MODELING HEALTH-DISEASE – Part I: The General Theory of Modes of Health

Naomar Almeida-Filho

(Instituto de Saúde Coletiva, Universidade Federal da Bahia, Brasil)

Scholars from several scientific fields (D’Amico 1995; Engelhardt 1995; Khushf 1995; Fedoryka 1997; Weed, 1998; Wulff, 1999) have emphasized the importance of theoretical work in order to help effectively change concrete health situations. Unfortunately, since Canguilhem’s and Dubo’s classical contributions to pathological and ecological models of disease, not so much investment, institutional and intellectual, has been devoted to theorizing about health-disease phenomena (Dubos 1960; Canguilhem 1966). The few attempts in this regard, such as Stempsey’s (2000) pathological view of disease or Gammelgaard’s (2000) evolutionary biology perspective, have resulted in reductionist, unidimensional models of disease and health. Exceptions are Christopher Boorse (1977, 1987, 1997) and Lennart Nordenfelt (1993) who proposed comprehensive approaches for health as a theoretical concept. Despite remarkable insights, the contribution of these authors is limited by their account of health as a linear object of knowledge, defined by the absence of disease. The most promising approach in this line of enquiry seems to be Kazem Sadegh-Zadeh’s Fuzzy Set Theory of Health – FSTH (Sadegh-Zadeh 2000), based on fuzzy logic, which was created as a critique to the notions of boundary and precision of formal set theory (Zadeh 1971).

I and colleagues have developed in several papers a conceptual framework for modeling health-disease-care phenomena and processes in the light of complexity approaches, formalizing a general theory of health, along with a restricted theory of disease (Mezzich & Almeida-Filho 1994; Almeida-Filho 1997, 1999, 2000, 2000a, 2000b, 2000c, 2006, 2006a; Almeida-Filho & Jucá 2002; Almeida-Filho & Coutinho, 2007). Results from this line of theoretical research made possible a preliminary model of health-disease based on Canguilhem’s ideas (Coelho & Almeida-Filho 2002), which provided grounds for a General Theory of Modes of Health – GTMH (Almeida-Filho 2001). From this baseline, we have developed a restricted approach of over-determination processes for several classes of illness-sickness-disease sets, named Holopathogenesis Theory – HPGT (Almeida-Filho & Andrade 2006).

This series of papers is intended to explore feasibility and possibilities of a unified theory of health-disease-care derived from the integration/convergence/fusion of the three approaches: GTMH, HPGT, FSTH. In Part I, the frame of reference of this work – complexity theory – is briefly introduced, followed by a summary of the General Theory of Modes of Health (GTMH). In Part II, an introduction to Holopathogenesis Theory (HPGT), taken as a restricted theory of disease-illness-sickness, is provided. In Part III, I review the principles of fuzzy logic and its derivation into a Fuzzy Set Theory of Health-Disease (FSTHD), present preliminary results on the inter-articulation of these theoretical models, and propose a prospective discussion of its application.

THEORETICAL FRAMEWORK

Two preliminary epistemological observations are pertinent. First, theories are made of objects of knowledge, which are not the representation of an “object-thing”, an abstract mirror reflex equivalent to concrete phenomena. According to Bunge (1972), theory components are “object-models”, which can be either ontological or heuristic. Ontological models are analogical devices

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for the reference of a given category of being, while heuristic models are explanatory devices of the determination or functioning of the studied object. Ontology is therefore descriptive or taxonomic by definition, while heuristics leads to explanation or understanding.

Second, the basic difference between a unified theory and a general theory is that the former is postulated as a global structure of all-encompassing explanations, valid for every level and context, while a general theory implies complementary, non-exclusive modes of understanding. In different ways, both kinds of theory tend to be respectful of the complexity of scientific objects. A general theory covers the totality or integrality of complex objects of knowledge. In turn, unified theories address the plurality of facets of complex objects, thus hosting different scientific approaches to an interdisciplinary problem.

