title: Algorithm for Identifying Nursing Home Days Using Medicare Claims and Minimum Data Set Assessment Data.
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.
  
  
  OBJECTIVE
  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.
  
  
  METHODS
  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.
authors.affiliation: Departments of *Pharmaceutical Health Services Research †Pharmacy Practice and Science, University of Maryland School of Pharmacy, Baltimore, MD ‡Department of Behavioral and Community Health, Seton Hall University College of Nursing, South Orange §Institute for Health, Health Care Policy, and Aging Research, Rutgers University, New Brunswick, NJ.
authors.forename: Yu-Jung
authors.initials: YJ
authors.lastname: Wei
authors.forename: Linda
authors.initials: L
authors.lastname: Simoni-Wastila
authors.forename: Ilene H
authors.initials: IH
authors.lastname: Zuckerman
authors.forename: Nicole
authors.initials: N
authors.lastname: Brandt
authors.forename: Judith A
authors.initials: JA
authors.lastname: Lucas
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D006801:Humans; D007182:Income / Q000706:statistics & numerical data; D007345:Insurance Claim Review; D007902:Length of Stay / Q000706:statistics & numerical data*; D008297:Male; D017533:Marital Status / Q000706:statistics & numerical data; D006278:Medicare / Q000706:statistics & numerical data*; D008875:Middle Aged; D009735:Nursing Homes / Q000706:statistics & numerical data*; D012737:Sex Factors; D013997:Time Factors; D014481:United States