If you’re vaccinated, why does it matter if I am not?

If you’re vaccinated, why does it matter if I am not?

By Kristen Panthagani, PhD

With the introduction of the COVID vaccine mandates, one common question has arisen: if vaccination works, then why should the vaccinated care if others don’t get the vaccine? If I’m vaccinated, then aren’t I protected? Why do I need my neighbor to be vaccinated, too? This line of thinking has led some to believe that the vaccines don’t actually work — if they truly worked, they argue, then those who are vaccinated shouldn’t worry about those around them choosing not to get vaccinated. If they are truly protected, then it shouldn’t matter if other people expose them to COVID, so the argument goes.

But, this line of reasoning falls into a type of ‘all or nothing’ thinking that doesn’t capture the reality of what is going on. It is simultaneously true that getting vaccinated helps protect me against COVID, and that it benefits me if my neighbor is vaccinated. These are not mutually exclusive.

There are three major reasons why your decision to get vaccinated impacts the people around you:

  1. Vaccinated people are less likely to transmit the virus to others.
  2. Vaccination helps reduce the risk of new variants developing.
  3. Vaccination reduces the burden on hospitals.

The COVID vaccines work, but aren't 100% perfect

“The COVID vaccines work.” This phrase has been stated over and over, but before we go any further, it’s important to clarify exactly what we mean by “work,” as the communication around this topic has been confusing. Does “the vaccines work” mean that if you get vaccinated, you’re guaranteed never to get COVID?


By saying that the vaccines “work,” we mean that the vaccines significantly reduce the risk of getting infected, hospitalized, or dying. We do not mean that they totally eliminate that risk. Vaccines, like all other medical interventions (and for that matter, most things in life) are not all or nothing. When we say seat belts “work,” we don’t mean that a seat belt is a guarantee that nobody will ever die in a car crash. We mean that compared to not wearing a seat belt, seat belts significantly reduce the risk of severe injury or death in a car crash. If someone was wearing a seat belt and still died in a car crash, we don’t instantly conclude that seat belts are worthless. We conclude that they help in many cases, but aren’t perfect, and this was one of the tragic exceptions.

So yes, the vaccines work, but they are not perfect. A vaccinated person is still at risk of being impacted by COVID, but that risk is much lower than if they were not vaccinated. For a more in depth discussion of breakthrough infections, check out this post

Reason #1: Vaccination reduces transmission.

Because vaccines are not a 100% guarantee of protection against COVID, my risk of getting COVID is not only impacted by my own vaccination status, but is also influenced by how many people around me are vaccinated. This can be a confusing concept, so here’s an example.

Let’s say there are two friends, Lincoln and Charlie (named after my adorable pets, pictured below.) Let’s say that Lincoln is vaccinated, and Charlie is not. Charlie and Lincoln walk into a room where, unfortunately, someone else with the delta variant had been coughing away moments earlier. There is lots of delta variant in the room, and they are both equally exposed to the virus.


All else being equal, who is more likely to develop symptoms? (To clarify, both are humans in this example.) Definitely Charlie, as he is not vaccinated. Assuming Lincoln got one of the mRNA vaccines, his risk of developing symptoms is ~60% lower than Charlie’s (range in studies is 39-84%), and his risk of ending up in the hospital is 75-95% lower than Charlie’s. Because Lincoln was vaccinated, his immune system had a head start fighting off the virus, so his risk of bad outcomes is much lower. It’s not zero, but it’s much lower. (Due to the range of estimates in the studies evaluating vaccine effectiveness against delta, as well as the strengths and weaknesses of each individual study, it is hard to put an exact number on vaccine effectiveness, which is why ranges are reported. In these types of studies, it’s normal to get some level of variation among the results from different studies. For a detailed discussion on the studies, I recommend reading through this.

In this situation, does the fact that Charlie is unvaccinated impact Lincoln’s chance of getting sick? No. I think this is where people are getting confused about how other people’s vaccination status impacts them. If I’m standing next to my unvaccinated friend breathing in SARS-CoV-2 particles from a different source, my friend’s vaccination status, in that moment, does not impact my risk of getting COVID from that exposure. That’s not how it works.

