I took the bait, I'm ashamed. I try to keep an open mind, so I read some of it.<p>The thrust of the article is that vaccines work well at preventing infection, but that if you do get infected, it is _possible_ that you might die at a higher rate. _Possible_ comes from a big spread in the standard deviation of P(death|infected & vaccinated). I didn't check his calculations, I took his numbers at face value and just looked at the charts.<p>The author uses big standard errors to make claims about what could or could not be true, because the standard errors straddle both favorable and unfavorable statistics for the vaccine.<p>However when the standard errors are too small for this dubious reasoning, they come out in favor of the vaccine.<p>The author appears to me to be using motivating reasoning to push an agenda.<p>Edit: sorry I am not completely right, because I wrote this comment before thoroughly reading the whole article. Sometimes his standard errors come out against the vaccine, and for all I know his math could be right. But I don't want to check the math because I have limited time and I don't care if my death rate goes up given conditions {x, y, z.}<p>I care if getting the vaccine makes me less likely to get sick and die, which it does, because if you look at all the tables and charts that's exactly what it shows.
There is a slippery joint conditional probability here. The article talks mostly of:<p>Pd1 = Prob(death GIVEN infected AND vaccinated)<p>vs<p>Pd2 = Prob(death GIVEN infected AND NOT vaccinated)<p>Which makes it easy to lose site of:<p>Pi1 = Prob(infected GIVEN vaccinated)<p>which is very very small compared to its opposite:<p>Pi2 = Prob(infected GIVEN NOT vaccinated)<p>Because Pi1 is so much smaller than Pi2 there are several consequences for Pd1 and Pd2:<p>- The small counts for "numerator" of Pd1 (zero in some cases) means large uncertainty in the ratio and that the centroid of the distribution is not particularly meaningful. Statistical fluctuations (just one more or one less death) will change Pd1 substantially.<p>- Statistically significant deviations between Pd1 and Pd2 do not point to a cause.<p>For example, it could be that those contributing to Pd1 all got a much higher viral load in order for the virus to break through the vaccine's defenses and high viral loads are known to correlate with death so once vax protection is defeated it is game over. The distribution of viral load exposure may even be the same in the Pd2 case, but for the unvaccinated a lesser load can be fatal. This explanation is consistent with P1d possibly being greater than P2d AND consistent with the given anecdotes of vaccinated tweeters saying covid is just the flu, bro.<p>The main take away is still: you do NOT want to get this shit and vaccine AND masking will help achieve that goal. And, unless you are a sociopath, you do NOT want to give this shit and vaccine AND masking will help achieve that goal.
This is a great post and you can immediately see where they are going with this as soon as he quotes Dr. Tom Frieden.<p>It makes tons of sense that those who are vaccinated and get infected are more likely to die, simply because it would indicate a failure to develop antibodies and mount an effective immune response to the virus.<p>That being said I’m not sure such a rant is justified over a tweet like this. Dr. Frieden is more or less correct if you slightly re-word his sentence to: “Getting vaccinated decreases the chance of dying by approximately 100x”.<p>What I take home from this is that everyone is human, can make simple mistakes, and that we take things written on Twitter far too seriously. I’m sure that Dr. Frieden would not have made such a mistake if he wrote an article that was published somewhere reputable instead of a tweet that could have been sent while waiting for his coffee at Starbucks, or using the toilet.<p>There is a trade off in communicating science to the public in that using social media, TV, and newspapers reaches more people but almost always distorts the message. I’m not sure how exactly to balance this trade off, but I also think that some amount of responsibility lies with the reader to seek the truth instead of demanding that every sentence they read on the internet be as accurate as a peer reviewed journal.
The author makes a critical mistake when analyzing the BNT/Pfizer clinical trial. Over and over when discussing the results they mention calculating efficacy against "infections" using this data. But this specific clinical trial provided no data about infections. The metric measured was symptomatic COVID-19 <i>disease</i>.<p>This kind of mistake is understandable, but doesn't really inspire much confidence in a screed about the mistakes others are making in analyzing COVID-19 data.
