Earlier this month, tech bro turned rabidest of rabid antivaxxers Steve Kirsch finally gave a talk that he had been hyping for a couple of weeks as the “definitive” evidence that COVID-19 vaccines are not just deadly, but so deadly that, by his estimation, they’ve killed well over 13 million people. He also made a big deal of giving the talk at MIT in the auditorium that had been named after him before he had turned into a raving antivax conspiracy theorist peddling pseudoscience who has become so proud of his status as “misinformation superspreader” that he had a T-shirt made featuring that saying.
Before I delve into Mr. Kirsch’s massively flawed analysis and findings, let’s briefly take a look at the dataset used and how Mr. Kirsch apparently got his grubby little hands on it.
A New Zealand “whistleblower”
As is the case with many good antivax conspiracy theories that involve torturing an existing dataset until it “confesses” that vaccines kill, Mr. Kirsch’s “mother of all revelations” (MOAR) about COVID-19 vaccines involves a “whistleblower,” who apparently passed on record-level data from Te Whatu Ora (the agency that manages the provision of healthcare services in New Zealand under the Ministry of Health) tallying deaths in vaccinated people, along with COVID-19 vaccination dates. (He also uses the pseudonym of Winston Smith, the protagonist in George Orwell’s dystopian novel Nineteen Eight-Four, because of course he does.) Antivaxxers trumpeting the “whistleblower” also claim that he is a statistician, which, as you will see, he appears not to be, at least not one with epidemiological or clinical knowledge.
Steve Kirsch being Steve Kirsch, a true prolific misinformation spreader who can’t resist anything that brings attention to him, he was hyping this talk and these “revelations” in the lead-up to his talk, revealing quite a bit along the way, because apparently he just can’t help himself. For instance, he revealed that he had illegally obtained the data on November 9 from the whistleblower, whose identity he knew. Of course, he didn’t actually admit that the data had been illegally obtained, but after he had started hyping his talk a number of people told him and his cronies that obtaining record-level data with personally identifiable health information was illegal in New Zealand, the US, and many other countries. At one point on X, the platform formerly known as Twitter, the “scientific advisor” of his antivax org Vaccine Safety Research Foundation (VSRF) even conceded with a metaphorical shrug of the shoulders that the data had been illegally obtained:
Indeed, apparently Te Whatu Ora was sure enough that the privacy breach had been illegal that it rapidly launched an investigation, which resulted in the arrest of a 56-year-old man, who had appeared in a video on Rumble with NZ presenter Liz Gunn to reveal the data, on suspicion of a “mass privacy breach“:
Police have arrested a man in connection with a mass privacy breach of Covid-19 vaccination data.
It comes after Te Whatu Ora- Health New Zealand launched an employment investigation, accusing a health sector worker of spreading misinformation using government data about Covid-19 vaccines.
A 56-year-old man was arrested this afternoon in relation to the “unauthorised disclosure and misuse of data”, police said.
He is charged with accessing a computer system for dishonest purposes and will appear in Wellington District Court tomorrow.
NZ authorities note that the man “had no clinical background or expert vaccine knowledge,” which means that he might be a statistician, but according to his interview as reported by the Brownstone Institute:
Smith was in an unusual position as the database administrator for the payment system. ‘Because New Zealand is a small country, you can get away with one database administrator. I am in a unique position, and because New Zealand is a Tier 1 country with really good IT, I was able to manage and build this system.’
So he was a database administrator, not a statistician, and appears to have no relevant scientific or clinical knowledge of public health or epidemiology, but rather acted on all sorts of assumptions that spikes in daily death rates were “not natural” and “had to be man-made.” Indeed, according to Mr. Kirsch, “Winston Smith” is an Oracle database administrator for the payments database:
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.
I love the naive assumption that, just because Barry Young risked his freedom to illegally download four million health records, that means there must be something to his suspicions and findings (and therefore to Mr. Kirsch’s “analysis”). No, just because Mr. Young apparently believed strongly enough that vaccines were killing large numbers of people in NZ that he risked prosecution and prison does not mean that he is correct in his belief. Strength of belief does not correlate with correctness of belief, even though a lot of people like Mr. Kirsch think that it does. (Actually, far too many people think that strength of belief correlates with correctness of belief.) Moreover, the revelation that Mr. Young is only a database administrator, and not just a database administrator. but apparently the payments database administrator, tells me all I need to know about his qualifications to judge what these data show: Zero.
According to the NZ authorities:
Earlier tonight, the ministry said the staff member had no clinical background or expert vaccine knowledge, and there was no evidence vaccination was responsible for “excess mortality in New Zealand”.
It’s alleged an individual downloaded a large amount of vaccine-related information, Te Whatu Ora chief executive Margie Apa said.
“The data, as published on an overseas site, appears to have been anonymised. Analysis of the released data is ongoing, but work so far has not found any National Health Index Numbers or personally identifiable information.”
Apa said an injunction had been used to have information taken down from an overseas website and cyber security specialists are continuing to scan extensively for any other places where the information may appear.
This was, of course, a completely expected reaction from a government agency after a computer breach and illegal download of a massive health department dataset, but, of course, Mr. Kirsch predictably saw it as more evidence of a “conspiracy” to “silence” him, particularly when Wasabi, the platform to which he had uploaded the illegal data, revoked his access to his account due to a TOS violation because of too much traffic (although it’s very likely that the New Zealand injunction also played a role in the decision):
Email to the CEO of Wasabi about my urgent situation was not returned. Wasabi tech support isn’t answering any of my emails either. The NZMH will do everything they can to stop this data from getting out because it reveals they are killing people with the vaccine.
What did Mr. Kirsch expect? He uploaded a dataset that, he knew, would likely result in a lot of traffic once he revealed on his Substack how to download it and that, he knew or should have known, had been illegally obtained. And, make no mistake, this was illegal. In the US, we refer to health information that has personally identifiable information in it as PHI (protected health information), and we have a law (HIPAA) that governs when such information may be shared and among whom. Suffice to say that anyone with access to PHI can’t just download and share it. Also note that “protected” doesn’t mean that the names have to be associated with the information; any form of personally identifiable information that might allow guessing whose information it is counts. Ever since HIPAA was passed, anyone in healthcare has had to go periodic mandatory training regarding patient data and not even inadvertently sharing it with entities who do not need it to provide healthcare services. Unsurprisingly, NZ has a similar health privacy law.
Of course NZ health authorities were going to act and seek to get an injunction against any private company hosting the data. If I thought Mr. Kirsch were sufficiently clever, I’d suspect that he had intentionally chosen a platform like Wasabi to host the dataset, knowing that once he publicized it health authorities in NZ would act to shut it down. Maybe he even knew that his talk would result in so many people downloading the dataset that bandwidth limits would likely be exceeded. (Hint: I doubt that he’s that clever.) I also note that, while perhaps Mr. Kirsch thought that anonymizing the dataset before releasing it into the wild would protect him and his “whistleblower,” that doesn’t matter. The original dataset, which he presumably possesses, still contains PHI, making its theft—yes, theft—a massive privacy breach, particularly when carried out by someone employed by Te Whatu Ora, which likely included in its employment contract conditions under which the employee was permitted to access the database.
Unsurprisingly, Mr. Kirsch is now bragging that he’s hosted the data on a “bulletproof” platform and that if “they try to take me down next time, they will regret it,” going on to say, “I actually hope they do; we’ll turn the tables on them.” Still, for a tech bro, Mr. Kirsch was either really stupid or more clever than we give him credit for. Given his history, I suspect the former.
