2009-2010 VIReC Database a

Methods Cyber Seminar Seri

RESEARCHERS’ GUIDE TO VA DATA
2009-2010 VIReC Database and Methods Cyber Semina

2010 VIReC Database and Methods Cyber Semina

Assessing Race and Ethnicity

Session 10

February 1, 2010

Presented by: Kevin Stroupe

RESEARCHERS’ GUIDE TO VA DATA
Session Objectives

Introduction

/Overview

Race and Ethnicity in the VA DataRace and Ethnicity in Medicare DataVA R D t Q lit VA Race D

a

t

a Qualit

y

RecommendationsWhere to Go for More Help

RESEARCHERS’ GUIDE TO VA DATA
Session Objectives

Introduction

/Overview

Race and Ethnicity in the VA DataRace and Ethnicity in Medicare DataVA R D t Q lit VA Race D

a

t

a Qualit

y

RecommendationsWhere to Go for More Help

RESEARCHERS’ GUIDE TO VA DATA
Audience Poll

Have you ever used race data?

– Yes– No

RESEARCHERS’ GUIDE TO VA DATA
Audience Poll

How would y y ou rate your overall knowledge of race data?

– 1 (Never Used)– 2– 3– 4– 5 (Used Frequently, Very familiar)

RESEARCHERS’ GUIDE TO VA DATA
Introduction/Overview

Importance of race and ethnicity information for research

– Research on racial/ethnic disparities in he

care

– Research on clinical factors associated wit

Problems identified with race and ethnicity data in the VA

– Completeness– C i t ti Consis

tency over time

RESEARCHERS’ GUIDE TO VA DATA
Introduction/Overview

Well-documented and persistent healthcadisparities in the U.S. (IOM, 2003; AHRQ, 2005)

– Quantity and quality of health care is related to r

ethnicity (particularly African Americans and His

Root causes and solutions not clear (IOM; AHealthcare disparities are also found in VA financial barriers to receiving care are min(Saha, 2008)Consequently, additional research on healdisparities is important

RESEARCHERS’ GUIDE TO VA DATA
Introduction/Overview

Obtaining race may also be important foaddressing clinical questions in researchFor example:

– Race used when studying chronic kidney disea

African American race (along with age, genderserum creatinine) is in the calculation of the eg () lomerular filtration rate 

(eGFR

), which is used 

determine patients stage of chronic kidney dis

– In studies of medications, it may be important 

race because appropriate medication managemdiffer by race in some cases (e.g., hypertensio

RESEARCHERS’ GUIDE TO VA DATA
Session Objectives

Introduction

/Overview

Race and Ethnicity in the VA DataRace and Ethnicity in Medicare DataVA R D t Q lit VA Race D

a

t

a Qualit

y

RecommendationsWhere to Go for More Help

RESEARCHERS’ GUIDE TO VA DATA
Race and Ethnicity in the VA

Race and Ethnicity Categories (2003-pr

Ethnicity

Hispanic or Latino

Race

American Indian or Alaskan NativeAsianBlack or African AmericanN ti H ii P ifi I l d N

ative Hawaiian or Pacifi

c I

slan

d

e

r

White

Current method of VA race and ethnicity 

2 question format: ethnicity, raceSelf-reported

RESEARCHERS’ GUIDE TO VA DATA

Multiple races
Race and Ethnicity in the VA

Di ti R di St d 

Directives Regarding Stan

d

OMB Directive No 15 revision OMB Directive No. 15 revision

– In 1997 the OMB revised Directive 15 and 

established the latest standards for maintcollecting and presenting federal data on collecting, and presenting federal data on and ethnicity (OMB, 1997) 

VHA im

plementation

– The VHA, through Directive 2003-027, 

implemented the mandated revisions in 2(DVA, 2003)

RESEARCHERS’ GUIDE TO VA DATA
Race and Ethnicity in the VA

The Who, What, Where, When, & How of race/ethnicity information acquisition in 

WHO: WHAT: WHEN: WHERE: 

Information Source:

• VA Form 

10-10EZ, 

• Enrollment• Hospital 

• Online• Telephone 

• VH

Source: En• Patient

(selfreport)

