The Evolution of Cybernetics

The Evolution of Cyberneticshttps://sifter.org/~simon/journalA Journal by Simon FunkSun, 22 Mar 2026 11:46:03 GMTPyRSS2Gen-1.1.0http://blogs.law.harvard.edu/tech/rssCovid Update: VFRhttps://sifter.org/~simon/journal/20211119.h.htmlCovid Update: VFRhttps://sifter.org/~simon/journal/20211119.h.htmlFri, 19 Nov 2021 00:00:00 GMT<blockquote style="margin: 0 auto; max-width: 800px;" morss_own_score="2.569937369519833" morss_score="123.5986750816098"> <center> [<a href="https://sifter.org/~simon/journal/20201219.h.html"><<</a> | <a href="https://sifter.org/~simon/journal/20211028.html">Prev</a> | <a href="https://sifter.org/~simon/journal/index.html">Index</a> | <a href="https://sifter.org/~simon/journal/20220125.1.h.html">Next</a> | <a href="https://sifter.org/~simon/journal/20220125.1.h.html">>></a>] <h2>Friday, November 19, 2021</h2> <h4>Covid Update: VFR</h4> </center> My Covid updates have stalled while I've been waiting for any sort of all-cause mortality data to come out. And waiting. And waiting. The stark absence of such data is itself very revealing<a href="https://sifter.org/~simon/journal/20211119.h.html#update2">[2]</a>. But it turns out some slipped out late October, brought to my attention yesterday by <a href="https://alexberenson.substack.com/p/another-major-red-flag-about-covid">Alex Berenson</a>. I thought it was worth a more thorough analysis, so here it is: TLDR: If this data holds up, the vaccines are killing more people than they are saving. Curious? Read on. The basic setup is this: <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3949410">The study</a> in question looks at Scandinavian-thorough data for some four million Swedes, and shows that vaccine efficacy starts out good but wanes over time, thus serving the current narrative push for boosters. It's a big study and, although not yet published, appears to be fairly well done. I expect it answers the questions it asks reasonably well. But, of course, they're only allowed to ask the wrong questions, because that's how sciencism works these days. So let's ask the right ones. Bear with me as I zip through this -- the reasons for it all will become evident below. On figure one (page 32 of the pdf at this time) they say: "From this cohort, 4,034,787 individuals were identified that had two shots of vaccine no later than August 5." From which: "Excluded: 3,939 Died within 14 days of [date of second dose]." Now, Alex does some back of the envelope calculations for what this number ought to be, but rightly observes that timing matters since, e.g., if these were all in the winter, the expected death rates would be higher (over 4k per 2 weeks). And he also relies on extrapolated data from prior years which is hazardous when death rates and numbers are steadily changing year to year as evidenced in my <a href="https://sifter.org/~simon/journal/20210724.1.html">prior covid update</a>. So, let's not make any guesses. Let's actually calculate how many people should have died in that window. Here's how we do it: First, we take the <a href="https://ourworldindata.org/covid-vaccinations#fully-vaccinated-by-age"> number of Swedes fully vaccinated, by date and age group</a>: <a href="https://sifter.org/~simon/journal/ims/20211119/covid-fully-vaccinated-by-age.png"><img src="https://sifter.org/~simon/journal/ims/20211119/covid-fully-vaccinated-by-age.png"></a> This results in about 4.5 million people who had their second dose during the time window covered by the study, with an average age of 59.1 and an average second-dose date of 2021-06-02. The larger study group in the paper, selected from their ~4 million cohort, had an average age of 59.4 and an average second-dose date of 2021-05-19. So, our sample (the full sample of all second-doses in Sweden) is very representative of the study sample. Next, we combine that with the <a href="https://www.mortality.org/">actual number of deaths</a> in Sweden during the study window: <a href="https://sifter.org/~simon/journal/ims/20211119/stmf.png"><img src="https://sifter.org/~simon/journal/ims/20211119/stmf.png"></a> This is not us extrapolating some expected number of deaths from past years--this is the actual number of all-cause deaths in Sweden for each week of the study, by age group. Also, we can (and I did) further extrapolate the distribution of those deaths within each age group using historical death distributions, which does not change the number of deaths at all but lets us (to a reasonably good approximation) distribute them more accurately among the mis-aligned vaccination age groups. After adjusting for the 4/4.5 million ratio (since they did not use quite all of the fully vaxxed people like we are), the net