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The Journals of Gerontology Series B: Psychological Sciences and Social Sciences logoLink to The Journals of Gerontology Series B: Psychological Sciences and Social Sciences
. 2017 Jun 27;73(2):258–266. doi: 10.1093/geronb/gbx090

Associations of Multimorbid Medical Conditions and Health-Related Quality of Life Among Older African American Men

Olivio J Clay 1,✉, Martinique Perkins 1, Gail Wallace 2, Michael Crowe 1, Patricia Sawyer 3,5, Cynthia J Brown 4,5
PMCID: PMC5927117  PMID: 28658936

Abstract

Background

African American (AA) men battling multiple morbidities are tasked with managing the components of each condition and are at greater risk for adverse outcomes such as poor health-related quality of life (QOL), disability, and higher mortality rates.

Method

Baseline data for AA men from the University of Alabama at Birmingham Study of Aging were utilized. Factor analysis was used to categorize medical conditions and create factor scores. Covariate-adjusted regression models assessed the relationships between categories of conditions and physical and mental health-related QOL as assessed by the SF-12.

Results

The mean age of the sample of 247 AA men was 75.36 years and 49% lived in rural areas. Medical conditions fit into three factors: metabolic syndrome, kidney failure and neurological complications, and COPD and heart disease. Covariate-adjusted models revealed that low education, higher levels of income difficulty, and higher scores on metabolic syndrome and COPD and heart disease factors were associated with lower scores on physical health-related QOL, p’s < .05. Higher levels of income difficulty were also associated with lower scores on mental health-related QOL.

Discussion

These findings suggest the importance of examining clusters of comorbid medical conditions and their relationships to outcomes within older African American men.

Keywords: Life course and developmental change, Minority and diverse populations, Multimorbidity, Social determinants of health

With increasing access to health care and advances in medical technology, the U.S. population is in a great position to improve its overall health profile. This is relevant since over 40 years of research has found that increased life expectancy does not necessarily translate to better functional health or quality of life (Molla, 2013). As a result, the U.S. population is in a precarious position of overall worsening health and increasing health care costs. More than half of the older adult population suffers from three or more chronic diseases: managing more than one chronic illness has aptly been termed multimorbidity (American Geriatrics Society Expert Panel on the Care of Older Adults with Multimorbidity, 2012). Multimorbidity has been associated with adverse outcomes such as poorer health-related quality of life, disability, ineffective treatments, greater health care expenditure, and death (Boyd & Fortin, 2010). Although there is an extensive evidence-based history on defining, measuring, and examining the impact of single diseases, as well as considerable guidelines for managing single diseases, these practices are not always transferable when individuals are experiencing more than one chronic illness.

Researchers have attempted to design measures to understand how multiple morbidities impact healthy life expectancies. An example is assessing how long an individual can expect to live without activity limitations due to poor health and how this relates to estimated life expectancy at birth (Katz et al., 1983). From 1999 to 2008, there were increases in the expected years free of activity limitations caused by chronic conditions (Molla, 2013). However, when examining racial and gender differences a marked disparity still remained, with men and African Americans having fewer estimated years without activity limitations.

The ability to live independently later in life is important for the physical, mental, and emotional health of older adults. Since African American men continually show the greatest risk factors for developing multiple chronic illnesses and disability, there is also a greater risk for poor health-related quality of life. There is also early onset in some cases such that this heightened risk of negative outcomes does not start at retirement age, but the impact of their environment, stress, and poor health behaviors begins to manifest early in the life course of African American men (Thorpe, Duru, & Hill, 2015).

