Virtual Population Obesity Prevention (VPOP) Labs: Simulating the impact of food environment changes on obesity risk
been linked to the food environment. Given the large scale and complexity of the obesity epidemic, simulation modeling
provides a timely and cost-effective way to evaluate program and policy options. We designed an agent-based model of
children in Baltimore City—a geospatially explicit computational model including detailed data on relevant food and
physical activity environments that influence child obesity risk, such as fast food restaurants, convenience stores,
schools, recreation centers, and more. The model was run with Baltimore City children (11 years old), each represented
by a virtual person assigned a set of socio-demographic characteristics (age, gender, household location, school location,
height, weight), and dietary and physical activity behaviors informed by the literature, existing data sources, and
empirical data. Each agent in the model also has embedded a dynamic metabolic model that integrates knowledge
about how the body responds to changes in diet and physical activity, and consequently, changes in body weight. We
follow child BMI over a 7-year period under the following scenarios: 1) no food environment changes; 2) implementation
of a staple foods policy; and 3) implementation of a healthy-zoning regulation. We demonstrate the utility of systems
modeling. Agent-based modeling provides decision makers with a novel tool to test the complex effects of programs and
policies in the safety of a virtual environment before investing in real-time implementation.
Learning Areas:Systems thinking models (conceptual and theoretical models), applications related to public health
Describe a systems science approach in obesity prevention. Explain the design process of a computer simulation model of residents interacting with the food and physical activity environments in a metropolitan city. Evaluate the potential impact of food environment changes on child obesity risk. Demonstrate how a simulation model can facilitate decision making at the policy level.
Keyword(s): Obesity, Public Health Policy
Qualified on the content I am responsible for because: I have over 15 years of experience in industry and academia in public health operations research, which involves developing and utilizing mathematical and computational methods, models, and tools to help stakeholders better understand decision making, processes, and systems. I have been the Principal Investigator for grants from a variety of sponsors including the Bill and Melinda Gates Foundation, the NIH, the Agency for Healthcare Quality and Research (AHRQ), UNICEF, Global Good, and the Global Fund.
Any relevant financial relationships? No
I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.