I take this approach because I'm not all that great with stats and data - I can "see" things much clearer with algebra.
My approach wasn't really demonstrating anything new - but trying to see the same things others had commented on but in a different (and simple) way.
Anyhoo - the point of the piece was really to highlight an effect that I'd noted : within my simple vaccination schedule model the end result for VE depended on the *percentage* vaxxed if the VE was calculated with a delay that effectively shunted the recently vaccinated deaths into the unvaxxed category.
You altered the percentage vaxxed here and found you could manipulate the efficacy. In other words you're seeing a dependence of the VE on the percentage vaxxed.
The efficacy of a vaccine should, of course, not depend on whether 10% or 20% or 80% of the population have been vaccinated. My question would be whether being able to demonstrate a dependence on vax percentage is sufficient to show "datacrime" with regards to VE?
Your question is exactly where I plan to go with my next set of explorations!
I have the OWID vaccination data for all the nations. Now, I need to find a few studies that compute VE through a defined time period in a few nations, then model it out.
One worry that I have is that levels of corruption in some nations may be so high that numbers are entirely fabricated. Heck, we saw the Surgisphere study out of the U.S., which looks 100% fabricated, so...
Still, to find even a few studies that perfectly match the model will be telling.
But first I need to build up a spreadsheet that follows this through two doses. Showing all that work won't be necessary for the book, but I plan to make spreadsheets that I use to make arguments publicly available for download upon publication.
I wonder if you'll see a big enough variation in the VE calculated from the 'official' data country by country? A big enough effect here would also be indicative of something 'not quite right'.
We wouldn't expect VE to vary by percentage vaxxed, but we also wouldn't expect it to depend on geography either (although I suppose one must consider demographics here - nation Blobistan might have a different initial health profile than Lampstandistan).
Thinking back to bits I haven't kept track of (one reason your book is very much needed), I seem to recall that some data came out of Alberta at one point which seemed to be in line with Prof. Fenton's efficacy illusion argument. I can't remember exactly which part of Fenton's argument it seemed to bear out, but Fenton's use of U.K. data to show the _apparent_ protective "effect" of the vaccines against _non-COVID deaths_, as described in the presentation that I link below, is one that I found particularly striking at the time: https://worldcouncilforhealth.org/multimedia/norman-fenton-vaccine-efficacy-uk/
You may have considerable difficulty assessing "VE" because reported "infections" were intentionally corrupted with high CtPCR. Joel Smalley's approach with All Cause Mortality is useful at one level, but again the diagnosis of Covid death was also gamed and corrupted.
As a followup, Dena Hinshaw, then Alberta's Public Health Officer, was noted to have lied under oath and was dismissed, but has happily found employment as a Deputy Health Officer in B.C., the adjoining jurisdiction.
I added a link to my comment above. I must admit that I can't really assess if what you are describing above is 100% the same phenomenon or a slightly different one. I tend to think a slightly different one.
In this other Gato Malo article it's also well explained, including the psychology. With an easy simile/metaphor.
This is a great article. I remember it. It does give a nice analogy as to why it's silly for people not to think of somebody as vaccinated during the time period, but what Fenton did was design a simulation to show the computational effects. And I'd like to use that model for some additional explorations now that I believe will help a wider audience understand better the high likelihood that this is exactly what has taken place.
Of course, you're adding a whole new element with the numerator-denominator but this is the revelation that got my video deleted from YT. It was called How to Lie with Statistics. Someone had been arguing with me about why his Wash State vaccine data and his brother's from Arizona both showed the unvaxxed were dying faster. I went to bed on it and sat bolt upright in the middle of the night and went to the computer. Eureka! They were both clearly from the same source with just a little formatting difference. And both said that those not fully vaxxed--2 weeks out--were counted as unvaxxed. A first year stats student couldn't get away with that. But this takes it to a whole new level, Mathew. Just to post my me-too from Rumble: https://rumble.com/vucisq-how-to-lie-with-statistics-on-dr.-john-abramson-and-russell-brand.html .
By the Grace of God, I barely persuaded 3 people from getting the jab. Well, God caused things to deflect them along the way, and possibly my explanations served as one of the deflections. It's a horror for everyone as the tragedies unfold as we knew it would; we tried to prepare ourselves and others.
