By Kurt Hoffman via Principia Scientific International
Early in the coronavirus pandemic, a survey of the world’s frontline physicians showed hydroxychloroquine to be the drug they considered the most effective at treating COVID-19 patients. That was in early April, shortly after a French study showed it was safe and effective in lowering the virus count, at times in combination with azithromycin.
Next we were told hydroxychloroquine was likely ineffective, and also dangerous, and that that French study was flawed and the scientist behind it worthy of mockery. More studies followed, with contradictory results, and then out came what was hailed by some as a definitive study of 96,000 patients showing the drug was most certainly dangerous and ineffective, and indeed that it killed 30% more people than those who didn’t take it.
Not only are lay people confused; professionals are. All that seems certain is that there is something disturbing going on in our science, and that if and when the “perfect study” were to ever come along, many won’t know what to believe.
We live in a culture that has uncritically accepted that every domain of life is political, and that even things we think are not political are so, that all human enterprises are merely power struggles, that even the idea of “truth” is a fantasy, and really a matter of imposing one’s view on others.
For a while, some held out hope that science remained an exception to this. That scientists would not bring their personal political biases into their science, and they would not be mobbed if what they said was unwelcome to one faction or another. But the sordid 2020 drama of hydroxychloroquine—which saw scientists routinely attacked for critically evaluating evidence and coming to politically inconvenient conclusions—has, for many, killed those hopes.
Phase 1 of the pandemic saw the near collapse of the credible authority of much of our public health officialdom at the highest levels, led by the exposure of the corruption of the World Health Organization. The crisis was deepened by the numerous reversals on recommendations, which led to the growing belief that too many officials were interpreting, bending, or speaking about the science relevant to the pandemic in a politicized way. Phase 2 is equally dangerous, for it shows that politicization has started to penetrate the peer review process, and how studies are reported in scientific journals, and of course in the press.
Those who have their doubts about hydroxychloroquine rightly point out that the public is scared, and we are longing for a magical potion to rescue us. The history of plagues is rife with such potions and the charlatans who sold them were well documented in Daniel Defoe’s Journal of a Plague Year. A pandemic is not a remedy for the innate tendency toward wishful thinking.
What is unique about the hydroxychloroquine discussion is that it is a story of “unwishful thinking”—to coin a term for the perverse hope that some good outcome that most sane people would earnestly desire, will never come to pass. It’s about how, in the midst of a pandemic, thousands started earnestly hoping—before the science was really in—that a drug, one that might save lives at a comparatively low cost, would not actually do so.
Reasonably good studies were depicted as sloppy work, fatally flawed. Many have excelled in making counterfeit bills that look real, but few have excelled at making real bills look counterfeit. As such, as we sort this out, we shall observe not only some “tricks” about how to make bad studies look like good ones, but also how to make good studies look like bad ones.
And why should anyone facing a pandemic wish to discredit potentially lifesaving medications? Well, in fact, this ability can come in very handy in this midst of a plague, when many medications and vaccines are competing to Save the World—and for the billions of dollars that will go along with that.
So this story is twofold. It’s about the discussion that unfolded (and is still unfolding) around hydroxychloroquine, but if you’re here for a definitive answer to a narrow question about one specific drug (“does hydroxychloroquine work?”), you will be disappointed.
Because what our tale is really concerned with is the perilous state of vulnerability of our scientific discourse, models, and institutions—which is arguably a much bigger, and more urgent problem, since there are other drugs that must be tested for safety and effectiveness (most complex illnesses like COVID-19 often require a group of medications) as well as vaccines, which would be slated to be given to billions of people.
“This misbegotten episode regarding hydroxychloroquine will be studied by sociologists of medicine as a classic example of how extra-scientific factors overrode clear-cut medical evidence,” Yale professor of epidemiology Harvey A. Risch recently argued. Why not start studying it now?
This inquiry concerns a molecule that has had so many accusations directed against it that it now has more than a whiff of scandal. As such, it might be thought of as a miniature of those eponymous 18th-century novels of reputation named after a single protagonist, such as Tom Jones, or Moll Flanders, where the hero’s good name is besmirched early in life and they must spend the rest of the story hoping to retrieve it.
These adventures are really every bit as much about the societies that surround the protagonist who, though no angel, has some redeeming features, and the writer has invented these imperfect, roguish heroes for the pleasure of seeing them abused and tormented chapter after chapter, often falsely accused.
We are held in suspense, watching the hero’s rises and falls, waiting to see whether fair play wins out in the end—or whether he or she is a scoundrel after all, who has pulled the wool over our eyes. So they are also morality tales.
