Thumb on the scale

How the numbers on vaccinated versus unvaccinated are manipulated

I have said for months that the numbers provided by the CDC just don’t add up.

Remember when they were claiming that 99% of all people in the hospital were unvaccinated? Simple math showed that was impossible.

They had to be putting their thumb on the scale somehow.

Since their introduction, the ratio of vaccinated to unvaccinated cases/hospitalizations/deaths reported by the CDC is far, far different than that from the rest of the world.

One way they do this manipulation has been to test/report unvaccinated cases differently than vaccinated cases. For example, the PCR test for a vaccinated person cannot exceed 28 cycles, but if you are unvaccinated they can run & report over 40 cycles as a positive. In addition, the vaccinated need an actual symptom (or two) of COVID along with a positive test to be reported as a ‘COVID hospitalization’; however, the unvaccinated can be declared ‘positive’ on the basis of a test alone - or even on the basis of symptoms with negative tests!

Turns out, there is another way of making vaccines look good (at the expense of the unvaccinated).

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Short version: You can make vaccinations look fantastic (and being unvaccinated terrible) by simply categorizing the two weeks after a first mRNA shot as ‘unvaccinated’ and shifting all cases/hospitalizations/deaths into the ‘unvaxxed’ category.

And then you can do it again for the second dose.

And then you can do it again for the first booster.

And then you can do it again for the next booster.

Etc. Etc. Etc.¹

You can see this definition explicitly laid out on the Ontario website:

If you go to their COVID page, and scroll down to the first graph (screenshot below) & click on ‘See what we mean by: Unvaccinated cases’

This definition pops up:

Thank you, Ontario. That couldn’t be more clear.

The CDC uses the same definition.

The definition of partially vaccinated is similar: 14 days after first dose until 14 days after second dose.

But look at the language “symptoms started…”.

That means someone who gets their first mRNA shot and then gets COVID symptoms on day 13, goes to the hospital 8 days after that, goes into the ICU 3 days after that, and dies 3 days after that…. is counted as an “unvaccinated” death! Despite the fact that they died 27 days after getting their shot.

Does that seem fair?

Obviously not, but the bigger issue is what these definitions do to the statistics.

Thanks to another Canadian province, Alberta, we have our first glimpse into exactly what happens after someone gets an mRNA shot. (They almost immediately took down the data - but enterprising internet sleuths found it and put it out for us all to see.)

Of the all the cases among people who got their first mRNA shot, 39.4% developed symptoms of COVID within 14 days - so these cases are wrongly categorized as ‘unvaccinated’ cases.

Of the all the hospitalizations among people who got their first mRNA shot, 47.6% developed symptoms of COVID within 14 days - so these hospitalizations are wrongly categorized as ‘unvaccinated’ hospitalizations.

Of the all the deaths among people who got their first mRNA shot, 56% developed symptoms of COVID within 14 days - so these deaths are wrongly categorized as ‘unvaccinated’ deaths.

About 40% of cases, 48% of hospitalizations, and 56% of deaths are miscategorized!!!

The effect of this mislabeling is even worse than it seems: not only do you remove a ‘vaccinated death’ from one side of the scale, you also add that death to the ‘unvaccinated’ side.

A double whammy.

The actual numbers are really hard to get (e.g., we know total COVID cases in Alberta and now know the number of cases that are miscategorized, but we don’t know what date people got their shot, so we cannot know how many of each week’s cases are wrongly classified).

Therefore, let me give you thought experiment/analogy from the world of teaching.

Let’s say I have a class of 200 students. 100 of them are in their first year of college and the other 100 are in their second year.

I have a theory that the second-year students (because of their experience in other courses) will have a lower rate of failure on my exams.

Let’s pretend that there is actually no difference between the two groups: first- and second-year students score EXACTLY the same on my exams. On average, 10% of students fail each exam.

However, it takes everybody a couple of tests to get used to my testing style, so the failure rates on my exams gets lower and lower as the semester goes along. From first to fifth exam, the failure rate is: 15%, 12%, 10%, 8%, & 5%, respectively.

The data would look like this:

The data reflect reality: the two groups each account for half of the Fs in the class.

Now, I will do what Canada and the CDC do…. I will miscategorize the groups.

I decide that the second-years will take some time to get used to my exams and are therefore no better off than the first-year students, I will put all the ‘second-year failures’ from the first two exams into the ‘first-year failures’ group.

You can see the results of moving the second-years’ failures into the wrong category (arrows).

It now appears that the first-year students account for over three-fourths of all the Fs in the class!!!

With one simple ‘re-categorization’, I have taken two equal groups and made them appear wildly different.

And this is just ONE of the things the CDC is doing to skew the data to make vaccines appear superior.

One more thing. Look at that same ‘miscategorized’ chart, but this time look at the ‘failures in each category’ column.

These numbers would be used to calculate ‘risk ratios’.

If my data were being reported by the media, based on this column the headline would be:

First-year students are over three times as likely to fail exams!!!

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Sound familiar?

Every decision made by the CDC (or Pfizer) to define this data skews it in favor of vaccines.

Every single one.

If I do to my student grade data what they do to the vaccine data - I could easily make it appear that one of the the two (exactly equal!) groups was a total failure and the other a rousing success.

This is why we cannot trust the data from the CDC (or Canada, apparently).

1. Evidence for this comes from Alberta, Canada. I am not sure if the data release was accidental, but it has all been hidden again (after just a couple of days). Thankfully, the internet is forever! (The original observation is here and El Gato has a long analysis.)