You wrote: "But vaccine brands were QUASI-RANDOMLY distributed in the Czech Republic. People didn’t get to choose. They got what was available. There was no systemic or systematic bias in the distribution."
It may have initially been true that people didn't get to choose the vaccine type in the Czech Republic, but later on in the online f…
You wrote: "But vaccine brands were QUASI-RANDOMLY distributed in the Czech Republic. People didn’t get to choose. They got what was available. There was no systemic or systematic bias in the distribution."
It may have initially been true that people didn't get to choose the vaccine type in the Czech Republic, but later on in the online form for booking a vaccine appointment, there was a dropdown menu where people were allowed to choose their preferred vaccine type: sars2.net/czech4.html#Kirschs_resurrected_claim_that_people_were_not_allowed_to_choose_if_they_got_a_Moderna_or_Pfizer_vaccine. But you should already know that, so you should stop repeating your old claim that people were not allowed to choose the vaccine brand.
And also how do you differentiate whether the vaccines were distributed quasi-randomly or not quasi-randomly?
There's a bunch of biases like that Moderna recipients were older, had higher age-normalized comorbidity index, were vaccinated later, had higher age-normalized rate of vaccinators whose name matched "senior home", and so on. So at what point do those biases become severe enough that you would say the vaccines were not distributed quasi-randomly?
Or how do you differentiate between a systematic bias and a non-systematic bias? ChatGPT said that "Systematic bias leads to consistently skewed results in a particular direction, whereas nonsystematic bias results in random fluctuations around the true value." ChatGPT said that the effect of nonsystematic biases averages out over time, and as an example of a nonsystematic bias, ChatGPT gave that "If a scale sometimes overestimates or underestimates weight by small amounts in random ways, it introduces nonsystematic bias."
However in the case of the Czech data, the reason why Moderna recipients have higher average age than Pfizer recipients is not only due to random noise caused by a small sample size, or what ChatGPT suggested was a "nonsystematic bias", but the age distribution seems to be systematically skewed.
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You wrote: "According to my bluepilled friend (ChatGPT), the vaccination rate was 60X lower in Palestine than Israel". However ChatGPT said it was in February 2021: "In February 2021, reports indicated that individuals in Israel were over 60 times more likely to be vaccinated than those in Palestine".
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In your spreadsheet you wrote that "the unvaccinated die at around 2.5X the rate of the vaccinated" but that "This is all selection bias. Vaccinated tend to be wealthier, better access to healthcare, have health insurance, health seeking behavior".
But if the difference is all due to selection bias, then is it not even 1% due to vaccine efficacy?
Then why did unvaccinated people have a much bigger increase in mortality during the COVID wave in November to December 2021? Between October 2021 and December 2021, the ASMR I calculated increased by about 84% among unvaccinated people, but it only increased by about 8% among people who got a Moderna vaccine for the first dose and about 23% among people who got a Pfizer vaccine for the first dose: sars2.net/czech2.html#ASMR_by_month_and_vaccine_type.
if you find a systemic or systematic bias to the distribution let me know. I said there wasn't one because nobody has shown there was one. Moderna came on later with supply, so that influenced the age mix. But people had NO CLUE which vaccine was safer and people could register for a brand, but nobody knew which brand to choose. There was nothing telling them which brand was safer for older people, for example. You can't explain how mortality rate consistently increases in every 5 year age group as you go younger.
I calculated 5 year age ranges to eliminate any distribution bias issues. And I took people with the exact same DCCI score as well. Same effect.
there is a huge amount of confounding for unvaccinated people. Their death rates went down in 2021 while vaccinated death rates were rising in most of the rest of 2021 on an ASMR basis, for example. I can't explain the peak with Omicron, but Omicron has a much lower CFR than previous variants. Why not compare with 2020 data when everyone was unvaccinated. Were there 80% peaks in 2020 from baseline mortality when EVERYONE was unvaccinated? I didn't look. Why don't you plot the curve starting in March 2020???
If the unvaccinated were so susceptible to COVID deaths, it would have shown in the US data when I looked at >65 for lowly vaccinated counties. It would have been HUGE. Instead, there was almost no difference.
The risk ratio depends on age. It's only 50% if you were born in 1955 to 1965 for example.
And finally, you didn't find any errors in my code or the numbers from the python script so those numbers are hard for you to explain away.
Here I calculated a risk ratio within 52 weeks from the first dose that was adjusted for age group and vaccination week.