Complexity versus Complication

To study health-disease-care phenomena, which are plural, non-linear, emergent events and processes, approaches alternative to Cartesian reductionism have been proposed in different scientific fields. The set of knowledge and methods developed by such approaches have been designated as complexity theory (Morin 1984, 1990; Edmonds 1996). Complexity theory is centered on the following key concepts:

Dynamic systems - open systemic structures, always changing, wholes formed by interrelated parts, mutant elements, connections and parameters (Lewin 1992)

Chaos - sensitivity to initial conditions plus unpredictability. In this regard, note that North American scholars, such as Lorenz (1993), propose that the whole approach is chaos theory, being complexity merely one of its properties.

Non-linearity – a property of interconnections between elements of a given system that operate beyond dose-response relationships (Percival 1994).

Emergence - the upcoming of the unpredicted, recognition of unknown laws of determination, engendering the “radically new” (Strogatz 2003).

Fractality - the new geometry of the micro-infinite, describing graphically repeated patterns of non-linear relations (Mandelbrot 1994).

Fuzziness - the property of blurred limits between systems and system elements, the quality of a boundary-less reality, resulting from the transgression of formal set logic (Klir & Yuan 1995).

Although being so characteristic of contemporary science, complexity theory is not a new paradigm. It is just a direct evolution of General Systems Theory, a remarkable conceptual framework that emerged in the early 1950s (Boulding 1956) and became influential in the scientific panorama worldwide in the 1960s (von Bertalanffy 1962). The systemic approach was updated in the light of conceptual and methodological developments, which occurred in practically all scientific fields (Hammond 2003). Such a perspective stands in opposition to the conventional and more familiar ways of knowing the world by slicing it into isolated pieces. Indeed, this new strategy for producing knowledge can provide theoretical devices that will be more effective for tackling real-world problems of higher levels of complexity, such as health and disease phenomena (Castellanos 1990; Schramm & Castiel 1992; Castiel 1994; Albrecht, Freeman & Higginbotham 1998; Gatrell 2005).

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Complex objects, events and processes form at the very least systemic objects, which means that they comprise part of a total system and can be understood as a system, incorporating partial totalities at a lower hierarchical level (Delattre & Thellier 1979). As such, they may be target of various forms of scrutiny, source of multiple discourses, overflowing the disciplinary segmentation of science. For modeling this order of scientific problems, we need a complex “mapping” approach. Of course, models are not direct representations of reality but result from a reduction of selected traits of concrete phenomena into general universal categories (Robson 1996). Like maps, models are useful as guides to travel through unexplored territories of reality (Godfrey-Smith 2003).

For modeling purposes, complexity may be better understood in contrast to ‘simplicity’, or parsimony. Simplicity is subsumed in the framework of complexity theory as basic elementary forms to any given explanatory model. Simple (elementary) forms of determination are:

a) Composition – Articulation of parts (A + B), components of a whole (D). This corresponds to a metaphor brought from the notion of synthesis in Chemistry. The corresponding mathematical formalization is the simple sum of elements:

A + B = Db) Anamorphosis – transformation or distortion of a component of a model into a different

form of itself, without losing its essential properties. This may be a change of state (e.g. from ice to liquid to gas, but still water) or a proxy variable in an explanatory network. The representation could be:

A A*c) Variation – this is the most used for the representation of causality in science. The basic

model of this elementary form of determination is the following: factor x, acting on a given health situation S, produces the outcome R (meaning Risk, for our purposes). The math formalization is the simplest case of a function, with a single-term equation:

R = f (x)d) Emergence – rupture or radical discontinuity in the dynamics of a given system,

occurrence of some process, object or phenomenon that was not there before. No formalization is available for that occurrence, as R evolves out of unpredictable sources:

=> R

In this set of definitions, ‘simplicity’ is the outcome of ana-lysis, i.e., the system is described by breaking it down in its basic elementary forms of determination. This is equivalent to Cartesian-like reduction of systemic processes and relationships into its simplest determination units. In this sense, the notation above per se constitutes simple models. The transition of simplicity to complexity is not linear and direct, but rather it has complication as an intermediate level. This point may be better understood with the help of a graphical example, as in Figure 1.

