MIPPA Section 185

1. MIPPA Section 185 Health Disparities Summit, May 22, 2009

2. Legislative Requirements • Evaluate data collection approaches with respect to disparities in health care on the basis of race, ethnicity, and gender [Section (a)] • Prepare a report to Congress that identifies approach to measuring and reporting on health disparities [Sections (b)(1)(A) and (b)(1)(B)] • Implement approach identified [Section (c)] • Evaluate disparities measurement and reporting effort and prepare reports to Congress, with recommendations for improvement [Section (b)(2)]

3. Examples of Potential Benefits Section 185 Measurement and Reporting • Benchmarking, awareness, and accountability • Quality improvement • Supporting consumer choice • Program monitoring and oversight

4. Issues We Need to Address for Report to Congress • Data on race and ethnicity • Measuring disparities • Public reporting • Evaluation

5. Data on Race and Ethnicity

6. Example of Disparities Data for Entity x

7. CMS Race/Ethnicity Data Sources • Enrollment Data Base (EDB) • Enrollment, eligibility, and characteristics data for entire Medicare population • Special data collection initiatives • Data collections for specific topics and subpopulations • E.g., Consumer Assessment of Healthcare Providers and Systems (CAHPS)

8. Race/Ethnicity Data in the EDB • Comes to CMS from the Social Security Administration • SSA captured race/ethnicity data when a person applied for a Social Security number (i.e., completed form SS-5) • SS-5 data (including person’s race/ethnicity) are stored at SSA in their Numident file • Data on race/ethnicity is transferred from SSA to CMS after person become eligible for Medicare • I.e., Data are transferred from Numident to EDB

9. Race/Ethnicity Data in the EDB, cont. • Some history: • From 1936 (when Social Security began) until 1980, the SS-5 was virtually unchanged. • The available race categories on the SS-5 were White, Black, Other. If the race field was left blank, the person was coded as unknown. • A large majority of current Medicare beneficiaries would have applied for a Social Security number between 1936-1980. • In 1980, the SS-5 was revised to expand the coding of race/ethnicity • New categories were: White; Black; Hispanic; Asian, Asian American, Pacific Islander; American Indian or Alaska native. • People who applied for a Social Security number between 1936-1980 would only submit a new SS-5 when seeking a replacement Social Security card or changing personal information (e.g., name change by marriage).

10. Assessing the Quality of Race/Ethnicity Data in the EDB

11. Assessing the Quality of Race/Ethnicity Data in the EDB, cont.

12. Initiatives to Improve Race/Ethnicity coding in the EDB • Short-term improvement • Probabilistic approach using surname • Intermediate-term improvement • Enhancements to probabilistic approach • SSA efforts to improve capture of race/ethnicity data • Long-term improvement • Capture race/ethnicity through on-going CMS primary data collection process (i.e., electronic health records)

13. Probabilistic Approach Using Surname • Using census data, the Census Bureau has identified the probability that people with a given surname will identify themselves with a given race/ethnic category • We have beneficiary name in our administrative data • Using the data from the Census Bureau, we associated the probability of being Hispanic with every Medicare beneficiary based on their surname. • We coded beneficiaries as being Hispanic if the probability associated with their surname was above a high threshold • We did the same thing for Asian American surnames • [Also used administrative data from other sources (e.g., if person requested Medicare & You Handbook in Spanish s/he was coded Hispanic)]

14. Assessing the Quality of Race/Ethnicity Coding Using Probabilistic Method

15. In short… • The current race/ethnicity data we have on the full Medicare population contains significant gaps • We have taken analytic steps to improve the data and will continue with these efforts • In some cases, more reliable data on race/ethnicity is captured separately for specific topics and subpopulations. We will use these data when available and appropriate • This will create something of a patchwork of race/ethnicity data that will be used in addressing Section 185 requirements

16. Measuring Disparities

17. Phase 1 • Health Plan Measurement with Fee-for-Service comparisons • CAHPS and HEDIS Analyses by Race/Ethnicity and Gender • Part D measures • Medicare Advantage Prescription Drug Plans • Stand-alone Prescription Drug Plans

18. Phase 1: CAHPS • The Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey is conducted annually by CMS to assess the experiences of beneficiaries in Medicare Advantage and Medicare Prescription Drug Plans. • The health plan measures are available for Medicare Advantage plans and fee-for-service. • The prescription drug plan measures are available for Medicare Advantage Prescription Drug Plans (MA-PDs) and stand-alone Prescriptions Drug Plans (PDPs).

