What’s in a Name? Accounting for Naming Conventions in NCHS Data Linkages

Eric A. MillerNational Center for Health Statistics (NCHS)

2012 FCSM Statistical Policy SeminarDecember 4, 2012

“Two men say they’re Jesus. One of them must be wrong.”

Mark Knopfler, Dire Straits

• 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?

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

Quick Background on Data Linkage

• Deterministic– Exact match on linkage

variables• Frank ≠ Francis

• Probabilistic– Accounts for imperfect data– Probability of a match

• Frank ≈ Francis

Caveats of Data Linkage

• It’s not perfect

Prince ?Prince Rogers Nelson

Prince

Some things are out of our control!

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

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

Mortality Rate per 100,000 Among Women in 1986-1990 National Health Interview Survey Linked to 1991 National Death Index

Age 18-44 Age 45-64 Age 65+0

1000

2000

3000

4000

80

642

3504

182

969

3928

97

480

2438

White-NH Black-NH Hispanic

Rat

e p

er 1

00,0

00

Liao et al. (1998). Mortality Patterns among Adult Hispanics: Findings from the NHIS, 1986 to 1990. AJPH.

Potential Reasons for Paradox• Health selective immigration• Salmon bias (return migration)• Advantageous health behaviors and social

support• Data quality / Insufficient linkage

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

Percent of “True” Matches for Hispanics and Non-Hispanic Whites by Foreign-Born Status

Hispanic Non-Hispanic White

Foreign-born US-born Foreign-born US-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.

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)

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

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

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

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

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

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

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

Nickname Lookup TableSEX NICKNAME PROPER NAME

M ABE ABRAHAM

F AGGIE AGNES

M AL ALBERT

M ALEX ALEXANDER

M ALF ALFRED

F ALLIE ALBERTA

M ANDY ANDREW

Example: If first name=‘Andy’ then alternate record first name=‘Andrew’

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

Hispanic Lookup TableSex Formal Name Nicknames

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

Alternate Records Example

Number First Middle Last

1 David Américo Arias Ortiz

2 David Américo Ortiz

3 David Américo Arias

4 David Américo

5 Big Papi

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

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

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?

Acknowledgements

• Dr. Jennifer Parker• Dr. Dean Judson

Thank you