Preventing Chronic Disease | Using Simulation to Compare Established and Emerging Interventions to Reduce Cardiovascular Disease Risk in the United States - CDC
Using Simulation to Compare Established and Emerging Interventions to Reduce Cardiovascular Disease Risk in the United States
Jack Homer, PhD; Kristina Wile, MS; Benjamin Yarnoff, PhD; Justin G. Trogdon, PhD; Gary Hirsch, SM; Lawton Cooper, MD, MPH; Robin Soler, PhD; Diane Orenstein, PhD
Suggested citation for this article: Homer J, Wile K, Yarnoff B, Trogdon JG, Hirsch G, Cooper L, et al. Using Simulation to Compare Established and Emerging Interventions to Reduce Cardiovascular Disease Risk in the United States. Prev Chronic Dis 2014;11:140130. DOI: http://dx.doi.org/10.5888/pcd11.140130
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Abstract
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.
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.
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.
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.
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.
Acknowledgments
This research was supported by contract no. 200-2008-27958 Task Order 12 from the Centers for Disease Control and Prevention and National Heart, Lung, and Blood Institute.
Author Information
Corresponding Author: Benjamin Yarnoff, PhD, RTI International, 3040 Cornwallis Road, PO Box 12194, Research Triangle Park, NC 27709. Telephone: 919-541-6640. E-mail: byarnoff@rti.org.
Author Affiliations: Jack Homer, Homer Consulting, Barrytown, New York; Kristina Wile, Sustainability Institute, Charleston, South Carolina; Justin G. Trogdon, University of North Carolina, Chapel Hill, North Carolina; Gary Hirsch, Creator Learning Environments, Wayland, Massachusetts; Lawton Cooper, National Institutes of Health, Bethesda, Maryland; Robin Soler, Diane Orenstein, Centers for Disease Control and Prevention, Atlanta, Georgia.
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