AI Article Synopsis
- Census data are crucial for health care research but need to ensure respondent confidentiality, which has led to the introduction of a Differential Privacy framework in the 2020 Census.
- The study investigates how this new privacy protection affects the accuracy of the Bayesian Improved Surname and Geocoding (BISG) algorithm, which estimates race and ethnicity when people don't provide that information themselves.
- Results show that while the Differential Privacy approach slightly reduces accuracy for American Indian and Alaska Native estimates, it has minimal impact on other racial and ethnic groups, indicating it won't significantly hinder research on health disparities.
Article Abstract
Census data are vital to health care research but must also protect respondents' confidentiality. The 2020 decennial Census employs a new Differential Privacy framework; this study examines its effect on the accuracy of an important tool for measuring health disparities, the Bayesian Improved Surname and Geocoding (BISG) algorithm, which uses Census Block Group data to estimate race and ethnicity when self-reported data are unavailable. Using self-reported race and ethnicity data as our standard, we compared the accuracy of BISG estimates calculated using the original 2010 Census counts to the accuracy of estimates calculated using 2010 data but with 2020 Differential Privacy in place. The Differential Privacy methodology slightly decreases BISG accuracy for American Indian and Alaska Native people but has little effect for other groups, suggesting that the methodology will not impede health disparities research that employs BISG and similar methods.
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| http://dx.doi.org/10.1177/10775587241251870 | DOI Listing |
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