Conflicting Stories About a Drug for COVID-19

Does Remdesivir Help or Not?

A drug called remdesivir has been approved for the treatment of hospitalized patients with COVID-19 by the Food and Drug Administration (FDA). The Infectious Disease Society of America (IDSA) recommends it for the treatment of severe COVID-19 infection.

         Yet despite these approvals and recommendations, the World Health Organization (WHO) recommended against the use of remdesivir in late November, stating that there is insufficient evidence to support its effectiveness in the treatment of COVID-19. What is going on here? Are these august bodies of medical and scientific experts looking at the same studies?

         The disagreement about whether to use remdesivir to treat COVID-19 highlights some of the principles of conducting clinical trials of medications that are important to understand. The WHO, IDSA, and FDA are in fact all reading the same papers about studies that had been completed as of this writing last month. In doing so, however, they come to very different conclusions, conclusions that could affect the lives of many people who are hospitalized with COVID-19. We think the difference of opinion hinges on the choice of what is called the “main outcome measure” for a clinical trial.

How Does Remdesivir Work?

         We’ll explain what difference the choice of the main outcome measure can make, but first a bit about what remdesivir is and does. Some say it is a drug that has been looking for a disease because it has been tested on patients with several different viral illnesses, including Ebola (for which it didn’t work). Remdesivir is marketed as Veklury by drug manufacturer Gilead and is an antiviral drug that can only be given intravenously. To understand how it works, we will first supply a short review of the virus that causes COVID-19, called SARS-CoV-2

         Cells in humans and mammals all contain the genetic molecule DNA, which holds all the instructions necessary to make the proteins that do the work in our bodies. The first step in that protein-making process is for the DNA to be “transcribed” into a molecule of RNA. RNA then binds to a protein called a ribosome and enzymes use its instructions to build proteins. SARS-CoV-2 and other coronaviruses skip the DNA stage and contain only a single strand of RNA, housed within a viral envelope. When the virus infects a human cell, say in the lung or gastrointestinal tract, it releases its RNA into the human cell, where it hijacks the human cells’ ribosomes to make its own enzymes and proteins. After making many copies of itself, the viral particles burst out of the human cell, killing it, and moving through the blood to infect other cells.

The virus that causes COVID-19 is an RNA virus, with a single strand of RNA contained within a viral envelope. When this RNA strand is released into an infected human cell, it uses the human cell’s machinery to make copies of itself (source: Shutterstock).

         But in order to make copies of itself, the viral RNA must make copies of the RNA itself and it does this by assembling a series of molecules called nucleosides it steals from the human cell. This is where remdesivir comes in; it looks like one of those nucleosides and the enzyme that makes RNA copies mistakenly inserts it into the newly forming RNA strands. Because remdesivir is really a “fake” nucleoside, the enzyme’s activity grinds to a halt, resulting in defective viral RNA molecules that are incapable of making viral proteins or assembling new viral particles. In this way remdesivir is able to stop viral infection, at least in the laboratory.

         Sounds like it should work in humans too, but there are some cautions. First, SARS-CoV-2 also has an enzyme that “proofreads” newly forming RNA strands and can clip out mistakes, like the insertion of a remdesivir molecule into the sequence instead of a proper nucleoside. Second, there are billions of viral particles fighting against billions of remdesivir molecules and it is possible that the virus will win out and cause severe infections even in people who get the drug.

What the Clinical Trials Show

         That’s where clinical trials come in. Even though a drug might work as predicted in a laboratory test tube or even in a laboratory animal, there is no guarantee it will work in the much more complex human body. So scientists need to do experiments to test a drug’s effectiveness and safety. Some parts of these clinical trials are fairly standard; the most rigorous ones compare the active drug under investigation either to a placebo (sugar pill) or another active drug under “double-blind” conditions, meaning that neither the patients nor the investigators know who is on the study drug and who is getting the comparator. That reduces the chance for biased assessments of whether the drug works. Patients are randomized to get either investigational drug or comparator, so at least theoretically the only thing that is different between the two groups is whether they get drug or placebo. This kind of study is called a randomized clinical trial (or RCT). Under some conditions, such as are presented by a raging pandemic, compromises are often made in this most rigorous study design, such as not maintaining a double blind or even not having a comparator.

         Some aspects of even the most rigorous RCT’s are left up to the investigators to decide about and one of these is what to declare before the study starts will be the main outcome measure. It is required that a main outcome measure—the most important outcome the investigators will use to evaluate whether the study drug worked—be set before starting the trial. There are two reasons for this. The first is statistical. There is an old saying that if you torture data sufficiently it will confess to anything. In this case, by simple chance if you keep testing data for whether drug worked better than placebo across multiple outcome measures–what scientists often call a “fishing expedition” –eventually one will turn up to be statistically significant. But that in itself is just a chance finding and of spurious value. The second is more obvious—if you wait until the trial is over and just pick the outcome variable that makes the trial look successful you may have avoided reporting many more that didn’t show any difference between drug and placebo. Scientists call this “cherry-picking the data.” It’s not allowed.

         In the case of testing if remdesivir works for COVID-19, then, scientists have many choices for a main outcome measure. It could be how quickly patients recover, mortality rates, number of patients who require life support, number of symptoms of the infection, and so forth.

         In the studies that FDA and the IDSA looked at, time to recovery in days was chosen as the main outcome measure by investigators conducting the trials, whereas in the studies WHO evaluated, mortality was the main outcome measure. It turns out that studies have shown a modest but statistically and clinically significant reduction in the number of days it takes for a hospitalized patient to recover from COVID-19 if the patient is given remdesivir compared to placebo. On the other hand, studies have not shown that remdesivir decreases the number of hospitalized patients who die from COVID-19.

Remdesivir is an antiviral drug that has been approved by the FDA for the treatment of hospitalized patients with COVID-19. The World Health Organization advises against using it (source: Shutterstock).

         For example, in one pivotal study called ACCT-1, 1062 hospitalized COVID-19 patients were randomized to receive either remdesivir or placebo. The patients who received remdesivir recovered in 10 days, compared to 15 days in the patients who received placebo. Giving remdesivir earlier in the course of illness was more effective than giving it after ten days, but the benefit of remdesivir persisted even when given later in the course.

         The experts at the WHO looked at four studies that evaluated remdesivir, each with different designs. One of these studies used as its main outcome measure clinical status after receiving a ten-day course of remdesivir, with death as the worst possible clinical status. This study found no significant difference between remdesivir and usual care (that is, patients were provided regular COVID-19 care in both groups, but only one group got remdesivir). There was no difference between groups in the rate of COVID-19 death.

         Two physicians wrote an op ed piece in the New York Times in November titled “Does remdesivir actually work against Covid-19?” They noted that Veklury (remdesivir’s brand name) is very expensive, doubted the evidence is strong enough for approval, and worried that its approval will discourage patients with COVID-19 from participating in trials of other potential treatments that might be more effective than remdesivir.

         Those are all good points. As you can see, however, there is clearly room for scientific debate on whether remdesivir works well enough to justify FDA approval. The debate seems to hinge on a reasonable difference of opinion about what main outcome measure to consider most important. Is it sufficient that the drug speeds recovery even if it doesn’t reduce mortality risk? Clearly some experts think it is while others do not.

         We highlight this issue in order to discuss the underlying concerns that inevitably arise whenever scientists disagree. In the case of remdesivir, and many other similar situations, the issue is not whether some experts are right and others wrong but rather that there are sometimes different ways to design studies and to interpret the results. The ultimate decision about whether to administer remdesivir to a hospitalized patient with COVID-19 will be made between individual patients and their physicians. Our prediction is that many in the U.S. will decide to take it based on the recovery data and that would not be a “wrong” decision. We also predict that remdesivir will be used less often in other countries where FDA approval is not relevant and WHO opinion more germane. That also will not be a “wrong” path to take. These are, of course, just guesses and only time will tell how much remdesivir is actually prescribed.

         In most cases in which such disagreements exist, scientists call for more studies and more data. It is possible that such work will help with decision-making in the present case with remdesivir, but these are not usual times and as the pandemic rages on it is not easy to assemble and conduct rigorous RCTs on various proposed interventions. Here, the art of medicine becomes of paramount importance and having each physician-patient pair makes its own judgments about relative risks and benefits becomes the most important route to decision-making.

         What would you do if you were hospitalized with COVID-19 and offered remdesivir?

Death and COVID-19

The Myth of Exaggerated Mortality Statistics

The tragic truth is that COVID-19 is a potentially fatal disease. Some have questioned, however, the official number of deaths attributed to the coronavirus pandemic in the U.S., which stood at the beginning of November at over 230,000. People have asked us how these rates are arrived at and what’s actually in a death certificate. Is there any truth to the rumor that the number of deaths from COVID-19 has been grossly exaggerated?

         There are several numbers to keep in mind here. First is the absolute number of deaths attributed to COVID-19, which as mentioned above stood at 230,000 on November 1, but was poised to go up as increasing numbers of cases of people testing positive for the virus were being reported. That number depends on accurate reporting of deaths, which we will discuss in a moment.

         The second statistic is the rate of deaths per people actually infected by the virus. Again as of November 1 there were approximately 9.3 million people reported to have been infected with the virus that causes COVID-19 (SARS-CoV-2) in the U.S.  since the pandemic began. With the above number of reported deaths, that would yield a U.S. mortality rate of 2.5% (also known as the case fatality rate). In other words, and consistent with the reported rate given by the Johns Hopkins University Coronavirus Resource Center, a bit more than 2 out of every 100 people who contract the virus would die.

Case Fatality Rate Likely an Overestimate

         But that case fatality rate is probably an overestimate because the denominator—the total number of people who have been infected with SARS-CoV-2—is likely an undercounting of the true number of infected people. This is because many asymptomatic people who are actually infected with the coronavirus are never tested and therefore not included in that 9.3 million number of cases. In fact, some estimates suggested that there may be as many as ten times the number of positive cases of SARS-CoV-2 infection in the U.S. than we know about, which would make the case fatality rate as low as 0.25% or 2 to 3 people dying per 1000 people infected. Of course, the case fatality rate is higher in more vulnerable people, including those with certain underlying health conditions, the elderly, and people of color. And even if the whole population mortality rate were at the low 0.25% number, it is of course unacceptable to let 2 to 3 people per 1000 die from a viral illness.

An Outrageous Claim

         What if, as some have claimed, the numerator for the COVID-19 case fatality rate is an overestimate? That would mean the death rate is potentially even lower than somewhere between 0.25% and 2.5%. It turns out that this claim is untrue and represents a misunderstanding about what is written on death certificates in the U.S.

         When someone dies, a death certificate is filed within the state in which the death occurs and once registered is then sent to the U.S. National Center for Health Statistics. Death certificates have a standard format that includes a page to list “causes of death.” There are two parts to this. In Part one, the responsible clinician filling out the form reports the sequence of events leading to death. This sequence ends on the top line of the death certificate, line a, which gives the most immediate reason the person died. Lines below that give the conditions that led to the death, with the actual underlying cause of death (UCOD) falling to the last line in Part one.

