The Quality of Reporting on The Quality of Reporting on Race & Ethnicity in US Hospital Race & Ethnicity in US Hospital

Discharge Abstract DataDischarge Abstract Data

Roxanne Andrews, PhDRoxanne Andrews, PhD

June 10, 2008June 10, 2008

BackgroundBackground

Statewide, all-payer hospital administrative Statewide, all-payer hospital administrative (claims) data are collected in nearly all states(claims) data are collected in nearly all states

Many state databases include race-ethnicity Many state databases include race-ethnicity as a mandatory or voluntary data elementas a mandatory or voluntary data element

AHRQ creates the Nationwide Inpatient AHRQ creates the Nationwide Inpatient Sample (NIS) for national estimates from Sample (NIS) for national estimates from statewide datastatewide data

About a quarter of NIS records are missing About a quarter of NIS records are missing race-ethnicityrace-ethnicity

National estimates by race-ethnicity are National estimates by race-ethnicity are needed for monitoring disparities, e.g. for the needed for monitoring disparities, e.g. for the National Healthcare Disparities ReportNational Healthcare Disparities Report

Study PurposesStudy Purposes

Examine the completeness & accuracy Examine the completeness & accuracy of race-ethnicity data collected in of race-ethnicity data collected in statewide discharge databasesstatewide discharge databases

Evaluate a method for making national Evaluate a method for making national estimates by race-ethnicity from estimates by race-ethnicity from statewide databasesstatewide databases

Data Sources:Data Sources: Healthcare Cost and Utilization Project, Healthcare Cost and Utilization Project,

20052005

State Inpatient Databases (SID) are evaluatedState Inpatient Databases (SID) are evaluated

– Voluntary Federal-State-Industry partnership Voluntary Federal-State-Industry partnership State governments, hospital associations, other private State governments, hospital associations, other private

– All inpatient discharge data in the stateAll inpatient discharge data in the state

– 37 states (in 2005); 37 states (in 2005); ~~ 90% of US discharges 90% of US discharges

Nationwide Inpatient Sample (NIS) is benchmark Nationwide Inpatient Sample (NIS) is benchmark for evaluation of national estimatesfor evaluation of national estimates

– 20% stratified sample of hospitals in US20% stratified sample of hospitals in US

– Uses SID hospitals as sample frameUses SID hospitals as sample frame

– Uses AHA Annual Survey as “universe”Uses AHA Annual Survey as “universe”

The Making of the SIDThe Making of the SID

Patient enters Patient enters hospitalhospital

Hospital sends Hospital sends billing data & billing data &

additional data (e.g. additional data (e.g. race-ethnicity) torace-ethnicity) to

Data OrganizationsData Organizations

States store data States store data in varying formatsin varying formats

Billing Billing record record createdcreated

AHRQ AHRQ standardizes data standardizes data to create uniform to create uniform HCUP databasesHCUP databases

NIS Is a Stratified Sample of NIS Is a Stratified Sample of Hospitals from the SIDHospitals from the SID

20% Stratified Sample of Hospitals

37 State Inpatient 37 State Inpatient DatabasesDatabases

20052005 N = ~ 1,000 HospitalsN = ~ 1,000 Hospitals

N = ~ 8 million dis.N = ~ 8 million dis.

Nationwide Nationwide Inpatient SampleInpatient Sample

Ownership/Control

U.S. Region

Urban/Rural

5 NIS Strata

Bed Size

Teaching Status

Ownership/Control

Bed Size

States Code Race & States Code Race & Ethnicity in Different WaysEthnicity in Different Ways

States are not bound by OMB standardsStates are not bound by OMB standards

Race is not included on the standard for Race is not included on the standard for the hospital bill (Uniform Bill) the hospital bill (Uniform Bill)

HCUP Race & Ethnicity HCUP Race & Ethnicity Data ElementsData Elements

HCUP uniform coding of race-ethnicityHCUP uniform coding of race-ethnicity– Hispanic, White, Black, Asian/Pacific Islander (API), Native Hispanic, White, Black, Asian/Pacific Islander (API), Native

American, otherAmerican, other

Separate indicator of Hispanic (when available)Separate indicator of Hispanic (when available)

State-specific coding of race-ethnicity retainedState-specific coding of race-ethnicity retained– Some states provide more detailed categoriesSome states provide more detailed categories

Methods: Completeness & Methods: Completeness & Accuracy AssessmentAccuracy Assessment

Determined the number of states collecting Determined the number of states collecting four race-ethnicity categoriesfour race-ethnicity categories– White, Black, Hispanic, API White, Black, Hispanic, API

(Native American group not included because of (Native American group not included because of known under-coding)known under-coding)

Percentage of records missing race-ethnicity Percentage of records missing race-ethnicity within states within states

