UK Govt Coronavirus Response: Sceptics Thread

[sancho panza]

thar she blows......

'Until government authorities can explain the dramatic rise in all cause-mortality among the vaccinated by some other explanation--it is the COVID-19 vaccine as the cause of death until proven otherwise. See Hulscher et al, 2023'

https://twitter.com/P_McCulloughMD/status/1728051042108014703

https://zenodo.org/records/8120771

A SYSTEMATIC REVIEW OF AUTOPSY FINDINGS IN DEATHS AFTER COVID-19 VACCINATION

Creators

• Nicolas Hulscher, BS¹
• Paul E. Alexander, PhD²
• Richard Amerling, MD²
• Heather Gessling, MD²
• Roger Hodkinson, MD²
• William Makis, MD³
• Harvey A. Risch, MD, PhD⁴
• Mark Trozzi, MD²
• Peter A. McCullough, MD, MPH⁵

 

 

Description

 

ABSTRACT

Background: The rapid development and widespread deployment of COVID-19 vaccines, combined with a high number of adverse event reports, have led to concerns over possible mechanisms of injury including systemic lipid nanoparticle (LNP) and mRNA distribution, spike protein-associated tissue damage, thrombogenicity, immune system dysfunction, and carcinogenicity. The aim of this systematic review is to investigate possible causal links between COVID-19 vaccine administration and death using autopsies and post-mortem analysis.

 

Methods: We searched for all published autopsy and necropsy reports relating to COVID-19 vaccination up until May 18^th, 2023. We initially identified 678 studies and, after screening for our inclusion criteria, included 44 papers that contained 325 autopsy cases and one necropsy case. Three physicians independently reviewed all deaths and determined whether COVID-19 vaccination was the direct cause or contributed significantly to death.

 

Findings: The most implicated organ system in COVID-19 vaccine-associated death was the cardiovascular system (53%), followed by the hematological system (17%), the respiratory system (8%), and multiple organ systems (7%). Three or more organ systems were affected in 21 cases. The mean time from vaccination to death was 14.3 days. Most deaths occurred within a week from last vaccine administration. A total of 240 deaths (73.9%) were independently adjudicated as directly due to or significantly contributed to by COVID-19 vaccination.

 

Interpretation: The consistency seen among cases in this review with known COVID-19 vaccine adverse events, their mechanisms, and related excess death, coupled with autopsy confirmation and physician-led death adjudication, suggests there is a high likelihood of a causal link between COVID-19 vaccines and death in most cases. Further urgent investigation is required for the purpose of clarifying our findings. 

[wherebee]

DOSBODS started in 2020 somewhat supportive of development treatments for COVID (I have gone back and checked);  It moved to interested in a vaccine development in late 2020 (perhaps partially because Trump was supportive) but then the speed of rollout and dystopian enforcement meant that by early 2021, almost every view on here was that the 'MRNA jabs' were not to be touched.

By late 2021, it was firmly obvious to anyone with a brain the vaccines were either dangerous, fucking dangerous, or a bio weapon.

By 2022, normos should have been informed with even the slightest checking.  Many 'mainstream' commentators broke ranks in 2022 and spoke up on vaccine damage

By now, end 2023, anyone outside DOSBODS who doesn't think the vaccines are shit and to be avoided never will, I think.  The Speccie Australia (which has been constantly of a DOSBODS mindset since 2020) can say all it wants, but until we get (as NZ just has) minor parties driving an investigation AND headline confessions, nothing is going to happen.

Of course, @NTB will tell me two more weeks, eh.

 

[sancho panza]

anotehr excellent John Campbell interview with Clare Craig.

 

[sancho panza]

https://dailysceptic.org/2023/11/27/new-evidence-that-covid-vaccines-may-promote-hyperprogressive-cancers/

New Evidence That Covid Vaccines May Promote ‘Hyperprogressive’ Cancers

A while ago, I explored a unique, rare class of antibodies called IgG4, caused by repeat injections of mRNA Covid vaccines.

These IgG4 antibodies are usually created in response to persistent irritants such as worms. Unfortunately, repeat injections of mRNA Covid vaccine are perceived by our immune systems as a ‘persistent irritant’ and cause the IgG4 antibody switch.

The ‘persistent irritation’ effect possibly occurs not only because of repeat injections but also due to mRNA gene expression never stopping in half of the vaccinated people.

Are these IgG4 antibodies harmless? Do they have any effects outside of our immune reactions to COVID-19? Is there something to worry about?

Unfortunately, a 2020 study published in the British Medical Journal’s Journal for Immunotherapy of Cancer suggests that having more IgG4 antibodies — of any kind – enhances cancer progression. The study by Wang et al. was done two years before the discovery of mRNA vaccine-related class switch to IgG4 antibodies.

The study authors found cancer-enhancing effects of any IgG4 antibodies in people and laboratory mice.

RESULTS: In a cohort of patients with esophageal cancer we found that IgG4-containing B lymphocytes and IgG4 concentration were significantly increased in cancer tissue and IgG4 concentrations increased in serum of patients with cancer. Both were positively related to increased cancer malignancy and poor prognoses, that is, more IgG4 appeared to associate with more aggressive cancer growth. We further found that IgG4, regardless of its antigen specificity, inhibited the classic immune reactions of antibody-dependent cell-mediated cytotoxicity, antibody-dependent cellular phagocytosis and complement-dependent cytotoxicity against cancer cells in vitro, and these effects were obtained through its Fc fragment reacting to the Fc fragments of cancer-specific IgG1 that has been bound to cancer antigens. … We found that local application of IgG4 significantly accelerated growth of inoculated breast and colorectal cancers and carcinogen-induced skin papilloma. We also tested the antibody drug for cancer immunotherapy nivolumab, which was IgG4 in nature with a stabilising S228P mutation, and found that it significantly promoted cancer growth in mice. This may provide an explanation to the newly appeared hyperprogressive disease sometimes associated with cancer immunotherapy. (emphasis added, here and below)

The scientists provide an excellent explanation of the IgG4 antibody subclass:

IgG4 is a unique antibody that has the lowest concentration among IgG subtypes in healthy individuals, and its function has not been well understood. IgG4 was regarded as a ‘blocking antibody’ because of its reduced ability to trigger effector immune reactions. Therefore whatever molecules IgG4 reacts to, the subsequent immune reaction was subdued.

The study details Wang et al.’s multidimensional investigation of IgG4 in a wide array of patients with cancer and tissues with both in vitro and in vivo experiments. Again, this research was done in 2020, well before the effects of Covid vaccines on IgG4 could be seen.

After collecting blood and tissue samples from 82 patients, scientists found that greater levels of IgG4 were associated with higher grade (cancer grade is the tumor’s degree of malignancy) and poor prognosis.

Do IgG4 antibodies cause worse cancer outcomes, or do worse cancers create more IgG4? What is the horse and what is the cart here?

The rest of the scientific study tries to answer this question, and scientists conclude that IgG4 drives malignancy and aggressiveness of the real-life cancers they observed.

They found that even non-cancer-specific IgG4 inhibited immune reactions to cancer cells. Since human experiments of this kind would be unethical, authors instead experimented with mouse models:

I illustrated the image scientists provided and circled larger tumors – and tumor size increases – they found to happen after IgG4 injections:

The authors describe hyperprogressive disease that occurs due to certain monoclonal IgG4-based antibody nivolumab, despite previous hopes of its potential usefulness.

Recent awareness of hyperprogressive disease (HPD) associated with anti-PD-1 and anti-PD-L1 monoclonal antibody treatment for cancer has caught widespread attention, but no consensual explanation for this phenomenon has arrived. HPD appeared to be a common complication for immunotherapy with nivolumab in many cancer types, including head and neck squamous cell carcinoma, non-small cell lung cancer, gastric cancer and so on. Our findings suggest that these IgG4 antibody drugs might have undesired side effects of inhibiting local immune responses and indirectly promote cancer growth. When the specific target molecule is present in cancer, these IgG4 antibody drugs might be effective. However, when the targets are absent or scanty, the IgG4’s immune inhibitory effect might prevail and accelerate cancer growth. This possible detrimental effect of IgG4 might contribute to HPD in patients treated with PD-1 targeting drugs with IgG4 structure.

What is ‘hyperprogressive disease’? It is the same thing as ‘turbo-cancer’, of course, but it is a more fitting scientific term.

The authors conclude:

Conclusion  There appears to be a previously unrecognised immune evasion mechanism with IgG4 playing an essential role in cancer microenvironment with implications in cancer diagnosis and immunotherapy.

Cancer Deaths Increase in Australia

Unfortunately, relatively few recent cancer statistics are officially available. An internet researcher named the Ethical Skeptic found some recent alarming numbers. I do not want to highlight any of his specific findings because I have not yet been able to verify them personally, but my readers may take a critical look of their own.

However, what is available is about a 7% increase in cancer deaths reported in Australia, a highly vaccinated country. Since cancers typically take years to develop and grow, such an increase is concerning, given that only two years passed since Australians received their ‘safe and effective’ vaccines.

A Hope for Vaccinated People

My work never takes cheap shots at vaccinated people and does not make unfounded, dire predictions not supported by evidence. I would rather forgo additional clicks and subscribers than misinform my readers. Let me summarise my reasons for hope that these biological findings will hopefully leave some people unscathed:

• We are only beginning to understand the effects of IgG4 antibodies on cancer
• Only about half of vaccinated people produce IgG4 antibodies in quantity
• Even though experiments showed increases in IgG4 over time, these antibodies may wane over the long run
• No evidence to date suggests that IgG4 antibodies cause cancer – the evidence only points to them enhancing and speeding up existing cancers

What the evidence shows is that some cancers, possibly treatable before mRNA injections, may become aggressive and difficult to treat, a condition that the BMJ study authors call “hyperprogressive disease”.