         That means if someone picks up a death certificate and looks at line a in Part one, they may not see the actual disease that was responsible for the person dying. Let’s first take the example of someone who died of a heart attack. The most immediate cause of death–the event that occurred right before the person died–might have been a rupture of the wall of the heart, called the myocardium. This was in turn caused by the death from lack of oxygen of some of the muscle tissue that forms the myocardium, which in turn was caused by a blood clot in one of the coronary arteries that line the outside of the heart that bring oxygen to the myocardium. All of this was caused by the UCOD, chronic heart disease (in this case coronary artery disease). So Part one would look like this:

a. Rupture of myocardium

b. Acute myocardial infarction

c. Coronary artery thrombosis

d. Atherosclerotic coronary artery disease

Notice that the reason this person died was because of hardening of the coronary arteries over many years, but you would not know that if you just glanced at line a.

          Then there is also Part two, which lists other conditions the individual may have had which contributed but were not part of the chain that led to. These might include kidney failure, for example, or diabetes.

         You can see that it is almost never correct to put only one thing down as the cause of death on a death certificate. This led some conspiracy theorists to make the incorrect claim that only 6% of cases of death said to be caused by COVID-19 were actually caused by COVID-19. What they missed is that in the bulk of cases in which someone dies from COVID-19 it is listed on line c or d of Part one as the underlying causes of death (UCOD). The 6% of death certificates that listed only COVID-19 are probably just incomplete. For example, Part one of someone who died from COVID-19 might look like this:

a. Acute respiratory distress

b. Pneumonia

c. COVID-19

Clearly, COVID-19 is the cause of death here because it is the reason the person got pneumonia and then couldn’t breathe and then died. There might also be contributing conditions in Part two, like diabetes and high blood pressure (hypertension).

 Excess Deaths

         Writing in Scientific American, Christie Aschwanden stated emphatically that “Researchers know beyond a doubt that the number of COVID-19 deaths in the U.S. have surpassed 200,000.” Aschwanden goes on to explain that there are multiple ways scientists know this in addition to the official death certificates. One of these is the excess deaths method. Here, scientists look to see if there are a significant number of deaths in any period above what has historically been the case. “In a paper published in JAMA this month,” Aschwanden writes, researchers “examined death records in the U.S. from March 1 through August 1 and compared them with the expected mortality numbers. They found that there was a 20 percent increase in deaths during this time period—for a total of 225,530 excess deaths—compared with previous years.” Since only two-thirds of those deaths were attributed to coronavirus on the death certificates, we can safely conclude that the number of deaths reported from COVID-19 is likely an underestimate.

         Charges that doctors deliberately inflate COVID fatalities by falsely listing it as the cause of death on death certificates have been soundly condemned by many medical associations. In fact, if anything there are probably more deaths from COVID-19 than we know about.

         As healthcare professionals become more and more adept at treating patients sickened with COVID-19 it may be that the mortality rate will fall. Still, some of those people will have suffered through stays in the ICU and some will continue to have symptoms for months after they leave the hospital. The data here are all too clear: SARS-CoV-2is a deadly virus that has killed more than 200,000 Americans and more than one million people worldwide. It is a scourge that thankfully most of us are taking seriously.

Rebuilding Trust in Our Institutions

What psychology can teach us about how to help people trust again.

Both leading up to and immediately following the much-anticipated 2020 U.S. presidential election, one word kept coming up over and over again in the media and in real-life conversation: trust. Many anxious Democratic voters worried before the election about whether they could trust polls predicting a Biden win. Indeed, polls in 2016 had projected Hillary Clinton as the winner, and this obviously did not pan out.

Immediately following the election, a broad contingency of Republicans, including Donald Trump himself, claimed that there was widespread election fraud and that the results were not to be trusted. This has wreaked havoc on the smooth transition of power that usually defines our democracy. 

Stratos Brilakis/Shutterstock
Source: Stratos Brilakis/Shutterstock

As we reflected on this historic occasion and the upheaval that followed, we found ourselves thinking more deeply about this concept of trust. For one thing, it is not specific to this election. Public trust in government, for example, has been in precipitous decline for decades, dropping from around 70 percent in 1978 to around 50 percent now. This is also not just an American phenomenon but exists worldwide. Which led us to a series of questions: What do we know about the psychology of trust? Why does it exist? What purpose does it serve? And what can be done to restore it? 

It should not come as a complete surprise that the main benefit of trust is a social one. Trust allows us to cooperate with others and, most importantly, to collaborate. This ability to collaborate and work with others, rather than to do everything ourselves, makes us a more efficient society and allows us to achieve considerably more using our collective knowledge and abilities than we ever could if we all operated independently. It may not be a complete exaggeration to say that trust is in fact a fundamental element in the success of the human species writ large. If anything, we can certainly posit that trust is extremely important to the fundamental functioning of human society. 

Some intriguing recent neuroscience research has shown us more specifically where trust might sit in the brain. In one experiment, people were assigned to various conditions, one in which they were told they were playing an economic investment game with a friend they trusted; one in which they told they were playing with a computer; and one in which they were told they were playing with a stranger. In all three instances, subjects were, in reality, playing with a computer that had equal odds in all cases of cooperating with or betraying the subject.

As expected, people were more likely to want to go in on an investment together with a trusted friend rather than with a stranger or a computer. What was more interesting here was that subjects’ brain activity was also tracked during this activity, and in the trusted friend condition, there was much higher activation in both the ventral striatum and the medial prefrontal cortex. The ventral striatum is associated with the brain’s reward pathway, and the media prefrontal cortex is associated with decision-making. This suggests that it is actually rewarding when we feel we can trust and in addition, it can be a vital element in how we make important decisions. 

If trust is essential to societal functioning and it has also been eroded in recent years, what can possibly be done to rebuild it? This is a more difficult question to answer, especially since most of the research and advice on rebuilding trust has to do with individual relationships rather than institutional trust. Nonetheless, there are some broad concepts and ideas that should be taken into account by our new leadership here in the U.S. and by leaders everywhere.

First of all, trust can be rebuilt through actions but is less likely to be restored through words alone. Of course, politicians can still make speeches, but the best way to rebuild trust is to show results. The more scientific and quantifiable the results, the better.

Second, politicians and other leaders should take the time to collect targeted feedback in areas where trust is low. When people are in a state of distrust, they often feel like their voices have been silenced, and having an opportunity to speak up can be therapeutic.

Finally, clear communication has to be prioritized above everything else. The importance of this cannot be overstated. Many Americans lost faith in the coronavirus pandemic response process when government officials were unclear about the need to wear masks and then failed to explain why there was a shift in thinking. Even if recommendations change over time, public officials must take the time to explain why rather than just reversing a recommendation and hoping everyone will follow suit. 

As the U.S. tries to move forward with a political process mired with doubt, it is time we think more clearly about how to heal the now deeply-rooted distrust of our system that has taken hold in recent years. We still have a lot to learn, especially about how institutional trust works, but in the meantime, there are a number of actions we can take to heal a system that has been seriously undermined.

Get the Flu Shot While We Wait for the COVID Vaccine

A Little About What the Flu Vaccine Is and How It Works

Every year, public health experts urge everyone six months old and up to get a seasonal flu shot. They are especially eager for all of us to be vaccinated against the virus that causes flu this year because of concerns that the healthcare system would be overwhelmed this winter by flu cases because of the surge in COVID-19 cases.

         Yet many people in past years have not gotten the flu shot (although the rates do seem higher this year) and some people think it is either unnecessary or causes the flu itself. Indeed, if you really start getting into the details about the flu and the vaccine against it you can run up against some complicated terminology and concepts. While the best place to get information about the flu shot is from the CDC, even their explanations for the general public can seem complicated. Given how important getting a flu shot is this year, we thought it might be wise to focus on it for the latest edition of our occasional series on vaccines.

First, What is the Flu?

         What we call the flu is an illness caused by the influenza virus. This is an RNA virus. In humans, our DNA makes another molecule called RNA, which then makes the proteins that do the body’s work. The influenza virus has no DNA, just a single strand of RNA inside a capsule. Like all viruses, it does not have the machinery to reproduce on its own, so it must enter cells of organisms higher up the chain. When it does this, the virus’ RNA hijacks the invaded cells’ reproductive machinery, causing it to manufacture viral proteins and many new copies of the viral RNA. That RNA is then put back into the capsules and released into the blood stream so it can infect other cells.

         Influenza virus becomes airborne when an infected person speaks, coughs or sneezes. Another person gets it either by breathing it in or touching a surface that has just been touched by someone with the flu. Hence, you can get infected by just shaking hands if you then immediately touch your face and the virus gains entry into your mouth or nose that way (which is why once again washing your hands right away if you do have contact with someone who has flu is a good idea). Most flu is transmitted from human to human, but some types of flu like bird or avian flu and swine or pig flu can be transmitted from animal to human by handling infected animals or eating undercooked food. For reasons that are not entirely clear, the flu is almost entirely seasonal and generally strikes only in colder months.

The influenza virus is shown here, with its single strand of RNA surrounded by an envelope. Two proteins on its surface, designated as NA and HA, are recognized by antibodies but also undergo rapid mutation (source: Shutterstock).

         The symptoms of flu are well-known and usually include fever, cough, sneezing, running nose, sore throat, muscle and body aches, and headaches. It usually lasts just a few days and most people recover. But some people, especially those at high risk like children under five (and especially under 2), elderly people, and people with underlying medical conditions, can get serious complications from the flu, requiring hospitalization and even admission to an intensive care unit (ICU). The most common of these severe complications is pneumonia, which can be caused either by the influenza virus itself invading the lungs or by another virus or bacteria that takes advantage of the situation because the immune system is involved in fighting the flu and is therefore in a weakened state to defend against other pathogens. Besides pneumonia, other complications include inflammation of the heart, brain, and muscles.

         Every year since 2010, between 12,000 and  61,000 people have died from the flu in the U.S. That’s why public health officials fight the uphill battle every year of trying to convince us that the flu is not always a mild illness that you just get over. People die from it, including small children.

The Complexities of Flu Vaccines

         Given the potential severity of the flu, it is no wonder that public health and infectious disease experts urge us all, with just a few exceptions, to be vaccinated. Yet here is where complexities and misunderstandings have gotten in the way. In the last 10 years, fewer than half of the American population received an annual flu vaccine, with children getting it much more often than average and those 18-49 getting the flu shot less often.

         There are very few people who should not get an annual flu vaccine—children under 6 months old and people who have had severe, life threatening allergic reactions to the vaccine or one of its components in the past are among them. So why do so many people shun the only known why to prevent the flu?

