How accurate antibody tests are for detecting COVID-19?
Cochrane recently published a review of studies looking at the accuracy of COVID-19 antibody tests. Here we summarise the main conclusions of the first review which provides authentic information to policymakers worldwide including those in India to make them aware of the status of the technology and the improvements needed for its proper use.
A few months ago our capability was to carry out only about 100 tests per day. With the introduction of rapid tests, the total number now is over 200,000 per day. With the present rate at which the virus is spreading across the country, the number of tests may also be scaled up.
The experts from the University of Birmingham led Cochrane researchers from universities across the world to search through 10,965 unique references on COVID-19 available at the end of April 2020 to find studies that reported results of antibody tests in groups of people known to have (or have had) COVID-19 and others knew not to have had COVID-19 infection.
Sensitivity and timeliness of the test
The researchers found that the sensitivity (the proportion of the people who have had COVID-19 that the test can detect) of antibody testing is very closely related to when the test is performed.
When we do the test:
• Between 8 to 14 days after onset of symptoms, tests of the IgG and IgM antibodies correctly identified only 70% of people who had COVID-19.
• Between 15 and 35 days after symptoms first began, antibody tests accurately detected over 90% of people who have COVID-19
• Beyond 35 days after the beginning of symptoms, the researchers found that there are insufficient studies to estimate the sensitivity of antibody tests.
The reviewers also found that tests only wrongly diagnosed COVID-19 in 1% to 2% of people without COVID-19.
A press release from Cochrane illustrated some typical results. In a sample of 1000 people where 200 people (20%) really have COVID-19 (this is typical of workers in a hospital setting where COVID-19 patients have been treated):
• 193 people would receive a positive test result but 10 (5%) of those people would not have COVID-19 (known as a false positive result)
• 807 people would receive a negative test result but 17 (2%) of those people would have COVID-19 (known as a false-negative result)
• In a population where COVID-19 was more common there would be more false negatives and fewer false positives.