Study: Chances of serious side effects from coronavirus vaccines | Hacker News Reader

Study: Chances of serious side effects from coronavirus vaccines

pubmed.ncbi.nlm.nih.gov

140 pointsEGreg165 comments

Pfizer and Moderna mRNA COVID-19 vaccines were associated with an excess risk of serious adverse events of special interest of 10.1 and 15.1 per 10,000 vaccinated over placebo baselines of 17.6 and 42.2 (95 % CI -0.4 to 20.6 and -3.6 to 33.8), respectively.

The placebo groups for the pfizer cohort and moderna cohort differed in baseline rate by the same magnitude (~25/10000) as the observed effect.

At best, that’s really noisy data.

>> (95 % CI -0.4 to 20.6 and -3.6 to 33.8)

> At best, that’s really noisy data.

Yes. That's exactly why each of the confidence intervals have a negative and a positive extreme. If there is a real effect, it's below the noise level.

The fact that the confidence intervals for the excess risks aren't roughly symmetric around zero, but rather biased to mostly positive, is worrying. Most of the probability is for positive excess risk. If it were just noise, wouldn't we expect the excess risk CI's to be roughly centered around zero?

The fact that benefits are not an absolute slam dunk over risks is also worrying given the coercion that was used to get people to take these vaccines.

This study doesn't talk about benefits at all.

Isn't "the risk reduction for COVID-19 hospitalization" a benefit?

"A" benefit? I'd say no. It's a wide range of benefits ranging from "not spending a night in hospital" to "surviving". If you know that the benefits and risks result in similar distributions of the risks, then you can just compare the numbers. If not, you have to check the distribution first.

that metric is not quantified here.

It's been a couple years since I had to report risk ratio (RR), and it's not something that I have used much in my research, but hear me out.

The RR CI must be >= 0. The calculation is based on the incidence in a population and the incidence in a sub-population, which are themselves fractions of positive integers (e.g., 1/1000 and 1/100). I don't think RR can ever be negative, or at least I'm not sure how you would get that without messing up your data or calculations?

RR of >1 means there is excess risk, and <1 means anti-excess risk which is a word I cannot think of right now (just, less risk than the baseline anyway). But RR is also just a measure of risk over baseline to begin with, so a 1.5 RR on something that happens 1/10,000 times is ... not that concerning? I wouldn't be concerned, anyway. Depending on what the risk is, I guess.

So, no, I don't think we would expect RR CI to be centered around zero at all.

The fact that the main effect is so small and crosses from neg to pos is by far more concerning. I would not submit results like that for publication.

Then if we throw a logarithm in there we should be seeking zero-centered intervals, I'm thinking, as opposed to non-negative intervals containing 1 (no effect). Any why normal distributions are even part of such discussions of ratios seems crazy, given that ratios can't be negative, but log-ratios can. Not my area; could it be that a log or similar non-linearity was already effectively included? If the confidence interval method was only using ratios, then the lower bound should have been zero, and negatives impossible.

Sanity checking results shouldn't be so difficult.

Actually, I would not expect them to be. There's inherently a risk from a vaccine because there is inherently a risk from riling up the immune system.

Note, however, that almost all the effects blamed on the vaccine are also effects we see with the virus. Does this not strongly suggest that what we are actually seeing is how the body reacts to the spike protein? If so, that means that if the patients got the real thing they would have the same or worse outcome?

Realistically, the comparison shouldn't be vaccine vs nothing, but vaccine vs infection--and by that yardstick they aren't even in the same ballpark.

If there is no effect, you expect that half of the times the confidence interval are biased to mostly positive values and you expect that half of the times the confidence interval are biased to mostly negative values.

Moreover, if there is no effect you expect that some of the intervals are almost centered but you also expect that many of them are very biased. Moreover^2, if there is no effect, You even expect that in a 5% of them the 95% confidence interval does not include 0.

This is a nice trick to detect fake data in blab reports from students. The intervals are too centered.

>> The excess risk of serious adverse events found in our study points to the need for formal harm-benefit analyses, particularly those that are stratified according to risk of serious COVID-19 outcomes.

This is the conclusion by the authors of the paper.

> At best, that’s really noisy data.

This is the reality of measuring rare events among moderately large groups of people. It’s also possible that any number of unexpected cofounders could have occurred for a small group of people receiving the placebo doses in one segment, such as a small number of placebo doses having an unexpected issue. Impossible to say, which is why large numbers of people are necessary in trials exploring relatively rare events like this.

Regardless, there doesn’t appear to be cause for concern among any of their observed serious events during the study period.

Is it possible that the data was tampered with? We are in the midst of a scientific fraud crisis and there are powerful interests heavily invested in making sure this result comes out negative or inconclusive.

For those about to downvote me: my general (but unscientific) opinion is that it doesn't make sense for the covid vaccine to be riskier than covid itself. But as a society we have shown that even "technical" people like researchers and scientists are willing to fudge, ignore, or misinterpret data for various political or personal purposes, not to mention that doing so in domains outside of public health is common practice in academia. So I believe it's only prudent and rational to be extremely skeptical of anything that looks "off".

