Fact-checking Dr. Frank Shallenberger’s COVID Vaccine Letter

Fact-checking Dr. Frank Shallenberger’s COVID Vaccine Letter
By Sana Zekri, MD

There has been a letter circulating written by Dr. Frank Shallenberger emphasizing the uncertainty and alleged danger of the COVID vaccine. However, much of the information is either blatantly false or taken out of context. Below is a point-by-point response to the claims of Dr. Shallenberger, including sources.

“Dear Patients and Friends,

Last week I must have been asked 20 times about the new COVID vaccines. Here are my thoughts. Please pass this information onto many as you can. People need to have fully informed consent when it comes to injecting foreign genetic material into their bodies. The COVID vaccines are mRNA vaccines. mRNA vaccines are a completely new type of vaccine. No mRNA vaccine has ever been licensed for human use before. In essence, we have absolutely no idea what to expect from this vaccine. We have no idea if it will be effective or safe.”

It is true that these are the first mRNA vaccines to be deployed. However, this is a culmination of years of research; the tech has been under research as a potential vaccine and cancer fighting methodology for years. Dr. Shallenberger’s claim that ‘we have absolutely no idea what to expect… if it will be effective or safe’ is not true. The short-term safety and efficacy are known because that’s what the clinical trials were for. This is how safety and efficacy of all vaccines and drugs are evaluated. Among the 22,000 people who received the Pfizer vaccine and the 15,000 who received the Moderna vaccine, there were no major safety issues, and the vaccine was ~95% effective at preventing COVID infection. For comparison, if a total of 37,000 people of similar demographics to the vaccine trials were infected with COVID, we would expect more than 350 deaths, based on a case-fatality of 1%, and an unknown number of people with persistent symptoms, respiratory disease, and other organ failures.  It is true that the long-term outcomes of the vaccine are not known. But, we also don’t know the long-term outcomes from COVID infection. So, as of right now, based on the data that we have, these mRNA vaccines are much, much safer than getting infected with COVID, by a huge margin. For a more thorough dissection of the safety data on the vaccine as well as a discussion of long-term side effects, see this video.


“Traditional vaccine simply introduce pieces of a virus to stimulate an immune reaction. The new mRNA vaccine is completely different. It actually injects (transfects) molecules of synthetic genetic material from non-humans sources into our cells. Once in the cells, the genetic material interacts with our transfer RNA (tRNA) to make a foreign protein that supposedly teaches the body to destroy the virus being coded for. Note that these newly created proteins are not regulated by our own DNA, and are thus completely foreign to our cells. What they are fully capable of doing is unknown.”

First of all, he keeps using the phrase “genetic material,” which is confusing. The vaccines use mRNA, which is very, very, very different than DNA. The injected mRNA encodes for the spike protein of the novel coronavirus. Getting into the details of how our cells convert mRNA to proteins using tRNA and liposomal fusion and whatnot is beyond the scope of this answer. What is important to know is that the mRNA cannot affect our DNA, and it cannot change our genetic code. Dr. Shallenberger also suggests that because the protein is completely foreign, we have no idea what impact it will have. But that’s not a valid argument, because that’s literally the same way that all other vaccines work: all vaccines expose you to “foreign” proteins from the virus, and your immune system responds to the foreign protein, forming immunity to it. This is also how our bodies generally create immunity – the body recognizes foreign proteins or particles (antigens) and the body produces antibodies that are designed to neutralize those antigens. Your body is exposed to foreign proteins constantly; this is why we have immune systems. The last part of his statement is fallacious due to the premise of the first part of his statement.

“The mRNA molecule is vulnerable to destruction. So, in order to protect the fragile mRNA strands while they are being inserted into our DNA they are coated with PEGylated lipid nanoparticles. This coating hides the mRNA from our immune system which ordinarily would kill any foreign material injected into the body. PEGylated lipid nanoparticles have been used in several different drugs for years. Because of their effect on immune system balance, several studies have shown them to induce allergies and autoimmune diseases. Additionally, PEGylated lipid nanoparticles have been shown to trigger their own immune reactions, and to cause damage to the liver.”

We must first address the quietly asserted idea that mRNA inserts into our DNA. The mRNA does not insert into our DNA. mRNA does not have the capability of inserting into DNA. DNA is scanned to make mRNA, but mRNA is not scanned to produce DNA in the cell. This is basic biology.

Next, we will address the claims regarding polyethylene glycol, or PEG. First, it is important to know, though, that using PEG to coat medicines has been around since the 1970s, and PEGylated medicines have been on the market since 1990, in the United States. PEG can also be used as a laxative when ingested, and is used in facial fillers for cosmetic procedures, and is a common component in beauty products. The medicines that are typically PEGylated are usually administered in much larger amounts than what is used in the vaccine. So there is more PEG dosage with other PEGylated medicines than with these mRNA vaccines.  The dosages of PEG in these vaccines are miniscule.

PEG coating allows certain medicines to last longer in our body and prevents those medicines from overstimulating the immune system and degrading quickly, as Dr. Shallenberger accurately posits. The medicines that are usually PEGylated actually become more inert by being PEGylated, because PEGylation tends to limit the amount of immune reaction and cross-reactivity. There are some rare case reports of PEG being associated with different auto-immune problems, but aside from these being so rare that it took thousands and thousands of people to get PEG drugs before these was found to be possible problems, there are no trials that actually demonstrate this effect, only case reports. Regarding his claim about liver toxicity — earlier, less refined versions of PEG were found to sometimes accumulate in the liver, but they did not demonstrate signs of causing liver toxicity. For a discussion of the rare incidences of anaphylaxis after COVID vaccination in individuals with a history of allergies, see this article.

“These new vaccines are additionally contaminated with aluminum, mercury, and possibly formaldehyde. The manufacturers have not yet disclosed what other toxins they contain.”

This is blatantly false, the ingredients are listed here for the Pfizer mRNA vaccine and here for the Moderna mRNA vaccine. Check out this explanation of what some of these ingredients are.

“Since viruses mutate frequently, the chance of any vaccine working for more than a year is unlikely. That is why the flu vaccine changes every year. Last year’s vaccine is no more valuable than last year’s newspaper.”

Dr. Shallenberger’s assertion is only partially true. Some viruses do mutate frequently, others do not. The polio vaccine and measles, mumps, rubella vaccine have not significantly changed since they were first introduced because the viruses are so stable. So far, unlike its coronavirus cousins, the novel coronavirus does not appear to undergo rapid mutation, particularly in the important spike protein domain, which is what the mRNA vaccine induces immunity against. For a more detailed discussion of the recent UK strain and what that means for vaccination, check out this article. However, regardless of how long the current vaccines provide immunity, the idea that it is somehow useless, even if immunity doesn’t last forever, is completely false. 

“Absolutely no long-term safety studies will have been done to ensure that any of these vaccines don’t cause the cancer, seizures, heart disease, allergies, and autoimmune diseases seen with other vaccines. If you ever wanted to be guinea pig for Big Pharma, now is your golden opportunity.”

Dr. Shallenberger is pointing out that this vaccine does not have long term safety data for it. He is 100% correct. It would be good to acknowledge that other vaccines do have rare adverse events associated with them, and very rarely those adverse cause chronic health problems. However, when you are looking at the risks of the vaccine, you have to weigh them against the risks of the disease it is protecting against. A good case to look at is the relationship between measles infection and the uniformly fatal pansclerosing encephalitis that rarely affects people years after they get measles. People vaccinated against measles don’t die of immediate measles-related disease, and also do not die of late onset pansclerosing encephalitis. Overall, more lives are saved and more morbidity is avoided by vaccinating against measles, despite adverse events from the vaccine, than by letting measles run rampant.

At the end of the day, neither COVID nor the COVID vaccine have long term data, but as a physician, I can personally tell you that people who get COVID and survive don’t always just go back to normal. And we still don’t know the longer-term outcomes associated with infection because… the virus has only been around for a year.

“Many experts question whether the mRNA technology is ready for prime time. In November 2020, Dr. Peter Jay Hotez said of the new mRNA vaccines, “I worry about innovation at the expense of practicality because they [the mRNA vaccines] are weighted toward technology platforms that have never made it to licensure before.” Dr. Hotez is Professor of Pediatrics and Molecular Virology & Microbiology at Baylor College of Medicine, where he is also Director of the Texas Children’s Hospital Center for Vaccine Development.”

I don’t know the context of Dr. Hotez’s quote and couldn’t find the interview where he said that – though I believe it’s something he could have said. Dr. Hotez has been quoted as recently as November 25th that he would take any effective vaccine that was developed including the Moderna one, with the expectation that additional vaccines will also be developed if the vaccine pans out to be less effective in the long term. Dr. Hotez says this not because immunity waning is an expected outcome, but because Dr. Hotez is a super practical man. He was my professor in medical school. Recently, he himself received the Pfizer mRNA vaccine, thus it is inaccurate to suggest he is somehow opposed to these vaccines.

‘Michal Linial, PhD is a Professor of Biochemistry. Because of her research and forecasts on COVID-19, Dr. Linial has been widely quoted in the media. She recently stated, “I won’t be taking it [the mRNA vaccine] immediately – probably not for at least the coming year. We have to wait and see whether it really works. We will have a safety profile for only a certain number of months, so if there is a long-term effect after two years, we cannot know.”’

This quote from Dr. Michal Linal is taken out of context. What she actually said was that she believes in the safety of mRNA vaccines, though she doesn’t know whether or not there will be prolonged immunogenicity, again, because of the lack of time-based data. Here’s the actual full interview for context.

‘In November 2020, The Washington Post reported on hesitancy among healthcare professionals in the United States to the mRNA vaccines, citing surveys which reported that: “some did not want to be in the first round, so they could wait and see if there are potential side effects”, and that “doctors and nurses want more data before championing vaccines to end the pandemic”.’

I don’t know what to tell you, people make bad bets all the time, including doctors and nurses. Many people were feeling hesitant about the vaccines until the safety data came out, and then made an informed decision based on that data. If the deluge of vaccine selfies in my social media feed is any indication, many health care providers are quite enthusiastic about getting the vaccine.

“Since the death rate from COVID resumed to the normal flu death rate way back in early September, the pandemic has been over since then. Therefore, at this point in time no vaccine is needed. The current scare tactics regarding “escalating cases” is based on a PCR test that because it exceeds 34 amplifications has a 100% false positive rate unless it is performed between the 3rd and 5th day after the first day of symptoms. It is therefor 100% inaccurate in people with no symptoms. This is well established in the scientific literature.”

This statement is blatantly false. Death rates from COVID-19 are consistently and considerably higher than seasonal flu and even the most recent epidemic flu. Also, COVID is not the flu. Furthermore, it’s not just cases that are increasing: hospitalizations and deaths are increasing as well. We have several articles on this very blog where Dr. Panthagani and I write about the difference between COVID and the flu, the difference in the death rate, and also the false assertion regarding ‘false positive pandemic’.

“The other reason you don’t need a vaccine for COVID-19 is that substantial herd immunity has already taken place in the United States. This is the primary reason for the end of the pandemic.”

I don’t know what the basis of this claim is, but it is also blatantly false. The fact that our hospitals are filling up with COVID patients speaks pretty definitively to the fact that we don’t have enough herd immunity to keep our ICUs and hospitals from filling up.

“Unfortunately, you cannot completely trust what you hear from the media. They have consistently got it wrong for the past year. Since they are all supported by Big Pharma and the other entities selling the COVID vaccines, they are not going to be fully forthcoming when it comes to mRNA vaccines. Every statement I have made here is fully backed by published scientific references.”

He doesn’t include any references.

“I would be very interested to see verification that Bill and Melinda Gates with their entire family including grandchildren, Joe Biden and President Trump and their entire families, and Anthony Fauci and his entire family all get the vaccine.”


“Anyone who after reading all this still wants to get injected with the mRNA vaccine, should at the very least have their blood checked for COVID-19 antibodies. There is no need for a vaccine in persons already naturally immunized.”

This is one of Dr. Shallenberger’s few reasonable points that might hold water. There is a different risk:benefit ratio for those who have already gone through COVID because there is likely a lower risk of infection in these people, and so the benefit of vaccination is lower. It should be noted; however, that there are documented cases where people have gotten COVID more than once and the second time was worse than the first time. 

Of course, as has been pointed out before, we do not know if vaccine-mediated immunity to COVID 19 will still be effective a year from now. That’s something only time will tell. Here is what the CDC currently says about the need to get vaccinated after getting COVID:

“Due to the severe health risks associated with COVID-19 and the fact that re-infection with COVID-19 is possible, people may be advised to get a COVID-19 vaccine even if they have been sick with COVID-19 before. At this time, experts do not know how long someone is protected from getting sick again after recovering from COVID-19. The immunity someone gains from having an infection, called natural immunity, varies from person to person. Some early evidence suggests natural immunity may not last very long. We won’t know how long immunity produced by vaccination lasts until we have a vaccine and more data on how well it works. Both natural immunity and vaccine-induced immunity are important aspects of COVID-19 that experts are trying to learn more about, and CDC will keep the public informed as new evidence becomes available.”

“Here’s my bottom line: I would much rather get a COVID infection than get a COVID vaccine. That would be safer and more effective. I have had a number of COVID positive flu cases this year. Some were old and had health concerns. Every single one has done really well with natural therapies including ozone therapy and IV vitamin C.. Just because modern medicine has no effective treatment for viral infections, doesn’t mean that there isn’t one.

