Category: COVID 19 deaths and injuries

Exclusive: VA Whistleblower Exposes the Official VA Medical Claims Data Showing the COVID Shots are a Healthcare Disaster

By STEVE KIRSCH

The VA strongly recommends the COVID vaccine and doesn’t warn veterans about the safety signals it knows about. Not giving veterans informed consent on the shots is not a problem for them.

Executive summary

In this video, Sonny explains how Veterans can get compensated for their injuries.

In this article, I’ll be releasing the data for the very first time so you can see for yourself whether you think the shots were helpful or harmful.

I’ll also show you conditions that were elevated by 50% or more from 2020 rates and I’ll go into detail on a few of them.

The VA is ignoring all these safety signals and not warning veterans about the possibility the shots could be unsafe. Instead, the VA is blindly trusting the CDC and ignoring their own data. They should be held accountable because by refusing to look at their own data, they are killing people. Too bad nobody in Congress will bring this data to their attention.

The data

What the data said

Over 75% of the claim types were elevated over baseline.

Here is a partial list of the key benefits that come at “no extra charge” with your “safe and effective” COVID vaccine

1. 7009 – Bradycardia (slow heartbeat)
2. 5244 – traumatic paralysis, complete
3. 9312 – Dementia, Alzheimer’s type
4. 7520 – Removal of half or more of the penis
5. 9918 – Neoplasm, hard and soft tissue, malignant
6. 6061 – Anatomical loss both eyes
7. 7630 – Malignant neoplasms of the breast
8. 6604 – Chronic obstructive pulmonary disease (COPD)
9. 7007 – Hypertensive heart disease
10. 7000 – Valvular heart disease

The following adverse events were elevated by less than 5X over baseline:

1. 7002 – Pericarditis
2. 6260 – Tinnitus
3. 6276 – Complete loss of sense of taste
4. 9901 – Complete loss of lower jaw
5. 9310 – Dementia, unknown etiology
6. 9913 – Loss of teeth
7. 6042 – Retinal dystrophy
8. 7020 – Cardiomyopathy
9. 6354 – Chronic fatigue syndrome
10. 7006 – Myocardial infarction (heart attack)
11. 8004 – Parkinson’s disease
12. 7099 – Heart condition – general
13. 7523 – Complete atrophy of the testis
14. 7626 – Surgery of breast

An example

In 2024, there were 134 claims.

I wonder what affected the heart of so many veterans. It would have to be something very powerful that was injected into a large number of veterans. The VA is of course baffled as to what it could be. It’s a medical mystery of course!

Summary

The VA leadership is completely ignoring all the safety signals in their own data and blindly trusting the CDC that the vaccines are perfectly safe. They should be held accountable by Congress.

Maybe someday.

Original Source: https://kirschsubstack.com/p/exclusive-va-whistleblower-exposes

Insight Into What US House Members Are Thinking about COVID Vaccine Injury

By STEVE KIRSCH

You’ll find this really interesting and I don’t think you’ll be surprised.

Executive summary

1. There is no appetite whatsoever to remove the liability protection for vaccines. Too many members are dependent on support from the drug companies.
2. It’s unlikely they will be able to create a $15B fund for the vaccine injured. Only about 5% of the members/staff believe vaccine injured people exist and there is no budget for this. It has to be allocated using a special mechanism that is hard to access.
3. There are only 6 members of Congress where the staff members have realized that the vaccine harms > non-existent benefits. There are 425 members of the House so that’s only 1.5% who have figured it out.
4. All the rest believe the benefits far outweigh the risks, so they have no regrets about the COVID vaccination program.
5. Those members of Congress who do acknowledge the harms privately are afraid to speak out publicly about them for fear of retribution from their colleagues and the mainstream media.
6. Members of the Freedom Caucus are reluctant to speak out because they know that if they support a bill, the Democrats will vote against it on that basis alone.

So there you go. This explains why we have to get Trump elected in November if there is going to be any hope of change.

Now, can you guess who I talked to? I won’t reveal the answer, but it will be interesting to see who you think I talked to.

Heiko Sepp vaccine injury story

He went from world-class athlete to severely disabled within hours of his second Pfizer shot.

Heiko Sepp website

11 minute Heiko Sepp story on X

His life was destroyed by the Covid vaccine https://t.co/cMUxayiZeb

— Steve Kirsch (@stkirsch) September 4, 2024

Full 36-minute Heiko Sepp documentary

Original Source: https://kirschsubstack.com/p/insight-into-what-us-house-members

The Official Data from British Columbia Shows the COVID Vaccine is Killing People

By STEVE KIRSCH

It’s important to preserve the evidence because they removed it from their website. Here’s the story.

Executive summary

But you have to do a few calculations on the data to figure out that the vaccines are killing people.

In this post, I will memorialize the data for everyone to see, and show you the calculations you have to make that show the harm of the COVID shots.

The data from the BC website before it was removed

and the overlapping screenshot with the rest of the data:

The takeaways here are if you measure the bars height are:

70+ vax status: 48 745 (unvax vax)
70+ died: 46 651 (unvax vax)

In other words, if you were vaccinated, it looks like you are nearly 10% less likely to die. Was I wrong about the vaccine?

Nope. There is no possible way I could be wrong about the COVID vaccines. What this data actually shows is that the vaccines made people more likely to die.

This is an illusion that fools almost everyone, including pretty much everyone in public health and most epidemiologists.

Most people never learn the magic trick. Can you figure it out?

They stopped publishing the data after that!

Do you want to know exactly how the magic trick was done?

COVID Vaccinated Kids are Dying Regularly from Cardiac Arrest

It’s baffling that they try to normalize this as always happening. It was never a thing before the COVID vaccines rolled out.

Executive summary

I did a CDC Wonder search for ICD-10 code I46 which is cardiac arrest.

It shows that those under age 23 don’t die from cardiac arrest:

Today, it is the new normal if you’ve had the COVID shots.

The latest death

Boy, 11, dies at waterpark after suffering heart attack while in line for slide as mom found him ‘blue and purple’

AN 11-year-old boy has tragically died following a heart attack he suffered at a waterpark.

Richard Weinert-Soto collapsed on Saturday while waiting in line for a slide at Adventure Park in Visalia, California , around 40 miles south of Fresno.

Richard Weinert-Soto, 11, died after he suffered a heart attack at a waterpark

Weinert-Soto collapsed at the Adventure Park in Visalia, California

Weinert-Soto had been at the top of a waterslide when the medical emergency happened.

Visalia Adventure Park officials shared that their CPR-certified staff member tried to save the boy before medical professionals arrived on the scene around 5 pm, according to a statement .

He was then taken to a hospital where he was pronounced dead at 9:26 pm.

The park later revealed that Weinert-Soto had suffered from a cardiac emergency.

His family clarified that Weinert-Soto’s heart had stopped at the waterpark.

His mother Maria Velasco shared the heartbreaking details of how she found her young son when speaking to NBC affiliate KSEE .

“This little girl comes and she tells me, ‘I think your little boy is laying up there,’” she said.

“And so I ran up there and he was blue and purple,” she added.

She revealed that Weinert-Soto had been born with a heart and lung condition.

Meanwhile, the boy’s father, Christopher Weinert, shared a heartbreaking tribute to his son, according to the news outlet.

“He was amazing. He was special. Everyone who knew him knew that,” he said.

His family shared messages about the young boy on a GoFundMe page that has been set up to help cover his funeral expenses.

“He was the happiest, most loving, and kind boy,” the family wrote.

“He enjoyed singing at his church, talking about God’s love, and making his friends and family laugh.”

This little girl comes and she tells me, ‘I think your little boy is laying up there’… and so I ran up there and he was blue and purple.

Maria Velasco Richard Weinert-Soto’s mom

“He fought until the end and left many beautiful memories behind,” the tribute said.

“He was a friend to all and we would appreciate any help or assistance to lay our Angel Richard to rest.”

His family thanked the local community as well as the park staff and emergency services for their work.

The fundraiser has amassed over $24,000 so far.

Visalia Adventure Park said in its statement that his death was not the result of a failure by the site.

“Please know that the park was and continues to be a safe environment for the whole family and that this emergency was not related to the facilities at the park,” Adventure Park said.

Apparently, 11-year-olds dying from cardiac arrest is the new normal. Check out this post

It’s been over a year since speaking at the Truth Be Told Rally in London. I remember every smile, tear & hug. I’ll never forget that day & the amazing warriors I stood with! #truthbetold #vaccineinjured #vaccineinjuries #catparkerphoto #bbc #pfizer #AstraZeneca https://t.co/ji9r8m5Icr

— Cat Parker Photography (@catparkerphoto) September 4, 2024

Summary

This is insane.

——————————

Resources:
https://kirschsubstack.com/p/covid-vaccinated-kids-are-dying-regularly
https://x.com/stkirsch/status/1831057770029920591
https://www.newsbreak.com/the-us-sun-513995/3577549792279-boy-11-dies-at-waterpark-after-suffering-heart-attack-while-in-line-for-slide-as-mom-found-him-blue-and-purple

The New Zealand COVID Vaccine Data Shows that the Shots Make You More Likely to Die

By STEVE KIRSCH

Have you seen any analysis of the NZ data posted by any prominent epidemiologist showing it is safe? I haven’t and it’s been almost 1 year since I set the data free.

