title: Simulated Models Suggest That Price per Calorie Is the Dominant Price Metric That Low-Income Individuals Use for Food Decision Making.
abstract: BACKGROUND
  The price of food has long been considered one of the major factors that affects food choices. However, the price metric (e.g., the price of food per calorie or the price of food per gram) that individuals predominantly use when making food choices is unclear. Understanding which price metric is used is especially important for studying individuals with severe budget constraints because food price then becomes even more important in food choice.
  
  
  OBJECTIVE
  We assessed which price metric is used by low-income individuals in deciding what to eat.
  
  
  METHODS
  With the use of data from NHANES and the USDA Food and Nutrient Database for Dietary Studies, we created an agent-based model that simulated an environment representing the US population, wherein individuals were modeled as agents with a specific weight, age, and income. In our model, agents made dietary food choices while meeting their budget limits with the use of 1 of 3 different metrics for decision making: energy cost (price per calorie), unit price (price per gram), and serving price (price per serving). The food consumption patterns generated by our model were compared to 3 independent data sets.
  
  
  RESULTS
  The food choice behaviors observed in 2 of the data sets were found to be closest to the simulated dietary patterns generated by the price per calorie metric. The behaviors observed in the third data set were equidistant from the patterns generated by price per calorie and price per serving metrics, whereas results generated by the price per gram metric were further away.
  
  
  CONCLUSIONS
  Our simulations suggest that dietary food choice based on price per calorie best matches actual consumption patterns and may therefore be the most salient price metric for low-income populations.
authors.affiliation: Global Obesity Prevention Center, r.b@jhu.edu.|Whiting School of Engineering.|Bloomberg School of Public Health, and.
authors.forename: Rahmatollah
authors.identifier: http://orcid.org/0000-0001-8912-3063
authors.initials: R
authors.lastname: Beheshti
authors.affiliation: Global Obesity Prevention Center.|Whiting School of Engineering.|Bloomberg School of Public Health, and.
authors.forename: Takeru
authors.identifier: http://orcid.org/0000-0002-3632-2867
authors.initials: T
authors.lastname: Igusa
authors.affiliation: Global Obesity Prevention Center.|Bloomberg School of Public Health, and.|Center for Human Nutrition, Johns Hopkins University, Baltimore, MD.
authors.forename: Jessica
authors.identifier: http://orcid.org/0000-0001-8962-1695
authors.initials: J
authors.lastname: Jones-Smith
mesh_terms: D003198:Computer Simulation; D003365:Costs and Cost Analysis; D003657:Decision Making; D002149:Energy Intake; D005502:Food / Q000191:economics*; D006801:Humans; D008962:Models, Theoretical; D009753:Nutritive Value; D011203:Poverty; D014481:United States
keywords: food decision making; food price; low income; price metric; simulation