Differences in Access to Care for Asian and White Adults Merrile Sing, Ph.D. September 8, 2008

Policy Context Many Asians face significant linguistic and cultural barriers Many Asians face significant linguistic and cultural barriers ~ 25% of Asians live in linguistically isolated households (Census 2000) ~ 63% of Asians are immigrants (Census 2000) Some Asian American subgroups are at greater risk than non-Hispanic Whites for certain diseases, such as diabetes, stomach and liver cancer, hepatitis B, and tuberculosis Some Asian American subgroups are at greater risk than non-Hispanic Whites for certain diseases, such as diabetes, stomach and liver cancer, hepatitis B, and tuberculosis 2

Research Objectives To estimate adjusted differences in access to care between non- Hispanic White and Asian adults To estimate adjusted differences in access to care between non- Hispanic White and Asian adults To identify factors that have the greatest marginal effects on improving access to care To identify factors that have the greatest marginal effects on improving access to care 3

Previous Research Moy et al. (2008). “Community Variation: Disparities in Health Care Quality Between Asian and White Medicare Beneficiaries.” Moy et al. (2008). “Community Variation: Disparities in Health Care Quality Between Asian and White Medicare Beneficiaries.” Miltiades and Wu (2008). “Factors Affecting Physician Visits in Chinese and Chinese Immigrant Samples.” Miltiades and Wu (2008). “Factors Affecting Physician Visits in Chinese and Chinese Immigrant Samples.” Snyder et al. (2000). “Access to Medical Care Reported by Asians and Pacific Islanders in a West Coast Physician Group Association” Snyder et al. (2000). “Access to Medical Care Reported by Asians and Pacific Islanders in a West Coast Physician Group Association” AHRQ (2007), National Healthcare Disparities Report AHRQ (2007), National Healthcare Disparities Report 4

Study Design Data are from the Medical Expenditure Panel Survey (MEPS) & Area Resource File, Data are from the Medical Expenditure Panel Survey (MEPS) & Area Resource File, – MEPS contains a nationally representative sample of households in the U.S. civilian, non-institutionalized population Sample includes non-Hispanic adults age 18 and older Sample includes non-Hispanic adults age 18 and older – There are 3,779 Asians and 52,498 Whites Andersen typology of access to care is used Andersen typology of access to care is used Outcome variables are binary Outcome variables are binary – Usual source of care (excluding emergency room) – At least one office visit during past year 5

Access to Care 6

Andersen Typology: Control variables Access depends on: – Predisposing characteristics – Enabling Resources – Illness level or perceived need 7

Predisposing Characteristics D emographic D emographic Age, sex, marital status Age, sex, marital status Social structure Social structure Education Education Acculturation Acculturation Difficulty speaking English Difficulty speaking English In linguistically isolated family In linguistically isolated family Immigrant < 5 years in U.S. Immigrant < 5 years in U.S. Immigrant 5 – 14 years in U.S. Immigrant 5 – 14 years in U.S. Attitudes Attitudes Overcome illness without medical professional Overcome illness without medical professional More willing to take risk More willing to take risk Always uses seat belt Always uses seat belt 8

Enabling Resources Family FamilyIncome Insurance coverage Community Community Urban-rural (using Metropolitan Statistical Areas) Census Region (4) Active non-federal MDs/ 1,000 population (county) Number of Federally Qualified Health Centers (county) Percent Asian population in county 9

Illness/Perceived Need – Self-rated general health – Poor mental health (Mental Component Summary) – Number of chronic conditions 10

Methods 11

Estimation Methods Unadjusted differences in means Unadjusted differences in means Adjusted differences (multivariate logistic regressions) Adjusted differences (multivariate logistic regressions) – Marginal effects estimated by method of recycled predictions – Standard errors estimated using balanced repeated replicates 12

Marginal effects on Access to care Which factors have the greatest marginal effects on improving access to care? Predisposing conditions Predisposing conditions with and without acculturation variables with and without acculturation variables Enabling resources Enabling resources Perceived need Perceived need All control variables All control variables 13

UnadjustedDifferences 14

Access to Care Adults Age ** * (**) Significantly different from White at 0.05 (0.01) level or better Source: MEPS 2002 – 2005, adults eligible for access supplement **

Acculturation Immigrants 16 ** * (**) Significantly different from White at 0.05 (0.01) level or better Source: MEPS 2002 – 2005, Adults eligible for Access Supplement **

Acculturation English Language 17 ** * (**) Significantly different from White at 0.05 (0.01) level or better Source: MEPS 2002 – 2005, Adults eligible for Access Supplement **

Factors Associated with Access to Care 18

Variables associated with Usual Source of Care Marginal effect Asian * (0.019) Asian * (0.019) Enabling Predisposing Income immigrant < 5 yrs in U.S. Income immigrant < 5 yrs in U.S. Insurance status immigrant yrs in U.S. Insurance status immigrant yrs in U.S. MSA Difficulty w/ English MSA Difficulty w/ English Census Region Asian * Difficulty w/English Census Region Asian * Difficulty w/English family size family size Perceived need age number of chronic cond. gender number of chronic cond. gender self-rated health marital status self-rated health marital status attitudes attitudes Year Year Source: MEPS

Variables associated with Office Visit(s) Marginal effect Asian ** (0.015) Asian ** (0.015) Enabling Predisposing Income immigrant < 5 yrs in U.S. Income immigrant < 5 yrs in U.S. Insurance status Difficulty w/ English Insurance status Difficulty w/ English MSA MSA Census Region education Census Region education Active MDs/ 1000 pop. family size Active MDs/ 1000 pop. family size age age Perceived need gender number of chronic cond. marital status number of chronic cond. marital status self-rated general health attitudes self-rated general health attitudes self-rated mental health self-rated mental health Year Source: MEPS

Estimated Marginal Effects 21

Marginal Effects on Access to Care Unadjusted Usual Source of Care Office Visit(s) White (0.004) (0.003) Asian (0.013) (0.011) Difference ** ** Adjusted differences: Marginal effects controlling for: Usual Source of Care Office Visit(s) Usual Source of Care Office Visit(s) Predisposing (w/o acculturation) ** ** Predisposing (w/o acculturation) ** ** Predisposing (w/ acculturation) ** ** Predisposing (w/ acculturation) ** ** Enabling ** ** Enabling ** ** Perceived need ** ** Perceived need ** ** All variables ** ** All variables ** ** 22

Conclusions Asian adults were less likely than Whites to have a usual source of care or an office visit, after controlling for predisposing and enabling characteristics and perceived need Asian adults were less likely than Whites to have a usual source of care or an office visit, after controlling for predisposing and enabling characteristics and perceived need Greatest Marginal Effects on Access to Care Greatest Marginal Effects on Access to Care Predisposing EnablingPerceived Predisposing EnablingPerceived w/ acculturation Need w/ acculturation Need Usual Source of Care √ Office Visit √ 23

Policy Relevance Findings suggest areas to focus on for improving access to care for Asian adults: – Translating general medical information and Medicaid applications into Asian languages may improve access to care for some Asians – Educating providers about differences in culture and disease incidence for Asians compared with non-Hispanic Whites 24