I'd like to respectively request that this mailing list be restricted to
astronomy/PAS-related discussions. I'll get my information on health,
politics, and other topics elsewhere.
Thanks,
Mark Johnston
On Wed, Mar 18, 2020 at 5:57 PM John Kundrat <dmarc-noreply@xxxxxxxxxxxxx>
wrote:
*This is the second item on Corona-Chan. This Stanford Medical Scientist,
an epidemiologist, points out that there is a dearth of information on
COVD-19. His message is: “we need unbiased prevalence and incidence data
for the evolving infectious load to guide decision-making.” Panic instead
has taken over.*
*John*
A fiasco in the making? As the coronavirus pandemic takes hold, we are
making decisions without reliable data
John P.A. Ioannidis
March 17, 2020
https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-as-the-coronavirus-pandemic-takes-hold-we-are-making-decisions-without-reliable-data/
[image: coronavirus testing]A nurse holds swabs and a test tube to test
people for Covid-19 at a drive-through station set up in the parking lot of
the Beaumont Hospital in Royal Oak, Mich. Paul Sancya/AP
The current coronavirus disease, Covid-19, has been called a
once-in-a-century pandemic
<https://www.statnews.com/2020/03/11/who-declares-the-coronavirus-outbreak-a-pandemic/>.
But it may also be a once-in-a-century evidence fiasco.
At a time when everyone needs better information, from disease modelers
and governments to people quarantined or just social distancing, we lack
reliable evidence on how many people have been infected with SARS-CoV-2 or
who continue to become infected. Better information is needed to guide
decisions and actions of monumental significance and to monitor their
impact.
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. 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?
Vaccines or affordable treatments take many months (or even years) to
develop and test properly. Given such timelines, the consequences of
long-term lockdowns are entirely unknown.
The data collected so far on how many people are infected and how the
epidemic is evolving are utterly unreliable. Given the limited testing to
date, some deaths and probably the vast majority of infections due to
SARS-CoV-2 are being missed. We don’t know if we are failing to capture
infections by a factor of three or 300. Three months after the outbreak
emerged, most countries, including the U.S., lack the ability to test a
large number of people and no countries have reliable data on the
prevalence of the virus in a representative random sample of the general
population.
This evidence fiasco creates tremendous uncertainty about the risk of
dying from Covid-19. Reported case fatality rates, like the official 3.4%
rate from the World Health Organization, cause horror — and are
meaningless. Patients who have been tested for SARS-CoV-2 are
disproportionately those with severe symptoms and bad outcomes. As most
health systems have limited testing capacity, selection bias may even
worsen in the near future.
The one situation where an entire, closed population was tested was the
Diamond Princess cruise ship and its quarantine passengers. The case
fatality rate there was 1.0%, but this was a largely elderly population, in
which the death rate from Covid-19 is much higher.
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. 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.
Could the Covid-19 case fatality rate be that low? No, some say, pointing
to the high rate in elderly people. However, 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%
<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2095096/> when they infect
elderly people in nursing homes. In fact, such “mild” coronaviruses infect
tens of millions of people every year, and account for 3% to 11%
<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3805243/> of those
hospitalized in the U.S. with lower respiratory infections each winter.
These “mild” coronaviruses may be implicated in several thousands of
deaths every year worldwide, though the vast majority of them are not
documented with precise testing. Instead, they are lost as noise among 60
million deaths from various causes every year.
Although successful surveillance systems have long existed for influenza,
the disease is confirmed by a laboratory in a tiny minority of cases. In
the U.S., for example, so far this season 1,073,976 specimens have been
tested <https://www.cdc.gov/flu/weekly/#ClinicalLaboratories> and 222,552
(20.7%) have tested positive for influenza. In the same period, the
estimated number of influenza-like illnesses is between 36,000,000 and
51,000,000, with an estimated 22,000 to 55,000 flu deaths.
Note the uncertainty about influenza-like illness deaths: a 2.5-fold
range, corresponding to tens of thousands of deaths. Every year, some of
these deaths are due to influenza and some to other viruses, like
common-cold coronaviruses.
