism) from its source to block a cerebral artery, resulting in lack of perfusion to part of the brain and ultimately cell death. The clots usually originate from the heart or arterial vessels leading from the heart to the brain, but they can occasionally originate from venous vessels as well, if there is a shunt in the heart, allowing the clot to travel from right to left side of the heart.

• Review of COVID-vaccination-associated strokes

Cerebral artery occlusion / Cerebral artery stenosis

• The cerebrum is the large walnut-like looking part making up around 80% of your brain mass. About 45% of strokes occur inside this region. The artery most frequently involved is the middle cerebral artery. When it becomes clogged this will usually present with signs most commonly associated with stroke, like hemiparesis and sensory loss.

• Review of COVID-vaccination-associated strokes

Cerebellar stroke

• The cerebellum is a small part of the brain located in the back, underneith the walnut-like structure that is the cerebrum. It is important for motorcoordination. Depending on which of the three large arteries is affected, cerebellar strokes can present with and without ataxia (loss of motorfunction). Other possible symptoms are headaches, nausea, nystagmus, but also sensory loss of pain and temperature.

• Case report of a cerebellar stroke after modRNA vaccination

Basal ganglia haemorrhage

• Basal ganglia are a group of nerve cell clusters inside the brain that are responsible for a large array of learning, cognitive, emotional and motorical functions. Haemorrhagic strokes are the most common form of stroke in basal ganglia, are often a result of high blood pressure and presents with cognitive, sensory and motorical dysfunction, as well as headaches.

• Case report of basal ganglia haemorrhage after modRNA vaccination

Lacunar infarction

• Lacunes are empty spaces in various deep regions of the brain, that arise when penetrating arteries supplying the respective region with blood become obstructed. While sometimes silent, when lacunar infarctions do present with symptoms these are usually motorical and sensory loss of function.

• Case 6 in this case series of patients with neurological adverse events had a thalamic lacunar infarction

Thalamic infarction

• The thalamus is a very small region in the center of the brain, which relays motor and sensory signals and also plays an important role in alertness, mood, speech and pain processing. Infarctions usually present with impaired sensory function, that can transition into a debilitating pain syndrome called Dejerine–Roussy syndrome.

• A case report of a modRNA-vaccinated patient suffering bilateral (both sides) thalamic stroke

Cerebral small vessel ischaemic disease

• CVSD (Cerebral Small Vessel Disease) describes a process in which the smallest of vessels in the cerebrum become clogged, that is very common among elders, contributing to about 20% of strokes and 45% of dementias.

• A link to COVID-19 has been discussed. It isn’t entirely unlikely that the same processes take place after COVID vaccination.

Ischaemic stroke

• Ischaemic strokes are caused by clots blocking the blood flow to parts of the brain.

• Review of COVID-vaccination-associated strokes

Haemorrhagic stroke

• Haemorrhagic strokes are caused by ruptured blood vessels in the brain.

• Review of COVID-vaccination-associated strokes

Cerebrovascular accident

• Cerebrovascular accident is a general term describing all of the above concepts. An interruption of blood flow to parts of the brain.

• Review of COVID-vaccination-associated strokes

Population-level Data

The signals that should be reflected in mortality figures can be grouped into three categories:

• Strokes (ICD-10 codes I60-I69)

• Myocardial infarctions (ICD-10 codes I21.0-I21.9)

• Lung embolisms (ICD-10 code I26.0-I26.9)

We are going to have a look at all three next to the number of administered vaccine doses per capita.

I will express the number of deaths where the respective diagnostic codes were mentioned anywhere on the death certificate as proportions among…

• all deaths

• non-COVID deaths

From these timeseries I am calculating an excess series with 2015-2019 as reference timeframe to remove seasonal influences.

Strokes

Due to the high number of COVID-associated deaths the proportion of cerebrovascular deaths saw a deficit of 0.7% in late 2020. Between onset and peak of the initial vaccination campaign this deficit turned into an excess of 0.3%. However, COVID mortality rates fell simultaneously, so we should expect the proportion of cerebrovascular deaths among all deaths to increase during this time.

Something similar happens during the booster campaign in the 4th quarter, but this was also accompanied by declining COVID mortality, so we are going to look at the proportion among non-COVID deaths next.

The proportion among non-COVID deaths started increasing shortly before the vaccination campaign commenced in December 2020 and peaks in early 2021. As soon as vaccination rates decline, so does the proportion of cerebrovascular deaths among non-COVID deaths.

The booster campaign seems to coincide with another increase, followed by a large excess throughout 2022. There was just so much going on at the time that it is hard to pin this on vaccines based on these observations alone.

However, considering the large number of cerebrovascular safety signals and the fact that the proportion of cerebrovascular deaths among non-COVID deaths is much higher in 2021 and 2022 compared to 2020, a causal link seems plausible.

Myocardial infarctions

The two largest vaccination rate peaks clearly correspond to peaks in the proportion of acute myocardial infarctions among deaths, but as I have mentioned before, COVID deaths were in decline, so the proportion is expected to increase. In mid-2021 there is another peak that does not correspond with vaccinations

We will now look at the proportion of decedents with heart attacks among non-COVID deaths.

The timeseries is corrected for the apparent linear downward trend between 2015-2019 that you saw in the previous chart. In 2020, this downward trend was clearly broken, which is to be expected when people have their “pants scared off”.

The situation relaxed a little in the course of 2020, but as soon as vaccinations commenced in December, the proportion of acute myocardial infarctions among non-COVID deaths shot up and remained elevated throughout 2021, when the largest share of vaccine doses was administered.

These upward deviations from the downward trend we are seeing throughout 2021 are more suggestive of a short-term effect on cardiac mortality, while cerebrovascular mortality continued to be elevated well into 2022.

Pulmonary embolisms

Despite the enormous increase in mortality that the USA saw in 2020, the proportion decedents with lung embolisms shot up after the initial “COVID wave” that began in March. A week or two after the start of the vaccination campaign the proportion increased steeply until February, then declines until mid-2021, before rising again along with the number of administered vaccine doses.

Proportions of embolisms among non-COVID deaths rise steepy with the onset of the vaccination campaign and peak shortly before vaccination rates peak. While proportions are highest in 2021, there is something happening in 2022 as well, which is not well-explained by acute vaccination effects.

Conclusion

There are four important signal clusters emerging from spontaneous reports:

• Cardiac signals made up of arrythmias and concepts surrounding heart attacks

• Cerebrovascular signals, both haemorrhagic and ischemic

• With 4 affected nerves, non-vascular neurological signals are almost exclusively related to cranial nerve dysfunction

• Reproductive system disorders centered around menstruation

Population-level data shows that proportions of decedents who had diagnoses for cerebrovascular disease, acute myocardial infarction or lung embolisms were either peaking in 2021 and 2022 or - in the case of acute myocardial infarction - diverging upwards from the previous downward trend.

While cardiac signals have been well-described in scientific literature, this is not the case for neuroinflammatory disorders. Cranial nerve dysfunction has received little attention from the scientific community.

Summary

Based on an analysis of proportion ratios of all diagnostic-keys mentioned in infant deaths between the years 2015 and 2022, the year 2022 stands out as highly unusual with a total of 55 diagnostic keys representing significant (p<0.001) safety signals, compared to a maximum of 11 safety signals in the preceding years.

One of the most striking signals is neonatal thrombocytopenia, with a 2022 proportion ratio of 12.9 (99.9% CI: 6.0-27.5) compared to the previous 7 years.

Further analysis revealed that the most common causes for the 68 neonatal deaths involving thrombocytopenia were intrauterine hypoxia and bacterial sepsis.

Furthermore, 29 cases resulted in intraventricular haemorrhage (bleeding into the intraventricular space of the brain), which likely represents the mechanism resulting in the deaths of these 29 infants. Intraventricular haemorrhage itself is an important 2022 safety signal that we isolated in the previous article of this series, involved in at least 80 excess infant deaths.

Almost a quarter (23.5%) of the 68 thrombocytopenia deaths occurred in Florida and Mississippi, which make up only 7.5% of the national population. These states were heavily vaccinated amidst the summer catastrophe of 2021, when Mississippi saw by far the largest number of fetal deaths per live birth.

Introduction / Recap

In part 1 of this series we identified a troubling signal in U.S. infant mortality, but trend-correction was neccessary to spot it and we weren’t able to explain the signal through maternal or infant COVID vaccinations.

In part 2 we got an overview of diagnoses by MCOD (Multiple Causes Of Death) that saw unusual increases in 2022.

Today I am going to do two things:

1. Look at the number of signals for all years between 2015 and 2022 to illustrate how unusual the year 2022 is in terms of changes in the distribution of infant mortality causes.

2. Take a closer look at the P61.0 signal (transient neonatal thrombocytopenia).

   1. Which other diagnostic keys are commonly found on death certificates of infants whose death involved a diagnosis thrombocytopenia?

   2. In which regions did these deaths occur?

Methods

I used CDC Wonder to download annual infant deaths occurring in the years 2015 through 2022, both by UCOD (Underlying Cause Of Death) and MCOD (Multiple Causes Of Death). Data for these 8 years was available for…

• 394 causes of death by UCOD

• 713 causes of death by MCOD

To identify signals I chose proportion ratios:

• Count the total number of infant deaths occurring throughout…

  • …the year in question (n1)

  • …the remaining 7 years (n2)

• Count the number of deaths with the respective diagnostic key throughout…

  • …the year in question (x1)

  • …the remaining 7 years (x2)

With these 4 figures I can calculate proportion ratios. The formula is:

pr = (x1/n1) / (x2/n2)

Since we are doing this for a total of 713 signals, we should expect a lot of false positives. To minimize the number of these false signals, I am calculating 99.9% confidence intervals for each diagnostic key.

Only keys which ratio is significantly (p<0.001) higher or lower than 1 generate a signal.

Results

First we take a look at the annual number of signals.

Negative signals (blue) are diagnostic keys that are mentioned in a significantly lower proportion of infant deaths than throughout the other 7 years. They range between 0 and 13 signals per year throughout the entire 8 years.

Positive signals (red) are actual safety signals - keys that are mentioned in a significantly higher proportion of infant deaths in the respective year compared to the other 7 years. They range between 0 and 11 from 2015 through 2021.

In 2022, the number of signals rises to 55. That’s five times as many safety signals as in 2021 or any other year since 2015.

This in itself is a very strong safety signal, that warrants immediate investigation by U.S. government authorities.

Sometimes changes to how deaths are coded can cause some diagnostic keys to be used more or less frequently than during previous years. This could be the case for some, but surely not all of these 55 signals.

There’s something happening - that’s for sure. The reason I showed you the chart is to make that crystal clear!

The strongest of these signals is transient neonatal thrombocytopenia (ICD-10 code P61.0), with a 2022 proportion ratio of 12.9 (99.9% CI: 6.0-27.5). That means it was mentioned on almost 13 times as many death certificates compared to the 7 years preceding 2022.

Transient neonatal thrombocytopenia (P61.0)

While thrombocytopenia (low platelet count) is a common occurrence in neonates, it rarely causes death. To give you an idea how unusual this cluster of deaths is, I’ve downloaded monthly data from 2018 through 2023.

There were 18 infant deaths in which thrombocytopenia was mentioned on the death certificate between 2018 and 2021. By the end of the first quarter of 2023 this number increased to 86.

While there is only a single diagnostic key coding for the problem, there is a large array of etiologies that can lead to neonatal thrombocytopenia. Some of these are more common when the problem occurs in the fetus, some are more commonly responsible in the first few days after birth (<72h), others are more likely to be responsible when the onset happens later (>72h).

Causes of transient neonatal thrombocytopenia

• Fetal thrombocytopenia

  • Alloimmune: NAIT (neonatal alloimmune thrombocytopenia) occurs when maternal antibodies are attacking paternal platelets and usually presents as a severe platelet deficiency

  • Autoimmune (maternal autoantibodies, in many cases due to the mother having idiopathic thrombocytopenic purpura or systemic lupus erythematosus)

  • Congenital infection (most often viral, e.g. cytolomegavirus, toxoplasma, rubella)

  • Chromosomal disorders (mostly trisomies or triploidies)

• Early-onset thrombocytopenia (<72h)

  • Alloimmune: NAIT (see fetal thrombocytopenia)

  • Autoimmune (see fetal thrombocytopenia)

  • Chronic intrauterine hypoxia, placental insufficiency (e.g. due to pre-eclampsia or diabetes)

  • Perinatal asphyxia

  • Disseminated intravascular coagulation (DIC)

  • Necrotizing enterocolitis (NEC)

  • Occult infection

• Late-onset thrombocytopenia (>72h)

  • Sepsis

  • Necrotizing enterocolitis (NEC)

A closer look at co-diagnoses

Since decedent-level data (death certificates) are not publically available and more detailed infant death data including information about the newborns are only available for 2021, I queried CDC Wonder to find out which other diagnostic keys were present in the 2022-2023 cluser of 68 infant deaths, where thrombocytopenia was mentioned on the death certificate.

• Causes

  • In 20 cases the cause was likely intrauterine hypoxia

  • In 6-9 cases the newborn developed necrotizing enterocolitis, which can be a cause for thrombocytopenia in the newborn

  • In 23 cases the newborn developed sepsis. In two more instances other perinatal infections were likely the cause.

  • Some other P-codes (perinatal) and Q-codes (congenital abnormalities) might have caused the problem to manifest, but we lack more detailed data on these codes

• Indirectly linked

  • Both hypotension and renal failure are relatively common in sepsis.

  • It is unclear whether or not there is a direct causal link between hypotension and renal failure, but they often co-occur

• Complications

  • Thrombocytopenia explains 29 of the roughly 80 excess P52 deaths (Intraventricular nontraumatic haemorrhage of newborn). This is bleeding into the intraventricular space of the brain.

So it seems the most common causes are intrauterine hypoxia and bacterial sepsis. While this alone won’t explain what is going on it helps to know that there isn’t just a single cause for these excess infant deaths involving thrombocytopenia.

What’s more important is, that we’ve identified the cause of 29 excess neonatal deaths involving intraventricular haemorrhage - a large significant (p<0.001) signal first isolated in the last part of this series.

Let’s have a look at where these deaths occurred.

Geographical distribution

First, a simple list of…

• all infant deaths 2022-2023

• infant deaths involving thrombocytopenia

• the proportion of the latter among the former

…by maternal state of residence.

The cluster of thrombocytopenia deaths did not occur in just a single state, suggesting a systemic problem.

However, what is very striking: Over 23% of the 68 thrombocytopenia deaths occurred in Florida and Mississippi, which make up less than 8% of the national population. These states were heavily vaccinated in Q3 2021, when Mississippi saw by far the largest number of fetal deaths per live birth.

Here is a look at the counties that registered these deaths.

Darker colors represent higher proportions among all infant deaths.

• Counties with more than 1 neonatal thrombocytopenia death in Florida:

  • 4 deaths in Miami-Dade County

  • 2 deaths in Palm Beach County

  • 2 deaths in Polk County

• Counties with exactly 2 neonatal thrombocytopenia deaths (other states):

  • Orange County, CA

  • Montgomery County, MD

  • Philadelphia County, PA

• The other 54 thrombocytopenia infant deaths were single occurrences across the remaining counties.

VAERS signal

I run a website called pervaers.com which lists safety signals generated from the publically available VAERS database. Signals for thrombocytopenia have been present in all age groups for some time now, but are rarely talked about.

These are proportional differences (along with 90% confidence intervals) between the proportion of U.S. Pfizer adverse event reports mentioning “immune thrombocytopenia”, relative to a age- and gender-adjusted pseudo-placebo report cohort. If you want to learn more about how the reference cohort is generated, you can find this information in the help section of pervaers.com.

The signal is present for Moderna as well, but not in the lowest age tiers, due to the low number of Moderna doses administered to children.

Introduction

In part 1 of this series I identified a troubling signal in U.S. infant mortality, but was not able to explain this signal through maternal or infant COVID vaccinations.

