Simulating and Evaluating Local Interventions to Improve Cardiovascular Health

Homer Consulting, Voorhees, NJ 08043, USA.
Preventing chronic disease (Impact Factor: 2.12). 01/2010; 7(1):A18.
Source: PubMed


Numerous local interventions for cardiovascular disease are available, but resources to deliver them are limited. Identifying the most effective interventions is challenging because cardiovascular risks develop through causal pathways and gradual accumulations that defy simple calculation. We created a simulation model for evaluating multiple approaches to preventing and managing cardiovascular risks. The model incorporates data from many sources to represent all US adults who have never had a cardiovascular event. It simulates trajectories for the leading direct and indirect risk factors from 1990 to 2040 and evaluates 19 interventions. The main outcomes are first-time cardiovascular events and consequent deaths, as well as total consequence costs, which combine medical expenditures and productivity costs associated with cardiovascular events and risk factors. We used sensitivity analyses to examine the significance of uncertain parameters. A base case scenario shows that population turnover and aging strongly influence the future trajectories of several risk factors. At least 15 of 19 interventions are potentially cost saving and could reduce deaths from first cardiovascular events by approximately 20% and total consequence costs by 26%. Some interventions act quickly to reduce deaths, while others more gradually reduce costs related to risk factors. Although the model is still evolving, the simulated experiments reported here can inform policy and spending decisions.

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    • "of how changes in the testable components affect the system behavior as a whole (Hirsch, Homer, Evans, & Zielinski, 2010; Homer et al., 2010; Richardson, 2011). Models have also been developed through collaborative or participatory processes with stakeholders to identify components, assign values, and test them, with occasional confirmation and adjustment through input from stakeholders (Hovmand et al., 2012; Stave, 2002). "
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    • "This holistic approach results in models that are small enough to describe easily, while containing feedback loops that can replicate complex counterintuitive behaviors (Ghaffarzadegan, Lyneis, and Richardson 2011). Healthrelated examples of system dynamics approaches include models of polio eradication (Thompson and Duintjer Tebbens 2008; Rahmandad et al. 2011), chronic illness (Homer et al. 2010), U.S. health care reform (Milstein, Homer, and Hirsch 2010), tobacco use (Tobias, Cavana, and Bloomfield 2010), and the pharmaceutical market (Paich, Peck, and Valant 2011). System dynamics can offer novel insights into the underlying processes of practice variation. "
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