Abstract

ObjectivesPublic health practitioners and policymakers often need to determine which strategies are most effective prior to investing in implementation. This is particularly important in urban neighborhoods where public health initiatives may have a significant impact on the health of a large population. This study aims to use an innovative agent-based model to evaluate and compare the potential impact of three food policies and programs on the consumption of fruits and vegetables in an urban neighborhood. MethodsWe developed an agent-based model that takes into account individual and neighborhood-level factors (e.g., age, gender, education, food environment) to predict fruit and vegetable consumption at the neighborhood level. Model parameters were estimated from the Food Attitudes and Behaviors Survey, United States Census data, and previous studies. We simulated three hypothetical interventions, including implementing a mass media and nutrition education campaign to alter healthy social norms, increasing the number of fruit and vegetable vendors, and reducing the price of fruits and vegetables. We predicted the impact of these interventions on the consumption of fruits and vegetables in the East Harlem neighborhood in New York City. ResultsThe simulation results suggested that a mass media and nutrition education campaign could increase the consumption of fruits and vegetables by 5.5%. In comparison, a program that increases the number of fruit and vegetable vendors by 10% and the one that reduces the price of fruits and vegetables by 10% could increase the consumption of fruits and vegetables by 1.6% and 1.2%, respectively. ConclusionsA mass media and nutrition education campaign may be more effective than increasing the access to or reducing the price of fruits and vegetables in East Harlem. A well-designed, validated agent-based model has the potential to provide insights on the impact of food policies and programs in a complex urban environment and aid policymakers and public health officials in making informed decisions for priority setting and program implementation. Funding SourcesThis study was supported by an R01 grant from the National Heart, Lung, and Blood Institute of the National Institutes of Health.

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