NIH antibody study uncovers millions of hidden, uncounted COVID-19 cases
During the first six months of the COVID-19 pandemic when diagnostic tests were scarce, the spread of the coronavirus reached far further than initially known—with the official count potentially missing the mark by millions to tens of millions of cases, according to a new antibody study conducted by the National Institutes of Health.
Researchers estimate that over the spring and summer of 2020, for every infection that was caught, 4.8 slipped by undiagnosed. That adds up to 16.8 million new hidden cases by July of that year alone.
The study builds on recent evidence from the NIH that showed the coronavirus reached U.S. shores long before it was considered an emergency, as early as December 2019. Using archived blood samples, these antibody studies are beginning to provide a clearer picture into how the disease spread undetected across the country.
The latest research also showed that the infection affected the country unevenly. Black participants in the study had the highest estimated rates of positive COVID-19 antibody tests, at 14.2%, followed by Native Americans and Alaska Natives at 6.8%, Hispanics at 6.1%, white and Caucasian participants at 2.5% and Asian at 2%.
At the same time, people living in urban areas saw higher rates than rural regions, at 5.3% vs. 1.1%, and respondents from the mid-Atlantic and Northeast states were the highest, at 8.6% and 7.5%, compared to the lowest rates in the Midwest, at 1.6%.
In addition, the disease was highly prevalent among the study’s youngest participants, between the ages of 18 and 44, with 5.9%.
“This study helps account for how quickly the virus spread to all corners of the country and the globe,” Bruce Tromberg, director of the NIH’s National Institute of Biomedical Imaging and Bioengineering, said in a statement. “The information will be invaluable as we assess the best public health measures needed to keep people safe, as new—and even more transmissible—variants emerge and vaccine antibody response changes over time.”
The study recruited over 240,000 volunteers, before selecting a sample of 8,058 individuals to ensure the analysis was representative of the U.S. population. Each participant mailed in a dab of dried blood or had blood drawn at an NIH clinic, with most specimens coming in between May and July 2020. The results were published in Science Translational Medicine.
“The estimate of COVID-19 cases in the United States in mid-July 2020, 3 million in a population of 330 million, should be revised upwards by almost 20 million when the percent of asymptomatic positive results is included,” said the study’s senior co-author, Kaitlyn Sadtler, chief of the NIBIB section on immunoengineering.
“This wide gap between the known cases at the time and these asymptomatic infections has implications not only for retrospectively understanding this pandemic but future pandemic preparedness,” Sadtler said.
Understanding the prevalence of an infectious disease is an important factor in gauging the overall accuracy of different diagnostic tests—the higher the chances are of finding the disease in the general population, the more likely it is that a physician can trust a positive result to be correct, depending on the test’s sensitivity.
During the early stages of the pandemic, the NIH and FDA noted that they could not fully interpret the performance of some diagnostic tests, because the full prevalence of COVID-19 was not known, and instead relied on round-number baselines.
However, the agency wasn’t too far off: in May 2020, the FDA began evaluating different tests’ predictive values based on an estimate of 5% prevalence—which would have translated to about 16.4 million people across the U.S.
Going forward, NIH researchers are following up with the study’s participants to gather blood samples at the six- and 12-month marks, and plan to compare the differences in antibodies developed from active infections and from vaccinations.