Analysis of Prescription Drug Utilization with Beta Regression Models

TL;DR

This paper demonstrates the effectiveness of Beta regression models, combined with INLA, in analyzing spatial variability of prescription drug utilization in the U.S., providing insights for health risk assessment.

Contribution

It introduces the application of Beta regression with INLA to model spatial variability in drug utilization, enhancing understanding of regional differences in healthcare analytics.

Findings

Beta regression models fit drug claim rates well.

Including spatial dependence improves model performance.

Models can inform health risk scoring.

Abstract

The healthcare sector in the U.S. is complex and is also a large sector that generates about 20% of the country's gross domestic product. Healthcare analytics has been used by researchers and practitioners to better understand the industry. In this paper, we examine and demonstrate the use of Beta regression models to study the utilization of brand name drugs in the U.S. to understand the variability of brand name drug utilization across different areas. The models are fitted to public datasets obtained from the Medicare & Medicaid Services and the Internal Revenue Service. Integrated Nested Laplace Approximation (INLA) is used to perform the inference. The numerical results show that Beta regression models can fit the brand name drug claim rates well and including spatial dependence improves the performance of the Beta regression models. Such models can be used to reflect the effect of prescription drug utilization when updating an insured's health risk in a risk scoring model.