System dynamics modeling for public health: background and opportunities

Abstract

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.

FIGURE 1—

A simple model of chronic disease prevention. Note. The rectangle represents a stock of people; thick arrows with valves and cloud symbols represent flows of people; thinner arrows indicate causal influence; arrows with minus signs indicate inverse causal influence.

FIGURE 2—

Chronic disease prevention model output for 3 scenarios over 50 simulated years, showing onset prevention fraction (a), complications prevention fraction (b), people with disease (c), and deaths from complications (d).

FIGURE 3—

A broad view of population health and the spectrum of possible responses. Note. Rectangles represent the stocks of people; thick arrows with circles represent flows of people; thinner arrows indicate causal influence; rounded rectangles indicate multidimensional concepts.

FIGURE 4—

Overview of the health care microworld. Source. Adapted from Hirsch and Immediato. Note. Episodes of illness in the bottom half of the diagram determine demand for care in the top half of the diagram. Rectangles in the bottom half represent stocks of ill patients. Rectangles in the top half represent stocks of provider capacity and workload. Arrows represent causal influences.