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Public Health Complexity and Systems Theory
by
Diane T. Finegood, Lee M. Johnston, Philippe Giabbanelli, Penny Deck, Sarah Frood, Lina Burgos-Liz, Marla Steinberg, Allan Best

Introduction

Public health embraces a holistic, “cell-to-society” approach to understanding both the direct and underlying causes of disease and the conditions that contribute to an absence of well-being. In the early 21st century, interdisciplinary methods that address the interconnected and overlapping determinants of health are needed more than ever for dealing with modern, intractable, complex health problems, such as obesity and chronic disease. The relatively recent explosion of complexity science and systems thinking across a broad range of disciplines promises to bring new insight into the nature of these challenges while providing new methods for grappling with the characteristics specific to complex problems (such as nonlinearity, feedback loops, and their chaotic nature). There are varying definitions across these disciplines of complex system, systems approach, and even systems thinking. Some researchers have called for a common language and logic to describe systems approaches; this will likely emerge in the coming years as the many disciplines working together establish common terminology. In this bibliography, “complexity science” refers to the methodologies and tools used to understand complex problems, such as agent-based and system-dynamic modeling. “Systems thinking” is used more broadly in reference to approaches that support thinking about the system, as both the causes of a complex problem and the solutions to it will be found within the structure and function of the system. Systems thinking tends to be integrative and solution oriented when compared with traditional reductionist science, which is more linear and focused on locating the causes of a problem. Complexity science provides frameworks and methods for integrating large amounts of data to enable development of a detailed picture of the workings of a complex system. Systems thinking differs in that it does not require a detailed understanding of specific system dynamics but may provide direction based on one or more of the characteristics common to complex problems. The works in this bibliography either introduce the reader to the general principles of systems thinking and complexity science or review their application to public health concerns. Applied methods include system dynamics, network analysis, and agent-based modeling. Applications in public health include behavior change, program planning, evaluation, and knowledge exchange.

Introductory Works

The works in this section tell the story of public health’s incorporation of novel methods and approaches drawn from complexity science and systems thinking in order to further strengthen its holistic approach. Plsek and Greenhalgh 2001 emphasizes the need for systems approaches and provides a guide to newcomers on the terminology and basic concepts required to understand complex systems. Leischow and Milstein 2006 introduces a collection of original and review material from leaders in systems thinking and modeling across a range of public health issues. Sterman 2006 offers an excellent introduction to the notion that dealing with complex problems requires a fundamental change in our mental models, whereas Trochim, et al. 2006 looks at new methods and tools for intervention. Finegood 2011 considers the application of a systems lens to the problem of obesity. Glass and McAtee 2006 reinforces the notion that we need to understand the many dimensions of the complexity inherent in public health challenges and calls for new metaphors and models to help us integrate a large amount of discipline-specific information. Although Mabry, et al. 2008 specifically addresses the work of the National Institutes of Health Office of Behavioral and Social Sciences Research, its account of the organization’s aggressive turn toward systems science speaks to the larger direction public health is taking to deal with issues associated with interdisciplinarity. Finally, Luke and Stamatakis 2012 argues that public health can build upon its already impressive adoption of system science research methods and presents case studies that demonstrate their utility for the uninitiated.

  • Finegood, Diane T. 2011. The complex systems science of obesity. In The Oxford handbook of the social science of obesity. Edited by J. Cawley, 208–236. New York: Oxford Univ. Press.

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    This chapter describes the characteristics of a complex system and uses the problem of obesity as an exemplar. The chapter also considers the usual responses to complex problems and provides two frameworks for intervention that do not depend on a detailed understanding of a system’s dynamics.

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  • Glass, Thomas A., and Matthew J. McAtee. 2006. Behavioral science at the crossroads in public health: Extending horizons, envisioning the future. Social Science and Medicine 62.7: 1650–1671.

    DOI: 10.1016/j.socscimed.2005.08.044Save Citation »Export Citation »E-mail Citation »

    This paper argues that biology and individual health behaviors must be studied within their broader social and environmental contexts. The authors offer a multilevel framework for examining behavior but remain rooted in the reductionist paradigm by suggesting that working out the causes of a problem will lead to solutions. Available online for purchase or by subscription.

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  • Leischow, Scott J., and Bobby Milstein. 2006. Systems thinking and modeling for public health practice. American Journal of Public Health 96.3: 403–405.

    DOI: 10.2105/AJPH.2005.082842Save Citation »Export Citation »E-mail Citation »

    Addresses the questions of what systems thinking and modeling are and why they are important with respect to public health. Notes that a systems view emphasizes relationships, transcends boundaries, bridges silos, and embraces heterogeneity. Introduces other articles in the issue as examples of applying systems thinking and complexity to public health.

