A Call to Address Complexity in Prevention Science Research

Abstract

The problems targeted by preventive interventions are often complex, embedded in multiple levels of social and environmental context, and span the developmental lifespan. Despite this appreciation for multiple levels and systems of influence, prevention science has yet to apply analytic approaches that can satisfactorily address the complexities with which it is faced. In this article, we introduce a systems science approach to problem solving and methods especially equipped to handle complex relationships and their evolution over time. Progress in prevention science may be significantly enhanced by applying approaches that can examine a wide array of complex systems interactions among biology, behavior, and environment that jointly yield unique combinations of developmental risk and protective factors and outcomes. To illustrate the potential utility of a systems science approach, we present examples of current prevention research challenges, and propose how to complement traditional methods and augment research objectives by applying systems science methodologies.

Translating research into prevention of high-risk behaviors in the presence of complex systems: definitions and systems frameworks

“…System dynamics is just one example of the larger class of systems science methods that may be useful in translational research, practice, and policy efforts [29,62,72,73]. Another approach, organizational network analysis, has already proven highly relevant to T4 translation and can improve our understanding of the adoption and implementation of evidence-based interventions [74].…”

Practice-Based Evidence in Public Health: Improving Reach, Relevance, and Results

“…Modeling organizes variables in a system and shows how they relate to one another. Remembering that "all models are wrong; the practical question is how wrong do they have to be to not be useful" (6, p. 424), researchers and practitioners can use models to address challenging public health issues while acknowledging that models are helpful simplifications of problems that can seem overwhelmingly complex (54,77).…”

“…For a complete review of these approaches, see Luke (55), "Systems Science Methods in Public Health: Dynamics, Networks, and Agents." Researchers have used system dynamics modeling, for example, to identify "leverage points" or "places one can intervene within a system to effect change" (p. 284) where an intervention is likely to have a positive effect on an outcome of interest while minimizing negative or unintended consequences (54,59). Agent-based modeling and network analysis are also systems methods that have useful applications in public health (55).…”

Policy, Work Organization and Sleep Health and Safety of Commercial Drivers: Introducing a Complex Systems Paradigm

“…Interacting and interrelated factors are problematic to reductionist approaches, and satisfactory means for incorporating time-delayed effects and phenomena such as emergence are lacking in the realm of reductionism [31,32]. This paradigm also cannot fully describe structural, multilevel pathways of influence, especially policy-level influences [33].…”

“…Interaction among system elements violates the assumption of independence of observations in regression [35]. Ultimately, reliance on reductionist approaches has proven prohibitive in understanding the true nature of system behaviour, with implications for intervention efforts [32].…”