# Bayesian reasoning in clinical diagnostics: a primer.

We know, from the source of eternal wisdom that is Saturday Morning Breakfast Cereal, that insufficient math education is the basis of the entire Western economy.1 This makes Bayesian logic and reasoning about probabilities almost like a dark art, a well-kept secret that only a few seem to know (and it shouldn’t be… but that’s a different story). This weird-wonderful argument, reflecting a much-reiterated meme about vaccines and vaccine efficacy, is a good example:

The argument, here, in case you are not familiar with the latest in anti-vaccination fallacies, is that vaccines don’t work, and they have not reduced the incidence of vaccine-preventable diseases. Rather, if a person is vaccinated for, say, measles, then despite displaying clinical signs of measles, he will be considered to have a different disease, and therefore all disease statistics proving the efficacy of vaccines are wrong. Now, that’s clearly nonsense, but it highlights one interesting point, one that has a massive bearing on computational systems drawing conclusions from evidence: namely, the internal Bayesian logic of the diagnostic process.

Which, incidentally, is the most important thing that they didn’t teach you in school. Bayesian logic, that is. Shockingly, they don’t even teach much of it in medical school unless you do research, and even there it’s seen as a predictive method, not a tool to make sense of analytical process. Which is a pity. The reason why idiotic arguments like the above by @Cattlechildren proliferate is that physicians have been taught how to diagnose well, but never how to explain and reason about the diagnostic process. This was true for the generations before me, and is more or less true for those still in med school today. What is often covered up with nebulous concepts like ‘clinical experience’ is in fact solid Bayesian reasoning. Knowing the mathematical fundamentals of the thought process you are using day to day, and which help you make the right decisions every day in the clinic, helps you reason about it, find weak points, answer challenges and respond to them. For this reason, my highest hope is that as many MDs, epidemiologists, med students, RNs, NPs and other clinical decision-makers will engage with this topic, even if it’s a little long. I promise, it’s worth it.

## Some basic ideas about probability

In probability, an event, usually denoted with a capital and customarily starting at $A$ (I have no idea why, as it makes things only more confusing!), is any outcome or incidence that we’re interested in – as long as they’re binary, that is, they either happen or don’t happen, and discrete, that is, there’s a clear definition for it, so that we can decide if it’s happened or not – no half-way events for now.2 In other words, an event can’t happen and not happen at the same time. Or, to get used to the notation of conditionality, $p(A \mid \neg A) = 0$.3 A thing cannot be both true and false.

Now, we may be interested in how likely it is for an event to happen if another event happens: how likely is $A$ if $B$ holds true? This is denoted as $p(A|B)$, and for now, the most important thing to keep in mind about it is that it is not necessarily the same as $p(B|A)$!4

Bayesian logic deals with the notion of conditional probabilities – in other words, the probability of one event, given another.5 It is one of the most widely misunderstood part of probability, yet it is crucial to understand to our own idea of the way we reason about things.

Just to understand how important this is, let us consider a classic example.

## Case study 1: speed cameras

Your local authority is broke. And so, it does what local authorities do when they’re broke: play poker with the borough credit card set up a bunch of speed cameras and fine drivers. Over this particular stretch of road, the speed limit is 60mph.

According to the manufacturer, the speed cameras are very sensitive, but not very specific. In other words, they never falsely indicate that a driver was below the speed limit, but they may falsely indicate that the driver was above it, in about 3% of the cases (the false positive rate).

One morning, you’re greeted by a message in your postbox, notifying you that you’ve driven too fast and fining you a rather respectable amount of cash. What is the probability that you have indeed driven too fast?

You may feel inclined to blurt out 97%. That, in fact, is wrong.

### Explanation

It’s rather counter-intuitive at first to understand why, until we consider the problem in formal terms. We know the probability $p(A|\not B)$, that is, the probability of being snapped ($A$) even though you were not speeding ($\not B$). But what the question asks is what the likelihood that you were, in fact, speeding ($B$) given the fact that you were snapped ($A$). And as we have learned, the conditional probability operator is not commutative, that is, $p(A|B)$ is not necessarily the same as $p(B|A)$.

Why is that the case? Because base rates matter. In other words, the probabilities of $A$ and $B$, in and of themselves, are material. Consider, for a moment, the unlikely scenario of living in that mythical wonderland of law-abiding citizens where nobody speeds. Then, it does not matter how many drivers are snapped – all of them are false positives, and thus $p(B|A)$, the probability of speeding ($B$) given that one got snapped by a speed camera ($A$), is actually zero.

In other words, if we want to reverse the conditional operator, we need to make allowances for the ‘base frequency’, the ordinary frequency with which each event occurs on its own. To overcome base frequency neglect,6 we have a mathematical tool, courtesy of the good Revd. Thomas Bayes, who sayeth that, verily,

$latex p(B \mid A) = \frac{p(A \mid B) p(B)}{p(A)} Or, in words: if you want to reverse the probabilities, you will have to take the base rates of each event into account. If what we know is the likelihood that you were not speeding if you were snapped and what we’re interested in is the likelihood that someone getting snapped is indeed speeding, we’ll need to know a few more things. ### Case study 1: Speed cameras – continued • We know that the speed cameras have a Type II (false negative) error rate of zero – in other words, if you are speeding ($B$), you are guaranteed to get snapped ($A$) – thus,$p(A \mid B)$is 1. • We also know from the Highway Authority, who were using a different and more accurate measurement system, that approximately one in 1,000 drivers is speeding ($p(B) = 0.001$). • Finally, we know that of 1,000 drivers, 31 will be snapped – the one speeder and 3% accounting for the false positive rate –, yielding $p(A) = 0.031$. Putting that into our equation, $p(B|A) = \frac{p(A \mid B) p(B)}{p(A)} = \frac{1 \cdot 0.001}{0.031} = 0.032$ In other words, the likelihood that we indeed did exceed the speed limit is just barely north of 3%. That’s a far cry from the ‘intuitive’ answer of 97% (quite accidentally, it’s almost the inverse). ## Diagnostics, probabilities and Bayesian logic The procedure of medical diagnostics is ultimately a relatively simple algorithm: 1. create a list of possibilities, however remote (the process of differential diagnostics), 2. order them in order of likelihood, 3. update priors as you run tests.7 From a statistical perspective, this is implemented as follows. 1. We begin by running a number of tests, specifically $m$ of them. It is assumed that the tests are independent from each other, i.e. the value of one does not affect the value of another. Let $R_j$ denote the results of test$j \leq m$. 1. For each test, we need to iterate over all our differentials $D_{i \ldots n}$, and determine the probability of each in light of the new evidence, i.e.$latex p(D_i \mid R_j).
2. So, let’s take the results of test $j$ that yielded the results $R_j$, and the putative diagnosis $D_i$. What we’re interested in is $p(D_i \mid R_j)$, that is, the probability of the putative diagnosis given the new evidence. Or, to use Bayesian lingo, we are updating our prior: we had a previous probability assigned to $D_i$, which may have been a uniform probability or some other probability, and we are now updating it – seeing how likely it is given the new evidence, getting what is referred to as a posterior.8
3. To calculate the posterior $P(D_i | R_j)$, we need to know three things – the sensitivity and specificity of the test $j$ (I’ll call these $S^+_j$ and $S^-_j$, respectively), the overall incidence of $D_i$,9 and the overall incidence of the particular result $R_j$.
4. Plugging these variables into our beloved Bayesian formula, we get $p(D_i \mid R_j) = \frac{p(R_j \mid D_i) p(D_i)}{p(R_j)}$.
5. We know that $p(R_j \mid D_i)$, that is, the probability that someone will test a particular way if they do have the condition $D_i$, is connected to sensitivity and specificity: if $R_j$ is supposed to be positive if the patient has $D_i$, then $p(R_j \mid D_i) = S^-_j$ (sensitivity), whereas if the test is supposed to be negative if the patient has $D_i$, then $p(R_j \mid D_i) = S^+_j$ (specificity).
6. We also know, or are supposed to know, the overall incidence of $D_i$ and the probability of a particular outcome, $R_j$. With that, we can update our prior for $D_i \mid R_j$.
2. We iterate over each of the tests, updating the priors every time new evidence comes in.

This may sound daunting and highly mathematical, but in fact most physicians have this down to an innate skill, so much so that when I explained this to a group of FY2 doctors, they couldn’t believe it – until they thought about how they thought. And that’s a key issue here: thinking about the way we arrive at results is important, because they are the bedrock of what we need to make those results intelligible to others.

## Case study 2: ATA testing for coeliac disease

For a worked example of this in the diagnosis of coeliac disease, check Notebook 1: ATA case study. It puts things in the context of sensitivity and specificity in medical testing, and is in many ways quite similar to the above example, except here, we’re working with a real-world test with real-world uncertainties.

There are several ways of testing for coeliac disease, a metabolic disorder in which the body responds to gluten proteins (gliadins and glutenins) in wheats, wheat hybrids, barley, oats and rye. One diagnostic approach looks at genetic markers in the HLA-DQ (Human Leukocyte Antigen type DQ), part of the MHC (Major Histocompatibility Complex) Class II receptor system. Genetic testing for a particular haplotype of the HLA-DQ2 gene, called DQ2.5, can lead to a diagnosis in most patients. Unfortunately, it’s slow and expensive. Another test, a colonoscopic biopsy of the intestines, looks at the intestinal villi, short protrusions (about 1mm long) into the intestine, for tell-tale damage – but this test is unpleasant, possibly painful and costly.

So, a more frequent way is by looking for evidence of an autoantibody called anti-tissue transglutaminase antibody (ATA) – unrelated to this gene, sadly. ATA testing is cheap and cheerful, and relatively good, with a sensitivity ($S^+_{ATA}$) of 85% and specificity ($S^+_{ATA}$) of 97%.10 We also know the rough probability of a sample being from someone who actually has coeliac disease – for a referral lab, it’s about 1%.

Let’s consider the following case study. A patient gets tested for coeliac disease using the ATA test described above. Depending on whether the test is positive or negative, what are the chances she has coeliac disease?

If you’ve read the notebook, you know by now that the probability of having coeliac disease if testing positive is around 22%, or a little better than one-fifth. And from the visualisation to the left, you could see that small incremental improvements in specificity would yield a lot more increase in accuracy (marginal accuracy gain) than increases in sensitivity.

While quite simple, this is a good case study because it emphasises a few essential things about Bayesian reasoning:

• Always know your baselines. In this case, we took a baseline of 1%, even though the average incidence of coeliac disease in the population is closer to about 0.25% of that. Why? Because we don’t spot-test people for coeliac disease. People who do get tested get tested because they exhibit symptoms that may or may not be coeliac disease, and by definition they have a higher prevalence11 of coeliac disease. The factor is, of course, entirely imaginary – you would, normally, need to know or have a way to figure out the true baseline values.
• Use independent baselines. It is absolutely crucial to make sure that you do not get the baselines from your own measurement process. In this case, for instance, the incidence of coeliac disease should not be calculated by reference to your own lab’s number of positive tests divided by total tests. This merely allows for further proliferation of false positives and negatives, however minuscule their effect. A good way is to do follow-up studies, checking how many of the patients tested positive or negative for ATA were further tested using other methodologies, many of which may be more reliable, and calculate the proportion of actual cases coming through your door by reference to that.

