Using a Spanish surname match to improve identification of Hispanic women in Medicare administrative data

Iris I. Wei, Beth A. Virnig, Dolly A. John, Robert O. Morgan

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

Objective. To assess the effectiveness of a Spanish surname match for improving the identification of Hispanic women in Medicare administrative data in which Hispanics are historically underrepresented. Data Sources. We collected self-identified race/ethnicity data (N= 2,997) from a mailed survey sent to elderly Medicare beneficiaries who resided in 11 geographic areas consisting of eight metropolitan counties and three nonmetropolitan areas (171 counties) in the fall of 2004. The 1990 Census Spanish Surname list was used to identify Hispanics in the Medicare data. In addition, we used data published on the U.S. Census Bureau website to obtain estimates of elderly Hispanics. Study Design. We used self-identified race/ethnicity as the gold standard to examine the agreement with Medicare race code alone, and with Medicare race code+Spanish surname match. Additionally, we estimated the proportions of Hispanic women and men, in each of the 11 geographic areas in our survey, using the Medicare race code alone and the Medicare race code+Spanish surname match, and compared those estimates with estimates derived from U.S. Census 2000 data. Principal Findings. The Spanish surname match dramatically increased the accuracy of the Medicare race code for identifying both Hispanic and white women, producing improvements comparable with those seen for men. Conclusions. We recommend the addition of a proxy race code in the Medicare data using the Spanish surname match to improve the accuracy of racial/ethnic representation.

Original languageEnglish (US)
Pages (from-to)1469-1481
Number of pages13
JournalHealth services research
Volume41
Issue number4 I
StatePublished - Aug 2006

Keywords

  • Medicare race code
  • Minorities
  • Sensitivity and specificity
  • Spanish surname
  • Women

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