[ENTER HERE: Figure 1 – Diagram of a Complicated Model]

Consider first the multi-factor variation of S => R, determined by factors x1 to xn. The math formulation is simply the notorious generalized multiple linear regression equation R = f1 (x1) + f2

(x2) ... fn (xn). This figure may also be taken as a graphical presentation of the epidemiological definition of multicausality, which clearly is a model of complication but not of complexity. The

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diagram provided in Figure 1 also includes this factor sub-model, a three-level graphical variation network, mathematically described by a system of linear regression equations, such as:

R = f1 (x1) + f2 (x2) + f3 (x3)

x2 = f1 (x1) + f4 (x4)

x3, x4 = fn (xn)

All components of the factor sub-model belong to the same class (and the interconnections between them as well) making it a monotonous system, with no place for diversity. This way of modeling reality reduces to standardized patterns the variety of links, elements and processes that shape real-world systems. This is another definition (or facet) of reductionism. Among other issues, diagrams representing complicated hierarchical models demonstrate one important point: multicausality and multi-levelness does not imply complexity. To multiply elements of a same kind and levels in a given system is not sufficient to “introduce” complexity in it. An immediate example of monotonous modeling taken from epidemiological theory is the classical “web of causation” (Krieger 1994).

On the other hand, Figure 1 is also a graphical, intuitive account of the articulation of different forms of determination, including the transformation of components into factors through “proxy” variable definitions. Steps or levels were introduced into the model, which has been hierarchically organized with the addition of composition and anamorphic sub-models articulated to the variation sub-model. The incorporation of varied elementary forms of determination into a same model makes it a complicated model of second order. The mathematical formulation provided is based more on logic than on applied calculus. However, because of its directionality, it is unsatisfactory regarding its potential to more closely apprehend reality processes. Indeed, all internal relationships are convergent toward the outcome, thus taken as the end of a process. Despite the superior heuristic power of this model, it still operates in the domain of complication (but not complexity) because no treatment of time-dependent phenomena or change of state can be found in it.

Modeling Complexity

Complicated explanatory networks are not complex models because, within them, there is no account of the complexity of concrete reality, manifested by the properties outlined above: dynamicity, non-linearity, emergence, fractality, and fuzziness. Complexity is concerned with change (Prigogine 1996). Complicated models, the more hierarchical and articulated they are, do not consider change in time. Even in more developed versions, they were reductionist, monotonous, and finalistic but, above all, approached complex reality by freezing it, that is, through paralysis of its most basic feature, which is the dynamical nature of being.

A system, as intricate and sophisticated as it can be, that always converge to a same fixed output, is not a complex dynamic system. The first generation of systemic models was, in some way, of this kind. For allowing conservative retroaction (or homeostasis), some models generated by early systems science research could be considered as paradoxical examples of linear complexity. This is why the notion of feed-back is so central for systems theory. By the same token, the idea of iteration has become indispensable to define non-linearity in dynamic systems.

The next set of diagrams organizes some propositions regarding the definition of complexity and recognition of complex models based on the above given assumptions.

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Figure 2 illustrates a complexity model of degree I. Structurally it is hierarchical, plural (with diversity of forms of determination), multilevel, and non-directional, therefore built under the first definition of complexity as dynamicity. In this model, the output is input. R is output in time 1 and input in time 2. In the classical theory of systemic models, this property has been described as retroaction, or feed-back. Mathematicians today prefer to call it “iteration”. Models of this kind are susceptible to mathematical descriptions based on more complex systems of differential equations. The algebraic formulation of this first-order model of complexity has a mainly descriptive utility and yet reveals the inclusion of retroaction, including iteration terms (c1, c2):

R = f1 (x1) + f2 (x2) + f3 (x3)

X1 = F|c1 ; X2 = f (X1 + X4) ; X3 = G|c2 ; X4 = E

E = D + C + B’ – G

B’ = B

c1, c2 = f (R)

[ENTER HERE: Figure 2 - Complexity Model of Degree I – Non-linearity]

This model implies a particular definition of non-linearity as recurrency, recursiveness or iterativity. It is drawn upon forms of representation that are intended to overcome the paralysis of reality of pre-complex, finalistic, linear models. Therefore, models such as this one are complex under the third definition of complexity: systemicity, dynamicity, non-linearity, with the presence of enlarged retroaction (heterostasis) and interactive connections.

Regarding our subject-matter of interest – health-disease models, this can be demonstrated in a very straightforward manner, using the incidence of a disease D as an example.