Health-Related Quality of Life in African American Men

The availability of research focused specifically on health-related quality of life in African American men is limited. There are studies that have defined health-related quality of life based on outcomes post specific disease diagnoses and surgery, for example prostate cancer (Jenkins et al., 2004; Kinlock et al., 2017; Lee, Marien, Laze, Aqalliu, & Lepor, 2012). Literature on general health-related quality of life reported in community-dwelling African American men is even more restricted. Calvert, Isaac, and Johnson (2012) used four health-related quality of life questions with a small sample of 16–64-year-old African American men. The majority of participants reported general health as good or better but higher levels of mentally unhealthy days were related to poor sleep, less physical activity, and poor nutrition (Calvert et al., 2012). To the best of our knowledge, Bukavina, Zaramo, Tarabonata, & Modlin (2015) study is the only published work focused specifically on the Short Form Health Survey (SF-36) in African American men. For their analysis, participants from the Cleveland Clinic Minority Men’s Health Fair were randomly selected. The African American men reported significantly lower health-related quality of life on six of the eight SF-36 subdomains compared to the U.S. population norms and those aged 65 years and older scored significantly lower across all domains compared to other age groups. Therefore, the current study provides a significant contribution to the limited knowledge of health-related quality of life among African American men over 65 years old.

Life Course Perspective

Psychological health research often provides us with a “snapshot” of the complex factors that influence our physical, mental, and emotional health. Oftentimes, not enough emphasis is placed on the cumulative effect of negative environmental factors that predispose individuals to experience poor outcomes. The importance of the life course perspective in health disparities research has been getting more attention over the past 10 years (Lynch, 2008). The life course perspective posits that disparities may fluctuate over time and are influenced by the micro- (e.g., health behaviors, household structure, and life experiences) and macro-level environmental factors (e.g., environment, society, and culture) consistently experienced by minorities. Proponents of the life course perspective argue that disparities in life experience may be cumulative and associated with the historical and social experiences and everyday stressors faced by a particular group of people (Pearlin, Nguyen, Schieman, & Milkie, 2007). Even within-group variation is evident, as Thorpe and colleagues (2013) reported that the relationships between health behaviors (e.g., smoking, physical inactivity, and weight status) and mortality varied depending on the age of African American men. Establishing the combined effects of multimorbidity and social determinants of health on health-related quality of life in the current study will provide additional knowledge relevant to improving the lives of older African American men.

Life Course Variables and Health Disparities

The life course perspective is concordant with the National Institute on Aging’s (NIA) call for an expanded health disparities research framework to include fundamental environmental, sociocultural, behavioral, and biological factors (Hill, Perez-Stable, Anderson, & Bernard, 2015) that was developed to organize the primary factors studied in health disparities research (Hill et al., 2015). A comprehensive framework identifies the populations at the highest risk for unequal health outcomes. This framework also provides a tracking system for macro-level organizations, establishes the directional influence of these factors, and highlights the areas where an intervention may be the most beneficial (Hill et al., 2015). The relationships examined in the current study specifically fall into three of the four categories proposed by the NIA Health Disparities Research Framework and will serve as the structure for defining multimorbidity in African American men.

Biological Factors

Older adults have higher rates of many different health conditions and assessing the relationships of these conditions with other outcome measures has become a priority. As the rates of obesity and hypertension continue to rise, it is not surprising that the number one cause of death is heart disease. Huffman and colleagues (2013) compared the lifetime risk for developing heart failure for Whites and African Americans with over 39,000 participants from three national studies. Interestingly, African American men did not have the highest lifetime risk for developing heart failure compared to other race-sex groups. Consequently, African American men were found to have greater burden of other causes of death besides heart failure (competing risks) and their lifetime risk of heart failure was lower than White men once these competing risks were accounted for (Huffman et al., 2013). Therefore, only focusing on heart disease would not adequately illustrate the health risks experienced by African American men. Utilizing data from the Health and Retirement Study, Pavela and Latham (2016) found that poor health during childhood was predictive of a higher number of chronic conditions later in life. To further support the idea of studying the combined effect of multimorbidity, researchers have found that factors of metabolic syndrome (Hari et al., 2012; Kwagyan et al., 2015; McNeill et al., 2006) and kidney disease (McCullough et al., 2008) are associated with also having cardiovascular disease. Along these same lines, Brown, Clark, Armstrong, Liping, & Dunbar (2010) found increased risk for chronic kidney disease among an African American cohort with metabolic syndrome; the presence of multimorbidities was associated with worse clinical outcomes for African Americans with chronic obstructive pulmonary disease (Putcha et al., 2014). The similarity in risk factors across many different chronic illnesses also necessitates the use of research methodology that examines the combined effect of multiple diseases on African American men.

