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What’s in a Name? Accounting for Naming Conventions in NCHS Data Linkages Eric A. Miller National Center for Health Statistics (NCHS) 2012 FCSM Statistical.

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Presentation on theme: "What’s in a Name? Accounting for Naming Conventions in NCHS Data Linkages Eric A. Miller National Center for Health Statistics (NCHS) 2012 FCSM Statistical."— Presentation transcript:

1 What’s in a Name? Accounting for Naming Conventions in NCHS Data Linkages Eric A. Miller National Center for Health Statistics (NCHS) 2012 FCSM Statistical Policy Seminar December 4, 2012

2 “Two men say they’re Jesus. One of them must be wrong.” Mark Knopfler, Dire Straits

3 One reason for data sharing is data linkage Assessing the quality of linked data is different from assessing a standalone dataset The quality of variables from a specific source doesn’t matter if the linkage is poor Problems with linkage can produce poor quality data –Are the data fit for use? + Are the data fit for linkage? What Does This Have to do With Data Quality?

4 Names Names are commonly used in data linkages Important to account for name differences and naming conventions to produce a high quality linked data file

5 Quick Background on Data Linkage Deterministic –Exact match on linkage variables Frank ≠ Francis Probabilistic –Accounts for imperfect data –Probability of a match Frank ≈ Francis

6 Caveats of Data Linkage It’s not perfect Prince? Prince Rogers Nelson Prince Some things are out of our control!

7 Caveats of Data Linkage Varying levels of quality for linkage variables can substantially increase workload –Clean-up, reformatting –Clerical review Analysis of insufficiently linked data can produce biased estimates

8 Example - Hispanic Paradox Despite having a higher risk profile, Hispanics have been found to have lower mortality rates compared to non-Hispanic whites Markides and Coreil (1986). Public Health Reports; 101: 253-265

9 Mortality Rate per 100,000 Among Women in 1986- 1990 National Health Interview Survey Linked to 1991 National Death Index Liao et al. (1998). Mortality Patterns among Adult Hispanics: Findings from the NHIS, 1986 to 1990. AJPH.

10 Potential Reasons for Paradox Health selective immigration Salmon bias (return migration) Advantageous health behaviors and social support Data quality / Insufficient linkage

11 Potential Reasons for Paradox Data quality / Insufficient linkage –Naming conventions for Hispanics differ from other US populations Use of mother’s and father’s surname May not have single middle name –Less likely to have social security number Especially among older adults and foreign born

12 Percent of “True” Matches for Hispanics and Non-Hispanic Whites by Foreign-Born Status HispanicNon-Hispanic White Foreign-bornUS-bornForeign-bornUS-born Class 1 (“True”) Matches 32.5%50.0%57.4%62.5% Class 1: records agree on at least 8 digits of SSN as well as first and last name, middle initial, and birth year (+/- 3 years) Joseph Lariscy. Differential record linkage by Hispanic ethnicity and age in linked mortality studies: Implications for the epidemiologic paradox. J of Aging and Health (2011); 23: 1263-1284.

13 What does this have to do with NCHS? NCHS Record Linkage Program –Links survey data with data collected from administrative records –Designed to maximize the scientific value of the NCHS population-based surveys –Examine factors that influence chronic disease, disability, health care utilization, morbidity, and mortality

14 Linked NCHS surveys National Health Interview Survey (NHIS) 1999-2004 NHANES, NHANES III, and NHANES II NHANES I Epidemiologic Follow-up Study (NHEFS) The Second Longitudinal Study of Aging (LSOA II) National Nursing Home Survey (NNHS) 14

15 15 National Death Index Medicare and Medicaid enrollment and claims Social Security Administration Retirement and Disability Pilot projects –Florida Cancer Data System –Texas Supplemental Nutrition Assistance Program (SNAP) Linked Administrative Records

16 Case Study: NCHS Survey linkage with the NDI National Death Index (NDI) –A national file of identifying death record information (beginning with 1979 deaths) –Every four years we send a file of survey participants to NDI to conduct a linkage and identify participant deaths –We take additional steps to try and improve the linkage

17 NDI Matching Algorithm Social Security Number First name Middle initial Last name Month of birth Year of birth Sex Father’s surname State of birth Race State of residence State of birth Marital Status

18 Unweighted percent of NHIS sample adults aged 18 or older, refusing to provide SSN, 1997-2009

19 NCHS Record Linkage Program To make sure we provide research quality data, we spend a lot of time processing the data to increase the chance of finding a true match –Try to increase the number of matches while minimizing false matches Addressing name clean-up and naming conventions is a major activity

20 Methods – Name Clean-up Fix invalid characters Compress spaces Remove titles/descriptors/suffixes –e.g. Mr., baby, jr. Linkage uses NYSIIS phonetic codes –Accounts for misspellings or unusual spellings

21 Methods – Name Clean-up Create alternate records –Sent with original record Among women substitute surnames for last name Nicknames (using a look-up table) –Substituting Elizabeth for Beth


23 Methods – Name Clean-up –Accounting for Hispanic and Asian naming conventions Hispanic –Hispanic nickname lookup table –switch middle and last Asian –switch first and last

24 Hispanic Lookup Table SexFormal NameNicknames F Adelina Deli Lina F Adelaida Ade Adela M Adrián Adri F Adriana Adri M Alberto Alber Albertito Beto Berto Tico Tuco Tito M Alejandro Ale Álex Alejo Jandro Jano Sandro F Alejandra Sandra Ale Álex Aleja Jandra Jana M Alfonso Alfon Fon Fonso Fonsi Poncho F Alicia Ali Licha

25 Alternate Records Example NumberFirstMiddleLast 1DavidAméricoArias Ortiz 2DavidAméricoOrtiz 3DavidAméricoArias 4DavidAmérico 5BigPapi

26 Conclusions Care needs to be taken to avoid false links –Alternate records increases the number of potential matches If two men claim they’re Jesus, they can both be wrong –Need a higher level of scrutiny to determine that a pair of records match

27 Conclusions Accounting for name differences and naming conventions improves quality of the linked-data product Hope our efforts to account for Hispanic and Asian naming conventions reduces potential bias –Need to evaluate

28 Important Considerations How are names are collected? How are the names recorded? More likely to have formal names versus nicknames? –Surveys may differ from official documents Are maiden names (surnames) available? Are there consistent rules for recording names?

29 Acknowledgements Dr. Jennifer Parker Dr. Dean Judson Thank you

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