Science Briefs

Do Psychosocial Factors Contribute to Socioeconomic Health Disparities?

The web of causation underlying health disparities is multi-faceted and complex, including factors such as differences in health care access and quality, health behaviors, biological factors, and gene-environment interactions.

By Linda Gallo, PhD

Applications of the Reserve Capacity Model 

Early in the 20th century, W.E.B. Dubois described differences in health between African Americans and Caucasians, concluding that they did not reflect disparate physical vulnerabilities, but were a reflection of social forces. More than a century later, health disparities persist, despite overall improvements in longevity and health. In 2000, the government called for the elimination of health disparities in the national health agenda “Healthy People 2010” (U.S.Department of Health and Human Services, 2000). Given mixed progress to date, this goal is likely to be re-instated for Healthy People 2020. A clear understanding of the processes that create and maintain health disparities and what we can do to address them remains elusive.

The web of causation underlying health disparities is multi-faceted and complex, including factors such as differences in health care access and quality, health behaviors, biological factors, and gene-environment interactions (Adler & Rehkopf, 2008). In recent years, researchers have also turned their attention to psychosocial factors in health disparities. Constructs such as stress, negative emotions, and social isolation relate to major health outcomes including cardiovascular disease (CVD) and all cause mortality (Krantz & McCeney, 2002). In addition, these factors vary by socioeconomic status (SES) and in some cases, ethnicity, so that individuals with low SES or minority ethnicity show relatively high psychosocial risk (Gallo & Matthews, 2003; Myers, 2008). Psychologists can help achieve the critical public health goal of addressing health disparities, via research that seeks to unravel the psychosocial pathways underlying health disparities, in order to inform related prevention and intervention efforts.

The Reserve Capacity Model

Gallo and Matthews developed the “Reserve Capacity Model” as a framework through which to examine psychosocial factors in SES-driven health disparities, using concepts from the aging and stress literatures (Gallo & Matthews, 2003). The model begins with the premise that stressful versus positive experiences and environments are unequally distributed according to SES. Individuals in disadvantaged circumstances endure more frequent exposure to risk, threat, conflict, ambiguity, daily hassles, and major life events (Gallo, Bogart, & Vranceanu, 2005; Hatch & Dohrenwend, 2007; Matthews, Raikkonen, Everson, Flory, Marco, Owens et al., 2000; Myers & Hwang, 2004; Stansfeld, Head, & Marmot, 1998; Turner & Turner, 2005). Moreover, social status may shape appraisals in a way that further increases stress burden. For example, prior research suggests that individuals with low SES (Chen, Langer, Raphaelson, & Matthews, 2004; Chen & Matthews, 2001) and those previously exposed to racial discrimination (Broudy, Brondolo, Coakley, Brady, Cassells, Tobin et al., 2007) formulate negative interpretations of even ambiguous social interactions, and that individuals with low SES view their social worlds as relatively hostile and unfriendly (Gallo, Smith, & Cox, 2006).

Over time, the wear and tear from repeated physiological stress responses, combined with unhealthy behavioral coping strategies, take their toll, increasing vulnerability to disease (McEwen, 1998; Myers & Hwang, 2004) and possibly accelerating the biological aging process (Epel, Lin, Wilhelm, Wolkowitz, Cawthon, Adler et al., 2006). In addition, stress may shape health indirectly through associations with emotional and attitudinal factors, which themselves affect health via bio-behavioral mechanisms (Cohen & Pressman, 2006; Everson-Rose & Lewis, 2005). Some research also suggests that persons with low social status experience enhanced emotional (Kessler & Cleary, 1980; McLeod & Kessler, 1990) and physiological (Lepore, Revenson, Weinberger, Weston, Frisina, Robertson et al., 2006; Williams, Marchuk, Siegler, Barefoot, Helms, Brummett et al., 2008) reactivity to stress, increasing the potential deleterious consequences.

The Reserve Capacity Model posits that this enhanced vulnerability reflects inadequate resource reserves that could otherwise attenuate negative appraisals or facilitate adaptive coping. We have been particularly interested in interpersonal (e.g., supportive social relationships) and intrapersonal resources (e.g., control perceptions; optimism) that may be scarce in individuals with low SES or in ethnic minorities. For example, persons with low SES or minority ethnicity may be exposed to discrimination, segregation, and unsafe neighborhoods, which could discourage trust and limit opportunities for supportive social interaction (Gehlert, Sohmer, Sacks, Mininger, McClintock, & Olopade, 2008). A lack of education may create a reduced sense of control and poor self-esteem through failure experiences wrought by less-developed communication and problem-solving skills (Mirowsky & Ross, 1998). Similarly, jobs held by individuals with low social status are often low in control and support. Given research showing that psychosocial resources relate directly to physical and mental health (Cohen, Gottlieb, & Underwood, 2001; Singer & Ryff, 1999; Taylor & Seeman, 1999), they may serve as mediators of health disparities as well as moderating stress responses.

