At this point the original phase 3 trials are pretty much irrelevant, we have so much more data now from the actual use of the vaccines. They really play no role at all now. In terms of effectivness we're three major SARS-CoV2 variants further, and for looking at rare side effects we have billions of doses now compared to tens of thousands in those trials.
I'm all for putting pressure on everyone in Science to publish more raw data. This kind of data is likely more complicated because it's really hard if not impossible to anonymize the actual patient-level data. It still should be as accessible as possible to other scientists.
Actually, I would argue we don't have good data from the use of the vaccines. Part of the reason is because all the mechanisms used for reporting things like side-effects, effectiveness, etc. have a litany of confounding variables which haven't been controlled for which could be far better corrected for/isolated with good studies.
For example, when it comes to vaccine side-effects, I don't think there exists a true account for how common the side-effects really are. The most common way to report side-effects (VAERS, and similar national databases) are dismissed due to the self-reporting nature, local GPs frequently dismiss side-effects and tell people to just go home and take a Panadol with zero reporting going on (I had this happen to me - started experiencing severe chest pain 2 days post-Pfizer. Subsequently saw a cardiologist after months of pain and his comment to me was "I'm seeing young people like you daily and your cases are going widely underreported"), etc.
Likewise, when it comes to vaccine effectiveness, there are a million and one confounding variables from % of the population that already had natural immunity, covid variants, health, age, seasonality, societal lockdowns, isolation, etc.
Also, it's important I think for us to raise the bar to the highest possible standard when you're talking about a medical intervention that was forced under significant duress (loss of job, social stigma, public/medical shaming) on a substantial percentage of the world's population. We should not be content as a society to come within inches of worldwide medical authoritarianism without asking some seriously hard fucking questions and imposing the absolute strictest and highest possible scientific standards to justify why.
> I had this happen to me - started experiencing severe chest pain 2 days post-Pfizer. Subsequently saw a cardiologist after months of pain and his comment to me was "I'm seeing young people like you daily and your cases are going widely underreported"
Orthogonal to the original conversation but have your cardiologist consultations yielded anything?
I also have chest pain for now more than 2 months after the second dose of the Biontech mRNA vaccine, but the tests revealed nothing abnormal. A few people in my entourage have been having similar symptoms but theirs has since receeded.
It doesn't help that search engine results for anything close to "Covid-19 Vaccine Chest Pain" are overran by both antivax conspiracy theorists and obvious propaganda. I couldn't find concrete information save from a few disparate accounts of similar conditions[1], despite the apparent frequency of those symptoms.
For me it took 6 months before the pain was reduced to "infrequent", I'm back to exercising almost to the full extent now as I was in July.
I received a diagnosis of pericarditis and had persistent tachycardia, mildest strain and my heart rate would shoot to 170 BPM not going below 85 while lying down. Now I'm back to normal and my resting heart rate is now 50-55 BPM.
But we actually don't need the VAERS data to examine if the vaccines are better than the alternative. We do need it to provide accurate labels for side effects, but that is all.
We can simply look at infection, hospitalization and death in vaccinated and unvaccinated populations. If we properly match the populations, we can determine if the vaccine saves lives, and it turns out that they do save a lot of lives.
That's true. And I agree that the vaccines have saved lives - mostly, of the old and the very sick/unhealthy.
However, what is less clear today is whether there has been a net positive or negative effect of the vaccine for young healthy people. You can only come to that conclusion if you actually had high quality data and studies on vaccine side-effects, effectiveness in population groups stratified by age, health, etc.
I suspect that the vaccines, mandates, lockdowns, etc. have been a net negative for the overall health of young (<50), and healthy people, and the body of scientific evidence will support this position in the future. It's just cloudy today because it's wrapped up in politics...but the science will eventually win out.
We have high-quality data on the vaccines and their side effects. We don't have RCTs with billions of people, but that is data we never had for anything and can't reasonably get.