A morality tale must have a central character that can arouse some of our sympathy. When the lead character’s name is the unapproachable and unpronounceable “C18H26C1N3O,” we are off to a bad start. That it often goes by “hydroxychloroquine sulfate” doesn’t help. So, like those English men and women of a certain era with embarrassing names who hid them behind initials or contemporaries so well known to their audiences that all one needs are a few letters to recognize them—like FDR or OBL or DMX—we shall call our protagonist simply: HCQ.
HCQ was first synthesized in 1946, but came from a distinguished European line. Its esteemed forebear, “quinine,” made from cinchona bark, had been used to treat malaria since at least the 1600s. In the 1700s, the Scottish physician and chemist William Cullen, an important Enlightenment figure, friend of David Hume, and physician to both Hume and the Scottish king, published his theory of how quinine cured malaria.
Another physician, Cullen’s near contemporary, Samuel Hahnemann, translated Cullen’s medical text, and decided to try some quinine himself, and found it gave him malaria-type symptoms. This so intrigued him, that it launched him on a new theory of his own—that diseases can sometimes be cured by substances that, if given to healthy individuals, give symptoms that are similar to the disease, but if given to people with the disease, they for some reason get better, such that “like cures like.”
Thereby, homeopathy was born, an approach that continues to be widely used in Europe and is considered a sign of insanity by many Western physicians—mature skeptics who prefer to champion effective and safe medications, like opioids.
For 300 years, quinine, which is not rare—it is in tonic water, for instance—was the only known remedy for malaria, albeit an imperfect one. In 1934, German chemists at Bayer synthesized chloroquine in the lab, and during World War II, that drug was widely used by American forces, and found more effective than quinine, for both preventing the disease, and for its treatment.
Chloroquine was used widely until the 1960s, when malaria—Plasmodium falciparum—cleverly had become resistant to it. But malaria couldn’t resist HCQ, which is nothing but chloroquine with a slight modification of its chemical structure, an added hydroxyl group. HCQ was approved for use in 1955, and found to be both more effective and less toxic than chloroquine, especially when taken for longer periods. As time passed, both chloroquine and HCQ were found to be helpful in treating autoimmune diseases like lupus and rheumatoid arthritis.
Early in the pandemic, in February and March, I was in Italy, where the death rate from corona (as it was then called) was astronomical, especially in the elderly and in frontline medical workers who had sustained contact with it (in what turned out to be infected hospitals), so I paid a lot of attention to what the Italian physicians and nurses and respiratory techs were doing for patients, and to protect themselves.
Stories emerged that often they, and the Chinese frontline physicians and health care workers, were treating corona patients with HCQ, hoping it would help, and similarly hoping that by taking it prophylactically, it would stop them from getting sick. But why?
While the Chinese Communist Party and government were engaged early in the pandemic in a well-documented deception of the naive West—withholding information about the virus, and even banning their physicians from publishing research on the corona outbreak—behind the scenes many brave Chinese physicians were nonetheless communicating with Western colleagues.
Wuhan was the epicenter, and the Chinese physicians at the People’s Hospital of Wuhan University told their Western counterparts that they got the idea of using HCQ because none of the 178 patients they had admitted for COVID-19 had lupus—a surprise, since lupus is an immune disease, and some thought it might have made these patients especially vulnerable. They wondered why this might be, and whether HCQ, which these patients had been taking for this preexisting condition, might in some way be protecting them against COVID-19.
Even in an age smitten by the idea that “Big Data Is Our Savior,” many of medicine’s greatest discoveries begin with precisely these kinds of chance observations, made by perspicacious frontline physicians looking at patients, and not from data sets or models, which can often be so abstract, that they generate only exalted nonsense. The question was: Could one extrapolate from these few patients—who might have been protected so far—to others?
A study in 10 Chinese hospitals was initiated, beginning as early as January 2020. When they found that 100 Chinese patients did better on chloroquine than controls, a conference was held on the subject on Feb. 15, in China. The preliminary results were published as a letter to an English-language journal, claiming they found the drug was effective against COVID-19-associated pneumonia. Chloroquine was included in the guidelines for the treatment of COVID-19 issued by the National Health Commission of the People’s Republic of China. By Feb. 23, seven Chinese studies of chloroquine or HCQ and COVID-19 had been added to the Chinese Clinical Trial Registry.
There was another reason studies were necessary. HCQ, though less toxic than chloroquine, can be dangerous in overdose, and for some people causes fatal heart arrhythmias, and it can cause retinal problems and blindness with long-term use (after 10 years of daily use, in 1% of patients), hearing loss, and even psychosis. Luckily, having been around so long, physicians had come to understand it very well, knew its dangers, how to screen for the approximately 1% of people who were vulnerable to these side effects, and also what drugs it interacted with that might trigger problems (such as antidepressants).
And so, despite all of that, it had been considered safe enough—if patients were properly screened and monitored by physicians or trained health care providers, and it was taken in the right amounts for the proper period of time—to be used throughout the world. They were still careful: COVID patients were poorly understood and being administered many new combinations of medications; rather than just giving HCQ and hoping for the best, scientists began carefully documenting their observations.