I hope and pray that the number of people affected by ‘hyperprogressive disease’ will be low – and I hope that my readers will agree with this statement.

[sancho panza]

I'm not sure it's workers too busy to get jabbed?

SPoke to our residnet jabber the other day as she was tkaing my name off the lsit and she was saying she'd had three x coof jab and had been really ill after each one.No more she said.....

I'm amazed it's as high as 20% for the coof given whats come out

https://www.dailymail.co.uk/health/article-12788381/As-FIFTH-NHS-staff-Covid-flu-jabs-winter.html

Concerns have also been raised that the low uptake is a result of NHS hospitals not making it easy enough for staff to roll up their sleeves, with workers simply too busy to get jabbed.

Of those that did, The Dudley Group NHS Foundation Trust recorded the lowest flu jab uptake, with only one in 20 of 4,700 frontline staff getting the vaccine. 

Cambridgeshire and Peterborough NHS Foundation Trust had the lowest Covid vaccine uptake, with just four of 3,773 frontline staff getting the jab (0.1 per cent).  

Edited November 27, 2023 by sancho panza

[sancho panza]

I don't know whats happened to the Daily Mail but reality jsut keeps coming

I've said this for sometime that all masks did was encourage people not to scoailly distance(one of the rpoven ways of reducing spread)

https://www.dailymail.co.uk/health/article-12804565/No-proof-face-masks-worked-against-Covid-UKHSA-boss.html

No proof face masks ever worked against Covid, claims UKHSA boss who warns they may have even had OPPOSITE effect on spread through 'false sense of security'

There is no solid proof masks ever slowed the spread of Covid, England's former deputy chief medical officer said today.

Professor Dame Jenny Harries, who now heads up the UK Health Security Agency, said the evidence that coverings reduced transmission is 'uncertain' because it is difficult to separate their effect from other Covid curbs.

She also told the UK's Covid inquiry that government advice on how to make a mask using two pieces of cloth was 'ineffective'.

Studies showed at least three were needed for even a small effect on the spread of viruses, Dame Jenny said. 

Meanwhile, she warned advice for the public to wear masks during the pandemic may even have given people a 'false sense of security' that they could reduce their risk of becoming infected if they wore one while mixing with others.

She told the inquiry that the evidence at the time said at least three layers were needed 'to give a positive impact' but even this finding 'was not very strong', so the advice was not effective.

She said, at the time, she and Professor Sir Jonathan Van Tam, England's former deputy chief medical officer, were 'really trying to highlight what we thought about the two metre and one metre rule discussions'. 

Dame Jenny added: 'What was being conceived was if you wear a face covering and reduce everything to a metre, the face covering will make up for the difference, and the answer was no, it won't, and it definitely won't if it's ever not evidence based.'

Dame Jenny also revealed that she wrote to cabinet secretary Simon Case in May 2020, when he was No10 permeant secretary, expressing concern that people may believe they 'could go back to normal' wearing face coverings made from t-shirts, when there was no evidence base around the measure.

[Chewing Grass]

49 minutes ago, sancho panza said:

Professor Dame Jenny Harries, who now heads up the UK Health Security Agency,

Another person with no professional integrity who should be put on trial to prove whether they lied or not.

Being a liar or being incompetent at this level is the same as there is.no excuse for professional incompetence because if you had integrity you wouldn't go there.

Catch 22.

Edited November 29, 2023 by Chewing Grass

[sancho panza]

Huge revelations from NZ.Huge rise in deaths post vaxx.

Also huge geogrpaical variance in vaxx injury apparently such that one pharmacy in Invercargill where 30% of pts who received the vaxx died

what the analyst measues is days where daily deaths are more than 120 in NZ

He also measures death rates from vaccination sites,death rates from batches.absolutely incredible data.

the guy explains he was in a unique postion because NZ is small enough to mean you can get all teh data

If true,this is it........ @dnb24

https://rumble.com/v3ynskd-operation-m.o.a.r-mother-of-all-revelations.html

 

Queens Park Medical is the vaxx site in Invercargill,30% of pts injected are apparently dead.Ave death rate in NZ is 0.75%

For some reason the south island has most of the high death rate vaxx sites.The analasyt states that some vaxx sites had fatality rates under 1%

 

and which batches killed the msot

and which vaxxinators had the highest death rates

singificant clusters of deaths per day in Invercargil

[dnb24]

https://www.reuters.com/legal/pfizer-is-sued-by-texas-over-covid-19-vaccine-claims-2023-11-30/
 

Texas suing Pfizer over vaccine claims

[sancho panza]

42 minutes ago, dnb24 said:

https://www.reuters.com/legal/pfizer-is-sued-by-texas-over-covid-19-vaccine-claims-2023-11-30/
 

Texas suing Pfizer over vaccine claims

from the artcile this data pointis similar to the recent JCampbell vid claiming 1.5% efficacy iirc.

The data will come out.

[sancho panza]

I've been follwoin tthe NZ whistle blower story and it's looking more and more legit.

The Sceptic finally talked about it,running a ...ahem...sceptical piece by Igor Chudov

Igor sets out to take apart the story but in the end accepts that after reassurances and explanations by Steve Kirsch that the data looks reliable.

for me the go to here is the speed with which NZ police have moved.

NZ govt is deep in the hole here.I think there's a chance the incoming govt will hang ardern out to dry

https://dailysceptic.org/2023/12/04/new-zealand-whistleblower-data-leak-linking-covid-vaccine-to-deaths-the-evidence/

By now, almost everyone has heard of the ‘New Zealand whistleblower data’. Many people are discussing it, and I want to weigh in with my sceptical opinion. While I oppose Covid vaccines, I owe my subscribers a duty to report truthfully. My post should not be interpreted as ‘pro-vaccine advocacy’.

Be aware that the ‘leaked New Zealand data’ is problematic; even the story accompanying it is less than entirely believable.

I spent an entire day analysing it.

I downloaded it as a CSV file, uploaded it to my MySQL database server, and analysed it. As I will show…

• The ‘whistleblower data’ is missing huge chunks of information that should logically be present
• Liz Gunn of NZ is misinterpreting it by trying to pass normal nursing home deaths as evidence of “super deadly batches” and “mass vaccine casualties”
• The data has problems that are incompatible with the story of its origin. It cannot be a full snapshot from a working payment database. Therefore, the story of its origin is suspect
• The actual vaccine casualties may reside in the missing pieces of data that the ‘database’ does not provide

The Story

You may have heard that a brave whistleblower, a database administrator in New Zealand, saved secret Covid vaccine death data and passed it to Liz Gunn, leader of the NZ Loyal Party.

New Zealand Covid-19 vaccination database admin turns whistleblower and reveals how many people died after taking bad batches of the Pfizer vaccine. This must be investigated. If this data of mass vaccine casualties is real there must be accountability. pic.twitter.com/2QjkmRGJca

— Kim Dotcom (@KimDotcom) November 30, 2023

The data he extracted includes four million records containing:

• Individual person ID
• Vaccine type
• Batch Number
• Vaccine dose number
• Vaccination date
• Date of birth
• Age
• Date of death, if applicable

In my post below, I will refer to the above video and question the ‘official’ story.

What I Did

I downloaded the data as a ZIP file. Inside was a large CSV file named “nz-record-level-data-4M-records.csv” with 4,193,438 records. I imported the data to a MySQL database. I chose the combination of individual ID and dose number to be the primary key. Due to 47 records with duplicate primary keys, only 4,193,391 rows were populated.

An example of one of such 47 duplications is shown here: Person 152,535 was recorded, in contradiction, as having received their first dose twice, on the same day, 12-07-2021; one AstraZeneca and one Pfizer:

Missing Pieces

The total number of ‘distinct individuals’ in the ‘leaked’ database is only 2,215,729, even though 4.3 million New Zealanders received Covid vaccines.

, half of vaccinated New Zealanders are missing.

A lot of doses are also missing, with some people having only dose five, or doses three and five, etc.:

461,900 people have only doses four and five recorded:

Only 966,989 records of dose one exist, even though the database contains information on 2.2 million people and 4.3 New Zealanders were vaccinated.

Over half of the people in the database (1,248,740) are missing records of dose one:

Out of 2,215,729 individuals in the database, a little under half (1,024,375) are missing the record of dose one and two:

Mess With Batches

The batch_id is supposed to refer to a particular batch of a particular vaccine, like Pfizer or Novavax. Instead, there are numerous ‘batches’ that contain multiple vaccine types:

Deadly Batches, Mass Murder Sites or a Total Misunderstanding?

The Liz Gunn video poignantly discusses “deadly sites” and “deadly batches”.

The video by Kim Dotcom reports that 21.38% of people receiving “Batch One” died:

However, the numbers do not match the leaked database, which has 2,979 people who received Batch One, and 375 of them died. That’s 12.59%, not 21%.

What kind of people who received Batch One died?

People who received Batch One were quite old. The average age of all Batch One recipients is 67 years old. The average age of all dead Batch One recipients is 86 years old. Batch One was given 2.5 years ago, so recipients had plenty of time to die naturally.

