Imputation of Race and Ethnicity in Health Insurance Marketplace Enrollment Data, 2015-2022 Open Enrollment Periods.

Health Care Access Health Equity Health Insurance Markets

Journal

Rand health quarterly
ISSN: 2162-8254
Titre abrégé: Rand Health Q
Pays: United States
ID NLM: 101622976

Informations de publication

Date de publication:
Nov 2022
Historique:
entrez: 9 12 2022
pubmed: 10 12 2022
medline: 10 12 2022
Statut: epublish

Résumé

Information on the race and ethnicity of individuals enrolled through the HealthCare.gov Health Insurance Marketplace is critical for assessing past enrollment efforts and determining whether outreach campaigns should be modified or tailored moving forward. However, approximately one-third of insurance applicants do not complete the race and Hispanic ethnicity questions on the Marketplace application. When self-reported race and ethnicity information is missing, other information about an individual can be used to infer race and ethnicity, such as surnames, first names, and addresses, with each characteristic contributing meaningfully to the identification of six mutually exclusive racial and ethnic groups: American Indian (AI)/Alaskan Native (AN); Asian American, Native Hawaiian, and Pacific Islander (AANHPI); Black; Hispanic; Multiracial; and White. Surnames are particularly useful for distinguishing people who identify as Hispanic and AANHPI from other racial and ethnic groups. Geocoded address information is particularly useful in distinguishing Black and White individuals who frequently reside in racially segregated neighborhoods. This article presents the results of imputing race and ethnicity for Marketplace enrollees from 2015 through 2022 using the modified Bayesian Improved First Name Surname and Geocoding (BIFSG) method, developed by the RAND Corporation, which uses surnames, first names, and residential addresses to indirectly estimate race and ethnicity.

Identifiants

pubmed: 36484074
pmc: PMC9718056

Types de publication

Journal Article

Langues

eng

Pagination

4

Informations de copyright

Copyright © 2022 RAND Corporation.

Auteurs

Classifications MeSH