result is, to a very good approximation, the number of deaths we should have seen in the study in the two weeks following the second doses, assuming there is no correlation with the vaccine and all-cause mortality at all. Indeed, if the vaccines lower all-cause mortality in that window, as the CDC recently claimed, then the expected number should be higher than the actual number (3,939). Running the calcuations, we get an expected 2,640 deaths. That means there were 1,300 more deaths in the two weeks following the second dose of the vaccine than there should be. I say "should" as opposed to merely "we might expect" because this is based on actual numbers of deaths (can I emphasize that too many times?) and actual second-dose dates by fairly narrow age bands. The most significant assumption in this particular calculation is that the vaccinated population is not a priori significantly more at risk of death than average. If you look at the study's demographics, the vaxxed group does in fact appear to be a bit less healthy than their age-matched unvaxxed counterparts, so this is a real confounder, however the impact is not likely to be large here<a href="https://sifter.org/~simon/journal/20211119.h.html#update1">[1]</a> -- even if they represented 100% of all Sweden's fatalities (an absurd extreme) there is still a significant deficit as Alex observed in his initial back-of-the-envelope upper limit. Furthermore, this is just for the second dose, and only for the immediately following two weeks, which means we are under-counting by however many causal deaths happened after the first dose or after two weeks (not unlikely for cardio issues). Let's call that a wash even though I suspect the true number is even worse. Now, we don't know anything about how those excess deaths were distributed (over time or age), but we do know how many doses likely caused them, so can calculate, for roughly the currently fully-vaccinated population of Sweden as a whole (since that's roughly our study cohort): VFR (vaccine fatality rate) = 0.03% Or about 1 extra death per 3,100 second doses, just in the first two weeks after the dose. In the original Pfizer trial, this would equate to about 6 excess deaths (of 19k vaxxed). But Pfizer used an atypically healthy cohort with an average age of only 50.4, so we should expect less. The <a href="https://www.fda.gov/media/151733/download">actual difference was 5</a> (not 1 like their public study release claimed), and that's assuming they didn't <a href="https://www.bmj.com/content/375/bmj.n2635">bury more</a>. (Yes, that's right -- all-cause mortality in the Pfizer RCT was 24% higher in the vaxxed arm. Nothing to see here, move along.) If we assume Americans are biologically similar to Swedes, then, based on the number of fully vaccinated, that translates to about 63,000 Americans killed so far by the second dose alone. This is on the same order as a number of other estimates (from VAERS, etc), which typically have put the figure at about 120,000 conservatively for all doses and latencies. So, yeah. But wait, there's more... The cited study also happens to show that vaccine efficacy for symptomatic infection goes negative by about 9 months--something we are also seeing in the UK data. <a href="https://sifter.org/~simon/journal/ims/20211119/VE.png"><img src="https://sifter.org/~simon/journal/ims/20211119/VE.png"></a> But the efficacy against hospitalization and death remains positive throughout, which is the high point of these vaccines: Even if they're doing <a href="https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(21)00648-4/fulltext">a crap job of preventing transmission</a>, they protect you, at least. Right? Right? Turns out, NOPE. What they appear to do is kill you if you're weak or especially sensitive to the spike protein, so that Covid can't kill you later. Okay, that's wild extrapolation, but amiwrong? Take the following with a grain of salt because it depends on how much covid is going around at the time, but: If we assume about 25% of hospitalizations lead to death (higher numbers make the vax look better so I'm conservatively erring on the high side), then even by six months after the vax, when its effectiveness had significantly waned and so the bulk of its benefit was over, fewer people in the above study had been spared death-by-covid than were killed in the first two weeks by the vaccine itself. As a ballpark, for every 5,000 second doses they killed one person on net. That would be equivalent to about 40,000 people needlessly dead in the US to date (depending on how similar the covid waves were). Bottom line, the vaccine efficacy for all-cause death appears to be negative across the board--you're less likely