         The first excuse is that the flu is not serious enough to require a vaccine. We’ve already pointed out how wrong that is, but because most people indeed do get over the flu without any complications it may seem like fearmongering to constantly warn about the severe cases and deaths from flu. That fortunate experience, however, does nothing to change the basic fact that some people, including those who have had mild cases in the past, do indeed get very sick with the flu and even die. Every year anywhere between 140,000 and nearly one million people are hospitalized with flu. Unlike COVID-19, flu can be a very serious illness in young children. We know that sometimes merely spouting large numbers like this is numbing and doesn’t really bring the seriousness of a situation home. So we need more stories of individuals who have gotten very sick from the flu in order to put real faces to the problem. If you send Critica such stories and give us permission, we will publish them on our website.

         A second incorrect notion about the flu vaccine is that it can cause the flu. You can get soreness around the area where the shot was administered and fever, headache, muscle aches, and fatigue, from the flu vaccine, but these reactions are almost always mild and last a day or so. You cannot get the flu from the flu shot and here is why.

         The influenza vaccine works by stimulating the production of antibodies against the virus that act to neutralize its effects. It takes about two weeks after receiving the vaccine for that to happen and that is why some people think they got the flu from the flu shot—they were probably either exposed just before getting the shot or during that two week period when the vaccine hasn’t yet become fully effective. In fact, the vaccine itself can be composed of one of three things, none of which can actually cause the flu.

         Remember that the first thing the viral RNA does when it enters one of your cells in your nose or mouth is to recruit your own cell machinery to make viral proteins. These proteins are what are technically called antigens, agents recognized by the immune system as foreign and therefore arousing an immune response that includes making antibodies. So vaccines work by exposing you to enough of the viral antigens to cause the immune system to react and remember what those antigens look like, but not enough to cause disease. That can be done in the case of flu vaccine by giving either:

1. Inactivated flu vaccine composed of viral particles that have been killed. They still have the proteins on them so that an immune response can be triggered, but because the virus is dead they can’t cause any disease. These are usually made in chicken eggs.

2. Attenuated live flu vaccine containing viral particles that, although still alive, are from a strain that does not reproduce at body temperature. It is given as a nasal spray and survives in the colder environment of the nose but cannot infect cells in the rest of the body. It is not recommended for children under two, pregnant women, or people with a compromised immune system. These are also usually made in chicken eggs.

3. Recombinant flu vaccine, which involves only the proteins, or antigens, from the virus, which are manufactured inside mammalian cell cultures (not egg cells). These are packaged inside a different virus particle, one that has no ability to infect humans, and thus no flu virus is given at all, just its proteins. Again, this cannot cause the flu or any other illness in people.

The influenza vaccine or flu shot cannot cause the flu, but it does reduce the likelihood of getting the flu or of getting very sick if one does get the flu (source: Shutterstock).

Why Isn’t it 100% effective?

         Perhaps the biggest misunderstanding about the flu vaccine is the concern that it doesn’t really work. After all, some insist, they always tell us it is not close to 100% effective and you have to have the shot every year. That does seem very different from so many other vaccines that provide years and sometimes a lifetime of protection. The measles vaccine is nearly 100% effective—almost no one who received the full course of MMR vaccines will ever get measles.

         Once again, a little explanation about the flu virus itself may be helpful. The first thing to know is that there are many different types or strains of flu virus. Three main types—A, B, and C—cause disease in humans and within each of those categories there are several subtypes. Each of these subtypes of flu virus has different proteins and antigens. Most modern flu vaccines are either trivalent or quadrivalent, meaning they contain the antigens of three or four subtypes respectively. That may not be enough, however, so it is always possible that some disease-causing flu virus types emerge that are not covered by a given year’s vaccine.

         To make matters even more complicated, flu viruses constantly change or mutate. That is, the RNA that controls the virus’ proteins changes itself and therefore slightly different proteins emerge. This process, called “antigenic drift” , occurs very rapidly in flu viruses, more rapidly than many other viruses such as the virus that causes COVID-19, which mutates much more slowly. The antigenic drift in the influenza virus occurs because of mutations that change the amino acid building block of proteins on the viral surface that are sites of antibody recognition. If sufficient antigenic drift occurs, antibodies stimulated by a flu shot in one year may not recognize the new viral strains. Every year, therefore, scientists must examine the virus to see what changes have occurred compared to the previous year. This allows updating of the vaccine and is the reason a new flu shot is needed every year. Even with this, it is still possible that unrecognized viral strains crop up not covered by a given year’s flu shot and therefore some people still get the flu even if they have been vaccinated.

         Fortunately, this kind of rapid antigenic drift does not occur in all viruses and some vaccines, like the measles vaccine, can confer life-long protection for people who receive all the recommended shots. Scientists have struggled to develop a universal flu vaccine that would stimulate antibodies against parts of the influenza virus that doesn’t change so rapidly and therefore obviate the need for annual vaccinations. This is a promising line of research that should ultimately yield a more permanent flu vaccine.

         There is no question that the flu vaccine is effective, just not 100% effective because of all the different viral strains and mutations. It is clear that the flu shot does not cause the flu, but it does substantially decrease the chances of getting infected with the influenza virus or getting very sick if one is infected. It isn’t a perfect vaccine, but it is far better than no vaccine.

         By now, everyone who can should have gotten a flu shot. If you haven’t gotten it yet, please do. At the time of this writing in November, cases of COVID-19 are surging and the last thing we need is people with the flu requiring hospital beds to even further overwhelm our healthcare system. We also do not want to see people simultaneously develop both flu and COVID-19, because this might make someone even sicker than either infection alone. While we wait for a COVID-19 vaccine, please do not ignore the flu.

Race in Medicine

It Is Not About Biology

The serum creatinine level is a standard blood test used as part of an equation to determine how well a person’s kidneys function. Creatinine itself is a protein produced by muscles that the kidney filters out of the blood and excretes in the urine. Creatinine levels vary with things like age, weight, and sex and therefore by themselves don’t tell us much about kidney function. But when the serum creatinine level is plugged into an algorithm that includes these other factors, you get something called the estimated glomerular filtration rate (eGFR), which does reflect kidney function. Low GFR means the kidneys are having trouble and can signal kidney disease.

         How does race factor into this? For any age and sex the algorithm for GFR tells us that Black people have a higher—that is more normal—level than a white person. In other words, In other words, race is put into the algorithm too. For example, a 50 year old Black man and a 50 year old white man could have exactly the same serum creatinine level (let’s say 1.0) but the equation says that the GFR for the Black man is higher than for the white man.

         What is going on here? Remember that this isn’t a direct measure of kidney function but rather an algorithm that uses a blood test and several demographic factors to estimate kidney function. What is the justification for the algorithm giving a higher GFR for Blacks than whites? The rationale commonly given is based on the fact that great muscle mass is associated with higher serum creatinine levels and Black people are said, on average, to have greater muscle mass than white people. The algorithm therefore is programmed to adjust for this supposed artifact that increases serum creatinine level in one group compared to the next.

A result of a blood test for a protein called creatinine is used to calculate a measure of kidney function, called the estimated glomerular filtration rate (eGFR). Race is part of the algorithm that determines the eGFR (image: Shutterstock).

         The problem is that the evidence that Black people have greater muscle mass than white people seems to be missing. As explained in a recent landmark article in the New England Journal of Medicine, “Explanations that have been given for this finding include the notion that black [sic] people release more creatinine into their blood at baseline, in part because they are reportedly more muscular. Analyses have cast doubt on this claim, but the ‘race-corrected’ eGFR remains the standard.” Now being under the impression that the GFR is higher in one person than another can mean that the person with the lower GFR gets attention for her kidney disease first, including priority for dialysis and kidney transplant. Indeed, as the article points out, Black people in the U.S. have longer waiting times for kidney transplants than white people.

The authors of the New England Journal of Medicine article actually find a number of instances in which algorithms change medical decision-making based on race and they note that “By embedding race into the basic data and decisions of health care, these algorithms propagate race-based medicine  Many of these race-adjusted algorithms guide decisions in ways that may direct more attention or resources to white patients than to members of racial and ethnic minorities.” These race-adjusted algorithms, they find, occur in cardiology, obstetrics and urology, in addition to the one explained above in kidney medicine (i.e. nephrology).

No One Thinks They Are Racist

         Perhaps we are inclined to think of racism as overt, conscious hatred of another group. We suspect that most readers of this Critica commentary do not hold such blatant prejudice.s Most white physicians are also not about to acknowledge that they don’t like Black patients or think they are inferior in some ways to white patients. A physician who publicly expressed such thoughts would appropriately be shunned by colleagues and effectively barred from practicing medicine, at least in most parts of the U.S.

         So we use this example of creatine and the GFR to illustrate a more insidious but still very harmful form of racism that exists in medicine. Scores of medical students have been taught about the GFR algorithm without thinking twice about the racial aspect of how it is calculated. When Critica President Jack Gorman was in medical school, the guidelines for writing up and presenting a case on rounds called for identifying the patient by age, sex, and race, e.g. “this 29-year-old Black man sustained a right humerus (arm) fracture as a passenger in a car crash.” One professor bravely asserted that in a case like this–and, he argued, most cases–the patient’s race had no bearing on the diagnosis or clinical management of the patient, so why mention it. The students and other professors were shocked. Isn’t it the case, they insisted, that some diseases are more common in one race than in another? When asked to name them, the students could actually come up with very few and for those few there was actually no need to state the person’s race; everyone knows that someone with sickle anemia is going to be black. Before long, race was taken out of those guidelines, but the story again highlights how racial bias exists in medicine without anyone actually realizing it.

         The fact is that race is a social construct, not a biological one. All humans originated in Africa and therefore we all share most of our genes in common with each other. A white person of Italian heritage and a white person of Eastern European heritage are likely to be more genetically different than either is with a Black person.

         But while the social construct of race tells us very little about biology and medicine, it does tell us a great deal about the ways in which racism affects health. Today in the U.S., Black people are more likely to have high blood pressure, heart disease, asthma, and diabetes than white people.[1] That turns out to have less to do with biology than it does with what are now called social determinants of health, things like poverty, living in crowded and segregated neighborhoods, poor availability of healthy food, and less access to healthcare. For instance, one study showed that Black people living in highly segregated neighborhoods have higher rates of hypertension than those living in more integrated neighborhoods. Moreover, when people moved from high segregated to less segregated neighborhoods, their blood pressure improved. Obviously, changing where you live does not change your genes, but it can lead to less stress, better healthcare, and healthier lifestyle because of cleaner air and more fruits and vegetables in local markets.

         The Black American experience with healthcare has often been told in terms of the infamous Tuskegee experiments, in which poor Black men with syphilis were deliberately not given treatment, without their knowledge, so that investigators could study the course of the disease. The study lasted from 1932 until 1972, even though penicillin, which effectively treats syphilis, was introduced in the late 1940s. Some of the men enrolled in the study died of syphilis. The scandal that arose when this egregious breach of medical ethics was finally disclosed led to reforms in medical research, but also created a lingering effect in the Black community that government and organized medicine cannot be trusted.

