Stanford scientist John Ioannidis finds himself under attack for questioning the prevailing wisdom about lockdowns.
By Allysia Finley
Defenders of coronavirus lockdown mandates keep talking about
science. “We are going to do the right thing, not judge by politics, not
judge by protests, but by science,” California’s Gov. Gavin Newsom said
this week. Michigan Gov. Gretchen Whitmer defended an order that, among
other things, banned the sale of paint and vegetable seeds but not
liquor or lottery tickets. “Each action has been informed by the best
science and epidemiology counsel there is,” she wrote in an op-ed. But scientists are almost never unanimous,
and many appeals to “science” are transparently political or
ideological. Consider the story of John Ioannidis, a professor at
Stanford’s School of Medicine. His expertise is wide-ranging—he juggles
appointments in statistics, biomedical data, prevention research and
health research and policy. Google Scholar ranks him among the world’s
100 most-cited scientists. He has published more than 1,000 papers, many
of them meta-analyses—reviews of other studies. Yet he’s now found
himself pilloried because he dissents from the theories behind the
lockdowns—because he’s looked at the data and found good news. In a March article for Stat News, Dr. Ioannidis argued that
Covid-19 is far less deadly than modelers were assuming. He considered
the experience of the Diamond Princess cruise ship, which was
quarantined Feb. 4 in Japan. Nine of 700 infected passengers and crew
died. Based on the demographics of the ship’s population, Dr. Ioannidis
estimated that the U.S. fatality rate could be as low as 0.025% to
0.625% and put the upper bound at 0.05% to 1%—comparable to that of
seasonal flu.
“If that is the true rate,” he wrote, “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.”
Dr. Ioannidis, 54, likes metaphors. A New York native who grew
up in Athens, he also teaches comparative literature and has published
seven literary works—poetry and fiction, the latest being an epistolary
novel—in Greek. In his spare time, he likes to fence, swim, hike and
play basketball.
Early in his career, he realized that “the common denominator
for everything that I was doing was that I was very interested in the
methods—not necessarily the results but how exactly you do that, how
exactly you try to avoid bias, how you avoid error.” When he began
examining studies, he discovered that few headline-grabbing findings
could be replicated, and many were later contradicted by new evidence.
Scientific studies are often infected by biases. “Several years
ago, along with one of my colleagues, we had mapped 235 biases across
science. And maybe the biggest cluster is biases that are trying to
generate significant, spectacular, fascinating, extraordinary results,”
he says. “Early results tend to be inflated. Claims for significance
tend to be exaggerated.”
An example is a 2012 meta-analysis
on nutritional research, in which he randomly selected 50 common
cooking ingredients, such as sugar, flour and milk. Eighty percent of
them had been studied for links to cancer, and 72% of the studies linked
an ingredient to a higher or lower risk. Yet three-quarters of the
findings were weak or statistically insignificant.
Dr. Ioannidis calls the coronavirus pandemic “the perfect storm
of that quest for very urgent, spectacular, exciting, apocalyptic
results. And as you see, apparently our early estimates seem to have
been tremendously exaggerated in many fronts.”
Chief among them was a study by modelers at Imperial College
London, which predicted more than 2.2 million coronavirus deaths in the
U.S. absent “any control measures or spontaneous changes in individual
behaviour.” The study was published March 16—the same day the Trump
administration released its “15 Days to Slow the Spread” initiative,
which included strict social-distancing guidelines.
Dr. Ioannidis says the Imperial projection now appears to be a
gross overestimate. “They used inputs that were completely off in some
of their calculation,” he says. “If data are limited or flawed, their
errors are being propagated through the model. . . . So if you have a
small error, and you exponentiate that error, the magnitude of the final
error in the prediction or whatever can be astronomical.”
“I love models,” he adds. “I do a lot of mathematical modeling
myself. But I think we need to recognize that they’re very, very low in
terms of how much weight we can place on them and how much we can trust
them. . . . They can give you a very first kind of mathematical
justification to a gut feeling, but beyond that point, depending on
models for evidence, I think it’s a very bad recipe.”
Modelers sometimes refuse to disclose their assumptions or
data, so their errors go undetected. Los Angeles County predicted last
week that 95.6% of its population would be infected by August if social
distancing orders were relaxed. (Confirmed cases were 0.17% of the
population as of Thursday.) But the basis for this projection is
unclear. “At a minimum, we need openness and transparency in order to be
able to say anything,” Dr. Ioannidis says.
Most important, “what we need is data. We need real data. We
need data on how many people are infected so far, how many people are
actively infected, what is really the death rate, how many beds do we
have to spare, how has this changed.”