Yours Always,

Frank Shallenberger, MD, HMD”

There are several problems with this statement. First of all, COVID is far, far, far more dangerous than the vaccine. Second, Dr. Shallenberger keeps calling COVID the flu. It is not the flu. Finally, it should be noted that Dr. Shallenberger’s ‘ozone therapy’ is not in any way a ‘natural treatment’, as it involves injecting ozone gas into the blood (ozone is a free-radical producing oxygen molecule) which does not happen ‘naturally’. Here is what the FDA has to say about ozone. For more information on Dr. Shallenberger’s background, check out this article.

Dr. Sana Zekri, MD is a Family Medicine with Obstetrics Physician. His particular interests are in public health, global health, women’s health and working towards justice in medicine. He is currently an Assistant Clinical Professor at SUNY Upstate, in Syracuse, New York. The views expressed on this website do not necessarily reflect the official views of the author’s employers or affiliated institutions.

A scientist looks at the COVID vaccine data

A scientist looks at the COVID vaccine data
By Kristen Panthagani, PhD

Trying to decide if you’re going to take the COVID vaccine? Me too. And I decided the best way to make that decision was to look through the data. I figured there are a lot of people like me who want to understand the data for themselves. So if that’s you, check out this hand-animated walk through of the Pfizer COVID vaccine safety and efficacy data.


A big thank you to Dr. Peter Hotez, MD, PhD for taking the time to answer some questions for this video!

Minor notes:

Side effects shown at @3:48 are common side effects for ages 16-55 after Dose 1. Side effects were generally lower for age 55+, and higher after Dose 2 for both age groups. I didn’t want to overwhelm people with graphs so didn’t draw out every single one; see all side effect data here. If you’re curious why the participant numbers @1:52 and @7:09 don’t perfectly match, see Figure 1 here. Also please note that when I refer to vaccine “approval,” I was using that term more colloquially — the vaccine has not yet obtained formal “FDA Approval” and currently has “FDA Emergency Use Authorization.” 

What are mRNA vaccines, and will they turn me into a GMO?

What are mRNA vaccines, and will they turn me into a GMO?
By Kristen Panthagani, PhD

COVID vaccines are on the horizon! And sadly, vaccine misinformation is on the rise as well. One claim that has been circulating for a while now is the idea that mRNA vaccines will somehow mess with people’s DNA and turn them into genetically modified organisms. Is this true?


Lol no. Not at all. Here’s why.


What is mRNA?

Before we get to the vaccine, let’s start with some basics of what mRNA is. You may remember that RNA has something to do with DNA, and they share two of the same letters so they’re probably related. Turns out you’re right… they are related, but they are not the same thing.

DNA is the stuff that makes up your genome. Think of DNA as the official master copy of your genetic code. Every cell (with a few exceptions) has its own master copy which contains the instructions to make all the machinery in the cell. Because it’s the master copy, DNA is guarded very, very carefully. And you probably remember that DNA is a code… there are only four letters in that code (A, C, G, and T), and with those four letters, all the instructions for the human body are spelled out. 

But DNA doesn’t actually do the stuff in the cell, it only has the instructions for it. So how does the cell turn those instructions into actions? The first step is to make a photocopy of the specific instructions needed for whatever task is at hand. Those photocopies are made out of RNA. There are different types of RNA, but for the sake of this blog, all you need to know about is messenger RNA (mRNA). mRNA is essentially a photocopy of small segments of your genome. 

So what does mRNA do (and why is it in a vaccine?). mRNA meets up with some machinery in the cell (ribosomes, to be specific), and translates the four letter mRNA code into proteins. Proteins are built of amino acids, and every three letter sequence in the mRNA code corresponds to a specific amino acid (for example, the sequence G-A-C encodes for the amino acid called aspartate.) As ribosomes read through the mRNA code, a protein is formed by making a chain of amino acids in exactly the right order based on the mRNA sequence. Those amino acid chains then fold up in just the right way to make a functional protein, which then go do their job inside the cell.

Why are we putting mRNA in vaccines?

Vaccines work by introducing a small part of a virus or bacteria (but not the whole thing) into the body so that our immune systems can learn to recognize it. In the past, scientists have done this by taking one of the viral proteins and putting that protein in the vaccine. Our immune systems learn to recognize the viral protein, and then we are ready to attack it when the real virus comes around. 

The idea behind mRNA vaccines is we are going one step upstream in this process. Instead of putting the viral protein in the vaccine, we put the instructions for the viral protein in the vaccine: the mRNA. Our cells automatically know what to do with mRNA and will translate it into the correct protein. In the case of the COVID vaccine, the mRNA encodes for the coronavirus spike protein. Then, just like with other types of vaccines, our immune systems will learn what that spike protein looks like and will form antibodies against it. Then, if the real virus comes around, we will already have the antibodies ready to destroy it.

So, will mRNA vaccines mess with my genome?

No. This process only runs in one direction. DNA encodes for mRNA which encodes for proteins; this doesn’t run in reverse. mRNA does not do anything to your DNA. And this is why your genome is 100% safe from any foreign mRNA you may encounter. Your cell knows not to edit something as important as its master copy (DNA) because a random photocopy (mRNA) came around. There are molecular fail-safes to make sure this never happens. So no, mRNA vaccines will definitely not turn you into a GMO.

Still confused? For this topic in particular, I think pictures are helpful. So I made my very first You Can Know Things video which explains what mRNA is, how it encodes for proteins, and why the COVID vaccines most definitely will not turn you into a GMO. Check it out!

Will the COVID vaccine sterilize women?

Will the COVID vaccine sterilize women?
By Kristen Panthagani, PhD

Yesterday someone sent me an article about an alleged side effect of the COVID vaccine: female sterilization. The argument goes like this: there is a protein expressed in the placenta (syncytin-1) that is similar to the coronavirus spike protein encoded by the vaccines. Because these proteins are allegedly so similar, an antibody response generated against the vaccine will allegedly attack this protein in the placenta as well, causing sterilization.

So, should we be worried that the COVID vaccine will sterilize women? 

Absolutely not. Here’s why.

The entire claim hinges on the assertion that the COVID spike protein and syncitin-1 (the placenta protein) are similar. One word we use for protein similarity is homology. Think of homology like genetic plagiarism. In plagiarism, if two journal articles have a common source (one was copied from the other), we would expect to see a high degree of similarity between the words and sentences in the two articles (or at least between some subset of the paragraphs.) Having a few words or phrases in common is not enough to suggest plagiarism… short phrases are bound to be repeated in totally unrelated pieces of writing. We would need to see long phrases and perhaps full sentences to suspect something was amiss. 

The same concept applies to proteins: when two proteins are homologous, they share a high degree of similarity in their amino acid sequence. (As a quick refresher, proteins are chains of amino acids that fold up in just the right way to do helpful stuff in your cells). If two proteins have only a few short strings of amino acids in common, this is not enough to suggest homology. We’d need long sections of matching amino acid sequences to suspect that the two proteins may be very similar (highly homologous). 

Ultimately what we care about, in the context of an aberrant antibody response against the placenta, is the shape of the proteins. If two proteins are extremely similar in shape, an antibody might mistake one for the other and accidentally attack the wrong thing. As the shape of a protein is ultimately determined by its amino acid sequence, in order for us to suspect that an antibody targeting the coronavirus spike protein will accidentally attack syncytin-1 instead, there would need to be a high degree of similarity between the amino acid sequences of these two proteins.

So, is there? Here is a sequence alignment of the two proteins. This is created by a program that attempts to detect homology, and will try to line up regions of the amino acid sequences that match. Each letter represents a specific amino acid, and the stars indicate “matches.” But remember that the amino acid alphabet is only 20 letters long, so there are bound to be some matches at random. This is what we see for these two proteins:

This is the top hits for homology between these two proteins. There are more that I couldn't capture in the screenshot, but they all have lower homology scores than these.

So is this enough “matching” to make us concerned? While back in February, one scientist looked at this and speculated there may be some similarity (which is probably what started this whole rumor), many argue it’s absolutely not. Why? Very simply, it’s because we need a very high degree of similarity to cause a problem. Because proteins are long and only have 20 letters in their alphabet, you can run these analyses for random proteins and find small regions that “match”, just by chance.

Don’t believe me? Well let’s pick another random protein and do the same thing. For this, to try to make it truly random, I typed “the best gene” into the NCBI genome browser and took the first human gene result, bestrophin-1. Then I performed the same type of alignment. The results are below.

Again, we see random matches. If you want to see this tested with even more proteins, check out this post (also links to some more resources going into more detail on this topic.) Also check out this post for a far more detailed explanation of this topic. Now, is it theoretically possible for two proteins with a low degree of homology to fold up in just the right way to cause antibody cross-reactivity? Yes it is, but this is very rare. And because of the prevalence of random matching, we would need more evidence than just a low degree of sequence homology to make us seriously concerned that there was a problem. If I had gone into my PhD qualifying exam with such minimal evidence to support my hypothesis, I probably would have failed.

But let’s suppose for a second that this hypothesis was correct, and there was cross-reactivity between the COVID spike protein and this placenta protein. Do you know what else causes humans to form antibodies against the COVID spike protein, besides the COVID vaccines? COVID. If this hypothesis were true, we would have this exact same concern about people who had COVID infections. This was the primary concern of the scientist who posted about this back in February, not the vaccine. But this only became news when other people (not that scientist) made a huge issue about the vaccine, but didn’t mention anything about COVID. Why go around telling people they’ll get sterilized if they take the vaccine, but forget to mention that, if they’re right and there really is antibody cross-reactivity against the placenta, COVID would do the exact same thing? Also, for the record, this would be a fairly easy hypothesis to test in vitro in a laboratory. If someone was really concerned about this, the responsible thing would have been to run the quick experiments testing for cross-reactivity, not jump to conclusions and declare to the entire world that the vaccine will make people sterile (and forget to mention that COVID infection would allegedly do the same thing).

But knowing that COVID infections would do the exact same thing gives us one other way to check this hypothesis about antibody cross-reactivity: if antibodies to the COVID spike protein attacked the placenta, we would expect to see higher rates of adverse pregnancy outcomes in pregnant women who got COVID. So, are women who get COVID more likely to lose their pregnancies? While this topic is still under investigation, there is not clear evidence to suggest that COVID increases the risk of fetal demise. Here is one recent study that showed no difference in adverse pregnancy outcomes between patients who had COVID during pregnancy versus those who didn’t. While we should certainly study the impact of COVID on pregnancy further, I think we can confidently say that COVID isn’t sterilizing everyone.

In conclusion, the entire claim about the risk of sterilization with COVID vaccination hinges on minimal evidence of homology between the COVID spike protein and syncytin-1, and forgets to mention that if this risk were real, it would happen with COVID infections as well. Given the minimal similarity between these proteins, the lack of any other laboratory evidence to suggest there may be cross-reactivity, and the observation that many pregnant women have gotten COVID without losing their pregnancies, this rumor does not concern me at all.

Are vaccines safe? A story about why I didn’t get vaccinated…

Are vaccines safe? A story about why I didn’t get vaccinated…
By Kristen Panthagani, PhD

With the announcement of effective COVID vaccines on the horizon, many are wondering about vaccine safety. How do we know the COVID vaccine(s) will be safe? Some have heard stories about crazy side effects from other vaccines, and may be concerned that rare side effects will be overlooked. So, how do we determine whether a new vaccine is safe or not?

To answer that question, I’m going to tell you a story… my senior year of college I had the chance to live in Ecuador for a couple of months. Ever since playing Amazon Trail as a kid, I had wanted to visit the jungle. So here was my chance… the Amazon was only an 8 hour bus ride away from where I was staying. So before leaving the US, I went to a travel clinic to get the necessary vaccines for the crazy tropical microbes I was about to confront. For most people, that includes the Yellow Fever vaccine, as the mosquitos in the jungle carry this disease. However, I soon learned that the medical community recommended I NOT RECEIVE the Yellow Fever vaccine. Why couldn’t I get this vaccine, when everybody else could?

Ecuadorian jungle with my buddy, 2010. Our guide said it would look cool if we put these flowers on our faces.

Well, it’s because of a weird medical thing that happened when I was a kid. When I was 12, a massive teratoma (a type of tumor) was discovered growing next to my heart. Teratomas are one of the weirdest types of tumors — they come from stem cells which can differentiate into any type of human cell, so they’re just a random mix of tissue. Mine had teeth and pancreases, among other things. The tumor was right next to my thymus (a small immune organ) and during the surgery, my thymus was so inflamed and my tumor was so crazy that my surgeon couldn’t tell the difference between the two, so he decided the best action was to take it all out, thymus and all. (I’m totally ok now – the thymus doesn’t contribute much to the immune system after early childhood.)

11-year-old me on a family vacation where I kept getting mysteriously sick. Turns out there was a teratoma growing next to my heart, making my other organs understandably unhappy.

Except, because my thymus was removed, I can’t get the Yellow Fever vaccine. Why?

If a new vaccine is approved based on clinical trials (where the COVID vaccines are right now), it becomes available for use in the general public. Clinical trials test two main things: is this vaccine safe and does this vaccine work? To answer these questions, they study thousands of people. Analysis of the data from clinical trials will catch the vast majority of side effects from the vaccine, answering the question is this vaccine safe? And of course, by comparing the disease incidence of people who got the vaccine versus people who were in the control group, they can answer the question does this vaccine work? If the data shows that the vaccine is safe and it works to protect against disease, then it will be approved by the FDA for use in the general public.

However, the safety monitoring doesn’t stop there. Sometimes vaccines (and other drugs) can cause super, super rare side effects. So even after a vaccine is FDA approved, it is still monitored for safety. How is this done? By a team effort: anyone (doctors or patients) can report any weird symptoms that could be related to the vaccine to a centralized database. (Additional monitoring methods discussed here.) Researchers on the back end of these databases look for patterns — if multiple people report the same side effect after the vaccine is administered, then the vaccine is re-evaluated. If after re-evaluation it is shown that the risk of the rare side effect is greater than the risk of the disease it’s preventing, then the vaccine is discontinued. Or, if they find that a certain group of individuals is at higher risk for the rare side effect, then the vaccine becomes contraindicated (vaccine should not be given) for that group. And this is why I can’t get the Yellow Fever vaccine.

