Food insecurity identification modeling for Medicare enrollees using administrative data

OBJECTIVES: Food insecurity is a critical health-related social need (HRSN) disproportionately impacting vulnerable populations. Although health care systems are expected to address social needs, many lack the infrastructure for universal screening. Predictive modeling offers a scalable alternative for targeting individuals. This study aimed to develop and evaluate a parsimonious, mixed-effects model to predict the likelihood of food insecurity among Elevance Health Medicare Advantage beneficiaries using administrative and social risk data. 

STUDY DESIGN: Retrospective cohort study using hierarchical generalized linear mixed modeling with cross-validation. 

METHODS: We analyzed data from 462,251 unique Medicare Advantage members with completed HRSN assessments between January 2021 and June 2024. Food insecurity, the dependent variable, was defined using self-reported health risk assessments. Predictors included demographic characteristics, prior social needs coded according to Logical Observation Identifiers Names and Codes, dual Medicare-Medicaid enrollment, Social Vulnerability Index (SVI) tertiles, and disability status. Models incorporated random intercepts by Medicare market state. Model performance was evaluated using 10-fold cross-validation. 

RESULTS: The final model demonstrated strong predictive performance (area under the curve = 0.82; SD = 0.002). The most influential predictors were documentation of multiple prior social needs (β = 3.52; 95% CI, 3.47-3.59) and dual enrollment (β = 2.96; 95% CI, 2.88-3.06). Chronic conditions were not significantly associated with food insecurity. SVI and disability status also contributed meaningfully. 

CONCLUSIONS: This mixed-effects model offers a scalable strategy for identifying food insecurity risk using existing data sources. It may enable managed care organizations to better target interventions and improve equity in addressing unmet social needs.