## Case study 3: Vaccines in differential diagnosis

This case is slightly different, as we are going to compare two different scenarios. Both concern $D_{VPD}$, a somewhat contrived vaccine-preventable illness. $D_{VPD}$ produces a very particular symptom or symptom set, $S$, and produces this symptom or symptom set in every case, without fail.12 The question is – how does the vaccination status affect the differential diagnosis of two identical patients,13 presenting with the same symptoms $S$, one of whom is unvaccinated?

It has been a regrettably enduring trope of the anti-vaccination movement that because doctors believe vaccines work, they will not diagnose a patient with a vaccine-preventable disease (VPD), simply striking it off the differential diagnosis or substitute a different diagnosis for it.14 The reality is explored in this notebook, which compares two scenarios, of the same condition, with two persons with the sole difference of vaccination status. That difference makes a massive – about 7,800x – difference between the likelihood of the vaccinated and the unvaccinated person having the disease. The result is that a 7,800 times less likely outcome slides down the differential. As NZ paediatrician Dr Greenhouse (@greenhousemd) noted in the tweet, “it’s good medical care”. In the words of British economist John Maynard Keynes,15 “when the facts change, I change my mind”. And so do diagnosticians.

Quite absolutely simply put: it’s not an exclusion or fudging data or in any sensible way proof that “no vaccine in history has ever worked”. It’s quite simply a reflection of the reality that if in a population a condition is almost 8,000 times less likely, then, yes, other more frequent conditions push ahead.

## Lessons learned

Bayesian analysis of the diagnostic procedure allows not only increased clarity about what one is doing as a clinician. Rather, it allows the full panoply of tools available to mathematical and logical reasoning to investigate claims, objections and contentions – and like in the case of the alleged non-diagnosis of vaccines, discard them.

The most powerful tool anyone who utilises any process of structured clinical reasoning – be it clinical reasoning in diagnostics, algorithmic analysis, detective work or intelligence analysis – is to be able to formally reason about one’s own toolkit of structured processes. It is my hope that if you’ve never thought about your clinical diagnostic process in these terms, you will now be able to see a new facet of it.

References   [ + ]

 1 ↑ The basis of non-Western economies tends to be worse. That’s about as much as Western economies have going for them. See: Venezuela and the DPRK. 2 ↑ There’s a whole branch of probability that deals with continuous probabilities, but discrete probabilities are crazy enough for the time being. 3 ↑ Read: The probability of A given not-A is zero. A being any arbitrary event: the stock market crashing, the temperature tomorrow exceeding 30ºC, &. 4 ↑ In other words, it may be the same, but that’s pure accident. Mathematically, they’re almost always different. 5 ↑ It’s tempting to assume that this implies causation, or that the second event must temporally succeed the first, but none of those are implied, and in fact only serve to confuse things more. 6 ↑ You will also hear this referred to as ‘base rate neglect’ or ‘base rate fallacy’. As an epidemiologist, ‘rate’ has a specific meaning for us – it generally means events over a span of time. It’s not a rate unless it’s necessarily over time. I know, we’re pedantic like that. 7 ↑ This presupposes that these tests are independent of each other, like observations of a random variable. They generally aren’t – for instance, we run the acute phase protein CRP, W/ESR (another acute phase marker) and a WBC count, but these are typically not independent from each other. In such cases, it’s legitimate to use $B = B_1 \cap B_2 \cap \ \ldots \cap B_n$$B = B_1 \cap B_2 \cap \ \ldots \cap B_n$ or, as my preferred notation goes, $B = \bigcap^n_{k=1} B_k$$B = \bigcap^n_{k=1} B_k$. I know ‘updating’ is the core mantra of Bayesianism, but knowing what to update and knowing where to simply calculate the conjoint probability is what experts in Bayesian reasoning rake in the big bucks for. 8 ↑ Note that a posterior from this step can, upon more new evidence, become the prior in the next round – the prior for $j$$j$ may be the inferred probability $p(D_i)$$p(D_i)$, but the prior for $j + 1$$j + 1$ is $p(D_i \mid R_j)$$p(D_i \mid R_j)$, and so on. More about multiple observations later. 9 ↑ It’s important to note that this is not necessarily the population incidence. For instance, the overall incidence and thus the relevant $D$$D$ for EBOV ($D_{EBOV}$$D_{EBOV}$) is going to be different for a haemorrhagic fever referral lab in Kinshasa and a county hospital microbiology lab in Michigan. 10 ↑ Lock, R.J. et al. (1999). IgA anti-tissue transglutaminase as a diagnostic marker of gluten sensitive enteropathy. J Clin Pathol 52(4):274-7. 11 ↑ More epidemiopedantry: ‘incidence’ refers to new cases over time, ‘prevalence’ refers to cases at a moment in time. 12 ↑ This is, of course, unrealistic. I will do a walkthrough of an example of multiple symptoms that each have an association with the illness in a later post. 13 ↑ It’s assumed gender is irrelevant to this disease. 14 ↑ Presumably hoping that refusing to diagnose a patient with diphtheria and instead diagnosing them with a throat staph infection will somehow get the patient okay enough that nobody will notice the insanely prominent pseudomembrane… 15 ↑ Or not…

# Ebola: a primer.

After this post was published, a lot of people have asked me to do a Reddit AMA, where I could answer some questions from a wide audience. The AMA has concluded by now, but you can read the questions and answers here.

As I’m writing this, the beginnings of what could well be a major outbreak are raging in Bikoro territory, Equateur province, in the northeast of the Democratic Republic of the Congo (DRC). Recent news indicate that Mbandaka, the capital of Equateur and home to a busy port and a million people, has now reported cases as of 17 May. The death toll has reached 25 as of the time of writing, and it’s anyone’s guess how bad it’ll get – having learned from the unexpectedly extensive devastation of the West African Zaire ebolavirus outbreak (2013-16), everybody is preparing for the worst case scenario. Me and ebolaviruses have a long relationship, going back over a decade – I sometimes tend to wistfully remark that I know more about virion protein (VP) 24 of the Zaire ebolavirus (EBOV) than I know about some of my own family members. The reverse of the medal is that reading some of the nonsense in the press is borderline physically painful. I’ve assembled these resources for interested laypeople – especially journalists intending to comment on the Bikoro outbreak, in hopes that it will somewhat reduce misunderstandings.

### Some taxonomy pedantry for starters

To start with, a point of pedantry: there are multiple ebolaviruses, so technically, ‘Ebola virus’ is a misnomer. Viral taxonomy is a complex thing, governed largely by the International Committee on the Taxonomy of Viruses (ICTV). The latter has preliminarily determined the taxonomy of filoviruses to look as follows:1

• Family Filoviridae
• Genus Ebolavirus
• Species Bundibugyo ebolavirus (BDBV)
• Species Reston ebolavirus (RESV or RESTV)
• Species Sudan ebolavirus (SUDV)
• Species Taï Forest ebolavirus, formerly Côte d’Ivoire ebolavirus (TAFV)
• Species Zaire ebolavirus (EBOV or ZEBOV)
• Genus Marburgvirus
• Species Marburg marburgvirus (MARV)

By far the most important of these are EBOV and SUDV. These have been responsible for almost all major outbreaks – TAFV had only one single human case (CFR:2 1/0, 0%), RESV killed a lot of monkeys3 but a number of humans, despite seroconverting,4 did not fall ill. SUDV is generally regarded as somewhat more benign than EBOV, with a CFR around 50% (range 41-65%, discounting the 2011 Luweero case, where the single patient died). EBOV is the type species of ebolavirus, and it commonly has mortalities up to 93%. It is almost definite that the current outbreak in the DRC is an EBOV outbreak.

Viral species are further subdivided into strains. This is important for ebolaviruses, EBOV in particular, because there seems to be an emerging divergence. Typically, ebolavirus outbreaks claim up to 3-400 lives at most, tend to be over in 3-4 months and are fairly localised. Because non-RESV ebolaviruses, at least in humans, need contact with bodily fluids, long chains of transmission are rare. The 2013-16 West African outbreak, however, seems to have upended this hypothesis. That outbreak lasted almost twelve times the average for all known outbreaks until then, and claimed more lives than all known ebolavirus outbreaks (since the index outbreak in Yambuku, DRC, in 1976) put together. Why this was the case is a bit of a mystery, but there is now an understanding that EBOV strains that are more similar to the Mayinga (EBOV/May) strain isolated in 1976 are different from strains more similar to the Makona strain (EBOV/Mak), which was the prevalent strain in the West African outbreak.

### Background and history

Ebolaviruses belong to the family of filoviridae, so named for their threadlike appearance – they are among some of the longest viruses, reaching a length of up to 14,000nm and a width of approximately 80nm. The genome of ebolaviruses is relatively simple, approximately 19,000 base pairs long, and stored as a single-strand negative sense RNA, making ebolaviruses, and all other filoviridae, (–)ssRNA or Baltimore V viruses. This is significant as negative-sense single-strand RNA viruses need to be translated into positive-sense RNA by RNA polymerase, and therefore aren’t directly infectious.

Ebolaviruses, and other filoviruses, are probably pretty old – in fact, the study by Taylor et al. (2014) has found genetic fossils5 of EBOV-like VP35 in the same location of several cricetid rodents’ (voles and hamsters) genomes, suggesting that ebolaviruses have diverged from marburgviruses around the time the common ancestor of hamsters and voles lived, sometime around the miocene (16-23 million years ago).6

We also know that EBOV only relatively recently diverged from other ebolaviruses (sometime in the last century), but the first acknowledged outbreak of an ebolavirus took place in 1976 in Yambuku, in what was then Zaïre and is today the DR Congo. The story of this outbreak is extensively told in a retrospective article by Joel Breman and a number of others who have been present at the initial outbreak, written four decades later. Arguably, we saw the emergence of a physiologically and epidemiologically different strain of EBOV during the West African EBOV epidemic, too – at least in the wild, EBOV/Mak behaved very differently from EBOV/May: characterised by long chains of transmission, a somewhat lower CFR7 and a much longer epidemic duration with a significantly larger number of cases – indeed, the 2013-16 outbreak claimed more lives than every single known filoviral outbreak since the first recorded filoviral epidemic, the 1967 Marburg outbreak, put together. Recent evidence seems to suggest that infection with EBOV/Mak does seem to exhibit some significant differences from the previously known strains that are clinically different to the point that they might explain the difference between the 2013-2016 West African outbreak and previous epidemics, which typically were regionally limited, originated in central Africa (Sudan and Zaire) rather than the coastal states of the Gulf of Guinea and lasted a few months with no more than 3-400 cases.8

• Two of the protagonists of the 1976 Yambuku outbreaks have written amazing autobiographies that are worth reading. No Time to Lose, by Peter Piot, is a fascinating book, although most of it – like Peter Piot’s career – is devoted to STDs, especially the fight against AIDS. His colleague and countryman, Guido van der Groen, has also written an engaging and well-written memoir, On the Trail of Ebola.
• Murphy, F.A. (2016): Historical perspective: what constitutes discovery (of a new virus)? In: Kielian, M. et al. (eds)., Advances in Virus Research95:197-220. – What’s it like to discover a virus? Fred Murphy, whose transfer electron micrograph graces the header of this blog  post and has become inextricably associated with ebolaviruses, was working as CDC’s chief viropathologist in 1976, and if not a father of EBOV’s discovery, he is at the very least its godfather. His experiences with Ebola specifically are summarised in section 5.8 of the chapter.
• Tropical medicine professor and ID physician David Brett-Major‘s book, A Year of Ebola, is an up-close-and-personal account of a year of the 2013-2016 West African outbreak, and the challenges that rendering assistance in the chaos of such an outbreak. For those unfamiliar with what a major, multi-party public health intervention involves, this book is a must-read.
• A good and somewhat lighthearted starter is my interview with Devon from the Bugs, Blood and Bones podcast: part 1 | part 2. This discusses many of the principal points you should know about ebolaviruses, especially the reason we can’t simply eliminate ebolaviruses as easily as, say, smallpox.

