Abstract
Christopher Boorse’s bio-statistical theory (BST) of health and disease argues that the central discipline on which theoretical medicine relies is physiology. His theory has been much discussed but little has been said about its focus on physiology or, conversely, about the role that other biomedical disciplines may play in establishing a theoretical concept of health. Since at least the 1950s, epidemiology has gained in strength and legitimacy as an independent medical science that contributes to our knowledge of health and disease. Indeed, it not only provides important information about disease distribution and aetiology, but the risk-factor approach it employs seems to challenge BST’s binary conception of health and disease. The objective of the article is to show, firstly, how important information deriving from descriptive and analytical epidemiology forms part of our contemporary medical concepts of health and disease, and secondly, that these elements are not taken into account by BST in a satisfactory way. The article’s central contention, therefore, is that if the project of defining the theoretical concept of health is to be maintained, more importance should be accorded to the contribution made by epidemiology—alongside physiology—in defining health.
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Notes
It could be objected that, whereas epidemiology concerns the health status of populations, BST aims to define individual health. One possible response to this objection is to say not only that population health impacts on individual health but also that the role of ‘modern epidemiology’ cannot any more be limited to public health: ‘risk-factor epidemiology’ now provides important information for individual health that is used in medicine [10, 11].
Alex Broadbent has argued that epidemiology has a proper explanatory role which relies on the contrastive model of explanation [12]. Contrastive explanation of disease consists in citing causes that are present in cases of disease and absent otherwise. According to Broadbent, epidemiology may thus suggest new kinds of disease, as in the case of, say, obesity, or as regards various risk factors (see [12, p. 146]), and can also help to redefine diseases thanks to its analysis of causation (see [12, p. 158]). See also Federica Russo who has examined what she calls ‘variational’ epistemology [13]. I also defended the characterization of the explanatory role of epidemiology as the one that is attached to ‘population thinking’ as described by Elliott Sober [10].
The main aspects of the last phase is the major reduction of infant mortality rates and the extension of average life expectancy, which, coupled with declines in fertility rates, induce or reflect the transition to chronic diseases. This transition occurs in developing countries as they undergo the process of modernisation and develop modern healthcare.
A cohort study is a study following a population of people over an extended period of time and recording exposures of interest and outcomes of interest. It is generally prospective, but it can also use historical data. A case-control study is a study comparing how often an exposure of interest occurs in empirical cases, as compared to controls.
The Framingham Heart Study, a prospective cohort study on cardiovascular disease carried out since 1948, is often seen as the paradigmatic model of such a design and method.
As Boorse points out, ‘What is judged health or disease is still an internal functional capacity of the individual. What has change is only species-typical functional capacity—the benchmark for whether the individual capacity is healthy or not’ [5, p. 715]. Thus, health is still an internal capacity, but it could be that its status changes without the internal capacity itself having changed.
Some clarification of the meaning of functional efficiency in the BST and of the confusion introduced by the frequent close correlation with some biological indicator of functioning has recently been given by Hausman [24] and by Boorse himself in an unpublished paper: ‘Clinical Normality’.
To quote Boorse: ‘the function of the thyroid is not merely to secrete hormones, but to secrete the right amount of them for current metabolic needs’ [3].
With this in mind, and as mentioned briefly above, Schwartz’s proposal to solve the problem of common diseases by adding a third axis, an evaluation of ‘negative consequences’, to the two axes of BST’s bell curve (statistical distribution and functional efficiency level) appears somewhat strange. Boorse’s notion of function already includes some consequences or effects of a process or trait, namely, its efficiency or contribution to individual survival and reproduction. See also Hausman [24, p. 527].
Likewise, the criticism of BST I made in the section ‘The Risk Approach and the Normal-Pathological Distinction’, where I pointed out the problem of using the statistical distribution in the case of the demarcation between normal and pathological blood pressure, did not yet take account of this aspect of BST. It should be recalled that, for Boorse, pathology consists in statistically subnormal functional efficiency and that the statistical distribution to which the definition refers is related to functional efficiency: the normal level of functioning is ‘at efficiency levels within or above some chosen central region of their population distribution’ whose two axes are statistical distribution and functional efficiency. This means that a disease is not reducible to a complete failure to function but could simply involve inadequate functioning.
See also Hausman [24]: ‘Boorse says little about what defines efficiency and how its levels are to be distinguished, as they must be before one can talk about their frequency and draw a graph’.
Hausman states, ‘statistically normal function often coincides with adequate functioning, but one should not identify the two’ [24, p. 525].
For an attempt in this direction, see Venkatapuram [26].
I rely here on the distinction introduced and stressed by Boorse himself between his naturalism and that of Murphy: ‘To Murphy, the best characterization of mental disorder as a theoretical concept must emerge from scientific theorizing, not analysis of scientific usage’ [28, p. 20]. Analysis of scientific usage is naturalism of one kind—that advocated by Boorse—whereas when physiology joins in scientific theorizing, it is naturalism of a different kind—that advocated in Murphy [29] or Lemoine [30]. My account resembles Murphy’s or Lemoine’s type of naturalism.
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Acknowledgements
I am grateful to Maël Lemoine and the several anonymous referees of this journal for valuable and precious comments on the different drafts of this article. I also warmly thank Henry Dicks for his precious linguistic help. Financial support from the Jean Moulin-Lyon 3 University is gratefully acknowledged. A previous version of this article was presented at the Bristol conference on concepts of health and illness in September 2010 (University of West England) organised by Havi Carel and Rachel Cooper. It benefited from discussions with the participants of this session.
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Giroux, É. Epidemiology and the bio-statistical theory of disease: a challenging perspective. Theor Med Bioeth 36, 175–195 (2015). https://doi.org/10.1007/s11017-015-9327-7
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DOI: https://doi.org/10.1007/s11017-015-9327-7