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

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.

 

Conclusion:

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.

 

Conclusion:

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.

 

Conclusion:

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.

 

Conclusion:

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.

 

Conclusion:

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.

 

Conclusion:

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.

 

Conclusion:

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.

 

Conclusion:

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

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.

5G Doesn’t Cause COVID: A Case Study in Misinformation

5G Doesn’t Cause COVID: A Case Study in Misinformation
By Kristen Panthagani

Ooooooooook. Deep breaths. Let’s begin.

 

I started this blog because I saw a lot of misinformation flying around, and well-meaning people are getting legitimately confused. And a lot of that misinformation is half truths or just quotes taken out of context (check out this post for examples of that), and I don’t blame readers for being misled.

 

But this paper is a whole other level. The paper entitled “5G Technology and induction of coronavirus in skin cells” was published in a PubMed-indexed journal (which is supposed to be a mark of credibility) trying to argue that SARS-CoV-2 can spontaneous generate in human skin cells due to exposure to 5G. The article has since been withdrawn due to its obvious flaws, but you can see an archived version here.

 

This is a whole other level of misinformation — it’s not some random person taking a legitimate study out of context. No — the actual authors are trying to sell you a completely implausible claim by dressing it up in sciency language and publishing it in (what is very likely) a predatory journal. Luckily scientists saw it and called it out, but I think it is still a valuable lesson in how people can dress up nonsense in fancy language to deceive readers, and that there are pay-for-play journals that will give it a legitimate-looking platform. Also there are still a lot of people that think 5G has something to do with COVID, and I hope walking them through the implausibility of these claims might help bring some perspective. So let’s take a look.

 

First let me note — this study is ignoring some really basic concepts of physics, chemistry, and biology, so in some ways it’s even harder to argue with this than less flawed studies because they are not working in the same physical universe as the rest of us. It’s kind of like trying to argue with someone who is saying there should be a border wall between the US and the moon… that… just doesn’t make sense.

What is the main argument of this article?

Before reading the summary below, take a gander at the actual article. It looks impressive, right? They have fancy diagrams and lots of math equations! They cite laws of physics! Alright, now taking away the science jargon, here is what they are actually arguing.

 

Plain language summary of what they are trying to say:

5G affects skin cells. COVID-19 affects skin cells, therefore COVID-19 and 5G have similar effects. DNA (which is inside skin cells) has a coil-like structure and is electrically charged. Inductors (coiled wire that can store energy when electricity passes through it) are also coil-shaped, therefore DNA is an inductor. As an inductor, DNA absorbs the 5G and then makes new electromagnetic waves that are DNA-shaped — not the shape of the DNA coils, mind you, but the shape of individual DNA molecules (bases). These DNA base-shaped waves punch DNA base-shaped holes in the liquid inside the cell. To fill these holes, a new DNA base is made, because it is the right shape. These individual DNA.. sorry, I mean RNA bases magically join together in exactly the right order to form the 30,000 RNA base-long coronavirus genome. For this to work, the wavelength of the electromagnetic radiation has to be smaller than the size of a cell. Also, btw, 5G is capable of killing every living thing except some forms of microorganisms. It causes 720 (factorial) diseases.

A similar level of scientific thinking as that displayed by this article.

Now the Science

Let’s walk through a few of the many, many things that are wrong with this argument. First, let’s start with some basic definitions. 5G is a form of electromagnetic radiation just like 4G, radio waves, and visible light. Its wave lengths generally range between 1 and 10 millimeters and are slightly shorter than 4G, but much longer than visible light. The shorter the wavelength, the more energy the wave carries. Thus sunlight carries way, way more energy than 5G.

It is true that some molecules absorb electromagnetic radiation like light (this is the reason color exists). However, whether or not a given molecule absorbs the electromagnetic radiation depends on the wavelength of the radiation. What wavelengths does DNA absorb? Ultraviolet light (~260 nanometer = 0.00026 millimeters). Is this in the range of 5G? No, it is 3,000X-30,000X shorter than 5G. This is why UV light is damaging to DNA (sunburns, skin cancer) and also why scientists are not at all concerned about 5G causing DNA damage.

 

The authors seem to understand that the wavelength matters — one of the main arguments of this paper is the reason 5G is so bad is because 5G wavelengths are smaller than the size of the cell. (And radio waves aren’t a problem because they are bigger than a cell.) But hold on… how big are human cells? 0.01-0.1 millimeters. How long are 5G wavelengths again? 1-10 millimeters. So, human cells are also way smaller than 5G wavelengths (10-1000X smaller). Already this paper is off to a self-conflicting start.

 

Alright, now their DNA inductor argument. There are lots of physics-y sounding arguments thrown together here, and I am guessing the authors are hoping that you’re not very familiar with electromagnetism and particle physics and will give up and say “ok maybe that could work?” But let’s distill the argument to something very simple: they are saying DNA emits DNA base-shaped radiation that punches holes in liquids.

 

Again, they use a partial truth: it is true that, in very specific circumstances, DNA molecules can emit electromagnetic radiation (this is called fluorescence). In fact, lots of molecules do this — this is not a unique property of DNA. However, these waves are not DNA base-shaped, they are…. wave-shaped. But perhaps more importantly, electromagnetic waves do not punch holes in liquids. Electromagnetic waves have no mass. They do not push matter out of the way. They just pass through it.

 

An example of fluorescence (emission of electromagnetic radiation) – glowing jellyfish.

But, just to humor them, let’s imagine, for a second, that somehow DNA did emit DNA base-shaped electromagnetic waves and those waves did punch DNA base-shaped holes in liquids. What happens when you punch a hole in a liquid? Try punching a swimming pool and see what happens. Is the hole instantly filled with a fist-shaped molecule? No, it just fills back in with water. So, if there were DNA base-shaped holes inside your cells, this would not trigger the cell to make a new DNA base. DNA bases are made through a highly complex series of chemical reactions guided by enzymes, and they are tightly regulated by your cell. They do not spontaneously generate because there is a hole.

 

Ok, but let’s pretend for a second that there were DNA shaped holes in your cells and those holes were filled in with DNA bases. Now the article switches to RNA because they forgot SARS-CoV-2 is an RNA virus. Ok now the holes are magically filled up with RNA bases. Could this lead to the construction of a RNA viral genome?

 

No. The SARS-CoV-2 RNA genome is ~30,000 bases long. 30,000 individual RNA bases would have to, by pure coincidence, line up and connect to each other in exactly the right order to build the SARS-CoV-2 genome. First, individual RNAs cannot connect to each other by themselves. They need an enzyme to help them, which, like everything else in the cell, is tightly regulated. Second, the chances of them lining up in the right order to make the SARS-CoV-2 genome is infinitesimal. This is like throwing thousands of fridge word magnets out your window and expecting to walk down and see a complete work of Shakespeare. The statistical chance of this happening in one cell is (1/4)^30,000, which is 0. (I put this in my calculator and literally it came out with 0… there weren’t enough decimal places allowed by my calculator to capture how small of a chance this is.) Ok… maybe let’s allow for some substitutions given that there is some variability in the SARS-CoV-2 genome. Let’s say only 90% of the bases have to be in the right order. That is (1/4)^(30,000*0.9). Still 0.

