Systems Thinking and Simulation Modeling to Inform Childhood Obesity Policy and Practice

Abstract

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.

Applications of systems modelling in obesity research

“…However, our review suggested that the desired level of comprehensive, in‐depth, interdisciplinary modelling practice has not really occurred yet. Except for a few studies that explicitly described the interdisciplinary expertise involved in the modelling , the synergistic effects of interdisciplinary collaboration have not generated new insights and significant progress in obesity research. This dearth exposes the challenge posed by a lack of well‐trained interdisciplinary researchers with diverse backgrounds, which will continue to pose major obstacles to applying SM approaches in public health research.…”

“…Based on the model developed by Homer and colleagues in 2006, a team comprising state legislators, legislative staff members and experts in nutrition, PA, epidemiology, economics and systems dynamics developed a system dynamics model to predict obesity among children in Georgia, and the team updated the model in 2014 . The updated model simulations suggested that, compared with a scenario with no policy change in which the prevalence of obesity among children in Georgia would remain 18% from 2014 to 2034, mandating daily school physical education (PE) would reduce the prevalence of obesity in childhood to 12%, integrating moderate to vigorous PA into elementary classrooms would reduce the prevalence to 10%, and if all policies were implemented simultaneously, the prevalence would be reduced to 3%.…”

A System Dynamics Simulation Applied to Healthcare: A Systematic Review

“…Many diseases and conditions, such as type 2 diabetes mellitus, coronary heart disease, cancer, hypertension, and musculoskeletal disorders, are associated with obesity [ 46 ]. The included papers investigate obesity among children [ 47 , 48 , 49 , 50 ], women of reproductive age [ 51 ], individuals in low-income urban areas [ 52 ], and lower-socioeconomic status (SES) groups [ 53 , 54 ]. The objectives of the selected papers can be categorized into two main groups: (a) reducing obesity (reactive responses) and (b) presenting interventions for obesity prevention (proactive responses).…”

“…Allender and associates [ 56 ] discussed the causes of obesity in four domains: fast food and junk food, social influences, physical activity, and participation in sports. Although a study places more emphasis on building outdoor activity spaces for children [ 48 ], it was also reported that physical education is more effective than physical activity in reducing obesity among children [ 49 ]. Interestingly, Chen and associates [ 54 ] concluded that enhancing an individuals’ status from lower- to middle-income levels would be effective in controlling obesity and being overweight.…”

Food and Nutrition Public Policy in Australia: A diversity of views amongst stakeholders

“…Approaches to systems modelling include 'systems dynamics modelling' and 'agent-based modelling', and although in the early stages, has been applied in a range of applications including the impact of physical activity and diet on weight gain/loss (Abdel-Hamid, 2003), policy effects of allocating revenue collected by SSB taxation (Liu, Osgood, Gao, Xue, & Wang, 2016), the impact of policy interventions on the prevalence of childhood obesity (Powell et al, 2017), and comparing obesity interventions using network-based targeting methods with interventions (Beheshti, Jalalpour, & Glass, 2017), among others. The use of systems modelling in obesity research is a growing area that provides a platform to model interactions between agents in a simulated world, proving invaluable in testing interventions and the effects of policy (Lal et al, 2017;Shoham, Hammond, Rahmandad, Wang, & Hovmand, 2015;Xue et al, 2018).…”