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April 24, 2020 - Health Ranger - Mike Adams

14:12

Antibody tests produce HIGH numbers of FALSE POSITIVES

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	This Health Ranger Report Pandemic Podcast is brought to you by NaturalNews.com for uncensored reporting and HealthRangerStore.com for lab tested, preparedness, supplies, such as storable food, full face medical masks, biostructured silver, first aid gel, and iodine, only while supplies last.
	April 23rd, Pandemic.News Update.
	This is Mike Adams here.
	Thank you for joining me.
	Today is the day that Governor Cuomo of New York announced Antibody test results that when projected out to the population at large, he says that now 13.9% of the people of New York already have antibodies against the coronavirus.
	That's 2.7 million people in the state.
	And of course, this is bad information.
	And here's why.
	We have a problem with false positives of antibody tests.
	And this comes down to something called specificity.
	So antibody tests, most of the tests being used today, including those by the Stanford researchers, are made in China by biotech companies that have horribly bad accuracy.
	Now think about this for a second.
	If you have an antibody test that is 95% accurate in terms of its specificity, that means that for every 100 people that you test, you're going to have five false positives.
	Now, these antibody tests can produce both false positives and false negatives.
	And false positives is from lack of specificity, and false negatives are from lack of sensitivity.
	But we're just going to talk about the lack of specificity here, which means that a positive result can be triggered by some other antibody in the body that isn't I think?
	Ends up being multiplied, amplified.
	And here's a good explanation of how this works.
	And this is true in testing for breast cancer and other diseases like that that have a very low actual prevalence in the population.
	but a very high number of people are screened for that disease.
	This is why mammograms mostly produce false positives for breast cancer.
	Most of the women diagnosed with breast cancer don't have it.
	Why is that?
	Well, here's why.
	Let's say that this antibody tests has a 95% accuracy in terms of its specificity.
	So that means for every 100 people you test, you're going to get five false positives.
	Got it?
	So five false positives out of 100.
	Now, if the actual infection rate in the population is, let's say, 1%, which is actually pretty, might be a good number, or maybe 2% across the U.S. right now, but let's just say 1% for simplicity here.
	That means that when you test 100 people with this testing kit, which again, most of them are made in China, most of them are not FDA approved, they're not accurate at all, You would get 6 positives.
	6 out of 100.
	5 of the 100 are false.
	1 out of the 100 is true.
	So you actually have, in that case, 5 false positives for every 1 true positive.
	So you would then be overestimating the number of positives in the population by 5 to 1.
	So that's obviously horrible.
	You know, if you find, let's say, 18 positives out of 100, and you think 18% of the population is infected with this, what you've actually found is something more like 15 false positives and three real positives, and that's how you got to 18.
	The problem with all of this Is that when you have a lot of false positives, as we saw in the Stanford study and also with Governor Newsom announcing this in New York, then when you do the math on it, it turns out that you would calculate the case fatality rate to be far lower than it actually is.
	And this results in complacency.
	So these false positives are being seized upon by the Wall Street Journal and basically a lot of the pandemic denialists to say, hey, this is not even very dangerous then.
	And a lot of them say, well, it's not even as dangerous as the regular flu, that it only has one-tenth the death rate as the regular flu, which is absurd.
	I mean, earlier this week, in one day, 2,800 people died from the coronavirus.
	Between 2000 and 2800 almost every day this week in the United States, the regular flu kills only an average of 94 people every day.
	Just 94.
	There's no comparison.
	We're talking about thousands of people dying from the coronavirus every day right now.
	In fact, I've said this before, more Americans died from the coronavirus in the last 17 days than died during an entire year of the flu season.
	So the coronavirus is far more dangerous.
	But even if you do the math on what Governor Cuomo announced, where he's saying it's a 13.9% antibody rate now across New Yorkers, i.e.
	2.7 million New Yorkers are already infected.
	If you look at the aggregate number of deaths in New York versus that 2.7 million, the death rate, and again, this is including the false positives.
	The death rate still comes out to 0.77%, which is almost 800% higher than the case fatality rate for the regular flu.
	And actually, it's even higher than that because if you look at the CDC website, cases for the regular flu are only considered to be infections if the person is symptomatic.
	So asymptomatic regular flu cases are never counted in this.
	And that's why you see the CDC saying, well, each year only, let's say, 65 million Americans get the flu.
	That's because there's only 65 million that are symptomatic.
	The actual number of people who have antibodies for the flu is no doubt much higher than that, maybe 150 million or 200 million or 250 million, something like that.
	So the case fatality rate actually for the regular flu It's way below 0.1%.
