Three-card Monte is a con game in which the victims, or "marks", are tricked into betting on the assumption that they can find the "money card" among three face-down playing cards.
In Three-card Monte a shill (read the CDC, FDA, and state and county health departments) pretends to conspire with the mark (insert you and me) to cheat the dealer (the pharmaceutical industry and the elite socialist globalist leaders) while in fact they are doing the exact opposite. The marks, you and I have no chance whatsoever of winning, at any point in the game. Here is how the CDC is playing the game.
Yesterday, I wrote about CDC’s cavalier acknowledgement that they have been withholding data on COVID 19. This is from The Washington Examiner’s Cassidy Morrison who wrote: “The Centers for Disease Control and Prevention has withheld valuable data on hospitalizations due to COVID-19 in the United States broken down by age, race, and vaccination status….”
Morrison continued, “The agency possesses but has not published critical information on hospitalizations, booster vaccines, and wastewater analyses, several people familiar with the data told the New York Times.”
So what does this mean for ALL of the data analysis presented by CDC and used to construct public health policies to control the spread of SARS-CoV-2? Its simple. NONE of it can be trusted. Robert Malone and Ryan Cole called it “scientific Fraud”.
What does this mean about the analysis I published yesterday? Who knows? I don’t. I can certainly guess because that’s all we are left with — guessing what the true data is.
One reader gently and correctly pointed that perhaps my comparison of hospitalizations among the vaccinated and unvaccinated to hospitalizations among those for whom VAERS reports have been submitted was an unfair comparison. It is a valid point. But as I wrote:
”What do we know about the accuracy of all of these numbers? Thanks to the “skillful”leadership of Rochelle Walensky, CDC Director we know that numbers that she offers us is not in the least bit reflective of the truth because Rochelle Walensky’s CDC has adopted a policy of withholding data on COVID-19 adverse event reports from the public lest we misinterpret that data.”
This nothing more than the typical misdirection from the Three Card Monte dealers and their shills.
Yesterday I also published this graph from the CDC:
CDC’s website discusses the data represented by this graph in three different ways. Here is the page headline:
”Rates of laboratory-confirmed COVID-19 hospitalizations by vaccination status”
This is how this data is described in the paragraph following the headline:
”laboratory-confirmed COVID-19-associated hospitalizations”
And here is the title of the graph:
”Age-Adjusted Rates of COVID-19-Associated Hospitalizations by Vaccination Status…”
Card-1, Card-2, and Card-3.
There is a difference between the cards. Card-1 is specific, firm, clear, it tells us that all of the children in this analysis were admitted because of a laboratory-confirmed case of COVID-19.
Card-2 is a bit nuanced. The cases are no longer simply laboratory confirmed they are now laboratory “confirmed” and “associated” hospitalizations.
Card-3 however is simply “COVID-19-associated”; the word “confirmed” is gone.
Does this really matter? After all, is “associated” really just another way of saying “confirmed”? No. It is not. Merriam-Webster lists 178 synonyms for the word “confirmed” and “associated” is not one of them. In fact, “associated” has an entirely different meaning. While “confirmed” is absolute “associated” is far more casual, in fact, “associated” might reasonably be considered to be lacking in causality.
In other words, we can’t come to a clear understanding of the true health status of the children who are behind the data. Were they hospitalized BECAUSE they had severe COVID-19 or because they had some other chronic illness that was the primary cause for their hospitalization and COVID-19 was secondary to that illness? OR as Card-3 suggests those children could have been hospitalized for almost any reason and we have no idea how COVID-19 was involved. They could have been admitted because of a car accident, a slip on the ice, or a pre-existing chronic illness and at some point either before or after admission they were found to be COVID-19 positive.
Here’s one more twist to our understanding of this data. The data doesn’t come from state health departments; it comes from CDC’s COVID-NET a network of over 250 acute-care hospitals in 14 states that are used to provide surveillance data to CDC. But there is a twist to this twist as well. Each of those 250 hospitals benefit financially from a COVID-19 diagnosis and even more from a COVID-19 hospitalization. Here is the language from the CARES Act (Coronavirus Aid, Relief, and Economic Security Act):
“For discharges occurring during the emergency period described in section 1135(g)(1)(B), in the case of a discharge that has a principal or secondary diagnosis of COVID–19, the Secretary shall increase the weighting factor for each diagnosis-related group (with such a principal or secondary diagnosis) by 15 percent.”
Now we have another way of looking at this graph:
The definition of who is included is obscure and it appears to be done intentionally.
The data set used is incomplete because CDC has told us that they have withheld data from the public.
The hospitals submitting data on “confirmed COVID-19” cases are receiving a 15% bonus for a COVID-19 diagnoses. The deck is stacked.
The only way to win Three Card Monte is to never play it OR put the dealer in jail.
Union, KY
28 Feb 2022