For each combination of age group and vaccination week, I calculated the 52-week mortality rate among all vaccinated people included in the dataset. And then for each vaccine type, I multiplied the population size for each combination of age group and vaccination week with the baseline mortality rate to get the expected deaths, and I added together the expected deaths for all combinations of age group and vaccination week to get the total expected deaths for the vaccine type, and I derived the risk ratio by dividing the actual deaths with the expected deaths.
So basically the risk ratio of each vaccine type shows the deaths as a multiple of deaths among all vaccinated people who were modified to have the same distribution of age groups and vaccination weeks as the vaccine type. The relative risk is the same as an excess mortality percent divided by 100 and then incremented by 1.
The risk ratio was about 0.94 for Pfizer and 1.12 for Moderna. So the Moderna-Pfizer ratio is only about 1.19, so it's much lower than 1.5:
The Moderna-Pfizer ratio inceased to about 1.25 when I disabled the normalization by vaccination week. And when I did the normalization by 4-week bins and not by individual weeks of vaccination, the Moderna-Pfizer ratio was about 1.22.
When I modified the code above to calculate the risk ratio during the entire observation period and not only the first 52 weeks, the Moderna-Pfizer ratio increased from about 1.19 to about 1.22, so it didn't make much difference.
You wrote: "But vaccine brands were QUASI-RANDOMLY distributed in the Czech Republic. People didn’t get to choose. They got what was available. There was no systemic or systematic bias in the distribution."
It may have initially been true that people didn't get to choose the vaccine type in the Czech Republic, but later on in the online form for booking a vaccine appointment, there was a dropdown menu where people were allowed to choose their preferred vaccine type: sars2.net/czech4.html#Kirschs_resurrected_claim_that_people_were_not_allowed_to_choose_if_they_got_a_Moderna_or_Pfizer_vaccine. But you should already know that, so you should stop repeating your old claim that people were not allowed to choose the vaccine brand.
And also how do you differentiate whether the vaccines were distributed quasi-randomly or not quasi-randomly?
There's a bunch of biases like that Moderna recipients were older, had higher age-normalized comorbidity index, were vaccinated later, had higher age-normalized rate of vaccinators whose name matched "senior home", and so on. So at what point do those biases become severe enough that you would say the vaccines were not distributed quasi-randomly?
Or how do you differentiate between a systematic bias and a non-systematic bias? ChatGPT said that "Systematic bias leads to consistently skewed results in a particular direction, whereas nonsystematic bias results in random fluctuations around the true value." ChatGPT said that the effect of nonsystematic biases averages out over time, and as an example of a nonsystematic bias, ChatGPT gave that "If a scale sometimes overestimates or underestimates weight by small amounts in random ways, it introduces nonsystematic bias."
However in the case of the Czech data, the reason why Moderna recipients have higher average age than Pfizer recipients is not only due to random noise caused by a small sample size, or what ChatGPT suggested was a "nonsystematic bias", but the age distribution seems to be systematically skewed.
---
You wrote: "According to my bluepilled friend (ChatGPT), the vaccination rate was 60X lower in Palestine than Israel". However ChatGPT said it was in February 2021: "In February 2021, reports indicated that individuals in Israel were over 60 times more likely to be vaccinated than those in Palestine".
---
In your spreadsheet you wrote that "the unvaccinated die at around 2.5X the rate of the vaccinated" but that "This is all selection bias. Vaccinated tend to be wealthier, better access to healthcare, have health insurance, health seeking behavior".
But if the difference is all due to selection bias, then is it not even 1% due to vaccine efficacy?
Then why did unvaccinated people have a much bigger increase in mortality during the COVID wave in November to December 2021? Between October 2021 and December 2021, the ASMR I calculated increased by about 84% among unvaccinated people, but it only increased by about 8% among people who got a Moderna vaccine for the first dose and about 23% among people who got a Pfizer vaccine for the first dose: sars2.net/czech2.html#ASMR_by_month_and_vaccine_type.
When I calculated age-standardized COVID hospitalization rates in the new Czech dataset, the rate in December 2021 was about 5,274 hopitalizations per 100,000 person-years for unvaccinated people but only 750 for Pfizer and 673 for Moderna: sars2.net/czech4.html#Age_standardized_hospitalization_rate_by_vaccine_type_in_new_UZIS_data.
if you find a systemic or systematic bias to the distribution let me know. I said there wasn't one because nobody has shown there was one. Moderna came on later with supply, so that influenced the age mix. But people had NO CLUE which vaccine was safer and people could register for a brand, but nobody knew which brand to choose. There was nothing telling them which brand was safer for older people, for example. You can't explain how mortality rate consistently increases in every 5 year age group as you go younger.