My neighbor's vaccination status impacts me

You may be confused, because aren’t people saying that the vaccination status of those around us impacts our risk of getting COVID? Yes it does. To see why, let’s follow Charlie and Lincoln’s story a little further. Charlie (unvaccinated) ends up getting COVID, and Lincoln (vaccinated) doesn’t. But Charlie doesn’t feel sick right away, and keeps going about his day. Pepsi, their friend, meets up with them for coffee. (Pepsi was one of my childhood cats. We had so many animals that we started running out of names, and began naming them after food products. She had an entire litter of kittens with soda-themed names.) While Pepsi, Charlie, and Lincoln are hanging out, Charlie is contagious, but isn’t showing symptoms yet. Lincoln is not contagious, because the vaccine gave him a head start beating the virus, and he killed it off very quickly. Charlie exposes Pepsi to the virus; Lincoln doesn’t. 

Vaccination reduces transmission (even for delta)

While multiple factors impact the likelihood that each person becomes infected and transmits the virus after an exposure, vaccination status plays a big role. It is true that vaccine effectiveness against infection and transmission is lower for delta than it was for previous variants. But even for delta, the vaccines reduce the risk of transmission relative to those who are unvaccinated. There has been debate about whether a vaccinated person with a breakthrough infection is just as likely as an infected unvaccinated person to transmit the virus. New data suggests that is not the case. Regardless, even if this were the case, vaccinated people are less likely to be infected in the first place, so they are overall less likely to transmit the virus. 

So in large part because Lincoln was vaccinated, he doesn’t expose Pepsi to the delta variant. And in large part because Charlie was unvaccinated, he does expose Pepsi to the delta variant. This is how Lincoln and Charlie’s vaccination status impacts the people around them. Those who are vaccinated are less likely to expose others. Those who are unvaccinated are more likely to expose others.

(You may be thinking, “but what about those who have already been infected with COVID and now have some level of natural immunity? How does that impact the risk of transmission?” It is certainly a fair question, but it’s a whole other complex topic of discussion, and one I hope to tackle in a future post.)

Having more COVID around is worse for everyone

But what if Pepsi is vaccinated? Isn’t she protected? Why should she care if Charlie exposes her, if the vaccine works? Again, because “works” doesn’t mean perfect. Her risk of a bad outcome from COVID is reduced because she’s vaccinated (just like Lincoln’s was), but it’s not eliminated. While her risk of getting COVID is lower because she is vaccinated, her risk would have been even lower if Charlie had not exposed her in the first place. Now that she is exposed, she may not get COVID at all, like Lincoln. Or she may only have a mild infection, but have to miss work. She may pass it on to her unvaccinated child or her immunocompromised sister. Or she may be one of those rare cases who does end up in the hospital. So even if a person is vaccinated, their likelihood of being negatively impacted by COVID is still influenced by the decisions of people around them.

To summarize this simply, there are multiple variables that impact a person’s risk of having a bad outcome from COVID, including 1. whether or not they are vaccinated and 2. how likely they are to be exposed to the virus. The first is entirely dependent on their own actions. The second is in part dependent on their own actions (choosing to social distance, wear a mask, etc.) but is also dependent on the actions of those around them.

We can see this clearly play out on a national level: where there are more unvaccinated people, there is more COVID. The graphs below show the number of COVID cases (left) and deaths (right) per 100,000 in each US county during the summer delta surge on the y-axis, and the percent of each county who was vaccinated at the beginning of the summer delta surge on the x-axis. Counties with lower vaccination rates had higher numbers of cases and deaths. I just spent a month in New England (which has a higher vaccination rate overall), and my risk of getting COVID there was much lower than my risk of getting COVID back home in Texas (which has a lower vaccination rate). My vaccination status did not change; the amount of COVID around me did. 

COVID cases and deaths data is from Johns Hopkins, vaccination data is from the CDC.

Vaccination data by state from Our World in Data.

Data from Johns Hopkins.

Reason #2: Vaccination helps decrease the risk of new variants

Increasing transmission is not the only way that the unvaccinated impact the vaccinated; getting vaccinated also helps reduce the risk of new variants developing. There has been a lot of confusion on this one, so let’s clear a few things up. 