In any event, before I move on to the dataset itself, let me just briefly point out that this whole shtick is very familiar. I very much get “CDC whistleblower” vibes from it. You remember the “CDC whistleblower,” don’t you? It was a scientist named William Thompson, who, while working for the CDC, decided that he disagreed with the analysis of a dataset examining whether there was any correlation between autism risk and vaccination with MMR (measles-mumps-rubella vaccine) and then started having phone conversations with an antivaxxer named Brian Hooker, apparently to vent. The result was a “reanalysis” of the dataset by Brian Hooker, a biochemical engineer turned incompetent epidemiologist who had at one point bragged about how he thought “simplicity” in statistical analyses was best and predictably found an increased risk of autism in African American boys due to MMR vaccination. (Of course, “simplicity” to antivaxxers means raw data analyses, without appropriate statistical adjustment for confounding factors.) The whole conspiracy theory was ultimately turned into an antivax epic in 2016, a pseudodocumentary by Andrew Wakefield and Del Bigtree entitled VAXXED. It’s an antivax conspiracy theory that persists to this day, promoted by—who else?—Steve Kirsch. One can’t help but wonder if his MOAR is nothing more than a pathetic attempt on his part to create his own CDC whistleblower.
But what about the dataset itself? Let’s, for the moment, ignore the illegal, unethical and immoral methods by which Mr. Kirsch got his sticky fingers on the dataset and just look at the dataset. Then we’ll move on to his talk.
The NZ dataset
The dataset stolen by the NZ Te Whatu Ora “whistleblower” and passed on to Mr. Kirsch consists health records from 12 million doses of COVID-19 vaccines. Specifically, according to Mr. Kirsch, it contains four million of these records from the the “Pay per dose” (PPD) program, the other eight million apparently not being from that program. Mr. Kirsch claims that whether “you got PPD or not is pretty random,” which sent up an immediate red flag to me regarding whether it is actually true or not that PPD was random or not. I could not ascertain that from my reading. However, I did immediately wonder. (Maybe one of our New Zealand readers could help me out.)
In addition, there are numerous other deficiencies that Mr. Kirsch was forced to admit but that, he claims, do not invalidate his analysis, for example:
- 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.
- Many people will not have all their doses in this database, e.g., there may just be dose 3 data for someone.
In other words, it would appear that some people got some doses through PPD and others not through PPD, which would explain why only some doses were covered. Mr. Kirsch just glosses over this, claiming ridiculously:
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.
To which I reacted:
Seriously, if you are going to assert that, for purposes of your analysis, incomplete records and uneven sampling in the dataset don’t matter, you really do need to show the receipts and mathematically prove that these deficiencies in the dataset don’t affect the results of your analysis. Epidemiologists do this all the time by doing multiple different analyses on datasets with missing records and/or uneven sampling, because there are statistical methods for dealing with uneven sampling and missing records that peer reviewers will expect to see if such analyses are to be published in the peer-reviewed biomedical literature. (It’s called sensitivity analysis, as in testing the sensitivity of the outcomes of the analysis to important assumptions made in doing the analysis.) Tellingly, Mr. Kirsch doesn’t even attempt to use them or mathematically justify his claim that these things don’t affect his analysis. He just asks you, with an overconfidence that bespeaks one of the most extreme examples of the Dunning-Kruger effect that I’ve ever seen, to “trust me on this,” so to speak.
Of course, there are other major deficiencies in the dataset, the key one being that the dataset includes only vaccinated people, when they were vaccinated and with what vaccine, as well as dates of death for those who died. We don’t know the baseline death rate in an unvaccinated cohort, which would almost certainly be much higher.
None of these deficiencies stops Mr. Kirsch, though. So let’s look at his analysis. His talk was posted to Rumble. (Where else?) His slides were posted to Google Documents and to his own website as a PDF. Feel free to watch it if you can handle the tsunami of Gish galloping misinformation.
Kirsch’s MOAR goes poof
Watching Mr. Kirsch talk for so long is rather painful. He uses entirely too many slides, and the aesthetics of his PowerPoint design reminds me, more than anything else, of late 1990s Time Cube. Let’s just say that I have a number of old antivax PowerPoint presentations that I saved from 15-20 years ago, and his reminded me a lot of them. But what about the substance? It’s also clear that Mr. Kirsch revels in his reputation as a “misinformation superspreader,” as his early slides claim that he’s written over 1,500 articles on COVID-19 (whoop-de-doo, I’ve probably written at least five times that number over the years on vaccines in general), before he whines:
Now let’s look at his key assumption, for which he provides no references, statistics, or math to justify:
Before I go on, let me just point out a very important principle. When you see a nonexpert analyze a dataset using methods that no expert uses to analyze the same type of dataset, be suspicious. It is, of course, possible that the nonexpert has stumbled onto an innovative way of analyzing data of this type. But is it likely? No, not really. It’s far more likely that he’s made a rookie mistake. Moreover, when the nonexpert doing the analysis is someone like Mr. Kirsch, who has a history of promoting the most outlandish antivax conspiracy theories, it’s far more likely that the nonexpert is doing nothing more than torturing the data until it confesses what he wants it to confess. Be even more suspicious that this is what is going on when, for example, the nonexpert, after stating his assumptions, doesn’t go straight into the data analysis but instead starts ranting at Moderna co-founder Robert Langer and claiming that as a board member of Moderna he has a responsibility to “stop the shots” and might be liable to prosecution under the PREP act. (That’s exactly what Mr. Kirsch did, referring to the vaccines as “kill shots.”)
Then he looks at another dataset for different vaccines, noting that his assumption is validated for the pneumococcal vaccine:
If you look at the original Substack upon which Mr. Kirsch bases this chart, you’ll note that there are data for unvaccinated people. You’ll also note that he hand waves away why the data are “confusing.” In any event, after Mr. Kirsch had published this analysis back in late February, a number of commenters pointed out that his results could be explained by the healthy vaccinee effect (people who are vaccinated tend to be the most at risk, as in older and less healthy, and those who are less healthy tend to self-select to be vaccinated regardless), seasonality (deaths tend to peak in winter, particularly among the elderly), and COVID-19 waves. It’s no surprise that Mr. Kirsch simply does the same thing with the New Zealand dataset.
But first, he has to cite anecdotes because:
My reaction was predictable:
Think I’m exaggerating? OK, then, let’s look at the next slide:
I discussed Mr. Bonnar’s “anecdote” and how implausibly unbelievable in the context of deconstructing Mr. Kirsch’s equally implausibly unbelievable claim that COVID-19 vaccines have killed 3.5 times more people than COVID-19. Mr. Kirsch sure does like to recycle his “greatest hits” of disinformation.
But, surely he must be about to get to his analysis of the NZ data, right? Sort of. First, he has to go on about getting the data on November 9 and then saying:
Actually, any dataset, “record-level” or otherwise, can be a “Rosetta Stone” for conspiracy mongers like Mr. Kirsch, who are determined enough to torture the dataset until it confesses what they want it to confess. That’s exactly what Mr. Kirsch does, but not before extrapolating wildly from his “findings” to claim that COVID-19 vaccines have killed over 13 million people worldwide, a false claim that he repeats later in his presentation with this slide:
I suppose that Mr. Kirsch thought that he was being a good showman in dragging out the presentation. Maybe he thought he was building suspense. To me, what he really did was make me fast forward through his talk because I was getting bored and frustrated that he wasn’t coming to the point. I know, I know, I’m sure it’s not entirely unlike some of my readers, who skim through until I get to the point. The difference is that I’m not entirely oblivious to the flaws in my communication style, the way that Mr. Kirsch appears to be when he decides to throw in some more slides from his bogus Medicare analysis, slides of other antivax “analyses” from other countries, and even slides about his utterly risible “survey” that led him to claim that COVID-19 vaccines have killed 3.5 times more people than COVID-19 into his talk before getting to what everyone wants to see, the NZ data.
