Dealing With Covid19: A Conversation with Stanford University Professor, John Ioannidis, America's Most Distinguished Epidemiologist

 By SAURABH JHA, MD

The COVID-19 pandemic has been a testing time for the already testy academic discourse. Decisions have had to be made with partial information. Information has come in drizzles, showers and downpours. The velocity with which new information has arrived has outstripped our ability to make sense of it. On top of that, the science has been politicized in a polarized country with a polarizing president at its helm.

As the country awoke to an unprecedented economic lockdown in the middle of March, John Ioannidis, professor of epidemiology at Stanford University and one of the most cited physician scientists who practically invented “metaresearch”, questioned the lockdown and wondered if we might cause more harm than good in trying to control coronavirus. What would normally pass for skepticism in the midst of uncertainty of a novel virus became tinder in the social media outrage fire.

Ioannidis was likened to the discredited anti-vax doctor, Andrew Wakefield. His colleagues in epidemiology could barely contain their disgust, which ranged from visceral disappointment – the sort one feels when their gifted child has lost their way in college, to deep anger. He was accused of misunderstanding risk, misunderstanding statistics, and cherry picking data to prove his point.

The pushback was partly a testament to the stature of Ioannidis, whose skepticism could have weakened the resoluteness with which people complied with the lockdown. Some academics defended him, or rather defended the need for a contrarian voice like his. The conservative media lauded him.

In this pandemic, where we have learnt as much about ourselves as we have about the virus, understanding the pushback to Ioannidis is critical to understanding how academic discourse shapes public’s perception of public policy.

Saurabh Jha (SJ): On March 17th, at the start of the lockdown, you wrote in STAT News cautioning us against overreacting to COVID-19. You likened our response to an elephant accidentally jumping off a cliff because it was attacked by a house cat. The lockdown had just begun. What motivated you to write that editorial?

John P.A. Ioannidis (JPA): March seems a long time ago. I should explain my thinking in the early days of the COVID-19 pandemic. Like many, I saw a train approaching. Like many, I couldn’t sense the train’s precise size and speed. Many said we should be bracing for a calamity and in many ways I agreed. But I was concerned that we might inflict undue damage, what I’d call “iatrogenic harm”, controlling the pandemic.

To answer your question specifically, I wrote the piece because I felt that the touted fatality rate of COVID-19 of 3.4 % was inflated, but we had so limited data and so much uncertainty that infection fatality rate values as different as 0.05% and 1% were clearly still possible. I was pleading for better data on COVID-19 to make our response more precise and proportionate.

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Covid Lies to Keep You Terrorized

Absent strong public health measures, we would expect it to kill something like 0.5% to 1.0% of a nation’s population, and whether or not that’s a large number is a matter of personal opinion.

So declared Ron Unz, publisher of the Unz Review. 

That claim is far from the truth as the case of Sweden demonstrates. There, in the absence of "strong public health measures" there have been 5,846 reported Covid deaths, or about 0.06% of the population. That must be close to the final toll, as Covid deaths in Sweden peaked in March and are now at or close to zero.

Why would a scientifically literate person such as Ron Unz make such a false claim? Mere confusion, perhaps*.

One way in which Covid death rates have been greatly exaggerated has been to confuse, deliberately or otherwise, two measures of the death rate; namely, the "Case Fatality Rate" and the  "Infection Fatality Rate." 

The Covid19 Case Fatality Rate (CFR) is a measure of deaths among confirmed Covid19 cases, the latter being mainly cases of serious illness, which thus came to the attention of the medical profession and were identified as due to Covid19 by a more or less reliable diagnostic methods.

The Covid19 Infection Fatality Rate (IFR) is a measure of deaths among all those infected with Covid19, whether they were seriously ill or not, or whether they were ill at all. The IFR can only be determined if there is population-wide testing for past or present Covid19 infection, for example by means of a reliable serological test for Covid19 antibodies. 

Evidence currently available suggests that the the IFR is only about one tenth of the CFR. Therefore, to mistake the CFR for the IFR will result in an exaggeration of the actual IFR by a factor of around ten. 

But even a ten-fold error does not explain Ron Unz's claim that "absent strong public health measures" Covid19 will "kill something like 0.5% to 1.0% of a nation’s population." To explain that, assuming it is not a straight lie, one must assume that Ron Unz confuses the Infection Fatality Rate with the Population Fatality Rate (PFR). Such confusion assumes a Covid death rate among the population as a whole equal to the Covid death rate among those made sick by a confirmed Covid19 infection, which is nonsense.

But perhaps Ron Unz's claim is a straight lie, which would be consistent with the fact that, when I pointed out the error on his Unz Review post, my comment was deleted.

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* Cf. Ronald B. Brown, 2000, Public Health Lessons Learned from Biases in Coronavirus Mortality Overestimation.

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