Quick summary

So here’s the short story…

The NZ data, when analyzed using a gold-standard methodology used by the UK government, clearly shows that the vaccines are increasing all-cause mortality in those who were vaccinated. It’s no more complicated than this:

• when seasonal mortality is strongly declining, the mortality of the vaccinated is increasing.
• when seasonal mortality is strongly increasing, the mortality of the vaccinated is increasing even more.

In short, if you chose to get vaccinated, your mortality increased. The mortality increase happened every time you got a shot. It’s crystal clear.

I’ve looked at other time series data for other vaccines and you simply don’t see this effect so it isn’t an artifact of vaccination (the Healthy Vaccinee effect).

We see the same effect in other countries after a shot is given. Your risk of death increases. Every time. Every country. Every dose. Every season.

The world’s epidemiologists are silent.

None are calling for data transparency.

None have published any written work claiming the New Zealand data proves the vaccines are safe. Is that how science works nowadays? You ignore data you don’t like?

Before I disclosed the NZ data publicly, I offered it to the CDC because they have never seen patient record-level data for a vaccine ever before. They said they didn’t want to see the data.

Even worse is that at a time when data transparency is key to eliminating misinformation, every one of the world’s health authorities refuses to disclose even the bare minimum, a time-series cohort summary report of their own data with weekly buckets and 5 year age categories, that would show the public whether the vaccines are safe or not. Members of Parliament asked the UK ONS for this and were turned down.

The lack of transparency is stunning.

If you want to show whether the vaccine is safe or not, all you have to do is compare the time-series data from New Zealand to the time-series data from a safe vaccine published in a paper in the peer-reviewed literature.

Except for one small problem… no such paper exists because the health authorities refuse to release it.

Why would they do that if the vaccines are safe?

No doctor in the world should be recommending these shots until there is the minimum requisite data transparency of the public health data. There is no excuse for this. The only reason you wouldn’t do this is if you knew you were killing people.

I also showed that even going further than just a time-series dataset and releasing the anonymized underlying patient record level data like I did with the New Zealand data, did not violate anyone’s privacy, yet allows for even deeper analysis than the time-series cohort summary records. That analysis shows the same safety signals as the time-series analysis.

Executive summary

Critical thinkers might wonder, “Why aren’t any prominent epidemiologist calling for data transparency of public health data? Don’t they want to know?”

Apparently the answer is “no.” Not a single mainstream epidemiologist in the world is calling for transparency of public health data. As far as I know, it has never happened.

Transparency is easy. The UK ONS did it by exposing the time-series cohort analysis of their data. But they just choose very wide “buckets” that completely obscured the damage. Using smaller buckets, one week in length, would reveal the truth. When I asked Sarah Caul of the UK ONS why they didn’t do this, she didn’t respond even though I cc’ed a member of Parliament on my e-mail. That member of Parliament is now escalating the issue with the ONS. We’ll see how well that works out for the ONS.

We finally have record-level patient data thanks to the efforts of Barry Young, the courageous whistleblower in New Zealand. It’s been publicly available since Nov 30, 2023. Have you noticed that not a single prominent epidemiologist in the world has come out and proclaimed that the NZ data proves the COVID vaccine is safe?

However, people have gone into over-drive to unfairly discredit this data.

In this post, I will show you:

1. That there is excess mortality in New Zealand and how you can determine that from the official death numbers.
2. That the COVID vaccine, and not COVID, is the most likely cause of these deaths.
3. That a variety of different methods, including a time-series cohort analysis, can be used to show the massive safety signal in the records that Barry released.

If you are looking for a safety signal for a vaccine, it’s really hard to beat a properly done cohort time-series analysis. This is the same method used by the UK Office of National Statistics for analyzing the UK vaccine safety data.

I published all the data and analysis tools for allowing people to prove to themselves that the COVID vaccines are deadly.

However, to date, not a single person in the world has looked at the data and published a proper analysis of what the cohort time-series analysis of the data reveals. They all chose to ignore the time series cohort analysis and do their own, ad hoc analysis. Wow.

More importantly though is this: To date, not a single respected epidemiologist in the entire world has looked at the data and published in written form, anything about it. There is complete silence from the community after this unprecedented release of patient record-level data. They’ve never had any data like this in the history of the world and they completely ignore it!?! That’s the stunning news. It’s objective. No comments. Just silence. With just one exception I’m aware of…

I just learned of a claim from an epidemiologist in New Zealand, Janine Paynter, that there will be two analyses of the data (likely the other being from Helen Petousis-Harris).

I hope they keep their word! But I seriously doubt it.

I predict that these epidemiologists will completely ignore the time-series data, cherry pick an analysis method that will obscure the signal, and then claim “there is nothing to see here” or that because “there is no control group” they can’t determine anything. They will provide no reason for avoiding the time-series methodology.

More likely scenario: They probably won’t even release their report. Ever.

In the time, in this article will show you how to interpret the time-series data.

The bottom line is that there is a very strong increase in the mortality rate of people who got the jab and it is a statistical certainty that this increase was not just random statistical noise. This means they should stop the shots immediately because it is a mathematical certainty that they are increasing all-cause mortality.

The only way the shots should be allowed is if there is at least 95% certainty that the vaccine improve all-cause mortality in the New Zealand. Mark my words: there is simply no chance of that. There is no possible way you can look at this data and come to that conclusion. Mortality rates always climb post-jab, even when seasonality would normally have them decrease

In other words, all the people pushing the shots are either bought off to look the other way or they are simply sheep following the herd and ignoring the record-level data that is now in full public view.

Thank you to Barry Young, the courageous New Zealand whistleblower for exposing the data that made these computations possible. No health authority in the entire world has ever made this information (patient record-level vaccine-death data) publicly available. They all keep it hidden from public view. That should tell you everything you need to know.

This is a dense article

Provenance of the data

I received the data directly from Barry Young. I verified it’s authenticity by randomly selecting data and verifying that it matched the death records for that person. I asked Barry questions that only a DBA would know. Barry’s employer acknowledged that Barry was fired after sending all the top leadership of HNZ that their data showed people were being harmed.

HNZ sought to stop the leaked data from being published. If the data was fake, why would they have bothered to go to extensive lengths to do that. They also threatened certain people in New Zealand with a $40,000 fine if they talk about the data:

I’m a US journalist. Giving me the data is basically publishing the data. This is very similar to Daniel Ellsberg giving the New York Times, The Washington Post, and other newspapers the Pentagon Papers. While Ellsberg was charged with several crimes, all charges against Ellsberg were dismissed due to government misconduct. The newspapers were never charged with a crime. The Nixon administration sought to prevent the publication of the Pentagon Papers through legal means, arguing that it jeopardized national security. They obtained temporary restraining orders but failed to permanently block publication.

Whether Barry Young violated the law will be determined in a court of law, not in a court of public opinion. He has pleaded innocent to the charges brought against him.

What I did in obfuscating the data and publishing my derivative work is not illegal, unethical, or immoral. I have not been charged with any crime nor I have violated any law according to any of the four attorneys I consulted with (just to be sure); two were US attorneys, one based in New Zealand, and one in the UK.

I have contacted HNZ and informed them that analysis of their data by myself and other respected individuals has shown very troubling safety signals that need to be explained. I asked them:

1. For a copy of all the vaccine-death analysis that have been done at HNZ so I can see if we got it wrong
2. If I could speak with one of their epidemiologists who is familiar with the data so they can explain how our analysis is flawed.
3. To make the full data public, e.g., as a time-series data dump that would expose any safety issues but 100% protect patient privacy to eliminate any concern about what the data actually says. The data leak is slightly over 4M records, but the full dataset is over 12M records.

I do not want to spread misinformation. I seek only to engage in a dialog to those responsible for monitoring this information so they can explain to me how all of us who have looked at the data and found it troubling got it wrong

They expressed no interest whatsoever in opening a dialog on what the data itself says.

Everyone, especially in New Zealand, should find this lack of openness and transparency very troubling.

Equally troubling is that not a single epidemiologist in New Zealand is calling for data transparency of public health data. This can be done without jeopardizing patient privacy as I have demonstrated here. Not a single person can identify any of their records.

In short, the epidemiologists in New Zealand are all captured and will never demand that the public health data be made public. Why? Because they will be fired and they need to keep their jobs.

I am speculating that if it becomes known that the HNZ leadership promoted a vaccine which killed thousands of people in New Zealand, faith in the health authorities, government officials, mainstream media, and the medical community would be devastated. This could lead to jail time for the people who deliberately looked the other way and lawmakers who promoted the shots that won’t get re-elected.

Official New Zealand data shows a huge increase in mortality starting right after the vaccine rollout!

If the vaccine is safe, how to they explain this New Zealand chart? It clearly shows that deaths weren’t “returning to baseline” or “negative excess mortality” as is often claimed.

The plot below was created by an epidemiologist at a top university who wishes to remain nameless so she can keep her job. It uses the latest weekly mortality rates in NZ from official data. She used the default values for the function to make the chart. She didn’t fine tune the parameters to get the results to fit her agenda. Can you believe that? You can download the R code and the spreadsheet of data from my Github and see for yourself.

