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does in the proximal biological environment evident through reductionist approaches.

The implication of these disparate world views of causation (biomedical and ecologic) calls for a broader integration of the disciplines than has occurred to date. OBSSR’s view is that there should be a “macro” inte- gration of the three broad disciplinary domains: the largely biomedical sciences, the largely individual be- havioral sciences, and the largely group or population- level sciences of the ecologic world view.

Recently there has been a call for a new integrative vision among the behavioral, social, and public health sciences that might loosely be termed systems socio- behavioral science, systems medicine, or, as one author has put it, populomics.52 This is being called vertical integra- tion, that is, integration across rather than within the three broad domains (i.e., the biomedical; the individ- ual behavioral [intra-individual variation]; and the pop- ulation [inter-individual or cluster variation] levels) of systems structure.20 The hope is that this type of vertical synthesis across varying levels of analysis will lead to a next generation of science enabling further break- throughs in the understanding and reduction of the burden and suffering of the major common, chronic diseases that afflict the U.S., other developed nations, and, increasingly, the developing nations. OBSSR’s call for systems science is a call for an increasingly global perspective on the interaction, connectivity, and rela- tionships within and across nations. The specific objec- tives for OBSSR with regard to systems science are:

  • To facilitate the development and application of the conceptual frameworks and tools needed for the application of systems methodologies to problems of health and its determinants;

  • To promote and support the development of in- formatics tools to facilitate the collaboration and dis- semination of data relevant to the behavioral, population, and social sciences (e.g., longitudinal epi- genetic, biomarker, social, and behavioral data related to health);

  • To contribute to the development of analytical frameworks, methods, and algorithms capable of integrating, analyzing, and interpreting highly di- verse data with varying metrics from research on genomic sequences, molecules, behavior, and social systems;

  • To collaborate in the development of the curricula, modules, and materials required to train health scientists in the application of systems science; and

  • To encourage the application of systems-organizing principles among stakeholder organizations in be- havioral and social sciences research, and to pro- mote the development of systems-organizing exper- tise among leaders, policymakers, and researchers. Bringing systems science to bear on public health

problems has the potential to explain how small

August 2008

changes at the individual level accumulate at the pop- ulation level to reveal significant shifts in the absolute causes of disease.2,3 System dynamics modeling and agent-based models are methods that can simulate the complex relationships among the components of a system and emergent behavior—that is, behavior that is observed at the bird’s-eye vantage point of the system emerging from the behavior of the individual compo- nents of the system (e.g., blood clotting and scab formation emerge at the systems level from the behav- ior of individual cells). Because of its unique ability to consider simultaneously both the whole system and its individual parts, systems science is capable of produc- ing solutions that take into account a broad range of factors pertinent to the problem under consideration; for instance, genetic-to-environmental–, cellular-to- behavioral–, and biological-to-social–systems approa- ches have proven extremely valuable when applied to problems identified in a variety of disciplines, including defense,53 business,54 and cellular biology.55,56 Systems science shows promise for unlocking the secrets of complex, multidimensional health issues and for trans- forming this knowledge into effective interventions that can fundamentally change population health. 57

An example of applying systems science to public health problems is illustrated by Jones et al.,58 who used system dynamics simulation modeling to explain type 2 diabetes prevalence since 1980 and to predict possible futures through 2050. The conceptual model (Figure 3) divided the U.S. population into those who do not have diabetes (normal glycemic levels); those at high risk for developing type 2 diabetes (i.e., people with prediabe- tes, divided into diagnosed and undiagnosed); and those who meet diagnostic criteria for type 2 diabetes (diagnosed and undiagnosed, subdivided into with and without medical complications from diabetes). The conceptual model included births (entry into the sys- tem); deaths (exit from the system); and individual members’ movements among the diagnostic categories over time (stocks and flows), as well as numerous factors contributing to diabetes outcomes (e.g., clini- cal management of diabetes, self-monitoring, healthy- lifestyle adoption, and medication use).

The relationships among all of these variables were quantified and the model was calibrated and validated in an iterative process using historical data from a variety of sources (e.g., the U.S. Census Bureau, the National Health Interview Survey, the National Health and Nutrition Examination Survey, and the Behavioral Risk Factor Surveillance System).

Simulations were then generated according to a variety of assumptions that were programmed into the model via algorithms. Figure 4 shows the results of the simulated population burden of diabetes (i.e., deaths) under various scenarios where an intervention is introduced that is designed to: (1) improve the clinical management of those diagnosed with diabetes; (2) improve pre-diabetes

Am J Prev Med 2008;35(2S)

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