If there is any value in the idea that disease is something other than the mere absence of health then that value must lie in the way that diseases are defined. Once we move from the knowledge that someone lacks health in respect of their fever and diarrhoea to the knowledge that they have cholera, we are able to make a number of inferences about the likely progression of the disease, the effectiveness of various possible treatments, and the relevance or otherwise of various circumstances (e.g. age, sex) to both these. These inferences depend on the way the disease “cholera” has been defined. They hold across most cases of cholera, but they are not reliable for diarrhoea or fever more generally. And they hold across most cases of cholera because cholera has been defined in terms of a cause that is, by definition, shared by every case of cholera, and is such that, by definition, no case of diarrhoea and fever can be a case of cholera without the cause, however symptomatically similar.

That cause is the presence of vibrio cholerae in the small intestine. K Codell Carter has done an excellent job of bringing out the extent to which the definition of diseases in terms of infectious agents really was an act of definition – a conceptual feat, not something to be settled by empirical evidence (Carter 2003). But this conceptual exercise has fallen out of fashion. The diseases of primary interest to modern medical science are “multifactorial”, meaning that, for whatever reason, they have not been classified according to particular causes. I have argued that multifactorial thinking has been endorsed too enthusiastically, and that there are merits to the old “monocausal” way of thinking about disease – even if the obsession with identifying just one explanatory cause is wrong. My “contrastive” model of disease is intended to retain the benefits of the monocausal model without the implausible commitment to classification in terms of just one cause (Broadbent 2009; a better-expressed version is forthcoming in Broadbent 2013, Ch 10).

An obvious difficulty for my account is that some kinds of ill health, such as instances of particular cancers, seem to be fruitfully treated as belonging together. Yet on my account they cannot be called instances of a disease unless a classificatory constellation of causes is known or at least suspected. (Of course one might prefer to mark the distinction with a word other than “disease”; but my hope is to get at an important distinction without getting tangled in semantic disputes.) This raises an objection of irrelevance: the objection would be that my account lays down distinctions that are irrelevant both to clinical practice and to scientific understanding.

A particular instance of this difficulty arises in the case of cancers that are caused by viruses. For example, cervical cancer seems to be caused by the human papillomavirus in over 90% of cases (Kumar et al. 2007) – but not 100%. According to the logic of my contrastive model of disease, we ought to say that those cases of cervical cancer caused by the virus form a disease – let’s call it “HPV-cancer” – while the small remainder do not. But is this really a good idea, either from a clinical perspective, or from the perspective of advancing our conceptual grasp on the health-condition(s) in question? Conversely, the circumstances in which the virus is sufficient to produce the cancer are not known: HPV infection does not always result in cervical cancer, and it is not known why. According to my contrastive model this means that the notional disease “HPV-cancer” is not well-defined, and that to call it a disease is to express the hope or conviction that some further causes can be found which are jointly sufficient for the development of cervical cancer. This raises questions, again, as to whether such a hope or conviction is very helpful from either a clinical or a scientific perspective; or whether, rather, we are better off thinking of cervical cancer as a multifactorial disease arising from various constellations of causes, and sometimes failing to arise from the very same constellations of causes.

In short, the question is whether a contrastive approach to disease classification really would help either scientific understanding or medical effectiveness, as I have claimed that it would. Cancers caused by viruses present a good case for studying this question because if the recommendations of the contrastive model can be followed for any multifactorial diseases, these are among the most viable candidates.

I’m starting work on a paper in which I attempt to get to grips with some of these questions and to answer this irrelevance objection to the contrastive model of disease. My gut feeling remains that there is an important distinction to be drawn between disease and mere lack of health (thus my starting point is the opposite of that of Boorse 1975). Yet I am not satisfied that I have made clear exactly what this importance is; nor have I given enough thought to exactly what one is supposed to do with cases that do not fit the model. And for the distinction to be important, as I claim, there must always be cases that do not fit the model – that is, there must be cases of ill health that are not disease, otherwise the distinction between them cannot be important. I hope to get further into these issues and I would welcome any thoughts.

Boorse, Christopher. 1975. “On the Distinction Between Disease and Illness.” Philosophy of Public Affairs 5: 49–68.

Broadbent, Alex. 2009. “Causation and Models of Disease in Epidemiology.” Studies in History and Philosophy of Biological and Biomedical Sciences 40: 302–311.

———. 2013. Philosophy of Epidemiology. New Directions in the Philosophy of Science. London and New York: Palgrave Macmillan.

Carter, K. Codell. 2003. The Rise of Causal Concepts of Disease. Aldershot: Ashgate.

Kumar, Vinay, Abul K. Abbas, Nelson Fausto, and Richard Mitchell. 2007. Robbins Basic Pathology. 8th ed. Philadelphia: Saunders Elsevier.

I’m working on a paper on prediction. It has a general philosophy of science focus and is not specific to epidemiology, but my interest in prediction was provoked by epidemiology. If anyone knows of existing work on prediction, that is philosophical in focus, I would be very interested to hear about it. I realise that there is a statistical literature on the topic but what strikes me is that there is very little treatment of prediction in the philosophy of science. There seems to be no “standard treatment”, as there is for explanation or confirmation; and nor are there clearly articulated competing positions on prediction, as there are concerning causation or laws of nature, for example – or so it seems to me. I would appreciate any confirmation or disconfirmation of these claims.