> <i>The real world data has shown that the death rate among the vaccinated, if infected with COVID, can be 3 to 5.7 times higher1 than the death rate of the unvaccinated.</i><p>Okay… but how much less likely are you to get “infected”?<p>How are you even defining “infected”, here? If virus gets in your body and then your immune system kills it before it does much damage, were you “infected”? If the answer is <i>no</i>, you've got pretty serious selection bias, because you're completely ignoring everyone who was <i>completely protected</i> by the vaccine in the “vaccinated” group, but paying attention to everyone who didn't really <i>need</i> a vaccine in the “unvaccinated” group: basically ignoring all the really-healthy, great-immune-systems, unlikely-to-die people in the “vaccinated” group so, proportionally, the sicker people take up more room. So you can't even say that the vaccine makes things worse for them! If it improves their chances by 100 times, as Dr. Tom said, but the selection bias is only paying attention to the 0.2% least immune people (those who got infected when exposed after vaccination), you'd expect to <i>see</i> a 5× higher death rate even though the <i>actual</i> death rate is 0.99× lower. (Made-up numbers.)
> 36 of 84611 in the unvaccinated versus 0 in 1066 in the vaccinated group. 36 in 84611 is roughly 1 in 2350, but we only had 1066 infected in the vaccinated group. There is not enough information to claim the death rate per infection is higher or lower, and that uncertainty is indicated in the graph above. That is worlds away from the relative immortality communicated by the efficacy number 100%.<p>Okay. But immediately after…<p>> In fact, if there had been 24 deaths in the vaccinated group the efficacy reported would have been 3%! Because it was looking at rates over time, 24 deaths would have been the death rate over time similar to 36 in the unvaccinated group. But clearly, among those infected, 36 in 84611 is a far lower death rate than 24 in 1066!<p>If? Now we’re onto hypotheticals. Here is an idea, you have so few because they were vaccinated!<p>Also the mental gymnastics of saying: “it’s better to not be vaccinated in case you get covid because you are less likely to die” is worthy of a mental gymnastics olympics gold medal.<p>I understand that the CDC guy may have worded things differently but directionally he is right.<p>But, again, one cannot just ignore the hypotheticals of Dr. Gator when it provides validation for your anti-vaxx stupid attitude.
In the “ What the numbers really showed” you use the lack of vaccinated sample size to justify something for the unvaccinated, but then immediately use that small number to your advantage to say “if 24 people had died then the vaccinated would have a higher death rate”. It’s disingenuous and doesn’t show anything.<p>Also if the vaccine prevents contraction then it should be counted as preventing death/hospitalization, but obviously those numbers aren’t obvious. Seems to me the vaccine IS preventing contraction so you would need to account for that wouldn’t you?
This article does actually make an interesting point, but its graphs are all bunk.<p>The vaccine works by priming the immune system to work quickly against a disease. It is quite likely that if someone develops symptoms despite their body being at peak alert for coronavirus then they are in trouble. The graphs bear that out, and do not alarm me. If we only select for cases where the person is physically susceptible enough for the virus to break through the vaccine protections to cause an infection it makes sense that the cases will be on average worse.<p>But there is an interesting point here that if the vaccine were killing people (eg, heart inflammation problems have been detected in some cases) then the "COVID-19-related-death" stats, etc, would be highly misleading. It would be more interesting to see all-cause mortality and the cited tables don't do that.
The case for the vaccine sells itself.<p>Why do professionals get in the business of exaggeration when it serves no useful purpose than to provide excuses and doubt to the hesitant.<p>Also. Often when officials state things with exaggerated and unwarranted certainty, take it with a grain of salt.<p>PS why isn’t Twitter marking such as misinformation? My guess, it fits their narrative, so it gets a pass.
