The Myth of Translation

Next week I am part of a symposium at EuroEpi in Porto, Portugal with the title Achieving More Effective Translation of Epidemiologic Findings into Policy when Facts are not the Whole Story.

My presentation is called “The Myth of Translation” and the central thesis is, as you would guess, that talk of “translating” data into policy, discoveries into applications, and so forth is unhelpful and inaccurate. Instead, I am arguing that the major challenge facing epidemiological research is assuring non-epidemiologists who might want to rely on those results that they are stable, meaning that they are not likely to be reversed in the near future.

I expect my claim to be provocative in two ways. First, the most obvious reasons I can think of for the popularity of the “translation” metaphor, given its clear inappropriateness (which I have not argued here but which I argue in the presentation), are unpleasant ones: claiming of scientific authority for dearly-held policy objectives; or blaming some sort of translational failing for what are actually shortcomings (or, perhaps, over-ambitious claims) in epidemiological research. This point is not, however, something I intend to emphasize; nor am I sure it is particularly important. Second, the claim that epidemiological results are reasonably regarded by non-epidemiologists as too unstable to be useful might be expected to raise a bit of resistance at an epidemiology conference.

Given the possibility that what I have to say will be provocative, I thought I would try my central positive argument out here.

(1) It is hard to use results which one reasonably suspects might soon be found incorrect.

(2) Often, epidemiological results are such that a prospective user reasonably suspects that they will soon be found incorrect.

(3) Therefore, often, it is hard to use epidemiological results.

I think this argument is valid, or close enough for these purposes. I think that (1) does not need supporting: it is obviously true (or obviously enough for these purposes). The weight is on (2), and my argument for (2) is that from the outside, it is simply too hard to tell whether a given issue – for example, the effect of HRT on heart disease, or the effect of acetaminophen (paracetamol) on asthma – is still part of an ongoing debate, or can reasonably be regarded as settled. The problem infects even results that epidemiologists would widely regard as settled: the credibility of the evidence on the effect of smoking on lung cancer is not helped by reversals over HRT, for example, because from the outside, it is not unreasonable to wonder what the relevant difference is between the pronouncements on HRT and the pronouncements on lung cancer and smoking. There is a difference: my point is that epidemiology lacks a clear framework for saying what it is.

My claim, then, is that the main challenge facing the use of epidemiological results is not “translation” in any sense, but stability; and that devising a framework for expressing to non-epidemiologists (“users”, if you like) how stable a given result is, given best available current knowledge, is where efforts currently being directed at “translation” would be better spent.

Comments on this line of thought would be very welcome. I am happy to share the slides for my talk with anyone who might be interested.

Link

I was recently interviewed on philosophy of epidemiology and the result is up here: http://www.minerva-podcast.com/

Direct link to the audio file is here: http://brainfood.lu/podcast/files/podcast_37.mp3

Taubes’ Tautology

In the once fertile garden of epidemiology, all is not well, according to some commentators. The low-hanging fruit has been plucked, and the epidemiological ladder is not long enough to bring the remainder within reach. Possibly the most famous expression of this dissatisfaction is a report by a journalist writing in Science in 1995 called “Epidemiology Faces Its Limits”. Gary Taubes cites a number of contrary findings, where exposures have been found to be harmful and then safe (or vice versa) in different studies, or harmful in different ways, or harmful when studied using one study design but not when using another. He interviews a number of eminent epidemiologists and reaches a simple diagnosis: epidemiology has spotted the big effects already, and is now scrabbling around trying to identify small ones. These are much harder to distinguish from biases or chance effects. Indeed, he hypothesizes that epidemiological methods may be unable to tell the difference at all, in some cases. In this sense, Taubes suggests, epidemiology is facing its limits.