The final version of my manuscript Philosophy of Epidemiology is off to the publishers, and I hope it counts as self-promotion rather than self-plagiarism if I post the Synopsis here:

The content of the book can be summarised as follows. Causation and causal inference are over-emphasized in epidemiology and in epidemiological methodology, and explanation and prediction deserve greater emphasis. Explanation is a much more useful concept for understanding measures of strength of association (Chapter 3) and the nature of causal inference (Chapters 4 and 5) than causation itself is, because causation is really only part of what we seek to measure and infer respectively. What epidemiologists really seek to do is explain, and their practices are seen much more clearly when described as such. Explanation is also the central notion to an adequate theory of prediction, a topic which has been sadly neglected by both philosophers and epidemiologists (Chapter 6). To predict, one must explain why what one predicts is going to happen, rather than some – but certainly not all – other possible outcomes (Chapter 7). Just like an adequate explanation, a good prediction must be sensitive to the alternative hypotheses it needs to dismiss and those it can safely ignore.

These themes are developed in Chapters 3-7. The remaining chapters tackle more specific problems, and apply the lessons learned where appropriate. Thus Chapter 8 concerns measures of attributability, which are not conceptually straightforward; and the lesson is that an outcome is attributable to an exposure to the extent that it is explained by it. Chapter 9 concerns “risk relativism”, an unfortunate degenerative tendency of thought some epidemiologists have identified in their peers. Risk relativism is diagnosed in Chapter 9 as a case of physics envy, exacerbated by a tendency to seek a univocal measure of causal strength, rather than a context-appropriate explanatory measure. Chapter 10 examines “multifactorialism”, another modern epidemiological ailment – though considered by most epidemiologists to be a profitable mutation. Multifactorialism is criticised for dropping the requirement that diseases be defined in relation to explanatory causes. Chapter 11 discusses the various embarrassments that lawyers have inflicted upon themselves in trying to use epidemiological evidence. Again, the lack of attendance to explanatory questions in favour of causal ones is partly to blame for the mess. Lawyers reasonably resist the explanation of particular trains of events by general statistical facts; but to refuse to admit those facts as evidence for the truth of particular causal explanations is plainly unreasonable. The theme, then, is that explanation deserves more attention epidemiological attention, and causation less. We will revisit the theme in the concluding Chapter 12.

[Philosophy of Epidemiology is being published by Palgrave Macmillan in the series New Directions in the Philosophy of Science and will appear in 2013.]

In 1965, Austin Bradford Hill identified nine “viewpoints” from which an association may be viewed in seeking to decide whether it is causal (Hill 1965). Hill’s nine viewpoints, often wrongly called “criteria”, have been repeated and rehashed very often indeed, with both approval and disapproval. Despite the profusion of developments in technical and non-technical literatures on causal inference since then, Hill’s viewpoints remain a starting point for discussions of causal inference in epidemiology.

There are good reasons for this. Technical advances do not eliminate the need for human judgement, and Hill’s nine viewpoints provide some structure for arriving at these judgements. And it is fair to say that the non-technical literature has not substantially advanced, at least in what it offers for practical purposes. There are other similar lists of guidelines, but it is hard to identify any clear advance, in the non-technical sphere, beyond the basic idea of identifying a few things to bear in mind when trying to decide if an association is causal. For example, Jon Williamson and Federica Russo suggest that, in the health sciences, evidence for causality must come from both population-level studies and experimental studies of underlying mechanisms (Russo and Williamson 2007). This claim may be theoretically interesting (for criticism see Broadbent 2011) but it is clearly intended as a theoretical analysis, and adds little from a practical perspective. Both items in question are covered by Hill’s list; the difference is that Hill does not think any item on his list is necessary, and the claim that evidence concerning underlying mechanisms, in particular, is necessary for a causal inference is highly doubtful in an epidemiological context, and identified as such by Hill. But however that difference is settled, as long as the debate is about what kind of evidence is or is not desired or required for a causal inference, we are not offering anything substantially more useful than what Hill has already offered.

Where should we start, if we wish to move beyond Hill-style lists? Lists of guidelines like Hill’s suffer from notable defects, despite their usefulness. They are open to misinterpretation as criteria, or as primitive algorithms for causal inference. They are a magnet for fruitless debate about exactly what should make the list, what should not, what order they should appear in, what weights to give the various components, and so forth. But most importantly – and this, I think, should be our starting point – they do not provide any clear bar that evidence must clear. The crucial question that making a decision imposes is: is the evidence good enough to act on?

A list of guidelines such as Hill’s has some heuristic value, but it does not tell us, in even the broadest terms, what constitutes enough of each item on the list. The guidelines tell us what the bar is made of, but they do not tell us how high it is. One way we might advance beyond Hill’s viewpoints, then, is to ask how good evidence needs to be before it warrants a causal inference, where that causal inference is important for a practical decision.

Broadbent, A. 2011. ‘Causal Inference in Epidemiology: Mechanisms, Black Boxes, and Contrasts’. In Causality in the Sciences, ed. Phyllis McKay Illari, Federica Russo, and Jon Williamson, 45–69. Oxford: Oxford University Press.

Hill, AB. 1965. ‘The Environment and Disease: Association or Causation?’ Proceedings of the Royal Society of Medicine 58: 259–300.

Russo, F and Williamson, J. 2007. ‘Interpreting Causality in the Health Sciences’. International Journal of the Philosophy of Science 21 (2): 157–170.

If anyone is interested in looking at a book manuscript on philosophy of epidemiology, or on any parts thereof, please get in touch. The manuscript is under contract and has been delivered to the publisher, so this is a good time for comments and criticism. Table of contents can be accessed here:

2012-06-29 contents

…with apologies for the “undefined bookmarks”. The publishers have already indicated that the title should be “Philosophy of Epidemiology” or something similarly descriptive – opinions on this also welcome.

Apologies for the recent inactivity on this blog – the book is a big part of the explanation.

James Woodward’s book “Making Things Happen” does not feature “prediction” in the index. But what is the point of making things happen if you don’t know what?