The murder of George Floyd and subsequent Black Lives Matter movement led to protests around the world. Medicine and the healthcare system are now facing the reality of structural racism in their domain as well (image: Shutterstock).

         As horrible as the Tuskegee experiments were, they are only part of the picture of Black people receiving substandard medical care in the U.S. Study after study has shown that Black Americans simply do not get the same quality of healthcare as white Americans, leading to shorter lifespans. As just one example, newborn mortality is three times higher in the US for Black than white babies. One telling study showed that death among Black newborns is reduced when the physicians caring for them are themselves Black. Another example is a study that looked at insulin pump utilization among children with diabetes. Instead of repeated injections, insulin pumps provide a regular administration of insulin that is coordinated with blood sugar levels. It is sadly unsurprising that Black children with diabetes are substantially less likely to have insulin pumps. In the study, this did not seem due to socioeconomic differences according to the study authors. Thus, a likely explanation is racial discrimination. Examples like this abound.

 This again is not a biological phenomenon but rather racism built into the U.S. healthcare system. So deep is the problem that although study after has documented the problem, most healthcare professionals are probably not aware of it. They do not consider themselves racist and therefore fail to see that a legacy of discrimination and inequities have denied Black people the quality healthcare that whites get.

Another Pandemic Dilemma

         Black Americans have less trust that science is acting for the public good than white Americans. Their mistrust of the healthcare system is causing a crisis right now in developing a COVID-19 vaccine. Although Black Americans are disproportionately affected by the pandemic, they are reluctant to volunteer for vaccine clinical trials. Without their involvement, it would be unclear whether the vaccine is effective and safe among Black people. There is consequently an urgent need for Black people to volunteer for vaccine clinical trials and to accept vaccination when it becomes available, but long-standing mistrust of the healthcare system borne of more than a century of racial discrimination stands in the way.

         There are now many suggestions for rooting out structural racism in the healthcare system, but still nothing that could be called comprehensive or well-funded. Various national accrediting bodies have called for health disparities curricula for residents training in internal medicine, but a recent study showed that the majority of programs do not have them. As physician Fola May put it in an op ed piece last June, “It is the responsibility of medical school and academic institutions to produce ‘woke’ doctors. Institution leaders are obligated to play an active role in identifying platforms to discuss race and locating the resources needed to provide training for all students and faculty on racial injustice, overt and implicit bias, and cultural competency.” Studies show, May writes, that the “medical education system…is ridden with structures that promulgate bias, especially against Black students.” This takes the form of disparities in grading and awarding honors.

         Critica thrives on well-conducted research studies and usually recommends that for every perceived problem in health and science, rigorous research is needed to provide data that will guide solutions. Racism in medicine is a problem, however, that does not need more empirical research. We already know it exists. Major medical and scientific societies like the American Public Health Association have stated this explicitly. We agree with the conclusions of four University of California, San Francisco scholars, two of whom are Black and two are white, that what is needed is a complete reckoning by every healthcare professional and institution of its own contributions to structural racism. They write:

“We believe our health professions colleagues, societies, and systems need to go beyond declarations—that each must review its own history, structures, workforce, and policies in an approach dedicated to truth and reconciliation and that we must all proactively engage in the battle against structural racism and health inequities to bring about a new era of antiracism in medicine.”

These are sentiments with which we fully agree.


[1] Nolen LT, Beckman AL, Sandoe E: How foundational moments in Medicaid’s history reinforced rather than eliminated racial health disparities. Health Affairs Blog, September 1, 2020

A World in Turmoil

By Peter McKenzie-Brown

Until the pandemic began, our globe was getting richer, but not becoming a happier place. Indeed, one of the great ironies of those pre-pandemic days was that, despite a growing world economy, turmoil around the planet was rising. Refugee camp numbers were growing rapidly, and would-be migrants used every imaginable tactic to migrate toward the world’s rich economies. This commentary draws widely from the ideas of political scientists – a field of science often confused with the rough-and-tumble of raw politics – to trace the growing turmoil in the world.

The three political scientists I cite begin their commentary with the dismantling of the Berlin Wall and the attendant collapse of the USSR. Think back on those events: After two world wars, communist takeovers in Russia and China and bitter East-versus-West encounters in Korea, Vietnam and other regions, the people of a soon-to-be-united Germany tore down the Berlin Wall.   

With those events, the future looked to be one shifting to liberal democracy, everywhere – at least, that was the view of political scientist Francis Fukuyama, who captured these moments of naïve optimism in The End of History and the Last Man. Western liberal democracy would rise after the Cold War and the dissolution of the Soviet Union, he wrote. “The end-point of mankind’s ideological evolution and the universalization of Western liberal democracy as the final form of human government” had begun. Most of his peers quickly rejected this notion.

Based on secularized Christianity, the view that history has a grand purpose for humanity is a classic Western conceit. As British philosopher John Gray pointed out, it is not found in Hinduism, Buddhism, Jainism, Taoism, nor in Greco-Roman antiquity. In those traditions, human history is seen as cyclical, with some version of salvation as the goal. By contrast, such Western secularisms as Marxism, liberal humanism, and global capitalism view history as linear trends which will ultimately lead to salvation.

Political scientist Samuel Huntington countered with The Clash of Civilizations and the Remarking of the World Order. This brave new world would face more, not fewer, conflicts between and among formerly communist powers and major civilizations, he wrote.

To understand the conflicts the world faced, we need to understand culture as the primary source of war. “In the emerging world of ethnic conflict and civilizational clash,” said Huntington, “Western belief in the universality of Western culture suffers three problems: it is false; it is immoral; and it is dangerous.” While peoples and countries with similar cultures would come together, those with different cultures would come apart. “Alignments defined by ideology and superpower relations are giving way to alignments defined by culture and civilization.”

Civilizations in conflict are more intransigent than countries advancing national interest or calculating the balance of power. During the cold war, the major global division was between liberal democracy and Marxism, both of which are Western notions. But when Russia became the core country of a civilization, its distance from the West became difficult to bridge.

As the relative power of the West declined, so did its cultural appeal. Meanwhile, in the dispersed Islamic world and the centralized Chinese state, renewed assertiveness and self-confidence arose. After four centuries of rapid growth in Europe and North America, a more hazardous stage of world history was emerging.

This led Huntington to a set of recommendations anchored in the idea that, unless the West recognizes the power of cultural conflict, it could perish from ignorance, overconfidence and complacency. “The principal responsibility of Western leaders,” he wrote, “is not to attempt to reshape other civilizations in the image of the West, which is beyond their declining power, but to preserve, protect and renew the unique qualities of Western civilization.”

These trends are in play in America’s capitol. There, the present Administration practices unilateralism and contempt for traditional allies, while it romances with traditional rivals. Washington seems to want the benefits of its traditional leadership without the inconvenience of shouldering the burdens they imply. In today’s White House, the gap between ends and means is increasing rapidly.

But America’s problems pale in comparison with those of countries with weaker institutions, less robust economies, and less democratic experience. Take Russia: Its collapse into a communist state led to control by a former KGB agent playing a similar role to those of the Tsars and such political premiers as Stalin.

For the best part of a century, people in the West believed Lenin had created a collectivist state with little resemblance to Marxism. Calling Russia or China a communist state benefitted two groups. Within the States, it conveyed the idea that those with power and wealth were using them for the benefit of all. Outside, it made the US and other countries that break unions to keep labor costs down seem to be doing so for the sake of freedom and not the benefit of those that already have wealth and power. You cannot associate Marxism with Russia’s or China’s government. Doing so is a dog-whistle.

The most recent important recent work in this area, Robert Kaplan’s The Revenge of Geography, carries the subtitle “What the map tells us about coming conflicts and the battle against fate.” It paints raw geography as a critical contributor to global conflict.

Acknowledging that people’s ideas and actions shape history, Kaplan described “constraints imposed by geography and the vast and varied phenomena that emanate from it…everything from persistent…national characteristics to the location of trade routes to the life-or-death requirements for natural resources – oil, water, strategic metals and minerals.”

And what about new technologies? “The advance of electronic communications [only made] the world smaller.” Such new media as the Internet made “geography more precious, more contested, more claustrophobic.”

The science of political science will now have to grapple with how these new technologies affect political structures and decisions. Countries can now influence each other and even wage “war” via the internet. Will that decrease or increase international tensions? Political scientists will do what all scientists do: develop new hypotheses and test them by observing what actually happens. Hopefully, what they learn will help us reduce conflict and improve human rights globally.

On the Front Lines

An Interview with a Physician Caring for COVID-19 Patients

Editor’s Note: In another commentary this month, “Large Numbers and Compassion Fatigue” we note that people have difficulty relating to tragedies that occur to large numbers of people. So we wondered if hearing the stories of a doctor taking care of individual patients with COVID-19, rather than just citing mortality statistics, might make it easier to comprehend the severity of the pandemic. So our Chief Operations Officer, Catherine DiDesidero, interviewed our Chief Medical Officer, David Scales, about his experiences as a hospitalist at a large New York City hospital. The transcript of the interview has been lightly edited for readability purposes. You can listen to the complete recording here.

Catherine:  
So I think the first thing we want to sort of cover is what does COVID look like from the inside? Because, you know, people do certain things because they understand the implications of it. I feel like with COVID, that’s not necessarily the case, so what does it look like? What turns it from something that’s as mild as a cold to something you are admitted to the hospital for?

David:  
Well, it’s a couple of different things. And what’s tough is kind of trying to look at COVID on an individual basis, like, if one patient in front of me has COVID, versus like looking at COVID at a population or like a statistical level. Because if I have one patient with COVID, in front of me, it’s not all that different from a lot of other things that I do in the hospital.  I have to look out for some specific things, like, they might have some breathing difficulties, they might have low oxygen levels, and also have to watch out for things like clots because the people with COVID kind of have a higher propensity for clots. But, to be honest, I would treat it in similar ways, as I treat people with other viral illnesses. Certain cold viruses, something we call RSV, rhinoviruses, which are cold viruses, influenza, so on a case by case basis, it’s not that different from those things, just with a couple of added things that I have to watch out for. Because in a typical winter, for example, I’m going to be taking care of a lot of patients that come in with some sort of respiratory virus. And sometimes I know what it is, and sometimes I don’t, and we treat them very similarly, we support their breathing. Like if they need oxygen, we give them oxygen, if they need things like nebulizers, to kind of help open up their airway a little bit, we can kind of give them medications, kind of similar medications that you might give somebody with asthma.

Catherine:  
Okay…

David: 
So any individual patient with COVID is actually like, not that unusual. We also look out for other infections, sometimes someone comes in with a virus, any virus and develops a bacterial pneumonia on top of that, so we’re always watching out for that, too. But if I only have one patient with COVID, on my service, it’s not any harder than taking care of a, you know, a patient with any other respiratory virus on my service. It’s something we have a decent idea now of how to treat.