Research Examining Tenets of the Reserve Capacity Model

In the first study designed to test components of the Reserve Capacity Model, we used ecological momentary assessment to monitor daily experiences of middle-aged women (N=108; 94% white) (Gallo et al., 2005). As shown in Figure 1, women with lower SES reported less control and positive affect, and more social conflict in their daily lives relative to those with higher SES. SES also showed an inverse linear association with aggregate resilient resources, which in turn predicted less control, positive affect, and more conflict and negative affect in everyday life. In part, resources helped explain associations between SES and daily experiences, although SES also had direct effects. On the other hand, the hypothesis that low SES would elicit excess emotional reactivity to stress was not well-supported, and SES was unrelated to ongoing negative affect in daily life. Overall, these results suggest that SES may foster a heightened burden of certain types of negative daily experiences due to direct effects and indirect effects through reserve capacity.

 
Another report from this study examined associations among SES, job characteristics, and ambulatory blood pressure (Gallo, Bogart, Vranceanu, & Walt, 2004) – an indicator that is strongly predictive of CVD (Conen & Bamberg, 2008; Verdecchia, 2000). In combination, occupational status and job characteristics accounted for 18% of the inter-individual variability in ambulatory systolic blood pressure. Interaction effects showed that women in low status jobs were more reactive to circumstances of high job demands; they also demonstrated greater physiological benefits from high control. As shown in Figure 2, regression equation estimates suggested that with low demands or high job control, women in low status jobs would evidence ambulatory blood pressure levels roughly equivalent to those of women in higher status jobs. Other studies have shown that some of the excess CVD risk associated with low SES can be attributed to perceptions of control at work (Bosma, Van Jaarsveld, Tuinstra, Sanderman, Ranchor, van Eijk et al., 2005; Marmot, Bosma, Hemingway, Brunner, & Stansfeld, 1997). Thus, efforts to build psychosocial resources, such as perceptions of control, could help attenuate the negative health impact of low status work environments.

Two other studies used the Reserve Capacity Model to examine the contributions of psychosocial factors to associations between SES and the metabolic syndrome – a constellation of risk factors that is associated with negative health outcomes such as CVD and all cause mortality (e.g., (Gami, Witt, Howard, Erwin, Gami, Somers et al., 2007; Räikkönen, Kajantie, Rautanen, & Eriksson, 2007). In a study of 401 middle-aged women from the Healthy Women Study (Matthews, Meilahn, Kuller, Kelsey, Caggiula, & Wing, 1989), SES showed an inverse, linear association with incident metabolic syndrome risk assessed across a 12 year follow-up period. As depicted in Figure 3, structural equation models revealed a direct effect of SES, and also, indirect pathways from low SES to low reserve capacity, to high negative emotions, to metabolic syndrome risk (Matthews, Räikkönen, Gallo, & Kuller, 2008). Contrary to predictions, SES, reserve capacity resources, and emotions did not interact with stress to predict the metabolic syndrome. Finally, a study performed in middle-aged Latinas recruited from a community health clinic near the San Diego/Mexico border identified a significant, inverse relationship between SES and most components of the metabolic syndrome (Gallo, Espinosa de los Monteros, Ferent, Urbina, & Talavera, 2007). Women with lower SES also reported lower levels of psychosocial resources, and in part, SES related indirectly to abdominal obesity (a central underlying determinant of the metabolic syndrome) through associations with reserve capacity. These studies suggest that a deficit in resilient resources, and a concomitant impact on emotional state, may link SES with risk for the metabolic syndrome, most likely via physiological stress responses and health behaviors.

Future Directions

Our research adds to the literature concerning the roles of psychosocial factors in health disparities, and demonstrates the potential utility of the Reserve Capacity Model as an integrative theoretical framework. However, additional research is clearly needed, and a number of unanswered questions remain.

