CDC fudged the 2021 data, claiming that 25-44y covid mortality was up 150% while 85+y covid mortality was DOWN 25% at the same time. Simply unbelievable.
I think the best debunk of this nonsense is Joel Smalley’s film showing more Covid deaths in the year after the rollout than in the year before, in almost every country in the world.
I concur, and I've seen many datasets that show higher deaths in vaccinated, but I was curious how exactly they had come up with a fudge that indicated the opposite. We must not let these hucksters mislead people with misleading stats!
The other trick to watch for is the disappearance of the single dose cohort.
'Fully vaccinated' is an Alice in Wonderland term where it means whatever they want it to mean. Some nations use it to define two shots (US, UK), some three (Canada, Israel), some 'with all boosters included'. Some will move the goalposts in real time, so it might mean both two or three or more depending on context.
Naturally single dose either get called 'not fully vaccinated' (with unvaccinated lumped in), or omitted entirely, as is the case in the CDC charts called out on your article.
Any time they fail to give an exact number of doses (which the UK government has switched to finally), you should automatically be suspicious and challenge the definition of terms.
'Fully Vaccinated' - "it means just what I choose it to mean — neither more nor less."
I'm not here to defend the v but I don't follow your logic in saying that splitting the v'd into dose numbers helps bring them lower, since they're per capita. Also believe when she says paid blue checks she means people who have bought it rather than 'earned' it, also, carrying the (possibly deliberate) alternative interpretation that they have been paid to spread FUD.
"but I don't follow your logic in saying that splitting the v'd into dose numbers helps bring them lower"
Okay, simple numbers example. We'll do V1, V2, V3 for vaccine categories 1, 2 and 3, and U1 for unvaccinated. We'll use an error deviation of +/-1 death (meaning the number can go up by 1 or down by 1). All numbers totals be 'even' (equal number of vaccinated v unvaccinated, equal number of deaths total).
Very unrealistic but will show what I mean.
V1: 9 out of 100 die
V2: 10 out of 100 die
V3: 11 out of 100 die
(V1+V2+V3: 30 out of 300)
U1: 30 out of 300 die (AKA 10 out of 100 die)
Study would then say something like: As you can see, only 9 out of 100 died in the V1 group, and in the U1 group 10 out of 100 died, therefore the V1 group has better odds of survival than the U1 group. V2 showed no increase in harms compared to U1. Therefore according to V1 and V2 data, vaccines are safe.
If you re-merge the V categories, you will find that justification vanishes (30 out of 300 die for both groups; break even). It's a variation on Texan sharpshooter fallacy, called 'stratification', where you essentially keep dividing your dataset into subgroups until you can cherry pick the subgroups that look favourable.
So sometimes you might stratify on dose, or your might stratify on age, or on region, or even brand of vaccine. It's one of the many tricks they use. Notice how the fudge becomes evident when I squash the age stratification (which oddly only stratifies for old people?) and squash the brand stratification?
You wouldn't have an error margin of just one or two deaths like in my example. Usually you'd see 100s or 1000s. Imagine if V1/V2/V3 were all from a placebo cohort stratified by age rather than vaccine dose - I bet you will see some skewering effect occurring.
Also notice that the CDC data omits the one dose cohort entirely. So they've stratified, then deleted a subset of the data. They also don't do a full age breakdown, so they've half-stratified age as well.
You understand how the stratification fallacy works, right?
Even though the vaccinated and unvaccinated groups, when unstratified, have *no* differences, when stratified, the differences emerge. The distinctions only emerge as a result of stratification itself, and not because of any actual effect. So the V1, V2, V3 should not be distinct groups.
You can replace '100' with '100k' if you prefer. You get the same distortion. You will either intuitively 'get' how stratification works in datasets and why it is dangerous, or you won't.
I'll flip the question: why aren't the unvaccinated stratified?
But they do have differences unstratified, that's the point. even if you collapse all the vaxed into one group in the above example you get a 10 per hundred rate.
