Specificity Vs. Sensitivity of COVID-19 Tests

When we are doing medical testing, there are a few questions we need to ask to understand the validity of any test. The validity of a test indicates if the test determines what we are looking for. For example, how valid is a home pregnancy test to determine if I am pregnant? In other words, does a positive or negative test give me the information I want:

Am I pregnant or not?

And if I test positive or negative, what are the chances the results are accurate?

Two important concepts in medical testing need to be understood: sensitivity and specificity.

Specificity and sensitivity are the yin and yang of the testing world and convey critical information about what a test can and cannot tell us. Both are needed to fully understand a test’s strengths as well as its shortcomings.

Sensitivity measures how often a test correctly generates a positive result for people who have the condition that’s being tested for (also known as the “true positive” rate). A test that’s highly sensitive will flag almost everyone who has the disease and not generate many false-negative results. (Example: a test with 90% sensitivity will correctly return a positive result for 90% of people who have the disease, but will return a negative result — a false-negative — for 10% of the people who have the disease and should have tested positive.)

Specificity measures a test’s ability to correctly generate a negative result for people who don’t have the condition that’s being tested for (also known as the “true negative” rate). A high-specificity test will correctly rule out almost everyone who doesn’t have the disease and won’t generate many false-positive results. (Example: a test with 90% specificity will correctly return a negative result for 90% of people who don’t have the disease, but will return a positive result — a false-positive — for 10% of the people who don’t have the disease and should have tested negative.)

The following graphic shows how these terms apply to one of the most commonly used tests: a pregnancy test.

It’s important to recognize that sensitivity and specificity exist in a state of balance. Increased sensitivity – the ability to correctly identify people who have the disease — usually comes at the expense of reduced specificity (meaning more false-positives). Likewise, high specificity — when a test does a good job of ruling out people who don’t have the disease – usually means that the test has lower sensitivity (more false-negatives).

Another everyday example

Airport security offers a good example of how these tradeoffs play out in practice. To ensure that truly dangerous items like weapons cannot be brought onboard an aircraft, scanners at a security checkpoint may also alarm for harmless items like belt buckles, watches, and jewelry. The scanner prioritizes sensitivity and will flag almost anything that seems like it could be dangerous. But that means it also has low specificity and is prone to false alarms; a positive result is much more likely to be a shampoo bottle than it is an explosive device.

What’s a ‘good’ test? It depends

The ideal test is one that has both high sensitivity and high specificity, but the value of a test depends on the situation.

Generally speaking, “a test with a sensitivity and specificity of around 90% would be considered to have good diagnostic performance.”

But just as important as the numbers, it’s crucial to consider what kind of patients the test is being applied to.

Mammograms are used to screen for breast cancer; a positive result requires follow-up with an invasive breast biopsy to confirm the diagnosis.

In our case, anyone who tested positive for IgM we refer to their primary care physician for further tests for active COVID-19.

It is important to take into account patient history and understand how the testing relates to the individual and how we use the test results to drive patient care.

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