National Center for Health Statistics

Race and Ethnicity Modeling Applied to Linked Health Data

Lisa B. Mirel, Dean Resnick, Jessie L. Parker, Cindy Zhang, and Christine Cox

FCSM Session D-2: Dying for Better Data: Using Administrative Records to 

Improve the Quality and Utility of Health Survey Data

October 26, 2022
Background

 Linked health data enable researchers to explore social determinants of health 

(SDOH) and health equity research

 However, some survey and administrative data lack key variables for SDOH and 

health equity research

 NCHS has a data linkage program that links NCHS survey data with 

administrative records

 To further enhance SDOH and health equity research our team explored 

imputing race and ethnicity information that could be applied to linked data
Background: NCHS Data Linkage Program

 Create linked data files that support high quality research and program 

evaluation 

 Utilize state of the art linkage methodologies and provide documentation 

and support for analyzing linked data files

 Explore innovative methods for providing researcher access to linked data
NCHS surveys used in linkages

National Health Interview Survey (NHIS)

A nationally representative, cross-sectional sample of the US civilian 

noninstitutionalized population, which includes a household interview survey 

that serves as an important source of information on the nation’s health

National Health and Nutrition Examination Survey (NHANES)

A nationally representative, cross-sectional sample of the US civilian 

noninstitutionalized population, which includes a household interview followed 

by an examination in a mobile examination center that serves as an important 

source of information on the health and nutritional status of adults and children

National Hospital Care Survey (NHCS) 

NHCS collects data on patient care in hospital-based settings (inpatient, 

emergency, and outpatient departments) to describe patterns of health care 

delivery and utilization in the US 
5

NCHS Data 

Linkage 

Program

NCHS Survey Data

Population-based 

household surveys

National healthcare surveys

Receipt/type of housing 

assistance

Housing and Urban 

Development

Cause of death from 

National Death Index

Mortality

Medicare and Medicaid 

Healthcare utilization and 

expenditures

Geocoded 

Addresses 

Add contextual 

information to standard 

Census geocoded areas

Service records and 

benefit utilization

Department of 

Veterans Affairs

Examine factors 

associated with ESRD

End Stage Renal Disease
Motivation: adding race and ethnicity data to linked files

 NHCS includes information on conditions and treatments of patients from 

sampled hospitals but data on race and ethnicity is limited

– In the 2016 NHCS, nearly 70% of patient records were missing race and 

ethnicity information

 The linked NHCS-NDI data provide race and ethnicity information for 

decedents, but it remains missing for those assumed alive

– This limits the analyses that can be conducted by race and ethnicity

 Analysis: assess mortality rates by race and ethnicity; the denominator 

should include both those who are alive and have died
Methods

 Use model for imputing race and ethnicity

 Apply model to NHIS which includes self-reported race and ethnicity

– Assess model overall and at precision thresholds, using positive/negative 

predictive value and kappa statistics

 Apply model to linked NHCS-NDI data and calculate mortality rates by race 

and ethnicity, overall and a precision threshold
Model for race and ethnicity imputation

 Model builds on work by Marc Elliott, et al., described as Bayesian 

Surname Geocoding (BSG) method

 Model leverages race and ethnicity proportions (priors) derived from 

Census block

 First Name – used in analysis (in addition to last name)

 Name proportions among race and ethnicity used

– e.g., P(Last Name = ‘Clemente’ | Hispanic),

P(First Name = ‘Anna’ | Hispanic)
Modeling strategy

 Posterior distribution computation:

PP~ PP RRRRRRRR▫EEEEE = RR | CC CCCCCCCC BBBBBB BB

� PP FFFF | RRRRRRRR▫EEEEE = RR � PP LL | RRRRRRRR▫EEEEE = RR

P: Probability, R: Race and Ethnicity, FN: First Name, LN: Last Name 

 Imputation category assigned to group with highest probability
Imputation categories

 Ethnicity

– Hispanic (takes precedence over race, e.g., persons described as Hispanic are not 

assigned a race group)

 Race

– White (non-Hispanic)