As of 2004, a total of 23 people worldwide (a rate of 3 people per 1 million vaccine doses given) developed a severe yellow fever-like disease after getting the yellow fever vaccine. 14 of the people died. A careful review of the situation was performed, and it was found that 4 out of the 23 people had a history of thymectomy (surgical removal of thymus). That’s 17% of all the cases, which is way higher than can be explained by chance, as thymectomies are quite uncommon. So because of this review, it was determined that a thymectomy is a significant risk factor for this vaccine reaction, and anyone who has had a thymectomy (like me) shouldn’t get the vaccine.

So I slept under an ethereal blue mosquito net and wore bug spray when I went to the jungle. A lot of bug spray.

Ethereal blue mosquito net in Ecuadorian jungle. I did not get one mosquito bite during that trip, and am incredibly proud of this accomplishment.

You may be wondering why this vaccine is still given to people without thymectomies, since 19 out of 23 people who had the side effect didn’t have thymectomies. Well, you have to remember that Yellow Fever can be really, really bad. So even though there is a small risk of serious side effects with the vaccine, for those traveling to areas where Yellow Fever is circulating, the risk of not getting vaccinated and dying from yellow fever is higher than the risk of side effects from the vaccine. For those who aren’t traveling to the jungle, there’s not a benefit of getting the vaccine, so it’s not worth the small risk of side effects. This is always the way any vaccine or medication is evaluated: no drug/vaccine is 100% without side effects, so it’s always an evaluation of the risks versus the benefits. If the risk of the disease it’s preventing is higher than the risk of side effects, then it makes sense to give it. If not, then you don’t give it.

In summary, while no vaccine is 100% without side effects, there are systems in place to continually monitor vaccine safety, detect rare reactions, and take action as necessary. So when the medical community says “these vaccines are safe,” or more explicitly, “the benefits of this vaccine far outweighs the risks,” they are doing their homework to make sure that’s actually true.

Are COVID death counts exaggerated?

Are COVID death counts exaggerated?
By Kristen Panthagani, PhD

Since the beginning of the pandemic, there has been confusion over the numbers: are cases being undercounted or overcounted? Are deaths being correctly attributed to COVID, or is the COVID death count grossly overestimating the true death toll? People hear stories of men dying from heart attacks being listed as COVID deaths, fueling suspicions that the official COVID death counts are inaccurate, including people who died “with” COVID rather than “from” COVID. Are these suspicions true?

First, let’s acknowledge the obvious: when you are trying to tally things up in real time across an entire country, the precise number is bound to be somewhat inaccurate. This is not a sign of negligence or deception; it’s a reflection of the difficulty of counting things in real time and coordinating across thousands of different health care systems simultaneously. If you want to argue with me that as of today, there are not precisely 224,292 people who have died from COVID in the US, I would say you’re probably correct. It is likely at least a little bit off from that number. We rarely have an exact number when it comes to death tolls from disease — even the seasonal flu is based on estimates, not precise counting.

I think most people realize this, and what they really want to know is this: is the official death count in the right ballpark of the true total? They want to know if it’s way off. If the true death total is actually 50,000 or 500,000, that would be important to know. If it’s actually 223,189 not 224,292, that’s really not something to make a fuss over.

How are COVID deaths counted?

So how do we count COVID deaths? The current method used by the CDC is to look at the causes of death listed on death certificates. These are filled out by doctors who took care of the patients who died, or sometimes medical coroners or medical examiners. In order to get as full of a picture as possible as to what happened, the death certificate asks the doctor to list the “final” cause of death (i.e. myocardial rupture) as well as the diseases/events that contributed to that happening (i.e. motor vehicle accident). Death certificates also have a place to list underlying conditions that likely contributed to death, but did not directly cause the death (i.e. heart disease). This form distills a complex patient story down to three or four words to describe what happened to them. From a medical perspective, this is very much an oversimplification — it misses the vast majority of the details of what happened to the person, which are documented in that person’s medical chart. From a data analysis perspective, distilling this information down into these simple diagnoses is incredibly helpful, as it allows organizations like the CDC to analyze what is going on with patients at a broad level. If we had the level of detail provided in medical charts on death certificates, the clarity on the COVID death count would be worse, not better, as there would be millions of medical notes to go through to figure out what happened to each patient. Thus it is helpful that the doctors summarize it on the death certificates. But again, it’s a simplification.

Here is an example death certificate — you can see it asks for both the underlying cause of death and the chain of events that the underlying cause triggered, ultimately leading to one or more vital organs failing. A person who gets COVID which causes pneumonia which then causes acute respiratory distress syndrome would have all three things listed under Part I: the chain of events that directly caused death. Part II of the form allows the doctor to indicate underlying health conditions that likely predisposed the patient to being ill, but are not the primary cause of death themselves.

Here is the CDC’s explanatory video for physicians on how to fill out these death certificates for COVID, which explains the process:

Up until the COVID pandemic, this process was not called into question. A person with chronic heart disease and decades to live who got into a car crash and later died due to a weak heart that couldn’t compensate for the blood loss might be listed as cardiac arrest (heart stopped beating) secondary to hypovolemic shock (not enough blood to the body) secondary to motor vehicle accident with a significant underlying condition of chronic heart disease. This would get counted as a motor vehicle death, and nobody would be upset. Did the person have heart disease? Yes. Would they still be alive if they hadn’t been in a car crash? Yes. Would they still be alive if they didn’t have heart disease? Perhaps, as maybe someone with a healthier heart could have survived the blood loss. Regardless, it is still appropriate to list car crash as a cause of death, even though their weak heart may have also contributed.

To doctors who have studied the full course of diseases and understand how different medical conditions are interconnected, this makes a lot of sense. However outside of the hospital, when we talk about what somebody “died from,” we usually only think of one cause like “car crash”. We don’t think of “what vital organ ultimately failed, what caused that, and what were the predisposing factors.”  That’s how doctors think because they are trained to do so; it’s not usually how the general public thinks. 

Table 3: Confuse America

This has led to substantial confusion over COVID death certificates: several weeks ago, the CDC released the data on the COVID death certificates, and of course, multiple things were listed (because that is the proper way to fill out a death certificate). You can see the table below. 6% of them had only one cause listed (COVID), which in reality probably meant that those death certificates were incomplete. But this data was rapidly misinterpreted: the general public saw that multiple causes were listed, and many assumed that those other diseases were the true causes of death, and COVID was only listed because they happened to test positive, but they were about to die anyways. Many seemed to assume that people who truly died from COVID should only have one thing listed on their death certificates, and attributed that 6% number to the “true” death toll. To make matters more confusing, the CDC did not separate out the two sections on the death certificate: Part I (which describes the chain of events leading to death like COVID => pneumonia => respiratory failure) and Part II (which describes the underlying health conditions that contributed to death like high blood pressure and obesity) were all mixed together into one table. This, in my opinion, is a mistake on the part of the CDC: this is a very confusing way to present the data. This confused many, and led to the widely circulated rumor that only 6% of COVID deaths were truly due to COVID, and everyone else was super sick already with all these other diseases. In reality, many of the other conditions listed in the table were directly caused by COVID (i.e. pneumonia, respiratory failure). But you couldn’t easily tell that because Part I and Part II were all are mixed together. Furthermore, one can’t determine how “sick” these people were based on this data. If someone has diabetes, they may be a well controlled diabetic with decades to live, or they may be someone who has not taken their medication or controlled their diet for years and only has 6 months to live. Knowing that they have diabetes does give you some information, but it cannot be used to argue that the person was already super sick and on death’s doorstep.

Source: CDC

Then why are heart attacks being counted as COVID deaths?

One of the most common arguments I’ve heard with regard to the accuracy of the COVID death certificates is the confusion over heart attacks. The argument goes something like: someone has a heart attack and also happens to test positive for COVID, and then they’re counting it as a COVID death! This is doesn’t make any sense! What’s going on here? Are these rumors true?

First of all, it’s false that everyone who tests positive for COVID and dies automatically has COVID listed on their death certificate.  Only conditions that caused or contributed to death are listed on the death certificates. Doctors don’t list every diagnosis a patient has, only the ones that, based on the details of that patient’s hospital course, the doctor knows to be pertinent to the disease process that ultimately killed them. This is stated directly on the CDC website:

So ultimately, doctors are using all their knowledge about how disease processes work to decide what actually killed someone. It is not the CDC who is deciding this, it’s doctors/medical examiners, all across the country. By writing “COVID-19” on the death certificate, they are not simply stating that the person who died was COVID positive; they are stating that “yes, COVID caused (or contributed) to this person’s death.” The CDC is not making this determination; the doctors are. Someone who is arguing that the COVID death count is wrong is not arguing with the CDC, they are arguing with all the doctors who actually took care of those patients, saw them die, and then filled out these forms describing what happened. Because these doctors had access to all the patients’ medical records, know the details, and are trained in medicine, they are in the best position to make this call. Looking at the limited information given by the death certificate and trying to argue that the doctor was wrong is like having access to three chapter titles of someone else’s book, not the full text, then trying to argue with the author of the book that they have the story wrong. Trying to do this from a conglomeration of thousands of these death certificates in the table above is even worse, as then you don’t even know whether each condition was listed under Part I (cause of death) or Part II (underlying condition), what other conditions each person had, nor how they were connected. 

Then where are these stories coming from about COVID and heart attacks? Well, it turns out that one of the weird complications of COVID is blood clots. For reasons we don’t fully understand, in addition to attacking the lung cells, the virus can also cause blood clots to form more easily than normal. Heart attacks are often caused by blood clots clogging the vessels that bring blood to the heart, which deprives the heart of oxygen and makes it stop working. So it is actually very plausible that COVID could directly cause a heart attack, especially in someone who has high blood pressure and high cholesterol, as these predispose the blood vessels to clot as well. But it can even in occur in someone without these risk factors, as reported here. So if both COVID and heart attack are listed on a death certificate, this does not mean the patient just happened to test positive for COVID right before their heart attack and died “with COVID, not from COVID.” It means that the doctor who filled out that death certificate, based on everything he or she knew about the patient, determined that COVID contributed to that person’s death. Are doctors perfect at assigning cause of death? Probably not, but if you would like to reasonably argue with them, you need both 1. a high level of medical knowledge and 2. access to the details of that patient’s medical records. Without these things, any arguments are pure speculation of what maybe happened based on minimal information, which are frivolous.

But what if doctors are lying?

It has been suggested that perhaps the doctors filling out these death certificates are doing so dishonestly, and falsely writing COVID diagnoses to make money. I’m not gonna lie, this one hurts. If you could see inside hospitals right now and know what doctors are sacrificing for their patients, you would know how much of a slap in the face this is. But let’s put emotions aside and see if this argument holds merit.

First, let’s note that falsely assigning an ICD-10 code for billing purposes is considered fraud, and doctors who do this lose their license. Second, let’s look at the impact COVID has had on hospitals and doctors: has the pandemic brought them lots of business making them rich? No, just the opposite: higher COVID cases has cost hospitals revenue, not increased profits. From a business persective, hospitals are financially incentivized to keep COVID numbers low so that they don’t have to divert resources away from other revenue streams. Falsely elevating their COVID numbers does not help them do this — it is not in the doctor’s nor the hospital’s financial interest to falsely inflate COVID numbers.

But even if there were truly financial incentives to falsely list COVID on the death certificate, would doctors do it? There are bound to be a few corrupt doctors out there, so maybe a handful of doctors would do so. Would this have a significant impact on the death count? No: in order for the effect of corrupt doctors allegedly falsely filling out COVID death certificates to significantly impact the COVID death count, you would need more than a handful of doctors committing this fraud… you’d need like half of the doctors in American doing it. This is usually where conspiracy theories fall apart: a few corrupt people doing something shady is plausible; half of an entire profession doing something shady, conspiring in secret, to deceive the public is not plausible at all. 50% of American doctors are not risking their jobs and purposefully deceiving the public in the middle of a pandemic just to make a little cash. No.

Cross-checking death counts: Are more people dying than normal?

One way we can cross check the COVID death numbers is by looking at total deaths during the pandemic, from all causes. If COVID deaths were being falsely inflated and those dying truly died from underlying causes that were going to kill them anyways, then we would not expect to see a big increase in overall deaths during the pandemic. So what do we see? 

Source: CDC

The CDC went back and looked at all deaths during the pandemic, and compared it to deaths during previous years to determine if their were “excess deaths” (deaths above what’s normally expected for that time of year). They found that as of Oct 3, 2020, there were 299,028 excess deaths, while only 198,061 deaths attributed to COVID on death certificates. So there were actually 100k more excess deaths than were being captured by the COVID death count. This could be explained by several possible factors — it could be that some of the excess deaths were due to COVID but it wasn’t reflected on their death certificate for one reason or another (maybe it was an abnormal presentation and the doctors didn’t realize it was COVID, or it was before widespread testing was available). Or perhaps some of these deaths are from the longer term complications of COVID that we don’t understand yet. Some are also likely due to other impacts of the pandemic, like people delaying care for other illnesses due to shutdowns or concerns about going to the hospital. We can’t say for sure, but the fact that the excess deaths are actually way higher than the official COVID count does not support the idea that the COVID death certificates are falsely inflating the death numbers. If anything, it supports the idea that COVID deaths may be undercounted.

