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Eliminating healthcare disparities remains a national priority. Large, population-based studies necessary to address healthcare disparities can be costly and difficult to perform, and may be compromised by sampling strategies and patient selection biases. An efficient alternative that is becoming increasingly attractive is the use of the Healthcare Cost & Utilization Project (HCUP) State Inpatient Databases (SID). A significant limitation of SID and other large databases is the quantity of missing data, in particular, "patient race", a key indicator for health disparities research. Two multiple imputation (MI) methods (1) the sequential regression multivariate imputation, and (2) the multivariate normal imputation are proposed for addressing the missing data issue in the SID. These approaches are compared through a comprehensive simulation study. Their advantages over the three commonly used missing data approaches (i.e. complete case analysis, random imputation, hot deck imputation) are also illustrated.