Dataset Information
Imputing race and ethnic information in administrative health data.
Ontology highlight
ABSTRACT: 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.
SUBMITTER: Xue Y
PROVIDER: S-EPMC6606547 | biostudies-literature | 2019 Aug
REPOSITORIES: biostudies-literature
Publications
Imputing race and ethnic information in administrative health data.
Health services research 20190517 4
<h4>Objective</h4>To improve on existing methods to infer race/ethnicity in health care data through an analysis of birth records from Connecticut.<h4>Data source</h4>A total of 162 467 Connecticut birth records from 2009 to 2013.<h4>Study design</h4>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.<h4>Principal findings ...[more]