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Policy Points Interventions in a regional system with intertwined threats and costs should address those threats that have the strongest, quickest, and most pervasive cross-impacts. Instead of focusing on an individual county's apparent shortcomings, a regional intervention portfolio can yield greater results when it is designed to counter those systemic threats, especially poverty and inadequate social support, that most undermine health and well-being virtually everywhere. Likewise, efforts to reduce smoking, addiction, and violent crime and to improve routine care, health insurance, and youth education are important for most counties to unlock both short- and long-term potential. CONTEXT Counties across the United States must contend with multiple, intertwined threats and costs that defy simple solutions. Decision makers face the necessary but difficult task of prioritizing those interventions with the greatest potential to produce equitable health and well-being. METHODS Using County Health Rankings data for a predefined peer group of 39 urban US counties, we performed statistical regressions to identify 37 cross-impacts among 15 threats to health and well-being. Adding appropriate time delays, we then developed a dynamic model of these cross-impacts and simulated each of the counties over 20 years to assess the likely impact of 12 potential interventions-individually and in a combined portfolio-for three outcomes: (1) years of potential life lost, (2) fraction of adults in fair or poor health, and (3) total spending on urgent services. FINDINGS The combined portfolio yielded improvements by year 20 that are considerably greater than those at year 5, indicating that the time delays have a major effect. Despite the wide variation in threat levels across counties, the list of top-ranked interventions is strikingly similar. Poverty reduction and social support were the most highly ranked interventions, even in the shorter term, for all outcomes in all counties. Interventions affecting smoking, addiction, routine care, health insurance, violent crime, and youth education also were important contributors to some outcomes. CONCLUSIONS To safeguard health and well-being in a system dominated by tangled threats and costs, the most important priorities for a county cannot be simply inferred from a profile of its relative strengths and weaknesses. Two interventions stood out as the top priorities for almost all the counties in this study, and six others also were important contributors. Interventions directed toward these priority areas are likely to yield the greatest impact, irrespective of the county's specifics. A significant concentration of resources in a regional portfolio therefore ought to go to these strongest contributors for equitable health and well-being.

In 2007, Jay (Forrester, 2007) surprised many of us by declaring the field, upon its 50th anniversary, to be on an aimless plateau of superficial and mediocre work. This dismal situation, he said, could be corrected only by breaking away from conventional institutions and doing things on our own. I wrote a rejoinder (Homer, 2007) stating that such a radical break was not necessary or advisable. I stated that if we only concentrated on doing “good, diligent work” within the existing system, often alongside our colleagues in other disciplines, things would work out fine. I gave as an example the progress a group of us had made at the Centers for Disease Control and Prevention (CDC) persuading leaders there that SD was a useful tool for public health planning and strategy. It’s six years later now, and, although good, diligent work is certainly being done, I now too have come to feel some sense of frustration with the field’s lack of progress and success. For one thing, growth of the SD Society has come nearly to a halt, with membership increasing by only 3 percent overall from 2005 to 2012. We see many fresh faces at the annual conference, but with Society attrition running at 22–26 percent per year (2010–2013) most of them are gone within a few years. Moreover, the quality of work presented at the annual conference remains generally sub-par, despite various efforts to address this problem. Two of the award committees I sit on have had difficulty in recent years finding work good enough to honor. It is similarly sad to observe how the large strategic consulting firms, so interested in SD in the 1990s and early 2000s, mostly seem to have backed away in recent years. We are also perennially at risk of losing traction in the public sector. For example, my team working at the CDC has hit roadblocks in the last couple of years. Distrust of SD in some quarters of the agency seems to have gained the upper hand and blocked our ability to publish and pursue new work. It is possible that in a few years our footprints within the institution, which were once deep, will be completely gone. What is happening to our field, and what should we do about it? Some might say that there is no fundamental problem and we’re basically on the right track. Maybe we’re simply observing the inevitable market ups and downs of any analytic field, especially one as subtle and nuanced as ours. If we want to prolong the ups and counteract the downs, we should simply do a better, more vigorous job of promoting our work. Under this view, we should keep quiet about our concerns and be cheerleaders rather than naysayers; we should be bold rather than shy or self-critical.

