With our two tutorials out of the way, we are ready to dig into this very complicated topic. Several reminders as we begin this journey:

1. We are looking at early data and early studies. I have no doubt that we will learn much more over the next few years that may change some of this information or confirm it and build upon it. Remember, our understanding of science evolves. So, too, will our knowledge and understanding of the health consequences from SARS-CoV-2 infection.
2. It does seem clear that health consequences can be highly variable and that Long COVID is not one disease process or syndrome, but likely many different pathophysiological processes that may operate alone or in concert with each other in different people leading to different manifestations of disease. No doubt many people who are struggling with long-term health effects from COVID-19 infection are looking for answers. I caution those so affected not to conclude that anything we review over this blog series must necessarily be the explanation for your personal health issues. It may or may not be, but that is a question to take up with your treating physician.
3. Finally, as I pointed out in the introductory blog piece to this blog series- I know a little about a lot of things, but I don’t know everything about anything. I am not an expert in all the disciplines and fields of study that we are going to review. I certainly can be mistaken at times, and I am happy for those who do have more expertise than me to please comment and let me know of things that I get wrong and I will then try to correct my mistakes in a future blog piece or the comment section. We are all learning through this time.
4. Finally, I will be pulling information from more than 100 studies for this blog series. Although I make an effort to be well read on these subjects, I am certain there are studies out there that I have missed. If I have missed an important one, please submit a comment and provide me with a citation or link to the study and I will then try to review it and add points that I have not previously made to a future blog post.
5. Long COVID encompasses a lot of long-term health effects from SARS-CoV-2 infection, but not all long-term health consequences. Thus, while I will devote a lot of time and effort in addressing Long COVID, I will be including all health consequences that I have seen studied, whether or not they fit under the umbrella of “Long COVID.”

Okay, with that stated, let’s dig in.

What is Long COVID?

There are a variety of names that have been used to refer to the long-term health effects resulting from COVID-19 – post-acute COVID-19, long-term effects of COVID, long COVID, post-acute COVID syndrome, chronic COVID, long-haul COVID, late sequelae, and others. One of the difficult things about gathering data and reviewing studies related to Long COVID (formal name post-acute sequela of SARS-CoV-2 infection or PASC in the U.S. or post COVID-19 condition by the WHO) is that a universal case definition for this illness has not been accepted. A case definition is what physicians use to make a diagnosis, e.g., the blood pressure levels we use to diagnose hypertension or the blood sugar levels we use to diagnose diabetes. Thus, some studies may include study subjects that would not be included in a different study due to differing criteria that may be used.

The World Health Organization (WHO) did come up with its case definition on October 6, 2021. Of course, many of the studies had already selected their study subjects by then, and even since then, not all researchers have accepted that definition, especially because neither the CDC nor the NIH have established a case definition, which are the agencies to whom American physicians and researchers would generally be looking to for that case definition.

Here is the WHO clinical case definition:

“Post COVID-19 condition occurs in individuals with a history of probable or confirmed SARS CoV-2 infection, usually 3 months from the onset of COVID-19 with symptoms and that last for at least 2 months and cannot be explained by an alternative diagnosis. Common symptoms include fatigue, shortness of breath, cognitive dysfunction but also others and generally have an impact on everyday functioning. Symptoms may be new onset following initial recovery from an acute COVID-19 episode or persist from the initial illness. Symptoms may also fluctuate or relapse over time.”

As you can see, this definition is very broad and is still a bit subjective. One feature that differs from many other commonly used study criteria is the duration of symptoms. The WHO definition requires persistence of symptoms for at least 2 months. Many others use 1 month. On the other hand, in some ways, this case definition may be more limiting than others in that it does not account for persons who had asymptomatic COVID-19 infection and it does not account for those who are experiencing aggravation of preexisting symptoms as their post-COVID condition as some other case definitions do.

Another problem in identifying Long COVID patients is that they may not have evidence of a SARS-CoV-2 infection. We now know that Long COVID can follow a mild case of COVID-19. That infection may have been so mild that the person did not realize that they were sick, did not realize that the symptoms might be related to COVID-19, or may have assumed that they did have COVID-19, and because it was mild that there was no need to get tested. There were also people infected during surges at which time it was difficult to find testing, so they did not get tested. Finally, we know that not everyone forms the tell-tale antibodies that we can test for to determine whether they may have had infection in the past, and others have antibody responses, but those antibodies fall below detectable limits with time, so when these patients are seen for evaluation of their Long COVID symptoms, we may not be able to establish with certainty that they had COVID-19, a precondition for the development of Long COVID.

The WHO definition attempts to account for the fact that many people with Long COVID may not have evidence of prior infection by including those with a probable infection. Of course, even this terminology can be subject to different interpretations. We often use the label “probable” when someone had close contact with a known infected person and developed symptoms consistent with COVID-19 within the typical timeframe for development of infection following exposure. Of course, this remains inadequate because there are many who are suffering with symptoms typical of Long COVID that not only did not realize that they had a prior COVID-19 infection, but also do not recall a close contact with a person known to have COVID-19. Similarly, studies have different criteria for who they include – some including only subjects with confirmed prior infection (+ PCR test), some including those with confirmed infection by PCR or antibody testing, and others with other criteria.

The CDC uses the term post-COVID conditions to describe health issues that persist more than four weeks after a person is first infected with SARS-CoV-2. https://www.cdc.gov/washington/testimony/2021/t20210428.htm. (Notice the CDC uses persistence of symptoms for more than 1 month versus the WHO criteria of 2 months.)

As I mentioned previously, not all health consequences following SARS-CoV-2 infection are generally considered Long COVID. For example, we have seen cases where a person appears to have recovered fully from their infection, but then suddenly has a heart attack or a massive pulmonary embolus (blood clot to the lungs). I just heard of another such case today involving a seemingly otherwise healthy person who appeared to have recovered from COVID-19 three months ago and then died of a massive heart attack today. We generally don’t refer to those cases as Long COVID. The CDC has come up with 3 categories of post-COVID-19 conditions, but acknowledges that these are not black-and-white and there certainly can be overlap between categories.

The first, called Long COVID, involves a range of symptoms that can last for months after first being infected with SARS-CoV-2 or can even first appear weeks after the acute phase of infection has resolved. Long COVID can happen to anyone infected with SARS-CoV-2, even if the illness was mild or entirely asymptomatic. People with Long COVID report experiencing varied symptoms, including tiredness or fatigue, abnormal sleep patterns, difficulty thinking or concentrating (sometimes referred to as “brain fog”), headache, loss of smell or taste, fast- beating or pounding heart (also known as heart palpitations), chest pain, shortness of breath, cough, joint or muscle pain, depression, anxiety, and fever. The causes of Long COVID are still unclear, although there are several hypotheses, including damage to blood vessels, autoimmune effects, and ongoing infection and there may be different causes in different people and even more than one cause at play in some patients. We will discuss these potential causes in much greater detail during this blog series.

Multiorgan effects of COVID-19 are the second type of post-COVID condition as described on the CDC’s website. COVID-19 can affect and cause long-term damage in multiple body systems including those involving the heart, lung, kidney, and brain. We will be reviewing all of these, and more, during the course of this blog series. These effects can include conditions that occur shortly after the acute phase of SARS-CoV-2 infection, like multisystem inflammatory syndrome (MIS) and autoimmune conditions. MIS is a condition where different body parts can become inflamed causing severe illness and even death. The CDC is studying inflammatory symptoms in both children (called MIS-C) and adults (called MIS-A). COVID-19 illness can also precede the development of autoimmune responses which cause the immune system to attack healthy cells by mistake and damage different parts of the body. Multiorgan effects include reports of neurological conditions, kidney damage or failure, diabetes, cardiovascular damage, fibrosis of the lungs (in some cases even requiring lung transplantation) and skin conditions.

Finally, post-COVID conditions also include the longer-term effects of COVID-19 treatment or hospitalization. Some of these longer-term effects for those who were hospitalized are similar to those seen in people hospitalized for other reasons, such as severe respiratory infections caused by other viruses or bacteria. Effects of COVID-19 treatment and hospitalization can also include post-intensive care syndrome, which refers to psychological and physical health effects that remain after a critical illness. Post-intensive care syndrome includes severe weakness, brain dysfunction, and mental health problems like stress disorders. Some of these symptoms can overlap with those observed with Long COVID.

As I write the blog series, I may occasionally describe findings related to one of these three categories, but most often, especially because of the arbitrariness of these distinctions, I will lump them altogether in our discussions as health consequences of COVID-19 or post-COVID conditions or some other more general description.

How many people are afflicted with Long COVID?

There are many reasons why this is a difficult question to answer. First, obviously it is difficult to quantify this number if we don’t even have a clear definition of what Long COVID is. Second, without a clear case definition, we cannot look to a common method we use to quantify illness – medical records and billing codes. And, unfortunately, with the relative newness of this condition, and the lack of a case definition, there are some doctors who have been dismissing these symptoms and failing to diagnose this condition. Thus, we are often left to surveys and self-reporting. Of course, when these studies are done, we often miss people who are in lower socioeconomic conditions, who in the case of COVID-19 have been disproportionately impacted, so these studies will often undercount the number of cases. Further, there are some people who are very hesitant to admit their symptoms, perhaps because of guilt in getting infected because they did not take steps to protect themselves or others, perhaps because of the fear of being stigmatized by friends, co-workers or even from family members, perhaps because of fear that it might impact their employment status and there are likely other reasons.

So, another way we can get to these numbers is by sampling and then extrapolating. For example, if we can sample a large enough group to determine what percentage of infections result in Long COVID, then we can apply that percentage to the general population to come up with estimates of the numbers of people with Long COVID. Of course, there are limitations to this methodology, as well. Not knowing what factors contribute to the development of Long COVID, we might select a group that will result in overestimating or underestimating the incidence of Long COVID. It is also complicated because we don’t know whether Long COVID might occur more or less often with changes in the variants, so the timing of this sampling may cause us to over- or underestimate the incidence. Further, although Long COVID can occur in children, it appears to be less common than in adults, so if we use a group of adults only, we might overestimate the incidence of Long COVID in the general population. On the other hand, if we our sample group is people of all ages, then we might underestimate the risk for Long COVID when adults try to make their personal risk decisions.

Another challenge is that if we try to apply a percentage of people that get Long COVID to a population based on the number of infections, we also may get an artificially low number because we know that Long COVID can develop in people who had mild COVID, people who didn’t get tested and therefore wouldn’t be counted in reported numbers, and people who didn’t ever realize they were infected. This problem has become even greater since at home tests became available and in much greater use. Recently, it is estimated that only 1 in 7 to 1 in 9 of all cases of COVID are currently diagnosed by a PCR test and reported to the state and CDC. Thus, if we use reported cases, we will undercount Long COVID cases.

Another way around that is to take a large population and test them for antibodies to determine who has previously had COVID-19 and determine what percentage of people have Long COVID. We can then apply that percentage to a larger population to determine the total number of Long COVID cases. However, even this methodology has limitations because of the fact that there are people who were infected in 2020, who developed Long COVID, but no longer have detectable antibodies. Therefore, this methodology may still underestimate the true incidence of Long COVID, though it should capture more cases than the method of just applying a percentage to the reported cases.