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  • Luke, Douglas A., and Katherine A. Stamatakis. 2012. Systems science methods in public health: Dynamics, networks, and agents. Annual Review of Public Health 33:357–376.

    DOI: 10.1146/annurev-publhealth-031210-101222Save Citation »Export Citation »E-mail Citation »

    Arguing for a broader inclusion of systems science study designs and analytic methods in public health training and curricula, the authors present three case studies, demonstrating their application to pressing public health issues (infectious disease, tobacco control, obesity). Available online for purchase or by subscription.

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  • Mabry, Patricia L., Deborah H. Olster, Glen D. Morgan, and David B. Abrams. 2008. Interdisciplinarity and systems science to improve population health: A view from the NIH Office of Behavioral and Social Sciences Research. American Journal of Preventive Medicine 35.2S: S211–S224.

    DOI: 10.1016/j.amepre.2008.05.018Save Citation »Export Citation »E-mail Citation »

    Overviews the four key programmatic directions (next-generation basic science, interdisciplinary research, systems science, a problem-based focus for population impact) of an organization leading the charge for bridging systems science and public health. Available online for purchase or by subscription.

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  • Plsek, Paul E., and Trisha Greenhalgh. 2001. Complexity science: The challenge of complexity in health care. British Medical Journal 323.7313: 625–628.

    DOI: 10.1136/bmj.323.7313.625Save Citation »Export Citation »E-mail Citation »

    Explains basic concepts for understanding complex adaptive systems within a health care context. An example of complexity facing a physician and patient runs throughout the paper and serves to make the concepts discussed more concrete.

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  • Sterman, John D. 2006. Learning from evidence in a complex world. American Journal of Public Health 96.3: 505–514.

    DOI: 10.2105/AJPH.2005.066043Save Citation »Export Citation »E-mail Citation »

    Describes three fundamental impediments to the goal of improving health policy: complexity, learning failures, and implementation challenges. Asserts that understanding complexity can help overcome policy resistance. Focuses on the role of feedback and time delays. Explores the use of simulations to facilitate learning through the creation of new feedback loops.

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  • Trochim, William M., Derek A. Cabrera, Bobby Milstein, Richard S. Gallagher, and Scott J. Leischow. 2006. Practical challenges of systems thinking and modeling in public health. American Journal of Public Health 96.3: 538–546.

    DOI: 10.2105/AJPH.2005.066001Save Citation »Export Citation »E-mail Citation »

    Gives brief overview of systems thinking, stressing that it goes beyond ecological models and the social determinants of health. Frames discussion with two organizing ideas: complexity and dynamics. Uses mechanical and biological metaphors to set context. Identifies eight challenges of systems thinking and modeling in public health; based on results of an empirical study.

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Monographs

Milstein 2008 presents a broad examination of the values and roles embedded in public health perspectives before turning specifically to systems thinking as a tool for modern-day practice. Savigny and Adam 2009 also addresses systems thinking in health care, whereas Best, et al. 2007 turns this lens to the public health challenge of tobacco control.

  • Best, Allan, Pamela I. Clark, Scott J. Leischow, and William M. K. Trochim, eds. 2007. Greater than the sum: Systems thinking in tobacco control. National Cancer Institute Tobacco Control Monograph. Bethesda, MD: National Cancer Institute.

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    This monograph is the combined effort of an interdisciplinary group of scientists funded by the National Cancer Institute and brought together under the Initiative on the Study and Implementation of Systems. This collaborative effort examined the application of systems thinking and systems methods in tobacco control and public health.

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  • Milstein, Bobby. 2008. Hygeia’s constellation: Navigating health futures in a dynamic and democratic world. Atlanta: Centers for Disease Control and Prevention.

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    In the first two sections of this monograph, Milstein studies the values embedded in public health through its early history and development. The third section expands on the whole of systems approach inherent in public health practice and considers how systems analysis will carry this work forward in addressing the complex problems of our time.

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  • Savigny, Don de, and Taghreed Adam, eds. 2009. Systems thinking for health systems strengthening. Geneva, Switzerland: Alliance for Health Policy and Systems Research.

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    This monograph gives the authors’ definition of systems thinking and describes how it can help us better understand complex problems and how researchers and decision makers can use it, including how it can be used to strengthen health systems. The monograph conflates systems thinking and complexity science, as they are used in this bibliography, but provides an introduction to the application of these ideas in health care systems.