Judging the risk/benefit ratio is the primary purpose of the regulatory agencies that approve vaccines. I don't see any reason to believe the claim that the vaccines are harmful for everyone below 50, that sounds quite outrageous to me. There have been adjustments based on new data for the vaccines a few times, e.g. younger people are generally recommended to be vaccinated with Biontech and not Moderna or AZ based on the side effects of these vaccines. That doesn't mean the risk/benefit ratio is bad there, it only means that we have vaccines with a more favorable profile for those age groups.
Exactly. They DID make adjustments to not give Moderna/AstraZeneca to younger people because they had the side effect data and compared it to the risk of getting COVID and realized that Pfizer was probably better at mitigating those risks
Another clear case of someone implying totally crazy things (younger folks without the vaccine would have been better without the vaccine) with absolutely NOTHING to support it.
.. If only we had clear data to make such a decision.
However, collecting and analyzing that data would likely have eaten a percent or two into the eighty billion dollars Pfizer made last year, so I guess there's nothing we can do but trust the same authorities that brought us 'natural origin for sure', 'masks don't work', 'NNVTs don't mean anything any more', and 'Covid isn't airborne'.
Why do you mix mandates and lockdowns in there? What does it have to do with the vaccine?
If you bring lockdowns into the picture, you have to compare to what would have happened WITHOUT a lockdown as well, how many more deaths in hospitals, etc. The countries that tried this strategy have a very high excess death to compared to those that tried to limit human contacts (especially PRE vaccine).
But we have a similar noise in COVID death and injury as we have incentivized hospitals to register everything and everyone as COVID related. And I believe we all know just about any PCR test can be made positive if you use enough cycles.
Sorry but I can't distrust VAERS and then trust the COVID injection complication data added by the same people but now with financial incentives.
You shouldn’t distrust VAERS per se, but it’s purpose is not risk assessment. It’s purpose is surfacing rare side effects for further study.
You don’t have to trust the drug co’s for that, we also have vaccine safety datalink system; so far the only notable side effect of the mRNA vaccines has been the myocarditis in younger people.
> Also your point about PCR testing is not accurate.
Source on that? Because all graphs I've seen show impossible to miss fluorescence around 35 cycles and up.
AFAICT VSD only does specific research at their own behest and currently don't have a section on COVID-19 vaccines.
Btw your link is dead.
I had pericarditis and some immediate reaction, my cardiologist thinks it was partially intravenously applied. Looking at the data on severe reactions from where I live I've been able to obviously tell that CDC must have used incredible criteria for their numbers. At least initially, I stopped caring when it eventually became clear to me that we do not really want to know how many are harmed.
And from the perspective of everyone involved I understand it, I too want this to be over, I too want this to be a safe magic bullet. But seems to me somewhere between 1:1000-10000 have significant heart issues from the Pfizer vaccine, but when we were rolling it out the numbers were claimed to be 1 in 230M.
I’m not going to argue that the US seemed to take longer and have worse communication about the myo/pericarditis issue than some other countries, but these things are being followed up on. The absolute timing I think is hard to discuss with a definite time frame
"If we properly match the populations," - that's a big if though. Without any matching, vaccines appear to have negative value because older people are more likely to get vaccinated, but even controlling for age there are a variety of potential confounding variables like general health, risk-avoidance, etc.
VAERS is for surfacing hypotheses about rare side effects
For serious risk quantification and causal analysis, we have things like the vaccine safety datalink, which links all electronic health records across a bunch of hospital systems, covering IIRC 3% of the US population. The UK has something similar I think. I think transparency in that system (VSD) could be better, but it has the same problem this thread is discussing, that anonymizing the records may be at odds with making the analysis reproducible.
The (small) controlled trial data can be combined with the (large) uncontrolled mass-population data, e.g. one can look at the larger dataset for corroboration of weak signals in the smaller dataset.
