Who doesn’t want to be this guy?!

Trigger warning: Long rant; gender and racial theory; I may use the qualifier “cis-” in a non-ironic way[1]; Since saying “male genitalia” or “female genitalia” is apparently bad, I may use the words “cunt” and “cock” to refer to the things they refer to; Aussie pride; excessive footnotes[2]; dead naming of dead dudes[3]; anti-Americanism; as always, sex positivity, along with a healthy dose of trans positivity (I hope, though maybe 800 people will judge me a bastard) and my usual disdain for radical feminism; insufficient or excessive trigger warnings

TLDR: WTF is going on with feminist philosophy?! Also, if you think that transgender people are serious and real and should be given full rights and respect, you probably also need to accept that transracialism is cool; but unless you’re American you probably already did, without even thinking that it was A Thing.

I just discovered a horrific conflagration overtaking the world of feminist philosophy, which has got me thinking about a concept that I didn’t even really know existed, but which is apparently A Thing: Transracialism. Transracialism is the practice of people of one race adopting the identity of another and living that identity even if they hadn’t been born into or raised with that identity, so superficially it has this transition process in common with being transgender. I’ve obviously been out of touch with left wing radical social ideals for a while, because I didn’t know that transracialism was A Thing, and that it is Bad while being transgender[4] is Good. In this post I want to talk about transracialism and the stultifying consequence of Americans hogging the debate about sex and race, and also about the disastrous state of modern leftist discourse[5] about so many things.

The controversy concerns an interesting paper in the philosophy journal Hypatia, discussing some of the logical consequences of accepting transgender as a real and serious issue[6]. The article, In Defense of Transracialism, examined the similarities between transitioning to a new gender and transitioning to a new race, and argued that logically if you accept one you really run onto rocky ground if you don’t accept the other. For case studies (and not, apparently, as the fundamental logical basis of the argument) the paper presented the case of Caitlyn Jenner as a transgender, and Rachel Dolezal as a transracial person (“transracer”?) As we know, Jenner got widespread public acceptance for her decision, while Dolezal received widespread public scorn. The article argues in what, to me at least, appears to be a quite tightly reasoned and accessible style, that it’s hard logically to accept one and reject the other, and maybe that means transracialism is actually okay.

The paper was published in March but recently a bunch of Associate Editors connected to the journal published an open letter demanding that the paper be retracted because its publication caused many “harms” to transgender people, and because it was academically poor. The outline of the case, and a solid takedown of the public letter, can be read at this New York Magazine post. It should be noted that the author of the paper is a non-tenured Assistant Professor, a woman, who is therefore quite vulnerable in a highly competitive field dominated by men, and that some of the signatories to the open letter were on the author’s dissertation assessment committee, which makes their signing the letter an extremely vicious act of treachery, from an academic standpoint. For more background on the viciousness of the letter and its implications for the author’s career and for the concept of academic freedom, see Leiter Reports, a well known philosophy blog (e.g. here) or the Daily Nous (e.g. here). It appears that the author has a strong case for defamation, and that many of the leading lights of feminist philosophy have really made themselves look very bad in this affair. (In case you haven’t gathered, I am fully supportive of the author’s right to publish this article and I think the open letter, demand for retraction, and pile-on by senior academics to an Assistant Professor near the beginning of her career is a vicious over-reaction of which they should all be deeply ashamed).

Beyond the obvious bullying and the ridiculous grandstanding and academic dishonesty involved in this attack on the author[7], I am disappointed in this whole issue because it is such a clear example of how Americans can dominate feminist (and broader social justice) debate in a really toxic way. I’ve discussed this before in regards to the issue of sex work and radical feminism, and I think it needs to be said again and again: American influence on left wing social debates is toxic, and needs to be contained. Just look at the list of signatories to this attack on this junior academic – they’re almost all American, and this is yet another example of how America’s conservatism, it’s religious puritanism, its lust for power, and its distorted republican politics, combined with its huge cultural output, is a negative influence on left wing politics globally.

I’m also really interested in this paper because I think it shows not just that transracialism may actually be an okay idea, but when I thought about the implications, I realized that I think most people on the planet already accept transracialism, and if Rachel Dolezal had occurred in any other country we would probably just have shrugged and got on with our lives. So in this post I’d like to discuss what Americans can learn from other countries’ approach to race.

Transracialism in Australia

Just to clarify, I was born in New Zealand to British parents and moved to Australia aged 13, taking Australian citizenship when I was 21. My grandfather was a Spanish war hero, a proud soldier in the losing side of the civil war and a man who spent nine years fighting fascism, and I was raised by him and my (deeply racist, white) British grandmother for two years as a child. So actually I’m a quarter Spanish, and so in theory I could have been raised Spanish but wasn’t, and don’t know anything about my birth race, which at various times in history has been defined as a separate race or just a culture. This makes me probably really normal in Australia, because Australia is a nation of immigrants making a new life in a land swept clean by genocide. It’s my guess that if you grew up in Australia you know a lot of mixed-race people, and if you paid any attention to the discussion of the Stolen Generations in the 2000s you’re aware that race is a very contested and contestable concept, and that Australian government policy has always assumed that race is a mutable concept subsidiary to culture. I think it’s likely that if you grew up in Australia you will know at least one of the following stereotypes:

  • An Aboriginal person who doesn’t “look” Aboriginal, and who maybe has no connection to their Aboriginal culture; you may even not be sure if they are Aboriginal, suspect they are but don’t know how to ask
  • A young Asian Australian who looks completely Asian, acts in ways that are stereotypically associated with Asian Australians (e.g. the guy holds his girlfriends bag for her, the girl is a complete flake in a very Asian Australian way) but is in every other way completely and utterly unconnected from their Asian heritage and is thoroughly through-and-through “whitebread” Australian
  • A completely Australian guy who speaks fluent Greek and goes back to Greece to “be with his family” every year
  • A person who has discovered that they have an ethnic heritage of some kind and is trying to recover that heritage in some way that might inform them about their own past, even though they are effectively completely disconnected from it, but they are clearly serious about rediscovering their heritage and all their friends and family support this apparent madness
  • A black or dark-skinned Australian who literally knows nothing about the culture of whatever race gave them their skin colour

If you’re a little older, like me, or know a wide range of older Australians, you may also have encountered an Aboriginal Australian who was stolen from their family at an early age and raised white but is on a bittersweet quest to recover the heritage they never had – and may have found that that heritage was extinguished before they could be led back to it. When I was 20 I was paid to provide maths tutoring to a bunch of 50-something women who were training to be Aboriginal Teaching Assistants – a kind of auxiliary teacher who will assist fully qualified teachers in remote Aboriginal communities – and some of them couldn’t even do fractions. When I asked how they missed such an early stage of education they told me they were taken to “the mission” when they were young, and didn’t get a proper education. I was young and this kind of issue wasn’t discussed then but now I understand that they were from the Stolen Generation, and were at various stages of understanding of their own racial heritage. They were going back to help their community, and recovering their own heritage, not just to settle the question of their own background but also to right wrongs done and change society[8]. These kinds of people are a normal thing in Australian cultural life. But can you look at that list of archetypes and say they aren’t all in their own way transracial? Indeed the underlying philosophy of the Stolen Generations was that you can eliminate racial traits of Aboriginality in half-Aboriginal people simply by raising them white; and the underlying principle of Multiculturalism is that culture transcends race, and we can all get along. Also in Australia there is a lot of tacit recognition of the problems second and third generation migrant children go through as they “transition” from the cultural heritage of their parents to that of their born country, where although racially they’re distinct from the majority they are clearly culturally more similar to the majority than to their parents. In the 1990s this was happening with Greek and Italian kids, in the 2000s with Vietnamese kids, and in the 2010s with Lebanese kids. Everyone in Australia knows that this happens, which surely means that everyone in Australia sees transracialism as a common pattern of multiculturalism.

Since I’ve moved to Japan I’ve seen this confirmed in many ways, but the best I can think of is a child I knew in a rural country town. His parents were both white New Zealanders but he had been brought to Japan at the age of 3 and raised in rural Japan, and when I met him at 17 he was thoroughly and completely Japanese. He didn’t speak English, communicating with his parents in a mixture of Japanese, really really bad English, and typical adolescent boy grunts. He hadn’t experienced much racism in Japan and had been sheltered in a very nice and welcoming rural environment, had a good group of close Japanese friends, communicated in the (ridiculously incomprehensible) local dialect, and was a typical cloistered Japanese boy. But he was also a big, white lump in his Japanese world, standing out like dogs balls. His race was irrelevant to his cultural background, except that he knew he was “white” and that therefore every Japanese person who ever meets him will engage in a boring conversation about why he is so. Fucking. Japanese. How is this not transracialism? Sure, a lot of transracial experience is not a choice per se, but whether it is a choice is surely irrelevant to the fact that it is completely possible and that for some of us – probably only a small proportion – changing “race” is a choice we feel compelled to make. I.e. not a choice. Rachel Dolezal might be a bad example, but whatever her motives might be, is her ability to do it under question? I would suggest that from an average Australian perspective, it is a completely ordinary concept. The only thing at issue is “why?” But since most well-meaning people don’t impugn the motives of strangers, who gives a fuck?

