Introduction Individual and environmental factors dynamically interact in shaping income inequalities in healthy food consumption. The agent-based model, Health Behaviors Simulation (HEBSIM), was developed to describe income inequalities in healthy food consumption. It simulates interactions between households and their environment. HEBSIM was used to explore the impact of interventions aimed at reducing food consumption inequalities. Methods HEBSIM includes households and food outlets. Households are characterized by location, composition, income, and preference for food. Decisions about where to shop for food (fruit/vegetable stores, supermarkets, or discount supermarkets) and whether to visit fast food outlets are based on distance, price, and food preference. Food outlets can close and new food outlets can enter the system. Three interventions to reduce healthy food consumption inequalities were tested: (1) eliminating residential segregation; (2) lowering the prices of healthy food; and (3) providing health education. HEBSIM was quantified using data from Statistics Netherlands, Statistics Eindhoven, and the GLOBE study (2011). Results The model mimicked food consumption in Eindhoven. High-income households visited healthy food shops more often than low-income households. Eliminating residential segregation had the largest impact in reducing income inequalities in food consumption, but resulted partly from a worsening in healthy food consumption in high-income households. Lowering prices and health education could also substantially reduce inequalities. Most interventions took 5-10 years to reach their (almost) full effects. Conclusions HEBSIM is a promising tool for studying dynamic interactions between households and their environment and for assessing the impact of interventions on income inequalities in food consumption.,
American Journal of Preventive Medicine
Department of Public Health

Blok, D., de Vlas, S., Bakker, R., & van Lenthe, F. (2015). Reducing income inequalities in food consumption: Explorations with an agent-based model. American Journal of Preventive Medicine, 49(4), 605–613. doi:10.1016/j.amepre.2015.03.042