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Motivation

Data and Analytic Approaches for Studies of Health and Health Care Disparities Alan M. Zaslavsky John Z. Ayanian Harvard Medical School. Motivation. Much evidence of disparities Health Health care Distinct issues and process One of many sources of health disparities

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Motivation

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  1. Data and Analytic Approaches for Studies of Health and Health Care DisparitiesAlan M. ZaslavskyJohn Z. AyanianHarvard Medical School

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

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

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

  6. 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

  7. 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

  8. 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

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

  10. 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%

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. (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

  20. 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

  21. Operationalizing the IOM Definition HealthStatus Race Utilization SES

  22. 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

  23. 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

  24. 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

  25. 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

  26. 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?

  27. 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?

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