Jack Homer

System Dynamics Modeling for Public Health: Background and Opportunities

The systems modeling methodology of system dynamics is well suited to address the dynamic complexity that characterizes many public health issues. The system dynamics approach involves the development of computer simulation models that portray processes of accumulation and feedback and that may be tested systematically to find effective policies for overcoming policy resistance. System dynamics modeling of chronic disease prevention should seek to incorporate all the basic elements of a modern ecological approach, including disease outcomes, health and risk behaviors, environmental factors, and health-related resources and delivery systems. System dynamics shows promise as a means of modeling multiple interacting diseases and risks, the interaction of delivery systems and diseased populations, and matters of national and state policy.

Understanding Diabetes Population Dynamics Through Simulation Modeling and Experimentation

Health planners in the Division of Diabetes Translation and others from the National Center for Chronic Disease Prevention and Health Promotion of the Centers for Disease Control and Prevention used system dynamics simulation modeling to gain a better understanding of diabetes population dynamics and to explore implications for public health strategy. A model was developed to explain the growth of diabetes since 1980 and portray possible futures through 2050. The model simulations suggest characteristic dynamics of the diabetes population, including unintended increases in diabetes prevalence due to diabetes control, the inability of diabetes control efforts alone to reduce diabetes-related deaths in the long term, and significant delays between primary prevention efforts and downstream improvements in diabetes outcomes.

Why we iterate: scientific modeling in theory and practice

An approach to system dynamics modeling is advocated that adheres to the scientific method, and that may be applied regardless of model scope or size. Scientific modeling is distinguished from other approaches largely by the quality of evaluation and revision performed and by an insistence upon empirical evidence to support hypotheses and formulations. Three case studies drawn from the authors experience are presented. Practical lessons for scientific modeling are given to help guide expectations and maximize effectiveness of the approach. Modelers and clients should clearly understand the level of rigor they wish to pursue and what this means for the degree of confidence that may be placed in model results and insights.

Analyzing National Health Reform Strategies With a Dynamic Simulation Model

Proposals to improve the US health system are commonly supported by models that have only a few variables and overlook certain processes that may delay, dilute, or defeat intervention effects. We use an evidence-based dynamic simulation model with a broad national scope to analyze 5 policy proposals. Our results suggest that expanding insurance coverage and improving health care quality would likely improve health status but would also raise costs and worsen health inequity, whereas a strategy that also strengthens primary care capacity and emphasizes health protection would improve health status, reduce inequities, and lower costs. A software interface allows diverse stakeholders to interact with the model through a policy simulation game called HealthBound.

A System Dynamics Model for Planning Cardiovascular Disease Interventions

Planning programs for the prevention and treatment of cardiovascular disease (CVD) is a challenge to every community that wants to make the best use of its limited resources. Selecting programs that provide the greatest impact is difficult because of the complex set of causal pathways and delays that link risk factors to CVD. We describe a system dynamics simulation model developed for a county health department that incorporates and tracks the effects of those risk factors over time on both first-time and recurrent events. We also describe how the model was used to evaluate the potential impacts of various intervention strategies for reducing the countys CVD burden and present the results of those policy tests.

Why Behavioral And Environmental Interventions Are Needed To Improve Health At Lower Cost

We used a dynamic simulation model of the US health system to test three proposed strategies to reduce deaths and improve the cost-effectiveness of interventions: expanding health insurance coverage, delivering better preventive and chronic care, and protecting health by enabling healthier behavior and improving environmental conditions. We found that each alone could save lives and provide good economic value, but they are likely to be more effective in combination. Although coverage and care save lives quickly, they tend to increase costs. The impact of protection grows more gradually, but it is a critical ingredient over time for lowering both the number of deaths and reducing costs. Only protection slows the growth in the prevalence of disease and injury and thereby alleviates rather than exacerbates demand on limited primary care capacity. When added to a simulated scenario with coverage and care, protection could save 90 percent more lives and reduce costs by 30 percent in year 10; by year 25, that same investment in protection could save about 140 percent more lives and reduce costs by 62 percent.