Let the whole loop of Figure 2 be a single feedback term in the system, provided that Rn (risk in time 1) is different from Rn+1 (risk in time 2). Given that the measure of R is the incidence I, a ratio dependent on the size of a population P, let also Pn = Pn+1, therefore fixing the population change parameter. This is the simplest way to represent the iteration of this particular kind of dynamical system.

The ultimate goal of epidemiological research is indeed to measure the variation of the “volume” of D (set of diseased among exposed) in time, which means basically to assess D n => Dn+1. Increase or decrease in number of cases of D is a function of, say, a factor m (for morbidity). Then, the function is

Dn+1 = m ( Dn )

But, considering that m is in fact dependent upon the conversion rate of D (non-diseased) into D, given the exposure e to susceptibility, predisposing or risk factors, and that Dn = 1 – Dn/Pn, therefore m = e (1 – Dn/Pn). Replacing m in the above equation:

Dn+1 = e [(1 – Dn/Pn) Dn ]

In order to standardize for the typical proportion-like disease estimators (making them vary from 0 to 1), we have

Dn+1 / Pn = e [ Dn / Pn ( 1 – Dn / Pn) ]

where all ( Dn / Pn ) = Rn, or the cumulative incidence of disease D in a given time. The final e-function of the incidence of disease D in time 2 (n + 1), taking into account the logistic control

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function (1 – Rn) resulting from exhaustion of susceptible subjects or reduction of exposed non-cases, is therefore

Rn+1 = e [ Rn ( 1 – Rn ) ]

This is equivalent to the general logistic function of population dynamics. Apparently, it represents a non-linear self-controlled growth function which tends to equilibrium by reaching an optimal point, or steady-state, after a few iterative cycles. The amazing aspect of this equation is that this is true only if e < 3.0. When e approximately equals to 3.0, a bifurcation appears, indicating a regular cycle of period two. Increasing the e-values to around 3.4, the bifurcation in turn bifurcates, doubling the period of the cycles. Increasing even more the e-values, and now to intervals shorter and faster at a logarithmic rate of bifurcations and period doublings, up to the value 3, when a regimen of unpredictability is installed in the system. This was demonstrated when the increasing capacity of computational devices enabled researchers to discover chaos out of simple equations, such as the classical example of Lorenz’s (1993) equations for meteorological prediction. Application of this analysis to epidemiology has been quite fruitful, especially concerning infectious disease epidemics (Philippe 1993).

Although complex models of first-order comply with a fundamental feature of complexity – non-linearity (by enlarged retroaction), they do not include other properties of complexity, such as emergence, fractality and fuzziness. In Figure 3, there is a graphical illustration of a complexity model of degree II. It is multi-modal, multi-level and, at the same time, non-finalistic, non-linear, and holds several points of emergence in its explanatory network. Therefore, it is a model simultaneously iterative and interactive, built under the second definition of complexity as emergence.

The corresponding system of equations of this second-order model of complexity again has a mainly descriptive utility but also reveals the inclusion of emergence, incorporating interaction terms (ei, ej, ek):

R =[ f1 (x1) + f2 (x2) + f3 (x3)] + (ei + ej + ek) | (c1... cn)

X1 = F|c1 ; X2 = f1 (x1) + f3 (x3) + ei ; X3 = 1/2 (E) ; X4 > 1/2 (E|c2) ; X6 = f (R)

E > D + C + B’

B’ = B

c1, c2 = f (R)

ei > (X1 + X2 + X3 ); ej < (X3 + X4 + X5 ); ek > (X4 + X6 + X7 )

[ENTER HERE: Figure 3 - Complexity Model of Degree II – Emergence]

Now let us explore the place of emergence in this stage of complexity. Taking the multivariate internal links between elements of the system, the combined effect e i, ej, ek is equivalent to more than the sum of isolated effects (X1 + X2 + X3), (X3 + X4 + X5) and (X4 + X6 + X7). Indeed, this is equivalent to what happens in real conditions, because frequently the outcome is bigger than the sum of individual variables. In this interpretation, when introduced into the model, these interactions have impact on the variation that is being evaluated as resulting from an effect conventionally predicted in the production of outcome.

