{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Xue Y"],"funding":["Centers for Medicare and Medicaid Services"],"pagination":["957-963"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6606547"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["54(4)"],"pubmed_abstract":["<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</h4>Our full model correctly classifies 81 percent of subjects and shows improvement over extant methods. We achieved substantially improved sensitivity in predicting black race.<h4>Conclusions</h4>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."],"journal":["Health services research"],"pubmed_title":["Imputing race and ethnic information in administrative health data."],"pmcid":["PMC6606547"],"funding_grant_id":["1G1CMS331404"],"pubmed_authors":["Harel O","Aseltine RH","Xue Y"],"additional_accession":[]},"is_claimable":false,"name":"Imputing race and ethnic information in administrative health data.","description":"<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</h4>Our full model correctly classifies 81 percent of subjects and shows improvement over extant methods. We achieved substantially improved sensitivity in predicting black race.<h4>Conclusions</h4>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.","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 Aug","modification":"2025-04-29T10:31:35.834Z","creation":"2025-04-06T19:30:32.847Z"},"accession":"S-EPMC6606547","cross_references":{"pubmed":["31099021"],"doi":["10.1111/1475-6773.13171"]}}