Imputing race and ethnic information in administrative health data

OBJECTIVE: To improve on existing methods to infer race/ethnicity in health care data through an analysis of birth records from Connecticut. DATA SOURCE: A total of 162 467 Connecticut birth records from 2009 to 2013. STUDY DESIGN: We developed a logistic model to predict race/ethnicity using data from US Census and patient-level information. Model performance was tested and compared to previous studies. Five performance measures were used for comparison. PRINCIPAL FINDINGS: Our full model correctly classifies 81 percent of subjects and shows improvement over extant methods. We achieved substantially improved sensitivity in predicting black race. CONCLUSIONS: Predictive models using Census information and patients' demographic characteristics can be used to accurately populate race/ethnicity information in health care databases, enhancing opportunities to investigate and address disparities in access to, utilization of, and outcomes of care.