title: Consistency of performance ranking of comorbidity adjustment scores in Canadian and U.S. utilization data.
abstract: OBJECTIVE
  The performance of standard comorbidity scores to control confounding is poorly defined in health care utilization data across elderly populations. We sought to evaluate and rank the performance of comorbidity scores across selected U.S. and Canadian elderly populations using health care utilization databases.
  
  
  METHODS
  Cross-population validation study.
  
  
  METHODS
  Study participants were residents age 65 years or older who had prescription drug coverage through state-funded programs selected from several large health care utilization databases available to the investigators: British Columbia, BC (N = 141,161), New Jersey, NJ (N = 235,881), and Pennsylvania, PA (N = 230,913).
  
  
  METHODS
  We calculated 6 commonly used comorbidity scores for all subjects during the baseline year (1994 for NJ and PA, and 1995 for BC). These included scores based on diagnoses (Romano, Deyo, D'Hoore, Ghali) and prescription drugs (CDS-1, CDS-2). The study outcome was 1-year mortality. The performance of scores was measured by c-statistics derived from multivariate logistic regression that included age and gender.
  
  
  RESULTS
  Across these 4 large elderly populations, we found the same rank order of performance in predicting 1-year mortality after including age and gender in each model: Romano (c-statistic 0.754 to 0.771), Deyo (c-statistic 0.753 to 0.768), D'Hoore (c-statistic 0.745 to 0.760), Ghali (c-statistic 0.733 to 0.745), CDS-1 (c-statistic 0.689 to 0.738), CDS-2 (c-statistic 0.677 to 0.718), and age and gender alone (c-statistic 0.664 to 0.681). Performance was improved by an average of 6% by adding the number of different prescription drugs received during the past year.
  
  
  CONCLUSIONS
  Performance ranking of 6 frequently used comorbidity scores was consistent across selected elderly populations. We recommend that investigators use these performance data as one important factor when selecting a comorbidity score for epidemiologic analyses of health care utilization data.
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Gigham and Women's Hospital and Harvard Medical School, Boston, MA 02120, USA. schneeweiss@post.harvard.edu
authors.forename: Sebastian
authors.initials: S
authors.lastname: Schneeweiss
authors.forename: Philip S
authors.initials: PS
authors.lastname: Wang
authors.forename: Jerry
authors.initials: J
authors.lastname: Avorn
authors.forename: Malcolm
authors.initials: M
authors.lastname: Maclure
authors.forename: Raia
authors.initials: R
authors.lastname: Levin
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Glynn
mesh_terms: D000368:Aged; D001955:British Columbia / Q000453:epidemiology; D002170:Canada / Q000453:epidemiology; D015897:Comorbidity; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D005260:Female; D005544:Forecasting / Q000379:methods; D006296:Health Services / Q000706:statistics & numerical data; D006302:Health Services Research / Q000379:methods*; D006801:Humans; D007345:Insurance Claim Review; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data; D009026:Mortality; D009515:New Jersey / Q000453:epidemiology; D010414:Pennsylvania / Q000453:epidemiology; D040442:Public Health Informatics; D020379:Risk Adjustment; D014481:United States / Q000453:epidemiology