Race is a social construct

The possibility of transracialism becomes even clearer when you recognize that race is a social construct. This doesn’t mean race doesn’t exist – it clearly does – but that it is an invention of humanity structured around clear physical lines, not a real thing. While there is a clear difference between black and white people, there is no boundary at which this difference can be defined, and no genetic markers that clearly distinguish between one and the other. This isn’t some weird fringe idea popular only amongst Black Panthers, but a fundamental plank of modern science, reasonably well accepted at least in the biological sciences and anthropology. When we talk about races what we really are referring to is distinct cultural identities that can be mostly distinguished by noticeable visual cues (e.g. Nigerians are black, and stress the first syllable of every word in a cool way). This also means that race has very little influence on the culture you can actually adopt, which is why although I’m a quarter Spanish I’m completely white, while there are Aboriginal or Maori people who are one quarter Aboriginal but completely wedded to the culture of that quarter.

In comparison, sex is an absolute category that is definable and distinct. It has a chromosomal origin, and multiple definable, distinct characteristics. It is also clear across cultures that men and women tend to be different in many physical and personality characteristics, though these aren’t always the same in every culture and there can be lots of differences between people of a single sex between and within cultures. But sex is a clear, binary concept that, for all its massive cultural baggage, is not independent of its biological underpinnings. This, by the way, is not an idea anathematic to feminism – lots of feminists accept that the sexes are fundamentally different, and although there may be argument about to what extent these differences are biological vs. cultural, there is a large body of feminist work that assumes these differences are real and important.

And yet still people can want to change sex. Really want to change sex! And this phenomenon is common across almost every culture, though it receives higher levels of acceptance in some cultures (e.g. some Asian and Indigenous cultures) than others (e.g. modern USA). It’s also clear that you can’t force someone to change sex the way you can race. You might be able to “breed out the colour” of “half-caste” Aboriginal people by stealing them from their parents and raising them in a white family, but you can’t breed out the pink by forcing a girl to grow up as a boy – she’ll still know that she’s a girl. The same is true of sexuality of course – most people can define their sexuality clearly by the gender of the people they fuck, but we have no evidence that you can change that, no matter how hard you try. We know in fact that down that road lies tragedy. And so most of us take people’s sexuality – and the right to express it freely – very seriously. Yet most of us also accept that the right to change sex, to express a desire to be the opposite sex to our birth sex or even to be a third sex, very seriously as well.

So why not race? It’s way more fluid than gender, it has no biological basis, and we have huge amounts of evidence that people do it by accident all the time. Yet when Rachel Dolezal was outed as white she attracted general derision across the political spectrum; and Trump trades on the Pocahontas slur for Elizabeth Warren, whose sole crime apparently is to have been raised thinking she might have Native American heritage. There’s clearly something wrong with this picture, especially if like me you grew up in a race-fluid environment. Why is it so wrong to be transracial?

The toxic American influence on sex and race debates

Of course in America race is not a simple issue, because of slavery. America has a complex, toxic and quite unique racial environment which makes it very hard for Americans to react reasonably to these debates. Just consider the “politically correct” term for black Americans – African American. How is this not a transracial identity? Africa is neither a country, nor a culture, nor a race. Being “African American” is a completely concocted identity, a race that didn’t exist until the 1970s and the advent of pan-Africanism. Nothing wrong with that per se, obviously, but it leads to strange contortions in which, for example, the previous president[9] was dismissed as not “African American” enough by some of his critics even though his dad was Kenyan. We also see unedifying moments like this, where we discover that one of Dolezal’s trenchant critics was raised in a white household from the age of 2, and has clearly made a conscious choice to be black – but rejects Dolezal’s choice on clearly spurious racial grounds.

I think the problem here is simply that Americans need to come to terms with their own racist history, and simultaneously with their role as centre of empire and cultural hegemon. It’s not just that white Americans are beneficiaries of a long history of slavery, or that a sizable portion of white Americans can’t even yet accept that slavery was really wrong, or that treason in defense of slavery was really bad. It’s also the case that black Americans are simultaneously deprived in their own country but hyper-privileged globally, benefiting from many of the profits of empire just as their white compatriots do. This is why, for example, in response to the water poisoning crisis in Flint, Michigan we heard so much about how this was happening “even in a developed country” – black Americans are used to certain basic things that many of the people in America’s tributary nations don’t get. Similarly, black Americans can talk about pan-Africanism while black Americans are bombing Libyans. This is a complex, messed up problem that Americans have to come to terms with before they preach to the rest of us about transracialism. Combine this with America’s well-established puritanism and religious extremism, and you have a perfect storm of stupid. It makes you wonder why they even bother doing philosophy.

It also makes me think that they don’t really have a proper grip on some of these issues. Instead of talking about their own race issues, I think a lot of American feminists could stand to look around the world and learn from others. Australia has a unique culture of multiculturalism and acceptance that, while far from perfect, offers important lessons on how to negotiate racial conflict. We also have a history of genocide and responding to genocide that is deeply entangled with old fashioned racial theories that still seem to have some influence on both the left and right of American politics. But as an Australian I think we have learnt a lot and grown a lot, both about sex and race, in ways that Americans need to learn from. Instead, however, these American philosophers seem to think that their experience of race is unique and universal. I even recently stumbled across a tweet by a “key” philosopher of transgender issues (American) who claimed that transracialism had never been practiced anywhere except by one person (Rachel Dolezal). What a joke! This shows deep ignorance of broader issues of race and culture and a kind of infantile understanding of what the rest of the world is doing. I bet right now there are huge debates going on in China in Chinese about people faking ethnic minority identity (or vice versa) that no American philosopher of race even knows about, let alone can turn into a lesson for American philosophical dialogue.

I think it’s time Americans learnt some humility. America is a nation of religious extremists with a history of slavery that just elected an orange shitgibbon for president. Some humility would be in order.

And a little less bullying too! So if, like me, you think that this article might have pointed you to a phenomenon that is more common than you think, that you didn’t even know existed, maybe you should read it. And then reconsider whatever passing judgement you might have made of Rachel Dolezal, and ask yourself how easily the media are fooled by ugly narratives, and what that says about their quality.

And then, I guess, be whatever race you want to be!

fn1: Google it!

fn2: Including but not limited to references to Aussie pride

fn3: Until today I didn’t know that this term existed, though I think that I probably tried to avoid doing what it refers to. Google it!

fn4: You’ll note that I am writing “transracialism” but not writing “transgenderism”. This is because apparently the latter term is offensive while the former is not; and this has nothing to say! Nothing at all! About how one of these processes is accepted by those who police our language in the name of social justice, while another is not.

fn5: Add “will non-ironically say ‘discourse'” to the trigger warnings! Too late!? Too bad!

fn6: Because for arbitrary and stupid reasons I can’t say “transgenderism”, every sentence where I want to refer to the process or state of being a person who is transgender is going to involve these slight awkwardnesses of English language. I’m going to stick to the politically correct phrasing here, but I hope that everyone sees how awkward this is, and how telling the acceptability of one -ism but not another -ism is.

fn7: I’m making a decision not to name the author because I suspect that if things go badly for her and the paper is retracted she is going to want her name not to be associated with the paper that she struggled over; I know that my actions won’t make a difference to the google search results, but I choose not to add to them. Nonetheless I think this is work she should be proud of and I hope she doesn’t have to retract or disavow it. Also what kind of budding philosopher wants their name turning up on a disreputable blog like this, associated with fantasy gaming and sex positivity?!

fn8: And they were being taught fractions by an ignorant white dude half their age. Can you imagine the indignity!? But they were very nice to me, and I think I did a good job of the teaching. But teaching fractions is HARD.

fn9: Please come back!

Recently I wrote a post criticizing an article at the National Bureau of Economic Research that found a legal ivory sale in 2008 led to an increase in elephant poaching. This paper used a measure of illegal poaching called commonly the PIKE, which is measured through counting carcasses discovered in searches of designated areas. This PIKE measures the proportion of deaths that are due to illegal poaching. I argued that the paper was wrong because the PIKE has a lot of zero or potentially missing values, and is bounded between 0 to 1 but the authors used ordinary least squares (OLS) regression to model this bounded number, which creates huge problems. As a result, I wasn’t convinced by their findings.

Since then I have read a few reports related to the problem. I downloaded the data and decided to have a look at an alternative, simple way of estimating the PIKE in which the estimate of the PIKE emerges naturally from a standard Poisson regression model of mortality. This analysis better handles the large number of zeros in the data, and also the non-linear nature of death counts. I estimated a very simple model, but I think a stronger model can be developed using all the standard properties of a well-designed Poisson regression model, rather than trying to massage a model misspecification with the wrong distributional assumptions to try and get the correct answer. This is an outline of my methods and results from the most basic model.


I obtained the data of legal and illegal elephant carcasses from the Monitoring Illegal Killing of Elephants (MIKE) website. This data indicates the region, country code, site number, and number of natural deaths and illegally killed elephants at each site in each year from 2002 – 2015. I used the full data set, although for comparing with the NBER paper I also used the 2003-2013 data only. I didn’t exclude any very large death numbers as the NBER authors did.

