Characterizing Geographic Variation in GLP-1 Receptor Agonist Prescribing Using Interpretable Machine Learning

Geographic disparities in glucagon-like peptide-1 receptor agonist (GLP-1 RA) prescribing remain poorly characterized, with prior studies yielding inconsistent findings regarding rural-urban patterns. The objective of this article is to characterize geographic variation in GLP-1 RA prescribing across the United States and identify neighborhood-level predictors using interpretable machine learning models. We conducted a retrospective analysis of IQVIA PharMetrics® Plus claims data (2010–2022) among commercially insured adults with type 2 diabetes or obesity. Prescriptions were aggregated to 667 three-digit ZIP code (ZIP-3) areas. We compared ridge-penalized linear regression, generalized additive models (GAMs), random forest, and gradient boosting to identify predictors of log-transformed prescribing rates per 100,000 population using 5-fold cross-validation. Among 44,150 individuals contributing 64,281 prescriptions, prescribing rates varied substantially across ZIP-3 areas (median: 2,487 per 100,000). GAMs achieved strong predictive performance (cross-validated R² = 0.335) while maintaining interpretability. Health uninsured rate emerged as the most influential predictor, showing a sharp inverse association with prescribing that attenuated beyond 8% uninsured. Education attainment and urban core fraction were also strongly predictive, with prescribing rates lower in more urbanized areas. Feature importance rankings showed moderate concordance across methods (Spearman ρ = 0.51–0.57). GLP-1 RA prescribing varies significantly across US regions, with patterns shaped by insurance coverage, educational environment, and urbanicity rather than rural-urban status alone. Interpretable machine learning methods provide actional insights for formulary planning and access equity monitoring.