[davem4616]

Originally Posted by Number 10 GI

How would universal health care have stopped this pandemic any sooner than the current system?????? How would it help the economy??? Let me help you out here, it wouldn't make any difference.

totally agree with you....UK and Canada both have universal healthcare...

oops they're facing the same challenges that we in the USA are facing...how can that be if universal single payer healthcare is the solution???

China and the WHO (the watchdog that wasn't) screwed everybody else by not telling everyone what needed to be said as quickly as possible

[Donb0975]

And the govt will tell you when and where and if you can get health care. No thank you to an ever bigger govt.

[Two Bills]

Originally Posted by rmd2

universal
Key word GOOD universal health care. No one on earth has GOOD universal health care. The reason we are a world leader in health care is competition and capitalism. Even in the UK which has a semi-universal health care system the wait for a biopsy can not days but many months.

If you can afford it!

[stevethepeddler]

Over 125,000 in TV and only a handful have been tested! Amazing. Save ourselves and distance and wear a mask.

[blueash]

Originally Posted by Aloha1

Here's a data point being ignored by most:
The Stanford study recently concluded tested over 3,000 Santa Clara County, CA residents for ANTIBODIES. The test results pointed to a positive (had the Wuhan virus) result 50 to 85 times the CDC daily positive number. As of this morning, 4/20, the CDC count is 760,000 people have, at some point, tested positive. If the count is off 50 fold then the true positive count is at least 38 million. If off 85 fold then 64.6 million. Why is this important? Two reasons, first it means we may be approaching "herd" immunity without which we'll never get off this merry go round. Second, it means the true death percentage from positive cases may very well be .1% to .2% and not 5, 8 10, or what ever number you see in the media.

Why is the CDC not doing relative population testing across the country. Within a week, we would have enough data to extrapolate and corroborate these findings. Relative testing is something the CDC does all the time. Why not now?

I don't know what "relative testing" means to you. But I can reply to your misunderstanding of the Stanford study. Click if you want to read it yourself.

The test results pointed to a positive (had the Wuhan virus) result 50 to 85 times the CDC daily positive number.

None of the tests were for the Wuhan virus That is not a medical term. The CDC has not ever produced a daily number. The numbers are produced by states individually and reported on media sources.

The Stanford study was NOT random. It was done in a county with the 4th highest number of COVID cases in California. They did not go out and randomly sample people for antibodies. Instead they placed an ad on Facebook to recruit people who volunteered to have their blood drawn for antibodies. Does this seem to be a good representation of the population, or maybe, just maybe, it would interest people who wanted to know if they'd been infected because of a mild illness or exposure where they couldn't get nasal swab tests done?

The study likely includes more positive people than a random sample would contain. The authors report that their volunteers were not representative of the county at large. It included far too many middle age white females. Of 3330 samples, only 50 were positive, a rate of 1.5% on a sample I believe tested higher than a random sample.

But there is more: The test kit being used is not FDA approved. The kit was tested for accuracy using known blood samples.

A combination of both data sources provides us with a combined sensitivity of 80.3% (95 CI 72.1-87.0%) and a specificity of 99.5% (95 CI 98.3-99.9%).

Sensitivity means it says positive if you are positive. So between 13 and 28% of people who are positive are mistakenly reported as negative. You are missing positives.
More importantly between 0.1 and 1.7% of negative are being mistakenly reported as positives.
If you test report 3330 tests, from 3 to 56 of your true negatives are being reported a positive. Guess how many positive tests they got.. 50. All of their positives could be false positives within the margin of error of the test. If you use the 99.5% specificity, then 16 of your positives are really negative, a full 1/3 wrong. Be very cautious when your finding is entirely in the margin of error.

And there's more to follow:

[Trophy25]

Take a look at how universal Healthcare works in Canada, Brazil and some European countries. Trust me you do not want it.

[blueash]

It would be very misleading not to do the same analysis for the negative tests. Here the utilized kit is only sensitive at about 80%. A good test is at least 90%, but it is what they were using. This means that of the 3280 negative results between 13% = 425 and 28 % = 918 were wrongly reported and should have been positive. Think about that, they reported 50 positives, but based on the poor quality of the test and of the sample, instead did calculations saying that there were really 139 positives [4.16% times 3330]. Do you see the possibility of a wide range of error?