In conclusion: no, the COVID death counts are not dramatically overestimating the number of COVID deaths. These numbers are based on the doctor’s evaluation of what killed the patient, and not simply counting people who just happened to test positive right before they died from something else. Additionally, there is no data to indicate that these were all super sick people who were going to die anyway; how “sick” they were prior to contracting COVID cannot be accurately gleaned from death certificates. The all cause mortality during the pandemic has dramatically increased above normal levels, confirming that way more people than normal are dying right now.

What is MIS-C, the weird post-COVID inflammatory thing in kids?

What is MIS-C, the weird post-COVID inflammatory thing in kids?
Kristen Panthagani, PhD

Hello! It’s been a little while since I’ve written a COVID-related post, as I have officially graduated with my PhD and am now back in medical school! (And thus am……. rather busy.) I am a third-year medical student now (if that’s confusing to you, check out this post), and I just finished my pediatrics rotation! While on pediatrics, one syndrome kept coming up over and over again: Multisystem Inflammatory Syndrome in Children (MIS-C). By now it’s well established that kids are at much lower risk of severe COVID infections, but you may have heard mention of a weird, sometimes severe inflammatory thing in kids several weeks after they’ve recovered from COVID, perhaps with the word “Kawasaki” thrown in. So, what exactly is this thing?

The very short, simple, and frustrating answer is: we don’t really know yet. Right now we’re still in the stage of describing what we’re seeing, and have very few answers as to why we’re seeing it. Here’s what we know so far, followed by a little speculation of what could be happening.

A Mysterious Kawasaki-like Illness

In April 2020, reports in the UK started coming out of kids with an illness like Kawasaki Disease, but with some differences. Kawasaki Disease is a relatively rare disorder where blood vessels become inflamed, leading to fever, inflammation in the eyes (red eyes), inflammation in the mouth (red tongue and dry/cracked lips), rashes, and swelling in the hands and feet. The disease can make kids quite sick and can progress to cause peeling of the skin as well as involvement of the digestive tract (vomiting, diarrhea). Often the most severe complication is heart damage (due to inflammation of the blood vessels that bring blood to the heart). The cause of Kawaski Disease is still a mystery; there is some data to suggest it’s triggered by an infection or environmental trigger, but we really don’t know.

Again, Kawasaki Disease is rare (and, for unknown reasons, it is more common in children of Asian ancestry.) In England, only about 5 out of every 100,000 kids under age 5 develop Kawaski disease. That’s why at the beginning of the pandemic, doctors in England were very surprised to see 8 cases of a severe Kawaski-like illness over the course of only 10 days, half of whom had reported family exposure to COVID. Something was up.

Since those initial reports, more reports have come in from all over the world of this weird, sometimes severe inflammatory disease in kids that seems to be associated with COVID. In some ways it looks like Kawasaki Disease, but in some ways it’s very different.

Here's what we know so far about MIS-C:

MIS-C seems to be a post-COVID disorder. Data from New York found that the peak of MIS-C cases came about 31 days after the peak in COVID cases, suggesting that MIS-C is a complication that occurs after the infection has resolved, not during the infection. Another analysis found that 60% of kids with MIS-C were negative for the SARS-CoV-2 PCR test (indicating they don’t have an active infection), but had COVID antibodies (meaning they were previously infected). The lag time of 3-4 weeks coincides with the development of antibodies, which has led to the hypothesis that MIS-C is caused by some sort of weird, dysfunctional immune response to a previous COVID infection, not the infection itself.

MIS-C disproportionately impacts Black and Hispanic children, while Kawasaki Disease disproportionately impacts Asian children. Children with MIS-C are also a little older (average age is 8 years old), while Kawasaki Disease usually impacts kids younger than 5.

MIS-C causes a wide variety of symptoms; more common ones are fever, GI symptoms (abdominal pain, vomiting, diarrhea), rash, inflammation of the eyes, neurocognitive symptoms (confusion, tiredness, headache), and rashes on mucosal surfaces (inside the mouth, etc.).

MIS-C can be a very serious disease, leading to heart damage, kidney damage, shock (not enough blood perfusion to the body), respiratory failure, and death. However, we often notice the most severe cases first, so as the months go on, we may find that MIS-C can be mild as we learn to diagnose it better. 

One key feature of MIS-C seems to be inflammation. Markers of inflammation in the blood are elevated in MIS-C cases, and a higher level of inflammation seems to be correlated to more severe disease.

Given that MIS-C can cause a laundry list of symptoms, the official diagnosis is also somewhat of a laundry list. To officially diagnose a child with MIS-C, they must have fever, elevated inflammatory markers in the blood, be sick enough to need admission to the hospital, have damage to at least two organ systems (i.e. signs of both kidney and heart damage, for example), have recent COVID exposure/infection, and no other plausible diagnosis (rule out everything else it could be). Hopefully as we learn more about it, we will have a more precise way of diagnosing it.

And lastly, the good news: thankfully, MIS-C seems to be relatively rare. As with most things COVID, we don’t have an exact number, but one report estimated 2 MIS-C cases per 322 COVID infections in people under age 21. However, given that it can cause so many different symptoms, it’s very possible it’s being under-diagnosed. 

Molecular Mimicry: Lessons from Little Women

So what is going on? Why are symptoms showing up after the infection? One possibility is that MIS-C is a disease like rheumatic fever. Rheumatic fever is a weird complication that can occur after your run-of-the-mill strep throat infection. Strep throat is caused by the bacteria Streptococcus pyogenes, which causes a fever and killer sore throat, and sometimes a rash as well (Scarlet Fever). These infections are easily treated by antibiotics, and for most people, that is the end of the story. But for some (usually those who weren’t treated or were under-treated), a few weeks after the infection, rheumatic fever can develop. The symptoms of rheumatic fever have nothing to do with the initial infection: they can include arthritis, heart damage, and even neurological disorders, and can lead to long-term health problems and even death. 


For the Louisa May Alcott fans, this may sound familiar as it is very likely what happened to Beth March in the story Little Women. Beth contracts Scarlet Fever early on in the story (unfortunately she lived in the era before antibiotics were discovered) and seemed to make a full recovery. But then slowly, she gets very, very sick, and ultimately passes away. While it’s not explicitly stated in the book, the course of her illness describes the progression of rheumatic fever (most likely rheumatic heart disease) — ultimately, her heart likely failed. Why did this happen?

My old, beloved copy of Little Women, inherited from my great grandmother.

Unlike MIS-C, we’ve had centuries to learn about rheumatic fever. It is caused by the immune response to a strep infection, not the infection itself. Our immune systems work by recognizing very specific pieces of bacteria and viruses, then making antibodies and immune cells that recognize those specific pieces and attack them if/when the microbes ever come around again (check out this post for a more detailed description of how this happens). It just so happens that some of the pieces of Streptococcus pyogenes happen to look very similar to pieces of human cells. Poor Beth didn’t have access to antibiotics, so her immune system had to do all the heavy lifting and created a very strong immune response to the Streptococcus pyogenes causing her Scarlet Fever. She beats the infection, but her antibodies are circulating in her body for life, looking for more Streptococcus pyogenes to attack. They come across her heart cells that have molecules that look so, so, so similar, and mistakenly attack them instead. This is called molecular mimicry: two unrelated molecules just happen to be very, very similar in shape, and the immune system can’t tell the difference. Ultimately, this immune response destroys her heart (and our hearts as well 😓). 

This may sound alarming — aren’t our immune systems supposed to be super precise? How can they get molecules mixed up? Yes, our immune systems are super precise. It is incredible how good they are at what they do. However, when you consider the millions of different pieces of microbes that our immune systems have to learn to recognize (we are CONSTANTLY exposed to microbes), it’s not so surprising that every now and then, there might be a problem like this. Just like every now and then, two completely unrelated people happen to look nearly identical.

The same thing could be happening in MIS-C: perhaps some molecule on SARS-CoV-2 just so happens to be the EXACT same shape as some other molecule on healthy human blood vessels, causing COVID antibodies to attack healthy human cells after the infection has resolved. But we really don’t know — for now, this is just a guess and one of several possible explanations. It could be molecular mimicry is at play, or it could be something else related to the COVID infection and its impact on the immune system. One case report detected actual virus in the damaged heart cells of a child with MIS-C, suggesting the MIS-C could be a direct effect of the viral infection; however this may simply be due to misdiagnosis of severe COVID infection as MIS-C in that particular patient. COVID is a really, really weird disease. I am studying for my pediatrics exam right now, and it’s striking to me how few infectious diseases affect as many organ systems in the body as COVID does. It will likely be some time before we fully understand this virus and how it impacts the human body.

Why is COVID different than the flu?

Why is COVID different than the flu?
Guest post by my friend Dr. Sana Zekri, MD

As the current pandemic continues to unfold, people have compared COVID to other diseases to help them evaluate the risks of the disease and to understand why the world’s top experts reacted the way they did to COVID-19. One theme that frequently arises among proponents of a more lax COVID policy is that the mandatory shut-downs, mask-wearing, and banning of gatherings is a symptom of a media-driven overemphasis on the dangers of COVID. Even after the spike in COVID-19 deaths in New York early in the pandemic, I was still hearing people compare these COVID-19 deaths to the seasonal flu, arguing that the deaths attributed to the yearly seasonal flu were comparable to the COVID attributed deaths, and wondering why we shut down for COVID while we didn’t shut down for influenza. And some have pointed to the H1N1 pandemic in 2009, questioning why we didn’t have strong lockdown measures then, but we do for COVID. So let’s talk about it! Why has the worldwide medical community reacted so strongly to COVID, while there was a more muted response to the next most recent respiratory pandemic, H1N1?

First, we will need to talk about some basics- what is H1N1, why was it different than regular flu and what is coronavirus? Then we can start doing some comparisons.

Note: This post focuses on the initial COVID shutdowns back in March and April 2020. While COVID-related restrictions obviously continued after these months into the present, they are highly variable by location and require geographic-specific discussions as to the rationale. So this post is focused on the early “major” shutdowns, not the nuances of every state and city’s individual ongoing restrictions.

What is “the flu?”

First, a note about the word “flu” – people use the word “flu” to describe a lot of different diseases, including true influenza infections as well as the common cold and the “stomach flu”. However, when doctors use this word, they are generally referring to influenza viruses, which are a family of viruses that include both the seasonal flu and pandemic strains like H1N1.  Influenza infections have the potential to be much worse than the common cold: the cold very rarely causes more than a stuffy nose and mild fever, while influenza more frequently causes considerable fatigue, body aches, fever, and chills. Influenza also has a nasty tendency to cause ‘post-viral pneumonia’, which is a much worse bacterial infection you get while your lungs are in a weakened state from having the influenza.


Influenza type viruses appear to have been with us since written history, if not longer, though it should be noted that before European colonization of North and South America, it appears that influenza was not endemic in Native American populations. Now the flu pushes through the entire world (except, maybe, for the Sentinelese) every year. Most years, we get a seasonal flu (though the specific strains change from year to year), but occasionally (typically about every 40 years), there is a pandemic flu. Why does the seasonal flu change every year? And what is it that makes a flu a pandemic flu?

How the seasonal flu changes every year

The reason we have to get flu vaccines every year for seasonal flu has primarily to do with the concept of antigen drift. An antigen is a part of the virus structure that the memory cells of your immune system can learn to recognize. For an explanation of how immune memory cells come to be and what role they play (also to look at cute gifs of puppies), I recommend the blog post by Dr. Caitlin Miller on this very site. The flu virus has multiple antigens on it, but two of them are really important- the H antigen and the N antigen.

Flu viruses are named after what subtypes of H and N antigens they have on their surface, which will come into play later in this article. The problem is that influenza is a highly mutable virus, meaning that every time a new virus copy is made, pieces of the virus change just a little bit, so the antigens typically look a little bit different when we check up on them every few months. To make a comparison, we can imagine that the flu antigen is like a key, and the immune cell that needs to recognize the flu antigen is like your house lock. Well, the house lock and the key go together and when you put the key in the lock you are able to access your house. Now imagine that you filed down one of the ridges on your key and tried to put it in your lock. What would happen? Sure, the key would slide in, but because the pins don’t fit in the right place, you wouldn’t be able to turn the lock and access your house. That’s basically what happens with antigenic drift with the seasonal flu.

As the influenza virus goes around the world every year, the antigens change just enough so that when it comes back to the United States, our memory immune cells (which are very specifically made) do not recognize it very well.  When our immune cells don’t recognize a virus, they aren’t able to mount a very fast immune response, so we get sick while our body figures out how to fight the virus. So how have we solved this problem? We depend on epidemiologists to figure out what the dominant strains of influenza are every year (usually by looking at flu strains in Australia), and we make a new annual flu vaccine that covers those specific strains. If you have ever been curious why your doctor tells you to get a flu vaccine every year, but other viruses you only need the shot when you’re a kid – this is why: viruses like measles have a stable genetic structure that doesn’t change much over time. This means that unlike influenza, we don’t have to worry about significant antigenic drift for measles, and a vaccine given early in life provides good immunity that lasts a lifetime.

Pandemic Influenza: Practically a Whole New Virus

But what about flu pandemics? How do those work? Pandemics are thought to work on the principle of antigen shift or reassortment/recombination. Essentially, the flu strain that becomes dominant has antigens that are so radically different from previous strains that the pandemic strain looks like a totally new virus to our immune systems. These novel strains can emerge by flu viruses mixing components, or when one of the animal influenza viruses gaining the ability to infect humans. Imagine the same key analogy, but your memory cells are all pin tumbler locks, like pictured above, and you’re instead presented with a barrel key. You would need to manufacture completely new locks to be able to recognize that key.