They're not making an honest comparison to Covid here.

Not only possible but proven. There are cases where people who suffered extremely severe adverse effects right after vaccination, were recorded by the trial operators as having minor ailments or even having had COVID despite negative tests, e.g. as in this case:

https://dailysceptic.org/2022/05/23/concerns-of-fraud-in-pfi...

Another similar problem in which a trial participant appears to have died a day or two after taking the shot, but it was ruled unrelated (by Pfizer!):

https://igorchudov.substack.com/p/pfizer-study-subject-c4591...

So? He had a multitude of health issues. People with a multitude of health issues sometimes die. And that report is obviously garbage--it refers to the "police report" of why he died. Nope, the police don't determine why someone died--they're only reporting what a medical examiner said. Why should Pfizer question it? Your link is clearly to a dishonest site.

The examiner wouldn't have known he was taking an experimental medical substance just a day or two before death, so obviously that assessment of cause of death would be incomplete. This is covered in the article and is basic common sense. It is dishonest of Pfizer to take the cause of death as assessed by someone who was lacking critical facts especially given the extremely strong temporal correlation.

indeed.

Yeah, I do wonder what might have caused such a massive difference between the two placebo groups (17.6 and 42.2 per 10K).

Small samples? The confidence intervals look pretty wide.

Here's the full paper: https://reader.elsevier.com/reader/sd/pii/S0264410X22010283

As I understand it, these are results from two different trials done separately by Pfizer and Moderna, with slightly different protocols and reporting standards:

"Moderna reported SAEs [serious adverse effects] from dose 1 whereas Pfizer limited reporting from dose 1 to 1 month after dose 2."

"For reasons that are not documented in the trial protocol, Moderna included efficacy outcomes in its SAE tabulations, while Pfizer excluded them. As a result, Moderna’s SAE table did not present a traditional SAE analysis but rather an all-cause SAE analysis. "

Thus, only numerical comparisons between treatment and control within the same trial are valid (assuming proper randomization), not between the two trials.

https://www.sciencedirect.com/science/article/pii/S0264410X2...

Pfizer reported serious adverse effects in 127 vaccine and 93 placebo. Moderna reported serious adverse effects in 206 vaccine and 195 placebo.

=> Headline: "Serious adverse events of special interest following placebo vaccination in randomized trials in adults"

Normally there aren't competing drugs racing to market, thus the FDA doesn't have any system in place to ensure apples-to-apples comparisons in a case like this. Each company decides it's test protocols and gets FDA approval, they aren't working in cooperation.

The number of test subjects isn't small, but the number that have something bad happen is small (they're not going to accept test subjects with obvious, serious problems because that makes the data noisier.) Reducing noise means increasing the sample size--to get clean data here you would need a truly huge test group. I wouldn't be surprised if you would have to use the whole nation as your test group--and what's the point??

Right. If I'm understanding those stats correctly, they indicate that even if a patient has an adverse reaction after taking the vaccine, the odds are >50% the reaction is caused by something else.

What's important for the discerning paranoid: _chances_

The most critical section from the paper [1]. AESI = Adverse Event of Special Interest:

---

3.4. Harm-benefit considerations

In the Moderna trial, the excess risk of serious AESIs (15.1 per 10,000 participants) was higher than the risk reduction for COVID-19 hospitalization relative to the placebo group (6.4 per 10,000 participants).

In the Pfizer trial, the excess risk of serious AESIs (10.1 per 10,000) was higher than the risk reduction for COVID-19 hospitalization relative to the placebo group (2.3 per 10,000 participants).

---

[1] - https://www.sciencedirect.com/science/article/pii/S0264410X2...

> higher than the risk reduction for COVID-19 hospitalization relative to the placebo group (6.4 per 10,000 participants).

This is a deceptive statistic.

They're only reporting adverse events due to COVID-19 hospitalization in the window of the study.

This will miss adverse events due to undiagnosed COVID-19 and it should be using the participants who contracted COVID-19 as a denominator, since essentially everyone will be infected by the virus eventually and the lifetime-horizon risk in the placebo group is much longer than the window of the study, while the vaccination risk obviously starts with the introduction of the antigen and then drops off rapidly after 3-6 months.

To make it more accurate the placebo groups should have been followed longitudinally for 5-10 years until nearly everyone had seroconverted or tested positive, and compare that against the vaccine group, and compare all outcomes and not apply the diagnosed COVID-19 hospitalization filter to only one arm.

Really bad "study" that is just some bad undergrad-level number crunching and misapplication of statistics to the vaccine trial numbers.

This. Every vaccination-is-dangerous paper I've seen suffers from this flaw. They always seriously downplay the risk of actually getting the infection, especially the risk of getting it for the first time.

To be fair, these studies are also downplaying any long-term effects of vaccination. Hard to say what direction the combined effect goes in.

There usually are no long term risks of vaccination. Drugs rarely have consequences that don't show up while taking them--and you don't keep taking a vaccine.