New controlled trials can be started, where further evidence is needed.
It's hard to analyse the data from treatments, as treatments don't have a control group. Trials are designed with control groups, placebos and double-blind process to gather data and then analyse it.
We may have loads of data from real people being treated, but good luck trying to analyse it in a scientific way.
We need effective vaccines, and we need to be able to trust them. Just treating people with a vaccine doesn't always give that trust and confidence.
(I hope I got my medical terminology right, I'm not a clinician, but have been close to covid-19 related medical research)
Depending on the phase of clinical trials, they don't need to be double blind or controlled.
You can absolutely analyze real world mass vaccination data in a scientific way. Just treat pre-vaccination and post-vaccination as two different patients. The numbers are so large that other factors (environmental, genetics, etc) cancel out. So if your post-vaccination population has a rate of heart attacks 80% lower than the established norm, you know that is worth further investigation.
(Full disclosure: my family used to own a company that ran clinical trials for pharmaceutical companies)
> You can absolutely analyze real world mass vaccination data in a scientific way. Just treat pre-vaccination and post-vaccination as two different patients.
Yes, you can do this. No, it's not the same.
With this kind of uncontrolled, longitudinal study, yes, some things are quasi-controlled: genetics, possibly lifestyle, other drugs, etc. Anything that plausibly doesn't change (much) in a single person from time A to time B.
Some things you can't control for, but still matter: changes in behavior due to the event itself. Placebo effect. Sample bias (e.g. the patients receiving treatment X were selected to receive treatment X because it was felt that they would benefit from it. This is subtle, but can really mislead. In the example of vaccination, imagine that the people most likely to vaccinate their young kids are also the ones most likely to keep their kids isolated at home, in a protective bubble...)
> So if your post-vaccination population has a rate of heart attacks 80% lower than the established norm, you know that is worth further investigation.
The key part of that is the last three words: worth further investigation. To get the final answer, you still need a controlled experiment.
It's much, much harder to understand the data if you don't have a control group. But once you have an effective treatment, you no longer have a control group if you're at all ethical. You cannot keep a working treatment from people just to do more science, that is deeply and fundamentally unethical.
So the moment we knew the vaccines worked, the science got harder. Well, that point also save an enormous amount of lives, so I don't think this is something to complain about. It's still possible to do a lot of good science now, even randomized controlled trials. They just can't compare to unvaccinated groups, but you can still do stuff like compare double-vaccinated with triple vaccinated.
> But once you have an effective treatment, you no longer have a control group if you're at all ethical. You cannot keep a working treatment from people just to do more science, that is deeply and fundamentally unethical.
You could easily find millions of potential study participants who don’t want the vaccine. The study wouldn’t be double-blind, but it would be single-blind and better than what they chose to do.
A lot of data now shows that double vaccinated are infected with Omikron more often than unvaccinated. I do not know what causes this or what it means, but imagine for a moment there were no unvaccinated left: Your vaccine could be arbitrarily harmful with a new variant and you would not be able to tell.
There is one study that listed negative effectiveness against Omicron for the vaccines. That study did not control for any confounding factors, it simply was not designed to measure this particular thing, it was focused on a different question.
There are some strong fundamental reasons why we would not expect the vaccine to be harmful with future variants. And even then we would still be able to detect if it was.
It is not one study, national data from Denmark, UK, Germany and Canada shows that double vaccinated are infected at a higher rate. I am not jumping to conclusions that this means efficacy is negative, because that would require a controlled trial with an unvaccinated group. Do you catch my drift?
Please show the data that claims that double vaccinated are more likely to be infected than unvaccinated people by Omicron. And raw infection counts are not an answer here as the vaccinated and unvaccinated people are clearly not matched populations you can compare without adjustments.
> And raw infection counts are not an answer here as the vaccinated and unvaccinated people are clearly not matched populations you can compare...
That's the point! That's why we need controlled trials. With an unvaccinated group. You know, the thing that you say we can't have, because it's unethical.