Surplus effects of risk factors, produced by synergic interaction processes, may be generally considered as examples of emergence in epidemiological systems. Consider selected findings on

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interaction effects of gender, social class and race/ethnicity on prevalence of depressive disorders (Almeida Filho et al 2004). In that study, gender followed a pattern, confirmed in different countries and studies: women have a risk for depression twice as high as for men. Each of the other isolated variables yielded low risk estimates: social class reached a relative risk estimate of 1.6 and race/ethnicity alone did not even reach significance levels. Considering the three factors together, with the relative risk estimates of gender, social class and race/ethnicity, the sum of isolated effects would be roughly 4,6 (= 2.0 + 1.6 + 1.0 respectively). Actually, the odds ratio found in the data analysis was double than expected: for poor black women the risk for depression was 9-times higher as compared to wealthy white men. This risk surplus, although unpredicted or unexplained by the model, emerges out of the internal structure of the data set (concrete referent of the research process) and its occurrence cannot de denied or suppressed.

Complex models are hierarchical, plural (with multiple elementary forms of determination), multi-modal, multi-level, non-finalistic, non-linear, simultaneously iterative and interactive, successfully including emergence in its explanatory network. To build up and operate models of this kind, transdisciplinary initiatives are definitely needed. These models may be prototypical for the desired integration between social sciences (e.g. with the macro-social processes represented as compositions at the basis of the model), logic and semantics (to justify the anamorphosis that links the distal upper level to the proximal factor sub-model) and public health or clinical sciences (responsible for modeling the health outcome R).

Nevertheless, all such models are still discrete (boundary-based), made of isolated components and far from being context-sensitive. Considering that the limits between events and processes in real-world phenomena actually are blurred and subject to arbitrary definition by the researcher, symbolization and theoretical representation of such object-models, either ontological or heuristic models, must go beyond formal logic. Newton da Costa’s (1980) para-consistent logic and Zadeh’s (1971) fuzzy set logic can be alternative systems of logic useful for this endeavor. General models and derived approaches of health-disease based on Fuzzy Set Logic will be discussed in more detail in the companion Part III of this paper.

Modeling health

In his treatise Epistemología, Mario Bunge (1980) sketched a comprehensive theory of health based on the following formal scheme:

Let a be a human being, with a certain number of properties Pi, i{1, . . ., n} and Fi be a function that represents the property Pi. In the simplest case Fi is a function of the system a at time T; Fi

could be blood pressure, body temperature, weight etc. If H and R stand for the sets of human beings therefore:

Fi : H x T → Rm

In addition, let the value s = F(a, t) for a system a at time point t be the state of this system at this time. Then, the n-tuple F = {F1, F2, ..., Fn} could be regarded as a vector in an n-dimensional Cartesian space. Along the time-arrow, the state s moves in the state space Σ of the system a:

Σ(a) = {F(a, t)|t T}

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The trajectory of vector F(a, t) in Σ(a) space describes the history or the lifeline of system a. If a is an organism, this lifeline starts at birth and ends at the death of the system. The set of all possible states of a – the allowed states of a that build the subset SL(a) of Σ(a) – is restricted by certain laws which the components Fi have to observe. For every organism, there are states of health and states of illness: if a biological system is in a state of health, the organism works optimally; if not, it is in a state of illness. Thus, in the case of a healthy organism the values of Fi, i.e. the corresponding function of the system’s property Pi, is restricted to a subinterval of values of the interval of all possible Fi values. Therefore, the states of health of a system a may be predicted in the set SL(a) of all allowed states.

This approach may be criticized for being too abstract and exclusively oriented towards the individual case; yet such drawbacks could be easily excused for being Bunge’s proposal a pioneer work in nobody’s land. Indeed, health, disease and related phenomena imply a much more complex object of knowledge than suppose our vain biomedtech thinking.

Philosopher Christopher Boorse (1977) proposed a linear articulation of four basic concepts: ‘reference class’, ‘normal function’, ‘disease’, and ‘health’. The reference class consists of the universe of members of a biological species of the same sex and age group. Normal function is defined as what is “statistically typical” in relation to the reference class for the species’ survival and reproduction. Disease is a reduction in the “typical efficiency” involved in normal function. Health means simply absence of disease. Boorse (1987) completed his “biostatistical theory of health” with an intentional tautology, indicating health as a concept can simply imply normalcy, always “in the sense of the absence of disease conditions”.

