While PCR tests tell you if you have the coronavirus right now, antibody tests tell you if you have had it in the past. These tests identify if your immune system has built up a response to the coronavirus by searching for antibodies, specific particles in your immune system that remember what a disease looks like, and help prevent you from getting sick with the same disease again. According to the CDC, it may take one to three weeks after a person is infected with the coronavirus for their body to produce an immune response and, as a result, for an antibody test to return a positive result.
As PCR and antibody tests measure different things and have different accuracy levels, it is important that any institution reporting them separates out the two numbers. For example, Texas reports about 5.5 million PCR tests and about 384,000 antibody tests as of Sept. 12. Of those Texans who took antibody tests, about 33,600 have tested positive; these individuals aren't included in Texas' case counts, since antibody tests cannot be used to diagnose a current case.
Antigen tests, similar to PCR tests, tell you if you have the coronavirus right now. These tests are new—the FDA has approved only three types as of September—but they work more quickly and are cheaper to make than PCR tests, and some public health experts predict that they will be used widely across the U.S. by this winter. The tradeoff for this speed and cost-effectiveness is that antigen tests are not as accurate as PCR tests. If you take an antigen test and have it come back positive, you would still need to test positive on a PCR test to be diagnosed with COVID-19 officially.
Any institution reporting antigen tests should separate these counts from PCR tests, just as they should for antibody tests. But as of September, most states and the federal government are not reporting numbers of antigen tests conducted at all. The Atlantic's Alexis Madrigal and Robinson Meyer call this "dark testing"; without these counts, it is difficult to see a clear picture of COVID-19 testing in America.
False positive, test specificity
A false-positive test is one that has labeled someone as infected with the novel coronavirus, when in fact they have not contracted the virus. Such an error can lead to people facing unnecessary quarantines and restrictions, as well as extra work for contact tracers who are attempting to track the virus' spread in a community by talking to people who have tested positive.
When a test doesn't return many false-positive results, it is said to have a high specificity. This means that the majority of people who test positive do, in fact, have the disease. PCR tests have incredibly high specificity; if you tested positive, your result is very trustworthy. Antibody and antigen tests have lower specificities, which is why positive results with these tests must be confirmed through PCR testing.
False negative, test sensitivity
On the other hand, a false-negative test occurs when someone's test labels them as not infected with the coronavirus, when, in fact, they are infected. False negatives are dangerous because they can lead people to visit friends and family or go out into public spaces with a false belief that they are safe from spreading the virus to others.
When a test doesn't return many false-negative results, it is said to have a high sensitivity. PCR tests have a high sensitivity when performed correctly, while antibody and antigen tests have lower sensitivities. For example, an antigen test by manufacturer Quidel, the first to receive FDA approval, has an 80% sensitivity; one in five people who take this test could have false negatives.
Many test numbers reported by public health agencies undergo a deduplication process, involving the removal of doubled results from the count. For example, if you went to a clinic in your town to get tested last week, and then went back to the same clinic for another test this week, your name would appear twice in the list of people tested at that clinic. Your local public health agency might take your second test off their list of people tested—deduplicating your tests—so that you only appear on the list once.
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When you see a COVID-19 testing number reported by your local public health agency, school, or some other institution, it is important to ask what units this institution is using. Units refer to the magnitudes used to describe values; if you say the distance between your house and your office is three miles, "miles" would be the unit for that data point.
An institution that reports its COVID-19 tests in units of specimens is telling you the number of samples, taken from people's noses or throats, that were sent off to labs for testing. Multiple specimens may sometimes be taken from one individual—for example, if both sides of your nose are swabbed—so test counts reported in specimens may be somewhat higher than the actual number of people tested.
An institution that reports its COVID-19 tests in units of people is telling you the number of unique individuals who were tested. Arriving at a "people tested" number involves deduplication, as was explained on a previous slide; anyone in a particular region or institution who was tested multiple times or who had multiple samples taken during their test would be reduced to a count of one in the dataset. "People tested" numbers are useful for evaluating who has access to testing, but they are less helpful in assessing how many tests are conducted over time, as people who get tested several times would be removed from the dataset at each test after their first.
Testing encounters are a new testing unit that allows for more accurate counts of test capacity over time. The COVID Tracking Project defines this unit as the number of people tested per day. This figure is not inflated by clinics that take multiple specimens from a patient, but it still includes multiple tests for people who are tested multiple times. As of mid-September, seven states are reporting testing encounters, according to the COVID Tracking Project.
Similar to daily cases, daily tests refer to the new COVID-19 tests conducted in a region or by an institution over a particular day. When looking at daily test numbers or testing time series, it is important to know in what unit these numbers are reported, so that you can interpret accurately. For example, Virginia reports testing encounters, allowing residents to examine a history of how many people have been tested in the state each day going back to March.
Test positivity rate
The test positivity rate is a calculated metric that shows what percentage of the tests conducted for a particular population has returned a positive result. This rate helps track test capacity over time. If a region has a lower test positivity rate, it likely means the region has testing available to all who need it and is more accurately diagnosing positive residents. Suppose a region has a higher test positivity rate. In that case, this likely means many people in this region are infected with the novel coronavirus. The region does not have enough testing available for its residents, and only people with symptoms are getting tested.
Similarly to examining units for test counts, when interpreting test positivity rates, it is crucial to examine units and ask for the underlying numbers behind the calculation: the total number of tests conducted and the number of those tests that were positive. In the past several months, some state public health agencies—and even the CDC itself—have artificially lowered test positivity rates by combining multiple test types in one number.
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