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 www.helsinki.fi/tint/aid.htm)

DISCUSSED BY:

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 (www.helsinki.fi/tintand the Finnish Epidemiological Society (www.finepi.org)

TIME AND PLACE:

Monday 9 February, 16:15-18

University Main Building, 3rd Floor, Room 5

http://www.helsinki.fi/teknos/opetustilat/keskusta/f33/ls5.htm

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.

QUESTIONS:

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?

READINGS:

Alex Broadent, Philosophy of Epidemiology (Palgrave Macmillan 2013)

http://www.palgrave.com/page/detail/?sf1=id_product&st1=535877

Alex Broadbent’s blog on the philosophy of epidemiology:

https://philosepi.wordpress.com/

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 http://www.ancr.nu.

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: http://www.bis.org/review/r090505e.pdf

Antonios Garas et al., Worldwide spreading of economic crisis: http://iopscience.iop.org/1367-2630/12/11/113043/pdf/1367-2630_12_11_113043.pdf

Christopher D. Carroll, The epidemiology of macroeconomic expectations: http://www.econ2.jhu.edu/people/ccarroll/epidemiologySFI.pdf

A Tale of Two Papers

I’m on my way back from the World Epi Congress in Anchorage, where causation and causal inference have been central topics of discussion. I wrote previously about a paper (Hernan and Taubman 2008) suggesting that obesity is not a cause of mortality. There is another, more recent paper published in July of this year, suggesting, more or less, that race is not a cause of health outcomes – or at least that it’s not a cause that can feature in causal models (Vanderweele and Robinson 2014). I can’t do justice to the paper here, of course, but I think this is a fair, if crude, summary of the strategy.

This paper is an interesting comparator for the 2008 obesity paper (Hernan and Taubman 2008). It shares the idea that there is a close link between (a) what can be humanly intervened on, (b) what counterfactuals we can entertain, and (c) what causes we can meaningfully talk about. This is a radical view about causation, much stronger than any position held by any contemporary philosopher of whom I’m aware. Philosophers who do think that agency or intervention are central to the concept of causation treat the interventions as in-principle ones, not things humans could actually do.

Yet feasibility of manipulating a variable really does seem to be a driver in this literature. In the paper on race, the authors consider what variables form the subject of humanly possible interventions, and suggest that rather than ask about the effect of race, we should ask what effect is left over after these factors are modelled and controlled for, under the umbrella of socioeconomic status. That sounds to me a bit like saying that we should identify the effects of being female on job candidates’ success by seeing what’s left after controlling for skirt wearing, longer average hair length, shorter stature, higher pitched voice, female names, etc. In other words, it’s very strange indeed. Perhaps it could be useful in some circumstances, but it doesn’t really get us any further with the question of interest – how to quantify the health effects of race, sex, and so forth.

Clearly, there are many conceptual difficulties with this line of reasoning. A good commentary was published with the paper (Glymour and Glymour 2014) which really dismantles the logic of the paper. But I think there are a number of deeper and more pervasive misunderstandings to be cleared up, misunderstandings which help explain why papers like this are being written at all. One is confusion between causation and causal inference; another is confusion between causal inference and particular methods of causal inference; and a third is a mix-up between fitting your methodological tool to your problem, and your problem to your tool.

The last point is particularly striking. What’s so interesting about these two papers (2008 & 2014) is that they seem to be trying to fit research problems to methods, not trying to develop methods to solve problems – even though this is ostensibly what they (at least VW&R 20114) are trying to do. To me, this is strongly reminiscent of Thomas Kuhn’s picture of science, according to which an “exemplary” bit of science occurs, and initiates a “paradigm”, which is a shared set of tools for solving “puzzles”. Kuhn was primarily influenced by physics, but this way of seeing things seems quite apt to explain what is otherwise, from the outside, really quite a remarkable, even bizarre about-turn. Age, sex, race – these are staple objects of epidemiological study as determinants of health; and they don’t fit easily into the potential outcomes paradigm. It’s fascinating to watch the subsequent negotiation. But I’m quite glad that it doesn’t look like epidemiologists are going to stop talking about these things any time soon.

References

Glymour C and Glymour MR. 2014. ‘Race and Sex Are Causes.’ Epidemiology 25 (4): 488-490.

Hernan M and Taubman S. 2008. ‘Does obesity shorten life? The importance of well-defined interventions to answer causal questions.’ International Journal of Obesity 32: S8–S14.

VanderWeele TJ and Robinson WR. 2014. ‘On the Causal Interpretation of Race in Regressions Adjusting for Confounding and Mediating Variables.’ Epidemiology 25(4): 473-484.

Comments sought: Risk relativism and physical law

The attached is a revise and resubmit, and will form part of a Debate in Journal of Epidemiology and Community Health. I have until 25 July to submit. Comments are very welcome. Text is 2100 words. Feel free to comment/track changes in the doc if so inclined.

2014-07-19 Risk relativism and physical law – version for comment

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.