Another fragment of the video discusses “deadly sites”. According to Liz Gunn, some sites were mass murder vaccine sites. For example, Liz and the whistleblower refer to one vaccination site, Te Hopai Hospital, where 32% of vaccinated individuals died after vaccination:

But Te Hopai Home and Hospital is a ‘nursing home’ for old and dying people unable to care for themselves.

Is the death rate of 32% in a nursing home where residents had 2.5 years of post-vaccine exposure excessive? I am not a nursing home expert, and I am not sure – but discussing a 32% death rate without mentioning that this is a nursing home is disingenuous.

Is This ‘Leak’ a Psyop?

I do a lot of things. One of them is administering the database for Algebra.com, a website with millions of monthly visitors and over a million of answered math questions.

, I understand database administration. The story of a bona fide ‘leak’ does not make sense to me. The data does not have the integrity that a full leaked data set would have.

This is supposed to be a payments database containing information for payments to vaccinators.

How can a payment database have such holes and missing data?

Was data selectively removed from the database before the leak?

How can batch IDs refer to multiple vaccines?

Did both the ‘whistleblower’ and Liz Gunn honestly forget to check that these ‘deadly vaccine mass murder sites’ are nursing homes?

Do the missing records of first vaccinations (doses one to two) hide real vaccine deaths, making Liz Gunn go on about “deadly nursing homes” instead of looking at deaths actually caused by the Covid vaccine?

Was the ‘leak’ a psyop and an intentional attempt to sow confusion, as it occurred with the old, pro-WEF and vaccine-crazy N.Z. Government still in place during the last days of it? This question is speculative, but something I would like to clarify.

There are many questions to which I do not have an answer.

I am thankful to Arkmedic and Nick Hudson for alerting me to the questionable nature of the ‘leak’.

I want to invite my readers to discuss this ‘leak’. I expect vigorous disagreements and hope my mistakes, if any, will be highlighted and corrected.

This article was first published on Igor’s Substack page. Subscribe here.

Update: A lot of things have happened since I wrote my previous post. First, I spoke with Steve Kirsch, who assured me that the data was genuine and the whistleblower was sincere.

A big discussion followed my post, as well.

Celia Farber also reported many additional facts today:

• This information was offered to other groups before (see VFF’s statement)
• The alleged whistleblower, identified as Barry Young, has been arrested

The New Zealand Government has obtained an injunction prohibiting the spread of the data. Steve Kirsch’s hosting account has been terminated, and he has moved the data to a new account.

In addition to offering a new way to download whistleblower data, Steve also provides additional details worth reading.

At this point, I believe that Barry Young was more likely to be sincere than insincere in his intentions and actions.

My previous questions and comments about Liz Gunn’s statements about nursing home deaths and data quality still apply, with one exception: The partial nature of the data is explained by the fact that some shots were not paid through the system that Barry Young was supposedly administering (I hope more clarity emerges).

This clarification is vital since I questioned the sincerity of the person who possibly risked his life to disclose data.

I greatly hope that, after thorough analysis, the data will yield useful information.

[sancho panza]

Here's STeve Kirsch on the matter via substack with hsi take and also referencing Prof Norman Fenton having had a loo

https://kirschsubstack.com/p/data-from-us-medicare-and-the-new

Data from the New Zealand Ministry of Health shows that the COVID vaccines have killed over 10 million worldwide

It's finally here: record-level data showing vaccine timing and death date. There is no confusion any longer: the vaccines are unsafe and have killed, on average, around 1 person per 1,000 doses.

Executive summary

Today you will get to see the data that nobody wants you to see. FINALLY.

No State or country has ever released record-level public health data on any vaccine.

Privacy is not the reason for this; the data can be easily obfuscated (which we did on this data) so that no record entry would match that of any person, living or dead.

The reason the data is kept secret is simple: it would expose the fact that the COVID vaccines are unsafe, as well as all the vaccines that I have been able to get record-level data on.

Today, thanks to a courageous whistleblower who works at the New Zealand Ministry of Health, we have record-level information from a large population of all ages and are making it public for the first time in history.

Here is the Rumble video announcing the leak:

 

There was a YouTube link as well, but YouTube censored it within minutes of posting, just like we knew they would.

Just as you suspected, the COVID vaccines have killed millions of people worldwide, an estimated 1 death per 1,000 doses on average in a standard population.

And now we have the data to prove it.

Short responses to criticisms about the data

I posted a link to this section of the doc on X so that people can list any objections I need to counter that aren’t already listed.

Here is are the response points to all the criticisms I’m aware of. The tl;dr is that all of the critiques don’t move the needle.

1. I know the leaker and got the data on Nov 9.

2. I verified the identity of the leaker and the authenticity of the data and chain of custody.

3. There is no doubt about the authenticity. NZMH would have said the data is fake if it was. That would immediately discredit it. But they didn’t because that would be criminal. So they decided to distract people by going after the leaker and calling him a misinformation spreader for leaking the ACTUAL records.

4. The data available publicly has been anonymized. That is why they acknowledged this and couldn’t find any matching records. Anyone who thinks their personal data was leaked is delusional.

5. There is only one way to analyze this data definitively and that’s with a time-series cohort analysis. This is the exact same methodology the UK ONS used to analyze their vaccine data. The difference is our buckets are one week long so we can see things they are trying to hide. So if you want to dispute the analysis, you either have to show one of 3 things: that 1) our buckets.py program has a bug or 2) that all the mortality curves are flat or relatively flat, or 3) that the dataset is somehow compromised by showing that one of the four graphs is nonsensical or 4) that the v4 xlsx file has a bug in it. Nobody has done #1, and #2 is impossible. Nobody has even attempted #3 or #4. So basically, all the “attacks” are a ridiculous waste of time. There is a way to attack this if it is wrong, but nobody seems capable of figuring it out. I just told you the answer. Go prove I’m wrong using this method and I’m all ears. I did it the professional way just like the UK did. If I made a mistake, tell me what it is. But having each person do their own “ad hoc” analysis is not how you analyze the data. The gold standard is time-series cohort.

6. There are 12M doses in New Zealand. The data drop is only for the “Pay per dose” (PPD) program in NZ which is 4M of the 12M records. Whether you got PPD or not is pretty random.

7. What cohort does this PPD programme cover? Answer: it’s completely random. It is not age biased or biased to any demographic. If you can prove I’m wrong, show me your data.

8. The reason the avg age of death is higher than average is because the older you are, the more the vaccine killed you. Duh.

9. KiwiCraig74 wrote: “Yes, these were mobile units used to give vaccinations to people in rest homes, which is where people generally are when they don't have long left. Average term of residence is only 20 months, so many die within his 10 months (vaccine or no vaccine).” This is just complete bullshit. There are 895,500,935 man days in people under 60 in the data. There are a total of 1,348,440,643 man days in the full data.

10. There is a disproportionate amount of records for the doses, i.e., they are not in direct proportion to the total number of each dose, e.g., they are not 33% of each dose. Some doses are over-represented, some are under represented.

11. Many people will not have all their doses in this database, e.g., there may just be dose 3 data for someone.

12. The fact that the sampling was uneven doesn’t matter if you analyze it the way I did. The fact that doses are missing is also irrelevant. These are gaslighting arguments made by people who are incompetent to analyze this data.

13. The data shows a mortality hump that peaks around 6 months after a dose is given.

14. If you limit the time period to before COVID, during COVID death wave (from April 1 to August 1, 2022) and after the wave, you find the same response curve. So it wasn’t COVID. People who claim that are misinformed and claim it without evidence.

15. Let’s take a simple example. From OWID, we know there were no COVID deaths from Aug 1, 2022 to Nov 1, 2022 in New Zealand. From our spreadsheet we see the average deaths for everyone during that time:

    

     

    So now lets look at that same period of time, except let’s look relative to the shot administration time

    

• Whoops! Ya got trouble my friend. Right here in River City. With a capital T and that rhymes with V for vaccine. The dots aren’t supposed to go above 1059.

• Nobody will debate me on this. I offered to debate the NZMH epidemiologists about the cohort time-series analysis I did and they don’t want to challenge me for some reason. I can’t figure it out. They should CRUSH me if the data shows the vaccines are safe.

• The NZMH should be releasing the full 12M record dataset to remove all doubt and prove to the world the vaccines are safe. They don’t want to do that. Nobody wants to do that. Nobody in the ##$#$#% world wants to do that. Can you figure out why? Use your brain folks!

• I offered to bet anyone $1M on the same terms as my bet with Saar Wilf that the NZ data is legit and it shows the vaccines increase risk of death. Nobody seems interested in taking my money which means all of them have no confidence whatsoever in their criticisms.

• The NZMH whisteblower, Oracle database admin Barry Young, is a hero. He knew he would risk his life and could spend the rest of his life in jail, but he made the courageous move to expose the data for all to see. This is a highly commendable act of public service. He basically threw the rest of his life away in order to save the lives of others. Why else do you think he would do that? Nobody can explain it.

• They tried to give this to lawmakers but nobody would meet with them.

• No New Zealand lawmaker is calling for an independent investigation. They all want to make Barry into the fall guy. Why not have a worldwide panel of top epidemiologists analyze the record-level data? We know why… Barry was right.

• The NZMH has never released the time-series cohort analysis showing the vaccines are safe. They have never released the record level data. Their goal is to keep the data hidden for as long as possible so that they kill as many people as possible before they are caught. The leadership of NZMH are the people who should be arrested.

• Professor Norman Fenton retracted his remarks about deaths being oversampled after I pointed out to him that there is nothing in the data that supports that. See his new article. He doesn’t dispute my analysis (which ignores the hot lot analysis).

