摘要：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 county's CVD burden and present the results of those policy tests. More than 27 million Americans have experienced an ischemic heart event, stroke, or hospitalization for heart failure or peripheral arterial disease. These cardiovascular events are the leading cause of death in the United States and are responsible for over 700 000 deaths each year. The American Heart Association estimates the annual cost of cardiovascular disease (CVD) to be $155 billion in direct health care costs and another $92 billion in indirect costs reflecting lost productivity. 1 Reducing the risk for CVD is a complex undertaking involving partnership among health providers, public and voluntary agencies, and the general public. Much of the responsibility for organizing local programs and systems of care for CVD falls on county health departments and other local agencies. We describe the application of system dynamics simulation modeling to support planning programs for CVD in El Paso County, Colorado. We used the system dynamics model to project trajectories for future incidence and prevalence of CVD under different plausible scenarios for population risk and different strategies for reducing the county's CVD burden. The work presented here was done in support of and was funded by the Cancer, Cardiovascular, and Pulmonary Disease (CCPD) Project of the El Paso County Department of Public Health and Environment. The CCPD project is a collaborative effort to reduce the burden of chronic illness through comprehensive prevention, early detection, and treatment services. System dynamics is an analytic approach for representing complex human systems and understanding how their components interact over time to create problems that resist easy solution. A system dynamics simulation model, which often consists of hundreds or thousands of equations, is developed uniquely to represent a dynamically changing problem situation as it is described by policymakers, subject matter experts, and available data. The participatory and iterative process of system dynamics model development are described elsewhere. 2 Mathematically, a system dynamics model is an interconnected system of differential equations that are simulated on a computer because they are too large and complex to have closed-form analytic solutions. In health applications, the state or stock variables in these equations typically include population subgroups moving among various categories of health or disease, but they may also include resources such as staff and their skill sets. One specifies the initial values for the stocks, plus algebraic equations describing how the various flows into and out of the stocks are determined, and numerical estimates for the coefficients in these equations. The computer does the rest, calculating through the entire set of equations and updating the stock variables through small increments of time, until the final simulation time is reached, often decades into the future. With modern simulation software packages, such as Vensim (Ventana Systems, Harvard, MA), which we used in the present study, it is possible to simulate even a large system dynamics model in no more than a few seconds. System dynamics simulation models help policymakers understand the impact of different interventions to find those that have the greatest leverage in the short term and in the longer term. To test an intervention, one simply makes appropriate changes to the corresponding input assumptions to the model and performs another simulation. System dynamics has been applied to various chronic disease issues since the 1970s. 3 , 4 The work described here builds most directly on a line of work that began in 2002 in Whatcom County, Washington, to help that community understand the potential impact of chronic disease programs focused on diabetes and heart failure and the financial impact of those programs on various providers and insurers. 5 That work was carried further by the US Centers for Disease Control and Prevention (CDC) in the areas of diabetes and obesity, and more recently, cardiovascular disease. 6 – 10