And, of course all of this fails to answer another question. We are now realizing that more and more people are getting reinfected. We know that our case counts significantly undercount reinfections and antibody testing does not distinguish between those who have been infected once and those who have had multiple reinfections. However, we are beginning to see evidence that those who are reinfected may have even higher risk for developing Long COVID. If true, this complicates applying whatever percentage of persons with Long COVID from those with a reported case of COVID or antibody evidence of prior infection to a general population.

Finally, it is also becoming clear that vaccination does not eliminate the risk of Long COVID, but it does decrease the risk of getting infected by 2.8 – 3.5-fold. And, it appears that if vaccinated and then infected, the risk for developing Long COVID is roughly half of the risk for those who are unvaccinated and get infected. https://www.bmj.com/content/376/bmj.o407. Therefore, if we determine the percentages of those infected who go on to develop Long COVID without regard to vaccination status, we may overestimate the risk in the general population for those who are vaccinated and underestimate the risk for those who are unvaccinated.

So, now understanding all of these limitations, let’s discuss what estimates currently are. First, we can look to a study done in the first year of the pandemic (so, this means no one was vaccinated and this would have been prior to the circulation of variants of concern). The authors concluded, “In this random sample of adults with a recent history of confirmed COVID-19, one third of participants reported post-acute sequelae 2 months after their SARS-CoV-2 positive test result, with higher odds among persons aged 40–54 years, females, and those with preexisting conditions. Persons aged ≥40 years, females, those with preexisting conditions, and Black persons also reported higher rates of post-acute sequelae.” https://www.cdc.gov/flu/weekly/index.htm.

A study that attempts to adjust for many of the limitations I mentioned above is the Long Covid Impact on Adult Americans: Early Indicators Estimating Prevalence and Cost by the Solve Long Covid Initiative www.solvelongcovid.org. In their white paper issued on April 5, 2022, they attempt to quantify the number of Americans with Long COVID, the proportion of those who are experiencing disabling Long COVID and the financial burden of disease. For their purposes, they defined Long COVID from the patient’s perspective of experiencing lingering or new symptoms following a suspected or confirmed case of COVID-19.  They considered disabling Long COVID as a patient’s experience of disabling or disruptive symptoms following a suspected or confirmed case of COVID-19. Disabling symptoms were considered to be those that resulted in the person being unable to fully function at their pre-infection level and experience of lingering or new symptoms resulting in disability or reduced ability to work, such that they could no longer work full-time or at their pre-illness work level.

These researchers used both the case model and the seroprevalence model (testing for antibodies) that I discussed above. The time period of their study ended January 31, 2022, so we would expect these estimates to undercount the number of persons today, both from the fact that our Omicron surge had not yet ended, but also the fact that those infected during January would not yet have been considered to have symptoms of a duration to constitute Long COVID.

Even so, using the seroprevalence model, they estimated that 43 million Americans (13.4% of the adult population have Long COVID, and another 14 million Americans (4.4% of adults) have disabling Long COVID. The financial burden (lost wages, lost savings and medical expenses) was estimated to total $511 billion.

The researchers do a very good job of explaining their methodology and how they make adjustments for many of the limitations of these kinds of studies that I wrote about above. For their reported case model (i.e., using confirmed cases reported to states and the CDC), they estimate 30% of those who were unvaccinated develop Long COVID, with 10% of those cases being severe enough to be classified as disabling Long COVID. They use lower rate calculations for those who have been vaccinated. In their seroprevalence model, using these percentages, they examine the cases and financial impacts for each state. For Idaho, they estimate 237,000 cases of Long COVID and 79,000 cases of disabling Long COVID, with a financial impact of $2.8 billion. These numbers are truly staggering, and keep in mind, they almost certainly undercount the true numbers of impacted people.

Of course, as we often have to remind some, this pandemic is not over. Unfortunately, many do not understand the potential for these long-term health consequences from getting infected at a time when many are abandoning almost all of the public health measures that we have to avoid infections, and many remain concerned that the harms of vaccination promoted by a group of doctors who spread disinformation that are not supported by science or evidence outweigh the real harms that we are seeing the evidence of in those who have been infected. Thus, I remain concerned about the amount of needless death and suffering, but also just the long-term economic implications of increased health care costs and decreased employee productivity, especially since Long COVID impacts many at the prime of their lives.

In my next blog piece, we will explore the pathophysiology of SARS-CoV-2 infection, i.e., the various disturbances to the body’s normal functioning that may result in death for some and long-term health consequences for others.

I am beginning my blog series on what is known about the medical and health consequences of COVID-19. Before jumping in, I indicated that readers would need two tutorials in preparation. The first we covered in the last blog piece. That covered different types of clinical trials and a brief tutorial on the statistics that we use to interpret the results of clinical studies.

Today, we will complete the other tutorial – a basic understanding of virology and immunology, one that is so brief and basic that it is sure to offend microbiologists, virologists and immunologists, because in being so basic, there is much that we won’t cover and do justice to, but also, we won’t be able to cover all the intricacies and the exceptions to general principles that a non-scientist will not need to know in order to gain an understanding of the health effects from infection with the SARS-CoV-2 virus.

So, let’s take on virology first. Let me first confess and let you know that in explaining viruses, I will do two things that are wrong, but I don’t know a better way to help non-scientists understand viruses. First, I will lead you to believe that viruses are living things. They are not. I will write things that discuss “killing” the virus, which would lead one to believe then, prior to killing, they must have been alive. It probably would be better for me to be more precise by stating that the virus is “inactivated” or “altered” to render it no longer capable of infecting a cell, but I find it a lot easier to just say the virus is killed, even though that is not technically correct. I am also in good company, because most of the virologists and microbiologists I talk to also use this phraseology, even though they know this is not technically correct. In fact, we even refer to “live” virus and “killed” virus vaccines as a simplistic reference to whether the vaccine can or cannot cause infection. (By the way, we do not use “live” vaccines for prevention of COVID-19).

The other thing I will do is give you the impression that viruses are intentional beings, which they are not. It is common when we discuss how viruses evolve to use language that suggests the virus is getting smarter and craftier, with the impression that the virus is purposefully trying to preserve its ability to transmit and infect its host. Certainly, we do see many viruses evolve in this manner, but there is no mind, consciousness or intent with respect to viruses. Again, it simply is a bit easier to understand and converse about these evolutionary changes by thinking about what is best for the virus and how it might choose to act if it could do so, because obviously, if a virus doesn’t evolve in a manner that preserves its ability to transmit and infect, then it is not going to pose much of a threat to us and we are not going to spend a lot of time worrying about those viruses.

With my confessions out of the way, we can begin. What is a virus? There are a number of ways we can divide viruses up into categories -the size of the virus, plant vs. animal viruses, human vs. non-human viruses, the family of viruses to which it belongs (e.g., coronaviruses), whether the virus has an envelope or not (SARS-CoV-2 does) and a number of other ways, but one common way is to divide them up according to the make-up of their genetic material – DNA viruses and RNA viruses. SARS-CoV-2 is an RNA virus. DNA and RNA contain the genetic instructions for the production of protein and new viruses. But, having the genetic code is not enough, and this is a big problem for viruses. (You may be wondering what does it matter whether a virus is a DNA virus or an RNA virus. The majority of examples of viruses that have a latent phase in humans, i.e., you get infected, but then the virus can hide out relatively dormant until manifesting itself years later are DNA viruses. As we will see, SARS-CoV-2 may be one of the exceptions to this rule. RNA viruses generally undergo much more rapid replication and often do not have the mechanisms that help prevent errors or repair these mistakes in the replication process. This leads to mutations, which can change the properties of the virus and may make antibodies formed to the version of the virus prior to the mutations less effective (immune escape or evasion). We have seen many examples of this with the SARS-CoV-2.)

Viruses need the machinery contained within a cell to make the proteins and to assemble all the pieces of newly reproduced viral particles into virus progeny. In other words, viruses can only replicate when they infect a cell so that they can take over the cell’s machinery. If a virus is airborne or on a surface like a countertop, it may or may not be able to infect a cell if it comes into contact with a host, but it is not replicating while in the air or on the surface. When the virus does infect a cell, instead of the cell’s machinery making proteins coded for by the cell’s DNA, the cell is now hijacked to use the virus’ genetic material to make the proteins needed to assemble new viruses. The virus has two more challenges, though.

First, if our immune systems are working properly, they are not going to welcome the virus into the body. As soon as our bodies recognize an invader, our immune systems launch an attack. From the time the virus enters our nose, mouth, lungs, gastrointestinal tract or blood until the time that it can invade a cell, it is particularly vulnerable to this attack. We’ll talk a lot more about this when we get to the immunology primer below, but keep in mind that before the virus enters a cell, antibodies can attack it. Once inside the cell, antibodies can’t get to the virus. (Keep in mind that there are millions of Americans who have immune deficiencies or states of immunocompromise where various portions of the immune system may not work well or work at all. For example, there are conditions where people don’t make certain antibodies or any antibodies. Also, many people have underlying health conditions for which the treatment has the effect of weakening the immune response. These folks can all be much more susceptible to infection than the rest of us, and if they get infected, they may be unable to clear the virus on their own.)

The second challenge for the virus is that it can’t just invade any old cell it wants to (see how I make it sound like viruses have minds and intentions!). Just like when we check into a hotel, we will get a room key that works just on one door and allows us to enter one room, the virus can only enter and infect cells that it has a “key” for, but in this case, we refer to the lock on the door as a receptor on a cell surface. With SARS-CoV-2, this is why you have heard so much about the ACE-2 receptor. This is the door lock to the cell for which the SARS-CoV-2 spike protein (specifically, something called the receptor binding domain or RBD) serves as the key. Now, this is where my analogy falls apart because unlike the single hotel room for our key, there are many, many cells that have the ACE-2 receptor, and thus they may all possibly be vulnerable to infection. In fact, this is in large part why we see so many different possible manifestations of SARS-CoV-2 infection.

The other way my analogy fails is that the SARS-CoV-2 virus also has a trick up its sleeve (see again how I am giving you the impression that the virus is alive, cognizant and tricky!) in that it has an alternative way into cells, kind of like if we forgot the key to our house, we might still be able to get in through an unlocked window. Nevertheless, knowing that the receptor for SARS-CoV-2 is the ACE-2 receptor and knowing which cells have ACE-2 receptors on their surface will help us a lot to understand all the ways the SARS-CoV-2 virus can wreak havoc on our bodies and why we see so many different manifestations of COVID-19 among people who get infected.

Above is a depiction of the SARS-CoV2 virus. First notice the red projections. These are the spike proteins that are able to bind to the ACE-2 receptor on cells that the virus can infect. This is the protein that the mRNA in the Pfizer and Moderna vaccines code for. That means that you can receive the genetic instructions that tell your cells to make the spike protein (but no other parts of the virus, so no infection can result), but your body will recognize the spike protein as an invader and form antibodies against it that will be at the ready should you be exposed and infected by the actual SARS-CoV-2 virus. Remember from above that the antibodies can only stop the virus before it enters the cell, so having premade antibodies (a process that can take 5 – 10 days) is a real advantage in fighting the virus and preventing significant infection.

