There is growing recognition that the solutions to the most vexing public health problems are likely to be those that embrace the behavioral and social sciences as key players. To address this recognition, in 2007 OBSSR adopted a new strategic prospectus¹ to guide future priorities in the behavioral and social sciences at NIH. At the core of OBSSR's vision is a vertical integration across the levels of scientific analysis, that is, a transdisciplinary integration of the biomedical paradigms of molecular and physiological causal mechanisms with the ecologic paradigms of multilevel (individual, group, community, societal, and global) “causes of the causes” of health and disease.^2,3

A note on terminology: As described by Stokols et al.,⁴

Population and biological sciences identified tobacco-use behavior as the primary cause of most lung cancers and a leading cause of many other diseases, including cardiovascular disease. Behavioral and social sciences research informed the smoking interventions (individual, community, and policy level) that have spurred a dramatic reduction in U.S. tobacco use since its peak in the 1960s. In fact, the past decade witnessed a decline in overall cancer death rates for the first time in a century,⁷ driven largely by the dramatic reduction in male smoking rates, from 54.1% at their peak in 1965⁸ to 23.9% today.⁹ Within the relatively short time span of 40 years, more than 45.7 million Americans have stopped smoking.⁹ This is arguably one of the most successful public health interventions in recorded history,^10,11 and it has reduced the burden of many other diseases and excess societal expense as well. Behavioral and social sciences research can take much of the credit for this. Such research also has been at the center of understanding the multiple determinants of smoking initiation and cessation. Findings from behavioral and social sciences research have informed a broad spectrum of approaches (e.g., policy, cessation and prevention programs, communication of the risks associated with tobacco use). Of these, policy interventions (e.g., smoking bans, cigarette taxes) have been found to be among the most effective strategies for reducing smoking prevalence. (For in-depth treatment of this topic, see Ending the Tobacco Problem: A Blueprint for the Nation.¹²) Because of behavioral and social sciences research, tobacco use has been changed on a massive scale despite the highly addictive nature of nicotine.

Another achievement of behavioral and social sciences research is the landmark NIH Diabetes Prevention Program (DPP), which showed that lifestyle changes (i.e., alterations in dietary intake and physical activity that led to a reduction in body weight) were nearly twice as effective as a common medication in reducing the risk of developing type 2 diabetes.¹³ An interdisciplinary effort to harness the power of the DPP intervention trial, together with lessons learned from tobacco control (especially around policy interventions), could help reverse the obesity and type 2 diabetes epidemics sweeping the developed world, and perhaps do so in less time than it took to cut smoking prevalence in half.

Research in the behavioral and social sciences has also spawned great progress in the development of effective treatments for the mental illnesses and disorders that are the leading contributors to disability. Meta-analyses show that cognitive–behavioral therapy is effective for unipolar depression, generalized anxiety disorder, panic disorder with or without agoraphobia, social phobia, posttraumatic stress disorder, childhood depressive and anxiety disorders, marital distress, anger, childhood somatic disorders, and chronic pain.¹⁴ Moreover, cognitive–behavioral therapy is superior to antidepressants in the treatment of adult depression.¹⁴ Finally, while a combination of cognitive–behavioral therapy and fluoxetine has been shown to be equal to fluoxetine alone in alleviating moderate-to-severe depression in adolescents, adding cognitive–behavioral therapy improves the safety of the medication by reducing suicidal ideation and events.¹⁵

Another major public health success to which behavioral and social sciences research on decision making, drug abuse, and sexual behaviors has made a significant contribution is the mitigation of the spread of HIV/AIDS.^16,17 As people have reduced their frequency of risky behaviors and new medications have become available, new AIDS cases in the U.S. have been cut almost in half, from a peak in 1992 of over 78,000 to approximately 40,000/year since 1998.¹⁸ The contributions from behavioral and social sciences research along with the development of effective pharmacotherapies have changed HIV from an imminent death sentence to a treatable, chronic disease. But for medications to be successful, they must be taken on a regular basis, and behavioral and social sciences research has contributed to significant, albeit modest, improvements in adherence.¹⁹ An effective partnership between the behavioral and social sciences and the biomedical sciences is at the core of the progress being made in the fight against HIV/AIDS worldwide.