To shed some light on the matter, we will be taking a brief look into the top-gainers among the causes of infant deaths.

This article may be very upsetting to some readers, so if you’re having a bad day, do yourself a favor and read something else.

This is just so much worse than I had ever imagined.

Methods

I used CDC Wonder to download annual infant deaths occurring in the years 2015 through 2022, both by UCOD (Underlying Cause Of Death) and MCOD (Multiple Causes Of Death). Data for these 8 years was available for…

• 394 causes of death by UCOD

• 713 causes of death by MCOD

There are countless options to isolate signals, but this is the method I decided to use:

• Determine the standard deviation for each time series

• Determine by how many standard deviations 2022 deviates from the 2015-2022 mean

• Sort in descending order

Downloads

You can download all annual time series for all diagnostic keys.

• By UCOD: TSV / XLS

• By MCOD: TSV / XLS

Since most of the signals are hidden when looking only at the UCOD’s I will limit the analysis to signals by MCOD.

Results: Select signals by MCOD

Before discussing the results, here is a visual representation of the 36 top signals, followed by a table listing the top 60 signals.

“σ (2022)” describes how far the year 2022 deviates from the 7 preceding years. It represents a very basic measure of signal strength.

“2022-2021” is simply the increase in the number of deaths with the respective ICD-10 code on the death certificate from 2021 to 2022.

I will now give you a brief overview of the signals I consider important.

Summary of safety signals

Some symptoms belong into multiple categories, so there is some overlap. When I mention “excess deaths/cases” I am referring to the increase from 2021 to 2022.

Signals related to the immune system

We’ve got quite a few signals for viral infections. Coronaviral infections unsurprisingly lead the list, but enteroviral, adenoviral, cytomegaloviral, herpesviral, RSV and influenza infections were also increased in 2022. It’s a long list which clearly indicates an issue with innate immunity.

Bacterial infections are most often secondary to viral infections, but they also occur isolated, particularly in immunocompromised patients. Since they are often not differentiated for coding purposes, all we’ve got here is pseudomonas and staphylococcus infections on top of unspecified bacterial sepses and congenital syphilis.

Bacterial sepses represent the largest of these signals, with an excess of 171 deaths compared to 2021.

The diagnostic code D81.9 deserves some attention. It is relatively uncommon for newborns to be diagnosed with “combined immunodeficiency”. The annual incidence increased from 4-5 cases between 2018 and 2021 to 11 cases in 2022.

Malformations

There are a signals for malformations of the spleen, circulatory system and limbs. None of these make up a very large proportion of death. Situs inversus (organs located on the wrong side of the body) was diagnosed 33 times in infant deaths throughout 2022, but only 21 times in 2020 and 2021 and tetralogy of Fallot (a complex cardiac malformation) saw an uptick of 31 deaths as well. Deaths involving open ductus arteriosus increased by 41 from 2021 to 2022.

Cardiovascular signals

The major signals here are:

• 118 excess cases of cardiac dysrhythmias

• 83 excess cases of cardiac failure.

• 31 excess cases of tetralogy of Fallot

• 20 excess cases of acquired cardiac septal defects

Cerebrovascular signals

• 100 excess cases of nontraumatic intraventricular haemorrhage (grade 3)

Accidental / external / psychosocial

Compared to 2021 we’ve got 108 more deaths where asphyxiation was mentioned on the death certificate, 76 accidental suffocations and strangulations in bed, and 34 hangings, strangulations and suffocations of undetermined intent.

It is hard to tell how many of these were assaults, how many were related to other health issues and how many represent actual accidents. Don’t expect these figures to be accurate.

Inhalation of gastric content is likely secondary to other disorders and increased from 7-11 cases between 2015 and 2021 to 17 cases in 2022.

Blood disorders

There are some extremely strong signals here, that should be cause for immediate government investigation into the matter.

• Transient neonatal thrombocytopenia: 45 extra deaths, an increase by 900%

• Primary thrombophilia: 17 extra deaths, up from 3-4 between 2019 and 2021

• Disseminated intravascular coagulation of newborn: There were 230 cases in 2022, up from a very stable 204-216 cases throughout the previous 7 years.

Perinatal (P* codes)

There is a very long list of signals representing disorders originating in the perinatal period. Some of these were frighteningly common, others saw large increases despite still being relatively rare overall.

Since I have already shown you charts for many of the less common disorders, I will just list the ones with the largest absolute increase from 2021 to 2022 here, all of which are also above the 2015-2022 mean.

• +333 deaths: P28.0 (Primary atelectasis of newborn)

• +171 deaths: P36.9 (Bacterial sepsis of newborn, unspecified)

• +168 deaths: P96.8 (Other specified conditions originating in the perinatal period)

• +118 deaths: P29.1 (Neonatal cardiac dysrhythmia)

• +106 deaths: P20.9 (Intrauterine hypoxia, unspecified)

• +100 deaths: P52.2 (Intraventricular (nontraumatic) haemorrhage, grade 3, of newborn)

• +97 deaths: P96.0 (Congenital renal failure)

• +83 deaths: P29.0 (Neonatal cardiac failure)

• +76 deaths: P03.9 (Newborn affected by complication of labour and delivery, unspecified)

• +57 deaths: P77 (Necrotizing enterocolitis of newborn)

• +53 deaths: P29.3 (Persistent fetal circulation)

• +52 deaths: P03.8 (Newborn affected by other specified complications of labour and delivery)

• +48 deaths: P27.1 (Bronchopulmonary dysplasia originating in the perinatal period)

• +47 deaths: P91.6 (Hypoxic ischemic encephalopathy of newborn)

• +45 deaths: P61.0 (Transient neonatal thrombocytopenia)

• +43 deaths: P22.0 (Respiratory distress syndrome of newborn)

• +42 deaths: P28.5 (Respiratory failure of newborn)

• +37 deaths: P01.2 (Newborn affected by oligohydramnios)

• +35 deaths: P01.7 (Newborn affected by malpresentation before labour)

• +30 deaths: P54.9 (Neonatal haemorrhage, unspecified)

• +26 deaths: P60 (Disseminated intravascular coagulation of newborn)

• +26 deaths: P78.0 (Perinatal intestinal perforation)

• +26 deaths: P22.9 (Respiratory distress of newborn, unspecified)

The strongest signal here are primary atelectases. Atelectase describes a condition in which part of the lung closes or collapses, limiting gas exchange. Think of pulmonary alveoli with very low volume, where the walls almost touch each other.

The most common cause for atelectases in the neonatal period are

• respiratory distress syndrome

• bacterial pneumonia

• meconium aspiration syndrome

• bronchopulmonary dysplasia

• pleural effusion

• pneumothorax

Mechanical ventilation often exacerbates the condition.

It is not unlikely that a large share of excess deaths involving atelectases were related to infections and mechanical ventilation. In any case the signal warrants further investigation.

Introduction

Igor Chudov recently wrote an article about Infant Mortality, which you can find here.

It motivated me to look into the matter. I have already downloaded a bunch of data from CDC Wonder and begin this article series by trying to find a signal on national level.

Methods

An infant death is defined as the death of a child between the age of 1 day and 1 year.

I calculate monthly infant deaths per 100k by dividing the number of monthly infant deaths by the number of live births occurring in the preceding year.

To give you an example, this is how infant deaths per 100k are calculated for January, 2015:

[infant deaths 2015/01] / [live births 2014/02-2015/01]

Trend-correction is performed by determining linear trends between 2015-2019 and then adjusting the entire time series for that trend, with past data receiving the strongest adjustment. This means that the last datapoint (2022/12) will always remain untouched.

For excess mortality rates I am also using 2015-2019 infant deaths per 100k as reference.

Results

First we are going to look at the number of infant deaths per 100k before and after trend-correction. This is very similar to what I did when I isolated the cancer signal.

In 2022, infant deaths per 100k are on the same level as they were in 2018 and significantly lower than in the 3 preceding years.

Calculating excess rates based on 2015-2019 would most definitely result in a mortality deficit in 2022.

But since we are seeing a very clear downward trend throughout these years, which can likely be attributed to advances in healthcare, I feel confident enough to adjust the data for this linear trend, which causes 2020, 2021 and 2022 to stand out, with very low infant mortality in 2020, but a clear excess in 2021 and an even larger excess in 2022.

The downward trend in infact mortality was clearly broken sometime in 2021 or 2022. This is our signal. It peaks at around +11% or 5 additional infant deaths per 100k in late 2022.

Let’s look at excess infant deaths per 100k between 2020 and 2022, using 2015-2019 as reference timeframe.

It seems infact excess mortality is surging twice. Once in 2021, shortly after vaccinations of 18-49 year old women commenced, then again in 2022, shortly before the maniacs started injecting infants with gene therapeutics.

I did the same thing for all 50 states and will now show you state-level excess infant mortality in 2022 plotted against…

• …cumulative doses administered to females aged 18-49 by the end of 2021

• …cumulative doses administered to infants by the end of 2022

Nothing to see here.

Again, nothing to see here, other than states like Maine, Delaware and Alaska having extremly high excess infant mortality rates. However, these are small states, so I would advise you not to overinterpret these figures.

Conclusion / Outlook

The downward trend in infant excess mortality was broken in 2021. Vaccinations alone do not explain this change in trends.

To get a better idea of what is going on, we are going to have to look into infant mortality by multiple cause of death.

Introduction

In this article I briefly summarize the impact vaccinations had on deaths during pregnancy (maternal mortality) and the link between maternal mortality and vaccine recommendations for pregnant women issued by the CDC (Centers for Disease Control) and ACOG (American College of Obstetricians and Gynecologists).

I also propose a mechanism by which vaccines may have increased maternal mortality.

All Weekly Maternal Deaths

The first chart we are looking at displays all deaths of pregnant women occurring between 2020 and 2022, color-coded into

• maternal COVID deaths

• all other maternal deaths

Every COVID death fulfills the following criteria:

1. The UCOD (Underlying Cause Of Death) is either O98.5 (Other viral diseases complicating pregnancy) or U07.1 (COVID-19)

2. U07.1 (COVID-19) is present anywhere on the death certificate

From this we can gather:

• With 20-30 maternal deaths per week, it is generally uncommon for pregnant women to die. (In 2019 only 1 in 5000 American women died during pregnancy)

• Very few pregnant women died of COVID in 2020

• The large number of excess deaths occurring in pregnant women during 2021 was almost exclusively attributed to COVID

Since this chart does not contain information on COVID mortality in the general population, I prepared another chart which expresses maternal COVID deaths relative to all COVID deaths, side by side with vaccination progress among females of reproductive age.

Deaths in Pregnancy And Vaccinations

Out of all decedents with a diagnosis of U07.1 (COVID-19) on the death certificate, less than 1 in 10,000 were pregnant throughout 2020. COVID mortality was a non-issue for pregnant women back then.

This changed in 2021. Around the peak of the “Delta wave” in Q3/2021, more than 1 in every 800 decedents with a diagnosis of COVID-19 was pregnant.

While 337 deaths may not seem like a whole lot, keep in mind it is not very common for pregnant women to die, as we have already seen in the first chart. This becomes even clearer when we express these COVID deaths in pregnant women as a proportion of all deaths in pregnant women.

Around the peak in Q3/2021 almost half of all deaths in pregnant women were attributed to COVID-19.

In the absence of a known pathomechanism that could explain how COVID suddenly became more dangerous to pregnant women, failing to at least consider so-called “COVID vaccines” as the reason for the sudden rise in COVID deaths in pregnant women would be nothing short of insane.

Since vaccination data for pregnant women is unavailable, we are going to take a look at how and when CDC and ACOG recommendations changed in the course of 2021.

CDC and ACOG recommendations

CDC’s mid-2020 assessment

Based on data collected between January 22 and June 7, 2020, the CDC found no increased risk for pregnant women to die of COVID, even though they allege pregnant women had a higher chance for a severe course and ending up on the ICU.

Sixteen (0.2%) COVID-19–related deaths were reported among pregnant women aged 15–44 years, and 208 (0.2%) such deaths were reported among nonpregnant women (aRR = 0.9, 95% CI = 0.5–1.5).

This lines up with the mortality data we looked at. Only 20 pregnant women died of COVID throughout all of 2020.

Now let’s look at ACOG’s and CDC’s stance on vaccinating pregnant women throughout 2021.

Early Public Messaging (CDC)

In early January, right when pregnant women started dying of COVID on a weekly basis, the CDC stated that “people who are pregnant and part of a group recommended to receive a COVID-19 vaccine […] may choose to be vaccinated”.

Early Public Messaging (ACOG)

In the first half of 2021, ACOG recommended that “pregnant patients who decline vaccination should be supported in their decision”.

Ironically ACOG has requested all cached (older) versions of their website to be deleted from web.archive by now.

A Change of Hearts

On June 29th, 2021 the CDC changed their messaging, now telling pregnant women that vaccines protect them from severe illness. One month later, on August 1st, they turned this into a vaccine recommendation for all pregnant women, breastfeeding women and those who might become pregnant in the future.

ACOG almost simultaneously (2 days earlier on July 30th) started recommending the vaccines to breastfeeding and pregnant women.

The consequences

The peak of COVID deaths in pregnant women occurred right after the CDC and ACOG started recommending vaccines to this subpopulation.

Based solely on visual comparison, it is tempting to assume these vaccines caused death with a delay, with the large vaccination spike (blue) being responsible for the large death spike (red).

This however is NOT what happened. Instead, it is a short-term effect.

The smaller vaccination wave - marked with a black arrow, coinciding with the wave of deaths in pregnant women - is what caused these deaths.

We can see this when comparing the number of administered first doses per woman aged 18-49 to COVID deaths in women aged 18-49 across all 50 states around this time (August 2021).

The more vaccine doses were administered to women aged 18-49 in August of 2021, the more women aged 18-49 died with COVID as the underlying cause of death on the death certificate.

The correlation is strongest when first doses are preceding COVID deaths by 8 days, suggesting a short-term causal effect from first dose reception towards “COVID death”.

Similarly, fetal deaths per live birth are also correlated with first doses administered to 18-49 year old women.

This is a snapshot of fetal deaths per live births on August 31st, generated from monthly data (roughly the mean of August and September), right around the time deaths in pregnant women peaked.

Again, the more first doses were administered to women of reproductive age, the more fetal deaths per live birth occurred in August 2021.

Proposed mechanism

First-dosing individuals with no previous exposure to SARS-CoV-2 must’ve created the perfect storm in those unfortunate enough to catch an infection around the time of vaccination.

• If the time of the first modRNA dose coincides with exposure to the virus, the body has not yet produced any antibodies, so the innate immune system is confronted with a very large amount of spike protein, more than would be produced upon transfection (vaccination) or infection (virus) alone

• In many cases innate immunity will not be able to compensate for the flood of antigen it encounters, which in turn can be expected to result in higher viral loads

• If eventually antibodies are being produced that lack the capacity to neutralize the virus, antibody-dependent enhancement could add to the problem. This has at least been demonstrated in silico and in vitro for Delta variants

They knew!

This is why individuals are not considered “partially vaccinated” before 14 infection-free days have passed. Pfizer and Moderna knew this from the start. The knowledge over the dangers of early infections are hard-wired into their clinical trial design.

In their study on VE (Vaccine Efficacy) and VMED (Vaccine-Mediated Enhanced Disease) the CDC categorize individuals who catch an infection within 14 days of their first dose as unvaccinated, along with individuals who have never received a dose.

And if you think about it… It’s a nobrainer!

What else did we think was gonna happen, when the body is flooded with spike protein from two sources at the same time?

Who in their right mind, would not assume that this worsens the prognosis relative to unvaccinated individuals?

At least the manufacturers knew exactly what they were doing.

Introduction

There has been a lot of talk about “turbo-cancer”. The way I see it, using such terms will only lower our credibility. Terms like “Rapidly progressing malignancies” or REP (Rapid Early Progression) are much closer to conventional medical language and can be understood by anyone in the field.