There are other proteins besides the S or spike protein, including the E, M and N proteins. You will recall from above that I indicated that the SARS-CoV-2 virus has an envelope (not all viruses do). An envelope is the outermost layer of the virus and it serves to help protect the virus’ genetic material. The E protein is part of that envelope. The M protein is associated with the virus membrane. The N protein relates to the nucleocapsid.

So, a person who has been infected with the virus will often have antibodies against most or all of these proteins, whereas someone who has been vaccinated, but not infected, will only have antibodies to the spike protein, since there is no viral membrane, envelope or nucleocapsid in the vaccines approved for use in the U.S.

So, here is a look at the SARS-CoV-2 virus in a clinical specimen from the first patient known to be infected in the U.S. using a special kind of electron microscope and a stain that turns the virus particles blue. The virus particles are the blue circles, most of which are inside cells that they are infecting:

In this case, the viruses that are inside of cells would no longer be susceptible to antibodies.

Here is another image from the same patient. In this case, the virus is not stained blue, but you can see them as the small black circles. But, notice that most of these have not yet entered a cell to infect the cell:

In this case, antibodies can bind to the virus and when those antibodies are effective in preventing the virus particle from entering a cell, we call those neutralizing antibodies. Not all antibodies are neutralizing; some bind to the virus, but don’t block the virus’ entry into the cell. That doesn’t mean that those antibodies don’t sill serve a purpose, but they may not be enough to prevent infection of cells.

Okay, just a little bit more and we will move on to the immunology tutorial. We will refer to viral load which is a reference to how many virus particles are in your nose or throat or wherever we are measuring it. But, what we are often more interested in is “infectious” viral load, something much more difficult to measure, but an indicator of not just how many viruses are present, but how many of those virus particles are infectious to someone else. Remember that if you are normal, you will begin attacking the viruses in your nose, throat, lungs, etc., so some of those viruses are rendered incapable of infecting someone else, and while they would be detected as part of the viral load, they would not be part of the infectious viral load.

Conversely, we talk about viral dose when we try to quantify how many viruses do you have to get in your nose or throat or lungs to cause infection or what amount were you exposed to. Oftentimes, being exposed to a higher amount of virus can produce more severe disease. This is where masks come in and the public has missed some of the nuance here. Masks don’t have to filter out every virus particle in order to help protect you. If they filter out enough of a virus, it may mean that you are exposed to so few virus particles that you do not become infected (although for SARS-CoV-2, a recent study shows that it doesn’t take many virus particles to cause infection), but even if you do get infected, having blocked a lot of the virus so that you received a low dose may make it more likely that you will only have a mild or moderate infection.

Now for a bit of immunology.

The immune system is actually very complex. Most often, people equate antibodies with immunity to something, but antibodies are just one part of a very complicated system, and having antibodies to something doesn’t always mean you are immune to it. I had a teacher ask me to explain the immune system as if I was teaching one of her 5^th grade students. That is not easy to do, but here is some of what we know about the immune response to SARS-CoV-2 for a 5^th or 6^th grader:

If a bacteria or virus invades your body, and you have never been exposed to that invader before, the first response of your body is to send in ground troops that will try to stop these invaders at your border (your skin or just under your skin if you get cut, or your nose and throat if it is trying to invade your body there, or your gut, if it is trying to make entry there).

The ground troops with their rifles are called white blood cells (or white cells, but not all white cells. We are going to talk about other white cells that are critical, but aren’t major players in this initial (innate) immune response later), and they don’t care who the enemy is, they just attack and try to shoot anyone (in this case a bacteria or virus) who doesn’t have the same uniform as the rest of the body (in this case, features that these white cells recognize as being your own body as opposed to an invader).

Just like we have different military forces (Army, Marines, Navy, Air Force, Coast Guard, National Guard, etc.), so too these white blood cells all have slightly different roles and tactics to attack an enemy. We have many different types of white cells in our blood, and they each conduct different kinds of warfare against these bacteria and viruses.

Just like our ground troops can throw a hand grenade or fire a cannon and blow up our enemies as well as things around them that we might otherwise not want blown up (like innocent bystanders or buildings, etc.), our white cells start releasing chemical warfare (chemokines, cytokines, etc.) against these invaders of our bodies and they cause some indiscriminate inflammation and surrounding tissue damage, as well, but as an attempt to kill these invaders or at least slow them down (if you got a cut in the past that got infected, you saw the redness and swelling that resulted, which is a result of this process).

What our ground troops (white cells) are trying to do is prevent these invaders, in this case a virus, from crossing that boarder (our nasal passages and throat in the case of SARS-CoV-2) and entering into our towns and cities (in this case our cells), where they can take over our food supplies and manufacturing plants that will allow them to make more invaders (viruses) that can then increase their attack on us.

This chemical attack is what makes us feel bad – fever, aches, cough, runny nose, etc.

Now, all this time that our ground troops (white cells) are fighting the invaders (virus) off at our borders with their rifles, hand grenades and cannons, they have already sent the message back to headquarters that we have invaders, a sample of what they look like, and a request that we need some weapons that will specifically target these invaders to stop them before they get into our towns and cities (cells) where they will make more invaders (virus). These weapons will be very specifically targeted to this invader (think like drones and laser-directed missiles) so that they only kill the invader and don’t cause all the collateral damage (destruction of property and injury or death to our own body’s cells and tissues as friendly fire, although like a drone, sometimes we target something we think is the enemy, but is not. In the case of our antibodies, this can mean that a part of our body becomes the target of the antibodies and this can result in auto-immune disease and we refer to those antibodies as autoantibodies).

HQ then revs up the manufacturing plant and starts making these highly targeted bombs (antibodies) that recognize something that is different that makes up these invaders that is not present in our normal body cells and tissues. This different thing is called an antigen and HQ manufactures these special bombs (antibodies) that only blow up anything that has that particular antigen and leaves everything else alone. It ordinarily takes HQ 5-14 days to make these specialized bombs (antibodies).

In the meantime, our ground troops (white cells) have to hold off the invaders. Sometimes they do, but often times, some of the invaders get into our towns and take over the food supply and start manufacturing new viruses.

Now, if you get an antibody test while you are sick (in this case, COVID-19), but before HQ has had time to make antibodies, the test will be negative, even though you are infected. This is called a false negative. It is also possible that you had some left-over antibodies from a prior invasion (COVID-19 infection), but you already defeated that invader, and perhaps now when the antibody test is measured, you were suffering from a cold virus or influenza virus. This positive test for SARS-CoV-2 antibodies would not indicate that you have acute SARS-CoV-2 virus infection at this particular time.

Now, these bombs (antibodies) come in a number of different kinds. Antibodies do often defeat invaders, but not always. We have examples of other virus invaders where HQ makes plenty of antibodies, but the invaders march on and take over our cities and don’t seem to be slowed down by the antibodies. In the case of this coronavirus, we think antibodies are important, but they are not the only thing that is important, and we still do not know how many antibodies you need to be protected from infection, which kinds of antibodies are needed, and if you have them, how long they will protect you.

Now, back to the types of bombs (antibodies). It turns out that you need one kind of antibody if the invader is crossing the skin (IgG) and you likely need a different antibody (IgA) if the invader is crossing a mucosal border (your nose or gut). Polio was a gut invader. We developed two different vaccines – a shot and a sugar cube, and it turned out that the sugar cube worked the best, because it caused HQ to make IgA better than the shot did. Everyone talks about IgG and that is what the COVID-19 antibody tests check for (much less commonly tests will include IgM levels), but it may be that IgA is very important in preventing SARS-CoV-2 – we don’t know (or at least I don’t). The good news is that in one of the first vaccine trials to be reported, it appears that the vaccine does stimulate a robust response of both IgG and IgA.

Okay, back to the types of bombs HQ is making. In addition to different types of antibodies like IgG and IgA (and there are others), some of these bombs are really powerful killer bombs called neutralizing antibodies, because in a test tube, they keep the enemy from entering into our towns and cities (cells), and if the invaders can’t get into our cells, they can’t make more invaders, so, when we shoot or bomb all of the invaders at our borders, its over because there are no more invaders.

Let me add that we don’t know that an antibody is truly a neutralizing antibody in someone’s body just because it is in a test tube, but in the case of the SARS-CoV-2, it does appear that these neutralizing antibodies are very important in our protection and that they do tend to be effective, though we saw that with omicron, these antibodies were less neutralizing than with prior variants. So, while neutralizing antibodies do seem to be important in the immune response to SARS-CoV-2 (this was not a given because there are other examples of viruses that induce lots of neutralizing antibodies to be produced, yet they don’t slow or stop the infection), other antibodies that bind to parts of the virus but don’t prevent cell entry (called binding antibodies) also seem to play an important role in our defense against SARS-CoV-2. It turns out that some of these other bombs (binding antibodies) are like paint balls/pellets, where you shoot the invader and it doesn’t kill them, but they are now marked. Marking these invaders can help other parts of our immune system go after them. This other part of the immune system is called the cellular immune system.

In this case, HQ is not only making highly specific bombs (antibodies – for extra credit, this part of the immune system is called humoral immunity and for credit to skip a grade, that part of the immune system with our troops on the ground at our borders is called innate immunity. It is innate because we are born with it and it does not require ever having been exposed to something to fight it. It is ready to fire on sight), but also plays a key role in messaging to HQ that we need to make highly specialized tanks (T-cells).

Remember, the humoral immunity – antibodies – takes time if you have never been exposed to that invader before. We have to get the body part to HQ, HQ has to design a blueprint for the bombs, and then we have to manufacture the bombs (antibodies) and that all takes about 5 – 14 days.

While HQ is mass producing bombs (antibodies), they have also been producing highly specialized tanks (T-cells – part of what we call cellular immunity).

These specialized tanks (T-cells) also come in several types. As, I mentioned previously, the goal of our innate immune system (our troops at the border) is to kill the invaders, or at least hold the invaders from getting to our towns and cities (our cells, where they can take over our manufacturing plants and make more invaders) until HQ has time to produce the specialized bombs (antibodies). Once an invader gets into a city, our innate immune system is not very effective and our specialized bombs (antibodies) generally can’t get inside to capture the invaders. It’s like ISIS getting into a town or city where they can create a stronghold and many barriers of protection as opposed to being out in the open in the unoccupied land by our borders.