Focus on the “trend” plot below. The trend shows increase of peak, compared to baseline of 0.0076/0.0068 = 11.8% increase. The mortality rise started abruptly at the start of Q4 2021 which was right after jab rollout. A coincidence? Unlikely in my opinion.

Here’s an independent assessment of the excess mortality. This looks like a problem to me. Thing go up starting right after Jan 2021 (which is normal).

People were told deaths were up in New Zealand because the population grew. True. But they deliberately misled the public because the deaths per capita (which is what matters) increased. So even though the population increased, deaths increased even more. When they deliberately deceive the people like this and the press lets them get away with it, it is an indicator of a very high level of corruption:

And finally, here’s the proof that the Deaths per 100K population was at a 12 year high in 2022 from the official New Zealand stats. See the number in the lower right in red.

An overview of the New Zealand data arguments

Here are the key arguments from the time-series data in a nutshell:

1. Not a single epidemiologist in the world has every called for a time series cohort analysis with weekly buckets such as the one I produced in just 2 hours using the New Zealand data. Nobody. Every in the history of the world. For any vaccine. In any country. Ever. What does that tell you? It tells you that the entire system is corrupt and bought off that there isn’t any transparency of public health data. This should make everyone furious. Every mainstream media outlet should be calling for this level of transparency so that the public learns the truth about vaccines. But they are all silent. There is absolutely no excuse for public health authorities not providing this.
2. The time series cohort analysis method, used by the UK ONS, is the definitive way to analyze a real-world intervention like a vaccine if you want to know the truth. There is only one way to do it. Everyone gets the same output file. Here’s are the time series cohort analysis done by the UK ONS. They stopped doing them in August 2023, probably because the results were too incriminating. When I asked Sarah Caul at the UK ONS to publish the same data but with weekly buckets so we could see what was going on, I received no response.
3. There is only one way to properly interpret a time-series analysis. If mortality climbs after every dose, regardless of seasonality, the vaccine should be stopped. Period. End of story.
4. The definitive nature of the time series analysis is why epidemiologists avoid it; they are focused on pushing the narrative and keeping their job. They can’t have a clear way to interpret the data. They need something they can game that is not objective.
5. There isn’t a way to interpret the HNZ time series data and conclude that the vaccines are safe. The safety signals are huge. For example, when mortality is declining for the population as a whole, if you were recently vaccinated, your mortality is going up when it should be going down. Nobody can explain how that is consistent with a safe vaccine.
6. In order for the shots to be recommended, the time-series cohort analysis would have to show that we have 95% confidence that the vaccines reduced all-cause mortality. There is no possible way for any serious epidemiologist to claim this.
7. Epidemiologists in New Zealand who support this analysis will lose their jobs if they speak out in favor of this data publicly. So they speak out privately.
8. Dishonest epidemiologists will likely resort to the handwaving assertions that the vaccine is safe and HVE must be lasting for months. This is not supported by the data at all as I point out in the HVE section where I show that the deaths per week level off at week 3, meaning the HVE effect ends by 3 weeks.
9. We restricted our focus in the analysis below to 60 years old and above, because that is where we have enough data to make a determination and that’s where the deaths from COVID are. It is a the target market for the vaccine. If it doesn’t work on this cohort, it’s over.
10. The mortality rate for this cohort (60 and over) appears to be from around 1,600 to to 2,400. This accounts for seasonality swings which are around a 20% plus/minus from the centerline mortality rate of 2,000 deaths per 100K person years. So anytime you see mortality rates above 3,000 for people who opted for vaccination, we have a serious problem. If mortality rates stay within this range, the vaccine is safe. In our case, we see mortalities rates as high as 4,000 deaths per 100K person years.
11. The time-series cohort analysis takes into account both number of deaths and number of people in each bucket, where each bucket represents people at a fixed number of weeks from the date of vaccination. It yields a mortality rate which we can then compare to the actual measured all-cause mortality in New Zealand at the time of the shots.
12. What we find is that the mortality rate in every Dose where we have sufficient data (Doses 1 through 4) exceeds what is expected from background effects based on the actual reported all-cause mortality numbers in New Zealand which takes into account COVID, seasonality, etc.

But there is more to the New Zealand data than just the time series analysis. For example:

1. If you have any fixed cohort of 80 year olds and older, the deaths per week must decline. It’s pretty close to a law of nature. But it doesn’t here. It’s a stunning violation of the way death works (it turns out that older people always die faster than their rate of death increases; this is true worldwide). Death counts increase over time. It’s something you rarely see.
2. New Zealand sex data: Sex differences in mortality after the COVID vaccine depend on which dose you got. That’s impossible for a safe vaccine.
3. New Zealand raw death count data: The raw death counts also rise each month when date of injection is restricted to a mortality peak (see the Raw Death Count Method below).
4. The New Zealand lot number analysis shown below showing deaths per week increased after certain lots were given.

To show safety, you also need to be able explain the other evidence from other countries which is consistent with what the NZ data shows

1. The Medicare data puts the “HVE lasts for months” argument to rest. There you can clearly see the HVE slope vs. the vaccine slope. You can also see the HVE timescale in the pneumococcal vaccine. In both cases, HVE, which has a high upward slope, is gone after 21 days. It shows the COVID vaccine is killing people.
2. There are also “impossible anecdotes” (such as Jay Bonnar) that can be 100% verified and there no counter-anecdotes. Anecdotes like that of Jay Bonnar are only possible if the vaccine is killing around 1 per 1,000 doses.
3. There are no “counter anecdotes,” i.e., people who lost 5 friends unexpectedly since the vaccine rolled out and all 5 friends were unvaccinated. These anecdotes should be easy to find it the vaccine wasn’t causing these unexpected deaths. I asked for anyone who knew 5 unvaccinated people who died unexpectedly and despite 100,000 views, there were no examples. However, I did get a lot of messages like this one which to any objective observer would be hard to explain.
4. The US Medicare charts show what happened over time to Medicare patients given two vaccines in the same year. In one case, deaths decline every day. In the other vaccine, death climb every day by a rate that has never been seen before. How will they explain that?
5. The rise in mortality reported by the Israeli MoH mimics the rise in mortality in New Zealand and Medicare. Hard to explain away. Where is this happening with any other vaccine?

Terminology used

Records are produced when people are born, die, and get vaccinated among other thing. Record-level data in this article refers to records contain the date and time of vaccination, what dose was given, and the birthdate and death date of the person. Generally, there is one record for every time a vaccination is given and those records have the date of birth (DOB) and date of death (DOD) as part of the record.

Death rate or DR
The death rate is the number of deaths per week or per month or per day. It is a measure of raw counts. This number can range all over the place and depends on what the age mix of the cohort you are following is and the number of people.

Mortality rate or MR
The mortality rate is a more normalized form of the death rate. It is expressed as deaths per 100K person-years. For example, the average mortality rate for 65-69 year olds in New Zealand is around 1,000 deaths per 100K person years. This means that 1% of this group would be expected to die in a year. So if you have 100 people in this age range at t=0, you’d expect to have 99 people alive after 1 year.

HVE
Healthy vaccinee effect refers to a 21 day period after a vaccination where mortality rates are artificially low because people who are very near death aren’t injected. But in New Zealand, this effect would be expected to be less than 21 days because every alive unvaccinated person was viewed as a threat to the living. See section below (“More on HVE”).

PPD
Pay Per Dose was the billing system the data is from. About 1/3 of all vaccinations were billed this way, slightly over 4M doses. There is no known systematic bias in who got a PPD shot.

HNZ
Health New Zealand aka Te Whatu Ora, the health agency which unwillingly provided the data to us, thanks to Barry Young. They have not published any epidemiological studies on their own data showing the vaccine is safe. Like most health agencies, they take a head in the sand approach to safety and trust the CDC.

Repository
Location of the data files from HNZ as well as derivative works to analyze the data. All this data has been anonymized so as not to reveal anyone’s personal information.

Time-series cohort analysis
A reliable method for analyzing interventions such as vaccines on a population. A database of patient records is analyzed by running through all the records multiple times (once per day). If a person is alive on that day, that person is tallied to exactly one alive “bucket”, e.g., the August, Dose 1, Week 2 , age 20-29 bucket. If the person died on that date, they are tallied to a death bucket and then no more tallies. Using this data, an appropriate visualization tool, and information about what is happening in the background (such as seasonal mortality, all-cause mortality measurements, mortality rates, COVID deaths, and more), skilled scientists can analyze the data very precisely.
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Here’s what the output of the time-series analysis looks like (this is only a small subset of the rows).

What the data is “supposed” to look like

Here is what a safe vaccine would look like in the ideal case where a vaccine is given evenly over time.

Note: If the vaccine is given over a short period such as in this case, the described curve below will simply modulate up and down depending on what is going on in the background (seasonality, COVID outbreaks, etc); the shorter the vax period, the larger the amplitude of the swings.