System dynamicists have long emphasized the use of multiple data sources and multiple methods to estimate parameters and test models. Ideally, one should estimate parameters using data " below the level of aggregation of the model" . For example, in a model of capital investment, one could estimate the length and order of the construction delay directly from data on the construction times for a large sample of relevant projects. Often, however, the needed data are not available. At the other end of the spectrum one can use " whole model estimation" in which the parameters are found by fitting the behavior of the full model to the available aggregate time series data. Whole model estimation, however, often suffers from identification problems. In this 1983 paper Jack Homer describes partial model testing, in which parameters are estimated within a subset of model structure rather than by calibration of the entire model. Homer illustrates with an example from his research on the adoption of new medical innovations, specifically, the cardiac pacemaker. He illustrates the partial model testing process with an important formulation representing how the clinical and research communities carry out and report follow-up data on the safety and efficacy of a medical innovation as it evolves. The paper also illustrates the necessity of careful empirical work to collect new data. Debates over methods to estimate parameters are sterile without the data to implement them. In the context of medical innovation, it would have been plausible to assume that follow-up data evaluating pacemaker efficacy would grow smoothly over time as use expanded, though perhaps with a lag. Not content with such easy assumptions, however, Homer examined every article on pacing published in every issue of the relevant cardiology journals, from the creation of the pacemaker through the (then) present. The work, done years before the advent of the Internet and online databases, was painstaking and time consuming—the journals had to be searched and articles coded by hand. The payoff was a unique dataset documenting the actual dynamics of evaluative reporting. Rather than smooth growth, the data showed a pronounced oscillation in the publication of evaluative studies, even though other data, which Homer also assembled from original sources, showed smooth growth in adoption, clinical indications and pacemaker use. Homer's model generates the same oscillation endogenously, and the partial model tests provide robust estimates of the parameters governing the institutional processes (such as research and publication delays) and behavioral decision rules (such as the decision by researchers and clinicians to initiate new follow-up studies) that determine the existence, period, and amplitude of the cycle. The structure identified through this process was not only important to accurately model the evolution of the pacemaker, but has important policy implications still relevant today. All modelers should follow Homer's example and put in the hard work to generate, from primary sources, the data needed to estimate the important parameters and relationships in our models. John Sterman Homer J. 1983. Partial-model testing as a validation tool for system dynamics. In Proceedings of the 1983 International System Dynamics Conference. System Dynamics Society, Chestnut Hill, MA; 919–932. Abstract This paper discusses an approach to model refinement that involves testing the behavior of individual pieces of a model in response to empirical input data for comparison with empirical output data. Partial-model tests should be used for selecting formulations or estimating parameters only when appropriate case-specific or logical information is not available for this purpose. The smaller the model components used for partial-model testing, the more likely it is that the model will prove useful for anticipating events outside historical experience and the less likely it is that observed behavior will be incorrectly attributed to certain relationships or parameters. Thus, from the standpoint of structural validity, partial-model testing is an improvement over whole-model testing for the purpose of structural adjustment. The paper presents a detailed example of partial-model testing in the context of a generic model of the evolving use of a new medical technology. Specifically, the technique is used for adjusting and validating a model subsystem that can explain why the reporting of clinical information on cardiac pacemakers has been marked by regular oscillations over time. Copyright © 2012 System Dynamics Society.