Research on disparities in stroke incidence and mortality is well-established due to large population-based studies like the REasons for Geographic and Racial Differences in Stroke (REGARDS) and the Atherosclerosis Risk in Communities (ARIC) studies. African American men have an estimated stroke death rate up to 68% higher than men of other racial groups (Ingram & Montresor-Lopez, 2015). Risk factors such as age, blood pressure, smoking, obesity, and inflammatory markers have been identified as contributors to the stroke disparity (Huxley et al., 2014). Cardiac abnormalities and diabetes have also been identified as risk factors for stroke incidence and based on the aforementioned research, provide another reason for considering the impact of multiple chronic illnesses (Howard et al., 2011; Huxley et al., 2014). Other researchers have reported similar findings for cardiometabolic risk factors (Kim, Diez Roux, Kiefe, Kawachi, & Liu, 2010; Pollack, Slaughter, Griffin, Dubowitz, & Bird, 2012; Ross & Mirowsky, 2001).

Environmental Factors

An individual’s surroundings and life experiences also have an influence on quality of life and health (Lei et al., 2017). Using data from the 1999 Large Health Survey of Veteran Enrollees, Weeks and colleagues (2004) examined health-related quality of life in a random sample of over 700,000 veterans who used the Veterans Health Administration in the past 3 years. A modified version of the SF-36 provided physical and mental health summary scores and medical records provided codes for mental health and medical diagnoses. Veterans living in rural areas reported worse health-related quality of life compared to veterans living in suburban and urban areas. Barber, Hickson, Kawachi, and Subramanian (2016) used data from the Jackson Heart Study to assess the relationship between neighborhood disadvantage and cumulative biological risk (CBR) which was measured with biomarkers representing metabolic, cardiovascular, neuroendocrine, and inflammatory levels. The strongest association between neighborhood disadvantage and CBR was found for African American men who reported living in neighborhoods with low social cohesion (e.g., low social connections between neighbors). Perceived discrimination has also been found to be related to poor outcomes. For example, higher levels of reported lifetime discrimination were found to be associated with elevated ambulatory blood pressure for middle and older aged African American and Latino adults (Beatty Moody et al., 2016).

Socioeconomic Factors

Measures of socioeconomic status are well-established as significant contributors to the marked differences observed in African American health. Chichlowska and colleagues (2009) embraced the life course perspective by examining the relationship between childhood, early adulthood, middle adulthood, and cumulative SES and metabolic syndrome in the ARIC study. The expectation that accumulated exposure to lower SES was related to increased prevalence of metabolic syndrome in adulthood was supported for women but, interestingly, not men. In a similar manner, the gender and racial differences in relationship between socioeconomic position and metabolic syndrome was studied in the Third National Health and Nutrition Examination Survey (Loucks, Rehkopf, Thurston, & Kawachi, 2007). Lower education was related to three indicators of metabolic syndrome (abdominal obesity, hypertension, and hyperglycemia) in men. Findings related to the association between socioeconomic factors and African American men’s health are inconsistent in the literature. The current analysis adds information about these important risk factors as social determinants of health.

The current study examines the additive effect of multimorbidity in a sample of African American men aged 65 years and older. The analyses explore and identify clusters of various physical health conditions that occurred within the sample and assess the relationships of socioeconomic status and health conditions with physical and mental health outcomes. Specifically, it is hypothesized that (a) older African American men who report lower SES will have poorer health-related quality of life and (b) older African American men with higher health conditions factor scores (more health conditions) will have worse health-related quality of life.

