Covid was already widespread before they started testing for it and definitely before they declared it a pandemic. Therefore, how dangerous it was is already measurable to an extent before they declared the pandemic. Which means at the noise level of excess mortality. Therefore, the fatality rate induced afterwards must be a product of reaction. Reaction is a broad term both full of volition and involuntary. They locked people into old homes and caused death in the vulnerable (80+ without proper treatment). Everyone else suffered from intentionally fraudulent treatment which spiked excess death. Remdesivir, lack of antibiotics, prophylactic antivirals. Iatrogenic harm and just maliciousness comes to mind.
Covid was already widespread before they started testing for it and definitely before they declared it a pandemic. Therefore, how dangerous it was is already measurable to an extent before they declared the pandemic. Which means at the noise level of excess mortality. Therefore, the fatality rate induced afterwards must be a product of reaction. Reaction is a broad term both full of volition and involuntary. They locked people into old homes and caused death in the vulnerable (80+ without proper treatment). Everyone else suffered from intentionally fraudulent treatment which spiked excess death. Remdesivir, lack of antibiotics, prophylactic antivirals. Iatrogenic harm and just maliciousness comes to mind.
Covid was already widespread before they started testing for it and definitely before they declared it a pandemic. Therefore, how dangerous it was is already measurable to an extent before they declared the pandemic. Which means at the noise level of excess mortality. Therefore, the fatality rate induced afterwards must be a product of reaction. Reaction is a broad term both full of volition and involuntary. They locked people into old homes and caused death in the vulnerable (80+ without proper treatment). Everyone else suffered from intentionally fraudulent treatment which spiked excess death. Remdesivir, lack of antibiotics, prophylactic antivirals. Iatrogenic harm and just maliciousness comes to mind.
Wow - the amount of patience and diligence it would take to produce this! I guess I am happy to be mentioned as a counter-example, though none of my arguments seem to be acknowledged directly.
I reread this stack after our back and forth about the PCR test (which makes me more confident accepting the number of infections).
So in making your case for severe efficacy, you talk about relative severe efficacy, and not absolute severe efficacy. What is the absolute severe efficacy in the raw numbers of the Pfizer trial?
Here's my take on the Pfizer trial numbers you cited:
Pfizer's trial probably has the most accurate numbers vaccinated versus unvaccinated people, which was 21,653 vaccinated, and 21,672 unvaccinated.
So in your first argument for severe efficacy citing Pfizer's trial, there were 2 vaccinated hospitalizations out of 21,653 people, and at most 83 unvaccinated hospitalizations out of 21,672 people (I say at most because the table you cited started counting 7 days post injection, but let's take that at face value).
So the risk of severe disease for the unvaccinated is .04%, and the risk of severe disease for the vaccinated is .009%.
So in absolute terms, the severe efficacy you refer to can't be more than the risk of severe disease, isn't that correct? So the severe efficacy is less than .04%, right? We're talking about a tiny, teensy, eency weency absolute severe efficacy, are we not?
When the absolute severe efficacy is so small, you can't say "the (transfection-vaccine) measures are effective but we shouldn't use them" when you're not taking into account adverse effects from the transfection.
Do you find an error in my thought process here?
There are many signs that the injury rate from this transfection is higher than the absolute severe efficacy of less than .04%:
Germany - The Paul Ehrlich Institute (PEI) studied suspected cases of adverse reactions that happened soon after the vaccine was injected. They found the side effect rate is one in every 500 vaccines given. A report and a sub stack article on this report:
German health insurance fund found that almost half a million people received medical treatment for side effects, out of 11 million people vaccinated, which is a 1 in 25 ratio, or 4%.
-The V safe program, a CDC smartphone app monitoring millions of people who took the covid vaccine injection, shows the rate of side effects requiring medical attention is 7.7%.
Assessment of Pfizer and Moderna's trial data show serious side effects 1 for every 800 injections with the data these companies have been willing to release so far. Teams of healthcare professionals and scientists have been closely assessing the trial data after a court ordered this data released for peer review. They have found that only a fraction of serious side effects were clearly accounted for because serious side effects were not accurately classified, people with serious side effects were removed from the trial, or side effects were hidden deeply in tables and data.
Pfizer and Moderna's trials show a greater risk of serious side effects injuries than people's risk of hospitalization and death from covid. Here is that paper, and two substack articles on this paper:
Here is an open letter to Pfizer and Moderna CEOs from the British medical journal asking for the raw patient data so risks can be age stratified: “The results showed the Pfizer and Moderna both exhibited an absolute risk increase of serious adverse events of special interest (combined, 1 per 800 vaccinated), raising concerns that mRNA vaccines are associated with more harm than initially estimated at the time of emergency authorization."
If evaluating risk/benefit based on the trial, then one way or another you need a model for the benefit going forward after the unblinding / cutoff. The crudest model would be "(unvax hospitalized - (same * v/uv/p-inf)) / (reported unvax infections / all unvax)," which makes the assumption that 100% of unvaxxed will eventually be infected, the severe outcome rate per infection doesn't change, and the per-infection prevention of severe outcomes by the vax doesn't change.
You could make different assumptions, come up with different models based on those assumptions. But to say there is a net negative based on the trial (overall or for specific risk group) isn't accurate, because the benefit keeps applying after the cutoff, because the virus doesn't go away, it's still out there, it's still going to hospitalize/kill some people, the vaccine is either 1) still going to reduce those hospitalizations/deaths or 2) stop doing this (you, the maker of the model, are deciding which one, if you decide a hard 2 then that's just another assumption).
Under the crudest model outlined above, using post dose-2 which understates prevented hospitalizations anyway, (21 - (21*0))/(873/22320) = 536 prevented hospitalizations (In plain language, with 4% of people infected there were ~21 prevented hospitalizations, so 100% is ~536 prevented). This is absolute reduction of 2% or 1 out of 50 people. Of course, this model overstates things and has ugly math (though if Delta had stayed around forever, this same model could have been an understatement), and doesn't make any sense from the perspective of individual risk for younger or older people, it's crude as said.
In your response, you're only considering the risks of 1) not taking the transfection vaccine and 2) the benefit of taking it in your risk benefit calculation.
FYI - I do believe your calculations that when using the model that you've put forth and that Pfizer used, taking the vaccine- transfection has prevented hospitalizations. So I'm not arguing that, I'm not arguing that there is a net negative from the Pfizer trial based on their model used.
I'm arguing the conclusion that you've drawn, specifically "this intervention is helpful but we shouldn't use it or mandate it.". Your statement applies to the real world, but you are basing your statement on a model that doesn't apply to the real world. This is an error.
In the real world, whenever people, doctors consider risk/ benefit of pharmaceutical products, they ALWAYS include the health risks from the product itself.
You aren't including the hospitalization risk from the transfection- vaccine. I know that Pfizer didn't include that risk either. Thus no one can conclude that the product is a net positive from the Pfizer trial itself.
It is known that there have been a significant number of hospitalizations and deaths from the product. We don't know the rate from the Pfizer trial. We do know pharmaceutical trials commonly hide adverse effects from their products, and the people seriously injured in Pfizer's trial have said their injuries weren't documented correctly. So to make a net positive or net negative evaluation of the product, we need to include information outside the trial.
We do have some evidence of the % risk from product. The V safe data puts it at 7%, the German health fund found 4%, PEI at .2%, others have said 1/800 or .1%.
If understand correctly, your 2% absolute efficacy was calculated by extending your calculations beyond the trial, so we would need to look at hospitalizations and deaths from the transfection- vaccine beyond the trial as well. Is the risk of hospitalization and death of the transfection vaccine greater than 2%? We really don't know, but since some data shows it's a possibility, the only rational thing we can say about the net positive or negative effect of this product in the real world is that it's "inconclusive". Wouldn't you agree?
Ultimately my position is that to critique the Covid vaccine on the grounds of risk/benefit, you *have* to concede that a positive risk/benefit means "Great! let's go crazy-nuts on mRNA vaccines against viruses that didn't exist two seconds ago, *if positive risk/benefit*." I reject this.
Thanks for all your responses, I've enjoyed looking through everyone's comments and reading your responses.
I didn't make this point because I didn't have hard evidence. But I knew there were reported discrepancies with how the hospitals and the CDC were counting people as vaccinated or unvaccinated. There were reports by healthcare workers that vaccinated people we're being counted as "unknown vaccination status", which were being counted as unvaccinated, in their electronic records. The electronic records didn't seem to have and unvaccinated category.
The highwire just did a pretty convincing segment on this from a study done. Apparently the study's authors wanted to do outreach to unvaccinated people, so delved into the electronic records and found 44% of those counted as "unknown vaccination status" (unvaccinated) we're actually vaccinated. The study was done in University healthcare systems in two states, so I'm not sure how widely it applies to other states. But along with Rochelle wollensky's sent a testimony that they never did get a count of covid vaccinated versus unvaccinated hospitalizations, she was admitting there was a problem. I put the link below, it's a 14 minute video. Of course this doesn't affect the Pfizer trial that you and I have been talking about, but it sure could affect the other data you referred to.
Thank you for the link. As for the CDC, I don't refer to them in this post, but they have come up in other comments. They and other, independent US studies don't use hospital records (EHR) for vaccination status, just for hospitalization. Then the names are cross-referenced to the state or local immunization registry. But, there is still a problem here because the immunization registries have a topline value for how many people were vaccinated and these values seem to be too high in a lot of places, e.g. King Co. Washington (Seattle) where the numbers where over 95% and they had to cap it there.