• Igor Chudov wrote a critical article but now is relooking at the data after I talked to him and explained to him how his original article is wrong.

• Jikkyleaks analysis is amazing crude. That’s not the way to analyze this data. I like Jikky but he’s wrong. Once again, nobody would analyze the data that way. There is a right way and a wrong way. The UK ONS got it right. Let’s stick to that way. NOBODY SEEMS TO WANT TO DO IT THE RIGHT WAY. They all want to use their own custom made analysis technique that they create on the seat of their pants, rather than the right way to do that. Why is that?

• UPenn Professor Jeffrey Morris claims you can’t find a signal here but all attempts for a public recorded discussion of the data were refused. He apparently hasn’t even seen my MIT presentation which is a pre-requisite if you are serious about understanding this data. He shoots first without taking the time to listen to what is being presented. Morris isn’t an honest actor. Someone looking to find the truth would be calling for every public health agency to release the record level data. Morris has never made any such call. He doesn’t want the truth exposed. I asked him to explain just one slide in my MIT slide deck (the most devasting slide in the deck), and he hadn’t seen it before which is proof he didn’t even listen to my presentation or even view the slides. Instead of explaining the Medicare data, Morris does his standard “switch the topic” technique to avoid answering a simple question. Click the link and scroll up to see the original question which nobody can answer:

  

   

• Morris has had the records in his hands for about 1 week before my MIT talk. He has not published any blog post explaining how this evidence is consistent with a safe vaccine. When I asked him, “So Jeffrey, what exactly would an “unsafe” vaccine look like, he said he doesn’t play games like that. Morris never saw a vaccine that is “unsafe.” In his eyes, no matter how many people die, the vaccine is safe and all safety signals are caused by confounders that he cannot quantify. This is all handwaving analysis to make your point. Where is his blog on this data? I’ll tear it apart inch by inch and show how corrupt he is.

• None of these people who claim to critique this will do a live discussion with me. No offers on the thread at all. If you want to debate me, simply post your argument and follow me, and say you want a debate. Everyone seems scared to do this, including KiwiCraig74.

• The official NZ death records show around 10,000 excess deaths since the vaccines rolled out. This is comparable to the number predicted from the vaccine data (1 excess death per 1,000 doses).

  

The MIT slide presentation

You can read my “Is it safe?” MIT presentation slides here. I highly recommend reading the slides and/or watching the livestream. I tried to make the slides self-standing, but the livestream can be helpful in explaining some of the slides.

I also periodically dump a PDF version of the presentation to my skirsch.com web server. The PDF version is searchable and you can copy/paste text from it (such as the access keys for the Wasabi server so you can download all the goodies).

The MIT talk livestream links

Here is the Twitter livestream.

Here is the Rumble livestream.

Downloading the data and tools

The MIT presentation listed above has everything you need including the credentials to download all the data.

Here are the credentials:

Server: kirsch.izt.world:9000
Note: do NOT use SSL (make sure this is unchecked)

Public API keys:
access-key= g42m54xwZS80yQpAO20Q
secret-key= Kq77gLL47mbypnnRc0UP7sPTvrvjn6y0D5FSEK5H

You can only access the data-transparency bucket for now. Trust me, there’s more that I’m not disclosing yet (including a new US source other than Medicare).

This bucket has data from New Zealand, US, and the Maldives.

You can use any S3 file browser to download such as CloudBerry Explorer or CyberDuck. Note: you may need to pick one of the default cloud providers, then change the Service point like I did here with CloudBerry Explorer:

Make sure your destination folder is writable when you copy files from the server.

You can also use rclone to make a local copy of the repository on your system:

mysystem% rclone config
mysystem% rclone -sync kirsch:/data-transparency /mylocal/file/destination-dir

Mirror sites (probably not as up to date)

See Kevin McKernan’s tweet which references this folder.

 

What you will find

1. The data: All the data in the data-transparency bucket is sanitized. Any matches to actual records is completely accidental. The data was sanitized in a way that preserves the statistics. We ran the bucket analysis on the original and obfuscated data and got nearly identical results. There is no reason any health authority couldn’t do the same thing we did.

2. The tools: We’ll give you our time-series cohort analysis software. This is the software that you’ll never get your State epidemiologist to use. Now, armed with record-level data, you can do your own analysis. We’ve made it super easy to use. When done, paste the output file into our v4 analysis .xlsx spreadsheet and you’ll see instantly whether the vaccine is safe or not.

3. The analysis documents: You’ll find annotated spreadsheets as well as word documents.

4. The description of the data: You’ll find documents describing the dataset (size, dates, average ages in each cohort, what the authorities claim, etc.

I encourage you to explore. Everything is “legal” in that jurisdiction. So you’ll see the full times of people who died in the Maldives, for example. In other places, the names are omitted.

Introduction

I was provided the data on November 8, 2023 when it was uploaded to my Wasabi file server.

I was asked by the whistleblower to keep the data confidential until November 30 in order to give the whistleblower time to work out the logistics of how the data would be made public.

I honored my commitment and only shared it with a handful of colleagues including Norman Fenton and his associates in the UK with the whistleblower’s consent.

The data from New Zealand is not perfect; it is not a complete sample. For example, for some people, the first record in the database is Dose #3. Also, only vaccinated people are in the database.

But, by using a cohort time-series analysis, it doesn’t matter. There is no possible way that this data is consistent with a safe vaccine. I estimated that the vaccine killed, on average, about 1 person per 1,000 doses. That means an estimated 675,000 Americans were killed by the COVID vaccines.

We have confirmation of the analysis from the US Medicare data thanks to another whistleblower.

The story of the data can be found in my presentation which has a link to the Wasabi server and access credentials, as well as how to download the free Wasabi File Explorers for PC and Mac. There is a large amount of data and analysis uploaded to the servers.

The cohort time-series analysis takes about 2 hours to run on the data. We’ve included the output files so you can start from that.

Analyzing the data takes about 5 minutes using the v4 spreadsheet in the analysis directory. Anyone can do it. You just plug in numbers to vary the parameters to look at anything you want to investigate. It has 8 visualizations: 4 main graphs (one for each independent variable) and 4 below each graph showing the number of deaths so you can use that to judge the reliability of the data points in the graph above.

Be sure to read the entire presentation to understand how to interpret the data.

Papers about the data

Papers will be coming out from various authors over the coming weeks. See this article which I will update over time.

Summary of what we found

Record level vaccination-date/death data obtained from a whistleblower in the New Zealand Ministry of Health was analyzed using a standard time-series cohort analysis. The results remained consistent even after varying all four of the key independent variables (observation time window, days after shot, age, and dose number). The only way that can happen is if the COVID vaccines significantly increased mortality for those aged 60 and older, the very population that the vaccine was supposed to help. All five Bradford Hill causality criteria are satisfied. From this data, we can accurately estimate that overall, the mRNA vaccines led to the premature death of more than 1 person per 1,000 doses on average over all doses.

This estimate is supported by COVID death data from Medicare obtained from another whistleblower. The data from Medicare was stunning: the number of people who died rose monotonically for those who got shot in 2021 or 2022. My whistleblower inside HHS had never seen anything like that before. It was a perfectly straight line sloping upwards for 365 days since the dose was given. A safe vaccine would see a decline in deaths by 4% to 5% after 1 year from the shot. The COVID vaccines had a 26% mortality increase, a net difference of 30%. This makes the COVID vaccine a competitor to heart disease as the leading cause of death among the elderly (which kills 20% of people per year).

The COVID vaccines are the deadliest vaccine of all time, killing an estimated 13 million people worldwide.

The precautionary principle of medicine requires that a vaccine which results in such a large net increase in all-cause mortality should be immediately revoked worldwide unless there is a more likely explanation for this “gold-standard” data. Nobody has come forward with a better explanation that fits all the data. In fact, nobody on the other side even wants to see this data: the FDA, CDC, Moderna, and Pfizer all refused to look at it. How is that responsible? That is reprehensible.

Researchers could have discovered the harms of these vaccines years earlier if any of the world’s health authorities released comparable record-level data to that released here. It is baffling to us why the medical community who is sworn to do no harm is not insisting on seeing any record-level data before recommending the use of any vaccine to their patients. It is the record-level data that is key to understanding whether a vaccine is safe or not. This is always hidden from public view.

Hidden from view?!?!

Clinical outcomes are never improved by keeping public health data hidden from public view. Yet every health authority in the world has kept this critical record-level safety data hidden from view.

And, to our knowledge, only one authority, the UK Office of National Statistics, had supplied even the most basic time-series analysis for a limited amount of time. The UK time-series analysis confirms the monotonic increase in mortality after each shot is given. But the UK ONS got to pick the bucket sizes whereas when we do the analysis, we have buckets for every week so we can see exactly what is going on. They can’t. And the ONS stopped responding to me when I asked to see the record-level data.

Other health authorities apparently refused to analyze their own data themselves to look for any safety signals which we found in abundance just minutes after receiving the data. After we received this data and analyzed this, we reached out to a number of health authorities in the US in Florida, California, and at the CDC and FDA. They all ignored the request to examine the data I obtained or look at their own data. This is the first time in history that vaccination-death record-level data has been made available to the public. And now we know why.

In addition, FOIA requests to the California Department of Public Health showed that they never analyzed their own data. There were no documents showing that they ever looked for any safety signals. They simply trusted the CDC even though the CDC doesn’t have any vaccine record level data, so it is IMPOSSIBLE for the CDC to do the proper safety analysis.