Catherine:  
Okay…

David:  
But the problem comes when you have more than one patient. You asked: how does it go from being a light illness to something that requires hospitalization? And the short answer is: we don’t actually know. Why do some people have no symptoms at all, and others have a little bit of a cold and other people are devastated and in the hospital and requiring life support. It’s really hard to predict why, I mean, we know certain things matter, right? Like age, diabetes, high blood pressure, some of those things matter. But you know, when a patient comes into the hospital, I can’t predict how they’re going to do. I can make an educated guess, based on some of those factors, to know if they’re going to do well or they’re not going to do well. And that’s what was really tough in the beginning of the outbreak was we didn’t know who was going to do well and who wouldn’t. We would generally hear that people might get sick about five days after they were exposed. I had a couple of patients that knew pretty much when they were exposed to the virus, they would get sick about five days later. And then they would kind of have some cold like symptoms, but between day five and day 10 that’s when things would get worse. I had a couple of people in the hospital who came in, they had some symptoms, a little bit of kind of like chest pressure, shortness of breath, and then they would wake up the next day or two days later, they were like, I feel better and we would check their oxygen and make sure everything was okay. And they would go home. But there are a lot of people that have some of those symptoms and then turn for the worse between day five and day 10. And that’s when they would get really sick, some would have a lot of fevers, just like sweating all the time, like wake up in the middle of the night drenched in sweat. And a lot of people would talk about they would feel a certain kind of chest pressure, just like their chest was heavy, not like a heart attack, or anything, but almost like there was a small weight that was sitting on their chest. And I had a number of people tell me that they would get out of breath doing things that used to be simple for them. Like, you know, New York, right? So nobody, people don’t have three level apartments or anything like that. So people would say, like, they would get up and go to the bathroom, or go to the kitchen, or they had a two story walk up and never had any problems. And they would do those simple things and find themselves short of breath. Why some people took a turn for the worse, we don’t know. And then it was a question of what does that worse mean? How bad would they get? And that was how it worked on an individual level. What became really hard is, as I said, it’s easy to do work with one patient that has this type of thing. But it’s really hard when you have, like a hospital full. Because what started to happen, especially at the peak of the epidemic, was pretty much everybody had COVID. And you had to manage some other things like people have diabetes or hypertension, you have to manage some of those other things while they’re in the hospital. But the hardest part was as the hospital got overwhelmed; it’s just hard to take care of people at the level that they needed it.

So like to give an example, like on a typical day in the hospital, like a typical week, even, I might have, you know, somewhere between 10 and 15 patients on my service. And I would say maybe once or twice during the week, someone is sick enough on my service where I have to talk to the intensive care unit. And like talk about “Oh, I’m worried about this person,” they might need to come to the intensive care unit. And maybe they go, maybe they don’t, I’d say it’s an unusual week if I’m sending even one, sometimes two people, to the intensive care unit. In the time of COVID, on a typical day, I would be talking to the ICU about somewhere between three and five patients that needed to go to the ICU. But there wasn’t room in the ICU. So we had to keep them on the medical floor, and just watch them more closely. So what made COVID really hard was not any individual patient, it was that we were taking care of so many more sick people than usual, in settings that weren’t really designed for people as sick as they were. And so like, that’s what made it really, really tough.

Catherine:  
Okay, and so, you know, you hear about how people who were admitted for COVID. And who you know, were in serious condition in terms of needing ventilators, whatever, like the family wasn’t permitted on the floor. So who made medical decisions for those people? Was it true that no family was able to visit? Or how did that work? It was this whole story about people just dying alone. Was that a reality that you saw?

David: 
Yeah, that was really tough. At the peak, and even for a few weeks after the peak, there were very severe visitor restrictions. And so only in extreme cases, like someone who was imminently dying, then we could get permission from the hospital who – they didn’t have to run this by the state – but they had to, there were state guidelines around this. And so the hospital was very strict about who was allowed. And so even if someone was imminently dying, then it was a battle, and we could only get one family member to come in for a brief period of time. And this was really challenging because someone had to be – the stipulation was someone had to – we had to expect that they might pass away within the next 24 hours. And the problem is like doctors can be horrible at predicting that and so we tried our best to try to get family members in when we knew that someone was not doing well or there were a number of people who had decided that they wouldn’t want to be intubated. Their oxygen levels are still dropping so you would normally intubate them, but that’s not what they want, then you do other things to try to make sure that they’re comfortable. So they don’t just feel like they’re suffocating. And you can give them medication so that they feel better. And, and that’s kind of a period of time that we call comfort measures. And that’s when it’s very clear that they are probably going to pass away, but who knows when? But that’s when we would talk to the hospital administration about like, we need to get a family member here. But I would say it was still a battle every time we wanted to do that. So there were a number of cases where people passed away in the hospital and didn’t have a family member close. Which was really hard. It’s hard to see. I mean, it’s, I’m sure it’s even harder for the families. So what did we do about this to try to make sure – especially for people that were on ventilators. Early on we were really concerned about this, and this was something like when I say we I mean, everybody in the hospital was worried about this. And because we’d heard reports out of Italy, where some people had gotten intubated, and like we’re on a ventilator so quickly, but like, no one knew who to call or where their family was. So, we had a policy where, whenever we went down to admit somebody, we would have a conversation with them. And I would say this is something we normally do in normal times, but there’s usually less urgency, we might like, go down and admit somebody, and then like have a conversation over the course of the next couple days, while they’re in the hospital, especially if it seems like they’re not doing well. But during the time of COVID, we had a policy where if you’re going down to admit somebody, you have to have a conversation about if they were unconscious, who would they want to make medical decisions for them and get the contact information? And also talk to them about – essentially make a backup plan of like, you know, if, if you did start to have a lot of difficulty breathing, what would you want us to do? Would you want to be intubated, and be on a ventilator? Or is that something that you would really like to avoid? And so those conversations had to happen immediately when someone came in the hospital. And still challenging. We would try to kind of like, have discussions with the person that the patient would nominate. That person is called a health care proxy, or a surrogate. And so we would try to talk to them on a daily basis. But as you can imagine, as the hospital got overwhelmed and staff were really stretched, it didn’t always happen. And so we just tried to kind of have discussions early. So like someone would come in the hospital, and we try to kind of update families almost daily, as much as we could. So that they wouldn’t only be called if there was bad news. We were in an overwhelmed hospital. But our hospital wasn’t nearly as bad as places like Elmhurst or New York Presbyterian Queens. And those hospitals were so overwhelmed, I’d heard from a number of people that being able to call family just wasn’t possible. These people were just trying to do whatever they could for patient care. And so I think in those places, it was even worse in terms of families being updated, conversations happening with families, and patients being alone if they passed away.

Catherine:  
So at what point in the symptoms, would hospitalization become necessary? Let’s say you come down with it, when did it turn from  what feels like a light flu to something that’s like, okay, you need to be admitted to a hospital now?

David: 
In the beginning of the pandemic, we didn’t know. So in the beginning, I remember the first patient I took care of with COVID wasn’t that sick, he was just COVID positive. And we just didn’t know what to do with him. It was like, well, how long do we watch him? Do we just wait until he gets worse? So there were a lot of questions initially. We want to admit someone to the hospital when they’re “sick enough,” right, but like, what does “sick enough” mean? And the short version is essentially, if you have a viral illness, like, you know, we don’t have a cure for COVID or other viral illnesses, right? So people need to come into a hospital in general, if the hospital has something that you can’t give yourself at home. This is different for everybody, right? So if you’re like 23 years old, and you have a great support system, so you live with your parents, and you have brothers and sisters at home, you really don’t need to be in the hospital, unless you need something beyond the care you’re getting at home, like maybe that’s a diagnostic test, or oxygen or intravenous antibiotics or something like that. But if you’re, say, homeless, or if you’re 85 years old and you live alone, then like, the threshold changes, right? Because it becomes unsafe for you to be by yourself much more quickly, like, if you’re 85, and you’re too weak to make yourself food or to make it to the bathroom on your own, then even if you’re “not that sick” as in, maybe you don’t need oxygen, maybe you don’t need intravenous antibiotics, then you might still need to come into the hospital for a few days, until you kind of get over the illness and get stronger. So, this was tough, because, at the peak of the epidemic, it was increasingly a question of – it would come down to, do you need oxygen? And so I would go down to the emergency room, and we would check your oxygen level. And if your oxygen level was okay, we would see if you could stand up and kind of walk around the emergency department. And if your oxygen levels didn’t drop, then we would often send people home with an oxygen monitor, and say, Look you have to monitor this, and if this gets worse come back, but you don’t right now meet criteria to come into the hospital. And that was more for the healthier people. Again, if it was an 85 year old, who couldn’t even get up and walk around the emergency department, then we were in a situation where the safest thing was to admit them. So every hospital has a little bit of a different criteria of when someone should be admitted to the hospital. And I think even now, now that we’re past the peak, I think the thresholds have changed a little bit more again. And like, you know, I wouldn’t be surprised if it’s a little bit back to kind of the normal criteria, which means that some people would get admitted to the hospital, even if they don’t require oxygen, but they’re just kind of in a situation where they can’t take care of themselves very well at home. 

Catherine:  
And so what is a typical day? I mean, were there COVID wards where everyone had it and what did it look like where that was happening? As a doctor what does it look like to you? Right? Because I can say, you know, what I know of COVID is the shortness of breath, and you can’t taste anything. But if that’s not exactly the reality of it, right? There’s much more to it?

