The 2020 US election will be vicious, with a nasty pandemonium following a nasty pandemic.
By SAURABH JHA, MD
When the COVID-19 pandemic is dissected in the 2020 presidential election debates, Donald Trump will be at a disadvantage. The coronavirus has killed over 100,000 Americans and maimed thousands more. The caveat is that deaths per capita, rather than total deaths, better measure national failure, and by that metric the US fares better than Belgium, Italy and the United Kingdom. New York City owns a disproportionate share of the deaths, but this hyperconnected megapolis is an outlier whose misfortunes can’t be used to draw conclusions about administrative competence for the country as a whole.
Nevertheless, even after introducing nuance, the numbers aren’t flattering. President Donald Trump may claim that the US dodged the calamity predicted by the epidemiological models, which foretold millions of deaths. To be fair, we don’t know the counterfactual — Jeremiads aren’t verifiable. The paradox of successful mitigation is that we can’t see the future we dodged, precisely because we avoided it.
Reducing the death count logarithmically, rather than merely arithmetically, won’t be celebrated because as bad as the worst case scenario could have been, the situation still looks awfully bad. Many still disbelieve the high death toll predicted by epidemiologists early on, particularly Trump supporters who believe the response to the virus, specifically the economic shutdown, has been criminally disproportionate. One can’t simultaneously believe that COVID-19 is no more dangerous than the seasonal flu and that Trump saved millions from the coronavirus. The constituency that acknowledges the lethality of COVID-19 and credits Trump for decisive action against it is small.
Triangle of Incompetence
Trump’s challenger, former Vice President Joe Biden, will charge that fewer Americans would have died had the Trump administration acted earlier. Trump may be accused of having blood on his hands, but such rhetoric is unnecessary. Biden’s team can simply show a montage of Trump’s bombast where he downplayed COVID-19’s lethality, dismissed doctors’ concerns about the shortage of personal protective equipment or exaggerated how well the US was containing the pandemic. Incidentally, the most iconic picture of the administration’s scornful indifference is the current vice president, Michael Pence, visiting a hospital without a mask, surrounded by health-care workers wearing masks.
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.
Episode 15 of “The THCB Gang” was live-streamed on Thursday, June 25th!
Joining Matthew Holt were our regulars: health futurist Ian Morrison (@seccurve), writer Kim Bellard (@kimbbellard), WTF Health Host Jessica DaMassa (@jessdamassa), radiologist Saurabh Jha (@RougeRad), policy expert Vince Kuraitis (@VinceKuraitis), and THCB’s Editor-in-Chief, Me (@zoyak1594)! We got into increasing COVID-19 rates, updates in health policy, what is the future of hospitals, and how the new generation is dealing with the health care industry. All while keeping an eye on the politics of the US.
If you’d rather listen, the audio is preserved as a weekly podcast available on our iTunes & Spotify channels — Zoya Khan
Episode 8 of “The THCB Gang” was live-streamed on Thursday, May 7th at 1pm PT- 4pm ET! You can see it below.
Joining me were our regulars: patient advocate Grace Cordovano (@GraceCordovano), data privacy lawyer Deven McGraw (@HealthPrivacy), policy expert Vince Kuraitis (@VinceKuraitis), radiologist Saurabh Jha (@RogueRad) (who snuck in late), and writer Kim Bellard (@Kimbbellard). We had a great conversation including a lot of detail around access to patient records, and some fun about infectious disease epidemiologists behaving badly! If you’d rather listen, the “audio only” version is preserved as a weekly podcast available on our iTunes & Spotify channels from Friday— Matthew Holt
Episode 6 of “The THCB Gang” was live-streamed on Thursday, April 23 at 1pm PT- 4pm ET! 4-6 semi-regular guests drawn from THCB authors and other assorted old friends of mine will shoot the sh*t about health care business, politics, practice, and tech. It’s available below and is preserved as a weekly podcast available on our iTunes & Spotify channels.
Our lineup included: Saurabh Jha (@roguerad), Ian Morrison (@seccurve), Kim Bellard (@kimbbellard), Grace Cordovano (@GraceCordovano),Vince Kuraitis (@VinceKuraitis), Brian Klepper (@bklepper1), and a special guest – Alexandra Drane (@adrane, founder of Eliza, Queen of the Unmentionables, CEO of ArchAngels and sometimes Walmart cashier). Lots of great conversation especially around palliative care, patient experience, the real prevalence of COVID-19 and much more.
Episode 4 of “The THCB Gang” was live-streamed Thursday April 9. You can see it below and it’s also preserved as a weekly podcast available on our iTunes & Spotify channels. Every Thursday at 1pm PT-4pm ET, 4-6 semi-regular guests drawn from THCB authors and other assorted old friends of mine will shoot the shit about health care business, politics, practice, and tech. It tries to be fun but serious and informative!
This week, joining me were Jane Sarasohn Kahn (@healthythinker), Anish Koka (@anish_koka), Saurabh Jha (@roguerad), Elizabeth Clayborne (@DrElizPC), and Ian Morrison (@seccurve). A fun and very informative discussion about where the COVID-19 crisis is right now and what it’s going to mean both now and in the near future — Matthew Holt
In a physician WhatsApp group, a doctor posted he had fever of 101° F and muscle ache, gently confessing that it felt like his typical “man flu” which heals with rest and scotch. Nevertheless, he worried that he had coronavirus. When the reverse transcription polymerase chain reaction (RT-PCR) for the virus on his nasal swab came back negative, he jubilantly announced his relief.