• Proxy

10 10EZ, Application for Health Benefits (online, 

• Hospital 

admission

• Outpatient 

visit or preregistration

• Telephone 

call from local VHA facility

• In person 

EnCodeAd

• Proxy (online, In

paper, or by interview)

registration

• In-person 

visit to local VHA facility

InEnSp

• Ou

pe

• Co

inen

RESEARCHERS’ GUIDE TO VA DATA
Race and Ethnicity in the VA

Race data collection: Enrollment

– Race and ethnicity information for VA patients is obt

through completion of VA Form 10-10EZ (Application Benefits) at enrollment 

– The information is then entered into the Veterans He

Information Systems and Technology Architecture (Vstored in the Race Information and Patient Informatifiles

– Race and other demographic information is transmit

patient healthcare encounter data to the Austin InfoTechnology ( ) Center 

(AITC

) where it is stored in the N

Patient Care Database (NPCD)

– National Data Systems directs the construction of ex

these data, known as the Medical SAS (MedSAS) dat

RESEARCHERS’ GUIDE TO VA DATA
Race and Ethnicity in the VA

Race data collection: Clinical setting

– R t i t t k th i it d Recent requirement t

o ask 

eth

nicit

y and rac

questions during pre-registration, if missing

– Race and ethnicity may also be obtained by 

personnel if missing in VistA

– This information is gathered directly from th

(or proxy) at hospital admission or clinic reg

– Race and ethnicity are then entered into Vis

stored in the Race Information and Patient 

Information Sub

-files

– Separate VistA field for method of data colle

RESEARCHERS’ GUIDE TO VA DATA
Race and Ethnicity in the VA

Historical Variables 

Historical method of race and ethnicreporting in the VA 

– Method of ascertainment uncertain, prima

observer

-reported

– No multiple race reporting option– Sin

gq p 

le 

question ca

ptured both race and e

• Hispanic, White• Hispanic, Black• American Indian• Black• White

RESEARCHERS’ GUIDE TO VA DATA
Sources of Race Data in the V

Medical SAS Datasets

Variable Name MedSAS Dataset Description RACE Inpatient (PTF Main File) Inpatient (PTF Main File) FY1970 - presen

presen

Outpatient (Visit File) FY1997 - presenOutpatient (Event File) FY1998 - presen

RACE1-RACE6 Inpatient (PTF Main) FY2003 - presenRACE1-RACE7 Outpatient (Visit, Event) FY2004 - presenETHNIC Inpatient (PTF Main) FY2003 - presen

Outpatient (Visit, Event) FY2004 - presen

RESEARCHERS’ GUIDE TO VA DATA
Race and Ethnicity in the VA

Sources of race data in the VA: Other data 

– VA Vital Status File

• Death dates from multiple VA and non-VA data 

(e.g., PTF, BIRLS, SSA, Medicare)

• Contains race data from Medicare• VSF file structure

– Master File contains one record for each SSN-dat

(DOB)-gender combination found in VA data. So

have more than one record.

– Mini File contains one record for each SSN. An a

used to select the “best” DOB, gender, and DOD SSN.

• Race is in Master File only

– Decision Support System National Data Extracts

RESEARCHERS’ GUIDE TO VA DATA
Session Objectives

Introduction

/Overview

Race and Ethnicity in the VA DataRace and Ethnicity in Medicare DataVA R D t Q lit VA Race D

a

t

a Qualit

y

RecommendationsWhere to Go for More Help

RESEARCHERS’ GUIDE TO VA DATA
Medicare Race/Ethnicity

Potentially useful source for veterans in Medicare

– Age 65 and older (>95% of VA elderly)– Disabled (~20% of VA patients <65 years)– End stage renal disease

Derived primarily from Social Security Administratiadministrative records

– Obtained at the time of application for SSN and/or replacem– Reporting sources: Usually self- or family

Distinctions from current VA race & ethnicity data 

– ‘Hispanic’ is a race category– No multiple race

RESEARCHERS’ GUIDE TO VA DATA
Medicare Race/Ethnicity

Medicare Race Data from SSA

Until 1980, 4 categories only:

White Black Othe

r U

In 1980, ‘Other’ replaced by 

Asian, Asian American or Pacific Islande

r

HispanicAmerican Indian or Alaskan Native

RESEARCHERS’ GUIDE TO VA DATA
Medicare Race/Ethnicity

Medicare Race Data from SSA

Medicare race data quality issues

– I f ti t ll (th h bt 

In

formation on most enrollees (those

w

h

o

obt

prior to 1980) is limited to 4 original categorie

– SSN application form – sin

g q le 

question format 

multiple race reporting

Initiatives to improve race and ethnicity completeness and quality

– Periodic updates on American Indians and Alas

Natives from Indian Health Service

– 1997 mailed survey to enrollees classified as ‘

‘Unknown’, or with Spanish surname, requestirace/ethnicity self-report

RESEARCHERS’ GUIDE TO VA DATA
Session Objectives

Introduction

/Overview

Race and Ethnicity in the VA DataRace and Ethnicity in Medicare DataVA R D t Q lit VA Race D

a

t

a Qualit

y

RecommendationsWhere to Go for More Help

RESEARCHERS’ GUIDE TO VA DATA
VA Race Data Quality

Consistency over time

– Due to implementation of revised OMB direc

is discontinuity over time in race categories

– The VA’s implementation of the revised OMB 

directs that

• Race and ethnicity be collected and reported • Race be collected by self-report or proxy• Race categories comply with the federal stand

– New race/ethnicity data standards implemen

• MedSAS inpatient files 2003• MedSAS outpatient files 2004

RESEARCHERS’ GUIDE TO VA DATA
VA Race Data Quality

Consistency Over Time

Sohn, et al. (2006) 

Compared all unique users of VA healthcare serv

self-reported race/ethnicity data in 2004 with therace/ethnicity data from a pre-VHA directive peri2002 (N 988 277) 2002 (N = 988,277)Results from comparison of self-report wVHA directive values:

– Non-Hispanic White patients: 98% agreement– NH African American patients: 94% agreemen– For non-African American minorities agreemen

less: American Indian/Alaska Native: 32% 

Asian/Pacific Islander: 35%

RESEARCHERS’ GUIDE TO VA DATA
VA Race Data Quality

Consistenc

y over time

– Agreement improved when categories we

combined

Categories Agreement KWhite, ca e ca African Am

erica

n, Ot ehr 95 3. 

White, Other 95.7 African American, Other 97.8 

RESEARCHERS’ GUIDE TO VA DATA
VA Race Data Quality

Completeness:A substantial 

p p ortion of 

patients do not have a “usa

value in the VA Medical SAS Inpatient and Outpatie

Year Usable Race, % Year Usable Race1997 57 2004 621998 57 2005 671999 58 2006 722000 58 2007 752001 56 2008 762002 55 2009 78

* A usable race value is any value that is not ‘missing’ or ‘unknown’

2002 55 2009 782003 49

RESEARCHERS’ GUIDE TO VA DATA
VIReC VA Race Data Quality Study

Background

Options for filling in missing race in VA databases

– Filling in missing race values using data from 

years of VA data

– Filling in missing race using non

-VA data sour

For patients enrolled in Medicare

– Medicare databases serve as a source of non

-VA 

information

– To evaluate the practice of using Medicare data 

missin

g VA race data, it is im

portant to assess t

agreement between the non-missing VA race da

Medicare race data 

RESEARCHERS’ GUIDE TO VA DATA
VIReC VA Race Data Quality Study

Ob

jectives

1. To estimate the extent missing “usable” race in 

data could be reduced using multiple years of Me

2. To estimate the extent missing usable race in Me

could be reduced after merging VA and Medicare 

3. To evaluate the agreement between Medicare an

reported race data in MedSAS files– Using data from 2004 to 2005 after VA implemented O– Examining sensitivities & specificities– Examining Kappa statistic to evaluate agreement