More importantly: when the spike protein and everything related to the vax has left your body, it's theoretically impossible for it to cause damage. It could be lingering damage that was undetected when first jabbed, but name a vax that discovered serious events years later.

This study answers a simple question: "If I deploy this treatment to x people, how will this affect the outcomes of those people over the next y months (where y is roughly the same as the study length)?" And this sort of study, when impartially carried out, can provide a reasonably accurate answer to that question.

I fully agree that there are plentiful problems with this. Such a study would conclude that smoking cigarettes is perfectly healthy! But going beyond that is generally not possible in any meaningful way, because one cannot see into the future. For instance you now claim, with hindsight, that effectively 100% of people will be infected by COVID. Yet at the same time these studies were happening, we were carrying out actions, ostensibly well informed and in good faith, that promised to be able to effectively eliminate COVID if we just e.g. shut down the country for 'x' weeks.

And so you now want to compare risk(COVID) vs risk(vaccine), but these vaccines ultimately proved themselves unable to meaningfully prevent COVID infection or spread. So instead you need to compare risk(COVID) vs risk(COVID + vaccine). And similarly the vaccines also ended up with brief periods of efficacy, so again you need to factor in yet another variable: risk(COVID + vaccine + boosters x time).

And that's really just getting started. Imagine trying to factor in completely unknowable, yet inevitable, viral mutations. Ultimately, these long term models are more likely to reflect the biases of the person building them than any actual relationship to expected outcomes. And when there are tens of billions of dollars at stake for a model to give the "right" answer, I don't think this is anything we ought ever aim for. Like in software, KISS. [1]

[1] - https://en.wikipedia.org/wiki/KISS_principle

> Yet at the same time these studies were happening, we were carrying out actions, ostensibly well informed and in good faith, that promised to be able to effectively eliminate COVID if we just e.g. shut down the country for 'x' weeks.

I don't think that's true. Pretty much within a month or two, most of the conversation was around "flatten the curve", which is explicitly not about stopping the total number of cases, but rather lowering the peaks of simultaneous cases.

And when the vaccines came out, it was clear fairly quickly (though maybe after this study? I don't know) that the major benefit of them would be to the severity of the illness, not to prevention of spread.

This study is looking at the data from the trials upon which the emergency authorization was granted. So you need to warp back to somewhere around August 2020 to December 2020. Extreme views at the time were more the norm than the exception at the time, both in terms of the potentially negative impact of the virus, and the potentially positive impact of various measures to combat it.

The data was, but the paper was published this month. The reviewers should have made them make note of the issue above.

The suggestion here is very clearly that the study can answer the question of the relative safety of the vaccines vs. the virus. Which it cannot. Which you absolutely do need a time machine to answer correctly. Which was exactly my point.

Insinuating that this study answers that question is just pure bullshit.

What I'm trying to emphasize is that by your standard of evidence, no study could ever answer anything. And that's not an entirely unreasonable position to take. For instance I know of one scientist, who is part of the National Academy of Sciences no less, who believes that the studies against smoking are insufficiently compelling. And it's for, more or less, similar rational to what you're putting forth here.

On the other hand, the viable ability to set up an insurmountable standard of proof does not really change the picture here. The data available, under the time frames studied, do not paint a positive picture of the safety or efficacy of these drugs. If you get into section 5 of the study, you can see how both the FDA and the pharmaceutical companies worked to pad the numbers to make them look better, including things like lumping thousands of individuals into the experimental group even though there had been no follow up on these individuals, meaning they had no idea of their outcomes.

> To make it more accurate the placebo groups should have been followed longitudinally for 5-10 years until nearly everyone had seroconverted or tested positive

Geez, you’re raging against the authors for not seeing into the future? The virus has only been here for less than 3 years This is worse than the job requirements that demand 20 years experience in node.js

I'm raging against the people here who are using it to suggest that this study answers the question of relative safety of the vaccine vs the virus.

This study doesn't do that, it can't do that, so it can't answer that question, so it really isn't worth posting that little nugget.

Those are the requirements for answering that question. The study cannot meet those requirements. The study cannot answer that question. Dunno why this math is so difficult.

Now stratify it by age and gender and see where everyone fits. Does it lead to the conclusion that a course of multiple injections reduces the risk of hospitalization for the average individual in each band?

Personally, I only followed the data for my own band, which was generally stratified as “Male 18 - 40.” I did not see any data that convinced me to begin a course of mRNA injections.

I got j&j because I would have lost my job otherwise. it was the only non mrna option available. also there was extreme social pressure from my immediate family. They refused to see me for over a year until I was vaccinated.

I still caught coronavirus about 2 months later.

I got all the same shit basically. Barred from all establishments except grocery shops, barred from taking exams & visiting my university campus, excluded from my cousins wedding, couldn’t board a plane… I just never gave in because I knew it wasn’t the right thing for me. I’m 25 now and never got the shot. My wife had it, and when we both got the virus she was sicker than me for a longer time.

