Advances in Understanding the Causes and Consequences of Health Disparities in Aging Minorities

Abstract

Purpose of Review:

The purpose was to discuss appropriate methods for advancing our understanding of health disparities or minority aging including life-course perspectives, biological measures, pain measurement, and generational approaches.

Recent Findings:

Life course perspectives provide an orientation for studying older minorities that concomitantly captures exposures and stressors that may lead to earlier onset of disease and premature mortality. The use of biological markers to study health disparities in older minorities is necessary in order to identify pathways between psychosocial factors and health outcomes. Work focusing on pain disparities should include explorations of relationships between psychosocial factors, and subjective and objective measures of pain. Studying families can provide insight into genetic associations and coping styles in older minorities.

Summary:

Methodological approaches that take life course, biology, and social factors into account may help identify causal pathways between social determinants of health and health outcomes among older minorities. Once these causal pathways have been identified, more strategies and interventions that strive toward health equity across older adults of all race/ethnic groups can be developed.

Introduction

Aging is a universal process but the factors that affect how we age are not uniform across demographic characteristics of the population [1]. The progression of aging and quality of health are affected by factors such as stress, lifestyle factors, and social determinants [2]. Within the aging population is the ever-growing subset of racial and ethnic minorities, which is expected to overtake the majority White group within this century [*3]. Even as the minority population grows in the United States, health disparities remain a problem that presents huge financial and social burdens on society [4]. Healthy People 2020 defines a health disparity as “… a particular type of health difference that is closely linked with economic, social, or environmental disadvantage” [5]. There are several socially defined groups who experience health disparities based on group status such as religion, socioeconomic status, gender, age, and sexual preference, in this article we focus on race/ethnicity.

Health disparities, which typically begin early in life and persist through to advanced ages, are prominent between White and racial/ethnic minorities in the United States. Although there is a substantial amount of literature on health disparities in late life [**6], there is limited attention paid to the potential problems that hinder our understanding of these disparities. In addition, advancing the understanding of health disparities in late life requires a shift from merely documenting between-group differences to understanding variations in pathways through environmental, sociocultural, behavioral, psychosocial, and biological factors that create and sustain health disparities [7–10]. Throughout this review we will refer to “healthy aging”, which is defined as “the process of developing and maintaining the functional ability that maintains well-being in older age” [11]. The inequality that results from health disparities has a profound impact on the ability for everyone to have equally healthy aging. Throughout the history of research in health disparities, methods for measuring and understanding disparities have evolved. Beginning with determinants as thresholds and moving to graded associations, then mechanistic relationships, followed by multilevel methods, and the current era of interactive effects [12]. Our understanding of the causes and consequence of unequal health has evolved as methods of measurement have evolved. The following review will focus on advances in approaches to better understand the causes and consequences of health disparities in aging minority populations including life-course methodology, biological methods, methods for measuring pain, and generatiotablenal methods.

Life Course Methods

It is important that disparities research in older adults takes into account a lifetime of unequal environmental and social exposures and the various social positions of older minorities, which may influence their biological, social, and psychological outcomes in middle and older adulthood [13, 14]. The typical study of aging starts at age 65 years. This approach may miss important aspects of the trajectory of outcomes and disparities [15–17], although these studies have provided knowledge about the natural history of health and functional outcomes. It is well known that many racial/ethnic groups have earlier onset of disease and premature mortality compared to Whites. These differences, however, tend to present themselves in middle-age rather than older age, so focusing solely on older ages may result in underestimation of health disparities [18–20]. For example, Szanton, Thorpe and Whitfield [21] identified that financial strain throughout the life course was significantly related to depression and disability. Zhang, Hayward, & Yu [22] reported that Blacks were more likely than Whites to report adverse childhood adversity, which increased the risk of cognitive impairment in later life. Further complicating measurement of health disparities in older ages are “exceptional survivors” or those persons who, despite structural disadvantages, have developed survival advantages through the interplay of genetics, and intrinsic and extrinsic factors such as personality traits an external coping methods [23]. If the African Americans that make it to older ages are disproportionately represented in these studies as exceptional survivors, the samples in many of these studies may not be representative of the general population of aging minorities.