Point being, there is a lot lot lot of "gotcha"-ing about this health record stuff but it isn't relevant to anything.
"They and other, independent US studies don't use hospital records (EHR) for vaccination status, just for hospitalization. Then the names are cross-referenced to the state or local immunization registry." Good to know. 👍🏽
I'll read it. But most US studies outside of the CDC, and some by the CDC, they aren't pulling vaccination status from hospital records. It's whatever's in the healthcare network that the study is based on. (So vax status and "hospitalized for Covid" are both in this bigger set of data.)
Brian, while I agree that mRNA vaccines prevented severe outcomes from the Alpha and Delta variants, the data are far less clear since omicron became the dominant variant more than 2 years ago. Both US CDC and UK ONS data skew their results by underestimating unvaccinated populations and counting all cases, hospitalizations and deaths of unknown vaccine status against the unvaccinated.
Is it a coincidence that all the best data supporting the efficacy of these injections against hospitalization stopped being published roughly 2 years ago?
This is why I say at a certain point there is no more (uninfected) unvaccinated control group - you can't measure severe efficacy because you have no yardstick (if you could teleport a million unvaccinated, uninfected adults back in from the void, maybe...). In this post I say after 2021 but really it's after spring, 2022. You can see this in the NSW dashboard, unvaccinated are still hospitalized at highest rate in May, 2022 and then total drop to zero. This is same time as transition in US to "Cleveland Clinic booster study era" where only the boosted are having first-time infections anymore. A couple studies by Nash et al. back up this idea (that USA boosted actually had largely put off first-time infections before June) - http://medrxiv.org/cgi/content/short/2023.09.29.23296142 fig 3 showing seroconversion up to March-June 2022 samples, note that 2-dosed are almost level with unvaxxed so this is nothing to do with the 'can't make N antibodies myth'.
The "CDC skew" - counting unknown as unvaxxed - can only dilute measured efficacy (or negative efficacy, if that were the case). Whatever the difference between vaxxed and unvaxxed are, having vaxxed counted as unvaxxed dilutes the difference, cannot create/exaggerate the difference (unless there is some bias in who has missing status, this isn't particularly likely since people less prone to be measured by whatever system are often less prone to get vaccine). *edit: CDC vaccinated denominators are the problem, this is distinct from case vax status classification but still a huge issue, see below.
The ONS doesn't have this problem. They exclude people they don't know about. And then the ONS critics kvetch about it, even though it makes the data better (and if more biased, only more biased for "people who are more similar to each other in terms of meeting ONS requirements" which is good).
OK, I don't quite understand your argument about the CDC skew. You do understand that unknown cases, hospitalizations, and deaths are counted as unvaccinated, but that this does not mean that individuals of unknown status are counted as unvaccinated. Right? In other words, the CDC moves the vaxxed numerators to unvaxxed, but that this in no way affects the denominators, which have their own issues in that the unvaxxed population is counted by subtracting the supposed vsxxed population (which is always overestimated) from total population measurments (which are always underestimated).
I phrased my ONS characterization lazily - the ONS excludes people who are not in GPES + HES + 2021 census. And then that's layered on NIMS+extract for vaccination, but the point is that they have chopped off "people" (or rather names and numbers) that are less likely to be in NIMS/e, so NIMS/e is going to be very accurate. So then what? Well, to turn this exclusion into a complaint, as the ONS Critics do, is bad faith. Likewise, 'gotcha'-ing the ONS for not, I don't know what, making the unvaccinated register actively (so they can live in prison camps next presumably? that would go over well) and instead using 'no vaccine in NIMS' for unvaccinated is contrary to what is realistic (imagine the insane bias that 'registered as unvaccinated' would introduce). It's just honestly the most robust approach possible, gives you good denominators and adds same biases to both sides (biased to be more alike because in GPES etc.), and I feel the ONS Critics are like angry drunks about it.
CDC
*comments on CDC methods removed due to errors, see below*
Brian, have you ever actually looked at official raw "ever vaccinated" rates for the senior citizens in any US locality that publishes these data?
Hint: These published rates are routinely over 100%. In fact, whole communities routinely report adult vaccination rates of over 100%.
This is why the CDC had to come up with its arbitrary "95%+" cut off. You know, because all those over 100% rates were sort of giving away the fact that they are overestimating the vaccinated population.
Yes - you are right. The CDC is just using the state's number for registered vaccinated even in the published studies. Individual status is still cross-referenced to state immunization registries (not depending on whatever hospital enters) but not the vaxxed denominator. "Age-specific vaccine administration data were used for incidence rate denominators; numbers of unvaccinated persons were estimated by subtracting the numbers of fully and partially vaccinated persons from 2019 U.S. intercensal population estimates." https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e2.htm - I am going to delete my previous characterization since it is inaccurate.
This is an important nuance and may explain why CDC rates disagree with other US studies (and may also be the easiest way for states to have thrown the CDC's numbers in pro-vax direction).
In regards to the main point about counting unknown as unvaccinated, this still wouldn't be a problem if the vaccinated denominator were "accurate" (pinned to individual records so names can be cross-matched to reported cases). So the problem is this bad denominator.
Likewise, the ONS data is still best approach to these aspects.
For vaxxed morbidity, hospitalization, and case rates:
(incidences of known vaxxed status only) / (locally reported overestimates of the vaxxed population based on local reports of the numbers of vaccines distributed)
Note that all local jurisdictions have every incentive to overreport the numbers of vaccines distributed since this is the way public health is DEFINED as far US medical establishment is concerned.
So all vaxxed of unknown status are subtracted from the numerator, which significantly shrinks the numerator AND all vaxxed populations are overstated (because vaccine distribution spoilage and free vaccine distribution to undocumented people are counted).
For unvaxxed morbidity, hospitalization, and case rates:
(incidences of known unvaxxed status plus all incidences of unknown vaxxed status) / (total population underestimates minus locally reported overestimates of the vaxxed population based on local reports of the numbers of vaccines distributed)
So all vaxxed of unknown status are added to the unvaxxed numerator, which significantly increases the numerator AND all unvaxxed populations are understated (because the population estimates are always too low AND the number of vaccinated individuals that are subtracted away are always too high)
Note this is only way that the CDC can produce its absurd MMWR statistics, such as unvaccinated were more 2.7 times more likely to test positive for COVID than the 2 dose only vaccinated from June 26th to September 27 of 2022: https://www.cdc.gov/mmwr/volumes/72/wr/pdfs/mm7206a3-h.pdf
We all know that these totally ginned case rate statistics are contrary to all other real world data even if only because (as you have mentioned several times) the young, healthy, and active unvaccinated were more likely to have developed recent and highly protective natural immunity than the 2 dose vaccinated by that point.
So basically, you have to throw out all the CDC data when it comes to estimating vaccinated anything vs. unvaccinated anything. They tried so hard to gin up their numbers that the their numbers disprove themselves.
Great article, I applaud the detail level of research that has gone into it.
However. I feel that like many others unfortunately you've fallen into a very common trap, and been tricked into accepting the narrow retrogade perspective of the vaccine fanatical public heath experts, allowing them to set the terms of debate accordingly.
I personally would rate the vaxx as only having a modest and very temporary efficacy. Why? Because real severe efficacy for Covid outcomes comes from having a young healthy immune system, and consider this; the durability of such an immune system is acceptable for around 50 /years/. And it requires no expensive experimental medical intervention with potentially disasterous side effects either.
How can this fraud have been perpetuated successfully on so many people, some of very considerable intelligence? But it's interesting that much of the vaccine critical side has fallen squarely for this trick into merely opposing the assertions of the vaccine fanatics, and so implicitly accepting their terms.
The thing I dispute is that there is an infection delaying effect. This is based purely on my observation of family and friends and people I know on Facebook who I know got infected ( with omicron or since) within 2-4 weeks of their booster. And I did pay attention to the timing with a certain amount of schadenfreude! Because I’m allergic to homework I can’t back up my argument with anything other than anecdotes!
Have you read Clare Craig’s excellent book Expired? In it she points out that each wave was roughly similar and that only about 15% ( think I’ve remembered that number correctly) of the population is susceptible in each wave. This then could explain what might be described as ‘delaying infection’ ability. She has a sequel due called something like ‘ A Covid Shot in the Dark’ about the vaccines which I’m very much looking forward to.
When it comes to boosters, say you have some number who get infected afterward due to either innate immune suppression or encounter with virus at vaccine clinic, it can still be true that the rate of infection for people getting boosters overall is lower in the day of and after than for previously infected people who don't get boosters. In fact this is what was found in both of the Bar-On, et al. booster studies in Israel. So for any anecdotal observations you have to ask "what is going on with the vaccinated but not boosted"? If the answer is that they are having more infections, then the result is still a delaying effect. This is especially important when it comes to the Omicron 2021/22 first wave, which was a bloodbath. I have my own anecdote of a relation with a post-booster infection, but at the time I wasn't personally seeing how things played out with anyone vaxxed who didn't get a booster.