I calculated 5 year age ranges to eliminate any distribution bias issues. And I took people with the exact same DCCI score as well. Same effect.
there is a huge amount of confounding for unvaccinated people. Their death rates went down in 2021 while vaccinated death rates were rising in most of the rest of 2021 on an ASMR basis, for example. I can't explain the peak with Omicron, but Omicron has a much lower CFR than previous variants. Why not compare with 2020 data when everyone was unvaccinated. Were there 80% peaks in 2020 from baseline mortality when EVERYONE was unvaccinated? I didn't look. Why don't you plot the curve starting in March 2020???
If the unvaccinated were so susceptible to COVID deaths, it would have shown in the US data when I looked at >65 for lowly vaccinated counties. It would have been HUGE. Instead, there was almost no difference.
The risk ratio depends on age. It's only 50% if you were born in 1955 to 1965 for example.
And finally, you didn't find any errors in my code or the numbers from the python script so those numbers are hard for you to explain away.
Here I calculated a risk ratio within 52 weeks from the first dose that was adjusted for age group and vaccination week.
For each combination of age group and vaccination week, I calculated the 52-week mortality rate among all vaccinated people included in the dataset. And then for each vaccine type, I multiplied the population size for each combination of age group and vaccination week with the baseline mortality rate to get the expected deaths, and I added together the expected deaths for all combinations of age group and vaccination week to get the total expected deaths for the vaccine type, and I derived the risk ratio by dividing the actual deaths with the expected deaths.
So basically the risk ratio of each vaccine type shows the deaths as a multiple of deaths among all vaccinated people who were modified to have the same distribution of age groups and vaccination weeks as the vaccine type. The relative risk is the same as an excess mortality percent divided by 100 and then incremented by 1.
The risk ratio was about 0.94 for Pfizer and 1.12 for Moderna. So the Moderna-Pfizer ratio is only about 1.19, so it's much lower than 1.5:
> t=fread("Otevrena-data-NR-26-30-COVID-19-prehled-populace-2024-01.csv")
> codes=rep("Other",26)
> codes[c(1,8,9,16,20,21,23)]="Pfizer"
> codes[c(2,15)]="Moderna"
> codes[3]="AstraZeneca"
> codes[4]="Janssen"
> codes[c(7,22)]="Novavax"
> names(codes)=sprintf("CO%02d",1:26)
> weeks=unique(format(seq(as.Date("2020-1-1"),as.Date("2024-12-31"),1),"%Y-%V"))
> d=t[Infekce<=1] # exclude duplicate entries for people with two or more cases
> d=d[Datum_Prvni_davka!=""] # exclude unvaccinated people
> d[,date:=match(Datum_Prvni_davka,weeks)] # convert year plus week number of first dose to integer
> d[,dead:=match(ifelse(DatumUmrtiLPZ=="",Umrti,DatumUmrtiLPZ),weeks)] # some people are missing Umrti but not DatumUmrtiLPZ, and vice versa
> d=d[date<min(grep("2023",weeks))] # exclude people with first dose in 2023 or 2024
> d=d[,.(date,dead,type=codes[OckovaciLatkaKod_Prvni_davka],born=RokNarozeni)]
> d=d[born!="-"&!born%like%"19(05|10)"] # exclude a few people with erroneous birth dates
> a=d[,.(dead=sum((dead-date)%in%0:51),pop=.N),.(date,type,born)] # count deaths within 52 weeks from first dose
> a=merge(a,a[,.(base=sum(dead)/sum(pop)),.(date,born)]) # get baseline mortality rate for each age and vaccination week
> o=a[,.(dead=sum(dead),pop=sum(pop),rr=sum(dead)/sum(base*pop)),type]
> print(o[order(-pop)],r=F)
type dead pop rr
Pfizer 14790 2183514 0.94
Moderna 2729 182240 1.12
Janssen 1395 152581 1.12
AstraZeneca 3474 123531 1.21
Novavax 9 3097 0.59
Other 0 8 0.00
The Moderna-Pfizer ratio inceased to about 1.25 when I disabled the normalization by vaccination week. And when I did the normalization by 4-week bins and not by individual weeks of vaccination, the Moderna-Pfizer ratio was about 1.22.
If I would've additionally normalized the calculation by the comorbidity index, the difference between Moderna and Pfizer would've become even smaller, but then I would've also had to exclude people with no COVID case listed (since the comorbidity index is only included for people with a COVID case): sars2.net/czech4.html#Mortality_rate_within_year_from_vaccination_normalized_by_comorbidity_index_in_new_UZIS_data.
When I modified the code above to calculate the risk ratio during the entire observation period and not only the first 52 weeks, the Moderna-Pfizer ratio increased from about 1.19 to about 1.22, so it didn't make much difference.