The Contribution of First-name Information to the Accuracy of Racial-and-Ethnic Imputations Varies by Sex and Race-and-Ethnicity Among Medicare Beneficiaries.

Background: 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.

Objective: 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.

A Comparison of Methods for Classifying and Modeling Respondents Who Endorse Multiple Racial/Ethnic Categories: A Health Care Experience Application.

Background: Race/ethnicity information is vital for measuring disparities across groups, and self-report is the gold standard. Many surveys assign simplified race/ethnicity based on responses to separate questions about Hispanic ethnicity and race and instruct respondents to "check all that apply." When multiple races are endorsed, standard classification methods either create a single heterogenous multiracial group, or attempt to impute the single choice that would have been selected had only one choice been allowed.

Teaching Business Demography Using Case Studies.

Many faculty members consider using case studies but not all end up using them. We provide a brief review of what cases are intended to do and identify three ways in which they can be used. We then use an example to illustrate how we have used the case study method in teaching business demography.

A new method for estimating race/ethnicity and associated disparities where administrative records lack self-reported race/ethnicity.

Objective: To efficiently estimate race/ethnicity using administrative records to facilitate health care organizations' efforts to address disparities when self-reported race/ethnicity data are unavailable.

Data Source: Surname, geocoded residential address, and self-reported race/ethnicity from 1,973,362 enrollees of a national health plan.

Study Design: We compare the accuracy of a Bayesian approach to combining surname and geocoded information to estimate race/ethnicity to two other indirect methods: a non-Bayesian method that combines surname and geocoded information and geocoded information alone.