### The (proteomic) nature of the beast

Ebolaviruses are remarkably simple for all the destruction they’re capable of. To understand the issues that curing ebolavirus infections raises, it’s important to understand how the virus itself is constructed and how it operates on a molecular level. The ebolavirus genome encodes seven proteins: a nucleoprotein (NP), a RNA polymerase (L), the glycoprotein GP, and four viral proteins (VPs): VP24, VP30, VP35 and VP40 (sometimes referred to as the matrix protein). For this reason, some of Ebola’s viral proteins ‘moonlight’ – that is, they fulfill multiple functions, depending on their polymerisation state.

• The overall structure of the virion is given by the ebola matrix protein or VP40. As a hexamer looking a bit like the S-shaped Tetris piece,9 it’s responsible for the structure of the virion, while as a crown-shaped octamer wrapped around the RNA, it regulates RNA transcription. The matrix protein’s main purpose, other than serving as a physical outer shell, is to connect the nucleocapsid with the target cell’s membrane, allowing penetration. VP40 also gives ebolaviruses the characteristic structure. For this reason, and the fact that it also coordinates some aspects of the viral lifecycle – in particular virion assembly and ‘budding’, that is, egress from infected cells –, it’s being considered as a therapeutic target.10
• The RNA is surrounded by a dynamic nucleocapsid, made up of VP35, VP30 and VP24. The purpose of this is to store and, at the necessary time, deliver, the genetic payload. The nucleoprotein NP is wrapped around the RNA genome.
• VP24 is also used to disrupt the innate immune system, specifically the STAT1 signalling pathway. Normally, in response to viral infections, interferons phosphorylate the STAT1 protein, which then binds to karyopherin alpha (KPNA). Karyopherin alpha is an ‘importin’, a shuttle protein. Once STAT1 is bound to KPNA, it is ferried to the nucleus, and stimulates gene transcription. VP24 selectively tricks this: it binds competitively to KPNA, so that STAT1 cannot bind to it. In a sense, VP24 is hijacking the cell’s internal shuttle system, preventing an adequate immune response but maintaining the ability to use the system for its own purposes.
• L, or RNA-dependent RNA polymerase, is required because ebolaviruses are negative-sense single strand RNA viruses, and thus a complementary, positive sense strand needs to be generated for transcription.
• GP, the ebolavirus glycoprotein, is perhaps the most essential part of the internal machinery of an ebolavirus. GP is responsible for infecting new cells, and for a cytopathogenic effect on endothelial cells – in other words, GP damages the cells that line blood vessels in particular and has been observed to cause endothelial cell loss. This in turn results in the haemorrhagic symptoms that characterise EVD’s haemorrhagic stage.11

### Ebola virus disease (EVD) and pathophysiology

Human and primate ebolavirus infection (regardless of species or strain) causes Ebola Virus Disease (EVD), sometimes referred to as Ebola haemorrhagic fever (EHF). EVD is more accurate as the well-known haemorrhagic manifestations are far from ubiquitous (about half the cases at best).12

EVD begins with nonspecific signs – like a bad flu: after an incubation time of about 4 days to two weeks, fatigue, fever, loss of appetite and muscle aches set in, along with vomiting, diarrhoea and more vomiting. Despite its apparent simplicity, ebolaviruses carry out a complex and multifactorial propgramme of destruction:

1. Prodromic stage: In the early, prodromic stage, the viral protein VP24 inhibits interferon type I and II signalling, effectively cutting the communication lines of the immune system and allowing the virus to proliferate in peace. During this time, the patient may be asymptomatic or have nonspecific symptoms like headaches, fatigue and a mild
2. Early disseminating stage: Ebolaviruses preferentially attack certain white blood cells that allow it to spread through the lymphatic system, in particular dendritic cells, macrophages and monocytes, and later on spread prolifically through liver cells and the adrenal gland, causing liver damage (leading to clotting issues and the diagnostically significant elevated transaminase levels). The death of the infected monocytes (called a cytopathic or cytopathogenic effect) causes immunosuppression through low lymphocyte counts and releases pro-inflammatory molecules, in particular TFN-alpha, and the interleukins IL-6 and IL-8, creating a state somewhat reminiscent of sepsis. GP also assists in inhibiting neutrophils, white blood cells crucial for immune reactions, from activating.
3. Vascular endothelial damage: Glycoprotein (GP) in vascular endothelial cells (the cells lining the walls of blood vessels) destroys the integrity of blood vessels around three to four days after infection, leading to bleeding.
4. Liver injury and DIC: GP, when expressed in the liver, causes liver damage, and also suppresses the production of integrins. Integrins are transmembrane proteins that allow cells to attach to the various molecules outside the cell, which is crucial for clotting. Together, these lead to a paradoxical state called disseminated intravascular coagulation (DIC): small blood clots form in the capillaries all over the body, leading to ischemia (lack of blood supply) and organ failure, while at the same time using up all the clotting factors and platelets. This is responsible for the later haemorrhagic manifestations.
5. At this stage, patients that do not recover succumb to the combined effects of multi-organ failure, volume loss from diarrhoea and massive haemorrhage.

Together, these have a damaging effect on vascular endothelial cells, the cells lining the walls of blood vessels, leading to internal bleeding and the haemorrhagic manifestations.

Eventually, the haemorrhagic (bleeding) symptoms – bleeding under the skin, uncontrollable bleeding from blood draws, bleeding into the sclerae (the whites of the eyes), blood in vomit and faeces – may begin, largely because damage to the liver and depletion of clotting factors.

Death usually occurs 8-14 days from onset of symptoms. Contrary to popular perception, death is actually not caused by bleeding out – the blood loss is quite simply not enough to be fatal, even in the haemorrhagic cases. Rather, ebolaviruses turn the body’s own inflammatory cascades on overdrive, causing a state that’s somewhat similar to septic shock. Survivors begin to feel better around 10-14 days after first symptoms, but recovery is slow and can take months.

• Geisbert, T.W. and Feldmann, H. (2011): Ebola haemorrhagic fever. Lancet 377:849-62. – a great summary, while intended for professional audiences, it is probably the most comprehensive article on what we know about ebolaviruses. Nb. that it was written before the 2013-16 West African outbreak.
• Munoz-Fontela, C. and McElroy, A.K. (2017): Ebola virus disease in humans: pathophysiology and immunity. In: Mühlberger E. et al. (eds.), Marburg- and Ebolaviruses. Springer, 2017. – This is a rather pricey book, and aimed at public health experts, but is probably the best summary of post-West African outbreak scholarship on all things ebola- and marburgviruses. For those writing for a professional audience or desiring a more comprehensive understanding of the underlying biology, it’s a must-have. Disclaimer: many of the chapter authors and editors are friends and/or valued colleagues.

### Ecology and reservoir hosts

Finding the reservoir host of ebolaviruses and Marburg marburgvirus has consumed an incredible amount of scientific effort during the 1980s and 1990s, with relatively little to show for it. It was clear from the very beginning that ebolaviruses are zoonotic – that is, there’s a reservoir host, an animal in which the virus can persist and multiply without causing disease. This explains why it sometimes appears as if ebolaviruses (and Marburg) came out of nothing, wreaked havoc, then disappeared as fast as they appeared. Using RT-PCR and qRT-PCR, it’s now clear that that the reservoir hosts are bats, and a number of species, in particular certain fruit bats. Bats have a complex interferon (IFN) system, much more complex than the human or NHP13 IFN system. This seems to give them an ability to manage the infection in their bodies (see the Kühl and Pöhlmann paper below).There’s a global increase of bat-borne pathogens causing outbreak – these are almost all viral (the related henipaviruses Hendra virus in Australia and Nipah virus in Malaysia/Bangladesh, the coronaviruses MERS-CoV and SARS-CoV, rabies, etc.). As humanity, in need of arable land across the world to feed the exploding population and mineral resources like diamonds and coltan, encroaches upon traditional habitats of Chiropteran species, especially the caves and jungles where they roost, interactions between bats and humans will become more and more frequent, raising the risk of infections. Clearly a strategy to manage ebolaviruses must also be able to manage the ecological problem of habitat loss.

• Kühl, A. and Pöhlmann, S. (2012): How Ebola Virus Counters the Interferon System. Zoon Pub Health 59:116-131. – great paper, but tough to digest for non-technical audiences. For those who prefer a slightly more relaxed version, see the next link.
• Fagre, A. (2016): Why don’t bats get Ebola? Scientific American Guest Blog, July 18, 2016. – same topic as above, just for more popular audiences.
• On ecology, the chapter Ecology of Filoviruses in Mühlberger et al. (eds.), op cit, is worth reading.
• For understanding zoonotic diseases, Spillover by David Quammen (2013) is an excellent read. Ebola: The Natural and Human History of a Deadly Virus, written in 2014, updates his chapter on ebolaviruses – largely EBOV – for an audience hungry for information after the 2013 West African outbreak. – Quammen has a great style and writes well, without Preston’s sensationalism. If this is your first foray into writing about, or trying to understand, filoviral zoonoses, both books are very much worth reading. The added value of whatever was added to the Ebola chapter in Spillover in Ebola: The Natural and Human History is, to me at least, dubious. It is, however, a much shorter read for those pressed for time.

### Treatment and prophylaxis

So far, no particular agent has proved to be conclusively effective against EBOV infection after symptoms have emerged, and treatment is mainly symptomatic. It is haunting that the state of the art in treating filoviral haemorrhagic fever 2018 is not much different from the approach Margaretha Isaäcson and her team used on the three Marburg cases – Cases 1 and 2, Australian hitchhikers, and Case 3, a nurse who took care of both Cases 1 and 2 – in 1975:

At this stage, it became clear that there would be no specific treatment that could be relied upon to attack and kill the virus responsible for this infection. The girls’ only chance of survival would, therefore, depend on meticulous, ongoing monitoring of various organ functions and managing clinical problems in anticipation or as they presented themselves. This approach required a large team in support of the core formed by the clinicians responsible for the daily evaluation, treatment and general management of the patients.
– from the notes of Margaretha Isaäcson, 26 February 1975

Treatment is focused on volume and electrolyte replacement (intravenously or using oral rehydration salts aka ORSs), pain management and blood transfusions to combat blood loss. To manage disseminated intravascular coagulation and the ensuing coagulopathy, heparin and clotting factors have both been used, with mixed success. Intensive care can greatly increase survival chances, but in low resource settings this remains a challenge. The West African outbreak has demonstrated the utility and sustainability of three-segment (four, if you count the morgue) Ebola Treatment Centres (ETCs, see image) as an easy and inexpensive way to reduce nosocomial spread (spread within a healthcare facility). The model ETC design, which separates confirmed, low-probability and high-probability cases, reduces the risk to lower probability cases by separating them from higher-probability or confirmed cases. One of the painful lessons of the 1976 Yambuku outbreak was that reuse of medical equipment, in particular of hypodermic needles and syringes, can greatly contribute to the spread of ebolaviruses, and this makes overcoming the logistic challenges of dealing with an ebolavirus outbreak in an isolated and ill-accessible location all the more acute.