to die if you don't take it. (And there's every reason to believe the boosters will be significantly worse. Early signs support this.) So, yeah. How'bout them mandates? <hr> <a>[1]</a> Update 2021-11-20: If we shuffle the deaths between groups so that our vaxxed population are twice as likely as the unvaxxed to die a priori, the excess deaths drop from 1300 to 1170--only about 10% and with negligible impact on the VFR. So this is unlikely a substantial bias. <a>[2]</a> Update 2021-11-20: Re. the recent all cause mortality data from the UK, see <a href="https://www.youtube.com/watch?v=6umArFc-fdc">this overview</a>. (Note in particular the very final graph.) Update 12/3: See also <a href="http://probabilityandlaw.blogspot.com/2021/12/possible-systematic-miscategorisation.html">Possible systematic miscategorisation of vaccine status raises concerns about claims of Covid-19 vaccination effectiveness</a> And <a href="https://bartram.substack.com/p/the-importance-of-the-delay-between">The importance of the delay between infection and death in official statistics</a> [3] Update 2021-11-22: Just found this study (October preprint) which tries to estimate the VFR from correlations of excess mortality with vax uptake across various countries, and comes up with 0.04%, which is very close to my 0.03%: <a href="https://www.researchgate.net/publication/355581860_COVID_vaccination_and_age-stratified_all-cause_mortality_risk">COVID vaccination and age-stratified all-cause mortality risk</a> [4] Update 2022-07-14: <a href="https://repec.its.waikato.ac.nz/wai/econwp/2211.pdf">The Rollout of COVID-19 Booster Vaccines is Associated With Rising Excess Mortality in New Zealand</a> estimates 0.016% VFR in New Zealand from the boosters. <center>[<a href="https://sifter.org/~simon/journal/20201219.h.html"><<</a> | <a href="https://sifter.org/~simon/journal/20211028.html">Prev</a> | <a href="https://sifter.org/~simon/journal/index.html">Index</a> | <a href="https://sifter.org/~simon/journal/20220125.1.h.html">Next</a> | <a href="https://sifter.org/~simon/journal/20220125.1.h.html">>></a>] </center> </blockquote> Derationalizing, Part 1: Question the Obvioushttps://sifter.org/~simon/journal/20220125.1.h.htmlDerationalizing, Part 1: Question the Obvioushttps://sifter.org/~simon/journal/20220125.1.h.htmlTue, 25 Jan 2022 00:00:00 GMT<blockquote style="margin: 0 auto; max-width: 800px;" morss_own_score="2.951871657754011" morss_score="107.15187165775401"> <center> [<a href="https://sifter.org/~simon/journal/20211119.h.html"><<</a> | <a href="https://sifter.org/~simon/journal/20211119.h.html">Prev</a> | <a href="https://sifter.org/~simon/journal/index.html">Index</a> | <a href="https://sifter.org/~simon/journal/20220125.2.h.html">Next</a> | <a href="https://sifter.org/~simon/journal/20220125.2.h.html">>></a>] <h2>Tuesday, January 25, 2022</h2> <h4>Derationalizing, Part 1: Question the Obvious</h4> </center> My reply to this question from an acquaintance: <blockquote> As a young person, it's difficult to know about every topic that is discussed. What do you do as a young skeptic who holds few opinions? Do you become the Socrates in every discussion/argument while offering very few of your own opinions? </blockquote> <blockquote> How can you ever be ready to say "I believe x" if there's so much room to say "I don't know"? It's unlikely that you'll go through every source about any subject, so at what point should you be confident enough to form an opinion? </blockquote> <blockquote> What happens when you don't know what you don't know? </blockquote> <blockquote> [..] </blockquote> <blockquote> I guess the question here is: which strategy are people most receptive to in an argument? Which will help you learn the most? Is there a combination of these that allows for maximum learning efficiency? </blockquote> Imo there is only one answer to any of these and it's the answer to all of these, and you touched on it up top w/Socrates: Make your only objective: to understand why people believe what they do. (And in particular, not to try to convince anyone of anything before you've done that.) Once you achieve that (which you may or may not ever with any given person...), depending on what the answer is there are usually one of three options: 1) You realize they are right, 2) You use what you've learned about why they believe what they do to guide them to a new belief, 3) You realize the reasons they believe things are entirely irrational and, chances are, you're too honest to do what it would take to change their minds. Generally speaking you will find that honest people will self-correct before you get to the end. That is, if you just keep asking them about whatever bit isn't making sense