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Journals

As of yet there is no journal dedicated to the intersection of public health and complexity. However, there have been several special issues of health-focused journals that have explored the area. A key landmark in the history of complexity science and systems thinking in public health is the March 2006 issue of the American Journal of Public Health (McLeroy, et al. 2006), which collected works from leaders in these domains, many of whom were involved in the National Cancer Institute’s systems thinking in tobacco control project (see Best, et al. 2007, cited under Monographs). A 2008 special issue of the American Journal of Preventive Medicine (Stokols, et al. 2008) reviews the role of team science in prevention, whereas another issue, in 2011 (American Journal of Preventive Medicine), deals with public health services and systems research. The 2007 American Journal of Community Psychology special issue on systems change (Foster-Fishman and Behrens 2007) bridges systems theory and the development of healthy communities. In 2011 the Lancet published a special series on obesity that applies a systems lens to this complex public health problem.

  • American Journal of Preventive Medicine. 2011. 41.1: 100–117.

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    This issue features an introduction and three papers about the emerging field of public health services and systems research. The papers document the growth of this research community in terms of people, organizations, and publications.

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  • Foster-Fishman, Pennie G., and Teresa R. Behrens, eds. 2007. Special issue: Systems change. American Journal of Community Psychology 39.3–4.

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    This special issue of the American Journal of Community Psychology focusing on systems change bridges theory and practice, integrating systems thinking concepts with the diverse work in community psychology. An introduction by Foster-Fishman and Behrens highlights the theoretical frameworks, methods, examples, and future directions discussed in the seventeen papers included in the issue. Articles available online for purchase or by subscription.

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  • McLeroy, Kenneth, S. J. Leischow, and B. Milstein, eds. 2006. Special issue: Thinking of systems. American Journal of Public Health 96.3.

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    This landmark issue presents the first comprehensive gathering of commentaries and original articles on systems approaches to public health. Frames public health and public health concerns with systems thinking and systems models. The authors present systems tools for facilitating further understanding, aiding in the development of new interventions, and improving outcome effectiveness.

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  • Special issue: Obesity. 2011. Lancet 378.9793.

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    This special issue puts the spotlight on obesity and includes several commentaries and original articles using systems science and systems thinking to consider solutions to this complex problem. Articles available online for purchase or by subscription.

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  • Stokols, Daniel, Kara L. Hall, Brandie K. Taylor, Richard P. Moser and S. Leonard Syme, eds. 2008. Special issue: The science of team science: Assessing the value of transdisciplinary research. American Journal of Preventive Medicine 35.2.

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    Provides an introduction to the science of team science, including the major conceptual, methodological, and translational issues. Examples of team science initiatives are used to highlight collaborative and cross-disciplinary approaches spanning a variety of topics. An introduction by Stokols and colleagues offers a road map to the articles. Articles available online for purchase or by subscription.

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Principles of Systems Thinking and Complexity Science

There are a number of relatively accessible introductions to systems thinking and complexity science. Although not all of these are health specific, all have interdisciplinary applicability. Bar-Yam 2004 and Meadows 2008 overview systems thinking principles from an engineering perspective and consider their potential application to complex problems. The popular work of Senge 2006 and Wheatley 2006 on the application of systems thinking to organizational issues has been adopted across a range of disciplines. Other authors present rigorous examinations of specific topics and methods, including Checkland and Scholes 1999, on soft systems methodology, and Miller and Page 2007, on the nature of complex adaptive systems.

  • Bar-Yam, Yaneer. 2004. Making things work: Solving complex problems in a complex world. Edited by C. Ramalingam, L. Burlingame, and C. Ogata. Cambridge, MA: Knowledge Press.

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    Bar-Yam presents a compelling case for using complex systems to address complex problems, explains some of the key concepts of complexity science, and gives specific examples in domains such as education, international development, and health.

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  • Checkland, Peter, and Jim Scholes. 1999. Soft systems methodology in action. Chichester, UK: Wiley.

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    A dense account of the origins and development of soft systems thinking, a flexible action-research approach that accounts for the construction, in part, of modern social problems by the observer’s drawing of system boundaries. Includes examples of application in health care settings.

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  • Meadows, Donella H. 2008. Thinking in systems: A primer. Edited by D. Wright. White River Junction, VT: Chelsea Green.

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    Meadows uses plain language and engaging examples to discuss the philosophy behind systems thinking and elements common to most complex systems. Emphasizing a solution-oriented approach, she distills her decades of experience into a list of leverage points with which to approach change in social systems.

