Systems Approaches to Health Research and Prevention

Abstract

There is a growing consensus among public health researchers that the complexities underlying population health and its distribution cannot be unraveled by traditional epidemiological methods alone. It is recognized that for interventions to be effective and sustainable, the complexities of the systems in which they are implemented must be considered. Systems approaches are increasingly being applied to grasp and address these complexities. Systems approaches imply that population health is an emergent outcome of complex systems that operate across spatial and temporal scales, from cells to society, and accordingly is not reducible to the impact of single factors such as smoking or diet. It is argued that this perspective allows for a more realistic assessment of how health at the level of the population arises. This chapter describes key features of complex systems, i.e., emergence, interactions, nonlinearity, interference, feedback loops, adaptation, and evolution. Moreover, it describes approaches to, first, understanding complex systems, with a focus on studying patterns, mechanisms, and dynamics, and, second, changing complex systems, with key concepts revolving around leverage points at different levels of the system. Specific methods arising from complexity science, such as causal loop diagrams or system dynamics models, are used to illustrate this approach. This chapter also maps current epidemiological methods in relation to key features and key concepts of systems approaches and shows how epidemiological methods might support systems approaches. The chapter concludes with future perspectives.