There are no specific treatment options for EVD that have stood the test of time and rigorous trials. A few of the most often discussed specific treatment options are outlined below:

• Convalescent plasma has for a long time been the best hope against filoviral infections, but is not always accessible and has its own risks, such as residual viral loads. It also doesn’t keep too well (like liquid plasma, it must be kept between +2ºC and +6ºC). It is taken from survivors of the infection using plasmapheresis, a process quite similar to haemodialysis except in this case, the dialysate is retained. This contains antibodies that the patient developed following his infection. Convalescent plasma also contains a range of other antibodies, and these can cause various immune reactions – importantly, convalescent plasma must come from healthy individuals (‘donor qualified’, i.e. adequate haemoglobin levels and free from bloodborne pathogens) that are compatible with the recipient’s blood type. In regions where ebolaviruses are endemic, this is one of the easiest treatment options to implement, but the efficacy of convalescent plasma may be hampered by epitopic dissimilarity (that is, if the strain the donor recovered from and the strain the recipient is suffering from are too dissimilar, the antibodies won’t work). The WHO has worked out a detailed guideline on using convalescent plasma, which also highlights one of its greatest drawbacks: it works best for patients with early stage disease.
• ZMapp is a biological drug, specifically a monoclonal antibody. Monoclonal antibodies are artificially created equivalents of the antibodies in convalescent plasma. The great benefit of ZMapp over convalescent plasma is that it only contains antibodies specifically against EBOV, and as such the risk of immune reactions is negligible. ZMapp’s efficacy is quite controversial, as due to the scarcity and cost of the drug, the number of patients treated was too low to really be able to draw conclusions from.
• Brincidofovir is a broad spectrum antiviral against DNA viruses, such as cytomegalovirus, smallpox and herpes simplex. For some reason, its lipid moiety appears to have shown some efficacy against EBOV, even though EBOV is not a DNA virus but a (-)ssRNA (negative single sense RNA, Baltimore Group V) virus. However, a very small (n=4) Phase II trial in Liberia was prematurely cancelled, and all enrolled subjects died of EVD, after the manufacturer decided to stop pursuing EVD as a target for brincidofovir.
• Favipiravir is also a broad spectrum antiviral, with specific activity against RNA viruses, initially developed against influenzaviruses. The JIKI trial was conducted in Gueckedou, the ground zero of the 2013-2016 outbreak, in September 2014, and has indicated some efficacy for patients with less severe disease (low to medium viral loads). Controversially, because the criteria weren’t met for a proper randomised clinical trial in late 2014, the JIKI trial was historically controlled, and this has drawn extensive professional criticism.

There are a range of ebolavirus vaccines, most specifically targeting EBOV. The two currently available vaccines are rVSV-ZEBOV and the cAd3-ZEBOV vaccine (colloquially referred to as the NIAID vaccine).

• rVSV-ZEBOV is a somewhat quirky viral vaccine. It is intended to create antibodies to GP, the virion glycoprotein of EBOV. Normally, vaccines contain an adjuvant and an antigen, such as a viral protein (e.g. the HPV vaccine contains the protein shell, called the L1 major capsid protein, of various HPV strains). The immune system then recognises this as foreign and generates antibodies against them. rVSV-ZEBOV works a little different – it actually contains a live virus, VSV (vesicular stomatitis virus or Indiana vesiculovirus, a distant relative of rabies), which is harmless in humans but causes a disease very similar to foot and mouth disease in cattle and horses. This recombinant (hence r) VSV expresses small amounts of GP, to which the body then generates antibodies. In a ring vaccination trial called Ebola ça Suffit-GRV Trial, 7,284 participants were recruited in Guinea and a parallel trial with the rVSV-ZEBOV vaccine was carried out in Sierra Leone by the CDC (the STRIVE VSV-EBOV trial). The trial faced complex ethical dilemmas. Placebo control would clearly not be ethically (or politically) acceptable, so instead the trial participants were randomised into two cohorts, some of whom received the vaccine after a three week delay. However, due to encouraging early results, the control arm was effectively dispensed with and everybody was vaccinated. The National Academies of Sciences, Engineering and Medicine published an report in which they assessed the trials, and found that much like in the case of favipiravir, it’s hard to do assess a life-saving treatment in the middle of a lethal epidemic. The WHO has announced that it will use the rVSV-ZEBOV vaccine to ring vaccinate contacts of known, laboratory confirmed cases, from 21 May onwards, and has a stock of 7,000 doses of the vaccine in cold storage in Kinshasa. Ring vaccination has been used successfully in the eradication of smallpox, and there is ample evidence to its efficacy and the ability to control further spread, provided contact tracing is successful.
• cAd3-ZEBOV aka the NIAID/GSK vaccine is a similarly structured vaccine, but derived from a chimpanzee adenovirus, ChAd3. Like the rVSV-ZEBOV vaccine, the cAd3-ZEBOV vaccine expresses glycoproteins from EBOV and, depending on configuration, SUDV.14 This vaccine is considered less ‘ready for use’, and while it’s been found safe, it is not clear what efficacy it will ultimately have.

• On Ebola treatment centres, Chowell, G. and Viboud, C. (2015): Controlling Ebola: key role of Ebola treatment centres. Lancet Inf Dis 15(2):139-141. – a good outline of the cheap yet surprisingly effective three-stage treatment centre model.
• Medecins Sans Frontieres, who have pioneered the three-stage treatment centre structure, have a great interactive guide to a treatment centre that reflects the idea of segregation by infection probability quite well.
• David Kroll’s article in Forbes asks the question on everyone’s mind: how will we know if the Ebola drugs used during the West African outbreak have indeed worked?Most patients received multiple different treatments, and the sample size was quite small – most of the patients in Africa have only received the usual symptomatic treatment. Clearly, there’s a huge ethical issue, and one of health equity, involved here: many drugs, high costs, many patients, and a willingness to give patients every possible chance at survival. The moral imperatives and the practicalities of the situation make it hard for researchers to gauge efficacy of individual treatments.
• Adebamowo, C. et al. (2014). Randomised controlled trials for Ebola: practical and ethical issues. Lancet 384:1423-1424. – when it comes to clinical trials for diseases with high mortality, complex ethical issues arise. This makes research and the traditional methods of evaluating treatments difficult. Randomised controlled trials, the gold standard when it comes to assessing the efficacy of medical interventions, are difficult to conduct in the middle of a devastating epidemic, and raises complex ethical issues.
• National Academies of Sciences, Engineering and Medicine (2017). Integrating Clinical Research into Epidemic Response: The Ebola Experience. The National Academies Press, Washington, DC. – this is probably the best overview of the current state of the art when it comes to vaccines for EBOV after the West African outbreak. Chapter 4 is a must-read for vaccines, and chapter 3 for clinical treatments. Furthermore, Chapter 2 is a great in-depth exploration of the Scylla and Charybdis of doing high-quality, evidence-based clinical research in the middle of an epidemic with a high-mortality viral disease.

### Keeping up to date & other stuff to read

The situation is currently quite rapidly evolving, and information flow is often quite restricted due to unreliable communication links. Perhaps the best source of information about what’s going on at the time is ProMED-mail, run by ISID. I also tweet pretty prolifically about the emerging crisis and other public health issues (you can find me at @chrisvcsefalvay), and of course you can find all my blog posts and public appearances that involve filoviruses on this page. I’m also always happy to answer questions, here in the comments thread or using the contact form (if you’re writing for a publication, please use the contact form).

I hope this primer to ebolaviruses was helpful, and if you intend to write about the subject, you now feel better informed. Please feel free to raise any questions that you think remain open in the comment thread below!

References   [ + ]

 1 ↑ See ICTV page on filoviral taxonomy. 2 ↑ Case-fatality rate, i.e. the number of cases versus the number of deaths. Typically given as case/fatality, percentage – e.g. 10/3 (30%) means 10 cases, 3 died, 30% CFR. 3 ↑ This is the outbreak dramatised in Preston’s Hot Zone. 4 ↑ Seroconversion refers to developing antibodies against a pathogen. It does not mean actually becoming sick as well, just that the body has encountered the pathogen and has responded to it. 5 ↑ A fossil gene is what happens when a virus does not infect or kill the host, but rather incorporates bits and pieces of the viral genome into its own. 6 ↑ Taylor, D.J. et al. (2014). Evidence that ebolaviruses and cuevaviruses have been diverging from marburgviruses since the Miocene. PeerJ 2 Sep 2014, 2:e556. 7 ↑ Case-fatality ratio or case-fatality rate, which is a misnomer, since it’s neither a rate nor a ratio in the epidemiological sense. Normally given as a percentage, it is defined as $\frac{C_d}{\Sigma C}$$\frac{C_d}{\Sigma C}$, where $C_d$$C_d$ describes all deceased cases and $\Sigma C$$\Sigma C$ is defined as the total of all cases that meet the inclusion criteria. 8 ↑ Versteeg, K. and Geisbert, T.W. (2017). Infection with the Makona variant results in a delayed and distinct host immune response compared to previous Ebola virus variants. Scientific Reports 7:9730. 9 ↑ Officially, a ‘mirrored Z free tetromino‘. Except, of course, it’s a hexomino. 10 ↑ Madara, J.J., Harty, R.N. et al. (2015). The multifunctional Ebola virus VP40 matrix protein is a promising therapeutic target. Future Virol (10)5: 537-546. 11 ↑ Yang, Z.Y., Nabel, G.J. et al. (2000). Identification of the Ebola virus glycoprotein as the main viral determinant of vascular cell cytotoxicity and injury. Nature Med 6(8):886-9. 12 ↑ The descriptions of ebolaviruses or even Marburg turning patients into bags of goo or exploding with blood, largely inspired by Preston’s Hot Zone, are wildly inaccurate. Still, it’s one nasty disease. 13 ↑ Non-human primate. 14 ↑ The vaccine is intended to express glycoproteins from both when in production use. The current Phase II UK trials, conducted by Oxford University’s Jenner Institute, are done with a variant expressing only EBOV GP.

# Herd immunity: how it really works

There are few concepts as trivial yet as widely misunderstood as herd immunity. In a sense, it’s not all that surprising, because frankly, there’s something almost magical about it – herd immunity means that in a population, some people who are not or cannot be immunized continue to reap the benefit of immunization. On its own, this may even be counter-intuitive. And so, unsurprisingly, like many evidently true concepts, herd immunity has its malcontents – going so far as to condemn the very idea as a ‘CDC lie’ – never mind that the concept was first used in 1923, well before the CDC was established.1

Now, let’s ignore for a moment what Dr Humphries, a nephrologist-turned-homeopath with a penchant for being economical with the truth when not outright lying, has to say – not because she’s a quack but because she has not the most basic idea of epidemiology. Instead, let’s look at this alleged ‘myth’ to begin with.

### Herd immunity: the cold, hard maths

Our current understanding of herd immunity is actually a result of increasing understanding of population dynamics in epidemiology, towards the second half of the 20th century. There are, on the whole, two ways to explain it. Both are actually the same thing, and one can be derived from the other.

#### The simple explanation: effective $R_0$$R_0$ depletion

The simple explanation rests on a simplification that makes it possible to describe herd immunity in terms that are intelligible at the level of high school maths. In epidemiology, $R_0$ (pron. ‘arr-nought‘, like a pirate), describes the basic reproduction rate of an infectious disease.2 To put it at its most simplistic: $R_0$ is the number of cases produced by each case. The illustration on the side shows the index case (IDX) and the first two generations of an infection with $R_0 = 3$.

