Does Dual Eligibility Drive Health Care Use Patterns? Evidence From MEPS Using Latent Class Analysis

双重资格是否会影响医疗保健使用模式?基于潜在类别分析的医疗支出调查证据

阅读:1

Abstract

Dually-eligible beneficiaries (or duals) are enrolled in both Medicare and Medicaid. Duals have high and complex healthcare needs, and face substantial barriers to quality healthcare access. Thus, it is important to understand how dual-eligibility status is associated with patterns of healthcare services use and the factors that drive these patterns. We used data from the Medical Expenditure Panel Survey (2008-2022) to evaluate the association of dual-eligibility status and health care use patterns for older Medicare beneficiaries (65+-years). We used Latent Class Analysis to sequentially fit and assess multiple class solutions based on 10 healthcare services (e.g. hospital use, dental visits, home healthcare, medications) and generated healthcare use patterns profiles. Subsequently, we fit survey-weighted unadjusted and adjusted multinomial logistic regression models to link dual-eligibility status to the best fitting use patterns profiles. We found that the three class solution (typical [67.8%], high [17.5%], and low [14.7%] users) provided the best statistical and substantive fit to the data. Unadjusted models showed that duals are more likely to be both high (RRR = 2.07, p < 0.001) and low users (RRR=1.45, p < 0.001) as compared to non-duals. After adjusting for sociodemographics, comorbidities, and access to usual source of care, duals remained more likely to be high users (RRR=1.7, p < 0.001), but no longer low users as compared to non-duals. These results highlight the need for more nuanced approaches, by stakeholders, to policies and interventions that streamline the use of health services in a way that aligns with efficient, quality and cost-effective healthcare for this complex need population.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。