The several different plans to reopen the economy share a common
element — more testing to identify who has or is at risk for infection,
and to help determine who can safely return to work. But, we still lack a
coherent national testing strategy. Continuation of what we are
currently doing will fail to identify some of the most crucial
information: what proportion of the population is currently infected and
whether the tests offer meaningful information about safely
reintegrating previously infected workers. We can do it better. Here’s
how.
Different Tests, Different Limitations
There are two types of tests. PCR-based tests use samples collected
using swabs of the nose or throat. Some patients can have positive tests
from the nose but negative tests from the throat (and vice versa), and
some patients with active infection can have negative tests at both
sites. [Ed.: After this article was submitted and accepted, the FDA authorized a viral protein antigen test.]
A different type of test is used to identify the presence of antibodies
in the bloodstream. IgM antibodies suggest a current infection. They
rise after a new infection and then decline. IgG antibodies imply a past
infection. They increase a couple of days to a few weeks after IgM and
remain elevated, but it is unknown for how long. Nor do we know if such
antibodies confer protection against reinfection. Not all patients
infected show positive antibody tests. Further, some positive antibody
tests can be falsely positive due to exposure to other viruses.
Who Should We Test?
Paul Romer, a Nobel laureate economist, proposed testing every person in the population
an average of 7 times over 500 days. But, is testing everyone feasible
or desirable? There are several reasons why the answer is “no.” First,
it simply is not feasible. The current testing capacity has expanded
greatly to about 200,000 tests per day. An ambitious goal is testing
three-quarters of the population. If we could double the current
capacity to 400,000 tests per day, it would take more than 21 months, or
625 days to test 250 million people a single time. A 21-month long
program does not tell us what to do today, nor will it necessarily be
instructive 21 months from now.
A second challenge is that clinical doctors and public health
agencies have different testing goals. Doctors test patients to make an
individual diagnosis; public health agencies need to understand
population prevalence and the dynamics of disease transmission in
communities. They do this not by testing everyone, but rather by testing
a scientifically determined reference sample of people from the
population, whether or not they have symptoms.
There are several reasons why trying to expand on our current
strategy will fail. First, swabs and reagents are often unavailable.
Second, clinical testing focuses on people with suspicious symptoms.
They were tested for individual diagnostic reasons and are not a
representative sample from the population. The fraction of people who
test positive in clinical settings does not provide an accurate picture
of the percentage of people who are infected now. So far, less than 1%
of the U.S. population have tested positive for the virus. The real
number of positives is undoubtedly higher. How much higher? We can’t say
with any confidence because testing only those with symptoms will never
offer unbiased estimates of how many people in the community at large
are infected.
Monitoring the Epidemic
From a public health perspective, trying to test everyone would be a
foolish waste of resources. There is a simpler, less costly, and
entirely feasible alternative. Public health practitioners use sample
surveys to estimate population rates. Similar to political polls, we can
test randomly selected people stratified by a few variables in order to
learn how many have been infected, or are currently infected, and among
them, how many have symptoms, or not.
The value of diagnostic tests is typically described in terms of
sensitivity (false negatives) and specificity (false positives). We have
surprisingly little information about the sensitivity and specificity
of our current and emerging tests. The one-peer reviewed study that has
received the most attention is based on only 205 patients in China. In
that evaluation, nearly 40% of people known to be actively infected with
the virus tested negative based on nasal swabs. To be fair, our
colleagues argue that the new tests used in America are much more
accurate. Are they? One evaluation suggested that a widely touted new test failed to detect infection in about 15% of the cases. Similar reports
have emerged from clinical experience. Because of the urgency
associated with the COVID emergency, the FDA has waived the requirement
for substantial evidence of test accuracy before the tests could be
marketed to the public. Emergency authorization was given to 85 different companies or laboratories. In response to concerns in Congress, FDA has now reversed course and is asking companies
to provide more quality evidence. But the cat is already out of the
bag. More than 85 different tests are out there, assuring that results
from different communities are not directly comparable.
Another reason we need smaller random-sample studies is to evaluate
the accuracy of the tests. Information on the accuracy of the tests and
the prevalence of the disease are linked. When the prevalence of a
disease is low, even small imperfections in test accuracy lead to
substantial numbers of misdiagnoses.
For example, if 1% of the population has the infection, and we use
the specificity estimates from the Chinese study, about five well people
will be incorrectly told they have the disease for each one person who
is accurately detected as being ill. In other words, for each person
that we justifiably quarantine, five uninfected people might be
restricted. Even if the sensitivity of the test is 95%, and the
specificity is 97%, about three people will be falsely told they are
infected for each one who is correctly detected. The reason – with a
prevalence of 1%, 99% are uninfected. A very low error rate is applied
to a very large number of uninfected people.
As the background prevalence increases in the population, the chance
that a positive test result is actually a true positive becomes more
likely. It is not until the prevalence rate reaches 5%, or about
quintuple the current estimate, that the number of false positives
reaches the level of true positives.
Still, a balance between false and true positives is miles from a
reasonable goal. For a test to be valuable, we would hope to have at
least 20 true positives for each false positive. More disturbing is that
our current testing strategy is not designed to tell us how many people
are infected. Without knowing that number, accurate estimates of test
quality are impossible.
Move Both Fast and Not So Fast
We are in an urgent situation and we need to take action. Given what
we know now, attempting to test everyone may cause more problems than it
solves. A feasible national strategy might have three components.
First, to determine which people with symptoms should be treated and
quarantined, diagnostic tests should continue to be used by clinical
facilities.
Second, it is necessary to divert an adequate portion of tests for
studies of the general population. We don’t need to test everyone, just
the minimal number to estimate how often the virus is present in people
without symptoms (diagnostic test) and how often people may have
previously been infected (antibody test). To assure enough tests are
available, asymptomatic people or those with minor symptoms might be
asked to defer testing and to self-quarantine.
The third component of the strategy is a nationally coordinated
research effort. Currently, we don’t know how many people are infected,
what fraction of infected people will die, or whether someone who has
been infected and has antibodies is immune to getting the disease if
they encounter the virus again. We need a strategy to figure it out so
that we can prevent more illness and death and get Americans back to
work.
Time is slipping. Let’s not let more weeks pass before we will
develop testing capacity to meet both clinical and public health needs.
Robert M. Kaplan, PhD,
is a distinguished research professor of Public Health and Medicine at
UCLA and a faculty member at the Stanford School of Medicine Clinical
Excellence Research Center. He is a former associate director of the NIH
and a former chief science officer at the Agency for Healthcare
Research and Quality. Kevin Winthrop, MD,
is principal investigator and director of the Center for Infectious
Disease Studies at the Oregon Health & Science University. He holds
positions as professor of public health at the School of Public Health
and professor of infectious diseases and ophthalmology at the School of
Medicine and is a former infectious disease epidemiologist at CDC. Michael H. Weisman, MD,
is distinguished professor of medicine emeritus at David Geffen School
of Medicine at UCLA and professor of medicine emeritus from Cedars-Sinai
Medical Center. His research interests involve genetic and
environmental triggers for chronic rheumatic diseases and as such he has
developed a keen interest in epidemiologic testing and assessment
methodologies.
https://www.medpagetoday.com/infectiousdisease/covid19/86450
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