And this is how you wind up in mid-2022 where only people who got boosters are lacking natural immunity and now, *going forward*, they are almost all the infected (famous Cleveland Clinic K-M graphs). This is what shows in a big seroprevelance survey from Nash, et al. http://medrxiv.org/cgi/content/short/2023.09.29.23296142
I would put the number infected in most waves in lockdown countries lower than 15%. And I don't think it's supportable to posit that lockdowns did not lower the AR of most waves. So for England, first wave 5%, double-second-wave (autumn + Alpha) 15% https://unglossed.substack.com/i/76838100/the-results Whereas in developing countries seroprevelance suggests everyone was already infected by 2020. So lockdowns matter, and one can only guess at what the natural threshold for waves would have been. My guess is something like a .4 decay of susceptibles for each wave in suburban geos, with a lot of passive immunity that protects in successive waves (e.g. abortive infection boosts innate antiviral immunity kind of thing).
I heard an ad on the radio this morning saying to get your "updated" c-vid shot if it has been more than two months since your last one or since your last infection. Six shots a year seems to indicate even they doubt the effectiveness.
I do appreciate your open-minded analysis. Given the tyranny exposed during the last few years it can be difficult to concede any point. Especially when the lies ("the vaccinated become a dead-end for the virus") are so obvious and result in such terrible policies.
As far as I can tell the ads are just psychological experimentation on retail employees at this point. But that's also what I thought about Toto songs when I used to work at Lowes
I will ask you this question instead of looking it up because I imagine you have the answer at the tip of your tongue: there was always a question for me of how the unvaccinated were counted. For example, people who received one dose of a two-dose series were counted as unvaccinated I think until 2 weeks after their second dose. Also as I recall, Israel was counting the unboosted as unvaccinated at some point during the first two covid years. Both are absurd, and do the vaccine efficacy studies/data count the unvaccinated these ways?
The topic can only be addressed case by case. But this is why I go back to the Pfizer trial over and over. The numbers are there for what happens counting from the first dose. In fact the recent review of the vaccines by Mead, Seneff, et al. *complains* about the fact that in the trials, the vaccines start to work a few days after the first dose. But this complaint makes no sense - the whole point of the immune system is to kick in in time to stop a virus from blowing up all your cells. It is only a minority of people who are "slow on the uptake." The reason vaccines have multiple doses and are "graded" on a delay is so this minority of slow responders doesn't muddy up the numbers. But the numbers with slow-responders included will still beat placebo / unvaccinated because most people kick out antibodies very rapidly.
Later real-world US studies, typically, "partially" vaccinated are not counted as unvaccinated but just excluded to keep comparisons tidy. UK ONS data, has been viciously attacked over delay windows, but there are empirical ways to show that everyone is being tracked in the right category and there are no delays (this is more for all-cause deaths). Israel, lots of reclassification on the dashboard, presumably the partially vaccinated were just censored after boosters because "partially vaccinated" now was a term for "not boosted." You take all these different cases together, there are no big changes to the difference between "fully" and "unvaccinated," then the treatment of "partially" doesn't drive the difference and isn't important.
Great, thanks for this explanation Brian. So if I'm understanding you correctly, you are working with Pfizer's raw data, is that correct? Because as I recall Pfizer counted people as unvaccinated until 2 weeks after their second dose.
Right, and results from immediately after dose 1 are separately reported in the clinical overview*. And when you see the graph of cases with the vaccinated going horizontal (very few new cases) at day 11, that's from dose 1. So you can measure efficacy from dose 1 and this just makes sense holistically in terms of "benefit" (thought the benefit only applies to the minority who would have a severe disease), and further, it should remembered that using/requiring two doses wasn't based on real data to begin with, just "winging it."
Thank you, I appreciate you helping me understand. I've been open to there being some efficacy, but there's been so many uncertain variables that are beyond my capability to get clear on. I downloaded the study link to get clearer on what you said above - that the number of cases went horizontal after taking the injection.
So the next obvious question is what is counted as a case and how that is measured. This will tell us what the injection had efficacy against. Pfizer counted a case if a person had one symptom of cold or flu, and a positive RT-PCR test (or NAAT test). More detailed information on the RT PCR test seems to be in module 2.7.1, which I don't see in this document, maybe I missed it. Have you written a stack on how RT-PCR was used in the Pfizer trial, and if so will you link it?
I wonder what RT-PCR tests was actually testing for in the Pfizer trial - what genetic sequences they were testing for, how many different genetic sequences they tested for, and the cycle threshold used. This will tell us if this injection had efficacy against infections from the common colds and flus - for example did one or more of the genetic sequences they tested for also belong to background coronaviruses? It's my understanding that the sequence of SARS Cov2 itself was not found in anyone in its entirety, and came from several different genetic strands found in people that were then strung together based on their percentage similarity to known pathogens. This in itself points to the possibility (probability?) that one or more of these genetic strands were already a part of a background swarm (background swarm of all the genetic material that can stimulate people's immune systems and cause symptoms). And we know about cycle thresholds; if they were too high they're bound to find common genetic material that is commonly within people's respiratory tracts.
I wonder if simply stimulating a person's immune system in any way would offer some protection against various pathogens and symptoms of infections, colds and flus...
From the Pfizer study:
"2.5.4.1.1.3. COVID-19 Case Determination
Participants who developed any potential COVID-19 symptoms listed in the protocol were to contact the site immediately and if confirmed to participate in an in-person or telehealth visit as soon as possible (optimally within 3 days of symptom onset, and at the latest 4 days after symptom resolution). At the visit (or prior to the visit, if a participant utilized a self-swab as permitted per protocol), investigators were to collect clinical information and results from local standard-of-care tests sufficient to confirm a COVID-19 diagnosis. Investigators were to obtain a nasal swab (mid-turbinate) for testing at a central laboratory using a validated reverse transcription–polymerase chain reaction (RT-PCR) test (Cepheid; EUA200047/A001) to detect SARS-CoV-2. If the evaluation was conducted by telehealth, the participant was to self-collect a nasal swab and ship for assessment at the central laboratory. A local nucleic acid amplification test (NAAT) result was only acceptable if it met protocol-specified criteria and if a central laboratory result was not available, in which case a local NAAT result could be used if obtained using one of the following assays:
Cepheid Xpert Xpress SARS-CoV-2
Roche cobas SARS-CoV-2 real-time RT-PCR test (EUA200009/A001)
Evidence of prior SARS-CoV-2 infection were determined by virological testing via NAAT
on mid-turbinate swab and serological testing for SARS-CoV-2 N-binding antibodies.
**2.5.2.3. Bioanalytical and Analytical Methods Used in Human Studies
Information on assays used to assess SARS-CoV-2 infection and immune response is in
Module 2.7.1. Only validated (PCR and neutralization immunoassay) or qualified (Luminex
immunoassay) methods were used.
*Case Definitions
COVID-19 cases (defined per FDA guidance)17 were based on SARS-CoV-2 positive test
result per central laboratory or local testing facility (using an acceptable test per protocol and if no central laboratory result was available) and presence of at least 1 of the following:
Fever
New or increased cough
New or increased shortness of breath
Chills
New or increased muscle pain
New loss of taste or smell
Sore throat
Diarrhea
Vomiting
CDC criteria-defined COVID-19 cases could include the following additional symptoms:
Swabs were mostly shipped by trial vendors to the central lab which used one brand, otherwise accepted if certain other types used.
This idea that PCR is not accurate, that it corresponds to random whatever, I have addressed it before.
1 If corresponds to "random" genes, then swabbing humans is going to throw a billion "human genes" at the primers. There is no reason that "inaccurate" (non-specific) primers would "trip up" on other random viruses *preferentially* compared to "tripping up" on human genes. To do so would imply that 1) viruses as a category have some patent on whatever random sequence of nucleotides (e.g. human genes can't go "cgatgat" or whatever, only other viruses can have this sequence) 2) PCR can selectively detect this sequence (when other viruses with the special patent are around) 3) PCR is so inaccurate, so trash, it actually can't tell *any* sequences apart. So that's incoherent.
2 When validating SARS-CoV-2 PCR kits, companies run the kits on samples of other viruses e.g. flu, adenovirus, other coronaviruses, etc., and show that they come up negative.
3 Mechanistic arguments aren't how we decide if things work. You get in your car, turn it on, it goes to the grocery store. Do you actually care about the nuts and bolts? No. For PCR, when you follow people with a previous PCR+ compared to people without, you see who gets another PCR+ afterward - the people with previous PCR+ get another PCR+ at 0-10% of the rate of those without. If PCR is just going + for "random" infections, then people with a previous + should be getting other +'s more often, just based on the fact that they have shown they test, they have shown they get sick. But instead they are *immune* to future PCR+. No one claiming "PCR is not accurate" can provide an explanation for this. It would require magic. The explanation is that PCR is accurate - the first plus corresponded to a specific virus, now the person who got that plus is 1/10 to 1/20 as likely to get another one, because they have immunity to that same specific virus. Just can't happen if PCR+ is for "any infection."
Thanks for your thorough response, I need a little repetition with these things to understand them. It's a little late for me and I think I'm going to have to re-read it tomorrow to get all I can from your response. I appreciate the back and forth.
Even in my tired state, while brushing my teeth, something came to mind that I think s is a valid consideration and question - how many RT-PCRs were positive without symptoms? We don't know this from the Pfizer trials because they only tested people who had symptoms. But say if you knew that 30% of PCR positives in a population were asymptomatic, would your above points hold?