RESEARCHERS’ GUIDE TO VA DATA
VIReC VA Race Data Quality Study

Methods

Patient cohort

– 10% representative sample of VHA patients w

obtained services during FY1997 – FY 2005

– A unique patient was defined as a unique SSN

date, and gender combination

Race data for patient cohort from 1997 2005 in

– M dSAS O t ti t Vi it fil M

edSAS O

u

tpatient Vi

sit files

– MedSAS Inpatient Acute Main files – N = 939,021

We obtained Medicare race data from Medicare Vital Status file

RESEARCHERS’ GUIDE TO VA DATA
VIReC VA Race Data Quality Study

Results: Characteristics of Patients with & without Usable Race Data VA MedSAS

U bl sa

e N U 

Race Value

(n=416,611)

N

o U

(n=

Age, mean (SD) 64.5 (15.6) 5Male, % 94 Number of In

p y atient Sta

ys: 

Mean (SD) 2.9 (3.4) 1Median 2 

Number of Outpatient Visits: 

Mean (SD) 61.6 (97.3) 12

Median 33 

RESEARCHERS’ GUIDE TO VA DATA

Years Followed in Study, Mean (SD)

4.9 (2.8) 2
Aim 1:

Improvement in Single Year Data with AdY f M dSAS D t Years of MedSAS Data

N Usable Race Usab

Single Yr Data, % All Yrs 

1997 300 272 300,272 57 1998 328,050 57 1999 342,525 58 2000 367,004 58 2001 407,993 56 2002 446,416 55 2003 471,794 49 2004 488 947 62 

RESEARCHERS’ GUIDE TO VA DATA

2004 488,947 62 2005 502,205 67 
Aim 1:

Improvement in Single Year Data witAdditi l Y f M dSAS D t Additional Years of MedSAS Data

This app ( 

roach resulted in a 3% (in 2

to 29% (in 1997) improvement in radata completenessThe largest increases were found in patient samples from earlier years

– Reflecting the relative utility of searching 

rather than earlier years of data 

RESEARCHERS’ GUIDE TO VA DATA
Aim 2:

Improvement in Race Completeness wi h ddi i f di f ith Addition of Medicare Data from 1997

Study Sample 

Yes No

(939,021) Usable Race Value in MedSAS?

522,410 (56%) 416,611 (44%)

Medicare Record Match?

Yes No

180,973 (43%) 235,6

Usable Race Value in Medicare?

Yes No

180,763 (99%) 210 (<1%)

RESEARCHERS’ GUIDE TO VA DATA
Aim 2: 

Improvement in Race Completeness wi h ddi i f di f ith Addition of Medicare Data from 1997

100% 99%

36%

19%

80%

Impact of Med

75%

63%

19%

40%60%

p

Race CompleMedSAS Data 

63% 56%

20%

0%

Medicare 

Enrollees in

All Study Sample

RESEARCHERS’ GUIDE TO VA DATA

Enrollees in

Study Sample
Aim 3: 

Comparison of Medicare with VA Data,

2004-2005

Analysis restricted to FY 2004 – 2005 order to use VA self-reported data as t“gold standard”151,721 patients enrolled in Medicare 2004-2005 with usable race value in band Medicare data

RESEARCHERS’ GUIDE TO VA DATA
Aim 3: 

Comparison of Medicare with VA Data, 20

Class

VA Race Medicare Race Constr

Consisten

White White W

Black or African American Black Black or Afr

American Indian or Alaska 

Native North American Native North Ame

Asian Asian

Asian, Pacif

Native Hawaiian or Other O

Pacific Islander Other

RESEARCHERS’ GUIDE TO VA DATA
Aim 3: 

Comparison of Medicare with VA Data, 20

Concordancebetween VA Race Values and Medicare Race Va

90%

100% 4%

(N=151,721)

60%70%80%90%

5%

36% 47%

30%40%50% 99%

55%

47%

96% 6% 

0%

10%20%

White Black North American Asian, Pacific

3%

RESEARCHERS’ GUIDE TO VA DATA

White Black North American

Native

Asian, Pacific

Islander, or Other

VA Race

aData values not 

percentages of l
Aim 3: 