For this article I tried to isolate cancer signals from U.S. population level data for the two vaccination age tiers of children 12 or older (12-17) and adults 75 or older (75+). If there is a signal, I want to know when it manifests and whether this coincides with the onset of the “COVID vaccination” campaign.

Methods

Deaths are based on UCOD (Underlying Cause Of Death). If a decedent had COVID (U07.1), then its death is unlikely to show up in neoplasm deaths, because the WHO advised examiners to prioritize U07.1 (COVID-19) over other underlying causes of death.

Monthly (2015-2017) and weekly (2018-2022) data are available through CDC Wonder. I stretch these to pseudo-daily data to get continuous time series that are better suited for being visualized as line graphs. The time series are smoothed by a period of +-15 days, so they should be treated as figures with monthly precision.

Data for all 50 states are available through the download section of my “Summer Deaths” article. I am using the same methodology to calculate rates per 100k residents outlined in the methods section of that article.

In order to isolate the signal I am performing a correction of linear trends which is explained in more detail in the results section.

Results

First I will show you how the trend-correction works, then we will proceed to look at excess mortality for both age tiers next to vaccine doses.

Finally I will show you a scatter chart, which displays cumulative doses per capita and all-age excess neoplasm mortality across 50 states in 2022.

Trend-correction

Trend-correction is very straight-forward. I calculate rates of deaths per 100k using annual population estimates released by the Census Bureau for all deaths where one of the ICD-10 codes from the Neoplasms chapter (C00-D48) was specified as the UCOD (Underlying Cause Of Death), perform a linear regression on the mortality data between 2015 and 2019 and then correct the entire time series for the slope determined by the regression.

Before trend-correction the mortality rates in 2021 and 2022 were roughly on the same level as they were in 2017 and 2018. If we tried to calculate excess mortality using 2015-2019 as reference, we would only find a very weak signal or none at all.

After trend-correction we can see the cancer rate shoot up right upon introduction of COVID shots in December 2021.

I did the same thing for ages 12-17. In both age tiers, neoplasm mortality was apparently trending downwards throughout 2015-2019, possibly due to improvements in cancer therapy and diagnostics.

Excess mortality and doses per capita

The neoplasm signal manifested as soon as vaccines started to be administered to the population.

Again, the neoplasm signal for children 12 or older manifested as soon as this age group started receiving their COVID shots, but five months later than the signal for individuals aged 75 or older.

So rising neoplasm excess mortality coincided with the onsets of vaccination campaigns.

Regressions

I applied the above process to state-level all-age neoplasm mortality, calculated relative excess mortality for 2022, plotted it against cumulative vaccine doses and performed a regression across the 50 states.

As you can see, you’re seeing nothing. There is no significant (p<0.5) correlation between doses administered per capita and excess neoplasm mortality.

Bummer.

Discussion

Correcting for linear trends is only semi-legit. We basically created new time series that do not represent reality. Climatologists and other scammers are doing it all the time to adjust the data to their needs - their needs being whatever they want to convince you of.

In the case of neoplasm mortality however, I would say it is justified, because cancer screenings and therapies have been improving for some time. We should expect to see negative trends in the data.

In both the youngest and highest age tier these negative trends were broken when COVID shots started to be administered to the respective age group. The signal in adults 75 or older precedes the signal in children 12 or older by around 5 months.

This is suggestive of the vaccines being responsible for the rise in cancer deaths.

Strangely there is no correlation across states between the number of doses administered per capita by the end of 2022 and cancer excess mortality in 2022.

Possible reasons for this could be:

• The vaccines aren’t what’s causing neoplasm deaths

• Another factor is increasing neoplasm mortality in regions with low vaccination coverage, e.g. mistrust in the medical establishment leading to less cancer screenings being performed

  • We should control for changes in the number of screenings per capita in a multivariate regression, but I do not have the required state-level data available

• Only some vaccine products are causing neoplasm deaths and a higher proportion of these products was used in states where demand was lower (states with lower “vaccine” coverage)

  • This is not unlikely. Only Pfizer’s shots are known to contain linearized plasmids coding for SV40 thought to have an impact on cancer growth

  • Regressions should be performed after stratifying doses per capita by manufacturer (Moderna, Johnson, Pfizer). Maybe I will get around to this.

Looking forward to your input!

Summary

"COVID vaccines" killed 114,134 Americans over a 3-month period from July to September 2021. These deaths were reported as “COVID deaths”.

Introduction

This is the shortest possible, most easily understandable version that I am going to write about the situation. The revelations about vaccine mortality in the summer of 2021 presented in the full version failed to reach people, presumably due to the highly technical nature of the article, its sheer length of 70 pages and its countless contradictions to other, more popular narratives in circulation.

I have dedicated many months of my life to this, looked at dozens of U.S. datasets, thousands of visualized timeseries and ran millions of regressions over the county- and state-level data available to me.

The findings presented in this article are not easy to accept. I kept breaking out in tears during the months it took me to come to grips with what happened. I don’t like it anymore than you do, which is why I consulted three seasoned statisticians before even beginning to write about it.

I am trying to report on this event as honestly as I can. When it comes to vaccine-mediated mass casualties, this is the strongest evidence I am aware of.

The Mass Casualty Event

I boiled the 30 charts presented in the main article down to two essential charts that I am going to explain to you. So let’s get right to it, starting with a visual comparison between national excess pneumonia mortality and first doses per capita across age groups in the summer of 2021.

While all of these excess pneumonia deaths were declared as “COVID deaths with pneumonia” (ICD-10 codes J12-J18), displaying “COVID deaths” among different age tiers within one chart poses scaling issues, so excess pneumonia mortality serves as a proxy here.

The more first doses were being administered throughout the third quarter, the higher pneumonia excess mortality was in the respective age tier. No sane person can deny this, but the mechanisms at play are debatable.

The lower the age tier, the higher the excess pneumonia mortality rates, which peaked at over 1200% in the youngest adult age tier of 18-49 year olds. This is because it is highly unusual for pneumonia to take the lives of young people, so even moderate increases in the number of deaths cause large increases in excess mortality.

There is a short delay between administration of first doses and the occurrence of excess pneumonia deaths. First doses are preceding pneumonia deaths by a couple of weeks. This temporal precedence is a strong indicator, that there is a causal link from reception of the first dose towards “dying a COVID death”.

Correlation Across 50 States

While the first chart might look quite convincing to some people, it does not represent strong evidence in itself. We need to find out, if those regions and age tiers that received the most first doses also suffered the highest number of “COVID deaths”.

We will approach this both visually and mathematically, by plotting the number of first doses per capita and the number of “COVID deaths” per 100k of each state onto a single chart. The closer the states are to being located on a straight line, the more likely a direct causal link between the variables is.

A correlation of 1 means that all states are located on a straight line. It would be extremely unusual to witness anything close to a correlation of 1 in population-level data, where countless factors are usually influencing each other and no single cause is ever solely responsible for whatever is being measured.

You can find charts for all age and gender groups in the main article. For this observation, we will only be looking at the age tier of 50-64 year olds, where the correlation is strongest. Correlations aren’t much weaker in the other age tiers, but I don’t want to overwhelm you with charts.

While not all vaccine deaths were declared “COVID deaths”, we only look at those where a diagnosis of U07.1 (the diagnostic key for COVID-19) was the designated UCOD (Underlying Cause Of Death) on the death certificate, since this is where the correlations with first dose administration rates are so strong, that a causal link is irrefutable.

The more first doses a state gave to its residents, the more people died whose deaths were labelled “COVID deaths”.

When first doses administered to individuals aged 50-64 are preceding “COVID deaths” in individuals of the same age by 9 days, the correlation reaches 0.95.

As I have already stated, it is extremely rare to see anything like this. The expression p<10^-24 above the chart means, that the probability of encountering a correlation like this by chance is lower than 0.0000000000000000000001%.

For every first dose administered to individuals aged 50-64, 0.010 (95% confidence interval: 0.009-0.011) people of this age died with a diagnosis of “U07.1” as the underlying cause of death.

That is more than 1 death between the ages of 50 and 64 for every 108 first doses administered to this age group during this time. So let’s have a look at how many people were killed in total throughout third quarter of 2021.

Total Number of Casualties

I summed up the total number of casualties among each age tier in a table. It looks like COVID mortality would've been negligible without first doses being administered at the time, so we can chalk all “COVID deaths” up to vaccinations.

More than 1000 deaths per day over a period of 3 months makes this one of the most devastating mass casualty events in American history.

By comparison, the Vietnam War took the lives of 58,220 U.S. military personnel over a period of 20 years - roughly half as many deaths over a period 80 times as long.

The most shocking aspect about all of this is the CDC’s apparent awareness of the situation.

The Cover-Up

The average delay between first vaccine dose and positive test result of those individuals later declared as a “COVID casualty” seems to be short enough (<14 days) for most decedents to be considered unvaccinated along with those who never received a dose, according to the CDC definition in their vaccine efficacy study and to be removed entirely from “all data shown” in their publication on hospitalizations by vaccine status.

An article by Maryanne Demasi about the study design of Moderna’s and Pfizer’s so-called “clinical trials” suggests that an event like this had been anticipated from day one.

In an interview with the NY Times in May of 2021, CDC director Dr. Walensky stated that she was preparing for a “Pandemic of the Unvaccinated”. When this anticipated situation - which should be understood as “Epidemics of the Recently Vaccinated” - did finally manifest in mid-July of 2021, the NYT proceeded to release her statement in an attempt to “prebunk” anyone stumbling over what would happen in the months that followed.

3368 cases of thrombocytopenia.

Anyone interested can download it here:

Letter by the EMA

908 pages safety report

908 pages safety reports text form (post OCR)

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Deaths of Pregnant Women (O98.5)

I am going to make this really snappy. I don’t think anyone has really talked about this before, so I just want to get it out there.

These two charts give you an idea how safe and effective COVID vaccines were in pregnant women:

Of all women who died during pregnancy throughout 2020, less than 2% died with a diagnosis of O98.5 (other viral disease complicating pregnancy).

Once vaccines were introduced this number shot up and peaked at almost 40% in September, 2021. The large spike in summer is not due to a delay of harmful effects, but because vaccinating in Q3 was MUCH more dangerous than vaccinating in Q1 or Q2 (and the proportion of pregnant women who considered gene therapeutics in pregnancy a good idea probably increased throughout the year - thanks to the marketing campaign).

The second half of 2021 is when it happened. If you want to understand WHY it happened, it is explained at length in this article:

The next chart is based on weekly data from CDC Wonder and represents the proportion of pregnant women dying with both a O98.5 and U07.1 diagnosis among all cumulative deaths of women who died between the ages of 18-49 from 2020-2022.

While we were first-dosing, the proportion kept increasing. When we stopped first-dosing, the proportion plummeted again.

First doses in Q1 and Q2 only had a minor effect, but the small bump in first doses in Q3 is causing the huge rise in deaths. I know this is hard to believe, but there is more evidence.

Since deaths in pregnancy are a rarity, we will look at first doses vs. COVID deaths in females in August of 2021.

The correlation between lagging age-stratified first doses and UCOD=U07.1 deaths in women ranges between 0.89 and 0.96.

You don’t get to see correlations like these very often in population-level data.

For every first dose administered to women aged 18-49 from 8 days before August 1st 2021 until 8 days before August 31st 2021 more than 1 in 740 women aged 18-49 died with a COVID diagnosis in August of 2021 - for women aged 75 or older there was more than 1 death per 19 first doses (p<10^-16).

Depending on age group it took between 8 and 15 days for women to die after the first shot. The second shot did not have the same effect.

This is why there is a 14-day delay before someone is considered “partially vaccinated” after the first dose. The effect is called infection enhancement, vaccine-mediated enhanced disease, vaccine-enhanced disease or disease enhancement and is the main reason why these vaccines should be illegal.

The CDC is blatantly covering this up by conducting studies on vaccine efficacy where they consider individuals who received their first shot and got a positive test within 14 days as unvaccinated, along with individuals who have never received a dose.

Their report on hospitalization rates by vaccination status [29] considers all individuals who do not provide vaccination records considered unvaccinated, and “persons who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before a positive SARS-CoV-2 test were excluded from all data shown.”

Data from the German hospital system confirms the findings.

Hospitalizations of Pregnant Women (O98.5) In Germany (2020-2021)

There were around 120 hospitalizations of pregnant women with diagnoses of both O98.5 and U07.1 in 2020.

This number rose to 850 women by the end of 2021.

Same data, but without accumulating the figures.

And here are two charts with just ICU cases:

The number went up from 8 to 84, a rise of 850% (8 in 2020 vs. 76 in 2021).

Doesn’t look as if these vaccines were safe and effective to me at all. Let’s look at fetal deaths.

Fetal Deaths Per Live Birth (USA)

Let’s look at the ratio of fetal deaths per live birth, first doses and how the two variables correlate across states.

So there was a rise in fetal deaths per live births of more than 10% in the third quarter. At the same time the correlation between first doses and fetal deaths per live births rose to 0.63. Let’s look at the last day of August, using pseudo-daily data.

The correlation isn’t super-high, because the proportion of pregnant women among those who took a dose during this time varies widely between states.

Safety Studies

Safety studies were conducted. The results of the clinical trial NCT04754594 were posted in July, 2023.

That’s right: July, 2023!

Don’t even bother looking at the results. Less subjects experienced headaches in the study group compared to the placebo group. I just can’t take this stuff seriously. We know the difference in incidence proportions of headaches should be over 20%.

Just another bogus study that only serves to retrospectively assess that we did everything right.

Obstreticians conducting these trials were paid handsomely:

Jeffrey Baker MD received 51 Payments from Pfizer for Comirnaty Research for a total of USD 1,834,390.

Look at his place! Isn’t it nice?

He’s been doing a loooot of vaccine research on pregnant women. In 2021 alone he received 142 payments from Pfizer - not just for injecting Biontech’s gene therapeutics into pregnant women, but a whole lot of other vaccines as well - for a total of USD 4,144,042.

That would buy anyone a smile like that, wouldn’t you agree?

Edwin Ricardo Ramirez received 34 Payments from Pfizer for Comirnaty Research for a total of USD 1,351,346.

That’s it from me. Make of this what you will.

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Summary

This is an investigation into mortality trends across the USA during the COVID-19 era and the impact of COVID vaccines on these trends, with particular emphasis on the wave of death that sweeped over the country in the summer of 2021.

U.S. mortality figures were soaring throughout the entire 3-year period from 2020 through 2022, with mortality of external causes dominating the picture among children and young adults and COVID accounting for the largest share of excess deaths among higher age tiers.

While first dose coverage was found to be negatively correlated (p<0.001) with COVID mortality in all age tiers across U.S. states throughout the summer of 2021, these negative correlations appear to represent an efficacy illusion resulting from increased vaccination efforts in regions and age groups that entered the third quarter with low first dose coverage.  

Per multivariate linear regression models, differences in COVID burden across U.S. states among adult age groups throughout July and August of 2021 are well-explained by first dose administration rates (p<10^-10), but not by first dose coverage (p>0.1).

The time-order relationship between first dose administration rates and COVID mortality suggests a direct causal link from first dose administration towards COVID mortality due to vaccine-mediated infection enhancement occurring in individuals who became infected with SARS-CoV-2 delta variants within days of receiving their first dose in the second half of 2021.

Infection enhancement not only negatively affects disease outcomes and infectiousness on the individual level, but can also have profound effects on epidemic behaviour through its effect on transmission dynamics.

Multivariate regression models suggest that the COVID outbreaks observed globally during the second half of 2021, resulting in COVID-attributed deaths of 200,000 Americans and 800,000 Europeans across Iceland, Liechtenstein, Norway and the 27 EU countries, would have had minimal impact on morbidity and mortality, if first doses had not been administered while Delta variants enjoyed predominance.