(I'm not saying that 30% of all positive tests were asymptomatic, but I understand that there were asymptomatic PCR positives. I remember Matt making the point that we don't know the false positive or false negative rates of the SARS Cov2 tests).
"So, I believe the Covid vaccines work (reduce severe disease even in the case of having an infection), but should not be used or promoted to the lay public. "
This is pretty alarming position for someone who is as deeply researched in the science and scientific fraud that causes so many folks to err on the side of misplaced trust. You're astute when it comes to false claims and fudged evidence so it boggles the mind that you see these mRNA transfections as remotely possible of producing an effective immune response to an airborne virus that requires T-cell response as an opener. Beyond that human bodies are acutely responsive to foreign proteins and transfected novel proteins & LNP have a long history of uncontrolled distribution in the body and triggering autoimmune disease. Holy Hell mate the entire transfection platform is a pipe dream of genetic knowledge & powers that do not exist. Watch Jay Couey's review of Cullis in the Nobel Prize pre-event tour.. this crap failed as cancer drug delivery!
They produce antibodies and T Cells. "Are these [antibodies and T Cells] helpful for severe disease" is a separate question from what is or isn't the "opener," since severe disease is an outcome of infection that takes some time to develop.
The mRNA transfections can't activate T-Cells and even their cartoon would theoretically activate B-cells that gets into science with antigen presenting functions that are way beyond my ability to explain. Bottom line is there is decades of failed science showing these transfections have random distribution, irritate the immune system & trigger a host of adverse events there is no science showing a mechanism of action for transfection to confer immunity only trigger autoimmune reactions.
As for the antibody measures as a correlate of immunity in any study my own suspicion is that the relationship is more hype that sound scientific theory because antibodies are cited as evidence of immunity except with HIV where antibodies are used as evidence of illness.
Reality is they put significance on the things they can measure and build their own reality around it! Thanks for the reply!! :~)
The immune system's fundamental role is to differentiate between self and non-self. Mighty T-cells are on constant patrol of the outside barriers of the body for all sorts of opportunistic invaders. Even in the case of a perfectly matched donor for a transplant with identical twins some level of auto-immune response occurs because Mother Nature makes each and every life form a little unique and our ability as humans to see similarity even at DNA levels is Fred Flintstone technology compared to the complexity that is the human body and its defense mechanisms. Why would an unnatural protein that could never be introduced to the body without first encountering the innate immune system do anything but wreak havoc inside the body? The whole idea of transfecting healthy humans is criminal...
Watch Jay's Cullis video I linked & be sure to listen for the part where Cullis says less than 0.01% of chemo drugs reach the intended target which is criminal and mRNA was hoped to improve that but they could never figure out how to control the distribution. The few trials of repairing genetic disease caused death w the "perfect DNA" rejected as Non-Self & destroying all the "repaired cells." Jesse Gelsinger is the poster boy for the failures.. the science didn't change just the PR and pesky problems of strict liability & informed consent.
Self / non-self recognition is regulated by T Cell negative selection (presumably T Cells go on to impose this regulation on B Cells in germinal centers). It is a question of "hey T Cell do you want to attack this self antigen?" not of "is antigen 'outside' or 'inside'", did antigen "first encounter innate immune system or not." It's all happening in the thymus and has nothing to do with foreign protein localization.
If vaccines cause autoimmunity, the question is whether they rendered self-antigens more "interesting" to APC or T Cells. Maybe, sure. And maybe Covid vaccines do this. But on the other hand SARS-CoV-2 packs more antigens (a baker's dozen of proteins). So there's no clear answer as far as which is worse for autoimmune hazard. Ultimately, most people are fine and the people who aren't, well, "tough luck."
Either way the default response to a foreign protein - why would it be detrimental? I don't keep my front door open at night. If the immune system sees an open door, it closes it (makes antibodies). This is generally good.
Unfortunately I can't explain why this is wrong aside from noting that mRNA transfections bear no similarity to traditional vaccines. I will ask Jay Couey if he can answer this, but based on your response it seems you did not watch Jay's video with Cullis because there is a clear delineation of transfection process with explanation why triggering the immune system is not creating immunity & difference from vaccine theory of exposing the system to an attenuated natural virus. What I know for sure is immunity is not produced the way you explain it here and abso-frigging-lutely not what mRNA transfection process is doing.
There's so much to learn I'm not sure where the truth is but one thing is for dang sure the virology field is like all the gmo science with lots of assumptions and models and "substantial equivalents" plucked from thin air and gaps as wide as the Grand Canyon in actual knowledge or evidence. It's like all the industrial chemicals where absence of evidence of harm is assumed to be safe and nobody looks for harms! Welcome to kleptocracy!
**Fun fact UC Davis pulled the database offline in 2020 Wayback has it!
Basic Testing to Identify Chemical Hazards
If an industrial chemical is allowed by law to be released into the environment, most people assume that it must have been tested and evaluated for its potential risks. Unfortunately, this is simply not true. Keeping chemical hazards under control requires information about what kinds of hazards each chemical poses. If the basic tests to check on a chemical's toxicity haven't been conducted, or if the results aren't publicly available, current laws tend to treat that chemical as if it were perfectly safe.
Information Needed for Safety Assessment
Could government assess a chemical's safety or risk? For most of the important industrial chemicals in U.S. commerce, government lacks the information to draw any scientifically based conclusion about the degree of risk--or lack of risk--that a chemical may pose when used. For every chemical in the database, Scorecard tells you whether or not the information needed to assess chemical risk is available. If it isn't, no one can accurately claim the chemical is "safe." https://web.archive.org/web/20120917041002/http://scorecard.goodguide.com/chemical-profiles/chems-profile-descriptions.tcl#basic_testing
Agree completely we had ordinary illness & extraordinary propaganda with protocol changes that predictably increased deaths & transferred wealth upward & rolled back rights.
Thanks; thought-provoking. I enjoyed reading your challenging content - hope this somehow makes up for you not enjoying writing this particular piece!!
Have now read. A lot of "shiny object" arguments (to use the phrase for the legal strategy of spinning a jury's head around).
There are interesting anomalies about the first spring wave, sure, but this shouldn't be treated as such a big problem - same thing happened with flu in 1918 and 1957, I have tried to make this point many times https://unglossed.substack.com/i/136573678/the-epidemiology - So do these interesting anomalies correspond to some kind of "op" to juice up pandemic fear? Maybe - but maybe they just happened because this is how it often goes with new (or reintroduced) viruses.
The "secret magic vaccinated sick people" trope does not apply in the examples used here. It typically isn't relevant in US data sources either, though the case by case impact may vary. But if vaccination status is missing, then the vaccinated are in the uninfected denominator until infected / hospitalized (when they move to the numerator). This because typically, the compiling of status in published research doesn't rely on whatever hospitals write down. The researchers have a "master" list of vaccinated.
So if the hospitalization rate is lower in the vaccinated on the "master" list, then any vaccinated who are missing vaccination status are in fact lowering the hospitalization rate in the unvaccinated as a whole, diluting and not "cooking" efficacy.
I don’t doubt the efficacy in older adults. I do appreciate your input and well written post.
It is the negative efficacy in younger adults and the SAEs that people died from. I’ve had over $500k in medical bills with injury to my heart lung and eyes. I will not take another vaccine again.
Here’s the nutshell. Let’s see what you think.
Covid was already widespread before they started testing for it and definitely before they declared it a pandemic. Therefore, how dangerous it was is already measurable to an extent before they declared the pandemic. Which means at the noise level of excess mortality. Therefore, the fatality rate induced afterwards must be a product of reaction. Reaction is a broad term both full of volition and involuntary. They locked people into old homes and caused death in the vulnerable (80+ without proper treatment). Everyone else suffered from intentionally fraudulent treatment which spiked excess death. Remdesivir, lack of antibiotics, prophylactic antivirals. Iatrogenic harm and just maliciousness comes to mind.
Here’s the nutshell. Let’s see what you think.
Covid was already widespread before they started testing for it and definitely before they declared it a pandemic. Therefore, how dangerous it was is already measurable to an extent before they declared the pandemic. Which means at the noise level of excess mortality. Therefore, the fatality rate induced afterwards must be a product of reaction. Reaction is a broad term both full of volition and involuntary. They locked people into old homes and caused death in the vulnerable (80+ without proper treatment). Everyone else suffered from intentionally fraudulent treatment which spiked excess death. Remdesivir, lack of antibiotics, prophylactic antivirals. Iatrogenic harm and just maliciousness comes to mind.
Here’s the nutshell. Let’s see what you think.
Covid was already widespread before they started testing for it and definitely before they declared it a pandemic. Therefore, how dangerous it was is already measurable to an extent before they declared the pandemic. Which means at the noise level of excess mortality. Therefore, the fatality rate induced afterwards must be a product of reaction. Reaction is a broad term both full of volition and involuntary. They locked people into old homes and caused death in the vulnerable (80+ without proper treatment). Everyone else suffered from intentionally fraudulent treatment which spiked excess death. Remdesivir, lack of antibiotics, prophylactic antivirals. Iatrogenic harm and just maliciousness comes to mind.
You have not made it until you have made it onto this guy's list:
https://sars2.net/nopandemic.html
Wow - the amount of patience and diligence it would take to produce this! I guess I am happy to be mentioned as a counter-example, though none of my arguments seem to be acknowledged directly.
I reread this stack after our back and forth about the PCR test (which makes me more confident accepting the number of infections).