Oh, there’s this:
As I’ve noted before, Dr. McCullough has not actually had his board certification revoked yet. I searched the ABIM website. You can search it too if you don’t believe me, but here’s my screenshot from yesterday:
Finally, there was this:
I only include this because Mr. Kirsch mentioned me. There’s also just one problem here. He blocked me, and I blocked him back (because I always block back) months ago. This is purely performative. Also notice the number in the lower righthand corner. That’s the slide number. Now go back and count how many slides he included before getting to the point, his big “MOAR” of the NZ data. Yes, it’s a lot.
it’s 11 more slides before he gets to the actual data. Now, before I present the slides, let me note that there’s a bit of a trick here let’s see if you can spot it:
Finally—finally!—Kirsch got to the point:
First, let’s again revisit Mr. Kirsch’s assumption, which is that the death rate should be flat. I immediately wondered about that assumption, particularly in the middle of a pandemic in which a contagious respiratory virus is ripping through the population at various intervals and to which the elderly and those with chronic health conditions are most susceptible to severe disease and death. I had a conversation with Prof. Jeffrey Morris about this, but first lets see what this Professor of Public Health & Preventive Medicine; Biostats, Stats & Data Science at Penn had to say publicly on X/Twitter:
There was, of course, more:
I also like to refer to this graph from Our World In Data:
Let’s quote a key element of Prof. Morris’ discussion, given that you might not want to have to go to the X/Twitter website to see this and you might not even have an account anymore:
As I would emphasize, any genuine attempt to assess potential causal effects of vaccines requires consideration of controls and adjust for confounding and other sources of bias inherent to these observational data in the pandemic (as may published studies do), but it might be useful for some to consider looking at these data as a basic plausibility filter for assessing whether they think the excess deaths are primarily driven by vaccination or by covid.
BTW the waves of covid infection waves coincide closely with the vaccine deaths in the plot, so even if you question covid death attribution, these times are precisely the times when each place experienced a massive wave of confirmed covid infections.
Note: It should read after the correction of an unfortunate typo:
BTW the waves of covid infection waves coincide closely with the COVID deaths in the plot, so even if you question covid death attribution, these times are precisely the times when each place experienced a massive wave of confirmed covid infections.
Everything old is new again, as I like to say. Basically, Robert F. Kennedy, Jr. “proved” that the CDC was “covering up” data supposedly showing that thimerosal-containing vaccines caused autism because appropriate adjustment of the analysis of the raw data for confounders made the effect go away. Ditto Brian Hooker “reanalyzing” the “CDC whistleblower” data. Basically, it’s a theme in antivax “reanalyses” of datasets like the Medicare and New Zealand datasets; they take raw results and fail to appropriately adjust for confounders. I’ve seen it again and again and again over the years. It’s a rookie mistake, and Mr. Kirsch falls for it like a day one rookie; either that, or he does it intentionally. (Take your pick.) In brief, Mr. Kirsch’s assumption that these curves should be absolutely flat in the middle of a pandemic is just that, an assumption. He does not adequately show that it is a justified assumption either, to put it mildly.
Even worse, Mr. Kirsch’s extrapolation is wildly inappropriate. Where do you think he got his claim that COVID-19 vaccines have killed over 13 million people? Ridiculously simple, although it’s really simply ridiculous. He extrapolated from his “estimate” from this dataset that COVID-19 vaccines kill one in a thousand people who receive them, to estimate that COVID-19 vaccines are responsible for 13 million dead worldwide and 675,000 in the US alone. Again, these are numbers so wildly implausible that they do not pass the smell test.
As Prof. Morris noted to me, the increase in death rate that Mr. Kirsch claims is everywhere in the data is, in actuality, only evident in the older age groups. Of course, the “all ages” death rate is driven primarily by the older age groups. There’s the trick! It’s the main one, but not the only one. In addition, Mr. Kirsch assumes that healthy vaccine effects “last exactly three weeks and not a moment longer,” which leads him to his erroneous assumption that any subsequent increase in deaths can only be explained by vaccine-caused deaths and no other factor. Prof. Morris also agreed with me that you can’t know the baseline death rate without having an unvaccinated control group, but he also educated me by pointing out this:
When you consider the actuarial baseline and the fact that 2020-2021 New Zealand had dramatically lower death rates than historical baseline, it seems like the long increase he sees (in the older groups), is not excess deaths but basically a return to baseline death rate.
In other words, it’s yet another confounder that Mr. Kirsch failed to consider, incompetent rookie epidemiologist and statistician that he is. He also misuses “proof by contradiction,” in which one exhaustively lists all the other possible explanations for an observation, until the “only one standing”—as Prof. Morris put it—must be true.
He also states erroneously:
Notably, he shows no documentation that this is, in fact, the “gold standard” for determining a safe and effective vaccine. (Also note the slide number. And people wonder why I was getting tired as I neared the end. Hint: He had more than 100 more slides to go, most of which included a lot of rants that are very old antivax tropes about how supposedly there is less autism in unvaccinated children and citing Robert F. Kennedy, Jr.’s false claim that vaccines are making this generation of children the “sickest generation.”) Truly, Mr. Kirsch’s crapfest of a talk was a shining example of a Gish gallop, a.k.a. firehosing and proof positive that Brandolini was an optimist. (Brandolini’s law, also called the Bullshit Asymmetry Principle, states that it takes an order of magnitude more energy to refute bullshit than it does to create it. I say it takes at least two, if not three, orders of magnitude more energy, as evidenced by my going through Mr. Kirsch’s talk and 285 slides, plus his Substack articles.)
Other antivaxxers react
One of the funny things about Mr. Kirsch’s talk was the reaction. Sure, credulous propagandist hacks at the Brownstone Institute took it at face value, because of course they did. However, Mr. Kirsch is so…out there…that the more “reasonable” antivaxxers—or at least the ones who either have a science background or desperately want to be perceived as “more reasonable” were a bit less enthusiastic.
For example:
Let’s quote the whole thing, shall we:
Steve @ichudov was able to obtain the NZ database. He has been combing through it and has raised serious concerns with the quality of the raw data set. Red flags were first noted by @jikkyleaks followed by others chiming in with established qualifications whom you should recognize. At first look –too much missing data and anomalies. Too many discrepancies with other data points and facts we have about NZ. No wonder the host for the data set wants no one else looking under the hood, rather just uncritical acceptance of the so-called conclusions.
@NickHudsonCT explained having a prior opportunity to look at the NZ data and that project fizzling out because of problems–that event wasn’t disclosed to you apparently by the owner whistleblower. @USMortality thinks the data are so riddled with missing and problematic inconsistencies that it is rendered unanalyzable for purpose. @DowdEdward responded to the conversation indicating there may indeed be a scam underfoot. One aim potentially to undermine you, leaders in the movement. I suspect the focus on high rates of death purported to be associated with “vaccinator” is to shift blame from product to human error and thus muddy the waters of liability. NZ findings don’t match those of other recognized data analyses around the world showing the mRNA C19 product harms. There has never been a robust, large effect size for “vaccinator” that couldn’t be attributed to the confound of bad lots. Just occasional egregious human error cases, but not of this magnitude. There is no concordance offered between NZ lot ID#s and formal Pfizer batch #’s so no analysis has yet been offered showing the same pattern of AEs with bad lots. Why not? That would be straightforward and the pattern and scale of harms by lot known from VAERS and well documented by Craig Paardekooper at http://howbad.info should line up closely, match like fingerprints.
