Perhaps "vaccine skeptics" say this because the Covid vaccines are not covered under VICP. They're covered under CICP, which is more stringent and has paid out one person $6 million, and a few hundred grand spread out between some dozen others.
My friend who was diagnosed, by multiple doctors in two hospitals with Myocarditis caused by the vaccine has yet to receive any money. It ruined his career.
"Tyranny of the minority" doesn't remotely apply here. No one has the authority to sacrifice one group of citizens to save another group of citizens.
> They're covered under CICP, which is more stringent and has paid out one person $6 million, and a few hundred grand spread out between some dozen others.
This is trying to play both sides. Appeal to emotion without having a rational thought process. Something bad happening is unfortunate and life changing. Then turning around and saying hundred grand isn’t life changing money for people.
What exactly is your remedy here - should people be not asked to provide proof for the harm and paid 10s of millions for every case? People have been asked proof for lesser things and paid even lesser for much bigger harm.
> My friend who was diagnosed, by multiple doctors in two hospitals with Myocarditis caused by the vaccine has yet to receive any money. It ruined his career.
Anecdotal evidence is not evidence of systematic wrongdoing. At least I wouldn’t expect to see on HN but here we are.
It's so sad to see that you are the first one with any kind of source in your comment. The rest are only saying scary stuff and you are supposed to argue with that. They are saying stuff like "my friend died because of the jab" and you come with studies links, which can sound unfriendly. And we are on HN, for fuck's sake.
The articles are fantastic, it's the unscientific claim that the data implies safety that's at issue. You can't post research about the fact that people misinterpret research right? But it's factually true. Most of the people on HN are not trained in doing or reviewing research. And not just on HN. Everyone can purchase an ad that claims to be the arbiter. Everyone can say they represent the consensus.
But the people with more money can buy more ads just like Google can stomp out any competitor, because they control the data channel.
It's a logical problem. Should we start requiring a license to hold an opinion about research? Who would we trust to govern that licensing process?
It's not really so simple as finding a source to back your claim. You need to be able to defend your interpretation of that source.
Bottom line "safety" is subjective. That's the critical argument. Compared to what is it safe?
Vaccine with a guarantee of infection, maybe the vaccine is safer.
Vaccinating everyone? Well, we didn't really study that. How do you study people in larger numbers with a guarantee that they don't get infected?
They didn't. They just assume everyone is infected and that number makes the research look valid and safe.
In fact, it's not.
The average person is more likely to have negative outcomes from vaccine than natural infection combined with non-infection.
So is it safe for someone who won't be infected at all? What about for someone that won't exhibit symptoms?
No. It's far less safe for those people.
300x-30,000x less safe. Depends on your estimation of asymptomatic and uninfected subjects in the real world.
It's more safe for someone in their 70s, with cancer, or whatever. Fine. But say that clearly. Don't try to bury that in summaries that obfuscate what you really studied.
Doing so destroys trust in science.
Not advocating for or against this vaccine platform, though. I'm advocating for laws about what meets and doesn't meet scientific rigor, as are most scientists right now.
In [1] "those who are not vaccinated" is a tiny subset of society that is actually composed of those who are infected, and also symptomatic, and also not vaccinated.
A large portion of society can't be counted because they are asymptomatic. How do you find them to count them? In fact that's nearly everyone. And for those people the vaccine increases their risk by 4/100,000. (More harm than good.)
If we accept your (extremely unrealistic) premise, then sure, but the numbers get worse at just a 5% chance of contracting the virus itself, and downhill from there.
Your premise is that COVID infection at just 5% is incredibly dangerous. But you can't actually say that without first knowing how many people were actually infected and asymptomatic. And you don't know that.
For all we know, everyone was infected, and infection is nearly harmless. Or it could be that it's way less contagious than that, and extremely deadly. You simply don't know. You'd need to make wild guesses without evidence.
We're supposed to be doing science, right? Seems to me your premise is the one that's unrealistic. Mine is firmly grounded in evidence that we DO have. Not guesses and doomsday religion.
70/100k is baseline. 5/100k is vaccination. 200/100k is unvaccinated with covid.
With vaccination you're at 75/100k.
