title: Comparing self-reported health status and diagnosis-based risk adjustment to predict 1- and 2 to 5-year mortality.
abstract: OBJECTIVE
  To compare the ability of two diagnosis-based risk adjustment systems and health self-report to predict short- and long-term mortality.
  
  
  METHODS
  Data were obtained from the Department of Veterans Affairs (VA) administrative databases. The study population was 78,164 VA beneficiaries at eight medical centers during fiscal year (FY) 1998, 35,337 of whom completed an 36-Item Short Form Health Survey for veterans (SF-36V) survey.
  
  
  METHODS
  We tested the ability of Diagnostic Cost Groups (DCGs), Adjusted Clinical Groups (ACGs), SF-36V Physical Component score (PCS) and Mental Component Score (MCS), and eight SF-36V scales to predict 1- and 2-5 year all-cause mortality. The additional predictive value of adding PCS and MCS to ACGs and DCGs was also evaluated. Logistic regression models were compared using Akaike's information criterion, the c-statistic, and the Hosmer-Lemeshow test.
  
  
  RESULTS
  The c-statistics for the eight scales combined with age and gender were 0.766 for 1-year mortality and 0.771 for 2-5-year mortality. For DCGs with age and gender the c-statistics for 1- and 2-5-year mortality were 0.778 and 0.771, respectively. Adding PCS and MCS to the DCG model increased the c-statistics to 0.798 for 1-year and 0.784 for 2-5-year mortality.
  
  
  CONCLUSIONS
  The DCG model showed slightly better performance than the eight-scale model in predicting 1-year mortality, but the two models showed similar performance for 2-5-year mortality. Health self-report may add health risk information in addition to age, gender, and diagnosis for predicting longer-term mortality.
authors.affiliation: Houston Center for Quality of Care and Utilization Studies, Health Services Research and Development Service, the Michael E. DeBakey VA Medical Center (152), 2002 Holcombe Blvd., Houston, TX 77030, USA.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Pietz
authors.forename: Laura A
authors.initials: LA
authors.lastname: Petersen
mesh_terms: D000367:Age Factors; D000368:Aged; D005260:Female; D006304:Health Status; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009026:Mortality; D011237:Predictive Value of Tests; D020379:Risk Adjustment / Q000379:methods* / Q000706:statistics & numerical data; D012737:Sex Factors; D014481:United States; D014493:United States Department of Veterans Affairs