We don’t have that confirmation. We don’t have analyses that show the overall rate of death and AEs for the population and for age groups by total and number of shots as @denisrancourt excellent new world report lays out. Why wasn’t this sort of analysis performed? Let’s involve @JesslovesMJK @lawrie_dr and protect good doctors like @PierreKory @molsjames @drcole12 @richardursomd @P_McCulloughMD and @MdBreathe from being hurt by this likely psy op. We don’t have reports from NZ provided in a way that provides any sort of denominators. If the data set is legitimate it is poorly administered, full of holes. Perhaps due to widespread faults in monitoring and recording of events that would comprise the data set. Talk to other NZ experts on those processes. Let’s examine the hype and drama that preceded the rollout of the “findings.” A proper well-vetted set of analyses would go through a rational serious process of being reviewed by other experts in the open before being proclaimed the “Mother of All Revelations” and presented in parliament by legislators like @ABridgen Winston’s demeanor and words are also highly suspect and caught the eye of many including those tagged here, and was my first tell. The full interview with Winston Smith was dripping with sniffly self-serving drama overkill and emotionally overplayed sensitivity to the plight of humanity. He is quite late to the party of outrage and has no believable excuse or response. It is a performance. No meaningful discussion of his real credentials, skills and experience. He is a database administrator but I see no evidence of skills as a statistician or analyst. He has worked with no one else by his own admission. By contrast @naomirwolf and her 2500 strong posse of experts worked months to sift through documents and data to produce interim and final reports. No one other than me has yet pointed out the highly strange clown world “coincidence” that Winston Smith is also a “fictional character and the protagonist of George Orwell’s dystopian 1949 novel Nineteen Eighty-Four…employed by Orwell as an everyman in the setting of the novel, a “central eye … [the reader] can readily identify with” https://en.wikipedia.org/wiki/Winston_Smith_(Nineteen_Eighty-Four) Dr. Malone @RWMaloneMD has been warning us about the advanced state of 5thGen warfare, and this entire event fits in with such a scheme from that playbook. @jjcouey has dubbed such shenanigans “Scooby Doo.”@HopeRising19 has expressed concern along with @chrismartenson who commit to staying firm on sidelines with respect to buying the conclusions until further inspection and analysis takes place.This is heartbreaking but fixable if the community works fast and together to self-correct. Take a breath, step back, and take a long second look. Work with Igor and a team of helpers (me included) and determine what really happened. What you discover at the end of that exploration may be much more useful to the world than what you think you found at the outset.
Jikkyleaks? She’s generally an antivaxxer at least as rabid as Mr. Kirsch is. As for Igor Chudov? Holy hell! He’s an antivaxxer whom, for reasons that I still don’t know, I’ve tolerated over in the comments section here, perhaps in the vain hope that he’s educable. If these two are balking at Mr. Kirsch’s analysis, it must be bad. I am also very much amused at how so many antivaxxers think that Mr. Kirsch’s analysis is so bad that he fell for a “psyop” in which bad data were intentionally leaked in order to get someone like him to take the bait, do a bad analysis, and thereby discredit antivaxxers.
I mean, Igor Chudov even thinks it’s 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. So, 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 1-2) 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 NZ government still in place during the last days of it? This question is speculative, but something I would like to clarify.
When a longtime antivax commenter here thinks you’re the victim of a psyop, my only reaction is: Pass the popcorn!
I didn’t even get much into the claims by Mr. Kirsch that certain batches of vaccines were more “deadly” than others, because of many of the reasons above. I’m also amused by how Chudov thinks that maybe the holes in the database actually hide more COVID-19 vaccine deaths. I was laughing as I read his post. I also note with amusement that Mr. Kirsch, as he characteristically does, is desperately trying to persuade Mr. Chudov that he’s wrong.
As I read his post, I was predicting that, because Mr. Chudov is an antivax conspiracy theorist, he would eventually succumb to Mr. Kirsch’s blandishments and “come home.” In the meantime, though I found it utterly hilarious that antivaxxers were holding him up as some sort of expert on data integrity:
Unsurprisingly…as I was writing this, a reader pointed out to me that Mr. Chudov was already backpedaling, and he did it even faster than I had expected, which is greatly satisfying, as it demonstrates to me that I do know the full depths of his antivax crankitude:
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!
Antivaxxers are so predictable. Also, once again, Mr. Young’s sincerity or lack of sincerity is completely irrelevant to whether the dataset is complete or incomplete, biased or unbiased, and, most importantly, whether his interpretation of it (and Mr. Kirsch’s “analysis” of it) show what antivaxxers claim.
Things have gotten even more hilarious, though. For instance, just yesterday, there was a post on his Substack Kim Dotcom discusses my New Zealand analysis with Dr. Shiva who claims it is worthless; I’m not allowed to speak. Yes, you read that right! That’s the same Dr. Shiva Ayyadurai, who deems himself the “inventor of email” and is an all-around conspiracy crank, and anti-GMO and antivaccine to boot, having also promoted claims that you can cure COVID-19 with vitamin C, being interviewed by Kim DotCom, who isn’t exactly a reliable source of information himself, if you know what I mean, being known as the entrepreneur behind the now defunct file-sharing platform MegaUpload and who is now involved in a paranoid crusade against the “deep state,” an occult entity that supposedly runs Western countries without their knowledge. Seriously, that’s how low Mr. Kirsch has fallen that he’s being dissed by the “inventor of email” with the help of Kim DotCom and whining that he was never invited to the X/Twitter Spaces interview to “defend” his work. This is seriously pathetic whining:
Kim Dotcom ran a space today with Dr. Shiva Ayyadurai.
I found out about it yesterday, contacted Kim, but I was not invited to speak in the space. The purpose of the space was for Dr. Shiva to discredit my analysis and the data. So naturally, I wouldn’t be allowed to speak.
Wow. Is that the way we do things now? With a one-sided presentation from a guy who clearly doesn’t understand the subject area?
And:
Shiva has an h-index of 13 and 1,644 citations. Harvey Risch has an h-index of 110 and has 51K citations. Risch is a Yale epidemiologist and one of the top epidemiologists in the world. Shiva… I couldn’t find a reference to him as an epidemiologist. The question you have to ask is why did Kim Dotcom seek out Shiva instead of Risch? I called Risch and he said Kim never contacted him. So much for seeking out subject matter experts to opine on the data. Kim Dotcom failed his followers big time on this choice and on not allowing me to speak.
Harvey Risch was perhaps at one time a respected epidemiologist, but he threw all that out the window when he embraced conspiracy theories about COVID-19 during that awful first summer of the pandemic in 2020. That’s when I first encountered him promoting truly awful “analyses” to claim that hydroxychloroquine was highly effective as a treatment for COVID-19. He’s only gone downhill from there; for example, he’s now all-in on the false claim that COVID-19 vaccines are causing “turbo cancers.” Basically, he took a reputation as an emeritus professor of epidemiology at Yale and descended in the course of a couple of years into the deepest depths of antivaccine and COVID-19 conspiracy theories. Seriously, at this point, Prof. Risch might as well be William Makis or Vinay Prasad, academics turned COVID-19 cranks.
Then there’s Norman Fenton, he of the p-hacking to find badness in COVID-19 vaccines fame (not to mention fame from his inability to distinguish postviral bacterial pneumonia from COVID-19), who notes about Mr. Kirsch’s claim of “deadly batches”:
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.