Upon manifestation of the phenomenon in population-level data maintained by the CDC in mid-July of 2021, Director Wallensky’s dubbed the outbreak a “Pandemic of the Unvaccinated”. Furthermore, the CDC are excluding individuals who became infected within 14 days of the initial inoculation from its vaccination status-stratified hospitalization data and are currently conducting a study on COVID VMED (Vaccine-Mediated Enhanced Disease) and VE (Vaccine Efficacy) spanning back to early 2021, that miscategorizes the problematic subpopulation in which disease enhancement appears to have occurred as “unvaccinated” along with individuals who have never received a dose, suggesting the CDC’s awareness of the risk for time-dependent VMED upon initial exposure to COVID vaccines.

Corroborating the aforementioned results from case and death data, autopsies performed on individuals who tested positive for SARS-CoV-2 within 4 days of receiving their first vaccine dose indicate higher viral loads in both lung and liver tissue (p<0.01) and nasopharynx (p<0.01) and a shorter time from symptom onset to death (p<0.01) compared to a control group of decedents who tested positive within 5-28 days of receiving their last dose.

The Ct-values of millions of positive RT-PCR tests conducted on nasopharyngeal swab specimens of SARS-CoV-2-infected first-dosed individuals could provide valuable insights into how infection enhancement contributed to COVID burden throughout different phases of the pandemic, but these data are absent from scientific literature.

Introduction

The United States did not perform well during the COVID era. Excess mortality figures for 2020 and 2021 far exceeded those of most European countries and its immediate neighbor Canada.

While the United States as a whole suffered the highest rate of excess deaths before the introduction of vaccines in December 2020, regional performance in the following year varied widely and the country saw a remarkable drop in the average age of death that warrants investigation (Supplemental Figure 1).

In this article, I examine the changes in mortality trends with focus on COVID burden among various age groups and states over the course of 2021 and analyze the impact of vaccinations on these patterns.

Methods

Terminology

• The terms VAED (VAccine-Enhanced Disease), infection enhancement, disease enhancement and VMED (Vaccine-Mediated Enhanced Disease) are used interchangably.

• Unless specified otherwise, whenever the terms COVID mortality, COVID mortality rates, first doses, first dose administration rates etc. are used they are referring to per-capita or per-100k variables.

Smoothing

• Smoothing is only applied for charting, but never for analytical purposes.

• The smoothing period of pseudo-daily data is +-3 days, so all visualized daily data should be understood as data of weekly precision.

Population estimates

• All population estimates are derived from the Census Bureau’s 1-year PUMS (Public Use Microdata Sample) mid-year estimates.

• The respective mid-year estimates are used for the entire duration of each calendar year.

• 2022 population estimates became available post-analysis, on October 19th 2023. 2021 estimates were used in their place.

Excess mortality rates

• Excess mortality variables are not used in any of the regression models.

• Method of calculating excess mortality from CDC Wonder data:

  • Monthly 2015-2017 figures are stretched to pseudo-daily time series of the respective month’s length in days.

  • MMWR (CDC Morbidity and Mortality Weekly Report) Weekly 2018-2022 data is stretched to pseudo-daily time series by a factor of 7.

  • Overlapping days are cut off weekly data before both time series are concatenated to represent daily time series spanning from 2015-01-01 to 2022-12-31.

  • Each daily value is multiplied by 100,000 and divided by the corresponding year’s population estimate.

  • Calendar day averages of pseudo-daily figures from 2015 through 2019 serve as projection for the calculation of (relative) excess mortality and (absolute) excess deaths per 100k.

Pre-processing of vaccination data

• Vaccination coverage data supplied by the CDC are based on 2019 population estimates.

• Values are multiplied by the 2019 population estimates included in the CDC dataset to yield the absolute number of cumulative doses administered in each state.

• Absolute figures are then divided by the appropriate 2021 population estimate to yield vaccination coverage data based on 2021 population estimates.

• For gender-stratified data the gender distribution of 2019 is applied to the 2021 population estimates.

Worldometer COVID cases and deaths

• To calculate per-capita variables I use 2021 population estimates for all 3 years. Only the 2021 data are used for analysis. 2020 and 2022 data are used for charting purposes only.

Data sources

• Daily dose series-, age- and gender-stratified state-level vaccination variables

• Monthly and weekly age- and gender-stratified state-level mortality

• Daily state-level COVID cases and deaths

• Wastewater data

• GISAID variant data

• VAERS data

• Population estimates

• Autopsy data [24]

Downloadable datasets

• Image pack

  • Uncompressed size: 22.9 MB

  • Compressed size: 19.6 MB

  • 33 figures contained in this article

  • 8 supplemental figures

  • ECDC country charts as pdf

• 4.3 million SARS-CoV-2 sequences scraped from GISAID

  • Uncompressed size: 14.9 GB

  • Compressed size: 1.4 GB

  • Includes all metadata and NEXTCLADE analysis results

  • 2020 data for all nations

  • U.S. data up to August 2022

• U.S. state-level dataset

  • Stacked CSV files

  • Uncompressed size: 356 MB

  • Compressed size: 59 MB

  • Annual population estimates :

    • National and state-level

    • By vaccination age 2015-2021

    • By vaccination age and gender 2021

  • Vaccination data

    • National and state-level

    • New doses and cumulative doses 2020-2022

    • Raw and per-capita figures

    • Age groups 0+, 12-17, 18-49, 50-64, 65-74, 75+

  • Mortality

    • No suppressed values

    • By vaccination age

    • CDC Wonder data:

      • Raw monthly figures 2015-2017

      • Raw weekly figures 2018-2022

    • Preprocessed data:

      • Pseudodaily figures 2015-2022

      • Pseudodaily figures 2015-2022 per 100k

    • Types:

      • National only: External causes

      • National and state-level by UCOD: U07.1 (COVID-19)

      • National and state-level by UCOD: C00-D48 (neoplasms)

      • National and state-level by MCOD: U07.1 (COVID-19)

      • National and state-level by MCOD: I00-I99 (circulatory system)

      • National and state-level by MCOD: J12-J18 (non-influenza penumonia)

  • Pneumonia mortality

    • Weekly data for 2018-2022

    • National, no age-stratification, per 100k

    • With UCOD=U07.1 and without UCOD=U07.1

    • J09-J11, J12, J13-J15, J16, J18

  • Worldometer data

    • National and state-level

    • No age-stratification

    • Cases and deaths

    • Daily data 2020-2022

    • Raw figures and per capita

Results

There is a lot of conflicting information circulating about U.S. mortality trends throughout the COVID years. I will try to clear up some common misconceptions by providing general information on the mortality patterns that emerged during this time, before shifting the focus towards investigating the impact COVID vaccines had on the surge in COVID mortality in the summer of 2021.

Since the subject matter is complex and not all readers are familiar with statistics, I will be explaining the most important techniques I use along the way, while walking you through the evidence step by step.

National Mortality 2020-2022

We begin by looking at the number of weekly COVID and non-COVID deaths that occurred in the United States from 2020 through 2022.

Non-COVID deaths remained consistently elevated beyond the 2015-2019 projections for the majority of the 3-year period. In contrast, COVID-19 deaths occurred in distinct waves. While the largest waves far exceeded non-COVID excess mortality rates, the national COVID mortality rate fell below 1 weekly death per 100,000 residents following the Omicron wave in the winter of 2022 and remained below non-COVID excess mortality rates ever since.

To assess the effect of vaccines on COVID mortality, we will examine the summer wave of 2021 which manifested at a time when over 50% of the population had already received the first vaccine dose.

Age-Stratified National Mortality

The FDA recommended [1] vaccination against COVID-19 in children below the age of 12 on October 29th 2021, which is why this age group will receive no further mention in this article.

Vaccination data supplied by the CDC for individuals aged 12 or older is limited to 5 age groups: 12-17, 18-49, 50-64, 65-74 and 75+

Ages 12-17

The following chart has a slightly different setup than the previous one. Mortality is split into deaths with and without external causes to allow for a direct comparison of the two.

Children aged 12 or older only started dying of COVID in mid-2021, at which point over 35% of them had already received their first COVID vaccine dose. While this is remarkable, the total number of COVID deaths only makes up a very small proportion of all deaths in this age group.

The projections tell us that around two thirds of deaths between 2015 and 2019 were attributed to external causes. The size of the red area in relation to the orange area indicates that this proportion increased further with the introduction of lockdowns.

This should come as no surprise since deaths in this category are linked to psychosocial distress in children and their guardians.

It’s child mortality with external causes that skyrocketed with the introduction of “measures to mitigate the spread of SARS-CoV-2”.

While every child death is a human tragedy, many articles vastly exaggerate the magnitude of American child excess mortality that occurred throughout the COVID years and more often than not fail to mention that the largest share can be explained by psychosocial distress caused by disastrous “public health measures” and social policies.

While the USA registered 298 excess deaths without external causes in 12-17 year olds throughout 2021 and most of these excess deaths occurred in summer, data are very sparse, with some states barely registering any COVID deaths at all during this time. It is difficult to yield meaningful results by running regressions across states based on such sparse data, which is why we are shifting our focus towards higher age tiers.

Ages 18-49, 60-64, 65-74 and 75+

These charts follow the same setup as the chart for all-age national mortality, except that the chart representing mortality in individuals aged 18-49 (top-left) has non-COVID excess deaths color-coded into deaths with (red) and without external causes (orange).

The most striking aspect of this chart is the thick red wall sitting on top of the projected number of deaths in individuals aged 18-49. These are the fruits of “public health measures” and inadequate social policies.

I show you these figures not just to make sure you are aware of the suffering young people went through during this time, but to illustrate how futile it is to measure the effect vaccinations had on all-cause mortality, particularly when using 2015-2019 as the reference timeframe for excess mortality calculations. There were just too many factors at play that bore an impact on how many people in this age tier lost their lives throughout the COVID era. The problem is illustrated in Supplemental Figure 2.

The higher up we move through the age tiers, the smaller the magnitude of non-COVID excess mortality relative to COVID mortality becomes. Individuals aged 75 or older saw prolonged phases of non-COVID mortality falling below the 2015-2019 projections. I summed up the UCOD proportions among excess deaths in the higher age tiers (50+) into a single chart.

External causes make up a much smaller proportion of excess deaths in adults aged 50 or older. The other UCOD’s making up non-COVID excess mortality are just as diverse und unspecific as they are in the lower tiers. There is no doubt in my mind that some of these deaths can be chalked up to the nucleotide-based vaccines introduced in late 2020 [2], but the potential impact of “public health measures” should not be underestimated. At least two aspects of public health policies and the messaging that accompanied them should have affected mortality rates:

• Lack of access to medical care due to…

  • …fear of COVID-19

  • …a bizarre sense of social responsibility

  • …distrust in the medical establishment

• Fear and social isolation have a negative impact on the prognosis of any illness. Loneliness, in particular, is a significant mortality factor not to be underestimated [3].

COVID mortality on the other hand is much less polluted. When comparing the fall/winter wave of 2020/21 to the summer wave of 2021, there is a clear pattern emerging:

The lower the age tier, the worse the performance was in Q3/2021 relative to the previous wave around the turn of the year. While individuals between the ages of 18 and 49 were subject to a much higher peak incidence of COVID mortality during the summer than during the winter wave, we are seeing a relative decrease in individuals aged 75 or older.

Seeing these summer waves - particularly in younger age tiers - should strike you as odd for two reasons:

1. Seasonality of Respiratory Mortality

Respiratory deaths have a tendency to increase during wintertime mainly for three reasons:

• Lack of sunlight exposure for extended periods of time reduces vitamin D levels

• The colder the air is that we breathe in, the stronger the reduction in relative humidity will be when it warms up inside our respiratory tracts

• We tend to spend more time indoors in close proximity to other people

All three of these mechanisms increase our susceptibility to respiratory infectious disease - particularly viral infections - during wintertime, but seeing spikes in respiratory deaths during summertime like the one in the third quarter of 2021 is highly unusual.

2. Vaccination Coverage

By the end of the 2nd quarter of 2021, more than 85% of American residents aged 65 or older and more than 60% of adults under 65 had already received at least one vaccine dose.

COVID vaccines were aggressively advertised both for reducing the risk of catching COVID and improving the prognosis to a degree that would make death a highly unlikely outcome.

If this rang true we should see much fewer COVID deaths from the third quarter of 2021 onwards, but we don’t.

Pneumonia, Influenza and COVID

Since a unified y-scaling reduces readability when plotting COVID deaths by age group, non-influenza pneumonia (J09-J18) excess mortality will be used as a proxy for COVID mortality in several charts.

Around half the death certificates on which COVID (U07.1) is specified as the UCOD (Underlying Cause Of Death) include a diagnosis of pneumonia. The ICD-10 subchapter “Influenza and Pneumonia” (J12-J18) is further divided into influenza (J09-J11), other viral pneumonia (J12), bacterial pneumonia (J13-J15) and unclassified/unspecified pneumonia (J16-J18).

Influenza was not diagnosed in the 2021 winter season and only made a very brief appearance around the turn of the year, before re-emerging earlier than usual in the fall of 2022.

In the largest share of pneumonia diagnoses the involved pathogen type is not specified (J16-J18), so diagnoses for both viral (J12) and bacterial pneumonia (J13-J15) only make up a very small share of all pneumonia diagnoses. Whereas bacterial pneumonia was more common among non-COVID decedents, viral pneumonia was more common among COVID decedents (UCOD=U07.1).

There was no large excess in pneumonia deaths where COVID was not the underlying cause of death and neither was there a deficit due to reclassification of unrelated pneumonia deaths to COVID deaths. The seasonal pattern in non-COVID pneumonia deaths was largely conserved throughout the COVID years, with the exception of elevated pneumonia rates during summertime, particularly in 2021.

The proportion of COVID decedents with a pneumonia diagnosis rose steadily from a level of 40% in early 2020 towards its peak value of 60% during the summer wave of 2021, before decreasing again when Omicron entered the stage in late 2021.

It’s time to find out if vaccination patterns can shed some light on the trends we saw in age-stratified mortality.

Age-Stratified Vaccination and Mortality Trends

In order to be visually comparable between age groups, weekly COVID mortality in the next chart is expressed relative to the weekly peak number of deaths during the fall/winter wave, while non-influenza pneumonia excess mortality is based on the weekly averages from 2015 through 2019. Because very few deaths were being attributed to influenza mortality in 2021, I decided to exclude influenza when calculating excess pneumonia mortality. Accumulated first dose administration rates per capita serve as a proxy for first dose coverage.

Non-influenza pneumonia excess mortality is peaking at +1200% in individuals aged 18-49 in the summer of 2021. That’s thirteen times the normal rate during this time of year and four times the peak rate during the previous summer.

We see a similar pattern in both of the bottom charts. Mortality trends among the various age groups are running almost perfectly parallel in 2020, but start diverging thereafter, reaching maximum dispersion in the third quarter of 2021.

It wouldn’t be irrational to assume that vaccines protected people from COVID death and that the larger decrease in COVID mortality seen in the highest age tier of individuals aged 75 or older compared to the age tier of individuals aged 65-74 resulted from a protective effect, but the difference in mortality decline is much larger than could be explained by the difference in first dose coverage between the two age tiers.

The difference in the cumulative number of first doses administered to individuals aged 65-74 and individuals aged 75 or older by the end of the second quarter of 2021 is below 0.05 doses per capita, but the reduction in peak COVID mortality is 35% and 70% respectively.

This doesn’t add up.

The youngest two tiers of adults below the age of 65 are seeing increases in COVID and pneumonia mortality. Vaccines had been deployed, so we should be seeing lower COVID mortality in all age tiers, particularly during summertime.

Differences in cumulative doses do not seem to explain the trends, but there is another way to interpret the situation.

Vaccination Coverage vs. Vaccination Rates

Age tiers in the bottom-right (COVID mortality trends), the bottom-left (pneumonia) and in the top-right (vaccination rates) charts are stacked in the same order throughout the second and third quarter of 2021, but the order in the top-left chart (vaccination coverage) is reversed relative to the other three charts.