First, where do variants come from? Variants arise from random mutations in the virus’ genetic code during replication. This is a normal process that occurs in every living organism. The molecular machines that copy the genetic code during replication are not perfect (and RNA viruses like SARS-CoV-2 are even more sloppy at copying), so every time a new copy of the virus is made, there is a new chance for mutations to arise. Not all mutations are bad. Some do nothing, some make the virus worse at being a virus, but a few rare ones will make the virus better at infecting people. The virus cannot “tell” which mutations are going to help it, and then mutate its genome accordingly. It’s just random chance. And the more times the viral genome is copied, the more mutations are produced, and the higher the chance a bad variant will arise.

Where does viral replication happen? Inside human bodies, when someone is infected. So where do new variants come from? Infections. More infected people = more viral replication = more random mutations = more chances for new variants.

Therefore vaccination reduces the risk of new variants arising because, if there are more vaccinated people, there will be fewer infections, which means there are fewer mutations happening. That means that the best way to reduce the risk of new variants arising is by reducing the number of infections. And vaccinations do that.

You may be thinking ‘but what about selective pressure? Don’t vaccines select for vaccine-resistant variants?’ Yes, but that’s not the whole story. Vaccines do not produce vaccine-resistant variants. Vaccine-resistant variants arise by chance and are more likely to arise in a world with fewer vaccinations, because there are overall more infected people (and thus more viral replication and mutations) in that world. But, if a vaccine-resistant variant were to arise because of the high number of infections happening due in large part to lack of vaccination, then yes, selective pressure may favor that variant to spread. But it’s essential to note that vaccination itself does not cause this variant to emerge.

Vaccines are not antibiotics

Some have argued that because vaccine-induced immunity doesn’t always completely destroy every last viral particle, the vaccines are prone to increasing the risk of developing variants that are vaccine-resistant. Some have likened it to antibiotic resistance — if someone has a bacterial infection and they take antibiotics but don’t fully kill off the infection, they are at risk of breeding bacteria that are resistant to that antibiotic. Is this true of the vaccine?

Thankfully, no. This comparison is not accurate because vaccines aren’t drugs like antibiotics, they’re tools that train an entire army of immune fighters. An antibiotic is like a single weapon that attacks the bacteria. The bacteria only need to figure out how to evade that single weapon to develop resistance. Vaccine-induced immunity is like an entire army with a diverse arsenal of weapons — we have a diverse set of antibodies as well as T cell-mediated immunity that attack the virus in different ways. Vaccine-induced immunity creates immune fighters that target different parts of the SARS-CoV-2 spike protein, so even if the spike protein mutates in one place to evade one set of immune fighters, there are other immune fighters that will still be effective. Said another way, vaccine-induced immunity is like taking many different antibiotics simultaneously, not just one. In order to develop resistance during infection, the virus would have to evade all of these at once. This is highly unlikely. So do not worry that getting vaccinated is going to speed up the development of vaccine-resistant variants. It will do just the opposite. Check out these explanations by Edward Nirenberg and Dr. Angela Rasmussen for more details.

Reason #3: With fewer COVID hospitalizations, there are more hospital beds for everyone else

And finally, one of the most critical reasons why my neighbor’s vaccinations status impacts me: hospital capacity. As delta has swept through the country, hospitals have been filling up with predominately unvaccinated patients. This has pushed some hospitals over capacity, requiring them to triage care, cancel surgeries and procedures for other patients, and be unable to provide the full quality of care to other patients needing it. In some cases, the issue isn’t physical space, it’s lack of qualified staff to care of patients.  This summer, one in four ICUs in the United States were over 95% capacity. Hospitals have had to send patients hundreds of miles away to find an open ICU bed. And it’s not only COVID patients who are impacted; when the hospitals are full, it impacts everybody’s care. A man in Texas died from gallstone pancreatitis (a treatable condition) due to a delay in care, as they searched across multiple states to find an open ICU bed. A man in Alabama with a cardiac emergency was turned away from 43 ICUs before being transferred out of state. He finally found an ICU that would accept him, but died shortly later. Stories of overwhelmed ICUs across the country have been commonplace this summer. Some ICUs became so full that they allowed “crisis standards of care”, meaning that instead of treating everybody as they normally would, when resources are scarce, they have to choose which patients they are going to treat based on who is most likely to survive. This is not normal. Hospital resources, including beds, equipment, and trained staff, are not infinite, and they can be (and have been) overwhelmed. By choosing to not get vaccinated, the unvaccinated are greatly impacting their community with their choices.