Methods

Procedures

The University of Alabama at Birmingham Study of Aging (UAB SOA) was designed to examine racial differences in patterns of mobility, examine short-term correlates and long-term predictors of mobility limitation, and determine if mobility limitation is predictive of longitudinal outcomes. Participants were selected from a list of Medicare beneficiaries from five counties in central Alabama and a total of 1,000 participants were recruited. As a longitudinal observational study, community-dwelling adults aged 65 years and older completed an in-home assessment and follow-up data were collected every 6 months via telephone interview for almost 10 years. The UAB SOA oversampled African Americans, men, and rural residents to provide a 50-50 stratified sample in each of these areas. The current analyses were restricted to African American men only (n = 247). Additional study details have been published regarding the UAB SOA design (Allman, Sawyer, & Roseman, 2006). All procedures were approved by the UAB Institutional Review Board.

Measures

The baseline questionnaire collected self-reported demographic data including age in years, gender, race, education, income difficulty, and household location. Race was self-reported as non-Hispanic African American and non-Hispanic White (with only African American men included in the current sample). Education was collected as completion of 6th grade or less and in an ordinal manner thereafter. For this analysis, completion of grade 6 or less corresponded to a score of 6, grades 7 through 11 were coded as the grade number, a high school diploma or GED was coded a 12, a college graduate was coded a 15, and graduate or professional degree was coded as 17. Income difficulty was assessed by inquiring, “All things considered, would you say your income (a) allows you to do more or less what you want, (b) keeps you comfortable but permits no luxuries, (c) gives you just enough to get by on, or (d) is not enough to make ends meet?” Higher scores correspond to greater income difficulty. We chose to include income difficulty instead of actual income because a number of participants reported income difficulty but not their specific incomes (44 African American men had missing income values). Household location was used to classify urban/rural status and was categorized using geocoding. Once a latitude–longitude coordinate was assigned, the address was linked to census data on population density. Participants were classified dichotomously as urban or rural status.

Multimorbidity

Individual medical conditions from the Charlson Comorbidity Index were assessed without consideration of severity (Charleson, Pompei, Ales, & MacKenzie, 1987). Participants were asked to respond yes or no if a physician had ever told them that they had: congestive heart failure (CHF), hypertension, diabetes mellitus, chronic obstructive pulmonary disease (COPD) or asthma, kidney failure, stroke, liver disease, cancer, peripheral neuropathy, or gall bladder disease. UAB SOA researchers verified conditions known to be associated with mobility limitation by the participant having a prescribed medication for the condition, or by the participant’s primary care physician confirming the condition via questionnaire, or if the condition was listed on a hospital or emergency room discharge summary. Only verified conditions were considered for the analyses. Height and weight were measured at the baseline interview and obesity was identified as a BMI score of 30 or greater.

Health-related quality of life

The 12-item Short Form Health Survey (SF-12) assessed health-related quality of life. A Physical Component Summary (PCS) score and a Mental Component Summary (MCS) score were computed using relevant items. Each component score represents a global measure of health-related quality of life and has been standardized to range from 0 to 100 and have a mean of 50 and an SD of 10 in the adult U.S. population (Ware, Kosinski, & Keller, 1998). Higher scores are indicative of better health functioning on both component scores.

Statistical Analyses

All statistical analyses were conducted using SAS V9.1.3. Frequencies for categorical measures and means for continuous measures were computed to describe the sample of African American men on variables of interest.

An exploratory factor analysis was performed to identify clusters of health conditions and to reduce the number of variables assessed in later multivariate models. A varimax rotation (orthogonal) was chosen rather than oblique rotation due to the investigator’s goal to produce factors of medical conditions that were as distinct from each other as possible and not correlated (Wood et al., 2005). Eigenvalues greater than 1 and an examination of the scree plot were used to determine the number of factors to retain. Standardized factor scores with a mean of 0 and standard deviation of 1 were extracted for the clusters of conditions retained from the results of the factor analysis. The use of factor analysis to create factors or groupings of chronic conditions is a novel approach that has recently been utilized by other investigators. For example, Basu, Zeber, Copeland, and Stevens (2015) utilized factor analysis to extract a multimorbidity factor using data from older adults who participated in the nationally representative Aging Demographic and Memory Study. Pearson’s r correlation coefficients were computed to assess the bivariate relationships of the variables of interest with health-related quality of life. Linear multivariable regression models were utilized to examine covariate-adjusted associations between variables of interest including the factor scores for the clusters of medical conditions and physical health-related quality of life and mental health-related quality of life in separate models.