The hospitals in my area are telling people who are coming in with strokes, heart issues, clotting issues that they are "seeing a lot of it in people with asymptomatic covid infections" (and the patients coming through the ER seem to all be + somehow, thanks to those fast result tests and still-not-very accurate PCR tests.).
After weeks of not feeling better after treatment, the patients are then being told they have "Long Covid." I haven't noted that they are given anything for their new diagnosis - for example the FLCCC post-spike protocols or the WCH "Spike Detox"(https://worldcouncilforhealth.org/resources/spike-protein-detox-guide/ It's excellent, great whether people have spike from the injections or the infection or both.).
I have given these resources to five people so far, and 2 of my doctors (who murmur, "Oh, thank you..." and to which I respond "Please, do not put this in the round file. You WILL be seeing injured patients. These give them something to look into.") fwiw. One of the pastors at the local church is starting to talk about it in passing to some people on one of his committees, so that is something. I'm meeting with him in another week or so to chat him up.
It would've been better off for humanity if the psychopaths had filled all the syringes with a saline solution rather than the experimental mRNA gene therapy concoction, but then the depraved sadists couldn't derive pleasure from torturing and slaughtering millions for profit.
Indeed. It was a combination of the intuition and Fenton's work that led me to work on other projects. As I said, I'd rather being doing something that adds unique value most of the time. But I think this turned out to add unique value, regardless. And there is more to come.
I made this comment under Norman’s piece just now, but relevant here also.
“Of course, for the actual covid injections as opposed to placebo, the illusion gets an additional “booster” (sorry but couldn’t resist) by virtue of the increased propensity of the injected to become infected during the period when they then become classed as unvaccinated.
And there’s a double booster if that infection then reduces the propensity to become infected later - once classed as vaccinated.”
My main memory of this goes back to an in-depth discussion of data issues on the radio so I didn't have a link to save off at the time. However, my weak google-fu managed to come up with an unrelated example study here
"Vaccination status was categorized based on number and type of vaccine doses received (1 Janssen dose, 2 Janssen doses, 1 Janssen/1 mRNA dose, and 3 mRNA doses§§). Patients with no record of vaccination were considered unvaccinated."
I would have preferred to come up with a better reference. I'm sure others with better research skills can do a better job.
I suppose one factor for the pro-VE side is that the quality control issues during the manufacturing process meant a significant number of the "vaccinated" were, in practice, unvaccinated. However, that would also affect adverse event rates so they probably don't want to go there.
The functionality (or, lack thereof) of the electronic medical record (EMR) and the uninspired ways in which it was originally built (at least where I worked), would likely have worsened during the initial phase of this vaccination classification system. Likely, chaos and administrative pressures existed to “add something” quickly in order to identify the vaccine status despite being uncertain about the best way to do that as definitions were shifting constantly. There would also have been likely disagreements about the most appropriate place to put “the data fields”in the EMR. The EMR designers would probably want something as automated as possible, so that if patients of an existing medical establishment had gotten vaccinated AT that institution massive campaign, then all such inputs would automatically populate “the data fields”. Easy peasy, especially if they are subsequently admitted to the system hospital, and you open the record and the answer is right there and easy to see.
I heard a hospital intake nurse in person and another on zoom complaining that the “automated” results were showing up in one part of the chart, while the same information being obtained verbally during a hospital admission would end up somewhere else, often in an unusable or incompatible or inconsistent format from a data mining standpoint. It also dissuaded overwhelmed practitioners from trying to find the information elsewhere.
Another iteration: Let’s say the patient being admitted was from a nursing home….which seems likely for that timeframe. One could assume quite rightly that they were probably vaccinated. Or, consider the patient who got vaccinated at Walgreens. However, with no evidence in hand/verbal not sufficient, and because those institutional data systems don’t link, the only other option for the human doing the hospital admission, was reportedly to choose “unknown” vaccination status. And from there, the “unknowns” were categorized as “unvaccinated”.
I also heard this during one of Pierre Kory’s FLCCC weekly updates where he observed this at the hospitals he was covering. He worked as a locums I believe when he was still “allowed” to fill hospital shifts at different places.
Whether or not this EMR glitch was eventually fixed, I do not know, but I do think it could have also contributed to that terrible phase known as “a pandemic of the unvaccinated.” It at least helped me to understand how they could improperly categorize so many of the hospitalized COVID patients as being “unvaccinated”.
