CrossFit | 200416
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200416

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Carlos Cobo
April 17th, 2020 at 2:34 pm
Commented on: Factors Associated With Hospitalization and Critical Illness Among 4,103 Patients With COVID-19 Disease in New York City

Something in the study reminds me of the Devor study, not necessarily re foul play, rather, re a number that doesn't sound right and could make a massive difference. Page 10, top: "The... Covid-19 positive study population... (26.8%) obesity." So let me get this straight. In a country where a third are obese, a LOWER PERCENTAGE than that got tested and tested positive? I would've expected the opposite.

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Robin Blankenbaker
April 17th, 2020 at 12:29 pm
Commented on: Factors Associated With Hospitalization and Critical Illness Among 4,103 Patients With COVID-19 Disease in New York City

These data do not account for biases in hospital admissions. Gender and race should be normalized.

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Paul Weber
April 16th, 2020 at 7:00 pm
Commented on: Factors Associated With Hospitalization and Critical Illness Among 4,103 Patients With COVID-19 Disease in New York City

This is the most helpful article I have found on COVID-19. Thank you for seeking to inform, rather than influence, and for analyzing and presenting pertinent data in a way that allows others to assess risk for themselves. We need more of this in mainstream media outlets.

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Jim Rix
April 16th, 2020 at 2:43 pm
Commented on: 200416

Did yesterday's fire-breather. Results there. I'm beat.

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Grant Shymske
April 16th, 2020 at 1:40 pm
Commented on: Factors Associated With Hospitalization and Critical Illness Among 4,103 Patients With COVID-19 Disease in New York City

Hopefully we can learn from this data, so that the next time this happens we can isolate like a scalpel instead of like a hand grenade.

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Lane Bowers
April 16th, 2020 at 1:15 pm
Commented on: Factors Associated With Hospitalization and Critical Illness Among 4,103 Patients With COVID-19 Disease in New York City

Crossfit, we need someone like you to find out why they are manipulating the data. The testing being done to determine whether or not someone is diagnosed as Covid-19 is a Test that was never designed to be used for virus. Furthermore, it does not actually determine whether or not you have Covid-19, but rather a response to stress by our immune system ("can only indicate the presence of viral material during infection and will not indicate if a person was infected and subsequently recovered." https://www.centerforhealthsecurity.org/resources/COVID-19/serology/Serology-based-tests-for-COVID-19.html#sec2).


99% of the deaths in Italy are from those who have 3-4 health complications. These deaths are being mislabeled as deaths because of Covid-19. This is fuzzy science like you tackled with the sugar industry.


I hope you can bring clarity and help the Crossfit community to be able to properly identify the real health concerns and not pseudo science that has been used to lock us down and put more at risk by impacting our community. We know that our best defense is to continue to be active. You guys are great at identifying scams and this may be too big for us to fight as a group, but I hope you can present great information.


The science can not even be properly reviewed if the data is incorrect. The diagnosing of Covid-19 is a mislabeling of the greatest proportion.


Thank you in advance.

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John Smith
April 16th, 2020 at 3:11 am
Commented on: Factors Associated With Hospitalization and Critical Illness Among 4,103 Patients With COVID-19 Disease in New York City

I'd like to know the prevalence of hospitalized that are on ACE-inhibitor drugs.

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Lane Wilson
April 16th, 2020 at 1:28 am
Commented on: Factors Associated With Hospitalization and Critical Illness Among 4,103 Patients With COVID-19 Disease in New York City

What does 'Ref' mean in table 3

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Js Smith
April 16th, 2020 at 12:50 pm

Lane, ref in statistics stands for reference or reference range.

In most medical applications, it’s the ‘normal’ range for most generally healthy portions of the population. Hope that helps.

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Robert Mauri
April 16th, 2020 at 2:25 pm

Also, the "odds ratio" is relative to the reference range. So, if the odds ratio is 2 for something (say, an age group), this means people in that age group are twice as likely to have something happen to them (such as being hospitalized) relative to people in the reference range. For normal epidemiology (eg, did people who ate butter fare worse than those who did not), you want odds ratios at least of two or preferably more. The larger the odds ratio, the more you can consider the association to be possibly causation. In this case, the odds ratios show what happens if you're older or have one of the listed characteristics.

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Michael Eades
April 16th, 2020 at 12:45 am
Commented on: Factors Associated With Hospitalization and Critical Illness Among 4,103 Patients With COVID-19 Disease in New York City

It is well known that aging typically associates with one or more co-morbidities. I would like to see an analysis in which the co-morbidities were separated from age. In other words, does a normal-weight 75 yo male with no hypertension, diabetes, heart disease, etc. still have the same risk of hospitalization as an obese, diabetic 75 yo male? I would assume not, but it seems that everyone stratifies by age alone and not by age and co-morbidities.

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Clarke Read
April 22nd, 2020 at 12:46 am

Mike,

We actually have this data, or at least a strong indication of it! The CDC has two reports - one of which I've read closely, one of which I just found now.

https://www.cdc.gov/mmwr/volumes/69/wr/mm6915e3.htm?s_cid=mm6915e3_w

https://www.cdc.gov/mmwr/volumes/69/wr/mm6913e2.htm?s_cid=mm6913e2_w


The latter is the one I know well. Table 2 provides the key - among those hospitalized, the rate of ICU entry among those age 19-64 WITH an underlying condition is HIGHER than the rate of ICU entry of those 65+ WITHOUT an underlying condition. Now, there is still absolutely an effect of age independent of comorbidity status, which we see in both those with and without comorbidities. But the impact of comorbidity on risk seems to be at least as great as that of age. (And who knows if we would see greater consistency if we started to suss out indicators of comorbidity that stop short of frank disease, like prediabetes)


The former, at a glance, shows that the share of all subjects with an underlying condition remains very high across all age groups. While you are right to suggest that the prevalence of these conditions increases with age, I know without looking it up that some number less than 94.4% of all Americans age 65+ have at least one of those underlying conditions.


The comorbidities are clearly increasing risk independent of age, across all age groups. And given that we now have two clear axes which are pushing increased disease risk - age and a handful of specific comorbidities - that probably begins to point us toward the mechanism.


There will be more discussion on this front, surely.

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