Comparison of Medicare with VA Data, 20

MedicareRace Among VA Self‐Reported Hispanics

60%70% 64%40%50%20%30% 25%10% 5%

0%

6%

RESEARCHERS’ GUIDE TO VA DATA

0%

Hispanic White Black North American

Native

Asian, Pacif

Islander, or Ot

VAH
Aim 3: 

Comparison of Medicare with VA Data,

2004-2005

99 96

91

99 99 99.8 100

80

47

40 3660

20

0

White Black Asian NA Native

n=129,305 n=17,142 n=991 n=183

RESEARCHERS’ GUIDE TO VA DATA
Aim 3: 

Comparison of Medicare with VA Data,

2004-2005

0 89

1 0.95

0.89

0.8

0.47

0.37 0.4

0.6

0.2

0

White Black Asian NA Native

n=129,305 n=17,142 n=991 n=183 

RESEARCHERS’ GUIDE TO VA DATA
Aim 3: 

Comparison of Medicare with VA Data,

Among patients self-identified as White or B

2004-2005

VA data

– 99% and 96%, respectively, had concordant 

information in Medicare records (Kappa 0.89 a

Among the 1,174 non-Black minority patient

– 36% of North American Natives and 47% of A

Pacific Islanders, or Others were coded the saMedicare

– For these minority groups, Medicare data had 

sensitivity g and low overall rates of agreement

– VA minorities misclassified in Medicare

– Were most often classified as White 

RESEARCHERS’ GUIDE TO VA DATA
Session Objectives

Introduction

/Overview

Race and Ethnicity in the VA DataRace and Ethnicity in Medicare DataVA R D t Q lit VA Race D

a

t

a Qualit

y

RecommendationsWhere to Go for More Help

RESEARCHERS’ GUIDE TO VA DATA
Recommendations

Use of Medicare race information wireduce the problem of missing race istudies using administrative dataWh i VA VSF t h d t When using VA VSF, ma

t

ch on d

a

t

e

o

birth and gender, in addition to (scrambled) SSN

– Researchers will be most likely to identify 

right individuals in the VSF if they use all 

elements when conducting their VSF

-stud

cohort record match

RESEARCHERS’ GUIDE TO VA DATA
Recommendations

Use of a dichotomous race classificaof Black/African American and OtheVA studies using Medicare race information results in higher rates oaccurate classification than other groupings

– Gi th f ith hi h VA N th Given th

e frequency

with 

whi

ch VA North 

American Natives and Hispanics were clasas White (and Non-Hispanic) in Medicare since Medicare White and African-Americacategories both had high predictive value

RESEARCHERS’ GUIDE TO VA DATA
Recommendations

To supp g lement missin

g VA sel

f-re

po

ethnicity data, it may be preferable tpre-2004 VA data, rather than Medicdata (Sohn 2006) (Sohn, 2006)

– Medicare data cannot be used to identify 

Hispanics with any degree of accuracy or 

completeness

Consider other supplementary data sources

– Department of Defense– Special surveys

RESEARCHERS’ GUIDE TO VA DATA
Session Objectives

Introduction

/Overview

Race and Ethnicity in the VA DataRace and Ethnicity in Medicare DataVA R D t Q lit VA Race D

a

t

a Qualit

y

RecommendationsWhere to Go for More Help

RESEARCHERS’ GUIDE TO VA DATA
VIReC Help

VIReC Webpage

http://www.virec.research.va.gov

– Information on VA data sources and how to a– Documentation on some VA datasets, e.g., M

datasets

HSRD t Li t HSRData Listserv

– Join at the VIReC Web site– Discussion among Discussion among 500 data stewards, ma

>500 data stewards, ma

and users

– Past messages in archive (on intranet)

VIR C H l D k VIReC Help Desk

– VIReC staff will answer your question and/

you to available resources on topics

RESEARCHERS’ GUIDE TO VA DATA

y p

– VIReC@va.gov– (708) 202-2413
Selected Recent References on 

Race

/ y Ethnicit

y Data

AHRQ (Agency for Healthcare Research and Quality) (2005). National healthcare disparities report, 2005 (Rep. No. AHRQ 

(Rep. No. AHRQ 

0017). Rockville, MD: U.S. Department of Health and Human Services, Agency for Healthcare Research and Quality.

