More accurate racial and ethnic codes for Medicare administrative data.

TLDR

Medicare disparity analyses using administrative race and ethnicity data have been limited to Black and White beneficiaries because of small other categories, coding inaccuracies, and concerns about bias in broader analyses. The study aims to develop an imputation algorithm that dramatically improves coding accuracy for Hispanic and Asian or Pacific Islander Medicare beneficiaries. The algorithm imputes missing race and ethnicity using available demographic and enrollment data, thereby enhancing coding accuracy for Hispanic and Asian or Pacific Islander beneficiaries. Compared with self‑reported data, the algorithm raised sensitivity for Hispanic beneficiaries from 29.5 % to 76.6 % and for Asian or Pacific Islander beneficiaries from 54.7 % to 79.2 %, achieved a Kappa of 0.80 without losing specificity, and recoded 2,245,792 beneficiaries to Hispanic and 336,363 to Asian or Pacific Islander.

Abstract

Analyses of health care disparities in Medicare using administrative race and ethnicity data have typically been limited to Black and White beneficiaries. This is in part due to the small size of the other categories, inaccuracies in the race and ethnicity codes, and caveats that more extensive analyses would produce biased results. While previous Medicare efforts certainly improved the accuracy of race and ethnicity coding, we have developed an imputation algorithm that dramatically improves the accuracy of coding for Hispanic and Asian or Pacific Islander beneficiaries. When compared with self-reported race and ethnicity, sensitivity increased from 29.5 to 76.6 percent for Hispanic and from 54.7 to 79.2 percent for Asian and Pacific Islander beneficiaries, with no loss of specificity, and Kappa coefficients reaching 0.80. As a result, 2,245,792 beneficiaries were recoded to Hispanic and 336,363 to Asian or Pacific Islander.