Workshop, Helsinki: What do diseases and financial crises have in common?

AID Forum: “Epidemiology: an approach with multidisciplinary applicability”

(Unfamiliar with AID forum? For the very idea and the programme of Agora for Interdisciplinary Debate, see


Mervi Toivanen (economics, Bank of Finland)

Jaakko Kaprio (genetic epidemiology, U of Helsinki)

Alex Broadbent (philosophy of science, U of Johannesburg)

Moderated by Academy professor Uskali Mäki

Session jointly organised by TINT ( the Finnish Epidemiological Society (


Monday 9 February, 16:15-18

University Main Building, 3rd Floor, Room 5

TOPIC: What do diseases and financial crises have in common?

Epidemiology has traditionally been used to model the spreading of diseases in populations at risk. By applying parameters related to agents’ responses to infection and network of contacts it helps to study how diseases occur, why they spread and how one could prevent epidemic outbreaks. For decades, epidemiology has studied also non-communicable diseases, such as cancer, cardiovascular disease, addictions and accidents. Descriptive epidemiology focuses on providing accurate information on the occurrence (incidence, prevalence and survival) of the condition. Etiological epidemiology seeks to identify the determinants be they infectious agents, environmental or social exposures, or genetic variants. A central goal is to identify determinants amenable to intervention, and hence prevention of disease.

There is thus a need to consider both reverse causation and confounding as possible alternative explanations to a causal one. Novel designs are providing new tools to address these issues. But epidemiology also provides an approach that has broad applicability to a number of domains covered by multiple disciplines. For example, it is widely and successfully used to explain the propagation of computer viruses, macroeconomic expectations and rumours in a population over time.

As a consequence, epidemiological concepts such as “super-spreader” have found their way also to economic literature that deals with financial stability issues. There is an obvious analogy between the prevention of diseases and the design of economic policies against the threat of financial crises. The purpose of this session is to discuss the applicability of epidemiology across various domains and the possibilities to mutually benefit from common concepts and methods.


1. Why is epidemiology so broadly applicable?

2. What similarities and differences prevail between these various disciplinary applications?

3. What can they learn from one another, and could the cooperation within disciplines be enhanced?

4. How could the endorsement of concepts and ideas across disciplines be improved?

5. Can epidemiology help to resolve causality?


Alex Broadent, Philosophy of Epidemiology (Palgrave Macmillan 2013)

Alex Broadbent’s blog on the philosophy of epidemiology:

Rothman KJ, Greenland S, Lash TL. Modern Epidemiology 3rd edition.

Lippincott, Philadelphia 2008

D’Onofrio BM, Lahey BB, Turkheimer E, Lichtenstein P. Critical need for family-based, quasi-experimental designs in integrating genetic and social science research. Am J Public Health. 2013 Oct;103 Suppl 1:S46-55. doi:10.2105/AJPH.2013.301252.

Taylor, AE, Davies, NM, Ware, JJ, Vanderweele, T, Smith, GD & Munafò, MR 2014, ‘Mendelian randomization in health research: Using appropriate genetic variants and avoiding biased estimates’. Economics and Human Biology, vol 13., pp. 99-106

Engholm G, Ferlay J, Christensen N, Kejs AMT, Johannesen TB, Khan S, Milter MC, Ólafsdóttir E, Petersen T, Pukkala E, Stenz F, Storm HH. NORDCAN: Cancer Incidence, Mortality, Prevalence and Survival in the Nordic Countries, Version 7.0 (17.12.2014). Association of the Nordic Cancer Registries. Danish Cancer Society. Available from

Andrew G. Haldane, Rethinking of financial networks; Speech by Mr Haldane, Executive Director, Financial Stability, Bank of England, at the Financial Student Association, Amsterdam, 28 April 2009:

Antonios Garas et al., Worldwide spreading of economic crisis:

Christopher D. Carroll, The epidemiology of macroeconomic expectations:

Snakes, statistics, and goals for the goal-setters

Cesar Victora gave a very interesting talk earlier today concerning the International Epidemiology Association’s position paper on the UN’s Sustainable Development Goals, which are currently being drafted (to replace the Millennium Development Goals post-2015). Victora is President of the IEA, for a few more hours at least (the new President takes office this evening). Many of his points were reiterated by the next speaker, Theodor Abelin, and in questions from the floor. There were no audible voices of dissent. (The talk reflects a fuller position paper, available here.)