If I’m overstating or misstating I ask for clarification, but at a minimum, I could see how the EMR design seemingly played a role, whether due to overwhelm, incompetence, or desire. Thank you, Mathew, for all you have taught me.
I'm no statistician, but back in 2021, the Province of Alberta, Canada posted on its official C19 stats website (or a brief time), a colourful 'heat' chart showing the number of C 19 cases diagnosed in the province as function of time since vaccination. The overwhelming majority (like 90%) occurred within 14-21 days days post vax. Uh oh. They proceeded to remove that chart from the website post haste and from then on, simply reclassified people having received their vaccines, as 'unvaccinated' for the first 14 days after vaccination- problem solved! Of course, I kept the screen shots of these charts for posterity and really hope that someday they re -emerge in a court of law.
This is really great Mathew. Although Norman correctly uses the ONS' stated definition of an unvaccinated case as one that occurs within 21 days of a jab, it is unclear what the CDC is doing. In this CDC statement, it doesn't seem like they are throwing those cases into the unvaccinated bucket. They are calling them "partially vaccinated" and are presumably excluded from the IR from both groups:
"Partially vaccinated case: SARS-CoV-2 RNA or antigen detected in a respiratory specimen collected from a person who received at least one FDA-authorized or approved vaccine dose but did not complete a primary series ≥14 days before collection of a respiratory specimen with SARS-CoV-2 RNA or antigen detected.
Unvaccinated case: SARS-CoV-2 RNA or antigen detected in a respiratory specimen from a person who has not been verified to have received any COVID-19 vaccine doses before the specimen collection date."
Even though the numerator in the unvaxxed IR won't be as exaggerated as in the UK, dropping 14 (not 21) days of infections from the vaxxed numerator still generates a fabricated efficacy--as long as they are including the partially vaccinated (i.e. Recently Treated) in the denominator. I am not sure if they are doing that.
Incidence rates estimates. The denominator is number of people who have received the primary series, no mention of elapsed time since completion. Interestingly, the Unvaxxed population is estimated by subtracting vaccinated + partially vaccinated from 2019 census data. This nudges IR in the unvaccinated up a tad.
Would enjoy seeing a plot of VE using CDC trickery with your magic dust sprinkled in...
1) It is important to clarify that the phenomenon of each groups' population sizes changing over time is only applicable to population level data. That aspect of this bias would not occur in a cohort study which would have both group sizes be fixed. I see some people citing Fenton's research erroneously in that regard. However other aspects of these biases would still be functioning. And also, in the event of acute negative efficacy, vaccine-caused covid cases would still be censored from the vaccine group (albeit not shifted into the unvaccinated group, as would happen in population level analysis).
[Edit: Unless they are doing some kind of person-years analysis, in which case the full extent of bias would be present]
2) One aspect I recently realized is that not only do the vaccinated have ever-so-slightly higher natural immunity due to the requirement to be healthy before vaccination, but for a second reason as well. In the first two weeks after vaccination, you have a chance to develop natural immunity, without getting a counted case of covid! The unvaccinated are not afforded this opportunity. This issue becomes worse if vaccines have an acute negative efficacy. In population level data, this would in essence not just cause covid to be assigned to the unvaccinated, but also cause the natural immunity be kept for the vaccinated! In a fixed-population-size cohort study, this would cause censoring of covid cases in the vaccinated, but would not "censor" the natural immunity they gained. But as Fenton stated, if infection rate is low, these biases relating to natural immunity may not matter a ton.
3) If you are going to develop this model further, some suggestions, if you are not already planning:
a) Do it for a fixed cohort study. The bias will be less, but since those are the studies people really cite which show the cyclical pattern of waning and boosting with new vaccines, it is the context we really need to understand.
b) Have a parameter for true vaccine efficacy that we can play with, not just a placebo.
c) Include the natural immunity bias I described. Have a parameter for natural immunity efficacy. We can dig up the best estimates for that later.
d) Also have a similar parameter for negative efficacy in the first two weeks that we can play with. That could amp things up.
d) Let's dig up some best estimates for covid infection rates during a specific rollout and use those real values. Of course, true prevalence is not the same as case count, but our goal is to do what they do.
e) Let's dig up estimates for real values for vaccination rates during that rollout and use them.
f) If you want to complicate things, model two doses. Each dose has a potentially negative VE period, and a potentially positive VE period.
g) Not all cohort studies use the 2-week misclassification. Most do, but some classify correctly and still report a benefit. Some of them do report negative acute efficacy, and some do not. Consider parameterizing the misclassification length as well.
