Systems science

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Systems science describes the use of systems science methodologies in complex health problems. For other users, you may find information from the following links:


Introduction

Approximately 50% of premature deaths and 70% of chronic illnesses in U.S. are preventable by changing behavioral risk factors and the related social and physical systems necessary to achieve and sustain those changes (Mokdad et al., 2004). Indeed chronic diseases, which are reaching epidemic proportions worldwide with 80% of chronic disease deaths occurring in low- and middle-income countries, are largely preventable (Jamison, et al., 2006). Globally, up to 80% of premature deaths from heart disease, stroke and diabetes can be averted with known behavioral and pharmaceutical interventions (WHO, 2005). There is a growing recognition that most major threats to the public’s health - including cardiovascular disease, pulmonary disease, cancer, diabetes, mental health problems, HIV, substance abuse, violence, emerging infectious diseases, obesity, sedentary lifestyle, poor diet, sleep disorders, and more—are complex in the sense that each one arises from an intricate mix of behavioral, economic and social factors interacting with biological factors, as well as each other, over the lifespan and across an array of settings (e.g., home, school, workplace, neighborhood, etc.). For example, tobacco use and successful cessation are influenced by host of interrelated factors, including: the tobacco product itself (e.g., percent free-base nicotine content, presence or absence of menthol/other flavoring, and other product constituents), the person (e.g., genetic predisposition), influences on the person (peer influence, media exposure - both tobacco promotion and health messages, cultural norms, prior tobacco exposure, availability and usage of pharmacotherapy, history of quit attempts, presence of workplace smoking bans), and the tobacco industry (product design, marketing, pricing; for a discussion of the myriad of factors in tobacco control, see NCI, 2007).

Most of the problems related to human health and disease are dynamically and relationally complex. Such problems have typically been approached using correlation-based analytic methods (e.g., regression), which are useful for identifying linear relationships but are limited because of their inability to set up and test a web of causal relationships. While such methods can be valuable in providing detailed information about various aspects of the problem, used alone they are insufficient for addressing complex problems that are dynamic (i.e., change over time) and complex in terms of the large number of relationships in the system.

Systems science methodologies provide a way to address complex problems, while taking into account the “big picture” and context of such problems. These methods enable investigators to examine the dynamic interrelationships of variables at multiple levels of analysis (e.g., from cells to society) simultaneously (often through causal feedback processes), while also studying the impact on the behavior of the system as a whole over time (Midgely, 2003). They are also useful for making implicit assumptions about complex phenomena explicit, which exposes gaps in knowledge about the problem. Moreover, simulation modeling can be used to generate “alternative futures” allowing decision makers (e.g., policy makers) to simulate the impact of various policy decisions and how they play out over time before actually putting them into practice (Sterman, 2006). For example, insights gained by the use of simulation models can help policy makers choose the most effective option among competing strategies when resources for combating the problem are limited. Systems science methodologies are also extremely useful for understanding why programs and interventions fail to have their intended effects (and in the worst cases magnify the problem; Sterman, 2000).

Systems science methodologies can also be used to refine and reform systems of care to enable planners to identify impediments to implementing proven innovations in everyday treatment and prevention practice. Dynamic models can facilitate the adoption of proven new therapeutic and business practices to ensure effective interface within existing complex systems of care. Decision tools and models can be developed to discover unanticipated effects of change on barriers to treatment and prevention services access, gaps in resource allocation, new training requirements, insufficient inter-organizational linkages, and numerous other factors affecting healthcare systems improvements.

Specific examples of systems science methodologies include, but are not limited to: systems dynamics modeling (Sterman, 2000), agent based modeling (Epstein, 2006), discrete event simulation (Banks et al., 2005), network analysis (Wasserman & Faust, 1994; Scott, 2000), dynamic microsimulation modeling (e.g., Mitton, Sutherland & Weeks, 2000), and Markov modeling (Sonnenberg & Beck, 1993). These techniques (among others) are particularly well-suited for understanding connections between a system’s structure and its behavior over time; anticipating a range of plausible futures based on explicit scenarios for action or inaction in certain areas; identifying unintended or counter-intuitive consequences of interventions; evaluating both the short- and long-term effects of policy options; and guiding investments in new research or data collection to address critical information needs. Such tools have proven heuristic power, typically integrating data from multiple prior studies and surveillance systems, and can offer innovative solutions to seemingly intractable problems. For example, systems modeling can enhance decision making and policy decisions by showing how to strike a more effective balance between treatment and prevention approaches.