I think the most critical was:

> The excess risk of serious adverse events found in our study points to the need for formal harm-benefit analyses

At no point in the pandemic was a harm-benefit analyses even considered for any intervention, action, anything.

Of course not--a harm/benefit analysis can't be done without a substantial window to gather data. Doctors take their best shot and the answers for what works or doesn't are written in the morgue.

Of course it was. Go to pub med search for "harm benefit covid" and look at all the research. A vax was even paused because of it: https://www.cdc.gov/vaccines/covid-19/info-by-product/jansse...

People that called for them had their careers destroyed and were deplatformed.

There's quite some difference between the placebo groups there...

Am I correct in reading this that the Pfizer vaccine was associated with fewer serious side effects (as defined) than the Moderna _placebo_?

It seems plausible that both vaccines cause slightly more serious side effects than their corresponding placebos (30–60% more if I understand correctly), but it's hard to know how meaningful this result is when one of the placebos is 2.5x more 'dangerous' than the other placebo.

> Am I correct in reading this that the Pfizer vaccine was associated with fewer serious side effects (as defined) than the Moderna _placebo_?

Given the small differences and wide confidence intervals, the correct interpretation is that the incidence of side effects isn’t really significantly different than placebo.

Or you could look at the numbers and decide that given the noise of the data that a massively larger study (10-100X more people) would be necessary to discover any practical differences, should they exist.

Not according to this part of the result:

"The Pfizer trial exhibited a 36 % higher risk of serious adverse events in the vaccine group; risk difference 18.0 per 10,000 vaccinated (95 % CI 1.2 to 34.9); risk ratio 1.36 (95 % CI 1.02 to 1.83). The Moderna trial exhibited a 6 % higher risk of serious adverse events in the vaccine group: risk difference 7.1 per 10,000 (95 % CI -23.2 to 37.4); risk ratio 1.06 (95 % CI 0.84 to 1.33)."

This seems to contradict the first sentence, which implies Moderna is worse:

"Pfizer and Moderna mRNA COVID-19 vaccines were associated with an excess risk of serious adverse events of special interest of 10.1 and 15.1 per 10,000 vaccinated over placebo baselines of 17.6 and 42.2 (95 % CI -0.4 to 20.6 and -3.6 to 33.8), respectively."

I'm not sure how to reconcile the apparent contradiction, perhaps others understand what's being said?

What it’s saying is that in both cases the unvaccinated group had less risk from not taking the vaccine than taking it.

The placebo isn’t relevant, just the fact they didn’t take the vaccine. Since it’s randomized, it’s unsurprising that both groups of unvaccinated people had diff risk outcomes. What IS surprising is that the vaccine performed worse than both random groups of unvaccinated people.

>What it’s saying is that in both cases the unvaccinated group had less risk from not taking the vaccine than taking it.

I get that, but why is the quantitative description of the risk different in the first sentence than it is later in the paragraph? The first sentence clearly indicates that the risk is higher with Moderna (excess risk of 15.1 vs 10.1 per 10,000), while later the excess risk of Pfizer is stated as 18.0 per 10,000, while Moderna's is a much lower 7.1 per 10,000. I assume the numbers describe different things, but it isn't clear what the difference is.

These two studies are not normalized on time so slightly more events in ~two months for A vs B verse a lot more in ~one month for C vs D means more total events in A than C.

Really, if you believe the primary difference is that pfizer was a lower dose and that this isn't all just noise, the data seems to be showing that there's a pretty short window where the risk of the jab wasn't made up for by benefit such that a longer study lost ground on finding a measurable risk.

> Combined, there was a 16 % higher risk of serious adverse events in mRNA vaccine recipients: risk difference 13.2 (95 % CI -3.2 to 29.6); risk ratio 1.16 (95 % CI 0.97 to 1.39).

I am not a statistician, but isn't this saying that there's not enough data to reject the null hypothesis (that the risk difference is 0 and the risk ratio is 1.0) with 95% confidence?

Not a statistician, either, but that's my understanding also. If your CI includes 0 you take the null hypothesis.

It looks like the largest effect found (from reading only the summary) is how many more adverse effects the Moderna placebo causes compared to the Pfizer placebo.

Given that, I won't regret my vaccinations just yet.

There has been decent research showing that a lot of Covid vaccine side effects appear when subjects are given placebo, which indicates that the subjects expected the side effects.

So we may someday discover that vaccine misinformation causes people to experience imaginary negative effects from vaccines or placebo.

I read too much FUD about the vaccines before my Moderna booster, particularly the myocarditis in younger men results. Started feeling tight chested and a lump in my throat later that day after the vaccine. Finally went to urgent care after a day or two and got an x-ray and EKG and it showed nothing of concern. Doctor suggested it was probably anxiety, suggested taking an NSAID and relaxing and I was better pretty soon after.

Looking at the list of adverse events is really interesting, and leads me to agree with the conclusion that this study lies somewhere between "inept bumbling" and "deliberate lying with statistics." Forget the red herring of comparing across the Pfizer and Moderna studies (which clearly have very different sample properties); the within-study comparisons are also a little suspect.