David:
Yeah. So like, let’s give an example of a day, at the peak of the pandemic. So I would bike to the hospital, because I wasn’t taking the subway anymore, right. And I would get there. And immediately when I would walk in, they would give me a mask. And that was my mask for the day. Under normal circumstances, I obviously wouldn’t be given a mask when I would walk in the hospital, and the masks would be outside of patient’s rooms. If a patient had the flu or some other virus, you would put a mask on before you walk into the room, and you would take the mask off and throw it away as you left the room. So it was a big change to walk into the hospital and be given a mask, and you were expected to wear this mask all the time. And then I would go to the floor where I work with other hospitalists where I would sit at a computer and review patient’s charts. Normally, we would all sit in a big room, right, and we’re all a little bit crowded in this one room but to try to do some social distancing, we started taking over  some other offices on the floor that we work in, so that people had a little bit more space. But we were still working in relatively close quarters having to wear a mask the entire time. And I would do kind of what I would do on typical mornings, which is review patient’s charts. And then I and my team–and that would sometimes be another redeployed, hospitalist. At one point, I had a gastroenterologist who came with me who was taking care of some additional patients. And I had a physician assistant that was working with me and we’d go and we’d round on the different patients. And initially, what was weird was we felt kind of in a tough situation because initially, you know, rounding is where I just go and I see every patient And that can take a couple of hours. Often more than that to have a conversation with the patient, have a conversation sometimes with the family member, sometimes use an interpreter. But the biggest challenge initially was we had to – every time we walked into a room we had to put on PPE [personal protective equipment]. And so many of us were like ” this is crazy,” right? Because you’re wasting so much PPE going in and out of rooms, is there a way that we can just kind of have like a hot zone where I put on PPE, and can just keep on seeing a lot of different patients without taking off a PPE, as long as they don’t have some other kind of restriction? If they’ve got some multi drug resistant bacteria or something like that, then maybe I should take off my gown after I see them so I don’t spread that bacteria to somebody else. But towards the peak of the pandemic, we finally got to a situation where we put some red tape on the floor, and basically cut the hallway in half, and we had a green zone and a red zone. And if you were in the green zone, that meant that you were not wearing PPE, and you have not touched anything that might be COVID contaminated. But if you were in the red zone, that meant you could and so what we would do is we would put on my PPE. I would walk into a patient’s room, I would do what I needed to do in there, talk to the patient, things like that, I would usually double glove and I would take off the first set of gloves, I would use some hand sanitizer, and then I put on a new set of gloves and go to the next patient. I would leave the room, stay in the red area, and then go see the next patient with my new set of gloves. And then I would just do that kind of down the hallway. We had to kind of improvise with a couple of things, for example, I would  put my phone in a plastic bag so I could call interpreter services, if I needed to talk. I had a lot of patients who spoke Spanish or Chinese. We had to navigate some of the challenges wearing gloves and a gown and two masks because we had  both an N95 and a mask on top of the N95. While juggling a phone and trying not to get it contaminated. So that would be how I would round. And then after rounding, I would do what I normally do, which is: you’ve made the plans during rounds. And then after rounds, you’re trying to implement those plans. So the physician assistant would go and write lots of orders, I would talk to a lot of consultants, I would spend a lot of time kind of talking to the intensive care unit folks about the patients that I was really the most worried about. And I would check in on them a lot because we just didn’t have enough room in the intensive care unit. We would spend a lot of time running and checking on patients that we were very concerned about. And if they looked like they were getting worse, we were kind of in constant contact with the intensive care unit. So we could be, we could say “look, I just saw them an hour ago, now they’re much worse, I really think we need to do something.” And, we could make things happen if patients were getting worse. And then in the afternoon, that’s when we would try to make a lot of phone calls. Just a little bit after the peak, we got a bunch of iPads. So we would try sometimes to go into a patient’s room, and be able to have conversations with the patient and their family members, sometimes on an iPad, or even just over the phone. Because that was always a lot better if you could talk to both the patient and the family member at the same time. That made things a lot easier. We would sometimes be outside the room to kind of limit the number of times we would go in, we would sometimes call the patient on the phone and just check in and ask “how are you doing? how’s your breathing? Has anything changed?” So that was something that I wouldn’t normally do, but we started to do increasingly during COVID to try to preserve some PPE and  minimize our risk of exposure.

Catherine:  
Wow. And did you see a lot of doctors and nurses coming down like contracting it? Or did you guys have a good enough system, it was sort of maintained or contained? I guess that’s the word.

David: 
Good question. So in our hospital, we actually, for a period of time, all of the COVID people were on two floors, and there was a floor that didn’t have any COVID patients for a while. But what was interesting is, it was that floor with no COVID patients that there was a bit of an outbreak of COVID among the staff and it was basically because in the  staff room, the staff would take off their masks to eat and to hang out. It was thought that like someone probably had COVID somehow, and spread it among a couple of the staff through the break room because the floors that had people, patients with COVID, it was an unusual thing – I heard of maybe I could think of one, maybe two cases of a provider that ended up getting COVID while they were working with COVID positive patients. I think it was because the PPE seemed to work pretty well. That’s what it seemed to us. I mean, at first, we were very skeptical because we were put in a lot of a lot of situations where it was like, “Man, I’m sure I got it.” We always talked about aerosolizing procedures. So if you did suctioning, or if you did a couple of other things, and I was wearing an N95 that I’d been wearing for four days, in rooms for a couple of hours sometimes because it was a very sick patient that we were trying to stabilize with aerosolizing procedures. And I was like, I am sure that I have been exposed. And yet I was shocked after the peak passed, and I got an antibody test and I was negative. A number of other people that were in similar risky situations were also negative. So it was surprising, but the PPE seemed to work. And to give an example of that is, my first week on the COVID service. This was a time very early in the outbreak, where we still kind of didn’t know what we were dealing with. And there was a lot of fear that we were going to run out of PPE. And so they actually did not let us wear N95 masks when we were going to see patients unless we were going to be involved in aerosolizing procedures. And so I remember that very clearly because I had a lot of patients with bad coughs. And I was only wearing a regular surgical mask for the entire week. And so I was really surprised because a number of the patients that I saw that week were very sick, and ended up intubated for a couple of weeks. So I was really surprised that, you know, if I didn’t get it that week, and I didn’t get it the weeks that I was exposed to aerosolizing procedures. I was like, “man, I guess the proof is in the pudding, I guess the PPE works.” 

Catherine:  
So in terms of the people who were family members of people who had COVID, and people who, maybe were not medical staff, but would have been exposed to it; but for some reason, were more resistant. Was there a common trait among those people that would have been exposed to it, but didn’t get it? That you guys were able to put together?

David: 
Hmm, not really. One of the things that was clear was, it was really challenging, just the living situations that people were in. Because it wasn’t uncommon that we had a couple of situations where husband and wives were both in the hospital. Because they were living in a situation where kind of there was no way to really socially distance. And one person in the family had the virus, and then all the sudden everybody had it. And that was not an uncommon thing. We would talk to people and they’d be like, “Oh, my husband also has the virus. He’s at home sick. And my son and daughter who live with us also are sick.” But there were varying degrees of who was very sick and who wasn’t. There was nothing that we could really pinpoint in terms of how the virus spread. But it made a lot of sense, people that were living in small apartments in New York, in close quarters where there were a bunch of people living there, it was a story we heard very commonly that everybody in the family had gotten sick. There weren’t situations that I’d encountered, where somehow somebody in the family had not gotten sick, even though everybody in the family was sick. That would have stood out because that would have been an unusual scenario. But that wasn’t something that I encountered personally with my patients.

Catherine:  
And was there anything in terms of who contracted it and the severity of it in regard to lifestyle? As a personal trainer, from a fitness point of view, I’m very curious to know if people fared better if they typically had a healthier, more active lifestyle? And was there a way to push out of it or to help along the healing process, let’s say, if you are a more active person?

David: 
Sure. I mean, this is completely anecdotal, but I did notice that, regardless of what age you were, if you were in kind of good shape, you seemed to weather the virus a little bit better. Especially for the younger people, all of the younger people I had. Anyone who was under the age of 50, they were generally obese, they may have had diabetes, maybe not, but they were generally obese, kind of out of shape.  I had one patient who was in his 30s, a couple of patients in their 40s, and they all kind of had that. And even some of the older people, it was less a hard and fast rule, but the older you got it did also seem – I didn’t have many 70 year olds who were pretty physically fit who came into the hospital. But the older you got, it wasn’t a hard and fast rule. Because there were definitely some older people who had no – I’m thinking of people in their 60s, early 70s who had no medical problems at all, no known medical problems, like no diabetes, no hypertension, just pretty healthy, pretty active and I knew a number of people in that age group that passed away.

Catherine:  
Okay. So it didn’t seem like there was any, like, there’s no predictability really on it, regardless of what your health or activity status is.

David: 
Whatever it is, I think this is a hypothesis, like I can’t say this for sure. It’d be really interesting to study this. But I think if you’re young and physically fit, I’d be really interested to know your risk of a severe COVID outcome. My suspicion is it would be lower, but I’m not sure. And then the older you get, I think the less your physical fitness protects you would be my suspicion just based on my experience. I’d be really interested to see if there are any studies that looked at that formally. Because I could be wrong, but it would be really, I’d be really interested to know. 

Catherine:  
All right. Um, I think that covered all the questions we talked about, is there anything that you feel like you wanted to add to it?

David:
There’s been some rumors out there about death certificates. And so I wanted to address that. What have you been hearing about these rumors?

Catherine:  
Um, so I’ve been hearing – and again, you know, it is anecdotal – however, my sister works, she’s a hospital administrator. She was working upstate, and she heard nurses on her staff that were going through something where they needed to alter the death certificate to reflect something other than COVID; that somehow the patient had tested negative for COVID but that was listed as their cause of death. I had heard things like that. And then there was something that had come out also about a motorcycle accident, and they put COVID as the cause of death. Now, would they have eventually died of COVID? Who knows? But, obviously, it would be the motorcycle accident that killed them. So, you know, there were questions about that where, you know, even if COVID wasn’t playing an active role in a person’s death, it was being listed as a cause, you know?

David: 
Yeah, that’s an interesting question. Because I had a couple of patients that it was like COVID was an incidental diagnosis, where they were clearly in the hospital with something else, like a hip fracture, right. But sometimes the link was there and COVID was very much part of it. For example, I had one patient who – I had a lot of patients, especially men, for some reason who the only presenting symptom they had, like the only symptom they had of COVID was they fainted. I had one guy who fainted at work, he was a young guy, obese security guard, he fainted at work, and so somebody called EMS. EMS brought him to the hospital, and he had COVID. And, he didn’t realize it, but he also had really low oxygen levels. He had fainted, probably because his oxygen was low. And even though he didn’t feel it, so it’s one of those things where it’s like, you know, just because of your symptoms, like, doesn’t mean that COVID wasn’t involved. So I had one patient who came in with a hip fracture, because he fainted, and broke his hip, right? Sure, the presenting diagnosis is a hip fracture, but he probably had the hip fracture because of COVID causing him to faint and lose consciousness. And that was one of the things that kind of happened a lot. It was an interesting thing to find patients that were doing well despite the COVID. I remember one patient that was, I think it was 93, and he was COVID positive, but it was like “wow, like he seems to be doing Okay, other than some of the other medical issues that we’re treating him for,” like he had some heart problems and kidney problems. And we tuned those up, and then he went home and the COVID was never really that big of an issue. For the people who got really sick, though. I can think of a couple of people where COVID  started the cascade. And so even though COVID probably didn’t kill them, it was what really started them down this pathway. And I can think of one person who – and this was not an atypical story – who had COVID, and his breathing was okay, but the COVID was bad enough that – this wasn’t uncommon – where his kidneys failed, and he got some clots and the clots ended up in the wrong places like in his lungs, and that’s what killed him. And so it wasn’t directly COVID in the sense that it wasn’t COVID attacking his lungs, and he died because he couldn’t breathe, right? That is how a lot of people died. But it was COVID setting off this cascade of other things that happened. And I also remember one patient I saw in one of the intensive care units, who was a very young guy, who thankfully he did not pass away, but he came in because of a motorcycle accident. But they had found that he had COVID. And so it was one of these things where it’s like, you know, did the COVID cause the motorcycle accident. And this was someone who he definitely had a lot of trauma. And so it was hard to tell whether or not some of the – he needed to be on a ventilator – well, did he need the ventilator because his body had experienced so much trauma? Or was it because of the COVID. And that’s, that’s actually something that can sometimes be difficult to tease out. Now, the other blood tests we did made me pretty certain that it was COVID. So even though he had a motorcycle accident and trauma, trauma wouldn’t cause all of these other signs of inflammation in his blood that we found. That was the type of stuff we found in COVID. 