There’s no way of running a controlled trial of this sort when 80% of the population have already been vaccinated. You can design better or larger observational studies though.
> you no longer have a control group if you're at all ethical.
> You cannot keep a working treatment from people just to do more science, that is deeply and fundamentally unethical.
I see this position put out there a lot and generally go unchallenged. For the record some people think the opposite: that it was unethical to unblind the placebo group so early. At the time you could estimate that continuing the placebo group might lead to ~60 unnecessary deaths from Covid-19 and a few hundred more serious cases. That is in the lucky scenario where the early success held. But in all scenarios, in return for a few dozen volunteers risking their lives, billions of people would benefit from randomized, placebo-controlled trial data which the NIH itself calls the "gold standard" as to "whether or not a treatment is safe and effective".
IMO the harms we have gone through from flying in the dark (people underhyping/overhyping the vaccines), multiplied by the number of people involved, make this a case where unblinding was unethical. It is very easy to imagine that far more life was lost in the general population from being in the dark than was saved by unblinding the volunteer study population.
(There's also the issue of whether or not it would have been practical to keep the participants blinded. I think that's a challenging topic of its own orthogonal to the ethical question.)
In phase 3 trial, there should be a regular follow up at set intervals, let’s say every 6 weeks, then every 9 weeks, etc. In the wild in the US healthcare realities if you receive a vaccine and then either have a breakthrough infection or any not immediate side effect, it’s not clear when this information will get back to CDC, so they can properly analyze it. Israel had much smaller data set(small population), yet they noticed myocarditis sooner.
> This kind of data is likely more complicated because it's really hard if not impossible to anonymize the actual patient-level data. It still should be as accessible to other scientists as possible.
I disagree with both parts of this. Why not associate each patient with a number then log all data against that? Patient names and other identifying information should simply never be attached to trial-related data (except for in a well-protected lookup file with highly restricted access).
Making scientific data accessible only to other scientists is highly anti-scientific. Feynman, one of the greatest scientists of all time has many quotes around exactly this mindset:
"Have no respect whatsoever for authority; forget who said it and instead look at what he starts with, where he ends up, and ask yourself, 'Is it reasonable?' ... we will doom humanity for a long time to the chains of authority, confined to the limits of our present imagination. It has been done so many times before."
"Science is the belief in the ignorance of experts"
"Our freedom to doubt was born out of a struggle against authority in the early days of science. It was a very deep and strong struggle: permit us to question - to doubt - to not be sure. I think that it is important that we do not forget this struggle and thus perhaps lose what we have gained."
> Why not associate each patient with a number then log all data against that? Patient names and other identifying information should simply never be attached to trial-related data
Because "patient name" isn't the only way a patient can be identified.
"Male, 27, admitted to Sunnybrook hospital for stitches to his forehead due to knife wound on January 20th 2022" is almost certainly uniquely identifying (actually it's made up so it probably identifies no one), is in the person's medical data for the trial, and clearly needs to either be redacted or in some other way separated from the other line that says "contracted HIV on January 19th 2022". Yet both are relevant when investigating causes of side effects.
I think HIPAA is something we should be re-evaluating as a nation. Back in '96 it was certainly a good idea given how devastating things like an HIV diagnosis could be for a family member in a fundamentalist family. Or the impact of said diagnosis on insurance coverage.
With the ACA, the insurance coverage problem is gone. Pre-existing conditions can't be used as a reason to deny or change insurance rates.
The Fundamentalist problem exists, but seems like much less of an issue with LGBT acceptance being so much better now than it was in the 90s.
I just don't think that health information is so valuable that guarding it like a state secret is warranted. I'm ok with the notion of putting in basic safeguards like not attaching a patient name with the information but I don't see it has horrible if some system can infer absolute identity from that stuff.
After all, seems far more scary that my web browsing generates a far clearer picture of who I am than what you could glean from my medical records.
