Impact of Perceived Impact of Perceived Discrimination on Use Discrimination on Use of Preventive Health of Preventive Health

ServicesServicesAmal Trivedi, M.D., M.P.H.Amal Trivedi, M.D., M.P.H.

John Z. Ayanian, M.D., M.P.P.John Z. Ayanian, M.D., M.P.P.

Harvard Medical School/Brigham and Women’s Harvard Medical School/Brigham and Women’s HospitalHospital

AcademyHealth Annual Research Meeting AcademyHealth Annual Research Meeting June 8, 2004June 8, 2004

BackgroundBackground Inequalities in health care based Inequalities in health care based

on race, gender, socioeconomic on race, gender, socioeconomic status, and geographystatus, and geography

Mechanisms for disparities not Mechanisms for disparities not knownknown

Institutional or individual biases Institutional or individual biases in delivery of carein delivery of care

Discrimination and Discrimination and HealthHealth

Growing body of research focusing Growing body of research focusing on experiences of discrimination on experiences of discrimination and their impact on healthand their impact on health

Several forms of discrimination Several forms of discrimination including institutional and including institutional and interpersonalinterpersonal

Evidence of association between Evidence of association between perceived discrimination and perceived discrimination and correlates of healthcorrelates of health

Research QuestionsResearch Questions What proportion of the population What proportion of the population

has had recent experience with has had recent experience with discrimination in the health care discrimination in the health care setting?setting?

What is the impact of perceived What is the impact of perceived discrimination on receipt of discrimination on receipt of appropriate preventive care?appropriate preventive care?

What proportion of observed health What proportion of observed health care disparities can be explained by care disparities can be explained by perceived discrimination?perceived discrimination?

Methods – Source of DataMethods – Source of Data Cross-sectional study of the 2001 Cross-sectional study of the 2001

California Health Interview Survey California Health Interview Survey (CHIS)(CHIS)

54,968 adult respondents 54,968 adult respondents representative of non-institutionalized representative of non-institutionalized populationpopulation

Oversampling of Asian, Latino, and Oversampling of Asian, Latino, and rural populationsrural populations

Unweighted response rate of 43% and Unweighted response rate of 43% and participation rate of 76%participation rate of 76%

Methods – Study Methods – Study VariablesVariables

Independent variable:Independent variable: Subjects asked Subjects asked “Thinking of your “Thinking of your

experiences receiving health care experiences receiving health care over the past twelve months, have over the past twelve months, have you ever felt discriminated you ever felt discriminated against?”against?”

If respondents answered yes, they If respondents answered yes, they were asked for specific reason for were asked for specific reason for discriminationdiscrimination

Methods – Outcome Methods – Outcome VariablesVariables

Preventive Preventive ServiceService

Time Time PeriodPeriod

PopulationPopulation

1. Aspirin Use1. Aspirin Use CurrentCurrent Persons with Persons with Heart Disease, Heart Disease, HTN>50HTN>50

2. Cholesterol Test2. Cholesterol Test 12 months12 months Persons with Persons with Heart Disease, Heart Disease, HTN>50HTN>50

3. HbA1C Testing3. HbA1C Testing 12 months12 months Persons with Persons with DiabetesDiabetes

4. Foot Exam4. Foot Exam 12 months12 months Persons with Persons with DiabetesDiabetes

5. Flu Vaccination5. Flu Vaccination 12 months12 months Adults > 65Adults > 656. Sigmoidoscopy6. Sigmoidoscopy 5 years5 years Adults > 50Adults > 507. PSA Testing7. PSA Testing 12 months12 months Men > 50Men > 508. Pap Testing8. Pap Testing 3 years3 years Women 18-65 Women 18-65

with no previous with no previous hysterectomyhysterectomy

9. Mammography9. Mammography 2 years2 years Women 50-79Women 50-79

Methods - AnalysesMethods - Analyses Bivariate analysis/chi-square tests: Bivariate analysis/chi-square tests:

perceived discrimination across perceived discrimination across subgroupssubgroups

Multivariable logistic regression Multivariable logistic regression using propensity scores predicting using propensity scores predicting likelihood of discriminationlikelihood of discrimination

Stratified by race, gender, and Stratified by race, gender, and insurance status and determined odds insurance status and determined odds ratios for receipt of services before ratios for receipt of services before and after adjusting for discriminationand after adjusting for discrimination

ResultsResults 4.7% of population reported recent 4.7% of population reported recent

discrimination in receiving health carediscrimination in receiving health care Population estimate of nearly 1.1 million Population estimate of nearly 1.1 million

Californian adultsCalifornian adults Most common reasons for discrimination Most common reasons for discrimination

were insurance type (28%), race (14%) and were insurance type (28%), race (14%) and income(7%)income(7%)