to you, and assuming their answers don't clear it up for you and change your mind, they will crash into their own inconsistencies or unsupported premises in their effort to answer your questions, and will say "Oh, now that you mention it.... maybe I had that part wrong." So in practice, it's rare you ever get a chance to exercise (2) because those people self-correct before you get there. There are like 10 such people on the planet. I know 5 of them. Most of the time you'll eventually end up at (3), which is a hard end to reach because you'll rightly be very reluctant to accept just how irrational people are, particularly the academics who've been trained to present as rational. Sadly (1) will be more rare than you hope, too, for the same reason. To give you a head start on understanding why (most) people believe what they do, here's my distillation after half a century of studying how people think: 1) No matter how many reasons most people have for what they believe, the reality is belief is a feeling and all the reasoning is post-rationalization. Deep down, certain things are "obvious" to them, and this feeling of obviousness is the stopping condition that keeps them from ruminating endlessly into the most minute details or remote possibilities. In other words, the feeling of truth or obviousness is a key and necessary feature of cognition, and without it we would be unable to look up from our navel in time to eat. But the problem is, very few people recognize it for what it is--a subconsciously acquired emotional response (as opposed to a necessarily sound inference)--and they blindly heed its impulse, which is to stop thinking about the "obvious" thing in question. So, most people most of the time are not in a learning mode at all, they are post-rationalizing what they already feel. 2) What most people feel is true is programmed into them by their tribe--in this age via their chosen media and social networks. People in media know this, and use it (the caveat on (3) above does not apply to them). But it doesn't happen the way you think: It's not about presenting evidence and making rational arguments, because as I said that's not how people actually think. It's about adopting the desired "truth" as a presupposition and then portraying everything using that light. So "the terrorists stormed the church and held fifty people captive for three days" brings our conscious attention to the described action, but hidden from scrutiny is the choice of "terrorists" vs. "revolutionaries", which carries with it a great deal of emotional implications which directly program the readers. Even if the depictions don't seem super credible, repetition will make them into reality, because every time someone is exposed to a phrase or story like that, they picture it in their mind's eye, and their cortex integrates that experience as if it had been seen in the real world. In other words, if you can get people to listen to your stories for enough minutes of the day, they will integrate the presuppositions (the background reality implied by your stories) of those stories as their model of the real world. Doesn't matter if they believe they are reading fact or fiction. And looping back, once those presuppositions are programmed in, they define what is "obvious", and in turn what people will no longer think about. Again, most people who make it to positions of serious power understand all of this very well, and the world is run via these strings. The people you'll encounter will mostly be on the wrong end of those strings. So, you can try asking people why they believe what they do, but mostly their answers won't make sense (they will form a logical chain, but that chain will be just one of many logical chains and not necessarily the most likely one by any obvious metric), so there's a fair chance you won't come to understand why they believe what they do by asking them. Instead, find out what media they consume, and then go pay attention to how that media is crafted, and you will probably have your answer. Which is why, for case (3), there's usually nothing you can do about it. Just beware, once you pay attention to media at this level enough to see what I'm talking about, you'll have learned to perceive it in the same way you perceive a chair when you walk into a room, without having to analyze the legs and seat and logically deduce that there's a chair. I.e., you won't be able to un-see it, and then you'll spend the rest of your life living in Clown World where you can't believe you're surrounded by people who blindly swallow/follow this crap. <a href="https://www.youtube.com/watch?v=g4XiKChyK7A">They Live</a> (But you won't be able to take these glasses off.) All that