Of course, when you see one claim that is so easily dismissed, you have to wonder about all the other claims. Moreover, Prof. Fenton clearly very much wants to believe Mr. Kirsch’s findings, so much so that he says that there is evidence “of increased risk the more doses one gets” but is oblivious to obvious confounding that comes from the fact that those at a higher risk of death are more likely to get the later boosters. That means that an increased death rate associated with a higher number of boosters does not necessarily imply that increasing number of doses causing increased risk of death. (Hat tip again to Prof. Morris, who helped me clarify something that I suspected but couldn’t quite put my finger on.)
In any event, it frequently amuses me when the self-identified “reasonable” antivaxxers appear to recognize a nonsensical analysis when they see it but, because they want to believe its results so badly, just can’t quite bring themselves to call it what it is, nonsense. That’s Fenton. He’s calling Mr. Kirsch’s analysis crap without actually being blunt about calling it crap.
I, on the other hand, have no such compunction. I call ’em as I see ’em, and I call this crap. I don’t know if it’s crap because Mr. Kirsch is so incompetent or if he’s a bit more competent than he appears and dishonest instead of clueless. Take your pick. Moreover, just because antivaxxers are claiming that this whole thing might be a psyop doesn’t mean that it isn’t organized. Barry “Winston Smith” Young was clearly working with groups that wanted to weaponize the dataset:
This actually reminds me of the whole “CDC whistleblower” conspiracy theory too. Remember that the “CDC whistleblower” William Thompson had been venting to Brian Hooker, who, apparently unable to resist chasing clout, had let Andrew Wakefield know that he was recording phone conversations with a CDC scientist. Andrew Wakefield, being Andrew Wakefield, couldn’t resist doing a brief video with clips from the phone conversations, thus blowing the lid off of the secrecy that Hooker had been keeping on his phone calls in the hopes of finding out still more information. Ultimately, Hooker decided to go all-in with Wakefield to publicize the “CDC whistleblower,” and, less than two years later, the saga produced the conspiracyfest of a pseudodocumentary VAXXED. I’m getting strong “CDC whistleblower” vibes, with Barry “Winston Smith” Young playing the role of William Thompson, whoever he had been working with playing the role of Brian Hooker, and, of course, Liz Gunn playing the role of Andrew Wakefield. It’s not perfect comparison, of course, because Mr. Young did apparently approach Mr. Kirsch, but you get the idea.
Conspiracy theorists tend to be proof positive of how difficult it is for any group of people to keep a secret, and Mr. Kirsch is just doing what antivaxxers have long done, “reanalyzing” a dataset based on incorrect assumptions and failing to correct for obvious confounders. To that tradition, he adds using a stolen dataset of uncertain provenance—remember, the CDC whistleblower’s dataset was the one ultimately used by the CDC to publish its results—whose acquisition involved a massive data breach and the violation of the privacy of millions of New Zealanders. Here’s hoping that Te Whatu Ora investigates fully, and we find out exactly how antivaxxers got their hands on such a dataset illegally and who besides Mr. Young was involved.
89 replies on “Steve Kirsch’s New Zealand “mother of all revelations” about COVID-19 vaccines fizzles”
It also reminds me of “Big Pharma Whistleblower” Bernard Dalbergue, who in 2014 claimed that the introduction of the HPV vaccine would be “The biggest medical scandal of all time”. Of course it turned out to be rather the most pathetic antivaxx dud we saw in a long time. Also see
https://www.respectfulinsolence.com/2018/01/17/did-gardasil-kill-colton-berrett/
Isn’t that, almost by definition, ‘The Secret?’
Not exactly. The Secret is thinking that if you wish for and want something badly enough the universe will give it to you.
I still go by what my first statistic teacher said the first day of class, Figures lie and liars figure. Saved me in many suspect paper read.
Readers who want to access X/ fka Twitter might try nitter.net plus the name or handle of the writer: it’s been working for me for months without sign-in or getting an account.
. . . . .
Steve Kirsch may not know how to analyse data BUT he knows how to analyse his potential readers/ subscribers and what they want to follow.
Like other alt med providers/ anti-vaxxers / contrarians** I survey, he understands that their cache is presenting mis-information in a manner that flatters the reader: they are incredibly smart and ahead of the times, being first with insider material, part of a small select group who look beyond the facades of media, government, corporations and university experts.
Being relatively clueless in an area of inquiry allows such writers to falsely believe that they have discovered something new that is really rather irrelevant or misguided. Thus, he’s also elevating himself to a rarified position beyond expertise to uber expertise ( or ultra expertise– I’m not sure which sounds better). Uncertainty and complexity are dismissed and he speaks as if ex cathedra although these paradigm breakers usually insult standard educators as merely parroting experts. However, experts usually have data and analysis.
** the list grows longer
To modify a famous comment: Every model is wrong, some are more wrong than others.
As a side note: I can’t say anything about the quality of algebra[dot]com because our school has it blocked due to repeated security risk warnings browsers throw over it. It seems to be [based on our IT folks] more than just using http instead of https. That, together with igor’s hand in it, should be enough to realize it should be avoided.
@ ldw56old:
re Igor
Like Orac ( in the OP) I sometimes feel as though he is salvageable but only slightly: he does sometimes reject the most outlandish woo ( autism from vaccines and Tech Bro Steve on NZ) BUT
he also presents disgraceful BS like Covid vaccines killing people.
He may have learned in his former home ( CCCP/ Rossiya) never to trust authorities and news sources but he now lives where he has easy access to international news and global SBM as well as every g-d-forsaken SubStack, Rumble and X account.
If you have access to everything and you trust the ( almost) worst?
Not a good look.
Yep, my balanced, fact-based content is thoroughly fact-checked and is grounded in science and reality. That’s why I am slowly becoming famous
But you discard any data that disagrees with you and accept data that backs up what you believe without proper investigation.
How is that balanced?
To believe what you posit is true the whole of our knowledge of medicine would have to be wrong and every practitioner of medicine, across the entire would would need to be in on it.
How do you propose such a large scale deception could have existed since before the origin of modern medicine?
@ Igor:
You may be becoming famous but for the wrong reasons amongst people who don’t really understand the material which they discuss. Mike Adams and RFK jr are ‘famous’ too.
Orac has repeatedly criticised your ideas and tried to help you see your errors. Do you think that you understand medicine/ biology/ statistical analysis better than he does? Really, he’s been very kind and patient with you.
You believe in conspiracy theories and some peeople love them. Reason of your “fame”.
If someone actually fact check you, you have been proven. at best, wrong.
“Grounded in science and reality.” You keep using that phrase. I do not think it means what you think it means. Actually, it most certainly doesn’t. You are grounded in conspiracy theories, pseudoscience, and misinformation. For instance, I saw your recent article on that study claiming that the vaccine does integrate in your DNA. Your lack of understanding of the study and basic molecular biology, as well as the many deficiencies in the study, was truly epic, worthy of much cringe and facepalming.
As he shows with his 12/15 post, igor’s not concerned with looking good to people who understand things: he’ll just lie and say he loves science.
He’s interested in looking good to the sheep who will toss away good money to subscribe to his disinformation blog.
I am a “science skeptic” and I popularize good science – while also pointing out bad science.
The genre is “popular science”, that is, explaining leading scientific findings to critically thinking readers.
Paid subscribers get no exclusive benefits whatsoever on my blog.
@Igor Chudov You do not understand papers you cite and not even understand that you do not understand ttha you do not understand. Some popularizing.
You typed explaining. In order to be factual about what you do you needed to type lying.
