Making the most of limited resources: predicting food insecurity

This research assesses the feasibility of developing an inferential model of individual-level food insecurity by linking health-related social need (HRSN) survey data with health care claims and area-level data on social drivers of health.

Logistic regression modeling linking attested food insecurity to individual-level health care claims data and area-level characteristics.

Food insecurity attestations were derived from a social risk survey deployed by a payer to be representative of membership by age, gender, urbanicity, and Social Vulnerability Index quartile and all insurance types aside from dual-eligible. Predictor variables were sourced from claims data and publicly available area-level data. Models were run with numerous selections of predictor variables and either including insurance type as predictors or stratifying by insurance type, in addition to several experimental models. Performance was primarily assessed by several goodness-of-fit measures, primarily area under the curve (AUC).

Model performance exceeded the 0.7 AUC threshold of acceptable performance for models including insurance type as a predictor, but not for insurance type-specific models, with the best-performing model including all available variables. This model classified no individuals in the commercial sample and 97% of the Medicaid sample as having food insecurity, compared with 19% and 65% in attestations, respectively.

The results suggest that insurance type is a strong predictor of food insecurity, with differentiating identification by insurance type highlighting the importance of additional HRSN screening among commercial members and further outreach among Medicaid populations. Use of multilevel modeling may strengthen analyses through increased ability to address consideration of sourcing, scale, and setting of data.