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  • Miller, John H., and Scott E. Page. 2007. Complex adaptive systems: An introduction to computational models of social life. Princeton studies in complexity. Princeton, NJ: Princeton Univ. Press.

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    Similar to the work of Bar-Yam and Meadows in its overviews of the characteristics that distinguish complex adaptive systems from merely complicated systems, but diverges in its focus on the role of modeling in furthering our understanding of the social world.

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  • Senge, Peter M. 2006. The fifth discipline: The art and practice of the learning organization. New York: Currency/Doubleday.

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    Although written for a mainstream audience, and specifically focused on business practices, Senge’s overview of learning practices in complex organizations is transferable to health care settings, where effective and nurturing management will help successful growth and collaboration.

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  • Wheatley, Margaret J. 2006. Leadership and the new science: Discovering order in a chaotic world. 3d ed. San Francisco: Berrett-Koehler.

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    Wheatley considers how the developments taking place in the fields of biology, chaos theory, and quantum physics are relevant to our understanding and management of complex organizations. A highly accessible and grounded introduction to challenging concepts.

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Theoretical Frameworks

The works in this section survey theories that have informed the application of systems thinking to public health issues. A common thread is the acknowledgment that the complex social problems of postindustrial societies will only be adequately addressed through the development and application of novel theory and methodologies. Systems engineering, as it was introduced in the 1950s, is generally viewed here as a reductionist, “hard systems” approach, lacking the flexibility to grapple with complex social issues. Rittel and Webber 1973 was among the first studies to identify the “wicked” nature of the modern social problem and the ways in which it might not be best served by traditional scientific means. The authors’ sentiments, particularly those regarding the subjective nature of problem definition in complex systems, are extended and formalized in the development of soft systems theory and methods in Checkland 2000. Soft systems theorists are sensitive to the nonlinear activities of human systems, which often function with unclear or competing objectives that preempt the application of classic systems engineering methodology. Whereas hard systems engineers observe systems as external, knowable, and ultimately malleable entities, soft systems theorists recognize the socially constructed aspect of problems and of the systems models developed to represent them. Although reflexivity is built into soft systems theory, the political role of the researcher is given further prominence in critical systems theory in Jackson 1991, an action research approach blending elements of hard and soft systems methodology. Olaya 2009 provides a countercritique of the claims made by soft and critical systems theorists in its overview of systems dynamics’ underlying theories. For a broader look at the integration of systems theory into an already established field, the reader can turn to Castellani and Hafferty 2009, a review of the “complexity turn” taking place in sociology.

  • Castellani, Brian, and Frederic W. Hafferty. 2009. Sociology and complexity science: A new field of inquiry. Berlin and Heidelberg, Germany: Springer.

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    A thorough account of the new field of sociology and complexity science and its five main areas of research: computational sociology, the British-based school of complexity, complex social network analysis, sociocybernetics, and the Luhmann school of complexity.

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  • Checkland, Peter. 2000. Soft systems methodology: A thirty year retrospective. Systems Research and Behavioral Science 17.S1: S11–S58.

    DOI: 10.1002/1099-174320001117:1+<::AID-SRES374>3.0.CO;2-OSave Citation »Export Citation »E-mail Citation »

    Overviews the emergence of soft systems methodology, in part as a response to the limitations of traditional (“hard”) systems thinking in developing action research to tackle the messy problems of social management. Includes a helpful bibliography. Available online for purchase or by subscription.

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  • Jackson, M. C. 1991. The origins and nature of critical systems thinking. Systemic Practice and Action Research 4.2: 131–149.

    DOI: 10.1007/BF01068246Save Citation »Export Citation »E-mail Citation »

    Reviews the origins of critical systems thinking as a further extension of soft systems methodology’s reaction to hard systems thinking and identifies its central commitments to social and critical awareness, theoretical and methodological complementarism, and human emancipation. Available online for purchase or by subscription.

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  • Olaya, Camilo. 2009. System dynamics philosophical background and underpinnings. In Encyclopedia of Complexity and System Science. Vol. 9. Edited by R. A. Meyers, 9057–9078. New York: Springer.

    DOI: 10.1007/978-0-387-30440-3Save Citation »Export Citation »E-mail Citation »

    Best approached as a broad introductory overview of the philosophical theory behind system dynamics and a reflection of the underlying assumptions of this methodology and their importance for practice. Provides a jumping-off point to other relevant literature, including works of Jay Forrester, David Lane, and Yaman Barlas, among others.

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  • Rittel, Horst W. J., and Melvin M. Webber. 1973. Dilemmas in a general theory of planning. Policy Sciences 4.2: 155–169.