Validity of individual hospital coding via Validity of individual hospital coding via questionable coding patternsquestionable coding patterns– White = 100% of recordsWhite = 100% of records– ““Other” > 30%Other” > 30%– Missing > 50%Missing > 50%– White + Other + Missing = 100%White + Other + Missing = 100%

Methods: Evaluate Approach for Methods: Evaluate Approach for National Estimates by Race-EthnicityNational Estimates by Race-Ethnicity

Develop SID disparities analysis fileDevelop SID disparities analysis file– Include hospitals in the sample frameInclude hospitals in the sample frame

From states with good R-E reportingFrom states with good R-E reporting Passing the 4 R-E edit checks Passing the 4 R-E edit checks

– Develop sample of US community hospitalsDevelop sample of US community hospitals Approximate a 40% stratified sampleApproximate a 40% stratified sample Use same sampling strategy as the NIS (AHA Annual Use same sampling strategy as the NIS (AHA Annual

Survey is the “universe”)Survey is the “universe”) Develop weights for making national estimatesDevelop weights for making national estimates

Compare national estimates from disparities Compare national estimates from disparities analysis file to estimates from NISanalysis file to estimates from NIS

Findings:Findings:Race & Ethnicity Coding, 2005 SIDRace & Ethnicity Coding, 2005 SID

Race & Ethnicity CodingRace & Ethnicity Coding StatesStates

Not collected (8)Not collected (8) IL KY MN NVIL KY MN NV

OH OR WA WVOH OR WA WV

No Hispanic group (3) No Hispanic group (3) IA NC SD IA NC SD

No Hispanic, API group (1)No Hispanic, API group (1) IN IN

Collects white, black, Collects white, black, AR AZ CA CT CO AR AZ CA CT CO Hispanic, API, AIAN (25)Hispanic, API, AIAN (25) FL GA HI KS MA FL GA HI KS MA MD MI MO MD MI MO NENE NHNH

NJ NY OK RI SCNJ NY OK RI SC TN TX TN TX UTUT VT VT WIWI

Completeness of Race-Ethnicity Completeness of Race-Ethnicity Coding, 2005Coding, 2005 SIDSID

Records MissingRecords Missing Number of StatesNumber of States Race/ethnicityRace/ethnicity

100%100% 8 891 - 99%91 - 99% 1 151 - 90%51 - 90% 1 131 - 50%31 - 50% 1 121 - 30 %21 - 30 % 3 311 - 20 %11 - 20 % 1 1

6 - 10 % 6 - 10 % 2 2 0 - 5 %0 - 5 % 20 20

States with Acceptable Race-States with Acceptable Race-Ethnicity Data, 2005 SIDEthnicity Data, 2005 SID

Criteria:Criteria:– Coding for white, black, Hispanic, APICoding for white, black, Hispanic, API– Fewer than 30% of records missing race-Fewer than 30% of records missing race-

ethnicity codingethnicity coding

23 States with acceptable race-ethnicity 23 States with acceptable race-ethnicity datadata

AR AZ CA CT CO FL GA HI AR AZ CA CT CO FL GA HI

KS MA MD MI MO NH NJ NYKS MA MD MI MO NH NJ NY OK RI SC TN TX VT WIOK RI SC TN TX VT WI

Race-Ethnicity Coding Problems in 23 Race-Ethnicity Coding Problems in 23 States with Acceptable ReportingStates with Acceptable Reporting

0

500

1000

1500

2000

2500

3000

Nu

mb

er o

f H

osp

ital

s

2459 0 61 62 25

No Problem All whiteOther race

>30%Missing

>50%Wh+ Oth+ Miss=100%

US Community Hospitals withUS Community Hospitals with“No Problem” R-E Coding, SID 2005“No Problem” R-E Coding, SID 2005

Percent of US Hospitals Available forDisparities File Sampling Frame

0

20

40

60

80

100

All NE MW S W

Per

cen

t

Hospitals Discharges

SID Disparities Analysis FileSID Disparities Analysis File

40% Stratified Sample of Hospitals

2323 State Inpatient State Inpatient Databases, 2005Databases, 2005

Hospitals with Hospitals with Good Race-Good Race-

Ethnicity CodingEthnicity Coding N = ~ N = ~ 1,9001,900 Hospitals Hospitals

N = ~ N = ~ 1515 million dis. million dis.