Catherine:  
In a case like that would they label the death as a COVID?

David: 
So if he had passed away, I would. Thankfully that patient didn’t. But other patients where COVID set off that cascade, or COVID might have – and thankfully this did not happen – but the patient I had with the hip fracture who came in because he fainted. He fainted from COVID that caused a hip fracture. Hip fractures, you need surgery. If he had passed away during surgery, I think COVID was a contributing cause. And that’s something that I would say in the time of COVID or if something similar had happened and someone had come in with some other kind of viral or bacterial illness. Yes, most proximally he died in surgery because of a hip fracture, but he wouldn’t have had that hip fracture unless he had this illness. So the two are obviously linked.

Catherine:  
That makes sense in a way, but at the same time, it almost feels like a “Schrodinger’s cat” kind of situation. Right? I mean, because It could be, but then, you know, I guess that’s where people become skeptical of what’s being told and what’s being reported.  Because if I have a friend who could have shortness of breath due to COVID, or they could have shortness of breath due to whatever, maybe they have high allergies, and, you know, so if they have high allergies, but they also have a COVID diagnosis and they get into a motorcycle accident, then it kind of seems like it could really go one way or the other. And so people are getting the impression that it’s an inflated number because it doesn’t make complete sense to them about why you would label that a COVID death because it also doesn’t sound like it is a 100% of a certainty.

David: 
The thing we run into in the hospital, and this is why some of this information about death certificates is taken out of context, because it is actually really rare that we have 100% certainty on the cause of death. If you talk to people that are used to filling out death certificates, there wasn’t anything unusual about the COVID situation except for probably the number. But in terms of how we attributed COVID as the likely cause of death and some of the other contributing factors, in the patients I saw that passed away in the hospital, it was quite routine. Now there’s a lot of variation in terms of how people fill out death certificates. I wouldn’t be surprised if there was a hospital or two that didn’t have well-trained staff and didn’t know what they were doing in this type of situation and maybe didn’t fill them out super well. But in situations where people were used to this kind of thing, it was relatively routine in terms of how we filled this out and very routine in terms of how much uncertainty there was surrounding the diagnosis.

In the hospital we’re very used to being uncertain and you show that uncertainty to people who have the assumption that that process would be certain and it feels very weird.

I’ve had this conversation with researchers because a lot of researchers use data from death certificates to study a bunch of things. What’s funny is when you talk with these researchers and you tell them just how much uncertainty there is in the process of filling out a death certificate, how little you know sometimes about what some of the underlying factors or even what the main factor was. They’re always very surprised. It is not a completely certain process, and there’s some subjectivity to it. Was COVID the primary cause of death? Or was the primary cause of death something else and COVID was a contributing factor? That’s a judgement call. But I think what is key is recognizing that for the people who were very sick who came in the hospital, COVID was pretty severe, and was very much a contributing factor. But that’s a hard thing to get across if you’re not used to filling out death certificates. 

Catherine: 
That makes sense, and I guess that’s also a public misconception of how that’s done. 

David:
In general, I think people believe there’s a lot more certainty in medicine than there is, and I think it can be very uncomfortable when people see just how much uncertainty there is. Physicians – a lot of our training – you can go on Google and learn all the stuff you need to know for a board exam. But what is really hard is the learning process we go through in med school is the learning process for how to make decisions in uncertainty. And you can’t learn that from a Google search. There’s a lot of debate about this, I wouldn’t say everybody does it perfectly. It’s something we always have to be conscious of, but there’s a large gap between how physicians think about uncertainty in situations in the hospital and how people perceive that uncertainty. And TV shows like House and ER and Scrubs haven’t really helped. 

COVID-19 and Compassion Fatigue

Why hearing about large numbers of victims blunts our empathy

In a public Facebook thread of comments we followed recently, people were posting their reactions to a news story about the development of a COVID-19 vaccine. It had just been announced that 200,000 Americans had died from the novel coronavirus since the pandemic began, but commentators seem to want to put that number in perspective. One brought up the number of people who die from cancer every year in the U.S.—about 600,000—and another the staggering global mortality rates from starvation—around 9 million.

Were they trying to say that the number of COVID-19 deaths is so small in comparison that we should somehow not be concerned? While we cannot dispute that 200,000 is a smaller number than 600,000 or 9 million, that comparison does not seem reasonable to us. The loss of 200,000 lives is tragic. We should pay a lot of attention to these other causes of death (in the case of cancer we actually do), but that doesn’t mean we should discount the loss of lives to a newly emergent pathogen that could be mitigated if people wore face masks while in public all the time and practiced social distancing. A vaccine will hopefully bring an end to the pandemic at last.

Zigres/Shutterstock
Source: Zigres/Shutterstock

It does seem that people have trouble feeling the impact of large numbers. An article by Sarah Elizabeth Richards at the end of September in National Geographic nicely laid out the problem. In “Why our minds can’t make sense of COVID-19’s enormous death toll,” Richards writes, “Researchers say our brains aren’t wired to make sense of big numbers.” A story about a single tragic death evokes waves of sadness and emotion in us. We focus on the individual’s details, their life story, and the circumstances of their death. As the number of victims increases, our ability to muster empathy fades, something often called compassion fatigue.

For example, consider a hypothetical example of a 28-year-old woman who has just died from cervical cancer. We will give her a name, Constance Johnson. Imagine that she was a nurse who worked long hours in a hospital intensive care unit taking care of very sick patients. Her superiors, colleagues, and patients all gave her the highest ratings. She was engaged to be married when the diagnosis of cervical cancer was first made and despite aggressive treatment, she died. This kind of story has been used to encourage people to have their sons and daughters receive the vaccine against the human papillomavirus (HPV), the virus that can cause cervical cancer. Constance wasn’t vaccinated, so it is very possible her death was preventable. Campaigns like this appear to be effective because they strike an empathic and compassionate chord within us; we can relate easily to the tragedy and feel the anguish of her family.

If we talk instead about multiple people like Constance Johnson at once or just give the numbers involved—about 4,200 women die every year from cervical cancer—the information loses its impact. We don’t comprehend a number like 4,200 the way we do the story of a single individual.

Compassion Diminishes Rapidly

As the results of a study by Paul Slovic and colleagues in 2014 showed, the tendency to be charitable and feel compassion diminishes rapidly as the number of people involved increases from one. The researchers write that “The results from four studies show that affective feelings about charitable causes were strongest for a single endangered person and began to decline as the number in danger grew larger. In support of compassion fatigue, both self-report and physiological measures of affect showed that positive affect declined substantially when the group size was two or more.”article continues after advertisement

What exactly is going on in the brain that accounts for this problem of compassion fatigue when the number of affected people becomes larger? As Richards notes, “Our neurons fire when something changes, but they stop after a while.” Indeed, our brains operate by the passing of signals from one brain cell, or neuron, to the next. There is a gap, or synapse, between two neurons that must be traversed by a chemical neurotransmitter. As an electrical signal reaches the end of one neuron, the presynaptic neuron, it triggers the release of one of the many varieties of neurotransmitters into the synapse and it then binds to a receptor on the next, or postsynaptic, neuron. If the neurotransmitter is an excitatory one like glutamate, it opens a channel and causes a series of events that propagates the electrical signal so that it continues moving down the postsynaptic neuron.

After several puffs of neurotransmitter are released, however, that channel may undergo a process called “desensitization” in which it closes and stops responding to signals from the presynaptic neuron. The electrical signal is thus blocked. We’ve actually described this process very simply (like everything else in the brain, the details become increasingly complicated), but it is one good way of understanding the physical process Richards is alluding to. Neurons respond to the story of Constance Johnson, but if we start increasing the number of affected individuals they desensitize to the stimulus so that we no longer feel its full impact.

The Medial Prefrontal Cortex and Large Numbers

We even know something about where in the brain all this is happening. In a 2020 study in the journal Scientific Reports, scientists (including Paul Slovic) first noted that there is a “core network of human empathy, composed of the medial prefrontal cortex (mPFC), middle cingulate cortex, and bilateral anterior insula. In particular, the mPFC and middle cingulate cortex have been linked with cognitive empathy and the right anterior insula with affective empathy.” Cognitive empathy is the thoughts we have about someone else’s situation and emotional empathy is how we feel about it.

In the study, titled “Brain imaging evidence for why we are numbed by numbers,” the researchers used the brain imaging technique functional magnetic resonance imaging (fMRI) to show that the mPFC was activated more by events involving a single person than it is by events involving multiple people. They concluded, “Observing that the tendency to mentalize with one person more than many people is built into our brains does not mean we should accept it as an excuse for acting passively when facing large-scale crises. This observation implies, however, that we can no longer rely on our moral intuitions.”article continues after advertisement

What the authors suggest is that we have to deliberately override the tendency to dismiss tragedies involving lots of people in order to act like moral people.

Most people do not know someone who has died from COVID-19 and therefore do not have stories immediately at hand that bring home the suffering the disease causes. Media sources usually refrain from showing us pictures and videos of people struggling to breathe or lying alone in ICU beds on respirators. And we don’t always get individual stories or images of the dead. Because of this, people may cope with the enormity of the pandemic by trying to find ways to minimize or even dismiss it. Saying that there are other diseases that cause more deaths than COVID-19 could be one such emotional mechanism.

We need to tell more stories about real people who have had COVID-19 and experienced it as more than mild symptoms, including stories about people who have been killed by the disease. The stories need to be told one by one. That way, we will be harnessing what we know from cognitive neuroscience to bring the sad message home.

The Convalescent Plasma Controversy

The Debate Highlights Some Basic Issues About Clinical Trial Research

Back in August, on the night before the Republican National Convention, the president of the United States and the director of the U.S. Food and Drug Administration (FDA) conducted a press conference in which they announced an emergency use authorization (EUA) for convalescent plasma to treat patients with COVID-19. Statements made at the press conference triggered a flood of criticism from medical and scientific experts. As it had when hydroxychloroquine was touted as a breakthrough treatment for COVID-19, politics once again entered the clinical trials arena.

         The controversy over convalescent plasma allows us to highlight some of the basics about how a new drug is tested and the results interpreted.

Convalescent Plasma Explained

         First, let’s explain what convalescent plasma is. Blood contains a host of different cells floating around in a liquid called plasma. These cells include the red blood cells that carry oxygen to the body’s organs, white blood cells that form the basis of the immune system, and platelets, necessary for blood to clot. If you spin blood in a centrifuge, the cells separate out from the plasma. What is left in plasma is a lot of proteins, some of which is composed of antibodies against a huge variety of foreign invaders, including viruses and bacteria. If you remove various clotting factors from the plasma, you get a clear, yellowish liquid called serum.