The point that stayed with me most from Victora’s rich talk was the importance of relating goals to appropriate measurement techniques. My own interest in epidemiology has tended to focus on efforts to identify causes (“analytic” epidemiology), since causation is a natural magnet for philosophical interest. But measurement is also a focus of philosophical interest, and Victora nicely pointed out that “descriptive” epidemiology – the business of measuring things like maternal mortality rate, for example – is extremely important if these Sustainable Development Goals are to be effective. A country cannot be held to a goal that cannot be measured, and it cannot be fairly be held to a goal when progress towards that goal is estimated rather than measured.

For example, I was not surprised to learn that in many countries where maternal mortality is high, data on maternal mortality rates (MMRs) are scarce. What did surprise me was hearing about the calculations that some august international organisations perform in the absence of data. A calculation is performed involving GDP per capita, general fertility rate and skilled birth attendance. MMR is estimated as a function of these and perhaps some other similar variables. This means that if the country goes through a recession, the estimated MMR will automatically go up. – Perhaps is really will go up, but it seems strange to think of that calculation as a measurement, at least in the absence of extremely good evidence for the reliability of the estimating equation – evidence which, of course, we don’t have.

MMR is measurable, of course. The problem with MMR is simply a lack of data, and this problem afflicts a large class of conditions. As Victora put it in relation to snakebite: “Where we have snakes, we don’t have statistics, and where we have statistics, we don’t have snakes.”

However, Victora’s most penetrating critique of the SDGs concerned the setting of goals in the absence of clear ideas about how progress towards the goals will be measured. The health-related goal is as follows:

Goal 3. Ensure healthy lives and promote well-being for all at all ages” (from the Outcome Document)

This overarching goal is broken down into 13 subgoals, some of which are very loosely specified. For instance, how are we to tell whether a country has managed to “strengthen prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcohol”? Ironically, those goals that are most clearly specified are wildly unattainable, such as halving global deaths and injuries from road traffic accidents by 2020. Those that are not well specified present measurement challenges for epidemiologists.

This made me wonder whether a body like the IEA could itself set some “goals for the goal-setters” – that is, criteria which any health-related goal must meet if, in the professional opinion of the IEA, they are to be useful. The simplest such criterion would be that outcomes must be specified in terms of a recognised epidemiological measure (mortality, for instance). Another might be to accompany each goal with information (perhaps in a corresponding entry in an appendix) concerning the trend over the past similar period: so if the goal is the halve road traffic deaths in 15 years, or 25, information on the growth of road traffic deaths over the past 15 or 25 years might be included. Goals of this kind will always be political, but there might be agreement on a set of simple rules for setting such goals, and if such rules existed, this might pull epidemiologists closer in to the goal-setting process – a kind of politicking which, as one of the questioners pointed out, is not part of standard epidemiological training.


Stability: an epidemiological ingredient in the realism debate?

I’m preparing a talk on stability for the New Thinking in Scientific Realism Conference that opens in Cape Town tomorrow. I introduced the notion of stability in my book, defined like this:

“A result, claim, theory, inference, or other scientific output is stable if and only if

(a) in fact, it is not soon contradicted by good scientific evidence; and

(b) given best current scientific knowledge, it would probably not be soon contradicted by good scientific evidence, if good research were done on the topic.” (Broadbent 2013, 63)

The introduction of this notion was a response to the perceived difficulties around “translating” epidemiological (or more generally biomedical) findings into good health policy. At Euroepi in Porto, 2012, I argued that translation was not the main or only difficulty for using epidemiological results, and that stability – or rather, the lack of it – was important. After all, one cannot comfortably rely on a result if one cannot be confident that the next study won’t completely contradict it, and that seems to happen pretty often in at least some areas of epidemiological investigation.

Thus the reasons for introducing the notion were thoroughly practical. More recently, though, I have been trying to tighten up the philosophical credentials of the notion, and that’s what I’m going to be talking about in Cape Town. Is stability epistemically significant? Can it be shown to be epistemically significant without collapsing into approximate truth? Can it be distinguished from approximate truth without collapsing into empirical adequacy? These are the questions I will seek to answer.

What’s interesting for me is that, as far as I can see, it’s pretty easy to answer these questions affirmatively. If I’m right about that, then this will be a nice case where studying actual science gives rise to new philosophical insights. The desire to make public health policy that will not have to be revised six months down the line is eminently practical; yet the proposal of a status that scientific hypotheses might have, distinct from truth and empirical adequacy and all the rest, is eminently abstract. If stability really is both defensible and novel, then it will illustrate the oft-repeated mantra that philosophers of science would benefit from looking more closely at science. I am personally put on guard when I hear that said, not because I disagree in principle, but because experience has taught me to suspect either lip service, or an excuse for poor philosophy. Perhaps I’m also guilty of one or both of these; I will be interested to see what Cape Town says.

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.

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.