Now, $R_0$ is a theoretical variable. It is usually observationally estimated, and don’t take measures intended to reduce it into account. And that’s where it gets interesting.

Consider the following scenario, where a third of the population is vaccinated, denoted by dark black circles around the nodes representing them. One would expect that of the 13 persons, a third, i.e. about. 4 , would remain disease-free. But in fact, over half of the people will remain disease-free, including three who are not vaccinated. This is because the person in the previous generation did not pass on the pathogen to them. In other words, preventing spread, e.g. by vaccination or by quarantine, can affect and reduce $R_0$. Thus in this case, the effective $R_0$ was closer to 1.66 than 3 – almost halving the $R_0$ by vaccinating only a third of the population.

We also know that for infections where the patient either dies or recovers, the infection has a simple ecology: every case must be ‘replaced’. In other words, if the effective $R_0$ falls below 1, the infection will eventually peter out. This happens quite often when everyone in a population is dead or immune after an infection has burned through it (more about that later).

Thus, the infection will be sustainable if and only if

$R_{0} \geq 1$

Under the assumption of a 100% efficient vaccine, the threshold value $\bar{p_V}$ after which the infection will no longer be able to sustain itself is calculated as

$\bar{p_V} = 1 - \frac{1}{R_0}$

Adjusting for vaccine efficacy, $E_V$, which is usually less than 100%, we get

$\bar{p_V} = \frac{1-\frac{1}{R_0}}{E_V} = \frac{R_0 - 1}{R_0 E_V}$

For a worked example, let’s consider measles. Measles has an $R_0$ around 15 (although a much higher value has been observed in the past, up to 30, in some communities), and the measles vaccine is about 96% effective. What percentage of the population needs to be vaccinated? Let’s consider $\bar{p_V}$, the minimum or threshold value above which herd immunity is effective:

$\bar{p_V} = \frac{R_0 - 1}{R_0 E_V} = \frac{15-1}{15 \cdot 0.96} = \frac{14}{14.4} \approx 97.22\%$

#### The more complex explanation: $\mathcal{SIR}$$\mathcal{SIR}$ models

Note: this is somewhat complex maths and is generally not recommended unless you’re a masochist and/or comfortable with calculus and differential equations. It does give you a more nuanced picture of matters, but is not necessary to understand the whole of the argumentation. So feel free to skip it.

The slightly more complex explanation relies on a three-compartment model, in which the population is allotted into one of three compartments: $\mathcal{S}$usceptible, $\mathcal{I}$nfectious and $\mathcal{R}$ecovered. This model makes certain assumptions, such as that persons are infectious from the moment they’re exposed and that once they recover, they’re immune. There are various twists on the idea of a multicompartment model that takes into account the fact that this is not true for every disease, but the overall idea is the same.3 In general, multicompartment models begin with everybody susceptible, and a seed population of infectious subjects. Vaccination in such models is usually accounted for by treating them as ‘recovered’, and thus immune, from $t = 0$ onwards.

Given an invariant population (i.e. it is assumed that no births, deaths or migration occurs), the population can be described as consisting of the sum of the mutually exclusive compartments: $P = \mathcal{S}(t) + \mathcal{I}(t) + \mathcal{R}(t)$. For the same reason, the total change is invariant over time, i.e.

$\frac{d \mathcal{S}}{d t} + \frac{d \mathcal{I}}{d t} + \frac{d \mathcal{R}}{d t} = 0$

Under this assumption of a closed system, we can relate the volumes of each of the compartment to the transition probabilities $\beta$ (from $\mathcal{S}$ to $\mathcal{I}$) and $\gamma$ (from $\mathcal{I}$ to $\mathcal{R}$), so that:

$\frac{d \mathcal{S}}{d t} = - \frac{\beta \mathcal{I} \mathcal{S}}{P}$

$\frac{d \mathcal{I}}{d t} = \frac{\beta \mathcal{I} \mathcal{S}}{P} - \gamma \mathcal{I}$

$\frac{d \mathcal{R}}{d t} = \gamma \mathcal{I}$

Incidentally, in case you were wondering how this connects to the previous explanation: $R_0 = \frac{\beta}{\gamma}$.

Now, let us consider the end of the infection. If $\mathcal{S}$ is reduced sufficiently, the disease will cease to be viable. This does not need every individual to be recovered or immune, however, as is evident from dividing the first by the third differential equation and integrating and substituting $R_0$, which yields

$\displaystyle \mathcal{S}(t) = \mathcal{S}(0) e^{\frac{-R_0 (\mathcal{R}(t)-\mathcal{R}(0))}{P}}$

Substituting this in, the limit of $\mathcal{R}$, as $t$ approaches infinity, is

$\displaystyle \lim_{t\to\infty}\mathcal{R}(t) = P - \lim_{t\to\infty}\mathcal{S}(t) = P - \mathcal{S}(0) e^{\frac{-R_0 (\mathcal{R}(t)-\mathcal{R}(0))}{P}}$

From the latter, it is evident that

$\displaystyle \lim_{t\to\infty}\mathcal{S}(t) \neq 0 \mid \mathcal{S}(0) \neq 0$

In other words, once the infection has burned out, there will still be some individuals who are not immune, not immunised and not vaccinated. These are the individuals protected by herd immunity. This is a pretty elegant explanation for why herd immunity happens and how it works. There are three points to take away from this.

First, herd immunity is not unique to vaccination. The above finding in relation to the nonzero limit of $\lim_{t\to\infty}\mathcal{S}(t)$ holds as long as $\mathcal{S}(0) \neq 0$, but regardless of what $\mathcal{R}(0)$ is. In other words, herd immunity is not something artificial.

Two, for any $i \in \mathcal{S}$ (that is, any susceptible person) at time $t$, the probability of which compartment he will be in at $t+1$ depends on whom he encounters. That, statistically, depends on the relative sizes of the compartments. In this model, the assumption is that the sample $i$ will encounter will reflect the relative proportions of the individual compartments’ sizes. Thus if $i$ meets $n$ people at time $t$, each compartment will be proportionally represented, i.e. for any compartment $\mathcal{C}$, the proportion will be $\frac{\mathcal{C}(t)}{P-1}$ for all $\mathcal{C} \neq \mathcal{S}$, for which the proportion will be $\frac{\mathcal{S}(t) - 1}{P - 1}$, since one cannot meet oneself. Given that the transition probability $\beta_{i}(t)$ is assumed to equal the probability of meeting at least one element of $\mathcal{I}$, the following can be said. $i$‘s risk of infection depends on the relationship of $n$ and $\mathcal{I}(t)$, so that $i$ is likely to get infected if

$\displaystyle n \frac{\mathcal{I}(t)}{P-1} \geq 1$

This elucidates two risk factors clearly, and the way to reduce them: reduce interactions (quarantine/self-quarantine), thereby reducing $n$, and reduce the proportion of infectious cases ($\frac{\mathcal{I}(t)}{P-1}$). The latter is where herd immunity from immunisation comes in. Recall that for a constant $n$, $i$‘s risk of infection at $t$ rises as $\mathcal{I}(t)$ rises.4 Recall also that while susceptible cases can turn into infectious cases, recovered (or vaccinated) cases cannot. And so, as $\mathcal{R}(0)$ converges to $P-1$,5 $i$‘s risk of infection at any time $t$, denoted by $\beta_{i}(t)$, falls. In other words,

$\displaystyle \lim_{\mathcal{R}(0) \to P-1} \beta_{i}(t) = 0$

Or to put it simply: the more are vaccinated at the start, the lower the probability, all things being equal, to meet someone who can pass on the infection.6

A final point to note is that this is primarily a model of statistical dynamics, and deals with average probabilities. It does not – it cannot – take account of facts like that some some susceptible people are just darn unlucky, and bump into a flock of unvaccinated, shiny-eyed snowflakes. Equally, in some places, susceptible people and infected people congregate, creating a viral breeding ground, also known as a Waldorf school. There are agent based models, which are basically attempts at brute force hacking reality, that can take account of such disparities. The takeaway is that herd immunity does not mean no susceptible individual will get infected. What it does mean is that their probability of getting infected is going to be significantly lower, for two reasons. First given a constant number of encounters ($n$), the likelihood of one of them being with an infectious individual is going to be much lower. More importantly, however, because of herd immunity, the disease is going to be able to persist in the population for a far shorter time – eventually it will burn through the small number of ‘accessible’ susceptible persons. Since the cumulative risk $\beta_{i}^T$ for $i \in \mathcal{S}$ for an infection that dies out after time $T$ is defined as

$\beta_i^T = \displaystyle \int\limits_0^T \beta_{i}(t) \, \mathrm{d}t$

– the sooner the infection dies out, the smaller the likelihood that $i$ will be infected. With that mathematical basis, let’s tackle a few of the myths about herd immunity.

### Myth #1: herd immunity only works with naturally acquired immunity

This argument goes roughly along the following lines: herd immunity does exist, but it only exists if and where the immunity is acquired the ‘natural’ way, i.e. by surviving the disease. Case in point:

The \$64,000 question, of course, is what the difference is between the residual immunity from a vaccine and the residual immunity from having survived the illness. A vaccine effectively ‘simulates’ the illness without actually causing the pathological symptoms. From the perspective of the immune system, it is largely irrelevant whether it has been exposed to an actual virus that can damage the body, or merely a capsid protein that is entirely harmless but will nonetheless elicit the same immune reaction. That should suffice to bust this myth, but it’s worth considering immunity quantitatively for a moment. As we have seen above, the source of immunity doesn’t matter. In fact, it doesn’t even have to be immunity: culling every animal except one in a herd is an entirely good way to reduce disease transmission. So is sealing oneself away from the rest of society and spending the evenings telling sexually explicit stories, as the heroes and heroines of Boccaccio’s Decameron have done, since we know that

$\displaystyle n \frac{\mathcal{I}(t)}{P-1} \geq 1$

Boccaccio’s crowd of assorted perverts knew nothing of all this, of course, but they did know that if they reduced $n$, the number of contacts with possibly infected persons, their chances of surviving the plague would increase. As it indeed did. Score one for medieval perverts. The bottom line is that it is entirely immaterial how immunity was obtained.

### Myth #2: Herd immunity is a concept deriving from animals. It doesn’t work on humans.

This is one of the more outlandish claims, but shockingly, it actually has a tiny kernel of truth.

Now, much of the above is a veritable storehouse of insanity, but the point it makes in the beginning has some truth to it. In human populations, herd immunity sometimes behaves anomalously, because humans are not homogenously distributed. This is true a fortiori for humans who decide not to vaccinate, who – for better or worse – tend to flock in small groups. The term of venery for a bunch of anti-vaxxers is, in case you were wondering, a ‘plague’.7

Herd immunity was, in fact, observed in a range of species. Humans are different as we can knowingly and consciously decide to create herd immunity in our population and protect our fellow men, women and children, the last of whom are particularly susceptible to infectious diseases, from some of the worst killers.

### Myth #3: If herd immunity can be obtained through natural immunity, surely we don’t need vaccines.

This argument has recently been peddled by the illustrious Kelly Brogan MD, who bills herself as a ‘holistic psychiatrist’ who threw away her script pad, which means she tends exclusively to the worried well and those with mild mental health issues where medication does not play as decisive a role as it does in, say, schizophrenia, severe PTSD, crippling anxiety disorders or complex neuropsychiatric post-insult phenomena.8 Here’s her foray into epidemiology, something she vaguely remembers studying in her first year of med school.