 

Furthermore, this has to happen by chance separately in everyone who has COVID. 30,000 bases magically lining up in the same way in people all across the globe. (Unless you think that most people have COVID because the virus emerged once and then was spread to other people. Congratulations! You have just discovered that 5G does not cause COVID).

 

As a side note — there truly are individual RNA and DNA bases floating around inside your cell — these are what’s used to build copies of your genome and various RNAs. We didn’t need 5G-induced DNA-shaped holes in liquid to get to this step. This is already a thing. But, they do not spontaneously join together to make 30,000 base long viral genomes. They can only do that when they have a template to work from and enzymes to guide the process, which is what a virus does inside the cell to make more copies of itself. Without the original template, nothing will happen.

 

How to dress up nonsense as real science

This paper does not obey the basic rules of physics, chemistry, or biology, yet on first glance it looks like it could be legitimate. I’m guessing many non-scientists who came across it wouldn’t be sure what to think. So let’s look at the tools they used to dress up their nonsense to fool their audience.

 

Step 1: Use lots of technical jargon.

Technical jargon serves two purposes: 1. it gives the writer an air of legitimacy (they’re using technical words! they must be an expert!) and 2. it hides what they’re actually saying (because the average person cannot understand them). To someone outside the field, it is very hard to read a paragraph with lots of jargon and process what it’s saying. Usually the reader walks away with a vague sense that ‘these people must know what they’re talking about,’ but they have little understanding of the details. This is the perfect combination to sell misinformation.

 

Step 2: Use half truths.

This article does contain some true scientific statements, and mixing these in with the false ones further serves to give it an air of legitimacy. Someone with a little science knowledge might recognize the true statements (DNA molecules are charged, etc.), which makes it seem more believable.

 

Step 3: Include lots of scary math.

This paper includes over two pages of formulas, which serves the same purpose as the technical jargon: it makes the paper look sophisticated, and most readers won’t understand it.

 

Step 4: Pay to have it published in a predatory journal that will give it a look of authenticity.

The reason this particular paper caused such a stir is that it was published in a PubMed-indexed journal. PubMed is a search engine of medical and scientific publications that is supposed to only include legitimate journals. Unfortunately, in the world of science, predatory publishing is a thing — people make up journals that have legitimate sounding names (like the “Academy of Science and Engineering (ASE)” and allow pretty much anyone to publish in them if they’re willing to pay the fee, just so they can make money. PubMed is supposed to include only legitimate journals to prevent the crap in these predatory journals from showing up in search results. That’s why it caused quite a fuss when this ridiculous 5G COVID paper showed up on PubMed — something went wrong with PubMed’s indexing system. It is an important lesson to realize that there is an entire world of predatory publishing out there that publishes fake and/or seriously flawed science in a legitimate looking package simply to make money. Unfortunately, that means that just because something looks like a real science publication doesn’t mean it is. And I absolutely don’t blame you if you don’t know how to tell the difference. Usually, checking if it’s on PubMed is a good first step.

 

It is important to remember we live in a world where people (even scientists with real credentials) make up completely ludicrous science, and that sketchy journals exist that are willing to publish that ludicrous science to make some cash. Unfortunately, that means it’s important not to trust something just because it sounds technical or looks official. If the claim sounds outrageous, it probably is. Also, 5G doesn’t cause COVID.

Masked Science: Fact-checking Mask Studies

Masked Science: Fact-checking Mask Studies
By Kristen Panthagani

I saw this post on the interwebs citing a bunch of studies, suggesting that masks are ineffective at preventing infection and/or are unsafe. (And also suggesting there is some sort of cover-up of this science). Since there is still controversy around mask-wearing (and the post gave me PubMed ID’s! thank you!) I decided to dig into to see if there was any merit to what they were saying. Here is what I found.

Quote #1

“Preliminary Report on Surgical Mask Induced Deoxygenation During Major Surgery…Our study revealed a decrease in the oxygen saturation of arterial pulsations (SpO2)”

PMID 18500410.

 

Is this study real?

Yes (full text here).

 

Is the quote above actually in the study?

Yes, but it is missing the second half of the sentence. Full quote reads “Our study revealed a decrease in the oxygen saturation of arterial pulsations (SpO2 ) and a slight increase in pulse rates compared to preoperative values in all surgeon groups.”

 

What did the study do?

This study measured SpO2 (blood oxygen saturation) of surgeons before and after surgery. All the surgeons were wearing masks during the surgery. As a control group, they also measured SpO2 of individuals in the operating room who were observing (not participating in surgery) and who were not wearing a mask.

 

What did they find?

For short operations (< 60 min) there was no decrease in SpO2 after surgery. For longer operations (1-2h, 2-3h, 3-4h) there were subtle but statistically significant decreases in the SpO2 after surgery in the mask-wearing surgeons who were operating, but not the non-mask wearing observers. The largest drop in SpO2 was recorded after the longest surgeries (3-4h), but was still quite small (it dropped from ~97.6% SpO2 before the surgery to ~96% SpO2 after the surgery — these are both within normal range.)

 

What does this tell us about masks?

SpO2 stayed well within normal range for every study participant, indicating it is safe to wear masks for extended periods of time. There was a slight drop in SpO2 in surgeons after long operations, but unfortunately, this study doesn’t tell us whether this was due to the mask or due to operating. The control group tells us this decrease in SpO2 was not from standing for that period of time, so we can rule out that explanation out. However, given that surgery requires intense focus and many of us tend to hold our breaths when we are focusing on fine motor tasks, it’s possible this slight reduction in SpO2 is due to changes in surgeon breathing and/or other effects of performing surgery, and not due to the mask. Alternatively, it is possible that wearing a mask for several hours slightly decreases SpO2, but not to a level that is unsafe. The authors do acknowledge that they cannot attribute this drop to the mask: “This change in SpO2 may be either due to the facial mask or the operational stress, since similar changes were observed in the group performing surgery without a mask.” I think it’s unfortunate they chose a title that didn’t reflect this uncertainty.

 

Simple Conclusion:

This study indicates that surgeons in their study group had a slight drop in blood oxygen saturation after long surgeries, but that this drop is minimal, and does not present a health concern. This study does not provide evidence regarding the effect of masks on blood oxygen saturation, as there were significant confounding variables in the study design.

 

Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?

No.

Quote #2

“We know that wearing a mask outside health care facilities offers little, if any, protection from infection.”

PMID: 32237672.