Without vaccination at 0% chance of covid you're at 70/100k.
Without vaccination at 5% chance you're at 77/100k (worse than 75 here), at 10% 83, etc, etc.
> We're supposed to be doing science, right? Seems to me your premise is the one that's unrealistic. Mine is firmly grounded in evidence that we DO have. Not guesses and doomsday religion.
There are plenty of studies linked in this thread, that you're simply ignoring.
See section 2.4. The "unvaccinated" group includes people with a history of myocarditis and pericarditis who had an episode during the study, and coupled with the fact that cases "without an episode" were excluded, we should expect that the numbers are very much inflated.
There indeed are plenty of studies linked. I'm not ignoring them, I'm using them to point out that they don't carry the implications necessary to recommend vaccination for everyone.
To make the claim that the vaccine LOWERED incidence of myocarditis even for those without covid, counter to so much other research (e.g. [0]), we would like to see that the study excludes people with a history of myocarditis.
Including them is cheating, quite simply.
Regardless, even if we did go with this Spanish study, you still can't say that a 5% chance of infection is worse. You can say that a 5% of symptomatic infection is worse. There isn't any study in the thread that includes asymptomatic infection, which we know is MOST infection.
So at least so far, I don't see that you have the math to back what appears to be an unscientific claim.
And when faced with the counter research below [0], defining the mechanism of action, we should assume that the risks are a valid concern. You can't just sweep them under the rug.
Most people who tested positive had no symptoms. Most people who didn't get tested also had no symptoms but were likely infected. All of those people shouldn't take on additional risk in their own self interest. At best you can claim that they should take on the risk to try to achieve herd immunity so as to protect others. And actually I'm not sure that we have the data to determine that either.
In retrospective cohort studies, researchers track newly occurring incidents during the study window.
If someone has a history, but suffers a new episode from covid, that is a medical event that should be counted.
> Regardless, even if we did go with this Spanish study, you still can't say that a 5% chance of infection is worse. You can say that a 5% of symptomatic infection is worse.
Yes you can. The mechanism for the side effect (as per your own source) is the same in both symptomatic and asymptomatic cases. Recipients of the vaccine do not get respiratory symptoms, and yet can contract the (very rare) side effects.
And as for your source, the author, Dr. Joseph Wu:
> “But COVID’s worse,” he added. A case of COVID-19 is about 10 times as likely to induce myocarditis as an mRNA-based COVID-19 vaccination, Wu said. That’s in addition to all the other trouble it causes.
5% of symptomatic COVID cases. So now you have to compare that to only people with injuries from the vaccine.
Otherwise you're comparing apples to oranges.
If you want to compare people who got the vaccine to people who didn't, then you need to include ALL people who didn't. Not just the ones who were symptomatic.
And no, you can't include those people who had previous symptoms in the "everyone" group. Obviously those people are way more likely to return for treatment than the average Joe. They are skewing the sample because they are immediately nearly 100% likely to return for treatment. That is not representative of a random sample of people in the general population.
The question was about the general population, and they didn't sample the general population. They sampled people who come to the hospital. That is, only people with symptoms of some sort or another that are bad enough to warrant a journey to get treatment.
So all you can conclude is that people who come to the hospital have a higher frequency of heart conditions. And even that claim is not firmly established by the data, it's just a sensible inference. We expect that people who need treatment are more likely to seek treatment than those that don't need it. But there are countless counter examples there too. Hypochondria, etc.
No doubt if we had the data we'd find more problems there.
Wu's statement is fine, because again, it's not ALL people with COVID. His statement holds for people seeking treatment with COVID which is what they sampled.
Wu has no clue how many people have COVID asymptomatically and how to separate them from people without COVID altogether, and makes no claim about that.
You're simply making a claim that isn't in the research. Vaccination for people without COVID or with asymptomatic COVID is not researched or compared here.
This research, if you want to use it for a comparison, is for vaccination of people already coming to a hospital with symptoms of some kind.
EDIT: forgot to mention this was during a pandemic, so you might see significantly lower numbers for heart conditions if the study was repeated today. And it might be the case that during the pandemic it would have been a good idea to vaccinate everyone coming to a hospital from a statistical standpoint. But an even better take would be to identify what was common amongst the group with bad outcomes and vaccinate them, more specifically.