This article fails to mention the BY FAR most significant benefit of the vaccine which is: <i>achieving herd immunity</i><p>We should be as accurate as possible with when talking about vaccine but throwing around statics(or should we say "Vaccine Statistics") like:<p>> "the death rate among the vaccinated, if infected with COVID, can be 3 to 5.7 times higher than the death rate of the unvaccinated."<p>is not exactly providing the big picture context needed for a productive social discussion.<p>The article suggests focusing "real efficacy". Before the vaccine program started there were ~4000 deaths per day in the US, now there are ~100. <i>Clearly</i> it is very effective at reducing death.
I do think it's very important to be clear what your statistics are saying, and <i>everyone</i> has done a pretty poor job of being clear about that.<p>But in this case, what people really want and need to know is how well the vaccine is going to prevent them from dying.<p>They don't care about the difference between "Prevent dying from covid" and "prevent dying from covid once you have covid". The latter simply doesn't matter to them until they have covid, and then <i>it's too late for the vaccine</i>. That statistic is meaningless to the general public.<p>People only want to know if the vaccine can help them not die. And it absolutely can because <i>it helps prevent getting infected in the first place</i>.<p>Edit: I just realized the statistic that people want: "How likely are you to die from Covid if exposed to it, vaxed vs unvaxed." Not if you <i>get</i> it. Just if you're exposed to it. And since we don't run tries where we deliberately expose people to viruses, it's going to be a hard number to get.
Here's what I'm failing to understand.<p>According to most major studies coming out (including this one: <a href="https://www.imperial.ac.uk/news/227713/coronavirus-infections-three-times-lower-double/" rel="nofollow">https://www.imperial.ac.uk/news/227713/coronavirus-infection...</a>), with the advent of delta variant, the vaccinated have about a 50% less chance of getting infected than unvaccinated people.<p>If we assume the vaccine offers no additional protection against hospitalization/death given that one is already infected (which is what the author seems to be arguing), then what we should be seeing is 67% of hospitalized COVID patients are unvaccinated, yet we're seeing the rate closer to 97%. So there's obviously something going on here, and the most likely culprit would be that the vaccine <i>does</i> offer protection against hospitalization/death, contrary to the author's argument.
The post is conflating vaccine efficacy and effectiveness which are two different epidemiological measures.<p>It also fails to consider the false positive and false negative rates inherent in these studies. In particular, lots of asymptomatic cases in the vaccinated arm are not caught by efficacy studies (false negatives). So claiming that low death rates are solely a result of reduced infection(as opposed to fighting off infections better) is also a conclusion made hastily.<p>Death rates and symptomatic cases are much measured much more accurately, and claiming that effectiveness studies have little point once there's vaccine efficacy is available underestimates the challenges of epidemiology.<p>But the overall point that statistics are hard to understand, easily misused in online arguments is a valid point.
100 people, unvaccinated, all contract covid, 10 die. That's 10%. 100 people, vaccinated, 10 contract covid, 1 die, That's 10% based on the number whom contracted covid. Not based on the number of vaccinated vs unvaccinated.
Not directly related to the article, but -
I am extremely disappointed that this article was flagged. It certainly displays the general inability for people to openly analyze ideas and narratives that (at first glance) appear to go against their beliefs. I feel like this type of censorship across platforms is only adding ammunition to the persecution complex of those who are already skeptical of the vaccine.
This post was flagged. I just vouched for it. I read the whole thing. As far as I can tell, it is accurate.<p>There is not a single comment pointing out any error or misinformation or lie or damned lie.<p>Unless somebody can show that the data and/or interpretation are wrong, it should not be flagged.
My theory - pandemic is mostly a religious event. Mind that much of world religions is mostly anti-epidemic advice presented as God's commands (with some world creation myth and some moral advice on top). So people are prone to react with religious fervor in response to each epidemics.<p>And since the main purpose of religion is dividing people on Us vs Them (Us - clean and moral, Them - stained and evil) this is exactly what is happening with pandemic. Plus some overzealous virtue signaling.<p>Rational arguments and rational people are doomed to fail in the circumstances.