Donglan Zhang1, Philippe J Giabbanelli, Onyebuchi A Arah, Frederick J Zimmerman. 1. Donglan Zhang and Frederick J. Zimmerman are with the Department of Health Policy and Management, Fielding School of Public Health, University of California, Los Angeles. Philippe J. Giabbanelli is with the Interdisciplinary Research in the Mathematical and Computational Sciences Centre, Simon Fraser University, Burnaby, British Columbia. Onyebuchi A. Arah is with the Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles.
Abstract
OBJECTIVES: Unhealthy eating is a complex-system problem. We used agent-based modeling to examine the effects of different policies on unhealthy eating behaviors. METHODS: We developed an agent-based simulation model to represent a synthetic population of adults in Pasadena, CA, and how they make dietary decisions. Data from the 2007 Food Attitudes and Behaviors Survey and other empirical studies were used to calibrate the parameters of the model. Simulations were performed to contrast the potential effects of various policies on the evolution of dietary decisions. RESULTS: Our model showed that a 20% increase in taxes on fast foods would lower the probability of fast-food consumption by 3 percentage points, whereas improving the visibility of positive social norms by 10%, either through community-based or mass-media campaigns, could improve the consumption of fruits and vegetables by 7 percentage points and lower fast-food consumption by 6 percentage points. Zoning policies had no significant impact. CONCLUSIONS: Interventions emphasizing healthy eating norms may be more effective than directly targeting food prices or regulating local food outlets. Agent-based modeling may be a useful tool for testing the population-level effects of various policies within complex systems.
OBJECTIVES: Unhealthy eating is a complex-system problem. We used agent-based modeling to examine the effects of different policies on unhealthy eating behaviors. METHODS: We developed an agent-based simulation model to represent a synthetic population of adults in Pasadena, CA, and how they make dietary decisions. Data from the 2007 Food Attitudes and Behaviors Survey and other empirical studies were used to calibrate the parameters of the model. Simulations were performed to contrast the potential effects of various policies on the evolution of dietary decisions. RESULTS: Our model showed that a 20% increase in taxes on fast foods would lower the probability of fast-food consumption by 3 percentage points, whereas improving the visibility of positive social norms by 10%, either through community-based or mass-media campaigns, could improve the consumption of fruits and vegetables by 7 percentage points and lower fast-food consumption by 6 percentage points. Zoning policies had no significant impact. CONCLUSIONS: Interventions emphasizing healthy eating norms may be more effective than directly targeting food prices or regulating local food outlets. Agent-based modeling may be a useful tool for testing the population-level effects of various policies within complex systems.
Authors: Brent A Langellier; Usama Bilal; Felipe Montes; Jose D Meisel; Letícia de Oliveira Cardoso; Ross A Hammond Journal: Am J Prev Med Date: 2019-08 Impact factor: 5.043
Authors: Stephanie Bell Jilcott Pitts; Mariel Leah Mayo Acheson; Rachel K Ward; Qiang Wu; Jared T McGuirt; Sally L Bullock; Mandee F Lancaster; Justin Raines; Alice S Ammerman Journal: Arch Public Health Date: 2015-08-25