Results

The mean age of the sample of 247 AA men was 75.36 with a range of 65 to 97 years. Of the 247 AA men, 122 (49%) were from rural areas. Only 1 participant was verified as having liver disease, and 3% of participants were verified as having gall bladder disease. These conditions were not included in the subsequent analyses. Additional descriptive statistics are reported in Table 1. Cancer was not included in the factor analysis because it represents a diverse set of diseases with numerous causes and all types of cancer (except skin cancer) were aggregated in the data collection. Results of the factor analysis revealed that physical health conditions fit into three factors which accounted for a combined 49.42% of the variability in our health condition construct: “metabolic syndrome”, “kidney failure and neurological complications”, and “COPD and heart disease” (Table 2). The co-occurrence of physical conditions for each factor is reported in Supplementary Table 1. Metabolic syndrome accounted for 16.84% of the variability among the health conditions with diabetes, obesity, and hypertension loading on this factor. Forty-four (17.81%) participants had neither condition, 105 (42.51%) had only one condition, and 98 (39.68%) had two or more. The second factor, kidney failure and neurological complications accounted for 16.60% of the variability and peripheral neuropathy, kidney failure, and stroke loaded on this factor. One hundred ninety-four (78.54%) participants had neither condition, 42 (17.01%) had only one condition, and 11 (4.45%) had two or more. Finally, COPD and heart disease accounted for 15.98% of the variability with COPD or asthma and congestive heart failure loading on this factor. One hundred seventy-three (70.04%) participants had neither condition, 61 (24.70%) had only one condition, and 13 (5.26%) had both.

Table 1.

Descriptive Statistics for African American Men in the UAB Study of Aging (N = 247)

Measures Mean (SD) n (%)
Age 75.36 (6.58) -
Education 9.01 (3.22) -
Income Difficulty
 1a - 134 (54.25)
 2a - 66 (26.72)
 3a - 47 (19.03)
Rural Community Status - 122 (49.39)
Diabetes Mellitus - 62 (25.10)
Obesity - 84 (34.01)
Hypertension - 182 (73.68)
Neuropathy - 14 (5.67)
Kidney Failure - 21 (8.50)
Stroke - 31 (12.55)
COPD or Asthma - 42 (17.00)
Congestive Heart Failure - 45 (18.22)
Physical health-related QOL 39.36 (12.16) -
Mental health-related QOL 53.93 (9.37) -
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Note: COPD = Chronic obstructive pulmonary disease; QOL = Quality of life.

aIncome Difficulty categories: (a) Allow you to do more or less what you want, (b) Keeps you comfortable but permits no luxuries, (c) Gives you just enough to make ends meet. A fourth category of “Not enough to make ends meet” had no responses.

Table 2.

Factor Loadings From Rotated Component Matrix After Varimax Rotation

Medical conditions Metabolic syndrome Kidney failure and neurological complications COPD and heart disease
Diabetes Mellitus 0.708 0.216 −0.246
Obesity 0.696 −0.177 0.071
Hypertension 0.515 0.157 0.292
Neuropathy −0.017 0.722 −0.157
Kidney Failure 0.121 0.670 0.184
Stroke −0.000 0.496 0.389
COPD or Asthma −0.153 0.101 0.719
Congestive Heart Failure 0.243 −0.010 0.633
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Note: COPD = Chronic obstructive pulmonary disease. Bold values refer to a medical condition loading on a specific factor.