With all that, we could do a best case, worst case, and middle case examples. This will show the huge uncertainty around observational studies. I
I made some of these comments on Fenton's article as well. I doubt there is a risk of you guys duplicating work though.
[Edit: It may be possible to avoid modeling 2 doses and also get some higher background infection rates if you modeled just the 3rd dose, which perhaps did not have a huge overlap with people getting first and second doses]
A while back I tried to figure out some possible sources of error in VE by taking an algebraic approach. You commented on the article back then.
https://rudolphrigger.substack.com/p/a-fascinating-result
I take this approach because I'm not all that great with stats and data - I can "see" things much clearer with algebra.
My approach wasn't really demonstrating anything new - but trying to see the same things others had commented on but in a different (and simple) way.
Anyhoo - the point of the piece was really to highlight an effect that I'd noted : within my simple vaccination schedule model the end result for VE depended on the *percentage* vaxxed if the VE was calculated with a delay that effectively shunted the recently vaccinated deaths into the unvaxxed category.
You altered the percentage vaxxed here and found you could manipulate the efficacy. In other words you're seeing a dependence of the VE on the percentage vaxxed.
The efficacy of a vaccine should, of course, not depend on whether 10% or 20% or 80% of the population have been vaccinated. My question would be whether being able to demonstrate a dependence on vax percentage is sufficient to show "datacrime" with regards to VE?
Bazinga!
Your question is exactly where I plan to go with my next set of explorations!
I have the OWID vaccination data for all the nations. Now, I need to find a few studies that compute VE through a defined time period in a few nations, then model it out.
One worry that I have is that levels of corruption in some nations may be so high that numbers are entirely fabricated. Heck, we saw the Surgisphere study out of the U.S., which looks 100% fabricated, so...
Still, to find even a few studies that perfectly match the model will be telling.
But first I need to build up a spreadsheet that follows this through two doses. Showing all that work won't be necessary for the book, but I plan to make spreadsheets that I use to make arguments publicly available for download upon publication.
Good luck!
Getting accurate covid data must be a nightmare.
I wonder if you'll see a big enough variation in the VE calculated from the 'official' data country by country? A big enough effect here would also be indicative of something 'not quite right'.
We wouldn't expect VE to vary by percentage vaxxed, but we also wouldn't expect it to depend on geography either (although I suppose one must consider demographics here - nation Blobistan might have a different initial health profile than Lampstandistan).
Thinking back to bits I haven't kept track of (one reason your book is very much needed), I seem to recall that some data came out of Alberta at one point which seemed to be in line with Prof. Fenton's efficacy illusion argument. I can't remember exactly which part of Fenton's argument it seemed to bear out, but Fenton's use of U.K. data to show the _apparent_ protective "effect" of the vaccines against _non-COVID deaths_, as described in the presentation that I link below, is one that I found particularly striking at the time: https://worldcouncilforhealth.org/multimedia/norman-fenton-vaccine-efficacy-uk/
Thanks for the link...I had missed this one, added it in.
https://totalityofevidence.com/professor-norman-fenton/
Possibly el Gato Malo from Jan. 20, 2022?https://boriquagato.substack.com/p/alberta-gets-caught-palming-cards
You may have considerable difficulty assessing "VE" because reported "infections" were intentionally corrupted with high CtPCR. Joel Smalley's approach with All Cause Mortality is useful at one level, but again the diagnosis of Covid death was also gamed and corrupted.
As a followup, Dena Hinshaw, then Alberta's Public Health Officer, was noted to have lied under oath and was dismissed, but has happily found employment as a Deputy Health Officer in B.C., the adjoining jurisdiction.
Does your book additionally also cover the - I think other, different from this - sleight of hand that Gato Malo called the "Bayesian Data crime"?
https://boriquagato.substack.com/p/bayesian-datacrime-defining-vaccine
Probably that is already covered in earlier parts of your article series.
You'll have to first show me what Gato Malo called the "Bayesian Data crime".