    DOI: 10.1007/BF01405730Save Citation »Export Citation »E-mail Citation »

    Early philosophical overview of the tricky nature of complex problems and of problem definition in and of itself. Available online for purchase or by subscription.

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Modeling

As shown in Epstein 2008, all people model, though most run implicit rather than explicit models. Epstein also points out the problems with implicit models, for example, the assumptions underlying them are mostly hidden, their internal consistency is untested, and their logical consequences and relation to data are unknown. When we make our implicit models more explicit by creating mathematical or computational models, we surface and can test assumptions, incorporate data, and check for consistency, thereby illuminating core dynamics and the importance of factors that may be difficult to change or that change rapidly in the real world. Two institutes with a focus on modeling and complexity that provide content relevant to public health audiences are the New England Complex Systems Institute (NECSI) and the Santa Fe Institute. Both institutes have websites with considerable resources for students and researchers interested in complexity science, modeling, and systems thinking. There are many different types of modeling, including several traditional statistical approaches, but the three methods most used at the interface of complex systems and public health are network analysis, agent-based modeling, and system dynamics. These three methods are a focus for the annual Institute on Systems Science and Health and have many possible applications for understanding and solving complex public health problems, such as epidemics, sexually transmitted diseases, and intervention cost-effectiveness. Hethcote 2000 discusses the fundamental mathematics used by several modeling methodologies, with an emphasis on infectious diseases, whereas Habbema, et al. 2008 gives a helpful introduction to modeling methods relevant to chronic disease. The Epiwork Consortium is integrating multiple methods, including network, multiscale, and agent-based models to support epidemic forecasting.

  • Epiwork.

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    Epiwork is a multidisciplinary project sponsored by the European Community under the Seventh Framework Programme aimed at developing the appropriate framework of tools and knowledge needed for the design of epidemic forecast infrastructures. Multiple modeling methods and data sources are being integrated.

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  • Epstein, Joshua M. 2008. Why model?. Journal of Artificial Societies and Social Simulation 11.4: 12.

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    This article is essentially the lecture notes from several of Epstein’s addresses on the topic. This document is a concise reminder of the many and varied reasons for modeling.

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  • Habbema, J. D. F., R. Boer, and J. J. Barendregt. 2008. Chronic disease modeling. In International encyclopedia of public health. Edited by H. K. Heggenhougen, 704–709. Amsterdam and Boston: Elsevier.

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    Walks the reader through the crucial steps of setting up models. Although many steps, such as simulation and validation, also hold for models in biology and meteorology, modeling diseases has its own needs, such as the importance of describing cohorts well. Available online for purchase.

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  • Hethcote, Herbert W. 2000. The mathematics of infectious diseases. SIAM Review 42.4: 599–663.

    DOI: 10.1137/S0036144500371907Save Citation »Export Citation »E-mail Citation »

    Exposes classical epidemic modeling, which started in the 1920s. These models became less common with the rise of agent- and network-based models, but the terminology still exists, and many of the ideas can be found in system dynamics models of infectious diseases. Available online for purchase or by subscription.

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  • Institute on Systems Science and Health.

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    The institute is a week-long course designed to introduce general principles of systems science and foster a deeper understanding of selected methodologies that may be useful in research on the behavioral and social drivers of population health. The website provides open access to course materials from each year the institute is run.

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  • New England Complex Systems Institute.

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    NECSI is an independent academic research institute led by Yaneer Bar-Yam at the Massachusetts Institute of Technology. NECSI is known for its applied work in many fields, including health care. Its website is fairly accessible and has many resources for newcomers to complexity science and systems thinking.

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  • Santa Fe Institute.

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    Established in 1984, the Santa Fe Institute is one of the premier research centers focused on complexity and has many well-known faculty. Its website offers resources on research topics relevant to health and health systems, such as behavioral dynamics and robustness and innovation.

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System Dynamics

System dynamics is a modeling method developed in the mid-1950s by Jay Forrester and his students at the Massachusetts Institute of Technology. The method evolved out of the need to understand increasingly complex industrial processes, and it is now applied to a wide range of social and health-related problems. Forrester 1994 details the process of system dynamics modeling and its intersection with systems thinking. Homer and Hirsch 2006 highlights that system dynamics depends on ongoing accumulation—of people, material or financial assets, information, or even biological or psychological states—and on both balancing and reinforcing feedback mechanisms. The work explores the potential of system dynamics for contributing to public health. Sterman 2000 illustrates how system dynamics enables consideration of dynamic complexity and disequilibrium and suggests that equilibrium (a stable state) is more the exception than the rule when it comes to the real world. As access to increasingly powerful computing systems has grown, so, too, have computational approaches, allowing for examination of the potential impact of public policies. Thompson and Tebbens 2007 looks at the impact of public policies on systems for disease eradication, and Granich, et al. 2009 discusses human immunodeficiency virus (HIV) testing. Other applications of system dynamics include Osgood, et al. 2011, an examination of early life influences on tuberculosis outcomes, and Hovmand and Gillespie 2007, a study of innovation in mental health service organizations. Richardson 1996, a key contribution to the development and application of system dynamics methods, cautions users and modelers about potential pitfalls of the approach.

