Ethnicity & Health: An Integrative Biopsychosocial Model of Risk & Resilience. Hector F. Myers, Ph.D. Professor of Psychology University of California,

Ethnicity & Health: An Integrative Biopsychosocial Model of Risk & Resilience.

Hector F. Myers, Ph.D.Professor of Psychology

University of California, Los Angeles

Outline

Race/Ethnicity, SES & Health Disparities Social Status-Related Stresses & Health Lifespan Model of Psychosocial

Adversities, Psychosocial Reserve Capacity & Cumulative Vulnerability & Health Disparities

Some Potential Contributors to Resilience

Implications & Future Directions

Ethnic Health Disparities Significant disparities in chronic diseases and death. Leading causes of death:

CVD & cancer leading cause of death in all groups. HIV/AIDS: in top 3 causes of death for AA men and

women and for HA men. Diabetes is among top 5 causes of death for

women of color, especially AA, HA & NA. Whites & APIs suffer disproportionate risk for

COPD. NAs suffer more from chronic liver disease. NA, AA and HA men at disproportionate risk for

violent death.

CHD & stroke mortality dropped in the US in past 20 yrs.

Sig. ethnic disparities in prevalence of CHD, stroke and related risk factors. Whites have highest rate of CHD, but AA

have higher mortality. Whites & AA have comparable rates of

stroke, but AA have higher stroke-related mortality.

AA have highest rate of hypertension (35% of pop compared to 22% of W and 23% of HAs).

Group differences in CHD related to differences in risk factors.

Low SES: highest rate and greatest clustering of risk factors.

Smoking:Men Women

Whites: 25.2% 20.7% AA: 27.0% 18.5% HA: 23.2% 12.5% Asians: 21.3% 6.9% NA: 32.0% 36.9% (Health, US. 2003-2004, CDC/NCHS)

Hypertension (140mmhg/90mhg or on anti-hbp meds) Men Women

Whites: 30.6% 31.0% AA: 41.8% 45.4% MA: 27.8% 28.7% Asians (overall): 16.7% NA (overall): 21.2% Note: up to age 55 M>W, but postmenopause: W>M.

Overweight (BMI = 25.0kg/m2+) Obesity (BMI = 30.0kg/m2+):

Men Women Whites: 69.4% 57.2%

28.2% 30.7%AA: 62.9% 77.2% 27.9% 49.0%

MA: 73.1% 71.7% 27.3% 38.4%

Asians (overall): 34.5% overweight, 7% obese.

Metabolic syndrome Men Women

Whites: 24.3% 22.9% AA: 13.9% 20.9% MA: 20.8% 27.2%

High Cholesterol (240mg/dl = high risk) Whites: 17.8% 19.9% AA: 10.6% 17.7% MA: 17.8% 13.9%

Physical inactivity: Men Women

Whites: 34.4% 38.3% AA: 45.1% 55.1% HA: 52.6% 57.2% Asians: 33.4% 42.6% NA: 42.5% 55.5%

Cancer Significant group differences

overall, in risk for specific types, and in cancer mortality.

Overall rate = 447.1 Whites = 471.2 AA = 456.0 NA = 298.5 Asians = 314.7 HA = 323.5

AA men highest rates of prostate, lung & oral cancers.

White women highest rate of breast cancer, but AA women have highest mortality.

Vietnamese women 2.5x rate of cervical cancer, Hispanic women 2nd highest rate.

AA and Hispanic women have highest rate of cervical cancer mortality.

NA and AA women highest rate of lung & colorectal cancers.

NA men have highest rate of colorectal cancers Asians have lowest cancer rates overall, but Korean

and Vietnamese men have highest rates of stomach & liver cancers.

Diabetes Minorities, women & Native Americans

disproportionately affected. Overall rate: 10.3%

Men Women Whites: 8.4% 7.8% AA: 8.5% 12.1% HA: 17.8% 19% Asians: 20% 16% NA: 49.4% 51.1%

Minorities suffer disproportionately from the diabetic complications

AA = 6.6x > W from end-stage renal disease. MA = 7.x > W and NA from end-stage renal disease. Non-Whites 2x > W to suffer from blindness. AA > W and H to suffer blindness. Except for Asians, minorities higher mortality rates

from diabetes than Whites. Group differences suggest possible differences in

genetic vulnerability. Differences in functional outcomes suggest

differences in lifestyle, early screening, treatment seeking, disease tolerance and quality of care.

HIV/AIDS Percent New cases, 2004

Whites: 28.3% AA: 49.3% HA: 20.4% NA: 0.5% Asians: 1.1%

Adverse Birth OutcomesPercent of live births, 2005 Preterm (< 37 weeks of gestation)

All groups: 12.7% Whites: 11.7% AA: 18.4% Hispanics: 12.1% NA: 14.1% Asians: 10.8%

Low birthweight (< 2,500 grams) All groups: 8.2% Whites: 7.3% AA: 13.7% HA: 6.9% NA: 7.4% Asians: 8.0%Note: Similar pattern for very preterm (<32 Wks)

& very low birthweight (<1,500 grams).

Ethnic Disparities in Health Care Differences in access to & quality of health

care important contributors to health disparities. Patient-level variables Health-care system variables Care process variables Ethnic differences reduced among higher

SES, those with insurance, medical care in the military & VA systems.

How do we explain these differences?

We argue, contrary to current direct effects studies, that the accumulating evidence supports a more complex, multi-dimensional model of risk and resilience.