 

Is this study real?

The article is real, but it’s not a study — it is a perspective/opinion piece. Here is the full text.

 

Is the quote above actually in the study?

Yes.

 

What did the article say?

This is an opinion piece discussing the pro’s and con’s of universal masking in hospitals during the COVID-19 pandemic, presumably written in March 2020 (published April 1, 2020). The quoted line above is the only mention of mask-wearing outside of hospitals, and the rest discusses whether every doctor/nurse/etc. needs to wear a mask in hospitals. The article does not question the effectiveness at proper mask use in hospitals, but discusses other factors that need to be weighed (supply availability, if people who wear a mask will touch their face more, etc.).

 

What does this tell us about masks?

They do not provide citations nor data backing the quoted claim above that wearing a mask outside health care facilities offers little protection, so this article does not provide any data on that topic. Overall, this piece is consistent with the thinking at the time it was written (the very beginning of the pandemic in the US) — it was not recommended that everyone wear masks at that point. (See below for discussion as to why.)

 

Simple Conclusion:

This article is an opinion piece written at the beginning of the pandemic that does not provide any data on whether masks are effective or not.

 

Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?

No, this is not a study, it’s outdated advice.

Quote #3

“.. both surgical and cotton masks seem to be ineffective in preventing the dissemination of SARS-CoV-2 from the coughs of patients with COVID-19 to the environment and external mask surface.” https://www.acpjournals.org/doi/10.7326/M20-1342.

 

Is this study real?

Yes (full text here), but it has now been retracted.

 

Is the quote above actually in the study?

Yes.

 

What did the study do?

They had 4 people with COVID-19 cough on petri dishes with and without masks (cotton and surgical, but not N95’s), and they measured the amount of virus they could detect with and without masks. The authors concluded that because they could not find a difference in the viral load on the petri dishes with and without masks and because they detected virus on the outside of the masks, surgical and cotton masks do not block SARS-CoV-2 when people are coughing.

 

Why was this study retracted?

Retraction means the study was so flawed that it can no longer be considered reliable — it is like “unpublishing” an article. The study was retracted because it had a Limit of Detection problem — the method they used to measure how much virus was present on the petri dishes could only measure viral loads above a certain threshold, and some of their samples were below that threshold. When samples are below the limit of detection, the experiment can’t tell the difference between no virus, a little bit of virus, and a little bit more virus. (This is kind of like squinting at a ruler from 20 feet away and trying to tell the difference between 0.4 inches and 0.33 inches… your eyesight is not reliable to detect subtle differences like that.) Unfortunately, the type of experiment they ran will still spit out numbers even below the limit of detection, even though those numbers aren’t reliable at all. It’s the scientists’ job to know when those numbers are reliable versus unreliable. These scientists made a mistake and thought their numbers were reliable, when in fact they weren’t.

 

Simple Conclusion

This study provides no evidence on the effectiveness or ineffectiveness of masks due to a problem in their methodology (there wasn’t enough virus to measure it accurately). Because of this issue, it was retracted.

 

Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?

No.

Quote #4

“Most healthcare workers develop de novo PPE (such as N95 face mask) associated headaches or exacerbation of their pre-existing headache disorders.”

PMID: 32232837

 

Is this study real?

Yes, full text here.

 

Is the quote above actually in the study?

Yes with a slight edit — the original quote reads “Most healthcare workers develop de novo PPE‐associated headaches or exacerbation of their pre‐existing headache disorders.” PPE stands for personal protective equipment.

 

What did the study do?

They gave questionnaires to 158 health care professionals working in high risk hospital areas in Singapore at the beginning of the COVID-19 pandemic. The questionnaires asked if they previously had headaches, if they had developed new headaches since the pandemic (and where the headaches were, how severe they were, and how often they had them), and what types of PPE they were wearing (at that hospital anyone caring for a COVID patient was required to wear goggles, close-fitting N95 masks, gowns, surgical gloves.)

 

What did they find?

The hospital workers wore goggles and face masks an average of ~6 hours per day. Over a 30 day period, 81% of participants reported sometimes having headaches when wearing the N95 mask with or without goggles, with the location of the headaches often corresponding to the points where the mask/goggles put pressure on the face. About half of the people attributed their headache to the mask, while half attributed it to the goggles. Most people graded their headache as mild and infrequent (1-4 days / 30 days). People who wore both N95 face masks and goggles for > 4 hours/day and those who had a pre-existing headache diagnosis were more likely to report headaches.

 

What does this tell us about masks?

Tight-fitting PPE like fit-tested N95 masks and goggles may cause mild headaches if worn for an extended period of time, likely due to the pressure from the straps/seal on the face.

 

Simple Conclusion:

Health care workers and other professions wearing tight-fitting masks and goggles for multiple hours may get mild headaches.

 

Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?

No.

Quote #5

“This study showed that the filtering efficiency of cloth face masks were relatively lower, and washing and drying practices deteriorated the efficiency.”

PMID: 31289698.

 

Is this study real?

Yes, here is the full text.

 

Is the quote above actually in the study?

Yes.

 

What did the study do?

The focus of this study was the effectiveness of cloth masks worn in developing countries on preventing inhalation of air pollution. They bought cloth masks from Kathmandu, Nepal and measured the pore size and filtering efficiency of air pollution compared to surgical masks. They found that cloth masks had larger pores and lower filtering efficiencies (63-84%) than surgical masks (94%). They also found that repeatedly washing cloth masks decreased the filtering efficiency further.

 

What does this tell us about masks?

While this study did not look at anything related to viruses or transmission of infectious diseases, it does tell us that cloth masks (at least those purchased in Kathmandu, Nepal) have larger pores than surgical masks, and that surgical masks are better at filtering small particles than cloth masks (though cloth masks still filter out a significant percentage of small particles in the air.) This is consistent with the idea that N95 > surgical mask > cloth mask > no mask.

 

Simple Conclusion

Surgical masks filter out more particles than cloth masks purchased in Nepal. Depending on the type of fabric, this may be true for cloth masks commonly used in the US as well.

 

Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?

No.

Quote #6

“None of the studies established a conclusive relationship between mask/respirator use and protection against influenza infection.”

PMID: 22188875.

 

Is this study real?

Yes, here is the full text.

 

Is the quote above actually in the study?

Yes.

 

What did the study do?

This study was a systematic review (a review of all studies on a particular topic) looking at whether or not the use of masks and/or respirators prevent transmission of influenza. One of the goals of this study was to figure out if it is worth it for people to wear masks every year to lower the transmission of seasonal influenza. They looked at 8 studies of masks use for preventing influenza, and 2 showed an effect (masks help prevent infection) and 6 did not show an effect (no difference in infection rates with and without masks.) Because there weren’t a lot of studies on influenza, they also looked at 9 studies of mask use during the first SARS epidemic. 8 out of 9 studies showed an effect (mask use was associated with reduced risk of SARS infection). However, the authors note that many of the SARS studies were suboptimal, and that the results can’t necessarily be generalized to influenza, since they are very different viruses. All together, they conclude that there wasn’t enough published evidence to support the idea of using masks to prevent transmission of the seasonal flu.

