By MICHEL ACCAD
Last month marked the 400th anniversary of the birth of John Graunt, commonly regarded as the father of epidemiology. His major published work, Natural and Political Observations Made upon the Bills of Mortality, called attention to the death statistics published weekly in London beginning in the late 16th century. Graunt was skeptical of how causes of death were ascribed, especially in times of plagues. Evidently, 400 years of scientific advances have done little to lessen his doubts!
A few days ago, Fox News reported that Colorado governor Jared Polis had “pushed back against recent coronavirus death counts, including those conducted by the Centers for Disease Control and Prevention.” The Centennial State had previously reported a COVID death count of 1,150 but then revised that number down to 878. That is but one of many reports raising questions about what counts as a COVID case or a COVID death. Beyond the raw numbers, many controversies also rage about derivative statistics such as “case fatality rates” and “infection fatality rates,” not just among the general public but between academics as well.
Of course, a large part of the wrangling is due not only to our unfamiliarity with this new disease but also to profound disagreements about how epidemics should be confronted. I don’t want to get into the weeds of those disputes here. Instead, I’d like to call attention to another problem, namely, the somewhat confused way in which we think about medical diagnosis in general, not just COVID diagnoses.
The way I see it, there are two concepts at play in how physicians view diagnoses and think about them in relation to medical practice. These two concepts—one more in line with the traditional role of the physician, the other adapted to modern healthcare demands—are at odds with one another even though they both shape the cognitive framework of doctors.
By MICHEL ACCAD
It is tempting to oppose the harmful effects of COVID-related lockdown orders with arguments couched in terms of trade-offs.
We may contend that when public authorities promote the benefits of “flattening the curve,” they fail to properly take into account the actual costs of imposing business closures and of forced social distancing: The coming economic depression will lead to mass unemployment, rising poverty, suicides, domestic abuse, alcoholism, and myriad other potential causes of death and suffering which could be considerably worse than the harms of the pandemic itself, especially if we consider the spontaneous mitigation that people normally apply under the circumstances.
While I have no doubt that lockdown policies can and will have very serious negative consequences, I believe that the emphasis on trade-offs is misguided and counterproductive. It immediately invites a utilitarian calculus: How many deaths and how much suffering will be caused by lockdowns? How many deaths and how much suffering will occur without the lockdowns? How exactly are we to measure the total harm? What time frame should we consider when we ponder the costs of one option versus the other?
On November 15, 2017, an epidemic of hypertension broke out and could rapidly affect tens of millions of Americans. The epicenter of the outbreak was traced back to the meeting of the American Heart Association in Anaheim, CA.
The pathogen was released in a special 488-page document labeled “Hypertension Guidelines.” The document’s suspicious content was apparently noted by meeting personnel, but initial attempts to contain it with an embargo failed and the virus was leaked to the press. Within minutes, the entire healthcare ecosystem was contaminated.
At this point, strong measures are necessary to stem the epidemic. Everyone is advised not to click on any document or any link connected to this virus. Instead, we are offering the following code that will serve both as a decoy and as an antidote for the virulent trojan horse.
Only a strong dose of common sense packed in a few lines of text can possibly save us from an otherwise lethal epidemic of nonsense. Please save the following text on your EHR cloud or hard-drive, commit it to memory or to a dot phrase, and copy and paste it on all relevant quality and pay-for-performance reports you are asked to submit.
Jim was at his desk, looking weary.
The last few weeks had been brutal. Despite working twelve-hour days, he felt that he had little to show for it. His annual board meeting was to take place the next day, and he expected it to be tense.
With a replacement bill for the ACA about to be voted on, and with Trump in the White House, the situation seemed particularly precarious. The board members had asked him to present a contingency plan, in case things in DC didn’t go well.
As CEO of a major health insurance company, Jim was well aware that business as usual had become unsustainable in his line of work. No matter what insurers had tried to do in the last few years—imposing onerous rules, setting high deductibles, pushing for government subsidies—prices had been going up and up.
Premiums, of course, had had to do the same but, evidently, the limit had now been reached. The horror stories being told at town hall meetings across the country were all too real. People were fed up, and politicians were feeling the heat.
Something needed to be done to change course, but what? He did not have any good plan to propose to the board.