Many system modeling methodologies are not new and indeed are now used routinely in fields such as corporate management, economics, engineering, physics, energy, ecology, biology, and others precisely because these methods add value compared with alternative techniques or unaided decision-making. System-oriented methods have been slower to diffuse in health-related behavioral and social science. Not surprisingly, as the appreciation for the complexity of many problems in the public health sphere has grown, there have been calls recently to address public health problems with systems science (Gerberding, 2007; Homer & Hirsch, 2006; Mabry, et al. 2008; Madon, et al., 2007; Milstein, 2008). For instance, systems science methodologies have already begun to be employed for planning and preparing against acute threats to public health (Lasker, 2004) such as global spread of a pandemic flu (Germann, Kadau, Longini, & Macken, 2006).

About Policy Resistance

Why do some public health problems seem intractable and resistant to the effects of planned interventions? Numerous well-known initiatives have greatly improved population health. But there are many other situations where health and social policy ventures ultimately were unsuccessful or made matters worse.

Typically labeled unintended consequences, side-effects (i.e., a secondary and usually adverse effect), or short-term results, our encounters with unsuccessful or less than fully successful interventions are rarely studied systematically. Those experiences cut across diverse content areas and their patterns and precursors are not well defined. Moreover, this phenomenon is not unique to the field of population health. The experience is so common, it has been given the generic name "policy resistance," defined as follows:

Policy resistance is the tendency for a policy, program, or an intervention to be delayed, diluted, or defeated by the responses of people, organizations, or the natural environment to the intervention itself. (adapted from Meadows, 1982).

(Note that policy resistance refers to “policy” in the broad sense to include policies as well as the interventions and programs that flow from them). Responses to significant health-related problems often fail to recognize and/or address complex conditions that prevail in neighborhoods across the nation, or in the health protection system at large. Thus it is increasingly important that we understand policy resistance where it affects the public's health.

Real-world examples of policy resistance operating in the public health domain

1) Low tar/nicotine cigarettes were designed by cigarette manufacturers, and promoted by public health officials as a way to reduce the health hazards associated with smoking. Consumers compensated by smoking more of these “light” cigarettes per day than the regular kind, took longer, more frequent drags, and held the smoke in their lungs longer. These changes in consumer behavior defeated the product’s design so that ultimately the health threat to smokers remained (adapted from Sterman, 2006; for expanded treatment of this issue see NCI, 2001).

2) Supported by scientific studies, and promoted by the federal government, the food industry, health practitioners and the popular media, low fat diets became very popular in the 1980’s and ‘90’s. However this significant shift in dietary intake did not have the desired effect on the national trends of overweight and obesity. Despite their increasing intake of low-fat food items, Americans grew heavier over the ensuing decades, not thinner, and now population rates of overweight and obesity are at or near historic highs. No one had predicted that the food industry would produce such an array of low-fat but relatively high calorie foods, nor that Americans would overindulge in them. Evidence is mixed about whether the introduction of lower-fat foods actually caused people to gain more weight (i.e., by increasing overall caloric intake). But one thing seems certain, the policy of introducing these novel foods was not effective enough to stem the rising tide overweight and obesity across the country (for full treatment of this subject, see La Berge, 2008).

3) Highly active antiretroviral therapy (HAART) works well to reduce mortality among those living with HIV/AIDS. But since the advent of HAART in 1996, high-risk sexual behaviors among men who have sex with men have markedly increased, further increasing this group’s risk of contracting HIV/AIDS. Paradoxically, some part of this increase in risk-taking behavior may have been triggered by the availability of the new therapy itself by conveying an optimism that HIV is not as devastating a diagnosis as it once was (see Sullivan, Drake, & Sanchez, 2007 for an extensive list of references). While the causes of this observed behavior change are likely multiply-determined (Elford, 2006), the post-HAART increase in high-risk sexual behavior was somewhat unanticipated and has contributed to a rebound in HIV incidence and the proliferation of multiply-resistant strains of HIV. In some geographic populations this increase in risky behavior appears to be significant enough to offset the beneficial effects afforded by HAART (e.g., Bezemer et al, 2008). (This example was adapted from Sterman, 2006; see also Imrie, et al., 2007).

See also

References

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Elford J. (2006). Changing patterns of sexual behaviour in the era of highly active antiretroviral therapy. Current Opinion in Infect Disease;19(1):26-32.

Epstein, J. M. (2006). Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton, N.J.: Princeton University Press.

Gerberding, J. (2007). Health Protectionomics: A New Science of People, Policy and Politics. , Public Health Grand Rounds, September 19, 2007. George Washington University School of Public Health and Health Services: Washington, D.C. Retrieved July 14, 2008 from http://www.kaisernetwork.org/health_cast/hcast_index.cfm?display=detail&hc=2349 click on “video” or “transcripts”.

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