I added a link to my comment above. I must admit that I can't really assess if what you are describing above is 100% the same phenomenon or a slightly different one. I tend to think a slightly different one.
In this other Gato Malo article it's also well explained, including the psychology. With an easy simile/metaphor.
https://boriquagato.substack.com/p/why-vaccinated-covid-deathshospitalizations?
This is a great article. I remember it. It does give a nice analogy as to why it's silly for people not to think of somebody as vaccinated during the time period, but what Fenton did was design a simulation to show the computational effects. And I'd like to use that model for some additional explorations now that I believe will help a wider audience understand better the high likelihood that this is exactly what has taken place.
And it appears that many adverse reactions occur in the RTVaxxed. Convenient…
Of course, you're adding a whole new element with the numerator-denominator but this is the revelation that got my video deleted from YT. It was called How to Lie with Statistics. Someone had been arguing with me about why his Wash State vaccine data and his brother's from Arizona both showed the unvaxxed were dying faster. I went to bed on it and sat bolt upright in the middle of the night and went to the computer. Eureka! They were both clearly from the same source with just a little formatting difference. And both said that those not fully vaxxed--2 weeks out--were counted as unvaxxed. A first year stats student couldn't get away with that. But this takes it to a whole new level, Mathew. Just to post my me-too from Rumble: https://rumble.com/vucisq-how-to-lie-with-statistics-on-dr.-john-abramson-and-russell-brand.html .
This post here also talks about magic, but in November 2020:
https://drmalcolmkendrick.org/2020/11/10/ninety-per-cent/
I will never understand this. Long before there is "data" some people "know" what is going to happen.
The deductionist sect.
People see things, abstract a pattern, and they learn.
Much later, when they see the first signs of the pattern emerging, they warn the other monkeys: don't fall for it.
The other monkeys get all indignant: How do you know? You don't have data! You are a denialist!
It's as if knowledge was more than science.
priceless link.
"Goodness me, you’re not one of those anti-product protestors are you."
Kendrick is so smart you wouldn't believe he's a real MD.
I'm with you. Perhaps seeing the patterns comes from living long enough to recognize them - like the heaven's constellations
OMG Agent, exactly!
By the Grace of God, I barely persuaded 3 people from getting the jab. Well, God caused things to deflect them along the way, and possibly my explanations served as one of the deflections. It's a horror for everyone as the tragedies unfold as we knew it would; we tried to prepare ourselves and others.
The hospitals in my area are telling people who are coming in with strokes, heart issues, clotting issues that they are "seeing a lot of it in people with asymptomatic covid infections" (and the patients coming through the ER seem to all be + somehow, thanks to those fast result tests and still-not-very accurate PCR tests.).
After weeks of not feeling better after treatment, the patients are then being told they have "Long Covid." I haven't noted that they are given anything for their new diagnosis - for example the FLCCC post-spike protocols or the WCH "Spike Detox"(https://worldcouncilforhealth.org/resources/spike-protein-detox-guide/ It's excellent, great whether people have spike from the injections or the infection or both.).
I have given these resources to five people so far, and 2 of my doctors (who murmur, "Oh, thank you..." and to which I respond "Please, do not put this in the round file. You WILL be seeing injured patients. These give them something to look into.") fwiw. One of the pastors at the local church is starting to talk about it in passing to some people on one of his committees, so that is something. I'm meeting with him in another week or so to chat him up.
For we numerically challenged these breakdowns are priceless thank you.
Glad to know this satisfies the goal for at least a chunk of people. Thank you.
It would've been better off for humanity if the psychopaths had filled all the syringes with a saline solution rather than the experimental mRNA gene therapy concoction, but then the depraved sadists couldn't derive pleasure from torturing and slaughtering millions for profit.
I figured it out via pure intuition. But then I had no proof
Indeed. It was a combination of the intuition and Fenton's work that led me to work on other projects. As I said, I'd rather being doing something that adds unique value most of the time. But I think this turned out to add unique value, regardless. And there is more to come.
Nice. If it wasn't so tragic it would be hilarious. Even still, it is kind of hilarious.
I made this comment under Norman’s piece just now, but relevant here also.
“Of course, for the actual covid injections as opposed to placebo, the illusion gets an additional “booster” (sorry but couldn’t resist) by virtue of the increased propensity of the injected to become infected during the period when they then become classed as unvaccinated.
