How Good Should You Feel About Those COVID Temperature Screenings?
By: David Hill
Tags: Covid 19, Health, Medicine, Evidence Based Medicine, Statistics

How Good Should You Feel About Those COVID Temperature Screenings?

Does a thermometer at the door mean you’re safe inside?

Photo by Gustavo Fring from Pexels

In mid-March, when COVID-19 concerns drove North Carolina into stay-at-home status, the hospital where I work posted two medical assistants at each entrance. Before any of us could pass, we had to submit to a temporal artery thermal scan, deny that we had a cough, and don a mask. We were not alone: businesses like stores and daycare centers started screening everyone similarly in an effort to assure patrons that they were safe.

Then, last week a local daycare was forced to shut down after exposing multiple children, parents, and staff members to COVID-19, including two of my friend’s children. Who was the vector? The very teacher charged with screening the kids every morning, a smoking thermal gun, so to speak.

In the meantime, my hospital replaced the MA’s at the door with a sign advising entrants to pinkie-promise we won’t work if we feel bad. So what happened?

If these screening measures work, why would a hospital abandon them? Could they actually be making things worse?

Trigger warning: the answer involves math. Spoiler alert: except for one itty bitty little equation, we’re just talking about basic concepts. Read on, math-phobics!

In order to answer our question, we need three pieces of information. First, we need to know the “sensitivity” of fever for COVID-19 infection. In other words, of all the people who have COVID-19, how many have a fever? That’s a hard number to pin down, but the best guess from the Centers for Disease Control and Prevention (CDC) is that around 65% of patients are symptomatic at the time they can infect others, although in other studies less than half of patients (45%) had a fever at the time they tested positive for COVID-19. Fever may be present in just over a quarter of elderly COVID-19 patients, so age makes a difference, too. To make life simple, let’s say that half of the infectious COVID-19 patients have a fever.

Taking that information alone, we might imagine that temperature screening reduces our risk of contracting COVID-19 by 50%. Studies conducted on passengers in airports, however, call into question how well that strategy works. The infrared thermometers commonly used also require re-calibration, especially with changes in ambient temperature, which are an hourly thing here in North Carolina.

When doctors and public health officials consider the ability of a test to tell you that a person does not have a disease, we call it the “negative predictive value” or NPV. You might think that fever screening has a negative predictive value of 50%, but that’s not the case, because negative predictive value depends on how common a condition is in the affected population, which we call the “prevalence.” When the prevalence is low, ie: few people have the condition, the negative predictive value goes up (for the actual equations, see here). But when the prevalence goes up, the negative predictive value falls. In other words, the more people get sick, the less useful fever screening becomes to keep us safe.

Various sources report prevalence for COVID-19 by county. Currently where I live in New Hanover County around 320 people have COVID-19 for every 100,000 residents. In many parts of the country, however, prevalence is rising rapidly, and in certain demographics (ages 11–20 for example), it’s exploding. If you live in a place on the down-swing, like Manhattan or Rome, temperature checks are getting better at protecting you. If you live in Texas, they’re getting much, much worse.

Which brings us to the last bit of information: how many people with a fever don’t have COVID-19? We know that positive testing rates in many parts of the country are now between 5% and 10%, so that’s a good guess, but not everyone with a fever is running out and getting a COVID-19 test (they should be, but they’re not). We need that number to calculate “specificity,” the chances that a person with a positive test has the disease you’re testing for and not something else.

For argument’s sake, let’s take our local COVID-19 test positivity rate of around 5% as our specificity. Now (look away, math-phobics) we can plug in our negative predictive value equation: NPV = (specificity x (1 — prevalence)) / [ (specificity x (1 — prevalence)) + ((1 — sensitivity) x prevalence) ] where sensitivity = 0.5, prevalence = 0.0032, and specificity = 0.05.

So, if you live where I do and accept the assumptions above (if you don’t accept that COVID-19 is real, please adjust your jaunty little tinfoil hat and move on), then you should feel really, really good that someone without a fever is not going to give you COVID-19: the NPV is around 99%. But, before you get too excited, some caveats.

The CDC recently reported that serology testing shows the prevalence of COVID-19 is ten times what’s recorded. If that’s true, then our NPV goes down to around 92%, still pretty good, but not as good as 99%. And as the disease spreads, that number will fall further.

The bottom line with any test in medicine is whether it changes what we actually do.

If I know that people are being screened for fever walking into a place, and I believe the thermometer is properly calibrated, do I stroll in without a mask? Do I touch my face? Do I stand close to people? Nope. To me personally, the fever check is about as useful as a Pap smear would be.

For you? Well, if you can find your local stats for prevalence and positive testing rates, you can run your own numbers here.

Me, I’ll be here in the hospital, waiting for my next COVID-19 admission.