That will require more testing. Dr. Ioannidis and colleagues at
Stanford last week published a study on the prevalence of coronavirus
antibodies in Santa Clara County. Based on blood tests of 3,300
volunteers in the county—which includes San Jose, California’s
third-largest city—during the first week of April, they estimated that
between 2.49% and 4.16% of the county population had been infected.
That’s 50 to 85 times the number of confirmed cases and implies a
fatality rate between 0.12% and 0.2%, consistent with that of the
Diamond Princess.
The study immediately came under attack. Some statisticians
questioned its methods. Critics noted the study sample was not randomly
selected, and white women under 64 were disproportionately represented.
The Stanford team adjusted for the sampling bias by weighting the
results by sex, race and ZIP Code, but the study acknowledges that
“other biases, such as bias favoring individuals in good health capable
of attending our testing sites, or bias favoring those with prior
Covid-like illnesses seeking antibody confirmation are also possible.
The overall effect of such biases is hard to ascertain.”
Dr. Ioannidis admits his study isn’t “bulletproof” and says he
welcomes scrutiny. But he’s confident the findings will hold up, and he
says antibody studies from around the world will yield more data. A
study published this week by the University of Southern California and
the Los Angeles County Department of Public Health estimated that the
virus is 28 to 55 times as prevalent in that county as confirmed cases
are. A New York study released Thursday estimated that 13.9% of the
state and 21.2% of the city had been infected, more than 10 times the
confirmed cases.
Yet most criticism of the Stanford study has been aimed at
defending the lockdown mandates against the implication that they’re an
overreaction. “There’s some sort of mob mentality here operating that
they just insist that this has to be the end of the world, and it has to
be that the sky is falling. It’s attacking studies with data based on
speculation and science fiction,” he says. “But dismissing real data in
favor of mathematical speculation is mind-boggling.”
In part he blames the media: “We have some evidence that bad
news, negative news [stories], are more attractive than positive
news—they lead to more clicks, they lead to people being more engaged.
And of course we know that fake news travels faster than true news. So
in the current environment, unfortunately, we have generated a very
heavily panic-driven, horror-driven, death-reality-show type of
situation.”
The news is filled with stories of healthy young people who die
of coronavirus. But Dr. Ioannidis recently published a paper with his
wife, Despina Contopoulos-Ioannidis, an infectious-disease specialist
at Stanford, that showed this to be a classic man-bites-dog story. The
couple found that people under 65 without underlying conditions
accounted for only 0.7% of coronavirus deaths in Italy and 1.8% in New
York City.
“Compared to almost any other cause of disease that I can think
of, it’s really sparing young people. I’m not saying that the lives of
80-year-olds do not have value—they do,” he says. “But there’s far, far,
far more . . . young people who commit suicide.” If the panic and
attendant disruption continue, he says, “we will see many young people
committing suicide . . . just because we are spreading horror stories
with Covid-19. There’s far, far more young people who get cancer and
will not be treated, because again, they will not go to the hospital to
get treated because of Covid-19. There’s far, far more people whose
mental health will collapse.”
He argues that public officials need to weigh these factors
when making public-health decisions, and more hard data from antibody
and other studies will help. “I think that we should just take
everything that we know, put it on the table, and try to see, OK, what’s
the next step, and see what happens when we take the next step. I think
this sort of data-driven feedback will be the best. So you start
opening, you start opening your schools. You can see what happens,” he
says. “We need to be open minded, we need to just be calm, allow for
some error, it’s unavoidable. We started knowing nothing. We know a lot
now, but we still don’t know everything.”
He cautions against drawing broad conclusions about the
efficacy of lockdowns based on national infection and fatality rates.
“It’s not that we have randomized 10 countries to go into lockdown and
another 10 countries to remain relatively open and see what happens, and
do that randomly. Different prime ministers, different presidents,
different task forces make decisions, they implement them in different
sequences, at different times, in different phases of the epidemic. And
then people start looking at this data and they say, ‘Oh look at that,
this place did very well. Why? Oh, because of this measure.’ This is
completely, completely opinion-based.”
People are making “big statements about ‘lockdowns save the
world.’ I think that they’re immature. They’re tremendously immature.
They may have worked in some cases, they may have had no effect in
others, and they may have been damaging still in others.”
Most disagreements among scientists, he notes, reflect
differences in perspective, not facts. Some find the Stanford study
worrisome because it suggests the virus is more easily transmitted,
while others are hopeful because it suggests the virus is far less
lethal. “It’s basically an issue of whether you’re an optimist or a
pessimist. Even scientists can be optimists and pessimists. Probably
usually I’m a pessimist, but in this case, I’m probably an optimist.”
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