said, to end on a more productive note and to tie in what I said at the start: Imo the best approach to take is to just ask questions. If you're actively debating someone, focus on the most important one or very few--people will throw a lot of chaff to distract you away from their inconsistencies. Just ignore the chaff (hard to do!) and laser focus on the important question--whatever most doesn't make sense to you about their stance. I have actually "won" a few debates this way, the sorts of debates that are not normally ever won by either side, which is as much a credit to them as to my approach because most will break down into full cognitive dissonance first: <blockquote> "I have learned that, for most people, if you inspire within them a conflict between what they feel is right and what they know is right, well, fireworks or waterworks are usually the result." -Rik Ling </blockquote> Equally important is to be that person who is willing to answer the important questions straight, and to admit error when you can't. You might just make it 11. Oh, and also actionable: To whatever degree all of the above is accurate and representative of humans in general including you and me, the primary control you have over what you believe, and the quality and accuracy of what you believe, is the media you expose yourself to. This is a very tricky decision to make well, because: <blockquote> "One thing I've learned in the half-century of living and learning is that if society establishes an organized process, cheats will enter, and game the system." -- <a href="https://jameslyonsweiler.com/2021/07/20/ghouling-targeted-study-retraction-is-biasing-scientific-literature/">James Lyons-Weiler</a> </blockquote> The most rigorous and seemingly trustworthy institutions are also the most insidiously corrupt. Independent bloggers and journalists are probably your best bet right now. Pick a wide range of ones who think well, and rely on their public position as aggregators to pull in what matters. Avoid the mainstream media like the NPC blue pill it is. (Once you start paying attention to slant, you won't be able to stomach mainstream media anyway. See Clown World.) Lastly... to make the implied explicit: You can do a lot to clean up your own belief system by simply being willing to entertain any challenge to something you consider "obvious" (at least long enough to make sure it's not a new angle you haven't honestly considered before; and even if you have, it's worth re-visiting if it comes from someone you consider sane). I.e., there's probably no way to avoid the emotional aspect of belief (and the subconscious absorption of presuppositions from media), but you can largely work around/patch it simply by understanding that that's how the (your) mind works. Which is a really long-winded way of saying: don't be over-confident about anything. <center>[<a href="https://sifter.org/~simon/journal/20211119.h.html"><<</a> | <a href="https://sifter.org/~simon/journal/20211119.h.html">Prev</a> | <a href="https://sifter.org/~simon/journal/index.html">Index</a> | <a href="https://sifter.org/~simon/journal/20220125.2.h.html">Next</a> | <a href="https://sifter.org/~simon/journal/20220125.2.h.html">>></a>] </center> </blockquote> Derationalizing, Part 2: Check your Blind Spothttps://sifter.org/~simon/journal/20220125.2.h.htmlDerationalizing, Part 2: Check your Blind Spothttps://sifter.org/~simon/journal/20220125.2.h.htmlTue, 25 Jan 2022 00:00:00 GMT<blockquote style="margin: 0 auto; max-width: 800px;" morss_own_score="2.9470588235294115" morss_score="44.94705882352941"> <center> [<a href="https://sifter.org/~simon/journal/20220125.1.h.html"><<</a> | <a href="https://sifter.org/~simon/journal/20220125.1.h.html">Prev</a> | <a href="https://sifter.org/~simon/journal/index.html">Index</a> | <a href="https://sifter.org/~simon/journal/20220204.html">Next</a> | <a href="https://sifter.org/~simon/journal/20230521.h.html">>></a>] <h2>Tuesday, January 25, 2022</h2> <h4>Derationalizing, Part 2: Check your Blind Spot</h4> </center> [Addendum to <a href="https://sifter.org/~simon/journal/20220125.1.h.html">last time</a>] I glossed over one key point that helps make more sense of the rest: Recall my example of walking into a room and seeing (perceiving) the chair in it. How do we do that, and what does "perception" mean exactly? The answer (my opinion, but one I'm very confident in) is that perception is the act of matching some inputs (the image hitting your retinas) with your existing model of the world. And by "model" here I mean (something equivalent to) a generative model--i.e. something that could "imagine" scenes from the world. I.e., you don't walk into a room and see a chair in a strictly