Causation and models of disease in epidemiology

Broadbent, Alex

doi:10.1016/j.shpsc.2009.09.006

Cited by 67 publications

(50 citation statements)

References 17 publications

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“…I take it as widely accepted that causation is not sufficient for explanation, and that for a causal association to explain, it must amount to a difference between fact and foil (Lewis 1986;Lipton 2004 Ch 3). In a public health context, health provides a plausible contrast class, as I have argued elsewhere (Broadbent 2009b If this sketch of a contrastive analysis is approximately correct, it would explain why mechanistic explanations are so useful in epidemiology and public health: because they…”

Section: Contrastsmentioning

confidence: 93%

Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts

Broadbent¹

2011

Causality in the Sciences

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This chapter explores the idea that causal inference is warranted if and only if the mechanism underlying the inferred causal association is identified. This mechanistic stance is discernible in the epidemiological literature, and in the strategies adopted by epidemiologists seeking to establish causal hypotheses. But the exact opposite methodology is also discernible, the black box stance, which asserts that epidemiologists can and should make causal inferences on the basis of their evidence, without worrying about the mechanisms that might underlie their hypotheses. I argue that the mechanistic stance is indeed a bad methodology for causal inference. However, I detach and defend a mechanistic interpretation of causal generalisations in epidemiology as existence claims about underlying mechanisms. Causal hypotheses in epidemiologyWhat does it take to establish a causal hypothesis in epidemiology? What standards need to be met? Or, if establishment comes in degrees, degrees of what?The most obvious aspect of this problem concerns inferring causation in a particular study. A study reveals a statistical association between smoking and lung cancer, or a certain gene and obesity. Statistical analysis reveals a low p-value -a measure of the chance that the association is due to chance. Study design controls for confounding variables (what philosophers would call common causes of the putative cause and effect).Can it be inferred that, for this group, a causal relationship exists between smoking and lung cancer, or having that gene and obesity?Oddly enough, this is not a question that epidemiologists like to answer. A single study would not normally be considered a sufficient basis for a causal inference. Replication is a guiding epidemiological principle. From a methodological point of view this is extremely interesting. Epidemiologists' credence in a causal hypothesis about Study Group A increases when the effect is replicated in Study Group B. Explaining (or, I suppose, refuting) this attitude is a central task for any methodological analysis. 2A second difficulty concerns the inference from a study, or a collection of studies, to a wider population. Epidemiologists are centrally concerned with extrapolating from the people they study to people they have not studied. Replication is important here too, because one way to argue that differences between the population studied and the target population are causally irrelevant is to replicate the study among people who are drawn from the target population. However, replication cannot solve the problem of generalisation. Often the study group will already be drawn from the target population:for example, when generalising from the Whitehall studies to the population of Britain. 1 Differences between those studied and those not studied will always remain; the difficulty is working out when these differences make a difference. On other occasions, studies on a subset of the target population may be impractical: for an obvious example, consider future populations. Q...

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Section: Contrastsmentioning

confidence: 93%

Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts

Broadbent¹

2011

Causality in the Sciences

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“…2) The multi-causal account of disease subsumes the mono-causal account Broadbent (2009Broadbent ( , 2013 suggests that the multi-causal account of disease subsumed the monocausal account, providing a second way of thinking about the relationship between monocausal and multi-causal accounts of disease. That is, the multi-causal account replaced the mono-causal account, such that the previously mono-causal sources of disease were placed on the web of causation as one cause amongst many.…”

Section: ) Different Accounts For Different Diseasesmentioning

confidence: 99%

Mono-Causal and Multi-Causal Theories of Disease: How to Think Virally and Socially about the Aetiology of AIDS

Furman

2017

J Med Humanit

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In this paper, I utilise the tools of analytic philosophy to amalgamate mono-causal and multi-causal theories of disease. My aim is to better integrate viral and socio-economic explanations of AIDS in particular, and to consider how the perceived divide between monocausal and multi-causal theories played a role in the tragedy of AIDS denialism in South Africa in the early 2000s. Currently, there is conceptual ambiguity surrounding the relationship between mono-causal and multi-causal theories in biomedicine and epidemiology. Monocausal theories focus on single, typically microbial, sources of illness and are most concerned with infectious diseases. By contrast, multi-causal theories allow for multiple factors to underpin a disease's aetiology, including socio-economic and behavioural factors, and they usually focus on chronic non-communicable diseases. However, if these theories are taken to be strictly distinct, this prevents the inclusion of both microbial and socio-economic factors in a single explanation of any particular disease. This strict distinction became a problem when trying to explain the disproportionate prevalence of AIDS in southern Africa and ultimately contributed to the tragedy of AIDS denialism in South Africa. In tandem with viewing how the perceived divide between multi-causal and mono-causal theories underpinned AIDS denialism, I examine Thabo Mbeki's specific role, while acknowledging that AIDS is being deprioritised on a broader international level. Overall, I will demonstrate that any long-term plan to eliminate AIDS will require viral and socio-economic factors to be considered simultaneously and that such a theoretical approach requires a clearer understanding of the underlying concepts of disease aetiology.

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“…7 (Broadbent, 2009). 8 For examples of suggested modifications of Koch's postulates, see: (Evans, 1976;Falkow, 1988;Fredericks & Relman, 1996;Smith, 2001).…”

Section: Th Century Theories Of Disease and Contagiamentioning

confidence: 99%

“…This is the interpretation favored by K. Codell Carter, considered the "foremost authority" in this area (Gradmann, 2009, 83), and most other historians and philosophers (Broadbent, 2009;Smith, 2001Smith, , 2007. Within this approach, the first postulate is equated with the claim that the contagion is necessary for the disease, and the second and third with the claim that the contagion is sufficient for the disease.…”

Section: Introductionmentioning

confidence: 99%

Koch’s postulates: An interventionist perspective

Ross

Woodward

2016

Studies in History and Philosophy of Science Part C: Studies in

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a b s t r a c tWe argue that Koch's postulates are best understood within an interventionist account of causation, in the sense described in Woodward (2003). We show how this treatment helps to resolve interpretive puzzles associated with Koch's work and how it clarifies the different roles the postulates play in providing useful, yet not universal criteria for disease causation. Our paper is an effort at rational reconstruction; we attempt to show how Koch's postulates and reasoning make sense and are normatively justified within an interventionist framework and more difficult to understand within alternative frameworks for thinking about causation.

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Causation and models of disease in epidemiology

Cited by 67 publications

References 17 publications

Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts

Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts

Mono-Causal and Multi-Causal Theories of Disease: How to Think Virally and Socially about the Aetiology of AIDS

Koch’s postulates: An interventionist perspective

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