Abstract
Our objective was to enhance existing methods for indirectly estimating race/ethnicity in health care data by exploring ways to improve imputation accuracy with a total of 9,812,306 hospital visits from the Connecticut statewide hospitalization claims database from 2012 to 2017. Using this data, we developed multinomial logistic regression models to predict patients’ race and ethnicity when assuming that 50% of race/ethnicity is missing completely at random. Our models included predictors derived from Connecticut birth records, US Census data, and demographic patient-level data, and were compared using performance measures. Our model correctly classified the race and ethnicity of approximately 85% of patients in the Connecticut hospitalization claims data. We found the following [sensitivities and specificities] for our five race/ethnicity categories: non-Hispanic White [94, 83], non-Hispanic Black [76, 97], non-Hispanic Asian or Pacific Islander [41, 99.6], Hispanic [87, 95], and non-Hispanic other race [5, 99.7]. First name, surname, census tract and insurance type were key predictors. Further, Connecticut-specific name dictionaries were better at identifying non-White race and ethnicity compared to the national 2010 US Census surname dictionary. Therefore, state-specific health records, census information, and patients’ demographic characteristics can be utilized to improve the prediction of missing racial and ethnic information in Connecticut hospitalization claims. In addition, this approach can be adapted to other state-specific healthcare databases, which enhances opportunities to investigate and address racial disparities in health outcomes.
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Acknowledgements
Funding to support this research was received through a cooperative agreement between the State of Connecticut and the Centers for Medicare and Medicaid Services (1G1CMS331404). Hospitalization and birth registry data were obtained from the Connecticut Department of Public Health.
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Funding to support this research was received through a cooperative agreement between the State of Connecticut and the Centers for Medicare and Medicaid Services (1G1CMS331404).
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OH and RA acquired funding for the project, developed the methodology, and substantively contributed to and revised the manuscript. KZ implemented the methodology and was a major contributor in writing the manuscript. The authors assume full responsibility for all such analyses, interpretations and conclusions. All authors read and approved the final manuscript.
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Hospitalization and birth registry data were obtained from the Connecticut Department of Public Health (CT DPH) which does not endorse or assume any responsibility for any analyses, interpretations or conclusions based on the data. The authors assume full responsibility for all such analyses, interpretations and conclusions. This study was approved by the CT DPH Human Investigations Committee. There data are not publicly available because they contain personally identifiable information. Contact the CT DPH to inquire about access to these data sets.
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Zavez, K., Harel, O. & Aseltine, R.H. Imputing race and ethnicity in healthcare claims databases. Health Serv Outcomes Res Method 22, 493–507 (2022). https://doi.org/10.1007/s10742-022-00273-z
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DOI: https://doi.org/10.1007/s10742-022-00273-z