Network Models

Network models represent connections between actors. Luke and Harris 2007 provides an overview of network models and indicates that they can be used to analyze the public health system, in which actors are individuals, organizations, programs, and other entities. Network models connect actors (nodes) through edges, based on relationships, such as social ties or information sharing. Bolland and Wilson 1994 demonstrates that interorganizational network analysis must consider specific organizational functions, such as service delivery, administration, and planning, when assessing coordination. With respect to individuals, Valente 2010 looks at how the methodology has uncovered important relationships between health and social/contact networks. Newman 2003 explains that individuals play different roles, such as being in contact with a large number of people or acting as a bridge connecting two social groups. Taking contact types and patterns into consideration can lead to different estimates for the course of a disease, as discussed in Bansal, et al. 2007. Kretzschmar and Wallinga 2007 demonstrates that most individuals have few connections, whereas few individuals have many, which could make it difficult to avoid the spread of a disease in a population. However, as the research in Piccardi and Casagrandi 2008 illustrates, the impact of network structures also depends on other parameters, such as the dynamics of mixing and dynamic changes in network structure. Gross, et al. 2006 shows the impact of changes when a network of individuals takes action to prevent becoming infected. When individual decision making matters more than the social ties of individuals, one should consider using an agent-based model instead; if both aspects are of equal importance, then network modeling techniques can include individual agents.

Agent-Based Models

Agent-based models are ideal for representing the behavior of each individual in a population in detail. The dynamics of a disease process are affected by an individual’s beliefs, experiences, and fears, which can all be modeled with this methodology, as illustrated by Brown, et al. 2011, in its study of H1N1, and by Barrett, et al. 2005, in its examination of smallpox. Epstein, et al. 2007 explains that whereas early models could only simulate small populations (thousands of individuals), advances in supercomputing are allowing much larger populations, on a global scale. To set up a virtual population, agent-based models need data on individual agents; however, most studies report aggregated population data. Wheaton, et al. 2009 argues that designing realistic agent-based models requires direct access to individual-level data or to synthesized population databases. Perez and Dragicevic 2009 and Auchincloss and Diez Roux 2008 demonstrate that this can be made even more accurate when data are available on the environment. Gibbons 2007 illustrates how agent-based models can be used to investigate the impact of network structures on diffusion of information throughout a health system.

Individual Behavior Change

Populations made up of individuals and Agent-Based Models are helping us simulate the aggregate behavior of small and large populations. But, these models are only as good as the data we have about the behavior of individuals. The topic of behavior change is both important and popular, as illustrated by the commercially successful works Heath and Heath 2010, Thaler and Sunstein 2008, and Ariely 2009, all useful entrees into the world of behavioral economics that demonstrate that behavior change is complex and not always rational. Resnicow and Vaughn 2006 describes behavior as chaotic and stochastic in nature and suggests that we need to understand this better in order to address behavior change. Hayes, et al. 2007 challenges the traditional linear, deterministic models of behavior change and identifies the importance of unplanned and random elements. Carver and Scheier 1998 examines the importance of feedback and feedback loops at the level of the individual. Bainbridge 1997 challenges the reductionist perspective espoused by most traditional behavior change theories and applies an integrative approach that views contributing factors within the context of the system as a whole. Together, these papers highlight the complex characteristics of behavior change and provide a theoretical framework for approaching behavior change with a complex systems lens.

  • Ariely, Dan. 2009. Predictably irrational: The hidden forces that shape our decisions. Rev. ed. New York: Harper.

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    One of many popular books on the topic of behavioral economics. Ariely describes his work on problems such as why cautious people make poor decisions about sex when aroused and why decisions are always based on comparisons. Ariely argues that a greater understanding of emotions, relativity, and social norms allows for a reexamination of economic and social policy.

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  • Bainbridge, L. 1997. The change in concepts needed to account for human behavior in complex dynamic tasks. Institute of Electrical and Electronics Engineers Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans 27.3: 351–359.