By March, there was evidence from China, published in Nature, that showed HCQ blocked not only malaria but also the COVID-19 virus in a test tube. The study showed that HCQ was effective at inhibiting COVID infections when the scientists put cells (stand-ins for our own) in culture, in a petri dish, then added the COVID-19 virus, and watched what happened. Compelling photos showed how the drug inhibited the development of COVID-19 infections in the cells, making it a potent “antiviral.”
It also decreased inflammation, which wasn’t that big of a surprise since this property was the reason HCQ was used to treat autoimmune diseases like lupus and rheumatoid arthritis, where the body’s inflammatory processes get out of control and attack its own cells. It was already clear by March that COVID-19 causes a wild autoimmune response in patients—the “cytokine storm”—which was often what killed them. But HCQ blocked cytokines, and moreover, it didn’t damage the cells in the process.
The lab scientists writing in Nature concluded that HCQ had three things going for it: It seemed safe for the cells (at least in the short term), was a promising antiviral against SARS-CoV-2 virus, and an anti-inflammatory compound that had potential for treating these patients. (It was soon learned that COVID-19 causes thrombosis or clots, which lead to stroke, and that HCQ also helped prevent these.)
Then on March 9, there was another Chinese study, published in Clinical Infectious Disease, which showed that HCQ was more effective in inhibiting the COVID virus in the test tube than chloroquine.
Did this mean the drug would cure COVID?
No, and the study wasn’t designed to demonstrate that. These tests done in the labs were what are called “proof of concept” studies: Preliminary studies designed to see if the “concept” that HCQ is an antiviral has any merit. To prove the drug could cure COVID would require studies in human beings, which followed patients for significant periods until they were better, or died, or left with aftereffects of the infection. Especially influential in much of the world in the early days (if not the U.S., which often, focuses, it seems, mostly on studies from the Anglosphere) were studies commissioned by the French government and led by the microbiologist, physician, and professor of infectious disease and virology, Didier Raoult, from l’Institut hospitalo-universitaire (IHU), which he directs in Marseille, and which had assembled one of the largest datasets in Europe.
Raoult is the most highly cited microbiologist in Europe, recognized for having identified 468 novel species of bacteria, most in humans, and for his team having discovered the largest virus ever documented at the time (so large it had been mistaken for an intracellular bacterium). He has boldly asserted that viruses—which had been classified as nonliving—are alive. He has published over 2,000 papers, many of them through the IHU, with him as a contributing or lead author. He has been given major awards, the French Legion d’honneur, and perhaps the most important one for a microbiologist, having a bacteria genus, “Raoutella,” named in his honor.
Raoult is a fascinating, eccentric, theatrical figure. He couldn’t be more colorful—a maverick who delights in opposing conventional thinking, his peers, and followership in science. He has hair to his shoulders, a long, pointed beard, and looks like a medieval knight in a lab coat. He loves a fight. At 68 years of age, he rides a Harley to work. He still treats patients. He sees himself as more like a philosopher or anthropologist than a typical French scientist, and teaches epistemology, the study of how we know that we know things, to his lab scientists, He believes an ever-increasing homogeneity is ruining scientific thought. He told Paris Match:
I am Nietzschean, I am looking for contradiction, trouble to strengthen myself. The worst is the comfort: It makes you silly … The more humans you have, the less they think differently. The “politically correct,” the “compliant thinking” are only a mass effect, to be avoided, even if it is difficult to resist! … To follow the herd no brain is needed, legs are sufficient … I don’t like movements, I run in the opposite direction. In general, that is where there are nuggets.
As a young man, he was, by his own account a poor student, and dropped out of school to join the French merchant marine. He eventually returned to the family trade, conventional medicine, but as is clear from his work on the interface of bacteria and viruses, the boundaries drawn by others are not his thing. His frank contempt for conformity is, not surprisingly, refreshing: He is a kind of Nassim Taleb of infectious disease. He is brilliant, doesn’t feign false humility, and claims rather persuasively that he is indifferent to many of his critics.
Raoult was the one in his lab who came up with the idea of combining the two older drugs, HCQ and azithromycin, for COVID-19. A contrarian specialty of his has been “repurposing” or “repositioning” inexpensive generic and already available medications to fight infections. Repurposing has huge advantages. If a drug can be repurposed as an antiviral in an outbreak, it provides an already approved drug on hand, one with which we have had years of experience, so we know its drug interactions, how to monitor its effects on the major organs, how to test for blood levels, as well as its “posology,” or the science of how a drug’s dosing changes in different situations, and its safety profile and side effects. Moreover, old drugs have huge advantages over new ones in this area, because often bad effects don’t show up for years after the drug is brought to market. For instance, we now know that methotrexate, which is used for certain kinds of arthritis, can cause cancer years later; certain chemotherapies for cancer can cause heart problems years later. New psychiatric drugs, often heralded to have better side effect profiles than the current ones on the market, turn out, as time passes to be far worse, and cause diabetes. The only way to learn about long term effects of anything is via time.