Most of the adverse effects have frequencies of 1-3, which is literally the lower limit of what kind of data we can use to estimate anything about anything (ask an actuary about modeling "rare events" or "extreme events"). Moreover, what is not reported is the number of affected individuals; there is no indication about whether or not the two poor souls who ended up with pancreatitis in the Moderna trial were also the two people who ended up with diarrhea in the same trial. I am very leery of looking at the sum of these effects and concluding that, in aggregate, the Moderna vaccine is more dangerous than Covid-19 infection (let alone more dangerous than the placebo) when it comes to these "rare" adverse events -- because it doesn't make a lot of sense to aggregate these numbers.

What stands out among all this rare-events noise is the risk of cardiac injury and coagulation disorder. There might actually be some meat on that bone. In the Pfizer trial, we have 8.5 instances of coagulation disorder per 10k versus 5.3 instances per 10k in the placebo. That's not nothing, and might even be "statistically significant" depending on your decision criteria. But again, without a comparison to Covid itself on this particular metric (the "risk reduction" numbers are buried in the text and not clearly enumerated), there's not much you can conclude here.

I actually went and plugged in the numbers for coagulation disorders into a frequentist likelihood ratio test for a difference in proportions, and got p = 0.23.

Feel free to check my work (in R):

    coag_vax <- 16
    p_vax <- 8.5 / 10000
    num_vax <- round(coag_vax / p_vax)
    cat("#(vaccine) = ", num_vax, "\n", sep = "")
    cat("#(coagulation disorder | vaccine) = ", coag_vax, "\n", sep = "")
    cat("P(coagulation disorder | vaccine) = ", p_vax, "\n", sep = "")
    cat("\n")

    coag_plac <- 10
    p_plac <- 5.3 / 10000
    num_plac <- round(coag_plac / p_plac)
    cat("#(placebo) = ", num_plac, "\n", sep = "")
    cat("#(coagulation disorder | placebo) = ", coag_plac, "\n", sep = "")
    cat("P(coagulation disorder | placebo) = ", p_plac, "\n", sep = "")
    cat("\n")

    coag_same <- coag_vax + coag_plac
    num_same <- num_vax + num_plac
    p_same <- coag_same / num_same
    cat("#(null) = ", num_same, "\n", sep = "")
    cat("#(coagulation disorder | null) = ", coag_same, "\n", sep = "")
    cat("P(coagulation disorder | null) = ", p_same, "\n", sep = "")
    cat("\n")


    ## Frequentist likelihood ratio test that the proportions are identical

    # https://stats.stackexchange.com/a/373448/36229
    # http://people.musc.edu/~bandyopd/bmtry711.11/lecture_02.pdf
    l_same <- dbinom(coag_vax, num_vax, p_same) * dbinom(coag_plac, num_plac, p_same)
    l_diff <- dbinom(coag_vax, num_vax, p_vax) * dbinom(coag_plac, num_plac, p_plac)
    t <- -2.0 * log(l_same / l_diff)
    p <- 1 - pchisq(t, 1)
    cat("LR test statistic = ", t, "\n", sep = "")
    cat("LR test p-value = ", p, "\n", sep = "")

Output:

    #(vaccine) = 18824
    #(coagulation disorder | vaccine) = 16
    P(coagulation disorder | vaccine) = 0.00085

    #(placebo) = 18868
    #(coagulation disorder | placebo) = 10
    P(coagulation disorder | placebo) = 0.00053

    #(null) = 37692
    #(coagulation disorder | null) = 26
    P(coagulation disorder | null) = 0.0006898015

    LR test statistic = 1.412184
    LR test p-value = 0.2346942

And for a 95% CI around the difference in proportions, I got [0, 8.5]:

    # https://pubmed.ncbi.nlm.nih.gov/25163425/
    # var_vax <- num_vax * p_vax * (1 - p_vax)
    # var_plac <- num_plac * p_plac * (1 - p_plac)
    var_pooled <- (p_vax * (1 - p_vax) / num_vax) + (p_plac * (1 - p_plac) / num_plac)
    std_pooled <- sqrt(var_pooled)
    ci_lo <- (p_vax - p_plac) - qnorm(0.975) * std_pooled
    ci_hi <- (p_vax - p_plac) + qnorm(0.975) * std_pooled
    cat("95% CI (Gaussian approximation) = [ ", max(ci_lo, 0.0) * 10000, " in 10k , ", min(ci_hi, 1.0) * 10000, " in 10k ]\n", sep = "")

Output:

    95% CI (Gaussian approximation) = [ 0 in 10k , 8.502481 in 10k ]

I also ran it through a completely-un-tuned Bayesian model and got a similarly inconclusive result:

    library(brms)

    coag_data <- data.frame(
      category = c("vaccine", "placebo"),
      n_sample = c(num_vax, num_plac),
      n_coag = c(coag_vax, coag_plac))

    fmla <- n_coag | trials(n_sample) ~ category

    prior <- get_prior(fmla, coag_data, family = binomial)
    cat("Prior:\n")
    print(prior)

    model <- brm(
      fmla,
      data = coag_data,
      family = binomial(link = "logit"),
      prior = prior,
      iter = 1000)
    cat("Model:\n")
    print(model)

Output (truncated):

    Population-Level Effects:
                    Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    Intercept          -7.58      0.32    -8.23    -6.98 1.00     1000     1278
    categoryvaccine     0.49      0.41    -0.27     1.32 1.00     1319     1239

    Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
    and Tail_ESS are effective sample size measures, and Rhat is the potential
    scale reduction factor on split chains (at convergence, Rhat = 1).