– Black (non-Hispanic)

– Asian or Pacific Islander (API, non-Hispanic)

– American Indian or Alaskan Native (AIAN, non-Hispanic)
Evaluation with 2018 NHIS data

 Imputed race and ethnicity was compared to the 2018 NHIS respondent 

reported race and ethnicity (“Gold Standard”)

 Assessed Positive Predictive Value (PPV) and Negative Predictive Value (NPV)

 Compared respondent reported to imputed race and ethnicity using Cohen’s 

Kappa

– overall and by sex and age

 Initial evaluation uses all records

 Refined evaluation uses records with precision (P IIIIII II RRRRRRRR = RR > 80%)
Respondent-reported versus imputed: PPV and NPV

PPV NPV

Hispanic

Overall 89.1 95.4

Precision>80% 94.2 96.6

Non-Hispanic Black

Overall 72.1 95.8

Precision>80% 87.8 97.5

Non-Hispanic White

Overall 87.3 87.3

Precision>80% 90.8 94.7
Respondent-reported versus imputed: PPV and NPV (cont.)

PPV NPV

Non-Hispanic Asian*

Overall 70.5 97.8

Precision>80% 84.4 98.4

Non-Hispanic Other*

Overall 56.8 98.6

Precision>80% 82.5 99.0

* Comparison was made using NHIS public use categories for race and ethnicity (e.g., respondent reported 

non-Hispanic Asian was compared to imputed non-Hispanic API and respondent reported non-Hispanic 

Other was compared to imputed non-Hispanic AIAN)
Kappa statistic: all records and precision >80%

Hispanic NonHispanic 

Black

NonHispanic 

White

NonHispanic 

Asian

Non-Hispanic 

Other

All 0.80 0.66 0.72 0.66 0.25

All precision (p) >80% 0.86 0.80 0.81 0.77 0.30

Female 0.78 0.68 0.71 0.64 0.25

Female (p>80%) 0.85 0.82 0.81 0.76 0.32

Male 0.82 0.64 0.73 0.68 0.24

Male (p>80%) 0.88 0.77 0.82 0.79 0.28

Age 65+ 0.82 0.71 0.76 0.69 0.26

Age 65+ (p>80%) 0.90 0.86 0.87 0.79 0.29
Implementation: race and ethnicity imputation model 

applied to linked NHCS-NDI data 

 70% of NHCS patients are missing race and ethnicity

 Analysis: assess mortality rates by race and ethnicity; the denominator 

should include both those who are alive and have died

 Post-hospitalization mortality rates calculated by time after discharge (0-

30 days), age (65 and over), sex and imputed race and ethnicity
Mortality rates 0-30 days post hospital discharge for 65 

and older

NOTE: small number of 2016 NHCS patients imputed to non-Hispanic, AIAN. They are not included in this tabulation.
Summary

 This research demonstrates that it is possible to reliably impute such 

information using Bayesian techniques applied to data obtained from 

other sources

– Precision estimates >80% seem to increase concordance

 Imputation strategy employed here is relatively straightforward and uses 

publicly available sources to develop the race and ethnicity distributions

 Applying statistical techniques to impute critically important health 

information can enable further study of the role of race and ethnicity in 

health outcomes
References

 Elliott, M. N., Fremont, A., Morrison, P. A., Pantoja, P., & Lurie, N. (2008). A 

new method for estimating race/ethnicity and associated disparities 

where administrative records lack self-reported race/ethnicity. Health 

services research, 43(5p1), 1722-1736.

 Tzioumis, K. (2018). Demographic aspects of first names. Scientific 

data, 5(1), 1-9. 

 Data:

– https://www2.census.gov/census_2010/redistricting_file--pl_94-171/

– https://www.census.gov/topics/population/genealogy/data/2010_sur

names.html

– https://www.nature.com/articles/sdata201825
NCHS Data Linkage Program

Contact: Lisa Mirel Lmirel@cdc.gov

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The findings and conclusions in this report are those of the authors and do not necessarily represent the 

official position of the Centers for Disease Control and Prevention.