1. The Quality of Reporting on Race & Ethnicity in US Hospital Discharge Abstract Data Roxanne Andrews, PhD June 10, 2008

2. Background Statewide, all-payer hospital administrative (claims) data are collected in nearly all states Statewide, all-payer hospital administrative (claims) data are collected in nearly all states Many state databases include race-ethnicity as a mandatory or voluntary data element Many state databases include race-ethnicity as a mandatory or voluntary data element AHRQ creates the Nationwide Inpatient Sample (NIS) for national estimates from statewide data AHRQ creates the Nationwide Inpatient Sample (NIS) for national estimates from statewide data About a quarter of NIS records are missing race-ethnicity About a quarter of NIS records are missing race-ethnicity National estimates by race-ethnicity are needed for monitoring disparities, e.g. for the National Healthcare Disparities Report National estimates by race-ethnicity are needed for monitoring disparities, e.g. for the National Healthcare Disparities Report

3. Study Purposes Examine the completeness & accuracy of race-ethnicity data collected in statewide discharge databases Examine the completeness & accuracy of race-ethnicity data collected in statewide discharge databases Evaluate a method for making national estimates by race-ethnicity from statewide databases Evaluate a method for making national estimates by race-ethnicity from statewide databases

4. Data Sources: Healthcare Cost and Utilization Project, 2005 State Inpatient Databases (SID) are evaluated State Inpatient Databases (SID) are evaluated – Voluntary Federal-State-Industry partnership  State governments, hospital associations, other private – All inpatient discharge data in the state – 37 states (in 2005); ~ 90% of US discharges Nationwide Inpatient Sample (NIS) is benchmark for evaluation of national estimates Nationwide Inpatient Sample (NIS) is benchmark for evaluation of national estimates – 20% stratified sample of hospitals in US – Uses SID hospitals as sample frame – Uses AHA Annual Survey as “universe”

5. The Making of the SID Patient enters hospital Hospital sends billing data & additional data (e.g. race-ethnicity) to Data Organizations States store data in varying formats Billing record created AHRQ standardizes data to create uniform HCUP databases

6. NIS Is a Stratified Sample of Hospitals from the SID 20% Stratified Sample of Hospitals 37 State Inpatient Databases 2005 N = ~ 1,000 Hospitals N = ~ 8 million dis. Nationwide Inpatient Sample Nationwide Inpatient Sample Ownership/ControlOwnership/Control U.S. Region Urban/Rural 5 NIS Strata Bed SizeBed Size Teaching Status Ownership/Control Bed Size

7. States Code Race & Ethnicity in Different Ways States are not bound by OMB standards States are not bound by OMB standards Race is not included on the standard for the hospital bill (Uniform Bill) Race is not included on the standard for the hospital bill (Uniform Bill)

8. HCUP Race & Ethnicity Data Elements HCUP uniform coding of race-ethnicity HCUP uniform coding of race-ethnicity – Hispanic, White, Black, Asian/Pacific Islander (API), Native American, other Separate indicator of Hispanic (when available) Separate indicator of Hispanic (when available) State-specific coding of race-ethnicity retained State-specific coding of race-ethnicity retained – Some states provide more detailed categories

9. Methods: Completeness & Accuracy Assessment Determined the number of states collecting four race-ethnicity categories Determined the number of states collecting four race-ethnicity categories – White, Black, Hispanic, API (Native American group not included because of known under-coding) Percentage of records missing race-ethnicity within states Percentage of records missing race-ethnicity within states Validity of individual hospital coding via questionable coding patterns Validity of individual hospital coding via questionable coding patterns – White = 100% of records – “Other” > 30% – Missing > 50% – White + Other + Missing = 100%

10. Methods: Evaluate Approach for National Estimates by Race-Ethnicity Develop SID disparities analysis file Develop SID disparities analysis file – Include hospitals in the sample frame  From states with good R-E reporting  Passing the 4 R-E edit checks – Develop sample of US community hospitals  Approximate a 40% stratified sample  Use same sampling strategy as the NIS (AHA Annual Survey is the “universe”)  Develop weights for making national estimates Compare national estimates from disparities analysis file to estimates from NIS Compare national estimates from disparities analysis file to estimates from NIS

11. Findings: Race & Ethnicity Coding, 2005 SID Race & Ethnicity Coding States Race & Ethnicity Coding States Not collected (8) IL KY MN NV OH OR WA WV OH OR WA WV No Hispanic group (3) IA NC SD No Hispanic, API group (1)IN Collects white, black, AR AZ CA CT CO Hispanic, API, AIAN (25) FL GA HI KS MA MD MI MO NE NH NJ NY OK RI SC TN TX UT VT WI NJ NY OK RI SC TN TX UT VT WI