The decision not to get vaccinated impacts both the individual making that decision as well as those around them. If someone chooses not to get vaccinated, they are more likely to transmit the virus to other people, they are more likely to use healthcare resources, and they are more likely to have the virus replicate inside them, which collectively increases the probability of a new variant arising. So even though I am vaccinated, my life is still impacted by the decisions of people in my community.

For answers to common questions about COVID vaccines, check out this page.

Visualizing contagion: how infectious is delta?

Visualizing contagion: how infectious is delta?
By Kristen Panthagani, PhD

This week’s CDC announcement came with a new revelation about delta: it’s even more contagious than we previously realized. 

The Washington Post reported the details of an internal CDC document which included new data on the R0 estimate for delta. R0 is a measure of how contagious a pathogen is — it measures how many people, on average, a single infected person will go on to infect. For example, an R0 of 2 would mean that on average, every infected person will infect 2 more people. R0 does not tell you how deadly the pathogen is; it only tells you how easily it spreads. The original SARS-CoV-2 virus was estimated to have an R0 of 2-3, making it far more infectious than the seasonal flu (R0 ~1.3). 

So just how contagious is delta? The CDC estimate says the R0 of delta is somewhere between 5 and 10. This puts delta in the range of the super infectious pathogens like chickenpox. (And, yes I am old enough that I actually *had* chickenpox. It was the worst. I have never been so itchy in my life.)

This jump in contagiousness between the original SARS-CoV-2 strain and delta may not seem like much: 5-10 isn’t that much higher than 2-3, is it? 

Unfortunately, it is. When it comes to R0, we’re not talking about linear growth, we’re talking about exponential growth. This is often hard to wrap ones head around, so here are some visualizations to help illustrate the difference. In each of these visualizations, one red dot is one new infected person.

Seasonal Flu

Original SARS-CoV-2 Strain

(Conservative Estimate)

(Less Conservative Estimate)

Why is delta so contagious?

Delta tricks human cells into making way more copies of itself compared to previous variants. One study revealed that the viral load (number of viral copies) is 1,000 times higher for delta compared to the original SARS-CoV-2 strain. Viral load plays a big role in whether or not a person gets sick: if a person is exposed to higher levels of the virus, they are more likely to become infected.

Is the R0 fixed?

Thankfully, no! We can reduce the R0 with our behavior. While inherent properties of the virus are part of what determines R0 (such as the amount of viral copies a given variant produces), human behavior also influences R0. Vaccination, masks, and social distancing all make it less likely than an infected person will transmit the virus to others, which means that these behaviors reduce the R0 in real world settings.

Do the vaccines still work against delta?

Thankfully, yes. While some breakthrough infections are expected, so far the data shows that vaccination significantly reduces the risk of symptomatic disease and death, even for delta. Check out this post for more details on breakthrough infections and vaccine efficacy against delta.

Would you like to share these visualizations on social media? You can share them on twitter, facebook, or instagram below (or just share this post!)

A major ivermectin study has signs suggestive of scientific fraud

A major ivermectin study has signs suggestive of scientific fraud
By Kristen Panthagani, PhD

Today is ‘World Ivermectin Day,’ a day to celebrate one of the most important antiparasitic drugs used to treat neglected tropical diseases, and the controversial drug many claim is an effective treatment for COVID-19. So here is a crazy story for World Ivermectin Day: one of the largest randomized-controlled trials to date showing benefit of ivermectin for COVID-19 has signs suggestive of scientific fraud.

It looks like the data may have been purposefully altered or fabricated. At the very least, as it stands, it cannot be trusted as a valid scientific study.

This story was discovered and originally reported by Jack Lawrence, Gideon Meyerowitz-Katz, and Nick Brown; please check out their posts for full details.