Kleinman, Eisenberg & Good (1978) defined disease as a biomedical concept which refers to alterations or dysfunction in biological and/or psychological processes, illness incorporates individual experience and perception of disease, and sickness refers to the social reaction to problems deriving from both disease and illness. Allan Young (1980) contends that the distinction between disease and illness in Kleinman-Good model is insufficient to account for the social dimensions of the process of becoming ill. To overcome these limitations, Young proposes to replace the scheme [sickness = disease + illness] with a triple series of categories (sickness, illness, disease) with equivalent hierarchical levels. Subsequently, Kleinman (1986) partially revised this original objectivist position, contending that also disease and illness are social constructs. Illness means the way sick individuals perceive, express, and deal with the process of becoming diseased.

Hereon, the Kleinman-Good model, modified by Young, will be designated as the DIS [disease-illness-sickness] Complex. Despite its interpretative sophistication, this frame of reference is congruent to Boorse’s biological theory of health as absence of disease.

Anthropologists Gilles Bibeau and Ellen Corin advanced a phenomenological framework to study local systems of signification and action vis-à-vis health problems, implying a dialogic view of the DIS Complex. Plural, fragmented, and even contradictory, popular semeiology and cultural models of interpretation do not exist as an explicit body of knowledge, but are formed by a varied set of imaginary and symbolic elements. According to these authors (Bibeau & Corin 1994), popular knowledge about disease and correlates (sickness in the DIS Complex) are linked and expressed in terms of socially and historically constructed “systems of signs, meanings, and practices” of health (SmpH). Such systems are rooted in the group’s social dynamics and cultural values underlying the individual construction of the falling-ill experience and the social production of sickness (Bibeau 1988; Bibeau & Corin 1994; Corin 1995). In the spheres of symbolic production, body, linguistic, and behavioral signs are transformed into symptoms of a

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given illness, acquiring specific causal meanings and generating given social reactions. In this sense, we can legitimately consider health as a social concept.

French philosopher Georges Canguilhem produced an epistemological foundation for biomedical knowledge with great potential for developing new theories in the health field. For Canguilhem (1966), normalcy includes health and sickness to the extent that both imply a certain life norm. The threshold between health and sickness is singular, although influenced by instances that transcend the strictly individual, like the cultural, socioeconomic and political planes. Particularly concerned with concepts of health, Canguilhem (1990) dwells with Kantian idea of health being an object outside the realm of scientific knowledge, a common-sense notion or a private matter, which could never become a concept. The eminent hermeneutician Hans-Georg Gadamer has also defended the idea of health as something private and personal. For Gadamer (1996), health cannot be measured or objectively apprehended, because it entails internal, subjective issues that cannot be controlled by external forces. In his opinion, the distinction between health and illness can never be clearly defined, and can only be accessed by the person who is no longer capable of dealing with the demands of life.

Canguilhem (1990) would agree that health is a philosophical issue that escapes the reach of instruments, protocols, and scientific methods. This “philosophical health” includes, but would not be confused with, subjective health. Yet philosophical health does not only incorporate individual health, but also its complement, recognizable as a public health (i.e., a health made public). This notion of public health, referring to ethical and metaphysical questions (which would result for example in the notions of utility, quality of life, and happiness), moves away from the expert’s concept of public health, which comprehends the state of health of populations and its determinants, both in the sense of a complement to the epidemiological concept of risk and as a reference to a broader concept of health. Figure 4 is an attempt at schematically merging Canguilhem’s position and the DIS Complex.

[ENTER HERE: Figure 4 – Canguilhem’s Model of the DIS Complex (adapted)]

Note that the graph center is occupied by Kleinman-Good-Young DIS model. The region of reality immediately related to the core-object gives some ground to the idea of health as complementary to the triple dimension of the illness-sickness-disease set. Normal health (Boorse’s diagnosed normalcy) is the complement of disease; private health (Gadamer’s enigma of health) is the complement of illness; social health (Bibeau-Corin’s notion of SmpH) is the complement of sickness. In addition, the outer field, where both regions of the DIS Complex and the dimensions of health are contained, hosts further modalities of the health concept.