1. Time series, no age bracketing: You’ll see a fixed upward slope of around 10% per annum because your fixed size cohort is aging. The slope depends on the age mix of the cohort. Under 30 is 5%; under 55 is 9%. 85 and older will be 15%. This is because your cohort is aging. So if you have a mixed cohort your slope will be between these values.
2. Time series, fixed age bracket: You see a perfectly FLAT line. This is because your cohort has a fixed age distribution. Every week you’ll have people leaving as they get older, but you’ll also have a roughly equal number of people coming into the bucket because they got older. The bucket is always the same age range so it always dies at the same rate over time.
3. DR plots. The deaths per month plots (which are a fixed cohort defined when someone is vaccinated) will have a slope that changes gradually over time. For a full sample snapshot of the NZ population, there will be a 4.2% slope in year one which reduces to 1.9% up in year 5. If you are looking at older cohorts, the slope will be lower and can easily go negative. In general, the slope of the deaths of a fixed cohort always goes more negative over time. For 70 to 90 year olds, the slope starts at 2.8% on year 1 and goes flat by year 4, and negative after that. There is a spreadsheet in the Generic analysis section of the repository (Age Standardized deaths) that allows you to calculate the expected slope for any age distribution. It also has the mortality rates for every 5 year age group as well as the annual increase in mortality.

The healthy vaccinee effect (HVE)

There are two different kinds of HVE: temporal and general.

The general type of HVE says that “healthier people opt for vaccination” so the baseline all-cause mortality of a vaccinated group can be lower than the population by as much as 65% for older age groups. This doesn’t mean the shots lowered their mortality. It means that relatively healthier people opted for the shots.

Sometimes these non-temporal HVE effects can be quite large (as high as a 65% reduction from baseline) in all age groups as was shown in this paper. For example, for age 85+, the unvaxxed mortality was 18.76 vs. 6.57 for those who opted for 2 doses. That is a huge 65% reduction from the underlying baseline mortality rate caused by selection bias.

This is why, when people look at the vaccinated in the New Zealand group, they will find that the mortality rate of the vaccinated is lower than the population as a whole. No surprise. It wasn’t the vaccine. It was selection bias.

The second type of HVE is known as temporal HVE. This is also a very real effect, but it only lasts around 3 weeks. HVE results in a significant depression of both death counts and mortality rates for weeks after a vaccine is given (generally 3 weeks). The and clause is very important.

HVE is caused by an exclusion bias where people who are going to die soon are excluded from the group who opts for vaccination. The deaths per day in the population don’t change of course, but you’ve pre-selected a cohort to get vaccinated who was less likely to die in the short run.

The primary causes of this selection bias are systemic which is why it remains constant for every vaccine and in every year:

1. When you are hospitalized, in general, they don’t give you vaccines. This is because vaccines challenge your immune system and they want your immune system focused on what you were hospitalized for. People in hospitals are more likely to die than a matched group of people who are not in hospitals.
2. When you are in hospice, the primary goal is to keep you comfortable as you pass away, not to extend your life. Half the people in hospice are dead by day 18. The others have a very long tail (and can be in hospice for 6 months or more). The people who are dead in 18 days are not vaccinated and removed from the vaccine rolls. The hospice stats are a key reason for the HVE largely disappearing after 18 days.

Temporal HVE is often abused by people who don’t understand this and used as the way to “explain way” the rise in the raw death counts when you look at death counts after a shot is given. A dishonest scientist will claim temporal HVE lasts for many months, rather than days. This is how they explain the rising mortality rate after the shots are given because they can’t blame it on the safe and effective vaccine, so it must be the mysterious HVE which they don’t understand.

Some have even speculated that HVE is small to non-existent for the COVID vaccines such as this person:

He’s 100% certain that there is no HVE effect.

But the data shows that there is such an effect and it’s super clear, the US Medicare data being the most convincing. So nothing is every 100% certain, no matter who you hear it from, including me.

The single most important part of temporal HVE is convincing yourself it only lasts around 21 days.

Here’s a magnified portion of the deaths after a COVID shot is given where you can verify the methods below yourself

There are at least 7 different ways to estimate the time the HVE lasts, and they all give the same result when used on both safe and unsafe vaccines (~21 days). The methods are most accurate if you have lots of deaths. For our purposes, the Medicare data is the most definitive and shows the effect lasts 21 days for the COVID vaccine, which is the same as other vaccines.

1. Diverging slopes intersection. You take the slope of the HVE line tangent and the vax tangent and look at where they intersect. This works well when the curve of the vaccine core effect slopes down (PNEU vax) or mildly up (such as the COVID vaccine). It fails for vaccine with a deadly response curve like the FLU vaccine because both slopes are large positive numbers just after vaccination.
2. Death rates stop climbing monotonically.
3. Mortality rates stop climbing each week and go down. The down point is a reasonable baseline mortality of the cohort.
4. Mortality rates enter the “strike zone” for that cohort, i.e., a point 40% below the average normal mortality for the cohort normally. This accounts for seasonal variations in mortality as well as the non-temporal HVE.
5. Mortality rates exceed a baseline threshold seen later in time, i.e., we are seeing a repetition of the same point or lower in a later time point. This is because the HVE values should never be repeated later in time since this is a one-time mortality reduction.
6. Death rates exceed a baseline threshold seen later in time
7. 4 half-lives from the start, i.e., you see how many days it takes for the HVE effect to diminish by 2X and multiply that by 4. This is a pretty good rule of thumb that works if you have little statistical noise on your datapoints, e.g., the Medicare COVID shot data above where it works brilliantly (~5 days between the first four half lives). This is because the HVE effect diminishes exponentially. So this is a great rule of thumb when looking at datasets where there is noise. You estimate the first half-life in terms of days and you have an approximate answer. 2^4 is a 16 fold reduction meaning the effect is basically de minimis at that point. You can use this method to verify the same 21 day effect in the NZ data by using Dose 4 data because the underlying mortality is going steeply down so the effect is much easier to see. The half live is around 5 days if you extrapolate between week points.

Here’s an example from the NZ data showing very clearly that HVE lasts only for 3 weeks which we can establish using multiple methods above.

Properties of temporal HVE:

1. The temporal HVE effect (where people who are about to die are not vaccinated which lowers all-cause mortality in the 3 weeks after the shots are given) always gets progressively smaller over time. This is because people are good at predicting death the next day (e.g., we’ve stopped feeding the patient), but predictability goes down the longer a person is expected to live.
2. People expected to live 21 days or longer are generally vaccinated. This is both to protect the living and the caregiver.
3. There aren’t any examples of HVE lasting more than around 3 weeks that I’m aware of. Not for any vaccine in history. Nobody has ever produced an example of this. Instead they just attack you when you ask them for evidence like Dr. Shiva did to me. When I challenged Dr. Shiva on this on Dec 17 in a recorded space on temporal HVE, Dr. Shiva REFUSED to answer the question and I asked at least a dozen times. Instead of citing evidence, he just came back with ad hominem attacks. Start listening at 1:23:15. It’s stunning on how he runs away from answering my simple question every time. I asked for the data, he responded with personal attacks every time. This is basically how it works. No “scientist” has ever shown a paper when HVE lasts >21 days.
4. HVE effect does not depend on the season
5. HVE does not depend on dose
6. When deaths go down in successive weeks or are flat, it is a sure sign that HVE is over
7. HVE is an exponential effect. You can see this from the COVID Medicare data below. So if HVE is reducing the baseline deaths by 80% on Day 0, by day 3, we’ve reduced deaths by half of baseline. By Day 21, the impact on deaths is <1%, so it’s gone. If you look at the daily NZ death data on a per day basis, you’ll see that a factor of .8 is generous; it drops faster than in the US. In other words, it’s gone in 21 days or less.

In New Zealand, like other countries, they vaccinated aggressively. So even if you were about to die, unless you were going to die in a few days, you got the shot because they were interested in protecting the caregivers, not the patient.

NOTE: There were absolutely no instructions given in New Zealand to avoid vaccinating people if they might die soon.

You can also see very clearly that HVE lasts 3 weeks or less by plotting raw deaths after dose in New Zealand. I chose Dose 2 and 4 because there was a lot of data and it wasn’t confounded by the background seasonality effect if we looked at Dose 3 (which would make it harder to see).

Here’s the chart for Dose 4 in New Zealand where there are plenty of deaths to see. At week 3 the HVE effect is gone because we can see the deaths stop rising… Week 4 is lower than week 3 (at 235 deaths) a clear signal of the end of the HVE period. The HVE, if there is any left, has diminished to the point it is no longer able to produce an increase in deaths each week.

You can also see this from the time-series plots looking at the raw death chart (and not the mortality chart) where the deaths per day stabilize and cluster starting at week #2. Note below that the week 8 number is the same as the week 2 number which suggests the effect ends at week #2 and using the mortality at Week #3 as a baseline (218 deaths in the table below) is a reasonable estimate since in the 5 weeks since then, only one observation was higher than 218 deaths (and it was higher by less than half a std deviation). So the week #3 mortality is likely representative of the group shortly after the vaccine was delivered to the group. You can confirm the HVE effect is over in the mortality rates as well since you see mortality rates are lower after week 3 in the chart below.

Here were the parameters I used:

I chose 70 and older since older people are much more likely to be subject to HVE. I chose Dose 4 since the seasonality acts against the mortality rise of the vaccine so the signal would be easier to see.