An examination of bivariate relationships revealed that advanced age (r = −0.160), lower levels of education (r = 0.336); higher levels of income difficulty (r = −0.317), rural living status (r = −0.190), and higher scores on the COPD and heart disease factor (r = −0.262) were each associated with lower levels of physical health-related quality of life, p’s < .02. Higher levels of income difficulty (r = −0.272) and rural living status (r = −0.151) were each associated with lower levels of mental-health related quality of life, p’s < .02. Assumptions were tested for the linear multivariate regression models. The outcome variables (physical and mental health-related quality of life) were both negatively skewed, with more values on the upper end of the distribution. This is due to the generally healthy status of the UAB Study of Aging sample. Observation of scatter plots revealed a linear relationship between variables and the assumptions of homoscedasticity and a lack of multicollinearity were not violated. Covariate-adjusted models revealed that low education, higher levels of income difficulty, and higher scores on the metabolic syndrome and COPD and heart disease factors were each uniquely associated with lower scores on physical health-related QOL, p’s < .05. The predictor with the strongest individual association was the COPD and heart disease factor, p < .001 (Table 3). Higher levels of income difficulty were also uniquely associated with lower scores on mental health-related quality of life. There were no significant associations between the medical condition factors and mental health-related QOL (Table 4).

Table 3.

Covariate-adjusted Relationships Between Variables of Interest and Physical Health-related Quality of Life

Variables B b t statistic p value
Age −0.079 −0.147 −1.34 .1807
Education 0.215 0.813 3.46 .0006
Income Difficulty −0.223 −3.477 −3.78 .0002
Rural Status −0.095 −2.296 −1.63 .1052
Metabolic Syndrome −0.126 −1.538 −2.23 .0268
Kidney Failure and Neurological Complications −0.085 −1.029 −1.50 .1346
COPD and Heart Disease −0.227 −2.757 −4.02 <.0001
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Note: B = Standardized β, b = unstandardized β; COPD = Chronic obstructive pulmonary disease.

Table 4.

Covariate-adjusted Relationships Between Variables of Interest and Mental Health-related Quality of Life

Variables B b t statistic p value
Age −0.038 −0.053 −0.58 .5647
Education −0.038 −0.110 −0.55 .5807
Income Difficulty −0.266 −3.192 −4.09 <.0001
Rural Status −0.105 −1.958 −1.64 .1033
Metabolic Syndrome 0.036 0.338 0.58 .5641
Kidney Failure and Neurological Complications −0.084 −0.790 −1.36 .1753
COPD and Heart Disease 0.021 0.200 0.34 .7317
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Note: B = Standardized β, b = unstandardized β; COPD = Chronic obstructive pulmonary disease.

Discussion

This investigation presented an innovative approach to categorizing multiple chronic conditions within older African men and assessed their associations with health-related quality of life. The strongest relationship was between the COPD and heart disease factor and physical health-related quality of life. COPD and heart disease are conditions that are commonly seen together. Potential explanations for the prevalence of this relationship include common risk factors such as smoking and individuals with COPD having poor lower extremity function (Clay et al., 2015) and reduced cardiorespiratory fitness (e.g., low peak oxygen uptake) which have been shown to be associated with lower exercise capacity (Putcha et al., 2014) and reduced mobility. Additionally, this combination can present many diagnostic challenges due to common symptoms (Hawkins et al., 2009).

Metabolic syndrome also had a significant unique association with physical health-related quality of life. African American men have not seen the decline in the prevalence of metabolic syndrome observed in other groups. The age-adjusted prevalence of metabolic syndrome in men has remained relatively stable over time while there has been an overall decrease found for women. Similarly, the prevalence of metabolic syndrome in African Americans and Mexican Americans has remained stable whereas, there has been a reduction found for Whites (Mozaffarian et al., 2015). The kidney disease and neurological complications factor did not have a significant bivariate or multivariate association with either health-related quality of life measure. This lack of an association points to a need for African American men to address risk factors for poor outcomes such as the metabolic syndrome variables (obesity, diabetes, and hypertension) which often precede kidney disease and neurological complications.