It is well documented that racial/ethnic minorities also experience different life circumstances from infancy onward compared to Whites [7, 24]. This is largely due to social and structural forces that produce or sustain inequalities at every stage of life. These structural forces create social disparities that contribute to cumulative disadvantage, or the sum of disadvantages over the life course [25, 26]. Cumulative disadvantage is linked to poor self-rated health [27] and multiple cardiovascular outcomes such as hypertension, stroke, heart attack, and diabetes [28]. A life course perspective offers three key features that are important to understanding health disparities and minority aging: 1) the opportunity to identify adverse experiences throughout the life course and their relationship to health outcomes in late adulthood; 2) the impact of when experiences happen over the life course and how the timing is related to health outcomes; and 3) the chance to identify possible points of intervention such as when risk factors or exposures are responsive to change [26]. An implicit assumption of the life course perspective directly relevant to understanding disparities in aging is that there is substantial variation in health status within age groups, particularly at later ages [29]. At a practical level these differences imply that cross-sectional models that use single level data (e.g., race differences in income in a cohort of adults aged 50 years and older) as opposed to longitudinal, multilevel data (e.g., race differences in income and education quality in adults aged 50 years and older by birth cohort) are not adequate to accurately model life course differences [30]. An understanding of how social, psychological, and economic experiences affect health outcomes studied in interventions is increasingly being found through biobehavioral approaches to aging [31–33]. Researchers are increasingly seeking to understand how to measure the embodiment of cumulative disadvantage and the repercussions of sustained inequality and chronic stress (e.g., Geronimus, Hicken, Keene, & Bound, 2006) [34].

Biological Methods

Within the field of health disparities, multiple measures of biology are currently being used to quantify the way in which adverse circumstances faced by minority groups affect health outcomes. The most recent methods include epigenetic aging measured through DNA methylation, biological age measured by length of telomeres, and physiological dysregulation measured through composite biomarkers including allostatic load and biological age [35].

Epigenetic aging is most often measured by inputting data on DNA methylation from multiple tissues and cell types into freely available calculators. These calculators use penalized regression to determine a functional age based on methylation of CpG sites throughout the body [36]. DNA methylation is a stable and valid marker of aging in mammals so epigenetic age is highly correlated with chronological aging. In current literature there are a number of epigenetic clocks [37] that all use DNA methylation data but use data from different areas (blood, tissue, plasma, phenotypes). There is no current consensus on which clocks are best for particular racial/ethnic groups and age groups. Recent research has shown that among African Americans, different clocks reflect different environmental stimuli, making the case for a better understanding of the environmental and psychosocial factors that affect DNA methylation [38]. Although research has been done that measures associations between environmental and psychosocial stressors and epigenetic age in African Americans [39, 38], until recently, many of the studies that looked at epigenetic aging and clinical outcomes either included all-White cohorts or did not expressly look at racial differences in outcomes related to epigenetic aging [40–43]. This is perhaps related to the general problem of lack of diversity in genomic research [44] though within the last few years research on epigenetic age and outcomes among aging African American cohorts has begun to increase [45–48]. Overall, epigenetic measures of aging, among African Americans especially, require more research to gain a better understanding of how these processes may be different by race/ethnicity.