Charles Ornstein is an award-winning healthcare journalist who recently wrote an article in the Boston Globe about an ongoing controversy regarding a top medical publication. Yet Ornstein still wonders about the current status of medical journals:
To help answer Mr. Ornstein’s query, I have asked the editors of top medical journals to submit responses to a simple questionnaire. Here are their answers.
What would an alternative title to your journal be? The Journal of Transparent Research
What is your tag line? “Leading the charge against conflicts of interest”
What happened at your most recent editorial staff meeting? We discussed possible strategic partnerships with healthcare journalists to get Freedom-of-Information-Act orders. Independent observers should be able to get patient-level research data released from the clutches of industry and their puppet scientists and journals.Continue reading…
While many doctors remain enamored with the promise of Big Data or hold their breath in anticipation of the next mega clinical trial, Koka skillfully puts the vagaries of medical progress in their right perspective. More often than not, Koka notes, big changes come from astute observations by little guys with small data sets.
In times past, an alert clinician would make advances using her powers of observation, her five senses (as well as the common one) and, most importantly, her clinical judgment. He would produce a case series of his experiences, and others could try to replicate the findings and judge for themselves.
A few weeks ago, the medical community received unexpected good news from the government about a “simplification of quality measures:”
Strictly speaking, and contrary to what Mr. Slavitt’s tweet would lead us to believe, the agreement to the new rules was primarily between commercial insurers and CMS, the Center for Medicare and Medicaid Services. Physicians were not actually party to the deal.
Nevertheless, doctors were expected to greet the news with cheers. As Rich Duszak reported, Adam Slavitt, acting administrator for CMS, also declared that “patients and care providers deserve a uniform approach to measure [sic] quality.”
Indeed, we all deserve uniform quality measures. Equality in quality!
A few days ago, cardiologist and master blogger John Mandrola wrote a piece that caught my attention. More precisely, it was the title of his blog post that grabbed me: “To Believe in Science Is To Believe in Data Sharing.”
Mandrola wrote about a proposal drafted by the International Committee of Medical Journal Editors (ICMJE) that would require authors of clinical research manuscripts to share patient-level data as a condition for publication. The data would be made available to other researchers who could then perform their own analyses, publish their own papers, etc.
The ICMJE proposal is obviously controversial, raising thorny questions about whether “data” are the kinds of things that can be subject to ownership and, if so, whether there are sufficient ethical or utilitarian grounds to demand that data be “forked over,” so to speak, for others to review and analyze.
Now all of that is of great interest, but I’d like to focus attention on the idea that conditions Mandrola’s endorsement of data sharing. And the question I have is this: Should we believe in science?
Mandrola’s belief in science must assume that medical science can reveal durable answers, truths upon which we can base our clinical decisions confidently. He comments:
I often find myself looking at a positive trial and thinking: “That’s a good result, but can I believe it?”…Are the authors, the keepers of the data sets, telling the whole story?
My last post was prompted by a reader’s comment where Victor Frankl’s Man’s Search for Meaning and Atul Gawande’s Being Mortal were juxtaposed. Since receiving that message, I have had occasion to notice that others also associate these two books.
For example, both are mentioned positively in this moving article by Dr. Clare Luz about a friend’s suicide, and in these tweets from Dr. Paddy Barrett’s podcast program:
Friends and patients of mine have likewise mentioned these two works to me, expressing praise and testifying to the deep impact the books have had on them.
I suspect that many readers of this blog will at least be familiar with these two books. If not, summaries are here (Frankl) and here (Gawande).
Frank Knight, risk and uncertainty
In this article, I wish to introduce the reader to the theory of entrepreneurship advanced by Frank Knight (1885-1972), and show that the common, everyday work of the physician could be considered a form of entrepreneurial activity in the Knightian sense.
Knight was an influential American economist. He is best known for his book Risk, Uncertainty, and Profit in which he proposed to distinguish risk and uncertainty as follows:
Risk pertains to situations where outcomes occur with a frequency that is quantifiable according to probability distributions.
Risk may be mathematical and a priori knowable, meaning that the probability function that governs the outcome is known with certainty, as in the case of a coin toss (assuming the coin to be well balanced).
Risk may also be statistical, where the outcome can be estimated according to an empirically discoverable probability function. This is the case in situations where we know the set of possible outcomes and can make observations under controlled conditions to determine the probability of occurrence of each outcome.