Chronic illness is the largest cause of death and source of health care costs in developed countries and a growing problem in developing countries. Here we build on past work in system dynamics and present a generic model of chronic illness, its treatment and prevention, applied to the U.S. population. The model explains the rising prevalence of illness and responses to it, including the treatment of complications and management activities designed to reduce complications. We show how progress in treatment and disease management has slowed since 1980 in the U.S., largely due to competition between health care payers and providers, resulting in price inflation and an unstable climate for health care investments. We demonstrate the impact of moving “upstream” by managing known risk factors to prevent illness onset, and moving even further upstream by addressing behaviors and living conditions linked to the initial development of these risk factors. Copyright © 2007 John Wiley & Sons, Ltd.

Chronic illness is a large and growing problem throughout the world. Experts agree that the U.S. health care system is poorly organized to care for chronic illnesses and, as a result, is wasteful and unresponsive to the needs of patients. This article describes a program to improve chronic care in a county of Washington State, and how system dynamics models focusing on diabetes and heart failure supported the planning of that program. The models project the program’s costs and benefits over 20 years and have given its leadership the ability to do resource planning, set realistic expectations, determine critical success factors, and evaluate the differential impacts on affected parties. Relying upon model projections, the leadership is seeking ways to address concerns about financial “winners” and “losers” so that all parties are willing to participate in and support the program. Copyright © 2004 John Wiley & Sons, Ltd.

Geoff Coyle has recently posed the question as to whether or not there may be situations in which computer simulation adds no value beyond that gained from qualitative causal-loop mapping. We argue that simulation nearly always adds value, even in the face of significant uncertainties about data and the formulation of soft variables. This value derives from the fact that simulation models are formally testable, making it possible to draw behavioral and policy inferences reliably through simulation in a way that is rarely possible with maps alone. Even in those cases in which the uncertainties are too great to reach firm conclusions from a model, simulation can provide value by indicating which pieces of information would be required in order to make firm conclusions possible. Though qualitative mapping is useful for describing a problem situation and its possible causes and solutions, the added value of simulation modeling suggests that it should be used for dynamic analysis whenever the stakes are significant and time and budget permit. Copyright © 2001 John Wiley & Sons, Ltd.

Many common bacterial pathogens have become increasingly resistant to the antibiotics used to treat them. The evidence suggests that the essential cause of the problem is the extensive and often inappropriate use of antibiotics, a practice that encourages the proliferation of resistant mutant strains of bacteria while suppressing the susceptible strains. However, it is not clear to what extent antibiotic use must be reduced to avoid or reverse an epidemic of antibiotic resistance, and how early the interventions must be made to be effective. To investigate these questions, we have developed a small system dynamics model that portrays changes over a period of years to three subsets of a bacterial population— antibiotic-susceptible, intermediately resistant, and highly resistant. The details and continuing refinement of this model are based on a case study of Streptococcus pneumoniae, a leading cause of illness and death worldwide. The paper presents the model's structure and behavior and identifies open questions for future work. Copyright © 2000 John Wiley & Sons, Ltd.

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

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 author's 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.

A system dynamics model reproduces a variety of national Indicator data reflecting cocaine use and supply over a 15-year period and provides detailed estimates of actual underlying prevalence. Sensitivity testing clarifies the source of observed trends, such as growth in the compulsive use of crack cocaine and decline in the casual use of cocaine powder. Alternative scenarios with possible policy implications are simulated and projected for 12 years, and the results are assessed.

This paper explores the dynamics of worker burnout, a process in which a hard-working individual becomes increasingly exhausted, frustrated, and unproductive. The author's own two-year experience with repeated cycles of burnout is qualitatively reproduced by a small system dynamics model that portrays the underlying psychology of workaholism. Model tests demonstrate that the limit cycle seen in the base run can be stabilized through techniques that diminish work-related stress or enhance relaxation. These stabilizing techniques also serve to raise overall productivity, since they support a higher level of energy and more working hours on the average. One important policy lever is the maximum workweek or work limit; an optimal work limit at which overall productivity is at its peak is shown to exist within a region of stability where burnout is avoided. The paper concludes with a strategy for preventing burnout, which emphasizes the individual's responsibility for understanding the self-inflicted nature of this problem and pursuing an effective course of stability.