Predicting hospitalization and functional decline in older health plan enrollees: are administrative data as accurate as self-report?

J Am Geriatr Soc. 1998 Apr;46(4):419-25. doi: 10.1111/j.1532-5415.1998.tb02460.x.


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.

Design: A longitudinal cohort study with 4 years of follow-up.

Setting: A large staff model HMO in western Washington State.

Participants: HMO Enrollees 65 years and older (n = 2174) selected at random to participate in a health promotion trial and who completed a baseline questionnaire.

Measurement: 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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Activities of Daily Living / classification*
  • Aged
  • Aged, 80 and over
  • Area Under Curve
  • Chronic Disease / epidemiology*
  • Cohort Studies
  • Data Collection*
  • Female
  • Forecasting
  • Frail Elderly / statistics & numerical data*
  • Geriatric Assessment / statistics & numerical data
  • Health Maintenance Organizations / statistics & numerical data*
  • Health Promotion / statistics & numerical data
  • Hospitalization / statistics & numerical data*
  • Humans
  • Longitudinal Studies
  • Male
  • ROC Curve
  • Reproducibility of Results
  • Risk Assessment
  • Washington / epidemiology