Insurance type most common reported Insurance type most common reported reason for men, women, uninsured, reason for men, women, uninsured, insured, Whites, Latinos, American-Indiansinsured, Whites, Latinos, American-Indians

Discrimination due to race most commonly Discrimination due to race most commonly reported by African-Americans and Asiansreported by African-Americans and Asians

Results – Rates of Results – Rates of DiscriminationDiscrimination

CharacteriCharacteristicstic

% % Reporting Reporting DiscriminatDiscriminationion

Race*Race* African-African-AmericanAmerican WhiteWhite LatinoLatino AsianAsian American-American-IndianIndian

6.1%6.1%4.2%4.2%5.8%5.8%2.9%2.9%8.8%8.8%

Gender*Gender* MaleMale FemaleFemale

4.0%4.0%5.3%5.3%

Income (% of Income (% of FPL)*FPL)* <100<100 100-199100-199 200-299200-299 300 or more300 or more

7.6%7.6%6.2%6.2%4.4%4.4%3.4%3.4%

CharacteristCharacteristicic

%Reportin%Reporting g DiscriminaDiscriminationtion

Insurance Insurance Status*Status* UninsuredUninsured MedicaidMedicaid Medicare/PrivatMedicare/Privatee

7.7%7.7%9.2%9.2%3.4%3.4%

Perceived Perceived Health Status*Health Status* ExcellentExcellent Very GoodVery Good GoodGood FairFair PoorPoor

2.7%2.7%3.0%3.0%4.5%4.5%9.0%9.0%15.4%15.4%

* P<0.01 (Chi-Square Test)

Unadjusted Estimates of Unadjusted Estimates of Preventive Service UsePreventive Service Use

0102030405060708090

ASAUse

CholTest*

FootExam

HbA1CTest*

Flu Shot

DiscriminationNo Discrimination

* P<0.05

Unadjusted Estimates of Unadjusted Estimates of Cancer Preventive Cancer Preventive

Service UseService Use

0102030405060708090

Sigmoidoscopy PSA Test* Pap Test Mammogram*

DiscriminationNo Discrimination

* P <0.05

Adjusted Odds Ratios for Adjusted Odds Ratios for Receipt of Age- and Disease-Receipt of Age- and Disease-Appropriate Preventive CareAppropriate Preventive Care

0 0.2 0.4 0.6 0.8 1 1.2

Flu Vaccine*

HbA1C Test*

Foot Exam*

CholesterolTest*

ASA Use

DiscriminationNo Discrimination

* P<0.05

Adjusted Odds Ratios for Adjusted Odds Ratios for Receipt of Cancer Preventive Receipt of Cancer Preventive

ServicesServices

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Mammogram

Pap Test

PSA Test

Sigmoidoscopy

DiscriminationNo Discrimination

Observed Disparities in Observed Disparities in Receipt of Preventive Receipt of Preventive

ServicesServices Women less likely to receive 5 of 6 Women less likely to receive 5 of 6

servicesservices African-Americans less likely to receive 2, African-Americans less likely to receive 2,

American Indians 3, Asians 6 and Latinos American Indians 3, Asians 6 and Latinos all 9 preventive services relative to Whitesall 9 preventive services relative to Whites

Uninsured less likely to receive all 9 Uninsured less likely to receive all 9 servicesservices

Adjusting for perceived discrimination Adjusting for perceived discrimination had a negligible impact on disparities by had a negligible impact on disparities by race, gender, and insurance statusrace, gender, and insurance status

LimitationsLimitations Lack of information about Lack of information about

specialized medical services; specialized medical services; intensity and frequency of intensity and frequency of experienced discrimination; and experienced discrimination; and other domains of discriminationother domains of discrimination

Cross-sectional study design Cross-sectional study design precluded assessment of causal precluded assessment of causal mechanisms between discrimination mechanisms between discrimination and use of health careand use of health care

ConclusionsConclusions Nearly 5% of a statewide sample report Nearly 5% of a statewide sample report

recent experience with health care recent experience with health care discrimination discrimination

Persons who report discrimination are Persons who report discrimination are less likely to receive some age- and less likely to receive some age- and disease appropriate preventive servicesdisease appropriate preventive services

Perceived discrimination is unlikely to Perceived discrimination is unlikely to account for a large portion of observed account for a large portion of observed disparities by race, gender, and disparities by race, gender, and insurance statusinsurance status

ImplicationsImplications Need to more clearly elucidate Need to more clearly elucidate

reasons for perceived reasons for perceived discrimination in obtaining health discrimination in obtaining health carecare

Examine other forms of Examine other forms of discrimination and their impact discrimination and their impact on health and other health care on health and other health care outcomes including use of outcomes including use of tertiary/specialty servicestertiary/specialty services