Briefly, here’s the story.  ‘Efficacy and Safety of Ivermectin for Treatment and prophylaxis of COVID-19 Pandemic‘ by Elgazzar et al. was a randomized controlled trial of ivermectin for COVID-19 from Egypt. It was never peer-reviewed and was posted on a preprint server in November 2020. The results claimed a huge benefit of ivermectin for COVID-19. But some of the details of the study (in particular the details of how patients were randomized) were incomplete, making the study overall poor quality or “high risk of bias.” If you are confused about why there is so much conflict over ivermectin, this essentially explains it right here: many who believe ivermectin works are looking at the promising statistical results reported by this and other studies, and those who remain skeptical are looking at the holes in the study methods and other technical issues, noting that these issues mean we can’t be confident in those promising statistical results. 

That is where this story stood for several months: scientists looked at this and other ivermectin studies and said ‘well, that’s interesting, but there are some major issues in the study designs, so we need better designed studies before concluding ivermectin is a beneficial treatment for COVID-19.’ This is in agreement with the current NIH recommendations: we can’t say definitively that ivermectin does or does not work for COVID-19, and we need a high quality trial to answer this question. The FDA issued this warning against taking ivermectin for COVID-19.

But no one suspected fraud at all — many studies are “poor quality” without being fraudulent.

In a more recent version of the Elgazzar preprint, a link to the raw data used for the study was made available. But – that raw data (an excel file) was password protected, so it couldn’t be readily accessed by the public.

Recently, Jack Lawrence, a biomedical science masters student and independent journalist, was assigned the Elgazzar preprint as a part of an assignment for his masters degree, and noticed that parts of the paper appeared to be possibly plagiarized from other sources. He found this concerning, and started digging a bit. He found the password protected raw data linked in the new version of the preprint. He decided to make an attempt at guessing the password and succeeded: it was 1234.

With the raw data now available, the many, many problems with this study, in part hidden by the vague methods reported in the paper, became evident. First, data from different patients matched nearly identically and appeared to be duplicated, as if someone had simply copied and pasted data from one set of patients into another. Second, the raw data did not even match all the results presented in the study. Many patients were listed as being hospitalized and dying before the study was reported to have begun. There were numerical patterns that are suggestive of human interference (humans are bad at making up random numbers, and there are telltale signs when they try to do it). And there were suspect statistics reported in the paper itself. I won’t go into all the details here, but please check out these posts to learn more [1, 2, 3]. In summary, this is really, really, really bad.

This story is not new. Last summer when I started digging into hydroxychloroquine studies, I found that one fairly commonly cited study had some statistics that simply didn’t add up (citation #4 here). For example, one of the analyses compared outcomes of patients who had gotten hydroxychloroquine early versus later in their disease course. The p-value reported by the paper was p < 0.0001 (very significant). The raw numbers used to calculate this were not made explicit in the paper (which is somewhat unusual), but with a little high school algebra the values could be back-calculated, and running the statistics on those values yielded p = 0.15 (not significant). That’s not good. That study was also posted as a preprint (not peer-reviewed), yet it was being used to advocate that hydroxychloroquine be widely prescribed for COVID-19. While peer-review is not perfect (and is not designed to catch fraud), these examples illustrate how important it is to have people digging into the numbers, especially when the results of the study are being used to advocate drugs be prescribed to millions of people.

So, what does this all mean for ivermectin? It’s almost always impossible to say what happened for certain in these cases, but at the very least, with these data issues now apparent, it is clear we can no longer rely upon the results from the Elgazzar study. And this was the largest randomized trial to date on ivermectin, which was driving the results of many of the meta-analyses assessing ivermectin for COVID-19. Gideon Meyerowitz-Katz, an epidemiologist, re-ran the meta-analyses after removing this study (and including a new, recently published study) and found the previous meta-analysis results showing benefit of ivermectin for COVID-19 are dramatically reduced or disappear.

As has been said all along, many of the studies we have on ivermectin for COVID-19 to date are low quality, so to really know if there is or is not a benefit, we need a well-designed randomized trial (which are ongoing now). Some have asked why the medical community readily adopted dexamethasone as a treatment for COVID-19 after only one major study assessing it, but have not adopted ivermectin despite many studies assessing it. The answer is the RECOVERY trial (which assessed dexamethasone for COVID-19) was very well designed, so we can be confident in its results. It’s not necessarily the number of studies that matters, it’s also the quality of those studies that are taken into consideration. So we will have to wait until the results from a well-designed randomized controlled trial are available to know if there is any benefit of ivermectin for COVID-19. 

