The health care sector in the United States is complex and is also a large sector that generates about 20% of the country’s gross domestic product. Health care analytics has been used by researchers and practitioners to better understand the industry. In this article, we examine and demonstrate the use of Beta regression models to study the utilization of brand name drugs in the United States 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.

美国的医疗保健部门很复杂，也是一个庞大的部门，约占该国国内生产总值的 20%。研究人员和从业人员已使用医疗保健分析来更好地了解该行业。在本文中，我们检验并展示了使用 Beta 回归模型来研究美国品牌药的使用情况，以了解不同地区品牌药使用的可变性。这些模型适用于从医疗保险和医疗补助服务以及美国国税局获得的公共数据集。集成嵌套拉普拉斯近似 (INLA) 用于执行推理。数值结果表明，Beta 回归模型可以很好地拟合品牌药物索赔率，并且包括空间依赖性提高了 Beta 回归模型的性能。当在风险评分模型中更新被保险人的健康风险时，此类模型可用于反映处方药使用的影响。