The data set contains a column for total deaths and a column for illegal deaths. I transformed the data into a data set with two records for each site-year, so that one row was illegal killings and one was total deaths. I then defined a covariate, we will call it X, that was 0 for illegal killings and 1 for total killings. We can then define a Poisson model as follows, where Y is the count of deaths in each row of the data set (each observation):

Y~Poisson(lambda)                               (1)

ln(lambda)=alpha+beta*X                   (2)

Here I have dropped various subscripts because I’m really bad at writing maths in wordpress, but there should be an i for each observation, obviously.

This simple model can appear confounding to people who’ve been raised on only OLS regression. It has two lines and no residuals, and it also introduces the quantity lambda and the natural logarithm, which in this case we call a link function. The twiddle in the first line indicates that Y is Poisson distributed. If you check the Poisson distribution entry in Wikipedia you will see that the parameter lambda gives the average of the distribution. In this case it measures the average number of deaths in the i-th row. When we solve the model, we use a method called maximum likelihood estimation to obtain the relationship between that average number of deaths and the parameters in the second line – but taking the natural log of that average first. The Poisson distribution is able to handle values of Y (observed deaths) that are 0, even though this average parameter lambda is not 0 – and the average parameter cannot be 0 for a Poisson distribution, which means the linear equation in the second line is always defined. If you look at the example probability distributions for the Poisson distribution at Wikipedia, you will note that for small values of lambda values of 0 and 1 are very common. The Poisson distribution is designed to handle data that gives large numbers of zeros, and carefully recalibrates the estimates of lambda to suit those zeros.

It’s not how Obama would do it, but it’s the correct method.

The value X has only two values in the basic model shown above: 0 for illegal deaths, and 1 for total deaths. When X=0 equation (2) becomes

ln(lambda)=alpha              (3)

and when X=1 it becomes

ln(lambda)=alpha+beta    (4)

But when X=0 the deaths are illegal; let’s denote these by lambda_ill. When X=1 we are looking at total deaths, which we denote lambda_tot. Then for X=1 we have



ln(lambda_tot)-ln(lambda_ill)=beta     (5)

because from equation (3) we have alpha=ln(lambda_ill). Note that the average PIKE can be defined as


Then, rearranging equation (5) slightly we have




So once we have used maximum likelihood estimation to solve for the value of beta, we can obtain


as our estimate of the average PIKE, with confidence intervals.

Note that this estimate of PIKE wasn’t obtained directly by modeling it as data – it emerged organically through estimation of the average mortality counts. This method of estimating average mortality rates is absolutely 100% statistically robust, and so too is our estimate of PIKE, as it is simply an interpretation of a mathematical expression derived from a statistically robust estimation method.

We can expand this model to enable various additional estimations. We can add a time term to equation (2), but most importantly we can add a term for the ivory sale and its interaction with poaching type, which enables us to get an estimate of the effect of the sale on the average number of illegal deaths and an estimate of the effect of the sale on the average value of the PIKE. This is the model I fitted, including a time term. To estimate the basic values of the PIKE in each year I fitted a model with dummy variables for each year, the illegal/legal killing term (X) and no other terms. All models were fitted in Stata/MP 14.


Figure 1 shows the average PIKE estimated from the mortality data with a simple model using dummy variables for each year and illegal/total killing term only.

Figure 1: Estimated Annual PIKE values

Figure 1: Estimated Annual PIKE values

The average PIKE before the sale was 0.43 with 95% Confidence Interval (CI) 0.40 to 0.44. Average PIKE after increased by 26%, to 0.54 (95% CI: 0.52 to 056). This isn’t a very large increase, and it should be noted that the general theory is that poaching is unsustainable when the PIKE value exceeds 0.5. The nature of this small increase can be seen in Figure 2, which plots average total and illegal carcasses over time.

Figure 2: Trend in illegal killings and total deaths

Figure 2: Trend in illegal killings and total deaths

Although numbers of illegal carcasses increased over time, so did the number of legal carcasses. After adjusting for the time trend, illegal killings appear to have increased by a factor of 2.05 (95% CI 1.95 – 2.16) after the sale, and total kills by 1.58 (95% CI 1.53 – 1.64). Note that there appears to have been a big temporary spike in 2011-2012, but my results don’t change much if the data is analyzed from only 2003-2013.


Using a properly-specified model to estimate the PIKE as a side-effect of a basic model of average carcass counts, I found only a 25% increase in the PIKE despite very large increases in the number of illegal carcasses observed. This increase doesn’t appear to be just the effect of time, since after adjusting for time the post-2008 step is still significant, but there are a couple of possible explanations for it, including the drought effect mentioned by the authors of the NBER paper; some kind of relationship between illegal killing and overall mortality (for example because the oldest elephants are killed and this affects survival of the whole tribe); or an increase in monitoring activity. The baseline CITES report identified a relationship between the number of person-hours devoted to searching and the number of carcasses found, and the PIKE is significantly higher in areas with easy monitoring access. It’s also possible that it is related to elephant density.

I didn’t use exactly the same data as the NBER team for this model, so it may not be directly comparable – I built this model as an example of how to properly estimate the PIKE, not to get precise estimates of the PIKE. This model still has many flaws, and a better model would use the same structure but with the following adjustments:

  • Random effects for site
  • A random effect for country
  • Incorporation of elephant populations, so that we are comparing rates rather than average carcasses. Population numbers are available in the baseline CITES report but I’m not sure if they’re still available
  • Adjustment for age. Apparently baby elephant carcasses are hard to find but poachers only kill adults. Analyzing within age groups or adjusting for age might help to identify where the highest kill rates are and their possible effect on population models
  • Adjustment for search effort and some other site-specific data (accessibility and size, perhaps)
  • Addition of rhino data to enable a difference-in-difference analysis comparing with a population that wasn’t subject to the sale

These adjustments to the model would allow adjustment for the degree of searching and intervention involved in each country, which probably affect the ability to identify carcasses. It would also enable adjustment for possible contemporaneous changes in for example search effort. I don’t know how much of this data is available, but someone should use this model to estimate PIKE and changes in mortality rates using whatever is available.

Without the correct models, estimates of the PIKE and the associated implications are impossible. OLS models may be good enough for Obama, but they aren’t going to help any elephants. Through a properly constructed and adjusted Poisson regression model, we can get statistically robust estimates of the PIKE and trends in the PIKE, and we can better understand the real effect of interventions to control poaching.

Today the Guardian reported on a new study that claims a large sale of legal ivory in 2008 actually led to an increase in illegal elephant poaching. Basically in 2008 China and Japan were allowed to pay for a large stockpile of legally-obtained ivory, in the hopes that this would crash the market and drive ivory traders out of business. Instead, the study claims, the sale led to a big increase in poaching – approximately a 66% increase in elephants killed, according to the study. This is interesting because it appears to put a big dent in a common libertarian idea for preserving endangered species – that allowing a regulated trade in them would lead to their preservation. It is also one of those cute findings that puts a hole in the standard just-so story of “Economics 101” that everything is driven by supply and demand. We all know that in reality there are many factors which moderate the effect of supply and demand on crucial markets, and on the surface this study appears to suggest a quite contradictory supply and demand relationship in illegal poaching markets, in which increasing supply boosts poaching. But is it true?

The Guardian report links to the original study, which is held at the National Bureau of Economic Research behind a paywall, but which I managed to get a copy of through my work. I thought I would check the statistical methods and see if the study really did support this conclusion. My judgment is that this study is quite poor, and that the data doesn’t support that conclusion at all, due primarily to three causes:

  • A poor choice of measure for illegal poaching that doesn’t clearly measure illegal poaching
  • The wrong choice of statistical method to analyze this measure
  • The wrong experimental design

I will go through each of these reasons in turn. Where equations are needed, I have used screenshots from the original paper because I’m terrible at writing equations in html. Let’s get started.

The PIKE is a terrible measure of illegal poaching

The study is based around analysis of a data set of “legal” and “illegal” carcasses observed at search sites in 40 countries. Basically a “legal” carcass is an elephant that died on its own, while an illegal one is one that was shot and looted. Apparently poachers don’t bother to clean up the corpse, they just cut off the ivory and run, so it’s easy to see when an elephant has been poached. However, because no one knows the full details of elephant populations, the authors study an outcome variable called the PIKE, which is defined as the ratio of illegal carcasses to total carcasses. In their words (screenshot):

PIKE equation

They say that this enables them to remove the unknown population from the outcome by “normalizing” it out in top and bottom of the ratio. They justify this with a little proof that I am not convinced by, since the proof assumes that probability of discovering carcasses is independent of the number of carcasses, and that legal mortality and illegal mortality are not related in any way. But even if it factors out population, this PIKE measure doesn’t tell you anything about illegal poaching. Consider the following hypothetical scenario, for example:

Imagine a population of elephants in which all the older elephants have been killed by poachers, so only the pre-adult elephants remain. Every time an elephant becomes mature enough to have decent tusks a poacher kills it and the corpse is found. Further, suppose that the population is not subject to predation or other causes of legal mortality – it is young, and the environment is in good shape so there are large stocks of easier prey animals for lions to target. This population is at high risk of collapse due to adults being killed as they mature; indeed, let’s suppose no babies are born because adults are poached as soon as they reach sexual maturity. Thus every time an elephant is killed, the population drops by one towards its inevitable crash.

In this case, at every time point the PIKE would be 1, because there are no legal carcasses. The PIKE will remain 1 until there are no elephants left to die, at which point it will jump to infinity. It doesn’t tell us anything about the impending population collapse.

