Stanford U. epidemiologist: Data Indicates We’re Severely Overreacting To Coronavirus
By James Barrett
In an analysis published Tuesday, Stanford’s John P.A. Ioannidis — co-director of the university’s Meta-Research Innovation Center and professor of medicine, biomedical data science, statistics, and epidemiology and population health — suggests that the response to the coronavirus pandemic may be “a fiasco in the making” because we are making seismic decisions based on “utterly unreliable” data. The data we do have, Ioannidis explains, indicates that we are likely severely overreacting.
“The current coronavirus disease, Covid-19, has been called a once-in-a-century pandemic. But it may also be a once-in-a-century evidence fiasco,” Ioannidis writes in an opinion piece published by STAT on Tuesday.
“Draconian countermeasures have been adopted in many countries. If the pandemic dissipates — either on its own or because of these measures — short-term extreme social distancing and lockdowns may be bearable,” the statistician writes. “How long, though, should measures like these be continued if the pandemic churns across the globe unabated? How can policymakers tell if they are doing more good than harm?”
The woefully inadequate data we have so far, the meta-research specialist argues, indicates that the extreme measures taken by many countries are likely way out of line and may result in ultimately unnecessary and catastrophic consequences. Due to extremely limited testing, we are likely missing “the vast majority of infections” from COVID-19, he states, thus making reported fatality rates from the World Health Organization “meaningless.”
“Patients who have been tested for SARS-CoV-2 are disproportionately those with severe symptoms and bad outcomes,” Ioannidis explains. With very limited testing in many health systems, he suggests, that “selection bias” may only get worse going forward.
Ioannidis then zooms in on the “one situation” where “an entire, closed population was tested”: the Diamond Princess cruise ship’s quarantined passengers. While the fatality rate was 1.0%, he points out, the population was largely elderly, the most at-risk demographic. Projected out onto the age structure of the U.S. population, he calculates, the death rate is more like 0.125%, with a range of 0.025% to 0.625% based on the sample size:
Projecting the Diamond Princess mortality rate onto the age structure of the U.S. population, the death rate among people infected with Covid-19 would be 0.125%. But since this estimate is based on extremely thin data — there were just seven deaths among the 700 infected passengers and crew — the real death rate could stretch from five times lower (0.025%) to five times higher (0.625%). It is also possible that some of the passengers who were infected might die later, and that tourists may have different frequencies of chronic diseases — a risk factor for worse outcomes with SARS-CoV-2 infection — than the general population. Adding these extra sources of uncertainty, reasonable estimates for the case fatality ratio in the general U.S. population vary from 0.05% to 1%.
“That huge range markedly affects how severe the pandemic is and what should be done,” Ioannidis stresses. “A population-wide case fatality rate of 0.05% is lower than seasonal influenza. If that is the true rate, locking down the world with potentially tremendous social and financial consequences may be totally irrational. It’s like an elephant being attacked by a house cat. Frustrated and trying to avoid the cat, the elephant accidentally jumps off a cliff and dies.”
For those who argue that the high fatality rate among elderly people indicates that the death rate cannot be as low as 0.05%, the professor notes that “even some so-called mild or common-cold-type coronaviruses that have been known for decades can have case fatality rates as high as 8% when they infect elderly people in nursing homes.” (Read the full opinion piece here.)