As it turns out, the different key type analogy can take us a bit further in understanding why flu pandemics have certain characteristics. If you recall the H1N1 flu pandemic in 2009 (also known as the Swine flu pandemic), you might remember that the elderly were less likely to die from the flu than usual, and younger people (especially children and teenagers) were unusually susceptible to death and morbidity from that particular flu. The reason was that the elderly (mostly those 60 and older) had already been exposed to a similar flu in their younger years, and their immune memory cells already had some idea what they were doing. Basically, they already happened to have barrel locks lying around, so the key that they were presented with was somewhat familiar. The elderly were hit with the standard antigen drift, while everyone else had to deal with antigen shift.

The H1N1 pandemic: what was different?

The Centers for Disease Control (CDC) has a timeline that gives a pretty good idea of how the response to the flu pandemic was carried out. Importantly, this was the first pandemic  since the foundation of the World Health Organization (WHO) and the CDC, this was the first public health emergency of international concern that had ever been declared by the WHO and CDC, and the proper response to an international problem like this had only been theorized to that point.

Timeline of the H1N1 Pandemic

In early April of 2009, the first case of novel human H1N1 flu was identified. Soon after, community spread was confirmed in multiple states and was reported to the WHO. Cooperative work started immediately on sequencing the virus strain and on developing a vaccine. By the end of April, the US government declared a public health disaster of international concern and started releasing stockpiles of anti-influenza drugs; the CDC also published guidelines of how to deal with laboratory-confirmed infections in schools. Soon after this (early May), multiple schools were shut down to try to mitigate further community spread of novel H1N1 flu. There was a brief downtrend in the number of flu cases by mid-July, and clinical trial started on the vaccine candidates- almost 4 months after vaccine work started. Schools started again by the end of August and beginning of September, followed by a second wave of H1N1 flu. School closures continued to happen all over the United States in response to laboratory confirmed diseases. The first H1N1 flu vaccine doses were distributed in early October. The peak of the second wave of H1N1 flu occurred at the end of October. By the time there was enough vaccine for everyone, in late December, the virus had already somewhat died down, though it persisted in the community for several more months. The pandemic was officially declared over in August of 2010.

So, to recap, the interventions that were obvious were:

  • Cooperation between the reporting country and the international public health community
  • Early vaccine development based on an already well-established infrastructure for developing influenza vaccines
  • Drugs that were known to be effective against influenza were released and used
  • Schools and facilities and summer camps that had cases or outbreaks were closed to prevent further spread.

The things that are a little less obvious are some of the characteristics of the virus:

  • The elderly were unusually immune to the virus.
  • The transmissibility of the virus (the R­­­­­0 or Rt of the virus) was estimated at 1.5 at the beginning of the pandemic, but decreased to about 1.2 during the summer school vacation months with natural social distancing.
  • People who got H1N1 had similar symptoms to people infected with seasonal flu, and had typical flu complications – the most common causes of death were respiratory failure from primary H1N1 infection, and post-influenza pneumonia.
  • Available data at the time showed that masks did not appreciably decrease transmissibility of pandemic influenza.
  • The estimate of case fatality rate in the United States was ~0.048%, or 48 deaths per 100,000 cases.

In the first year of the pandemic, 12,469 people were estimated to have died from H1N1 influenza in the United States. That’s about a thousand people per month. 80% of global deaths were younger than age 65.

SARS-CoV-2: The COVID-19 Pandemic

Now let’s talk about the thing on everybody’s mind and newsfeed.

Importantly, this is still an area of active research. We have had more than 10 years to study the mechanisms and transmissibility of H1N1 influenza, so we have significant retrospective bias. If may feel like it’s been forever, but remember that at the time of this writing, COVID-19 has only been known to exist for 9 months, and has only been known to be in the United States for 8 months. Real hub-bub about COVID-19 didn’t start until about 6 months ago, as of this writing. So, with that said, let’s dive into it:

SARS-CoV-2 virology compared to the flu

SARS-CoV-2 is from an entirely different family of viruses, the coronaviridae. We are actually quite frequently exposed to different coronaviridae. For the most part, these viruses just cause cold symptoms, or our body fights it off without making a fuss at all. Coronavirdae have their genetic code written in RNA, like influenza virus, and also undergo antigenic drift and antigenic shift (also known as reassortment/recombination). COVID-19 appears to have undergone reassortment/recombination. As far as we can tell, COVID-19 arose from a bat coronavirus that recombined with a related coronavirus from an another animal and was then able to jump to humans. 


What were the early factors in decision making for COVID-19 policy?

Again, let’s remember that this is an evolving story, and the data we have are constantly being collected, updated and revised to better approximate the truth. The website Think Global Health has an exhaustive timeline that encompasses global coronavirus status, and I will be using that for my summarization of the events that may have led to the current public health policy guidelines of widespread shutdowns of various strictness. 


At the beginning of December 2019, a ‘pneumonia of unknown etiology’ emerges in Wuhan, a city of 11 million people, in the province of Hubei, in China. The WHO is informed of a string of infections at the beginning of January 2020. By the middle of January, viral transmission is found in the neighboring countries of Thailand and Japan, and community spread (rather than direct contraction by exposure to animal sources) is suspected. By the end of January, the first case of novel coronavirus is identified in the state of Washington. Around the same time, Wuhan and a lot of the Hubei province is put under strict quarantine by the Chinese government to reduce further spread in mainland China. There are 830 confirmed cases and 25 deaths total (3% mortality rate), all in China, by this point.


Following major airline suspension of flights to and from mainland China, the United States imposes a ban on entry of ‘immigrants and non-immigrants’ from China to the United States secondary to known community viral spread of the pneumonia of unknown etiology. The ban does not prevent citizens, non-citizen spouses, asylum seekers, or permanent residents from returning from China. The WHO declares a public health emergency of international significance on the same day as the U.S. travel ban. Evacuation of foreign nationals from China begins in early February 2020. Soon after, the public health agencies of the G7 countries agree to coordinate their responses to the COVID-19 outbreak. Tests distributed by the CDC were found to be defective in middle February.  Iran and South Korea confirm that they have cases in their countries- in Iran, the two confirmed patients died of COVID-19, while in South Korea it was found that 20 cases were linked to a single COVID positive woman. Iran and South Korea simultaneously recognize more and more cases with South Korea noting doubling of case numbers within 24 hours- both Iran and South Korea begin to restrict travel between cities and within individual cities. Within days, Italy pops up with 16 confirmed cases and immediately closes public areas. By the end of February, Iran shuts down universities and public spaces in 14 major cities; multiple Eurasian, middle Eastern, Asian and European countries have sentinel cases (mostly travelers from countries that had already declared infections); states of emergency have been announced on the U.S. west coast, many countries have banned large public gatherings. By this point, by WHO accounts, mainland China is developing fewer cases per day than the rest of the world. More than 2800 people have died from COVID-19 out of more than 84,000 cases (3.4% case fatality rate).

By this point, Iran and Italy have emerged as secondary epicenters of COVID-19. The United States sees small, but steadily increasing caseloads, but is not nearly as bad off as Europe. What happens next particularly shapes the view of how big a deal this virus is.


At the beginning of March, Italy imposes a nation-wide lockdown – the Vatican also closes St. Peter’s square and the Basilica to all tourists. On March 11, after 120 countries have declared infections totaling more than 142,000 over the course of about 12 weeks, including more than 5300 fatalities (3.7%), the WHO declares that COVID-19 is a pandemic. Stories pour in from Italy and Iran describing physicians having to make life-and-death decisions in the hallways of the hospitals because there are not enough hospital beds or enough ventilators to give everyone the care they need. Italian physicians write stories warning the world of the seriousness of this infection and the coming storm, and begging for social distancing guidelines to prevent a similar tragedy in other countries. The case fatality rate in Italy is particularly high, averaging 7%. At the same time China reports no new COVID-19 cases for the first time in 4 months, after stringent lockdown.  By the end of March, Italy is sustaining more than 600-900 daily deaths secondary to COVID-19.


As New York, California and Washington act as sentinel cases in the United States, the public health experts of the nation come together to make recommendations. They recommend social distancing, and also begin to recommend general lockdown to slow undetected community spread, especially to nursing homes and care facilities where the most vulnerable population stays. The rationale is twofold: rapid spread of the virus will result in overload of existing healthcare structures leading to excess mortality simply because of insufficient machines and resources and staff to care for the number of sick; and countries that were effective in lockdown and contact tracing have controlled their case loads. Modeling estimates of the mortality of COVID-19 and associated conditions runs in the 200,000 to 1 million persons range. The advisement to lockdown is taken differently by different groups- many citing concerns about the economic impact of hampering travel and consumption. Despite public health recommendations from the COVID-19 Task Force, other high-level political figures send mixed messages about the seriousness of the COVID-19 pandemic.

The rest of the story is important too, but the purpose of this section is to see what led the public health officials of the United States to recommend lockdown.

So, why was COVID different?

  1. There were concerns that there was not early enough reporting from China that there might be a novel emerging respiratory illness. It seems like China reported that there was something going on before the virus was known to have spread to other countries, but it was several weeks before the WHO was informed. Regardless of this, even when a virus is reported as early as possible, as with H1N1, the virus has already entered the community and is spreading.
  2. COVID-19 is far more infectious than influenza. The transmissibility of the virus (the R­­­­­0 or Rt of the virus) was estimated at 2-3 without social intervention. In countries that instituted strong social distancing interventions and shutdowns, the R­t of the virus was driven down to less than 1, and curves would flatten and decline. You can see maps of the calculated Rt of the virus for each of the 50 states over time at this website– it even includes when lockdowns were implemented and removed. Places that had infection but subsequently did strict contact tracing and that population level commerce and social shut down showed improvements in COVID-19 case rates.

Model of H1N1 spread
(R0 = 1.5)

Model of COVID-19 spread
(R0 = 2.0)

3. The Case Fatality Rate is far higher than H1N1.  The case fatality of COVID-19 ranged between 1.2% to 10.8% in different countries. In the United States, the case fatality, as of this time, is 3.1%, or 3100 deaths per 100,000 confirmed cases. Remember the true mortality rate of an infection is difficult to calculate early in a pandemic (and improves over time as doctors learn how to treat the disease).

Deaths from COVID-19 vs Influenza

4. People who got moderate to severe disease from COVID-19 did not behave like people infected with other coronaviridae. This was, for all intents and purposes, a totally new disease. The complications were new and unpredictable, the best treatments and the best drugs were a question mark for the first 4 months, and the disease course was totally unfamiliar to us.


5. Drugs that worked against other coronaviridae were hypothesized (such as zinc) but were not know to work against COVID-19, so unlike influenza, we had pretty much no drugs known to be effective against COVID-19.


6. Vaccine development was started as soon as it was understood what we were dealing with, but unlike influenza, we have never made a vaccine to a coronavirus before, so we had less existing vaccine infrastructure to get us off the ground.


7. It was not predictable who would be immune to the virus, even those who had antibodies to other coronaviridae could still get the virus. Unlike H1N1, the elderly were not immune.


8. Initial data on masks was questionable (largely because we were basing our ideas on data based on influenza transmission), but over time it was found that masks and social distancing were more and more important in reducing viral spread.

My experience treating COVID patients

I’d like to tell you about my experience treating COVID-19 patients in the hospital. Now, let’s remember that anecdotes do not equate to evidence. What I was seeing may have been much better or much worse than what others were dealing with. What I say here is just the account of one senior resident physician who took care of patients on the hospital floor, and in our dedicated COVID ICU.


When COVID-19 first started being reported broadly in the press, I was in Uganda, and the cases were almost exclusively in China. By the time I made my way back to the U.S., there were increasing calls to begin social distancing. I remember, at the time, thinking that this was a large over-reaction. My only experience with the coronaviridae was when I was in medical school and I had learned that coronaviridae usually cause cold-type illnesses. It took my roommate (also a physician) making a public statement, and talking to me about the need for social distancing to get me on board. Even at that time; however, I remember social media posts abounding that ‘the flu kills more people every year’, and ‘cardiovascular deaths and cancer deaths per day are still greater than COVID deaths’, and ‘we haven’t even lost as many people as with H1N1, and they’re freaking out way more’. I even recall one of my bosses (a high level OB-Gyn) commenting that we were putting so much energy into making accommodations in the hospital for the feared influx of COVID patients, and were putting so many restrictions on activities despite the virus not yet causing as many deaths as H1N1. Physicians were not nearly uniform, initially, in their endorsement of social distancing, even though it seemed like almost everyone was worried about the PPE situation. Then, the cases started to mount. At the worst I saw it, my hospital had about 100 patients on the regular floors requiring oxygen just because of COVID-19, and about 20 people in the newly appropriated negative-pressure COVID Intensive Care Unit (ICU). At first, it was still kind of a distant experience for me though, because residents weren’t allowed to treat COVID-19 patients on the floor, and I had not been called to rotate in the COVID-ICU yet. It all changed when I joined the COVID ICU team. Now, keep in mind, I only served on that team for a week and a half. I had co-workers who were on the COVID-ICU team for an entire month, sometimes two months. Whatever experiences I had pale in comparison to what they lived.