Baker, D. W., Cameron, K. A., Baker, D. W., Cameron, K. A., Feinglass Feinglass, J., Thompson, J. A., Georgas, P., Foster, S. et al. (2006). A system for rapidly and 

, J., Thompson, J. A., Georgas, P., Foster, S. et al. (2006). A system for rapidly and 

patients' race and ethnicity. Am J Public Health, 96, 532-537.

Bertolli, J., LeeLisa M., & Sullivan, P. S. (2007). Racial Misidentification of American

Indians/Alaska Natives in the HIV/AIDSReporting Systems of Five States and One

U b H lth J i di ti U S 1984 Urban Health Jurisdiction, U.S., 1984–2002 . P bli H lth R t 122 Public Health Reports, 122, 382-392.

Blustein, J. (1994). The Reliability of Racial Classifications in Hospital Discharge Abstract Data. American Journal of Public 

1021.

Boehmer, U., Kressin, N. R., Berlowitz, D. R., Christiansen, C. L., Kazis, L. E., & Jones, J. A. (2002). Self en, C. L., Kazis, L. E., & Jones, J. A. (2002). Self-reported reported vs admini

admini

data and study results. Am J Public Health, 92, 1471-1472.

Brahan, D. & Bauchner, H. (2005). Changes in reporting of race/ethnicity, socioeconomic status, gender, and age over 10 

/ethnicity, socioeconomic status, gender, and age over 10 

e163-e166.

Clegg, L. X., Reichman, M. E., Hankey, B. F., Miller, B. A., Lin, Y. D., Johnson, N. J. et al. (2007). Quality of race, Hispa Clegg, L. X., Reichman, M. E., Hankey, B. F., Miller, B. A., Lin, Y. D., Johnson, N. J. et al. (2007). Quality of race, Hispanic et

c et

stat s in pop lation status in population-based cancer registr data implicat based cancer registr based cancer registry data: implications for health disparit implications for health disparit ions for health disparit st dies y studies. Cancer Ca ses Control 18 Cancer Ca Cancer Causes Control ses Control, 18

Eicheldinger, C. & Bonito, A. (2008). More accurate racial and ethnic codes for Medicare administrative data. Health Care F

Elliott, M. N., Fremont, A., Morrison, P. A., Pantoja, P., & Lurie Elliott, M. N., Fremont, A., Morrison, P. A., Pantoja, P., & Lurie, N. (2008). A New , N. (2008). A New Method for Estimating Race/Ethnicity and 

Method for Estimating Race/Ethnicity and 

Where Administrative Records Lack Self Where Administrative Records Lack Self-Reported Race/Ethnicity. Reported Race/Ethnicity. Health Serv Res.

Ford, M. E. & Kelly, P. A. (2005). Conceptualizing and categorizing race and ethnicity in health services research. Health S

1675.

Friedman, D. J., Cohen, B. B., Averbach, A. R., & Norton, J. M. Friedman, D. J., Cohen, B. B., Averbach, A. R., & Norton, J. M. (2000). Race/ethnicity and OMB (2000). Race/ethnicity and OMB Directive 15: implications fo

rective 15: implications fo

practice. Am.J Public Health, 90, Public Health, 90, 1714-1719.

Gomez, S. L., Ke Gomez, S. L., Kel l id ( ) i i b lf lsey, J. L., G sey, J. L., Glaser, S. L., Lee, M. M., & S aser, S. L., Lee, M. M., & Sidney, S. ney, S. (2005). Incons . Inconsistencies between se etween self-reporte reported h ii 

d ethnicity an

a health maintenance organization. Ann Epidemiol, 15, 71-79.

Gomez, S. L. & Glaser, S. L. (2006). Misclassification of race/ethnicity in a population Gomez, S. L. & Glaser, S. L. (2006). Misclassification of race/ethnicity in a population-based cancer registry (United States

based cancer registry (United States

Control, 17, 771-781.