So, HQ makes these T-cell tanks while they are making the antibodies. One of these tanks has the ability to find pieces of the invaders and it amplifies the attack in those areas (helper T-cells). Another type of tank can identify which towns or cities (our cells) have been invaded, and while our antibodies can’t penetrate the invader’s hold on the towns, these tanks just blow up the cell and kills all the invaders who are occupying the town (our cells) (these are called cytotoxic or killer T-cells – in the studies we are going to look at, these will often be referred to as CD8+ cells reflecting that we can identify these specific cells based on them having the CD8 marker on them). And, thinking ahead, HQ makes tanks with advanced radar, infrared detection capabilities and other abilities to quickly detect these same invaders again should they ever try to cross our border again once we have defeated them (memory T-cells – these are often referred to as CD4+ T-cells because they are positive for this marker). The long-lasting antibodies and the helper and memory T-cells are useful, because while the first time we face an invader, the entire range of our arsenal (humoral- antibodies- and cellular – T-cells) takes 5 – 14 days to mount our full response, the next time we see the invader, all of these parts of our immune response can be called to duty almost immediately, so much so, that we often will not get sick or have any symptoms, or if we do, with some unusual exceptions (like Dengue fever- due to a phenomenon called antibody-dependent enhancement or ADE, something you may have heard about early in 2020 when we feared this might also be the case with SARS-CoV-2, but we were relieved to find does not happen contrary to some doctors who still suggest it does in their disinformation campaigns), we will only have a mild case.

What we also found out is that while the antibody response to SARS-Co-V-2 infection is not always robust or long-lasting, the cellular response in nearly everyone was. Not only did those who did not mount a very good antibody response develop a good cellular response, but even family members who lived with someone who was infected, but to the best of our knowledge, did not get infected themselves, still developed a good cellular immune response! And, for many viruses, we know that the cellular immune response tends to be more important for viruses, because there are diseases that you don’t produce antibodies, and these patients tend to get serious bacterial infections, but not severe viral illnesses; while there are other diseases for which patients have problems with their T-cells and they tend to get bad and prolonged viral infections, like shingles that will occur in multiple locations (whereas shingles tends to occur only in a single area in those with otherwise healthy immune systems). COVID-19 appears to be a disease for which both the humoral (antibody) and cellular (T-cells) responses are important.

Vaccines can often be engineered to trigger specific antibody responses that we want (like neutralizing antibodies against a specific part of the virus that appear to be especially protective against viruses getting into cells), but they also often trigger the cellular immune response. Even if the antibody response declines over a few months, we have many examples (e.g., measles) where the memory cells specific for that virus can persist for many decades, if not the remainder of your life. With COVID-19, the immune response thus far, to the currently available vaccines, does not appear to be long-lasting (certainly not life-time), but it does appear that the cellular immune protection may outlast the humoral (antibody) immune response, which may explain why, over time, we may be more prone to breakthrough infections (if previously vaccinated) or reinfections (if previously infected), but yet don’t seem to be as likely to become severely ill. However, as you will see, recent studies are showing that some people who get infected are developing immune disturbances that can result in those people having more severe disease with reinfection, despite the common misinformation that infections build up your immune system. We have to remember that the immune system is a delicate balance between many different chemicals, antibodies and cells that can all work together in the right balance to protect us, but call also easily become out of balance and actually cause harm in an uncontrolled and overly exuberant response which appears to be playing a role in why certain children develop MIS-C (Multisystem Inflammatory Syndrome – Children) and certain adults develop critical illness with manifestations of cytokine storm.

I think this is enough for now. In my next post, we will dig into what health effects we are seeing in those who survive COVID-19.

A Brief Tutorial – Clinical Trials and Statistics

In yesterday’s blog post, I explained that I would begin a blog series in which we would examine studies, data and reports that are appearing in the medical literature about long-term potential harms that can result from SARS-CoV-2 infection. Let me qualify that when I refer to “long-term,” realize that the longest interval from the first known infections from this novel virus to now are not quite 2 ½ years (not a long time when it comes to viruses). It certainly may be the case that some of the health effects we are seeing now will resolve over time. It is also possible that we won’t begin to identify other health effects for years from now.

But, before I launch into this series, we need to cover some concepts so that those without a scientific or medical background can understand what we are talking about. We will start with a brief overview of clinical study design, how to interpret clinical studies, and a smattering of cell biology, virology and immunology. I promise to try to make it all interesting and not overly complicated! In doing so, my apologies in advance to statisticians, cell biologists, virologists, immunologists and all other experts in the fields for which I cannot even begin to do justice.

Let’s start by understanding clinical trials/study design. If you want to know why it is important, merely look to the drama surrounding the ivermectin clinical studies. If you don’t understand the concepts I am going to cover, you could very well look at these trials and believe that there is a lot of evidence to support the use of ivermectin in the treatment of COVID-19. However, once you understand how to look at clinical trials, you realize that the weight of the evidence is pretty convincing that ivermectin is not effective.

Here we go. There are many ways that a clinical study can be designed. They often fit nicely into a number of categories and the weight of the evidence from the study should take into account the study design.

Let’s start with a few principles:

1. When you want to apply the findings of a clinical trial to practice, you need to know the population that was tested in the study and understand how the patient you are treating or the person to whom you are providing advice might differ. As an example, a study done to look at the health effects of SARS-CoV-2 infection in nursing home residents may not allow one to conclude that the same health effects would be seen in children or college students, or even middle-aged adults. Why? Nursing home residents would be older and the very fact that they are in a nursing home suggests that they have significant limitations to care for themselves either due to comorbid health conditions or physical limitations or both. We know that older people as well as those with certain health conditions are at increased risk for severe disease with SARS-CoV-2 infection.
2. When we are dealing with a subject like SARS-CoV-2, it is also helpful to know what the study period was and the countries the subjects were from. If we were doing a study to measure the effectiveness of the vaccines or a monoclonal antibody treatment, this would be critical information because that effectiveness can vary depending on the variant that was circulating at the time. A test of the Regeneron monoclonal antibody treatment effectiveness was excellent early in the pandemic, but almost zero today due to the shift in variants.
3. You will also want to look at what question the authors of the study are trying to answer. For example, when we look at COVID-19 vaccine effectiveness studies, it is very important to know “effectiveness of what?” If a study is conducted to look at the effectiveness of preventing severe disease and the authors define severe disease as the rate of hospitalizations and death, then they likely are not conducting the kind of tests that would be necessary to answer the question of vaccine effectiveness against infection or vaccine effectiveness against symptomatic infection. Let’s now say that the authors are conducting a study to determine the effectiveness of vaccines in preventing symptomatic infection. Well, then likely the study design will inform you that they only tested subjects when they exhibited symptoms. Therefore, that study will not answer the question as to how well vaccines prevent all infections, since we know that with COVID-19, many people have no symptoms or symptoms that they don’t realize are cause for testing. If we wanted a study to evaluate how effective the vaccines were in preventing all infections, then we would vaccinate subjects and then routinely test them to look for evidence of infection regardless of how they felt.
4. Finally, we need to be careful about not confusing an association with causation. When I was early in my medical practice, an observational study had found that treating menopausal women with hormone replacement led to better cardiovascular outcomes. So, we all prescribed women hormone replacement treatment referring to its “cardio-protective effect.” Years later, randomized controlled trials demonstrated that at best the effect was insignificant and at worst, the hormone replacement treatment was actually placing women at risk for worse cardiovascular outcomes. We’ll discuss these different types of studies below, but why would they come to such different conclusions? Observational studies are prone to identifying associations rather than causation. In this case, women who were most likely to seek and receive hormone replacement treatment at that time were in higher socioeconomic strata, with better access to health care, better nutrition, less likely to be smokers and more likely to belong to a gym or participate in regular exercise, and all of those factors would likely have contributed to those study participants’ lower risk for cardiovascular disease.

Types of clinical studies:

1. Randomized trials – These are generally the highest quality trials and ones that are better designed to answer the question of whether something is an association or a cause. These trials are of higher quality because they divide study participants into groups of test subjects and so-called “controls.” The test subjects will receive an intervention, let’s say a vaccine, whereas the control group receives perhaps an injection of normal saline. We call the trial randomized because we take all the people who have signed up for the trial and “randomize” them into one of these two groups. Often that is done these days with the benefit of a computer. The best studies will make sure that the two groups look as similar as possible, e.g., same age ranges and average age between the test group and the control group. We can even make these studies better when the study participants are “blinded,” that is to say they don’t know whether they are receiving the intervention or a placebo. Why would that be better? Because people can filter the symptoms they report or their perception of how they feel based upon whether they believe they are being exposed to something or treated with something. We can make it even stronger by making the study “double-blinded,” meaning that neither the study participants nor the investigators know whether someone has received the intervention or the placebo, because now the investigators do not filter the evaluation of the study participants based upon knowing whether they received the intervention or not.

Let’s take a real-life example to see why a randomized trial can be so helpful. No doubt you heard a few doctors who stated something to the effect that “I treated all my COVID-19 patients with ivermectin and they all did great!” So, is that pretty good proof that ivermectin works? No, because maybe they treat a young, healthy population that was going to do great anyway, whether they received ivermectin or not. We would need to know more – how many patients did they treat, what were their conditions when they began treatment, how did they follow them up and for how long, how do they know whether any of these patients got sick and went to a hospital or died? This is where having a control group helps you answer this question.

Suppose I told you that I told all my friends to eat an orange every day during the pandemic and none of us got COVID. Does that mean that an orange a day will prevent COVID? No, because you don’t know the characteristics of my friends. They are likely older individuals and may not have school-aged children in the home. They are likely to be in health-care related fields and probably more likely to mask, avoid large crowds and get vaccinated.

• Observational studies. These are studies where we don’t make an intervention, but rather just observe differences between groups to see if there are different outcomes. These studies are prone to errors and particularly to identifying an association or correlation rather than causation. That doesn’t mean that they aren’t helpful and can’t provide us with insights. An observational study could be comparing COVID-19 disease transmission rates in a school with a mask requirement against a school without one.

• A common type of study we will look at will be a case-control study. In these studies, we are usually comparing a group with the disease or condition to a group without. For example, we could follow a group of college athletes who got COVID-19 and compare their exercise tolerance with a group of college athletes who did not report getting COVID-19 to see if there are differences.

There are a number of other refinements of how studies can be designed, but I think this gives you a sufficient background for now.

Let’s finish with a few concepts from statistics as to how to interpret the findings of a clinical trial.

With apologies in advance to any statisticians who may read this for the extreme oversimplification, when we look at the results of a clinical trial, we want to know how likely these results could have happened by chance. For example, if I want to know what percentage of the population has blue eyes, it is not practical for me to check every person’s eye color. But, if I have a representative sample, it is possible to get to this percentage number. The key is “representative.” If I just sample people in my neighborhood, that is unlikely to be representative of the entire population. So, when we look at the results from studies, we look for statistical measures of how likely these really are “statistically significant” results and if we repeated the experiment 100 times, how wide of a range of results might we be likely to get?

So, in determining statistical significance, we often look at the “p” values. A p-value represents the probability that the sample result was produced from random sampling of a population, given a set of assumptions about the population. When we make an intervention in a clinical trial, we are usually hoping that the result of the prevention or treatment would be unlikely to occur randomly in a population. For example, if we vaccinate 1,000 people and 2 people develop Bell’s palsy, is that similar to the rate of Bell’s palsy in the general population (i.e, might have occurred by chance) or is it significantly lower (maybe the vaccine helps prevent the occurrence of Bell’s palsy) or is it significantly higher (maybe the vaccine causes Bell’s palsy as a adverse event)? Statisticians (I am told) consider a p-value of 0.05 or less to be statistically significant. In clinical trials, we look for much lower p-values (in the thousandths) to give us confidence that a treatment really works or really doesn’t work. For example, if we had a randomized, control trial with the study group taking an antibiotic and the control group just using symptomatic treatment (rest, anti-inflammatory medications, etc.), and the outcome in the antibiotic-treated group was superior to the control group with a p<0.001, then we would feel very confident that the antibiotic worked with the p-value telling us that if we repeated this trial 1,000 times with just symptomatic management, we would only get the result we did with antibiotics at most one time.