Conclusion: It's suggestive that the vaccine is more dangerous than the placebo but not at all a strong confirmation.

I did skim back over the paper and it looks like they categorized "serious" in such a way that maybe hospitalization is not a terrible baseline for comparison. But it's still pretty questionable that the incidence of "serious" coagulation disorders in the placebo group is higher than the incidence of hospitalization in the placebo group, and it makes me think that they're making an invalid comparison there. So this is an inconclusive result, presented in what is at best a hand-wavey framework.

Please be aware that in these trials the placebo is not an inert known safe substance like water but rather another closely related vaccine. The goal is to ensure that any reactions you get are similar to the primary vaccine. So, people experiencing side effects from placebos is not really proof that they're self-created or 'expected', just that whatever vaccine was used as the placebo could also cause side effects.

There was considerable chatter among the “vaccine hesitant” about Pfizer being “better” than Moderna, which could play into your point.

Yeah, Pfizer optimized for acceptable performance with fewer side effects, Moderna optimized for best performance with less regard for side effects.

Hence the general pattern that Moderna hits you harder than Pfizer and of course the hesitant go with Pfizer if given a choice. The flip side is those of us who don't react badly will tend to go Moderna for the boosters. That also means that going forward we can't compare real world effects because the distribution will be decidedly non-random.

I'm not sure why this is downvoted; it's almost certainly the case. Many of the folks who fainted shortly after vaccine administration were probably in this category.

Someone pointed out above, those numbers are from 2 different trials, and they counted placebos and SAEs differently. The two trials are internally consistent, but can't be compared against each other due to differences in procedure and methodology.

Clearly, we really need to improve quality control on the Moderna placebo. /s

One more thing that bothered me was that lot of medical staffs world over were not informed about aspiration for intramuscular injection.

Additionally, many doctors have said that 2 weeks between shots was too quick and would not be enough time for the first shot to "primer". 2 weeks acted more like a single larger dose than 2 spread out doses.

Edit: My memory failed me, the spacing was 3 weeks and 4 weeks (Pfizer and Moderna respectively). Point still stands though.

Ref: https://www.medpagetoday.com/opinion/marty-makary/94315

What vaccine was given with 2 weeks between shots?

Updated my original comment, I thought it was 2 and 3 weeks, not 3 and 4 weeks.

But they're certainly aware of the general issue. Also, there does not appear to have been conclusive evidence that aspiration was warranted, especially not in 2020.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8783631/

It's an interesting publication, and a necessary one. However, there are a couple of thoughts to keep in mind:

1. The confidence intervals for the risk differences are wide, and some categories include negative numbers. This means that perhaps the vaccines were protective (?) against these other outcomes, but overall the trend based on this analysis is the vaccines were associated with increased risk of these other bad outcomes.

2. Capturing and reporting SAEs in the study designs are sensitive but not specific (i.e. they try to capture everything and aren't particularly concerned with verifying the cause of the SAE). For example, in the Pfizer study, they captured data related to any SAE from the start of consent to 6 months after the last dose of trial vaccine [1]. That is a long time for typical medical problems to crop up. I would like to know a comparison between the intervention group and the estimated rates of such illnesses for an age-matched population.

3. In this study they looked at all SAEs as compared to any SAE (which is what the FDA did). I don't want to discredit they study in any way and it is a valid analysis. However, it is important to keep in mind that when someone becomes ill, injury to one organ system can cause downstream injury to a second (for example a heart attack reduces cardiac output, which causes kidney injury). One way to explain the findings here is that the vaccine triggered more secondary, unintended, bad outcomes. Another way to explain the data is that a small number of people became very ill in the peri-vaccination period and had a lot of bad stuff happen. Also keep in mind from point 2 that the time period for collecting SAEs was from consent to 6 months after the last dose of vaccine.

The data presented here is interesting, and it's possible that the vaccines could be more harmful than initially thought. Overall, I am skeptical. The numbers here are still very low compared to the total number of people in the trial. If patient level data is released and there is more meat to the findings here it might change how I think about vaccination, but at this point I wouldn't change.

[1] https://www.nejm.org/doi/suppl/10.1056/NEJMoa2034577/suppl_f...

Assuming (for the sake of argument) that covid vaccines were more harmful than typical vaccines, I have a few questions [based entirely on my ignorance]:

1. Were the doses too high? or ...

2. It is something about what was being vaccinating against, or ...

3. the mRNA technology

mRNA vaccine technology seems to be really exciting; what might be done to make them safer? or is there really not special risk to that technology at all?