Canguilhem contends that health is realized simultaneously in the genotype, in the subject’s life history, in the human population and in the individual’s relationship to the environment. Hence, while philosophical health in a sense encompasses individual health as a human right, it does not displace the possibility of measuring the health-related quality of life of human subjects. By analogy, the idea of philosophical health (e.g., health as a value, political or ethical) would not preclude taking health as a scientific object (e.g., epidemiological or population health). In this sense, scientific health would be public or collective health, that is, healthiness constituted in opposition to the classical notion of morbidity.

For Juan Samaja, eminent Argentine epistemologist, the health concept must be conceived as an object referred to what he calls “complex systems with history” with distinct facets and multiple dimensions, leaving room for the recognition of various planes of emergence, structural and functional hierarchies, and simultaneous levels of integration in complex adaptive processes (Samaja 2004: 46). For Samaja, the fundamental epistemological question for the Health Sciences

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consists precisely of identifying structuring interfaces for approaching the multifaceted totality of the object-model ‘health’.

Since the body is the product of complex processes of exchange with the environment, to the extent that these processes can contribute to determine the phenotype, the concept of health would correspond to a diffuse object cutting across the biological sphere of living organisms, the mode of life of human subjects and the perceptions and feelings of diseased persons. This point is well depicted in Sol Levine’s insight of levels of “health reality”, in the following quotation: “Health is, first of all, a conceptual construct that we develop to encompass a range of different classes of phenomena [... in] three levels of reality: the physiological, the perceptual, and the behavioral” (Levine, 1995: 8).

From these preliminary epistemological foundations, now it is possible to briefly consider the accumulated heuristic potential in the interfaces among biosciences, social sciences and health sciences, providing objective conditions for a proposal to systematize the problem of Health as a concept, along with the following points:

a) Converging with Canguilhem’s stance, selected forms of the ‘health’ concept can legitimately subsidize the ontology of health as a scientific object, shaping descriptors capable of empirical reference.

b) Overcoming Gadamer’s impasse, yet retrieving his argument regarding the holistic nature of health, the object-model ‘health’ should incorporate a meta-synthetic component into its construction, respecting its integrity-totality.

c) Incorporating Samaja’s contribution, a constructive approach to the scientific quality of ‘health’ should contemplate hierarchical interfaces, organizing the concept’s explanatory structures as a heuristic object-model.

Considering the definitions of hierarchical interfaces and planes of emergence and integrating the contributions by applied social sciences, as reviewed above, I have developed a conceptual framework that may be useful as a basis for a general theory of health. This framework organizes the terminology used for categories of non-health available to the various health sciences, in addition to distinguishing between the varied definitions of normalcy and health and their potential empirical descriptors. This implies an effort at theoretical specification of what may be called Modes of Health, as shown in Table I.

[ENTER HERE: Table I – Planes of Emergence and Modes of Health]

The scheme proposed is an expanded glossary of categories for non-health, which, in a sense, incorporates and articulates the preliminary semantic demarcation of disease-illness-sickness. The various categories of non-health and the corresponding modes of health are organized according to hierarchical planes of emergence: subindividual, individual, collective. Also, equivalent descriptors are indicated for the respective level and sphere.

On the subindividual and individual planes of emergence, at any level of complexity, the health object can be examined based on a deterministic explanatory approach, producing structured causal metaphors. In this case, the aim is to construe partial facets of the object-model Health: biomolecular processes in the normal systems or sustained physiological processes in healthy subjects, equivalent to pathological processes as manifested in a ‘case’ of a sick person. Thus, at the subindividual level, normalcy and pathology (in the original Canguilhemian sense) correspond to the descriptor “state”.

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At the individual level, in the clinical sphere, normal health corresponds to disease (structural) and disorder (functional), having “signs & symptoms” as descriptors. Note that the category ‘disorder’ (a disease without structural anatomo-pathological lesion) occupies a level equivalent to the definition of disease in the clinical sphere. Private health, with Gadamerian phenomenology, as well as individual health, object of an “epidemiology of health”, both refer to the category ‘illness’, according to the distinction proposed under Kleinman-Young line of thought. Note that in each of these instances the descriptors display a certain sense of antagonism: “health status” as the intent to objectify the individual mode of health and “feeling” as the intimate, particular, private mode of health, which cannot be made public.