The other very helpful statistical tool in interpreting how much to rely on the results of a study is the confidence interval (CI). People will recall that when the initial results for COVID-19 vaccine efficacy came out, one of the trials showed that the efficacy was 94.1%. People threw that number around like it was handed down from above. However, if you repeated that study many times, you wouldn’t end up with exactly the same result because there would be different people in different studies. For example, another trial might give us 92% and another one 95%. Statisticians can calculate an interval 95% CI, a range of numbers that we can be 95% sure contains the true mean for the population. So, for example, the result might be 94.1%, but then we can look to the 95% CI, and let’s say that is 92 – 97%. That would mean that the vaccine efficacy could realistically be as low as 92% or as high as 97%. Now, 95% confidence intervals also help us judge how much “confidence” (pardon the pun) we should place in the results. If a clinical trial shows that the effectiveness of a treatment is 64%, but the 95% CI is 33 – 76%, then we know that we cannot place a lot of trust in the 64% number, because it could be a little as half that. When the confidence interval is that wide, usually, in my experience, it means that the study population was small.

The most common statistic we are going to look at when we look at the subject of adverse health effects following COVID-19 will be risk ratios. We will see several versions. One will be relative risk (RR). This is the risk of one population relative to the risk of a different, or in terms of our study group and the control group, the risk of developing an outcome in the study group vs. developing the outcome in the control group. Back to my vaccine example, if the rate of development of Bell’s palsy in the vaccine group was the same as the rate of developing it in the control group, then the relative risk is 1 and the vaccine is unlikely to be the cause of the Bell’s palsy. Another ratio we will deal with is the odds ratio – the odds of an outcome occurring vs. the odds of it not occurring. For example, we will look at a study that shows 20 adverse cardiovascular events that can occur as a result of SARS-CoV-2 infection and for each event, there will be an odds ratio indicating how much having COVID-19 elevates the odds of you developing a cardiovascular condition than had you not had COVID.

Well, this very basic level of understanding is pretty much all you will need to understand the clinical studies we are going to review in this blog series. Now that I have probably irritated all the statisticians out there with my oversimplification, and probably not technically correct explanations, with the next blog piece, I will see if I can similarly irritate the biologists, virologists and immunologists. But, that will be our last tutorial, and then we are ready to dive into the studies!

In medicine, death is commonly considered the worst outcome of illness. As a physician, I have taken care of patients whose conditions and sufferings made their ultimate deaths merciful. Despite my efforts over the past two years to bring awareness to the potential long-term health consequences of having COVID-19, the discussion about COVID-19 has all-to-often been a binary one – you survive or die. But for 10 – 30% of those who have survived COVID-19, they will tell you that they didn’t get their lives back, even though they survived. They suffer from what has been called colloquially, “long COVID” or medically, “post-acute sequelae of SARS-CoV-2 Infection” or “post-acute sequelae of COVID-19 (PAS-C).” In some tragic instances, the health effects following recovery from the acute infection have been so disabling that the person has taken their own life.

The problem with discussing PAS-C is that we don’t yet have a set of criteria for the diagnosis, so studies and reports in the literature may establish different inclusion criteria. Even more concerning to me are all the changes to anatomic structures, physiologic functions and the immune system we are detecting following infection, but don’t yet know whether these changes are reversable or permanent and what problems they may cause long-term, if any.

We also don’t yet know, and likely won’t for years, what all the long-term consequences of infection other than PAS-C may be. As an example, the human papillomavirus (HPV) is the most common sexually-transmitted infection in the U.S. An association between cervical cancer and sexual activity had long been suspected. It was epidemiologically established in the 1960s. HPV was then first identified in cervical cancer specimens in 1985. The role of HPV in causing cervical cancer was then established in the 1990s and the first vaccine to guard against HPV infection was then available in 2006. As another example, the Epstein-Barr Virus (EBV) was first discovered in 1964. In 1968, we determined that EBV was the cause of infectious mononucleosis. In 1976, we established that EBV was the cause of a specific type of lymphoma. Since then, we have identified its causal relationship to a number of other cancers and just this year, we finally established that EBV plays a causative role in the development of multiple sclerosis (MS), raising the possibility that we might be able to eliminate the risk of developing MS by developing a vaccine to prevent EBV infection.

This all becomes a critical issue because I constantly am asked by people whether to take certain steps to prevent COVID-19 infection when they perceive themselves to be at rather low risk for dying. I find that few people are weighing the risks of chronic health conditions resulting from COVID-19 because it is seldom talked about and we don’t yet know what those risks are and who specifically is at risk for developing them. Further, I find that physicians and public health experts often are reluctant to talk about these risks because we don’t yet know much about them and many are hesitant to mention these issues for fear of being accused of fear-mongering or for ultimately turning out to have been wrong.

So, I am starting an entire blog series on what we know, what we think we know, what we are seeing and what we fear could be long-term health implications from having been infected with SARS-CoV-2, and most recently, fears related to people being repeatedly infected with SARS-CoV-2.

I am dedicating this series to my mom – Charlene Pate. I told my mom some time ago that I was thinking about doing such a blog series. She thought this would be great. In fact, she has friends with various issues following COVID-19 and she knew that many were interested in answers and were loyal readers of my blog.

As I dealt with the other demands on my time, I kept putting this off and putting it off. Its not easy to explain this to the public. Many of these studies are complicated and will take a lot of effort on my part to try to make them understandable. Other studies show concerning things, but we don’t know what they mean. Nevertheless, my mom kept asking me and gently pushing me in the ways that moms do. So, now I am embarking on this intimidating subject matter and I do so dedicating this to my mother who has always been my supporter and encourager. Thanks, Mom!

Now, before we go further, let’s set out the understandings:

Here’s what I am committing to:

1. I have no agenda here. I am not trying to get anyone to do anything. I am not advocating for mask or vaccine requirements or mandates as part of this blog series. The purpose of the blog series will not be to scare you into wearing masks or getting vaccinated. While I often have opinions and usually freely share them, this blog series will not be for opinions. I am just going to try to share studies, data and facts. You can do with them what you will.
2. I am going to present a lot of information. I will try to provide plenty of references for those who want to read the studies for themselves or want to learn more.
3. I am going to try to translate the medical and scientific terms for you as much as I can, but I am sure that there will be terms or phrases I will overlook, so feel free to submit a comment asking me to explain whatever the term, phrase or concept is that you don’t understand. And, if you don’t understand it, that means many others reading it won’t understand it, so you will be doing them a favor by asking.
4. I am going to try to resist editorializing or adding my opinions to these blog posts. However, this will be hard for me to do and I may slip.
5. Keep in mind that just because a study shows x or y happens, it doesn’t mean that will happen to you. There is still a lot we don’t understand about this virus and the disease it causes. What will or will not happen in the event you have been or will get infected has a lot to do with your age, your gender, your underlying medical conditions, your genetics, the specific variant you were or will be infected with, the dose of virus you were infected with, whether you were vaccinated prior to being infected, whether this is a reinfection, whether you were treated with antivirals in a timely manner, and no doubt many other factors.
6. I am a board-certified internal medicine physician. I know a little about a lot of diseases and a lot about a number of diseases, but I don’t know everything about anything. So, I may from time to time make a mistake or I may overlook a point that a specialist in a particular area would think important. Therefore, I invite experts in all of these various specialties, as well as virologists, immunologists and other scientists to please write in comments to either correct me or to amplify some points with my readers. I will share those comments with you, correct any of my mistakes and may even write further about an area that I am getting a lot of comments about.
7. Science evolves. Sometimes science proves something we thought we knew wrong. Sometimes, science further explains or gives us new perspective on something that we already know some things about. This is certain to be the case with this blog series. I may present a study next week that may be corrected, clarified or expanded upon next month. If so, I will update you.
8. I probably read studies 4-5 hours a day on average. Even so, I miss plenty of them. So, if I miss something that you have seen or think is important, send me the link in a comment.
9. Some of the studies I will cite to will be peer-reviewed and published in respected journals. Because this area is so rapidly evolving, I will also be presenting some studies that have not yet been peer-reviewed. Obviously, if I have already determined that I think there are concerns with the study, I will not use them. However, keep in mind, when I am presenting a study that has been published on a pre-print server, but not yet peer-reviewed, you should realize that reviewers might subsequently find issues with the study.
10. This effort is already so overwhelming for me that I am not sure how much I will get into the study methodology and study design or limitations of the studies, but I will certainly make an effort to try to remember to do so. I’ll also try to remember to address some of the statistical methods that we use to help you in interpreting the data without making you think you are taking a statistics class.
11. I am going to try to present these studies and the information we are getting in some kind of logical manner – I suspect by writing about the organs or organ systems that are involved, e.g., a blog on cardiovascular effects, another on neurological effects, etc. But, please understand that in medicine, these distinctions can be blurry. For example, a stroke is generally thought of as a neurological problem, but some strokes are caused by cardiovascular issues. So, I am sure that my efforts to categorize these findings will not be perfect and will overlap between blog posts.
12. No doubt, I will write something about an organ system and then something new will come out weeks later. So, I may end up revisiting a prior blog post and if the article is significant enough, I might devote an entire blog piece to that article.

Now, here is your part:

1. Please be sensitive to other readers. There are many who are suffering with profound effects that they don’t understand and they are looking for answers, and more importantly, hope. Please be kind. Please don’t diminish what they are experiencing and what they are feeling. If you have had COVID and you feel 100%, then thank your lucky stars, but don’t dismiss what others are experiencing.
2. Keep things in perspective. Just because I share a study with some concerning health effects doesn’t mean that you have them or will get them. It doesn’t even mean that if you have similar symptoms that whatever I am presenting is what is wrong with you. The same symptoms can be caused by wildly different disease processes, for example, light-headedness and dizziness upon arising from bed can be POTS (we’ll cover that in an upcoming blog piece) or benign positional vertigo involving entirely different body systems and treated entirely differently. So, don’t self-diagnose from these blog pieces. Certainly, feel free to point it out to your doctor and inquire whether this might be applicable to your case, but don’t assume so on your own.
3. As I stated above, I am just going to try to present studies, data and information. If some of it upsets you, I am sorry; that is not my intent. But, like I will stick to facts, I will ask you to do so as well. This is not the place to voice your skepticism of the virus, who or what is responsible for it, what the government is or is not doing, or the latest conspiracy theory. There are other venues for that. This blog series is merely an effort to try to equip people with knowledge so that they can make their own health decision in a more informed manner.

So, for the next blog piece, I will cover some terms and concepts that you will need to understand for almost all of the studies we are going to review.

