Systems Theory in Public Health Chapter uri icon

abstract

  • The recognition of the complexity of many public health problems has led to the search for analytic methods capable of capturing more fully the underlying dynamic processes at work compared to traditional study designs and statistical tests. Similarly, those with an interest in public health interventions have begun to see the limitations of standard methods, such as randomized trials and quasi-experiments, in understanding the consequences (both intended and unintended) of programs and policies designed to influence population-level health. This search for methods that move beyond a simple mechanistic cause-and-effect (or risk factor–outcome) representation of the world has stimulated interest among public health researchers in analytic techniques that have been developed within the evolving field of system sciences. These methods have proved successful in understanding complex adaptive phenomena in fields such as operations research, engineering, ecology, biology, and sociology. While there are a number of system science methods that have the potential to further public health research, to date, three methods have been most often applied in the field: agent-based modeling, social network analysis, and system dynamics modeling. Each method has strengths and weaknesses, and each is better suited to studying some aspects of complex dynamic phenomena than others. Agent-based models are especially good in addressing issues that involve heterogeneous actors and exploring phenomena that are thought to emerge from the interaction between such actors and their interactions with their environments. The agents in the model can learn and adapt over the course of the simulation, and the environment can change as a result of the interactions of the agents. The models can be used to test purely theoretical ideas or those grounded in real-life events or situations (e.g., the occurrence of violence in a specific city). In each case the goal is to identify the mechanisms through which the phenomena of interest emerged. System dynamics models typically group actors into categories or stocks and are concerned with the flow between these conditions and the factors that influence the rate at which these flows occur. This method is especially interested in feedback loops and the unintended consequences that can occur from well-intentioned attempts to change a system. Another key concept in system dynamics modeling is that of leverage points for successful interventions. These two aspects of the approach make it especially useful in policy analysis. Although social network analysis can also be used to understand basic theoretical principles of interactions between actors (e.g., six degrees of separation, weak and strong ties) it has primarily been used in public health research to examine relationships within datasets pertaining to either individuals (e.g., the spread of smoking over time within a large cohort study containing data on connectivity) or organizations (e.g., the exchange of resources among tobacco control agencies). This article provides practical guidance for those who wish to use these system methods in their own research, especially in the areas of social epidemiology, social and behavioral health, and policy analysis.

author list (cited authors)

  • Elkins, A. D., & Gorman, D. M.

editor list (cited editors)

  • McQueen, D. V.

Book Title

  • Oxford Bibliographies in Public Health

publication date

  • January 2014