In this, Dr Brogan has successfully found almost a century old evidence for what everybody knew, namely that herd immunity can be naturally obtained. To anyone who has read the maths part above, this should evoke a sensation of ‘DUH!’. The problem is twofold. One, the ‘actual virus’ has an unsavoury fatality rate of 0.1%, not including the horribly tragic, heartbreaking late consequence of measles known as SSPE.9 Two, and perhaps more important: you don’t get lifelong, natural immunity if you die. This may have somehow escaped Dr Brogan’s attention, but part of the point of herd immunity is to protect those who would not survive, or would suffer serious sequelae, if they contracted the infection. What we don’t know, of course, how many of that 68% suffered permanent injuries, and how many are not included because they died. What we do know is that all 68% probably had a miserable time. Anyone who thinks measles is so fantastic should start by contracting it themselves.

### Myth #4: Herd immunity means 95% need to be vaccinated to prevent a disease.

This one comes courtesy of Sarah aka the Healthy Home Economist,10, who, to what I presume must be the chagrin of her alma mater, states she has a Master’s from UPenn. Suspiciously enough, she does not state what in. I am somehow pretty sure it’s not public health.

The tedious conspiracy theory aside, it is quite evident just how little she understands of herd immunity. No – herd immunity is not based upon11 the idea that 95% must be vaccinated, and it is most definitely not based on the idea that 100% must be vaccinated. Indeed, the whole bloody point of herd immunity is that you do not need to vaccinate 100% to protect 100%. In fact, given the $R_0$ and vaccine efficacy $E_V$, we can predict the threshold vaccination rate for herd immunity quite simply, as demonstrated earlier: the threshold value, $\bar{p_V}$, can be calculated as

$\bar{p_V} = \frac{R_0 - 1}{R_0 E_V}$

As an illustration, the herd immunity threshold $\bar{p_V}$ for mumps, with an efficacy of 88%12 and an $R_0$ of around 5.5, is $\approx 92.98\%$, while for Ebola, which has a very low $R_0$ around 2.0, herd immunity sets in once about 50% are immune.13

And those ‘conventional health authorities’? That’s what we call health authorities whose ideas work.

### Myth #5: If vaccines work, why do we need herd immunity?

This argument is delightfully idiotic, because it, too, ignores the fundamental underlying facts of herd immunity. Quite apart from the fact that some people cannot receive some or all vaccines and other people can receive vaccines but may not generate sufficient antibody titres to have effective immunity, sometimes vaccines simply fail. Few things are 100%, and while vaccines are designed to be resilient, they can degrade due to inappropriate storage or fail to elicit a sufficient response for some other reason. Unlike wearing deodorant (or ‘deoderant’, as spelling-challenged anti-vaxxers would say), infections can sometimes be imagined as a chain of transmission. This is a frequently used model to explain the consequences of not vaccinating on others.

In this illustration, an index patient (IDX) is infected and comes in contact with G1, who in turn comes into contact with G2 who in turn comes into contact with G3. In the first case, G1, G2 and G3 are all vaccinated. The vaccine may have a small failure rate – 2% in this case – but by the time we get to G3, his chances of contracting the infection are 1:125,000 or 0.0008%. In the second case, G2 is unvaccinated – if G1’s vaccine fails, G2 is almost guaranteed to also fall ill. By not vaccinating, his own risk has increased 50-fold, from 0.04% to 2%. But that’s not all – due to G2’s failure to vaccinate, G3 will also be affected – instead of the lottery odds of 1:125,000, his risk has also risen 50-fold, to 1:2,500. And this 50-fold increase of risk will carry down the chain of potential transmission due to G2’s failure to vaccinate. No matter how well vaccines work, there’s always a small residual risk of failure, just as there is a residual risk of failure with everything. But it takes not vaccinating to make that risk hike up 50-fold. Makes that deodorant (‘deoderant’?) analogy sound rather silly, right?

## Conclusion

Admittedly, the mathematical basis of herd immunity is complex. And the idea itself is somewhat counterintuitive. None of these are fit excuses for spreading lies and misinformation about herd immunity.

I have not engaged with the blatantly insane arguments (NWO, Zionists, Masonic conspiracies, Georgia Guidestones), nor with the blatantly untrue ones (doctors and public health officers are evil and guided just by money as they cash in on the suffering of innocent children). I was too busy booking my next flight paid for by Big Pharma.14 Envy is a powerful force, and it’s a good way to motivate people to suspect and hate people who sacrificed their 20s and 30s to work healing others and are eventually finally getting paid in their 40s. But it’s the myths that sway the well-meaning and uncommitted, and I firmly believe it’s part of our job as public health experts to counter them with truth.15

In every social structure, part of co-existence is assuming responsibility not just for oneself but for those who are affected by our decisions. Herd immunity is one of those instances where it’s no longer about just ourselves. Many have taken the language of herd immunity to suggest that it is some sort of favour or sacrifice done for the communal good, when it is in in fact the very opposite – it is preventing (inadvertent but often unavoidable) harm to others from ourselves.

And when the stakes are this high, when it’s about life and death of millions who for whatever reason cannot be vaccinated or cannot form an immune response, getting the facts right is paramount. I hope this has helped you, and if you know someone who would benefit from it, please do pass it on to them.

References   [ + ]

 1 ↑ Topley, W. W. C. and Wilson, G. S. (1923). The spread of bacterial infection; the problem of herd immunity. J Hyg 21:243-249. The CDC was founded 23 years later, in 1946. 2 ↑ Why $R_0$$R_0$? Because it is unrelated to $\mathcal{R}$$\mathcal{R}$, the quantity denoting recovered cases in $\mathcal{S(E)IR}$$\mathcal{S(E)IR}$ models – which is entirely unrelated. To emphasize the distinction, I will use mathcal fonts for the compartments in compartment models. 3 ↑ I hope to write about SIS, SEIR and vital dynamic models in the near future, but for this argument, it really doesn’t matter. 4 ↑ Technically, as $\frac{\mathcal{I}(t)}{P - 1}$$\frac{\mathcal{I}(t)}{P - 1}$ rises, but since the model presupposes that $P$$P$ is constant, it doesn’t matter. 5 ↑ Since otherwise $\mathcal{R} = P$$\mathcal{R} = P$ and $\mathcal{S} = 0$$\mathcal{S} = 0$, and the whole model is moot, as noted above. 6 ↑ Note that this does not, unlike the $R_0$$R_0$ explanation, presuppose any degree of vaccine efficacy. An inefficiently vaccinated person is simply in $\mathcal{S}$$\mathcal{S}$ rather than $\mathcal{R}$$\mathcal{R}$. 7 ↑ Initially, ‘a culture’ was proposed, but looking at the average opponent of vaccination, it was clear this could not possibly work. 8 ↑ In other words, if you have actual mental health issues, try an actual psychiatrist who follows evidence-based protocols. 9 ↑ Subacute sclerosing panencephalitis is a long-term delayed consequence of measles infection, manifesting as a disseminated encephalitis that is invariably fatal. There are no adjectives that do the horror that is SSPE justice, so here’s a good summary paper on it. 10 ↑ As a rule, I don’t link to blogs and websites disseminating harmful information that endangers public health. 11 ↑ Correct term: ‘on’ 12 ↑ As per the CDC. 13 ↑ Efficacy $E_V$$E_V$ is presumed to be 100% where immunity is not acquired via vaccination but by survival. 14 ↑ Anyone in public health is happy to tell you those things don’t merely no longer exist, they never even existed in our field. 15 ↑ And, if need be, maths.

# MedDRA + VAERS: A marriage made in hell?

This post is a Golden DDoS Award winner

So far, this blog was DDoS’d only three times within 24 hours of its publication. That deserves a prize.

Quick: what do a broken femur, Henoch-Schönlein purpura, fainting, an expired vaccine and a healthy childbirth have in common? If your answer was “they’re all valid MedDRA codes”, you’re doing pretty well. If you, from that, deduced that they all can be logged on VAERS as adverse effects of vaccination, you’re exceeding expectations. And if you also realise that the idea that Jane got an expired HPV vaccine, and as a consequence broke her femur, developed Henoch-Schönlein purpura, and suddenly gave birth to a healthy baby boy is completely idiotic and yet can be logged on VAERS, you’re getting where I’m going.

MedDRA is a medical nomenclature specifically developed for the purposes of pharmacovigilance. The idea is, actually, not dreadful – there are some things in a usual medical nomenclature like ICD-10 that are not appropriate for a nomenclature used for pharmacovigilance reporting (V97.33: sucked into jet engine comes to my mind), and then there are things that are specific to pharmacovigilance, such as “oh shoot, that was not supposed to go up his bum!” (MedDRA 10013659: vaccine administered at inappropriate site), “we overdosed little Johnny on the flu vaccine!” (MedDRA 10000381: drug overdose, accidental) and other joys that generally do only happen in the context of pharmacovigilance. So far, so good.

At the same time, MedDRA is non-hierarchical, at least on the coding level. Thus, while the ICD code V97.33 tells you that you’re dealing with an external cause of mortality and morbidity (V and Y codes), specifically air and space transport (V95-97), more specifically ‘other’ specific air transport accidents, specifically getting sucked into a jet engine (V97.33), there’s no way to extract from MedDRA 10000381 what the hell we’re dealing with. Not only do we not know if it’s a test result, a procedure, a test or a disease, we are hopelessly lost as to figuring out what larger categories it belongs to. To make matters worse, MedDRA is proprietary – which in and of itself is offensive to the extreme to the idea of open research on VAERS and other public databases: a public database should not rely on proprietary encoding! -, and it lacks the inherent logic of ICD-10. Consider the encoding of the clinical diagnosis of unilateral headache in both:

We know that an ICD code beginning with F will be something psychiatric and G will be neurological, and from that alone we can get some easy analytical approaches (a popular one is looking at billed codes and drilling down by hierarchical level of ICD-10 codes, something in which the ICD-10 is vastly superior to its predecessor). MedDRA, alas, does not help us such.

## Garbage in, garbage out

OK, so we’ve got a nomenclature where the codes for needlestick injury, death, pneumonia, congenital myopathy and a CBC look all the same. That’s already bad enough. It gets worse when you can enter any and all of these into the one single field. Meet VAERS.

The idea of VAERS is to allow physicians, non-physicians and ‘members of the public’ to report incidents. These are then coded by the CDC and depending on seriousness, they may or may not be investigated (all reports that are regarded as ‘serious’ are investigated, according to the CDC). The problem is that this approach is susceptible to three particular vulnerabilities:

• The single field problem: VAERS has a single field for ‘symptoms’. Everything’s a symptom. This includes pre-existing conditions, new onset conditions, vaccination errors, lab tests (not merely results, just the tests themselves!), interventions (without specifying if they’re before or after the vaccine), and so on. There is also no way to filter out factors that definitely have nothing to do with the vaccine, such as a pre-existing birth defect. The History/Allergies field is not coded.
• The coding problem: what gets coded and what does not is sometimes imperfect. This being a human process, it’s impossible to expect perfection, but the ramifications to this to certain methods of analysis are immense. For instance. if there are 100 cases of uncontrollable vomiting, that may be a signal. But if half of those are coded as ‘gastrointestinal disorder’ (also an existing code), you have two values of 50, neither of which may end up being a signal.
• The issue of multiple coding: because MedDRA is non-hierarchical, it is not possible to normalise at a higher level (say, with ICD-10 codes, at chapter or block level), and it is not clear if two codes are hierarchically related. In ICD-10, if a record contains I07 (rheumatic tricuspid valve disease) and I07.2 (tricuspid stenosis with tricuspid insufficiency), one can decide to retain the more specific or the less specific entry, depending on intended purpose of the analysis.