The problem in answering this question is that there are no precise definitions or criteria for determining whether a disease is endemic. But, generally, public health professionals refer to a disease being endemic when it is present throughout the year in relatively low and stable levels, and does not result in unexpected surges that would overwhelm health care capacity. The CDC defines endemic as “the constant presence of an agent or health condition within a given geographic area or population.”

I think it is helpful to better understand this by considering an example. I suspect that most all experts would agree that hepatitis C infection has been endemic in the U.S. since its identification and our ability to test for it in the early 1980s. To see this visually, let’s look at the epidemiologic curve “epi curve”) for hepatitis C. This is a graphic representation of the disease prevalence or disease transmission by plotting the number of cases along the ordinate (y-axis) and the progression over time on the abscissa (x-axis).

What you can see here is what would otherwise appear to be an unexpected outbreak or epidemic of hepatitis C over the decade of the 1980s. But, in reality, this was the result of testing the population for hepatitis C (screenings including blood donations and testing of those with liver disease) with newly developed testing and finding many people who had previously been infected. In other words, these were not all new infections.

Once we identified all of these previous infections, but continued testing, you see that from about 1995 on, we are identifying a relatively stable number of new infections every year. The epi curve from 1995 on gives us a typical graphic display of an endemic disease.

We can look at a different example – measles. Measles was quite common in the U.S. and is highly infectious, but an effective vaccine was developed and the U.S. made a concerted effort to eliminate measles from the U.S., which we did in 2000. That doesn’t mean that we don’t ever see measles cases, but these generally occur in people returning to the U.S. who are unvaccinated and infected in another country, or in groups of unvaccinated individuals when one of these travelers returns and is in contact with these unvaccinated individuals, often at school or in religious communities. Let’s look at Australia’s epi curve for measles.

The epi curve, reading left to right shows you the disease activity of measles in Australia prior to a national vaccination effort to control measles (Measles Control Campaign- MCC), then the disease activity during that campaign in the middle of the epi curve, and then the manifestation of measles as an endemic disease at the right of the curve following the campaign. Notice that when endemic, there still is some variation, but you no longer see the wide swings and surges that you see on the left hand portion of the epi curve.

So, now let’s look at the U.S. epi curve for COVID-19:

It should be immediately apparent to you that this is not representative of an endemic disease. This epi curve demonstrates a series of surges followed by low levels of disease transmission followed by more surges, and in fact, the most recent surge having been our largest yet. Another way to answer the question as to whether COVID-19 is now endemic is to ask whether the disease activity has (1) been predictable, (2) been relatively stable, and (3) has avoided overwhelming the health care system. I think the answer to all 3 would have to be no.

Well, if COVID-19 is not yet endemic, will it become so? I am not so sure. Remember the measles epi curve for Australia above and the development of an endemic following a vaccination campaign? Now, look at the U.S. COVID-19 epi curve and realize that we started our COVID-19 vaccination program in early 2021. Does it look like the pattern is evolving towards an endemic one? I don’t think so.

So, let’s look at the epi curve for another viral infection:

This is the epi curve for influenza from 2007 – 2010. Influenza is seasonal with fairly predictable times for outbreaks each year depending upon where in the world you live. Some people suggest the COVID-19 is also seasonal, but you can look back to the epi curve for COVID-19 above and see that it clearly is not. This influenza epi curve demonstrates an annual recurring epidemic. It returns each year and infects large numbers of people because that virus mutates frequently and not enough people get influenza vaccinations.

So, do you think that the COVID-19 epi curve looks more like (1) the U.S. hepatitis C epi curve and the Australian measles epic curve after the vaccination campaign, or (2) like the influenza epi curve? If you answered (1), then you would be in agreement with those talking heads that want you to believe COVID-19 is already endemic (though I would recommend an eye examination!). If you answered (2), then you understand why I disagree that COVID-19 is currently endemic. You may also now understand why I am not sure that it is headed towards being endemic, but rather I anticipate that it will be epidemic (like the influenza epi curve, though not seasonal) for the foreseeable future.

Of course COVID-19 might become endemic at some time in the future. It could be that a future variant might produce long-term natural immunity. It could be that much larger numbers of people will choose to get vaccinated. It could be that new vaccines will induce longer-term immunity. Lots of things could happen, but until something does, I don’t expect our epi curves over the next year or two to look like hepatitis C, but rather more like influenza, but with more frequent surges since COVID-19 is not seasonal (at least not at this time).

The CDC published their last Science Brief on the Community Use of Masks on December 6, 2021. https://www.cdc.gov/coronavirus/2019-ncov/science/science-briefs/masking-science-sars-cov2.html. At that time the CDC recommended the use of masks to “prevent transmission of SARS-CoV-2” especially in light of asymptomatic and pre-symptomatic disease.

Here are the benefits of that strategy:

1.  Decreasing transmission decreases the potential for new variants of concern, which as we have seen have tended to become more transmissible with each new version and most recently developed some degree of immune escape/evasion, making protection from prior infection and vaccination less effective and making some of our therapies less effective or not effective at all.
2. Decreasing transmission rates benefit those who are vulnerable (the elderly, those who are unable to be vaccinated, those with underlying medical conditions, the immunocompromised and those in institutional settings [nursing homes, prisons]) safer because the chance of encountering someone who is infected in their own families or when out in public is diminished.
3. Decreasing transmission is also beneficial with a virus that is capable of causing long-term complications and disability.

Now, the CDC states that “community measures should focus on minimizing the impact of severe COVID-19 illness on health and society.” So immediately, you can see that the focus is no longer in preventing illness, but rather preventing severe illness. However, the strategy isn’t great for achieving that goal, because often the people who spread the infection to those who are at high risk for severe infection are those who are not themselves at high risk.

Dr. Walensky (Director of the CDC) stated, “This new framework moves beyond just looking at cases and test positivity to evaluate factors that reflect the severity of disease, including hospitalizations and hospital capacity, and helps to determine whether the level of COVID-19 and severe disease are low, medium, or high in a community.” In principle, I don’t disagree with this. I have always assessed multiple data points and different impacts as I have assessed Idaho’s or our local risk. The problem is the significant increase in disease transmission that the CDC is now willing to just accept (see below).

She also states, “This updated approach focuses on directing our prevention efforts toward protecting people at high risk of severe illness and preventing hospital and health care systems from being overwhelmed.” My problem with this statement is:

1. It was a lot easier to identify those at high risk of severe disease back in 2020. Very typically it was people with advanced age and people with multiple medical conditions. We became much less able to identify these people as of last year as we saw more and more people in their 30s and 40s hospitalized, and often the only risk factor, if any, was being overweight. This kind of talk also resulted in pregnant moms not appreciating that they were now in the high risk group even though they were in their 20s and 30s, and we saw all kinds of disasterous outcomes for mom and baby. And, of course, since last year, we have gradually seen more and more children becoming ill and being hospitalized.
2. This statement focuses on severe illness and preventing hospitalization. But, what about trying to prevent long COVID, MIS-C and many different long term complications that we are beginning to understand can result even from mild infection? I fear that we will see a significant rise in long-term medical conditions in those who have been infected, and likely especially those who have been multiply reinfected, along with the attendant decrease in employee productivity and increase in health care costs, which of course will then be reflected in everyone’s cost for health insurance.
3. Again, the point I made above – one of the best ways to prevent people from developing severe disease and being hospitalized is to prevent mild infections in younger, lower risk. Why? If you look at infections in patients in nursing homes or inmates in prison, it was not fellow patients or inmates or visitors that infected them, it was the otherwise healthy workers who got infected and brought the infection in to the patients or inmates when they came to work. A lot of grandparents and other at-risk family members took many precautions with their every day activities, but they were infected by young family members who got infected at school or other healthy family members who got infected at work or gatherings and then infected the at-risk persons at home or during family gettogethers.

So, how big of a change is this guidance?

Here is our country’s risks levels immediately before the new guidance went into effect:

About 94% of the country was in areas that were high or substantial risk of disease transmission and advised to wear masks under the strategy of trying to prevent infections.

Here is the country’s risk levels immediately after the new guidance went into effect:

Wow! Now only about 30% of the country lives in areas of high transmission for which masks would be advised when the strategy is to accept a lot of infections, but try to limit overburdening of hospitals.

So, how did the risk levels fall so dramatically? The CDC accepted a higher level of disease transmission – up to 200 new cases in a week per 100,000 population. According to the CDC – the determination of COVID-19 Community Level begins by determining whether new cases per 100,000 in the past 7 days were < 200 or > 200. (For those who, like me, like to follow the 7-day moving average of daily new cases, this cut-off at 200 roughly translates to 28.6, which has generally been associated with accelerated community spread.)

One of the reasons for the CDC allowing higher transmission is that we now have more testing and therapeutics. That is true now that cases have come down so much, but ask anyone who tried to find at-home tests a month ago how easy that was and ask anyone who got sick with omicron who was at higher risk whether they were able to find an infusion center that was able to administer monoclonal antibodies or a pharmacy that had stocks of antivirals. I tried to help a number of people in Idaho and in Texas who got ill and was unsuccessful in all but one case (and, even then, I spent hours on the internet and on the phone trying to locate the treatments). What assurance do we have that we will have plenty of therapies with the next surge, assuming that the next variant is still susceptible to these treatments?

Another reason the CDC suggests as to why it can tolerate many more infections is the “high rate” of vaccination in the U.S. Really? We have 65% of the population fully vaccinated and there have been many countries with far higher levels of vaccination who have had huge surges and large numbers of hospitalizations in the past few months.

So, here are a couple of graphs that will help illustrate what a dramatic shift the CDC is making:

This next graph (an epi curve) will show you how this masking guidance would have played out had it been in effect for the entire pandemic with the surges the U.S. went through.

Here is how one social scientist paraphrased what the CDC’s new guidance was in essence stating: “It’s safe for you to take off your mask because when you get (COVID-19) there is still plenty of room at the hospital.”

I understand the loss of patience for masking by the general public. However, I fear that it is because we haven’t done a good job of educating the public that bad stuff can still happen even if you survive the infection. I talk to a number of these patients. That is why, along with the fact that I want to protect babies, toddlers, grandparents and the immunocompromised, I will be sticking to the old guidance for my own personal masking decision.

Vitamin D is essential for normal human functioning, but just how much is necessary has been a subject of debate and whether supplementing vitamin D in people who have “normal” levels can be beneficial in the prevention or treatment of certain diseases has been a subject of controversy for decades, largely due to the lack of high-quality studies.

While some will argue that the level of normal vitamin D should be set higher than current guidelines, I think that you would find a general consensus that an adult is vitamin D deficient if their serum 25-hydroxyvitamin D level is < 50 nmol/L or 20 ng/ml. There would also likely be agreement that treatment with vitamin D supplementation in individuals with serum 25-hydroxyvitamin D levels of < 30 nmol/L or 12 ng/ml is beneficial and very important for overall health.