The epidemiological garden is still growing nearly two decades later. Either the gardeners did not listen, and continued to tend fruitless trees, or Taube’s diagnosis was wrong. But epidemiologists did listen: the piece is well-known. Moreover epidemiologists are among the most methodologically reflective and self-critical of scientists, which is evident from the fact that most of Taubes’ criticism is drawn directly from interviews with epidemiologists (and which is one reason epidemiologists are such a pleasure to engage with philosophically). The implication is that Taubes’ low-hanging fruit hypothesis is mistaken.

Taubes’ hypothesis is tempting because it is true that big discoveries lie in the past. It is, however, a fallacy to suppose that this means no big discoveries lie in the future. On inspection, the tempting hypothesis reveals itself as an instance of a very common theme: that we are nearing the end of what inquiry can tell us. This has been said before, most famously in physics shortly before Einstein’s impact. If the history of science tells us anything it is that this claim is always false. We know more about the past than the future, and so we know what the big discoveries of the past are, but not the big discoveries of the future. If there were low-hanging fruit that epidemiology has not yet plucked, then we would not know it, even if they were going to be plucked tomorrow afternoon. More is needed to prove that epidemiology faces its limits than that the tautologous claim that its most striking discoveries to date lie in the past.

“The Exposome” – a lab for epidemiology?

In February 2011, Nature ran a journalistic piece on the development of technologies designed to increase the accuracy of measuring exposures, spurred by various dissatisfactions with questionnaires. (Thanks to Thad Metz for pointing me to this.) The “exposome” is presented in that piece as the logical conclusion of improved measurement techniques. It is supposed to be a device (I am imagining an enormous plastic bubble) capable of measuring every exposure of study subjects. A quick hunt around the internet reveals that the idea is capturing at least a few imaginations, including some at the US Centers for Disease Control.

The CDC’s Overview of the exposome defines the exposome like this:

The exposome can be defined as the measure of all the exposures of an individual in a lifetime and how those exposures relate to health.

The idea of the exposome suggested two questions to me.

First, the idea of the exposome puts pressure on the concept of an exposure. In most epidemiological practice, the question “What is an exposure?” is of no practical importance. But if the aim is to measure every exposure, then we must answer the question in order to know whether we have succeeded.

The CDC article contrasts the target of the exposome with genetic risk factors, suggesting that exposures exclude genetic make-up. But the CDC article also suggests that exposures measured by the exposome may begin before birth. (I am imagining babies born in little plastic bags.) So it is not clear exactly what the rationale for excluding genetic make-up from “exposures” would be. If the goal is simply to measure anything that might affect a given health outcome then we should include genetics. We should also include our entire solar system, indeed the galaxy, so as to account for the effects of solar flares, meteorites, and so forth. (The plastic bubble in my imagination is getting very big.)

My first worry, then, about “exposomics” is that it will not get very far without circumscribing the notion of exposure, so as to be something less than what the authors of the CDC overview probably think they mean – that is, something less than all factors potentially affecting health outcomes.

My second question is whether striving for an exposome is a good idea, judged by the goals of epidemiology, which I take to be providing information which can be used to improve public health.

One central point of epidemiology is that it studies people, not in labs, but as they actually live their lives. The exposome is a sort of lab, and striving for it is nothing other than striving for the controlled experiment. Aside from the complete fantasy of ever achieving an exposome (my imaginary bubble just burst), it does not seem helpful even to “study” the exposome, or whatever else it is “exposomists” are supposed to do. (And, incidentally, it does not seem that the exposome is a logical extension of increasing accuracy of measurements of exposure.) Epidemiologists want to know what happens in reality, not in the exposome.

Epidemiology and laboratory sciences complement each other in this way. Tar may be shown to produce cancer in the skin of laboratory rats, but epidemiology tells us what happens when humans smoke cigarettes. The two sources of knowledge complement each other. Each has flaws. Causal inference is harder in epidemiology because of the lack of control over potentially relevant variables: exposures, for short. But lab sciences suffer a different inferential limitation: not in making a causal inference, but in inferring that the results obtained in the lab will apply outside. So it is hard to see how doing away with either source of knowledge could be a good idea, and hard to see what “exposomics” could add to epidemiology, except another buzz word.