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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 patientlevel 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.

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