bottom-up (concrete-to-abstract) way. Rather, you walk into a room expecting (being able to imagine, top-down) various things that might be there, and expecting what they might look like, and in effect you cull this set of possible things down to the ones that match the inputs you get. Why this matters is: People are utterly incapable of perceiving anything outside of their existing model. (People can update their models with time but this is slow and.. rare.) It's a little harder to illustrate for something like a chair (because we all have very detailed models of 3d objects in general) although it's more applicable there than one might guess, but probably the most accessible example is if you've ever tried to learn a new language with a different phonetic base than you are used to: You will initially perceive the language in terms of your existing base, and in general won't be able to properly group and differentiate some of the sounds. And by that I mean, someone native to the language might be able to say two sounds that are clearly different to them, and to you they will sound qualitatively the same. With practice those sounds begin to emerge as distinct, and eventually you will (effortlessly) hear them as two different things (as your model slowly updates to include them). Now, someone with a more elaborate low-level model of phonetics might be able to differentiate sounds from a new language immediately -- so it's not strictly true that you need experience with a particular thing to be able to perceive it; rather, it just depends on where your particular model has what level of detail. For someone who grew up in a single accent of a single language, there is no need to have a finer-grained model than the phonemes of that dialect, and so the brain generally won't waste resources on that, and they will be the most "blind" to the nuances of a novel language. Anyway, where this becomes more relevant is with more abstract things. Consider all the nuance of life, social interaction, politics, and so on, which are "invisible" to children, and which you learn to see more and more of in better and better detail as you get older. My point here is to reflect on what you could see a few years back and compare to what you can see now; there must be some examples of things you just naturally perceive now which you remember being completely unaware of a few years back? If you can hone in on that contrast, then realize the same applies between any two people -- there are ways that the people around you are or will be like children compared to you in terms of what you can easily see that is entirely invisible to them. I'm being long-winded on this, but it's super important, because we naturally tend to imagine that everybody sees the same world that we do. But the reality is they don't--things can be plain as day to you (not even a judgment call, just what seems to be a direct perception) and totally invisible to them, and vice versa. If you don't account for this, it's a lot harder to understand other people's perspectives. Once you do account for this, you can generally learn the limits of other people's models--typically by tribe since people's models are, as I mentioned, mostly absorbed from the presuppositions of their social/media context. And you can be on the lookout for anywhere that others seem to be able to see something you don't, and can proactively learn to see that thing. The example I gave before was media slant. I have now witnessed multiple people transition from being blind to it to seeing it. It is the closest real-world analogy to the "red pill" Matrix moment. In general, this only happens when they have been so personally familiar with something that they were forced to see the media version vs. the real version, especially as both evolved in real time. This serves as a training example, via the correlates between the nuances of the media and the particular ways and places they are deviating from or misrepresenting reality. For some reason, some people go through this but never develop that model. That's called Gell-Mann Amnesia. But people whose brains haven't calcified, or whatever is the issue, update their models and gain a new and permanent insight. The same situation with media exists with academia and science, unfortunately. (I've been collecting the patterns of bias and fraud for years; thinking of assembling them into a book some day.) And the same principle applies here: Most people simply can't see it, because they've not spent the time looking closely enough at it to build a model of how it works; and without that model, they simply can't see it even when it's right in front of them. Again, I've seen people transition (red