Environmental Health
Ecological Health Complex Science, New Models, and Public Health Ted Schettler, MD, MPH Many different pathways lead to complex diseases, such as diabetes, cancer, dementia, and cardiovascular disorders. Diverse combinations of risk factors from
multiple levels—cellular, individual, neighborhood, community, ecosystem, and society—determine disease risks in individuals and disease patterns in populations. Dynamic interactions within and across levels are the rule, influenced by time, scale, and proportion. Scientists from many disciplines have long studied this complexity by simplifying it, looking for an effect when selected variables change while others are constant. They have focused mostly on proximal causes of effects rather than causes of causes. This has yielded important insights but has limited predictive and explanatory power. The real world is more complex, and interest in new, system-wide models is growing.
New Models
New models for understanding disease mechanisms are rapidly developing in the field of systems biology. Similarly, epidemiologists see the need for more systems-based models to help clarify causes of disease patterns in populations.1,2 They recognize the importance of a life-course perspective, since early life exposures to environmental chemicals, air pollution, poor nutrition, or chronic stress uniquely influence health status not only in childhood but also decades later. This also sets a context for risks from exposures later in life. Beyond that, relationships among variables in a dynamic, systems-based model can change over time. For example, lack of exercise increases the risk of obesity, but obesity can then influence activity levels. Dynamic complex systems feature feedback loops and nonlinear, emergent behavior, challenging epidemiologists and public health practitioners whose interest is in identifying interventions that will make a difference in health outcomes.
Complexity Science
Some attempts to develop system-wide models draw on complexity science, chaos, and self-organizing system theories.3 These models interpret biologic systems as adaptive and open, relying on a continual flux of matter and energy from the environment in order to stay alive. Interactions within and across levels of organization are richly complex, as in ecologic systems. Nonlinear system behavior is the rule, with changes in the dynamics of feedback loops, the abrupt appearance of thresholds, and newly emergent properties. Chaos theory is sometimes invoked to help explain the behavior of complex systems. In 1961, Edward Lorenz, a meteorologist, used computer modeling to predict weather patterns. He discovered that rounding 0.506127 to 0.506 during a comwww.sfms.org
puter run resulted in large changes in the long-term outcome prediction.4 He showed that small changes in initial conditions can profoundly affect the trajectory of a dynamic, highly-complex system—a consequence that has subsequently been extensively validated. Complex, chaotic systems also exhibit fractal structure— that is, they show self-similarity at progressively smaller scales, such as in the branching of a tree or the respiratory tract. Thus, with close inspection within what appears to be chaotic activity, one can often find repetitive patterns, suggesting behavior on the edge of chaos—far from equilibrium yet not wildly random. Chaos theory leads to the conclusion that the behavior of an open adaptive system will be difficult to quantify, although over time it will lie within what ecologists call a basin of attraction or what Lorenz called an attractor. Here, while dynamic and far from equilibrium, system behavior lies somewhere within a set of boundaries and has certain qualities. In short, although the behavior of a system on the edge of chaos is difficult to predict precisely, it is subject to a probabilistic set of outcomes. Chaos and complexity theories are increasingly used in the natural sciences, business-cycle analysis, and economics. Applications in medicine and public health are in their early stages but are sparked by the need to develop models that reflect a more integrated, dynamic, multilevel understanding of human biology and population health.
Applications of System-Wide Models in Medicine and Public Health
Complex dynamic models have been used primarily in understanding infectious disease processes and patterns. Some analysts have applied chaos theory to the interpretation of electroencephalograms5 and heart function, where heart rate variability often shows fractal patterns, and loss of variability may predict the progression of cardiovascular disease.6,7,8 Obesity: The emergence of obesity as a major public health threat has led to the development of many models intended to explain why this has happened and help prioritize among possible interventions. Most people agree that the trends reflect combined effects of the interaction of multiple factors at many levels—genetic, metabolic, behavioral, psychological, social network-related, built environment-related, institutional, food system-related, and food policy-related. Environmental chemicals, called obesogens, are a relatively recent and potentially important addition to the list.9 The Foresight project in the U.K. developed a causal loop model that incorporates input and interactions among these multilevel factors.10 Although the graphic depiction of the com-
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