Why the death rates of an elderly fixed cohort over time always decline over time

1. By age 86, the average mortality is 10% per year or higher.
2. On average, the increase in mortality per year is around 10% give or take.

These are two opposing forces. The first one causes the number of deaths per week of a fixed cohort to decline by that percentage per year. The second one acts to increase the number of deaths per week.

At age around 85, these forces are in equilibrium and the death rate is relatively flat for a fixed composition cohort at t=0 followed over time.

For ages > 85, the slope is down. The older the cohort, the steeper the slope.

For ages <85, the slope is up. But the slope can NEVER increase by over 10% per year no matter what due to point #2. This is critical. So when we see the deaths at the end of the year 25% higher than at t=0, we know we either 1) have a deadly vaccine or 2) there is a huge background effect that is causing the lift.

For real world cohorts, such as Medicare, for any practical Medicare cohort, the slope is always flat to down. But for the COVID vaccine it is up by 25%. It’s stunning.

We’ll see this later on when we talk about Medicare.

The gaslighting arguments

I’ll address these techniques in a subsequent post.

One of my favorites is this response to the unambiguous huge signal on Dose 4: “there must be confounders” which you can see here:

This is the closest you’ll ever see anyone on the pro-vax side admitting that they were wrong. They’ll always come up with a fallback like this when their narrative falls apart.

Real scientists find value in any data

A real scientist will always find a way to maximize the value of any data they get. Sadly, there are too few of these scientists nowadays. See this excellent article by Norman Doidge for more on this. It’s one of my favorite articles.

The New Zealand data is a strong teaching point: it show you just how corrupt the system is that in the 3 weeks since I made the data available, not a single epidemiologist has said, “this data is consistent with a safe vaccine.”

Instead, they all keep their mouths shut and say absolutely nothing about the definitive time-series cohort analysis that was done. Most people ignore it and proclaim their own analysis method is the right approach. It’s because they can’t refute the time-series analysis.

Things you should know before looking at the data

1. NOBODY has found an error in the extraction code or the visualization spreadsheet. In fact, the extraction code (buckets.py) has even been independently replicated from scratch and found to be error free: “We re-wrote Steve’s approach in a different programming language in order to learn the intricacies of this approach. We were able to perfectly match Steve’s results (at the underlying fine-grained “bucket” level), give-or-take some floating point precision differences between the runtimes.”
2. Here is the vaccination schedule in New Zealand from the Health New Zealand site:

   

3. Death numbers by month and sex are found here: Monthly death registrations by ethnicity, age, and sex: January 2010–September 2023 which can be found on this page.
4. Seasonality is very small for people under 60. It’s mostly Poisson noise. If we look at all the people dying in New Zealand, seasonality can make a huge difference in all-cause mortality from peak to trough of 50% as happened from Feb to August 2022 (a rise) and then again from August 2022 to Feb 2023 where it went back down by a similar amount. This is the largest seasonal effect during the vaccination period and can be used to compare with what is happening to the vaccinated during this time. If the vaccinated flow with the trend, the vaccine is safe. If the vaccinated die higher than the trend, the vaccine is killing people. If the vaccinated are dying lower than the trend, the vaccine is saving lives.
5. 80 year olds (and older) double their mortality rate over 5 years. This is a 15% mortality increase over 1 year and a 7% increase per year. This would be considered very normal. So if nothing is going on in the background (like seasonality), we should expect to see the mortality rate line increase by no more than 7% in 6 months.
6. The cohort time-series analysis is used by the UK government to assess vaccine safety because it computes accurate mortality rates. If the vaccine is given evenly over a 1 year period (which wasn’t the case here), the mortality rates should increase by 7% evenly over 6 months.
7. In the cohort time-series analysis, older people tended to go for their next dose starting around 5 months after getting their shot. This is why the mortality rate begins to level off and starts declining at 6 months; it’s a Simpson Paradox effect because the age of the cohort after 6 months is becoming younger and younger. This is because after 6 months, old people recycle to the next shot. Say in 12 months, all the old people opted for the next shot. So at 12 months, the cohort is very young. So after 6 months, the cohort left in the “after 6 months in that dose” are younger and younger over time. However, if you restrict your view to those within a narrow age range, such as 60 and older, you see a completely different story: mortality continues to rise monotonically for around 1 year since the shot was given which is stunning. This happens for Dose 3 and 4, as well Dose 2 to 4 and Dose 2 to 5. Dose 1 can be added to any of those ranges as well and doesn’t change anything. So what is stunning is Doses 3 and 4 are given at opposite calendar dates (6 months apart) so in one case you benefit from seasonality and the other you don’t, so the two doses should cancel each other out from a mortality point of view.
8. So looking at doses 3 and 4 if the line slopes higher than 7% in 6 months you have a huge problem since these two cohorts roughly cancel out the seasonal mortality since they are 6 months apart. In fact the Dose 4 cohort (where seasonal deaths are dropping) is actually 20% greater than the dose 3 cohort (3.6M person-days in week 0 for dose 3 vs. 3M person days in week 0 for dose 4). The seasonality effect for Doses 3 and 4 are equal and opposite.

   

About the data below

So you don’t have to believe me. You can verify this yourself.

I confine my analysis to those age 60 and over to avoid Simpson paradox effects (since a lot of young people didn’t get jabs after dose 2 and that would pull down mortality because the “after 6 month” cohort would get dramatically younger).

Overall mortality rate for New Zealand for the entire population is around 680 per 100K, but it rose to 752 overall after the vaccines rolled out, a 10% rise.

But since we are looking at ages 60 and above, so the mortality will be much higher. Mortality for this age group can range between 1,628 and 3,000 depending on what is happening in the background (like deaths from the vaccines and seasonality and how vaccinated people were; lower mortality at the start of the vax program and significant higher mortality for the same cohort after they got more doses).

For example, for Doses 1 and 2, mortality ranged from 1622 to 2586 (see the dose1.xlsx and dose2.xlsx spreadsheets).

But for for Dose 3, the Q1 rate is 2,080 and the Q2 rate is 3,021. You can see this in the Dose3.xlsx spreadsheet in the repository.

For Dose 4, the mortality rates ranged from 2,274 to 2,652 because we are fighting the seasonality trend so the rates will be more compressed as expected.

It certainly appears that our 60 and older cohort mortality rose over time. This should be very a very concerning red flag.

Dose 1 analysis

Look at the first 5 weeks in this graph. Does this look like mortality rate of the people who got the shot are falling? No way. It show a huge safety signal.

Only the first five weeks are reliable here because after that, the death numbers drop below 34 deaths because these people are moving over to shot 2 after 3 weeks, just like they are told.

There is no Simpson’s Paradox in the first 3 weeks since the cohort age mix is stable.

Look at the rise in the first three weeks *the first three dots). Mortality climbs from 766 (week 0) to more than double to 1,620 (week 2). This is all temporal HVE which lasts 3 weeks. Nothing to be worried about here. This was totally expected. We hit baseline mortality in 3 weeks after the shot which is normal.

What’s unexplainable is the rise above 3,000 (deaths per 100K person years). That is trouble with a capital T. That’s very hard to explain, even with a very strong seasonality effect for 60 and older in New Zealand.

Note that the total deaths here past week 5 are small, so the numbers are noisy but the trend, even with the noise, is above 4,000.

Dose 2 analysis

This is not nearly as big an effect as for Doses 3 and 4. It reaches a minimum in February 2022 (at week 16), then we see background rates start to rise from there so by April (week 24) we are about even with rates at the start of the period.

So what happened to mortality at the start and end when background deaths are flat?

Week 23: 2,986 (April, 2022 deaths are 3042)
Week 3: 1,621 (November, 2021 deaths 2952)

Wow. Mortality went up a whopping 84% even though the background death rates on those two points went up by 2%. That’s really stunning.

Note: Week 23 shows as point 24 in the graph because of the way Excel did the graph. I’m using Week 23 from the v4 spreadsheet.

By the 4th week, we have a mortality of 1621 which is within our normal range for this age. The rate should never climb above 3,000 but look at what happens after week 20. We have a serious problem. At a time when deaths should be declining by 20%, we are seeing deaths climbing

Dose 3 analysis

Feb 2022: 2607
Aug 2022: 3972

Here’s where we aren’t fighting the trend; where death rates are going up. The question is: do mortality rates in the cohort go even higher than the 52% increase we’d expect from seasonality?

Baseline mortality rate: 1,936 (week 3)
Peak mortality rate: 4,161 (week 30)

Ratio: 2.15
Expected 1.5

In other words, the vaccine generated a 43% excess mortality at the end of the day in an uptrend. So it seems like no matter what is happening in the background, if you’ve had the shot, your mortality is going up.