And there’s a double booster if that infection then reduces the propensity to become infected later - once classed as vaccinated.”
https://wherearethenumbers.substack.com/p/the-illusion-of-vaccine-efficacy/comment/15698818
Then there is the 3rd VE "Bonus" where hospitals would count people with unknown vax status as "unvaccinated"
Can you cite, please?
I know I've seen this a bit, but have never been close to caught up organizing notes.
My main memory of this goes back to an in-depth discussion of data issues on the radio so I didn't have a link to save off at the time. However, my weak google-fu managed to come up with an unrelated example study here
https://www.cdc.gov/mmwr/volumes/71/wr/mm7113e2.htm?s_cid=mm7113e2_w
"Vaccination status was categorized based on number and type of vaccine doses received (1 Janssen dose, 2 Janssen doses, 1 Janssen/1 mRNA dose, and 3 mRNA doses§§). Patients with no record of vaccination were considered unvaccinated."
I would have preferred to come up with a better reference. I'm sure others with better research skills can do a better job.
I suppose one factor for the pro-VE side is that the quality control issues during the manufacturing process meant a significant number of the "vaccinated" were, in practice, unvaccinated. However, that would also affect adverse event rates so they probably don't want to go there.
The functionality (or, lack thereof) of the electronic medical record (EMR) and the uninspired ways in which it was originally built (at least where I worked), would likely have worsened during the initial phase of this vaccination classification system. Likely, chaos and administrative pressures existed to “add something” quickly in order to identify the vaccine status despite being uncertain about the best way to do that as definitions were shifting constantly. There would also have been likely disagreements about the most appropriate place to put “the data fields”in the EMR. The EMR designers would probably want something as automated as possible, so that if patients of an existing medical establishment had gotten vaccinated AT that institution massive campaign, then all such inputs would automatically populate “the data fields”. Easy peasy, especially if they are subsequently admitted to the system hospital, and you open the record and the answer is right there and easy to see.
I heard a hospital intake nurse in person and another on zoom complaining that the “automated” results were showing up in one part of the chart, while the same information being obtained verbally during a hospital admission would end up somewhere else, often in an unusable or incompatible or inconsistent format from a data mining standpoint. It also dissuaded overwhelmed practitioners from trying to find the information elsewhere.
Another iteration: Let’s say the patient being admitted was from a nursing home….which seems likely for that timeframe. One could assume quite rightly that they were probably vaccinated. Or, consider the patient who got vaccinated at Walgreens. However, with no evidence in hand/verbal not sufficient, and because those institutional data systems don’t link, the only other option for the human doing the hospital admission, was reportedly to choose “unknown” vaccination status. And from there, the “unknowns” were categorized as “unvaccinated”.
I also heard this during one of Pierre Kory’s FLCCC weekly updates where he observed this at the hospitals he was covering. He worked as a locums I believe when he was still “allowed” to fill hospital shifts at different places.
Whether or not this EMR glitch was eventually fixed, I do not know, but I do think it could have also contributed to that terrible phase known as “a pandemic of the unvaccinated.” It at least helped me to understand how they could improperly categorize so many of the hospitalized COVID patients as being “unvaccinated”.
If I’m overstating or misstating I ask for clarification, but at a minimum, I could see how the EMR design seemingly played a role, whether due to overwhelm, incompetence, or desire. Thank you, Mathew, for all you have taught me.
Excellent work. Truly excellent.
Mathew, you’re cheeky.
Nice easy summary to understand, many wished that they got the saline
I'm no statistician, but back in 2021, the Province of Alberta, Canada posted on its official C19 stats website (or a brief time), a colourful 'heat' chart showing the number of C 19 cases diagnosed in the province as function of time since vaccination. The overwhelming majority (like 90%) occurred within 14-21 days days post vax. Uh oh. They proceeded to remove that chart from the website post haste and from then on, simply reclassified people having received their vaccines, as 'unvaccinated' for the first 14 days after vaccination- problem solved! Of course, I kept the screen shots of these charts for posterity and really hope that someday they re -emerge in a court of law.
Joel Smalley did an analysis of that here:
https://metatron.substack.com/p/alberta-just-inadvertently-confessed
Kudos! It's like a primer on how to lie with statistical brilliance! Thank you. God bless you. Amen.