Repurposed drugs are often generics, and so if one worked during an epidemic, a society would not have to spend hundreds of millions on developing new ones, which may or may not work, and may or may not be safe in the long term. The cost of HCQ for a course of COVID treatment is under $10, and the cost of another new medication, being evaluated now, remdesivir, is about $3,500 (which is an entire year’s annual income in some developing countries, and will not be affordable). So, repurposing also has the effect of pissing off Big Pharma and those academic courtiers who make their living from its untold generosity to them.
The public has almost been trained to think that drugs can only be used for the purpose for which they are primarily known. People who get cold viruses and ask their physicians if they can have an antibiotic are told that old adage: “Antibiotics kill bacteria, not viruses.” And that is true for most antibiotics. But Raoult’s team was able to show that azithromycin, classically described as “an antibiotic that fights bacteria,” was effective in protecting cells that were infected with the Zika virus. His team also had 20 years of experience of repurposing HCQ for the long-term treatment of a kind of Q fever—another infectious disease.
Sometimes drugs developed for noninfectious disease turn out to fight infection. Some antihypertensives, for example, have antiviral properties, it turns out. By investigating these relationships systematically—simply trying old drugs on new conditions and seeing what happened—Raoult was making a career of, or increasing the probability of, making the kinds of “chance” observations that the Chinese physicians had made when they saw that lupus patients on HCQ seemed not to be getting COVID-19. He was making his luck.
The idea of studying HCQ as part of a “drug cocktail” to treat COVID-19 had a personal resonance for Raoult. Part of his childhood was spent in French Senegal (in Dakar) where his father, a military physician, was stationed, and as a kid Raoult took chloroquine to prevent malaria. He had a realistic sense of its long-term side-effect profile, and didn’t take at all seriously the media characterization of its safer version, HCQ, as especially dangerous, if taken for several weeks to treat COVID-19, if patients were properly monitored.
When the pandemic broke, the first thing that Raoult studied was the effects of HCQ and azithromycin on “viral load,” or how much COVID-19 virus a given patient had. Leaving aside other factors—including the patient’s general health, immune system, diet, Vitamin D status, age, and more—Chinese physicians knew the amount of virus present correlated with severity of symptoms in sick patients, and doctors were beginning to think that “how much virus” the patient has to deal with was likely a factor in how they would ultimately fare. The longer that virus had to replicate in the body, especially in a vulnerable person, the harder it might be to defeat. So, early in the battle against the virus physicians realized that if a medication was to work, the earlier it was given to an infected person the better.
The first small study by Raoult’s group was begun with 36 COVID-19 patients divided into three groups: 14 who got HCQ and six who got HCQ and azithromycin for 10 days. (The azithromycin was only added when patients were showing signs of a lower respiratory tract infection). The third was a control group of 16 people. These patients were from another hospital that didn’t offer the new treatment, or people who were offered the treatment but refused. As we shall see, this approach is very important to Raoult: On moral grounds he refuses to set up a control group that withholds a possibly effective treatment from a patient with a lethal illness.
Patients who might be vulnerable to the potential cardiac side effects were screened and not included in the study, and EKGs were done as required. It too was a kind of “proof of concept” study, like the studies of HCQ in the test tube, but taking it to the next level, to see if the drugs might work to lower the amount of virus in an infected person’s nasopharynx. COVID-19 was seen as primarily a respiratory disease at that time, so it made sense to measure the virus there. This study was answering questions like: Would the drug actually lower the viral load in the respiratory tract? What would be the appropriate dose? Would the drugs in combination work synergistically?
Lowering viral load in the nasopharynx alone would not prove that these drugs would save lives. But without it happening, it would be hard to imagine saving lives with the drugs would be possible.
The patients’ noses were swabbed every day, to check for presence of the virus. By day six, 70% of the HCQ group no longer tested positive for the virus in the nasopharynx, versus only 12.5% of the control group who were virus-free. 100% of those who were on both drugs had no virus by day five. That sounded very good, and seemed to “prove” that the drugs lower viral load in most patients, in a short time.
Some criticized the study. While the combination of HCQ and azithromycin’s effect was dramatic, there were only six patients in that group—not a big number. Another criticism was that there were six dropouts in the treatment arm, and they were not included in the final analysis, which weakened the results. This was not hidden in the paper but discussed and explained. Studies usually have dropouts. One dropout stopped taking the medication because of nausea. One person died on day three of getting the medication.
