Wang Sijiu, Qi Mingyu, Konetzka R Tamara
University of Chicago, Chicago, USA.
Tsinghua University, Beijing, China.
Health Serv Outcomes Res Methodol. 2025;25(1):29-41. doi: 10.1007/s10742-024-00325-6. Epub 2024 May 23.
This study aimed to assess the quality of Home and Community-Based Services (HCBS) data elements within the Transformed Medicaid Statistical Information System Analytical File (TAF) and to devise strategies for improving its research usability. Analyzing Medicaid TAF data from 2016 to 2018, we conducted a retrospective data quality analysis, focusing specifically on HCBS-related data elements. Through secondary data analysis, we identified significant challenges, including considerable missingness and inconsistencies that hamper the effective use of TAF for research purposes. Despite these issues, we developed three approaches that enabled us to identify 94% of known 1915(c) waiver claims as HCBS. Our study also revealed considerable cross-state variations in data quality, prompting specific recommendations for utilizing HCBS data within TAF. Ultimately, the study concludes that while challenges exist, applying our recommended strategies can yield data of acceptable quality for most states, particularly in identifying HCBS usage and classifying them into service categories. Given the growing importance of home-based care, there is a pressing need to prioritize improvements in TAF's HCBS data quality to better inform policy and practice.
The online version contains supplementary material available at 10.1007/s10742-024-00325-6.
本研究旨在评估转型医疗补助统计信息系统分析文件（TAF）中家庭和社区基础服务（HCBS）数据元素的质量，并制定提高其研究可用性的策略。通过分析2016年至2018年医疗补助TAF数据，我们进行了一项回顾性数据质量分析，特别关注与HCBS相关的数据元素。通过二次数据分析，我们发现了重大挑战，包括大量数据缺失和不一致性，这阻碍了TAF在研究中的有效使用。尽管存在这些问题，我们开发了三种方法，使我们能够将已知的1915（c）豁免申请中的94%识别为HCBS。我们的研究还揭示了数据质量在州与州之间存在相当大的差异，从而针对在TAF中使用HCBS数据提出了具体建议。最终，该研究得出结论，尽管存在挑战，但应用我们推荐的策略可以为大多数州产生质量可接受的数据，特别是在识别HCBS使用情况并将其分类到服务类别方面。鉴于居家护理的重要性日益增加，迫切需要优先改进TAF中HCBS数据的质量，以便更好地为政策和实践提供信息。
在线版本包含可在10.1007/s10742-024-00325-6获取的补充材料。
