The nonsense of CDC 'guidance'
An illustration using Cleveland State University and Cuyahoga County
I’ve pointed out multiple times now, ever since the new CDC Map of Fear came out in February of this year, just what a disaster the CDC map and ‘guidance’ would be - the last time I brought it up was just over two weeks ago (link here).
It all seems logical on the surface. ‘Data-driven’, ‘evidence-based’ triggers that apply everywhere in the US sounds simple and efficient, but I’d like to walk through how this works in the real world and why it matters how the data is collected and how it can create havoc and nonsense behavior down the line.
Cleveland State University got trapped in this mess this past week. See the next two images showing the announcements on Cleveland State University’s Facebook page.
Note the dates.
The first post, requiring masks, was put up July 5th, to go into effect on July 6th. The second post was put up on July 9th, just 4 days later, returning to masks optional for the next school day, Monday, July 11th. The classroom mask mandate was in place for all of 3 days.
So what happened here?
They were sucked in by the ease and lack of institutional responsibility offered by the CDC’s Map of Fear, that’s what happened.
Following the links on the Facebook announcements, we are led to Cleveland State University’s masking policy, seen below:
It looks so simple, so clean, so unassailable, right? They aren’t the ones making the masking decisions! It’s all data driven, straight from the CDC!
So how about we examine that ‘data’ that determined the color of Cuyahoga County (the home of Cleveland State University, population ~1.2 million)
On June 30th, the CDC had the information below on Cuyahoga County, the metrics determining the ‘yellow’ designation that week:
One week later, on July 7th, the CDC updated its maps, and Cuyahoga County had the following numbers and a ‘green’ designation:
Now I’ve marked on this second image what has changed. We can see that the cases per 100k population actually increased week over week, while new ‘COVID-19’ admissions and percentage of inpatient beds in use by ‘COVID-19’ patients dropped by a very tiny amount each.
To understand how such minute changes changed the alert level, first see the CDC’s explanatory table below:
Seems simple enough, right? If there are over 200 cases per 100,000 population, the county is automatically medium or high risk. If the county has fewer than 200 cases per 100k population, then you need to look at the new admissions or % occupancy.
In Cuyahoga County’s case, the color of the county rested solely on the new ‘COVID-19’ hospital admissions that I’ve boxed in above. For the first week, there were 10.3 new admissions per 100k population putting the county in yellow, while one week later it had 9 admissions per 100k population, dropping it below the CDC threshold of significance.
But what kind of ‘hospitalizations’ are these? Is there any way to know?
I’m so glad you asked! Because there is a way to get a good idea of the scale of admission for COVID-19 vs. with COVID-19 by looking of the specifics of those individual hospitalizations from the downloadable csv data file from coronavirus.ohio.gov and comparing when ‘onset’ occurred vs. the admission date.
There were 33 total hospital admission in Cuyahoga County between 6/22/22 and 6/30/22 and I have plotted the result of comparing onset vs. admission dates below.
Now there’s a lot to unpack here. Those who have been following me for a while are familiar with this kind of graph showing days between onset and admission, with the negative numbers indicating that onset occurred before admission to the hospital — the way we would expect it to if a person was being admitted for COVID-19. But as you can see from the graph, the vast majority of these admissions did not occur after onset. Most had ‘onset’ on date of admission (shown by 0 days between onset and admission date). Now, ‘onset’ in Ohio means either the date that the individual started exhibiting symptoms or when the individual tested positive if that individual had no symptoms.
The likelihood of these hospitalized individuals experiencing symptoms and going immediately to the hospital are vanishingly low, and it is far more likely that they are admitted to the hospital for other reasons, but coincidentally test positive on admission. They are not there for COVID-19. Even more obvious are the two individuals who are marked as ‘COVID-19’ admissions who had ‘onset’ after they were admitted to the hospital. Those two were clearly not admitted to the hospital for COVID.
So in the end, only 10 out of the 33 hospital admissions in that week could have even remotely have been for COVID, all others were almost certainly incidental positives. But even those 10 potential hospitalizations need to be examined carefully. In order to do so, I have listed the exact demographics of those potential COVID patients in the graph above.
What exactly are the odds that a 20-29 year old male starts experiencing COVID symptoms and one day later is admitted to the hospital for COVID? I’d say probably slim to none, and if he really was, then I expect he probably has some more severe underlying conditions that he is dealing with.
Overall, we are looking at Cleveland State University setting in motion mandatory masking when there may have been 10 people over 7 days who were potentially admitted to the hospital for COVID-19. In a county with over 1.2 million residents.
That’s how insane this system is.
Appreciate all your graphs, Kathryn, but deception by these hospitals will always be suspect now. We will never believe anything they say again. We know they were paid handsomely for covid patients and giving them Remdesivir and putting them on vents. It had nothing to do with actually healing these patients, plus the denial on their part of vaccine injuries is also a major concern. I helped personally save a friends x-wife in an Akron hospital that told him to accept she was going to die, they gave her Remdesivir and wanted to put her on a ventilator which they finally got it stopped. All her symptoms were from the Remdesivir. They put her on the Remdesivir when she told them no! We also had some friends who lost three family members to this protocol as well and they were under 60. So they never should have died. Unfortunately it was too late when we heard about it and they had already passed away. It’s a shame all these kids being forced to mask again, don’t just go in and refuse and ask for refunds. But they don’t equate it to reducing their immunity by wearing them. We also have a relative through marriage, age 27, had a stroke and can no longer function. They were on vacation when it happened. She was first hospitalized in South Carolina, but now she is at Cleveland Clinic and they refuse to admit its vax related. They can find nothing else that contributed to it. She has a 3 month old and 3 year old! We will never trust the healthcare system again.
Yesterday . I was at a local store in Westlake. There was one person wearing a mask from time they came into store and left . In car with mask on and windows rolled up .. wow ! You really can’t fix stupid ! I took a picture and was laughing the whole time …are the stupid leaving in an alternate universe?? 😂😂😂. What is this phenomenon??