Covid Test Interpretation and Response
I am seeing stories about people who are taking leave from work because they are infected with covid, and they are expected to return when they are well. So as a kind of ready-reference let me review what the decision process is for ending isolation, according to the CDC:
That is because negative test results don't really mean anything. They do not tell you whether you are sick or well.
One problem with developing an accurate test is determining the standard. To tell if the test works on somebody, you need to know if they are sick or not when you test them. How can you know that -- especially if they are asymptomatic? Researchers test and then test again the next day, to see how consistent a test is. They figure if you're sick the second day, you were probably sick the day before. They know they underestimate the number of errors this way but it is the best method they have.
A physician interpreting the test takes prior probability into account. For instance, they will consider the infection rate in the region. They will adjust their prior probability estimate if you have traveled to a place with a high rate, or have engaged in risky behaviors like going out without a mask or attending a concert or party, have been exposed to a sick person, or if you have symptoms of covid-19. Then they take the test result and make a diagnosis. The Bayesian math is more than we need here, but the point is that they do not just look at the test result.
The positive result is pretty good on its own -- if the test sees enough viral RNA to trigger a reaction then the result is positive. It seems that positive test results are about 98 percent accurate; if it says you're sick, you're sick.
But what if the result is negative? A false negative test has serious consequences, for instance it can lead an infected person to interact with others with minimal precautions. Because there is no true gold standard, researchers can only estimate the false negative rate. One review of five studies estimated false negative rates up to 29 per cent, but those estimates have been questioned and are probably low.
The point is that a negative test result does not mean you are not infected.
The BMJ (formerly British Medical Journal) has published guidelines in an article titled, sensibly, "Interpreting a covid-19 test result." Their interest is professional, they are mostly advising doctors about when they can return to their practice without infecting their patients. Here's what they say:
The worrisome thing to me is that employers are using the test as a binary decision-maker. If your test is positive, then they let you stay home, but if it's negative they interpret that to mean that you should get back to the office and get to work. That kind of decision is going to send a lot of sick people back into a risky environment, which will in turn drive up the numbers, put more people in danger, and perpetuate the chaos of this pandemic.
In the face of a negative test result, a responsible physician should make an informed diagnosis.
Here is how the BMJ piece suggests doctors explain it to patients:
We should play this safe. Supplement a negative test result with a medical diagnosis. If you are sick, remain isolated until you meet the CDC criteria given above.
You can be around others after:You will notice that there is nothing in that list about "getting a negative test result."
- 10 days since symptoms first appeared and
- 24 hours with no fever without the use of fever-reducing medications and
- Other symptoms of COVID-19 are improving*
*Loss of taste and smell may persist for weeks or months after recovery and need not delay the end of isolation
That is because negative test results don't really mean anything. They do not tell you whether you are sick or well.
One problem with developing an accurate test is determining the standard. To tell if the test works on somebody, you need to know if they are sick or not when you test them. How can you know that -- especially if they are asymptomatic? Researchers test and then test again the next day, to see how consistent a test is. They figure if you're sick the second day, you were probably sick the day before. They know they underestimate the number of errors this way but it is the best method they have.
A physician interpreting the test takes prior probability into account. For instance, they will consider the infection rate in the region. They will adjust their prior probability estimate if you have traveled to a place with a high rate, or have engaged in risky behaviors like going out without a mask or attending a concert or party, have been exposed to a sick person, or if you have symptoms of covid-19. Then they take the test result and make a diagnosis. The Bayesian math is more than we need here, but the point is that they do not just look at the test result.
The positive result is pretty good on its own -- if the test sees enough viral RNA to trigger a reaction then the result is positive. It seems that positive test results are about 98 percent accurate; if it says you're sick, you're sick.
But what if the result is negative? A false negative test has serious consequences, for instance it can lead an infected person to interact with others with minimal precautions. Because there is no true gold standard, researchers can only estimate the false negative rate. One review of five studies estimated false negative rates up to 29 per cent, but those estimates have been questioned and are probably low.
The point is that a negative test result does not mean you are not infected.
The BMJ (formerly British Medical Journal) has published guidelines in an article titled, sensibly, "Interpreting a covid-19 test result." Their interest is professional, they are mostly advising doctors about when they can return to their practice without infecting their patients. Here's what they say:
While positive tests for covid-19 are clinically useful, negative tests need to be interpreted with caution, taking into account the pre-test probability of disease. This has important implications for clinicians interpreting tests and policymakers designing diagnostic algorithms for covid-19... False negatives carry substantial risks; patients may be moved into non-covid-19 wards leading to spread of hospital acquired covid-19 infection, carers could spread infection to vulnerable dependents, and healthcare workers risk spreading covid-19 to multiple vulnerable individuals. Clear evidence-based guidelines on repeat testing are needed, to reduce the risk of false negatives.
The worrisome thing to me is that employers are using the test as a binary decision-maker. If your test is positive, then they let you stay home, but if it's negative they interpret that to mean that you should get back to the office and get to work. That kind of decision is going to send a lot of sick people back into a risky environment, which will in turn drive up the numbers, put more people in danger, and perpetuate the chaos of this pandemic.
In the face of a negative test result, a responsible physician should make an informed diagnosis.
Here is how the BMJ piece suggests doctors explain it to patients:
- No test is 100% accurate
- If your swab test comes back positive for covid-19 then we can be very confident that you do have covid-19
- However, people with covid-19 can be missed by these swab tests. If you have strong symptoms of covid-19, it is safest to self-isolate, even if the swab test does not show covid-19
We should play this safe. Supplement a negative test result with a medical diagnosis. If you are sick, remain isolated until you meet the CDC criteria given above.