    DOI: 10.1109/3468.568743Save Citation »Export Citation »E-mail Citation »

    Discusses models of behavior change, noting that human behavior does not conform to sequential (linear) stages, but incorporates dynamic cognitive processes. Suggests that management of complex behavior requires an overview of the whole task at hand and development of skills specific to dealing with that task. Available online by subscription.

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  • Carver, Charles. S., and M. F. Scheier. 1998. On the self-regulation of behavior. Cambridge, UK, and New York: Cambridge Univ. Press.

    DOI: 10.1017/CBO9781139174794Save Citation »Export Citation »E-mail Citation »

    Uses concepts from control theory to explore individual behavior change. Emphasizes the systems characteristic of feedback through self-regulation to support behavior change.

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  • Hayes, Adele M., Jean-Philippe Laurenceau, Greg Feldman, Jennifer L. Strauss, and LeeAnn Cardaciotto. 2007. Change is not always linear: The study of nonlinear and discontinuous patterns of change in psychotherapy. Clinical Psychology Review 27.6: 715–723.

    DOI: 10.1016/j.cpr.2007.01.008Save Citation »Export Citation »E-mail Citation »

    Applies dynamical systems theory to challenge the notion that change is linear and gradual. Offers examples from the psychology literature in which change occurs randomly and is unplanned. Available online for purchase or by subscription.

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  • Heath, Chip, and Dan Heath. 2010. Switch: How to change things when change is hard. New York: Broadway.

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    An enjoyable read that also provides a nice analogy for behavior change, the rider (rational) and his elephant (emotional). Gives many examples that illustrate important ideas, such as the impact of too much choice when designing behavior change interventions.

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  • Resnicow, Ken, and Roger Vaughan. 2006. A chaotic view of behavior change: A quantum leap for health promotion. International Journal of Behavioral Nutrition and Physical Activity 3.1:25.

    DOI: 10.1186/1479-5868-3-25Save Citation »Export Citation »E-mail Citation »

    Applies systems concepts, including chaos and quantum change, to individual behavior change. Posits that a new conceptualization of health behavior is needed in order to influence change more effectively. The ideas were debated in subsequent issues of the journal by Tom Baranowski and Johannes Brug.

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  • Thaler, Richard H., and Cass R. Sunstein. 2008. Nudge: Improving decisions about health, wealth, and happiness. New Haven, CT: Yale Univ. Press.

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    The authors present their ideas on libertarian paternalism and choice architecture in the design of population interventions. Nudge takes behavioral economics into the realm of politics and public policy and helps us understand how we can shape more effective public policy by understanding how people interact with their environments.

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Program Planning and Implementation

The planning and implementation of interventions in complex environments are challenging tasks for organizations and governments, and the tool kit that has been applied in the public health domain is limited. Probably the most advanced tool is the use of system dynamics as an instrument of planning, as illustrated by Huz, et al. 1997. The authors also provide a ten-point framework for evaluating systems thinking, using group model building and system dynamics, and demonstrate the application of the framework in evaluating a systems change intervention in mental health services in New York State. Tseng and Seidman 2007 presents another framework for consideration in planning interventions, though its focus is social process, resources, and organization of resources. Keshavarz, et al. 2010 and Suarez-Balcazar, et al. 2007 look at specific interventions in the school setting, each taking a different approach to the process of applying systems thinking. On a larger scale, Thompson and Tebbens 2008 discusses the successful use of systems dynamics and modeling as a tool for the analysis and planning of global polio eradication policies. Together, these papers provide a sense of the potential for systems thinking and complexity science in the planning, and implementation, and evaluation of programs and policies.

  • Huz, Steven, David F. Andersen, George P. Richardson, and Roger Boothroyd. 1997. A framework for evaluating systems thinking interventions: An experimental approach to mental health system change. In Special issue: Group model building. Edited by Jac A. M. Vennix, David F. Andersen, and George P. Richardson. System Dynamics Review 13.2: 149–169.

    DOI: 10.1002/SICI1099-172719972213:2<149::AID-SDR122>3.0.CO;2-SSave Citation »Export Citation »E-mail Citation »

    Proposes a framework for evaluating the impact of systems thinking interventions. The framework consists of ten domains of measurement and analysis and is designed to evaluate measurable outcomes. The findings from the piloting identify a number of challenging issues for organizations attempting to apply systems thinking interventions and highlight what should be the focus of future research. Available online for purchase or by subscription.

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  • Keshavarz, Nastaran, Don Nutbeam, Louise Rowling, and Freidoon Khavarpour. 2010. Schools as social complex adaptive systems: A new way to understand the challenges of introducing the health promoting schools concept. Social Science and Medicine 70.10: 1467–1474.