Hahn, R. A. (1992). The state of federal health statistics on racial and ethnic groups. JAMA, 267, 268-271.Hahn, R. A. & Stroup, D. F. (1994). Race and ethnicity in public health surveillance: criteria for the scientific use of soci Hahn, R. A. & Stroup, D. F. (1994). Race and ethnicity in public health surveillance: criteria for the scientific use of social c

c

Health Rep, 109, 7-15.

Hamilton, N. S., Edelman, D., Weinberger, M., & Hamilton, N. S., Edelman, D., Weinberger, M., & Jackson, G. L. (2009). Concordance between self kson, G. L. (2009). Concordance between self-reported race/ethnicity 

reported race/ethnicity 

V Aff i l i di l d Veteran Affairs electronic medical record. N C M d J 70 N C Med J, 70, 296-300.

IOM (Institute of Medicine) (2003). Unequal treatment: Confronting racial and ethnic disparities in health care Washingto

Academies Press.

Jones, C. P., Truman, B. I., Elam Jones, C. P., Truman, B. I., Elam-Evans, L. D., Jones, C. A., Jone Evans, L. D., Jones, C. A., Jones, C. Y., Jiles, R. et s, C. Y., Jiles, R. et al. (2008). Using "socially assigned ra al. (2008). Using "socially assigned race

ce

advantages in health status. Ethn Dis, 18, 496-504.

Kashner, T. M. (1998). Agreement between administrative files and written medical records: a case of the Department of VKashner, T. M. (1998). Agreement between administrative files and written medical records: a case of the Department of V

Care, 36, 1324-1336.

Kramer, B. J., Wang, M., Hoang, T., Harker, J. O., Finke, B., & Kramer, B. J., Wang, M., Hoang, T., Harker, J. O., Finke, B., & Saliba, D. (2006). Identification of American Indian and Alas liba, D. (2006). Identification of American Indian and Alas

d i i t ti d t f th V t H lth Ad i i t ti d th I di H lth

RESEARCHERS’ GUIDE TO VA DATA

administrative data of the Veterans Health Ad

ministration and the Indian Health
Selected Recent References on 

Race

/ y Ethnicit

y Data

Laws, M. B. & Heckscher, R. A. (2002). Racial and ethnic identification practices in public health data systems in New EnglLaws, M. B. & Heckscher, R. A. (2002). Racial and ethnic identification practices in public health data systems in New Engl

117, 50-61.

Mays, V. M., Ponce, N. A., Washington, D. L., & Cochran, S. D. (2003). Classification of race and ethnicity: implications for and ethnicity: implications for p

Public Health, 24, 83-110.

McAlpine McAlpine D D Beebe T J Davern M & Call K T (2007) Agreement between self McAlpine, D. D., Beebe, T. J., Davern, M., & Call, K. T. (2007). Agreement between self Agreement between self-reported and administrative race an

reported and administrative race an

Medicaid enrollees in Minnesota. Health Serv Res, 42, 2373-2388.

McBean, A. M. (2006). Improving Medicare's Data on Race and Ethnicity. National Academy of Social Insurance. Medicare McBean, A. M. (2006). Improving Medicare's Data on Race and Ethnicity. National Academy of Social Insurance. Medicare 

Ref Type: Serial ( Ref Type: Serial (Book,Monograph Book,Monograph)

Morgan, R. O., Wei, I. I., & Virnig, B. A. (2004). Improving identificati (2004). Improving identification of Hispanic males in Medicare: use of surname ma

on of Hispanic males in Medicare: use of surname ma

810-816.

Office of Management and Budget Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity, 

No. 62).

Pan, C. X., Glynn, R. J., Mogun, H., Choodnovskiy, I., & Avorn, J. (1999). Definition of race an J. (1999). Definition of race and ethnicity in older people i d ethnicity in older people in 

J Am Geriatr Soc, 47, 730-733.

Polednak, A. P. (2001). Agreement in race Polednak, A. P. (2001). Agreement in race-ethnicity coding between a hospital di ethnicity coding between a hospital discharge database and another database. 

Rhoades, D. (2005). Racial Misclassification and Disparities in Rhoades, D. (2005). Racial Misclassification and Disparities inCardiovascular Disease Among American Indians and Alask

Cardiovascular Disease Among American Indians and Alask

111, 1250-1256.

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