That's my point, the author uses a different demoninator for the different groups, but the graph he's criticising isn't doing that. You still just take the average of the combined strata if you just want to count the vaxed as a single group.
If they're hiding something then it's in the group they've excluded, which is the partially vaxxed or something like that from memory. If they're cheating it's not the way he's saying.
"if you collapse all the vaxed into one group in the above example you get a 10 per hundred rate. "
*only* if you collapse it together. You understand the differences emerge only when separated, yes?
Otherwise if it's stratified you get evident variations which allows for all sorts of comparison fallacies.
"You still just take the average of the combined strata"
Except the graph does not do this. It separates them. You understand what I am saying, yes? You're saying 'you should combine them', which is what I'm saying should be done, but you recognise they don't do.
But the combined rate is still much better is the point. The stratification isn't being used the way you're implying, the point they're making still stands even when you combine.
To be clear, I don't actually believe what they're saying. They're just not hiding the bodies that way.
They stratify to make it look like people should get more shots.
By the way, hocking generally means ‘pawning’ here. Trading assets for money at an alarming interest rate in the hopes of buying it back at a later date.
Although the full description says 'to later buy back', in colloquial usage it means to 'sell something', often with implied shill-like or desperate tendencies in a sort of marketstall style context.
Edgar Allan Poe meets the specter of Jonas Salk and his polio vaccine in Turfseer’s lockdown dirge “NEVERMORE.” Listen to it here: https://turfseer.substack.com/p/nevermore.
CDC fudged the 2021 data, claiming that 25-44y covid mortality was up 150% while 85+y covid mortality was DOWN 25% at the same time. Simply unbelievable.
With continual misrepresentations of data, it does make me wonder if there's a lawsuit in there somewhere.
There absolutely is.
I think the best debunk of this nonsense is Joel Smalley’s film showing more Covid deaths in the year after the rollout than in the year before, in almost every country in the world.
https://open.substack.com/pub/metatron/p/whats-the-story-of-the-covid-19-vaccines?r=peo1w&utm_medium=ios&utm_campaign=post
I concur, and I've seen many datasets that show higher deaths in vaccinated, but I was curious how exactly they had come up with a fudge that indicated the opposite. We must not let these hucksters mislead people with misleading stats!
The problems go much deeper when you look at how thy come up with these numbers.
You're right about the Denominator shenanigans, and thankfully their footnotes tell us several ways they create those false denominators.
I wrote about it here: https://www.theblaze.com/news/denominatorgate-how-public-health-agencies-are-skewing-the-statistics-on-vaccine-effectiveness
The other trick to watch for is the disappearance of the single dose cohort.
'Fully vaccinated' is an Alice in Wonderland term where it means whatever they want it to mean. Some nations use it to define two shots (US, UK), some three (Canada, Israel), some 'with all boosters included'. Some will move the goalposts in real time, so it might mean both two or three or more depending on context.
Naturally single dose either get called 'not fully vaccinated' (with unvaccinated lumped in), or omitted entirely, as is the case in the CDC charts called out on your article.
Any time they fail to give an exact number of doses (which the UK government has switched to finally), you should automatically be suspicious and challenge the definition of terms.
'Fully Vaccinated' - "it means just what I choose it to mean — neither more nor less."
nice work exposing that quack Bik
I'm not here to defend the v but I don't follow your logic in saying that splitting the v'd into dose numbers helps bring them lower, since they're per capita. Also believe when she says paid blue checks she means people who have bought it rather than 'earned' it, also, carrying the (possibly deliberate) alternative interpretation that they have been paid to spread FUD.
"but I don't follow your logic in saying that splitting the v'd into dose numbers helps bring them lower"
Okay, simple numbers example. We'll do V1, V2, V3 for vaccine categories 1, 2 and 3, and U1 for unvaccinated. We'll use an error deviation of +/-1 death (meaning the number can go up by 1 or down by 1). All numbers totals be 'even' (equal number of vaccinated v unvaccinated, equal number of deaths total).
Very unrealistic but will show what I mean.