 

What does this study tell us about masks?

This study tells us that (at least as of 2012 when this study was published), there was not evidence to support wearing masks every year to prevent the seasonal flu. It does suggest that masks work against SARS, as 8/9 studies showed a protective effect, however better studies were warranted as there were some methodological flaws in those studies. Overall (as of 2012), the authors highlight that more research is needed.

 

Simple Conclusion

As of 2012, there was not enough evidence published to support the use of masks to prevent seasonal influenza. The authors want more (and better) research on the topic. There are quite a few studies indicating masks helped protect people in the first SARS epidemic, but those studies have some methodological weaknesses.

 

Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?

No.

Quote #7

“Face mask use in health care workers has not been demonstrated to provide benefit in terms of cold symptoms or getting colds.”

PMID: 19216002.

 

Is this study real?

Yes, abstract here. I couldn’t access the full-text so this is based off the abstract.

 

Is the quote above actually in the study?

Yes.

 

What did the study do?

A total of 32 health care workers were studied for 77 consecutive days — they were randomized to either wear masks or not wear masks, and the presence of cold symptoms was tracked. Only two people got colds, (one in each group). They also found that the group who wore masks were more likely to experience headaches.

 

What does this study tell us about masks?

This study doesn’t really tell us much about the effectiveness of masks at preventing infection because it didn’t study enough people — if only two total people got colds throughout the course of the study, if there was a difference in cold prevention we would need far more people to detect this difference. It does suggest wearing masks may increase risk of headaches in health care workers.

 

Simple Conclusion

This very small study suggests healthcare workers who wear masks daily may have increased risk of headaches. It does not give us reliable information on whether or not masks prevent colds. A bigger study is needed to address this question.

 

Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?

No.

Quote #8

“There is little evidence to support the effectiveness of face masks to reduce the risk of infection.”

PMID: 20092668.

 

Is this study real?

Yes, full text here.

 

Is the quote above actually in the study?

Yes, but it makes the conclusion sound simpler than it is. Here is the full paragraph:

 

“In conclusion there remains a substantial gap in the scientific literature on the effectiveness of face masks to reduce transmission of influenza virus infection. While there is some experimental evidence that masks should be able to reduce infectiousness under controlled conditions [7], there is less evidence on whether this translates to effectiveness in natural settings. There is little evidence to support the effectiveness of face masks to reduce the risk of infection. Current research has several limitations including underpowered samples, limited generalizability, narrow intervention targeting and inconsistent testing protocols, different laboratory methods, and case definitions.”

 

What did the study do?

This is a systematic review of studies that have looked at whether or not face masks, often in combination with other protective measures like hand washing, help protect against influenza infection. They looked at 12 different studies — some were underpowered and didn’t provide any useful information, some did not show a protective effect of mask use from influenza and/or cold symptoms, and some showed a protective effect of mask use or face shield. Sometimes this effect was only observed when combined with hand hygiene (hand washing). There were significant limitations in many of the studies, and the authors call for further research.

 

What does this tell us about masks?

As of 2010 when this review was published, there wasn’t strong evidence to support the idea that healthy people should wear face masks to protect against influenza infection. Many studies had limitations, and more research is needed. Remember not to confuse absence of evidence with evidence of absence — sometimes there is “not strong evidence” for something because we simply haven’t done the studies needed to find the evidence, and sometimes there is not strong evidence for something because we looked and there was not evidence to be found. (Check out this post for more on that.) Here is the conclusion provided by the authors: “There is some evidence to support the wearing of masks or respirators during illness to protect others, and public health emphasis on mask wearing during illness may help to reduce influenza virus transmission. There are fewer data to support the use of masks or respirators to prevent becoming infected. Further studies in controlled settings and studies of natural infections in healthcare and community settings are required to better define the effectiveness of face masks and respirators in preventing influenza virus transmission.

 

Simple Conclusion:

As of 2010, there was not strong evidence to support mask-wearing by healthy people to prevent influenza infection in a non-pandemic setting. Some of the studies were pretty crappy, and more research is needed.

 

Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?

No.

 

Quote #9

“..laboratory-confirmed virus were significantly higher in the cloth masks group.. Penetration of cloth masks by particles was almost 97%.. This study is the first RCT of cloth masks, and the results caution against the use of cloth masks.. Moisture retention, reuse of cloth masks and poor filtration may result in increased risk of infection.”

PMID: 25903751.

 

Is this study real?

Yes, here is the full text.

 

Is the quote above actually in the study?

Yes, but it is somewhat taken out of context.

 

What did the study do?

This is a randomized-controlled trial looking at the effect of different types of masks on preventing infections in medical personnel in a Vietnam hospital. There were three groups: those who wore medical masks (surgical masks), those who wore cloth masks, and a control group, which, very importantly, was “usual practice, which included mask wearing.” So many in the control group were also wearing masks, which makes the results of this study very difficult to interpret (i.e. there is no “no mask” group). They looked at infections (respiratory illness, influenza-like lillness, and laboratory-confirmed respiratory virus infection) and found that the rates of infection were lowest in those who wore surgical masks and highest in those who wore cloth masks. The “control” group (which isn’t really a control group but more of a mystery group) was in the middle. Notably, a lot of people in the study didn’t actually wear the masks all the time like they were supposed to, which also limits the conclusions one can draw from this study.

 

What does this tell us about masks?

This study suggests that surgical masks may work better than cloth masks (at least those used by hospital workers in this study) at preventing influenza-like illness in a non-pandemic hospital setting. However many of the participants weren’t compliant with mask wearing, so I don’t put a lot of weight on the results of this study. The control arm is completely useless, so this study tells us nothing about the effectiveness of mask versus no mask.

 

Simple conclusion.

Once again, surgical mask > cloth mask (probably, but mostly this study was just messy).

 

Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?

No.

Quote #10

“Respiratory acidosis develops when air inhaled into and exhaled from the lungs does not get adequately exchanged between the carbon dioxide from the body and oxygen from the air.” https://www.medicalnewstoday.com/articles/313110

 

Is this real?

Yes, but this is just a website about respiratory acidosis, and has nothing to do with masks.

 

What does this tell us about masks?

Absolutely nothing.

 

Simple Conclusion

This is just a medical definition.

 

Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?

No, this is not a study, it is a lesson in medical vocabulary.

