Predicting Hospitalization and Functional Decline in Older Health Plan Enrollees: Are Administrative Data as Accurate as Self-Report?

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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.


A longitudinal cohort study with 4 years of followup.


A large staff model HMO in western Washington State.


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


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.


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).


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.

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