The OBSSR at NIH has historically embraced a biopsychosocial perspective on the causes and correlates of health and illness.^23,24 Extending the biopsychosocial model, Glass and McAtee²⁰ provide an even stronger rationale for OBSSR's taking an interdisicplinary and systems science perspective to improve understanding of the forces that determine optimal health promotion and prevention, reduced disease burden, and improved chronic disease management across the human lifespan and across generations.

Interdisciplinarity is an explicit, programmatic theme within the OBSSR strategic prospectus that, in fact, pervades all other themes. A number of other cross-cutting themes also underlie OBSSR's programmatic directions. These themes include: (1) the elimination of health disparities²²; (2) the strengthening of the science of dissemination (the quest for scientific evidence to determine the most effective ways to translate findings from basic research and clinical trials performed under ideal conditions to the successful widespread adoption and implementation by all target audiences and in national health policy)^27,28; (3) capitalizing on recent advances in informatics, communications, imaging, sensor technology, and data-visualization techniques that aid data analysis and interpretation²⁹; and (4) investigating commonality among theories and mechanisms of behavior change and sustained maintenance of change. Another goal of OBSSR is to enhance the interdisciplinary training of the current and next generation of behavioral and social scientists.

A critical milestone for enhancing interdisciplinary science and systems science is the rapid deployment of various components of cyber-infrastructure, making connectivity possible from the local to the global scale.^29,30 The National Science Foundation's landmark Atkins report³⁰ enumerates the potential and the critical base technologies underlying cyber-infrastructure, including the integrated electro-optical components of computation, storage, and communication that continue to advance in raw capacity at exponential rates. Above the cyber-infrastructure layer are the software programs, services, instruments, data, information, knowledge, and social practices applicable to specific projects, disciplines, and communities of practice. Between these two layers is the cyber-infrastructure layer of enabling hardware, algorithms, software, communications, institutions, and personnel. This layer should provide an effective and efficient platform for the empowerment of specific communities of researchers to innovate and eventually revolutionize what they do, how they do it, and who participates.

At the same time, OBSSR recognizes that behavioral factors and social conditions have profound effects on the development and progression of common chronic diseases, premature disability, and mortality. Humans are both agents of change and affected by the process of change over time. This reciprocal determinism³¹ is a dynamic process and is often nonlinear, multi-determined, and multilevel in nature. Patterns of behavior, exposures to pathogens, and the social and physical built environments are rapidly changing as a result of human agency. For example, tobacco use, diet, physical activity, obesity, and HIV/AIDS have all changed dramatically within the relatively short period of 1 or 2 decades during the 20th Century. Many changes in lifestyle and living conditions have had large impacts on subgroups of the population and on the absolute rates of disease burden within the whole population. On the positive side, from 1900 to 2004, the U.S. population witnessed a dramatic increase in life expectancy, from 47.3 years to 77.8 years, due primarily to changes in life circumstances and, more recently, due to improvements in health care.³² On the negative side, between 1976–1980 and 2003–2004, the prevalence of obesity—a risk factor for type 2 diabetes, heart disease, cancer, and other serious health problems—rose dramatically. Obesity more than doubled in adults (from 15% to 34%), while overweight in children also more than doubled during this period (from 7% to 19% in ages 6–11), and overweight more than tripled in adolescents (from 5% to 17% in ages 12–19).³² Moreover, persistent problems like tobacco use and disparities in health remain as leading causes of preventable disease burden, disability, and death.