Another method for measuring biological aging, telomere length, has been in vogue for decades. Telomeres are the caps at the end of chromosomes and shorten each time a cell divides (as with aging) until they are so short that a cell can no longer divide, causing cell death. Thus, telomere length (TL) is associated with general aging and diseases of aging [49]. Measurement of leukocyte telomere length (LTL) as an indicator of accelerated biological aging has been around for many years with more than 6,000 publications on the topic [50]. Shorter LTL has been linked to mortality, cancer, cardiovascular disease, and Alzheimer’s disease [51]. The relationship between LTL and factors among minorities thought to contribute to health disparities (e.g., discrimination, adverse childhood experiences, neighborhood disadvantage, poverty, educational attainment, etc.) have been well characterized in the literature as well [52–54] though more often in middle age than older age. The repeated associations of LTL minority stressors make it a compelling choice for health disparities research, however certain inconsistencies give researchers pause. For example, although research has shown that the stressors that are especially common among Blacks in the US are related to shorter telomeres, multiple studies have shown that Blacks have longer telomeres than Whites both in adulthood [55] and as newborns [56]. There is also research showing shorter telomeres among Blacks compared to Whites [57]. Similarly, only a small number of studies have examined telomere length in participants that are older than 65 years [58, 59] so the exact way in which telomeres are related to aging in older minorities isn’t well characterized. Further, there is no consensus on whether shortened telomeres are a cause or consequence of aging. One inherent issue with epigenetic aging and telomere length is their distance from the disease process. Cumulative biological measures may offer a solution to this problem.

Cumulative biological measures such as allostatic load and biological age have the benefit of being more proximal to the disease process in that their components tend to be markers of disease as opposed to DNA. These markers such as blood pressure, cholesterol, glucose, and triglycerides are often easier to obtain than genetic material. Such cumulative measures are predictive of health outcomes of aging such as mortality [60, 61], cardiovascular disease [62, 63], and Alzheimer’s disease [64]. Similar to genetic measures, these measures are associated with stressors most common among aging minorities such as discrimination [65–67], low socioeconomic status [*68–72], and neighborhood factors [73, 74]. The problem with these measures is reflected in how many different definitions of cumulative dysregulation are found in the literature. The measure of allostatic load used the MacArthur studies of Successful Aging [75] included blood pressure, waist-hip ratio, serum high-density lipoprotein and total cholesterol, plasma total glycosylated hemoglobin, serum dehydroepiandrosterone sulfate, urinary cortisol, urinary norepinephrine, and urinary epinephrine. However, as there is no consensus on the practical definition of allostatic load, a number of studies have been published that use physiological dysregulation as measured by biomarkers but have used a number of different biomarkers, mainly due to availability, and different methods of calculation [76, 60]. However, most studies, regardless of the biomarkers and method of calculation used tend to have similar overall conclusions regarding the relationship between higher physiological dysregulation and poor health outcomes [43, 51, 60].

When biological markers are utilized in health disparities studies, they are often used to explain how non-biological factors such as environment and psychosocial factors can contribute to disparate health outcomes. Particular factors that tend to be most detrimental to minority populations include discrimination [77, 78], interpersonal racism [79, 52, 80], poverty [57, 81], social stratification [82], socioeconomic status [83], and general environment and stress [84, 85]. Although all of the biological measures mentioned herein are being used in aging research and, to a degree, in health disparities research, the issues mentioned with each method have to be taken into account. To this point, we have discussed issues in understanding general biological aging in minority populations. However, there are also issues related to understanding specific health conditions and the disabling effects of these conditions in minority populations. Pain is one condition that is particularly disabling in minorities, yet further work is needed in measurement and our understanding of pain in older minorities.

Measurement of Pain

Older minorities experience poorer pain management and poorer physical function and disabilities related to their pain than non-Hispanic White older adults [86–91]. Although there are many factors that may contribute to disparities in pain in older minorities (e.g., higher rates of comorbidities, and cognitive impairments, limited access to pain management, communication barriers within health care) [92, 93], pain assessment plays a large role in identifying and understanding pain disparities in older minorities [*94, 95]. Self-report of pain is the first step in pain assessment. Therefore, it is essential that self-report assessments of pain in older minorities are adequately and appropriately done in order to identify pain disparities, within group pain outcomes, and identify effectiveness of pain management in older minority populations.