So in making your case for severe efficacy, you talk about relative severe efficacy, and not absolute severe efficacy. What is the absolute severe efficacy in the raw numbers of the Pfizer trial?
Here's my take on the Pfizer trial numbers you cited:
Pfizer's trial probably has the most accurate numbers vaccinated versus unvaccinated people, which was 21,653 vaccinated, and 21,672 unvaccinated.
So in your first argument for severe efficacy citing Pfizer's trial, there were 2 vaccinated hospitalizations out of 21,653 people, and at most 83 unvaccinated hospitalizations out of 21,672 people (I say at most because the table you cited started counting 7 days post injection, but let's take that at face value).
So the risk of severe disease for the unvaccinated is .04%, and the risk of severe disease for the vaccinated is .009%.
So in absolute terms, the severe efficacy you refer to can't be more than the risk of severe disease, isn't that correct? So the severe efficacy is less than .04%, right? We're talking about a tiny, teensy, eency weency absolute severe efficacy, are we not?
When the absolute severe efficacy is so small, you can't say "the (transfection-vaccine) measures are effective but we shouldn't use them" when you're not taking into account adverse effects from the transfection.
Do you find an error in my thought process here?
There are many signs that the injury rate from this transfection is higher than the absolute severe efficacy of less than .04%:
Germany - The Paul Ehrlich Institute (PEI) studied suspected cases of adverse reactions that happened soon after the vaccine was injected. They found the side effect rate is one in every 500 vaccines given. A report and a sub stack article on this report:
https://coronakrise-europa.net/en/2022/02/21/safety-report-paul-ehrlich-institute-february-2022/
https://nakedemperor.substack.com/p/german-ministry-of-health-one-in?utm_source=%2Fprofile%2F45856071-ne-nakedemperorsubstackcom&utm_medium=reader2
German health insurance fund found that almost half a million people received medical treatment for side effects, out of 11 million people vaccinated, which is a 1 in 25 ratio, or 4%.
https://rairfoundation.com/germanys-largest-health-insurer-reveals-1-in-25-clients-underwent-medical-treatment-in-2021-for-covid-vaccine-side-effects/
-The V safe program, a CDC smartphone app monitoring millions of people who took the covid vaccine injection, shows the rate of side effects requiring medical attention is 7.7%.
https://www.icandecide.org/ican_press/breaking-news-ican-obtains-cdc-v-safe-data/
https://jessica5b3.substack.com/p/v-safe-data-release
Assessment of Pfizer and Moderna's trial data show serious side effects 1 for every 800 injections with the data these companies have been willing to release so far. Teams of healthcare professionals and scientists have been closely assessing the trial data after a court ordered this data released for peer review. They have found that only a fraction of serious side effects were clearly accounted for because serious side effects were not accurately classified, people with serious side effects were removed from the trial, or side effects were hidden deeply in tables and data.
Pfizer and Moderna's trials show a greater risk of serious side effects injuries than people's risk of hospitalization and death from covid. Here is that paper, and two substack articles on this paper:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4125239#
https://nakedemperor.substack.com/p/editor-of-the-british-medical-journal-6d1
Here is an open letter to Pfizer and Moderna CEOs from the British medical journal asking for the raw patient data so risks can be age stratified: “The results showed the Pfizer and Moderna both exhibited an absolute risk increase of serious adverse events of special interest (combined, 1 per 800 vaccinated), raising concerns that mRNA vaccines are associated with more harm than initially estimated at the time of emergency authorization."
https://www.bmj.com/content/378/bmj.o1731/rr-0
If evaluating risk/benefit based on the trial, then one way or another you need a model for the benefit going forward after the unblinding / cutoff. The crudest model would be "(unvax hospitalized - (same * v/uv/p-inf)) / (reported unvax infections / all unvax)," which makes the assumption that 100% of unvaxxed will eventually be infected, the severe outcome rate per infection doesn't change, and the per-infection prevention of severe outcomes by the vax doesn't change.
You could make different assumptions, come up with different models based on those assumptions. But to say there is a net negative based on the trial (overall or for specific risk group) isn't accurate, because the benefit keeps applying after the cutoff, because the virus doesn't go away, it's still out there, it's still going to hospitalize/kill some people, the vaccine is either 1) still going to reduce those hospitalizations/deaths or 2) stop doing this (you, the maker of the model, are deciding which one, if you decide a hard 2 then that's just another assumption).
Under the crudest model outlined above, using post dose-2 which understates prevented hospitalizations anyway, (21 - (21*0))/(873/22320) = 536 prevented hospitalizations (In plain language, with 4% of people infected there were ~21 prevented hospitalizations, so 100% is ~536 prevented). This is absolute reduction of 2% or 1 out of 50 people. Of course, this model overstates things and has ugly math (though if Delta had stayed around forever, this same model could have been an understatement), and doesn't make any sense from the perspective of individual risk for younger or older people, it's crude as said.
Thanks so much for your response.
In your response, you're only considering the risks of 1) not taking the transfection vaccine and 2) the benefit of taking it in your risk benefit calculation.
FYI - I do believe your calculations that when using the model that you've put forth and that Pfizer used, taking the vaccine- transfection has prevented hospitalizations. So I'm not arguing that, I'm not arguing that there is a net negative from the Pfizer trial based on their model used.
I'm arguing the conclusion that you've drawn, specifically "this intervention is helpful but we shouldn't use it or mandate it.". Your statement applies to the real world, but you are basing your statement on a model that doesn't apply to the real world. This is an error.
In the real world, whenever people, doctors consider risk/ benefit of pharmaceutical products, they ALWAYS include the health risks from the product itself.
You aren't including the hospitalization risk from the transfection- vaccine. I know that Pfizer didn't include that risk either. Thus no one can conclude that the product is a net positive from the Pfizer trial itself.
It is known that there have been a significant number of hospitalizations and deaths from the product. We don't know the rate from the Pfizer trial. We do know pharmaceutical trials commonly hide adverse effects from their products, and the people seriously injured in Pfizer's trial have said their injuries weren't documented correctly. So to make a net positive or net negative evaluation of the product, we need to include information outside the trial.
We do have some evidence of the % risk from product. The V safe data puts it at 7%, the German health fund found 4%, PEI at .2%, others have said 1/800 or .1%.
If understand correctly, your 2% absolute efficacy was calculated by extending your calculations beyond the trial, so we would need to look at hospitalizations and deaths from the transfection- vaccine beyond the trial as well. Is the risk of hospitalization and death of the transfection vaccine greater than 2%? We really don't know, but since some data shows it's a possibility, the only rational thing we can say about the net positive or negative effect of this product in the real world is that it's "inconclusive". Wouldn't you agree?
Ultimately my position is that to critique the Covid vaccine on the grounds of risk/benefit, you *have* to concede that a positive risk/benefit means "Great! let's go crazy-nuts on mRNA vaccines against viruses that didn't exist two seconds ago, *if positive risk/benefit*." I reject this.
Thanks for all your responses, I've enjoyed looking through everyone's comments and reading your responses.
I didn't make this point because I didn't have hard evidence. But I knew there were reported discrepancies with how the hospitals and the CDC were counting people as vaccinated or unvaccinated. There were reports by healthcare workers that vaccinated people we're being counted as "unknown vaccination status", which were being counted as unvaccinated, in their electronic records. The electronic records didn't seem to have and unvaccinated category.
The highwire just did a pretty convincing segment on this from a study done. Apparently the study's authors wanted to do outreach to unvaccinated people, so delved into the electronic records and found 44% of those counted as "unknown vaccination status" (unvaccinated) we're actually vaccinated. The study was done in University healthcare systems in two states, so I'm not sure how widely it applies to other states. But along with Rochelle wollensky's sent a testimony that they never did get a count of covid vaccinated versus unvaccinated hospitalizations, she was admitting there was a problem. I put the link below, it's a 14 minute video. Of course this doesn't affect the Pfizer trial that you and I have been talking about, but it sure could affect the other data you referred to.
https://rumble.com/v4azk2r-cdc-in-hot-seat-over-skewed-covid-data.html
Here's the study, unfortunately it doesn't have a lot of details.
https://www.researchgate.net/figure/Vaccine-status-breakdown-before-and-after-manual-query-of-IIS_tbl2_370011210
Thank you for the link. As for the CDC, I don't refer to them in this post, but they have come up in other comments. They and other, independent US studies don't use hospital records (EHR) for vaccination status, just for hospitalization. Then the names are cross-referenced to the state or local immunization registry. But, there is still a problem here because the immunization registries have a topline value for how many people were vaccinated and these values seem to be too high in a lot of places, e.g. King Co. Washington (Seattle) where the numbers where over 95% and they had to cap it there.
Point being, there is a lot lot lot of "gotcha"-ing about this health record stuff but it isn't relevant to anything.
"They and other, independent US studies don't use hospital records (EHR) for vaccination status, just for hospitalization. Then the names are cross-referenced to the state or local immunization registry." Good to know. 👍🏽
You should check out this recent article by Pierre Kory: https://open.substack.com/pub/pierrekory/p/proof-the-cdc-knowingly-lied-when?r=clqze&utm_medium=ios&utm_campaign=post
I'll read it. But most US studies outside of the CDC, and some by the CDC, they aren't pulling vaccination status from hospital records. It's whatever's in the healthcare network that the study is based on. (So vax status and "hospitalized for Covid" are both in this bigger set of data.)
