Using a Spanish Surname Match to Improve Identification of Hispanic Women in Medicare Administrative Data

As the largest health provider in the United States, the Centers for Medicare and Medicaid Services (CMS) maintains Medicare administrative/claims data that have been used extensively by researchers and federal/state governments for assessing equity among Medicare beneficiaries (Eggers and Prihoda 1982; Gornick et al. 1996; Eggers and Greenberg 2000; Schneider, Zaslavsky, and Epstein 2004; Jha et al. 2005; Trivedi et al. 2005). However, prior works have documented that Hispanics are substantially underrepresented in Medicare data (Arday et al. 2000; Morgan, Wei, and Virnig 2004; Waldo 2004). In brief, the underrepresentation stemmed from the fact that the race code contained in the Medicare Enrollment Database is obtained from the Social Security Administration Master Beneficiary Record (SSA-MBR), which is collected from the SSA application form (SS-5) (Lauderdale and Goldberg 1996). Before 1980, the SS-5 form had only three race options: white, black, and other. In 1980, the race options were expanded to six: white, black, Hispanic, Asian/Asian American/Pacific Islander, North American Indian/Alaskan Native, and unknown. However, the SSA-MBR has continued to assign individuals into white, black, and other, with Hispanics and nonwhite/nonblack individuals (e.g., Asians) being forced into one of these three categories. Detailed descriptions on the SSA race codes are provided elsewhere (Lauderdale and Goldberg 1996; Arday et al. 2000). The Medicare race code has been reported to identify less than 40 percent of Hispanic Medicare beneficiaries (Arday et al. 2000). Historically, the U.S. Bureau of the Census has used Spanish surnames to identify Hispanic population when self-identification is not attainable. The Spanish surname list was first constructed in 1950 using Hispanic surnames found in Arizona, California, Colorado, New Mexico, and Texas (Perkins 1993). Later, Passel and Word used the 1980 Decennial Census and the 1977 Federal Income Tax Returns to develop the Passel–Word Spanish surname list (i.e., the 1980 Census list of Spanish Surnames) (Perkins 1993). Word and Perkins then did additional refinement of the list by linking ethnicity and names directly using the 1990 Spanish Origin Research (SOR) data (Perkins 1993; Word and Perkins 1996). The SOR provided 1,868,781 householder (i.e., males or never married females) records that contain both surname and Hispanic origin. Based on the proportion of householders who are Hispanic, they classified distinct surnames into one of six classifications: heavily Hispanic (i.e., >75 percent of householders with a surname in this classification are Hispanics), generally Hispanic (50–75 percent), moderately Hispanic (25–50 percent), occasionally Hispanic (5–25 percent), rarely Hispanic (<5 percent), and indeterminate. This is referred to as the 1990 Census Spanish Surname list. An earlier study by Howard et al. (1983) used the 1980 Census list of Spanish Surnames and the Generally Useful Ethnic Search System (GUESS) to compare the surname-based identification with Hispanic self-identification obtained from a 1980 mailed survey examining the prevalence of respiratory diseases in Albuquerque, New Mexico. Their study found sensitivities of 85 and 87 percent from male respondents for the census list and GUESS methods, respectively, and slightly lower sensitivities of 79 and 82 percent from female respondents. A more recent publication reported the results of two studies assessing the accuracy of surname matching using the 1990 Census Spanish Surname list for identifying elderly Hispanic males in both regional and national Medicare data (Morgan, Wei, and Virnig 2004). Comparing with self-identified race/ethnicity collected from 1,530 male veterans aged 65 or older, the Spanish surname match augmented Medicare race code substantially improved the identification of Hispanic males. Sensitivity increased from 43 to 87 percent, while the overall κ statistic improved from 0.54 (95 percent CI: 0.46–0.62) to 0.86 (0.82–0.91). Corresponding improvements were seen among whites, where specificity improved from 61 to 87 percent and the overall κ improved from 0.69 (0.64–0.75) to 0.87 (0.83–0.90). Additionally, using surname augmented Medicare race code brought the estimated proportions of elderly Hispanic males in 16 counties from California, Colorado, Florida, Illinois, New York, and Texas (Spring 2002) into close correspondence with the county-level proportions shown in the U.S. Census 2000. The mean difference (±standard deviation) in proportion of Hispanics was reduced to 0.7 percent (±2.0 percent) from a difference of 11.0 percent (±9.8 percent) obtained when the Medicare race code alone was used. In parallel, the estimates of whites improved from an average difference of 9.3 percent (±9.1 percent) to 0.9 percent (±2.1 percent). This prior work was limited by its exclusive focus on male Medicare beneficiaries. Other investigators have reported that a Spanish surname match appears to work better among men and never-married women, who typically retain their parental surname, than among women who have been married and who may have adopted their husband's surname (Howard et al. 1983; Perkins 1993). However, most of the reported differences in accuracy between men and women have been restricted to the geographic locations of the studies. For example, differences in sensitivity for men and women range from small (0.99 versus 0.95) in San Antonio, TX (Hazuda et al. 1986) to sizeable (0.88 versus 0.70) in the San Francisco Bay Area (Perez-Stable et al. 1995). Thus, further work is necessary to determine to what extent a Spanish surname match would improve the identification of Hispanic women in Medicare data. In this paper, we report the results of a study using a combination of survey data, Medicare administrative files, and data from the U.S. Census 2000, to compare the effectiveness of a Spanish surname match among elderly women and men in geographically diverse metropolitan and nonmetropolitan areas.