Quelle surprise that a site run by Igor would be dodgy.
Newbie: “This is the obvious way to look at, just look how simple it is!”
Experienced person in field: “Yes, it’s so obvious that we tried that a hundred years ago and discovered it didn’t actually work, so we use our current method now because it does.”
EXACTLY!
This is the sort of thing I encountered with creationists a lot. They’d bring up some criticism or other of evolution, as though they had been the first to think of it, and a very quick bit of study would rapidly show me that evolutionary biologists had considered the criticism ages ago, done experiments based on it, and found that it was not a valid weakness in the theory. Antivaxxers do the same thing.
Kirsch embodies this phenomenon of resurrecting long-disproven criticisms and using newbie methodology at a magnitude that I’ve never seen before.
I call this the “Just how stupid do you think scientists are?” fallacy.
Another way of putting it is the “look how brilliant I am compared to the experts” fallacy.
Explained for the umpteenth time to Dr Gorski…. Where the CDC Whistleblower affair is concerned, Brian Hooker’s reanalysis is largely irrelevant. What is relevant is the CDC found a race effect for black boys that they planned to report, but they didn’t! Hooker’s ‘incompetent’ reanalysis and all found the same effect.
As to Kirsch’s own ‘incompetent’ analysis of the New Zealand data, perhaps we should be kind and permit him this rebuttal.
https://kirschsubstack.com/p/data-from-us-medicare-and-the-new
Silly antivaxxer. No, the government did not find a race effect for autism risk attributable to MMR. When the CDC investigators did the appropriate analysis and controlled appropriately for confounders, the race effect found in the raw analysis disappeared, and they found no increased risk attributable to MMR vaccination. (Effects seen in the first pass analysis of raw data frequently disappear when appropriate adjustment is made for confounders; that’s epidemiology.) In contrast, Brian Hooker “found” that elevated “risk” of autism in black boys attributable to MMR by “reanalyzing” the dataset but failing—as antivaxxers almost always due—to bother even to try to control appropriately for confounders. Then Andrew Wakefield, Brian Hooker, and antivaxxers used that result to claim that the CDC had “covered up” a race effect.
Seriously. I was writing about this the entire “CDC whistleblower” affair from the very day that it was created when Andrew Wakefield posted a video featuring snippets of audio of William Thompson’s phone calls with Brian Hooker. I wrote about Hooker’s study. I pointed out how Hooker had bragged on a YouTube video about how “simplicity” was key to epidemiology and statistics and then further observed that Hooker’s “simplicity” appeared to involve not doing appropriate analyses to account for confounders, which is a common theme in antivax “reanalyses” dating back to the Simpsonwood conspiracy theory in which antivaxxers claimed that the disappearance of thimerosal-containing vaccines as a risk factor for autism after adjustment for confounders was actually the CDC trying to “cover up” the link.
Seriously, dude. You’re way out of your league here discussing this. I’m not the only one here who has been following the “CDC whistleblower” conspiracy theory since the very beginning.
As for Kirsch’s “rebuttal,” I’m laughing at it. It’s just so ludicrous. No control needed? I laugh at that bullshit response. Just because Kirsch thinks a control isn’t necessary doesn’t make it so, and if he wants to make that argument he really has to do the work to demonstrate that a control group isn’t necessary, which he hasn’t done. Show that the dataset isn’t complete? Kirsch himself admits that it’s not complete because it’s only 1/3 of vaccine doses. Moreover, it’s not a random set. Kirsch can claim all he wants that who got vaccines by the PPD program was random, but that doesn’t make it so. New Zealanders have been trying to explain to him exactly how and why who got vaccines through the PPD program was not random, but Kirsch, being Kirsch, just doesn’t listen. Moreover, if you claim that a dataset you are analyzing is a random sample, YOU have to show it, which Kirsch doesn’t. So just with that at least one of Kirsch’s conditions for falsifying his analysis has easily been met.
I had a prof during my MPH say the only time you don’t need a control group is if you are testing whether shooting someone in the back of the head causes death, everything else is too complex (Or something to that effect.)
As an aside, I had an ER case years back where a hunter accidentally shot in the occiput survived so maybe you would need a control…morbid though that joke was.
The is a real problem with the way confounders are used in these type of analysis. Confounding variables are important, but they can also be used to explain away inconvenient findings. That seems to be the case in many vaccine studies. There no way of knowing which variables can be considered a root cause and which are correlations only. Confounding variables provide plausible contenders for causality, but they do NOT show the finding to be false or unimportant.
I imagine you can provide studies to show this and are able to demonstrate mathematically where the application of correction parameters failed?
Or is this one of those, it doesn’t show what I think it should show so I’ll assume it must have been fiddled?
Ah yes, the old hiding the data in the statistics canard. Seriously, if you don’t know why confounders are used, you probably shouldn’t be commenting on them.
It is not as if confounders are chosen arbitrarily.
Or perhaps you do know, but desperately want the opposite result to occur.
Beth returns to do the “vaccine studies don’t show the results I want them to therefore they’re flawed but I can’t say how” dance.
@ NumberWang & squirrelelite
I haven’t said the application of correction parameters failed. I’m saying the conclusion drawn – that vaccines are not causal connected to autism – is too strong. What are statistical tests tell us is akin to not guilty which is not the same as innocent.
Confounding variables only means that we cannot conclude which, if either, of the confounded variables are causally connected to autism. I remained struck at the lack of interest in exploring those confounding variables to better understand what, if any, causal connections exist.
@Beth Clarkson,
The largest meta-analysis of studies about possible connections between vaccines and autism was Taylor et al from 2014.
https://www.sciencedirect.com/science/article/abs/pii/S0264410X14006367?via%3Dihub
It summarized
Do you agree with that phrasing of the conclusion?
Here is one definition of confounding variables from Lauren Thomas
One of those “confounded variables” is genetics, which Spark for Autism has found to be strongly linked to autism or an ASD diagnosis.
Correction for confounders can be done poorly, incorrectly, or even misused, just like any other statistical test. Antivaxxers, however, have never been able to show that in the case of the Atlanta study “reanalyzed” by Brian Hooker, the methodology to correct for confounders was, from a statistical standpoint, incorrect or intentionally misused to eliminate an “inconvenient” finding.
Also, IIRC, the effect was only seen in boys who were vaccinated later than the recommended schedule and they had to tweak the age ranges to get a significant effect to show up. And it was a relatively small population size.
And none of these people have been touting the remaining conclusion, that the MMR vaccine dose NOT cause autism in non-black children.
@ squirrelelite:
Exactly.
In addition, we know that:
1. other studies do not show that Black boys are more susceptible
2. usually, when studying drug/ poison effects, younger kids are more, not less vulnerable- even if it is due solely to size
3. we know that the brain differences seen in autism occur prenatally and that precise timing of development is shown
4. we know that certain genes predispose to autism
5. trained observers can spot which very young infants are more likely to be dxed with autism later
6. brain differences can be shown prenatally ( MRI, EEG)
7. facial physiognomy varies in autism because the brain and face develop in synch
8. brain areas implicated in autism show patches of disorganisation, faulty interconnection and incomplete cells.
9. drugs, poisons and infection occurring during gestation affect whether autism happens or not. Also precise timing.
…..For insight into the workings of Steve Kirsch’s mind:
he was interviewed by Mike Adams today ( NN, 40 minutes)
which illustrates the depth of their cluelessness and naivete concerning autism, vaccines and epidemiology.
…..I’ve often said that I could create a stir in geology denialism circles with a theory that tectonic plates don’t exist but unlike Kirsch et al, I actually studied the area I attempt to overturn.