19. CAHPS Health Care Measures • Getting Needed Care (composite) • Getting Care Quickly (composite) • Doctors who Communicate Well (composite) • Health Plan Customer Service (composite) • Overall Rating of Health Plan • Overall Rating of Care Received • Influenza Vaccination • Pneumonia Shot

20. CAHPS Prescription Drug Measures • Ease of Getting Needed Prescription Drugs (composite) • Getting Information from the Plan about Prescription Drug Coverage and Cost (composite) • Overall Rating • Willingness to Recommend Drug Plan

21. Phase 1: HEDIS • The Healthcare Effectiveness Data and Information Set (HEDIS) is a set of health plan performance measures that were developed by the National Committee for Quality Assurance (NCQA). • CMS requires the annual submission of HEDIS data from Medicare Advantage contracts. • A subset of HEDIS measures are available for the fee-for-service population by geographic area. • Based on administrative data

22. HEDIS Measures • Breast cancer screening • LDL testing for diabetics • Retinal eye exams for diabetics • HbA1c testing for diabetics • LDL testing for cardiovascular conditions • Persistence of beta blockers • Medical attention for nephropathy • Annual monitoring of patients on persistent medications • Antidepressant medication management • Beta blocker treatment after a heart attack • Disease modifying anti-rheumatic drug therapy in rheumatoid arthritis • Colorectal cancer screening This is a subset of HEDIS measures where FFS comparisons are feasible.

23. Identifying Race/Ethnicity for CAHPS/HEDIS • CAHPS • Self-reported information about race/ethnicity • Are you of Spanish, Hispanic or Latino origin or descent? • No, not Spanish/Hispanic/Latino • Yes, Spanish/Hispanic/Latino • What is your race? Please choose one or more. • White • Black or African American • Asian • Native Hawaiian or other Pacific Islander • American Indian or Alaska Native • HEDIS • Use imputed race/ethnicity values

24. Next Steps for Phase 1 • Develop methodology for producing plan-level/geographic estimates by race/ethnicity and gender • Sample size issues • Combining multiple years of data • Level of detail for race/ethnicity • Make recommendations for measure selection • Produce estimates

25. Phase 2 • Expand to additional provider settings • Home Health Agencies • Nursing Homes • Hospitals • ESRD Facilities

26. Public Reporting

27. Medicare Options Compare • Currently, CMS presents HEDIS, CAHPS and other performance information by contract on Medicare Options Compare. • The information is presented at three levels • Summary measure of quality and performance • Topic/domain level scores • Individual measure scores

32. Current Directions for Medicare Options Compare • Currently, conducting consumer testing to determine the best way to add Fee-for-Service comparisons to the website, where available, for Fall 2009. • Once the measures are selected for MIPPA Section 185, we plan to conduct testing to determine the best way to present this information to the public.

33. Consumer Testing: Approaches • Respondents • Medicare beneficiaries, family caregivers • Clinicians who serve as Information Intermediaries • Individuals who are interested in the use of the data for Quality Improvement • Methods • One-on-one in-depth interviews for cognitive testing • Small group discussions

34. Evaluation

35. Examples of Potential Analyses • Examine changes in disparities measures • Cross-sectional time series analysis • Include measures that are reported and some that are not • Monitor usage of web pages • Obtain feedback from sample of users who came to the website • Obtain feedback from sample of entities being measured • What are they doing to improve • Obtain feedback from other stakeholders

36. Discussion/Ideas from the Group • Data on Race and Ethnicity • Measuring Disparities • Public Reporting • Evaluation • Other considerations

37. Contacts • Elizabeth Goldstein, Ph.D. • Center for Drug and Health Plan Choice • 410-786-6665 • Thomas Reilly, Ph.D. • Office of Research, Development, and Information • 410-786-0631