Readers of MH Blog are familiar with my interest in disease and epidemiologic causality, which has significant implications for matters of population health and inequities. (Why this is so should be obvious: if we want to improve population health and compress health inequities, it is important to have some understanding of what are the prime causes of health and its distribution within and across populations).
For many years I have been attracted to models of complexity (formerly known as chaos theory) for conceptualizing health and illness, in large part because linear models map out very poorly onto the complex (nonlinear dynamic) adaptive systems that collectively and iteratively constitute population health and its distribution. There has been some excellent work on this in recent years, but I wanted to highlight a new article written by Saroj Jayasinghe entitled Conceptualising population health: from mechanistic thinking to complexity science, in Emerging Themes in Epidemiology. Like all articles published in BMC journals, the article is available full-text open-access. Here is the Abstract:
The mechanistic interpretation of reality can be traced to the influential work by René Descartes and Sir Isaac Newton. Their theories were able to accurately predict most physical phenomena relating to motion, optics and gravity. This paradigm had at least three principles and approaches: reductionism, linearity and hierarchy. These ideas appear to have influenced social scientists and the discourse on population health. In contrast, Complexity Science takes a more holistic view of systems. It views natural systems as being 'open', with fuzzy borders, constantly adapting to cope with pressures from the environment. These are called Complex Adaptive Systems (CAS). The sub-systems within it lack stable hierarchies, and the roles of agency keep changing. The interactions with the environment and among sub-systems are non-linear interactions and lead to self-organisation and emergent properties. Theoretical frameworks such as epi+demos+cracy and the ecosocial approach to health have implicitly used some of these concepts of interacting dynamic sub-systems. Using Complexity Science we can view population health outcomes as an emergent property of CAS, which has numerous dynamic non-linear interactions among its interconnected sub-systems or agents. In order to appreciate these sub-systems and determinants, one should acquire a basic knowledge of diverse disciplines and interact with experts from different disciplines. Strategies to improve health should be multi-pronged, and take into account the diversity of actors, determinants and contexts. The dynamic nature of the system requires that the interventions are constantly monitored to provide early feedback to a flexible system that takes quick corrections.
The article is highly recommended.
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