An enormous scientific challenge now presents itself: What are the best ways to understand, prevent, and treat common, chronic diseases like heart disease, cancer, addiction, and mental illness when it is apparent that they are the result of interactions between individuals—in all their biological complexity—and their ever-changing physical, behavioral, and social environments? To maximally improve population health, the individual's genome and biology must be viewed in its much broader environment. Human genetic sequences are static, but the functional expression of that DNA sequence is influenced by the environment. To begin unraveling this complexity, NIH launched its Genes, Environment and Health initiative³³ and the Genetic Association Information Network.³⁴ These trans-NIH efforts seek to identify how gene–environment interactions contribute to common diseases by supporting genomewide association studies to link particular genetic variants to specific diseases and the development of environmental and biomarker-sensor technologies to measure behavioral and chemical exposures.

These activities are an excellent start, but significant challenges remain. The massive amounts of genetic and exposure data that will be collected will make sense only with improved basic behavioral and social sciences research, which can address questions such as these: How should statistical power calculations and the interpretation of significant versus spurious associations be handled when so many variables can now be explored simultaneously? What is the best way to measure human phenotypes and the intermediate phenotypes that underlie complex clinical disease categories? What are the health-relevant physical, behavioral, and social environments, and how should these environmental exposures be measured over an entire lifespan? How can true gene–environment interactions be captured, and what are the mechanisms underlying these interactions?³⁵How might environments be changed so that they foster, instead of assail, health?

The above considerations, as well as others, have led OBSSR to the following research priority areas in next-generation basic behavioral and social sciences research:

Research on stress and cancer is an excellent example of interdisciplinary research involving the behavioral and biomedical sciences. Antoni et al.⁴¹ recently integrated a number of biomedical, behavioral, and clinical studies into a proposed mechanistic cascade underlying the links among behavior, biology, and cancer. Evidence is accumulating to suggest that stress, depression, and lack of social support influence the risk of cancer. For example, the breakup of a marriage has been associated with a twofold increase in the risk of breast cancer,⁴² and long-term chronic depression appears to increase general cancer risks.^43,44 Basic research in physiology established a long time ago that the stress response is characterized by the activation of the sympatho-adrenal system, which releases the catecholamines, epinephrine, and norepinephrine, and the HPA axis, which releases glucocorticoids. More recently, animal models have shown that catecholamines, glucocorticoids, and other stress hormones influence multiple aspects of the tumor microenvironment, including: (1) the alteration of numerous aspects of immune function, (2) the promotion of tumor cell growth, (3) the migration and invasive capacity of cancer cells, (4) the stimulation of angiogenesis by the induction of pro-angiogenic cytokine production, and (5) the activation of oncogenic viruses (Figure 2).⁴¹

The advantages of utilizing systems science as a complementary method for addressing complex problems include the fact that nonlinear relationships, the unintended effects of intervening in the system, and time-delayed effects are often missed with traditional reductionist approaches, whereas systems approaches excel at detecting these. The common conceptual orientation that defines a systems approach can be summarized as follows:

Systems science is not a single discipline; rather, it is a linkage of disciplines to bring about problem understanding and solving under the paradigm described above.

Systems science does not refer to a single methodology; rather, it encompasses a wide range of methods and tools (e.g., system dynamics simulation, agent-based modeling, network analysis, Markov modeling, soft-systems analysis, discrete-event modeling). While technology is used to maximize the effectiveness of systems approaches, systems science is not a technology. For an in-depth introduction to this topic, readers are encouraged to view webcasts of the 2007 Symposia Series on Systems Science and Health.⁴⁷

By embracing systems science, the research community will be better equipped to handle the policy-resistant problems that abound in public health. Policy resistance refers to the “tendency for interventions to be defeated by the system's response to the intervention itself.” ²¹ In the last decades of the 20th Century, almost in parallel to the developments that spawned systems biology, the social–ecologic model emerged as a dominant world view in searching for explanations of the broader population-level causes of the very same common, chronic diseases that are the focus of biomedicine today.^48–51