But many have claimed that we already know ivermectin works and need to give it now (and some have argued that it can be used instead of vaccination.) The data does not support these claims. This whole saga feels like a repeat of last summer when hydroxychloroquine was touted as “the key to defeating COVID” based on extremely low quality evidence, and illustrates how important it is to have robust data backing medical decision making. This is the essence of evidence-based medicine: it is imperative that we know the drugs and treatments we give patients are actually helping them. That’s what separates medicine from wishful thinking.

Are COVID vaccines killing thousands of people?

Are COVID vaccines killing thousands of people?
By Kristen Panthagani, PhD

Rumors have been circulating claiming that the COVID vaccines have killed thousands of people.  These claims are often based on reports to the Vaccine Adverse Events Reporting System (VAERS), a system designed to track ongoing safety of all vaccines and assess for side effects that were too rare to be detected in clinical trials. Anyone can file a report to VAERS: any person who experiences any negative health event after receiving a vaccine can report it, whether or not they know if the vaccine truly caused it. And this system works — it has caught rare side effects for other vaccines, which in turn were used to refine vaccine recommendations. 

Correlation ≠ Causation

Deaths after COVID vaccination have been reported to VAERS (the reports are freely available, you can download them and see for yourself). Does this mean that the COVID vaccines caused all these deaths?

No. VAERS reports can be made regardless of whether or not the vaccine had anything to do with the negative health event. Someone could die in a car accident after getting the vaccine, and that could be reported to VAERS. That does not mean that a vaccine caused a car accident. Someone could also die of a heart attack shortly after getting vaccinated, but this wouldn’t necessarily mean the vaccine caused the heart attack, as heart attacks happen frequently, independently of vaccines. 

Then how do we tell if VAERS reports are actually connected to the vaccine? By analyzing the reports (and getting more information as needed, as the VAERS reports provided pretty limited information), and then comparing the rate of the reported health event to background levels in the population. Let’s look at miscarriages as an example. Miscarriage is quite common: an estimated 1 in 8 pregnancies ends in miscarriage. If you tracked 80 different pregnant women, about 10 of them would report miscarriages due to natural causes. Now let’s say all of those 80 woman got vaccinated. You would still expect about 10 of them to have miscarriages for reasons unrelated to the vaccine. But to each individual, the reason for miscarriage is often unknown, so those women might report their miscarriages to VAERS, uncertain if the vaccine caused it or not. Because millions of people are getting vaccinated right now, we expect that some negative health events will happen near the time of vaccination, just due to chance. (It would be a statistical anomaly if this didn’t happen.) But we also want to be on the look out for true vaccine side effects. The way we tell the difference is by looking at the VAERS reports and seeing if the rate of reported health events is higher than normally expected. If it is, then that tells us the negative health event might be connected to the vaccine. If it’s not, then that tells us that these VAERS reports are likely capturing background levels of that health event, unrelated to the vaccine. And so far, that is what we see for miscarriages: rates of miscarriage after vaccination are not above expected background rates in the population.

What does analysis of the VAERS data say about deaths?

So what does analysis of the VAERS data show about deaths after vaccination? Is there any indication in the data that these deaths might be truly linked to the vaccine? Here’s what the data shows:

More than 302 million doses of COVID-19 vaccines were administered in the United States from December 14, 2020, through June 7, 2021. During this time, VAERS received 5,208 reports of death (0.0017%) among people who received a COVID-19 vaccine. FDA requires healthcare providers to report any death after COVID-19 vaccination to VAERS, even if it’s unclear whether the vaccine was the cause. A review of available clinical information, including death certificates, autopsy, and medical records, has not established a causal link to COVID-19 vaccines. However, recent reports indicate a plausible causal relationship between the J&J/Janssen COVID-19 Vaccine and TTS, a rare and serious adverse event—blood clots with low platelets—which has caused deaths.” (Source: CDC)

Doctors are required to report

Finally, it is important to note that health care professionals are required to report any death that occurs after vaccination to VAERS, whether or not the death could be plausibly connected to the vaccine. This has led to VAERS “death reports” like this one (an actual VAERS report):

Jan 2021, Female, age 90:

“At the time of vaccination, there was an outbreak of residents who had already tested positive for COVID 19 at the nursing home where patient was a resident. About a week later, patient tested positive for COVID 19. She had a number of chronic, underlying health conditions. The vaccine did not have enough time to prevent COVID 19. There is no evidence that the vaccination caused patient’s death. It simply didn’t have time to save her life.”