The biggest problem with the COVID that I saw was that patients who needed hospitalization often had long stays. Some patients had been intubated and in the ICU for an entire month. I ended up feeling that one of the blessings of other diseases and pathologies was that people would ‘declare themselves’- they would often show clear signs that they were going to die soon, or that they would get better. COVID didn’t act like that. People would go the COVID ICU because they needed BIPAP or CPAP (non-intubation methods of helping people breathe), and they would get worse and need to be intubated, and then their organs would start to fail one by one. But you could never tell who would slowly get better, who was going to die despite your best efforts, and who was going to be stuck unconscious, probably uncomfortable, lonely and without any human dignity for a month at a time before they eventually died or made some minimal recovery that let them leave the ICU. We couldn’t allow visitors in the COVID ICU, so I would video conference with patient families while in my PAPR suit (basically like a HAZMAT suit but with a filtered air supply) and show them their loved ones just so they could talk to them in their drug-induced slumber. These people with bad COVID were in a completely unrecognizable form – people with wires and tubes, surrounded by machines; honestly, it was awful. We had several young people die, several people who were previously healthy leave the COVID ICU having suffered strokes from effects of the virus, or worse yet because of the therapies we were giving them, we had tens of people who had normal kidneys before who needed dialysis now, and frequent death in the elderly. To be clear – there were people who made great recoveries and left the COVID ICU a little debilitated but otherwise ok, but there were many, many more who suffered a great deal before leaving the ICU in very bad shape with new chronic health conditions from their stint with COVID.


Because of my experience, I am personally in the camp that believes that every prevented COVID-19 ICU hospitalization is a victory.

Dr. Sana Zekri, MD is a Family Medicine with Obstetrics Physician. His particular interests are in public health, global health, women’s health and working towards justice in medicine. He is currently an Assistant Clinical Professor at SUNY Upstate, in Syracuse, New York. The views expressed on this website do not necessarily reflect the official views of the author’s employers or affiliated institutions.

That Newsweek Article: Review of Yale Epidemiologist’s Key to Defeating COVID

That Newsweek Article: Review of Yale Epidemiologist’s Key to Defeating COVID
By Kristen Panthagani, PhD

After writing about Dr. Stella Immanuel’s viral video, the most common request I got was to assess this Newsweek Opinion piece circulating by Dr. Harvey Risch, a Professor of Epidemiology at Yale, claiming that we already have the key to defeating COVID (hydroxychloroquine), and we need to start using it. So let’s assess his argument and see if it holds merit.


He argues that hydroxychloroquine has proven to be effective against COVID, in particular when it is given early on in the disease course and when combined with azithromycin (or doxycycline, another antibiotic) and zinc. This is based on 5 studies summarized in his publication in the American Journal of Epidemiology (AJE) Early Outpatient Treatment of Symptomatic, High-Risk Covid-19 Patients that Should be Ramped-Up Immediately as Key to the Pandemic Crisis and 7 more studies published in a follow-up letter. He additionally points to two examples of correlation between hydroxychloroquine prescription and mortality rate in Pará, Brazil and Switzerland. He further argues that the reason that other studies have not shown benefit is that they were not used in the proper setting: it should be given early in the course of disease to high risk patients (although he does point to two studies done in hospitalized patients that show benefit; so it seems he is also arguing that there is some efficacy even when the drug is given later in the disease course after patients are already quite sick).


Now, let me clarify the purpose of this blog post. My goal is to evaluate Dr. Risch’s claim that we already have evidence that this treatment is effective based on the studies he has cited. He is not saying ‘maybe this works let’s study this more,’ he is arguing that we already have enough evidence to show that it works (at least enough evidence for a pandemic setting), so we need to start prescribing it now. Therefore, my goal is to evaluate that claim. Do we have enough evidence to show that it works? Do the studies he cited truly demonstrate efficacy of the drug(s)? Can we reliably say that the hydroxychloroquine drug combo is the “key to defeating COVID-19” based on the data he cited? These are the questions this post is tackling, not whether or not more hydroxychloroquine combo studies are warranted. That is another discussion for another day.


First, let’s nail down what treatment combo he is saying is effective. In his Newsweek editorial he seems to argue that hydroxychloroquine + azithromycin (or doxycycline) + zinc + given early (outpatient) + high risk patients is the best combo. To clarify a few terms — outpatient describes patients who are treated outside of a hospital (like at a clinic/doctor’s office), and inpatient describes patients who are admitted to a hospital for treatment (i.e. they have their own bed). Generally, outpatient patients are less sick / early in their disease course, and inpatients are quite sick / later in their disease course. ‘High risk’ is not explicitly defined, but he seems to mean patients that are older or have underlying conditions (those who are at higher risk of dying from COVID). However, given that the criteria for what ‘high risk’ means isn’t precisely defined, I’m not going to try to determine if the studies he cites really study a ‘high risk’ population, as without clear criteria of precisely what that means it becomes a bit subjective.


While he strongly advocates for use of the hydroxychloroquine drug combo for outpatients, his comments about use of hydroxychloroquine alone or in hospitalized patients are conflicting… sometimes he says that data is irrelevant: “Evidence about use of hydroxychloroquine alone, or of hydroxychloroquine+azithromycin in inpatients, is irrelevant concerning efficacy of the pair in early high-risk outpatient disease” and sometimes he seems to cite the data as evidence to support his argument “Even so, it has demonstrated significant benefit in large hospital studies in Michigan and New York City when started within the first 24 to 48 hours after admission.” So his opinion on hydroxychloroquine +/- other drugs in a hospital setting (very sick patients) is a little unclear, but perhaps he thinks there is some benefit. Additionally, in his original publication in the AJE, he doesn’t emphasize the importance of zinc: “all of these reviews have omitted the two critical aspects of reasoning about these drugs: use of HCQ combined with AZ or with doxycycline, and use in the outpatient setting,” but in his Newsweek editorial he does. So for each study below, I will evaluate three different scenarios:


  1. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + zinc + given early (outpatient) has a clinical benefit?
  2. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + given early (outpatient) has a clinical benefit?
  3. Does this study provide reliable evidence that hydroxychloroquine (any combo, any population) has a clinical benefit?

One of the main things we learn to do in MD-PhD training is to evaluate study design. One of my favorite classes in grad school was “Method and Logic,” where we ripped apart studies and evaluated whether or not the data they provide actually supports the conclusions they made. Before taking that class, I naively thought that nearly every scientific paper’s claims were reliably supported by their data. But that is not true — many do support it (perhaps with a few minor weaknesses), but a surprising number have significant and sometimes severe methodological flaws. It is our job as scientists to not just blindly accept the conclusions provided by the authors of the study, but to see if their data and their study design really support the conclusions they are making. So that is what I’m going to do with the studies cited by this epidemiologist.


Before we look at the studies, let’s talk about a few things that are essential for a scientific study. Note I’m not even talking about the strengths of different types of studies (observational versus randomized), I’m talking about what are basic criteria that any type of study needs to have in order to be considered valid. These are more like bare minimum standards:


1. The details of the data are made available.

2. We know the people being studied actually have the disease we’re trying to study.

3. Patients aren’t eliminated from analysis because they got sick or died.

4. The statistics are sound.

5. There is an adequate control group.


What is an ‘adequate’ control group and why is it important? A study must have a control group (a similar group of patients who did not get the treatment) to know if any benefit you see is actually from the drug(s) and not from something else about the population you are studying. As a hypothetical example, if you did an analysis of lollypop consumption in all COVID patients, you would likely find that it is associated with good COVID outcomes. Would that mean lollypops cure COVID? No, it just means that children are more likely eat lollypops than adults, and for reasons unrelated to lollypop consumption, they are also less likely to get severely sick from COVID. This example is obvious because nobody actually thinks lollypops can cure COVID, but similar things can happen with drug treatments. Maybe doctors were more likely to give hydroxychloroquine drug combos to patients who were less sick? Or more sick? Or maybe the particular hospital or clinic giving the drugs serves a different demographic of patients with different underlying conditions? All of these can impact clinical outcomes, thus an adequate control group is essential to make any meaningful conclusions about whether or not a drug really works. So what do I mean by ‘adequate control group?’ I mean that we can be reasonably convinced that the patients in the control group are similar enough to the patients in the treatment group that we can think of them as roughly equal groups of people (at least, equal in terms of factors that impact how sick they become / their risk of dying).

Now, here are the studies:

Citation #1: Hydroxychloroquine and azithromycin as a treatment of COVID-19: results of an open-label non-randomized clinical trial

Type of Study: Observational

Outcome: Positive for Virus after 6 days

Sample Size: 36 patients

Hospitalized or Outpatient: Mix*

Treatment Regimen: Hydroxychloroquine +/- Azithromycin

Summary: This was the first hydroxychloroquine study to get a lot of attention, published by Dr. Raoult back in March. They gave hydroxychloroquine +/- azithromycin to 20 people, and included 16 people that either refused treatment or were from a different medical center as controls. They tested for the presence of the virus in nasal swabs, and concluded that after several days, those who got the treatment were more likely to test negative for the virus.


There are many issues with this study and I will not be able to address all of them (check out this post for a more detailed discussion of some of them.) However I will highlight what I consider to be the biggest flaw: there were originally 26 people in the hydroxychloroquine (+/- azithromycin) group, but 6 of those people weren’t included in the final data analysis. Why? Because they didn’t finish the treatment. Why didn’t they finish the treatment? Because one got too nauseous from the medication, one went home, three were transferred to the ICU (meaning they got very, very sick), and one of them died. That means 15% of people who got the drug(s) had very bad outcomes (ICU or death), compared to 0% in the control group. If people are clinically deteriorating in the treatment group and not the control group, I don’t really care if there are differences in whether or not their nasal swabs were positive for the virus. Clinical outcomes are what ultimately matters, not nasal PCR tests. Notably, the person who died was PCR-negative the day before he/she died, which also makes me question whether or not the results of their PCR test (which is what their entire results are based on) are reliable and/or have anything to do with how sick the people were. Additionally, the control group was not a good control group. The controls were either children who were not sick, or adults from another medical center. Why does it matter if they were from another medical center? Maybe that medical center served a different demographic group and there were more people with pre-existing conditions. Maybe the PCR test used to measure the presence of the virus was different at the other medical center (from an earlier version of the paper, the way the PCR results were reported was different for some of the control subjects, making me think they used a different method. This is not good). In short, we know very little about how comparable the treatment and control groups actually were, and what we do know is not comforting. An experiment is only as good as its controls. You simply cannot conclude anything without adequate controls in your experiment.


*While the study states it was done it a “hospital setting”, it sounds like some in the control group may have not been hospitalized. Additionally, some patients in both the treatment and control group were asymptomatic, which makes me think they were not hospitalized.



1. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + zinc + given early (outpatient) has a clinical benefit?

No – not tested

2. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + given early (outpatient) has a clinical benefit?

No – not tested (not outpatient)

3. Does this study provide reliable evidence that hydroxychloroquine (any combo, any population) has a clinical benefit?

Not really – main outcome (viral shedding) was not a measure of clinical severity, and more patients in treatment group had bad clinical outcomes (ICU/death). Also lots of other problems.

Citation #2: Early Treatment of COVID-19 Patients With Hydroxychloroquine and Azithromycin: A Retrospective Analysis of 1061 Cases in Marseille, France

Type of Study: Uncontrolled

Outcome: Death, Clinical Worsening, Viral Shedding

Sample Size: 1061 patients

Outpatient or Hospitalized: Mixed (Inpatient and ‘day-care’ hospital)

Treatment Regimen: Hydroxychloroquine + Azithromycin

Summary: This study was run by the same group as Study #1. They gave hydroxychloroquine and azithromycin to 1061 patients and tracked a variety of parameters. However, this study has no control group to compare to (patients who did not get the treatment), so it does not provide any useful evidence about the effect of the treatment.



1. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + zinc + given early (outpatient) has a clinical benefit?

No – not tested

2. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + given early (outpatient) has a clinical benefit?

No – not exclusively outpatient, and no control group

3. Does this study provide reliable evidence that hydroxychloroquine (any combo, any population) has a clinical benefit?

No – no control group

Citation #3: Dr. Zelenko’s Clinical Protocol (google doc)

Type of Study: Uncontrolled

Outcome: Death/Hospitalization/Intubation

Sample Size: 405

Population: Outpatient, some high risk

Treatment Regimen: Hydroxychloroquine + Azithromycin + Zinc

Summary: This isn’t a publication, it’s a google doc by Dr. Zelenko describing his treatment regimen. He does provide a few sentences about the patients he has treated — he reports treating 405 cases in an outpatient setting that are either confirmed or suspected to have COVID. He argues that treatment should be started before the diagnosis is confirmed, so it is not clear whether every patient was eventually confirmed to have COVID or not. Two patients died, six were hospitalized, and four were intubated. It is unclear how robust his follow-up is to track the outcome of patients. There is no control group.



1. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + zinc + given early (outpatient) has a clinical benefit?

No – no control group, uncertain if patients had COVID

2. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + given early (outpatient) has a clinical benefit?

No – not tested

3. Does this study provide reliable evidence that hydroxychloroquine (any combo, any population) has a clinical benefit?

No – no control group, uncertain if patients had COVID

Citation #4: Empirical treatment with hydroxychloroquine and azithromycin for suspected cases of COVID-19 followed-up by telemedicine

Type of Study: Observational

Outcome: Need for Hospitalization

Sample Size: 636

Outpatient or Hospitalized: Outpatient (Telemedicine)

Treatment Regimen: Hydroxychloroquine + Azithromycin

Summary: This non-peer reviewed study evaluated patients who had mild COVID-like symptoms (but the diagnosis was not confirmed) in an outpatient setting (via telemedicine). All patients were offered hydroxychloroquine/azithromycin, and those who refused served as the control group (224 patients) while those who accepted were the treatment group (412 patients). They report lower hospitalization in the treatment group (1.9%) versus control group (5.4%). They also look at differences in how early patients in the treatment group started their treatment, and report that those who started it earlier (< 7 days from start of symptoms) were less likely to go to the hospital (1.17%) than those who started it later (3.2%).