Other troubling causes of poor health and shortened life expectancy, such as access to care and disparities and inequality in healthcare delivery, have also been studied. The population, behavioral, and social sciences advanced beyond single discipline and simple causal views toward another valid systems view of understanding health and disease. In this world view, human behavior can be broadly defined as hierarchically organized along levels of complexity, from individual behavior to collective behavioral patterns within groups to higher levels of the clustering of patterns of behavior that are embodied in neighborhoods, worksites, schools, communities, cultural, ethnic, or religious affiliations, to even broader patterns determined by societal norms, financial incentives, and policies. These higher-order levels of factors interact in complex, dynamic, and multifactorial ways to produce the so-called “causes of the causes” of the complex common, chronic diseases.² In this ecologic perspective, the view of the ultimate “causes of the causes” lies as much in the behavioral–social–ecologic environment as it 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” integration of the three broad disciplinary domains: the largely biomedical sciences, the largely individual behavioral 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 integration, that is, integration across rather than within the three broad domains (i.e., the biomedical; the individual behavioral [intra-individual variation]; and the population [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 breakthroughs 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 relationships within and across nations. The specific objectives for OBSSR with regard to systems science are:

Bringing systems science to bear on public health problems has the potential to explain how small changes at the individual level accumulate at the population 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 components of the system (e.g., blood clotting and scab formation emerge at the systems level from the behavior of individual cells). Because of its unique ability to consider simultaneously both the whole system and its individual parts, systems science is capable of producing 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 approaches 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 transforming 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 prediabetes, 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 system); 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., clinical 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 management; and (3) prevent diabetes (through the prevention of obesity). These three hypothetical scenarios are compared to “baseline,” a predictive model in which the status quo of diabetes clinical practices and prevention activities is maintained at baseline levels.

The following outcomes were predicted under each of the three scenarios:

However, even this powerful step alone (i.e., reducing rates of obesity without concurrent changes in prediabetes management or clinical diabetes management) would not reduce the overall burden of diabetes in terms of both the number of unhealthy days (not pictured) and the number of deaths due to diabetes right away (Figure 4). In fact, the number of deaths attributable to diabetes would actually rise through at least the year 2020, although during subsequent decades, a significant decrease in diabetes prevalence and deaths would occur. Thus, the time perspective is vital to determining the value of a strategy—that is, disease management works in the short term, but primary prevention is more effective in the long term. This example illustrates the potential of systems science to inform healthcare and policy decisions to improve population health.

In another example of adopting a systems approach to improving the understanding of a public health problem, Levy and colleagues developed SimSmoke,⁵⁹ a simulation model for guiding policy to make a population impact on reducing smoking prevalence. SimSmoke uses historical and current data to model the multiple sources and complex interrelationships that determine tobacco-use prevalence and its health effects. A discrete-time dynamic model was developed that simulated smoking prevalence and tobacco-related deaths over a 40-year period. The model employed a first-order Markov process that modeled population growth and age-based rates of tobacco initiation, cessation, and relapse. This model simulated the impact of five policy-level interventions on smoking prevalence: taxes, clean indoor-air laws, strategies to reduce youth access to cigarettes, strategies to promote cessation treatments, and mass-media policies. Researchers used empirical and predicted data for the effects of each of these areas on model parameters. SimSmoke showed the relative contributions made by a variety of different policy interventions (i.e., increasing cigarette prices, introducing smoking bans, introducing media campaigns to encourage cessation and prevention, implementing additional restrictions on youth access to tobacco, and introducing proactive quitlines) toward the desired outcomes (i.e., reduction in smoking prevalence and reduction in deaths attributable to tobacco). Such models can be used to inform decisions about how best to allocate financial resources and formulate policies to optimize a desired public health impact. The focus is on making an efficient population impact to address a complex societal problem (tobacco-use behavior) with an emphasis on outcomes and on multiple causal pathways, feedback loops, and control-systems dynamics that underlie the way the tobacco industry and the public health constituencies vie for their respective goals.