Here's an interesting theory to consider. The covid-19 vaccines are intended to administered intramuscularly. The resulting immune reaction is intended to be localized to that area and generally the expected result is some localized discomfort (and the creation of antibodies) -- so far so good, right?

Medical professionals are recommended to perform a technique called intramuscular aspiration. It helps ensure the vaccine is delivered to a localized area. The technique involves injecting the needle, sucking back on the syringe slightly and looking for blood. If there is no blood, push the contents of the syringe into the muscle and complete the vaccine administration.

If during aspiration there IS blood, it means the needle may have hit a vein or artery in or near the muscle. At that point, administration should be stopped and the medical professional should re-administer the vaccine in a different location.

Here is the theory: Intramuscular aspiration has not been consistently practiced. A certain percentage of covid-19 vaccinations have not been administered locally, but into the circulatory system via a vein or artery. This is not how the vaccine was designed to be administered, and it's not fully clear on what (if any) negative affects may result.

Interesting. Given how many people were giving out shots, wouldn't surprise me at all if best practices were not adhered to.

And my first two were definitely by people that weren't exactly routine injectors and I think one of my wife's shots was placed too high.

I did a lot of research on this last year. Here are the best answers I could find out to your questions:

1. This is possible. No real information is public on how dose ranging was done. The closest I could get is the suggestion that doses were calculated based on animal experiments by simply starting high and reducing it until the animals didn't seem to get sick or die anymore. So, doses appear to be the highest tolerable in whatever sample size they used to do this. If there's deeper theory behind the doses it's unclear what it can be because Moderna dosage is 3x stronger than Pfizer for no obvious reason (they are advertised as granting equivalent protection).

2. Yes, vaccinating against CoVs is ~useless and can backfire. There was evidence in the research literature about this before COVID. There are two related problems:

2a. Useless: respiratory viruses like CoVs and influenza can mutate very fast. Nobody ever made a vaccine against the common cold because the viruses mutate beyond it immediately, rendering the vaccine useless. Flu vaccines routinely have efficacy of <20% or even zero because they get made for a variant that doesn't then emerge. It was not a huge leap to realize that the fast mutation problem was also going to apply here, and indeed just months after vaccination started Omicron appeared and replaced Delta completely.

2b. Can backfire: There can be a problem called OAS or antigenic fixation. The immune system appears to lack fine grained resolution. After the immune system memorizes a virus it's possible for it to get confused and think it's detected that virus when in reality it's seeing a slight mutation. When this happens it produces antibodies to the older antigens, and if the virus mutated in such a way that they're now less effective this can lead to the virus not being stopped properly. In other words it can make things worse. Not all viruses are as unstable as CoVs so this problem doesn't occur with e.g. smallpox, but SARS-CoV-2 is very unstable. There's some evidence that COVID vaccines can cause this effect unfortunately, whereby the immune system produces antibodies to the long extinct Wuhan 2019 strain instead of the one the body was actually exposed to.

3. mRNA tech is very neat in theory but there are two major possible issues:

3a. It never launched before COVID because it was plagued with extremely severe toxicity problems that caused any drug using it to fail trials / safety approvals. The problem seemed to be the lipid nanoparticle wrapper which became toxic on repeat doses. Most drugs require repeat doses so that's a problem. Moderna was a failing biotech firm before COVID because of this. They couldn't find a solution so pivoted to vaccines. Why? Because - pre COVID - vaccines were assumed to be something you take once and then you're done for many years or for life, so it was a way to dodge the repeat-dose-toxicity problem rather than solve it. Well, then COVID mass hysteria infected the globe and people are being forced to take 2, 3, 4 doses. So we're well into repeat doses territory. How many doses did Moderna's tech become toxic after, exactly? That's not public AFAIK.

https://www.statnews.com/2017/01/10/moderna-trouble-mrna/

3b. We were assured that the mRNA disappears from the body within a few days. That turned out to be false and mRNA spike can be found collecting in different parts of the body weeks or even months after injection. Because research that could undermine vaccines is so rare and hard to get published, and because mRNA vaccines are so new, why this happens is unclear. However some doctors have speculated that it's to do with the pseudo-uridine substitutions they do. (mRNA vaccines disable the immune system's normal defenses against foreign mRNA using some chemical tricks).

On the other hand, mRNA itself is probably not the cause of the heart/clotting problems. AstraZeneca's vaccine doesn't use mRNA and has the same issues. The problem is more likely that the spike protein is toxic and can cause these problems if it gets into the bloodstream. Doesn't matter how you make it.

Thank you for your reply. People who disagree with you should be less lazy and give their own answers, or rebut yours. I take your commen as a reasonable lay person's conclusion based on their best effort (given whatever constraints).

Thank you, and you're welcome.

2--exactly the same thing happens if you get the disease. Thus it's not a reason against the vaccine unless the probably of it mutating before you catch it is high.