First, to date, relatively little research has directly addressed the contribution of psychosocial processes in connecting SES with health, particularly in relation to objective physical health outcomes. Thus, additional research is required to develop a more precise understanding of the specific roles and relationships among the psychosocial components encompassed by the Reserve Capacity Model. For example, initial evidence (Gallo et al., 2005; Matthews et al., 2008) suggests that resources may play a more direct role than originally theorized, whereas their moderating influence might be less salient than anticipated. In addition, to date our efforts to test various tenets of the Reserve Capacity model have not indicated a clear role of stress or stress reactivity (Gallo et al., 2005; Matthews et al., 2008). Although stress is implicated as a primary culprit in psychosocial explanations for health disparities, a close look at the literature reveals a complex association. One study found that individuals with low SES evidenced fewer, but more severe, hassles in daily life when compared to those with high SES (Grzywacz, Almeida, Neupert, & Ettner, 2004). Another study revealed the importance of nuanced contextual factors, such as stressor domain, severity, timing, or perceived risk (Almeida, Neupert, Banks, & Serido, 2005). Further, over time, some individuals may become habituated to disadvantaged status (Nguyen & Peschard, 2003), and others may be resilient to stress, which would alter stress appraisals or consequences. Additional research is needed to better understand how stress contributes to health disparities, and to determine factors underlying resilience versus vulnerability. Likewise, given limited (Gallo & Matthews, 2003) and inconsistent (Thurston, Kubzansky, Kawachi, & Berkman, 2006) evidence, further research is needed to test the mediating roles of negative emotions in connecting SES with health.

In the studies performed to date, we have conceptualized reserve capacity as an aggregate “bank” of interpersonal and intrapersonal resources, consistent with the view that overall resource reserves are most relevant to understanding peoples’ stress responses, rather than any specific deficit or advantage (Hobfoll, 2001). However, in considering possible avenues for intervention (Gehlert et al., 2008), it may be helpful to examine the relative importance of specific protective factors. A number of studies suggest that social support and perceived control can guard against the negative emotional and physical consequences of low social status (Chen, 2007; Lachman & Weaver, 1998; Marmot et al., 1997; Singer & Ryff, 1999; Turner & Noh, 1983), and both are potentially amenable to intervention. For example, existing school-based intervention programs focused on problem solving or communication skills for at-risk children could also foster perceptions of control. Community-based interventions designed to build collective efficacy and increase neighborhood safety could reduce social isolation. Given the close inter-relationships among resources (Hobfoll, 1998), interventions focused on any given pathway are likely to have a broad-based impact. Because associations between SES and psychosocial resources and risks are rooted early in the life course (Chen, 2004; Repetti, Taylor, & Seeman, 2002), prevention and intervention efforts would optimally target young, at-risk children and their families. To the extent that reserve capacity can be maintained, individuals with low social status might experience fewer negative health outcomes across the lifespan.

Future research is also needed to examine how culture and ethnicity interact with other constructs in the Reserve Capacity Model to influence health and disease. Although intended to guide research on socioeconomic health disparities, concepts in the model can potentially contribute to understanding disparities due to ethnicity/race or other demographic characteristics (Broudy et al., 2007; Gallo, Penedo, Espinosa de los Monteros, & Arguelles, 2008; Myers, 2008). In addition, the combined associations between minority ethnicity and SES and intermediate psychosocial processes and subsequent health outcomes must be carefully considered. Given added stressors such as discrimination, acculturation, or immigration, low SES may be especially “toxic” in individuals with minority ethnicity (Myers, 2008; Williams, 1999). In support of this assertion, African Americans experience more stress and negative health outcomes at any given level of SES relative to non-Hispanic whites (Williams, 1999; Williams & Rucker, 1996). On the other hand, the repeatedly observed inverse association between SES and health is sometimes flattened, or even reversed, in specific ethnic minority (e.g., Hispanic) and immigrant populations (Chen, Martin, & Matthews, 2006; Kimbro, Bzostek, Goldman, & Rodriguez, 2008). Migration patterns or recording errors may contribute to these findings, but certain groups may also benefit from culturally-driven resources (community or family support; better health behaviors) that could augment reserve capacity and alter the implications of low SES (Gallo et al., 2008).

Conclusions

The existence of unjust differences in health is clearly at odds with deeply held American values of fairness and equality. Should they persist, health disparities will have an amplified economic and social impact in the coming decades, as our population diversifies. Thus, for reasons of social justice as well as economic well-being, continued efforts to understand and eliminate health disparities are critical. Psychosocial risk and resilient factors represent one strand in the web of causation underlying health disparities that could be amenable to change through individual, community, work, or school-based intervention approaches. However, before effective interventions can be designed, additional research is needed to understand the precise roles that psychosocial variables have in health disparities. Our research program is one of many that seeks to contribute to this effort via a theory-based approach to examining the chain of events that leads from social disadvantage, to intermediate psychosocial risk and resilient factors, to bio-behavioral pathways, and ultimately, to health.

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