Regardless, nothing here implies better outcomes for everyone. It's not claimed by and it's not inferable from the research. And certainly not when there is a bunch of research showing contrary results to this study an example of which I linked earlier.
Respectfully, I think you've mistaken the definition of the word cohort . A cohort specifically does not include everyone. Please check the definition of cohort and then read section 2.4.
They tested people treated at a small collection of hospitals (the cohort).
And, you have made several other mistakes aside from that.
The 2020 studies are all retracted. They were over-spinning the centrifuges, I think by a factor of 10 or 100 maybe? They got insanely high positive numbers and the guidance was all updated to correct for that. You can ask an LLM to help you find the CDC publications on that. Don't use those numbers.
Additionally, even if your interpretation was accurate, it would fly in the face of thousands of other studies, just like the one I posted earlier in the thread.
Make sure your beliefs are based on peer reviewed research, that is not retracted, has been established long enough to withstand challenges from the scientific community, and that you're interpreting it honestly, using the established academic criteria for the scientific method and publication standards. Be careful to check the definition words that you're not familiar with, and even ones that you think you're familiar with. They might have a different meaning when used in a scientific context.
Out of curiosity, are you a journalist, college educated? I'm trying to understand where science has failed here. I would like our institutions to produce adults that can identify sound research and draw logical conclusions. There are some basic methods for doing that. I don't mean any offense by that. I'm just trying to understand what's broken. And more to the point, whether or not you are actually interested in science or you're just trying to prove that the media knows more than the scientists. Or maybe you just can't believe that journalists lie about what scientists say.
Is the media in the business of selling truth for profit, even if it means they won't profit? Do you believe that enough to take drugs that legally restrict you from taking legal action against the manufacturer when you're injured? (See CARES act)
Is your motivation to continue discussion political? Or scientific? If it's scientific, let's stick to the facts and follow the science. Don't try to make the science say what you want. You'll find that doesn't work in the broader forum.
> Respectfully, I think you've mistaken the definition of the word cohort . A cohort specifically does not include everyone. Please check the definition of cohort and then read section 2.4.
> They tested people treated at a small collection of hospitals (the cohort).
In epidemiology, a cohort is simply a defined group of people followed over time. In a population-based cohort study (which this is), the cohort is the entire population of the health district (over 500,000 people).
The study did not just evaluate people treated at the hospital. It used the hospitals EHR to identify the numerator (the heart inflammation cases) out of the denominator (the entire regional population).
> The 2020 studies are all retracted. They were over-spinning the centrifuges, I think by a factor of 10 or 100 maybe? They got insanely high positive numbers and the guidance was all updated to correct for that. You can ask an LLM to help you find the CDC publications on that. Don't use those numbers.
You are confusing PCR testing with serology. The "over-spinning" you are referencing relates to PCR cycle thresholds (Ct values), which are used to detect active viral RNA swabs. Seroprevalence studies—which track historic asymptomatic spread—do not look for active RNA. They test blood serum for antibodies using immunoassays (like ELISA). They do not use PCR amplification, and they are absolutely not "all retracted." They are the standard of how we track the infection rate of a population.
> Is your motivation to continue discussion political? Or scientific? If it's scientific, let's stick to the facts and follow the science. Don't try to make the science say what you want.
This is... literally what I've been doing the entire time.
> a cohort is simply a defined group of people followed over time.
Yes, and the group is defined very clearly here, as is usually the case. I appreciate that you've conceded that it does not include everyone, as we see in your next quote:
> It used the hospitals EHR to identify the numerator (the heart inflammation cases) out of the denominator (the entire regional population).
So it compared something studied in the cohort (the numerator) to something not studied and outside the cohort (the denominator). So now let's establish whether or not the denominator can be used without calling ourselves science deniers.
And as we go, let's consider that a slight adjustment to that denominator has a multiplicative impact on the result! So we really want to get that dialed otherwise our interpretation could lie very, very far from the truth.
Your next quote leads us right to the most convincing point about the denominator.