The order of first dose administration rates and first dose coverage in different age tiers flipping at some point should not surprise us, because once a subpopulation has been fully first-dosed, it can not receive anymore first doses. The lower the first dose coverage, the more first doses can still be administered.

We are seeing the highest vaccination rates in those age groups that were previously vaccinated at the lowest rates and vice versa, which allows for more than one possible causal link between COVID mortality and first doses:

1. High first dose coverage (cumulative first doses per capita) is associated with lower COVID death rates:

   • This would suggest the vaccines work

2. High first dose administration rates (daily first doses per capita) are associated with higher COVID death rates:

   • This would suggest the vaccines transiently increase the risk for severe disease and death upon infection

3. A combination of 3. and 4.

   • Vaccines could transiently increase the risk for severe disease and death upon infection, but then later elicit a protective effect.

   • Transient increases in the risk for disease enhancement could cause an efficacy illusion through pull-forward effects

To get to the bottom of this, we are going to look at whether or not this pattern is present in regional data.

Regional Differences

The following chart displays daily COVID case data from worldometer as well as death certificate-based COVID mortality from CDC Wonder on top of the two first-dose variables.

This doesn’t pose the same scaling issues as age-stratified data, so all variables are plotted without applying any of the previous transformations.

The South is leading in COVID cases, deaths and vaccination rates throughout the summer wave. It entered the third quarter with the lowest first dose coverage of all census regions, but emerges with more cumulative first doses per capita than the Midwest (bottom-right).

The difference in cumulative first doses between the most (Northeast) and the least (South) vaccinated regions at the end of June 2021 was below 0.15 doses per capita, which shouldn’t result in the huge difference in COVID deaths per capita seen throughout the third quarter.

Not only were COVID deaths in the South peaking at least 4 times as high as in the Northeast, but regional case and mortality waves are also shaped differently, with the South dropping as fast as it’s peaking, while the other regions see their waves either grow more gradually (Northeast and Midwest) or peak lower without declining as far (West), before all trends were cut short by the rise of Omicron in December of 2021.

Census regions are not stacked quite as neatly as the age-stratified graphs in Figure 9. The Northeast seems to have gotten off easy, despite administering just as many first doses per capita as the West, which fared much worse in terms of COVID mortality.

The association between first dose administration rates and COVID variables seems to dissolve as time progresses. For a more detailed view I prepared a chart displaying pneumonia excess mortality and first dose administration rates across census regions and age groups in the second half of 2021.

The pneumonia excess mortality rate peaks rapidly in the South in late August, but the peaks in the other regions are delayed and less pronounced. Regional vaccination rate graphs exhibit similar shapes within each age group, with peaks occurring between early and mid-August.

Since deaths across census regions stop increasing at different times in September and there is no apparent association between first doses and pneumonia excess mortality in Q4, our focus will be on the months of July and August.

We are going to find out how the state-level variables we have at our disposal relate to each other by performing linear regressions between them.

Identifying Linear Relationships

Not all readers will be familiar with statistics, so I will briefly explain some of the terms and techniques that you are going to encounter to those who need it. Everyone else can skip ahead.

If you would like to know more about the math behind linear regressions, I recommend visiting this page hosted by the Duke University [4].

Linear relationship

If changing one variable results in another variable changing by an amount proportional to the change in the first variable the two variables are sharing a linear relationship.
When such variables from different units of observation, such as regions or individuals, are plotted onto an XY-diagram, they are located on a straight line.

Pearson correlation coefficient r

The Pearson correlation coefficient r measures the strength of a linear relationship. It can range from -1 to 1 where a value of -1 or 1 would indicate a perfect linear relationship, with all points being located on a straight line. Its sign indicates the slope of the line. When it’s negative, the line is pointing downwards and increases of one variable are associated with decreases in the other variable, but when it’s positive the line is pointing upwards and increases in one variable are associated with increases in the other variable.

Simple linear regression (SLR)

In a SLR (Simple Linear Regression) we explain one variable with another by assuming a linear relationship between the two. The result describes the best-fitting line through the datapoints and how close the points are to the line.

The variable that is being explained is called the dependent y and the variable that is being used to explain it is called the independent x.

Multiple linear regression (MLR)

A MLR (Multiple Linear Regression) uses more than one independent variable (x₁, x_2, …, x_n) to explain the dependent y.

Controlling for a variable in a MLR means adding it as an independent to separate its effect on the dependent from the effects of other independents used in the regression.

Two variables might appear positively correlated when performing a simple or multiple linear regression, but a MLR could indicate a negative correlation or no significant correlation at all between them after an additional independent variable is controlled for that better explains the variance seen in the dependent.

Regression coefficients

The regression coefficient estimate b of an independent variable describes its estimated relationship to the dependent variable. In linear regressions each coefficient describes the ratio at which changes of the respective independent variable are estimated to be associated with changes in the dependent variable. This ratio is the slope of the regression line in a SLR.

Standard error (SE)

The standard error of a regression coefficient estimate describes how well a regression coefficient can be estimated. It can never be negative. The lower it is, the more precise the estimate of the regression coefficient. Standard errors between coefficients can not be compared directly. One coefficient estimate could have a higher standard error than another, despite being more precise.

t-statistic (hypothesis test statistic)

The t-statistic of the regression coefficient estimate is the ratio of the regression coefficient estimate to its standard error, which results in t-statistics of different regression coefficient estimates within a model being directly comparable. Its sign indicates if the coefficient estimate is positive or negative. The further from zero it is, the more precise the estimate.

p-value

The p-value of each regression coefficient estimate is determined by the t-statistic, the number of datapoints n and the number of variables involved.
It describes the probability of encountering a t-stat with an absolute value as large or larger than the value of the respective t-stat by chance in a set of n random datapoints.

Significance level

When testing a hypothesis like “independent variable x₁ is positively correlated with the dependent variable y”, we have to arbitrarily define what we consider significant. A common significance level is 0.05 or 5%. When the p-value is higher than the significance level we choose, we reject the hypothesis, because we consider it too likely that the correlation we are observing is explained by chance.

Confidence level

Similar to the significance level, we arbitrarily define a confidence level used for confidence interval calculation. A common confidence level is 95%, which corresponds to a p-value of 5%.

Confidence intervals (CI)

The confidence interval of a regression coefficient’s estimate is the range which we assume the coefficient to be in with the probability designated by the confidence level. Both the upper and lower bound of this interval should have the same sign or we aren’t confident enough that the apparent positive or negative correlation between two variables isn’t just owed to chance.

Intercept

The regression’s intercept is the mean value of the dependent variable when all independent variables are zero.

We can calculate the intercept estimate’s t-stat from its standard error the same way we calculate it for regression coefficients and derive its p-value and confidence interval from the t-stat and the number of variables and datapoints involved in the model.

R² (R-Squared)

The coefficient of determination R² tells us how well a model can predict the dependent variable, with a value of 1 indicating a perfect prediction. The lower R² is, the less explanatory power our model has. It can be lower than 0, but we will not encounter any such situation. In our regressions it will always range between 0 and 1.
There are many other measures of model-fit that can be more appropriate to use depending on the situation, but we will only be looking at R².

This is everything you need to understand in order to interpret the results of the regressions I am performing.

Linear Regressions Across States

The regressions in the next section will focus on the relationship between state-level COVID mortality and first doses. Correlations between first doses and excess pneumonia mortality are weaker and pneumonia is only present on half the death certificates where U07.1 is specified as the UCOD.

Simple Linear Regressions

I plotted daily first doses per capita and daily COVID deaths per 100k along with their correlation coefficient. To determine and plot the coefficient’s time series I stretched weekly COVID deaths to pseudo-daily figures.

The Pearson correlation coefficient r between the non-age-stratified variables is hovering around 0.8 throughout most of July and August and is peaking at over 0.85 in early August right after the vaccination rate peaks, but almost a month before COVID deaths peak at the turn of the month.

First we are going to perform simple linear regressions between age-stratified COVID deaths that occurred in August of 2021 and first dose coverage in the beginning of the month.

All SLR charts are simple scatter charts, where the x-axis represents the independent variable and the y-axis represents the dependent variable.

While first dose coverage is negatively correlated with August COVID mortality across all age groups, the correlations are weak. Lots of datapoints are located far away from the regression lines on all four charts. Low model fits of R²<0.39 reflect this.

Regional charts have already revealed, that the regions administering the highest number of first doses per capita during the third quarter of 2021 were the ones entering this period with the lowest first dose coverage, so we are now going to perform regressions against vaccination rates to find out if they can better explain why some states saw COVID mortality surge while others did not.

First doses per capita are strongly correlated with COVID deaths in all four age groups. R² was ranging between 0.26 and 0.38 when using vaccination coverage as the independent variable, but is ranging between 0.64 and 0.86 when vaccination rates are used instead. However, the existence of a correlation alone doesn’t necessarily mean there is a causal link between the two variables.

“Correlation Does Not Imply Causation”

I am sure you have heard the above statement many times throughout the past years. While this is true, it is still not unreasonable to suspect a causal link between two variables when they are strongly correlated.

So how can the correlation between first dose and COVID mortality rates be interpreted?

1. First doses were causing COVID deaths

2. COVID deaths were causing people to get vaccinated

3. Other variables are causally linked to both first dose and COVID mortality rates in August, but the two variables don’t share a direct causal link

4. There is no causal link between the variables. The observed correlations are merely coincidental.

The probabilities for correlations this strong to occur by chance are represented by the p-values of the regression coefficients which range between 10^-11 and 10^-21, so it would be unreasonable to assume that what we are witnessing is just coincidence.

There might be other variables causally linked to both COVID mortality and vaccination rates, but finding such factors is beyond the scope of this article.

It is plausible to assume a direct causal link between the two variables, because…

1. …the vaccines were deployed to stimulate an immune response against the encoded spike protein which should at least in theory have an impact on how the body responds to viral spike protein.

2. …witnessing friends, family members or other people die with a COVID diagnosis might have tipped the scales in favor of vaccination in individuals who were indecisive about whether or not to get vaccinated.

In order to determine the direction of the causation we can time shift our data to see how this affects the regressions.

Time Shifts

By implementing a lag into one of the two variables we can compare past data of this variable to future data of the other variable.

Since mortality rates drop after August and age-stratified vaccination rates are available in daily resolution, the latter variable is the one that will be shifted. Once the time shift is implemented, I sum vaccination rates up into monthly data with daily precision to compare them to COVID mortality.

Time shifts span from -28 days (lagging vaccinations) to +28 days (forward-shifted vaccinations). For every shift and every age group I performed a simple linear regression between the number of administered first doses per capita and COVID deaths per 100k in August 2021.

I’ve done this for both first and second doses and plotted the results onto an XY-diagram where the y-axis represents the t-statistic of the regression coefficient. Since we are using 50 units of observation (states) and a single independent variable for every regression, each coefficient’s t-statistic corresponds to a specific p-value which indicates the probability of encountering a correlation of this strength in random data. The t-statistics are shown in the y-axis labels on the left, and the p-values are shown in the y-axis labels on the right, but they apply to both charts.

The center of the x-axis in each chart represents regressions without time shift. To the left of this we find regressions performed against vaccination rates lagging by 1-28 days. When vaccination rates are lagging, we are comparing past vaccination rates with August COVID mortality. When vaccination rates are shifted forward, we are comparing future vaccination rates with August COVID mortality.

Among all age tiers correlations between first dose administration and COVID mortality rates peak when first doses are preceding COVID mortality by 8-15 days and are unusually strong among all adult age tiers (r between 0.88 and 0.95). This supports the notion of COVID deaths being caused by newly administered first doses.

Correlations between second doses and vaccinations are weaker in comparison and have less pronounced peaks that are shifted towards the right-hand side by a period of 3-4 weeks, which was the recommended interval [5] between the first and second modRNA vaccine dose at the time. This does not indicate a reversed causation, but rather that 2nd doses did not elicit the same effect on COVID mortality first doses seem to have had. The second dose correlations are only this strong, because first dose administration rates reliably predict second dose administration rates.

Summary

• First doses are strongly correlated with COVID deaths in all four adult age tiers with a delay of 8-15 days during the rise of the summer wave

• A direct causal link from first doses towards COVID deaths is highly probable

• COVID deaths do not appear to have promoted vaccinations

• Second doses are not linked to COVID deaths the same way that first doses are

We can now use the lags we have determined to re-run the simple linear regressions between age-stratified COVID mortality and the number of first doses administered to the respective age group.

When first doses are lagging by 8-15 days all correlations with COVID mortality are much stronger than without time shift.

It looks a lot like first doses were increasing the risk of dying with a diagnosis of U07.1 as the underlying cause of death in a time-dependent manner.

We will now investigate if previous vaccinations provided any form of protection by controlling for first dose coverage.

Multiple Linear Regressions

MLR charts follow a different setup. The y-axis still represents the number of observed COVID deaths per 100k, but the x-axis represents the number of COVID deaths per 100k as predicted by the multiple linear regression model denoted in the x-axis label.

The vertical distances of datapoints from the regression line represent the errors between the observed and the predicted number of COVID deaths, from which the model fit R² is derived.

Controlling for vaccination coverage barely increased R². We are seeing an increase from 0.78 to 0.82 and 0.77 to 0.78 in the age tiers 18-49 and 75+ respectively, but the model fit in the other two age tiers did not increase when R² is rounded to two digits.

A closer look at the regression coefficients will reveal how each the independent variables relates to the dependent variable.

Even after controlling for first dose coverage (x₂) lagging first dose rates in August (x₁) were strongly associated (p₁<10^-10) with COVID deaths per 100k in all adult age tiers in August of 2021.

• For every resident 75 or older first-dosed between July 18th 2021 and August 17th 2021 0.07341 (95% CI: 0.05683 - 0.08999) residents 75 or older died with a diagnosis of U07.1 as underlying cause of death in August 2021 (more than 1 COVID death for every 18 doses)

• For every resident aged 65-74 first-dosed between July 17th 2021 and August 16th 2021 0.02993 (95% CI: 0.02575 - 0.03411) residents aged 65-74 died with a diagnosis of U07.1 as underlying cause of death in August 2021 (more than 1 COVID death for every 39 doses)

• For every resident aged 50-64 first-dosed between July 24th 2021 and August 23rd 2021 0.00987 (95% CI: 0.00865 - 0.01109) residents aged 50-64 died with a diagnosis of U07.1 as underlying cause of death in August 2021 (more than 1 COVID death for every 116 doses)

• For every resident aged 18-49 first-dosed between July 24th 2021 and August 23rd 2021 0.00178 (95% CI: 0.00146 - 0.00210) residents aged 18-49 died with a diagnosis of U07.1 as underlying cause of death in August 2021 (more than 1 COVID death for every 685 doses)

• For every resident aged 12-17 first-dosed between July 23rd 2021 and August 22nd 2021 0.00003 (95% CI: 0.00001 - 0.00004) residents aged 12-17 died with a diagnosis of U07.1 as underlying cause of death in August 2021 (more than 1 COVID death for every 181,800 doses)

First dose coverage (x₂) is negatively correlated with COVID deaths in the non-age-stratified regression model, but this correlation disappears when running the regressions over age-stratified data instead. The only significantly (p<0.05) negative correlations between first dose coverage and COVID mortality are found in the youngest two age tiers of children aged 12 or older and adults under 50. It is highly unlikely that vaccines only protected individuals under 50 from COVID death, so these negative correlations don’t make a strong case for vaccine immunity.

Gender stratification

I further stratified the data by gender. Model fits for females increased, while model fits for males decreased.

Gender is among the strongest predictors of criminal behavior, with males tending to exhibit both a higher prevalence and frequency of offenses (Rebellon et al. [6]), hence one possible explanation for the effect could be gender differences in the proportion of individuals who acquired proof of vaccination without actually having received a vaccine dose in order to circumvent restrictions to constitutional rights.