And why would I want to mis-inform readers?
Dr Gorski, wrong wrong wrong! The study’s protocol called for doing subgroup analysis for race and gender, as well as for the entire sample. The findings for the entire sample and some of the subgroup findings were reported. The one for black boys was conspicuously left out. You can’t spell fraud any other way.
So, if there was an increased risk for that particular (and extremely specific subgroup) why isn’t that borne out in real world numbers, huh Fred?
The unadjusted analysis conducted by Thompson found a significant effect for African American boys vaccinated late, but not for those vaccinated on time.
This effect disappeared when potential confounders were included in the model. As DeStefano et al. mention, the likely reason is that children being vaccinated late were being vaccinated so they could access intervention programs.
No, Fred, it wasn’t.
Seriously, I’ve been writing about the whole “CDC whistleblower” thing since it first surfaced in 2014. Yours is a common misrepresentation that I’ve seen a thousand times before.
Yo, Fred, if “tens of millions” of people were dying from COVID vaccines, that would be a colossal worldwide die-off that no amount of statistics or state power would be able to cover up. No one has seen such a die-off, therefore all these wild-ass claims about vaccines killing people are just plain false. That’s all anyone really needs to say to the anti-vax morons.
Note that he has been increasingly promoting to them alleged money making themes, too.
He may not be grifting by selling fake treatments and supplements, but he is certainly hoping to have a grift of his own.
Steve Kirsch is boasting about an e-mail he sent to Health New Zealand and its senior epidemiologist Michael Walsh, to “put (them) on notice they are killing people.” According to Steve, if they can’t explain his charts (he thoughtfully linked to one of his Substack articles), they’ve got to withdraw Covid vaccines or face charges of criminal negligence.
I’m sure they will be duly impressed.
Indeed. I’m sure they will give his letter all the consideration that it merits.
Methinks Kirsch – & by extension Mr Young – doth protest too much. Young was hardly putting “his life at risk” by stealing that information (though I’m sure a lot of people would like to give him a good hard kick in the derriere), & I highly doubt he’ll get life in jail for it either. But I guess it sounds good to the muppets who follow him.
In other anti-vax news…
The “US Supreme Court refuses RFK jr’s request to intervene in social media case” The Hill, yesterday
He wanted to join states who claim Biden interfered with free speech on social media. Only one dissent.
I’m sure he had a cinematic vision of how he might argue the case or at least, headlines it would create.
I guess it’s a relief that this time Justices Gorsuch and Thomas did not join Justice Alito in going down the completely extreme route. But the fact that any justice on the Supreme Court goes there is still worrying.
Also:
Naomi Wolf Substack etc:
her Daily Clout was erased from Face Book and Instagram. Censors!
Yes, a major Wolfian freakout is underway. Naomi says that if we don’t overcome such dreadful censorship “We’ll be a parking lot, with a gas chamber”.
As long as it’s free parking…
Recently, she said that what Covid has wrought is WORSE than the ( actual) Holocaust ( on one of her taped messages, maybe on Daily Clout).
Her political stances veering right may have a source:
a few years ago, she married her private investigator who was an army intelligence and operations officer. He is Brian O’Shea and he also has a SubStack called Investigate Everything.
Sound like a fun couple.
According to Te Whatu Ora/New Zealand Health and Te Aka Whai Ora/Māori Health Authority’s adult immunisation page:
https://www.immunise.health.nz/when-to-immunise/adults/#covid
Though I don’t know whether that was always the case. Other vaccines may not be free for people who aren’t eligible for free treatment under New Zealand’s health system.
Record level stupidity – Steve Kirsch and the New Zealand data
Back to the Science
10.3K subscribers
Dec 12, 2023
Recently, Barry Young stole confidential healthcare record level data from Te Whatu Ora in New Zealand and used it to spread disinformation about Covid vaccines. He also shared this confidential data with misinformation superspreader, Steve Kirsch, who incorrectly analysed this record level data to share even more disinformation.
In this video, Dr Susan Oliver will be going back to the science and showing why Steve Kirsch’s claims and wrong, and how the data from New Zealand provides further evidence that vaccines are safe and effective.
Orac handwaves so much that you often miss the sleight of hands. Kirsch’s analysis of the Medicare data is different than the New Zealand analysis. One is dealing with AMOUNTS of daily deaths after COVID vaccination, and the other is dealing with RATE of daily deaths after vaccination. Indeed, with the Medicare data, the stated confounders can influence the numbers and leading to deaths peaking at 300 days after vaccination. Dealing with daily rates however is totally different. Whichever way you slice it, with the NZ data, if the vaccine was not causing the deaths, we shouldn’t see that upward slope. Reread Orac’s digression laced, adhom spiel if you dare; nowhere does he make a concerted effort to account for the slope.
So…COVID vaccines are killing “over 13 MILLION” people WORLDWIDE, but you had to go to New Zealand to find evidence of that? Instafail, Dismissed.
BREAKING: Steve gets a trove of Record Level Data from a whistleblower in Liechtenstein showing that in August 2022 three people died in the country compared to only two in August 2019, a 50% INCREASE that proves THE VACCINES ARE KILLING PEOPLE!!!!!
The mRNA vaccines may be responsible for deaths/injury. Hard to tell since the control group was given the vaccine during the trial and then there was no control group…a convenient way to possibly never know.
Because of course we have no data at all about mortality rates in different demographics that predate the vaccines. All data on the likelihood of people dying in a given time frame only started being collected after vaccines were administered. Such a shame that there are no historical tends to look back to and compare against.
Hitchens’s razor: “What can be asserted without evidence can also be dismissed without evidence.”
RFK Jr. got caught out in his own lies.
https://www.huffpost.com/entry/cnn-kasie-hunt-robert-f-kennedy-jr_n_657d4506e4b08e9b410a78a2
He seems to have some severe memory issues. Also, he claimed that there was no vaccine to prevent cancer. Except there are two: HepB and HPV.
I suspect that he is so used to lying that he automatically lies, like Donald Trump.
Not only is he used to lying, he’s used to having his audience swallow those lies without question, so having someone call him out on one of them surprised him and tossed him off balance.
@ Naftali
I was a volunteer in the clinical trials for the Moderna COVID vaccine. First, the control group was NOT vaccinated before they had two months following receiving a placebo. And we have data on how many in control group sickened from COVID. More importantly, we now have massive data from US and around the world that clearly proves that those vaccinated were far less likely to be hospitalized, show up at Emergency departments, etc. And the vaccines and control groups were first tested on senior citizens, those most at risk.
@ Joel Harrison – Two months doesn’t seem long enough to determine efficacy and safety. There was still so much unknown about the coronavirus at the time. Seems like a lot of assumptions were made and conclusions drawn with limited data. It’s like the mRNA is too big to fail. Those who promote the vaccine won’t admit its failings and limitations. Most of the rest of the world has stopped giving it to children but not in the U. S. Giving the vaccine to the control group 2 months in was a bad decision.
“Two months doesn’t seem long enough to determine efficacy and safety.”
The three years that mRNA Covid vaccines have been in use provide ample evidence of safety and efficacy.
Antivaxers whine about about trial limitations while ignoring much more extensive data from clinical applications.
@ Naftali
The reason rest of world stopped giving vaccine to children was not because of risks from vaccine; but simply children are at less risk from COVID to be hospitalized and even dying. However, some children have been hospitalized, etc. See CNN “WHO experts revise Covid-19 vaccine advice, say healthy kids and teens low risk” Note. low risk doesn’t mean no risk.
