Since my articles (here and here) on vaccine-induced mortality made the rounds, and a bounty was placed on my head, some interesting things have occurred and not occurred. What has occurred is that I was put in touch with a number of academics, including some at top-five ranked research universities, making similar observations. In some cases, they are working toward publication, which is great, though the experimental vaccines face potential approval at the end of this month while our regulatory agencies seem content to pretend there are no safety signals and dodge all questions about why they're suppressing autopsies.
In some cases, research indicating vaccine-induced mortality and low-to-negative risk-benefit has been suppressed (here and here). Some very famous researchers feel under threat of physical harm for themselves and their families, with mentions of specific threats made. Such threats were made against Professor Didier Raoult last year, though U.S. researchers may have reason to take such threats even more seriously.
What hasn't happened is any serious kind of challenge to the data arguments themselves. We will take a look at those arguments that have been made, but first let's take a moment frame what our regulatory agencies should be doing to understand the problem.
Causality of Mortality
We have suffered through 18 months of Pandemonium during which all manner of absurdities have been thrust upon us. Two of those that stand out include, (1) the use of PCR testing at such high cycles that exponentially scale up tiny mistakes in sequence-matching to detect other genetic sequences, and (2) almost no autopsies have been performed.
Thinking about (2) is actually quite chilling. It makes no sense, whatsoever. Finding out from a friend that we've had robots standing by that could have been performing autopsies with blood analysis with essentially zero risk (far less than tending to an ICU patient or intubing somebody) this whole time, but that authorities apparently send strict "stand down" orders for those use of those tools makes me sick to my stomach. Meanwhile, vaccine-induced death causality has been done in Europe, pointing to large numbers (here and here), though that information has been suppressed and denied in the U.S.
Of course, this overly strict requirement of "proof of causality" (by regulatory organizations that seem to be sabotaging the process in service of an industry that works in tandem with media that sabotages clean information signals) comes during a pandemic in which a positive PCR turns falling off a ladder into a COVID death.
Unrelated reminder that multidrug early treatment regimens save nearly all COVID patients.
Many of those not paying attention or not capable of paying attention build cognitive dissonance throughout the process. Perhaps that explains the following flimsy arguments.
Arguments Against Vaccine-Induced Mortality: Wrong Answers Only
I'll start with Economist Steven Postrel.
I've debated with Steven a few times (or observed him debate against my points while I was unable to discuss due to Facebook bans) before and feel he makes quick judgments before deeply examining many complex topics. Of course, we all do that to a degree [because it's economical to have fast/slow thinking gears], but in conversations during the pandemic, he seems to jump to justification by authority on such topics instead of expressing interest in seeing the work, then not respond to deeper discussion [when I've done the actual work].
I'm not a fan of using terms like "alarmism" in association with data, and I don't think I've used alarmist language relative to the observations, but it is noteworthy that he does not at all argue the data. I sent him a PM asking what the "official number of vaccinated COVID-19 deaths" is, but he did not respond. I will note now that everyone I've asked, in government and academia has either not answered the question or given a different answer. Every single one. That includes most recently a conversation with a Ro Khanna staffer who told me that there were zero deaths caused by vaccines and incorrectly claimed without citation (in fact, all she made were uncited statements during a half-hour call) that (official) numbers were similar around the world. This seems to be what happens in a world in which the CDC confidently makes statements that aren't actually about real numbers (the made up 99% number, for instance), and suppresses all attempts at attaining that data, and nobody in Congress or the press ever even asks questions.
It depresses me to see a libertarianish economist doing exactly nothing to push the government on the veracity of its data claims. I would also welcome a more serious argument by Steven.
Many who have taken shots at my analyses misunderstood some of the numbers. To be fair, I gave only enough explanation that experienced data minds would understand easily.