Quote #11

“Conversely, surgical and hand-made masks, and face shields, generate significant leakage jets that have the potential to disperse virus-laden fluid particles by several metres. The different nature of the masks and shields makes the direction of these jets difficult to be predicted, but the directionality of these jets should be a main design consideration for these covers. They all showed an intense backward jet for heavy breathing and coughing conditions. It is important to be aware of this jet, to avoid a false sense of security that may arise when standing to the side of, or behind, a person wearing a surgical, or handmade mask, or shield.” https://arxiv.org/ftp/arxiv/papers/2005/2005.10720.pdf

 

Is this study real?

Yes (though it has not yet been peer-reviewed.)

 

Is the quote above actually in the study?

Yes.

 

What did the study do?

They used some fancy methods to track where a person’s breath goes when they breath and cough while wearing different types of masks/PPE (surgical mask, handmade mask, FFP1, FFP2, respirator, face shields.) They found that all masks (except the respirator, which is not designed to stop exhaled air) significantly decrease how far a person’s breath travels in the forward direction. They found that masks that seal (like FFP1, FFP2 masks) don’t have significant leakage to the side and back, while masks that don’t seal (surgical masks, cloth masks) do have leakage to the side and back. They caution people to remember that non-sealing masks can leak to the side and back.

 

What does this tell us about masks?

Non-sealing masks do not block everything and redirect some airflow to the side and back. This was based on a study of one person, so it should be verified in a larger sample size. It is not yet peer-reviewed, so there may be flaws that have not been caught yet.

 

Simple Conclusion

Non-sealing masks may leak to the side/back.

 

Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?

No.

Quote #12

“Face masks should not be worn by healthy individuals to protect themselves from acquiring respiratory infection because there is no evidence to suggest that face masks worn by healthy individuals are effective in preventing people from becoming ill.” https://jamanetwork.com/journals/jama/fullarticle/2762694

 

Is this study real?

Yes, but it’s not a study — it’s a summary of information about masks published in early March 2020.

 

Is the quote above actually in the publication?

Yes.

 

What does this tell us about masks?

This article reflects the thinking at the time it was written — reserve masks for healthcare workers and people who are sick. It does not provide new data on the topic, only summary recommendations.

 

Simple Conclusion

At the beginning of the pandemic, the recommendation was that healthy people not wear masks. (More details on that below).

 

Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?

No, this is not a study, it’s outdated advice.

Summary: What did we learn from these studies?

  1. There is not enough evidence to suggest everyone wear masks every year during influenza season in a non-pandemic setting (and we don’t.)

  2. Surgical masks are probably better than cloth masks at preventing infection (though there may be variation on the type of cloth mask used). Both leak somewhat.

  3. Use of tight-fitting masks for long hours by health care workers may be associated with increased risk of mild headaches.

  4. While many of the quotes (as well as the title “masked science” with the crying masked person) make it sound like these articles provide evidence that masks are unsafe and/or don’t work against SARS-CoV-2, none of these articles actually provide any evidence to support this claim (and in some cases, provide evidence to the contrary.)

 

Was this post misinformation?

While none of the quotes or studies were fabricated, they were often taken out of context and/or presented in a misleading way. Read the quotes again now that you’ve gone through the details of the studies.

The quotes often avoid key details about the studies which results in misleading the reader.. for example, the studies on cloth masks were often compared to surgical masks, but you couldn’t tell that from the quotes. So reading these quotes you might assume they were comparing to not wearing masks at all, which changes the conclusion from “surgical masks work better than cloth masks” (what the studies actually showed) to “cloth masks don’t work” (what this post seems to be trying to imply). This is obviously very misleading, and most people would only catch it if they went and looked up the studies. Additionally, some of the quotes feel cherry-picked to reveal a specific point of view and don’t really reflect the conclusion of the authors of the studies. A lot of the studies are about influenza/common cold, but the way they’re presented makes it seem like these results should apply to SARS-CoV-2, which isn’t an accurate assumption (why? There’s probably many reasons, but a major one is SARS-CoV-2 is way more infectious than the seasonal flu). The random inclusion of a respiratory acidosis definition is just silly — thrown in there with all these quotes from studies, it would make many readers think there was a study that had shown respiratory acidosis was associated with mask use (which is a myth that has been circulating), when in fact this had absolutely nothing to do with masks. All together, this feels like it was put together by someone who was trying to convince people masks aren’t safe/don’t work against SARS-CoV-2 in spite of the evidence, not because of it. It is an important reminder that you can’t trust something just because it has lots of citations.

Are masks effective in slowing the transmission of SARS-CoV-2?

None of these studies actually looked at the effectiveness of masks for SARS-CoV-2 (except the retracted one, which is useless) — are there studies indicating that masks do protect against SARS-CoV-2 transmission? Yes, check out this meta-analysis of SARS-CoV-2, SARS, and MERS studies and this summary about cloth masks (note the study being asked about at the beginning of this summary is the problematic study from quote #9).

Why were we told not to wear masks at the beginning of the pandemic?

So if masks work against SARS-CoV-2, why were they not recommended for everyone at the beginning of the pandemic in the US? (They were recommended for healthcare workers and those who were showing symptoms). While I was not involved in making these recommendations (of course), here is my best guess. At the beginning of the pandemic, we were in the following situation: 1. we didn’t understand the virus, 2. we thought that the prevalence of COVID-19 in the US was quite low, 3. we didn’t have strong evidence that people could be infected without showing symptoms and 4. people were freaking out and panic-buying toilet paper. From that perspective, the risk of telling “healthy” people (people who weren’t showing symptoms) to wear masks outweighed the benefit… we weren’t sure how much it would help (we didn’t know so many “healthy” people were actually infected), and it would likely deplete mask supplies for healthcare workers if everyone started panic-buying N95s. Some argue that we should have told everyone to wear cloth masks back then as to not confuse everyone about the effectiveness of masks; in retrospect I think that would have been a good idea. Now, our situation is different: 1. we have data to show that wearing masks helps slow transmission, 2. the prevalence of COVID-19 in the US is way, way higher than it was in March (and we know a lot of cases are going undetected), 3. we know that many people are infectious without showing symptoms (meaning they can spread it when they think they are healthy), 4. we’re not as concerned about people panic-buying all the N95 masks, and 5. many places are trying to re-open, so masks provide a way to lower the rate of transmission without resorting to a full shutdown again. Given these changes, it makes sense for everyone to wear masks when around others, even if they aren’t showing symptoms, because they may be infected without knowing it.

Dealing with the Coronacoaster

Dealing with the Coronacoaster
By Kristen Panthagani

I started this blog because I saw a lot of people getting confused about science topics surrounding the COVID pandemic, and I wanted to help clarify things where I could. So the majority of my focus has been science. But one of the biggest side effects of this pandemic has been the impact on people’s emotional well-being. I have been working from home since mid-March, and will continue to do so until the end of August, and this isolation is certainly taking its toll on me (even though I am one of the most introverted introverts you’ve probably ever met). So with that said, this post is dedicated to everyone stuck at home and feels like they’re doing great (and maybe even enjoys working from home, like me), but occasionally (or maybe not so occasionally) has seemingly random bursts of sadness/anxiety/depression due to the stress of this whole thing. Some mystery person on the internet came up with a perfect word for this:

While I didn’t have gin for breakfast, today was one of those days for me. So this post is my attempt to deal with it (and maybe it will help you too).

