Imagine an articulate
chief lemming bragging that not only had his followers jumped off a cliff, but
that they had done so in far greater numbers than any other slice of the
rodents. This is the position occupied by the US regarding testing for
COVID-19.We’ve done more testing than any other country and bragged a lot about
doing so; but no one seems to have
survived to give a proper interpretation of the results.
To begin with, the tests
currently in use do not test for the entire virus, rather they just test for
various fragments of it. Many of the results are thus false, sometimes false
positives and sometimes false negatives. This means one has to interpret their
results with caution. Our medical authorities, to say nothing of our political
ones, don’t seem to be able to do this.
All medical students are
taught the basics of screening in their introductory statistics course. The
problem is that most of them either didn’t go or slept through the course. The
rest immediately forgot what they had learned.
When
testing for anything, a medical professional needs to know the positive
predicative value (PPV) of the test as well as the negative predictive value.
I’ll focus on the former.
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In
order to know the PPV—i.e., the percent likelihood that a positive test is a
true positive—the sensitivity of the test must be known as well as prevalence
of the disease, at least to an approximate degree. According to a recent article in the New England Journal of Medicine, the
sensitivity of the tests for COVID-19 is about 70 percent. The prevalence in
any of the tested populations is not yet known, so we cannot calculate the PPV,
although we can calculate what it would be at any prevalence level we want to
assume. I’ll get back to this below.
A test for COVID-19 that is
70 percent sensitive will only catch 70 percent of the tested subjects with the
disease. Therefore, 30 percent will falsely test negative. Additionally,
Bayes’s theorem, the mere mention of which defeats the numeracy of all but the
most resolute of physicians, says that a 70 percent sensitive test will be
positive in 30 percent of the tested population that doesn’t have the disease.
(I am assuming a specificity that is also 70 percent. The specificity of the various
tests used has not been given.) Consider testing for COVID-19 in 1 million
subjects, none of whom harbors the virus. Three hundred thousand will test
positive.
This
is the reason I doubt this report from Asturias,
Spain, which tells us the region has gone fourteen days
without recording a single new case of COVID-19. Either they have stopped
testing—you can’t get zero positive tests on a sizable population with a 70
percent sensitive test—or they are not telling the truth.
The Johns Hopkins COVID-19
tracker that is widely used and quoted considers a confirmed test to be equal
to a positive test. This is an error of epic dimensions for the reasons just
stated.
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The
professional and college athletics departments are making the same error. They
are testing their athletes daily or every other day using a test that is no
better than 70 percent sensitive. Eventually all the players will test
positive. They will be isolated for fourteen days, after which they may again
test positive, ad
infinitum.
How does one estimate the PPV of a COVID-19 test? At 70 percent
sensitive there’s no need to. Such a test is so contaminated with both false
positives and negatives that its use is virtually without utility. Suppose we
had a test that was 95 percent sensitive and specific. The PPV is the number of
true positives divided by the sum of true and false positives. To make this
calculation we must assume a prevalence of the virus in the sampled group.
Let’s start with a prevalence of 1 percent. If we test 10,000 subjects, 100
will carry the virus. Of these, 95 will test positive. Bayes’s theorem says 495
(5 percent of the 9,900 patients) without the virus will also have a positive
result. That’s 495 false positive tests. The PPV in this hypothetical sample is
95/95+495, or about 16 percent.
Now assume our population
(again 10,000 subjects) has 50 percent true positives. Here our PPV is 95
percent (4700/ 4700+250). The relationship of PPV to prevalence is given in the
figure below. Note that even with a 95 percent sensitive test we’ll be overwhelmed
with false positive results if our tested population has a low prevalence for
the virus. The more we test, the more false positives we’re likely to get if
our testing is not focused.
It
should be obvious from the data above that all the testing we have done and
continue to do has likely confused more than enlightened. The virus is real and
in the wild. How should we effectively deal with it? The best indicator of our
status is how many people are in the hospital because of a clinical diagnosis
of viral pneumonia. More specifically, how many are in the ICU. Note that
testing here is unnecessary, as the assumption today is that any case of viral
pneumonia is caused by the coronavirus.
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If
our situation regarding the epidemic improves, widespread testing will have
played no role in this improvement. Why any improvement? We recognized who the
at-risk population was and they took shelter and continue to do so. The
Centers for Disease Control and Preventon (CDC) estimates that mortality from
COVID-19 in patients younger than 50 is 0.05 percent. Virtually all
of this mortality in younger patients comes from those with comorbidity. We
also have gotten better at treating patients with severe pneumonia caused by
the coronavirus.
The virus is likely to be
with us for some time. Epidemics end either when those most susceptible to the
pathogen have been exposed to it or when an effective and safe vaccine is
available. We don’t have such a vaccine. It’s hard to know when or if one will
be available. And the logistics of manufacturing and administering billions of
doses are formidable. In the meanwhile, we have to coexist with it while not
destroying society, socially and economically, in the process. We will also
have to admit that our current testing regime has alarmed the planet without
contributing a health benefit.
Note: The
views expressed on Mises.org are
not necessarily those of the Mises Institute.
Neil A Kurtzman, MD, is Grover E. Murray Professor Emeritus and
University Distinguished Professor Emeritus at Texas Tech University Health
Sciences Center.
