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Analysis

Study Puts U.S. COVID-19 Infection Fatality Rate at 1.3%

By John Commins  
   May 07, 2020

As alarming as the numbers appear, the researchers said, their estimates "may be slightly conservative."

COVID-19 kills 1.3% of symptomatic people and could kill 500,000 Americans in the coming months if as many people contract the highly-contagious virus this year as contracted the seasonal flu last year, according to a caveat-laden estimate published Thursday in Health Affairs.

"After modeling the available national data on cumulative deaths and detected COVID-19 cases in the United States, the IFR-S (Infection Fatality Rate – Symptomatic) from COVID-19 was estimated to be 1.3%," said the researchers, led by Anirban Basu, Stergachis Family endowed director and professor in the Department of Pharmacy, CHOICE Institute, University of Washington, Seattle.

"This estimated rate is substantially higher than the approximate IFR-S of seasonal influenza, which is about 0.1% (34,200 deaths among 35.5 million patients who got sick with influenza)."

"If we carry out a thought experiment where 35.5 million individuals would contract COVID-19 illness this year in the US (i.e., the same number as flu last year) then, in the absence of any mitigation strategies or social distancing behaviors and the supply of health care services under typical conditions, our IFR-S estimate predicts that there would have been nearly 500,000 COVID-19 deaths this year."

As alarming as the numbers appear, the researchers said, their estimates "may be slightly conservative."

"To the extent that COVID-19 is more infectious than flu and does not have any protection from a vaccine or treatment, the number of infections, and hence the number of deaths, would be higher," they said.

Using GitHub data from the Johns Hopkins Repository and data from The New York Times, the researchers looked at 116 counties in 33 states and found 40,835 confirmed cases and 1,620 confirmed deaths through April 20.

Asymptomatic COVID-19 patients who recovered with no major symptoms were not counted in the data, which the researchers acknowledged skewed results. 

Limitations and Caveats

The researchers acknowledged limitations in their analysis that could skew results.

"First, we acknowledge that our estimate of IFR-S would be higher than the true overall IFR. This is because our model relies on identified cases who are presumably all symptomatic COVID-19 patients," the researcher said. "Therefore, even at the limit, our estimated rate would not include the fraction of patients who may have the infection but remain and recover asymptomatically."

The researchers also said they could not estimate age-adjusted IFR-S because the data isn't available "to assess the distribution of IFR-S across age and comorbidity profiles of patients."

"One would need, ideally, individual-level data, and at the least group-specific data to estimate such dispersion, which are not publicly available," they said.  

The researchers also went with the assumption that the supply of healthcare services, including hospital beds, ventilators, and access to providers, would continue into the future.

 "Constraints in the supply of health care services could surely increase IFR and the overall fatality rates," they said. "We hope that simulations to understand and forecast the impact of such shortages can be improved using our estimates of IFR-S as the baseline."

“To the extent that COVID-19 is more infectious than flu and does not have any protection from a vaccine or treatment, the number of infections, and hence the number of deaths, would be higher.”

John Commins is a content specialist and online news editor for HealthLeaders, a Simplify Compliance brand.


KEY TAKEAWAYS

The researchers looked at 116 counties in 33 states and found 40,835 confirmed cases and 1,620 confirmed deaths through April 20.

Asymptomatic COVID-19 patients who recovered with no symptoms were not counted in the data, which could have skewed results.

The researchers also said they could not estimate age-adjusted IFR-S because the data isn't available.


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