SID disparities SID disparities analysis fileanalysis file

Ownership/Control

U.S. Region

Urban/Rural

5 Strata (same as NIS)

Bed Size

Teaching Status

Ownership/Control

Bed Size

2005 NIS vs SID Disparities Analysis File 2005 NIS vs SID Disparities Analysis File National Estimates of Discharges by National Estimates of Discharges by

Race-Ethnicity Race-Ethnicity

0

20

40

60

80

100

NIS SID Disparities File

Pe

rce

nt

Missing White Black Hispanic API

2005 NIS vs SID Disparities Analysis File: 2005 NIS vs SID Disparities Analysis File: National Estimates That Are SimilarNational Estimates That Are Similar

Type of EstimateType of EstimateDifferences in Differences in

National EstimatesNational EstimatesNumber of Discharges:Number of Discharges:

Total Total NoneNone

Sample stratifiers (hospital region, urban-Sample stratifiers (hospital region, urban-rural, teaching, ownership, size)rural, teaching, ownership, size)

NoneNone

GenderGender 1%1%

Ages: 18-44, 45-64, 65+Ages: 18-44, 45-64, 65+ 3 % or less3 % or less

Expected Payer GroupExpected Payer Group 3 % or less3 % or less

Mdn Inc of Pt Zip- highest 3 of 4 quartilesMdn Inc of Pt Zip- highest 3 of 4 quartiles 3% or less3% or less

DRGs- 23 of the 25 highest volume DRGsDRGs- 23 of the 25 highest volume DRGs 3% or less3% or less

Mean LOSMean LOS 1%1%

2005 NIS vs SID Disparities Analysis File: 2005 NIS vs SID Disparities Analysis File: National Estimates That Are >3% DifferentNational Estimates That Are >3% Different

Type of EstimateType of EstimateDifferences in Differences in

National EstimatesNational Estimates

Number of Discharges:Number of Discharges:Percent Percent

DifferenceDifference

SID SID Disparities Disparities

File is:File is:

Ages: 0-17 Ages: 0-17 7%7% LowerLower

Mdn Income of Pt Zip- lowest quartile*Mdn Income of Pt Zip- lowest quartile* 12%12% HigherHigher

DRGs- 2 of top 25DRGs- 2 of top 25

- Psychosis- Psychosis

- Major joint & limb reattachment - Major joint & limb reattachment procedures of lower extremity procedures of lower extremity

6%6%

5%5%

HigherHigher

LowerLower

* Note: For Disparities Analysis File, but not the NIS comparison file, * Note: For Disparities Analysis File, but not the NIS comparison file, median income of patient zipcode was imputed for discharges with median income of patient zipcode was imputed for discharges with missing zipcode.missing zipcode.

ConclusionsConclusions

Acceptable discharge data with race-Acceptable discharge data with race-ethnicity is available in HCUP for half of ethnicity is available in HCUP for half of the U.S. hospitals and discharges the U.S. hospitals and discharges

Through data cleaning, sampling and Through data cleaning, sampling and weighting these data can be used to weighting these data can be used to examine disparities nationallyexamine disparities nationally

Comparisons between the disparities file Comparisons between the disparities file and a benchmark (e.g. the NIS) are and a benchmark (e.g. the NIS) are important to identify possible biased important to identify possible biased estimatesestimates

ImplicationsImplications

Hospital discharge abstract data are Hospital discharge abstract data are – Generally readily available from state data Generally readily available from state data

organizationsorganizations

– Can support a wide range of health services Can support a wide range of health services research, policy analysis and planning.research, policy analysis and planning.

With careful design and analyses, these data With careful design and analyses, these data can support national disparities studiescan support national disparities studies

State/local disparities analyses may be State/local disparities analyses may be hindered by the lack of data, particularly in the hindered by the lack of data, particularly in the midwestmidwest

Other SID Disparities Analysis File Design Team Other SID Disparities Analysis File Design Team MembersMembers::

Marguerite Barrett, Rosanna Coffey, Robert Houchens (Thomson Marguerite Barrett, Rosanna Coffey, Robert Houchens (Thomson Reuters)Reuters)

Ernest Moy (AHRQ)Ernest Moy (AHRQ)

For More Information:For More Information:Coffey R, Barrett M, Houchens R, Moy E, Andrews, R. Coffey R, Barrett M, Houchens R, Moy E, Andrews, R. Methods Methods Applying AHRQ Quality Indicators to Healthcare Cost and Utilization Applying AHRQ Quality Indicators to Healthcare Cost and Utilization Project (HCUP) Data for the Fifth (2007) National Healthcare Project (HCUP) Data for the Fifth (2007) National Healthcare Disparities ReportDisparities Report. HCUP Methods Series Report # 2007-07. Online . HCUP Methods Series Report # 2007-07. Online January 4, 2008. U.S. Agency for Healthcare Research and Quality.January 4, 2008. U.S. Agency for Healthcare Research and Quality.Available: http://www.hcup-us.ahrq.gov/reports/methods.jsp.Available: http://www.hcup-us.ahrq.gov/reports/methods.jsp.