keskiviikko 16. joulukuuta 2020

Reporting of COVID-19 Ct Values Can Better Shape Public Policy

  • The cycle threshold (Ct) value is the number of cycles needed for the virus to be detected from the person’s sample.
  • The higher the Ct value the smaller the amount of virus present in the sample.
  • Most medical experts agree that Ct values over 33 likely indicate non-infectious, non-contagious levels of the virus.



 https://rifreedom.org/wp-content/uploads/RI-COVID19-Ct-AveDaily-022920-063020.jpg


Reporting of COVID-19 Ct Values Can Better Shape Public Policy

Not All Positive Tests Are the Same

Despite months of stonewalling of prior APRA requests … and just one day after a petition campaign by the RI Center for Freedom & Prosperity sent over 1,000 emails to various state-government officials, including the DOH and the governor … the Center has obtained information released to a persistent citizen by the state Executive Office of Health and Human Services; partial data covering a limited number of positive Covid-19 tests performed before July 2020.

According to Dr. Andrew Bostom, Covid-19 advisor to the Center and Rhode Island resident, a Brown University degreed epidemiologist, academic internist, and clinical trialist:

This first public revelation of cycle threshold (Ct) data, from over 5,000 RIDOH covid-19 rtPCR positive tests from late February through the end of June, reveals that a considerable number (36.2%) of these positive tests occurred at a Ct > 33, a level generally considered by the medical community to be associated with extremely low Covid-19 infectiousness

Initial analysis of this data also confirms the need for all future RIDOH reports of daily positive test results to include breakdowns of the Ct data

Deeper analysis of even more data can be immensely important in determining more data-driven, targeted, and effective public policies. I join with the Center in petitioning the state of Rhode Island to follow Florida’s lead and to immediately begin collecting and reporting Ct data for all Covid-19 PCR tests.”

The data includes 9,878 COVID-19 tests divided into two “N” groups corresponding to each other by day from February 29, 2020, through June 30, 2020.

The cycle threshold (Ct) value is the number of cycles needed for the virus to be detected from the person’s sample. The higher the Ct value the smaller the amount of virus present in the sample.
Most medical experts agree that Ct values over 33 likely indicate non-infectious, non-contagious levels of the virus. In practice, for example, the country of India only triages (treats or closely monitors) cases where the Ct values are less than 25.

Routine government collection and detailed reporting of this data can be enormously helpful in determining public policy, and in informing patients and their doctors when it comes to determining more appropriate individual quarantine and medical treatment regimens.

Of the total tests reported in Rhode Island, 51% had Ct values of 30 or higher, while 66% had values of 25 or higher.

Considered over the span of the report, Ct values gradually rose through May, after which they dropped along with the number of tests being conducted per day.

 

 

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221 views


TNK Twitter logo

15 Dec, 8 tweets, 3 min read
More important findings from the newly uncovered RI PCR test Ct data!

Recall I previously showed the Ct values of more than 5000 C19 PCR tests from the RI state health lab. Here they are again color coded for estimated infectivity. While all these folks were “positive”...Image
The green folks were likely not infective and the yellows may not have been. The higher the Ct score, the lower the viral load - the person is “less sick” or has remnant viral rna which can be detected for months while infectivity lasts maybe a week. 
Yet it is nearly impossible to obtain Ct score data! Go ahead and ask for it- you will likely get a blank stare or a weak excuse about authorization or data storage. But you won’t get your Ct score. Ridiculous. 
Now look what happens if we take the mean Ct values for each day- a clear trend emerges. As we moved into May the means climbed over 30. The average person receiving a positive test result was likely not infectious! What I believe @MichaelYeadon3 has termed a “cold positive”Image
The plot thickens when we add daily fatalities (7d ma). Here I have offset them by 21 days. The appropriate lag from test to fatality is arguable, but the point is as Ct values increase (avg viral load decreases), fatalities 3 weeks later decrease. Significantly...Image
As the average Ct rises past 30, deaths almost disappear.

Seems like reporting and tracking of Ct values would be very important for tracking the severity and progress of the pandemic! Trends in Ct PREDICT severity - and why wouldn’t they as Ct tracks viral load! 
Yet we only receive a binary yes/no test result. PCR testing w/o Ct tracking is a blunt instrument used to bludgeon us into compliance and create a casedemic while the very important insights Ct scores could provide is ignored by “public health experts.” This must change! 
Thanks again to @andrewbostom for suppling the data; uncovered via a FOIA request by

https://threadreaderapp.com/thread/1338698633336774657.html

https://archive.vn/TwVem


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449 views


TNK
Twitter logo
11 Dec, 8 tweets, 2 min read
PCR Ct data revealed! For the first time we get a look at the Ct values from a state health lab - these cover March-June 2020. First the scatter of all 5036 positive tests:
Next we look at the distribution of Ct values for all tests:
Finally, the temporal shift in percentage of tests with Ct > 32 (arguably a fair cutoff for infectious viral loads).
We can see even during the peak pandemic months 25% of tests were > 32 - and this was when mainly symptomatic folks were tested. In May-June as many as 2/3 of tests were non-infectious. The virus was waning AND testing protocols changing. 
So we have clear indication that over the entire period, at least a quarter of tests were effectively false positives, with as many as 2/3 being false positives in May/June. 
And the temporal shift in Ct distributions should be highly valuable in tracking the disease. Certainly these data are very important yet accessing it is extremely difficult! Why? 


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