12. Completeness of Race-Ethnicity Coding, 2005 SID Records Missing Number of States Race/ethnicity 100% 8 100% 8 91 - 99% 1 51 - 90% 1 31 - 50% 1 21 - 30 % 3 11 - 20 % 1 6 - 10 % 2 6 - 10 % 2 0 - 5 % 20 0 - 5 % 20

13. States with Acceptable Race- Ethnicity Data, 2005 SID Criteria: Criteria: – Coding for white, black, Hispanic, API – Fewer than 30% of records missing race- ethnicity coding 23 States with acceptable race-ethnicity data 23 States with acceptable race-ethnicity data AR AZ CA CT CO FL GA HI AR AZ CA CT CO FL GA HI KS MA MD MI MO NH NJ NY KS MA MD MI MO NH NJ NY OK RI SC TN TX VT WI OK RI SC TN TX VT WI

14. Race-Ethnicity Coding Problems in 23 States with Acceptable Reporting

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

16. SID Disparities Analysis File 40% Stratified Sample of Hospitals 23 State Inpatient Databases, 2005 Hospitals with Good Race- Ethnicity Coding N = ~ 1,900 Hospitals N = ~ 15 million dis. SID disparities analysis file SID disparities analysis file Ownership/ControlOwnership/Control U.S. Region Urban/Rural 5 Strata (same as NIS) Bed SizeBed Size Teaching Status Ownership/Control Bed Size

17. 2005 NIS vs SID Disparities Analysis File National Estimates of Discharges by Race-Ethnicity

18. 2005 NIS vs SID Disparities Analysis File: National Estimates That Are Similar Type of Estimate Differences in National Estimates Number of Discharges: TotalNone Sample stratifiers (hospital region, urban- rural, teaching, ownership, size) None Gender1% Ages: 18-44, 45-64, 65+ 3 % or less Expected Payer Group 3 % or less Mdn Inc of Pt Zip- highest 3 of 4 quartiles 3% or less DRGs- 23 of the 25 highest volume DRGs 3% or less Mean LOS 1%

19. 2005 NIS vs SID Disparities Analysis File: National Estimates That Are >3% Different Type of Estimate Differences in National Estimates Number of Discharges: Percent Difference SID Disparities File is: Ages: 0-17 7%Lower Mdn Income of Pt Zip- lowest quartile* 12%Higher DRGs- 2 of top 25 - Psychosis - Psychosis - Major joint & limb reattachment procedures of lower extremity - Major joint & limb reattachment procedures of lower extremity6%5%HigherLower * Note: For Disparities Analysis File, but not the NIS comparison file, median income of patient zipcode was imputed for discharges with missing zipcode.

20. Conclusions Acceptable discharge data with race- ethnicity is available in HCUP for half of the U.S. hospitals and discharges Acceptable discharge data with race- ethnicity is available in HCUP for half of the U.S. hospitals and discharges Through data cleaning, sampling and weighting these data can be used to examine disparities nationally Through data cleaning, sampling and weighting these data can be used to examine disparities nationally Comparisons between the disparities file and a benchmark (e.g. the NIS) are important to identify possible biased estimates Comparisons between the disparities file and a benchmark (e.g. the NIS) are important to identify possible biased estimates

21. Implications Hospital discharge abstract data are Hospital discharge abstract data are – Generally readily available from state data organizations – Can support a wide range of health services research, policy analysis and planning. With careful design and analyses, these data can support national disparities studies With careful design and analyses, these data can support national disparities studies State/local disparities analyses may be hindered by the lack of data, particularly in the midwest State/local disparities analyses may be hindered by the lack of data, particularly in the midwest

22. Other SID Disparities Analysis File Design Team Members : Marguerite Barrett, Rosanna Coffey, Robert Houchens (Thomson Reuters) Ernest Moy (AHRQ) For More Information: Coffey R, Barrett M, Houchens R, Moy E, Andrews, R. Methods Applying AHRQ Quality Indicators to Healthcare Cost and Utilization Project (HCUP) Data for the Fifth (2007) National Healthcare Disparities Report. HCUP Methods Series Report # 2007-07. Online January 4, 2008. U.S. Agency for Healthcare Research and Quality. Available: http://www.hcup-us.ahrq.gov/reports/methods.jsp.