You write: “Two months doesn’t seem long enough to determine efficacy and safety.” Maybe it doesn’t seem long enough to you; but to myself and others who have studied and researched infectious diseases and vaccines for decades, the risk from any vaccine occurs within a few weeks. And, again, we have a lot of data, both from US and internationally that has found the mRNA vaccine to be quite safe. Not perfectly safe; but comparing risk from vaccine to risk from actual COVID, vaccine much much safer.
Instead of posting comments, why don’t you first try to learn the basics of immunology and infectious diseases. A good starting point is an inexpensive 160 pages book: Lauren Sompayrac’s “How the Immune System Works”
@Joel A Harrison – Thank you for your response. I was reading it takes an average of 5-10 years to get a vaccine to market. Well I pulled this off the web, “ A typical vaccine development timeline takes 5 to 10 years, and sometimes longer, to assess whether the vaccine is safe and efficacious in clinical trials, complete the regulatory approval processes, and manufacture sufficient quantity of vaccine doses for widespread distribution.” President Trumps Warp Speed helped that process along. Now, with the speed of science, only a few mice need to be tested to approve the updated vaccines. Fantastic!
I don’t think it’s that simple. Tests were run perhaps more parallel and in the end the same amount of testing was done, as with other vaccines and medicines. There was an emergency.
But the words of president Trump about operation warpspeed might have fooled people in thinking corners were cut.
” complete the regulatory approval processes, and manufacture sufficient quantity of vaccine doses for widespread distribution.”
Since most people haven’t had any experience with what it is like to get regulatory approval by the FDA, let me tell you part of the reason that it takes a very long time is that the FDA is processing many applications at once, and they don’t have enough staff to do it all immediately. For the COVID vaccines they dropped pretty much everything else to concentrate on processing those submissions. So the vaccines got through the approval process much faster by everything else being put on hold so everyone at FDA could work on analyzing the data.
Manufacturing is another place where you can just throw more money and more people and more resources to get the work done faster. Vaccines aren’t big money makers compared to other medications, so industry has to take the slow and steady route to manufacturing. For the COVID vaccines it was all hands on deck, paying massive overtime, to get enough made quickly enough.
Another thing that makes vaccine approval take a long time is getting enough people to enroll in the clinical trials. That alone can take more than a year. But in the case of COVID people were eager and excited to be part of the vaccine trials, so again, that could be faster without sacrificing quality.
Years refer to live vaccines. Attenuating indeed takes timw. Youshouukd check story of Salk polio vaccine.
@ Silex
You write: “Because of course we have no data at all about mortality rates in different demographics that predate the vaccines. All data on the likelihood of people dying in a given time frame only started being collected after vaccines were administered.”
I suggest type in Google “Trends in United States COVID-19 Hospitalizations, Deaths, Emergency Department (ED) Visits, and Test Positivity by Geographic Area” https://covid.cdc.gov/covid-data-tracker/#trends_weeklyhospitaladmissions_select_00
Note. numbers started before vaccines available! ! !
@ Silex
You write: “Because of course we have no data at all about mortality rates in different demographics that predate the vaccines. All data on the likelihood of people dying in a given time frame only started being collected after vaccines were administered.”
I suggest type in Google “Trends in United States COVID-19 Hospitalizations, Deaths, Emergency Department (ED) Visits, and Test Positivity by Geographic Area” https://covid.cdc.gov/covid-data-tracker/#trends_weeklyhospitaladmissions_select_00
Note. numbers started before vaccines available! ! !
I think Silex was sarcastic in responce to Naftali.
@ Joel A. Harrison, PhD, MPH:
I think that Silex is being sarcastic.
@ Renate & Denice
Maybe and maybe not???
I was being sarcastic.
Sorry about causing confusion. I just like pointing out the absurdity of what some of the people here post by making equally absurd statements.
@ Silex
Thanks ever so much for clarification. Yep, antivaxers especially post absurd statements, not backed by science, etc.
re what Chris and ldw56old wrote.
RFk jr will say whatever gets him noticed. Like other anti-vaxxers/ alt med proselytisers/ contrarians though, his strong suit is frightening people… about vaccines, chemicals, technological advances and a variety of legal, environmental and societal issues.
That’s all they have! Their audience would be vastly diminished if they presented truthful, realistic information about these topics. People wouldn’t listen or sign on. Amongst those I survey, genocide, holocaust, financial meltdown, civil war and apocalypse are frequent descriptors of everyday problems which are magnified to prime audiences for continued adherence.
Physicians, counsellors and therapists usually calm clients down when a possible negative outcome is somewhat likely so that they can deal with it in an informed manner which will ultimately benefit them. Fear mongering interferes with how they assimilate information and make decisions. This is not sugar coating possible problems but presenting them in a realistic fashion that includes data.
This tendency makes me think that they don’t really have much respect for their audience because they are not treating them as educated and responsible adults but as people who need to be led and shown the way usually in contradiction to most or all legitimate sources by someone who knows it all.
There is a quick tell about which writers are misleading the public:
if anyone asserts that vaccines cause autism, they are lying, unable to research information or lost in a sub-universe of denialism and self-aggrandisement. Students of physiology will know that autistic brains are different from average ones and that these differences occur prenatally. Researchers from diverse fields have assembled a body of evidence that illustrates these facts. This is not new stuff but is decades old. There is no way a vaccine given postnatally can totally *re-arrange the architecture of the brain regions associated with autism and the number and quality of the neurons and their interconnection*. For g-d’s sake, researchers have counted the number of cells and quality of layers involved!
Thank you, Steve!
https://kirschsubstack.com/p/yet-another-flawed-fact-check-on
Nobody likes a kissarse.
Well, maybe prominent anti-vaxxers do. You can always dream.
He still does not analyse deaths among unvaccinated. I wonder what this data would be,
UK does:
https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/deathsbyvaccinationstatusengland
Check it.
Antivaxers seem to be in a continual race to the bottom as they jockey for attention and revenue from their followers.
Steve is now threatening to expose names of dead people from his collection of filched New Zealand data, unless NZ’s public health epidemiologists agree to play games with him.
Toby Rogers meanwhile is poised to make inroads on Steve’s loony Substack subscriber base with posts like this:
“Now people just watch TV, absorb endless propaganda 24/7/365, and drive themselves and their families to their genocide appointments.” (the latter of course refers to going to the doctor).
The real problem is that it’s so hard to get a genocide appointment on short notice, requiring you to visit an Urgi-Genocide.
“Now people just watch TV, absorb endless propaganda 24/7/365”
As opposed to the endless propaganda available round the clock on the internet. Especially if you go looking for it.
Maybe Toby gets his propaganda delivered by pony express relay, hand printed in a bunker in Montana.
URGI-GENOCIDE
Coming soon to a shopping mall or downtown near you!
Are your toxin-infused pHarma prescriptions running out? Or do you need inflammatory frame-shifting jabs IMMEDIATELY? Anaphylactic shock?
Well, we have it all for a price!
Our “health” care workers operate as they’re programmed assisting you to find optimal “results”.
All of our services come with a 30 day guarantee regarding viability ( 12 hour escape clause for our convenience, profit margin and legal security)
No fruits, vegetables, supplements or re-purposed meds on the premises.
( Zyklon B available on request)
Yep. And I notice that our very own Igor has ignorantly misinterpreted another study. As much as I hate to draw more attention to him, maybe he might merit a bit of not-so-Respectful Insolence next week. (Yeah, as of now I’m done posting until after Christmas, other than maybe a seasons greetings post. Even a box of blinky lights needs a holiday break every now and then.)
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