Integrating multilevel data to assess Massachusetts food vulnerability

Music City Center 

Food insecurity, which includes lack of consistent access to enough food, or reduced quality, variety, and desirability of diet, is a pressing issue in the United States. Data on local food insecurity is crucial to identifying locations with high food insecurity and formulating interventions. However, due to insufficient individual data at county level, current food insecurity estimates are only available at the state level, thus cannot reflect heterogeneities within a state. We present a methodology to integrate multilevel data to estimate food insecurity at a more granular level. We use individual data to estimate food insecurity based on household characteristics. We further estimate the distribution of household characteristics within a county by combining marginal data at the county level with dependency structure at individual level. The first two steps are combined to obtain a county-level food insecurity estimate. We illustrate the method through Massachusetts as a case study. This methodology can be applied to estimations of other quantities, e.g., household food budget, which facilitates a more comprehensive view of local food affordability.

probabilistic graphical model

data integration

iterative proportional fitting