Daley Sean, Kajendrakumar Bakthameera, Nandhakumar Samyuktha, Personett Christine, Sholes Michael, Thapa Swornim, Xue Chen, Korvink Michael, Gunn Laura H
Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
Healthcare (Basel). 2021 Oct 22;9(11):1424. doi: 10.3390/healthcare9111424.
The U.S. Centers for Medicare and Medicaid Services' (CMS's) Hospital Compare (HC) data provides a collection of risk-adjusted hospital performance metrics intended to allow comparison of hospital-provided care. However, CMS does not adjust for socioeconomic status (SES) factors, which have been found to be associated with disparate health outcomes. Associations between county-level SES factors and CMS's risk-adjusted 30-day acute myocardial infarction (AMI) mortality rates are explored for = 2462 hospitals using a variety of sources for county-level SES information. Upon performing multiple imputation, a stepwise backward elimination model selection approach using Akaike's information criteria was used to identify the optimal model. The resulting model, comprised of 14 predictors mostly at the county level, provides an additional 8% explanatory power to capture the variability in 30-day risk-standardized AMI mortality rates, which already account for patient-level clinical differences. SES factors may be an important feature for inclusion in future risk-adjustment models, which will have system and policy implications for distributing resources to hospitals, such as reimbursements. It also serves as a stepping stone to identify and address long-standing SES-related inequities.
美国医疗保险和医疗补助服务中心（CMS）的医院比较（HC）数据提供了一系列经风险调整的医院绩效指标，旨在实现对医院所提供医疗服务的比较。然而，CMS并未对社会经济地位（SES）因素进行调整，而这些因素已被发现与不同的健康结果相关。利用多种县级SES信息来源，对2462家医院探讨了县级SES因素与CMS经风险调整的30天急性心肌梗死（AMI）死亡率之间的关联。在进行多次插补后，采用基于赤池信息准则的逐步向后消除模型选择方法来确定最优模型。最终得到的模型由14个主要为县级层面的预测变量组成，该模型在捕捉30天风险标准化AMI死亡率的变异性方面提供了额外8%的解释力，而这些死亡率已考虑了患者层面的临床差异。SES因素可能是未来风险调整模型中一个重要的纳入特征，这将对向医院分配资源（如报销）产生系统和政策影响。它也为识别和解决长期存在的与SES相关的不平等问题提供了一个切入点。
