Antibody tests for COVID-19 may be wrong up to half of the time, according to updated information from the Centers for Disease Control.
The CDC now warns that the antibody testing is not accurate enough for it to be used for any policy-making decisions, as even with high test specificity, ‘less than half of those testing positive will truly have antibodies’.
This is because a test will always average a certain number of false positive results, and a smaller number of true positive results to compare them to can throw off the statistics by a large margin.
The CDC urges caution with the test results as many false positives could lead people to believe they have an immunity to and act accordingly.
Health care providers may need to test patients at least twice to give a more accurate reading, the new guidance posted to the adds.
Antibody studies, also known as seroprevalence research, are considered critical to understanding where an outbreak is spreading and can help guide decisions on restrictions needed to contain it.
Antibody tests for COVID-19 may be wrong up to half of the time, producing many false positives, according to updated information from the Centers for Disease Control. Pictured, a man getting a coronavirus antibody testing at the NYPD Community Center on May 15
There is currently a high level of inaccuracy in the testing, however, caused by how uncommon the virus is within the population.
If the infection has affected only a small number of people tested, it will have a magnified margin of error, the CDC explains.
It means that even a test with more than 90 percent accuracy can still miss half the cases if only five percent of the population has been infected.
‘In most of the country, including areas that have been heavily impacted, the prevalence of SARS-CoV-2 antibody is expected to be low, ranging from less than 5% to 25%, so that testing at this point might result in relatively more false positive results and fewer false-negative results,’ the CDC states.
‘For example, in a population where the prevalence is 5%, a test with 90% sensitivity and 95% specificity will yield a positive predictive value of 49%. In other words, less than half of those testing positive will truly have antibodies,’ it adds.
‘Alternatively, the same test in a population with an antibody prevalence exceeding 52% will yield a positive predictive greater than 95%, meaning that less than one in 20 people testing positive will have a false positive test result.
‘Therefore, its best to use tests with high specificity – which are unlikely to throw up a lot of false positives – and in populations where doctors suspect there are many cases,’ it concludes.
The way this maths works is that a 95% specific test, for example, will always produce five false positive results from a group of 100 people.
Even if it is sensitive enough to detect all the people who have genuinely had the disease, it will still return five false positives.