On first glance this study looks much better than the previous two, as it includes a control group. This is not an ideal control group as there may be significant differences in people who refused treatment versus not, but it’s certainly better than nothing. However, as I dug into the study, I found a couple things that were pretty funky and didn’t quite add up. First, they do not actually confirm that their patients had COVID. They enrolled anyone with “flu-like symptoms,” and do not do any diagnostic testing. Some of the patients underwent CT scans, and they found results of 40% of scanned patients in the control group were suggestive of COVID versus 70% in the treatment group. Over half of the patients in the study were not scanned, so all we really know about them is that they had flu-like symptoms for at least 2-3 days and weren’t sick enough to go the hospital. Given that lots of viruses cause mild flu-like symptoms, it is very likely that not all of these patients actually had COVID. If the percent of true COVID patients was different between the two groups, this could invalidate the results.


There are also some funky things with their statistics. The main conclusion they report is that 1.9% of the patients who got the treatment went to the hospital versus 5.4% of those who didn’t get treatment, with a reported p-value of p < 0.0001. It is standard in studies like this to not only report the percentage of patients, but to also include the actual number of patients who were hospitalized in each group. The authors do this for most of their data with the exception of their main results, which I find odd. For their main results, they only report the percentage and a p-value. But assuming this is the percentage of the total patients in each group (which is what the study implies), we can calculate it: 412 x 1.9% = 8 hospitalized patients in the treatment group and 224 x 5.4% = 12 hospitalized patients in the control group. They don’t explicitly state which statistical test they use to get the p-value < 0.0001, but the only two tests included in their methods that are used for this type of analysis would be the Fisher Exact Test or the Chi-square Test. I ran these statistical tests on their data, and the p-values they return are p = 0.02 and p = 0.03, respectively. So something is very wrong, because that is a very different result than the p < 0.0001 they report. It is still modestly significant, but it gives me a significant pause as to how they ran their analysis and the transparency of their data. We additionally do not get the numbers for their analysis of hospitalization rates in the early versus late treatment groups. But thankfully, with a little high school algebra, we can back-calculate them as well based on a total of 8 hospitalizations in the treatment group and the percentages provided in Figure 2. This gets us 255 patients treated early (3 hospitalized) and 157 treated late (5 hospitalized). If we run a chi-square on this data, though they report p < 0.0001, the result is not significant (p = 0.15). This is very alarming; something is very wrong. Some of their p-values in Table 1 seem to have a similar problem.


In conclusion, my main criticism of this study is that we don’t know if all the patients actually had COVID, which really limits the conclusions we can draw from it. My secondary criticism is that some of the numbers and statistics don’t add up. If the authors could be more transparent in their data and calculations, that could solve the second problem, but not the first.



1. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + zinc + given early (outpatient) has a clinical benefit?

No – not tested

2. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + given early (outpatient) has a clinical benefit?

No – unknown if patients had COVID, stats questionable

3. Does this study provide reliable evidence that hydroxychloroquine (any combo, any population) has a clinical benefit?

No – unknown if patients had COVID, stats questionable

Citation #5: Long Island Long-Term Care Facility

Type of Study: Uncontrolled

Outcome: Hospitalization / Death

Sample Size: 45 (news report), 200 (unofficial update)

Outpatient or Hospitalized: Outpatient (Long-term care facility)

Treatment Regimen: Hydroxychloroquine + Doxycycline

Summary: This is a news report as well as personal correspondence about a long-term care facility who is giving hydroxychloroquine + doxychycline to its residents. It sounds like they are giving it to residents diagnosed with COVID (rather than prophylactically), but I am not certain, as there are not very many details provided. 5.6% and 4.5% of patients died according to the news report and unofficial update, respectively. This does not have a control group.



1. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + zinc + given early (outpatient) has a clinical benefit?

No – no not tested

2. Does this study provide reliable evidence that hydroxychloroquine + azithromycin (or doxycycline) + given early (outpatient) has a clinical benefit?

No – no control group

3. Does this study provide reliable evidence that hydroxychloroquine (any combo, any population) has a clinical benefit?

No – no control group

Citation #5.5: Doxycycline and Hydroxychloroquine as Treatment for High-Risk COVID-19 Patients: Experience from Case Series of 54 Patients in Long-Term Care Facilities

Type of Study: Uncontrolled

Outcome: Hospitalization / Death

Sample Size: 54

Outpatient or Hospitalized: Outpatient (Long-term care facility)

Treatment Regimen: Hydroxychloroquine + Doxycycline

Summary: This is a non-peer reviewed case study (that I believe includes some of the same patients in Study #5, which is why I called this Study #5.5). Residents of a long-term care facility who were diagnosed or presumed to have COVID were given hydroxychloroquine + doxycycline. 11% went to the hospital and 6% died. This does not have a control group.



1. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + zinc + given early (outpatient) has a clinical benefit?

No – no not tested

2. Does this study provide reliable evidence that hydroxychloroquine + azithromycin (or doxycycline) + given early (outpatient) has a clinical benefit?

No – no control group

3. Does this study provide reliable evidence that hydroxychloroquine (any combo, any population) has a clinical benefit?

No – no control group

Citation #6: Outcomes of 3,737 COVID-19 patients treated with hydroxychloroquine/azithromycin and other regimens in Marseille, France: A retrospective analysis Type of Study: Observational

Outcome: Death / Hospital Stay ≥ 10 days / Transfer to ICU / Viral Shedding

Sample Size: 3737 patients

Outpatient or Hospitalized: Mixed — some Hospitalized, Some in Hospital “day-care” (don’t stay overnight)

Treatment Regimen: Hydroxychloroquine + Azithromycin

Summary: This is a study done by the same group as Study #1 and Study #2 in France. It is a retrospective study, meaning there was no design set up beforehand, rather they just looked back to see what happened with their patients. In that hospital they were trying to give hydroxychloroquine + azithromycin (HCQ-AZ) to nearly everyone with COVID, and most got that combo for ≥ 3 days (83%), some got it < 3 days (6%), some got just hydroxychlroqouine (3%), some got just azithromycin (4%), and some got neither (4%). They compare various outcomes between these groups. To make it simple, let’s look at their summary category: poor clinical outcome (Death, ICU, and/or Hospitalization ≥ 10 days). They looked at what percent of people in each of the groups above had at least one of these bad outcomes, and found people treated with HCQ-AZ ≥ 3 days had the fewest percent of people with a bad outcome (3.9%), followed by HCQ-only (7.9%), neither drug (8%), HCQ-AZ < 3 days (23.4%), and azithromycin only (27%). They sliced and diced the data quite a few ways, but going through all of that is beyond the scope of this post.


First off, after reading all the other studies cited, this one is a breath of fresh air. This is an actual study. It still has significant flaws/limitations, but they are the normal kinds of flaws and limitations we expect from science, not weird things like no control group, statistics that don’t add up, and dropping out people from analysis because they had a bad outcome. So what do we make of this study? How strong is the evidence that hydroxychloroquine/azithromycin works?


It is an observational study, meaning the authors didn’t assign people to study groups ahead of time, rather they just let life happen and then look back to see what happened organically. These studies are nice because they are easier to do than randomized trials, but they provide much weaker evidence than a randomized trial because there is no guarantee that people who got the treatment versus the control group are truly equivalent groups of people. And that is what we find in this study — while the people who did not get either drug had worse outcomes than those who got the HCQ-AZ combo for ≥ 3 days, those people were also older, had more underlying conditions (heart disease, high blood pressure), were already sicker, and a higher percentage of them were already hospitalized compared to the people who got HCQ-AZ ≥ 3 days. This could very likely explain why these people had worse outcomes, rather than any effect of the drug treatment. The authors do acknowledge this imbalance in the groups and try to correct for it by doing various types of adjusted statistical analysis. (Adjusted just means they controlled for some of these variables, then looked to see if there was still an effect associated with treatment). They report a significant benefit of HCQ-AZ for ≥ 3 days; however, when they did this analysis, they grouped everyone else together into one group (so those who got one drug, no drugs, or the drugs for < 3 days were all lumped together and treated equally). They likely did this in order to have enough subject numbers to run their analysis, which I can understand, but it also means it’s very hard to determine what the difference between drug(s) and no drugs really is, since it mixes a lot of different treatment regimens together. They do adjust for a score that is a measure of how sick the patients were before treatment (the NEWS score), which is a good step. Statistical “adjustments” like these are supposed to correct for skew between groups, but they are only as good as the clinical measure being used, and they’re not a guarantee that they’ll truly capture all the differences in severity between the two groups. Given all these considerations and caveats, I find the results of this study to be only modestly convincing. I’d give it a ‘hmmmm… maybe.’ There could be something there, but it’s also possible the results could be explained by factors other than the drugs.


Finally, this study does not seem to really be reflective of an outpatient setting / people early in their disease course. Some of the patients were hospitalized in inpatient units (they stayed overnight), and the rest were inpatients in the ‘day-care’ hospital. If they needed to stay the full day in the hospital, even if they didn’t spend the night, that makes me think they were probably sicker than somebody who is early in their disease and goes to their doctor’s office. But they do say many were “mild” cases, so it could be that some of the patients were comparable to an outpatient setting. However, the results aren’t analyzed separately for mild versus severe cases, so we can’t necessarily apply their results to mild cases only. This is important because Dr. Risch’s main argument is that this drug combo is effective when given early. We already have studies showing hydroxychloroquine + azithromycin doesn’t work when given to hospitalized patients, so if that is the group of people this study looked at, then we have to take into consideration all those other studies that say it doesn’t work as well.



1. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + zinc + given early (outpatient) has a clinical benefit?

No – no not tested

2. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + given early (outpatient) has a clinical benefit?

No – some patients were hospitalized, and no analysis of mild cases only was provided

3. Does this study provide reliable evidence that hydroxychloroquine (any combo, any population) has a clinical benefit?

Maybe? Something could be there, but results could potentially be explained by underlying differences between the study groups.

Citation #7: Dr. Crawford’s patients (radio interview)

Type of Study: Uncontrolled

Outcome: Death

Sample Size: 52

Population: Nursing Home

Treatment Regimen: Hydroxychloroquine + rehydration

Summary: 52 patients COVID-19 patients (it sounds like they were confirmed infections) at a nursing home were given hydroxychloroquine early on in their disease course, and they report 0% died. No more details are available, and there is no control group.


1. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + zinc + given early (outpatient) has a clinical benefit?

No – no not tested

2. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + given early (outpatient) has a clinical benefit?

No – no not tested

3. Does this study provide reliable evidence that hydroxychloroquine (any combo, any population) has a clinical benefit?

No – no control group


This study wasn’t actually cited in the NewsWeek editorial or linked articles, but I know someone is going to ask about it so I included it.

Study #8: COVID-19 Outpatients – Early Risk-Stratified Treatment with Zinc Plus Low Dose Hydroxychloroquine and Azithromycin: A Retrospective Case Series Study

Type of Study: Observational

Outcome: Hospitalization / Death

Sample Size: 518

Population: Outpatient (General Practice)

Treatment Regimen: Hydroxychloroquine + Azithromycin + Zinc

Summary: This is another study presumably run by Dr. Zelenko (he is the senior author) in which 141 risk-stratified COVID-19 patients were given the treatment regimen, and 4 were hospitalized and 1 died. As a control group, they use “Independent public reference data from 377 confirmed COVID-19 patients of the same community,” which had a significantly higher rate of hospitalization but not death. My big, big issue with this study is we do not have any information about the control group. We don’t know how sick they were, their age, their underlying conditions, etc. So while the hospitalization rate was higher in that group (15.4% vs 2.8%), we have no way of knowing if this has anything to do with the treatment. Maybe that reference group was already sicker? (Notably, subjects older than 60 didn’t even have to have symptoms to be included in Dr. Zelenko’s treatment group). Maybe the reference group was older? Maybe they had more underlying conditions? It is standard in observational studies to include a table comparing the relevant characteristics of the treatment and control groups to see if the groups are comparable, and that was impossible to provide in this study, since they had no information about the control patients. Overall, this makes the results essentially impossible to interpret.



1. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + zinc + given early (outpatient) has a clinical benefit?

No – adequate control group not provided

2. Does this study provide reliable evidence that hydroxychloroquine + azithromycin + given early (outpatient) has a clinical benefit?

No – adequate control group not provided

3. Does this study provide reliable evidence that hydroxychloroquine (any combo, any population) has a clinical benefit?

No – adequate control group not provided

Those are all the data I could find cited in Dr. Risch’s articles. In his letter, 6 other citations of results reported by “Personal Communication” are also included. Most are uncontrolled observations like #7 above, and as the data are not available, I have not included them here.


The end of the editorial also addresses two other claims of evidence: ‘natural experiments’ where regulatory changes in use of hydroxychloroquine +/- azithromycin or shipment of drug doses to the region is correlated with changes in regional deaths in Pará, Brazil and Switzerland. No links to the data are made available, so these claims are difficult to assess. But on a general level, there are so many variables that can affect case/death rates on a population level (lockdowns, mask use, social distancing measures, other changes in treatment, etc.) that it is not feasible to confidently attribute changes like these to a single event. Check out this website to see more examples of correlation ≠ causation.


So, what did we find? Overall, the evidence was very underwhelming. All but one of the studies failed to meet basic scientific standards of confirming the subjects actually had COVID, not dropping out subjects who got sick or died, including a control group, and having accurate statistics. Much of the data cited do not have a control group — some have argued it’s unethical to do a control group during a pandemic if we have enough evidence to say that the treatment works. However, this becomes a bit of a circular argument, because we can only know if it works with an adequate control group. (Or in reality, at least a couple studies with adequate control groups.) You can’t simultaneously argue that a study done without a control group was justified because we already know it works and also argue we know it works because of those same studies without control groups. In his AJE article, Dr. Risch tries to get around the need for a control group by making an estimate of the mortality in a similar population, but as Dr. Fleury points out in a published rebuttal to the article, this leads to many problematic assumptions. Simply put, it is very, very difficult to reliably ‘estimate’ a mortality rate that is truly reflective of a relatively small group of individuals (< 1000). There are so many variables that come into play that could affect that number. This is why a control group of people who are part of the same group of individuals being treated is essential. That is the most reliable way to assess what the mortality rate would have been without treatment. Control groups are Science 101. You have to have them, and it is very much possible to include them, even during a pandemic.


