Food insecurity identification modeling for Medicare enrollees using administrative data

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.

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

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.

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.

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.