I did appreciate the conversation, but it’s telling that one of the main data points @drsanjaygupta , chief medical correspondent @CNN ,chose to educate @joerogan on probably isn’t correct.
Pretty simple math : (myocarditis diagnosed / ppl with Covid) was found to be 16x higher than (myocarditis diagnosed / ppl without COVID)
But did the study get the denominator of people who had COVID right?
Ppl with COVID was based on those who received a diagnosis of COVID-19 in an encounter w/ the health system.
That mild cold the 5 year old had that u didn’t call anyone about?
Not included in the denominator per this CDC reported.
The row of interest in this CDC table is the <16 cohort and is what @drsanjaygupta is quoting. 86 myocarditis cases are identified in those diagnosed with COVID (64,898). This predicts 1/754 kids will have myocarditis if they get COVID.
But that denominator — 64,898 — is clearly an undercount because if only includes symptomatic people who had a positive COVID test.
We can estimate how far off they may be by using seroprevalence data from the @CDCgov
Testing for antibodies in a population sample is generally accepted (unless its done in Santa Clara in Spring 2020) as a way of gauging prevalence of disease in a population. That’s what the CDC did in Missisippi with COVID. cdc.gov/mmwr/volumes/7…
This CDC study estimated a ~10% prevalence (with a wide range based on ethnicity) in ppl < 18yo in Mississippi between May - Sep 2020.
Keep in mind the Cdc paper we’re analyzing covers Mar-Jan 2021 (6 more months)
If you assumed a minimum of 10% prevalence (an underestimate since the Cdc myocarditis report covers 2x more months than the Mississippi study) in this population of ~3.6million kids < 16 , that comes to ~360,000 who had COVID .. not 64,898..
The numerator would change 2 of course, but the 1:754 (86:64,898) estimate as it relates to risk of covid myocarditis wld seem to be a pretty sig. overestimate.
Interestingly, 1 way of checking these CDC #s wld be to see if the without COVID myocarditis rates are close to the prior baseline prevalence of myocarditis (132:3670762). They aren’t..the rate here ~4/100k, is much higher than xpected 0.25-2:100k/yr
Myocarditis was determined by ICD10 code only and was not confirmed by clinical data (laboratory tests or cardiac imaging)
Myocarditis has many causes other than COVID… no chart review took place to exclude alternative possibilities
Also.. sick inpatients are understandably investigated/ probed with diagnostic tests (like troponins and imaging) at much higher rates than those not encountering the health system
So the rate of pulmonary nodules in patients admitted to the hospital with a pneumonia are sure to be much higher than a control population not encountering the health system.
This does not mean pneumonias cause pulmonary nodules
It does mean that every novel virus that results in a Bolus of sick patients in hospitals will be found to have higher rates of myocarditis, especially if the only thing used to identify cases are ICD 10 codes.
Look what happens in an EMR study where they require an ICD diagnosis of myocarditis AND supportive laboratory / imaging data.
Notice myocarditis case spikes above the prepandemic baseline happens in the post vaccine era, not during COVID..
@joerogan referred to a preprint that suggested an elevated myocarditis risk in young boys (in line with a later @NEJM study) in the US to ? how necessary vaccines were for that group.
Gupta’s counter to rogan that COVID is 16x more likely in the unvaxxed based on the CDC report we’ve been discussing isn’t unexpected, but brings the analytical chops of both the CDC and Gupta into ?.
Gupta either knows , but thinks the larger goal of max vaccination is 2 important, or he doesn’t have the ability to go beyond what the cdc proclaims..
Both are problematic, and make Rogan’s point about trust in the MSM and the public health community they parrot.
If you can’t trust the Cdc/public health/MSM to communicate about issues related to vaccines in young boys, why trust their analysis on anything?
Here’s a visualization of just how deep the hole public health has dug itself..
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In order to be a vaccine provider in philly, months long application process, hours of webinar (mid-day), upload vaxx administered, wasted, in stock to 2 different websites every 24 hours, unpredictable allocation from local DOH, 30 day expiration in a -4 fridge. 1/
After we vaxxed everyone who wanted vax in the practice, walked to almost every business <1mile, street cleaning crews, random passerby’s etc .. declining demand / reg. requirements made it 2 hard to maintain vax stock.
Federal allocation schemes that have wide popular support generally favor big players that can navigate regulatory thicket, grease the right wheels to get early disbursements of product, (and make a small killing doing it)
In this preprint , the VAERS database was interrogated for anyone given a diagnosis of myocarditis/pericarditis/myopericarditis/chest pain AND appears to require an abnormal very sensitive blood marker of cardiac damage (troponin)
A few bites about the VAERs database. It was legislated into existence via the National Childhood Vaccine Injury Act (NCVIA) in 1986, which was a mechanism to shield vax manufacturers from litigation related to potential adverse events after getting vaccinated
Increasingly difficult to break through to many patients skeptical of recommendations seen coming from the medical establishment or through the mainstream media 🧵
Why is there a growing segment of the population that doesn’t believe experts/media ? Let’s start with a tragic non-med story of the last week.
Here’s the @AP account on a drone attack & what comes to light after someone actually investigates.
Everyone now knows about the fake ivermectin overdoses flooding ER story.
Journalists clearly know the headlines their audience will lap up — almost anything that will paint the half of the country that didn’t vote with them in a bad light will do
Great 🧵 on recent neuro-COVID paper making the rounds.
1. Nothing in the paper speaks to the veracity of CSF/grey matter volumes derived by T1/T2. (This is most definitely not like measuring the size of the aorta over time)
2. Whatever measure they do derive overlaps w controls a lot when looking at the box/scatter plot. While we use criteria with this type of poor discrimination not infrequently, we would seldom make a therapeutic decision with a diagnostic test with this much overlap with normal
3. These findings are derived based on defining regions of interest (ROI) in the brain. Anyone that’s done this in the heart to define EF, or in MUGA studies knows how fraught that is.. so would want a core lab, multiple interpreters b4 calling a real difference ..