Cited by 78Medical Coding and Health InformationCensus and Population EstimationData Quality and Management

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

Open access
Wei, Iris I., Virnig, Beth A., John, Dolly A., Morgan, Robert O.·2006·Health Services Research

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/j.1475-6773.2006.00550.x

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Imputation was recommended not only as a solution for missing data but also to improve the accuracy of implausible data.

    “Imputation was recommended not only as a solution for missing data but also to improve the accuracy of implausible data.29,30,36,37,66,71”

    From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [66]

    The Conclusions recommend adding a Spanish-surname-based proxy race code to improve the accuracy of racial and ethnic representation in Medicare data. This supports using a proxy to address inaccurate coding, but the paper does not recommend imputation for missing data or describe the codes as implausible data. (LLM, conf=0.77)

    Experimental — accuracy not yet measured
  • Methods involving Spanish surname coding were investigated in several studies to increase the reliability and consistency of data from Hispanic patients.

    “Methods involving Spanish surname coding, a close relative of BISG that uses a patient’s last name to guess their ethnicity, were investigated in several studies to increase the reliability and consistency of data from Hispanic patients.29,36,37,66”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Research works printed but not linked: 13

  1. Arday, S. L., D. R. Arday, S. Monroe, and J. Zhang. 2000. ''HCFA's Racial and Ethnic Data: Current Accuracy and Recent Improvements.'' Health Care Financing Review 21 (4): 107-16.
  2. Eggers, P. W., and L. G. Greenberg. 2000. ''Racial and Ethnic Differences in Hos- pitalization Rates among Aged Medicare Beneficiaries, 1998.'' Health Care Financing Review 21 (4): 91-105.
  3. Eggers, P. W., and R. Prihoda. 1982. ''Pre-Enrollment Reimbursement Patterns of Medicare Beneficiaries Enrolled in 'At-Risk' HMOs.'' Health Care Financing Review 4 (1): 55-73.
  4. Gornick, M. E., P. W. Eggers, T. W. Reilly, R. M. Mentnech, L. K. Fitterman, L. E. Kucken, and B. C. Vladeck. 1996. ''Effects of Race and Income on Mortality and Use of Services among Medicare Beneficiaries.'' New England Journal of Medicine 335 (11): 791-9.
  5. Hazuda, H. P., P. J. Comeaux, M. P. Stern, S. M. Haffner, C. W. Eifler, and M. Rosenthal. 1986. ''A Comparison of Three Indicators for Identifying Mexican Americans in Epidemiologic Research. Methodological Findings from the San Antonio Heart Study.'' American Journal of Epidemiology 123 (1): 96-112.
  6. Howard, C. A., J. M. Samet, R. W. Buechley, S. D. Schrag, and C. R. Key. 1983. ''Survey Research in New Mexico Hispanics: Some Methodological Issues.'' American Journal of Epidemiology 117 (1): 27-34.
  7. Jha, A. K., E. S. Fisher, Z. Li, E. J. Orav, and A. M. Epstein. 2005. ''Racial Trends in the Use of Major Procedures among the Elderly.'' The New England Journal of Medicine 353: 683-91.
  8. Lauderdale, D. S., and J. Goldberg. 1996. ''The Expanded Racial and Ethnic Codes in the Medicare Data Files: Their Completeness of Coverage and Accuracy.'' American Journal of Public Health 86 (5): 712-6.
  9. Morgan, R. O., I. I. Wei, and B. A. Virnig. 2004. ''Improving Identification of Hispanic Males in Medicare: Use of Surname Matching.'' Medical Care 42 (8): 810-6.
  10. Perez-Stable, E. J., R. A. Hiatt, F. Sabogal, and R. Otero-Sabogal. 1995. ''Use of Spanish Surnames to Identify Latinos: Comparison to Self-Identification.'' Journal of the National Cancer Institute Monographs 18: 11-5.
  11. Schneider, E. C., A. M. Zaslavsky, and A. M. Epstein. 2004. ''Use of High-Cost Op- erative Procedures by Medicare Beneficiaries Enrolled in For-Profit and Not-for- Profit Health Plans.'' New England Journal of Medicine 350: 143-50.
  12. Trivedi, A. N., A. M. Zaslavsky, E. C. Schneider, and J. Z. Ayanian. 2005. ''Trends in the Quality of Care and Racial Disparities in Medicare Managed Care.'' New England Journal of Medicine 353: 692-700.
  13. Waldo, D. R. 2004. ''Accuracy and Bias of Race/Ethnicity Codes in the Medicare Enrollment Database.'' Health Care Financing Review 26 (2): 61-72.

Cited by

Sorted by citations

Authors
Wei, I. I., Virnig, B. A., John, D. A., & Morgan, R. O.
Affiliations
  • Michael E. DeBakey VA Medical Center (US)
  • University of Minnesota (US)
  • University of Washington (US)
Corresponding author
Wei, Iris I.
Venue
Health Services Research
Publisher
Wiley
Published
2006-05-05
Volume
41
Issue
4p1
Pages
1469-1481
ISSN
  • 0017-9124 (ISSN-L)
  • 1475-6773
OpenAlex
W2110385097
Cited by
78
References
0
Citations by year
2026: 2 · 2025: 3 · 2024: 4 · 2023: 7 · 2022: 2 · 2021: 4 · 2020: 3 · 2019: 7 · 2017: 2 · 2016: 5 · 2015: 6 · 2014: 4 · 2013: 8 · 2012: 3
FWCI
5.85
Type
journal article
Language
en
Open access
Locations
  • Health Services Research (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • U.S. Department of Veterans Affairs
  • Health Services Research and Development
  • National Institute on Aging (R01AG019284)
Topics
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
  • Physical Sciences › Mathematics › Statistics and Probability › Census and Population Estimation
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
Keywords
Hispanic women, elderly Medicare beneficiaries
MeSH terms
Data Collection, Female, Medicare, Hispanic or Latino, Humans, Male, Names, Sensitivity and Specificity, United States

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review