The Quality of Reporting on Race & Ethnicity in Medicare Data: Assessing the Effect of Improved Coding

1. The Quality of Reporting on Race & Ethnicity in Medicare Data: Assessing the Effect of Improved Coding Ernest Moy, Linda G. Greenberg Center for Quality Improvement and Patient Safety The analytic database was created by the Research Triangle Institute (RTI) for CMS and AHRQ under CMS Contract Number: 500-00-0024, Task No. 8 (2005) and an AHRQ/CMS Interagency Agreement CMS Contract Number 500-00-0024 Task No. 21 (2006).

2. Background • Assessing health care disparities is often hindered by • Small samples • Lack of detailed geographic information • Inaccurate racial identification • Lack of socioeconomic information • Medicare administrative data can address 1 & 2 but need enrichment to address 3 & 4

3. Project Goals • Builds on project to enrich Medicare data & allow better disparities assessments • Enrich identification of Hispanics & Asians • Add information on socioeconomic status • Quantifies impact of improved coding compared with standard Medicare enrollment database (EDB) coding

4. Methods for Improving Data • Identification of Hispanics & Asians • Enriched racial coding of enrollment database using combination of surname, first name, and State of residence • Validated against self-reported race data (Medicare Current Beneficiary Survey) • Sensitivity improved dramatically • Hispanics from 30% to 77% • Asians from 55% to 80% • Specificity unchanged

5. Methods for Improving Data • Added area SES info at block group level • % labor force unemployed • % people living below poverty level • Median household income • Median value of dwellings • % adults w/ < high school education • % adults w/ 4+ years of college • % households w/ >1 person per room • Created SES index • Sorted people by index into quartiles

6. Enriched Database • Significant population shifts • Hispanics +2.0 million • Asians +260,000 • Whites -1.7 million • Other/Unknown -460,000 • Stratified sample of Medicare fee-for-service beneficiaries to allow estimates of all racial groups

7. Measures of Health Care • Datasource: 2002 CMS inpatient, outpatient, physican, & DME claims • Cancer screening • Colorectal cancer screening (FOBT or lower endoscopy) • Mammography • Prostate specific antigen • Diabetes care • Diabetic testing (HbA1c, lipids, urine) • Eye examination • Instruction in self-care

8. Analysis • Compared measures of health care by • Standard Medicare race coding (EDB race) • Enriched race coding (enriched race) • Compared magnitude of racial differences compared with Non-Hispanic whites for EDB vs. enriched race • Compared racial differences stratified by SES for EDB vs. enriched race

9. No Colorectal Cancer Screening by Race: EDB vs. Enriched Race

10. No Mammography (l) & PSA (r) by Race: EDB vs. Enriched Race

11. No Diabetic Testing by Race: EDB vs. Enriched Race

12. No Diabetic Eye Exam & Self-care Ed by Race: EDB vs. Enriched Race

13. No PSA by Race: EDB vs. Enriched Race, High (l) vs. Low SES (r)

14. Summary of Effects • Overall, almost all disparities underestimated • Asians: 4/6 measures, direction of disparity changed • Blacks: 3/6 measures, disparity underestimated >100% • Hispanics: 4/6 measures, disparity underestimated >100% • For cancer screening, racial disparity among low SES underestimated the most

15. Conclusions • Standard Medicare coding of race/ethnicity is unreliable • Medicare coding can be enriched • Enrichment is important to correct • Wrong direction of disparity among Asians • Large underestimates of disparities among Blacks, Hispanics, AI/AN • Especially those of low SES • CMS does use enriched coding for some analyses, but does not do so routinely