Algorithm for Identifying Nursing Home Days Using Medicare Claims and Minimum Data Set Assessment Data. Issue 11 (November 2016)

• Record Type: Journal Article
• Authors: Wei, Yu-Jung; Simoni-Wastila, Linda; Zuckerman, Ilene H.; Brandt, Nicole; Lucas, Judith A.
• Is Part Of: Medical care. Volume 54:Issue 11(2016)
• Publication Date: 2016-11
• Subjects: long-term care -- nursing home stay -- Medicare -- administrative data -- minimal data set; Economics, Medical -- Periodicals; Insurance, Health -- Periodicals; Santé, Services de -- Administration -- Périodiques; Soins médicaux -- Périodiques; Medical economics -- Periodicals; Health insurance -- Periodicals; Medical economics -- United States -- Periodicals; Health insurance -- United States -- Periodicals; Comprehensive Health Care -- Periodicals; Personal Health Services -- Periodicals; Gezondheidszorg; Économie de la santé -- Périodiques; Santé, Services de -- Périodiques; Health insurance; Medical economics; United States; Periodicals; 362.10973
• DOI: 10.1097/MLR.0000000000000109
• Languages: English
• ISSNs: 0025-7079

Abstract

Abstract : Background: No consensus exists about methods of measuring nursing home (NH) length-of-stay for Medicare beneficiaries to identify long-stay and short-stay NH residents. Objectives: To develop an algorithm measuring NH days of stay to differentiate between residents with long and short stay (≥101 and <101 consecutive days, respectively) and to compare the algorithm with Minimum Data Set (MDS) alone and Medicare claims data. Research Design: We linked 2006–2009 MDS assessments to Medicare Part A skilled nursing facility (SNF) data. This algorithm determined the daily NH stay evidence by MDS and SNF dates. NH length-of-stay and characteristics were reported in the total, long-stay, and short-stay residents. Long-stay residents identified by the algorithm were compared with the NH evidence from MDS-alone and Medicare parts A and B data. Results: Of 276, 844 residents identified by our algorithm, 40.8% were long stay. Long-stay versus short-stay residents tended to be older, male, white, unmarried, low-income subsidy recipients, have multiple comorbidities, and have higher mortality but have fewer hospitalizations and SNF services. Higher proportions of long-stay and short-stay residents identified by the MDS/SNF algorithm were classified in the same group using MDS-only (98.9% and 100%, respectively), compared with the parts A and B data (95.0% and 67.1%, respectively). NH length-of-stay was similar between MDS/SNF and MDS-only long-stay residents (mean±SD: 717±422 vs. 720±441 d), but the lengths were longer compared with the parts A and B data (approximately 474±393 d). Conclusions: Our MDS/SNF algorithm allows the differentiation of long-stay and short-stay residents, resulting in an NH group more precise than using Medicare claims data only. Abstract : Supplemental Digital Content is available in the text.