Hold on, those are not the numbers used because the authors elected to try to use their sample and re-balance so it would better represent the population of Santa Clara county. This is standard. Their sample was badly unbalanced, again possibly reflection selection bias. 63% were female where the county is 50%, 76 % were between 19-64 where the county is 62%, 5% were over 64 where the county is 13%.

The authors balanced 63% female: the real county = female 50, balanced data 50
They balanced the ethnicity tested white 64%; real white = 33%, balanced data 35%
They strangely "balanced" tested over 64 5% : real over 64 = 13% to over 64 = 4.5 % I see no explanation why they failed to correct their data for age which is a major risk factor for disease. And the elderly represent much of the county reported positives.

The study does not break down how many in each tested group were positive. They obviously did not test people in nursing homes or hospitals. The authors did not correct for age but felt they needed to correct for zip code.

Using the correction to balance for the atypical sample population, they then estimate the "real" rate of persons who have had COVID in the county is 50 to 85 times higher than the positive PCR results reported. As you can see there are some significant problems, IMO, with the test used and the way they ignored age as a factor in their analysis. As of today, 26% of positive tests PCR tests in Santa Clara are in people over 64. If you undercount the elderly in your sample, of course you will miss positive cases. PM me if you want to run through some of the math.

And there is more:

[CoachKandSportsguy]

Blueash ! keep going! Spot on!

I was going to add the same!

sportsguy

[Curtisbwp]

Your words should provoke a self examination and soul searching. How many of your neighbors have checked on your wellbeing? Why do some of my neighbors have parties in the evening? Mine do.

[blueash]

Enough about the Stanford study. Now as to your analysis. I do not know if this is your original extrapolation or you read it elsewhere but it is so wrong.

As of this morning, 4/20, the CDC count is 760,000 people have, at some point, tested positive. If the count is off 50 fold then the true positive count is at least 38 million. If off 85 fold then 64.6 million. Why is this important? Two reasons, first it means we may be approaching "herd" immunity without which we'll never get off this merry go round. Second, it means the true death percentage from positive cases may very well be .1% to .2% and not 5, 8 10, or what ever number you see in the media.

You cannot use the national numbers the way you did. And the Stanford study which you are claiming supports your contention, refutes you directly. If the highest estimate of COVID in the study is accurate being 85 times the figures reported, that only applies to a community with the population and incidence reported that is similar to Santa Clara. So use their top number of 85 times, that percentage in Santa Clara is 4.16 % of the population. Stop, read it again.

If the study is perfect, the highest calculated number of people who are immune to COVID in Santa Clara is 4.16% of the population. If you want to use a multiple for the USA that is the one to focus on. 4.16 times 330 million = 14 million. And 96% of the US is not immune if only 4.16% are immune. Nationally today 750,000 positives are reported, out of 330,000,000. That is a rate of 0.23 % If the real rate is 85 times you get 19.2% of Americans have been infected. This still is nowhere near your figure and it completely ignores the huge fraction that is represented by the NYC metro epidemic. If you apply the 85 times figure to hard hit Westchester county NY, population 1 million, known cases 24,000, times 85 = 200,000 which is still only 20% of the people, nowhere near herd immunity. Apply it to Sumter FL, cases 147 * 85 = 12,500, less than 10% of our population. And all of these are using Stanford's highest numbers.

[perrjojo]

Is it my neighbors responsibility to keep me safe / healthy? it brings to mind the Hippocratic oath. First do no harm.

[blueash]

Lastly, and thanks for your patience and persistence..
Your choice to mention the death rate

the true death percentage from positive cases may very well be .1% to .2% and not 5, 8 10, or what ever number you see in the media.

is so misleading. I believe the point you are making is that influenza has an estimated death rate of 0.1%, so if that is the "true death percentage" for COVID, it is no worse than seasonal flu.

Fail.
The calculation by the CDC of the fatality rate for influenza is complex. You can read about how they do it HERE and HERE. Please do so and return. I'll wait

Welcome back. As you learned the flu estimate is based on a calculation, a model, which has as its inputs many factors. How many people are presenting to hospitals and selected outpatient clinics with flu-like illness, how many people after the end of flu season report that had significant flu symptoms even if they did not go for medical care, how many excess deaths there are from diseases known to have flu be a possible trigger, such as death from pneumonia, and information about how often flu tests are being done and how often they are positive at selected hospitals.