Among adults first dose rate coefficient estimates are significantly (p<0.0001) higher for males than for females. Whereas the coefficient estimate for male adults under 50 is 69% higher than for females, it is 41%, 36% and 73% higher in the age tiers 50-64, 65-74 and 75+ respectively, with the oldest group of males aged 75 or older registering 0.100 (95% CI: 0.076-1.123) COVID deaths per lagging first dose (more than 1 death per 13 first doses).

You can find MLR charts and coefficient tables in the supplemental figures 3, 4, and 5.

Infection Enhancement And Transmission

Infection enhancement occurs when the immune system’s attempt to neutralize a virus results in higher viral replication rates. It can lead to poorer outcomes, higher transmission rates and extend the duration for which an individual remains infectious. Ferguson et al. have shown how this can lead to “complex and persistent cyclical or chaotic epidemic behavior” [7] by granting growth advantages to strains that might otherwise lack the potential to cause epidemics.

If first doses caused infection enhancement in those vaccinees who were exposed to SARS-CoV-2 around the time of inoculation, we should see this reflected not just in COVID mortality, but in case rates as well.

While first-dose correlations with age-stratified COVID death rates max out when first doses are lagging by a period of 8-15 days, the correlation with COVID cases is highest when first doses are lagging by a period of 0-2 days, depending on the observation period.

Lagging first dose administration rates were strongly correlated with case rates throughout July and August of 2021, but not with rates of performed PCR tests per capita, so testing behaviour is unlikely to be responsible for the correlation between first doses and cases.

The following scatter charts illustrate the relationship between case rates and first dose administration rates throughout July and August of 2021.

As vaccinations rates were ramped up in July case rates were increasing proportionately, with plotted datapoints of each state largely following linear paths up to the end of the month (top-right). When daily first dose administration rates started declining in the Southern states throughout early August, case rates remained elevated for the first couple of weeks (bottom-left) - which is to be expected unless the virus absolutely depends on freshly first-dosed hosts to spread - before finally dropping in the second half of August (bottom-right).

While case rates in the South were likely dropping because the virus was running out of susceptible individuals to infect, case rates in the other regions kept on rising, as indicated by the late upward trends of those states that had administered fewer doses throughout July and August (bottom-right).

If freshly first-dosed individuals infected with SARS-CoV-2 were more infectious than other infected individuals, then those states that administered the most first doses at the time must have achieved herd immunity against Delta strains sooner than other regions. This provides a plausible explanation for the drop in correlations between first dose administration rates and COVID case/mortality rates in the course of August 2021.

We can recycle the MLR model we used to explain COVID deaths per 100k in August to instead explain COVID cases per capita. Since there is apparently an age-dependent delay of 6-13 days between registered COVID cases and COVID deaths and reliable age-stratified case data is not available, we are shifting our observation period backwards by a period of 10 days. Whereas we previously explained August COVID deaths, we now explain COVID cases registered between July 22nd and August 21st.

First dose administration rates lagging by a period of 2 days are strongly correlated (p<10^-14) with COVID case rates, where each first-dosed resident corresponds to an increase in 0.39 (95% CI: 0.32-0.46) cases per capita. It is unlikely that freshly first-dosed individuals were the only ones affected by the surge in cases. Age-stratified case data might shed more light on the situation, but the public age-stratified case dataset provided by the CDC does not match vaccination age tiers, comes in monthly resolution and is missing data for a large array of counties.

The relationship with first dose coverage is negative, but barely significant (p<0.05). While we can not exclude the possibility that previously vaccinated individuals exposed to the virus were less likely to register a case, there is a high chance that the apparent negative association is better explained by other variables that were not controlled for. Regardless of whether or not efficacy does eventually manifest, any potential beneficial effect of vaccinations becomes clinically irrelevant in the face of time-dependent infection enhancement ocurring after the initial inoculation.

Confirmation: European Data

Regional Delta outbreaks happened asynchronously throughout the second half of 2021. Perhaps the differences in their timing can be explained by first dose administration rates, so we are going to take a look at European data.

The ONS (Office for National Statistics UK) supplies all the variables we need in daily resolution. I picked Upper Tier Local Authorities as observation units.

The ECDC (European Centers for Disease Control) supply data for 30 European nations, but the relevant variables come in weekly resolution, so I’ve converted them to pseudo-daily time series for the next chart.

For the USA I am using daily COVID mortality data from worldometer which is sufficient for this purpose, since CDC Wonder is only making weekly data available.

We will be comparing ONS, CDC and ECDC data by looking at daily COVID deaths, daily first doses and the correlations between these two variables and cumulative first doses throughout 2021.

The black lines represent the correlation coefficients between first doses per capita (vaccination rates) and cumulative first doses per capita (vaccination coverage). When they’re above zero, we see a continuation of regional vaccination trends. Regions with high first dose coverage are administering more doses per capita than regions with low first dose coverage in these phases.

• In the UK (left) vaccination trends are maintained throughout the entire year of 2021. The Delta wave in the second half of the year is unremarkable in comparison to the winter wave which peaked almost 9 times as high.

• In The USA (middle) the correlation turns negative in the third quarter of 2021, meaning that the least vaccinated states were now catching up with states that had been leading in first dose coverage. This coincides with the onset of the summer wave.

• Something similar happened in the rest of Europe (right), where the divergence between first dose administration rates and first dose coverage occurred a few months later than in the USA and is also accompanied by a large Delta wave, despite lower first doses administration rates.

While vaccination trends are maintained (black line far above zero), the blue (vaccination rates vs. daily COVID deaths per capita) and orange (vaccination coverage vs. daily COVID deaths per capita) lines run closely parallel. Both cumulative first doses and new first doses appear to be correlated with COVID mortality in the same way, but the correlations are weak. Correlation coefficients between -0.4 and 0.4 don’t deserve much attention.

It is hard to tell which impact each of these variables has on COVID mortality as long as they are strongly correlated. However once their correlation dissolves (black line closer to zero), the orange and blue lines diverge, suggesting that newly administered first doses (rates) promote COVID mortality while previously administered first doses (coverage) are inversely related to COVID mortality.

It is quite puzzling that explosive growth of COVID deaths only manifested when regions with low first dose coverage increase their vaccination efforts. Something about vaccinating these regions was apparently problematic.

• First dose coverage might have provided some protection against COVID.

• Uncontrolled factors might be negatively associated with COVID burden, but positively associated with first dose coverage (e.g. healthy user bias [8])

• A milder form of infection enhancement in pre-Delta times might have been increasing natural immunity in regions with higher first dose coverage

It’s hard to tell why exactly this is happening.

The correlation between first dose administration rates and COVID mortality rates across European countries is peaking in calendar week 43/2021, so we are taking a closer look at this week.

It seems that Romania (RO), Bulgaria (BG) and Latvia (LV) are almost solely responsible for the variance in first dose administration and COVID mortality rates. Let’s see what happens when we control for first dose coverage.

The model fit increases from 0.82 to 0.85, but the estimate of the first dose coverage coefficient (b₂) is barely significant (p<0.05). There is a good chance that it will turn insignificant when using age-stratified data instead, which is what happened when we used the same model to explain U.S. COVID mortality rates in August of 2021. Alas the age-stratified data we need to do that is not available.

The confidence interval of the first dose administration rate coefficient estimate overlaps with the respective coefficient estimate derived from U.S. data. Whereas we calculated 0.00596 (95% CI: 0.00482-0.00711) COVID deaths for each first dose administered in the USA throughout August, there were 0.00457 (95% CI: 0.00340-0.00574) COVID deaths for each first dose administered in week 2021/43 across Europe.

The largest outlier in this model is Lithuania (LT), as indicated by its vertical distance to the regression line (the error between predicted and observed COVID deaths per 100k). First dose administration rates in Lithuania fail to explain the local surge in COVID mortality, but the country happens to share 380 miles of border with Latvia (LV), which administered more first doses per capita than any other nation in calendar week 2021/43.

For a more detailed view of the situation we are going to look at the three countries with the highest first dose and COVID death rates (Bulgaria, Romania, Latvia) next to Austria - one of the less conspicuous nations in this chart.

You can find similar charts for 28 out of the 30 European nations the ECDC provide data for in the supplemental figures. Hungary and Malta are not included due to their lack of sequencing data.

Vaccination coverage for Bulgaria, Latvia, Romania and Austria was at 24%, 26%, 35% and 75% respectively at the end of the third quarter of 2021. The first three nations simultaneously suffered comparable rates of COVID deaths per capita at the peak of the wave, and higher rates than during previous waves in these regions, while Austria peaked two weeks later and got off easier.

It’s possible that the higher first dose coverage prevented Austria from suffering the same fate as the other three nations, but based on what we’ve seen in U.S. data I lack confidence to make any such claim. Higher first dose coverage leads to lower first dose administration rates. This is a recurring pattern and it applies to Austria the same way it applied to U.S. states and age groups.

Take special note of the irregularly shaped Delta wave in Bulgaria. During the initial phase of exponential growth vaccination rates decelerated and so did the COVID mortality rate growth. Only after vaccination rates started increasing again did the COVID mortality rate continue its rapid growth towards the peak. A hint of these dynamics can be spotted in Austria as well, where COVID mortality rates stopped growing for a few weeks in September and October until first dose administration rates increased again. This is just not how infectious disease outbreaks usually progress [9].

These observations support the hypothesis that transient infection enhancement in recently first-dosed individuals was a causal factor in the explosive growth of COVID mortality rates seen in many countries throughout the second half of 2021.

Confirmation: VAERS

If vaccinees had an increased chance to register a case upon exposure to the virus shortly after receiving their first dose, then this should be reflected in VAERS reports.

The highest rate of adverse events should be expected when plasma levels of a drug peak. In the case of gene therapeutics we should expect another adverse event rate peak when translation rates peak. Spontaneous events are most likely to be reported, if they occur shortly after a drug was taken. Together these effects are responsible for the adverse event reporting incidence to decline over time. However this should not apply to infections in the same way it applies to other side effects.

The highest number of COVID cases was reported on the day of inoculation, which is remarkable, because the risk for an individual to be exposed to the virus on this day should not be much higher than on any other given day.

By day 7 the number of filed infection reports dropped to 16% in the first half of 2021, but to 10% in the second half of the year, suggesting that the relative risk to register a case on the day of inoculation increased in the course of the year.

If vaccine efficacy was responsible for the decrease in reports from day 1 to day 7, the decrease should be less pronounced in the second half of 2021 when Delta variants were circulating against which any alleged vaccine efficacy is said to have decreased.

If infection enhancement occurred in vaccinees exposed to the virus shortly after receiving their first dose, these individuals can be expected to suffer higher viral loads, more severe symptoms and be more infectious than other infected individuals, which would increase the chance for infection reports to be filed shortly after inoculation and is precisely what we are seeing in VAERS report patterns.

Confirmation: Literature Search

The Cycle threshold (Ct-value) in a RT-PCR (real-time PCR) test represents the number of amplification cycles that were required for the assay to yield a positive result. Since the Ct-value is inversely related to the amount of target genetic material in the sample, the test is also referred to as a quantitative PCR (qPCR) test.

Lower Ct-values are associated with higher viral loads. What we need to find in order to confirm that time-dependent infection enhancement in first-dosed individuals enabled Delta strains to exacerbate the U.S. outbreak in the summer of 2021 are Ct-values of RT-PCR tests performed on samples from patients who caught an infection between August and November of 2021 - when Delta variants enjoyed full predominance - ideally in August of 2021, within days of receiving their first dose.

More than 4.3 million RT-PCR tests that yielded a positive result were performed on samples from U.S. residents in August. If the probabilities to be tested and for the test to come back positive are the same among all subpopulations, then the accumulated product of daily proportion of residents first-dosed within the past week and daily positive PCR tests per capita, multiplied by the number of residents, approximates how many positive tests were performed on people who received their first dose in the week preceding the test:

40,259 tests performed in August of 2021 could indicate infection enhancement in recently first-dosed individuals. If we assume the critical timeframe during which vaccine-associated infection enhancement occured is limited to 2 days after the initial dose, the figure is still over 11,000. I was hoping that some of these test results found their way into scientific literature.

Alas none of over 100,000 peer-reviewed articles published on COVID vaccines seem to focus on what happened to first-dosed individuals who registered early infections before reaching the status “partially vaccinated” 14 days post-inoculation. Those articles that do look at infections in vaccinated individuals are usually limiting their observation to “breakthrough infections” which are defined as symptomatic infections occurring in fully vaccinated individuals - those who developed COVID-19 at least 14 days after receiving their second dose. Other articles include infections in “partially vaccinated” individuals who received their first dose at least 14 days before symptom onset, but early infections are either not mentioned at all or excluded without further elaboration of the reasons for the exclusion.

"Patients who […] developed infection within 14 days after the first dose were excluded." [10]

Some studies [11] even go as far as combining both partially vaccinated individuals (>14d) and unvaccinated individuals into one cohort.

The lack of RT-PCR data in the subpopulation of interest should not be understood as a result of carelessness. Selecting data is an active process that is absolutely crucial for yielding valid, generalizable results.

There is no requirement to categorize patients according to the legal definitions of vaccination status, but this is what’s usually being done. Decision-making processes were involved that caused hundreds of teams to assess it would be best to exclude early infections in first-dosed individuals from their analyses altogether.

The absence of these data from scientific literature is suggestive of data being actively suppressed that would let COVID vaccines appear in an unfavorable light.

Anyone interested in the effects a pharmaceutical product has on patients should be paying special attention to what happens in the first hours and days after the initial exposure. This is when the magic happens. Excluding this most critical period of time from observation is testament of a complete disregard for patient safety and not reporting on it constitutes willful misrepresentation of data to skew study results in favor of the vaccines.

Professors Norman Fenton and Martin Neil explained how the bizarre 14-day delay in the definition of vaccination status should be expected to introduce selection bias [12] and thereby create an efficacy illusion, even without assuming an increased risk for infection enhancement in recently first-dosed individuals.

There are a few articles however, that do provide some insights.

Lack of Efficacy Against Delta Infections

It has repeatedly been shown that vaccine efficacy was very low against Delta infections, possibly even negative. Luo et al. [13] investigated 2,785 clinical isolates linked to inpatient and outpatients treated in the Johns Hopkins Medical System between January and July 2021 and found…

• insignificantly (p>0.05) higher proportions of samples with recoverable virus in vaccinated (70.4%) vs. unvaccinated (66.7%) patients

• significantly (p<0.00001) higher proportions of samples with recoverable virus in samples from fully vaccinated Delta breakthrough patients (70.4%) vs. fully vaccinated Alpha breakthrough patients (17.4%) patients

• lower mean Ct-values in samples from fully vaccinated patients compared to samples from unvaccinated patients, in Delta samples with (16.1 vs. 17.6) and without (24.4 vs. 25.3) recoverable virus

Delta Variants, Original Antigenic Sin and ADE (Antibody-Dependent Enhancement)

When the immune system first encounters a pathogen it tailors a specific response to it. If at a later time the immune system encounters a different but related version of the pathogen, instead of generating an optimal immune response to this new strain, it might rely more heavily on the memory from the first exposure. This effect is called immune imprinting [14], Original Antigenic Sin [15,16] or Hoskins Effect [17].

Röltgen et al. found “that prior vaccination with Wuhan-Hu-1-like antigens followed by infection with Alpha or Delta variants gives rise to plasma antibody responses with apparent Wuhan-Hu-1-specific imprinting manifesting as relatively decreased responses to the variant virus epitopes, compared with unvaccinated patients infected with those variant viruses.” [18]

Mutations in viral epitopes can result in changes in the affinity that antibodies resulting from exposure to previous strains or vaccines have for those epitopes.

