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.

Acetaminophen (paracetamol), Asthma, and the Causal Fallacy

In November 2011, a senior American pediatrician suggested that there was enough evidence to warrant restricting acetaminophen (paracetamol) use among children at risk of asthma, despite inadequate evidence for a causal inference. His argument was based on an ethical principle. However neither his argument nor the evidence he surveys are sufficient to warrant the recommendation, which therefore has the status, not of a sensible precaution, but a stab in the dark. I have written to the editors of Pediatrics to explain why – the link is here:

http://pediatrics.aappublications.org/content/128/6/1181.full/reply#pediatrics_el_53669

The theoretical point underlying this is one under-emphasized in both philosophical and epidemiological thinking, namely, that causal inference is something rather different from making a prediction based on the causal knowledge so obtained. The temptation to suppose that we have even a hunch what we happen when we restrict acetaminophen use on the basis that we have a hunch that it causes asthma is fallacious. It all depends on what consequences the non-use of acetaminophen has, and that in turn depends on the form that non-use takes. The point is familiar from philosophical studies of counterfactuals, but those studies arguably either do not offer much of practical use for epidemiology or else have not received an epidemiological audience. (I favour the former option, although I realise many philosophers will disagree.)

The result is a common fallacy of reasoning which we might call The Causal Fallacy: epidemiologists, policy makers, and probably the public assume that because we have causal knowledge, we have knowledge of what will happen when we manipulate those causes. In practice we do not. (This under-appreciated point has been emphasized by Sander Greenland among epidemiologists and Nancy Cartwright among philosophers, and as I see it tells heavily against the programme of manipulationist or interventionist theories of causation.) Establishing whether an exposure such as acetaminophen is a cause of an outcome such as asthma is not sufficient to predict the outcome of a given recommendation on the use of acetaminophen, for the simple reason that more than one such policy is possible, and each may in principle have a different outcome.