John Ioannidis laid bare the foibles of medical science. Now medical science is returning the favor.
By DAVID H. FREEDMAN – Wired Magazine
Original article at Wired Magazine
I’M STARING AT a small sea of frowning faces on Zoom. “I’m really angry about this,” says one of them. These are medical students at Columbia University, and I’m speaking to a class on communicating medicine. They’ve been friendly up until now, but that all changed when I brought up Stanford University epidemiologist John Ioannidis.
Ioannidis has been a fixture in medical-school curricula for years, achieving something akin to hero status. He’s one of the most-cited scientists of any type in the world, and may be peerless on this metric among physicians. Amazingly, he’s earned all this acclaim by dedicating his career to telling the fields of biomedicine (and others, too) how shoddy they are, and how little trust one should have in their published research.
But now the scientist celebrated for showing colleagues how their studies are screwed up has a new claim to fame. Its very different vibe is reflected in the faces of the medical students I’m addressing. Almost literally overnight Ioannidis has himself become a case study in how to screw up a medical study. And not just any study: This one concludes that Covid-19 isn’t all that dangerous; that the current lockdowns to prevent its spread are a bigger threat to public health than the actual disease. In other words, Ioannidis’ views on the pandemic sound closer to those of the governor of Georgia than to Anthony Fauci’s.
Let’s face it, the field of epidemiology hasn’t so far covered itself in glory with regard to the coronavirus crisis. The field was for the most part fatefully slow to recognize that a pandemic was aborning; later on, it produced a stream of conflicting and sometimes wildly off-the-mark assessments of infection and mortality rates, and where they might be heading.
But even in this fast-paced and sloppy context, Ioannidis’ study is seen as standing out. Not just for its methodological weaknesses but for the apparent wrongness of its main conclusions—and the risk that these could have a harmful influence on public health recommendations. In a nutshell, Ioannidis and his study coauthors tested about 3,300 residents of California’s Santa Clara County for antibodies to the new coronavirus. The results, according to Ioannidis, imply that the disease isn’t nearly as deadly as believed. “Based on what we’re seeing now, the fatality of the virus is more or less the same as influenza, about 0.1 percent,” he says. “Most of the earlier data was completely bogus.”
The study, posted as a preprint on April 17, has been pilloried nonstop. Critics noted problems in the way subjects were recruited, potential defects in the antibody test, and apparent mistakes in the statistical analysis. Ioannidis might have received a pass if his involvement went no further than being listed among the suspect study’s 17 coauthors. But he’d already needled colleagues with an essay that he wrote in March, calling the response to Covid-19 “a once-in-a-century evidence fiasco”; and now, again, he took to the airwaves to hawk these new results as evidence that stay-at-home measures are misguided.
It wasn’t all the airwaves, though. Thanks to the fact that his advice happens to align with the right-wing message that it’s time to open up the economy, Ioannidis had his star turn on Fox News. Hosts Laura Ingraham and Tucker Carlson must have been thrilled to have some cover, for once, from a bona fide, mega-credentialed scientist, instead of having to trot out the likes of Drs. Oz and Phil on their shows. Meanwhile, The Wall Street Journal’s mostly right-wing-friendly opinion pages described Ioannidis as a scientist “under attack for questioning the prevailing wisdom about lockdowns,” and labeled him “the bearer of good coronavirus news.”
I’M FRIENDLY WITH Ioannidis. I got to know him 10 years ago, when I spent three days with him in his native Greece, as well as in the US, for a long profile for The Atlantic. In that article I suggested he “may be one of the most influential scientists alive”—a line that’s quoted in his official Stanford bio, and follows him through media accounts.
That assessment has held up, at least until the last few weeks. Starting with his iconic 2005 paper, “Why Most Published Research Findings Are False,” Ioannidis raised the consciousness of researchers about how their humanity can skew their work. Science is a messy process even under the best of circumstances, argued Ioannidis, and it gets nudged in the wrong direction because scientists have careers. They want to get published so they can win grants, jobs, tenure and respect.
To get published, they need to come up with exciting, novel, important findings. Unfortunately, reality rarely obliges. Because scientists are so biased toward producing those rare results, they tilt their studies to produce them, often in unconscious ways, and then exaggerate the significance of what they’ve done. Ioannidis provided both mathematical and data-analytic proof that these biases warp science in general, and medicine especially. He has been a reliable gauge ever since of what’s likely real, and what isn’t, among controversial medical and public-health pronouncements.
I’ve stayed in loose touch with Ioannidis over the years, since visiting him in Ioannina in northwest Greece; and I spoke to him again by phone a few days before the Santa Clara study came out. It was immediately clear from the conversation that serious trouble was brewing, and not just because the study’s findings—and the public-health conclusions he was drawing from it—were startling outliers. Absent from his comments were the qualifications he normally sprinkles liberally throughout his claims; his thoughtful concerns about whether he was getting things right, and about the limitations of the data at hand; his praise for others’ differing points of view; and his constant habit of chuckling while offering even mild criticism of the field, as if to emphasize his interest in keeping everything on a friendly footing.
Instead, Ioannidis sounded sure of himself. He was right; the others had it wrong. He called out other research teams by name—Johns Hopkins, Imperial College London—to berate their findings as “astronomically wrong,” and “constantly dialed back to match reality.” Here he was, about to come out with an exciting and important finding—if he were right, it could change almost everything about how we deal with this virus—and he seemed unworried by the possibility that something might be amiss with the project.