Consider now a situation where there are a great many more legal deaths than illegal deaths. Denoting illegal carcasses by y and legal carcasses by x, we have y/(y+x) where y<<x. In this case we can approximate the PIKE by y/x, and if e.g. the number of illegal carcasses suddenly doubles we will see an approximate doubling in the PIKE. But suppose y is approximately the same as x. Then we have that the PIKE is approximately 1/2. Now suppose that the number of illegal carcasses doubles; then the PIKE increases to 2/3, i.e. it nowhere near doubles. If the number of illegal carcasses again doubles, it increases to 4/5. But if all deaths drop to 0 it then increases to infinity … So the magnitude of the increase in PIKE is not a direct reflection of the size of the change in poaching, and in at least one case even the direction is not meaningful. That is not a well-designed measure of poaching. It is also scale free, which in this case is a bad thing because it means we cannot tell whether a value of 1 indicates a single illegal carcass or 10 illegal carcasses. Similarly we don’t know if a value of 1/2 corresponds to 1 or a million illegal carcasses; only that however many there are, they are half of the total.

The authors say that this variable is constrained between 0 and 1, but this is not strictly true; it actually has an additional non-zero probability mass at infinity. This strange distribution of the variable has implications for model choice, which leads us to the second problem with their data.

All the models in this study were poorly chosen

The authors choose to model the PIKE using an ordinary least squares (OLS) model with fixed effects for country and a (separate) fixed effect for each year. An OLS model is only valid if the residuals of the model are normally distributed, which is a very strong assumption to make about a variable that has lots of values of 0 or 1. The authors claim their residuals are normally distributed, but only by pooling them across years – when you look at residuals within individual years you can see that many years have much more normally distributed residuals. They also don’t show us the crucial plot of residuals against predicted values, which is where you get a real idea of whether the residuals are well-behaved.

An additional consequence of using an OLS model is that it is possible to predict values of the PIKE that are unphysical – values bigger than 1 or less than 0 – and indeed the authors report this in 5.6% of their data points. This is indicative of another problem – the PIKE shows a non-linear response to increased illegal kills (see my example from 1/2 to 2/3 to 4/5 above), so that for a fixed number of legal kills each additional illegal kill has a diminishing effect on the value of PIKE, but a linear OLS model assumes that the PIKE changes by a uniform amount across its range. Given that the goal here is to identify increases in the PIKE over time, this runs the risk of the model over- or under-estimating the true effect of the 2008 ivory sale, because it is not properly modeling the response of the PIKE score.

The authors try to test this by fitting a new model that regresses ln(illegal carcasses+1) against a function that includes ln(legal carcasses+1) like so:

PIKE alternative model

This introduces a new set of problems. The “+1” has been added to both variables here because there are many zero-valued observations, and ln(0) doesn’t exist. But if there are lots of zero-valued observations, adding one to them is introducing a big bias – it’s effectively saying there was an illegal carcass where previously there wasn’t one. This distorts low numbers and changes the patterns in the data. The authors claim, furthermore, that “The coefficient on legal carcasses φ will be equal to unity if the ratio of illegal carcasses to legal carcasses is fixed”, but this is both nonsensical and obscures the fact that this model is no longer testing PIKE. It’s nonsensical because that is not how we interpret φ. If φ=1, then we can rewrite their equation (8) so that the left hand side becomes the natural logarithm of (illegal carcasses+1)/(legal carcasses+1). Then we are fitting a linear model of a new variable that is not the PIKE. We are not, however, assuming the ratio of illegal carcasses to legal carcasses is fixed. If φ is not 1, we are modeling the natural logarithm of (illegal carcasses+1)/(legal carcasses+1)^φ. The ratio here is still fixed, but the denominator has been raised to the power φ. What does “fixed” even mean in such a context, and why would we want to model this particular strange construction?

The authors do, finally, propose one sensible model, which is similar to equation (8) (they say) but uses a Poisson distribution for the illegal carcasses, and still fits the same right hand side. This is better but it still distorts the relationship between illegal and legal carcasses by adding a 1 to all the legal (but not the illegal) carcasses. It also doesn’t properly account for elephant populations, which is really what the legal carcasses serve as a proxy for. There is a much better way to use the legal carcass data and this is not it.

Finally there are two other big problems with the model: It uses fixed rather than random effects for country and site, which reduces its power, and also it doesn’t include any covariates. The authors instead chose to model these covariates separately and look for similar spikes in specific possible predictors of ivory usage, such as Chinese affluence. The problem with this is that you might not see a strong spike in any single covariate, but multiple covariates could move together at the same time to cause a jump in poaching. It’s better to include them in the model and report adjusted poaching numbers.

The wrong experimental design

An expert cited in the original article noted this interesting fact:

The Cites spokesman also noted that there had never been a one-off sale of rhino horn: “However, the spike in the number of rhinos poached for horn largely mirrors what has been seen with ivory. The illegal killing of rhino for its horn in South Africa alone increased from 13 in 2007 to close to 1,200 last year.”

This suggests that there has been an upsurge in illegal poaching across Africa that is independent of the ivory sale, and could reflect changing economic conditions in Africa (though it could also reflect different markets for ivory and rhino horn). It’s possible to test this using a difference-in-difference approach, in which rhino poaching data is also modeled, but is treated as not having been exposed to an intervention. The correct model specification then enables the analyst to use the rhino data to estimate a general cross-species increase in poaching; the elephant data identifies an additional, elephant-specific increase that could be said to be due to the ivory sale. The authors chose not to do this, which means that they haven’t rigorously ruled out a common change in poaching practice across Africa. If the CITES spokesman’s point is correct, then I think it likely that we would conclude the opposite to what this study found: that compared to rhinos, elephant poaching did not increase nearly as much, and in fact the ivory sale protected them from the kind of increased poaching observed with rhinos.

Indeed, it’s possible that there were poachers flooding into the market at around that time for other reasons (probably connected to development and increasing demand in Asia), but after the ivory sale most of them switched to killing rhinos. That would suggest the sale was successful, provided you aren’t judging that success from the standpoint of a rhino.

A better model: Bayesian population estimation followed by Poisson regression

It’s possible to build a better model using this data, by putting the legal carcass data to proper use and then using a correctly-specified Poisson regression model on the illegal carcass data. To see how different the results might then look, consider Figure 1, taken from the Appendix of the paper, which shows the actual numbers of illegal carcasses in each year.

Figure 1

Figure 1: Distribution of illegal elephant kills, 2002 – 2013 (year is above its corresponding histogram)

Does it look to you like the number of elephants killed has increased? It certainly doesn’t to me. Note that between 20 and 50% of observed data are 0 kills in all years except 2002 (which the authors say was the start year of the data, and exclude from their analysis). Can you strongly conclude any change from these figures? I haven’t shown the legal kill data but it is broadly similar in scale. Certainly, if there is any upward step in illegal kills in 2008, it could potentially be explained simply by changes in populations of elephants – if even a small change in elephant density leads to an extra 1 or 2 extra kills per site per year, it would lead to distributions like those in Figure 1. To me it seems likely that the single biggest determinant of elephant kills will be the number of elephants and the number of poachers. If we assume the number of poachers (or the pace of their activity) changed after 2008, then surely we need to consider what happened to the population of elephants overall in 2008. If it declined, then poachers might catch the same number as 2007; if it increased, they would catch more.

The best way to analyze this data is to directly adjust for the population of elephants. We can use the legal kill data to do this, assuming that it is mostly reflective of elephant population dynamics. It’s not easy, but if from published sources one can obtain some estimate of the mortality rate of wild elephants (or their life expectancy), a Bayesian model could be built to estimate total population of elephants from carcasses. This would give a credible interval for the population that could then be used as what is called an offset in a Poisson regression model that simply modeled counts of illegal kills directly against time. The advantage of this is that it uses all 0 count events, because a Poisson model allows for zeros, but it adjusts for the estimated population. I think the whole thing could be done in a single modeling process, but if not one could obtain first a distribution of the elephant population, then use this to simulate many different possible regression model coefficients for the effect of the ivory sale. In this model, the effect of the ivory sale would simply represent a direct estimate of the relative increase in mortality of elephants due to poaching.

Then, to complete the process, one would add in the rhino data and use a difference-in-difference approach to estimate the additional effect of the ivory sale on elephant mortality compared to rhinos. In this case one would find that the sale was protective for elephants, but potentially catastrophic for rhinos.


Based on looking at this data and my critical review of the model, I cannot conclude that the ivory sale led to an increase in poaching. I think CITES should continue to consider ivory sales as a tool to reduce elephant poaching, though with caution and further ongoing evaluation. In addition, based on the cited unnamed CITES spokesman, evidence from rhino culling at the time suggests the sale may even have been protective of elephants during a period of increased poaching; if so, a further big sale might actually crush the business, although there would be little benefit to this if it simply drove poachers to kill more rhinos.

With regard to the poor model design here, it shows a lot of what I have come to expect from economics research: poor definition of an outcome variable that seems intuitive but is mathematically useless (in health economics, the incremental cost effectiveness ratio shows a similar set of problems); over-reliance on OLS models when they are clearly inappropriate; poor model specification and covariate adjustment; and unwillingness to use Poisson or survival models when they are clearly most suited to the data.