Brian, while I agree that mRNA vaccines prevented severe outcomes from the Alpha and Delta variants, the data are far less clear since omicron became the dominant variant more than 2 years ago. Both US CDC and UK ONS data skew their results by underestimating unvaccinated populations and counting all cases, hospitalizations and deaths of unknown vaccine status against the unvaccinated.
When Australian data was available, it showed far more vaccinated hospitalizations: https://app.powerbi.com/view?r=eyJrIjoiOTczMTkxZTYtNGIwMS00Y2I0LTkxMTItYzQ1OThjZmNhN2Y0IiwidCI6ImFjNDA2NDcyLWRhNjgtNGQ5ZC04NmU4LTkyMjM1ZDBhNjI3NiJ9
Is it a coincidence that all the best data supporting the efficacy of these injections against hospitalization stopped being published roughly 2 years ago?
This is why I say at a certain point there is no more (uninfected) unvaccinated control group - you can't measure severe efficacy because you have no yardstick (if you could teleport a million unvaccinated, uninfected adults back in from the void, maybe...). In this post I say after 2021 but really it's after spring, 2022. You can see this in the NSW dashboard, unvaccinated are still hospitalized at highest rate in May, 2022 and then total drop to zero. This is same time as transition in US to "Cleveland Clinic booster study era" where only the boosted are having first-time infections anymore. A couple studies by Nash et al. back up this idea (that USA boosted actually had largely put off first-time infections before June) - http://medrxiv.org/cgi/content/short/2023.09.29.23296142 fig 3 showing seroconversion up to March-June 2022 samples, note that 2-dosed are almost level with unvaxxed so this is nothing to do with the 'can't make N antibodies myth'.
The "CDC skew" - counting unknown as unvaxxed - can only dilute measured efficacy (or negative efficacy, if that were the case). Whatever the difference between vaxxed and unvaxxed are, having vaxxed counted as unvaxxed dilutes the difference, cannot create/exaggerate the difference (unless there is some bias in who has missing status, this isn't particularly likely since people less prone to be measured by whatever system are often less prone to get vaccine). *edit: CDC vaccinated denominators are the problem, this is distinct from case vax status classification but still a huge issue, see below.
The ONS doesn't have this problem. They exclude people they don't know about. And then the ONS critics kvetch about it, even though it makes the data better (and if more biased, only more biased for "people who are more similar to each other in terms of meeting ONS requirements" which is good).
OK, I don't quite understand your argument about the CDC skew. You do understand that unknown cases, hospitalizations, and deaths are counted as unvaccinated, but that this does not mean that individuals of unknown status are counted as unvaccinated. Right? In other words, the CDC moves the vaxxed numerators to unvaxxed, but that this in no way affects the denominators, which have their own issues in that the unvaxxed population is counted by subtracting the supposed vsxxed population (which is always overestimated) from total population measurments (which are always underestimated).
And the ONS does NOT exclude those of unknown status. It also counts them against the unvaccinated: https://twitter.com/USMortality/status/1720872486135534009#m
ONS
I phrased my ONS characterization lazily - the ONS excludes people who are not in GPES + HES + 2021 census. And then that's layered on NIMS+extract for vaccination, but the point is that they have chopped off "people" (or rather names and numbers) that are less likely to be in NIMS/e, so NIMS/e is going to be very accurate. So then what? Well, to turn this exclusion into a complaint, as the ONS Critics do, is bad faith. Likewise, 'gotcha'-ing the ONS for not, I don't know what, making the unvaccinated register actively (so they can live in prison camps next presumably? that would go over well) and instead using 'no vaccine in NIMS' for unvaccinated is contrary to what is realistic (imagine the insane bias that 'registered as unvaccinated' would introduce). It's just honestly the most robust approach possible, gives you good denominators and adds same biases to both sides (biased to be more alike because in GPES etc.), and I feel the ONS Critics are like angry drunks about it.
CDC
*comments on CDC methods removed due to errors, see below*
Brian, have you ever actually looked at official raw "ever vaccinated" rates for the senior citizens in any US locality that publishes these data?
Hint: These published rates are routinely over 100%. In fact, whole communities routinely report adult vaccination rates of over 100%.
This is why the CDC had to come up with its arbitrary "95%+" cut off. You know, because all those over 100% rates were sort of giving away the fact that they are overestimating the vaccinated population.
Yes - you are right. The CDC is just using the state's number for registered vaccinated even in the published studies. Individual status is still cross-referenced to state immunization registries (not depending on whatever hospital enters) but not the vaxxed denominator. "Age-specific vaccine administration data were used for incidence rate denominators; numbers of unvaccinated persons were estimated by subtracting the numbers of fully and partially vaccinated persons from 2019 U.S. intercensal population estimates." https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e2.htm - I am going to delete my previous characterization since it is inaccurate.
This is an important nuance and may explain why CDC rates disagree with other US studies (and may also be the easiest way for states to have thrown the CDC's numbers in pro-vax direction).
In regards to the main point about counting unknown as unvaccinated, this still wouldn't be a problem if the vaccinated denominator were "accurate" (pinned to individual records so names can be cross-matched to reported cases). So the problem is this bad denominator.
Likewise, the ONS data is still best approach to these aspects.
More about all the CDC's data chicanery: https://pierrekorymedicalmusings.com/p/proof-the-cdc-knowingly-lied-when
Not quite.
Here is how the CDC plays the game --
For vaxxed morbidity, hospitalization, and case rates:
(incidences of known vaxxed status only) / (locally reported overestimates of the vaxxed population based on local reports of the numbers of vaccines distributed)
Note that all local jurisdictions have every incentive to overreport the numbers of vaccines distributed since this is the way public health is DEFINED as far US medical establishment is concerned.
So all vaxxed of unknown status are subtracted from the numerator, which significantly shrinks the numerator AND all vaxxed populations are overstated (because vaccine distribution spoilage and free vaccine distribution to undocumented people are counted).
For unvaxxed morbidity, hospitalization, and case rates:
(incidences of known unvaxxed status plus all incidences of unknown vaxxed status) / (total population underestimates minus locally reported overestimates of the vaxxed population based on local reports of the numbers of vaccines distributed)
So all vaxxed of unknown status are added to the unvaxxed numerator, which significantly increases the numerator AND all unvaxxed populations are understated (because the population estimates are always too low AND the number of vaccinated individuals that are subtracted away are always too high)
You can confirm all of this right here: https://www.cdc.gov/mmwr/volumes/72/wr/mm7206a3.htm
Note this is only way that the CDC can produce its absurd MMWR statistics, such as unvaccinated were more 2.7 times more likely to test positive for COVID than the 2 dose only vaccinated from June 26th to September 27 of 2022: https://www.cdc.gov/mmwr/volumes/72/wr/pdfs/mm7206a3-h.pdf
We all know that these totally ginned case rate statistics are contrary to all other real world data even if only because (as you have mentioned several times) the young, healthy, and active unvaccinated were more likely to have developed recent and highly protective natural immunity than the 2 dose vaccinated by that point.
In contrast to the CDC, here is what the Walgreen's data were saying about vaccinated vs. unvaccinated case rates during September 2022: https://www.reddit.com/r/WayOfTheBern/comments/xquta3/newest_walgreens_data_again_show_that_mrna/
And here is what the UK-HSA data were saying about about boosted vs. unvaccinated case rates until they pulled the plug on these data in March 2022: https://www.reddit.com/media?url=https%3A%2F%2Fi.redd.it%2Foixjgdkliyq81.png
So basically, you have to throw out all the CDC data when it comes to estimating vaccinated anything vs. unvaccinated anything. They tried so hard to gin up their numbers that the their numbers disprove themselves.
Great article, I applaud the detail level of research that has gone into it.
However. I feel that like many others unfortunately you've fallen into a very common trap, and been tricked into accepting the narrow retrogade perspective of the vaccine fanatical public heath experts, allowing them to set the terms of debate accordingly.
I personally would rate the vaxx as only having a modest and very temporary efficacy. Why? Because real severe efficacy for Covid outcomes comes from having a young healthy immune system, and consider this; the durability of such an immune system is acceptable for around 50 /years/. And it requires no expensive experimental medical intervention with potentially disasterous side effects either.
How can this fraud have been perpetuated successfully on so many people, some of very considerable intelligence? But it's interesting that much of the vaccine critical side has fallen squarely for this trick into merely opposing the assertions of the vaccine fanatics, and so implicitly accepting their terms.
Thanks! As for falling into etc:, well… that sort of goes into why I never made a post on this topic to begin with, haha
A* for doing your homework!
The thing I dispute is that there is an infection delaying effect. This is based purely on my observation of family and friends and people I know on Facebook who I know got infected ( with omicron or since) within 2-4 weeks of their booster. And I did pay attention to the timing with a certain amount of schadenfreude! Because I’m allergic to homework I can’t back up my argument with anything other than anecdotes!
Have you read Clare Craig’s excellent book Expired? In it she points out that each wave was roughly similar and that only about 15% ( think I’ve remembered that number correctly) of the population is susceptible in each wave. This then could explain what might be described as ‘delaying infection’ ability. She has a sequel due called something like ‘ A Covid Shot in the Dark’ about the vaccines which I’m very much looking forward to.