 

Step 1: Validate to yourself that this sucks.

The pandemic absolutely sucks. There are lots of very stressful things happening all at the same time. Some of us have been trained to deal with hard things by “looking on the bright side!” At least I’m not sick! At least I still have my job! At least there are no hurricanes in the forecast! While it is good to be thankful for the positive things in our lives, if we don’t allow ourselves to feel the negative emotions of this being as hard as it is, that sadness will just get buried inside and not really go away. So, if you’re on the coronacoaster, and today is a hard day, don’t try to just snap yourself out of it by distracting yourself with positive things. That’s not how humans work. Instead, first validate to yourself that what you’re going through is hard, and know you don’t have to pretend it’s not. (Also you are allowed to feel unhappy even if someone else has it worse than you right now. This isn’t a competition.)

 

Step 2: Feel sad for a little bit.

First, just sit in it. Let yourself feel sad.

Don’t scroll further yet. Sit in it.

Step 3: Ok, now just for a second, look at this cozy kitten.

Step 4: Don't expect that kitten to have magically cheered you up; your feelings are valid and this thing is hard.

 

Sit in the sadness a little longer if you need to.

 

Step 5: Let Mr. Rogers remind you that there are lots of people who sometimes feel like this.

The last 30 seconds of this song feels written for the pandemic.

 

Step 6: For just a second, look at this giddy puppy.

(disclaimer: this is my puppy ❤️)

 

Step 7: Ok, now pick one thing in your house to clean.

Step 8: Now tell yourself you don't actually have to clean it.

Working harder is not the solution to sadness.

 

Step 9: Watch this Brené Brown video on Empathy.

Step 10: Until you're feeling better, don't talk to people who don't know how to empathize.

Empathy is a skill people can learn, and not everyone has learned it yet. I find it is helpful to avoid talking to people who haven’t demonstrated skill in empathizing when I am feeling sad, since I know their words might be unhelpful.

 

Step 11: Pull out your phone and look at a picture from exactly one year ago.

This was our wedding reception ❤️

 

Step 12. Look at pictures from your last trip when things were "normal."

NOLA, February 2020.

 

Step 13. Make a list of the things that you were really looking forward to, but that got cancelled because of the pandemic.

For me the big ones were my parents weren’t able to come to my thesis defense, and my husband and I were planning a trip to Europe this summer to celebrate our first anniversary, which obviously isn’t happening now :/

 

Step 14. Name one good thing that has happened because of the shut downs/other changes caused by the pandemic.

I planted a garden on my balcony and now it is so green and beautiful.

 

Step 15: Don't expect that good thing to make up for all the hard things and stuff you've missed.

You can simultaneously be thankful for the good things and grieved over the hard things. You are allowed to have multiple conflicting emotions at the same time.

 

Final Step: Read these Strange Planet comics, and know it's ok if you're having a hard day.

Virology 101: Ask a Virologist!

Virology 101: Ask a Virologist!
Guest post from virologist Dr. Alex Chang-Graham!

For those of you whose last biology class was decades ago, the differences between bacteria, viruses, and other microbes is probably a bit hazy. I asked my friend Dr. Alex Chang-Graham, who studied viruses for her PhD thesis, to give a refresher on what what viruses are and how they make people sick. Here is her virology 101 course for you!

What is a virus anyway?

Viruses are one of the strangest things in biology – unlike bacteria, mold, and other microbes, they are not exactly “alive.” But that doesn’t stop many of them from making people sick. A virus is a package of genetic material that hijacks the human body to make more copies of itself.

 

All organisms have genetic information that allows that organism to make more of itself: the genome. In human cells, the genome is made of DNA molecules. Human cells use DNA as the master guidebook to tell the cell how to work and first by transcribing the DNA as RNA, a different kind of genetic material molecule. Then RNA is used as the template to build proteins, which are little machines that do the work in the cell. To use a broad sports metaphor, a team uses a playbook (DNA) to run plays that are given names (RNA) and then the players executes the play (protein) on the court/field (the cell).

DNA is the master guidebook used to make transcripts of RNA, which are then used as the instructions to make proteins, which keep the cells running.

Viruses are special in that, depending on the species of virus, they use DNA or RNA for their genetic material. SARS-CoV-2 is an example of an RNA virus, and the RNA is used to make its own special viral proteins. These viral proteins can help the virus do a variety of tasks including: make more copies of its genome, become the protein capsid or nucleocapsid (the proteins that surrounds the virus’ DNA or RNA), or interfere with host (human) cell’s normal activities. Some viruses, like SARS-CoV-2, also have a fat membrane with viral proteins embedded in it that surrounds the virus capsid. This outer layer of fat is called the envelope. This envelope is essential for SARS-CoV-2 to infect new cells. This is the reason washing your hands with soap or using hand sanitizer works so well against SARS-CoV-2… both soap and hand sanitizer break apart the virus’ fat layer, which inactivates it.

Unlike human cells, viruses cannot replicate or function by themselves. They require entering a host (human) cell first. In other words, a virus needs a “field” (host cell) in order to run any “plays” (make proteins). Once a virus is inside a host cell, it takes over the normal host cell processes. It’s as if Team Virus interferes with the Team Host plays and forces the Team Host to run Team Virus plays instead!

Viruses interfere with the cell’s plays,

just like this tragic ending to Super Bowl 49.

How are viruses different from bacteria?

Viruses require a host cell to replicate while most bacteria can replicate on their own. Viruses are the ultimate intracellular parasite (a parasite that lives inside people’s cells) and have no energy source or way to make copies of itself outside of its host cell. Without host cells, they can do nothing. In contrast, most bacteria can live, get energy, and reproduce without needing the help of human cells.

 

Despite this limitation, viruses are among the most ubiquitous and successful type of organisms in the world. Different species of virus infect every other kind of life on Earth, including animals, plants, insects, and bacteria. Once a virus invades its host cell, it can be extremely efficient and create many thousands of copies of itself that can then go on to invade new host cells and start new rounds of replication.

How do viruses like SARS-CoV-2 make people sick?

Once a virus invades a human cell, it disrupts all the cell’s normal functions and takes them over to make more copies of itself. However, the human cell can often detect there is something wrong when the virus invades. It sends out danger and mayday signals to other nearby cells, like an emergency flare. These signals include cytokines, which are special kinds of molecules that cause inflammation and activate the immune system. This inflammatory response sounds the alarm and mobilizes immune cells to recognize the viral threat and mount defenses against the virus and the human cells it has already infected. One of the immune responses is antibodies, which neutralize the virus or kill the infected cells to stop viral replication from going any further. (Check out this post to learn more about how our bodies make antibodies and become immune to viruses).