Like Twitter, in WhatsApp emotions quickly outstrip facts. After he received a flurry of cheerful emojis, I ruined the party, advising that despite the negative test he assume he’s infected and quarantine for two weeks, with a bottle of scotch.
It’s conventional wisdom that the secret sauce to fighting the pandemic is testing for the virus. To gauge the breadth of the response against the pandemic we must know who and how many are infected. The depth of the response will be different if 25% of the population is infected than 1%. Testing is the third way, rejecting the false choice between death and economic depression. Without testing, strategy is faith-based.
Our reliance on testing has clinical precedence – scarcely any decision in medicine is made without laboratory tests or imaging. Testing is as ingrained in medicine as the GPS is in driving. We use it even when we know our way home. But tests impose a question – what’ll you do differently if the test is negative?
That depends on the test’s performance and the consequences of being wrong. Though coronavirus damages the lungs with reckless abandon, it’s oddly a shy virus. In many patients, it takes three to four swabs to get a positive RT-PCR. The Chinese ophthalmologist, Li Wenliang, who originally sounded the alarm about coronavirus, had several negative tests. He died from the infection.
This is the part two of a three-part series. Catch up on Part One here.
Preetham Srinivas, the head of the
chest radiograph project in Qure.ai, summoned Bhargava Reddy, Manoj Tadepalli, and
Tarun Raj to the meeting room.
“Get ready for an all-nighter, boys,”
Qure’s scientists began investigating
the algorithm’s mysteriously high performance on chest radiographs from a new
hospital. To recap, the algorithm had an area under the receiver operating
characteristic curve (AUC) of 1 – that’s 100 % on multiple-choice question
“Someone leaked the paper to AI,”
“It’s an engineering college joke,”
explained Bhargava. “It means that you saw the questions before the exam. It
happens sometimes in India when rich people buy the exam papers.”
Just because you know the questions
doesn’t mean you know the answers. And AI wasn’t rich enough to buy the AUC.
The four lads were school friends from
Andhra Pradesh. They had all studied computer science at the Indian Institute
of Technology (IIT), a freaky improbability given that only hundred out of a
million aspiring youths are selected to this most coveted discipline in India’s
most coveted institute. They had revised for exams together, pulling
all-nighters – in working together, they worked harder and made work more fun.
No one knows who gave Rahul Roy
tuberculosis. Roy’s charmed life as a successful trader involved traveling in his
Mercedes C class between his apartment on the plush Nepean Sea Road in South
Mumbai and offices in Bombay Stock Exchange. He cared little for Mumbai’s weather.
He seldom rolled down his car windows – his ambient atmosphere, optimized for
his comfort, rarely changed.
Historically TB, or
“consumption” as it was known, was a Bohemian malady; the chronic suffering produced
a rhapsody which produced fine art. TB was fashionable in Victorian Britain, in
part, because consumption, like aristocracy, was thought to be hereditary. Even
after Robert Koch discovered that the cause of TB was a rod-shaped bacterium –
Mycobacterium Tuberculosis (MTB), TB had a special status denied to its immoral
peer, Syphilis, and unaesthetic cousin, leprosy.
TB became egalitarian in the early twentieth
century but retained an aristocratic noblesse oblige. George Orwell may have
contracted TB when he voluntarily lived with miners in crowded squalor to
understand poverty. Unlike Orwell, Roy had no pretentions of solidarity with
poor people. For Roy, there was nothing heroic about getting TB. He was
embarrassed not because of TB’s infectivity; TB sanitariums are a thing of the
past. TB signaled social class decline. He believed rickshawallahs, not
traders, got TB.
Despite an area under the ROC curve of 1, Cassandra’s
prophesies were never believed. She neither hedged nor relied on retrospective
data – her predictions, such as the Trojan war, were prospectively validated. In
medicine, a new type of Cassandra has emerged –
one who speaks in probabilistic tongue, forked unevenly between the
probability of being right and the possibility of being wrong. One who, by conceding
that she may be categorically wrong, is technically never wrong. We call these
new Minervas “predictions.” The Owl of Minerva flies above its denominator.
Deep learning (DL) promises to transform the prediction
industry from a stepping stone for academic promotion and tenure to something
vaguely useful for clinicians at the patient’s bedside. Economists studying AI believe that AI is revolutionary,
revolutionary like the steam engine and the internet, because it better predicts.
Recently published in Nature, a sophisticated DL algorithm was able to predict acute kidney injury (AKI), continuously, in hospitalized patients by extracting data from their electronic health records (EHRs). The algorithm interrogated nearly million EHRS of patients in Veteran Affairs hospitals. As intriguing as their methodology is, it’s less interesting than their results. For every correct prediction of AKI, there were two false positives. The false alarms would have made Cassandra blush, but they’re not bad for prognostic medicine. The DL- generated ROC curve stands head and shoulders above the diagonal representing randomness.
The researchers used a technique called “ablation analysis.”
I have no idea how that works but it sounds clever. Let me make a humble
prophesy of my own – if unleashed at the bedside the AKI-specific, DL-augmented
Cassandra could unleash havoc of a scale one struggles to comprehend.
Leaving aside that the accuracy of algorithms trained
retrospectively falls in the real world – as doctors know, there’s a difference
between book knowledge and practical knowledge – the major problem is the
effect availability of information has on decision making. Prediction is
fundamentally information. Information changes us.