Pat @JubanPat@VaccineTruth2 Ignoring the 18 day lag - and taking Vietnam as an example - 35 prior deaths in 2501 prior cases = 0.014 CFR. Applied to 157507 cases =2204 expected deaths total vs 1306 actual deaths. A decrease of 898 or -128/m jabs. How do you get an increase of 782/m? Lag?
The "implied mortality" numbers in my "naive nations analysis" take currently unresolved cases and apply the post-vaccination-start CFR to estimate future deaths from those cases. I've used this estimation method many times during the pandemic and it has consistently worked out well in prediction models. In the case of Vietnam, my estimate proved slightly low this time, which raises the mortality per dose results.
Another wrong answer comes from Taison Tan:
Testing is a long and tricky conversation that would be worthy of its own deep dive data article. However, there is not likely much link between mass testing and symptomatic cases, and far less with cases that progress in severity or death. But most pertinent to the point is that the CFR analysis on Europe involves an 18-day case lag (the denominator) where COVID deaths (the numerator) drive the broad rise in CFR. The rise in CFR took less than three weeks during which there was no broad change in testing regimes.
I'd be open to seeing somebody take on my two analyses based on testing regimes most of all because I strongly believe they'd find no significant effect. It is a well-understood publication bias that suggests that papers are less likely to be published when they do not confirm researcher predictions or desires, so to the extent that we do not see such an analysis completed, we should update our priors in a way that supports my analyses.
Google software engineer Alyssa Vance makes the argument that my identified vaccine mortality estimates don't match the vaccination program rollouts. Aside from the fact that she posted U.S. vaccine rollout rates (which differ significantly from European nations) I don't think she read my argument well enough to really absorb it. Since most of the vaccine-induced deaths (like COVID) come from the elderly (on a fairly extreme distribution), we need to take a look at vaccine rollout as a vector, not a single scalar. I confirmed in the OWID data set that I used that vaccine rollout among the most elderly groups matched the CFR rise I noted. My claim is easily falsifiable, but nobody is arguing that. Here is the UK rollout curve as an example:
Again, I welcome outside analysis of the data, but please do the actual analysis. I strongly believe outside checks will reconfirm my results. In fact, my spreadsheet has been passed around among data geeks and university researchers with no complaints to this point.
Matt's argument is interesting in that, as stated, it should apply to all deaths. All deaths are easily refuted because "this chart would look different if they existed". It's a bizarre argument, but I can see how he manages the self-deception. It starts with assuming that I'm wrong. What his cognitive dissonance doesn't allow him to see is that the vaccine-induced deaths are already part of this chart. They were simply categorized as COVID-19 deaths, which I explained in my analysis, which I'm guessing he didn't actually read.
Here he makes the same fail that Alyssa did, not focusing on the vaccination of the elderly, which absolutely fits my analysis. I invite him to download the OWID data, compute an age-impact vector from the VAERS numbers, and perhaps actually perform the analysis before assuming the result.
Addendum: While most of the focus of my European analysis is on the elderly, because that's where we see the signal of larger numbers, it is also worth noting that many signals have pointed toward increased excess mortality among the young (here and here for instance) once vaccination campaigns reached them.
I answered a few other weaker arguments here, though they likely aren't interesting enough for most people to want to wade through. A couple were deleted by authors with credentials to protect because (I assume) they were made to look foolish. Wish I'd saved them.
A few dozen statisticians and data scientists read this newsletter/blog, but none have offered observations of flaws, though one very good scientist I've worked with some this year is nearing publication with a substantial-but-lower mortality estimate based on U.S. data, and I will mention that my own currently-incomplete analysis of U.S. data points so far to a substantial-but-lower mortality estimate.
It is time that the U.S. regulatory agencies (CDC and FDA) come to the table for real discussion, or that we write them off as corrupt, extralegal organizations. What other choice do we have?
Meanwhile, your moment of Zen: There is no [significant] correlation between rates of COVID cases in U.S. counties and vaccination rates (Texas numbers excluded here, but on their own show a similar result). I am thinking through theories that might explain this result and will write more about this in depth in a future article.