Finally, the safety signals are limited to those 60 and over simply because there wasn’t enough data to make a firm determination for people under 60; the data was simply too noisy because we were only given 4M of the 12M records in New Zealand.

However, since the vaccine provides no benefits whatsoever for infection, hospitalization, or death, there is no reason for anyone in the world to take these vaccines. See the presentation for details.

In any sane world, the COVID vaccines would be immediately halted and inquiries should begin as to why no health authority in the world did a thorough cohort time-series analysis on the data which would have uncovered the safety signal very early in the deployment. Are they all corrupt? Or are they all incompetent? Or both?

Can Moderna survive this? Why would anyone buy their stock?

These results have implications for Moderna stock as the failure of their underlying technology casts serious doubt on their viability as a going concern. Even if governments continue to buy their products, the breach of the public trust and the unwillingness of the company to look at the record-level data shows that the company is more interested in making a profit than ensuring the safety of their customers. A head in the sand approach to safety is despicable.

Pfizer is no different. Both companies were offered an opportunity to view this safety data and they all refused. So did the FDA and CDC. The offer was made by a respected journalist in the medical new community, not by me.

What did Professor Norman Fenton say about this new data?

Nobody should take my word on this. Those are my opinions based on examination of the data.

Anyone can analyze this data. Come to your own conclusions.

Finally, here is what famed British Mathematician Professor Norman Fenton said, “This confirms what we also saw in the most recent ONS data once.

Whatever uncertainty there may be in the younger age groups there is now no doubt the vaccine is increasing the mortality rate in older people.”

I agree. In spades. I’d bet my life on it.

Yale epidemiologist Professor Harvey Risch had this to say:

“I think that you've made a very strong case that the Covid genetic vaccines are associated with appreciably increased mortality rates for 6-12 months after each dose.  This is particularly compelling in people over age 65.  I am not aware of actual evidence that the increased post-vaccine mortality that you've shown has a different cause.”

The English translation of what he wrote is “the vaccines are killing people,” but scientists aren’t allowed to be blunt so they have to qualify everything they say.

This is how today’s “scientists” come to conclusions

If there was a mass shooting and everyone died, a scientist would want to have a control group and complete medical histories of each person (including a list of comorbidities) and then want to do a Cox proportional hazards analysis before concluding that the gunman could be the cause of death of these people. Without a control group, the scientist would be unable to say whether the shooting actually caused the deaths.

Nobody with respectable credentials wants to defend the vaccine as being safe

I offered to engage in a public recorded debate with anyone who thinks we got it wrong. Nobody was willing to do that to date, although Professor Jovo Vogelstein offered to give it a try to play devil’s advocate.

If you think we got it wrong, I have a $500K bet pending with Saar Wilf in Israel. I’d love to increase the stakes on that bet. Any takers?

Some people are just never going to figure this out

UPenn Professor Jeffrey Morris has had the data for a while. He doesn’t agree with our analysis (as expected). But when I asked him to explain the Medicare data where the mortality monotonically increases every day for 365 days straight, he said he refused to speculate. Professor Morris never is able to see a vaccine that is unsafe. I proposed all sorts of unsafe hypotheses to him, and he said none of them were convincing. So in his mind, no matter which way the deaths go, even if they go sky high after the vaccine is given, you cannot tell if a vaccine is safe or not; there will always be a confounder that he will find. And he’ll always insist on getting additional data that is never available, so he’ll argue that all data, no matter how strong, is not good enough.

Nearly half of America has already figured out the COVID vaccines are not safe; they want to sue the drug companies!

Fortunately most people figure it out pretty quickly. Did you know that 42% of Americans would join a class action lawsuit against the COVID vax makers if they were allowed under law to do so? That is an unprecedented level of customer dissatisfaction. This is why I shorted Moderna stock. That is not a sustainable business. The markets will eventually figure this out.

Their attempts to gaslight you

Some people will try to convince you that the data isn’t complete and is confounded for that reason. That’s bullshit. If it’s a safe vaccine, you can be missing 99% of the shot data and still get the right answer. Doses don’t matter; a safe vaccine is like a saline shot: they cause no impact.

They won’t get away with stupid arguments like that with me. That’s why they won’t debate me.

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Summary

It’s over. They’ve lost. The vaccines are unsafe. This data is the nail in the coffin. Gold standard, official records. There is no better ground truth than this. There is no comparable ground-truth data showing the vaccines are safe. Zero. There can be only one right answer.

If you think the vaccines are safe, accept my bet, debate me publicly, or release the record level data in your state. Nobody will do any of those things it seems.

Sooner or later top epidemiologists will weigh in on this data.

Now we’ll see just how broken science is if the world’s top epidemiologists cannot agree that the vaccines are unsafe. For example, will John Ioannidis weigh in? Or will he remain silent? Will Martin Kulldorff say anything? Or will he also ignore this data?

In the meantime, the medical community and mainstream media will keep recommending the jabs as if nothing has happened. They should be ashamed of themselves.

[sancho panza]

Prof Nroman Fenton offers his opinion

https://wherearethenumbers.substack.com/p/the-new-zealand-vaccine-data-what?utm_source=profile&utm_medium=reader2

The New Zealand vaccine data: what I actually saw and analysed and what the limitations are

Note: As new information becomes available I have been updating this article, so some of the conclusions are different to the original version.

The data and its provenance

Anybody who has followed our writings over the last 3 years will know that we believe there is overwhelming evidence that the covid vaccines are neither effective nor safe. Contrary to ludicrous claims made by official Government sources there is no evidence the vaccines have saved any lives (let alone the millions claimed), but rather evidence that all-cause mortality in the vaccinated is now higher than that in the unvaccinated.

In the last few days there have been claims that leaked vaccination record data from New Zealand provides the most comprehensive evidence yet of how unsafe the vaccines are. A description and analysis of the data is provided in this article and MIT presentation by Steve Kirsch. Because Steve quoted me it appears that I have unwittingly become a key figure in the debate over the data.

I, along with numerous colleagues, was asked to look at an anonymised version of the dataset. I was told this was accurate, reliable record-level data on a very large, random subset of the vaccinated population (it contains over 2 million vaccination records) for the first 2 years of the covid vaccination programme. Any conclusions (including those below) about vaccine safety based on the dataset are only valid if these assumptions about the data are true. Since looking at the data there have been claims that the dataset may not be random since it contains a disproportionate number vacinees who died during the 2-year period. This would mean the data is biased, in which case most of the conclusions made about it would not be generalisable. Igor Chudov has also expressed concerns about the provenance of the data. However, having spoken with Steve Kirsch today, he says that there are widespread misunderstandings about the data and it is not biased. For a start he says that the datatset is the complete set of ‘pay-per-dose’ vaccination records and therefore there is no biased sampling at all. He says:

The people within the group is representative of the total population. There are 2.2 million people in the group, and there are 4 million records. Each of those records is a Vaccination Record.

There were 1.348 billion person days in the "vaccinated state" in the sample. There were 37,283 deaths. This is an average mortality rate of 1009 for the vaccinated people. An average population is 800 deaths per 100K person years. But this population is more elderly skewed let's call it 900 deaths per 100K/p-y. Well, vaccinated people will die, on average, about 10% higher than normal. So that's 990 deaths per 100K p-y. Well our numbers are 1,009 deaths per person year which is 1.9% higher. That is hardly biased.

 

What is in the dataset

Each record in the dataset corresponded to a vaccination and included information about: age of vaccinee, batch number, vaccination centre, date and dose number, and number of days to death if that person died during the 2-year period. However, there were always fundamental limitations of the dataset if we were to draw any firm conclusions about vaccine safety from it. The most obvious was the absence of any records of unvaccinated people during the period. This meant that, for example, comparisons of all-cause mortality between vaccinated and unvaccinated could only be made based on external, more general (and often disparate) population mortality and excess death data. For those who died there was also no information about whether cause of death was recorded as covid.

It is a very large dataset - far too big to be opened in Excel - and so required specialist database tools and skills to analyse it thoroughly. Because of my rather extreme personal circumstances (and because the kind of database skills required in this case are outside my area of expertise) I relied on others to provide some summary information that I could only analyse briefly.

The safety signals in the data

I had been told that the most significant safety issues for the vaccine in the dataset was revealed by a) time series analysis of the various doses by age group and b) clusters of deaths associated with certain batches.

I looked at Steve Kirsch’s various time series analyses and felt that, even accounting for inevitable ‘survivor bias’ (the more jabs a person gets, the quicker they are likely to die after their last jab) there was some evidence of increased risk the more doses a person gets. Moreover, given Steve’s comments about the datatset being the complete set of ‘pay-per-dose’ vaccination records, this conclusion seems robust even if there were a biased proportion of vaccinee deaths in the dataset. Also (as per my above quote in Steve’s presentation) I felt that the data provided further support for the hypothesis that the vaccine was increasing the mortality rate in the older population (something which we had already concluded based on the most recent ONS data). This latter observation also seems to be robust even after taking account of the confounding effect of disproportionate number of vaccinee deaths.

What I am now much more concerned about are the conclusions I originally stated about the clusters of deaths associated with a small number of Pfizer batches. Because I was told this was especially revealing I asked for the following very simple summary information for each batch at each vaccination site:

• the total number of people vaccinated

• the total number of deaths

• average days from last vaccination to death

• the average age of the vaccinated

I said I would do a basic probabilistic analysis of the clusters with that summary information.

