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.
Highlights
Population health is defined as ‘the health outcomes of a group of individuals, including the distribution of such outcomes within the group’ [1]
This paper proposes that the discourse in population health is dominated by a Newtonian mechanistic view, and there is much to gain by embracing concepts in Complexity Science
Analysis We begin with a brief description and analysis of the mechanistic model, its influence on social sciences and on the way we perceive population health
Summary
Population health is defined as ‘the health outcomes of a group of individuals, including the distribution of such outcomes within the group’ [1]. Embodiment could be taken as a form of adaptation of the system to environmental pressures; b) Pathways of embodiment: these are structured by the societal arrangement of power and resources and constrained by biological characteristics we have gained through evolutions and individual histories This denotes how an ‘open’ system evolves through time, constantly adapting to the dynamic environment; c) Cumulative interplay between exposure, susceptibility and resistance: there exist multiple levels of sub-systems or factors, and their distributions from sub-cellular levels, going through levels such as individual, community, social group, country and global. They interact across a wide range of time scales from nanoseconds (in the case of sub-cellular or biochemical reactions) to millions of years that are relevant time-scales in human evolution; d) Accountability and agency accepts the reality of multiple roles played by agents, and varying causal explanations at different scales of time and space These constructs are analogous to features of a complex system: ‘open’ systems constantly adapting to the environment, fuzzy borders, lacking clear-cut hierarchies, and non-reductionist in approach. One has to be cautious when generalising from the experiences of one location
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