As predicted within the sample of older African American men, lower socioeconomic status, particularly income difficulty, was associated with lower scores on physical and mental health-related quality of life. Although the models control for education, income difficulty may serve as a proxy for a variety of different constructs including neighborhood disadvantage, access to services, and access to healthy food. For example, an examination of data from the 2003–2004 National Health and Nutrition Examination Survey revealed that adults in the highest income group had higher total scores on the Healthy Eating Index-2005 than the lowest income group (Hiza, Casavale, Guenther, & Davis, 2013). The relationship between financial strain and mental health was previously documented in an analysis of data from the Health and Retirement Study. Participants were asked about financial strain during the economic recession lasting from 2007 until 2009. It was found that high levels of financial strain were associated with high levels of anxiety and depressive symptoms over 4 years of follow-up (Wilkinson, 2016). Finding ways to address the negative outcomes associated with adverse social determinants of health is a multidimensional task that will require input from medical professionals and policy makers (Bryant, Hess, & Bowen, 2015).

There are limitations associated with the current study. The original UAB SOA has offered researchers a rich data set to understand mobility difficulties and their correlates among African American and White older adults. Understanding the relationship between multimorbidity of physical health conditions and health-related quality of life was not the primary purpose of the UAB SOA. Because mental health conditions were not verified, the researchers were unable to assess their associations with health-related quality of life in a similar manner to physical health. The oversampling of African American men by the UAB SOA provided the current investigation with a substantial sample size to study health-related quality of life within this population. However, the use of only African American men limits generalizability to the broader older adult population which contains other racial groups and women. Additionally, these findings may not be the same for younger African American men. The researchers are also limited in generalizing our findings to the overall older adult population of the United States since the UAB SOA only recruited from Medicare beneficiaries within the state of Alabama.

The data set included an extensive list of illnesses that was utilized in an attempt to identify conditions associated with the participants’ ratings of their physical and mental health. The verification process of the self-reported participant illnesses provided additional confirmation of the reported conditions. However, health-related quality of life was entirely self-reported and could have been under- or over-reported by some of the participants. The UAB SOA sample was relatively healthy overall and our findings may not provide the full picture of how multiple chronic illnesses impact African American men’s perception of their health. This, however, relates to selection bias for most studies as those who are sicker and more disabled may not participate.

This investigation has provided additional evidence of the importance of not just providing a count of medical conditions, but assessing the effects of clusters of commonly occurring conditions. A life course approach was used to identify older African American men at risk for lower levels of physical and mental health-related quality of life. The results point to the importance of early recognition of individuals with multimorbidity to prevent reductions in health-related quality of life, and the development of strategies by health care systems and providers to help ensure these individuals have access to appropriate treatments and medications.

An understanding of complex clusters of conditions such as COPD and heart disease is important for both medical professionals and patients to achieve optimal health outcomes. This is particularly supported by these analyses, since the COPD and heart disease factor had the strongest association with physical health-related quality of life. Finally, there is an emphasis on not only determining if a specific intervention can be effective, but also establishing which physical health conditions should be addressed first and identifying potential modifications that may be necessary within the health care system (American Geriatrics Society Expert Panel on the Care of Older Adults with Multimorbidity, 2012). Findings from this investigation point to COPD and heart disease as important factors that should be prioritized in an attempt to maintain or improve health-related quality of life within older African American men.

Supplementary Material

Supplementary data are available at The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences online.

Funding

This work was supported by the National Institute on Aging at the National Institutes of Health awards R01 AG015062 (UAB Study of Aging), P30AG031054 (Deep South RCMAR), and 3P30AG031054-02S1 (Research Supplement to Promote Diversity in Health Related Research). Additional funding was provided by the UAB Mid-South Transdisciplinary Collaborative Center for Health Disparities, an NIH, National Institute on Minority Health and Health Disparities funded initiative (U54MD008176). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Supplementary Material

Supplemental_Table_1
Click here for additional data file. (18.4KB, docx)

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