Premium
Imputing race and ethnic information in administrative health data
Author(s) -
Xue Yishu,
Harel Ofer,
Aseltine Robert H.
Publication year - 2019
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/1475-6773.13171
Subject(s) - ethnic group , census , race (biology) , health care , logistic regression , race and health , medicine , health equity , demography , computer science , gerontology , public health , environmental health , population , machine learning , nursing , political science , sociology , gender studies , law
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.