In the following, I will demonstrate each of these based on randomly selected reports from VAERS.

### The Single Field Problem (SFP)

The core of the SFP is that there is only one codeable field, ‘symptoms’.

VAERS ID 375693-1 involves a report, in which the patient claims she developed, between the first and second round of Gardasil,

severe stomach pain, cramping, and burning that lasted weeks. Muscle aches and overall feeling of not being well. In August 2009 patient had flu like symptoms, anxiety, depression, fatigue, ulcers, acne, overall feeling of illness or impending death.

Below is the patient’s symptom transposition into MedDRA entities (under Symptoms):

The above example shows the mixture of symptoms, diagnostic procedures and diagnostic entities that are coded in the ‘Symptoms’ field. The principal problem with this is that when considering mass correlations (all drugs vs all symptoms, for instance), this system would treat a blood test just as much as a contributor to a safety signal as anxiety or myalgia, which might be true issues, or depression, which is a true diagnosis. Unfiltered, this makes VAERS effectively useless for market basket analysis based (cooccurrence frequency) analyses.

Consider for instance, that $PRR$ is calculated as

$PRR_{V,R} = \frac{\Sigma (R \mid V) \/ \Sigma (V)}{\Sigma (R \mid \neg V) \/ \Sigma (\neg V)} = \frac{\Sigma (R \mid V)}{\Sigma (V)} \cdot \frac{\Sigma (\neg V)}{\Sigma (R \mid \neg V)}$

where $V$ denotes the vaccine of interest, $R$ denotes the reaction of interest, and the $\Sigma$ operator denotes the sum of rows or columns that fulfill the requisite criteria (a more detailed, matrix-based version of this equation is presented here). But if $\{R\}$, the set of all $R$, contains not merely diagnoses but also various ‘non-diagnoses’, the PRR calculation will be distorted. For constant $V$ and an unduly large $R$, the values computationally obtained from the VAERS data that ought to be $\Sigma(R \mid V)$ and $\Sigma(R \mid \neg V)$ will both be inaccurately inflated. This will yield inaccurate final results.

Just how bad IS this problem? About 30% bad, if not more. A manual tagging of the top 1,000 symptoms (by $N$, i.e. by the number of occurrences) was used as an estimate for how many of the diagnostic entities do not disclose an actual problem with the vaccine.

According to the survey of the top 1,000 codes, only a little more than 70% of the codes themselves disclose a relevant issue with the vaccine. In other words, almost a third of disclosed symptoms must be pruned, and these cannot be categorically pruned because unlike ICD-10, MedDRA does not disclose hierarchies based on which such pruning would be possible. As far as the use of MedDRA goes, this alone should be a complete disaster.

Again, for effect: a third of the codes do not disclose an actual side effect of the medication. These are not separate or identifiable in any way other than manually classifying them and seeing whether they disclose an actual side effect or just an ancillary issue. Pharmacovigilance relies on accurate source data, and VAERS is not set up, with its current use of MedDRA, to deliver that.

### The coding problem

Once a VAERS report is received, it is MedDRA coded at the CDC. Now, no manual coding is perfect, but that’s not the point here. The problem is that a MedDRA code does not, in and of itself,  indicate the level of detail it holds. For instance, 10025169 and 10021881 look all alike, where in fact the first is a lowest-level entity (an LLT – Lower-Level Term – in MedDRA lingo) representing Lyme disease, while the former is the top-level class (SOC – System Organ Class) corresponding to infectious diseases. What this means is that once we see a MedDRA coded entity as its code, we don’t know what level of specificity we are dealing with.

The problem gets worse with named entities. You see, MedDRA has a ‘leaf’ structure: every branch must terminate in one or more (usually one) LLT. Often enough, LLTs have the same name as their parent PT, so you get PT Lyme disease and LLT Lyme disease. Not that it terrifically matters for most applications, but when you see only the verbose output, as is the case in VAERS, you don’t know if this is a PT, an LLT, or, God forbid, a higher level concept with a similar name.

Finally, to put the cherry on top of the cake, where a PT is also the LLT, they have the same code. So for Lyme disease, the PT and LLT both have the code 10025169. I’m sure this seemed like a good idea at the time.

### The issue of multiple coding

As this has been touched upon previously, because MedDRA lacks an inherent hierarchy, a code cannot be converted into its next upper level without using a lookup table, whereas with, say, ICD-10, one can simply normalise to the chapter and block (the ‘part left of the dot’). More problematically, however, the same code may be a PT or an LLT, as is the case for Lyme disease (10025169).

Let’s look at this formally. Let the operator $\in^*$ denote membership under the transitive closure of the set membership relation, so that

1. if $x \in A$, then $x \in^* A$,
2. if $x \in A$ and $A \subseteq B$, then $x \in^* B$.

and so on, recursively, ad infinitum. Let furthermore $\in^*_{m}$ denote the depth of recursion, so that

1. for $x \in A$:  $x \in^*_{0} A$,
2. for $x \in A \mid A \subseteq B$:  $x \in^*_{1} B$,

and, once again, so on, recursively, ad infinitum.

Then let a coding scheme $\{S_{1...n}\}$ exhibit the Definite Degree of Transitiveness (DDoT) property iff (if and only if) for any $S_m \mid m \leq n$, there exists exactly one $p$ for which it is true that $S_m \in^*_{p} S$.

Or, in other words, two codes $S_q, S_r \mid q, r \leq n$, may not be representable identically if $p_q \neq p_r$. Less formally: two codes on different levels may not be identical. This is clearly violated in MedDRA, as the example below shows.

### Bonus: the ethical problem

To me as a public health researcher, there is a huge ethical problem with the use of MedDRA in VAERS. I believe very strongly in open data and in the openness of biomedical information. I’m not alone: for better or worse, the wealth – terabytes upon terabytes – of biomedical data, genetics, X-ray crystallography, models, sequences  prove that if I’m a dreamer, I’m not the only one.

Which is why it’s little short of an insult to the public that a pharmacovigilance system is using a proprietary encoding model.

Downloads from VAERS, of course, provide the verbose names of the conditions or symptoms, but not what hierarchical level they are, nor what structure they are on. For that, unless you are a regulatory authority or a ‘non-profit’ or ‘non-commercial’ (which would already exclude a blogger who unlike me has ads on their blog to pay for hosting, or indeed most individual researchers, who by their nature could not provide the documentation to prove they aren’t making any money), you have to shell out some serious money.

Worse, the ‘non-profit’ definition does not include a non-profit research institution or an individual non-profit researcher, or any of the research bodies that are not medical libraries or affiliated with educational institutions but are funded by third party non-profit funding:

There is something rotten with the use of MedDRA, and it’s not just how unsuitable it is for the purpose, it is also the sheer obscenity of a public database of grave public interest being tied to a (vastly unsuitable and flawed, as I hope it has been demonstrated above) nomenclature.

## Is VAERS lost?

### Resolving the MedDRA issue

Unlike quite a few people in the field, I don’t think VAERS is hopelessly lost. There’s, in fact, great potential in it. But the way it integrates with MedDRA has to be changed. This is both a moral point – a point of commitment to opening up government information – and one of facilitating research.

There are two alternatives at this point for the CDC.

1. MedDRA has to open up at least the 17% of codes, complete with hierarchy, that are used within VAERS. These should be accessible, complete with the hierarchy, within VAERS, including the CDC WONDER interface.
2. The CDC has to switch to a more suitable system. ICD-10 alone is not necessarily the best solution, and there are few alternatives, which puts MedDRA into a monopoly position that it seems to mercilessly exploit at the time. This can – and should – change.

### Moving past the Single Field Problem

MedDRA apart, it is crucial for VAERS to resolve the Single Field Problem. It is clear that from the issues presented in the first paragraph – a broken femur, Henoch-Schönlein purpura, fainting, an expired vaccine and a healthy childbirth – that there is a range of issues that need to be logged. A good structure would be

1. pre-existing conditions and risk factors,
2. symptoms that arose within 6 hours of administration,
3. symptoms that arose within 48 hours of administration,
4. symptoms that arose later than 48 hours of administration,
5. non-symptoms,
6. clinical tests without results,
7. clinical tests segmented by positive and negative results, and
8. ancillary circumstances, esp. circumstances pertaining to vaccination errors such as wrong vaccine administered, expired vaccine, etc.

The use of this segmentation would be able to differentiate not only time of occurrence, but also allow for adequate filtering to identify the correct denominators for the $PRR$.

### A future with (for?) MedDRA

As said, I am not necessarily hostile to MedDRA, even if the closet libertarian in me bristles at the fact that MedDRA is mercilessly exploiting what is an effective monopoly position. But MedDRA can be better, and needs to be better – if not for its own economic interests, then for the interests of those it serves. There are three particular suggestions MedDRA needs to seriously consider.

1. MedDRA’s entity structure is valuable – arguably, it’s the value in the entire project. If coding can be structured to reflect its internal hierarchy, MedDRA becomes parseable without a LUT,1 and kinship structures become parseable without the extra step of a LUT.
2. MedDRA needs to open up, especially to researchers not falling within its narrowly defined confines of access. Especially given the inherent public nature of its use – PhV and regulation are quintessentially public functions, and this needs an open system.
3. MedDRA’s entity structure’s biggest strength is that it comprises a range of different things, from administrative errors through physical injuries to test results and the simple fact of tests.

## Conclusion

VAERS is a valuable system with a range of flaws. All of them are avoidable and correctable – but would require the requisite level of will and commitment – both on CDC’s side and that of MedDRA. For any progress in this field, it is imperative that the CDC understand that a public resource maintained in the public interest cannot be driven by a proprietary nomenclature, least of all one that is priced out of the range of the average interested individual: and if they cannot be served, does the entire system even fulfill its governmental function of being of the people and for the people? It is ultimately CDC’s asset, and it has a unique chance to leverage its position to ensure that at least as far as the 17% of MedDRA codes go that are used in VAERS, these are released openly.

In the end, however sophisticated our dissimilarity metrics, when 30% of all entities are non-symptoms and we need to manually prune the key terms to avoid denominator bloat due to non-symptom entities, such as diagnostic tests without results or clearly unconnected causes of morbidity and mortality like motor vehicle accidents, dissimilarity based approaches will suffer from serious flaws. In the absence of detailed administration and symptom tracking at an individual or institutional level, dissimilarity metrics are the cheapest and most feasible ways of creating value out of post marketing passive reports. If VAERS is to be a useful research tool, as I firmly believe it was intended to be, it must evolve to that capability for all.

References   [ + ]

 1 ↑ Look-up table

# Ebola! Graph databases! Contact tracing! Bad puns!

Thanks to the awesome folks at Neo4j Budapest and GraphAware, I will be talking tonight about Ebola, contact tracing, how graph databases help us understand epidemics and maybe prevent them someday. Now, if flying to Budapest on short notice might not work for you, you can listen to a livestream of the whole event here! It starts today, 13 February, at 1830 CET, 1730 GMT or 1230 Eastern Time, and I sincerely hope you will listen to it, live or later from the recording, also accessible here.