One was transferred to the ICU on the second day of medication, one on the third day, and one on the fourth day. In fact, there was nothing original in this discovery of dropouts, since the authors had pointed them out. These patients had a lethal disease, and it is not surprising some couldn’t complete the trial. Those who went to the ICU would then be getting other treatments, which would confuse the analysis, not clarify it, had they been included.
In a case of “unwishful thinking,” some people said, in a knee-jerk way, that the dropouts were obviously a fatal flaw in the study. But, in fact, the only way to know that would be to check the actual numbers. Epidemiologist Harry Risch from Yale reanalyzed the raw data—this time including the dropouts in the analysis.
Risch found that their inclusion “does not much change the 50-fold benefit.” His analyses also reconfirmed that the drug had to be given earlier in the illness, to patients with a lower viral load, and that Raoult’s drug combination did indeed seem to help many patients lower their viral load.
So: Those dropouts were not a “fatal flaw” for the study, nor was the sample size, given its purpose. These “proof of concept studies” are often small when human beings are involved. Where no effective treatment exists, you have to start somewhere. And “where” you start—i.e., in which country or ecology—may also be relevant.
Raoult, also an expert in the history of epidemics, believes that that scholarly discipline teaches us that ecology—local environmental factors that we don’t completely understand such as climate, or the presence of other organisms in a region—influence epidemics, and affect when the peaks occur and when they recede. Different strains of the coronavirus have arisen in different parts of the world, for instance. Indeed, sometimes epidemics do recede, as happened before humanity had medications or vaccines, again for reasons we don’t totally understand.
We also know genetic factors in different groups can influence differential responses to medications and perhaps even resistance. Thus it is important to do studies in different countries, and in different ecological situations. This was a French beginning, done at the point when there were only 4,500 COVID-19 patients in the country, but already the team had enough very promising results to be gearing up for the next study of over 1,000 treated patients.
A larger and longer follow-up clinical trial—what is known as an outcome study— would now definitely be worth the effort, and might show whether lowering the virus in the nasopharynx correlated with a lasting benefit, such as saving lives, at least in some patients.
What the proof of concept study didn’t do was what so many desperate people, including those in the media who were also personally scared of COVID-19, wanted it to do: declare that we had a medication combo that would entirely defeat virus in any and all who were infected. They wanted a study that would declare that all our troubles were over.
Those people were skipping steps. In fact, they were skipping science, because science is about just this kind incrementalism. So here then is a lesson: When scientific competitors, politicians, and the media, dump on a study for not showing X, make sure you know whether that study was even designed with the primary purpose of showing X to begin with.
Raoult’s clinical group found that for the medications to work, they had to be given early—something since replicated. This happens with anti-flu drugs as well—there is a need to stop the virus in its tracks before it overwhelms the body. This was not only a viral load issue.
It had to be given, it seemed, before the cytokine storms got fully underway. COVID-19 is almost like two illnesses—one before the storm, and one after. Any evidence about the use of HCQ and azithromycin given after the storm starts might well be irrelevant to its effectiveness before the storm. As well, HCQ is cleared out of the body in significant part by the kidneys.
But the COVID-19 disease process can attack small blood vessels, and seriously harm the kidneys (and other organs, including the heart and brain). Basic physiology suggests that giving HCQ after the kidneys are destroyed would likely mean they would not be able to filter and clear many of the medications the patients were on, including HCQ, and so those patients would be more vulnerable to overdose complications.
Meanwhile, some American physicians and specialists in infectious disease working on the frontlines began reporting to American media that they were seeing HCQ benefits in their own patients too, from some large groups of physicians at the Henry Ford Health Systems in Detroit, to ones in private clinics. Two physicians with decades of experience with epidemics—Drs. Jeff Colyer and Daniel Hinthorn—wrote in the Wall Street Journal, “the therapy [HCQ plus azithromycin] appears to be making a difference. It isn’t a silver bullet, but if deployed quickly and strategically the drug could potentially help bend the pandemic’s ‘hockey stick’ curve.”
Given that the American political class and pharmaceutical industry had outsourced the making of essential medications abroad, chiefly to China and India, Colyer and Hinthorn publicly asked for federal help to secure the supply.
Hydroxychloroquine was not yet a household word. It was just another molecule, making its way through the world, with a good-enough reputation.
On March 21, President Donald Trump, referring to Raoult’s group’s study (which had appeared just days before), tweeted: “HYDROXYCHLOROQUINE & AZITHROMYCIN, taken together, have a real chance to be one of the biggest game-changers in the history of medicine… Hopefully they will BOTH (H works better with A, International Journal of Antimicrobial Agents)….. be put in use IMMEDIATELY…”
A week later, Trump announced that he was going to make sure that the United States had a huge stockpile of HCQ. He quickly made a deal with India—which produced most of the world’s supply, and which had hoped to keep it for its own citizens—and stockpiled 29 million tablets. This would make it available for Americans if it turned out it was as effective as hoped, and also protect supplies for patients with lupus and rheumatoid arthritis.
