title: Predicting hospitalization and functional decline in older health plan enrollees: are administrative data as accurate as self-report?
abstract: OBJECTIVE
  To compare the predictive accuracy of two validated indices, one that uses self-reported variables and a second that uses variables derived from administrative data sources, to predict future hospitalization. To compare the predictive accuracy of these same two indices for predicting future functional decline.
  
  
  METHODS
  A longitudinal cohort study with 4 years of follow-up.
  
  
  METHODS
  A large staff model HMO in western Washington State.
  
  
  METHODS
  HMO Enrollees 65 years and older (n = 2174) selected at random to participate in a health promotion trial and who completed a baseline questionnaire.
  
  
  METHODS
  Predicted probabilities from the two indices were determined for study participants for each of two outcomes: hospitalization two or more times in 4 years and functional decline in 4 years, measured by Restricted Activity Days. The two indices included similar demographic characteristics, diagnoses, and utilization predictors. The probabilities from each index were entered into a Receiver Operating Characteristic (ROC) curve program to obtain the Area Under the Curve (AUC) for comparison of predictive accuracy.
  
  
  RESULTS
  For hospitalization, the AUC of the self-report and administrative indices were .696 and .694, respectively (difference between curves, P = .828). For functional decline, the AUC of the two indices were .714 and .691, respectively (difference between curves, P = .144).
  
  
  CONCLUSIONS
  Compared with a self-report index, the administrative index affords wider population coverage, freedom from nonresponse bias, lower cost, and similar predictive accuracy. A screening strategy utilizing administrative data sources may thus prove more valuable for identifying high risk older health plan enrollees for population-based interventions designed to improve their health status.
authors.affiliation: Department of Medicine, University of Washington, and VA Puget Sound Health Care System, Seattle, USA.
authors.forename: E A
authors.initials: EA
authors.lastname: Coleman
authors.forename: E H
authors.initials: EH
authors.lastname: Wagner
authors.forename: L C
authors.initials: LC
authors.lastname: Grothaus
authors.forename: J
authors.initials: J
authors.lastname: Hecht
authors.forename: J
authors.initials: J
authors.lastname: Savarino
authors.forename: D M
authors.initials: DM
authors.lastname: Buchner
mesh_terms: D000203:Activities of Daily Living / Q000145:classification*; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D002908:Chronic Disease / Q000453:epidemiology*; D015331:Cohort Studies; D003625:Data Collection; D005260:Female; D005544:Forecasting; D016330:Frail Elderly / Q000706:statistics & numerical data*; D015577:Geriatric Assessment / Q000706:statistics & numerical data; D006279:Health Maintenance Organizations / Q000706:statistics & numerical data*; D006293:Health Promotion / Q000706:statistics & numerical data; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D012372:ROC Curve; D015203:Reproducibility of Results; D018570:Risk Assessment; D014861:Washington / Q000453:epidemiology