Some people might claim that week 30 is an unfair comparison week because by week 30, almost 1/3 of the people had migrated to get the next dose leaving the “unhealthiest” behind. But that doesn’t seem to be the case for several reasons:

1. As you can see from the chart below, the slope isn’t changing at all after week 20 when people started to migrate to the next dose; it just kept climbing at the same rate. If the “unhealthy were being left behind” then we’d expect a shift upwards.
2. If we compute the HVE effect magnitude for Dose 4, it’s around 150 that were “left behind” in the Dose 3 cohort that would artificially increase the mortality rate. Those 150 are roughly evenly distributed over time so let’s be very conservative and assume it was distributed over 7 weeks. This would add 21 deaths to each of the weeks. So Week 30 with 2,061,416 man days and 235 deaths should really be 214 deaths which gives a mortality rate of 214/2,061,416*365*100000=3789. So 2.19 becomes 1.96 which is still a 30% increase over the expected 50% seasonality increase in mortality (which is very conservative since it would never be as large as the peak to trough since the vaccines were given over time which would tend to diminish the seasonality extremes).

Dose 4

With Dose 4 we have a lot of data and the slope of the mortality effect is relatively flat due to the downdraft, so we can now very clearly see the temporal HVE which was harder to see clearly on earlier doses. After the third dot, it’s over. The fourth dot goes up (statistical noise) and the 5th dot is lower than the third dot confirming, once again, that HVE ended by the third dot, i.e., that the HVE (which is a very strong effect) is largely gone by the end of week 3.

Week 3: 2,370 mortality rate
Week 24: 2,741

Yet deaths in a month go from:
August 2022: 3972
Feb 2023: 2,691

So our peak is 47% higher than the trough.

Let’s do a Fisher Exact test on this using the death numbers 6 months apart to see if they track and the differences are statistically significant.

Our Fisher matrix using weeks 3-6 and 24-37 (without normalizing for the cohort size to be super conservative is (3972, 919, 2691, 907)

Statistics for dose 4 3972 919 2691 907 8489
One-sided p-value 8.4287274053816e-13
Two-sided p-value 1.5944061943595884e-12
Odds ratio= 1.4566920525719358
95% Confidence Interval(low=1.3110223439620594, high=1.6185720629913125)

So it is virtually certain that the disparity didn’t happen by pure chance. Something caused a very large shift.

Doses 3 and 4 combined

Looking at this over the first 24 weeks when the effects should more than cancel out so that basically the background deaths are dropping, we see an increase from

Week 4: 2,109 (so we start after HVE is gone)
Week 24: 3,008

This is a whopping 43% increase in mortality at a time when the background death rates should be going down.

Dose 5 and 6 combined

Doses 1 through 4 combined

All doses over all time (ages 60 and older)

Over time this cohort gets younger because the oldest people die off. But we never have a return to baseline mortality rates established at week #3 (slightly above 2,000).

Raw death count method examples

Below is another stunning example. This is people 80 and older who were vaxxed in Jul, Aug, Sept 2021 showing the number of deaths per week since shot #1 was given (regardless of any other shots). Those are peak death months in NZ. Two years later, there’s another death peak; 6% higher. Yet the deaths per week 2 years after the shot are running around 50% higher than at baseline. This is impossible for anyone to explain because there should be fewer people dying each day in that age range because the mortality of the cohort exceeds the annual increase in mortality of the cohort. This is why the deaths in the pneumococcal (PNEU) vaccine below decline every day.

The Lot number analyses are truly stunning

I have not had a chance to look at the lot number analysis in detail. There are 124 lots to look at.

Here’s an example of a huge safety signal in Lot #10 (corrected as of 12/28/23 to eliminate duplicates):

This pattern is impossible for a safe vaccine. I really don’t need anything more than this one chart to prove the vaccines are unsafe.

But here’s another one with the same story: an impossible rise in deaths per month at the 1 yr and 18 month mark. Deaths should be heading down not up.

And you will find this same pattern in every country, in every season, after every COVID vaccine: the deaths are supposed to trend down, but they don’t.

Analysis by sex

Do we see a difference in how males and females die relative to the shot? If so, we have an unsafe vaccine. We do!

See this article:

New Zealand headlines

So when you see headlines in the mainstream New Zealand media like this, you have to take it very seriously:

And then there is this headline:

So clearly I’m not alone in believing there are excess deaths.

The US Medicare data

Note also in these plots that the temporal “healthy vaccinee effect” ends around day 21. This is quite obvious from the COVID data.

For the PNEU curve, this curve acts as expected with fewer deaths every day. This is appears to be true for all general population or elderly cohorts. The reason for this is that the people who mostly die are generally 80 and older and their annual mortality exceeds the annual increase in their mortality so fewer people die a day because there are fewer people available to die if you are starting with a fixed size cohort. Restricting the cohort to those 80 and older ensures a downward slope for deaths per day (subject to some seasonality waves if the cohort is vaccinated over a small number of months).

If you restrict your cohort to ages 65-75, there is a slight downward slope in deaths per day. If you allow ages above that in your cohort, the downward slope increases.

There are very few people in the world who are aware of this effect.

Data from Israel

Here’s what happened to deaths per day after Dose 2 in Israel: deaths per day went way up. Either something huge happened in the background that exactly corresponded to 40 days after the elderly got their shots and was sustained for 5 months, or it was the shot.

I wonder what it could be? It couldn’t be COVID, because all these people were 30 days after their second dose of the vaccine and you can’t die from COVID if you’ve had the vaccine as everyone knows (just kidding).

More importantly, starting from March 1, 2021 for the next 5 months, deaths from COVID were falling. So it couldn’t be COVID causing the rise in the chart above.

The original Israeli MoH report can be found here. Here’s the English translated study. Look at the very steep and sustained rise in the number of deaths after the shot is given. And this can’t be caused by COVID because they have all been doubly vaccinated! So what is killing these people? The steep rise is correlated with vaccine administration since this chart is relative to the time Dose 2 was given.

The report covered people who got the shot from Jan 2021 to October 2021.

Like all studies of death since dose, it’s a fixed cohort on Day 0: fixed in size and age mix. Between Dec 19, 2020 and Mar 16, 2021, about 90% of all initial vaccinations (dose 1 and 2) were given. That means the cohort death rates should show some seasonality.

During the 3 months of vaccination (Jan to Mar), the death count in Israel averaged 4,138/month. The next 3 months averaged 3,300. The next 3 months were 3,666/month. So we should see deaths dip in the first half of the chart, and then rise in the second half. But we don’t see that at all. We see deaths that defy the background all-cause mortality if you were vaccinated.

The Israeli scientists speculate that HVE causes the steep increase in the number of deaths after day 36. They cite no evidence for that and want us to believe that everyone in Israel could precisely predict when they were going to die for the next 36 days, and then their magical predictive skills simply evaporated if it was over 36 days.

As we noted in the HVE section above, HVE doesn’t work like that. The biggest reductions in death are always on Day 0, and the savings get smaller and smaller each day after that, exactly like the first 21 days in the Medicare data above. The effect is quite strong so as soon as you get a lower count on a per day basis (in Medicare where the numbers are high), the effect is basically gone. The HVE effect appears gone in Israel by Day 7. They basically vaccinated everyone who moved.

So how do they explain the huge rise in mortality from Day 30 to Day 50?

This was never explained in the report. They just glossed over it. Which tells you everything you need to know.

Younger ages

For example, for 20-24 year olds, the mortality rate post Dose 2 can be as high as .86 deaths per 1K person years when measured over a 180 day period. The 2022 New Zealand mortality rate for that age group is .55 which is a 56% mortality increase.

Kill rate on average

This is one way to get a rough estimate. It means that the vaccine kills about 1 person for every 1,000 doses on average. This suggests that in New Zealand that there were 12,000 excess deaths. It’s unlikely this estimate is off by more than a factor of 2. It’s certainly not off by a by a factor of 10.

Which means at this point, we are rearranging deck chairs on the Titanic. The vaccines should be immediately stopped.

The 1 per 1,000 estimate is very comparable to numbers using other methods including VAERS, polling Americans as Mark Skidmore did, or looking at countries where confounders are minimized as Denis Rancourt did, or looking at large numbers of countries like Mathew Crawford did (he estimated .411 deaths per 1,000 doses).

Other proposed explanations for the excess mortality

1. COVID
2. Immigration that changes the average age to be older

Some of the excess deaths in New Zealand could be ascribed to COVID. OWID lists 3,600 deaths. We pointed out before that the absolute number was significantly higher than that.

Some deaths could also be due to an age mix in New Zealand changing. People who make that argument never actually show any data backing their statement. Why would they not do this?

Using a standard technique used by the UK Office of National Statistics, we showed that after every dose of the vaccine, the mortality rates increase relative to whatever the all-cause mortality rates in New Zealand were doing at the time. Thus, we accounted for all COVID deaths, return to baseline mortality effects, seasonality, and more because we used the actual mortality statistics reported by the New Zealand authorities as the control group.

Even more stunning is when we combined doses 3 and 4 which had equal and opposing forces happening in the background, we also saw a very strong increase in mortality over time since the shot was given.

Here we limited our analysis to ages 60 and older where we had sufficient data to make a clear assessment. But this was the very age group that was targeted by the vaccine. What we showed is that the COVID vaccines have killed thousands of people in New Zealand which translates to over 10M people worldwide..

If New Zealand were to release all the records instead of just 1/3 of the records, we could do an assessment for even younger ages.