Trump was clearly very excited (and would, according to reports, ultimately take the drug prophylactically himself), and like many a politician, wanted to be the bearer of good news in a frightening time. But as so many had, he slid into seeing Raoult’s very hopeful proof of concept study as an outcome study.
Let us leave aside that the biggest game-changer in the history of medicine probably occurred on the day that physicians and surgeons learned to wash their hands between patients, and thus stopped killing them while curing them, and leave aside considerations of how to best convey such information to a frightened populace as the last few pages show. There was a very serious line of reasoning, and a case to be made for:
1. Allocating resources to study HCQ and azithromycin in early cases of COVID-19 on a large scale
2. Making both drugs available on compassionate grounds for an illness that had no other effective treatment, as was already now routine in other countries
3. Securing the national supply in case the combo turned out to be as effective as hoped, for COVID-19 patients and for those with lupus and other conditions where it was needed
4. Making clear that the current studies were as of yet small
5. Making clear these studies were not of HCQ for prophylaxis (studies that take a lot of time, because the subjects must take the drug and then be exposed to the virus), but instead that they were of its use in treating people already infected
Trump’s political base cheered for HCQ and his opponents booed and accused him of practicing medicine without a license—and began dredging up any evidence, or “experts,” they could find, who might emphasize that HCQ was dangerous, or useless, or both, and thus they responded to his hyperbole with their own, and then some. As Risch observed in Newsweek, for many HCQ became “viewed as a marker of political identity, on both sides of the political spectrum.”
CNN began a nonstop campaign criticizing the safety of the drug, holding Trump responsible for three people who overdosed on it in Nigeria. Rivals went after Raoult, now tainted because Trump had mentioned his work. A New York Times profile depicted the scholar-physician as a Trump doppelganger, with his, “funny hair” and, being a man “who thinks almost everyone else is stupid,” who “is beloved by the angry and the conspiracy-minded.”
Headlines such as, “Why does Trump call an 86-year-old unproven drug a game-changer against coronavirus?” were common. Stories began equating HCQ with Trump (“Trump’s drug”) and emphasized not only that it was dangerous, but that HCQ was old. And old was definitely not good. The implication was that far better than old was some new drug—that wasn’t yet invented, never mind tested—that might be in the utopian “pipeline” to the always better medical future.
What the media, and public health officials, did not report at the time was how poor people’s chances were should they go to hospital and need intensive care for the illness. Hospitals were finding that 80% of people put on mechanical ventilators died. All the commentators who railed that HCQ was “unproven” because there had been no randomized control trials (RCTs) didn’t mention that standard ventilation treatment for COVID-19, which had become treatment-as-usual overnight for severe cases, had no RCTs supporting it either. There was a double standard as far as HCQ was concerned.
Our poor protagonist, HCQ, could now go nowhere in a hyperpoliticized America without being hectored and called “Trump’s drug.” In the media, HCQ was now “touted,” “hyped,” and not “recommended” or “prescribed,” by the physicians who advocated for it. If someone took the do-it-yourself approach, as in the sad story of the Arizona man who, terrified out of his wits of the coronavirus, along with his wife, drank fish tank cleaner mixed with soda, because she had noticed it had among its ingredients, “chloroquine phosphate.” His death was blamed on “a chemical that has been hailed recently by President Trump …”
This was all happening at a moment when clinicians working 12- to 15-hour shifts, seven days a week with COVID patients, probably had more knowledge of the disease and its treatment than any studies could yet provide. During this first-wave HCQ-chastisement by the American media, a survey study of 6,200 frontline physicians in 30 countries showed that, worldwide, HCQ was chosen by the physicians, from among 15 options, as what they thought was the most effective treatment for patients (37% chose HCQ). The other drug the physicians thought highly of was azithromycin.
But in the United States, HCQ was embroiled in the Republican-Democratic rivalry. On March 12, Michigan state Rep. Karen Whitsett, a Democrat representing the 9th Michigan House District in Detroit, went into quarantine for cornavirus symptoms, and by March 31 got her test results and was diagnosed with such a serious case of COVID-19 that she thought she was dying. She and her physician, Dr. Mohammed Arsiwala, sought permission to use HCQ but could not get it, because the Michigan Department of Licensing and Regulatory Affairs, under Democratic Gov. Gretchen Whitmer, had issued an order prohibiting the use of HCQ for COVID-19.
What an interesting twist: Plagues always give rise to new customs, practices, and regulations. If the state can give a medication to some poor decent citizen on compassionate grounds, indeed why can’t it withhold it on vindictive grounds from a traitor and a fool (as someone who wanted the Trump drug must obviously be)?