Dr. Risch does acknowledge at least some of the weakness of the studies (though not all that I have highlighted here) and argues this is acceptable because we are in a pandemic and don’t have the luxury of perfect data. He states “Each piece of evidence, contained in each study, must be carefully considered and not dismissed because in an ideal world such evidence would fall in a lower part of the evidence-quality triangle.” While it is true we are in a pandemic setting which makes everything more challenging, it’s not true that we are limited to very, very poorly designed studies. It is very much possible to do a reasonably good study, even during a pandemic — there are many that have already been done. We should (and will) give more weight to better designed studies and give minimal weight to the results of critically flawed studies. Yes, just as with non-pandemic times, no study is perfect, but some are far, far, far worse than others.


Based on the above discussed data, Dr. Risch argues that high-risk patients should get the hydroxychloroquine drug combo immediately upon clinical suspicion of COVID-19: ‘These medications need to be widely available and promoted immediately for physicians to prescribe.’ He is not saying ‘this might work let’s wait for more research’, he is saying that we already have enough data to justify prescribing these medications, presumably to all high risk patients with suspected COVID infection. However, all but one of the studies cited were critically flawed, failing to meet basic standards of scientific enquiry. Furthermore, most of these studies did not even test the proposed regimen (hydroxychloroquine + azithromycin + zinc + outpatient setting). Only one study was not critically flawed (Study #6), and does provide modest evidence of the efficacy of hydroxychloroquine + azithromycin. However, it was done in a hospital setting where the underlying conditions and disease severity were quite skewed between the treatment and control groups, and requires us to trust that the statistical methods used to adjust for these differences were fully adequate. Furthermore, this study was not done in an outpatient setting, many of the patients were not mild cases, and those that were mild cases were not analyzed separately. Thus, these results cannot be applied as evidence the drugs work for mild cases and should be consider along with the multiple studies failing to find an effect of this regimen in a hospital setting. Overall, the cited data is not nearly sufficient evidence* to declare hydroxychloroquine + azithromycin +/- zinc as the “key to defeating COVID-19” nor justify prescribing it to all high-risk mild/outpatient COVID cases, which over the coming months would amount to thousands, if not millions, of Americans. A higher standard of evidence is required* given the risk of side effects (even rare side effects become a significant burden once many, many people get the drugs) and the risk of breeding antibiotic resistance to azithromycin. We need a higher standard of evidence* to justify these risks, and that standard is attainable, even during a pandemic.


*This blog is intended to help people understand the scientific literature and is NOT intended to provide medical advice. Please consult with your physician for any questions about health concerns or medical treatments. The American College of Physician’s statement on hydroxychloroquine for COVID-19 can be found here.

Fact-check: Dr. Stella Immanuel’s hydroxychloroquine cure

Fact-check: Dr. Stella Immanuel’s hydroxychloroquine cure
By Kristen Panthagani, PhD

This morning I got a request to address one of the latest viral videos going around from a doctor claiming that we already have a cure for COVID-19: hydroxychloroquine, azithromycin, and zinc. While the video has been taken down on many platforms (and for the record, I have very mixed feelings about this type of censorship — that is a whole other discussion), it has rekindled the hydroxychloroquine fire set by Dr. Raoult back in March, the idea that hydroxychloroquine is the silver bullet for COVID-19, and all these masks and lockdowns are unnecessary. So let’s take a look at her claims and see if what she’s saying has any merit.

Her basic arguments are this:

1. She has treated over 350 COVID-19 patients with hydroxychloroquine + azithromycin + zinc, and none of them have died, therefore this treatment is a cure.


2. She and her staff and some other doctors have been taking this drug combo as prophylaxis and none of them have gotten sick, therefore it is also effective as a prophylaxis.

She then goes on to say that any study saying otherwise is fake science, it’s unethical not to give the drug now because people are dying, and doctors who are standing by, and not giving this treatment are like the ‘good Germans’ who stood by and let the holocaust happen. We’ll tackle some of these follow-up claims in a minute, but first let’s look at her dataset that she is basing these claims on.


Her argument is that treating 350 COVID-19 patients and all of them surviving is evidence that the treatment is a cure. Usually we want a control group to compare to, but it seems Dr. Immanuel believes this would be unethical, so we have none. This is big flaw #1 of her data set. But, let’s work with we what we got: whenever we look at any outcome in science, we always first look to see what is the probability of getting that outcome by pure chance. So what is the chance of having 350 COVID-19 patients in a row all survive? While the COVID-19 mortality rate is a tricky number to nail down, let’s use an estimate of 1% (i.e. on average, across the entire population, 1% of people who contract COVID die from it). If we look at 350 COVID-19 patients at random, the chance of having every one of them survive is (1-0.01)^350 = ~3%. Seems small right? Not necessarily — if we consider the fact that millions of people are getting this disease all across the country, the chances of this happening at least once becomes quite large. As of today there are ~4.38 million total confirmed COVID-19 cases in the US — if you broke all of those people into groups of 350 patients (that’s about ~12,000 groups of 350 patients), we would expect ~360 of those groups to be all patients who survive. So this result is expected to happen by chance 360 times across the US. This indicates her data set really isn’t strong evidence of anything, as the chances of this happening aren’t too improbable when you are looking at a disease that is so prevalent across the US.


But, as everybody knows, the mortality rate is highly dependent on the population you are looking at. So what patients is she treating? Are they representative of the average population? Is 1% mortality a reasonable estimate for them?


To listen to her talk, you might think she is working in a hospital taking care of very sick COVID-19 patients and miraculously seeing them all get better with the hydroxychloroquine combo. But she keeps using the word “clinic”.. which is not where sick hospital patients are treated. ‘Clinic’ generally refers to an outpatient doctor’s office or perhaps an urgent care center. So what “clinic” is she talking about?


After doing a little googling, I found that she works at Rehoboth Medical Center, which, though the name sounds like it might be a rather large medical operation, is in fact a walk-in clinic in a Houston strip mall.

Google street view of strip mall with “Rehoboth Medical Center.” This seems to be the right image — it matches the video on the clinic’s facebook page, where the name of the medical center appears to be added electronically.

Edit: earlier version of this post included an image a few shops down, which is what google pulls up for “Rehoboth Medical Center.” However, based on closer review of the clinic’s facebook video, I believe this is the correct image of the clinic.


So these are 350 COVID-19 patients who came to her walk-in clinic. This very much skews her data set. First, it means that the people she is studying are not very sick patients (because they are going to a walk-in clinic for treatment, not a hospital.) This is confirmed by the video on the clinic’s facebook page, where she says they “screen and treat mild cases of COVID-19.” The chances of having 350 mild COVID-19 patients all survive is much, much higher than the chances of 350 very sick hospitalized COVID-19 patients all survive. Second, and perhaps more problematic, it is unlikely that she is able to follow-up with all of her patients to see whether they did well or not. Is she regularly calling all the patients who came to her clinic to see if they went to the hospital and died? I guarantee you medical records are not coordinated enough for her to follow up with them that way. If she has a patient who comes in on Tuesday, gets his hydroxychloroquine/azithromycin/zinc combo, then falls very ill on a Friday and goes to a hospital across town, how would Dr. Immanuel know? Unless she is faithfully following up with every walk-in patient and has backup plans if those patients become too sick to speak on the phone, it is unlikely she could rigorously track whether or not her patients became sick and died. So in essence, it seems like Dr. Immanuel may be saying that nobody died at her walk in clinic, or called to let her know that one of her patients died. The fact that this happened for 350 people in a row now becomes highly, highly probable, not improbable.


And now her prophylaxis argument. She adds that masks are not necessary because we already have a COVID-19 prophylaxis: hydroxychloroquine + azithromycin + zinc. It is a little confusing watching the viral video of her making this claim and then watching the video on her clinic’s facebook page where she is encouraging everyone to wear masks, stay 6 feet away, and use hand sanitizer. But, let’s address her argument. She argues that because herself and her staff and some other doctors have used the hydroxychloroquine drug combo as prophylaxis and they haven’t gotten sick, that proves that the drug is effective as a prophylaxis for everybody. But how many staff does she have? Based on the picture of the clinic, this is a fairly small operation, and they likely only have a few staff. Maybe ~10 staff work there. That is a very small data set to make such a bold claim. She said ‘some other doctors’ are taking it too… how many other doctors is she referring to? We can only guess, but let’s say it’s as many as 20. The chances of 20 health care workers not getting sick from COVID, if they are wearing masks and other PPE as the staff in her clinic are in the video, is not that small.


In summary, her “evidence” that hydroxychloroquine/azithromycin/zinc is a cure and prophylaxis for COVID-19 does not hold up at all. We would expect these same results by pure chance.


Now let’s look at the details of a few of her other claims.


She argues there is a 2005 NIH study that says ‘it works.’

While there are numerous in vitro studies looking at the effect of hydroxychloroquine on various viruses, I guarantee you that whatever 2005 study she is referring to was not studying SARS-CoV-2, as the virus did not exist back then. I’m not sure what study she is referring to (perhaps it was this 2005 in vitro study of chloroquine efficacy against SARS), but please remember that different types of studies carry different levels of weight. In vitro studies are considered very, very preliminary, and you can’t conclude a drug works in humans just because it worked in an in vitro study.


She says the NIH knows that hydroxychloroquine works because of a COVID hiccup study. “If the NIH knows that treating a patient with hydroxychloroquine proves that hiccups is a symptom of COVID then they definitely know that hydroxychloroquine works.”

She says to google hiccups and COVID to see what she is talking about, so I did. This is the study that came up: it is a case report (description of a single patient) of a man in China who presented with hiccups as an atypical presentation of COVID. That man was given hydroxychloroquine, and his hiccups did go away. However, I hope this doesn’t need to be said — this is not a study, it’s a story about what happened to a single patient. You can’t make sweeping conclusions about the efficacy of a drug based on one patient. If that were true, then any single patient who got the hydroxychloroquine drug combo and died would be evidence that it’s 100% lethal. This is considered anecdotal evidence and is not proof of anything.


She says she sees people sitting in her office knowing that this is a death sentence.

This is a very dramatic claim for someone who treats mild COVID patients. Not everyone who gets COVID-19 dies. Yes, an upsetting percentage of them do… but “death sentence” is over the top.


She says there is no way she can treat 350 patients and they all live, but other doctors/scientists are going to tell her that they treated 20 people, 40 people and it didn’t work.

This, I believe, is her criticism of other studies showing that hydroxychloroquine doesn’t work, which she asserts are fake science. She seems to be arguing that she has the biggest study of hydroxychloroquine effectiveness, and that studies of 20 – 40 people aren’t strong evidence to show lack of efficacy. While she is correct that studies of 20 – 40 people aren’t very strong evidence, she is mistaken in thinking that this is the sample size of hydroxychloroquine studies to date. Here is a randomized controlled trial of 4716 patients showing no benefit of hydroxychloroquine treatment, and here is a meta-analysis of 26 different studies (including a total of 103,486 patients) showing no clinical benefit of hydroxychloroquine treatment (with or without azithromycin). These are two of the strongest studies we have on hydroxychloroquine for COVID-19 to date. Check out this post for more details, as well as other published studies on hydroxychloroquine +/- azithromycin for COVID-19.


She says you don’t need masks — there is a prevention and a cure.

Again, her data “proving” that the hydroxychloroquine drug combo works as a prophylaxis is based on herself and her staff and some unknown number of other doctors, which is not very many people. Here is a randomized double-blinded placebo-controlled trial of hydroxyhcloroquine prophylaxis (studying 821 people) demonstrating that hydroxychloroquine prophylaxis did not protect against COVID-19. Check out this post for more details on hydroxychloroquine prophylaxis studies.


She says that for all the doctors waiting for data — if 6 months down the road they find out the drugs work, its unethical not to have treated them now. She also compares doctors standing by watching patients die to the ‘good Germans’ standing by letting the holocaust happen.

No. First, we already have lots of data on hydroxychloroquine and COVID-19, and there is not strong evidence to suggest it works against COVID-19 (see studies in previous paragraphs). But even if we didn’t have this data yet — that doesn’t mean it would be unethical to withhold hydroxychloroquine treatment until we know if it works or not. The way doctors decide whether or not to give any treatment is by weighing the benefits versus the risks. For benefit, we look at the evidence that the drug works (which is very little). For the risks, we look at the side effects (which include risks of heart problems). If you have a drug that lacks evidence that it works and has side effects, it is not unethical to avoid prescribing it.


In conclusion, this doctor is making claims based on a deeply flawed data set and ignores the other studies on hydroxychloroquine that contradict her conclusions. This is not helpful. I am not sure why she is doing this — it is very possible that she genuinely believes what she is saying and is trying to get the word out. But that doesn’t make her arguments valid.


Disclaimer: This blog is intended to help people understand scientific concepts and is NOT intended to provide medical advice. Please consult with your physician for any questions about health concerns or medical treatments. The American College of Physicians’ statement on hydroxychloroquine for COVID-19 can be found here.


Edit: But what about the Yale epidemiologist’s Newsweek article calling hydroxychloroquine the key to defeating COVID? Read about that here.