Impact of different policies on unhealthy dietary behaviors in an urban adult population: an agent-based simulation model

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.

FIGURE 1—

Impact of (a) taxes on the consumption of fast foods and (b) zoning policies on the consumption of fruit and vegetables. Note. Impact of zoning policies was defined as altering the ratio of stores selling fast food vs fruit and vegetables in the district. We evaluated zoning policies by assuming 3 different food accessibility buffers: 1 mile (squares), 0.5 miles (triangles), and 0.25 miles (circles).

FIGURE 2—

Impact of promoting healthy norms on (a) consumption of fast foods and (b) consumption of fruit and vegetables. Note. We evaluated 4 scenarios for each outcome. The first 3 scenarios involved a small-world network and varied the strength of the peer influence value (α; see Table 1) from 0.3 (squares) to 0.2 (triangles) and 0.1 (circles). The fourth scenario involved a random network with a peer influence value of up to 0.3.