> You are confusing PCR testing with serology.
I said all methods measuring prevalence in 2020 were wrong. That doesn't imply confusion with PCR. Serology also was wrong.
Just to find something agreeable to cite, here is the CDC stating very clearly that we shouldn't use serology for this kind of decision making. [0] "Negative results do not rule out SARS-CoV-2 infection and should not be used as the sole basis for treatment or patient management decisions, including infection control decisions."
So, assuming you trust the CDC publishes good science, then you know damned well you can't trust that denominator you keep throwing around as fact. At the very least, you're in opposition to CDC guidance if you want to keep using that denominator. You're not a science denier are you?
> This is... literally what I've been doing the entire time.
Well, you may want to read back. The claim that a cohort includes everyone is not remotely in the realm of science. Neither of us can conceive of an experiment that would obtain the denominator to be used in the math above. Not as it is, and definitely not if you defined it as "everyone else." And that's been my point for the last few comments. It's an illogical, fantasy based approach to interpreting the data. You did not test everyone, and you never will for any research ever in the past or future. It's not even fathomably possible.
The denominator, implicitly, makes broad assumptions about cultural, environmental, and economic factors, and that's just the beginning. There are countless other factors that we haven't even thought of yet. So that denominator, can be seen as a wild guess at best, and at worst it's a blatant falsehood if the study was published after whatever research the CDC is relying on to tell us not to use it. And therefore, it must be considered with all of the ambiguity that it implicates, as a wild guess or a blatant falsehood.
I don't like to take things that are wild guesses and stuff them into my math. There's a special word for that, pseudoscience. I have more respect for science than to do that.
So, all things considered, I'll take your word for it that sticking to the science is literally what you've been doing. And you can literally do it better by not using wild guesses to try to make the math say what you want.
If you will use numbers that are based on measured (or even measurable!) data, I'll be more inclined to lend belief to what insight you have that's worth exploring. And I hope you will.
--
Summary: If you have an argument that makes sense, I'll gladly follow it. But you're not there yet.
Your argument is entirely based on a denominator that doesn't remotely meet what modern philosophers of science would refer to as worth believing in. It's a wild guess that the CDC says is inaccurate for weighing this kind of decision.
I'm probably misunderstanding something, but if the overall population risk for myocarditis is bigger than 4/100,000, wouldn't that indicate that mRNA vaccinated people have a lower risk? The number I found for the global population is 10~20/100,000. If taking the vaccine increases the risk, wouldn't the risk be bigger than 10~20/100k?
If the risk went down the term for that is "inverse correlation." In that case it would be preventative, rather than causative. Here we are seeing a correlation and we have a known mechanism of action that's driving that (described in the Stanford study).
Is that making more sense?
In part though, you are right to be noticing that the Pfizer study and the Stanford study disagree on the risk profile.
Why might that be?
Well, for one thing, if you look up the peer reviewers for the Pfizer study, it's the same list of names as the ones that performed the research, and each one of them is a Pfizer employee. Unsurprisingly, the lower risk profile was found in a study conducted by the same drug company that stands to profit from a lower risk profile. And it's not peer reviewed. Typically we rely on peer reviewed research in the world of science and medicine. Without it, it becomes very difficult to differentiate a science article from advertising, and pseudoscience.
Conversely the Stanford study was done by various researchers from various schools with no conflict of interest and no apparent profit from Pfizer, as best I can tell. And as we'd expect, they found not only a causal link, not only a higher incidence rate, but also the mechanism of action that drives it.
See my other comment answering this. It's only higher if you don't trust peer reviewed research, and instead use the numbers provided by the drug company in a study that was not peer reviewed. See the list of peer reviewers in Pfizer's study and notice that they are the same as the researchers, and that they've made claims without providing their results (those are still being released over an 80 year period by FOIA court order, you'll get the results when we're all dead).
If you can't find a link to the court case on this, let me know. I'll find it for you.
My friend who was diagnosed, by multiple doctors in two hospitals with Myocarditis caused by the vaccine has yet to receive any money. It ruined his career.
"Tyranny of the minority" doesn't remotely apply here. No one has the authority to sacrifice one group of citizens to save another group of citizens.