Our primary outcomes were all-cause mortality and first hospitalization. Of the 34,103 subjects who were eligible for ACQUIP, 12,388 subjects returned both a health inventory and the SF-36 at entry to the study and were eligible for this analysis. The health inventory asked whether a provider had told patients if they had any of 25 common chronic illnesses. We used Coxmodeling to estimate the hazard ratio for mortality and for first hospitalization. Because patients below 50 years of age violated the proportional hazards assumptions, the analysis was restricted to the 10,947 patients over 50 years of age. These patients were followed for a mean of 722.5 (plus or minus 84.3) days. RESULTS: Using a derivation set of 5,469 patients, the SIC was constructed using age, smoking status and 7 of 25 self-reported medical conditions that were univariately associated with increased mortality. The SIC was predictive of both mortality and hospitalizations when tested in a validation set of 5,478 patients. Multiple imputation methods were compared with a strategy of enhancing the clinical database by merging it with diagnoses derived from administrative ICD-9 codes in hospital discharge data and day procedure data (Norris et al., 2000) . Logistic regression models predicting death at one year were based on the different missing data strategies applied to the 1995 data and were evaluated using measures of discrimination and goodness of fit. The strategies were further evaluated by examining how well the logistic regression models predicted outcomes in data collected from patients in 1996.