Based on molecular modeling Yahi et al. assessed, that “in the case of the Delta variant, neutralizing antibodies have a decreased affinity for the spike protein, whereas facilitating antibodies display a strikingly increased affinity. Thus, ADE may be a concern for people receiving vaccines based on the original Wuhan strain spike sequence (either mRNA or viral vectors).” [19]

Junqueira et al. concluded “that antibody-mediated SARS-CoV-2 uptake by monocytes and macrophages triggers inflammatory cell death that aborts the production of infectious virus but causes systemic inflammation that contributes to COVID-19 pathogenesis.” [20], but Maemura et al. came up with different findings, concluding that “although ADE of infection was observed in monocyte-derived macrophages infected with SARS-CoV-2, including its variants, proinflammatory cytokine/chemokine expression was not upregulated in macrophages. SARS-CoV-2 infection thus produces antibodies that elicit ADE of infection, but these antibodies do not contribute to excess cytokine production by macrophages.” [21]

Making matters more complicated, there are other types of infection enhancement, like the CD4 receptor - dependent route described by Guillon et al. for HIV [22]. Arkmedic has written extensively about the gp120 peptide inserts [23] found in both viral and vaccine-encoded spike protein that are critical for this type of antibody-mediated infection enhancement.

It is hard to come to a conclusion about the clinical relevance of infection enhancement to Delta outbreaks. What is crucial to understand is, that when infection enhancement does occur, it is not merely a concern for the individual, but can through its effect on viral replication rates affect transmission dynamics across entire populations, as Ferguson et. al have demonstrated in their modeling of Dengue outbreaks [7].

None of the studies published on the topic take into account the situation of simultaneous exposure to both vaccine- and virus-encoded spike protein. By vaccinating right into the exponential growth phase of the Delta outbreak the immune systems of ten thousands of Americans were thrown into a situation where they not only encountered two different versions of the same protein, but were also confronted with much larger net amounts of this protein than would have resulted from infection or injection alone

Overcoming exposure to larger quantities of pathogenic spike protein should be challenging for any immune system, regardless of the occurrence of the described mechanisms responsible for infection enhancement.

Autopsy Findings

Hirschbühl et al. performed autopsies on 16 partially and 13 fully vaccinated COVID patients [24]. Their supplementary tables include detailled information on these patients, including:

• Time from last vaccination to positive SARS-CoV-2 test (26 patients)

• Time from first symptom to death (29 patients)

• Lowest Ct-value of tests performed on organ tissue samples collected during autopsy (lung: 24 patients, liver: 2 patients)

• Ct-value of test performed on nasopharyngeal swab specimen collected during autopsy (23 patients)

I compared 2 groups of deceased patients:

• 3 patients had a positive test within 4 days of their last dose.

• Another 11 patients had a positive test within 5-28 days of their last dose.

All except for one patient in the 5-28 day cohort had only received 1 dose. I compared the mean values of the 3 variables “time from symptom onset to death”, “lowest organ tissue Ct-value”, “nasopharyngeal swab Ct-value”.

All means are significantly (p<0.01) lower in patients who became infected within 4 days of their last dose.

Furthermore I found a significant (p<0.05) correlation between the timespan from last dose to first positive SARS-CoV-2 test and the timespan from symptom onset to death.

For each day between vaccination and positive SARS-CoV-2 test, the duration between symptom onset and death was extended by 0.68 days (95% CI: 0.18-1.18 days).

Patients in the 5-28 day group were slightly older than patients in the 1-4 day group (avg. decade 7.8 vs. 6.7) and a higher proportion was infected with variant B.1.1.7 (Alpha, 8/11 vs. 1/3). None of the patients in these two groups were infected with Delta variants and all patients had relevant comorbidities.

I expect to find the same patterns in healthy first-dosed individuals who became infected with Delta variants within days of their first dose, but data on this subpopulation is not available, which is concerning.

Discussion

The data we have evaluated are highly suggestive of VAED (vaccine-enhanced disease [25]) occurring in first-dosed vaccinees in a time-dependent manner, causing a surge in COVID mortality throughout the summer of 2021, but a lot of questions remain unanswered:

• Why did vaccination trends change in the second half of 2021?

• Were public health authorities aware that first dose administrations were associated with higher COVID burden?

• Why are COVID mortality rates unaffected by first doses administered in the fourth quarter of 2021?

• How can we uncover more evidence that vaccines were driving outbreaks in 2021?

I will try to address these questions one by one.

What Drove The Change In Vaccination Trends?

Looking at trends in the popularity of google search terms can provide clues as to why some states were vaccinating at higher rates than others and why these trends reversed in the summer of 2021.

Regional variance in the degree of trust in nucleotide-based vaccines likely lead to states with high first dose coverage maintaining higher first dose administration rates than states with lower first dose coverage in the first half of 2021, as suggested by the rise in populatity of the search term “vaccine side effects” during the initial formation of vaccination trends.

While the early public debate around vaccine passports was accompanied by trends intensifying further until mid-May, less vaccinated states started catching up in late May as more and more states, workplaces and public venues announced or implemented vaccine mandates.

The requirements for providing proof of vaccination seemed to be the driving force behind the inversion of regional vaccination trends. At the same time concerns over vaccine side effects flared up once more, as people were forced to choose between their health and their freedom.

Were The CDC Aware?

In recent interviews Wallensky has repeatedly used the term “prebunking”, which she describes as follows:

We spent a lot of time and energy thinking about how we could anticipate what the next piece of mis- or disinformation would be and how we would combat it.

What’s the information that we need to put out there today, and can we anticipate some of those? We call that prebunking

CDC Director Rochelle Wallensky
https://www.thecrimson.com/article/2023/10/13/rochelle-walensky

While we should not overestimate the competence of CDC employees, spending so much time and energy on anticipating misinformation must include taking a brief look at the very data they supply to the public.

So how did Dr. Wallensky react, when the explosive rise in COVID cases and deaths manifested in those regions that were leading in first dose administration rates throughout July 2021?

She coined the term “Pandemic of the Unvaccinated” [26] and other authorities - like president Joe Biden or NIAID director Anthony Fauci [27] - were quick to chime in.

The status “partially vaccinated” was never reached by those who caught a SARS-CoV-2 infection within 14 days of receiving the first dose according to the CDC definition [28], but calling them “unvaccinated” seems like a stretch.

If Wallensky saw a “Pandemic of the Unvaccinated” on the horizon, then this begs the question…

Who Do The CDC Consider Unvaccinated?

This is where things get exciting.

The CDC report on hospitalization rates by vaccination status [29] is highly suggestive of a certain degree of awareness. Not only are all individuals who do not provide vaccination records considered unvaccinated, but “persons who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before a positive SARS-CoV-2 test were excluded from all data shown.”

Moreover, the CDC are currently conducting a study titled “COVID-19 Vaccine-Mediated Enhanced Disease and Vaccine Effectiveness” [30]. This is an excerpt from the study rationale:

Vaccine-mediated enhanced disease (VMED) can result from immune complex formation and Th2 biased cellular immune response when vaccinated individuals become infected with the target pathogen. […] This study in the Vaccine Safety Datalink (VSD) will estimate [Vaccine Effectiveness] VE against COVID-19 hospitalization and provide data to assess the risk of VMED in vaccine recipients.

The study aim 1 is defined as follows:

1. Estimate class- and product-specific VE against COVID-19 hospitalization and severe respiratory illness in a cohort of VSD enrollees. This aim will generate a signal for VMED if VE <0 with a 95% confidence interval that excludes 0.

And this is how the “Unvaccinated” cohort is defined:

Unvaccinated (all unvaccinated and vaccinated person-time until <14 days after dose 1)

Endpoints:

The primary endpoint is hospitalization with an ICD-10 diagnosis code for COVID-19. These include U07.1 (COVID-19 infection or disease) or J12.82 (pneumonia due to COVID-19). We will not require laboratory confirmation of SARS-CoV-2 infection since diagnostic tests may be performed prior to hospital admission and not available in VSD data files.

The secondary Aim 1 endpoint is respiratory failure or acute respiratory distress syndrome (ARDS) due to COVID-19, identified by the combination of U07.1 or J12.82 and a diagnosis code for either ARDS (J80) or acute respiratory failure (J96.0*) during the same hospitalization

So the CDC are conducting a study on patient-level data looking for the very mechanism I have highlighted in population-level data throughout this article.

However, the subpopulation which I allege fell victim to vaccine-mediated enhanced disease (infection enhancement) - single-dosed individuals who became infected within days of receiving the first dose - is considered “unvaccinated” in the CDC study.

This is beginning to look an awful lot like a cover-up.

The patient data are automatically processed and uploaded to the VSD Hub from the trial site locations. Collaborating site investigators and project managers from Denver Health, Harvard Pilgrim, HealthPartners and Kaiser Permanente probably never got to see it, but at least one person in the MCRI team should have.

While none of the states in which the participating trial sites are located saw particularly large Delta outbreaks in August, the data the MCRI team is processing should still contain evidence of vaccine-enhanced disease among recently first-dosed COVID hospitalizations. Evidence that is going to be used to frame “The Unvaccinated” as spreaders of disease.

Lack of Correlation in Q4/2021

While the drop in correlations between first doses and COVID cases/deaths in September can be explained by states in the Southern census region being the first to reach herd immunity after fuelling the outbreak with first doses, this hypothesis provides no explanation as to why COVID variables show little reaction in the fourth quarter, when the Northeast starts first-dosing residents at higher rates than during the third quarter (Figure 10).

1. Different Products

The vaccination variables used in the regression models do not contain information about the product type (Moderna / Pfizer / Johnson & Johnson) or batch quality. Different products should provoke different immune responses.

However, it seems unlikely that only certain batches were responsible for the surge in deaths in the third quarter or the lack thereof in the fourth quarter, because this should be reflected in VAERS reports.

None of the batches mentioned inside reports about adverse events occurring within 14 days of inoculation in July or August of 2021 are responsible for more than 5% of adverse event reports during this time period. The 25 most-mentioned batches make up 50% of adverse event reports.

Similarly, none of the batches administered in October or November make up more than 5% of the 40k adverse event reports occurring within 14 days of inoculation during this time period, where the 27 most-mentioned batches made up half of reports.

2. Different Delta Variants

I downloaded over 4 million SARS-CoV-2 genomes from GISAID that were collected in the USA and analyzed those with a length of at least 28k base pairs with Nextclade. The software found 53,661 unique mutations in samples matching Delta variants of which over 200 were sequenced more than 5000 times.

The prevalence of mutations that are not known to have affected SARS-CoV-2’s transmissibility or virulence often varied between regions. Some of the mutations I looked at were present in more than 5% of sequenced genomes during the outbreak in the South, but had gone extinct by the time first dose administration rates in the Northeast were ramped up in October (e.g. S:K77T, ORF1a:F3138S).

It’s possible that recently first-dosed individuals were only at risk to suffer infection enhancement when infected with strains containing specific mutations, but given the low differences in prevalence of mutations that could be responsible and the lack of strong positive correlations between first dose administration rates and the prevalence of these mutations, I consider it unlikely.

3. Infectious Disease Dynamics

In a modified SI model (Susceptible Infectious model [31]), where…

• susceptible individuals S, infectious individuals I and recently first-dosed individuals V are expressed as proportions of the total population

• only a proportion of the population is susceptible to a strain

• there are two groups of infected individuals I_{recently first-dosed} and I_{not recently first-dosed} with different degrees of infectiousness β_{recently first-dosed} and β_{not recently first-dosed}

…increasing the number of recently first-dosed individuals during different phases of an outbreak affects the course of the outbreak differently, depending on the current infection rate of the problematic strains and the remaining proportion of susceptible individuals among the population.

The highest impact of first doses on transmission dynamics is observed when the product of infection rate and proportion of susceptibles among the population is at its maximum. The later in the outbreak first dose administration rates are increased, the further the proportion of susceptibles will have decreased by that time and the larger the remaining proportion of susceptibles will be when the outbreak has reached a point where it can not sustain itself, given a sufficiently low degree of infectiousness β_{not recently first-dosed}. The larger the proportion of susceptibles is when first dose administration rates are increased, the larger the impact of infection enhancement on transmission dynamics is and the more mortality this will cause.

4. Unmeasured / Unknown Confounders

ONS and ECDC data indicated that the catastrophic impact of Delta waves only manifested when first dose administration rates were increased in regions with low first dose coverage.

Autopsy data suggest that infection enhancement had already been an issue for infected individuals recently first-dosed before the advent of Delta. It is possible that this manifested in increased transmission and thus a higher degree of natural immunity in highly vaccinated regions, without manifesting in case data due to differences in testing behaviour between vaccinated and unvaccinated individuals.

Wastewater data could supply information about virus prevalence as a proxy for natural immunity across US states, but pre-2022 data are not available for the vast majority of states.

Factors other than immunity that I have not taken into account might be inhibiting or promoting COVID mortality, e.g. a decline in the use of medical services, temperature peaks, economical factors, psychosocial distress, tourism or other cross-state travel. Finding such factors however is beyond the scope of this article.

Where To Go From Here?

SI model

A modified SI model should be best-suited to answer questions about how infection enhancement affected the timing and further course of Delta outbreaks in various regions.

Causal inference

Causal inference is the "identification of the cause or causes of a phenomenon, by establishing covariation of cause and effect, a time-order relationship with the cause preceding the effect, and the elimination of plausible alternative causes." [32]

We have already used causal inference techniques in this analysis, but the selection of variables used in the regressions was very limited. There might be “plausible alternative causes” explaining what we are seeing. DAG’s (Directed Acyclic Graphs) can be used to illustrate the relationships between all relevant variables. Demonstrating that all aspects have been taken into account before jumping to conclusions assists in convincing sceptics.

Linked death certificates and vaccination records

Linked death certificates and vaccination records are not easy to acquire. So far all requests from US state-level health authorities were denied.

With such data available it would be trivial to match first-dosed COVID decedents to a control cohort of unvaccinated decedents by age, gender and relevant comorbidites to show that first doses lead to a time-dependent COVID mortality increase during the Delta outbreak in the second half of 2021.

Sub-national data from other nations

While the dynamics observed in the USA and across Europe were not apparent in ONS data, things might become clearer when regional data from other nations is investigated. Alas I have been unsuccessful in my attempts to acquire regional data for Bulgaria, Latvia and Romania.

Such data should further improve our understanding of the impact first doses had on Delta outbreaks and can possibly answer why signs of infection enhancement are not visible in ONS data. This knowledge can aid in all future modeling attempts.

RT-PCR data

Many of my readers are highly sceptical of RT-PCR tests and I can understand why:

• RT-PCR tests should never be used to diagnose an asymptomatic person with a disease.

• RT-PCR test results without Ct-values are meaningless.

Other than that they provide an extremely cost-effective way for comparative quantification of a target genetic sequence. Exceptionally low Ct-values in 10,000-40,000 positive tests performed on individuals first-dosed within days of their infection out of a total of 4.3 million positive RT-PCR tests conducted on Americans throughout August 2021 should indicate disease enhancement.

The data are out there. In Germany individuals who had RT-PCR tests performed usually received a Ct-value along with the information whether or not they were considered SARS-CoV-2 positive.

Even if some laboratories collected information on vaccination status, these data would be protected by privacy laws, so RT-PCR test results might have to be crowd-sourced. There should be tens of thousands of American VAED (vaccine-enhanced disease) victims currently unaware that they were vaccine-injured.

Conclusion

Population-level data supplied by the CDC (Centers for Disease Control) and ECDC (European Centers for Disease Control) are highly suggestive of vaccine-mediated infection enhancement occurring in individuals who became infected with SARS-CoV-2 delta variants around the time of receiving the first dose in the second half of 2021. 

After a large nationwide vaccination campaign in the US during the first half of 2021, the surge in COVID-19 cases and deaths during the third quarter of 2021 disproportionately impacted younger age groups and states situated in the Southern census region.

While first dose coverage is negatively correlated (p<0.001) with COVID mortality in all age tiers across US states throughout July and August of 2021, these negative correlations appear to represent an efficacy illusion resulting from increased vaccination efforts in regions and age groups that entered the month of July with lower first dose coverage.  

