title: The performance of administrative and self-reported measures for risk adjustment of Veterans Affairs expenditures.
abstract: OBJECTIVE
  To evaluate the performance of different prospective risk adjustment models of outpatient, inpatient, and total expenditures of veterans who regularly use Veterans Affairs (VA) primary care.
  
  
  METHODS
  We utilized administrative, survey and expenditure data on 14,449 VA patients enrolled in a randomized trial that gave providers regular patient health assessments.
  
  
  METHODS
  This cohort study compared five administrative data-based, two self-report risk adjusters, and base year expenditures in prospective models.
  
  
  METHODS
  VA outpatient care and nonacute inpatient care expenditures were based on unit expenditures and utilization, while VA expenditures for acute inpatient care were calculated from a Medicare-based inpatient cost function. Risk adjusters for this sample were constructed from diagnosis, medication and self-report data collected during a clinical trial. Model performance was compared using adjusted R2 and predictive ratios.
  
  
  RESULTS
  In all expenditure models, administrative-based measures performed better than self-reported measures, which performed better than age and gender. The Diagnosis Cost Groups (DCG) model explained total expenditure variation (R2=7.2 percent) better than other models. Prior outpatient expenditures predicted outpatient expenditures best by far (R2=42 percent). Models with multiple measures improved overall prediction, reduced over-prediction of low expenditure quintiles, and reduced under-prediction in the highest quintile of expenditures.
  
  
  CONCLUSIONS
  Prediction of VA total expenditures was poor because expenditure variation reflected utilization variation, but not patient severity. Base year expenditures were the best predictor of outpatient expenditures and nearly the best for total expenditures. Models that combined two or more risk adjusters predicted expenditures better than single-measure models, but are more difficult and expensive to apply.
authors.affiliation: Health Services Research and Development Northwest Center of Excellence, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA.
authors.forename: Matthew L
authors.initials: ML
authors.lastname: Maciejewski
authors.forename: Chuan-Fen
authors.initials: CF
authors.lastname: Liu
authors.forename: Ann
authors.initials: A
authors.lastname: Derleth
authors.forename: Mary
authors.initials: M
authors.lastname: McDonell
authors.forename: Steve
authors.initials: S
authors.lastname: Anderson
authors.forename: Stephan D
authors.initials: SD
authors.lastname: Fihn
mesh_terms: D000368:Aged; D015331:Cohort Studies; D005260:Female; D019538:Health Care Surveys; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006301:Health Services Needs and Demand / Q000639:trends; D006304:Health Status; D006787:Hospitals, Veterans / Q000191:economics / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008875:Middle Aged; D011320:Primary Health Care / Q000191:economics / Q000706:statistics & numerical data*; D011446:Prospective Studies; D016032:Randomized Controlled Trials as Topic; D020379:Risk Adjustment / Q000379:methods*; D012650:Self Disclosure; D014481:United States; D014493:United States Department of Veterans Affairs; D014600:Utilization Review / Q000379:methods*