title: Predicting Disability among Community-Dwelling Medicare Beneficiaries Using Claims-Based Indicators.
abstract: OBJECTIVE
  To assess the feasibility of using existing claims-based algorithms to identify community-dwelling Medicare beneficiaries with disability based solely on the conditions for which they are being treated, and improving on these algorithms by combining them in predictive models.
  
  
  METHODS
  Data on 12,415 community-dwelling fee-for-service Medicare beneficiaries who first responded to the Medicare Current Beneficiary Survey (MCBS) in 2003-2006.
  
  
  METHODS
  Logistic regression models in which six claims-based disability indicators are used to predict self-reported disability. Receiver operating characteristic (ROC) curves were used to assess the performance of the predictive models.
  
  
  RESULTS
  The predictive performance of the regression-based models is better than that of the individual claims-based indicators. At a predicted probability threshold chosen to maximize the sum of sensitivity and specificity, sensitivity is 0.72 for beneficiaries age 65 or older and specificity is 0.65. For those under 65, sensitivity is 0.54 and specificity is 0.67. The findings also suggest ways to improve predictive performance for specific disability populations of interest to researchers.
  
  
  CONCLUSIONS
  Predictive models that incorporate multiple claims-based indicators provide an improved tool for researchers seeking to identify people with disabilities in claims data.
authors.affiliation: Mathematica Policy Research, Washington, DC.
authors.forename: Yonatan
authors.initials: Y
authors.lastname: Ben-Shalom
authors.affiliation: Mathematica Policy Research, Washington, DC.
authors.forename: David C
authors.initials: DC
authors.lastname: Stapleton
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000465:Algorithms; D006233:Persons with Disabilities / Q000706:statistics & numerical data*; D018588:Fee-for-Service Plans / Q000706:statistics & numerical data; D005260:Female; D006304:Health Status; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D016015:Logistic Models; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data*; D008603:Mental Health; D008875:Middle Aged; D015233:Models, Statistical; D012372:ROC Curve; D012107:Research Design; D012111:Residence Characteristics; D057566:Self Report; D012680:Sensitivity and Specificity; D012737:Sex Factors; D014481:United States; D055815:Young Adult
keywords: Medicare; Self-reported disability; claims-based algorithms; receiver operating characteristic (ROC) curves