My small analysis of the strange batch data

What I received was a file with only the total number of people vaccinated and the total number of deaths for 118 batches (so I did not have the critical information about average days from last vaccination to death and average age of the vaccinated, but was assured there were no major differences between the batches):

Batch Ratio

43.5KB ∙ PDF file

Download

 

The total number of vaccinees associated with these 118 batches was 2,213,062. The largest number in any batch was 101,896 and the smallest was 38. The total number of deaths is 37,073 so the average death rate is 1.68%

There were 1228 deaths in the largest batch, so the death rate for the largest batch is 1.21%.

However, five batches have a death rate of over 10%:

The question is: what can we conclude about vaccine safety given the fact that these batches had such a high death rate compared to the average?

It is important to note that there are possible causal explanations unrelated to vaccine safety that could lead to batches with such unusually high death rates. Specifically:

• Explanation A (very elderly cohort given early) : Most obviously, we would likely observe such results if these batches were given to cohorts of the very elderly/very ill early in the vaccination programme. In this case it is inevitable that many more than average would die anyway within a two-year period after vaccination.

• Explanation B (incomplete/biased batch data): It is also possible that batches with such exceptionally unusual death rates would be observed if the data were incomplete and non random; for example, if the data does not include all those vaccinated from specific batches, with those dying disproportionally included in the data.

• Explanation C (seasonality): It is possible that batches were given during periods of especially high population death rates unrelated to vaccine reactions.

If, as had been suggested to me, that the average days from last vaccination to death and average age of the vaccinated was similar across all batches, then it would be reasonable to rule out explanations A and B. But, it now appears that this is not the case. I did an analysis before knowing this. I said that, if these explanations can be ruled out (so we are assuming all batches are similar in terms of distribution of age of vaccinees and time vaccines were given) then we can estimate the probability that, by chance alone, we would see such batches with exceptionally high death rates. Specifically, let H be the hypothesis that “the vaccine and its delivery is similar across batches”, then:

• Under hypothesis H it is reasonable to assume that the ‘true’ average death rate is approximately 1.68% since this is the overall death rate for all those vaccinated.

• Under Binomial distribution assumptions, the probability that in 118 batches we would observe at least one batch whose size is at least 500, with a death rate above 20% is infinitesimally small (less than 1 in 10 billion). , the batch 1 observation would be essentially impossible under hypothesis H.

• Even if we ignore batch 1, we have 4 other batches out of 118 (all of size >220) that have a death rate above 10%. The probability that, in a specific batch of size 221 the death rate would be above 10% is less than 1 in 6 million. The probability that at least 1 out of 118 batches would have a death rate above 10% is about 1 in 53,000. However, the probability that at least 4 out of 118 batches would have a death rate above 10% is less than 1 in 100 billion.

Therefore, if explanations A, B and C could be ruled out, the hypothesis of similarity of batches could be rejected with near certainty. In that case the conclusion would be that chance alone cannot be the cause of the exceptionally high death rates in these batches. In the absence of any other causal explanation, that would mean that in these batches the vaccines (or their delivery) were genuinely more deadly than expected.

Conclusions

If the dataset is a real, unbiased and representative subset of those vaccinated, then it is potentially one of the most important publicly available datasets for examining covid vaccine safety, despite the fundamental limitation imposed by absence of data on the unvaccinated. It provides evidence of lack of safety of the vaccine. However, some of the conclusions drawn from the data would be confounded if the claims that it contains a disproportionate number of vaccinee deaths is true. Steve Kirsch is adamant that there is no such bias because it a complete record of all ‘pay-per-dose’ vaccinations. If Steve is correct about this, then his conclusions about the evidence of lack of safety are reasonable.

What we can probably discount is the claim concerning batches with exceptionally high mortality rates. The claim that these batches were especially deadly due to the contents of the vaccine or its delivery is confounded by their very different age and time of vaccination profiles.

[dnb24]

Just a glitch

 

https://www.telegraph.co.uk/news/2023/12/06/mrna-jabs-modena-pfizer-quarter-unintended-response/

[apples]

13 minutes ago, dnb24 said:

Just a glitch

https://www.telegraph.co.uk/news/2023/12/06/mrna-jabs-modena-pfizer-quarter-unintended-response/

Compare the language!

Quoting Professor Fenton, not @sancho panza, emphasis mine:

On 05/12/2023 at 22:39, sancho panza said:

we believe there is overwhelming evidence that the covid vaccines are neither effective nor safe

Quoting Professor Willis in @dnb24's Telegraph article:

Quote

Professor Anne Willis, co-senior study author and director of the MRC Toxicology Unit told reporters.

“Ribosomes are somehow sensing the modified RNAs, but the Covid vaccines are very, very safe and very, very efficacious.

Not just one, but two intensifiers, twice! Must be safe and effective if they're "very, very" "safe and effective"

Source study about the pseudouridine frameshifting:

https://www.nature.com/articles/s41586-023-06800-3

Edited December 7, 2023 by apples

[sancho panza]

anoterh great interview with Clare Craig.

A particualrly interesting bit is at 15 mins when she discusses the 13 year old who was widely publicised to have died of covid and was butired alone with no family present.

It turns out-as she reveals-that the boy died from a bungled intubation rather than the effects of covid.

she also covers psot mrotems and death certificates(she's a pathologist)

at 21 minutes she details 3000 people who's death certificate stated covid as the main cause when these people were expected to die of other causes.

24 mins tlaks about the spread of fear,mass psychosis,shame etc

26 mins-first covid winter ,40,000 deaths from otehr causes she calls 'missing deaths' which werent recorded in the data as they,peak coivd deaths peaked at the same time as peak 'missing deaths' leading to huge inflation of covid deaths and therefore huge inflation of fear.

30 mins-funding bias-incl Fauci

37 mins-difference between political truth and scientific truth-

50 mins -great discussion about viruses not killing the host and how some viruses alter to survive and perpetuate themselves.

60 mins-people like Hancock/Ferguson saying that if we hadnt locked down more would have died.they have to.

62 mins-good discussion ontesting and whether using a test on it's own is of any use.Clare saying very easy to get false positives with covid testing.

69 mins-discussion about how people were testing positive for covid months after having had it.

 

Edited December 12, 2023 by sancho panza

[sancho panza]

New research from Austria shows no benefit in protecting against death from the 4th vaxx

Also casuing more people to get infected.

Campbell

'They found that those who had more vaccines had more infections.What are we doing? '

Peer reviewed paper by Prof Pils and Prof Ioaniddis

https://onlinelibrary.wiley.com/doi/10.1111/eci.14136

Effectiveness of a fourth SARS-CoV-2 vaccine dose in previously infected individuals from Austria

 

Alena Chalupka, Lukas Richter, Ali Chakeri, Ziad El-Khatib, Verena Theiler-Schwetz, Christian Trummer, Robert Krause, Peter Willeit, Bernhard Benka, John P. A. Ioannidis, Stefan Pilz

4 DISCUSSION

During the Omicron wave by the end of 2022 in Austria, individuals with a previous SARS-CoV-2 infection showed no significant difference in COVID-19 mortality in groups receiving four versus three vaccine doses. For SARS-CoV-2 infections, we observed a small rVE of a fourth vaccine dose with evidence for rapidly waning immunity and reversal of this effect in 2023. Repeated previous and recent infections were both associated with reduced infection risk. All-cause mortality data indicate modest healthy vaccinee bias.

Our findings on COVID-19 mortality extend the few investigations on the effectiveness of a fourth vaccine dose on clinically significant outcomes in previously SARS-CoV-2 infected persons.^{9, 10, 13} A nationwide study from Italy showed that from 12 September to 11 December 2022 (i.e. 7 to 90 days after receiving the second booster), the rVE of a second bivalent mRNA booster dose versus a first monovalent mRNA booster was approximately 62% in reducing severe COVID-19 in persons ≥60 years with previous infection.⁹ A study from the US during mid-2022 reported that in adults aged ≥50 years with previous SARS-CoV-2 infection, rVE of four versus three mRNA-1273 vaccines was 34.8% (95% CI: 26.5–42.1) for a combined outcome (SARS-CoV-2 infections, COVID-19 hospitalizations and COVID-19 hospital deaths).¹⁰ A study in adults from Singapore between 14 October 2022 and 31 January 2023, showed that in previously infected individuals, a fourth bivalent vaccine dose reduced symptomatic SARS-CoV-2 infections and COVID-19-related hospital admissions by 86% and 96%, respectively, during 2 months after vaccination.¹³ These studies had significantly shorter follow-up times after vaccination than our study, and used combined endpoints including hospitalized patients with SARS-CoV-2 to classify severe COVID-19. Such classifications for severe COVID-19 must be interpreted with caution, because during Omicron waves, many hospitalized patients with positive SARS-CoV-2 tests presented with mild symptoms or were even asymptomatic and detected only because of routine admission screening.^{2, 18, 24} Thus, there is a need for investigations on vaccine effectiveness that assess also COVID-19 mortality, as in our study, to disentangle rVE for hard clinical outcomes versus positive laboratory tests for SARS-CoV-2 with unclear and probably no adverse consequences for most individuals (even for hospitalized patients) in an endemic phase.^{2, 18, 24}

Our results on a significant rVE of four versus three vaccine doses with regard to laboratory confirmed SARS-CoV-2 infections corroborate findings from several other studies.^{5, 8, 12, 22, 25} Estimates of rVE were larger in previous studies, but this may be due, at least partly, to shorter follow-up.^{5, 8, 12, 22, 25} Evidence of peak effectiveness about 3–5 weeks after receiving the fourth vaccine dose, but then decreasing effectiveness towards no remaining effect beyond 15 weeks was previously reported and fits well to our findings.^{5, 25} The public health significance of this transient risk reduction in SARS-CoV-2 infections lasting for several weeks after receiving the fourth vaccine dose remains unclear. Although this reduced infection risk did not translate into prevention of COVID-19 deaths according to our data, we cannot exclude other benefits related to non-fatal adverse health outcomes following SARS-CoV-2 infections. While rapidly waning vaccine protection is observed for laboratory confirmed SARS-CoV-2 infections, previous studies documented that vaccine effectiveness seems to be long lasting for protection against severe and lethal COVID-19.^{5, 17} Similarly, data from Qatar suggest that natural immunity confers a very strong protection against severe COVID-19 with no evidence of waning immunity, a conclusion that is supported by a systematic review and meta-analyses.^{14, 16} Thus, SARS-CoV-2 infections and/or vaccinations, have contributed to the transition of this COVID-19 pandemic into endemicity with very low case fatality rates, as documented in our investigation.^{2, 14} The relative contribution to this protection against COVID-19 mortality by natural and/or vaccine induced immunity, by the characteristics of the Omicron variant, by advances in COVID-19 therapy or by other factors, remains speculative.