pill) and almost always it is due to some direct personal experience where they knew something so well that they could not escape seeing the contrast between what was true and what was broadly and universally accepted, supported, and defended to the death by "science". (Until years later, when science finally comes around, and declares that those who thought otherwise before were right for the wrong reasons.) So, bottom line, don't assume the person you're talking to sees the same thing even if you're both looking right at the same thing. Study their tribe and how it models the world, and you'll eventually figure out what they will and won't be able to see; and once you know that, people make a lot more sense... (And you still won't be able to teach them anything, alas.) <center>[<a href="https://sifter.org/~simon/journal/20220125.1.h.html"><<</a> | <a href="https://sifter.org/~simon/journal/20220125.1.h.html">Prev</a> | <a href="https://sifter.org/~simon/journal/index.html">Index</a> | <a href="https://sifter.org/~simon/journal/20220204.html">Next</a> | <a href="https://sifter.org/~simon/journal/20230521.h.html">>></a>] </center> </blockquote> Covid Update: Vitamin Dhttps://sifter.org/~simon/journal/20220204.htmlCovid Update: Vitamin Dhttps://sifter.org/~simon/journal/20220204.htmlFri, 04 Feb 2022 00:00:00 GMT<blockquote style="margin: 0 auto; max-width: 800px;" morss_own_score="2.4871794871794872" morss_score="37.72792022792023"> <center> [<a href="https://sifter.org/~simon/journal/20220125.2.h.html"><<</a> | <a href="https://sifter.org/~simon/journal/20220125.2.h.html">Prev</a> | <a href="https://sifter.org/~simon/journal/index.html">Index</a> | <a href="https://sifter.org/~simon/journal/20220707.html">Next</a> | <a href="https://sifter.org/~simon/journal/20230521.h.html">>></a>] <h2>Friday, February 04, 2022</h2> <h4>Covid Update: Vitamin D</h4> </center> I've been mentioning vitamin D as a likely severe-Covid preventative with <a href="https://sifter.org/~simon/journal/20200904.html">increasing confidence</a> since <a href="https://sifter.org/~simon/journal/20200429.html">April, 2020</a>. Science Tribe representatives have consistently pushed back, adamant that it's woo woo nonsense, that there is "no statistically significant effect" and other such fine tools of motivated incompetence. (Their supporting evidence is always lame, but they refuse to peel that onion any further than the dry skin on the outside which has "vitamin D is not the cure you're looking for" sharpied on it.) Well here's the latest: <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0263069">Pre-infection 25-hydroxyvitamin D3 levels and association with severity of COVID-19 illness</a> <blockquote> "Patients with vitamin D deficiency (<20 ng/mL) were 14 times more likely to have severe or critical disease than patients with 25(OH)D >=40 ng/mL" (14x: 95% CI 4-51; p<0.001) </blockquote> That alone would be not very interesting if very few people had poor vitamin D status, but alas that's common enough not only to account for many Covid deaths, but to account for nearly all of them. See particularly figure 2: <a href="https://sifter.org/~simon/journal/ims/20220204/figure2.png"><img src="https://sifter.org/~simon/journal/ims/20220204/figure2.png"></a> Remarkably similar to the plot I posted April 2020, and once again showing that there are essentially no critical (and very few severe) Covid cases with strong (prior!) vitamin-D status. (And recall that <a href="https://www.sciencedirect.com/science/article/pii/S0960076020302764">this study</a> among others showed it to be a causal and effective intervention, not merely an indicator of underlying poor health.) It would seem that most Covid deaths were (and continue to be) easily avoidable. Along with billions of pharma profits, but I digress. <hr> [Update Feb 6: Since multiple people have replied that vit-D status is just a proxy for BMI, I will highlight this excerpt from the above study: "Finally, among patients with a BMI <30, 49.5% have a 25(OH)D <20 ng/mL compared to 63.6% among patients with a BMI >= 30+ (p = 0.07, 2-sided; p = 0.04, 1-sided). The intensity of the correlation between BMI and vitamin D deficiency does not indicate a strong correlation (Crammer's V = 0.117). As a result, 25(OH)D was not, and cannot be, used as a surrogate value for BMI."] <center>[<a href="https://sifter.org/~simon/journal/20220125.2.h.html"><<</a> | <a href="https://sifter.org/~simon/journal/20220125.2.h.html">Prev</a> | <a href="https://sifter.org/~simon/journal/index.html">Index</a> | <a href="https://sifter.org/~simon/journal/20220707.html">Next</a> | <a href="https://sifter.org/~simon/journal/20230521.h.html">>></a>] </center> </blockquote>