Unfortunately, this seems unlikely as it would expose the fact that the health authorities were advising parents to kill their kids. This is why they will fight to keep the full dataset secret and keep the public in the dark.

These epidemiologists now have an interesting task ahead which is to show everything I wrote is exactly the opposite of what I described. They will show you that the NZ data shows with at least 95% confidence that the vaccines lowered all-cause mortality. If they can’t do that with this data (which is more than enough to see an impact), they should halt the vaccine. I cannot wait to see their work.

Original Source: https://kirschsubstack.com/p/the-new-zealand-covid-vaccine-data

The Simplest Way to Show the COVID Shots Increase ACM

By STEVE KIRSCH

Vaccines are supposed to lower all-cause mortality. The COVID shots don’t do that. Here’s the clearest evidence I’ve seen to date.

The vaccines didn’t work. It doesn’t get any clearer than that.

Executive summary

Introduction

The vaccinated should have had a significantly lower ACM even if the vaccine doesn’t work at all.

If the vaccinated have a higher ACM (or statistically the same), that’s a disaster.

That’s why I generally refrain from comparing vaccinated with the unvaccinated.

So when I see a post like the one below where the vaccinated have a higher ACM, it’s a showstopper.

The message is very clear: the vaccines were a total failure: they increased all-cause mortality.

Can you spot the error in the graph?

Ben showed me that the ASMR in his chart is MONTHLY average ASMR.

So if you want the annual ASMR which is what I thought I was looking at, multiple the numbers by 12.

For some fun, try this on your friends…

Summary

If the COVID shots did nothing, the ACM would be substantially lower for the vaccinated due to the effects I talked about (at least 10% or more).

But as you can see, the shots increased all-cause mortality significantly since the vaccinated ASMR was slightly above the unvaccinated ASMR.

This is a disaster.

It means the shots killed people.

How many more people will die before we can have a discussion about the data?

Original source: https://kirschsubstack.com/p/the-simplest-way-to-show-the-covid

Denmark, Finland, and Norway Graphs Show All-cause Mortality Kept Rising After the Shots Rolled Out

By STEVE KIRSCH

I got a tip from one of my colleagues that Denmark, Finland, and Norway had very similar vaccine policies. Mortality kept going up after the shots.

Executive summary

I heard from a scientist in Denmark that the death stats were flat until the shots rolled out.

She also said that Denmark stopped reporting daily deaths and has switched to reporting deaths quarterly. Does having less information lead to better health outcomes? Or are they hiding something? X

Similarly, in Norway, they stopped reporting COVID stats due to budget cuts; it had absolutely nothing to do with the numbers getting worse and worse (14X more COVID, 3X more hospitalisations, 3X as much death).

Here are the numbers. You decide. I think the numbers are pretty incriminating.

The chart

Excess deaths are relative to start of the period and excess is measured relative to a 2017-2019 baseline.

If what they are doing is working, cumulative excess deaths should turn around and fall back to zero. But this just doesn’t happen.

Deaths monotonically increase. This means that what they did either had no effect or made things worse.

How they can possibly claim success with numbers like these is a mystery to me.

The deaths started getting worse after the COVID shots rolled out. If it wasn’t the COVID shots, what is fueling the monotonic rise in excess deaths?

It can’t be “this was due to lack of access to healthcare during lockdowns” since that was turned on after the shots rolled out which means the numbers should have started going down after the shots rolled out. That’s why they never show you any numbers and correlation coefficients for their “explanation.’

Here is the original graph I got from my friend. Notice how similar the shapes are and that it flatlines at the same time. Nature simply doesn’t work like that in different countries. These are essentially man-made effects.

Essentially, these charts are saying we started in 2021 making things worse and didn’t back off until the end of 2022.

Norway’s post-vaccine results have been terrible. The authorities cannot explain it so they won’t be publishing stats anymore.

They won’t be publishing stats anymore due to budget cuts.

They have had a clear continuous decline in mortality among those under 60 in the period from 2000 to 2019. Now it seems that this trend has reversed.

Details here:

Summary

We have evidence that the authorities can’t explain it (since they aren’t allowed to blame the vaccine, it goes unexplained).

We can’t do it ourselves because they keep the data hidden from public view.

Original source: https://kirschsubstack.com/p/denmark-finland-and-norway-graphs

BREAKING BBC NEWS: AstraZeneca Vaccine Deaths: Families Ask Why Safety Warnings Not Shared?

BREAKING BBC NEWS:

AstraZeneca vaccine deaths: families ask why safety warnings not shared?

Why? It’s because that’s what corporate psychopathic entities do: ‘callous disregard for the safety of others’

Stop Big Pharma’s mass manslaughter

Another family whose son, Oli Akram Hoque, died from the same complications a few days after Marina, are also calling for lessons to be learned.

A Department of Health and Social Care (DHSC) spokesperson said: “Throughout the pandemic the health system responded quickly to reports of extremely rare cases of complications following vaccination.”

The Oxford-AstraZeneca Covid vaccine was estimated to have saved millions of lives during the pandemic, but as in Marina and Oli’s cases, caused rare – and sometimes fatal – blood clots.

An AstraZeneca spokesperson said the vaccine had been “recognized by governments around the world” as helping bring the pandemic to an end.

Max and Liz Waldron believe earlier awareness of the jab’s rare complications could have saved Marina

Marina, from the Forest of Dean in Gloucestershire, was just starting her career in film when she was given her first dose of the AstraZeneca jab on 11 March 2021.

She had developed a headache and sickness by 22 March and went to a London hospital only to be sent home with migraine tablets, despite mentioning she had had the jab recently.

Mrs Waldron, 64, said Marina deteriorated the following day and was this time discharged by an A&E department, again with a migraine diagnosis and no brain scan.

On 27 March, her parents were so worried they took her to A&E in Gloucester. She died on 31 March at Southmead Hospital, Bristol, after suffering a heart attack and brain haemorrhage.

“We weren’t able to go in. We weren’t able to say ‘don’t you understand she hasn’t drunk or eaten’. They [the hospital] just sent her back,” said Mrs Waldron.

“But the next day it was worse and she started having issues with her arm – she was lifting up her arm and holding it, and that’s when it all went mad.”

The family’s solicitor said Marina’s case fell into a gap between risks of the jab being known and frontline staff being warned

At Marina’s inquest in December 2023, a coroner determined her death had been caused by a combination of factors: intracerebral haemorrhage, cerebral venous sinus thrombosis, and vaccine-induced immune thrombocytopenia and thrombosis due to the AstraZeneca vaccine.

While the coroner found Marina had died from a recognised but very rare complication of the AstraZeneca vaccine, he did not find fault with the NHS for failing to diagnose it quickly enough.

The Waldrons want to know why medical professionals were not informed about the risks associated with the vaccine earlier when other countries were putting a halt on the vaccine and why appropriate diagnostic measures were not taken promptly.

They believe that earlier awareness could have made a significant difference in Marina’s case.

‘She could have been saved’

Mrs Waldron said: “It was only after a couple of days in hospital that they took us to one side and said ‘we think it’s the vaccine’, and we said ‘we’ve been banging on about this vaccine right from the beginning and every single person said it wasn’t anything to do with that’.

“They didn’t even bother to scan her.

“There was a possibility, a chance she could have been saved.”

Their solicitor, Lynda Reynolds from the Hugh James practice, said there seemed to have been a gap between advice being published on the government’s public website on 18 March to urge people with concerns to attend A&E and clinicians being told on April 7 to investigate any potential symptoms.

She said: “Unfortunately for Marina, she fell in that gap. She’s not the only one.”

Could doctors have been warned earlier?

15 March: Germany, France and Italy halt Astra Zeneca rollouts

A week before Marina fell ill, Germany, France and Italy had halted rollouts of the AstraZeneca jab after a series of incidents in Europe involving blood clots, even though the World Health Organisation did not feel there was enough evidence to prove a link.

18 March: five cases identified of blood clots in the brain (aged 18-59) – one death

A few days later, five cases of blood clots in the brain had been identified in the UK – one of them fatal. The NHS carried on giving the vaccine to young people, although it advised anyone with a headache lasting more than four days after vaccination should seek medical advice, as a precaution.

25 March: NHS Blood and Transplant issues alert to specialist staff

The alert was issued after a rise in the number of organ donors dying from blood clots was noticed. The statement said: “I would ask you to be alert to the possibility of this syndrome in any patient within 28 days of receiving Covid vaccination with thrombosis or unexplained thrombocytopaenia.”

5 April: Oli Akram Hoque goes back to hospital

Oli had been suffering from progressively worse headaches before being turned away by a GP. He died 10 days later of cerebral venous sinus thrombosis.

7 April: New advice to healthcare professionals

Advice from the Medicines and Healthcare Regulatory Authority (MRHA) mentioned a possible link between the AstraZeneca Covid vaccine and specific types of blood clot. Both families say this should have been issued earlier.

‘An amazing girl’

During the inquest expert evidence suggested that had Marina’s brain scans been carried out earlier, it would not have made a difference.

But Ms Reynolds said a side-effect of the vaccine is to make blood platelets clump together, adding: “The scan may or may not have shown what was developing.