I think there is lots of evidence that legal markets don’t necessary protect animals from over-exploitation (exhibit A, the fishing industry), but it is also obviously possible that economic levers of supply and demand could be used to kill an illegal industry. I suspect that more effective, sustainable solutions to the poaching problem will involve proper enforcement of sales bans in China and Japan, development in the regions where poaching happens, and better monitoring and implementation of anti-poaching measures. If market-crushing strategies like the 2008 ivory sale are going to be deployed, development is needed to offer affected communities an opportunity to move into other industries. But I certainly don’t think on the evidence presented here that such market-crushing strategies would have the exact opposite of the intended effect, and I hope this poor quality, non-peer-reviewed article in the NBER doesn’t discourage CITES from deploying a potentially effective strategy to stop an industry that is destroying a majestic and beautiful wild animal.

This week 700 asylum seekers drowned when their boat capsized somewhere in the Mediterranean sea; reports suggest that a large number of these poor souls were locked in the hold of the ship and had no chance of escape. A year ago the people on this ship might have been found rescued earlier by the European Union’s large, integrated emergency response program Mare Nostrum, but unfortunately it was defunded and replaced with a much weaker local Italian response under the explicit rhetoric of “deterrent,” pioneered so effectively by Australia. Countries with significant anti-immigrant political parties and communities, most notably the UK and Germany, refused to fund the continuation of a coordinated Mediterranean-wide rescue program on the basis that rescuing asylum seekers at sea encourages people smugglers to simply send more, and the best way to save lives is to refuse to help, so that the people smugglers’ business collapses when their customers realize the risks.

The events of the last week – 400 drowned last week, 700 this week, and it’s only Monday – show how effective that tactic has been. So does the record so far this year, with 30 times the deaths recorded in the equivalent period last year under Mare Nostrum. Record numbers are crossing the Mediterranean, fleeing persecution in Libya and chaos in Syria and Iraq. These people appear not to have got the Home Office memo, and apparently think that any risk is better than staying where they are. The ideology of “pull factors,” based on the assumption that these asylum seekers aren’t really that desperate and are just looking for the best country to settle rather than a place of safety, has been shown to be completely wrong.

Last year, before the end of Mare Nostrum, I wrote that Europe has been presenting evidence against the Australian ideology of reducing “pull” factors. Since I wrote that blog post Mare Nostrum has ended and the flow of refugees has exploded. Either there is no relationship between the border control policies in place at sea, or the defenders of this ideology – if they are being honest – will have to accept that the evidence shows that the only “pull” factor at work here is going in the opposite direction of their claims, and that rescuing asylum seekers at sea is a more effective deterrent than letting them drown. Of course they won’t accept such a conclusion, and will continue to argue that we “encourage” these desperate people by saving them, when all the evidence now shows that their plight is so desperate that they don’t care about our search and rescue plans, they just want to get out. But our political masters don’t care about these people, and indeed why should they when popular columnists refer to them as vermin and cockroaches? So instead mealy-mouthed politicians in Europe try to maintain their ideology of deterrence through callousness, and maintain that they will end the flow of refugees by targeting the people smugglers – rhetoric they have used for years to no effect, probably because they aren’t even bothering to do that. And how can they affect migration policy in North Africa? Libya is a chaotic mess that the last Italians fled from months ago, leaving the people of Libya and especially its most vulnerable stateless displaced to their bloody fate. How do you target people smuggling when you don’t even have an embassy? Europe is powerless to affect events on the ground in Syria, and refugee flows through that part of the world are now so huge that it would be impossible to identify the people smugglers, let alone stop them.

Japan is another example of the emptiness of “pull factor” rhetoric. Even though Japan has only approved a handful of asylum applications in the last decade, numbers of people claiming asylum have increased ten-fold over that time. How can it be that a country which offers zero chance of resettlement is seeing unprecedented application numbers, if asylum policy at the destination is a major determinant of asylum seekers’ choices?

Abandoning people to drown is cheap and politically easy in modern Europe, but it will not deter these people, because they are desperate. It’s time for Europe to recognize that its neighbourhood has gone to hell, and Europe won’t be able to keep ignoring this problem forever, or pretending that it can stand by and let people drown out of simple callousness. If Europe is not willing to invest the time, money and lives in stabilizing Syria and Libya, then it needs to recognize that it has at least a moral responsibility to save the lives of the desperate and stateless when they put to sea. Maybe then Australian politicians will also rethink their cruel and vicious policies towards the stateless. This problem is not going to end anytime soon, but if we keep lurching towards the moral event horizon, our humanity will …

Today’s media are breathlessly reporting that the WHO is predicting 5,000 – 10,000 new cases of Ebola virus disease per week by the beginning of December. There is no written documentation on this, but I did find this study in the New England Journal of Medicine (NEJM) from the WHO’s rapid response unit which suggested 20,000 cumulative cases by 2nd November, which would be 10,000 more cases than we are seeing now (roughly) in just two weeks, so 5,000 per week in November. Given the doubling times in that study were estimated to be approximately one month, that does suggest a rough number of 10,000 cases per week by December (if anything that number is probably slightly optimistic). If so we can expect to see 40,000 cumulative cases by the start of December (20,000 to 2nd November, then 20,000 more in November), and 80,000 by the end of the year. Assuming the same doubling, we will see another 20,000 a week in January, which takes us to a rough 150,000 by the beginning of February, assuming that there is no successful intervention by then.

The case fatality rate is now estimated to be about 70%, so those time frames would give respectively (and approximately); 30,000 deaths by the start of December; 55,000 by the end of the year; and about 100,000 by February. Those are large numbers, but on a national basis what does that mean? In this post I want to look at the implications of these numbers under three different scenarios, but first let’s just look at the number of deaths by the end of the year, and do some rough calculations of the implications.

First, let’s look at just Liberia. The NEJM article puts about 50% of all cases in Liberia, so if we follow that proportion forward, we can expect about 27,000 deaths by the end of the year, and 40,000 cases. It’s not necessarily wise to assume that proportion is static, since the disease appears to have taken hold in the capital of Liberia and Liberia seems to be the worst affected, so the disease may spread faster there or may burn out sooner; but for lack of better evidence let’s just go with that proportion. Liberia, according to Wikipedia, has a population of 4 million, and its capital Monrovia has a population of about 1 million. At first blush, 27,000 deaths is not a lot of people in a country of 4 million … but in 2005-2010 Liberia’s mortality rate was 12 per 1000, for a total of 48,000 deaths in 2014 (my estimate). The 30,000 extra cases of Ebola in Liberia that will occur by the end of the year will cause 21,000 deaths, 50% of its annual total. In just 2.5 months the disease will kill as many people as usually die in 6 months. That’s a traumatic increase in mortality, such as usually happens only in times of natural disaster and war.

In addition, however, controlling the epidemic requires isolation and monitoring of an enormous number of people. Consider this report of an outbreak of Marburg disease in Uganda in September. The disease – which is very similar to Ebola – was identified in a single person in a small town in Uganda, and killed the index case after 17 days. Contact tracing was carried out, and the WHO reports that

As of today, a total of 146 contacts have been identified and are being monitored for signs and symptoms compatible with MVD. Eleven of the contacts developed signs and symptoms compatible with Marburg virus disease.

In order to properly contain this disease, the doctors had to track down 146 people, and of those 11 developed signs and symptoms (fortunately in this case none of them were positive). In the Liberian context this would mean that for every case, more than 100 people need to be traced and 11 isolated as suspected Ebola. Even if we assume that people are starting to catch on to the risks, and so are having less contacts and less need for isolation, we can probably still safely assume that to properly control the disease we need to isolate 4-10 people and trace 100 or so. For 30,000 new cases from now till the end of the year that will mean isolating 120-300,000 individuals, for a period of as long as 21 days. The top end of that figure is about 8% of the population of the country.

Finally, the toll on health care workers of the first 10,000 cases has been genuinely shocking. The latest WHO situation report tells us that 201 health care workers in Liberia have caught the disease, and 95 have died. Assuming that rate persists, adding 30,000 more cases will lead to the death of 300 more health care workers. Wikipedia, again, tells us that Liberia had 5000 full- or part-time health care workers in 2006, of whom 51 were doctors. By December Liberia will have lost almost 20% of its entire health workforce, leading to huge setbacks for health in one of only seven countries in Africa to have met its Millenium Development Goal 4 (under-5 mortality) targets.

So let’s bear those basic figures in mind. 40,000 cases= 28,000 deaths, 120-300,000 isolated individuals, 1.2 – 3.0 million individuals being monitored for signs and symptoms, 20% of the health workforce dead. Also, a very large number of foreign health workers coming in to help, and entire new hospitals being constructed in a country with no suitable infrastructure. Now let’s consider three different scenarios, based around the UN’s 70-70-60 goal, which is to be able to isolate 70% of cases and bury 70% of bodies safely, within 60 days. The low basic reproductive number of Ebola – below 2.0 in most cases – means that preventing 70% of secondary cases should be sufficient to kill the epidemic (just!) So let’s assume that if this goal is reached and maintained, the epidemic will plateau and then begin to decline, in about the same time that it took to escalate. For simplicity we’ll count that time period in terms of numbers of cases – so after the disease peaks, we will assume as many new cases occur as the disease fades away as occurred in its growth. This is not unreasonable – most epidemic patterns don’t crash, but tend to go through a decline that looks roughly symmetric to the increase. This may not apply with a disease as fatal as Ebola, but no one will know till we come out the other side, so let’s assume it will behave as most other epidemic patterns do. This means that if we have x cases by new year, and the UN goal is attained at new year, we should expect to see a further x cases before the disease is gone.

