Findings from RAND Corporation Provides New Data on Insurance (The Contribution of First-name Information To the Accuracy of Racial-and-ethnic Imputations Varies By Sex and Race-and-ethnicity Among Medicare Beneficiaries): Insurance

2022 JUL 28 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- New research on Insurance is the subject of a report. According to news reporting originating from Santa Monica, California, by NewsRx correspondents, research stated, “Data on race-and-ethnicity that are needed to measure health equity are often limited or missing. The importance of first name and sex in predicting race-and-ethnicity is not well understood.”

Financial support for this research came from Centers for Medicare & Medicaid Services (CMS).

Our news editors obtained a quote from the research from RAND Corporation, “The objective of this study was to compare the contribution of first-name information to the accuracy of basic and more complex racial-and-ethnic imputations that incorporate surname information. We imputed race-and-ethnicity in a sample of Medicare beneficiaries under 2 scenarios: (1) with only sparse predictors (name, address, sex) and (2) with a rich set (adding limited administrative race-and-ethnicity, demographics, and insurance). A total of 284,627 Medicare beneficiaries who completed the 2014 Medicare Consumer Assessment of Healthcare Providers and Systems survey and reported race-and-ethnicity were included. Hispanic, non-Hispanic Asian/Pacific Islander, and non-Hispanic White racial-and-ethnic imputations are more accurate for males than females under both sparse-predictor and rich-predictor scenarios; adding first-name information increases accuracy more for females than males. In contrast, imputations of non-Hispanic Black race-and-ethnicity are similarly accurate for females and males, and first names increase accuracy equally for each sex in both sparse-predictor and rich-predictor scenarios. For all 4 racial-and-ethnic groups, incorporating first-name information improves prediction accuracy more under the sparse-predictor scenario than under the rich-predictor scenario.”

According to the news editors, the research concluded: “First-name information contributes more to the accuracy of racial-and-ethnic imputations in a sparse-predictor scenario than in a rich-predictor scenario and generally narrows sex gaps in accuracy of imputations.”

This research has been peer-reviewed.

For more information on this research see: The Contribution of First-name Information To the Accuracy of Racial-and-ethnic Imputations Varies By Sex and Race-and-ethnicity Among Medicare Beneficiaries. Medical Care, 2022;60(8):556-562. Medical Care can be contacted at: Lippincott Williams & Wilkins, Two Commerce Sq, 2001 Market St, Philadelphia, PA 19103, USA. (Lippincott Williams and Wilkins - www.lww.com; Medical Care - http://journals.lww.com/lww-medicalcare/pages/default.aspx)

The news editors report that additional information may be obtained by contacting Marc N. Elliott, RAND Corporation, 1776 Main St, Santa Monica, CA 90401, United States. Additional authors for this research include Ann Haas, Amelia M. Haviland, Jacob W. Dembosky, John L. Adams, Peter A. Morrison, Sarah Gaillot and Loida Tamayo.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1097/MLR.0000000000001732. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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