title: Health care expenditure prediction with a single item, self-rated health measure.
abstract: BACKGROUND
  Prediction models that identify populations at risk for high health expenditures can guide the management and allocation of financial resources.
  
  
  OBJECTIVE
  To compare the ability for identifying individuals at risk for high health expenditures between the single-item assessment of general self-rated health (GSRH), "In general, would you say your health is Excellent, Very Good, Good, Fair, or Poor?," and 3 more complex measures.
  
  
  METHODS
  We used data from a prospective cohort, representative of the US civilian noninstitutionalized population, to compare the predictive ability of GSRH to: (1) the Short Form-12, (2) the Seattle Index of Comorbidity, and (3) the Diagnostic Cost-Related Groups/Hierarchal Condition Categories Relative-Risk Score. The outcomes were total, pharmacy, and office-based annualized expenditures in the top quintile, decile, and fifth percentile and any inpatient expenditures.
  
  
  METHODS
  Medical Expenditure Panel Survey panels 8 (2003-2004, n = 7948) and 9 (2004-2005, n = 7921).
  
  
  RESULTS
  The GSRH model predicted the top quintile of expenditures, as well as the SF-12, Seattle Index of Comorbidity, though not as well as the Diagnostic Cost-Related Groups/Hierarchal Condition Categories Relative-Risk Score: total expenditures [area under the curve (AUC): 0.79, 0.80, 0.74, and 0.84, respectively], pharmacy expenditures (AUC: 0.83, 0.83, 0.76, and 0.87, respectively), and office-based expenditures (AUC: 0.73, 0.74, 0.68, and 0.78, respectively), as well as any hospital inpatient expenditures (AUC: 0.74, 0.76, 0.72, and 0.78, respectively). Results were similar for the decile and fifth percentile expenditure cut-points.
  
  
  CONCLUSIONS
  A simple model of GSRH and age robustly stratifies populations and predicts future health expenditures generally as well as more complex models.
authors.affiliation: Division of General Internal Medicine, Tulane University School of Medicine, New Orleans, Louisiana 70112, USA. kdesalv@tulane.edu
authors.forename: Karen B
authors.initials: KB
authors.lastname: DeSalvo
authors.forename: Tiffany M
authors.initials: TM
authors.lastname: Jones
authors.forename: John
authors.initials: J
authors.lastname: Peabody
authors.forename: Jay
authors.initials: J
authors.lastname: McDonald
authors.forename: Stephan
authors.initials: S
authors.lastname: Fihn
authors.forename: Vincent
authors.initials: V
authors.lastname: Fan
authors.forename: Jiang
authors.initials: J
authors.lastname: He
authors.forename: Paul
authors.initials: P
authors.lastname: Muntner
mesh_terms: D000328:Adult; D015331:Cohort Studies; D005260:Female; D005544:Forecasting; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006304:Health Status; D006306:Health Surveys; D006801:Humans; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D018570:Risk Assessment; D014481:United States