The best case scenario: Liberia meets the UN goal on time

If the sudden inrush of aid workers and soldiers enables Liberia to meet the UN goal on time, we will reach 70-70 in 60 days from now, i.e. by mid-December. That means there will have been 60,000 cases by the time the epidemic begins to decline, or maybe 100,000 by its end. This means 70,000 deaths, 300-700,000 isolated individuals, and pretty much everyone in the country being monitored. About 50% of the workforce will be dead. If we assume the decline takes a few months, say until March, we can guess that we will see nearly two years’ mortality in just 6 months. Between 10-25% of the population will have been isolated for about one month during this period, unable to work or care for others. The goal of safely burying 70% of the dead means 50,000 bodies will need to be buried by specialist teams. The difficulty of their work can be seen in this excellent brief report from the NY Times, but I think it’s obvious that burying 50,000 bodies is going to have to be done in a very different way to this. I wonder if there is even a protocol for mass burial of highly-infectious bodies?

This is the best case scenario. On the basis of the numbers alone it is clearly a catastrophe for Liberia, but it isn’t enough to bring the country to its knees (at least with outside help). Less than 2.5% of the country is dead, and although the economic effects are alarming and the long-term destruction of the health system will set the country back years from its health goals, it doesn’t appear to be a recipe for total collapse (at least on paper). There is hope here, and if the containment efforts are good so that the epidemic crashes really fast, then we can expect it to have a much less significant effect on the health workforce.

The realistic scenario: Attaining 70-70-60 a month late

Suppose instead that the UN goal is missed by a month, taking us to mid-January. That will correspond with about (very roughly) 100,000 new cases by the time the epidemic peaks, or 200,000 by the time it finishes in probably March or April next year. From our calculations, this means 140,000 deaths, 600,000 – 1.5 million isolated individuals, and the remainder being monitored. The entire health workforce will die in this scenario, and about 100,000 bodies will need to be buried safely. Only 54% of the Liberian population are working age, or about 2 million; it’s quite possible that a large part of the adult workforce will be in isolation for more than 3 months, with a large part of the rest involved in basic Ebola-combat activities (burying bodies, contact tracing, logistics). The death toll is equivalent to three years’ mortality in 6 months. What this would mean for the agriculture sector I cannot guess, but it doesn’t seem good. At this level of disease spread, I think we are looking at a society on the verge of collapse, where trade-offs have to be made between isolation/contact tracing on the one hand, and maintaining basic functions of civil society on the other. If the UN goal is missed by a month, alternatives will need to be found to isolation systems, and a huge increase in available health workers will be needed.

The worst case scenario: Failure to contain the epidemic by February

Failure to contain the epidemic by February means 150,000 cumulative cases by February, and probably at least 300,000 (maybe more) over the next few months, with no sign of a slowdown. Every month we will see another doubling of the rate (40,000 per week in March, and so on). Just taking the minimum value here of 300,000 cases, there are 210,000 deaths, 840,000 – 2 million individuals in isolation, and the entire health workforce decimated. In this scenario most of the adult working-age population is isolated, and the entire economy has shut down. Without a huge influx of foreign aid – in food, water, field hospitals, and probably thousands of medical staff – the disease will break out of any containment system that might be left in place, and the only limit on its spread will be its own voracity. This suggests to me that we have until January to get an effective containment system in place, or Liberia as a country will cease to exist in any functional sense. We should assume, furthermore, that in the general breakdown of the social order that will ensue many people will leave the country, and the risk of the epidemic spreading to Nigeria will be very great.

Caveats and limitations

These figures are all rough guesses based on huge assumptions. The number of people who need to be isolated will not scale linearly with disease spread, for example, because one individual will begin to have multiple case connections, and as the disease spreads and social contact reduces, the number of people a new case will have actually touched or been near will decline rapidly. So my estimates of effects on the working age population are inflated, and these are the key cause of social breakdown, I think. Without the effect of isolation and disease containment efforts, even 300,000 cases and 210,000 deaths is not a society-ending event in a country of 4 million people, though nobody wants to think about how horrible that will be. My assumption that the downward side of the epidemic will cause as many cases as the upward side is based on the assumption that the basic reproduction number will be reduced only just below 1 by the 70-70-60 plan; this means each existing case gets a chance to cause another, but if the epidemic is contained more effectively once the plan is in place that assumption could be an over-estimate. Also there are geographical limits on the spread of the disease (especially once things get desperate and all travel within the country is shut down); this will mean that the disease rapidly burns through its available cases and dies out before it can spread fully. And finally, I don’t know what the time trend in deaths of health workers is, but I suspect these deaths were mostly in the early stage of the disease before the word was out, and that deaths are now declining rapidly towards zero. Given all these constraints, I think that an aggressive plan enacted now, aiming to achieve the 70-70-60 goal, and followed through aggressively thereafter, will probably stop the disease somewhere before the numbers provided in my best case scenario. This will still cause a years’ worth of mortality in a couple of months, take up to 10% of the working age population out of work for isolation, and kill up to 20% of the Liberian health workforce. It won’t cause a national collapse, but it is a catastrophe easily as bad as a tsunami or some other huge natural disaster.

What should this mean for the future of health planning in Africa?

We often talk about “fragile health systems” and “extreme poverty” in Africa, but in the rich nations we’re used to thinking of health system failure as poorly-managed diseases and unpleasant medical experiences, but it’s worth remembering that at the extremes of medicine there are disasters: car accidents, pandemic influenza, and incredibly horrible diseases like Ebola. In the best of times in Africa, “fragile health systems” means excess deaths due to preventable infant, maternal, HIV- and malaria-related mortality. But in the worst of times it means huge waves of mortality due to natural disasters, war or epidemics. This Ebola outbreak is showing the rich world what “fragile health systems” really means, and also showing us that we are not able to completely disconnect ourselves from these failures. We can’t expect to isolate ourselves from emerging infectious diseases forever, except perhaps at the cost of our humanity, so instead of trying to isolate ourselves we should try to seriously tackle the fundamental problems affecting health systems in Africa and some parts of South Asia. This is not like a military intervention where the best of intentions can bring about the worst of results; we know what works and we simply need to find the political will to make it happen. Once this disease is back in its box, and all three affected countries are able to contemplate a return to normality, we in the rich world should make a serious, final effort to fix global poverty and most especially to end the grotesque inequality in health systems around the world. It’s almost certainly not going to happen, but we have to recognize that any country with a fragile health system is one weird event away from a terrible humanitarian catastrophe, and we need to start thinking about how to stop this from happening again. That means we have to act to help those countries to genuinely strengthen their health systems, and achieve the kind of economic state that is able to sustain them. This may mean we fat, rich westerners pay a little more for our chocolate, coffee and clothes, but it’s a price I hope we are all going to be a little more willing to pay now that the threat of dying horribly in our own body fluids has begun to make itself felt.

This situation should also serve as a warning about the dangers of ignoring very rare but high-risk events. Ebola has been known for 40 years, and this is the first time it has ever escaped containment. Work on a vaccine has been delayed or ignored partly, I think, because the risk of this disease escaping its bounds is so low that people considered it negligible. I hope my calculations show that the cost of this disease is only negligible provided it never happens, and that once it does happen all our risk assessments look incredibly stupid. We need a new way of assessing risk which puts a serious value on low-probability events. In the era of climate change the implications of this are obvious. At the tail end of some of the global climate models there are some extreme, civilization-ending events that have been largely overlooked by policy-makers because they are so unlikely to happen. Hopefully this Ebola outbreak will convince the world that it is time we started looking more at the tails of our probability distributions, and not at the comforting bulges down near the low-cost events.

Commencing case isolation protocol 666

Commencing case isolation protocol 666

Media reports today that the Spanish government has killed a dog. Not just any dog – this was Excalibur, the hapless pet of the nurse who is quarantined for Ebola in Spain. The nurse, Teresa Romero Ramos, is being treated for Ebola after contracting it while treating a returned missionary; her husband is in isolation to be monitored for signs of the disease, and there are fears that the dog might have it too. It’s not clear whether dogs can get or transmit Ebola, though there is some vague evidence that they are at least at risk, so in theory there was some justification for the execution of an innocent dog, but in my opinion this is a huge public health mistake.

Because there is no treatment for or vaccine against Ebola, our only effective intervention to prevent its spread is case isolation, which in turn depends on early identification of cases, and rapid and effective contact tracing. This method alone has been effective in every previous outbreak. Although not airborne, Ebola is highly infectious with close contacts, so early identification is important to reduce the subsequent contact tracing burden, but symptoms are vague (fever) and easily confused with other possible illnesses – especially as influenza season approaches. So it’s really important that people with fever be willing to attend a doctor early, and that they be willing to risk putting their lives into the hands of public authorities on the basis of nothing more than a suspicious fever.