In an autopsy series
<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3805243/> that tested for
respiratory viruses in specimens from 57 elderly persons who died during
the 2016 to 2017 influenza season, influenza viruses were detected in 18%
of the specimens, while any kind of respiratory virus was found in 47%. In
some people who die from viral respiratory pathogens, more than one virus
is found upon autopsy and bacteria are often superimposed. A positive test
for coronavirus does not mean necessarily that this virus is always
primarily responsible for a patient’s demise.
If we assume that case fatality rate among individuals infected by
SARS-CoV-2 is 0.3% in the general population — a mid-range guess from my
Diamond Princess analysis — and that 1% of the U.S. population gets
infected (about 3.3 million people), this would translate to about 10,000
deaths. This sounds like a huge number, but it is buried within the noise
of the estimate of deaths from “influenza-like illness.” If we had not
known about a new virus out there, and had not checked individuals with PCR
tests, the number of total deaths due to “influenza-like illness” would not
seem unusual this year. At most, we might have casually noted that flu this
season seems to be a bit worse than average. The media coverage would have
been less than for an NBA game between the two most indifferent teams.
Some worry that the 68 deaths from Covid-19 in the U.S. as of March 16
<https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html>
will
increase exponentially to 680, 6,800, 68,000, 680,000 … along with similar
catastrophic patterns around the globe. Is that a realistic scenario, or
bad science fiction? How can we tell at what point such a curve might stop?
The most valuable piece of information for answering those questions would
be to know the current prevalence of the infection in a random sample of a
population and to repeat this exercise at regular time intervals to
estimate the incidence of new infections. Sadly, that’s information we
don’t have.
In the absence of data, prepare-for-the-worst reasoning leads to extreme
measures of social distancing and lockdowns. Unfortunately, we do not know
<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6993921/> if these measures
work. School closures, for example, may reduce transmission rates. But they
may also backfire if children socialize anyhow, if school closure leads
children to spend more time with susceptible elderly family members, if
children at home disrupt their parents ability to work, and more. School
closures may also diminish the chances of developing herd immunity in an
age group that is spared serious disease.
This has been the perspective behind the different stance of the United
Kingdom keeping schools open
<https://www.theguardian.com/world/2020/mar/13/herd-immunity-will-the-uks-coronavirus-strategy-work>,
at least until as I write this. In the absence of data on the real course
of the epidemic, we don’t know whether this perspective was brilliant or
catastrophic.
Flattening the curve
<https://www.statnews.com/2020/03/11/flattening-curve-coronavirus/> to
avoid overwhelming the health system is conceptually sound — in theory. A
visual that has become viral in media and social media shows how flattening
the curve reduces the volume of the epidemic that is above the threshold of
what the health system can handle at any moment.
Yet if the health system does become overwhelmed, the majority of the
extra deaths may not be due to coronavirus but to other common diseases and
conditions such as heart attacks, strokes, trauma, bleeding, and the like
that are not adequately treated. If the level of the epidemic does
overwhelm the health system and extreme measures have only modest
effectiveness, then flattening the curve may make things worse: Instead of
being overwhelmed during a short, acute phase, the health system will
remain overwhelmed for a more protracted period. That’s another reason we
need data about the exact level of the epidemic activity.
One of the bottom lines is that we don’t know how long social distancing
measures and lockdowns can be maintained without major consequences to the
economy, society, and mental health. Unpredictable evolutions may ensue,
including financial crisis, unrest, civil strife, war, and a meltdown of
the social fabric. At a minimum, we need unbiased prevalence and
incidence data for the evolving infectious load to guide decision-making.
In the most pessimistic scenario, which I do not espouse, if the new
coronavirus infects 60% of the global population and 1% of the infected
people die, that will translate into more than 40 million deaths globally,
matching the 1918 influenza pandemic.
The vast majority of this hecatomb would be people with limited life
expectancies. That’s in contrast to 1918, when many young people died.
One can only hope that, much like in 1918, life will continue. Conversely,
with lockdowns of months, if not years, life largely stops, short-term and
long-term consequences are entirely unknown, and billions, not just
millions, of lives may be eventually at stake.
If we decide to jump off the cliff, we need some data to inform us about
the rationale of such an action and the chances of landing somewhere safe.
*John P.A. Ioannidis is professor of medicine, of epidemiology and
population health, of biomedical data science, and of statistics at
Stanford University and co-director of Stanford’s Meta-Research Innovation
Center.*
John P.A. Ioannidis