In this scheme, it is also possible to place the conventional epidemiological perspective of risk factors, founded on inductive logic with a probabilistic basis. From this perspective, the health-disease object is reproduced as a specific concept, with risk production models based on the direct action or interaction of risk factors. In the epidemiological sphere of risk analyses, quantitative descriptors (rates, coefficients) can deal with the subset’s counter-domain [of sick population groups], equivalent to the population residue [1 – risk].

The notion of public health in Canguilhem, which may be called ‘healthiness’ in contrast to the idea of morbidity in traditional public health discourse, may have “health situation” as an efficient descriptor. Finally, the modes of “social health”, which are equivalent to the concept of sickness in interpretative medical anthropology, could be approached through Bibeau-Corin’s systems of signs, meanings, and practices of Health (SmpH). Indeed, the SmpH theory provides the possibility of incorporating sickness into the health concept itself, to the extent that it takes the experience of sickness as a way of structuring the social representation of health by constructing subjectivity and the subject’s relationship to the material and symbolic world.

Needless to say that, as any schematic representation, this depiction is necessarily partial and impoverished in comparison to the richness and complexity underlying reality.

CONCLUDING REMARKS

A synthesis of our approach to the problem of defining Health theoretically is:

(i) The concept of ‘health’ will be heuristically more powerful if construed with a clear, close reference to its inevitable pair, ‘disease’;

(ii) Given its diversity and complexity, ‘disease’ will be heuristically more powerful if expressed in the semantic chain pathology-disease-illness-sickness;

(iii) Therefore, conceptually, neither health nor disease should be referred in the singular, but rather will be conceptually more robust if referred to with their plural, multi-modal, multi-level, non-linear, emergent properties.

The health-as-absence-of-disease perspective, albeit comfortable in theory and methodologically feasible in many instances, cannot fully cover processes and phenomena referring to life, health, sickness, suffering, and death at any of the levels of reality identified by Levine (1995). To the same extent that the whole is always greater than the sum of its parts, health is much more than the absence or inverse of sickness. This is a crucially interesting logical problem, to be solved by overcoming the antinomy health vs. sickness inherited from traditional biomedical thought.

For social theorist Talcott Parsons, health is not a capacity or capability (term used by Amartya Sen [1993]) that may be found in the body, nor even does it refer to the individual organism, but

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rather it is a mediator in the interaction between social subjects. Health is not something that can be “stored”; it only exists while it circulates, when it is “enjoyed”. Health, as stated succinctly by Parsons (1978:69), “is the teleonomic capacity of an individual living system... the capacity to cope with disturbances... that come either from the internal operations of the living system itself or from interaction with one (or) more of its environments.” Health is thus not the inverse or absence of disease; for Parsons, disease should be the “obverse” of health.

On the other hand, I presented above a systematic attempt for outlining the notion that health is not a single, universal, all-encompassing category. Rather, depending on the levels or planes of emergence and on the facets of the object at stake, it will be more respectful of the complexity of health states to refer to “healths” or, more precisely, to various “modes of health”. The scheme proposed above in Table I may be read as building equivalences between health as a plural concept and the contemporary notion of dimensions of disease (or the DIS Complex).

In sum, the epistemological question pursued herewith is meant as an instance of the central problem of levels of complexity, planes of emergence and angles of a multi-faceted object of knowledge. The core argument is that health-disease phenomena cannot be defined as essentially an individual-clinical or a subindividual-biological issue. Indeed, the objects of Health are polysemous, plural, multifaceted, transdisciplinary, simultaneously ontological and heuristic models capable of cutting across (and being traversed by) spheres and domains referring to different levels of complexity.

At this still preliminary stage of exploration and theoretical formulation, one point is out of doubt. Health-disease processes and phenomena imply a new family of scientific objects, i.e., object-models defined not by their components, functional principles, and dimensions but rather by their totality, general principles, and planes of emergence. As such, they are not amenable to the production of knowledge by way of fragmentation (hence, objects adverse to analytical processes) but by way of synthesis and complex modeling. Synthetic object-models of this kind tend towards a higher degree of formal ascension to become meta-synthetic objects, construed for (and by) reference to the facts produced by the so-called Health Sciences.

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Table I – Planes of Emergence and Modes of Health

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