You mean OAS? That can happen yes and is theorised to be why flu gets deadlier as people get older, their immune system gets more and more confused by memories of flus from people's youth.

In this case, the problem is that it accelerates the spread. Normally in any epidemic there's a large population of people who were never infected, so it's their first time and the body becomes highly immune. They act as buffers that stop the virus from spreading so fast to those unlucky enough to have got the earlier version. You end up with a diverse population of people immune to different variants which slows them all down.

If you fix everyone on a variant that hasn't existed for years then this buffering effect is eliminated. Everyone becomes equally vulnerable to reinfection. This is posited as the explanation for why do many vaccinated people seem to be susceptible to rapid reinfection. Although it can be happen naturally too, the wider social diversity that would make that happen say every few years is gone, so now it can happen every few months instead.

It's different depending on which vaccine technology was used.

Some vaccines used a lipid shell to get around the problems of raw MRNA being really fragile. However, it seems they made the protection too good, and MRNA that was supposed to just stay in the arm easily travelled further and got into the bloodstream. That means that MRNA was getting into cells all around the body and causing spike protein production everywhere, instead of just in the arm like designed.

This means that you got inflammation problems everywhere in the body, including in the heart (myocarditis/pericarditis), vascular systems ("brain fog", general unwellness), and reproductive systems (especially for women).

Inflammation in the injection site for a vaccine is pretty normal, inflammation elsewhere is terrible.

Initial optimistic assumptions of the MRNA being permanently consumed by the body relatively quickly also don't seem to be true - even the CDC recently edited a page on MRNA vaxxes to remove a statement that the MRNA is used up quickly. The very worst case scenario could be people who got vaxxed just have cells pumping out inflammatory spike protein at a low rate for years.

Other vaccines used a real virus vector that is modified version to express spike protein. This of course has the issue of a virus being not entirely controllable once it's in the body. Again, you'll have the problem of the virus (and thus the spike protein it expresses) ending up in strange places. However, the immune profile is slightly different, as the immune system will start to attack the vaccine vector as it progresses in the body.

Several vaccines were associated with strange, systemic blood clotting in some people - but I'm not sure if they ever found the mechanism of action.

Novavax uses a traditional protein+adjuvant mix seen in many previous vaxxes. There's no widespread report of nasty side effects with Novavax - but there's not that much usage of Novavax in the wild either.

Aside from that, there's also the issue that in targeting a variant of the rapidly-mutating spike protein, you set people up to be victim to the long-known "original antigenic sin" effect.

Immune system works off a principle of first impression - it targets a response to whatever it sees first. This also applies to infections that are somewhat close but not the same as what is 'memorised' by the immune system already. So an infection by a COVID variant when the person has been exposed to a vaccine for the originals will produce an immune response for the modified original protein, not the real infection that is currently happening. (The vaccines target a modified original spike protein, and not the S-shape protein as well, so the distance is even greater.) Since the spike protein keeps shifting so fast in the wild, hitting the OAS effect is really likely over time.

Some people think the S-protein in the middle of COVID should have been targeted by the vaccines, not the spikes, as it's more stable. It's hard to know if that would have been more effective.

So it was a hoax 2 years ago. Now we have studies for that. But media still wont talk about it. Instead our government plans to distribute 4th dose in spring because they already bough 17 milion doses for 10 milion people. From Pfizer with unknown prices and hidden contracts. In time of inflation caused by lockdowns and energy crisis. I'm not surprised that people are on streets, despise the government and do not trust any traditional media.

Anyone remember how this data was treated 2 years ago? Bannings, Doctors losing their licenses/being fired etc.

Without the actual study to read I'm going to actively assert there is nothing here for a general audience. We know vaccines have adverse effects. And the rate they occur is useless info.

We really need cost-benefits of adverse effects v. COVID outcomes stratified by ages, and it takes more than one study to build the picture.

> We really need cost-benefits of adverse effects v. COVID outcomes stratified by ages, and it takes more than one study to build the picture.

Also when taking this into account, one must take note that many who have had the vaccine still end up getting COVID at some point, so the additional adverse effects that COVID brings may add onto any risks the vaccine brought.

I disagree. Classic trolley problem. You don’t get to decide for other people how they’d like to approach the intervention ethical dilemma.

Oh boy, this comment section is going to be lit.

What kind of serious adverse events are they?

In the paper [1] they're all listed. The most common, by far, were "coagulation disorder" or "other forms of acute cardiac injury". The give examples of the former as including pulmonary embolism, acute myocardial infarction, immune thrombocytopenia, and disseminated intravascular coagulation.

[1] - https://www.sciencedirect.com/science/article/pii/S0264410X2...

So basically life threatening severe problems, nice.

> adverse events of special interest.

This feels like we’re looking at some sort of subset rather than overall risk profile?

Don’t think anyone was expecting vaccines to be entirely risk free.

Yet they (Government, CDC and media) downplayed it blatantly. They certainly made people believe forcing it to young people (<40), especially teenagers and kids was ok. Risks were higher than benefit and it was obvious from the data.

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