Like emergency flares, many cytokines are signals made by human cells that sound the alarm that there is a problem and recruit the immune system to action.

However, sometimes the body’s immune response can be too enthusiastic and overwhelm the body as a whole. This is called a cytokine storm. Studies so far suggest that SARS-CoV-2 can cause cytokine storms (though this is an area of active research). Fever and cough, the most common symptoms from SARS-CoV-2 infection, can escalate into more serious conditions when the body’s initial immune response fails to contain the virus, and the inflammatory response increases uncontrollably. Acute respiratory distress syndrome occurs when the delicate lung cells cannot exchange oxygen due to damage caused by the virus as well as immune system-driven secretions. Healthy lungs are mostly empty space with cells that are very, very thin to allow oxygen to pass from the air in the lungs into the bloodstream, which then delivers the oxygen all over the body. When the lungs fill up with fluid, the oxygen can’t easily get from the lungs to the bloodstream because there is a bunch of fluid, dead cell debris, and other gunk in the way. This condition is very dangerous and can lead to intubation and high mortality.

Cytokine storms happen when the emergency flares get out of control, and the immune system ends up harming the body instead of helping it.

How do antiviral drugs work?

Antiviral drugs are small molecules that interfere with the actions of viral proteins while leaving human proteins alone. Since viral proteins are often very different from human proteins, they make attractive targets for designing drugs that are targeted specifically to the virus and have limited side effects (side effects happen when drugs interfere with the normal (healthy) actions of human proteins in addition to interfering with their target).

 

For example, the investigatory drug remdesivir blocks the SARS-CoV-2 viral protein called RNA-dependent RNA-polymerase that makes more copies of the virus’ RNA genome. With reduced replication, there will be fewer copies of the virus to infect new cells, which will help buy the immune system space and time to eliminate the virus.

What is your favorite virus to study?

There are many fascinating viruses that cause a large spectrum of diseases! A group of diverse viruses that have made the news in recent years are broadly called “arboviruses” because they are transmitted to humans by insects. These viruses have adapted to survive and thrive in multiple hosts, very commonly mosquitoes. Arboviruses often cause fever and rash, but are known to also cause muscle and joint pain (Chikungunya virus), fetal abnormalities (Zika virus), meningitis (West Nile virus), or hemorrhagic fever (Dengue virus).

 

However, a special mention is needed for rotavirus, norovirus, and other enteric viruses, which infect the digestive system. They amazingly survive the harsh environments of the acidic stomach or even when flooded with digestive enzymes. They cause vomiting and diarrhea, which are both our bodies’ defense mechanism to get rid of the virus AND how the virus can spread to new hosts. Very clever!

Dr. Chang-Graham completed her Ph.D. studying how rotavirus causes life-threatening diarrhea particularly in children. While she loves studying infectious diseases in general, she is especially interested in viruses and how they take over host cells to cause physical disease. She is currently finishing her medical degree.

Why does it take so long to develop medical treatments?

Why does it take so long to develop medical treatments?
By Kristen Panthagani

Medical research is a loooooooooong process. Like, really long. There are many different steps along the way, and from the outside it can be confusing why there was a study published years ago saying Drug X works for Disease Y, but a decade later we still don’t have a treatment. Why is this?

Medical Research is a Process

In an ideal world, developing medical treatments would look like this.

Somebody has a great idea for a new treatment, and that new treatment quickly gets tested and developed. But because biology is super complicated, and there are lots of variables that we still don’t understand well, it usually ends up looking like this:

Lots of different types of studies build upon each other, ultimately leading to a well-tested, effective, and safe treatment. But even this doesn’t always work out. The vast majority of drug ideas end up failing somewhere along this path.

 

[Note: depending on the treatment under investigation, these steps aren’t always in exactly this order, and it’s often even messier and more squiggly path than the line drawn above. But for the sake of simplicity, I’m breaking it down into these simple steps illustrating the different types of studies that build upon each other to ultimately develop a new therapeutic.]

Step 1: Do the very first study.

Say a scientist or a doctor gets a great idea for a new therapeutic while drinking a beer at the pub. So they design an experiment (or in reality, more like 5-20 different experiments) and test out their idea. Depending on the type of study, the experiments that go into a single research article can take months (or more often years) to complete. Then, it’s published! The world knows! And, the PhD student who did the work gets a thesis. A success!!

 

But many of these ideas don’t end up working out… maybe after several months of experimenting, the data just doesn’t back it up. Some ideas sound great on paper, but fail to work right out of the gate. Even talented scientists come up with lots of ideas that don’t work.

Step 2: Reproduce it.

Ok let’s say the idea worked… in a single study. It’s published! But now comes the most central tenet of scientific research: it must be reproducible. Lots of studies end up failing this test (check out this book if you’re curious why). Depending on the type of scientific question, scientists might try to reproduce the results in a cell line (cells grown in a dish). These are very common experiments and are called in vitro studies (vitro is latin for glass, so basically it translates as: in a glass tube/dish/some other glass container). In reality, these are now usually made of plastic, but that’s ok. While in vitro studies provide valuable information (and are much easier to do than trials in animals or humans), it’s important to note how early they occur on this timeline… they are considered very preliminary evidence for drug efficacy, and you can’t definitively conclude that a drug works based only on in vitro studies. (Much of the early excitement about hydroxychloroquine was based on in vitro studies, and more than one confident gent on the internet sent me these studies as evidence that “it works.” But you can see how early it is on the timeline, and why many scientists remained skeptical of the in vitro studies on hydroxychloroquine, waiting for more rigorous human trials to decide if it really works for COVID-19.)

Step 3: Look at the data in humans.

The next step in the process (again depending on the therapeutic) might be to look for epidemiological data to see if the drug works. This is what happened more recently with hydroxychloroquine — because it was being prescribed as an emergency treatment, we could look back and see if the patients who got the drug did better than those who didn’t. (Usually drugs aren’t prescribed before they’ve gone all the way through this process, the pandemic was an exception). These type of observational studies provide better data than studies done in a dish of cells, but they still have problems — the main one is there is no guarantee that the people who got the drug are similar to those who didn’t. For example, maybe hydroxychloroquine was prescribed to patients who were sicker to begin with… this would confound the results of these types of studies. And studies may fail when they reach this level… just because cells in a dish are cured by the drug, that doesn’t mean that real breathing humans will be cured.

Step 4: Test it out in animals.

Most treatments will be evaluated in animals before going to human trials. Animal studies are usually one of the first major steps to test causation — is the thing you are studying really causing the effect that you think it is? Association studies in humans might fool you — some things are associated, but that doesn’t mean one is the cause of the other. In animal studies, we give them the treatment, and we can clearly tell if it’s causing the effect we expect.