Structure, data, and compelling conclusions: notes from the field†

Some system dynamics models are more effective than others in changing the thinking and actions of their audiences. In my experience, the models that prove most compelling to clients generally have two things in common: a potent stock and flow structure and a rich fabric of numerical data for calibrating that structure. Stock and flow structures focus attention on the intrinsic momentum of a situation and allow one to track movements of people and things in a clear and systematic way. Numerical data not only help to build a client’s confidence in a model, but also can materially affect the final structure and key parameter values of a model. Three examples are presented that demonstrate the strong inferences one may draw when stock and flow structures are combined with sufficient numerical data. System dynamics models should be built on a foundation of straightforward core structures and the full range of available evidence.

A systems view of the smoking problem : perspective and limitations of the role of science in decision-making

The complex issues and relationships surrounding the smoking problem indicate the desirability of a system dynamics computer simulation model for policy development and analysis. This paper describes an initial model-building effort, including reports of initial policy and sensitivity testing of the model. The lack of scientific research on most of the relationships and parameters required in such a model forced heavy reliance upon intuition in the model development. The sensitivity of simulated model outcomes to many of these assumptions demonstrates the need for a more concentrated multi-disciplinary research effort if forecasting and policy determination are to be carried out with confidence.

Macro- and micro-modeling of field service dynamics

A system dynamics model to investigate field service issues was developed for a major producer of equipment for semiconductor manufacturing. This strategic model has a broad scope and multi-year time horizon, and treats variables in an aggregate and deterministic way that is typical for such models. The high-level approach is adequate in most respects, but lacks the detail necessary to resolve a key issue regarding the impact of product crosstraining on service readiness. As a result, it proved useful to supplement the strategic ‘macro’ model with a ‘micro’, OR-type model that portrays the daily queuing and assignment of service jobs. The micro model provides detailed what-if results that were used for calibrating the strategic model and may also be used for making tactical manpower decisions at the local level. Traditional OR tools may have a role to play in supporting strategic modeling eAorts when important operations-level relationships are not adequately understood. Copyright * c 1999 John Wiley & Sons, Ltd. Syst. Dyn. Rev. 15, 139‐162, (1999) A system dynamics modeling project was undertaken by a major producer of diagnostic equipment used in semiconductor wafer fabrication. The company’s many products include both simple machines requiring little maintenance and complex systems requiring more frequent maintenance. All products come with an initial warranty on parts and service, after which the customer has the option of continuing with a service contract. Customers often select service contracts for the more complex and essential pieces of equipment, for which downtime must be minimized and do-it-yourself repairs are diAcult at best. The company’s new product sales have grown rapidly over the last several years, though they do follow the ups and downs of the semiconductor industry’s persistent two-year business cycle. This overall growth in sales has led to robust growth in the installed base of equipment and similar expansion of the workforce of field service engineers. Although field service does not generate much for the company in the way of profits, it is nearly as important as product performance and competitive price are for the company’s continued success in the marketplace. As the field service workforce has grown, its planning, organization, and management have taken on increasing complexity and significance for the company. (Richmond 1994 describes the inevitable evolution of a high-tech manufacturer from a primary sales focus to an increasing field-service focus). For example, while the workforce has always been segmented by local territory or hub, it has only recently been segmented by customer account as well. This action was taken so that major customers

Using simulation to compare established and emerging interventions to reduce cardiovascular disease risk in the United States.

Introduction Computer simulation offers the ability to compare diverse interventions for reducing cardiovascular disease risks in a controlled and systematic way that cannot be done in the real world. Methods We used the Prevention Impacts Simulation Model (PRISM) to analyze the effect of 50 intervention levers, grouped into 6 (2 x 3) clusters on the basis of whether they were established or emerging and whether they acted in the policy domains of care (clinical, mental health, and behavioral services), air (smoking, secondhand smoke, and air pollution), or lifestyle (nutrition and physical activity). Uncertainty ranges were established through probabilistic sensitivity analysis. Results Results indicate that by 2040, all 6 intervention clusters combined could result in cumulative reductions of 49% to 54% in the cardiovascular risk-related death rate and of 13% to 21% in risk factor-attributable costs. A majority of the death reduction would come from Established interventions, but Emerging interventions would also contribute strongly. A slim majority of the cost reduction would come from Emerging interventions. Conclusion PRISM allows public health officials to examine the potential influence of different types of interventions — both established and emerging — for reducing cardiovascular risks. Our modeling suggests that established interventions could still contribute much to reducing deaths and costs, especially through greater use of well-known approaches to preventive and acute clinical care, whereas emerging interventions have the potential to contribute significantly, especially through certain types of preventive care and improved nutrition.