Karen Whitsett didn’t feel like assenting to this new reality. Her physician got his hands on some and dared to put her on it. After she recovered, in early April, she thanked President Trump for having spoken out about the drug, and visited him at the White House to look into ways it might be made available for others.
In response, her Michigan Democratic colleagues voted unanimously to censure her, the resolution stating she had “misrepresented the needs and priorities” of the Michigan Democratic leadership to the president and public “in contradiction with the scientifically based and action-oriented response” of themselves—i.e., the Michigan leadership—thereby “endangering the health, safety and welfare of her constituents, the city of Detroit, and the state of Michigan.”
On April 9, Dr. Raoult’s French center released the initial abstract reporting their team had now put 1,061 patients on HCQ (for 10 days) and azithromycin (for five days), and it was ultimately published in Travel Medicine and Infectious Disease on May 1. All the patients had had viral tests, to establish the diagnosis, and had electrocardiograms. Genetic analysis of their viruses was also performed. By publication time 91.7% of those patients had a good clinical outcome and a virological cure. Eight patients (0.75%) died, ranging from 74-90 years of age, often having several other complicating illnesses. These were far better results than in most centers. They also found that only 5% of the patients were shedding the virus after the first week of treatment. They reported that none of the patients had the dreaded cardiac side effects that were being discussed by some.
Was this the last word on HCQ? No. According to Raoult’s own scholarly interest in how epidemics are expressed differently in different locales, other studies would have to be done. For instance, in Marseille, Raoult found hardly any obesity in his study population. But in America, the COVID-19 epidemic was happening on top of another epidemic: According to the CDC, 71.6% of American adults are overweight, and 39.8% are overweight to the point of being obese; and obesity, often associated with diabetes, are two huge risks factors for COVID-19.
They might somehow lower the drug’s effectiveness. One couldn’t assume that because a study showed the drugs worked in Marseille, they would work in the United States. By the same token, just because an American study might get poor results for the combo, wouldn’t mean the Marseille study was inaccurate.
This study also wasn’t a randomized control trial, intentionally. As noted, Raoult doesn’t believe in them during a pandemic (nor at some other times). As he told the Times: “We’re not going to tell someone, ‘Listen, today’s not your lucky day, you’re getting the placebo, you’re going to be dying.”
What was emerging in scientific circles now was a debate about “methodology,” or what kind of scientific study of HCQ was appropriate in an emerging, lethal pandemic.
We tend to think of methodology as a dry question that has nothing to do with morality. The methodologist asks what is the best technique to get at the most certainty most quickly, and usually answers: a randomized control trial, or RCT. But in medicine, moral concerns can’t be humanely divorced from methodology. Early in a pandemic, when we know little, there is a moral imperative to start gathering data.
While RCTs are often (but not always, see below) the best kind of study, they take more time, and involve randomly assigning, say, half the patients to a new unknown but promising treatment, and half to either a placebo (sugar pill) or treatment-as-usual (which might be nothing). They are a type of experiment. With a milder disease, slow to overtake its victims, with some viable treatments to compare, one would perform RCTs sooner rather than later. If the disease is slow to kill, and patients don’t get better in the study, they might try another treatment or two after the study ends.
But COVID-19 is lethal, kills within weeks when it does, and there was no good standard treatment for very sick patients, which meant that in a randomized study, some people would most likely get no effective treatment, and no second chance with another treatment after the study was done. Raoult was saying those people were being randomly assigned to death.
That is one reason why so many researchers, like Raoult, opted for observational studies, in which as many patients as possible are treated. This is not a matter of choosing a design that is “fatally flawed,” it is a matter of choosing a design that is not unnecessarily fatal to the patients. It’s is not sloppiness (as some of his critics would allege), but being true to the study question as he saw it: How can we save as many lives as possible.
These observational studies could begin almost immediately, and didn’t require the slow approval process that RCTs require, in part because of the moral dilemmas they raise.
Still, given that pandemics kill tens of thousands, if not millions, why not favor the cold-hearted methodologist, who is willing to stand back on a high hill, like a general in a war, and take some casualties to get a win sooner? Isn’t that more moral in the long run?
Not necessarily. It is a common conceit of methodologists that they alone can improve the quality of medicine, which, without them, would be hopelessly unscientific. But diseases are very complicated. I know, from personal experience, that pure methodologists—like “armchair generals”—i.e., researchers who have perhaps have never treated a single patient with the relevant illness—often make very elementary errors in design because they don’t understand how people react to illness, the illnesses themselves, or the burden of side effects, but rather work from models. Here is just one kind of such elementary methodological error.