Clearly, this death was not caused by the vaccine; her death was caused by a COVID outbreak in her nursing home during the surge in January. But because the death occurred shortly after vaccination, the doctor was required to report it to VAERS. This is one of the ‘VAERS death reports’ being used as evidence that the vaccines have caused thousands of deaths. 

Do you remember several months ago when many were claiming that COVID deaths were over-counted? They argued that elderly patients who were already sick and about to die just happened to test positive for COVID, and then they were counted as a COVID death, but COVID wasn’t truly the cause. (This wasn’t true, by the way.) This argument has now flipped a complete 180 with the vaccines, as people are now assuming that anybody who has had a vaccine and then died, the vaccine must be the cause, regardless of their other medical problems or circumstances of death. Both of these are overly simplistic thinking, and both ignore how cause of death is determined.

In summary, when people are saying that there have been “thousands of deaths from the vaccine,” they are logging onto VAERS and counting up the “deaths”, and assuming that they were all caused by the vaccine, without actually analyzing the data (or in the case above, even reading what the report says.) This is not how the reporting system works, and it is not good science.

What is MD-PhD training (and why am I still a student?)

What is MD-PhD training (and why am I still a student?)
Kristen Panthagani, PhD

Hello!!! It has been a little while since I’ve written anything as I have OFFICIALLY GRADUATED WITH MY PHD!!!! (And am now back in medical school). You may now call me Dr. Panthagani.

While more COVID science explanatory goodness will be coming soon, I thought I’d write a quick post explaining what it is I do, as I hope that this pandemic has shown the importance of science and medicine and maybe has inspired a few students to pursue careers in biomedical research. While most people are familiar with medical school, I have found that many people are confused about what MD-PhD training actually is, why it takes so long, and why it’s worth doing. So here it goes: what exactly is an MD-PhD program and why, despite being in school my entire life, do I find myself in my 30’s but am stilllllll a student?

Very simply, an MD-PhD program is a combined program where you do both medical school (the MD part) and graduate school (the PhD part). Neither of those are shortened beyond the standard time it takes to do them individually, so MD-PhD programs are quite long (8-9 years). A 4-year undergraduate degree is required to apply, so all together (if you don’t take any breaks), it’s 12-13 years of post-secondary education, not including residency.

When I moved to Texas to start my program (waaaay back in 2012), most of my friends didn’t really understand what I was doing, so at my going away party I drew out this diagram (like the nerd that I am.)

Why on earth would anyone sign up for this? A fair question. The reason these programs exist is to train people to become physician-scientists: people who have two very different skill sets in the same brain: 

          1. how to take care of patients (the MD part) and

         2. how to do scientific research (the PhD part)

While deeply interconnected, the jobs of science and medicine are very different. While medical school provides some training in how to evaluate medical studies, the majority of the time is spent learning the details of all the different diseases, how to take patient histories and do physical exams, how to navigate hospitals and clinics, and how to take care of real breathing patients. Learning how to do scientific research and design experiments is not at all the focus of medical school. Contrast that with graduate school, where all you do all day every day is scientific experiments. There are no patients to care for, only test tubes, data analysis, and the broken hearts of crushed hypotheses. By the end of medical school, you have a good idea how to take care of patients (well… sort of, you really hone that in residency). By the end of graduate school, you know how to do a major scientific study.

Simplifying this to meme format, this is med school:

And this is grad school….


So where am I on this road of endless studentship? I am 8 years in (12 years including college). I did a bachelors in Chemistry, then the first two years of med school, then went to grad school for 6 years and just finished my PhD, and am now back in med school to finish it up. Then, on to residency.


