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Antibody Tests Point To Lower Death Rate For The Coronavirus Than First Thought

People line up in mid-April in Chelsea, Mass., to get antibody tests for the coronavirus that causes COVID-19.
Stan Grossfeld
The Boston Globe via Getty Images
People line up in mid-April in Chelsea, Mass., to get antibody tests for the coronavirus that causes COVID-19.

Mounting evidence suggests the coronavirus is more common and less deadly than it first appeared.

The evidence comes from tests that detect antibodies to the coronavirus in a person's blood rather than the virus itself.

The tests are finding large numbers of people in the U.S. who were infected but never became seriously ill. And when these mild infections are included in coronavirus statistics, the virus appears less dangerous.

"The current best estimates for the infection fatality risk are between 0.5% and 1%," says Caitlin Rivers, an epidemiologist at the Johns Hopkins Center for Health Security.

That's in contrast with death rates of 5% or more based on calculations that included only people who got sick enough to be diagnosed with tests that detect the presence of virus in a person's body.

And the revised estimates support an early prediction by Dr. Anthony Fauci, director of the National Institute of Allergy and Infectious Diseases and a leading member of the White House coronavirus task force. In an editorial published in late March in The New England Journal of Medicine, Fauci and colleagues wrote that the case fatality rate for COVID-19 "may be considerably less than 1%."

But even a virus with a fatality rate less than 1% presents a formidable threat, Rivers says. "That is many times more deadly than seasonal influenza," she says.

The new evidence is coming from places such as Indiana, which completed the first phase of a massive testing effort early in May.

Indiana's program began soon after coronavirus cases began appearing in the state. The governor's office contacted Nir Menachemi, who chairs the health policy and management department at Indiana University's Richard M. Fairbanks School of Public Health.

The governor wanted basic information, such as how many people had been infected, and how many would die.

At the time, "it was really difficult to know for sure," Menachemi says. "And frankly, not just in our state, but in any state."

That was because health officials only knew about people who had been sick enough to get tested for the virus. And that number can be misleading, Menachemi says.

"It doesn't capture the vast number of people out there who might be infected but not seeking medical care," he says.

So starting in late April, Menachemi, working with the Indiana State Department of Health, led a study of more than 4,600 people statewide. Most were selected at random.

Participants got two tests. The first was the standard test that looks for the virus. It shows whether you have an active infection. The second was a test that looks for antibodies to the virus in a person's blood. It detects people who were infected but have recovered.

Preliminary results showed that the coronavirus had infected about 3% of the state's population, or 188,000 people.

"That 188,000 people represented about 11 times more people than conventional selective testing had identified in the state to that point," Menachemi says.

And 45% of the infected people reported having no symptoms at all.

For Menachemi and his team, it was like finally getting a glimpse of the entire coronavirus iceberg, instead of just the part above the water.

And the data allowed them to calculate something called the infection fatality rate — the odds that an infected person will die. Previously, scientists had relied on what's known as the case fatality rate, which calculates the odds that someone who develops symptoms will die.

Indiana's infection fatality rate turned out to be about 0.58%, or roughly one death for every 172 people who got infected.

And the results in Indiana are similar to those suggested by antibody studies in several other areas. In New York, for example, an antibody study indicated the state has an infection fatality rate around 0.5%.

Studies in Florida and California have suggested even lower fatality rates, but the results are less certain, Rivers says.

"They may have enrolled people who are more likely to have been infected than would be ideal," she says, which would lead to an overestimate of infections and an underestimate of the infection fatality rate.

An antibody study in Santa Clara County, Calif., used Facebook ads to find participants — a tactic unlikely to attract a random sample. Also antibody studies become less accurate when conducted in areas where the prevalence of infections is low.

Calculating infection fatality rates in the U.S. is useful for researchers but less so for individuals who have been infected, Rivers says.

"Thankfully, children and young adults are at low risk of severe illness and death," she says. "But older adults are at quite high risk."

Studies suggest a healthy young person's chance of dying from an infection is less than 1 in 1,000. But for someone in poor health in their 90s, it can be greater than 1 in 10.

And that means different states in the U.S. should expect different infection fatality rates, says Juliette Unwin, a research fellow at Imperial College London.

"Places like Maine and Florida, we find that the infection fatality ratio is higher than in other places where the demographic is younger," she says.

Unwin is part of a large team in the United Kingdom that is monitoring both infection and mortality from the coronavirus in the United States. The team puts the infection fatality rate for the U.S. at somewhere between 0.7% and 1.2%.

"That will be subject to change and revision, as is everything in science," says Samir Bhatt, a senior lecturer at Imperial College London. "But I don't think we have it an order of magnitude out."

To get a more precise estimate of infections and the infection fatality rate nationwide, the National Institutes of Health has launched an antibody study that will test 10,000 people. Results will be released on a rolling basis. The study is expected to wrap up in early 2022.

Copyright 2020 NPR. To see more, visit https://www.npr.org.

Jon Hamilton is a correspondent for NPR's Science Desk. Currently he focuses on neuroscience and health risks.