When it comes to boosters, say you have some number who get infected afterward due to either innate immune suppression or encounter with virus at vaccine clinic, it can still be true that the rate of infection for people getting boosters overall is lower in the day of and after than for previously infected people who don't get boosters. In fact this is what was found in both of the Bar-On, et al. booster studies in Israel. So for any anecdotal observations you have to ask "what is going on with the vaccinated but not boosted"? If the answer is that they are having more infections, then the result is still a delaying effect. This is especially important when it comes to the Omicron 2021/22 first wave, which was a bloodbath. I have my own anecdote of a relation with a post-booster infection, but at the time I wasn't personally seeing how things played out with anyone vaxxed who didn't get a booster.
And this is how you wind up in mid-2022 where only people who got boosters are lacking natural immunity and now, *going forward*, they are almost all the infected (famous Cleveland Clinic K-M graphs). This is what shows in a big seroprevelance survey from Nash, et al. http://medrxiv.org/cgi/content/short/2023.09.29.23296142
I would put the number infected in most waves in lockdown countries lower than 15%. And I don't think it's supportable to posit that lockdowns did not lower the AR of most waves. So for England, first wave 5%, double-second-wave (autumn + Alpha) 15% https://unglossed.substack.com/i/76838100/the-results Whereas in developing countries seroprevelance suggests everyone was already infected by 2020. So lockdowns matter, and one can only guess at what the natural threshold for waves would have been. My guess is something like a .4 decay of susceptibles for each wave in suburban geos, with a lot of passive immunity that protects in successive waves (e.g. abortive infection boosts innate antiviral immunity kind of thing).
I heard an ad on the radio this morning saying to get your "updated" c-vid shot if it has been more than two months since your last one or since your last infection. Six shots a year seems to indicate even they doubt the effectiveness.
I do appreciate your open-minded analysis. Given the tyranny exposed during the last few years it can be difficult to concede any point. Especially when the lies ("the vaccinated become a dead-end for the virus") are so obvious and result in such terrible policies.
Please be well and be vigilant.
As far as I can tell the ads are just psychological experimentation on retail employees at this point. But that's also what I thought about Toto songs when I used to work at Lowes
😅 did Toto songs cause people to buy more home improvement products?
Thanks for rehashing this Brian. 👍🏽
I will ask you this question instead of looking it up because I imagine you have the answer at the tip of your tongue: there was always a question for me of how the unvaccinated were counted. For example, people who received one dose of a two-dose series were counted as unvaccinated I think until 2 weeks after their second dose. Also as I recall, Israel was counting the unboosted as unvaccinated at some point during the first two covid years. Both are absurd, and do the vaccine efficacy studies/data count the unvaccinated these ways?
The topic can only be addressed case by case. But this is why I go back to the Pfizer trial over and over. The numbers are there for what happens counting from the first dose. In fact the recent review of the vaccines by Mead, Seneff, et al. *complains* about the fact that in the trials, the vaccines start to work a few days after the first dose. But this complaint makes no sense - the whole point of the immune system is to kick in in time to stop a virus from blowing up all your cells. It is only a minority of people who are "slow on the uptake." The reason vaccines have multiple doses and are "graded" on a delay is so this minority of slow responders doesn't muddy up the numbers. But the numbers with slow-responders included will still beat placebo / unvaccinated because most people kick out antibodies very rapidly.
Later real-world US studies, typically, "partially" vaccinated are not counted as unvaccinated but just excluded to keep comparisons tidy. UK ONS data, has been viciously attacked over delay windows, but there are empirical ways to show that everyone is being tracked in the right category and there are no delays (this is more for all-cause deaths). Israel, lots of reclassification on the dashboard, presumably the partially vaccinated were just censored after boosters because "partially vaccinated" now was a term for "not boosted." You take all these different cases together, there are no big changes to the difference between "fully" and "unvaccinated," then the treatment of "partially" doesn't drive the difference and isn't important.
Great, thanks for this explanation Brian. So if I'm understanding you correctly, you are working with Pfizer's raw data, is that correct? Because as I recall Pfizer counted people as unvaccinated until 2 weeks after their second dose.
Right, and results from immediately after dose 1 are separately reported in the clinical overview*. And when you see the graph of cases with the vaccinated going horizontal (very few new cases) at day 11, that's from dose 1. So you can measure efficacy from dose 1 and this just makes sense holistically in terms of "benefit" (thought the benefit only applies to the minority who would have a severe disease), and further, it should remembered that using/requiring two doses wasn't based on real data to begin with, just "winging it."
*This is online at https://phmpt.org/wp-content/uploads/2021/12/STN-125742_0_0-Section-2.5-Clinical-Overview.pdf I missed the link in my post, have now added
Thank you, I appreciate you helping me understand. I've been open to there being some efficacy, but there's been so many uncertain variables that are beyond my capability to get clear on. I downloaded the study link to get clearer on what you said above - that the number of cases went horizontal after taking the injection.
So the next obvious question is what is counted as a case and how that is measured. This will tell us what the injection had efficacy against. Pfizer counted a case if a person had one symptom of cold or flu, and a positive RT-PCR test (or NAAT test). More detailed information on the RT PCR test seems to be in module 2.7.1, which I don't see in this document, maybe I missed it. Have you written a stack on how RT-PCR was used in the Pfizer trial, and if so will you link it?
I wonder what RT-PCR tests was actually testing for in the Pfizer trial - what genetic sequences they were testing for, how many different genetic sequences they tested for, and the cycle threshold used. This will tell us if this injection had efficacy against infections from the common colds and flus - for example did one or more of the genetic sequences they tested for also belong to background coronaviruses? It's my understanding that the sequence of SARS Cov2 itself was not found in anyone in its entirety, and came from several different genetic strands found in people that were then strung together based on their percentage similarity to known pathogens. This in itself points to the possibility (probability?) that one or more of these genetic strands were already a part of a background swarm (background swarm of all the genetic material that can stimulate people's immune systems and cause symptoms). And we know about cycle thresholds; if they were too high they're bound to find common genetic material that is commonly within people's respiratory tracts.
I wonder if simply stimulating a person's immune system in any way would offer some protection against various pathogens and symptoms of infections, colds and flus...
From the Pfizer study:
"2.5.4.1.1.3. COVID-19 Case Determination
Participants who developed any potential COVID-19 symptoms listed in the protocol were to contact the site immediately and if confirmed to participate in an in-person or telehealth visit as soon as possible (optimally within 3 days of symptom onset, and at the latest 4 days after symptom resolution). At the visit (or prior to the visit, if a participant utilized a self-swab as permitted per protocol), investigators were to collect clinical information and results from local standard-of-care tests sufficient to confirm a COVID-19 diagnosis. Investigators were to obtain a nasal swab (mid-turbinate) for testing at a central laboratory using a validated reverse transcription–polymerase chain reaction (RT-PCR) test (Cepheid; EUA200047/A001) to detect SARS-CoV-2. If the evaluation was conducted by telehealth, the participant was to self-collect a nasal swab and ship for assessment at the central laboratory. A local nucleic acid amplification test (NAAT) result was only acceptable if it met protocol-specified criteria and if a central laboratory result was not available, in which case a local NAAT result could be used if obtained using one of the following assays:
Cepheid Xpert Xpress SARS-CoV-2
Roche cobas SARS-CoV-2 real-time RT-PCR test (EUA200009/A001)
Abbott Molecular/RealTime SARS-CoV-2 assay (EUA200023/A001).
Evidence of prior SARS-CoV-2 infection were determined by virological testing via NAAT
on mid-turbinate swab and serological testing for SARS-CoV-2 N-binding antibodies.
**2.5.2.3. Bioanalytical and Analytical Methods Used in Human Studies
Information on assays used to assess SARS-CoV-2 infection and immune response is in
Module 2.7.1. Only validated (PCR and neutralization immunoassay) or qualified (Luminex
immunoassay) methods were used.
*Case Definitions
COVID-19 cases (defined per FDA guidance)17 were based on SARS-CoV-2 positive test
result per central laboratory or local testing facility (using an acceptable test per protocol and if no central laboratory result was available) and presence of at least 1 of the following:
Fever
New or increased cough
New or increased shortness of breath
Chills
New or increased muscle pain
New loss of taste or smell
Sore throat
Diarrhea
Vomiting
CDC criteria-defined COVID-19 cases could include the following additional symptoms:
Fatigue
Headache
Nasal congestion or runny nose
Nausea
Swabs were mostly shipped by trial vendors to the central lab which used one brand, otherwise accepted if certain other types used.
This idea that PCR is not accurate, that it corresponds to random whatever, I have addressed it before.
1 If corresponds to "random" genes, then swabbing humans is going to throw a billion "human genes" at the primers. There is no reason that "inaccurate" (non-specific) primers would "trip up" on other random viruses *preferentially* compared to "tripping up" on human genes. To do so would imply that 1) viruses as a category have some patent on whatever random sequence of nucleotides (e.g. human genes can't go "cgatgat" or whatever, only other viruses can have this sequence) 2) PCR can selectively detect this sequence (when other viruses with the special patent are around) 3) PCR is so inaccurate, so trash, it actually can't tell *any* sequences apart. So that's incoherent.
2 When validating SARS-CoV-2 PCR kits, companies run the kits on samples of other viruses e.g. flu, adenovirus, other coronaviruses, etc., and show that they come up negative.