Social Status Stresses

The first factor in such a model is exposure to social status stresses & adversities

Social status stresses are stresses that are experienced because of attributes that define our status in the social hierarchy, such as race/ethnicity, social class, gender, sexual orientation, and age.

Social Status-Related Sources of Risk

Socio-Economic Status & History Current Status SES History & Trajectory

Race/Ethnicity Ethnic Identity and Ethnic Self-Esteem Cultural affiliation and Level of Acculturation

Race/Ethnicity and SES interact such that SES conditions race/ethnicity in predicting health & functional outcomes (Williams & Jackson, 2005).

Race/Ethnicity

SES

Health Outcomes

However, we argue that the race/ethnicity-SES interaction effects are likely mediated through exposure to psychosocial adversities. Chronic Daily Stresses Stressful Life Events Socio-Ecologic Stresses Racism & Discrimination

Race/Ethnicity

SES

Psychosocial Adversities

Health Outcomes

Racism, Discrimination & Health

Racism/discrimination is not “just another stressor”.

Despite important social changes, racial discrimination continues to be pervasive, expressed at multiple levels, and includes a range of attitudes and acts (Harrell, 2000).

While most studies have been conducted on African Americans, exposure to discrimination has been linked to functional outcomes in all ethnic groups.

However, the effect is greater and more pervasive in persons of color, especially in African Americans (Greene et al., 2006; Mays et al., 2007).

These experiences are linked to a variety of medical (e.g Hypertension, CVD), mental health (e.g. depression, anxiety, hostility) & behavioral disorders (e.g. violence, substance abuse) (Paradies et al., 2003; Brondolo et al., 2003; Landrine et al., 2006).

Exposure to discrimination are frequent for many groups, especially the poor and persons of color, and vary in the causal attributions made (e.g. race, color, nationality, immigration status, size, gender, etc.).

Exposure produces direct negative effects that are moderated by appraisal & coping.

These experiences are usually not forgotten and condition future appraisals, expectations and coping with future experiences (Brondolo et al., 2005).

Exposure to race-related and other stresses linked to disease in part by activating pre-existing or creating new biological vulnerabilities.

Biological heterogeneity of racial groups, yet there is clustering of genetically-mediated disorders within groups (e.g. hypertension, diabetes).

Evidence is also growing of environmental contributions to the acquisition of biological vulnerabilities (e.g. multi-generational exposure to pathogens & toxins).

Race Psychosocial Adversities

SES Biological Vulnerabilities

Health Outcomes

Strong evidence suggests, however, that these biological vulnerabilities are often manifested in patterns of physiological hyperreactivity or dysregulation of stress response mechanisms (e.g. BP, HRV, cortisol, immune changes, etc.).

Biological Stress Processes Allostatic Load

Stress Hormones Blood Pressure, Heart Rate & Heart Rate

Variability Metabolic Factors

Neurophysiologic Processes Immune Processes Disease-Specific Biomarkers

Race Psychosocial Adversities Health

Outcomes

Biological Biological SES Vulnerabilities Stress

Processes

Other evidence also identify processing of thoughts & emotions as implicated in health outcomes

Negative thoughts & emotions as risk enhancers Depression Fear & Anxiety Anger/Hostility Hopelessness

Positive thoughts & emotions as resilience enhancers Faith & Hope Optimism Tranquility

Race Processing thoughts & emotions

Psychosocial Adversities

SES Biological Biological

Vulnerabilities Stress Processes

Health Outcomes

Strong evidence notes that social adversities also exert their effects in part through the clustering of health risk behaviors.

These behaviors are disproportionately evident among the poor and persons of color.

Clustering of these behaviors have synergist rather than simply additive effects.

Health risk behaviors Physical Inactivity Poor dietary habits & excess weight Smoking Alcohol and recreational drugs Risky sexual behavior Poor health & illness management

Processing Race Thoughts & Emotions

Health Psychosocial Behaviors Health Adversities Outcomes

SES Biological Biological Vulnerabilities Stress

Processes

Recent evidence provide initial support for the role of histories of social resources (Singer & Riff, 1999) and psychosocial reserve capacity (Gallo & Mathews, 2003) in moderating or mediating the effects of race/ethnicity, SES and social adversities in predicting health outcomes.

Psychosocial Reserve Capacity Cultural Resources

Spirituality/Religiosity Cultural Wisdom

Interpersonal Resources Social Support Network of Social Relationships

Intrapersonal Resources Perceived Control Self-Esteem & Self-Efficacy Resilience(Gallo & Mathews, 2003; Gallo et al., 2005)

ProcessingRace Thoughts Health

& Emotions Behaviors

Health

Psychosocial Outcomes

Adversities Reserve

SES Biological Capacity Vulnerabilities

Biological Stress Processes

Finally, we argue that health disparities are the byproducts of the interactions of all of these risk factors moderated or mediated by reserve capacity to lead to Cumulative Vulnerabilities over time, and it is these relationships that in turn predict adverse health outcomes.

Implications & Future Directions

This is a reciprocal & recursive, lifespan meta-model that cannot be tested in a single study.

Serves as a framework to inform new hypothesis-driven studies of smaller models.

Model emphasizes the interplay of social context factors that exert their effects through cognitive-emotional, psychosocial risks & resources and biological pathways over the life course to predict current health status & functioning.

This framework asserts the value of both within and between group studies and recognizes the possibility that group-specific models may be worth investigating.

Specific models will also need to test to predict resilience in the face of race- and SES-related psychosocial adversities.

Finally, testing models such as this will likely require larger and more representative samples, new measures, longitudinal designs, multidisciplinary teams and advanced statistical procedures.