The influenza illness death rate is not a count based on what is on a death certificate. It is a much much higher number based on a statistical process looking for any deaths that might be, perhaps for lack of a better term, flu adjacent. The estimate of how many people had the flu includes in its number a best guess of all those with flu, not just those seen in a hospital, or those with a positive flu test, but everyone who had a flu-like illness. This give a large number for the denominator, with estimated deaths as the numerator.

This is totally, completely, astronomically, amazingly, different than the way COVID is now being calculated. At this time, except in some places, only deaths where there is a proven positive test are being listed as COVID deaths. This is the case in Florida. If you have all the COVID symptoms but you didn't get tested, you are NOT a COVID death. This of course artificially keeps the numbers down. Things look better than they really are. And this is especially true if the COVID tests miss a significant number of people who are really positive. This is widely reported. Note that in the Stanford study they missed about 20% of known positive controls. If these had been real patients who died, the official records of that 20% would not have COVID as a cause of death.

This is not how flu deaths were calculated, see above. Flu rate of death is an estimate of how many people died with influenza even if they never knew that had flu divided by an estimate of every person who had influenza like illness over the entire flu season. . It might be interesting to do a community wide test for antibodies to a particular circulating strain of influenza to see how many minimally ill or symptom free people were actually infected. It is not typically done. But that is what the Stanford Study is trying to do. Don't you think the denominator on the flu death calculation would be so much higher if asymptomatic people were included. Yup.
And HERE is an article asking the same question as the Stanford study.. How different is the evidence from blood testing for flu from the patient report of a flu illness

in longitudinal studies in which infections were identified using serology the point estimates of the asymptomatic fraction adjusted for illness from other causes fell in the range 65%–85%.

And that is the asymptomatic people, not including the low symptomatic people. None of these asymptomatic people are captured in the CDC's estimate of the number of persons who had influenza. And they need not be for the purposes of what the CDC is following. The CDC is very clear that the usual 0.1 % death rate is for persons with influenza like illnesses, and does not included lesser illness or no illness. You are of course saying you'd like to include lesser and no illness in your calculation to prove COVID is just like influenza. It is not. You are in error.

[OrangeBlossomBaby]

blueash I understood maybe 1/10 of what you posted. It was strangely compelling, nonetheless! Probability/statistics was my second-favorite math subject (after computer programming), and the only thing I -really- got out of prob/stat was how to play by the rules in Blackjack and craps.

Great posts, blueash. Thank you!

[GoodLife]

Originally Posted by blueash

Lastly, and thanks for your patience and persistence..
Your choice to mention the death rate
is so misleading. I believe the point you are making is that influenza has an estimated death rate of 0.1%, so if that is the "true death percentage" for COVID, it is no worse than seasonal flu.

Fail.
The calculation by the CDC of the fatality rate for influenza is complex. You can read about how they do it HERE and HERE. Please do so and return. I'll wait

Welcome back. As you learned the flu estimate is based on a calculation, a model, which has as its inputs many factors. How many people are presenting to hospitals and selected outpatient clinics with flu-like illness, how many people after the end of flu season report that had significant flu symptoms even if they did not go for medical care, how many excess deaths there are from diseases known to have flu be a possible trigger, such as death from pneumonia, and information about how often flu tests are being done and how often they are positive at selected hospitals.

The influenza illness death rate is not a count based on what is on a death certificate. It is a much much higher number based on a statistical process looking for any deaths that might be, perhaps for lack of a better term, flu adjacent. The estimate of how many people had the flu includes in its number a best guess of all those with flu, not just those seen in a hospital, or those with a positive flu test, but everyone who had a flu-like illness. This give a large number for the denominator, with estimated deaths as the numerator.

This is totally, completely, astronomically, amazingly, different than the way COVID is now being calculated. At this time, except in some places, only deaths where there is a proven positive test are being listed as COVID deaths. This is the case in Florida. If you have all the COVID symptoms but you didn't get tested, you are NOT a COVID death. This of course artificially keeps the numbers down. Things look better than they really are. And this is especially true if the COVID tests miss a significant number of people who are really positive. This is widely reported. Note that in the Stanford study they missed about 20% of known positive controls. If these had been real patients who died, the official records of that 20% would not have COVID as a cause of death.

