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

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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. 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.