         Convalescent plasma is plasma obtained from someone who has recovered from an illness that contains antibodies against the specific virus or bacteria that caused the illness. It has been used to treat infectious diseases since the late 19th century, including during the 1918 flue epidemic. Antibodies, also called immunoglobulins, are proteins that latch onto pathogens like viruses that get into our bodies and neutralizes them. They are produced by a specific type of white blood cell, called the B cell, and form the main engine of what is called the humoral immune system.

Blood plasma is what is left after red and white blood cells have been removed. It contains antibodies against viruses and bacteria (source: Shutterstock).

         It is known that at least some people who contract COVID-19 develop neutralizing antibodies against the virus that causes it, SARS-CoV-2. For months, studies have been ongoing to test the possibility that convalescent plasma containing neutralizing antibodies against SARS-CoV-2 could be taken from people who recovered from COVID-19 and administered to patients acutely ill with the disease in order to reduce the severity of symptoms, prevent death, and speed recovery. One such study was published online in August. It was conducted by the Mayo Clinic and involved multiple centers across the country, enrolling at that point about 35,000 patients hospitalized with severe COVID-19 symptoms.

Misstating Trial Results

         The data from that study formed the basis of the EUA for convalescent plasma and showed that among patients who received convalescent plasma (within three days of being diagnosed with COVID-19, 8.7% died by seven days compared to 11.9% who received the antibody transfusion four or more days after diagnosis. That difference is statistically significant. At 30 days, those mortality figures were 21.6% and 26.7% respectively for patients who received antibodies less than and more than four days after diagnosis, also a statistically significant difference. Importantly, the higher the amount (or titer) of antibodies against the coronavirus in the plasma received, the greater were the chances of survival.

         As was widely reported, the director of the FDA, Stephen Hahn, misstated the size of the benefit from convalescent serum in this study when he said that it produced a 35% reduction in deaths, or, as he put it, for every 100 patients with COVID-19 transfused with convalescent serum, 35 would survive who would have died without receiving plasma.

         We can use this clinical trial and the controversy surrounding statements about it to illustrate several important points about clinical trial research, starting with why Hahn’s statement was incorrect (note that he admitted publicly a few days later that he had made a mistake). It is based on the difference between relative and absolute risk. The relative reduction in risk for death by 7 days in the Mayo Clinic study is indeed 35% in the subgroup of patients included in the analysis. But that only tells you that a person with COVID-19 has a 35% chance of doing better if they receive convalescent serum. This does not tell us how many actually would survive if given the treatment.

Let’s say that an imaginary virus kills one person who gets an imaginary antiviral drug in a study that enrolls 10,000 people compared to two people who don’t get the drug. That’s obviously a relative reduction in mortality of 50%, which sounds like a very big number and one can imagine headlines blaring that the drug “reduces death by 50%.” In fact, of course, death from the virus in this imaginary case turns out to be rare and the drug only saved one more person out of 10,000. The headline should read “drug does not produce a meaningful survival benefit.” On the other hand, if in the same study 100 people die without drug compared to only 50 people given drug, that is still a 50% reduction, but this time the difference is 50 patients and the drug would appear to offer an important benefit.

So instead of looking at relative differences, we need to look at absolute differences. In the case of the Mayo Clinic study, the difference in death rates at 7 days is actually 3.2%, meaning that instead of 35 out of 100 potentially being saved, it is really only three patients out of 100. Saving three people isn’t bad but relying on this finding now leads us to two additional problems, one having to do with study design and the other with multiple testing. We’ll consider these in turn.

An RCT is Needed

         In the Mayo Clinic study all of the patients received the antibody transfusion. Why did some people get it by three days and others at four or more days? Apparently, that was based on a clinical decision, which means that some set of unknown and unmeasured factors were in play influencing the decision. It could be simply how quickly the hospital was able to obtain convalescent plasma. But other factors could also be involved that might have influenced the decision. Did the doctors taking care of these patients perhaps decide that people with some characteristics were more likely to benefit from immediate transfusion and could those factors, rather than the transfusion itself, be responsible for the difference in survival outcome? In order for us to be certain that no such confounding factors influencing the decision about when to transfuse occurred, the decision about how many days after diagnosing someone would have to be made completely randomly and the two groups would have to be similar in most characteristics except for the number of days after diagnosis that they received the transfusion.

         The best way to satisfy these criteria would be to randomize the patients to receive either a transfusion of serum or plasma containing antibodies or to a transfusion of a similar appearing substance that does not contain antibodies at the same number of days after diagnosis. This would be the most powerful test of whether transfusing convalescent plasma is beneficial because the only difference between the two groups would be whether antibodies are received.

In a randomized placebo-controlled study with two arms, study participants have an equal chance of receiving the treatment under investigation or a comparison substance, such as a placebo (source: Shutterstock).

         Without a randomized, placebo-controlled clinical trial, we simply do not know whether the small difference in survival observed in the Mayo study is real. Such studies are now being conducted and the first one reported failed to find a difference in any outcome between those transfused with antibodies and those given a placebo. However, in that study participants received transfusions more than three days after diagnosis, so it is not directly comparable to the Mayo Clinic. More about the possible importance of the number of days that elapse between diagnosis and transfusion later, but first let’s tackle the question of multiple testing of the data.

Don’t Test Too Often

         In a clinical trial with a randomized, placebo-controlled design (known as an RCT), researchers must declare before starting the study what their primary outcome measure is, for example the difference in 7-day survival. Absent that declaration, investigators could pick any outcome measure they wish after the study is completed. Why not try survival at three days and if that does produce a statistically significant difference between drug and placebo, let’s try four days and then 7 days and then 30 days. Or let’s look only at the subgroup of patients who needed to be on a mechanical respirator. Or perhaps just the group who developed severe respiratory distress.

         This is what statisticians sometimes call “data massage”: keep dividing the study group into different subgroups until a statistically significant finding emerges. The problem is that for statistical reasons if you keep doing that you will ultimately get a statistically significant finding purely by chance. And then once again you have no idea whether the finding is real or fluke. It turns out that the subgroup of patients included in the Mayo analysis was comprised of only a small subgroup of the total approximately 35,000 enrolled in the study and there is no indication that this group and the three day cutoff were part of a primary outcome measure declared before the study started. So if those investigators kept testing the data until something emerged, what they have is an interesting—or more technically, exploratory—preliminary finding that may or may not be a real finding. How do you solve that problem? Conduct an RCT next.

         Experts pointed out that at the point the president and FDA director held their press conference and the EUA was granted, we did not have (and still at the time of this writing last month do not have) enough data to reasonably conclude whether or not convalescent serum works to treat patients with COVID-19. The risk of prematurely issuing a EUA, especially when the drug it is applied to is hyped by high ranking federal officials, is that patients will shun RCT’s in which there is a 50% chance they will receive a placebo. That means that large numbers of COVID-19 patients who are very sick and hospitalized will be given a treatment about which we are not yet sure it works. It could be, for instance, that antibodies, which as we mentioned above are part of the humoral immune response based in B cells, are not the only or even main component of a successful anti-COVID-19 immune response. There is evidence that the other main branch of the immune system, called cellular immunity and driven by T cells, is important, but T cells are deliberately removed from convalescent plasma and serum. It is clearly a bad idea to keep throwing unproven treatments at patients, as was the case with the hydroxychloroquine experience, without ensuring that RCTs are done. We need to be resolute about completing clinical trials that will tell us what does and doesn’t work and what is safe.

         None of this means, of course, that convalescent plasma doesn’t work. The findings that it works best if given early and with high titers of neutralizing antibodies make sense. It is possible that in early days of COVID infection the damage is done by the virus itself and neutralizing antibodies are one of the body’s defenses in this phase. Then, after a few days an overwhelming immune response may get out of control and become the problem, rather than the virus itself. At that point, neutralizing antibodies would no longer offer benefit and anti-inflammatory drugs, some of which are being tested now, could be the best avenue to symptom reduction. Hopefully, something like this will prove true in the RCTs now being conducted and a valuable intervention to prevent death from COVID-19 will be found.

         Politicians need to take their lead from scientists when drug development is concerned and not the other way around. The intrusion of politics into decisions about what constitutes an effective and safe treatment increases the chance that useless and/or dangerous medications will be foisted on very ill people. One of our missions at Critica is to increase the use of scientific evidence in public policymaking. Right now, that is needed more than ever.

Why Do We Resist Fact-Checking?

The strange psychology behind when and why we ignore the facts.

It’s probably fair to say that we live in an era in which fact-checking would seem to be of paramount importance. Especially with the onset of the COVID-19 pandemic, but even before that, flat-out incorrect information spreads like fire throughout the internet and social media. In recent months, however, we’ve heard a lot about how fact-checking “doesn’t work.” 

So if fact-checking doesn’t work, should we stop spending so much time doing it? Not so fast. Whether or not fact-checking works in particular instances is actually quite nuanced. So when and how does fact-checking work? And what are the psychological drivers that lead people to resist it? 

For starters, it’s important to state one thing: fact-checking is still important, even if it doesn’t work in every instance. Fact-checking is both often very effective and necessary. Political fact-checking is also a phenomenon that supports democracy. Fact-checking has grown tremendously in the past 10 to 15 years, in part because there is a real appetite for it and people who depend upon it. Indeed, for the vast majority of people, fact-checking will work the majority of the time on the majority of topics. 

Mega Pixel/Shutterstock
Source: Mega Pixel/Shutterstock

There is a crucial caveat to this though—very vocal people on contested political issues (even ones that have clear factual answers like climate change) are not likely to respond to fact-checking on those issues. What’s more, because they are so vocal, it can seem like there are more of them than there actually are. In these cases, human cognition does not filter out emotions before looking at information and so even fact-checks may not be particularly persuasive on their own.

But that still doesn’t mean fact-checks serve no purpose in these cases, because they may still be effective as part of a multi-faceted intervention. People are most resistant when something comes up that challenges their world view, and since there isn’t a large study of the effects of fact-checking on everyone in the world, it’s hard to say that it “doesn’t work” even if we still hear a lot of misinformation circulating.

There are still things we can do to make fact-checking more effective. One is to share sources of disagreement that generally agree with people’s worldview overall (e.g. getting a Republican to refute an idea to a Republican rather than a Democrat). It also seems that graphical information, in the form of charts and other data visualization techniques, can be helpful.

Importantly, providing an alternative narrative rather than just a refutation on its own can be particularly salient. There are not a ton of data on the efficacy of real-time fact checks but it’s reasonable to think they’re still useful—after all, repeating misinformation can make it stronger so intervening before too much of that happens might help to reduce it.

Other effective methods include: aiming for the middle rather than people at the extremes, who are less likely to be swayed; and fact-checking that says things like “if you believe this, you are right” is more likely to be effective because people love to be told they are right—so including that actual phrasing can be motivating. 

There are major challenges to truth and facts in our world right now. But just because we are faced with resistance to facts does not mean we should essentially abandon them altogether. Better understanding the psychology that makes people cling to incorrect notions can help us build more effective multi-faceted strategies that include an understanding and appreciation of the importance of facts.