The above examples illustrate the potential for systems science to radically transform the behavioral, social, and population sciences to a degree similar in magnitude to the transformation that systems biology and bioinformatics are now bringing about in biology. This sentiment is captured in the broad vision for cyber-infrastructure outlined in the Atkins report of the National Science Foundation³⁰:

A variety of research disciplines, including social psychology, nutrition science, marketing, education, primary care medicine, policy, and tobacco control were brought together to design this multilevel intervention. The results were impressive: By the early 2000s, the number of deaths of working-age Finnish men from coronary heart disease had plummeted 75%. In North Karelia, the effects were even more pronounced (an 82% reduction in deaths), and life expectancy for men increased 7 years. Much of this reduction in mortality came from reductions in risk factors like high blood pressure, high cholesterol, and smoking, because of nutritional changes and smoking cessation. Today, this project continues to sustain itself with a modest level of public resources.

Another problem with tremendous population impact is that of health disparities. If this problem were widely addressed, enormous benefit could be conferred on those affected by these inequalities. Transdisciplinary and systems science perspectives may be valuable approaches for addressing health disparities and inequality.²² These approaches permit researchers in the field to step back and consider the ways that their science has been framed by historical, disciplinary perspectives (i.e., a focus on intra-individual, molecular, genetic “causes” within biomedical frames of reference versus a focus on the socioeconomic forces and the levels of socio-environmental context, such as social position and poverty, as the “causes of the causes” within ecologic frameworks). For example, a recent study used county-level geospatial and racial-group coding to categorize into clusters the population of the U.S. according to expected longevity; these clusters are called the “Eight Americas.”⁶¹ An incredible gap of 35 years of life expectancy was reported between the highest and lowest life-expectancy ranks among the eight clusters. The lowest cluster is grouped among nations of the world with the lowest life expectancy (sub-Saharan Africa and Russia) and can be viewed as excluded from the gains made in average life expectancy in the U.S. during the entire 20th Century. Life expectancy in the cluster at the high end of the Eight Americas exceeds that of nations whose life expectancy is the highest in the world (3 years better than Japan for females and 4 years better than Iceland for males).

Abrams²² suggests a new framework for integrating historically disparate frames of reference from individual and population sciences into a new synthesis. This framework would embrace a model of genes and the social and physical environments in a complex, nonlinear, reciprocal interaction of risk and protective factors, over the lifespan and across generations.^2,20,39,62 Interdisciplinary research and systems science can perhaps clarify the extent to which gene–environment interactions account for racial and ethnic health disparities and improve the development of effective interventions and policies to eliminate those disparities.⁶³

Interdisciplinary research, one of the components of the Research Teams of the Future theme, has included several initiatives specifically targeting the behavioral and social sciences. The OBSSR-led initiatives Supplements for Methodological Innovations in the Behavioral and Social Sciences (RFA RM-04-013)⁶⁴ and Meetings and Networks for Methodological Development in Interdisciplinary Research (RFA RM-04-014)⁶⁵ supported research on dietary intake, physical activity, child development, stress–immune interactions, environmental exposures, treatment decision making, patient quality of life, gene–environment interactions, pain, and aging. Seven postdoctoral institutional-training grants were awarded under another NIH Roadmap initiative, Interdisciplinary Health Research Training: Behavior, Environment and Biology (RFA RM-05-010).⁶⁶ These programs provide formal coursework and research training in a new interdisciplinary field for individuals holding advanced degrees in a different discipline. The Exploratory Centers for Interdisciplinary Research (RFA RM-04-004)⁶⁷ program is supporting the centers that are investigating cognition, elder self-neglect, or youth vulnerability to sexually transmitted infections and unintended pregnancies. Another center focuses on the pathways through which the environment, genetic, and psychosocial domains jointly shape child health and well-being. A 2007 initiative, Facilitating Interdisciplinary Research via Methodological and Technological Innovation in the Behavioral and Social Sciences (RFA RM-07-004),⁶⁸ supports the development of new and innovative measures, methods, and technologies that underlie the interdisciplinary integration of human social science, behavioral science, or both, with other disciplines across varying levels of analysis. Links to descriptions of the projects funded under these and other interdisciplinary NIH Roadmap initiatives can be found on the NIH Roadmap website (www.nihroadmap.nih.gov/interdisciplinary/fundedresearch.asp).