In public health, complexity science can be harnessed to “conceptualise (…) poor health and health inequalities as outcomes of a multitude of interdependent elements within a connected whole” (Rutter et al. 2017). This “connected whole” can be perceived as a system, where seeing the world through a complexity science lens means looking at factors and their cause-and-effect relationships as constituting a system that operates across spatial and temporal scales, from cells to society. This perspective implies that socioeconomic inequalities in health are a symptom of deeper, underlying problems that require systemic action – and accordingly are not reducible to single factors such as smoking or diet.
At the Institute for Advanced Study, we employ qualitative and quantitative complexity science methods, such as group model building, causal loop diagramming and computational (system dynamics) modelling, as well as epidemiological methods to study population health and its distribution as the outcome of a complex system of factors and actors that interact with each other across multiple scales.
Rutter H, Savona N, Glonti K, Bibby J, Cummins S, Finegood DT, et al. The need for a complex systems model of evidence for public health. Lancet [Internet]. 2017 Dec;390(10112):2602–4. Available from: http://www.ncbi.nlm.nih.gov/pubmed/28622953