Impact of Perceived Discrimination on Use of Preventive Health Services Amal Trivedi, M.D., M.P.H. John Z. Ayanian, M.D., M.P.P. Harvard Medical School/Brigham and Women’s Hospital AcademyHealth Annual Research Meeting June 8, 2004

Background Inequalities in health care based on race, gender, socioeconomic status, and geography Inequalities in health care based on race, gender, socioeconomic status, and geography Mechanisms for disparities not known Mechanisms for disparities not known Institutional or individual biases in delivery of care Institutional or individual biases in delivery of care

Discrimination and Health Growing body of research focusing on experiences of discrimination and their impact on health Growing body of research focusing on experiences of discrimination and their impact on health Several forms of discrimination including institutional and interpersonal Several forms of discrimination including institutional and interpersonal Evidence of association between perceived discrimination and correlates of health Evidence of association between perceived discrimination and correlates of health

Research Questions What proportion of the population has had recent experience with discrimination in the health care setting? What proportion of the population has had recent experience with discrimination in the health care setting? What is the impact of perceived discrimination on receipt of appropriate preventive care? What is the impact of perceived discrimination on receipt of appropriate preventive care? What proportion of observed health care disparities can be explained by perceived discrimination? What proportion of observed health care disparities can be explained by perceived discrimination?

Methods – Source of Data Cross-sectional study of the 2001 California Health Interview Survey (CHIS) Cross-sectional study of the 2001 California Health Interview Survey (CHIS) 54,968 adult respondents representative of non-institutionalized population 54,968 adult respondents representative of non-institutionalized population Oversampling of Asian, Latino, and rural populations Oversampling of Asian, Latino, and rural populations Unweighted response rate of 43% and participation rate of 76% Unweighted response rate of 43% and participation rate of 76%

Methods – Study Variables Independent variable: Independent variable: Subjects asked “Thinking of your experiences receiving health care over the past twelve months, have you ever felt discriminated against?” Subjects asked “Thinking of your experiences receiving health care over the past twelve months, have you ever felt discriminated against?” If respondents answered yes, they were asked for specific reason for discrimination If respondents answered yes, they were asked for specific reason for discrimination

Methods – Outcome Variables Preventive Service Time Period Population 1. Aspirin Use Current Persons with Heart Disease, HTN>50 2. Cholesterol Test 12 months Persons with Heart Disease, HTN>50 3. HbA1C Testing 12 months Persons with Diabetes 4. Foot Exam 12 months Persons with Diabetes 5. Flu Vaccination 12 months Adults > Sigmoidoscopy 5 years Adults > PSA Testing 12 months Men > Pap Testing 3 years Women with no previous hysterectomy 9. Mammography 2 years Women 50-79

Methods - Analyses Bivariate analysis/chi-square tests: perceived discrimination across subgroups Bivariate analysis/chi-square tests: perceived discrimination across subgroups Multivariable logistic regression using propensity scores predicting likelihood of discrimination Multivariable logistic regression using propensity scores predicting likelihood of discrimination Stratified by race, gender, and insurance status and determined odds ratios for receipt of services before and after adjusting for discrimination Stratified by race, gender, and insurance status and determined odds ratios for receipt of services before and after adjusting for discrimination

Results 4.7% of population reported recent discrimination in receiving health care 4.7% of population reported recent discrimination in receiving health care Population estimate of nearly 1.1 million Californian adults Population estimate of nearly 1.1 million Californian adults Most common reasons for discrimination were insurance type (28%), race (14%) and income(7%) Most common reasons for discrimination were insurance type (28%), race (14%) and income(7%) Insurance type most common reported reason for men, women, uninsured, insured, Whites, Latinos, American-Indians Insurance type most common reported reason for men, women, uninsured, insured, Whites, Latinos, American-Indians Discrimination due to race most commonly reported by African-Americans and Asians Discrimination due to race most commonly reported by African-Americans and Asians

Results – Rates of Discrimination Characteristic % Reporting Discrimination Race* African-American African-American White White Latino Latino Asian Asian American-Indian American-Indian6.1%4.2%5.8%2.9%8.8% Gender* Male Male Female Female4.0%5.3% Income (% of FPL)* <100 < or more 300 or more7.6%6.2%4.4%3.4% Characteristic %Reporting Discrimination Insurance Status* Uninsured Uninsured Medicaid Medicaid Medicare/Private Medicare/Private7.7%9.2%3.4% Perceived Health Status* Excellent Excellent Very Good Very Good Good Good Fair Fair Poor Poor2.7%3.0%4.5%9.0%15.4% * P<0.01 (Chi-Square Test)

Unadjusted Estimates of Preventive Service Use * P<0.05

Unadjusted Estimates of Cancer Preventive Service Use * P <0.05

Adjusted Odds Ratios for Receipt of Age- and Disease-Appropriate Preventive Care * P<0.05

Adjusted Odds Ratios for Receipt of Cancer Preventive Services

Observed Disparities in Receipt of Preventive Services Women less likely to receive 5 of 6 services Women less likely to receive 5 of 6 services African-Americans less likely to receive 2, American Indians 3, Asians 6 and Latinos all 9 preventive services relative to Whites African-Americans less likely to receive 2, American Indians 3, Asians 6 and Latinos all 9 preventive services relative to Whites Uninsured less likely to receive all 9 services Uninsured less likely to receive all 9 services Adjusting for perceived discrimination had a negligible impact on disparities by race, gender, and insurance status Adjusting for perceived discrimination had a negligible impact on disparities by race, gender, and insurance status

Limitations Lack of information about specialized medical services; intensity and frequency of experienced discrimination; and other domains of discrimination Lack of information about specialized medical services; intensity and frequency of experienced discrimination; and other domains of discrimination Cross-sectional study design precluded assessment of causal mechanisms between discrimination and use of health care Cross-sectional study design precluded assessment of causal mechanisms between discrimination and use of health care

Conclusions Nearly 5% of a statewide sample report recent experience with health care discrimination Nearly 5% of a statewide sample report recent experience with health care discrimination Persons who report discrimination are less likely to receive some age- and disease appropriate preventive services Persons who report discrimination are less likely to receive some age- and disease appropriate preventive services Perceived discrimination is unlikely to account for a large portion of observed disparities by race, gender, and insurance status Perceived discrimination is unlikely to account for a large portion of observed disparities by race, gender, and insurance status

Implications Need to more clearly elucidate reasons for perceived discrimination in obtaining health care Need to more clearly elucidate reasons for perceived discrimination in obtaining health care Examine other forms of discrimination and their impact on health and other health care outcomes including use of tertiary/specialty services Examine other forms of discrimination and their impact on health and other health care outcomes including use of tertiary/specialty services