Per multivariate linear regression models, differences in COVID burden across US states among adult age groups throughout July and August of 2021 are well-explained by first dose administration rates (p<10^-10), but not by first dose coverage (p>0.1).

Correlations between COVID variables and time-shifted first dose administration rates among all adult age tiers are very strongly positive (r between 0.88 and 0.95) and peak when first doses are preceding COVID cases and mortality by 2 days and 8-15 days (depending on age group) respectively, but decline sharply when first doses are shifted forward, suggesting a direct causal link from first dose administration rates towards COVID case and mortality rates.

Infection enhancement not only negatively affects disease outcomes and infectiousness on the individual level, but can also have profound effects on epidemic behaviour through its effect on transmission dynamics.

The multivariate regression models suggest that the COVID outbreaks observed globally during the second half of 2021, resulting in COVID-attributed deaths of 200,000 Americans and 800,000 Europeans across Iceland, Liechtenstein, Norway and the 27 EU countries, would have had minimal impact on morbidity and mortality, if first doses had not been administered while Delta variants enjoyed predominance.

Upon manifestation of the phenomenon in population-level data maintained by the CDC in mid-July of 2021, Director Wallensky’s coined the outbreak a “Pandemic of the Unvaccinated”. Exclusion of individuals who became infected within 14 days of the initial inoculation from the CDC’s vaccination status-stratified hospitalization data is suggestive of the agency’s awareness of the real situation. Furthermore, the CDC are currently conducting a study on COVID VMED (Vaccine-Mediated Enhanced Disease) and VE (Vaccine Efficacy) spanning back to early 2021, which miscategorizes the problematic subpopulation in which disease enhancement appears to have occurred as “unvaccinated” along with individuals who have never received a dose.

Corroborating the aforementioned results from case and death data, autopsies performed on individuals who tested positive for SARS-CoV-2 within 4 days of receiving their first vaccine dose indicate higher viral loads in both lung and liver tissue (p<0.01) and nasopharynx (p<0.01) and a shorter time from symptom onset to death (p<0.01) compared to a control group of decedents who tested positive within 5-28 days of receiving their last dose.

The Ct-values of millions of positive RT-PCR tests conducted on nasopharyngeal swab samples of SARS-CoV-2-infected first-dosed individuals could provide valuable insights into how infection enhancement contributed to COVID burden throughout different phases of the pandemic, but these data are absent from scientific literature.

References

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17. Hoskins, T. W., Davies, JoanR., Allchin, A., Miller, ChristineL., & Pollock, T. M. (1973). CONTROLLED TRIAL OF INACTIVATED INFLUENZA VACCINE CONTAINING THE A/HONG KONG STRAIN DURING AN OUTBREAK OF INFLUENZA DUE TO THE A/ENGLAND/42/72 STRAIN. In The Lancet (Vol. 302, Issue 7821, pp. 116–120). Elsevier BV. https://doi.org/10.1016/s0140-6736(73)93062-6

18. Röltgen, K., Nielsen, S. C. A., Silva, O., Younes, S. F., Zaslavsky, M., Costales, C., Yang, F., Wirz, O. F., Solis, D., Hoh, R. A., Wang, A., Arunachalam, P. S., Colburg, D., Zhao, S., Haraguchi, E., Lee, A. S., Shah, M. M., Manohar, M., Chang, I., … Boyd, S. D. (2022). Immune imprinting, breadth of variant recognition, and germinal center response in human SARS-CoV-2 infection and vaccination. In Cell (Vol. 185, Issue 6, pp. 1025-1040.e14). Elsevier BV. https://doi.org/10.1016/j.cell.2022.01.018

19. Yahi, N., Chahinian, H., & Fantini, J. (2021). Infection-enhancing anti-SARS-CoV-2 antibodies recognize both the original Wuhan/D614G strain and Delta variants. A potential risk for mass vaccination? In Journal of Infection (Vol. 83, Issue 5, pp. 607–635). Elsevier BV. https://doi.org/10.1016/j.jinf.2021.08.010

20. Junqueira, C., Crespo, Â., Ranjbar, S., de Lacerda, L. B., Lewandrowski, M., Ingber, J., Parry, B., Ravid, S., Clark, S., Schrimpf, M. R., Ho, F., Beakes, C., Margolin, J., Russell, N., Kays, K., Boucau, J., Das Adhikari, U., Vora, S. M., Leger, V., … Lieberman, J. (2022). FcγR-mediated SARS-CoV-2 infection of monocytes activates inflammation. In Nature (Vol. 606, Issue 7914, pp. 576–584). Springer Science and Business Media LLC. https://doi.org/10.1038/s41586-022-04702-4

21. Maemura, T., Kuroda, M., Armbrust, T., Yamayoshi, S., Halfmann, P. J., & Kawaoka, Y. (2021). Antibody-Dependent Enhancement of SARS-CoV-2 Infection Is Mediated by the IgG Receptors FcγRIIA and FcγRIIIA but Does Not Contribute to Aberrant Cytokine Production by Macrophages. In S. Schultz-Cherry (Ed.), mBio (Vol. 12, Issue 5). American Society for Microbiology. https://doi.org/10.1128/mbio.01987-21

22. Guillon, C., Schutten, M., Boers, P. H. M., Gruters, R. A., & Osterhaus, A. D. M. E. (2002). Antibody-Mediated Enhancement of Human Immunodeficiency Virus Type 1 Infectivity Is Determined by the Structure of gp120 and Depends on Modulation of the gp120-CCR5 Interaction. In Journal of Virology (Vol. 76, Issue 6, pp. 2827–2834). American Society for Microbiology. https://doi.org/10.1128/jvi.76.6.2827-2834.2002

23. Syed, A. K. (2022). Absolute proof: The Gp-120 sequences prove beyond all doubt that "COVID-19" was man-made. https://arkmedic.substack.com/p/absolute-proof-the-gp-120-sequences

24. Hirschbühl, K., Schaller, T., Märkl, B., Claus, R., Sipos, E., Rentschler, L., Maccagno, A., Grosser, B., Kling, E., Neidig, M., Kröncke, T., Spring, O., Braun, G., Bösmüller, H., Seidl, M., Esposito, I., Pablik, J., Hilsenbeck, J., Boor, P., … Wylezich, C. (2022). High viral loads: what drives fatal cases of COVID-19 in vaccinees? – an autopsy study. In Modern Pathology (Vol. 35, Issue 8, pp. 1013–1021). Elsevier BV. https://doi.org/10.1038/s41379-022-01069-9

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Acknowledgements

I wrote the article in first person singular because it comes more natural to me, but this doesn’t mean I had no support from other people.

I thank Bobby Bounds and Marius Lohden for their help in acquiring sequencing data from GISAID and death certificates from US state authorities.

I thank Mark Gorton for motivating me to look into US data, introducing me to hands-on statistical methodology, spending countless hours discussing ideas and most importantly keeping me sane in a very difficult phase of my life.

Christian Meyer and Russ Wolfinger shared invaluable insights on data visualization techniques with me, helped me understand the mathematical concepts behind what I was doing and provided some reassurance that I wasn’t barking up the wrong tree, for all of which I am equally grateful.

Last but not least I thank Andreas Schramm, Dr. Hans-Joachim Kremer and Dr. Sabine Stebel for proof-reading the article and evaluating my charts without cutting me any slack.

Disclaimer: None of the individuals named are responsible for the content of this article.

How You Can Help

COVID vaccines were likely responsible for millions of avoidable deaths worldwide by facilitating Delta outbreaks throughout the third and fourth quarter of 2021. RT-PCR test results can provide evidentiary support for this claim.

I am looking for test results that fulfill the following criteria:

• The test was performed on a nasopharyngeal swab specimen

• The individual tested positive within a month of the first vaccine dose, before receiving the second dose

• The Ct-value is known

• The collection date is known

• The vaccination date is known

• The test was performed between July and December of 2021

If you are connected to a laboratory that might be willing to cooperate on sourcing such data, please leave a comment or contact me directly.

If you know anyone who became infected within a month of receiving the first dose and is able and willing to share the required data, then this too will help.

You can also support my efforts by sharing this article or becoming a paid subscriber to my newsletter.

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Thank you for helping get the word out about Vigilance per VAERS!

This Delta outbreak is still giving me headaches. Since I have only recently decided to integrate county-level case data, I wanted to see if the outbreak behaved like any other outbreak. I’d expect to see a few epicenters with the virus then spreading to the surrounding counties.

Video Description

• Displayed are the first 6 weeks of the third quarter of 2021

• The red color represents accumulated cases per capita

• The blue color represents accumulated vaccinations per capita

• 100% color opacity is assigned to the peak number of cases p.c. and vaccinations p.c. respectively

• 0% color intensity equals 0 cases/vaccinations p.c.

  Vigilance per VAERS is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Another Useless CDC Dataset

Alas it turns out the CDC dataset only contained data for 1752 our of 3143 counties, so the results were hard to interpret visually.

To give you an idea, the white counties are not listed at all in the case dataset in question.

State-Level Data

Since I had spent all morning in a coding frenzy to visualize county-level data this way - doing a pretty awesome job if I do say so myself - I at least wanted to take a look at state level data which I had already visualized in scatter chart videos (I literally made hundreds of these - and I am not using the term literally lightly).

My Offer To You

If you have any county-level or state-level data that you would like to see visualized this way, I will gladly help you with that, since it only costs me a few minutes. It doesn’t have to be epidemiological data.

All I need from you is:

• A CSV file with…

  • …up to two timeseries per county (or state)

  • …with the geo or FIPS code of each county in the first column

  • …the variable name in the second column if there are two variables

• Information about which color (RGB code or color name) you want assigned to which variable (variable name + outline/fill)

  Thank you for reading Vigilance per VAERS. This post is public so feel free to share it.

  Share

Introduction

This morning I received an email from someone who suffers the delusion that the US excess mortality of 2021 and 2022 could be explained by an “epidemic of sudden deaths”.

I decided to create a few charts to establish that while these sudden deaths have apparently become more frequent, they do not nearly explain the millions of excess deaths we see across the USA.

Methods

I am going to to leave all the thinking to you and will only display 2x6 charts representing weekly excess deaths and weekly excess mortality respectively for two age groups each, based on Minnesota death certificates of the years 2015 through 2018.

Each chart displays

• either weekly relative excess mortality

• or weekly absolute excess deaths,

contains two line graphs representing

• deaths of individuals 65 and older

• deaths of individuals under 65

and features only deaths occurring in one of 6 types of locations:

• Hospital Care (inpatients of hospitals)

• Emergency Room (either dead on arrival or emergency room)

• Home (either the decedent’s home or a relative’s home)

• Hospice

• Nursing Home

• Other (any other location, mostly outdoors)

Results

Conclusion

The situation is far more complex than some people would like you to believe. Don’t trust analysts who are cashing in on the situation by publishing books with sensationalist titles - not even if they’ve worked for Blackrock.

Summary

Changes in the proportion of children who received all recommended vaccines in the first two years of their lives (lag: 6 years) and changes in the prevalence of autism or speech/language impairment among 8 year old children are positively correlated across U.S. states (p<0.002).

Every percentage point increase in vaccination rates of 2 year olds between 1995 and 2001 was associated with an increase of 0.035 (95% CI: 0.014-0.057) percentage points in the rates of autism or speech impairment disorders among 8 year olds between 2001 and 2007.

Introduction

Last night I received an email asking me to take a look at an article about the association between vaccination rates and the prevalence of childhood autism and speech disorders.

I postponed fulfillment of the request to the morning hours, since I was coincidentally spending time with an autistic friend at the time who happened to tell me that he considers me autistic as well.

The study came up with a weird model:

Autism = a + b1∗Vaccination + b2∗Log(Income) + b3∗Hispanic + b4∗African American + b5∗Other + b6∗2002 + b7∗2003 + b8∗2004 + b9∗2005 + b10∗2006 + b11∗2007

I just wanted to run a simple linear regression without all those other variables which probably just served to maximize their model fit.

Methods

I chose a very simple approach which should eliminate the need to correct for confounders like race and income, as was done in the original study.

4:3:1:3:3:1:4 means fully immunized with 4 doses of DTaP, 3 doses IPV, 1 dose MMR, 3 doses Hib, 3 doses HepB, 1 dose Varicella, and 4 doses PCV. This is the official childhood vaccination series before children turn 3.

For each state, I determined the linear trend for each of the following two variables over the respective time periods.

• 1995-2001: [rate of 2 year olds who received the recommended 4:3:1:3:3 series of vaccinations]

• 2001-2007: [rate of autism among 8 year olds] + [rate of speech/language impairment among 8 year olds]

I then ran a linear regression between the two resulting variables across the 50 US states + District of Columbia.

Results

Trends represent the average absolute change per annum of the respective rate.

These are the regression results:

The intercept has a very low significance, with it’s regression coefficient (B) being right around zero.

The coefficient of our independent variable was found to be 0.03527 (95% CI: 0.01380-0.05675) which I interpret as follows:

An increase from 0% to 100% vaccination rate among under 3 year olds would result in an additional 3.5% (95% CI: 1.4% - 5.7%) of children being diagnosed with speech impairment and/or autism before reaching their 9th birthday.

Cross-validation with VAERS

Compared with a pseudo-placebo reference cohort, an excess 2.0% of reports (90% CI: 1.8% - 2.2%) about MMR vaccinations mention autism spectrum disorder. Report proportions often approximate incidence proportions. For the age groups 0–4 and 5–11 proportional differences are 4.6% (90% CI: 4.0%-5.1%) and 2.6% (90% CI: 2.0%-3.3%). All confidence intervals overlap with the regression coefficient's 95% confidence interval, which confirms our results.

Autism is much more frequently reported in boys than in girls. You can look up gender-stratified proportional differences if you follow this link.

Other vaccines for which I found weaker autism spectrum disorder safety signals are:

• Polio

• DTP

• Hepatitis B

Conclusion

If you ask me, this is enough evidence to withdraw products from the market until double-blindly randomized placebo-controlled trials have been performed. But I’m not the science, so we are unlikely to see that happen.

All we can really do is speak truth whenever we get a chance.

What’s next

I’m not saying I will be doing these things, but here are a few ideas what one could do:

• Do an analysis not by antigen type, but by other constituents (adjuvants and preversatives)

• Get updated government data and see how things have changed since 2007

You’ve all seen these disclaimers that pop up whenever you navigate to a Youtube video that might contain dangerous COVID-19 misinformation.

This is what you usually see right below the video frame, when you are located in Germany:

This is how Google translates the disclaimer into English:

Until a friend told me about this earlier today I had never bothered clicking the link labelled “Further information”. It directed me to the url “infektionsschutz.de” (infection protection), which is run by the German Health Ministry.

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A familiar logo

The logo they are using looked strangely familiar…

…but I couldn’t quite put my finger on it.

The friend who asked me to click the link helped me out. It’s a slightly modified version of the Umbrella Corporation’s logo.

What Is The Umbrella Corporation?

For those not familiar with the Resident Evil video game series from the 90s, here is an excerpt from the Resident Evil Wiki.

The Umbrella Corporation was a multinational conglomerate with subsidiaries active in a variety of industries from the 1980s to the early 2000s. Umbrella's large array of subsidiaries was […] purposely built to cover up illegal activities.

Soon after the opening of its pharmaceutical subsidiary […] Umbrella began developing biological weaponry for militaries across the world as part of a worldwide conspiracy to accumulate deadly viruses directly prohibited by the 1972 Biological Weapons Convention. Umbrella Pharmaceuticals was able to cover their true intentions by researching vaccines for the same viruses as a front.

Umbrella's true goal was not, in fact, a capitalistic urge for monopolization of a lucrative industry. It was eugenics.

What a great find. I thank André Graf for pointing this out to me.

I’ve already written an email to the communication agency Hauptweg-Nebenwege to find out who the designer is.

Whoever came up with this deserves a personal thank-you letter, because this absolutely made my day.

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