Comparison of Number and Overlap of Diagnostic Information for Risk Adjustment for Dually Enrolled Veterans in Medicaid.

IF 3.2 2区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Patrick N O'Mahen, Chase S Eck, Suja S Rajan, Cheng Rebecca Jiang, Christine Yang, Laura A Petersen
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引用次数: 0

Abstract

Objective: To measure discrepancies in risk adjustment scores using only Medicaid or Veterans Health Administration (VA) diagnoses for Veterans dually enrolled in VA and Medicaid.

Study setting and design: Veterans aged 18-64 enrolled in the VA and Medicaid for at least one full calendar year during 2017-2020. We compared the number and overlap of annual diagnoses derived from VA and Medicaid data. We also calculated Charlson, Elixhauser, and Centers for Medicare and Medicaid Hierarchical Condition Categories Version 21 (CMS-V21) risk scores using VA-only, Medicaid-only, and combined VA-Medicaid data for each person-year. We used intraclass correlations within risk measures to compare scores across risk measures.

Data sources and analytic sample: We used data from the VA's Assistant Deputy Undersecretary for Health's (ADUSH) enrollment files regarding age and VA Priority Group to select our cohort of VA enrollees. We used T-MSIS Analytic Files (TAF) and the Demographics and Enrollment (DE) file to determine Medicaid enrollment.

Principal findings: Our study cohort contained 183,018 dual-enrollees with service-connected disabilities representing 405,318 person years and 219,977 dual enrollees without service-connected disabilities (531,948 person years). On average, service-connected individuals had 9.1 fewer diagnoses from Medicaid-only data than from VA-only data (95% Confidence Interval (CI): [9.0, 9.1]) and 5.0 fewer for non-service-connected Veterans (95% CI: [4.9, 5.1]). Intraclass correlations between VA-only data and combined VA-Medicaid scores had higher correlations for Charlson (0.816 vs. 0.591 for service connected, 0.722 vs. 0.638 for non-service connected) and Elixhauser (0.818 vs. 0.609 for service-connected, 0.723 to 0.702 non-service-connected) scores, while Medicaid-only scores had higher correlations for CMS V21 (0.756 vs. 0.666 for service-connected, 0.795 to 0.542 for non service-connected).

Conclusions: Medicaid and VA data represent non-overlapping diagnoses data in three common risk scores. Researchers should consider combining records to calculate disease burden for dual-enrolled Veterans to ensure complete capture of risk.

医疗补助双登记退伍军人风险调整诊断信息的数量和重叠比较。
目的:衡量仅使用医疗补助或退伍军人健康管理局(VA)诊断的退伍军人双重参加VA和Medicaid的风险调整评分的差异。研究设置和设计:年龄在18-64岁之间的退伍军人在2017-2020年期间至少注册了一个完整的日历年。我们比较了来自退伍军人管理局和医疗补助计划数据的年度诊断的数量和重叠。我们还计算了Charlson, Elixhauser和医疗保险和医疗补助分层疾病分类中心版本21 (CMS-V21)的风险评分,使用仅va,仅医疗补助和合并VA-Medicaid数据。我们使用风险度量中的类内相关性来比较不同风险度量的得分。数据来源和分析样本:我们使用了退伍军人事务部负责卫生的助理副部长(ADUSH)关于年龄和退伍军人事务部优先组的登记文件中的数据来选择我们的退伍军人事务部登记队列。我们使用T-MSIS分析文件(TAF)和人口统计和登记(DE)文件来确定医疗补助登记。主要发现:我们的研究队列包含183,018名患有服务相关残疾的双入组患者(405,318人年)和219,977名没有服务相关残疾的双入组患者(531,948人年)。平均而言,只有医疗补助的数据比只有va的数据少9.1个诊断(95%置信区间(CI):[9.0, 9.1]),没有服务的退伍军人少5.0个诊断(95% CI:[4.9, 5.1])。仅va数据与VA-Medicaid评分之间的类内相关性在Charlson(服务连接的0.816比0.591,0.722比0.638)和Elixhauser(服务连接的0.818比0.609,非服务连接的0.723到0.702)评分中具有较高的相关性,而仅医疗补助评分在CMS V21中具有较高的相关性(服务连接的0.756比0.666,非服务连接的0.795到0.542)。结论:医疗补助和退伍军人事务部的数据在三个常见的风险评分中代表了非重叠的诊断数据。研究人员应考虑结合记录来计算双重登记退伍军人的疾病负担,以确保完全捕获风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Health Services Research
Health Services Research 医学-卫生保健
CiteScore
4.80
自引率
5.90%
发文量
193
审稿时长
4-8 weeks
期刊介绍: Health Services Research (HSR) is a peer-reviewed scholarly journal that provides researchers and public and private policymakers with the latest research findings, methods, and concepts related to the financing, organization, delivery, evaluation, and outcomes of health services. Rated as one of the top journals in the fields of health policy and services and health care administration, HSR publishes outstanding articles reporting the findings of original investigations that expand knowledge and understanding of the wide-ranging field of health care and that will help to improve the health of individuals and communities.
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