Many people promoted the need for vitamin D supplementation to prevent and/or treat COVID-19 when the pandemic first began (in fact, a number of people continue to promote this idea). Unfortunately, we had little data or clinical studies at that time to answer that question. However, two years later, we know a lot more. Here are some key points:

1. A study from Italy showed that a low serum vitamin D level was an independent risk factor for developing severe COVID-19 (mean 18.2 ng/ml) and dying (mean 13.2 ng/ml) from it, if a patient with low vitamin D gets infected.
2. Unfortunately, studies have not been able to demonstrate that giving high doses of vitamin D once the patient with low vitamin D gets infected will reverse the risks for severe disease, hospitalization, the need for intensive care, mechanical ventilation or death.
3. Some persons have a genetic abnormality that causes them to have high levels of vitamin D, but studies have demonstrated that these folks do not have a lower risk of getting infected or if infected, having less severe disease or risk of hospitalization and death.

Guidelines from the Endocrine Society recommend the following dietary intake of vitamin D:

• Children aged 1-18 years: ≥ 600 IU/d
• Adults aged 19-70 years: ≥ 600 IU/d
• Adults older than 70 years: ≥ 800 IU/d

So, above, I have provided you with the state of the science. But now, here are my thoughts and how I put all this together:

1. If you have vitamin D deficiency, it makes sense to take a daily vitamin D supplement. My vitamin D levels have been low and I take a daily supplement. If you have low levels of vitamin D, you may be at higher risk if infected.
2. The fact that people with low vitamin D levels are at increased risk for severe disease if infected, but supplementing with vitamin D once infected doesn’t improve outcomes, as well as the fact that those with high vitamin D levels are not protected from developing severe disease suggests to me that vitamin D is likely only one factor in this risk for severe disease and may not even be the most important factor. Thus, if you get infected, please don’t rely on vitamin D and other supplements to keep you from getting severely ill. Seek medical attention from a physician and explore options that have been proven to improve outcomes if you are at high risk for severe disease, such as antiviral medications and monoclonal antibodies. I have no objection to taking vitamin D if you get infected, my point is simply that you should not rely on vitamin D or any other supplements to keep you from getting severely ill.

This a document that I participated in developing as a briefing document for the Idaho Legislature from the IMA’s Public Health Committee. Dr. Laura McGeorge from St. Luke’s was the lead author. I thought this would be of interest for followers of my blog.
• The Vaccine Adverse Event Reporting System (VAERS) was developed by the Food and Drug
Administration (FDA) and Centers for Disease Control and Prevention (CDC) in 1990.
• VAERS was developed to get post-vaccine data from across the country to monitor for rare
safety concerns. More common safety concerns are found in clinical trials. However, if a trial
studies 40,000 vaccine doses, for example, and a safety event occurs once every 100,000 doses,
then the event may not occur in the study. Thus, VAERS allows for continued safety oversight of
any vaccine.
• Post-vaccine information may be entered in the VAERS national repository by anyone, including
health care providers, vaccine manufacturers, patients, and their families.
• Often the information entered is incomplete and is usually not validated. Vaccine safety experts
validate the data when there is a serious event reported. To validate the information, the expert
must obtain the medical records and review them to see if the event was likely caused by the
vaccine or an unrelated event.
o For example, if a reported death was from an allergic reaction immediately after
vaccination, that death would be determined to be caused by the vaccine. If the death
was from a car accident following the vaccine, that death would not be attributed to the
vaccine. If a death were from a heart attack, that would be noted for further analysis.
• A common misconception about VAERS is that the high number of reported deaths were caused
by vaccines. Keep in mind that given the U.S. population of 330 million people, thousands of
people are born each day and thousands die every day. In 2017, prior to the pandemic, on
average 7,708 American deaths occurred every day.
• For deaths among those who received the COVID-19 vaccine, it is important to note that those
people tended to be older and were disproportionately people with underlying medical
conditions and their deaths were statistically likely whether vaccinated or not.
• The number of deaths reported to VAERS is less than the number expected and the FDA and
CDC’s review of these deaths has determined that almost all these deaths were unrelated to
COVID-19 vaccines.
• 496 million doses of COVID-19 vaccines were administered in the United States from December
14, 2020, through December 20, 2021. During this time, VAERS received 10,688 reports of death
(0.0022%) among people who received a COVID-19 vaccine.
• In reviewing data from over ten million people, comparing vaccinated versus unvaccinated
mortality rates, those vaccinated had about 30% the mortality rate of those that were
unvaccinated.
Today, the FDA and CDC use several tools to evaluate vaccine safety. VAERS is just one of the tools. The
CDC does not just depend on VAERS to monitor for vaccine safety. V-safe is a new smartphone-based
voluntary health check tool. The v-safe program now also has a pregnancy monitoring registry. In
addition, the Vaccine Safety Datalink has been in place since 1990 and is a collaboration between the
CDC and nine large health care organizations. The CISA project is a collaboration between the CDC and
seven medical research centers.

The scientific and medical community want the COVID-19 pandemic to end just as much, and likely more than you do. First of all, preserving health and life is why we all went into these professions. Second, we are tired and exhausted. Not only have we been subjected to the same limitations on our own activities and family get togethers as you have, but we have been caring for COVID-19 patients for almost two years now, under some of the most difficult of circumstances. If there was a magic pill that would prevent people from getting COVID-19 and treat patients with COVID-19 to prevent our hospitals from being overwhelmed, believe us, we would be the first ones championing it and promoting it to the public and prescribing it to our patients and taking it ourselves. Keep in mind that all of us worry about getting infected when we are seeing patients and inadvertently bringing the virus home to our families. So, no way would we try to prevent our family, our friends and neighbors and our communities from having access to a medication that would keep them healthy or save their lives.

• The primary reason that we do not support the treatment of people with ivermectin for COVID-19 is that we have no high-quality studies that point to its benefit in either preventing or treating COVID-19. Those who advocate for the use of ivermectin will point to various studies that seem to suggest a benefit. However, that is not how we make treatment decisions for patients who have serious threats to their health or for whom we are recommending a medication that can have adverse effects such that the old adage might apply of the treatment being worse than the disease. We evaluate those studies to determine whether they are of high quality, in other words, results that can be trusted. This is why you will hear the phrase “peer review.” This is a rigorous process in which experts in the field review the study design, the size of the study, the process by which participants in the study were divided into the group that would receive the medication and the group that would not to make sure we are really comparing apples to apples, the data collected during the study (including the proper statistical analysis) and the conclusions drawn from those data. Unfortunately, it is not uncommon for studies to have major flaws in one or more of these elements that undermine the validity of the study or the confidence we can have in the results. Most alarming is when the authors of the study are contacted during this peer review process, but refuse to engage with the peer reviewers or their data doesn’t seem right and those peer reviewers request the data, but the request is declined. There was some of all of these concerns in most of the studies that those who advocate for ivermectin base their recommendation upon. For a very nice summary of the clinical trials that are among the better designed studies with an explanation of the findings and limitations of those trials, see https://www.covid19treatmentguidelines.nih.gov/tables/ivermectin-data/.

• Much of the excitement about a potential therapeutic benefit for ivermectin comes from studies showing an antiviral effect of ivermectin in a test tube. In fact, this is not new information. This test tube result led to enthusiasm for the possible benefit of ivermectin to treat many other viruses in the past such as the viruses that cause HIV/AIDS, dengue fever, Zika, and yellow fever. Given that many poor countries struggle with these diseases, the potential for an inexpensive pill to treat these infections was very exciting. The problem is that the benefits we see in the test tube did not occur in humans when we conducted clinical trials, and so far, we have not seen those benefits against the virus that causes COVID-19, either. It is not uncommon for benefits that we see in a laboratory do not occur when we test those treatments in humans. That is why the FDA never approves medications simply based on laboratory tests. There must be clinical trials involving humans to ensure that those treatments are safe when given to humans (you wouldn’t see adverse effects in test tube studies) and that they actually do provide a measurable benefit when given to humans. The reasons why a medication might seem to offer hope in a test tube, but not when actually administered to humans are many because the human body and the interactions of medications with all the fluids, cells and organs of the body can seldom be predicted in a test tube. In the case of ivermectin, one problem seems to be that the level of ivermectin needed to get the antiviral effect we see in the test tube cannot be attained in human cells without excessive doses and excessive toxicity.

• We aren’t saying that ivermectin should not be considered as a therapeutic option. We are saying that (1) it should be tried in the setting of well-designed clinical trials so that we can determine once and for all whether it has a benefit and (2) given there is no high-quality evidence to suggest benefit in either preventing or treating COVID-19, it is not responsible for those prescribing it to suggest to patients that they can rely on this drug instead of those public health measures and therapeutics that do have proven benefit in either preventing or treating COVID-19.

• There is a large NIH (National Institutes of Health) -sponsored trial currently underway to evaluate ivermectin’s effect on the treatment of persons with COVID-19 who do not require hospitalization. We hope to have preliminary results as soon as March of 2022.

• The biggest threat caused by many who tout ivermectin is not the prescription of ivermectin itself, it is the fact that they often encourage the use of ivermectin as an alternative to measures that are proven to help prevent COVID-19 (e.g., masks and vaccines). Unfortunately, we all too often hospitalize people who were told not to get vaccinated and that ivermectin would protect them.

• FDA – Ivermectin is neither authorized or approved for use in preventing or treating COVID-19. In fact, the FDA has warned against its use https://www.fda.gov/consumers/consumer-updates/why-you-should-not-use-ivermectin-treat-or-prevent-covid-19.

• Unfortunately, because there are some who promote ivermectin as safe and very effective in preventing or treating COVID-19, some people have resorted to ways other than being evaluated by a physician to get the drug that are not safe. As with most things that get promoted and have high demand, a black market emerges to sell products advertised as ivermectin that do not have the same safety oversight and for which the ingredients cannot be guaranteed. Others have turned to veterinary suppliers of ivermectin for animals, without understanding that the dosage recommendations for a horse can be very dangerous for humans.
• Bottom line:
• World Health Organization (WHO) recommendation: ”We recommend not to use ivermectin in patients with COVID-19 except in the context of a clinical trial. (Recommended only in research settings).” https://app.magicapp.org/#/guideline/nBkO1E/section/LAQX7L.

• National Institutes of Health’s (NIH) COVID-19 Treatment Guidelines Panel has also determined that there are currently insufficient data to recommend ivermectin for treatment of COVID-19 https://www.covid19treatmentguidelines.nih.gov/.

• Centers for Disease Control and Prevention (CDC) has warned of severe illness resulting from the use of products containing ivermectin https://emergency.cdc.gov/han/2021/han00449.asp.

• The American Medical Association recommends against the use of ivermectin https://www.ama-assn.org/delivering-care/public-health/why-ivermectin-should-not-be-used-prevent-or-treat-covid-19.

• The Infectious Diseases Society of America (IDSA) recommends that ivermectin not be used for inpatients or outpatients outside of the context of a clinical trial.

• The American Academy of Clinical Toxicology recommends against the routine use of ivermectin https://www.clintox.org/news/aact-recommends-against-the-routine-use-of-ivermectin-for-treatment-for-covid-19.