This kind of early identification, case isolation and contact tracing depends fundamentally on trust. The person with a fever needs to trust that they and their loved ones will be treated well, and that people contacted through them will be treated well. In general – I’m going to go out on a limb here – shooting someone’s dog does not fall under the definition of “treating them well.” It is, in fact, kind of mean.

Of course we all know that in times of emergency, the government will kill our dogs. If Ebola jumps the shark, you can bet that pets of all kinds will be seen as mere collateral damage in an extremely authoritarian and aggressive public health response. But since we don’t want our society to get to that point, our first goal in public health responses should be to ensure that everyone who might need to attend a doctor does so as early as possible, without fear of the consequences. Notice the emphasis on might – that is an important word in this context. If you want to give people the impression that they don’t need to fear the consequences of reporting their fever, you probably shouldn’t shoot their dog.

Now, many people might think that this is a public health emergency and in public health emergencies dogs aren’t very important. This is probably very true. But a public health response has to be built on the possibility that not everyone will agree with you about that; or that they might not understand the dynamics of infectious diseases enough to realize the dangers of letting their dog go; or that they might not have the same understanding of their own disease risk that you do. If anyone who thinks in any of those ways gets Ebola, and you have given them reason not to trust the authorities, they will delay their attendance to a hospital, and/or lie about their circumstances. This doesn’t just extend to crazy scenarios like refusing to admit they have Ebola because they don’t want you to kill their dog. The most likely scenario is much more bland: someone with a fever misjudges the risk that it is Ebola, and because they have a general worry that their dog will be shot if they go to hospital, they decide to just “wait and see” for a few days. During that few days they definitely infect their dog, and a few other people, before they finally accept that the bleeding eyes are the giveaway that they really do have Ebola.

But shooting a dog isn’t just about dogs: it’s about the general possibility that you’ll be treated like shit just because you have a fever. There are lots of other situations where such a fear could cause delay: the dude who has a fever but spent last night cheating on his wife, and is worried that a government that shoots dogs won’t be particularly discreet about contact tracing; the potential Ebolaee who has friends with prizewinning breed dogs, and doesn’t want to have the government shoot their friends’ dogs so decides to just wait a few days to be sure it isn’t Ebola; the person who gets really sick at work, but whose cat is outside, decides to check himself in to hospital but figures cats don’t talk to strangers, and doesn’t want all the cats in the neighbourhood being shot, so doesn’t mention it; the dog lover who doesn’t think they have Ebola and doesn’t want to take the risk, so hands their dog to a neighbour before going to hospital. Any one of these scenarios is a potential nightmare of contagion, and they can break down at any point in that identification-isolation-contact tracing process.

Obviously when the outbreak goes epidemic, this will all become academic, but right now it’s not epidemic: there are a few people under observation, and two people in quarantine. The decision to kill the dog sparked a global protest. Would it really have been so difficult to tranquilize the dog, put it in some kind of quarantine, then tweet pictures of it with a dumb-arsed chewy toy and the phrase “Spanish healthcare: no dog left behind”? A tiny bit of extra work, for a huge public relations win. You can always shoot the dog a few days later and claim it got Ebola and it was the “humane thing to do.” If you really really can’t figure out a way to keep a dog alive for a few days without touching it, I think you aren’t really trying – and I think every pet owner will agree with me about this. Also – and this might prove important later in the epidemic – we don’t know if dogs can transmit or even become symptomatic for Ebola. It might be nice to know that, and right here we have a dog with potential Ebola. More specifically: there are a lot of cat owners out there, and cats wander, and fight. If one of those cat owners has Ebola and lets their cat out at night, it would be really really handy to know whether domestic pets are a risk. If only we had a dog with Ebola … oh, but we shot it.

Basically the Spanish government just told everyone who thinks they might have Ebola that even though they are nowhere near emergency stage, they’re already willing to act like complete dickheads. So anyone who has a fever and something to hide, a pet, or a group of people they really don’t want to annoy, is going to be thinking that maybe they should be really sure that it’s Ebola before they cash in everyone they know to a pack of ruthless dog killers. That suspicion may only delay their presentation for a day or two, it may only make them lie a bit during the contact tracing phase, but that’s enough – the disease gets spread. And as we have seen from Africa, stopping the spread of this disease early is crucial to stopping it at all.

Also, if I survive Ebola, I would quite like to go home to rapturous greeting from my (uninfected) dog. Shooting Excalibur was just a dick move.

Since I’ve been talking a bit about HIV lately, I’ve also been thinking about Ebola, and so while I’m here I thought I’d make a few other points about the media treatment of Ebola, and the associated public perception, that I think are important. I also would like to share the Science collection of articles on Ebola, which have been made open access for the duration of the epidemic. These include some fairly accessible media descriptions of the issues, and also some interesting survivor interviews. The Guardian has also devoted one of its (horrible) live “Blogs” to a day of coverage of Ebola, which is reasonably informative (it also includes survivor interviews). Make no mistake: this disease is easy to prevent and really, in the modern era, should not be a serious public health threat, but it is a terrifying phenomenon once it gets wild.

Ebola is not less important than Malaria and HIV

Quite a few media articles have been complaining that Ebola is getting more attention than malaria and HIV, which are the worst killers in Africa. Articles on this theme usually show  a mixture of motives, primarily a desire to criticize media sensationalism, complaints about westerners just throwing money at dramatic attention-grabbing problems rather than core health problems, criticisms of the amount of money available in aid[2] for these major diseases, general bullshit about the WHO[1], or misjudgments about risk. But let’s be clear about this: it’s a completely bullshit argument, probably racist and definitely annoying. First of all, huge amounts of aid money are committed to malaria and HIV every year: the Global Fund, Bill and Melinda Gates Foundation, WHO, PEPFAR, GAVI – there are billions and billions of dollars, whole inter-governmental organizations (e.g. UNAIDS!) and large portions of international aid budgets devoted to the biggest killers in Africa. They are not under-resourced, though of course all these diseases could (and should) have more money. Also this disease is not something you can sensationalize enough: read the reports from survivors, and you see that it is a truly terrifying and destructive phenomenon. It is also possible for us to walk and chew gum at the same time: pouring resources into Ebola doesn’t suddenly mean HIV will lose its money, and if anything the opposite will happen: a society forced to commit all its medical resources to a sudden wildfire epidemic will not be able to maintain routine health care, and other conditions (in Africa, maternal and child mortality) will get worse. This is a fairly obvious thing to say, but because opinion writers are usually idiots, it needs to be spelled out: a society facing a medical apocalypse cannot also maintain routine maternity services. As an example of this, I know a man whose cousin had arranged work as a paediatrician in Sierra Leone, starting in November. She’s now changed her plans, and will be starting work in an Ebola containment ward next week. That’s what happens when a hemorrhagic virus goes full retard: paediatricians don 77 layers of rubber and head into the hot zone.

But the thing that’s most annoying about this article is its reduction of all of Africa to a single entity, or as the infectious disease blog haba na haba put it, Ebola is only the Kardashian of diseases if you think Africa is a country. Yes, malaria and HIV kill lots of people in Africa, but the death numbers for these diseases cover the whole continent. Ebola is killing people in just three countries, and it has probably now killed more people this year than HIV and malaria combined in those countries. Unless you think national boundaries don’t matter for health and economic policy, it should be fairly obvious that while most of Africa is struggling primarily with HIV and/or malaria, in these three countries Ebola is a catastrophe unfolding on a grand scale.

This last argument comes down to another simple problem with modern media and their interpretation of health policy: misinterpretation of risk.

Ebola is only harmless while we make it so

Ebola is not as infectious as measles or mumps, or even HIV, but it is remarkably virulent and its ability to infect people after death means its growth is not necessarily constrained by its high case fatality rate. This makes it a rather unique virus. But there are many articles in the media suggesting that we are over-reacting to Ebola, and that it is not that serious a concern. These articles are largely based on past experience of Ebola, but they miss an important point about how we manage disease outbreaks: Ebola is only not a threat so long as we take it very seriously. Provided we take Ebola seriously, and act quickly to stamp out even the smallest evidence of it, it is not a serious concern. If we decide that therefore it is not a concern, and lower our guard, it will spread and cause huge damage. But the various critics of epidemic policy are always looking for the latest disease threat that didn’t materialize – SARS, avian flu, H1N1 – and claiming that the health authorities overreacted, when in fact that “overreaction” is the main bulwark between civilization and chaos.

And if you want to see what happens when that bulwark collapses, visit the Ebola zone now. In this article, Senga Omeonga talks about his colleagues who were struck down by Ebola. He is a doctor, and only just survived the disease. He says, of his small unit,

In total two brothers, a Spanish priest, a sister, two nurses, one x-ray tech, one lab tech, and one social worker died. Two other doctors, two sisters, and one orthopedic tech survived. They closed the hospital after the outbreak.

So many skilled health workers died because of one index case. Ebola preferentially targets healthcare workers, and the associated people who are needed to support the work of doctors. Even if these countries manage to defeat the disease, they are facing a future with a massively depleted healthcare workforce. Some of these countries have less than 100 doctors, and less than 1000 nurses: every single death in this workforce is a huge loss, and the loss of a massive amount of national capital. Even if the disease doesn’t spread enough to decimate the population – a possibility that is looking increasingly likely – it is probably going to set the health development program in these countries back by decades. The result of this epidemic will be a long-term reduction in capacity to handle HIV/AIDS, malaria and maternal and child mortality. But a lot of coverage of this disease is predicated on the assumption that health systems are overreacting, and that the disease can be assessed simply in numbers of deaths, rather than their strategic location; and a lot of media reports (and let’s face it, probably a lot of government policy) has been focused on the risk of rich nations being infected, rather than on the threat to health systems in poor countries.