V1: 9 out of 100 die
V2: 10 out of 100 die
V3: 11 out of 100 die
(V1+V2+V3: 30 out of 300)
U1: 30 out of 300 die (AKA 10 out of 100 die)
Study would then say something like: As you can see, only 9 out of 100 died in the V1 group, and in the U1 group 10 out of 100 died, therefore the V1 group has better odds of survival than the U1 group. V2 showed no increase in harms compared to U1. Therefore according to V1 and V2 data, vaccines are safe.
If you re-merge the V categories, you will find that justification vanishes (30 out of 300 die for both groups; break even). It's a variation on Texan sharpshooter fallacy, called 'stratification', where you essentially keep dividing your dataset into subgroups until you can cherry pick the subgroups that look favourable.
So sometimes you might stratify on dose, or your might stratify on age, or on region, or even brand of vaccine. It's one of the many tricks they use. Notice how the fudge becomes evident when I squash the age stratification (which oddly only stratifies for old people?) and squash the brand stratification?
You wouldn't have an error margin of just one or two deaths like in my example. Usually you'd see 100s or 1000s. Imagine if V1/V2/V3 were all from a placebo cohort stratified by age rather than vaccine dose - I bet you will see some skewering effect occurring.
Also notice that the CDC data omits the one dose cohort entirely. So they've stratified, then deleted a subset of the data. They also don't do a full age breakdown, so they've half-stratified age as well.
But the numbers in the graph are all given per 100k. They don't seem to be doing what you say.
They're doing:
V1: 9 out of 100 die
V2: 10 out of 100 die
V3: 11 out of 100 die
U1: 30 out of 100 die
"They don't seem to be doing what you say"
You understand how the stratification fallacy works, right?
Even though the vaccinated and unvaccinated groups, when unstratified, have *no* differences, when stratified, the differences emerge. The distinctions only emerge as a result of stratification itself, and not because of any actual effect. So the V1, V2, V3 should not be distinct groups.
You can replace '100' with '100k' if you prefer. You get the same distortion. You will either intuitively 'get' how stratification works in datasets and why it is dangerous, or you won't.
I'll flip the question: why aren't the unvaccinated stratified?
But they do have differences unstratified, that's the point. even if you collapse all the vaxed into one group in the above example you get a 10 per hundred rate.
That's my point, the author uses a different demoninator for the different groups, but the graph he's criticising isn't doing that. You still just take the average of the combined strata if you just want to count the vaxed as a single group.
If they're hiding something then it's in the group they've excluded, which is the partially vaxxed or something like that from memory. If they're cheating it's not the way he's saying.
"if you collapse all the vaxed into one group in the above example you get a 10 per hundred rate. "
*only* if you collapse it together. You understand the differences emerge only when separated, yes?
Otherwise if it's stratified you get evident variations which allows for all sorts of comparison fallacies.
"You still just take the average of the combined strata"
Except the graph does not do this. It separates them. You understand what I am saying, yes? You're saying 'you should combine them', which is what I'm saying should be done, but you recognise they don't do.
If there's no difference then *why stratify*?
But the combined rate is still much better is the point. The stratification isn't being used the way you're implying, the point they're making still stands even when you combine.
To be clear, I don't actually believe what they're saying. They're just not hiding the bodies that way.
They stratify to make it look like people should get more shots.
By the way, hocking generally means ‘pawning’ here. Trading assets for money at an alarming interest rate in the hopes of buying it back at a later date.
I think you mean ‘hawk,’ not ‘hock.’
A minor point, I know.
Hock is my British slang showing.
https://dictionary.cambridge.org/us/dictionary/english/hocking
Although the full description says 'to later buy back', in colloquial usage it means to 'sell something', often with implied shill-like or desperate tendencies in a sort of marketstall style context.
'I saw Mark hocking his wares down at the stall'.
Thank you for expanding my international lexicon.
Amazing work.
I've noticed similar things with California's pediatric data—it's a complete mess.
They are fudging it all, and they think we're too stupid to see it.
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