How Public Health Educational Initiatives Can Reduce the Prevalence of Diabetes among Blacks

Social and Behavioral Aspects of Public Health

1. The relationship between socioeconomic status (SES) and health is one of the most robust in the public health literature. Whereas high SES is almost invariably linked to lower morbidity and mortality, low SES is associated with higher morbidity and mortality. What theories, variables, and processes help to explain this inverse relationship between SES and health outcomes?

Money may not buy happiness, but it does buy better health and therefore longer life. The multiple variables that are involved in this calculus include the fact that affluent people can afford healthier foods, visit health care providers for preventive checkups as well as whenever ill health requires it, enjoy a less stress- and depression-filled lifestyle due to fewer worries over money, and actively participate in those activities that provide humans with physical fitness and joy. This is not to say, of course, that less affluent people never experience joy or a decent meal, but it is to say that poor people might avoid visiting a health care provider or purchasing needed medicine due to a lack of funds, or go without meals so that their children can have enough to eat.

A growing body of scholarship confirms the positive relationship between higher SES levels and better health and longevity. For example, a seminal study by Prus (2004) used divergence theory to test the hypothesis that higher SES levels serve to reduce morbidity and mortality rates over the lifespan. The key finding that emerged from this study was that, “While adults from all socio-economic status (SES) levels generally encounter a decline in health as they grow older, research shows that health status is tied to SES at all stages of life” (Prus, 2004, p. 145).

Furthermore, the strength of this positive relationship tends to increase as affluent people grow older. For instance, Prus (2004) also notes that, “Multiple linear regression analyses show support for this assumption; that is, the relationship between SES (measured by years of education and annual household income) and health (measured by self-rated and functional health indexes) strengthens with age” (p. 145). These findings indicate the reverse is also true: lower SES levels adversely affect morbidity and mortality rates over the lifespan and this effect intensifies with age as they grow older and vulnerable to age-related disorders.

An interesting finding by Rarick et al. (2016), though, was that the subjective perception of affluence can also have an effect on morbidity and mortality rates. In other words, individuals’ views about their own SES in relation to others has many of the same effects as clear-cut differences in income levels. This is an especially noteworthy finding since, ceteris paribus, the subjects’ income levels did not allow them to receive any additional or better health care, diet or lifestyle and the subjective perception of being affluent was the only operative variable. This finding suggests that, irrespective of reality, people who believe they are well off financially may enjoy more stress-free lives which contributes to their improved health and longevity (Rarick et al., 2016).

The subjective sense of life satisfaction’s effect on health and longevity that was tested by Rarick et al. (2016) was also the focus of a study by Moreno-Agostino et al. (2021). The findings that emerged from this study were also noteworthy since they shed some light on how subjectivity concerning SES status affects health and lifespan. For example, Moreno-Agostino et al. (2021) point out that, “One of the predictors of life satisfaction in older adulthood is SES. While some SES indicators (e.g., education) may reflect socioeconomic experiences in earlier life stages, others (e.g., income) represent current socioeconomic conditions that are more susceptible to change with age” (p. 585). Although research has shown that the relationship between actual SES levels and health strengthens with age, the reverse is true of the subjective perception of SES and corresponding life satisfaction levels. In this regard, Moreno-Agostino et al. (2021) conclude that, “Life satisfaction (i.e., evaluative wellbeing) has shown a particularly strong association with health [but] life satisfaction exhibits a declining pattern with age in most countries worldwide” (p. 585).

2. Discuss briefly the differences in rates of mental illness by race, SES, and gender. How might the concept of stress help us to better understand these observed differences?

Although everyone’s response to stress manifests in unique ways, there are some race, SES and gender-related commonalities involved that warrant further attention. In fact, researchers have shown that there is even an intergenerational effect from lower family SES levels on susceptibility to developing mental illness, with children from low SES families tending to develop these disorders as higher rates compared to their more affluent counterparts (Silva et al., 2022). This outcome is attributed to children’s experiences with and perceptions of their parents or caregivers suffering from mental illnesses (Silva et al., 2022).

The current views concerning the effects of SES on mental illness, however, may be skewed, perhaps even substantially, by the fact that people with higher levels of SES may avoid seeking medical care for their mental illnesses due to the powerful stigma this involves and are therefore underrepresented in the statistics. While there are some cross-cultural differences involved, this stigma appears salient in many nations today. In this regard, Foster and O’Mealey (2022) report that, “Prior research has suggested that higher levels of SES may be linked with heightened mental illness stigma. It has been posited that this link is due to the resource-rich environment high-SES individuals live in, which predisposes them to seeing the certain phenomena as being more controllable in nature than low-SES individuals” (p. 58). In other words, the same affluent SES levels that may have exacerbated a mental disorder operate to discourage sufferers from seeking assistance since they feel they should have been able to resolve the issue on their own with the support of professional – and typically expensive – health care providers.

In addition, racial differences have also been shown time and again as having an effect on the likelihood of developing mental illness. For instance, a study by Hope et al. (2020) assessed the manner in which the psychological and physiological anticipation of encountering racism in society and corresponding race-related stress levels affected mental health. Based on their findings, Hope and his associates (2020) determined that stress levels among younger black men were more pronounced, but the potential for developing mental health disorders in response to race-related stress were mitigated when community support from older black males was available.

Likewise, stigma is also a race-based factor that affects the likelihood of individuals developing a mental disorder. For instance, a study by Misra et al. (2021) identified four major race-related themes that could have an effect on racial minorities and their vulnerability to mental illness. These four factors were: 1) service barriers including access and quality (structural stigma); 2) family experiences including concealment for family\\\\\\\'s sake, fear of being a burden, and stigma extending to family (affiliative stigma); 3) lack of knowledge about mental illness and specific cultural beliefs (public stigma); and, 4) negative emotional responses and coping (self-stigma) (Misra et al., 2021, p. 486).

Beyond the foregoing differences, there are also some gender-based differences that affect the likelihood of people developing a mental disorder. In fact, even the same types and levels of stress can exact a dramatically different toll on men compared to women. For example, Boyd et al. (2015) determined that, “Stress appears to be differently experienced between genders: emotional exhaustion prevails in women, while men tend to feel more depersonalized” (p. 245). Moreover, adult women appear to be at higher risk of developing psychological disorders, and this outcome is attributed to other variables such as social inequities, social segregation, negative gender stereotypes and the combination of various social and biological determinants (Boyd et al., 2015).

3. Compare and contrast the Health Belief Model and the Theory of Reasoned Action/Theory of Planned Behavior. Then, choose one theory and describe how you would apply it in developing the proposed intervention for the Public Health Intervention and Policy Project (i.e., Decreasing Diabetes in the African American Community through Health Education).

Delivering evidence-based strategies is an integral part of any public health intervention, and the health belief model provides a useful framework for this purpose. In this regard, Moradi et al. (2022) report that, “The health belief model is an effective framework for designing educational interventions and promoting preventive behaviors act and considers behavior as a function of the individual’s knowledge and attitude” (p. 2). The health belief model is based on the key tenets that the perception of the respective costs and benefits of engaging in different behaviors, including lifestyle choices that may be harmful to health. For example, Moradi et al. (2022) note that according to the health belief model, “A persons’ behavior changes when he understands the level of danger that threatens him (perceived sensitivity and severity) and also has a proper assessment of health barriers and behaviors (perceived barriers and benefits)” (Moradi et al., 2022, p. 2). These attributes indicate that the health belief model can be used appropriately for public health interventions of various types.