title: Using claims data to predict dependency in activities of daily living as a proxy for frailty.
abstract: OBJECTIVE
  Estimating drug effectiveness and safety among older adults in population-based studies using administrative health care claims can be hampered by unmeasured confounding as a result of frailty. A claims-based algorithm that identifies patients likely to be dependent, a proxy for frailty, may improve confounding control. Our objective was to develop an algorithm to predict dependency in activities of daily living (ADL) in a sample of Medicare beneficiaries.
  
  
  METHODS
  Community-dwelling respondents to the 2006 Medicare Current Beneficiary Survey, >65 years old, with Medicare Part A, B, home health, and hospice claims were included. ADL dependency was defined as needing help with bathing, eating, walking, dressing, toileting, or transferring. Potential predictors were demographics, International Classification of Diseases, Ninth Revision Clinical Modification diagnosis/procedure and durable medical equipment codes for frailty-associated conditions. Multivariable logistic regression was used to predict ADL dependency. Cox models estimated hazard ratios for death as a function of observed and predicted ADL dependency.
  
  
  RESULTS
  Of 6391 respondents, 57% were female, 88% white, and 38% were ≥80. The prevalence of ADL dependency was 9.5%. Strong predictors of ADL dependency were charges for a home hospital bed (OR = 5.44, 95%CI = 3.28-9.03) and wheelchair (OR = 3.91, 95%CI = 2.78-5.51). The c-statistic of the final model was 0.845. Model-predicted ADL dependency of 20% or greater was associated with a hazard ratio for death of 3.19 (95%CI: 2.78, 3.68).
  
  
  CONCLUSIONS
  An algorithm for predicting ADL dependency using health care claims was developed to measure some aspects of frailty. Accounting for variation in frailty among older adults could lead to more valid conclusions about treatment use, safety, and effectiveness.
authors.affiliation: Department of Physical Medicine and Rehabilitation, School of Medicine, University of North Carolina, Chapel Hill, NC, USA; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
authors.forename: Keturah R
authors.initials: KR
authors.lastname: Faurot
authors.forename: Michele
authors.initials: M
authors.lastname: Jonsson Funk
authors.forename: Virginia
authors.initials: V
authors.lastname: Pate
authors.forename: M Alan
authors.initials: MA
authors.lastname: Brookhart
authors.forename: Amanda
authors.initials: A
authors.lastname: Patrick
authors.forename: Laura C
authors.initials: LC
authors.lastname: Hanson
authors.forename: Wendy Camelo
authors.initials: WC
authors.lastname: Castillo
authors.forename: Til
authors.initials: T
authors.lastname: Stürmer
mesh_terms: D000203:Activities of Daily Living; D000368:Aged; D000369:Aged, 80 and over; D003625:Data Collection / Q000706:statistics & numerical data; D005260:Female; D016330:Frail Elderly / Q000706:statistics & numerical data*; D015577:Geriatric Assessment / Q000379:methods / Q000706:statistics & numerical data*; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D012111:Residence Characteristics / Q000706:statistics & numerical data; D014481:United States / Q000453:epidemiology
keywords: epidemiologic methods; frailty; functional limitation; pharmacoepidemiology; unmeasured confounding