    DOI: 10.1016/j.socscimed.2010.01.034Save Citation »Export Citation »E-mail Citation »

    Examines the usefulness of complex adaptive systems as a framework for understanding the potential impact of health promotion interventions in primary schools. Primary data were from semistructured interviews with twenty-six school principals and teachers.

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  • Suarez-Balcazar, Yolanda, Ladonna Redmond, Joanne Kouba, et al. 2007. Introducing systems change in the schools: The case of school luncheons and vending machines. American Journal of Community Psychology 39.3–4: 335–345.

    DOI: 10.1007/s10464-007-9102-7Save Citation »Export Citation »E-mail Citation »

    This paper uses the social ecological model to illustrate the interaction between the individual and different systems that impact individual behavior. Explores the specific conditions that facilitate change in a system, in this case the luncheon program and food vending machines in schools. Also looks at the challenges to creating change in an organization and the critical antecedent factors leading to systems change.

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  • Thompson, Kimberly M., and Radboud J. Duintjer Tebbens. 2008. Using system dynamics to develop policies that matter: Global management of poliomyelitis and beyond. Systems Dynamics Review 24.4: 433–439.

    DOI: 10.1002/sdr.419Save Citation »Export Citation »E-mail Citation »

    Provides an example of how system dynamics and modeling can help influence decision makers and shape policies. The authors reflect on their previous work, using modeling to assess the risk, costs, and benefits of polio eradication versus control. This paper also stresses the importance of understanding the audience and the complex physical and social systems within which large projects operate. Available online for purchase or by subscription.

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  • Tseng, Vivian, and Edward Seidman. 2007. A systems framework for understanding social settings. American Journal of Community Psychology 39.3–4: 217–228.

    DOI: 10.1007/s10464-007-9101-8Save Citation »Export Citation »E-mail Citation »

    Presents a systems framework that describes important aspects of social settings and how those aspects are interrelated in a system. Focuses on three aspects of settings that represent intervention targets: social process, resources, and organization of resources.

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Evaluation

The incorporation of systems thinking and concepts into evaluation has grown tremendously since the late 20th century, with new approaches continuing to emerge. Green 1977 introduces the complex characteristics of interventions in public health that make evaluation and measurement a challenge. Patton 2011 offers a thorough and readable explanation of system concepts and a new approach for evaluators to support innovation and evaluative thinking throughout a program life cycle. Two publications formally mark the beginning of the active promotion of systems thinking in evaluation, even though many evaluators had applied system concepts to their evaluations without explicitly naming them, and a smattering of published evaluations did articulate an explicit adoption of system thinking. Parsons 2007 is a guide to designing systems-oriented evaluation that helps evaluators incorporate systems thinking into their evaluations. Williams and Imam 2007 showcases the application of system concepts in a variety of evaluations. The Cornell Office for Research on Evaluation 2009 report, led by William Trochim, presents a comprehensive, step-by-step guide to planning an evaluation. Hargreaves 2010 provides quantitative and qualitative research and evaluation methods that are appropriate under different system dynamics. Best, et al. 2003 explores the role of theory and its practical contribution to understanding complex interventions and guiding evaluation. Examples of the types of indicators evaluators use to study and assess system change can be found in Huz, et al. 1997.

Knowledge Translation, Dissemination, and Implementation

Application of complexity and systems thinking perspectives in public health to knowledge translation, dissemination, and implementation (KTDI) offers a practical case study of the utility of the models and tools described in other sections. KTDI has grown significantly in the early 21st century, integrating systems thinking to an ever-increasing extent. However, it has also been plagued by the same key challenge encountered by complexity and systems thinking in general: the critical need to develop a common language and logic for understanding core principles and how best to apply them. This section includes references that illustrate the application of systems thinking to conceptual models for KTDI. Best and Holmes 2010 shows how an increased focus on translation of knowledge, to action in particular, has evolved. Elliott, et al. 2003; Graham, et al. 2006; Greenhalgh, et al. 2004; Brownson, et al. 2009; and Wandersman, et al. 2008 provide a sampling of models that address complexity issues. There is no “one size fits all” model for applying systems thinking to KTDI; this sampling is designed to give useful examples that can be selected or tailored for particular situations. Ward, et al. 2009 presents a way to organize this tailoring process by synthesizing several published models. Finally, Greenhalgh, et al. 2009 offers an outstanding illustration of how models like these can guide evaluation.

LAST MODIFIED: 10/25/2012

DOI: 10.1093/OBO/9780199756797-0049

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