The various self-reported pain clinical assessment instruments that have been used to measure pain in older adults include the Faces Pain Scale (FPS), Numeric Rating Scale (NRS), Visual Analogue Scale (VAS) and the Verbal Descriptor (VDS) [*96, 97]. The FPS, NRS, and VDS have demonstrated good reliability in older minorities with and without cognitive impairment [97]. Common self-report pain scales in research studies include the McGill Pain Questionnaire (MPQ), the Brief Pain Inventory (BPI) and the Patient Reported Outcomes (PROMIS) pain measures [98–100]. The MPQ, BPI, and the PROMIS pain measures have demonstrated good reliabilities in studies that included older minorities [101, 102, 100, 103]. The Brief Pain Inventory has been validated in many languages [104].

There are limitations in current subjective measures of pain in clinical settings and in research studies. For example, further research is needed to determine if the FPS, NRS, VAS and the VDS are all validated across various older minority populations [*94, 97]. The McGill Pain Questionnaire and the PROMIS pain Questionnaires [98–100] were also developed with predominantly non-Hispanic White samples and/or the distribution of race/ethnicity of the samples was not described [86, 99]. It is unknown if these tools accurately capture as intended the pain intensity, related behaviors, and impact that pain has on health in older minorities [*94]. Further refinement of these pain assessment scales is needed to increase the applicability and usability among older minority populations [*94, 105].

Quantitative sensory testing is a method used to assess pain threshold by exposure to cold and warm temperatures and vibration sensations applied to the skin and can be an effective objective measure of pain [106]. Studies using quantitative sensory testing have demonstrated than minorities have higher pain sensitivity and lower pain tolerance than non-Hispanic Whites [107, 108]. Many studies that use these methods have been done with healthy young adults that do not include minorities or only compare non-Hispanic Whites to African Americans/Blacks. Few studies using quantitative sensory testing have included older adults from Asian, Native American and/or Hispanic populations [109].

Tailoring the scales that measure pain may be useful in older minorities; however, researchers and clinicians should be aware of stereotyping or misrepresentation of racial/ethnic minorities [*94, 109]. One way to improve the accuracy of scales and self-report is to include older adults from the target racial/ethnic groups in development and/or adaptation of pain scales. Researchers can also use cognitive interviewing and focus groups to assess validity of self-report pain scales [*94].

More studies are needed that conduct quantitative sensory testing in older racial/ethnic minorities with various types of pain. Large studies that examine the complex relationships between sociodemographic, self-reported pain, and objective pain in mixed race/ethnic samples of older adults are needed to tease out causal pathways of pain disparities [110–112]. These studies can provide further background information on modifiable factors that can be addressed in order to improve pain management in older minorities with pain. Finally, randomized-controlled trials that test pain interventions should be tailored to meet the needs of older racial/ethnic minorities. Additional methods might prove to be useful in providing insights on how to improve pain measurement and management among older minorities.

Generational Methods and Family Designs

In order to understand the causes and consequences of health disparities on aging, it can be very helpful to study generations and families within aging minority populations. When looking at minority aging populations an understanding of family methods and dynamics can lend insight into genetic associations as well as into sources of stress and support. Family designs, such as twin studies, are seldom used in health disparities research and this represents a significant missed opportunity to fully understand health disparities [31, 33]. When looking at the pattern of health disparities over the years, there is a considerable amount of consistency regardless of the advances in medical technology, treatments, and preventative programs. One source of possible inquiry is the influence of family. The impact of family can influence health in a number of ways. For example, health behaviors are significantly influenced by traditional beliefs and health practices.

Twin designs are useful for understanding the contribution of genetic and environmental influences on behaviors as well as health conditions. The design is based on genetic differences in identical and fraternal twins in which identical twins share 100% of their genes and fraternal twins 50%. The further assumptions are that twins are raised in the same family with similar parental influences across their childhood. There are also unique influences on the individual such as friends or other experiences not shared across twins [113]. These assumptions allow one to estimate the percent of genetic, shared and non-shared environmental influences. Within the last decade or so, twin studies of older adult twins have been used in health disparities research to look at especially disparate predictors of health such as educational attainment [113] and self-reported health [114], as well as disparate outcomes of aging such as cognitive impairment [115]. In addition to designs that take advantage of the family unit, research on multiple generations living in the same household has revealed the effects of custodial grandparenting on aging, especially among African American grandparents.