Ioannidis sounded sure of himself. He was right; the others had it wrong.
If anyone should understand how the pressure to contribute to the science of the crisis might lead to flawed work and exaggerated claims, it ought to be Ioannidis, arguably the world’s most famous epidemiologist. Who knows? Perhaps like so many of us, he’s just stressed out by the whole damned thing. Maybe he’s just off his game.
On the other hand, Ioannidis’ track record is such that it may not be wise to dismiss his claims too quickly. There really aren’t any solid studies out there that can help settle the question of Covid-19 fatality rates, and what data we do have remains all over the place. Yes, Ioannidis’ results look to be an outlier—but they may be an outlier in the right direction, suggesting a need to revise the infection fatality rate downwards, even if not all the way to 0.1 percent.
In fact, since the study came out, more researchers and physicians have been citing likely fatality rates of around 0.5 percent, which is closer to Ioannidis’ estimate than it is to the 1-percent-and-way-up numbers that were once bandied about. Recent antibody testing results in New York support the 0.5 figure.
But the real essence of Ioannidis’ claims is about the need to shift the conversation from how to avoid infection to reckoning how many people would ultimately die from the virus depending on how long and tightly the stay-at-home policies are maintained. The lockup was supposed to be a delaying tactic to avoid overwhelming local healthcare systems, and in particular their critically short supply of ventilators. But Ioannidis points out the worse-case scenario for hospitals hasn’t come to pass, except at a few locations in New York City during the period when it was the worst hot-spot in the world.
Yes, that may be partly because we managed to get enough people to stay home quickly enough; and yes, it has taken heroic sacrifice and risk on the parts of front-line healthcare workers to keep the system functioning. But Ioannidis insists there’s little evidence that hospitals couldn’t now handle the surges that might come from relaxing stay-at-home policies, assuming people who go out take moderate protection measures such as masks and social distancing. If we’re not ready to stay locked up for a year or more, he argues, we’re just delaying the inevitable spread of the disease without doing much to change the death rate.
On the other side, he says, are the health costs of the lockup. “If we prolong these measures too long, the premature deaths from that policy could be 100-fold larger than what we see with Covid-19 itself,” Ioannidis told me. The fear of leaving home to go to a hospital is alone almost certainly leading to thousands of unnecessary deaths from heart attacks, strokes and cancer.
“If I did a study and the results showed that in fact there wasn’t really much bias in research, would I be willing to publish it?”
JOHN IOANNIDIS, STANFORD UNIVERSITY
Other epidemiologists’ assessment of Ioannidis’ claim, that staying at home will likely kill far more people than Covid-19, might best be summed up the way physics giant Wolfgang Pauli is said to have dismissed the lesser work of a colleague: It’s not even wrong. To be promoted to wrong, the Ioannidis position would have to be based on data and analysis that scientists could argue over. Even allowing his 0.1 percent fatality rate for the disease—which most epidemiologists think is way too low, but not beyond-the-realm-of-possibility low—there is almost no data to go on for the likely cost in human life of the lockdown. We know Covid-19 is killing tens of thousands of people, and that staying at home is slowing the spread; but we know virtually nothing about the number deaths caused by staying at home. As such, what Ioannidis is promoting simply isn’t science, says Loren Lipworth, a Vanderbilt University epidemiologist. “It’s impossible to do that risk-benefit analysis,” she says. “It’s just relying on anecdote and common sense.” In other words, Ioannidis is pitting his gut against the collective data-driven wisdom and analysis of medicine and public health.
It’s not that Ioannidis isn’t asking the right questions. I watch CNN and CBS News every day, and I’ve only seen the issue of unnecessary deaths due to the lockdown come up a few times, in the context of chronically-ill people having trouble getting treatment. But I’ve watched hundreds of stories of individuals dying horribly from Covid-19, and of anti-stay-at-home protesters excoriated by experts. That’s how most Americans see this disease: through a terrifying filter of widespread death that only fools would leave home to risk.
If Ioannidis’ claims even slightly alter the conversation toward a more balanced, thoughtful view of what we really gain, and what we might lose, from the lockdown, then maybe it’s mission accomplished. If he’s even partly right that we’re too biased toward staying at home, and the disease isn’t as deadly as we thought, the resulting shift could ultimately save tens of thousands of lives.
A WEEK AGO, Ioannidis’ legacy in medical science seemed unassailable. Today, not so much. I saw it on the faces of those medical students. To them Ioannidis may always be the fringe scientist who pumped up a bad study that supported a crazy right-wing conspiracy theory in the middle of a massive health crisis.
The prevailing take now is that Ioannidis has fallen prey to the very sorts of biases and distortions that he became revered for exposing in others. If that’s what happened, it will be a twist that Ioannidis himself had prophesied to me 10 years ago in Greece. “If I did a study and the results showed that in fact there wasn’t really much bias in research, would I be willing to publish it?” he said then. “That would create a real psychological conflict for me.” Ioannidis was acknowledging that he’s invested in showing that other scientists tend to get it wrong, and that he might end up being skeptical of data suggesting they are, in fact, getting it right.
Now Ioannidis’ claims about Covid-19 may be pulled by the gravity of his commitment to being the one who sees where everyone else went wrong. There’s a meta-meta-science lesson in there, too, and one we’ve sometimes seen before: Bias is so powerful a force in scientific research that even a grandmaster of research into bias can eventually trip over it.
Photographs: Getty Images; Library of Congress