Once the health system collapses, any disease gets a free run. The health systems in these countries are on the brink. Even the World Bank – which has spent years resisting Universal Health Coverage – has been forced to recognize that these health systems are fragile and underfunded. When these countries emerge from this epidemic, let’s hope that western governments will have finally learnt the lesson global health policy makers have been pushing for years, and recognize that in an interconnected world robust health systems are a social good. Maybe then they will start to find creative ways to create the fiscal space for effective health systems in even the poorest countries. Any program that looks for such a fiscal space is going to need to recognize that poverty and underdevelopment do not support universal health coverage, and make policies to genuinely support economic growth. Let’s hope Ebola is a turning point towards shifting the economic relations between low- and high-income countries, to the unequivocal betterment of the former.

fn1: If you google “ebola WHO priorities” you’ll find this article by Henry I Miller being syndicated across the world. It’s incredibly negative about the WHO – the organization that eradicated smallpox! – and also incredibly wrong. It’s worth noting that Henry I Miller was specifically identified as an advocate for Big Tobacco in the Tobacco Papers. The campaign against tobacco is one of the WHO’s greater success stories, so it’s no surprise that he takes every opportunity to slander the organization, and no surprise that the Hoover Institute is willing to employ someone this oily. It should come as no surprise, then, given the history of Big Tobacco in funding global warming denialists, that this greasy little man is also a global warming denialist. Yet idiot newspapers around the world have reproduced the anti-WHO rantings of this paid defender of Big Tobacco. Do they have any understanding at all of how to check sources?

fn2: I particularly like the use of a picture of a semi-naked dead person being sprayed with disinfectant at the top of an article about our “empathy deficit.” Stay classy, Huffington Post!

He likes the smell of new viruses in the morning

He likes the smell of new viruses in the morning

This week the journal Science reports a new study finding HIV first emerged in Kinshasa (now the Democratic Republic of the Congo) in the 1920s – not the 1970s or 1980s as previously suspected. The disease was likely introduced to Kinshasa through bush-meat, but spread rapidly across the Congo through mobile workers moving on Belgian-built train networks. At that time the region was a Belgian colony, and labourers were moving across large areas of the country as they moved to and from the capital and large mining areas in the hinterland. The article also reports that Kinshasa itself had a large and active sex industry in support of he transient labourers, and this may have helped to spread the disease. It’s an interesting story of virology, archaeology and globalization.

What I find fascinating about this story is that HIV took hold in the 1920s, but wasn’t identified as a disease until the 1980s, despite the presence of medical and public hygiene programs in Kinshasa, the growth of tropical medicine as a discipline, and the presence of major militaries in the area during both world wars (most notably the Force Publique, a force of some tens of thousands of black Congolese soldiers led by white Belgian officers). Typically the military establishment pays careful attention to hygiene and to STIs, especially since the work of Florence Nightingale, but somehow during all this period they missed HIV as a disease. In fact, this new research suggests that the success of the entire discipline of Tropical Medicine should probably be reassessed.

The reason that HIV was not identified is, I think, quite simple: it has a very long asymptomatic period, up to 12 years, and it does not manifest through a single set of coherent symptoms, like measles or flu, but through a complex of opportunistic infections. The case definition for AIDS is complex and depends on a list of AIDS-defining conditions that have few commonalities, so it is extremely hard for a doctor seeing these cases in disparate people to identify a single underlying condition. Instead the symptoms are treated, and the patient dies. From the point of view of a doctor in 1920s Belgian Congo, finding an underlying cause would be almost impossible. First the doctor might see a soldier with recurrent herpes, then a miner with a rare and untreatable cancer, then a sex-worker with repeated bacterial infections. Some of these people might have got the disease sexually, some through infected needles during a vaccination drive, perhaps the soldier might have exchanged blood in a fight – 10 years ago. It’s just not possible to identify a cause in this case, or to see a common pattern.

So why do we even know about the existence of HIV at all? It was first identified in 1984, but if it had been around since the 1920s it should surely have been identifiable in the modern era, at least since the program to eradicate smallpox, when modern public health was really beginning to come to terms with infectious disease. Why so late? I think it was identified because of a stroke of luck: a group of cases in the USA that all happened in gay men, and with a disproportionate number of Karposi’s Sarcoma (KS) cases. KS is usually limited to elderly southern European men, and so its presence in young American men was highly unusual. But the real trigger was that it occurred in gay men. Its presence in gay men meant that they were all visiting the same small number of gay-friendly clinics, and they were definably different to other men. They all shared a single common factor: their sexual identity. Of course all those patients in the Congo also shared a common sexual identity but nobody thinks of heterosexuality as a defining characteristic. It’s a background property, a default setting. Whereas homosexuality is a definable strand of difference. I think this coincidence set people thinking, first because a small number of doctors saw all the cases, the diseases these cases were experiencing were very unusual for men of their age and race, and they all shared a different sexuality. This of course tripped the doctors into thinking that they must have a common condition, and that it must be related to their sexuality. This in turn sparked a search for a common cause, probably infectious, and in 1987 HIV was identified. Had HIV instead spread into America through heterosexual carriers those carriers would not all have gone to the same doctors and the disease would not have been linked to their sexual identity. This link is essential for HIV because the symptoms occur so long after the transmissive act that it is not possible to connect them without a symbolic link. Without the sexual link, doctors would not have considered an infectious cause of the range of AIDS-defining conditions they were witnessing, and they would not have sought a virus. Had the Morbidity and Mortality Weekly Review reported on a sudden rash of deaths due to Karposi’s Sarcoma, there might have been discussion, but occurring in only heterosexual people widely separated in the community, an infectious cause might not have been considered. This is especially likely since KS is just the first manifestation of AIDS, and not necessarily the killer – people travel through different trajectories of opportunistic infections to their eventual (horrible) death, and in the absence of deaths, given KS is not notifiable, it would probably simply never have come to anyone’s attention – or would have taken so long to be noticed that HIV would have been entrenched in the wider community before it was identified, if it were identified at all.

So I guess we have the unfortunate sacrifices of a significant proportion of gay men in one generation in the USA to thank for our discovery of HIV. By the time the full scope of the disease and its origins were understood, HIV was already out of control in Africa, to the point where it was causing major social and economic problems, and it’s possible to imagine real economic and social collapse happening in some parts of Africa if the disease hadn’t been identified for another 10 or 15 years – especially if by the time of its identification the rich countries were also burdened with a generalized epidemic and facing their own public health (and potentially economic) emergencies.

Which leads to a horrible speculation about the past. Would human society have survived if HIV had emerged 500 or 1000 years earlier? With death following a pattern similar to non-communicable disease and old age, no coherent virological or bacteriological principles, and the point of infection distal from the point of symptom onset, it would have been almost impossible for human society to identify the existence of the disease, let alone its cause. Worse still, HIV is transmitted from mother to child, with very high mortality rates in children, so it would have spread rapidly over generations and had huge mortality rates. Once widespread the disease is economically highly destructive, since it forces communities to divert adult resources to caring for sick adults who should be in the most productive part of their lives. In the absence of a known cause it would simply be seen as “the Scourge,” but in the absence of well-kept statistics on life expectancy and mortality rates, it might be difficult for societies to realize how much worse their health was than previous generations.

In that period there were other diseases – like the Black Death – that had an unknown transmission mechanism, but these were identified as diseases and (mostly erroneous) methods put in place to prevent them, with of course the final method being case isolation and quarantine, a technique that usually has some success with almost all diseases. But these diseases differ from HIV in that there is a rapid progression from symptom onset to mortality and the symptoms are visible and consistent, making the Black Death clearly definable as a disease, which at least makes quarantine possible. With a diverse range of symptoms, a long period from symptom onset to death (often 2-3 years) involving an array of different infections, in a society where death from common infectious diseases was normal, people just would not notice that they were falling prey to a single, easily preventable disease, so even quarantine or case isolation would be unlikely to be implemented. Another difference between HIV and the Black Death is the long asymptomatic phase of HIV guarantees its persistence even though it has a nearly 100% case fatality rate; whereas the Black Death spread through communities so fast that it soon burnt out its susceptible population, leaving a community with some immunity to the disease. HIV is not so virulent, or so kind.

I think if HIV had spread from Africa 500 years earlier, it’s possible that the majority of the human race would have died out within a century or two, leaving whole continents almost empty of people. I guess the Indigenous peoples of the “new” world would have escaped the scourge, leaving the earth to be inherited by native Americans, and most of Europe and Africa to fall to waste and ruin. It’s interesting to think how different the world might have been then, and also chilling to think how vulnerable our society was in the past through ignorance and happenstance. A salutary lesson in a world where we live ever closer to nature, but where many societies still have health systems that are too fragile to handle the challenge presented by relatively preventable diseases like Ebola virus. The Science paper also presents a timely reminder of the importance of being prepared for the unexpected, and the dangers of complacency about the threats the natural world might offer up to us in future …

Next Page »