Custodial grandparents are grandparents that provide primary care to their grandchildren with or without their children in the home. The 1990 Census reported a 44% increase over the preceding decade in the number of children living with their grandparents or other relatives [116]. In one-third of these homes, neither parent was present, typically making the grandparent or other relative the primary caregiver. The 2000 census estimated that 1 in 12 children (roughly 8.3%) are living in households headed by grandparents or other relatives [117]. As of 2016, approximately 2.7 million grandparents were raising grandchildren with or without the birth parents in the household, and 62% are custodial grandmothers [118]. Custodial grandparents are more likely to be African American or Hispanic and to be of lower income classes [*119]. In other another study, almost 30% of African American grandmothers and 14% of African American grandfathers reported being the primary caregiver for a grandchild for at least 6 months [120], compared to 10.9% of all grandparents [121]. In sum, the research implies that grandparents who raise their grandchildren are more likely to be African American, female, poor, and live in the South.

The southern region of the United States had the highest proportion of custodial grandparents [122]. Furthermore, a higher proportion of custodial grandparents are in frontier and rural areas than metro areas [123]. One-quarter of custodial grandparents live in rural areas [124] and rural custodial grandparenting has its own set of challenges including transportation and childcare issues [125]. In 2003, Alabama ranked 17^th in the country for percent of custodial grandparents (49.4%). In 2004, Alabama ranked 6^th (57.7%). In Alabama, 100,765 children live in households headed by grandparents, with 56,369 grandparents reporting that they are the primary caregivers for the grandchildren living with them. Fifty percent of these grandparents are African American, 48% are White, and 1% are Hispanic/Latino [122]. Despite the proportion of custodial grandparents in the rural southern region of the US, these grandparents have rarely been studied [126, 127]. It is important to understand the factors that comprise rural custodial grandparenting and even more important to understand how custodial grandparenting impacts the cognitive functioning of these primary caregivers.

The type of support found in custodial grandparent situations has been studied in research that examined everyday problem solving. Whitfield and Wiggins (2002) [128] found that after controlling for age, education, and physical limitations, the level of social support given to others was a significant predictor of performance on an everyday problem-solving task. The results indicate that there may be differences in the cognitive abilities of those actively involved in social activities. These findings are intriguing in the search to better understand cognitive functioning in understudied populations. Social support (to others) appears to perhaps be a protective factor for cognitive functioning among African Americans and may be a part of successful aging.

Conclusions

Health disparities in aging and issues specific to minority aging are well-documented [**6, *129]. Precise methodological approaches are necessary to capture the aspects of healthy aging and to understand why healthy aging appears unattainable for some while present for others. Life course methods offer the dual benefits of understanding how early life experiences affect health in later life and how the accumulation of social stress affects biological and aging processes. Although there are multiple methods of quantifying how accumulated stress dysregulates biology, each method has its own advantages and pitfalls. Most notably, a considerable amount of the biological and genomic research to date has been done on mainly White samples making it difficult to generalize it to aging among minorities who are affected by different experiences. Recently, more work involving genes and environment has been done on older African American adult twins that gives us a better idea of the mechanisms through which some health disparities may be perpetuated [130]. However, the overall lack of diversity in research with clinical applications is problematic, which is evident in poorer pain management in minority populations and the lack of clarity about pain experiences in minorities. Underlying all of these issues is the key concern that aging minorities have been affected by unique emotional, social, and psychological experiences that affect their aging both positively and negatively. Each of the methods reviewed here are intended to gain a better understanding of the proximal causes of unhealthy aging in order to gain a better understanding of how and when to intervene for maximum success in healthy aging.