This is really great Mathew. Although Norman correctly uses the ONS' stated definition of an unvaccinated case as one that occurs within 21 days of a jab, it is unclear what the CDC is doing. In this CDC statement, it doesn't seem like they are throwing those cases into the unvaccinated bucket. They are calling them "partially vaccinated" and are presumably excluded from the IR from both groups:
"Partially vaccinated case: SARS-CoV-2 RNA or antigen detected in a respiratory specimen collected from a person who received at least one FDA-authorized or approved vaccine dose but did not complete a primary series ≥14 days before collection of a respiratory specimen with SARS-CoV-2 RNA or antigen detected.
Unvaccinated case: SARS-CoV-2 RNA or antigen detected in a respiratory specimen from a person who has not been verified to have received any COVID-19 vaccine doses before the specimen collection date."
https://www.cdc.gov/coronavirus/2019-ncov/php/hd-breakthrough.html
Even though the numerator in the unvaxxed IR won't be as exaggerated as in the UK, dropping 14 (not 21) days of infections from the vaxxed numerator still generates a fabricated efficacy--as long as they are including the partially vaccinated (i.e. Recently Treated) in the denominator. I am not sure if they are doing that.
I believe the answer is here:
https://covid.cdc.gov/covid-data-tracker/#rates-by-vaccine-status
Incidence rates estimates. The denominator is number of people who have received the primary series, no mention of elapsed time since completion. Interestingly, the Unvaxxed population is estimated by subtracting vaccinated + partially vaccinated from 2019 census data. This nudges IR in the unvaccinated up a tad.
Would enjoy seeing a plot of VE using CDC trickery with your magic dust sprinkled in...
1) It is important to clarify that the phenomenon of each groups' population sizes changing over time is only applicable to population level data. That aspect of this bias would not occur in a cohort study which would have both group sizes be fixed. I see some people citing Fenton's research erroneously in that regard. However other aspects of these biases would still be functioning. And also, in the event of acute negative efficacy, vaccine-caused covid cases would still be censored from the vaccine group (albeit not shifted into the unvaccinated group, as would happen in population level analysis).
[Edit: Unless they are doing some kind of person-years analysis, in which case the full extent of bias would be present]
2) One aspect I recently realized is that not only do the vaccinated have ever-so-slightly higher natural immunity due to the requirement to be healthy before vaccination, but for a second reason as well. In the first two weeks after vaccination, you have a chance to develop natural immunity, without getting a counted case of covid! The unvaccinated are not afforded this opportunity. This issue becomes worse if vaccines have an acute negative efficacy. In population level data, this would in essence not just cause covid to be assigned to the unvaccinated, but also cause the natural immunity be kept for the vaccinated! In a fixed-population-size cohort study, this would cause censoring of covid cases in the vaccinated, but would not "censor" the natural immunity they gained. But as Fenton stated, if infection rate is low, these biases relating to natural immunity may not matter a ton.
3) If you are going to develop this model further, some suggestions, if you are not already planning:
a) Do it for a fixed cohort study. The bias will be less, but since those are the studies people really cite which show the cyclical pattern of waning and boosting with new vaccines, it is the context we really need to understand.
b) Have a parameter for true vaccine efficacy that we can play with, not just a placebo.
c) Include the natural immunity bias I described. Have a parameter for natural immunity efficacy. We can dig up the best estimates for that later.
d) Also have a similar parameter for negative efficacy in the first two weeks that we can play with. That could amp things up.
d) Let's dig up some best estimates for covid infection rates during a specific rollout and use those real values. Of course, true prevalence is not the same as case count, but our goal is to do what they do.
e) Let's dig up estimates for real values for vaccination rates during that rollout and use them.
f) If you want to complicate things, model two doses. Each dose has a potentially negative VE period, and a potentially positive VE period.
g) Not all cohort studies use the 2-week misclassification. Most do, but some classify correctly and still report a benefit. Some of them do report negative acute efficacy, and some do not. Consider parameterizing the misclassification length as well.
With all that, we could do a best case, worst case, and middle case examples. This will show the huge uncertainty around observational studies. I
I made some of these comments on Fenton's article as well. I doubt there is a risk of you guys duplicating work though.
[Edit: It may be possible to avoid modeling 2 doses and also get some higher background infection rates if you modeled just the 3rd dose, which perhaps did not have a huge overlap with people getting first and second doses]