The kind of Russian Roulette RCT I described above, which involves withholding a possible treatment from a lethal disease, is a methodologist’s dream design. But you won’t likely volunteer yourself or loved ones for it if there is a more direct access to a promising treatment in a dire situation. Almost no sane, nonsuicidal person will, if properly informed about what is going on (which doesn’t always happen).
This is why the role of the “clinician-researcher” developed. A union of humane medicine with the certitude-seeking scientific researcher, these people don’t solve all research design problems; rather their role is, ideally, not to lose sight of the inherent tension of the enterprise.
Anyone who has performed both sides of that compound discipline in good faith knows there are profound ongoing moral conflicts between the good doctor, who thinks of the patient in front of him or her, and the scientist who thinks of the ideal methodology, which—it is hoped—might benefit other patients in the future. The randomization conflict almost always exists in serious illness, because we don’t generally study treatments on dying people that we think have no chance of working. Any clinician-researcher deserving the name knows that being a researcher does not cancel out the clinician’s Hippocratic oath to do no harm, or give them permission not to do what is best for the patient.
So, how does one sort out what kind of study is appropriate for testing a new treatment for a lethal disease? Let’s take a little detour to discuss the models.
Once a proof of concept study has established that a treatment has a chance of being effective, then one goes on to do an “outcome study.” Of these, there are two major kinds: observational studies and randomized control trials.
The aim in RCTs, as we’ve said, is to compare those who get that new medication to those who don’t (or who get another medication). It is especially important that the two groups are very similar. If the two groups are very different, it is impossible to tell if the group that did better did so because of the medication, or perhaps because of some other characteristic.
For instance, we know that advanced age is a huge risk factor for COVID-19 death. Say one group got the drug, and the other got placebo, and the group that got the drug had a better survival rate, but on closer look, was also younger on average. It would be hard to know if they survived because of the drug or their relative youth.
Age, here, is considered a “confounding factor.” It is called a confounding factor because a naive researcher might think that in the above study, he or she was measuring “the power of the medication to protect one from COVID-19 death,” but may actually have also been measuring the role of youth in protecting the patient from a COVID death.
Other confounding factors we know about now could include how advanced the illness is at the time of the study, heart disease, diabetes, obesity, or the person’s vitamin D levels. There could easily be many other confounding factors we don’t yet know about.
This is where randomization can be very helpful. In a randomized control trial, one takes a large group of patients and randomly assigns them to either the treatment group, or the nontreatment “placebo” control group, for instance. It is hoped that by randomly assigning this large number of patients to either the treatment or nontreatment condition, that each of the confounding factors will have an equal chance of appearing in both groups.
Observational studies don’t randomly assign patients to another group. Sometimes they take people with a chronic illness (which by definition doesn’t improve) and give them a treatment, and see if they improve. They compare the patients before the medication and after they got it. Sometimes they find a control group too.
One way they might do so is by comparing patients in two different settings, where one setting provides the treatment, and the other setting doesn’t. (This is what Raoult did). This is a way to get around the moral problem of “withholding” treatment from the control group—they weren’t going to get it anyway. Care can (hopefully) be taken to make sure the patients in both groups are as similar as possible, and are “matched” (say in terms of severity of illness), but the risk of yet-unknown confounding factors is higher.
For such reasons, many scientists confidently assert that RCTs are generally better, and many researchers often say—about HCQ for instance—that we will only know if it works when we get the results of a large number of large RCTs.
This implies two things. First, that they are not as confident in the RCT design sorting out the HCQ problem as they say they are: If the design is so unshakable and so bias resistant, why would we need to repeat it many times over? Why wouldn’t one good study be sufficient? Which brings us to the second implication—namely, that there is safety in numbers: The more studies show a similar outcome, the more comfortable scientists will be with it.
Except, sometimes more studies create more confusion, and are part of a bandwagon going in the wrong direction. In 2005, Dr. John Iaonnides published a paper called “Why Most Published Research Findings Are False” that became the most downloaded paper in the journal PLOS’s history, and demonstrated that all study designs can, and often do, have problems—including replication problems, meaning that in a disturbing number of cases when one group repeats a study or experiment of another group, they do not get the same findings. He proposed that this was due to various kinds of bias sneaking in.
He also showed there is often a tendency for a first study to be biased in a certain way, and for that bias to be picked up and repeated in subsequent studies so that they all have the same flaw. In this way, a massive library of falsity can build up, until it is exposed, and overthrown. So, we can’t assume just because many studies show a particular outcome, that it is true.
One might think frontline physicians would have protested Ioannides’ findings. But many were not at all surprised, since they too had witnessed the many reversals of major findings. This is now called the “replication crisis” in science, or what Nature calls a crisis of “reproducibility,” and is widely accepted to be a crisis in many fields, but particularly in the life sciences, psychology, and in medicine—and much less so in engineering, physics, and astrophysics.
Read the full story at www-tabletmag-com.