Consistent with the literature on waning natural immunity, we observed increasing risk of SARS-CoV-2 infections with time elapsed after the last prior infection. As not only time but also virus variants may underlie the observed declining immunity after previous infections, we note that the end of 2021 and beginning of 2022 marked the beginning of the Omicron wave in Austria. The magnitude of the changes in infection risk as a function of time elapsed after the last previous infection suggest that natural immunity may be a main determinant of immunological protection in a population (Table S3).

Compared to three vaccine doses, those with fewer or no vaccinations did not differ with regard to COVID-19 mortality but had reduced risk of SARS-CoV-2 infections. Of note, less vaccinated groups yielded also significantly lower SARS-CoV-2 infection risk compared to the four vaccine dose group in 2023, a finding that fits well to a relatively long-term follow-up study from Qatar.²⁶ In that study, comparing the third versus the second vaccination, the rVE for SARS-CoV-2 infections was highest with 61.4% (95% CI: 60.2–62.6) in the first month of follow-up and gradually declined to a negative rVE with −45.7% (95% CI: approximately −60 to −30) after 11 months follow-up.²⁶ It was hypothesized that immune imprinting might explain this effect, as prior exposure to a primary antigen (e.g. the ancestral SARS-CoV-2 vaccine) can attenuate the immunity against subsequent infections (or vaccinations) of related but novel antigens (e.g. new virus variants), because the immune response is skewed towards the ancestral antigen.^26-28 Compared to unvaccinated controls, compromised humoral immunity against SARS-CoV-2 variants of concern (e.g. Omicron) in triple vaccinated humans and animals has been documented, and may, at least in part, explain our findings.^{27, 28} To what extent other factors such as a hypothetically reduced willingness to test for SARS-CoV-2 in those who refuse vaccinations, bias, or other factors (e.g. stronger infection derived immunity) may explain the particularly low infection risk in unvaccinated or less vaccinated persons, remains speculative. We consider the higher prevalence of repeated previous infections in these less vaccinated individuals to be consistent with protective effects of vaccinations during the course of this pandemic.

We observed a 21% lower all-cause mortality risk in individuals with four versus three vaccine doses (37% when we excluded nursing home residents), suggesting healthy vaccinee bias especially in the community-dwelling population, albeit much lower as in a study from Israel.²³ In that study, individuals who received three versus two vaccinations had a 94.6% lower risk of COVID-19 deaths, but also a 94.8% lower risk of non-COVID-19 mortality.²³ Healthy vaccinee bias may cause overestimation of rVE, but this would not materially alter our findings, as we have not observed a protective effect for COVID-19 deaths anyhow. As huge differences in all-cause mortality are likewise also paralleled by differences in hospitalizations, this might also affect other endpoints such as severe COVID-19 (that includes hospitalizations). On the other hand, group differences in all-cause mortality may have an impact on COVID-19 outcomes, for example, by competing risks, as someone who died due to non-COVID-19 diseases cannot die due to COVID-19 anymore. Moreover, there can be some misclassification between COVID-19 and non-COVID-19 deaths.²⁹

Our findings are limited due to the observational design that precludes definite conclusions regarding causality. In general, observational studies of COVID-19 vaccine effectiveness are subject to multiple possible biases.^{30, 31} The low number of COVID-19 deaths warrant caution regarding data interpretation and we also have to note the unequally sized groups and the relatively long time elapsed after the last vaccination in individuals who received three vaccine doses. However, a long lapse with waning immune protection would suggest better chances, if anything, for showing benefits from a 4th dose. We have to acknowledge the strong dependence on the data quality of the EMS with subsequent potential sources of bias and/or confounding. These include among others, limitations regarding reporting of data, access and indications for SARS-CoV-2 tests with missing data on testing frequencies and persons who moved away from Austria during the study, potential behaviour changes in response to vaccination and/or SARS-CoV-2 infection, and test results, as well as test accuracy that may all vary over time. We did not have access to data regarding co-morbidities and medications and could therefore not adjust for them. While we had no detailed data on co-morbidities, we could use national data on nursing home residency, which represents a surrogate for many co-morbidities and the strongest factor that may affect infection fatality rate.³² Thus, we could also perform analyses excluding nursing home residents that give fair estimates of rVE in the free, community-living general population. These sensitivity analyses yielded similar results and therefore strengthen our findings. Subgroup analyses according to age did also not materially change our findings. Unavailability of data on monovalent versus bivalent vaccinations precluded separate analyses of these two different vaccine types. Bivalent vaccines were mainly recommended in Austria from mid-September 2022 on and were thus likewise the predominant vaccine received as the fourth vaccination in our study. Of note, superior effectiveness of bivalent versus monovalent mRNA vaccines against SARS-CoV-2 has been documented.^{11, 13} The lack of effectiveness of the fourth vaccination during 2023 in our study is, however, consistent with the notion of rapidly waning immunity by this second, mainly bivalent, booster. Finally, our findings do not apply to previously uninfected individuals, a population group that is vanishingly small by late 2023.

In conclusion, in the general population of Austria with a history of a SARS-CoV-2 infection we did not observe a significant rVE of a fourth vaccine dose for COVID-19 deaths during a time with already very low absolute risk for this outcome. We documented a transient rVE for SARS-CoV-2 infections, but this effect was reversed during extended follow-up in 2023. Repeated previous and more recent SARS-CoV-2 infections were both associated with significantly reduced reinfections. In general, our study results question whether recommendations for repeated vaccine boosters against SARS-CoV-2 are currently justified for large parts of the general population with a history of previous infections. This does not contradict the health benefit of the initial vaccinations of unprotected populations in the early phase of the COVID-19-pandemic and of vaccinations of very high-risk populations at any time.⁴ Our findings fit well to the hypothesis of diminishing effectiveness and thus shifting risk–benefit ratios from additional vaccinations during the transition of the COVID-19 pandemic to its endemic phase.² In view of the strong and population-wide immunological protection due to previous infections and vaccinations, it is tempting to speculate that SARS-CoV-2 infections may already resemble by 2023 other human coronaviruses.² Our data require confirmation in other national populations and are important to inform future public health and vaccine policy regarding COVID-19, but also underscore the critical role of active national health surveillance during a pandemic.

[sancho panza]

Steve Kirsch,US govt have been stduying the Amish ror decades so lets' see the data.

 

[Noallegiance]

Thank you for keeping this thread going.

[Pinkpanther]

On 15/12/2023 at 12:23, sancho panza said:

Steve Kirsch,US govt have been stduying the Amish ror decades so lets' see the data.

 

The book "Turtles all the way down: Vaccines science and myth" covers the Amish "control group" in some detail.

[sancho panza]

On 15/12/2023 at 14:20, Noallegiance said:

Thank you for keeping this thread going.

I like having specific threads for lines of discssuion hence we can catalgoue lockdown scepticism since the first reading of Ioannidis Stat News article on page 1... 3yaers ago.

 

here's anotehr cracker

https://www.conservativewoman.co.uk/vaccine-studies-find-a-comprehensive-catalogue-of-harm/

Vaccine studies find a comprehensive catalogue of harm

THREE studies which compared millions of Covid-19 vaccinated people with unvaccinated people have concluded that the unvaccinated are less likely to suffer from many diseases including inflammatory musculoskeletal disorders, gynaecological disorders, and blood disorders. Findings also showed that those who had received Covid vaccinations were at risk of developing immune-related adverse events.

The study which investigated musculoskeletal disorders (injuries or disorders of the muscles, nerves, tendons, joints, cartilage, and spinal discs) said: ‘All Covid-19 vaccines were identified as significant risk factors for each inflammatory musculoskeletal disorder. This cohort study found that individuals who received any Covid-19 vaccine were more likely to be diagnosed with inflammatory musculoskeletal disorders than those who did not.’

The studies looked at all mRNA and viral vector vaccines delivered in South Korea. These included Pfizer/BioNTech, Moderna, Janssen (Johnson & Johnson), Novavax (from a US company), and AstraZeneca. More than 194million doses were injected; Pfizer was the most used with 67million doses delivered and Janssen the least with 7million. Forty million doses each of AstraZeneca, Novavax and Moderna were used.

Three separate South Korean universities combed millions of records from the country’s National Health Insurance Service (NHIS) database, a compulsory health insurance scheme covering the whole population (52million) which is used to produce various health statistics. The results are startling and shocking. They tally with what many of the vaccine injured are reporting. Despite mounting evidence, authorities are still in denial and intent on gaslighting them.