• Here is the most damning argument against taking ivermectin. The pharmaceutical company that makes ivermectin would stand to make huge profits if sales of its drug were promoted across the world for prevention and treatment of COVID-19. Despite this, the company has warned the public not to take its drug for this purpose because even its own scientists have seen no credible evidence that the drug has these benefits. https://www.merck.com/news/merck-statement-on-ivermectin-use-during-the-covid-19-pandemic/. It is instructive to read the results from Merck’s own analysis of ivermectin for COVID-19:
• No scientific basis for a potential therapeutic effect against COVID-19 from pre-clinical studies; 
• No meaningful evidence for clinical activity or clinical efficacy in patients with COVID-19 disease, and; 
• A concerning lack of safety data in the majority of studies.

Frequently asked questions:

1. There are doctors claiming that they have treated hundreds of patients with ivermectin who have all done well. Why isn’t this good enough evidence for the use of ivermectin?

These are called anecdotes and they are not strong enough for us to make medical decisions based on them. As an example, if I were to tell 100 of my friends to eat 10 M&Ms a day, they did so, and none of them ended up hospitalized with COVID-19, we should not jump to the conclusion that M&Ms prevent severe disease. When we are dealing with a disease like COVID-19 for which a large number of people get asymptomatic or mild disease, it would not be surprising if these doctors were treating 100 or 200 healthy, young adults with ivermectin that they would not develop serious illness, and would not have developed severe illness even if they weren’t taking ivermectin.

2. What would be the harm in taking ivermectin?

While the majority of people would probably tolerate prescription strength ivermectin perfectly well, like all medications, some will experience adverse reactions. But, the bigger harms are not related to the ivermectin itself. If it turns out that ivermectin is not effective, will people taking ivermectin take chances they otherwise would not assuming that they are being protected by the ivermectin only to end up sick and perhaps severely so? Are people taking ivermectin to prevent COVID-19 instead of the vaccines that are proven to help prevent COVID-19? One doctor who touts the benefits of ivermectin got infected with COVID-19 and indicated that it was likely due to the fact that he missed a couple of doses of the medication while traveling. Patients often miss doses of their regular medicines. If missing just a couple of doses of ivermectin can lead to infection, how practical of a solution is ivermectin compared to getting vaccinated? If you have to continue taking ivermectin for years to prevent getting infected, then that will add up in costs for a medication that has no evidence of benefit and further, we do not know the long-term adverse effects of ivermectin, since ivermectin is intended for short courses of treatment of certain conditions.

I am going to bring this series of blog posts on identifying disinformation to a conclusion. In this part V, I will summarize the key messages from the first four blog posts and add a few other pointers.

During this pandemic, those of us dedicating significant amounts of time to educate the public found that we still couldn’t keep up with all the false information. In this case, I am referring to addressing “disinformation,” the spread of incorrect information with the intent to harm, confuse, mislead, or otherwise affect people’s hearts and minds to accept lies and/or support the position of those who are spreading the false information. This is opposed to “misinformation” that is incorrect information, but the person spreading it did not realize that the information was incorrect or incorrect at the time. Whether it be misinformation or disinformation, the other thing we found is that combating it was like playing the game whack a mole, in that every time we successfully countered incorrect information, new misinformation or disinformation popped up to replace it. Because of this, we cannot successfully win this battle for truth on our own; we must educate the public how to recognize suspicious information. Fortunately, we had so many examples of people spreading disinformation that patterns that can alert us to disinformation emerged.

Disinformation comes from many sources, including foreign countries. The motives of those who spread disinformation can be diverse. For a foreign country, it often is the intent to sow distrust in the U.S. government and discontent among the populace. For home-grown purveyors of disinformation, perhaps they stand to financially benefit, sometimes there are political aspirations or motivations, and sometimes these sowers of disinformation like the public attention and adoration they receive from those who want to believe the lies. There may be other reasons as well. But, a common thread was narcissism and lack of empathy. These persons do not care if they harm others so long as they gain whatever benefit it is they are seeking for themselves.

What are the patterns that can alert the public to be suspicious of the information being conveyed?

1. Credentials.

We often saw that physicians and other health professionals who were promoting misinformation and disinformation were not in specialties that generally treat patients with COVID-19 or have expertise in treating acute infections, respiratory disorders and hospitalized patients. Examples were pathologists, neuroradiologists, ophthalmologists and chiropractors. Certainly, that does not mean that the physician may not have very specialized knowledge about COVID-19, but this should be a red flag when that advice is contrary to physicians who are specialists in those areas of practice in much the same way that a dermatologist offering advice about prenatal care that is inconsistent with information put out by the American College of Obstetricians and Gynecologists should be suspect.

2. The use of grandiose, overly dramatic or inflammatory language.

Reputable physicians and scientists generally try to maintain objectivity and professionalism. It would be greatly out of character for these experts to use inflammatory or offensive language in explaining science or public health recommendations to the public. On the other hand, one physician who was spreading disinformation repeatedly referred to the COVID-19 vaccines as “needle rape.” To compare vaccination to one of the most psychologically and physically traumatizing events that can happen to a person is beyond the pale and offensive in its trivialization of the dehumanizing trauma that these victims of sexual assault have suffered. The language is obviously intended to inflame the hearts and passions of those they are spreading disinformation to rather than to stimulate the mind. We also heard many of those spreading disinformation about the vaccines using language to conjure up images of Nazi Germany, making reference to the Nuremburg Code, and often using variations of the phrase “crimes against humanity,” all of which were intended to create a false equivalency between vaccinations and various historical atrocities. When you hear or read this kind of language, a red flag should be raised to suggest that an attempt is being made to emotionally manipulate you and that the information is likely unreliable.

3. The use of emphatic and absolute statements.

There are very few things that we can say are absolutes in medicine – always happen or never happen. That is because diseases can manifest themselves and behave differently between children and adults, between young adults and the elderly, between men and women, between the immunocompromised and the healthy, and even in individuals of the same age and gender. So, another warning sign is when sources make statements that are absolutes. Physicians and scientists will often qualify their statements and advice – “based upon what we know today” (understanding that new data may come along in the future that could change our understanding of the disease or its treatment), or “based on a recent study” (leaving open that more studies or larger studies might change our understanding), or “based on the limited data we have available” (acknowledging that we have some data, but it may not apply to people that are different than those who were study participants, or to people with additional medical conditions than those studied). An example of an absolute statement that should raise a red flag that we heard during this pandemic was “masks do nothing.” Now, we can debate the effectiveness of different kinds of masks, their effectiveness under different conditions, or the effectiveness with different variants, but if someone tells you masks provide absolutely no protection or if someone tells you that masks provide complete protection, this should be reason enough to look for another source.

Another very helpful indicator as to whether you can trust a source is that reputable physicians and scientists will often answer questions when there is a new development with some variation of “we don’t know” or “we need more information before we can answer that,” whereas those spreading disinformation rarely answer questions by admitting that the science is evolving or that it is too early to answer the question with any certainty.

4. The use of anecdotes as evidence.

We often heard those promoting misinformation making comments such as “I treated [fill in the number] patients with ivermectin and they all did great.” That is an anecdote; that is not science. Were all of those patients young, healthy and low risk and would be expected to have good outcomes even without treatment? Did all of them have confirmed infection? How do you know none of them deteriorated and didn’t go straight to the hospital without notifying you, especially since a number of physicians making this claim did not treat patients in hospitals?

5. Internal inconsistencies in their arguments.

Think about a person in an interrogation room at a police station. A skilled detective can tell when the person is being truthful, because truth has internal consistency to it. The various pieces fit together and make sense that they would occur in the manner and the order the witness describes. On the other hand, when you can get a person to talk for an extended period of time and that person starts out with a lie, it is incredibly difficult to maintain the lie because things eventually don’t make sense and cannot be connected in a logical way. So, too, if you give someone spreading disinformation enough time to talk, you will generally see them get tripped up. Let’s look at an example. A very common internal inconsistency arose when some doctors made the argument that people should not take the COVID-19 vaccine because it was only authorized for emergency use, but not yet fully approved by the FDA. That could certainly be a reasonable concern for some people and worthy of discussion. However, what they suggested as the alternative, and what patients seemed more than willing to do, was to take various medications that were neither authorized or approved by the FDA for prevention or treatment of COVID-19. In fact, the FDA even warned the public not to take one of the recommended medications. The inconsistency was using FDA approval as the criteria for prevention of COVID-19 and using it to arguing against one measure, but then disregarding it in recommending another.

6. Misuse of data.

One common tactic used to create fear of getting vaccinated was for purveyors of disinformation to point to the thousands of deaths reported to VAERS (the Vaccine Adverse Event Reporting System) as being due to the vaccines. This would make sense to the majority of Americans who are not familiar with VAERS, but can tell from the name that this is a system that reports vaccine adverse events. But, this was intentional deception by the physicians who were suggesting causality, when in fact, the site is for reporting by the public so that the CDC and FDA can be alerted if the frequency of an adverse reaction exceeds the background rate of those symptoms or conditions. Prior to the pandemic, more than 8,000 persons in the U.S. die each day on average. To keep the math simple, if you consider that about half of the U.S. was vaccinated this year, you would expect roughly 4,000 deaths each day unrelated to COVID-19 among the vaccinated, in fact, even higher than 4,000 because those being targeted for vaccination were the elderly and those with underlying medical conditions. Those perpetuating fears of vaccination attributed all of the deaths in VAERS that would be expected under pre-COVID conditions and unrelated to COVID-19 and the vaccines to the COVID-19 vaccines. Even just resorting to common sense, if the FDA temporarily stopped vaccinations with the J&J COVID-19 vaccine when only three cases of Thrombosis with Thrombocytopenia Syndrome were identified through the VAERS reporting, it should cause everyone to question that the CDC and FDA could then believe that there were thousands of deaths caused by the vaccine, yet take no action or even issue a warning.

7. Failure to cite studies and identify any limitations to those studies.

It is not uncommon for purveyors of disinformation to indicate that studies show support for their position, but then not produce those studies when asked. Further, we have never heard these disseminators of falsehoods ever describe any limitations to the studies they are referring to. Reputable physicians and scientists are always willing to provide the evidence for their statements or opinions. They also are quick to point out limitations of these studies, such as patient selection or study design, and usually will provide caveats that we cannot be certain that the findings will apply to patients that are different than those who made up the study participants or who are treated under different conditions than those used in the study.

Health professionals can certainly have differing opinions about the best treatment for a particular patient. These differences can arise because clinical trials have not yet provided us with evidence of the best treatment or because the particular circumstances of the patient are different than those who have been enrolled in clinical trials. These professional differences of opinion are appropriate.

My objection is to health professionals who offer the public and their patients advice that is clearly and demonstrably erroneous, contrary to the guidance of the CDC, FDA, professional associations and their own peers, and has great potential to harm patients. At the heart of this, physicians always have a duty to act in the best interests of our patients and we have an obligation to inform our patients and tell them the truth, as best we understand it. Lying to and deceiving patients should be abhorrent to physicians.

We are starting to see physicians held accountable for purposeful and repeated dissemination of disinformation. I believe appropriate actions can include discipline by state medical boards, loss of hospital privileges and revocation of board certification.

However, the disciplinary process is a lengthy one because of the need for investigations and hearings. In the meantime, it is essential for the public to be aware of the red flags of disinformation that we have listed above in order to protect themselves until the system can, because in an internet and social media age, disinformation spreads faster than the virus.