This is not how flu deaths were calculated, see above. Flu rate of death is an estimate of how many people died with influenza even if they never knew that had flu divided by an estimate of every person who had influenza like illness over the entire flu season. . It might be interesting to do a community wide test for antibodies to a particular circulating strain of influenza to see how many minimally ill or symptom free people were actually infected. It is not typically done. But that is what the Stanford Study is trying to do. Don't you think the denominator on the flu death calculation would be so much higher if asymptomatic people were included. Yup.
And HERE is an article asking the same question as the Stanford study.. How different is the evidence from blood testing for flu from the patient report of a flu illness

And that is the asymptomatic people, not including the low symptomatic people. None of these asymptomatic people are captured in the CDC's estimate of the number of persons who had influenza. And they need not be for the purposes of what the CDC is following. The CDC is very clear that the usual 0.1 % death rate is for persons with influenza like illnesses, and does not included lesser illness or no illness. You are of course saying you'd like to include lesser and no illness in your calculation to prove COVID is just like influenza. It is not. You are in error.

Here is another antibody study in LA county done by USC and LA county health

USC-LA County Study: Early Results of Antibody Testing

And a study of realtime R1 for covid 19

covid-19/Realtime R0.ipynb at master * k-sys/covid-19 * GitHub

[ColdNoMore]

Originally Posted by blueash

Lastly, and thanks for your patience and persistence..
Your choice to mention the death rate
is so misleading. I believe the point you are making is that influenza has an estimated death rate of 0.1%, so if that is the "true death percentage" for COVID, it is no worse than seasonal flu.

Fail.
The calculation by the CDC of the fatality rate for influenza is complex. You can read about how they do it HERE and HERE. Please do so and return. I'll wait

Welcome back. As you learned the flu estimate is based on a calculation, a model, which has as its inputs many factors. How many people are presenting to hospitals and selected outpatient clinics with flu-like illness, how many people after the end of flu season report that had significant flu symptoms even if they did not go for medical care, how many excess deaths there are from diseases known to have flu be a possible trigger, such as death from pneumonia, and information about how often flu tests are being done and how often they are positive at selected hospitals.

The influenza illness death rate is not a count based on what is on a death certificate. It is a much much higher number based on a statistical process looking for any deaths that might be, perhaps for lack of a better term, flu adjacent. The estimate of how many people had the flu includes in its number a best guess of all those with flu, not just those seen in a hospital, or those with a positive flu test, but everyone who had a flu-like illness. This give a large number for the denominator, with estimated deaths as the numerator.

This is totally, completely, astronomically, amazingly, different than the way COVID is now being calculated. At this time, except in some places, only deaths where there is a proven positive test are being listed as COVID deaths. This is the case in Florida. If you have all the COVID symptoms but you didn't get tested, you are NOT a COVID death. This of course artificially keeps the numbers down. Things look better than they really are. And this is especially true if the COVID tests miss a significant number of people who are really positive. This is widely reported. Note that in the Stanford study they missed about 20% of known positive controls. If these had been real patients who died, the official records of that 20% would not have COVID as a cause of death.

This is not how flu deaths were calculated, see above. Flu rate of death is an estimate of how many people died with influenza even if they never knew that had flu divided by an estimate of every person who had influenza like illness over the entire flu season. . It might be interesting to do a community wide test for antibodies to a particular circulating strain of influenza to see how many minimally ill or symptom free people were actually infected. It is not typically done. But that is what the Stanford Study is trying to do. Don't you think the denominator on the flu death calculation would be so much higher if asymptomatic people were included. Yup.
And HERE is an article asking the same question as the Stanford study.. How different is the evidence from blood testing for flu from the patient report of a flu illness

And that is the asymptomatic people, not including the low symptomatic people. None of these asymptomatic people are captured in the CDC's estimate of the number of persons who had influenza. And they need not be for the purposes of what the CDC is following. The CDC is very clear that the usual 0.1 % death rate is for persons with influenza like illnesses, and does not included lesser illness or no illness. You are of course saying you'd like to include lesser and no illness in your calculation to prove COVID is just like influenza. It is not. You are in error.

As is your usual...well done.