After reports of false-positive tests, 77 NFL players from 11 different teams who apparently tested positive for the China-originated novel coronavirus had their tests re-examined at New Jersey lab BioReference, which found every single test to be negative.
According to The Associated Press, which first reported the re-testing from BioReference, the New Jersey lab will re-examine the tests for second time to be sure they are false positives.
“Among teams reporting false positives, the Minnesota Vikings said they had 12, the New York Jets 10 and the Chicago Bears nine,” the report outlines. And the New York Jets, after canceling a walk-through practice on Saturday night, had a full practice Sunday morning “after the previously positive tests came back negative.”
“The Pittsburgh Steelers said they had six false positives and those players will be back at practice Monday,” the report noted. “The Philadelphia Eagles held out four players from working out Sunday. The Cleveland Browns initially canceled practice, but after re-testing turned up negative tests they decided to have their workout.”
An anonymous source told the AP that a player from the Detroit Lions was found to have a false positive test, confirmed by BioReference: “The person spoke on condition of anonymity because the league and team were not disclosing test results.”
Buffalo Bills general manager Brandon Beane told the AP “tests in the Northeast had gone ‘haywire,’ and called it ‘a lab issue and not a true issue with our guys currently’ after several Bills were held out of practice,” the report said.
“Definitely probably better that this happened now than three weeks from now,” said Beane. “But it seems like every few weeks, or even every week, something’s going on. Who knows what the next curveball will be?”
Players who test positive are required to test negative two more times before they’re cleared to play, even if the test was initially a false positive.
Donald Trump Jr. reacted to the report on Twitter Monday morning by condemning the media.
“WOW. All were false positives?!?” he posted. “I wonder how many across America we’re the same? I’m sure very few in media will talk about this because they think it bodes well for [Democrats] if they keep All Americans, not just those more vulnerable, in fear.”
🚨🚨🚨 WOW. All were false positives?!? I wonder how many across America we’re the same? I’m sure very few in media will talk about this because they think it bodes well for Ds if they keep All Americans, not just those more vulnerable, in fear. https://t.co/iX4tBDFOt0
— Donald Trump Jr. (@DonaldJTrumpJr) August 24, 2020
It’s unclear the number of false positives or false negatives that have been reported to the Center for Disease and Prevention (CDC).
Last Sunday, the CDC posted a “lab alert” on their website, asking for reports of false-positive and false-negative testing.
Under CDC “frequently asked questions” regarding COVID and laboratories, the CDC explains “what factors have the greatest impact on false-positive rates” as follows:
Positive predictive value is the probability that a person who has a positive test result most likely has the infection. Pretest probability and test specificity have the greatest impact on false-positive rates. As the pretest probability and the specificity of the test increases, the false-positive rate decreases and the positive predictive value increases.
“The likelihood of obtaining a false-positive or false-negative diagnostic test result is influenced by factors related to the testing scenario and the test being used (e.g., sensitivity and specificity of the diagnostic test). Diagnostic tests perform optimally for detecting an infection when the pretest probability is high,” the CDC says (emphasis theirs).
“Pretest probability is the likelihood that the person being tested actually has the infection. This likelihood is based on both the proportion of people in the test population or group who have the infection at a given time (prevalence) and the clinical presentation (including symptoms and known exposure) of the person being tested. In other words, the pretest probability increases with increasing prevalence in the population and clinical indications of illness in the person being tested,” the agency continues. “In contrast, tests typically perform best for excluding an infection when the pretest probability is low. Test sensitivity is the ability of a test to correctly identify persons with infection, whereas test specificity is the ability to correctly rule-out infection.”