Having health information public and easy to gather would (potentially) be a significant boon to the study of health information. It would also make it a lot easier for us to make health information sharing systems so you don't have to fill out the same 500 forms every time you go to a different hospital or doctor.
Assuming name isn't attached to the data, that'd require the spouse (or someone else) to do the legwork to link up the medical history.
When I say re-evaluate HIPAA, I don't mean "Hey, let's put everyone's name right next to every checkup and list it in a wikipedia like DB and email relatives about the results of every checkup". I mean "Hey, let's consider limiting the scope of the law and the penalties associated with it". It doesn't have to be a black and white thing.
That all seems like a worthy risk if it enables researchers to find that "Hey, looks like people prescribed medication X with medication Y tend to develop cancer Z way more frequently than the general public" or "Hey, looks like people with gene X respond way better to cancer treatment Y than the general public".
Exact dates and exact hospitals should also not be included. Doctors do not manually write their hospital location and the date in each of their notes (instead these are fields added by software like Epic) so this should be easy to just not export those fields.
There will also be some risk that someone has a very unique combination of visits or treatments that does make them identifiable but that should be an incredibly low percentage and we need to weigh the pros/cons. I can also get a good idea of who has cancer by sitting on a bench outside a cancer center and watching who goes in.
Anonymization is difficult and sometimes impossible because reidentification attacks are extremely difficult to prevent. Anonymization is much more complex than people typically assume; see [1,2] for surveys.
The more data you release, the more difficult it is to ensure that subjects cannot be reidentified by combining the "anonymized" dataset with other publicly available data.
Important note: It's not just a case of putting in the work and being willing to share. There are impossibility results in this space. That is, there are reasonable formal threat models for which it is mathematically impossible to release any version of the dataset that is (a) useful (ie contains enough data to replicate findings) and (b) not subject to unacceptable levels of reidentification.
I am generally in favor of releasing data and code/spreadsheets, but anything involving patients becomes difficult quickly. There are also reasonable middle grounds between "no access for anyone" and "throw it in a public s3 bucket". E.g., making data more available to researchers and medical practitioners -- or even sufficiently interested & motivated members of the public -- but under strict data handling rules, enforceable audit trails, and legal consequences for being reckless with sensitive patient data.
Hell, odds are better than even that your employer made you sign a document that would allow them to sue you into oblivion for forwarding some sure-to-be-doomed product launch draft to the wrong person. Being at least that careful with fine-grained medical data on hundreds/thousands of patients isn't exactly unreasonable...
Drawing a hard line on 100% complete, anonymous, and consequence-free access to troves of patient data isn't a reasonable position.
I'm not fully up to speed with all the research here, but as far as I understand it's quite easy to de-anonymize many datasets of exactly the kind you describe. If you're not really, really careful there will be lots of information in simple demographic data that could easily be matched to other data sources.
Simply removing the name isn't enough to anonymize the data, because as another commenter noted, the data submitted includes detailed patient histories and information about all of their other conditions, which is more than enough to uniquely identify someone.
And you can't simply strip out that data, because a lot of the subsequent analysis of effectiveness is dependent on it.
Speculation is made either way without datapoints. What's the harm in being transparent if at worst some spurious (but provably spurious) correlations mislead a few people already prone to mistrust? Higher transparency would also dissolve a lot of the claims that the pharmaceutical companies are "hiding" something regarding the actual effectiveness of their vaccines and therapeutics.
I don’t think people claiming pharma companies are going to be satisfied by data dumps. They will simply claim the data are fabricated.
There is so little correspondence between reality and claims already- I think you’re making the mistake that the discourse is in good faith. It doesn’t seem to be to me.
I'm all for putting pressure on everyone in Science to publish more raw data. This kind of data is likely more complicated because it's really hard if not impossible to anonymize the actual patient-level data. It still should be as accessible as possible to other scientists.