# In extremis

It’s not frequent for a State of the Union address to delve into drug approval policy in any depth. Yet that’s exactly what President Trump did when, for the first time, he spoke publicly about legislation allowing terminally ill patients to access experimental treatments that have passed only FDA Phase I trials, often referred to as ‘right to try’ legislation:

We also believe that patients with terminal conditions should have access to experimental treatments that could potentially save their lives. People who are terminally ill should not have to go from country to country to seek a cure — I want to give them a chance right here at home. It is time for the Congress to give these wonderful Americans the ‘right to try’.

The Right to Try is unsurprisingly controversial. On one hand, patient groups see it as a chance to access treatments that are too far in the pipeline for them. It is hard not to have sympathy with this argument. It is especially hard for me to do so so, because my life was saved by an experimental drug that at the time did not have general approval for my condition, though it was known to be safe. At the same time, FDA Commissioner Scott Gottlieb is right to be skeptical about this policy effectively usurping the FDA’s authority to ensure that pharmaceuticals administered to all patients in the United States are safe and effective. Like all great moral quandaries, both sides are, to an extent, right.

### What is ‘right to try’?

Quite simply put, right to try laws allow terminally ill patients access to treatments, medications and devices that have passed FDA Phase I testing, but are not yet approved by the FDA. The libertarian Goldwater Institute, which has been pushing and lobbying for right to try, has created a model legislation, variants of which have by now been accepted by 38 states. It provides, in short, an exception for patients suffering from “advanced illness”, defined as

a progressive disease or medical or surgical condition that entails significant functional impairment, that is not considered by a treating physician to be reversible even with administration of current federal drug administration approved and available treatments, and that, without life-sustaining procedures, will soon result in death.1

Patients that qualify under this definition would then be allowed access to any treatment, pharmaceutical or device as long as it has passed Phase I testing,2 although the manufacturer or provider would be under no obligation to sell or provide that treatment to the patient.

### Ethical issues

The ‘right to try’ legislation is far from uncontroversial. @gorskon, whom I greatly respect even when I disagree with him, has gone so far as to call it a ‘cruel sham’ and a libertarian attack on the FDA, and his points merit consideration:

I’ve written many times before over the last three years about how “right-to-try” laws have swept the states. When last I wrote about right-to-try, 37 states had passed such laws over the course of a mere three years, and I observed at the time that it wouldn’t surprise me in the least if most or all of the remaining states were to pass such laws within the next year or two. Basically, the idea behind these laws is that the FDA is killing patients (I’m only exaggerating slightly) through its slow drug approval, overcaution, and bureaucratic inertia, or at least letting them die because life-saving drugs are being held up. So the idea, hatched by the Goldwater Institute was that terminally ill patients should have the “right-to-try” experimental drugs not yet approved by the FDA because they have nothing more to lose. Of course, it’s not true that they have nothing more to lose, but I’ll discuss that more later. Basically, right-to-try laws purport to allow the terminally ill “one last shot” by letting them access experimental therapeutics outside of FDA-sanctioned clinical trials. However, these laws operate under a number of false assumptions, not the least of which is the caricature of the FDA as being slow, inefficient, and unwilling to bend, as you will see. They also strip away a number of protections for patients, as you will also see.

While I am not sure I’m on board with the idea of there being a libertarian conspiracy to curb the FDA’s powers – especially given how limited the ambit of right to try legislations would be -, Orac makes an excellent point.

Much of the Goldwater Institute’s position is premised on the FDA being ‘slow’ and inefficient – as they like to present their case, they merely seek to remedy an instance of the state failing to serve citizens adequately. Speaking from personal experience, when you’re dying, everything is too slow and no approval process can come fast enough. It is hard not to have a lot of sympathy towards the patients who know there may be a promising drug in the pipeline but like Moses of old, they will never get to see the promised land. But realistically, the FDA is not slow – indeed, it is as fast as, or sometimes even faster, than regulatory agencies in many other countries.3

I would also add that the benefits of investigational therapies has rarely been particularly high, with only about 10% yielding a clinical improvement.4. For 90% of patients, then, the right to try would mean putting themselves through another round of torturous treatment instead of spending their last weeks or months focusing on appropriate symptom relief, quality of life and putting their affairs in order. In the end, these might be more important than a forlorn hope of extending one’s life by another few months.

Patients are subject to a high degree of informational asymmetry. When I had to decide between various treatments, I spent days on PubMed, reading every single study, building my own little mini-metaanalysis from my hospital bed. I was lucky – I had access to all the academic literature I could want and I was trained in evaluating that evidence. But most patients aren’t (and there’s no reason why they would be!),5 and what takes the place of sound knowledge is often less healthy. Patients may feel emotional pressure to try every treatment, however modest the chance of success: be it because they would feel that not doing so is ungrateful towards the doctors who ‘fought for them’, or because they feel they owe it to their family, the psychological pressure to try potentially ineffective treatments is immense, and might rob the patient from their chance to exercise some degree of autonomy over the last moments of their life.

### The reverse of the medal

At the same time, many note, respect for the patient’s autonomy should extend to allowing treatment that a competent patient wants, even if the physician disagrees. And, in addition, many argue that it would be paradoxical to allow patients to outright request physician-assisted suicide but not the administration of a treatment that may just save their lives. These arguments are not pointless, and any policy needs to justify why paternalism is particularly justified in this case, and while treatment would be inappropriate where suicide would be permitted.

More importantly, it is arguable that the absence of a ‘right to try’ leads to its own set of tragic adverse consequences, by directing patients to ‘try’ treatments in the unregulated sector of outright quackery. I had the distinct misfortune of witnessing one of these.

Jillian Mai Thi Epperly is an unqualified naturopathic healer with no educational background in nutrition who is running what she describes as a large-scale experiment on volunteers (aka marks). Her victims – around 30,000 – joined her Facebook group, which is closer to a cult than anything else, and consume vast quantities of a concoction that contains an unhealthy amount of salt and fermented cabbage juice. This is supposed to rid the body of ‘weaponised mutant candida and parasites’, which she claims is responsible for all or most pathological processes in the human body. Ms Epperly’s Facebook group is replete with images generally for the strong of stomach (including gut lining which her acolytes believe are parasites), but that’s nothing compared to the damage she has done to human lives. None is more tragic than the story of J. (name redacted in the interests of privacy), who is suffering from an unspecified cancer, and who was one of the biggest supporters of the ‘protocol’… until the placebo effect wore off, and she realised it is all a fraud. But valuable time spent on a miserable, painful treatment that bore no benefit, and might well accelerated J.’s disease progression.

There are, as we speak, thousands, if not millions, of Jillian Epperlys, peddling their fraudulent wares to an uninformed public. When the chips are down and conventional treatment options have been exhausted, patients will always turn to alternatives. With Right to Try, they could do so under medical supervision, adequately counseled and with their side effects managed. Moreover, the medications administered would have to adhere to standards of manufacture (GMP) and have a well-understood mechanism of action in most cases. There will always be desperate patients – and a well-designed Right to Try policy may keep them away from quacks and within the traditional medical system that would cater better for their needs and handle the transition from trying salvage/last-ditch treatments to palliative care and ensuring adequate end-of-life care.

Another undesirable aspect is the existence of an informal right to try. Darrow et al. describe the case of Josh Hardy, a 7-year-old boy who received the experimental antiviral drug brincidofovir after the media drew sufficient attention to his case for the manufacturer to ‘add’ Josh to an open-label study.6 Similarly, public sympathy for the aid workers from Samaritan’s Purse, including Kent Brantly, allowed for the use of the chimeric monoclonal antibody ZMapp. From the perspective of health equity, it is concerning that this informal procedure is amenable only to those with the means and connections to launch a massive social media campaign. In this sense, it is eerily reminiscent of the case of Sarah Murnaghan, whose lung transplant ineligibility was supervened by a large public campaign. It is fair to question whether the effects of a discretionary scheme that ultimately favours those with social, political and economic influence would not be better supplanted by a formal, equitable system available to all on equal terms.

### The light and the dark

I don’t normally discuss end-of-life policy or bioethics: my days in that field are long gone, and my priority now is to try to avert those situations. However, to me, Right to Try will always be more than an abstract issue. A few years ago, a last-ditch therapy ended up working so well, it saved my life and put me into remission. After failing two different treatment regimens, we were out of conventional options, and things looked bleak – until a dedicated consultant oncologist took on the drug manufacturer, the hospital board and even the government, so as to be allowed to administer a drug still not approved for the particular indication. It was a huge gamble, and it worked. I will forever be grateful for the chance I’ve been given – but I’m also aware that I was the exception, not the rule, and $n=1$ doth not a good rule make.

I believe that even if the current version of Right to Try is, as Orac says, a ‘cruel sham’, it does not inherently have to be so.

There is enormous potential in Right to Try policies, not only for patients but also for drug development and future patients. Well implemented, it does not have to be a cruel sham. Nor does it necessarily have to be a wholesale ouster of the FDA’s competence.

But if it is to be anything other than that, it has to come with a comprehensive institutional structure that ensures that consent is truly free and adequate. Crucially, an independent physician must be available to honestly explain the odds and assess the patient’s understanding and capacity.7 The process must focus on balancing respect for patient autonomy against a degree of paternalism needed to protect a vulnerable patient. And in the end, it is paramount to have a sensitive understanding of the potential pressures the patient is under. It is not an easy task. But it is not an impossible one.

Many states now speak of ‘death with dignity’ as a euphemism for physician-assisted suicide. Perhaps to some people, that indeed is dignity, and it is a choice that deserves consideration. It is not cowardice or refusal to fight. But what about patients whose concept of dignity would closer encompass ‘staying in the fight’? Whether it is right or wrong, the practice of physician assisted suicide has shown that true consent can be separated from impaired consent in such a difficult scenario. Why, then, would it be impossible to separate instances where the Right to Try would merely engender false hope from those where it might have a small but not unrealistic clinical chance to succeed?

In the end, one needs to be able to separate the present rules from the principle. The present rules, and much of the motivation behind it, are clearly imperfect. But the potential behind Right to Try is significant. Regulated Right to Try can curb quackery and unregulated charlatans preying on the incurably ill by providing more legitimate last-ditch treatments carried out under medical supervision. It can accelerate research without prejudicing patient welfare if the pharmaceutical manufacturer is kept at arm’s length. And maybe, just maybe, it can save lives.

The current legislative framework might not be there yet. But it has the potential to make a difference not just to research but for millions of patients who have exhausted all possibilities, who, like me, might strike gold. Just as the history of science is one of incremental development, procedures and practices should be given the chance to develop over time.

References   [ + ]

 1 ↑ Right to Try Model Legislation, sec.1(2)(a). 2 ↑ Ibid., sec.2(1). 3 ↑ Downing, N.S. et al. Regulatory review of novel therapeutics – comparison of three regulatory agencies. N Engl J Med 366:2284-2293. 4 ↑ Freireich, E.J. et al. The role of investigational therapy in management of patients with advanced metastatic malignancy. J Clin Oncol 27:304-306. 5 ↑ Woloshin S, Schwartz LM, Welch HG. Patients and medical statistics: interest, confidence, and ability. J Gen Intern Med 20:996-1000. 6 ↑ Darrow, J.J. et al. Practical, Legal and Ethical Issues in Expanded Access to Investigational Drugs. N Engl J Med 372:279-286. 7 ↑ In all honesty, I am not entirely sure that all too many patients in that emotionally and physically difficult situation are lucid enough to comprehend the entirety of what is involved in such a decision!