	It might be 0.04% or something like that, which means that the case fatality rate for the coronavirus is way more than 10 times higher, at least an order of magnitude higher if you're comparing apples to apples, that is, i.e., people who carry the virus and who have the antibodies, whether or not they're symptomatic.
	So again, even if you use Governor Cuomo's numbers, the coronavirus is still vastly more dangerous than the regular flu.
	But since we now live in this era where a lot of journalists, conservative journalists, especially in independent media, they can't do basic math.
	They can't even take the number of deaths in New York and divide it by 2.7 million To get.0077, you know?
	They can't even do fractions.
	So, a lot of times, when I'm even describing podcasts or writing articles, to a certain audience of Other publishers, readers, and so on.
	It doesn't even matter because they can't do math anyway.
	They don't follow integers.
	All they know is emotionally they want this to not be bad.
	And so there's this tribalism of denialism to say, well, we don't want it to be bad.
	Therefore, we're just not even going to look at any numbers unless we can use numbers that we think satisfy an emotional need.
	And that's what it's all based on, just emotions and political agendas.
	But if you really look at the numbers, it tells a very different story.
	And if you really look at the science behind the antibody test, you'll realize that most of the positives are false positives.
	Now, what else is interesting about that is, as the number of infections rises in the population, let's say you hit 50% of people who are actually infected and recovered.
	Well, then the number of false positives when you test 100 people is Much lower, okay?
	Because here you go.
	Out of 100 people, remember that you get five false positives out of the 100 because of the lack of specificity of the test.
	So if you test 100 people and 50 of them actually have the infection, you would get 50 true positives and roughly five false positives.
	Now, in truth, I'm simplifying this.
	In truth, you would really only get 2.5.
	False positives because half of the false positives that would happen are already covered by the real positives because it's a 50%.
	But whatever, I'm simplifying it.
	The point is that you would have 50 real positives and a small number of false positives.
	So in that case, once you have that 50% penetration, you would be able to use the exact same antibody test, which isn't that accurate, but still get far more accurate numbers.
	That's what's interesting about this.
	You see, the inaccuracy of the test is amplified when you have mass testing of a population that has a very low infection rate, such as 1 or 2%.
	And that's kind of where we are across most of America today.
	We don't even have A 2% penetration rate or even a 3% penetration rate in most areas.
	Now, possibly we do have that in New York City, for example.
	In fact, it seems likely that it's higher than that in New York City.
	But for most of America, we're not even at the probably 2% or 3% infection rate yet.
	So that means that we're going to have a lot of false positives.
	We're going to have a lot of bad science, such as the Stanford study that went out there.
	And perhaps Governor Cuomo's announcement is exaggerated in terms of reality versus the fake positives.
	And we're going to see a lot of bad policy decisions that are based on these bad numbers with bad antibody tests.
	And again, this is true whether you're talking about AIDS testing or Ebola testing or measles testing or breast cancer.
	When the condition has a very low prevalence in the population, errors of specificity in the tests are amplified to create the impression of a large number of positives that actually don't exist.
	This is a well-known phenomenon in medical testing.
	Any statistician in medicine or epidemiology would tell you the exact same thing.
	I mean, frankly, go listen to Michael Savage.
	He knows his stuff on this.
	I'm just explaining to you how the numbers work.
	So this is very important stuff to keep in mind.
	It's probably not as widespread in New York as we're being told, which means we still have a very long ways to go.
	And the case fatality rate is probably much higher than what is being reported by the media.
	Remember that most members of the media can't do math and don't understand science and don't understand medicine anyway.
	So they don't even read the studies.
	They just kind of run the headline.
	Whatever the study says in the headline, they think that's true.
	If you read the study, a lot of these studies fall apart very rapidly.
	Bad sampling, no randomization, faulty antibody tests and so on.
	So read the science, actually read the studies, you'll find a very different picture from what's being reported in the mainstream media and even in much of the independent media.
	So if you want the real story on this, good, accurate analysis without political bias, that's my website, naturalnews.com and pandemic.news.
	And yeah, we're taking it to both Republicans and Democrats on this.
	As you can tell, I'm being very critical of a lot of conservatives on this issue.
	So we are not joining the tribe to defend Trump with bad science.
	If it's bad science, We're going to call it out regardless of whether it's good or bad for Trump's election.
	We're sticking with the science and the real numbers and the solid analysis.
	That's the goal.
	That's what I'm trying to do and bring you that information each and every day.
	So thank you for listening.
	Mike Adams here, The Health Ranger, pandemic.news.
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