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Objectives: In 2007, 31.7% of Georgia adolescents in grades 9-12 were overweight or obese. Understanding the impact of policies and interventions on obesity prevalence among young people can help determine statewide public health and policy strategies. This article describes a systems model, originally launched in 2008 and updated in 2014, that simulates the impact of policy interventions on the prevalence of childhood obesity in Georgia through 2034., Methods: In 2008, using information from peer-reviewed reports and quantitative estimates by experts in childhood obesity, physical activity, nutrition, and health economics and policy, a group of legislators, legislative staff members, and experts trained in systems thinking and system dynamics modeling constructed a model simulating the impact of policy interventions on the prevalence of childhood obesity in Georgia through 2034. Use of the 2008 model contributed to passage of a bill requiring annual fitness testing of schoolchildren and stricter enforcement of physical education requirements. We updated the model in 2014., Results: With no policy change, the updated model projects that the prevalence of obesity among children and adolescents aged ≤18 in Georgia would hold at 18% from 2014 through 2034. Mandating daily school physical education (which would reduce prevalence to 12%) and integrating moderate to vigorous physical activity into elementary classrooms (which would reduce prevalence to 10%) would have the largest projected impact. Enacting all policies simultaneously would lower the prevalence of childhood obesity from 18% to 3%., Conclusions: Systems thinking, especially with simulation models, facilitates understanding of complex health policy problems. Using a simulation model to educate legislators, educators, and health experts about the policies that have the greatest short- and long-term impact should encourage strategic investment in low-cost, high-return policies.

Context: Few studies have described the range and health impacts of obesity prevention strategies in local communities supported by the Communities Putting Prevention to Work program., Objective: To address this gap, we reviewed implemented strategies in Los Angeles County (LAC) for 3 program focus areas: physical activity-promotion, health marketing, and creation of healthy food environments. Local context and results from an impact simulation are presented., Design: Information on population reach and program milestones was synthesized to describe historical and programmatic progress of the obesity prevention efforts during 2010-2012. To forecast health impacts, the Prevention Impacts Simulation Model (PRISM) was used to simulate population health outcomes, including projected changes in obesity burden and health behaviors 30 years into the future., Setting: LAC with more than 9.8 million residents., Participants: Low-income adults and youth who were the intended audiences of the Communities Putting Prevention to Work program in LAC., Intervention: Implemented strategies for the 3 focus areas., Main Outcome Measures: Documentation of program reach and PRISM forecasting of obesity rates and health impacts., Results: Implemented strategies in LAC ranged from best practices in healthy food procurement (estimated reach: 600 000 students, 300 000 meals per day) to completed shared-use agreements (10+ agreements across 5 school districts) to a series of strategically designed health marketing campaigns on healthy eating (>515 million impressions). On the basis of PRISM simulations, these highlighted program activities have the potential to reduce by 2040 the number of youth (-29 870) and adults (-94 136) with obesity, youth (-112 453) and adults (-855 855) below recommended levels of physical activity, and youth (-14 544) and adults (-28 835) who consumed excess junk food, as compared with baseline (2010-2011)., Conclusions: Program context and PRISM-simulated health impacts showed modest but promising results in LAC, which may lead to further population health improvements in the future. Downstream health and behavioral surveillance data are needed to confirm these estimates.