So, why do MD-PhD programs exist? Why would somebody need to learn how to do both medicine and scientific research? Isn’t one enough? Well, yes. It’s not like everyone needs to do both, but there are some advantages to having both of those skill sets in the same person. For people who want to do medical research, a deep understanding of the details of how to do research is obviously essential (study design, appropriate controls, appropriate statistics, etc.), but it is also very helpful to have a broad understanding of medicine, a deep clinical understanding of the disease that they’re studying, and also knowledge of more practical things like the details of how hospitals work and what’s feasible from a patient’s perspective, etc. That’s where the MD part comes in. The idea is that by having a foot in both the hospital and the laboratory, physician-scientists can more rapidly bring medical questions to the laboratory and study them, and also more rapidly bring medical breakthroughs from the laboratory back to the hospital. How often that works in real life I’m not sure, but I can tell you that having training in both sure does help me understand what research questions are important and what therapeutics might actually be feasible. 

Now some caveats before I offend a bunch of people: having both degrees is not essential; you can gain these skills in other ways. There are many MD’s who do medical research without a PhD and many PhD’s who do medical research without an MD. The idea behind the MD-PhD programs is that they are a (relatively) streamlined way to get students to the place where they have both skill sets. (Another advantage is that many come with a full-ride scholarship, which means graduates aren’t burdened by medical school debt when they’re done and are thus free to take lower paying research jobs.) 

When I was trying to decide whether or not to apply to MD-PhD programs after college, I scoured the internet looking for helpful advice, and didn’t find much to be honest. So if you’re reading this trying to figure out if this career path is right for you, here are the thoughts of someone 85% through.


  1. A lot of people go into these programs simply because they’re indecisive and couldn’t pick between med school and research. That’s ok. If you genuinely like both, just do both. 
  2. I remember one piece of advice I read was something like “you know the physician-scientist path is right for you if you can’t imagine doing anything else.” I think that’s terrible advice. I could be happy doing a lot of things, I just chose this one. Don’t feel like you have to have some grand sense of calling to this career path; just do it if you want to do it.  In fact, it is very helpful if your identity is not wrapped up in your education/career, otherwise you may feel like you’re failing at your life’s grand purpose when you encounter academic challenges (which will happen). Having your success as a human being wrapped up in your score on your first med school exam is quite a load to carry; I don’t recommend it.
  3. You very much need to be ok with delayed gratification. It is a long training path, and nearly everyone thinks about quitting at some point (for me it was every other month for the first four years). Perseverance is a must.
  4. My dad, who did his PhD in wind tunnels, gave me a very useful piece of advice before I started… he said PhD training is like going through a long tunnel, and there is a big chunk in the middle where you can’t see the light at the beginning nor can you see the light at the end, and it feels like the darkness will go on forever. This is very, very true. It does feel that way. So just know that going in, and decide if you want to deal with that. It is hard, but it is worth it.
  5. Don’t worry about the fact that you’ll be in your 30’s when you graduate. I remember this seemed like a really big deal when I started. As a college kid that feels like an eternity and like you can’t start your life until you’re “done with school.” That’s just not true. Another great piece of advice I got when I started was that no matter what I do, I’ll be in my 30’s in 8 years, so the question is really whether or not I’d like to have two doctoral degrees when I get there? I decided yes. Your 30’s are coming whether you like it or not, so just decide what you want to do with the time you have between now and then.
  6. One of the ways pre-med students are encouraged to explore a career in medicine is by shadowing physicians. However, if you don’t really enjoy shadowing doctors, don’t sweat it. I shadowed several doctors and to be honest found it very boring, because I didn’t yet have the skills to be helpful, and I hate standing around doing nothing. I remember wondering if this was a sign that I shouldn’t be a doctor. If you found this too, don’t count yourself out. If you’re thinking about becoming a doctor and you like to study, learn, and serve, don’t overthink it too much. It’s a job where you can do those things. There are other jobs where you can do those things too. Again, this isn’t your destiny, it’s your occupation. Just commit to one, and go for it.
  7. If you don’t like research, don’t do a PhD. It’s not worth it. Yes the MD-PhD scholarships are tantalizing, but remember that those who don’t do a PhD start earning a physician’s salary 4-5 years earlier, which if you compare to the 30k stipend you’ll get in grad school, is a much better deal. So only do the PhD if you actually want to do it; don’t do it for the med school scholarship.
  8. My MD-PhD road has been pretty rough, but I can honestly say I’m really glad I did it. I’m a person who likes science, serving people, and taking on very big challenges. If that describes you, go for it.