1. 1 Data and Analytic Approaches for Studies of Health and Health Care Disparities Alan M. Zaslavsky John Z. Ayanian Harvard Medical School

2. 2 Motivation Much evidence of disparities –Health –Health care Distinct issues and process One of many sources of health disparities What data are needed to detect disparities? What data to understand and correct processes leading to disparities? Conceptual approach to disparities Analytic approaches for disparities

3. 3 NAS/IOM Studies Envisioning the National Healthcare Quality Report (2001) Unequal Treatment: Confronting Racial and Ethnic Disparities in Healthcare (2003) Guidance for the National Healthcare Disparities Report (2003) NHQR, NHDR now issued by AHRQ

4. 4 Institute of Medicine, 2003 IOM “Unequal Treatment” Report: Documented disparities in health care Framework for definition and analysis of disparities

5. 5 NAS/CNSTAT data report Eliminating Health Disparities: Measurement and Data Needs (2004)

6. 6 NAS/CNSTAT data report Focus on collecting personal characteristics relevant to disparities research –Race/ethnicity –Socioeconomic position –Acculturation

7. 7 Meaning of race/ethnicity Socially-constructed groupings Ethnicity: common culture, origin, history Race: defined by putative genetic relationship, broad areas of origin Many possible levels of detail –E.g. Hispanic vs. Salvadorean ethnicity –“Asian” race? –“Inside” vs. “outside” self-identification –Growing non-European immigration since 1965

8. 8 Measuring race/ethnicity Self-report as gold standard OMB 1997 categories: –5 racial categories: white, African-American, Asian-American, Native America/Alaskan Native, Hawaiian/Pacific Islander –“Check all that apply” format (~2% multiracial in Census) –Hispanic/Latino ethnicity as separate question

9. 9 Changes in categories Before 1997: “select one” race Standard allows for more detailed reporting –Must be collapsible to basic categories –More specific desired for state/local programs

10. 10 Medicare race data Primarily based on self-identification at Social Security enrollment Little detail for older cohorts [See Arday et al. (HCFR 2000)]

11. 11 Comparisons to self report White, African-American fairly accurate –White: EDB sensitivity=99%, PV+ = 93% –Black: EDB sensitivity=95%, PV+ = 89% Less so for other groups (smaller, “newer”) –Hispanic: sensitivity=32%, PV+ = 93% –Asian: sensitivity=42%, PV+ = 81%

12. 12 Collecting race/ethnicity at point of service Self report versus staff report Level of detail –Tailor to local populations Avoiding duplication of effort Sensitivity –Research and experience suggests: acceptable if properly motivated

13. 13 Socioeconomic Position (SEP) Critical to disparities research –Mediator of disparities: Race → Lower SEP → Poorer health care –Control variable to distinguish mechanisms –Source of disparities in itself –Complex interactions E.g. SEP gradients for some outcomes are different across racial/ethnic groups

14. 14 Dimensions of SEP Current resources –Current income –Wealth/Assets (especially for elderly) “Permanent” income: ability to gain income –Education is an important & measurable component Occupation: prestige, stress (UK research) Life-course experience of deprivation/plenty

15. 15 Collection of SEP data Education –Relatively nonsensitive, simple to ask Income –Highly sensitive in surveys, may be complex Occupation –Less sensitive, complicated to code Assets –Scales ask about key assets: home, car

16. 16 Acculturation/language Complex concept characterizing meeting of cultures – especially among immigrants –Ability to use health care systems –Discrimination based on “foreignness” –Protective and detrimental effects of culturally- specific practices –Changing expectations and needs English language proficiency a key component –Barriers to communication and recognition

17. 17 Acculturation measures Place of birth, age at immigration Generations since immigration Language –Proficiency –Preference: English or other language –Might be useful to providers/plans for communication, targeted outreach Cultural identity scales

18. 18 General issues Broad range of data systems have different strengths and weaknesses –Coverage, sample size Less for surveys –Level of detail and control over data collection Less for administrative data Many levels of detail possible –Different for research, federal and state programs

19. 19 Linkages Linkages across systems can bring together characteristics and outcomes –Linkages based on names or other keys –Geocoded census linkages: contextual variables or approximate individual characteristics Due regard to confidentiality concerns –Separate research and administrative uses

20. 20 (Analytically) defining healthcare disparities IOM Definition (Unequal Treatment) Break race/ethnic differences into three parts –Due to health status-related variables NOT part of disparity –Due to socioeconomic variables Part of disparity –Remaining race/ethnic effects Part of disparity

21. 21 3 components Health status-related variables –Age, sex, conditions, preferences, congenital susceptibilities Socioeconomic variables –Income, education, employment/insurance Remaining race/ethnic effects –Discrimination, “statistical discrimination”, poor communication

22. 22 Operationalizing the IOM Definition Race Health Status SES Utilization

23. 23 SEP & Disparities Analytic approach suggested by IOM Unequal Treatment report: –Include SEP variables in models –Disparity = Differences mediated through SEP + “Residual race effect” –Differs from race coefficient in regression model

24. 24 Respect for Preferences: A Value or an Excuse? Preferences may reflect: –Personal and cultural values –Effects of past and present discrimination, patients’s awareness of limited resources & access, etc. Ideal of “informed preferences” –Only attainable with adequate information, communication, access to care

25. 25 Geographic Variation: Alternative Treatments Allow to mediate (like SES) –Geographical differences may be the result of historical patterns of oppression and discrimination. –Allows us to make comparisons across areas Adjust (like health status) –Consider geography to be immutable if we are looking for improvements within an area –Consider geography as a preference: an individual makes a decision to live in a given area

26. 26 Multilevel analysis Distinguish effects arise at various levels of healthcare system –Geography –Health plans –Providers (hospitals, doctors, clinics) –Patients Effects of service patterns versus differential quality within units

27. 27 Discussion questions (1) Preferences: –How would you distinguish between informed preferences and effects of past/current inequities? –What factors might contribute to apparent “noncompliance”? Discrimination –Is it always conscious? –How would you prove discrimination?

28. 28 Discussion questions (2) Responsibility –Who is responsible for closing disparities: entire system, or units that serve the most members of underserved groups? –Should we penalize or bolster underperforming providers who serve many minority patients? –How much responsibility does the health care system have for remedying the effects of social inequalities?