3 Mechanistic arguments aren't how we decide if things work. You get in your car, turn it on, it goes to the grocery store. Do you actually care about the nuts and bolts? No. For PCR, when you follow people with a previous PCR+ compared to people without, you see who gets another PCR+ afterward - the people with previous PCR+ get another PCR+ at 0-10% of the rate of those without. If PCR is just going + for "random" infections, then people with a previous + should be getting other +'s more often, just based on the fact that they have shown they test, they have shown they get sick. But instead they are *immune* to future PCR+. No one claiming "PCR is not accurate" can provide an explanation for this. It would require magic. The explanation is that PCR is accurate - the first plus corresponded to a specific virus, now the person who got that plus is 1/10 to 1/20 as likely to get another one, because they have immunity to that same specific virus. Just can't happen if PCR+ is for "any infection."
Thanks for your thorough response, I need a little repetition with these things to understand them. It's a little late for me and I think I'm going to have to re-read it tomorrow to get all I can from your response. I appreciate the back and forth.
Even in my tired state, while brushing my teeth, something came to mind that I think s is a valid consideration and question - how many RT-PCRs were positive without symptoms? We don't know this from the Pfizer trials because they only tested people who had symptoms. But say if you knew that 30% of PCR positives in a population were asymptomatic, would your above points hold?
(I'm not saying that 30% of all positive tests were asymptomatic, but I understand that there were asymptomatic PCR positives. I remember Matt making the point that we don't know the false positive or false negative rates of the SARS Cov2 tests).
"So, I believe the Covid vaccines work (reduce severe disease even in the case of having an infection), but should not be used or promoted to the lay public. "
This is pretty alarming position for someone who is as deeply researched in the science and scientific fraud that causes so many folks to err on the side of misplaced trust. You're astute when it comes to false claims and fudged evidence so it boggles the mind that you see these mRNA transfections as remotely possible of producing an effective immune response to an airborne virus that requires T-cell response as an opener. Beyond that human bodies are acutely responsive to foreign proteins and transfected novel proteins & LNP have a long history of uncontrolled distribution in the body and triggering autoimmune disease. Holy Hell mate the entire transfection platform is a pipe dream of genetic knowledge & powers that do not exist. Watch Jay Couey's review of Cullis in the Nobel Prize pre-event tour.. this crap failed as cancer drug delivery!
https://rumble.com/v3q5vbq-2023-10-17-pieter-cullis-2022-study-hall-16-oct-2023-brief-twitch1953665426.html
They produce antibodies and T Cells. "Are these [antibodies and T Cells] helpful for severe disease" is a separate question from what is or isn't the "opener," since severe disease is an outcome of infection that takes some time to develop.
The mRNA transfections can't activate T-Cells and even their cartoon would theoretically activate B-cells that gets into science with antigen presenting functions that are way beyond my ability to explain. Bottom line is there is decades of failed science showing these transfections have random distribution, irritate the immune system & trigger a host of adverse events there is no science showing a mechanism of action for transfection to confer immunity only trigger autoimmune reactions.
As for the antibody measures as a correlate of immunity in any study my own suspicion is that the relationship is more hype that sound scientific theory because antibodies are cited as evidence of immunity except with HIV where antibodies are used as evidence of illness.
Reality is they put significance on the things they can measure and build their own reality around it! Thanks for the reply!! :~)
Why would the immune system react to foreign proteins, as a default, by amplifying harms?
The immune system's fundamental role is to differentiate between self and non-self. Mighty T-cells are on constant patrol of the outside barriers of the body for all sorts of opportunistic invaders. Even in the case of a perfectly matched donor for a transplant with identical twins some level of auto-immune response occurs because Mother Nature makes each and every life form a little unique and our ability as humans to see similarity even at DNA levels is Fred Flintstone technology compared to the complexity that is the human body and its defense mechanisms. Why would an unnatural protein that could never be introduced to the body without first encountering the innate immune system do anything but wreak havoc inside the body? The whole idea of transfecting healthy humans is criminal...
Watch Jay's Cullis video I linked & be sure to listen for the part where Cullis says less than 0.01% of chemo drugs reach the intended target which is criminal and mRNA was hoped to improve that but they could never figure out how to control the distribution. The few trials of repairing genetic disease caused death w the "perfect DNA" rejected as Non-Self & destroying all the "repaired cells." Jesse Gelsinger is the poster boy for the failures.. the science didn't change just the PR and pesky problems of strict liability & informed consent.
https://web.archive.org/web/20121025034826/https://www.nytimes.com/1999/11/28/magazine/the-biotech-death-of-jesse-gelsinger.html
Self / non-self recognition is regulated by T Cell negative selection (presumably T Cells go on to impose this regulation on B Cells in germinal centers). It is a question of "hey T Cell do you want to attack this self antigen?" not of "is antigen 'outside' or 'inside'", did antigen "first encounter innate immune system or not." It's all happening in the thymus and has nothing to do with foreign protein localization.
If vaccines cause autoimmunity, the question is whether they rendered self-antigens more "interesting" to APC or T Cells. Maybe, sure. And maybe Covid vaccines do this. But on the other hand SARS-CoV-2 packs more antigens (a baker's dozen of proteins). So there's no clear answer as far as which is worse for autoimmune hazard. Ultimately, most people are fine and the people who aren't, well, "tough luck."
Either way the default response to a foreign protein - why would it be detrimental? I don't keep my front door open at night. If the immune system sees an open door, it closes it (makes antibodies). This is generally good.
Unfortunately I can't explain why this is wrong aside from noting that mRNA transfections bear no similarity to traditional vaccines. I will ask Jay Couey if he can answer this, but based on your response it seems you did not watch Jay's video with Cullis because there is a clear delineation of transfection process with explanation why triggering the immune system is not creating immunity & difference from vaccine theory of exposing the system to an attenuated natural virus. What I know for sure is immunity is not produced the way you explain it here and abso-frigging-lutely not what mRNA transfection process is doing.
There's so much to learn I'm not sure where the truth is but one thing is for dang sure the virology field is like all the gmo science with lots of assumptions and models and "substantial equivalents" plucked from thin air and gaps as wide as the Grand Canyon in actual knowledge or evidence. It's like all the industrial chemicals where absence of evidence of harm is assumed to be safe and nobody looks for harms! Welcome to kleptocracy!
**Fun fact UC Davis pulled the database offline in 2020 Wayback has it!
Basic Testing to Identify Chemical Hazards
If an industrial chemical is allowed by law to be released into the environment, most people assume that it must have been tested and evaluated for its potential risks. Unfortunately, this is simply not true. Keeping chemical hazards under control requires information about what kinds of hazards each chemical poses. If the basic tests to check on a chemical's toxicity haven't been conducted, or if the results aren't publicly available, current laws tend to treat that chemical as if it were perfectly safe.
Information Needed for Safety Assessment
Could government assess a chemical's safety or risk? For most of the important industrial chemicals in U.S. commerce, government lacks the information to draw any scientifically based conclusion about the degree of risk--or lack of risk--that a chemical may pose when used. For every chemical in the database, Scorecard tells you whether or not the information needed to assess chemical risk is available. If it isn't, no one can accurately claim the chemical is "safe." https://web.archive.org/web/20120917041002/http://scorecard.goodguide.com/chemical-profiles/chems-profile-descriptions.tcl#basic_testing
Side of beans and cobtagiios please!
Agree completely we had ordinary illness & extraordinary propaganda with protocol changes that predictably increased deaths & transferred wealth upward & rolled back rights.
Thanks; thought-provoking. I enjoyed reading your challenging content - hope this somehow makes up for you not enjoying writing this particular piece!!
PS. Not sure if you read John Dee's output? This (ongoing series) also thought-provoking... https://jdee.substack.com/p/what-was-covid-19-exactly-part-2
Have now read. A lot of "shiny object" arguments (to use the phrase for the legal strategy of spinning a jury's head around).
There are interesting anomalies about the first spring wave, sure, but this shouldn't be treated as such a big problem - same thing happened with flu in 1918 and 1957, I have tried to make this point many times https://unglossed.substack.com/i/136573678/the-epidemiology - So do these interesting anomalies correspond to some kind of "op" to juice up pandemic fear? Maybe - but maybe they just happened because this is how it often goes with new (or reintroduced) viruses.
Haven't had his work sent my way in a while. I'll take a look
Quite. Once you appreciate that there’s no readily identifiable novel disease entity the whole fraud falls into place.
Reporting matters
“about five unvaccinated individuals are hospitalized for every vaccinated individual of the same age.“
Who was being considered “unvaccinated”? Therein lies the rub: the data was cooked
The "secret magic vaccinated sick people" trope does not apply in the examples used here. It typically isn't relevant in US data sources either, though the case by case impact may vary. But if vaccination status is missing, then the vaccinated are in the uninfected denominator until infected / hospitalized (when they move to the numerator). This because typically, the compiling of status in published research doesn't rely on whatever hospitals write down. The researchers have a "master" list of vaccinated.
So if the hospitalization rate is lower in the vaccinated on the "master" list, then any vaccinated who are missing vaccination status are in fact lowering the hospitalization rate in the unvaccinated as a whole, diluting and not "cooking" efficacy.
I don’t doubt the efficacy in older adults. I do appreciate your input and well written post.
It is the negative efficacy in younger adults and the SAEs that people died from. I’ve had over $500k in medical bills with injury to my heart lung and eyes. I will not take another vaccine again.