Step 5: See if it's safe in humans.

Once we have lots of in vitro experiments and animal experiments showing high promise for a drug, it’s time to test it out in humans. For new drugs that have never been used in humans before, the very first test in humans is always safety. This is called a Phase I Trial — where the primary goal is to determine if the drug is safe. These studies usually start with just a few people who are given a very low dose of the drug, and they are monitored extremely carefully for any side effects. If no side effects are detected, the dose is slowly increased until the target (treatment) dose is tested. Sometimes an extremely promising treatment will fail at this step if it has significant side effects. It does not matter how many mice the drug has cured, if people’s livers don’t tolerate it, we’re not going to use it. About one third of Phase l trials fail at this step.

Step 6: See if it works in humans.

If a drug proves to be safe in a Phase I Trial, the next step is to see if it works in humans. Remember, just because a drug works in a dish of cells or in a bunch of mice does NOT mean it will work in human beings. Phase II Trials test this. These studies usually enroll 50-100 patients who have the disease of interest, and half are given the treatment while half are given the normal care they would get for the disease. If the people who got the treatment do better, then that is evidence the drugs works! They are also closely monitored for safety to see if there are rare side effects not detected in the Phase I Trial. About half of Phase II trials fail and don’t go onto the next step.

Step 7: Test it out in... more humans.

Phase III Trials test if the drug works better than the best treatment currently available. They are generally bigger than Phase II Trials (usually several hundred people) and include a placebo group to control for the placebo effect. Sadly, even though they’re so close to the finish line, even sometimes these studies end up failing (about 60% succeed). They fail for a variety of reasons — the placebo effect can be one of them.

Step 8: Approve it, but keep monitoring it.

Usually once a drug has made it through a Phase III Trials it can be approved by the FDA for use. However, that isn’t the end of the story… drugs that are approved are still monitored for safety and long-term efficacy. This is sometimes called a Phase IV Trial. Maybe there was a side effect that was rare but serious, and couldn’t be detected in the earlier trials. Maybe there are severe drug interactions that weren’t detected. If there are significant side effects that prove to outweigh the benefit of the drug, the drug can be recalled even after getting FDA approval.

And, voila! After decades of work and millions of research dollars, your great idea has actually turned into a treatment that doctors can prescribe! You can see this is a very long process, and the vast majority of treatment ideas end up failing. However, this does not stop the press and the general public from getting excited (which is fine) and confused (which is not fine) at every step along the way.

So now you know… if a study has the words in vitro, mouse, or really anything other than ‘randomized controlled trial’ in the title, it probably still has a very long way to go before we know if it’s going to work as a treatment in humans. (Check out @justsayinmice for one man’s effort to remind the world that many studies in mice are reported as if they have just saved humanity from disease.)

Concluding remarks to a PhD thesis written during two historic crises in the United States

Concluding remarks to a PhD thesis written during two historic crises in the United States
Kristen Panthagani

I just finished a draft of my PhD thesis, and we are allowed to include a “Concluding Remarks” section, in which we offer some perspective on science based on our thesis training. Here is what I wrote:*

 

In the weeks I have taken to write this thesis, turmoil has raged across the United States surrounding two separate but related crises: the COVID-19 pandemic and systemic racism. While the protests of the hour center around the death of George Floyd and police brutality, these protests along with the disproportionate impact of the COVID-19 on minority communities (1) bring to light other aspects of American society that have resulted in long-standing and egregious disadvantages for racial and ethnic minority groups in the United States. Studies of public health disasters like Hurricane Harvey (the topic of my thesis) reveal that many health outcomes are influenced by the racial, ethnic, and socioeconomic characteristics of the impacted individuals (2,3). The current crises in our nation highlight the need to analyze and address the root causes of health disparities in the United States, including the higher all-cause mortality for non-white individuals, the higher risk of pregnancy-associated death for non-Hispanic Black and American Indian/Alaska Native mothers, and the higher infant mortality rate for non-Hispanic Black children, just to name a few (4-6). The US clinical and biomedical research system is also not immune to racial and ethnic bias, as the vast majority of studies have failed to include representative numbers of non-white individuals, leaving critical knowledge gaps for other races and ethnicities (7). While the turmoil of the hour is unprecedented in my lifetime, I am hopeful that this outrage at injustice will lead to lasting change and inspire an entire generation of doctors and scientists to study, identify, address, and eliminate the root causes of systemic injustice against minority communities in the US biomedical research and healthcare system.

 

*modified to eliminate unpublished results from my thesis work.

Some ideas that might help in a swirling, confusing world

Some ideas that might help in a swirling, confusing world
Kristen Panthagani

There is a lot of confusion and division swirling around these days, and sometimes it feels like the world is spinning, and it’s hard to know up from down. I’ve found that when I can name what I am experiencing, it can help me process what’s going on and re-center my grasp on truth. Here are two concepts that I think may prove helpful in processing some of the events going on right now.

Gaslighting

Gaslighting is a form of manipulation where a person makes false statements that cause their audience to doubt their own memory, perception, or judgement. It is classically part of an abusive relationship — many abusers use gaslighting to confuse and disorient those they are abusing, trying to distract them from the fact they are being abused, or ultimately convince them that they are not being abused. A simple example might be: Person A told Person B they would go grocery shopping. Person A does not go grocery shopping, and Person B asks why. Person A responds by saying “I never said I would go grocery shopping” and accuses Person B of “misremembering.” Or Person A might say “I was only joking when I said that, you should have known I wasn’t seriously planning on going grocery shopping,” even though Person A was clearly not joking. Or even, Person A might get angry that Person B didn’t go grocery shopping, and blame them for the fact that there is now no food. When this goes on repeatedly, it is extremely disorienting and can be surprisingly powerful. It can make a smart, logical, confident person start questioning their own judgement and memory and sow a lot of self doubt. It feels like one’s mind is in a fog, and one is getting hit and hurt by words, but it’s hard to even put your finger on where they are coming from. Every time one tries to combat the lie that was said, the abuser tries to flip everything around, so there is no fair playing field of reason and truth. It is extremely disorienting.

Cognitive Dissonance

Cognitive dissonance is the state of believing or acting upon two or more conflicting ideas at once. This is when we as humans hold conflicting beliefs, and it makes our brains hurt. An example might be Belief 1: “I believe that person A is knowledgeable and trustworthy.” Belief 2: “What Person A just said is not true.” We’re not sure what to do with that, and it leads to confusion. Until we can reconcile our cognitive dissonance, usually our brains hurt and our responses may not be fully logical, because we are trying to respond based on conflicting ideas.

 

I have found that naming what is happening can help me process what I am experiencing. If you are experiencing either of these things, I hope this helps you too. Also if you see other people experiencing these things, have lots of grace for them. This stuff is hard.