“It was clear that Marina’s platelets were continuing to deteriorate, and obviously, had she been properly assessed and reviewed, that would have been found because it was in her previous blood test.”

Mrs Waldron is keen people do not forget her daughter, whose organs were donated after her death.

“We don’t want the amount of people who have died to be swept under the carpet because it’s taken over three years to get to the coroner’s court,” she said.

“Her death had not been registered [until then] as a vaccine death.

“She was an amazing girl. She wasn’t frightened of anything.”

Mr Waldron added: “We don’t know how many other people went through the same thing and died from the same thing as Marina.”

Oli Akram Hoque, 26, from London, also suffered rare and fatal complications after receiving the AstraZeneca vaccine, dying from a blood clot on the brain on 15 April.

He had received his jab on 19 March 2021 and began to suffer headaches from 1 April.

His headaches became progressively worse and he went to an A&E department at a London hospital on 4 April.

At his inquest, the coroner concluded that on his first visit to hospital, Oli had not presented with any symptoms which would have required an emergency department assessment.

The next evening he was taken to the same London hospital which Marina had attended the month before with a severe headache.

He saw a GP there who did not think he needed an assessment but the next day he was rushed back to hospital after having seizures.

His sister Anika Hoque said: “He was saying ‘why do I feel like this?’ and he was pressing his hands… the doctor didn’t think that it was anything to be concerned about.”

The MHRA told Oli’s inquest that starting from 25 February 2021 it had identified three cases of blood clots to the brain associated with the AstraZeneca jab.

But the MHRA could not fully consider these cases as they did not receive all the necessary clinical information from hospitals.

The coroner raised concerns about this with the DHSC, saying the MHRA should be able to compel hospitals to give them such information as soon as possible.

The MHRA said: “Regarding the scope of MHRA’s powers to compel information to be provided by hospitals to patients, the Agency does not have legal powers to compel healthcare professionals to provide additional information.”

Miss Hoque said the AstraZeneca jab had already been suspended in many parts of Europe at the time of her brother’s death.

“This is information that I had to find through the internet,” she added.

“My GP she said the exact same thing, so it was clear that information was available at the time but the doctors in A&E said they were not aware, which was surprising… so obviously it cost lives.”

The DHSC said: “Our sympathies are with the families of Marina Waldron and Oli Hoque.

“This government is committed to learning lessons through the Covid-19 inquiry.”

It said vaccines would be discussed in Module 4 of the Covid-19 Inquiry, with hearings planned to start from 14 January to 30 January.

It added the “health system quickly responded to reports from the MHRA of extremely rare cases of concurrent thrombosis and thrombocytopenia following vaccination with the first dose of AstraZeneca.”

In April 2021, the Joint Commission on Vaccination and Immunisation advised adults under 30 without underlying health issues should be offered an alternative vaccine to AstraZeneca if available.

This was later extended in May 2021 to adults under 40 without underlying health issues.

‘Six million lives saved’

A spokesperson for AstraZeneca told the BBC “Our sympathy goes out to anyone who has lost loved ones or reported health problems. Patient safety is our highest priority.

“From the body of evidence in clinical trials and real-world data, the Oxford-AstraZeneca vaccine has continuously been shown to have an acceptable safety profile and regulators around the world consistently state that the benefits of vaccination outweigh the risks of extremely rare potential side effects.

“We are incredibly proud of the role the Oxford-AstraZeneca played in ending the global pandemic.

“According to independent estimates, over six million lives were saved in the first year of use alone and over three billion doses were supplied globally.

“Our efforts have been recognized by governments around the world and are widely regarded as being a critical component of ending the global pandemic.”

——————————–

Resources:
https://x.com/DrAseemMalhotra/status/1826907728490451281
https://www.bbc.co.uk/news/articles/cx2g921rd2lo.amp

New Oxford Study Shows ONLY the Kids who Took the COVID Shots had Heart Problems and Died

We now know kids should NOT get the COVID vaccine.

Executive summary

Expect health authorities to ignore this because otherwise it would make them look bad.

Oxford study shows only vaccinated kids are dying and getting myocarditis and pericarditis

The new study looked at the official government data of more than 1 million English children and adolescents aged between five and 11 and 12 and 15.

The study compared vaccinated and unvaccinated subjects.

Vaxxed: 12 cases of myocarditis or pericarditis, 3 deaths
Unvaxxed: 0 cases; 0 deaths

Considering there were no COVID deaths in either cohort, health authorities such as the CDC should be IMMEDIATELY telling parents not to vaccinate their kids.

The myo and pericarditis cases difference is highly statistically significant (p=0.00024). The death differences (3 vs. 0) were just short of statistical significance (p=.13).

Summary

Expect the medical community to ignore this study and continue to advise parents to vaccinate their kids, even though the data says otherwise.

Major Study: Covid Shots Are SOLE Cause of Child Heart Failure Surge

An explosive study has finally confirmed that Covid mRNA shots are the sole cause of an unprecedented global surge of heart failure among children.

The study shuts down previous claims from the corporate media and so-called experts who have tried to blame the phenomenon on COVID-19 and other factors such as sugar intake, video games, and even so-called “climate change.”

As Slay News has been reporting, cases of cardiac-related deaths and heart failure such as myocarditis and pericarditis have been soaring around the world since the Covid injections were rolled out to the public in early 2021.

Myocarditis and pericarditis are inflammatory conditions of the heart and are generally considered to be rare, especially among children.

Severe cases can lead to serious complications and even sudden death.

Both conditions are known side effects of the Covid mRNA shots.

In response, health officials and the corporate media have been pushing the claim that the COVID-19 virus, and not the shots, is behind the phenomenon.

However, a new large-scale study from renowned scientists at the prestigious University of Oxford has just confirmed that myocarditis and pericarditis only appear in children and adolescents after Covid vaccination and not after infection from the virus.

The new study looked at the official government data of more than 1 million English children and adolescents aged between five and 11 and 12 and 15.

The study compared vaccinated and unvaccinated subjects.

The researchers also took into account the number of doses of vaccine received.

In detailing their findings, the researchers wrote:

“All myocarditis and pericarditis events during the study period occurred in vaccinated individuals.”

The study also noted that hospitalization related to COVID-19 was extremely rare among children and adolescents.

Additionally, there were no deaths from the virus recorded among the entire subject population.

The English data show that myocarditis and pericarditis were only recorded in vaccinated children and adolescents.

Other studies have claimed that myocarditis risk is higher after infection with the virus.

Adolescents had a significantly higher risk of contracting the conditions than children.

The vast majority of myocarditis and pericarditis cases occurred after the first dose of the vaccine, the study found.

Over half of the adolescents who suffered from the conditions visited the hospital as a result.

This news follows another recent study, from some of America’s most well-respected researchers, that found that Covid shots were responsible for soaring sudden deaths around the world.

As Slay News reported, the case study was conducted by Harvard Medical School and chronicled soaring cases of fatal cerebral ischemia over the past three years.

Cerebral ischemia is a form of deadly brain damage triggered by insufficient blood flow to the brain.

The Harvard researchers found that Moderna’s Covid mRNA injections specifically caused the recent spike in the fatal disorder.

Leading experts have been responding to the explosive study by raising the alarm about the mRNA vaccines.

The Harvard study is the latest in a long line of studies linking the shots to sudden death and potentially fatal side effects.

In response to the Harvard study, Dr. John Campbell published a video breaking down the results.

“This could not be a more serious report,” Campbell warns.

Dr. Peter McCullough also discussed this case study on his Substack.

“We are becoming accustomed to unexpected death after COVID-19 among young persons who have taken one or more injections of the COVID-19 vaccine,” McCullough said.

“It is important to realize that not all deaths after vaccination are cardiac.”

Slay News recently reported on the bombshell admission from the U.S. Centers for Disease Control and Prevention (CDC) that the Covid mRNA sots have killed hundreds of thousands of American children and young people.

The explosive admission was uncovered in a secret CDC report showing that a staggering half a million American children and young adults have been killed by Covid shots.

The CDC report has revealed that almost 500,000 Americans aged between 0 and 44 years old died from the dangerous side effects of the Covid mRNA injections.

The recorded deaths occurred between the start of the public rollout of the shots in early 2021 and October 9, 2022.

However, due to the timeline of the data, the already shocking figures do not include deaths recorded over the past year and a half.

———————————–

Resources:
https://kirschsubstack.com/p/new-oxford-study-shows-only-the-kids
https://slaynews.com/news/major-study-covid-shots-sole-cause-child-heart-failure-surge/

Leading Pharmaceutical Scientists in Japan have Just Exposed a Staggering Surge in the Risk of Heart Failure for people who have been Injected with a Covid mRNA Shot

By AussieCossack

Leading pharmaceutical scientists in Japan have just exposed a staggering surge in the risk of heart failure for people who have been injected with a Covid mRNA shot.

According to a team of Japanese researchers from the Division of Pharmacodynamics at Keio University Faculty of Pharmacy and Yokohama General Hospital, the risk of heart failure surges by up to 4,900% after a person receives a Covid mRNA injection.

The research team, led by Professor Keisuke Takada, found that the risk of myocarditis is 20-50 times greater after receiving the shot.

Original source: https://t.me/AussieCossack/20989