One team of doctors and scientists from Ewha Womans University, Seoul, concluded that ‘the three-month risks of incidental, non-fatal adverse events (AEs) are substantially higher in the Covid-19 vaccinated subjects than in non-vaccinated controls’.

The lead author, Dr Jee Hyun Suh from the Department of Rehabilitation Medicine at Ewha Womans University, examined the incidence rate and risk in several medical areas including gynaecology (reproductive system disorders), haematology (blood disorders), dermatology (skin-related diseases), ophthalmology (eye-related disorders), otology (ear-related disorders), and dental problems. They said that vaccination significantly increased the risks of all non-fatal AEs, with the risk of inner ear disease being the highest.

Many complain that they developed tinnitus post vaccination, and it is already accepted that medication can cause a constant ringing in the ears, described by some as white noise. These include antidepressants, some antibiotics and anti-malaria medications.

The university found increased risks across all areas except for endometriosis and visual impairment. In fact visual problems were raised but numbers were small and considered not statistically significant. That does not mean that the vaccine does not cause eye problems.

A team from Kyung Hee University Hospital, Seoul, led by Hye Sook Choi from the Department of Internal Medicine, looked at blood disorders in more than 4.2million people aged 20-plus. They found many abnormalities affecting the production of red and white blood cells as well as damage to the bone marrow.

These include nutritional anaemia, which is iron deficiency, hemolytic anaemia, when your red blood cells are destroyed faster than they are replaced, aplastic anaemia, a rare condition that stops the body producing enough new blood cells which usually develops because of bone marrow damage, coagulation defects, the most common of which is vaccine-induced thrombotic thrombocytopenia (VITT), a blood clotting disorder characterised by low platelets, and neutropenia, which is an abnormally low count of white blood cells called neutrophils also caused by damage to the bone marrow. Neutrophils help your immune system fight infections and heal injuries.

They said that the risk of coagulation defects increased regardless of whether someone received mRNA vaccines such as Pfizer and Moderna, or viral vector vaccines such as AstraZeneca, Novavax, and Janssen (Johnson and Johnson).

The orthopaedic surgery department at Korea University Guro Hospital in Seoul looked at inflammatory musculoskeletal disorders in more than 2.2million people. The team led by Dr Young Hwan Park noted that earlier research into Covid-19 vaccines identified a range of adverse reactions that caused inflammation and an excessive immune response.

They also discovered increased incidence of plantar fasciitis, a painful condition of the foot in the soft tissue connecting your heel bone to your toes, rotator cuff syndrome: an injury or degenerative condition affecting the rotator cuff, a group of muscles and tendons that surround the shoulder joint, adhesive capsulitis or frozen shoulder, herniated intervertebral disc or slipped disc, spondylosis, a term used to describe osteoarthritis (degeneration of the joint cartilage) of the spine, bursitis, inflammation of the cushions (bursae) at the joints, Achilles tendinitis (the Achilles tendon connects the calf muscle to the heel bone) and De Quervain tenosynovitis, a painful condition affecting the tendons on the thumb side of the wrist.

Only 15.1 per cent had not received two vaccines. As well as looking at mRNA, and viral vector vaccines, they also studied those who mixed both types of vaccine. They concluded: ‘All Covid-19 vaccines were identified as significant risk factors for each inflammatory musculoskeletal disorder, except for mixing and matching vaccines for De Quervain tenosynovitis. This cohort study found that individuals who received any Covid-19 vaccine were more likely to be diagnosed with inflammatory musculoskeletal disorders than those who did not.’

For some reason, none of the studies looked at heart conditions. The mRNA vaccines are known to cause myocarditis and pericarditis, most commonly in young males.

The studies have not yet been peer-reviewed and opposition to their findings will be fierce. However, the results are clear at this stage: the vaccinated who report health problems only to be told they are nothing to do with the vaccine are not going mad after all.

[sancho panza]

incredible to see the level of misinformation aimed at the unvaccinated.

How these words may come to haunt these poeple as the situation develops

Well worht a wathc -2 mins-

The reason I didn't take the vaxx was that I saw some(what looked like clear adverse events) vaxx injuries within the the first cohorts that got vaxxed.

Edited December 18, 2023 by sancho panza

[tlc]

20 minutes ago, sancho panza said:

The reason I didn't take the vaxx was that I saw some(what looked like clear adverse events) vaxx injuries within the the first cohorts that got vaxxed.

 

I rejected it long before anyone was vaxxed with the poison.

mRNA animal experiments=death 

Thanks Dolores 

[XswampyX]

7 minutes ago, tlc said:

I rejected it long before anyone was vaxxed with the poison.

mRNA animal experiments=death 

Thanks Dolores 

This explains it... if you speak Hindi?

 

 

[sancho panza]

This jsut sums up the coof more generally.IWas it really all about cool hard cash in the end?

https://www.theguardian.com/uk-news/2023/dec/10/captain-tom-moore-hannah-ingram-moore-daughter-spa

The curious case of Captain Tom: how did the feelgood story of lockdown turn sour?

His fundraising efforts offered a glimmer of light in a world darkened by Covid. But now his family are portrayed as freeloaders and a garden building created in his name faces enforced demolition

 There has never been much of a dividing line between effective public relations and the spread of religious fervour, and for 25 days in April 2020 the good news of Captain Tom sounded a lot like The Greatest Story Ever Told. The essence of PR is to take the local and the particular and make it universal; marketeers like to talk about the “toothbrush test” in regard to new product lines, the implied question being: “does everyone need one?” In the early lockdown days of the pandemic, twinkly 99-year-old war veteran Tom Moore walking lengths of his garden on a Zimmer frame in support of NHS Charities Together passed that toothbrush test with flying colours: everybody needed him. The trick lay in recognising that fact.

Hannah Ingram-Moore, Captain Tom’s daughter, with a background in retail branding, understood something of that trick. The chain of events that led to her father’s sudden and unlikely global stardom was set out in one of his several hastily ghosted books – the gospel according to Tom – Tomorrow Will Be a Good Day (the £800,000 proceeds from which books the family eventually took to be their own).

The following Tuesday, Morgan stole a march on his BBC rivals; he staked £10,000 of his own money on Captain Tom, live on air, and used his 8.7m Twitter following to seed the idea of a knighthood for the captain #AriseSirTom. Nearly £8m was donated to the cause two days later when Captain Tom completed his 100 laps, and donations peaked again on his 100th birthday a fortnight later with the surreal flypast of a second world war Hurricane and Spitfire over the garden in Marston Moretaine, his elevation to the honorary rank of colonel, the confirmation of a knighthood from the Queen, and a No 1 record, You’ll Never Walk Alone, with Michael Ball.

Looking back on all this now, was a different kind of ending to Captain Tom’s story always inevitable? Was it a near certainty that sooner or later another law of PR would kick in, a version of that tabloid and social media truism that, eventually, no good deed ever goes unpunished? It is hard to pinpoint exactly when that different ending started to become a probability – the one that concludes with the current ongoing statutory inquiry from the Charity Commission into the conduct of the Captain Tom Foundation and the enforced demolition of a garden building created in his name – but that other captain who became prominent at that time, Captain Hindsight, might argue for 18 May 2020. That was the date, two weeks after the hero’s birthday, on which the Ingram-Moore family first applied to trademark Captain Tom’s name.

The family, seeking to protect Captain Tom’s newly acquired IP, set up a commercial business, Club Nook Ltd (named after the home in which Tom had grown up), alongside the charitable foundation. Through this enterprise they licensed Captain Tom gin from a distiller in his native Yorkshire, and negotiated a film deal to tell the epic story of the 25 days in April, with the writers and producers of that summer’s feelgood movie Fisherman’s Friends, about the Cornish trawlermen who topped the charts. And then there was a three-book contract with Penguin Random House with designs on the Christmas bestseller list.

The sudden unfathomable scale of the events that the Ingram-Moores had set in motion in April 2020 always threatened these kinds of contradictions. Another of Tom’s late-life ambitions had, apparently, been to build a resistance pool in the garden to improve his strength. Somehow, after his death, that germ of an idea evolved first into an L-shaped spa room approved by Bedfordshire council from a planning application that made liberal use of the imprimatur of the “Captain Tom Foundation”, without any kind of approval from the charity’s trustees. The eventual £200,000 building, for which the family applied for retrospective planning permission in 2022, was a much larger C-shaped structure built significantly closer to neighbouring gardens. One of those neighbours spoke for many in saying: “It feels as if they thought that their goodwill gave them cover to do whatever they wanted.” The weird global enormity of the story appeared to have gone to their heads. The evidence for that belief – the infamous indoor spa – gave that part of the public hungry to confirm its worst suspicions about Hannah Ingram-Moore the precise mundane detail it needed. The PR campaign that magnified a local act of fortitude to epic proportion returned to earth.

One result of those events is that the feelgood film of Captain Tom’s month of miracles is now on hold. The Charity Commission has yet to report into the conduct of the Captain Tom Foundation, but even in advance of its findings, the foundation has stopped taking donations and will discontinue operations.

If the original story arc provided a manual for successful PR, the reversal of fortunes may well prompt some urgent case studies in how not to manage a reputation. I don’t imagine there is an acronym for it, but one age-old lesson seems to apply: if you do decide to build a spa pool in memory of your recently sainted father, perhaps don’t go to court to argue it is partly “targeted at helping local elderly people struggling with loneliness”. Just stop digging.