A Comparison of Variances in Age Cohorts to Understand Longevity in African Americans

Abstract

Background

African American life expectancy at age 65 is about 2 years less than that of Caucasians, but by age 85, African Americans may have a longevity advantage. One possible explanation for this cross-over effect is that African Americans who make it to the oldest ages have done so by handling stressful contextual and health disadvantages. The purpose of this study was to examine possible within group cohort differences that lead to exceptional longevity among older African Americans.

Methods

Data came from three cohorts of older African Americans: the Carolina African American Twin Study of Aging (CAATSA), the Baltimore Study of Black Aging-Patterns of Cognitive Aging (BSBA-PCA), and the Study of Longevity and Stress in African American Families (SOLSAA). Of the 533 participants, we compared two age cohorts (60–79 and 80+) with an average age of 73.2 (SD = 8.33) and 26.3% are men. Variables included measures of stress, depression, coping, cognition, and health indicators.

Results

The variance for depression and average peak expiratory flow (APEF) was significantly larger for the older cohort but after controlling for demographic factors, the measure of depressive symptoms was not. The Alpha Span test showed a significant difference with the older cohort having larger variances after controlling for demographic factors.

Conclusions

The findings suggest that there are changes in the characteristics of who makes it to later life, but counter to our hypothesis, there was greater variability in the oldest group relative to the younger.

Although remaining life expectancy of African American men and women at age 65 is about 2 years less than that of Caucasian men and women at the same age, some estimates suggest that by age 85, African Americans may have a longevity advantage (1–4). One possible explanation for this convergence, or cross-over effect, is that African Americans who make it to the oldest ages have done so despite many disadvantages by effectively managing sources of stress such as discrimination. They represent “exceptional survivors” who have developed or possess physiological and/or psychological survival advantages (5). The African Americans who survive into late life have done so by handling a lifetime of stressful contextual and health disadvantages. Exceptional survivorship is often thought to be the result of genetic predisposition. While the National Institute on Aging has presented a fairly substantial list of candidate genes for longevity (cf. http://elcapitan.ucsd.edu/cgi-bin/longevity/GeneData.cgi?org=Hs) thought to explain population longevity, heritability of life span has been found to vary significantly by age and by ethnic group with African Americans having the lowest heritability (6). This suggests that environmental sources of variability account for more of the individual variability in longevity in African Americans. This does not suggest that genes do not impact survival among this group, but rather, complex relationships likely exist between genes and other salient factors, such as family environment and unique environmental factors to play critical roles in longevity in this population. These environmental components are likely to represent complex interactions that result from interactions between individual intrinsic and shared extrinsic factors. Intrinsic factors, for example, might be personality characteristics and extrinsic factors such as family income that result in an individual by family interaction creating variability in longevity.

Social scientists have identified many different causes of health disparities and mortality (7). One of the most formidable and well recognized biobehavioral variables related to physiological damage and risk for poor health is stress. Stress can be considered both an intrinsic and extrinsic factor. The relationship between health and stress, particularly perceived stress, is a central mechanism that accounts for the health disparities experienced by African Americans. Perceived stress is typically studied in relation to racial discrimination, social inequities, disadvantaged contexts (poor neighborhoods), and perceptions about the health care system (8). What has not been clear is how the impact of perceptions of stress from our environment that have changed over time and different sources across generations continue to exert the negative persistent influence on longevity of African Americans. These generational similarities and differences can be implicitly observed in age group comparisons.

The purpose of this study was to examine whether variances in health and psychosocial factors change with age. We hypothesized that age group variances will be different and decrease from younger to older cohorts. Our conjecture is that selection effects produce reductions in the variability in older adults. This is based on the well-known patterns of mortality observed in older African Americans. We believe that excess mortality results in older adult populations who have survived into later life, who do not have a variety of health conditions and are similar to one another in how they perform on psychosocial measures. We also believe that stress and depression will account for the differences between the age groups. Psychosocial factors such as stress contribute to the development of chronic health conditions and lifetime stress and discrimination can lead to poor mental health (depression), which produces poor physical health (9).

Methods

Data for this study comes from individuals 60 years of age and older from three cohorts of older African Americans: the Carolina African American Twin Study of Aging (CAATSA) (10), the Baltimore Study of Black Aging-Patterns of Cognitive Aging (BSBA-PCA) (11) and the Study of Stress and Longevity in African American Families (SOLSAA). CAATSA was designed to examine the sources of individual variability in health status, cognition, and physical and psychosocial functioning of adult African American twins) (10). This population-based sample of participants was identified from birth records between the years of 1913 and 1975 from 23 vital statistics offices in North Carolina counties. Birth records were then entered into a computerized database of twin births. After the records were computerized, potential participants were located through voter registries and telephone White page searches. The protocol for data collection consisted of two parts. The survey was administered in person by a trained interviewer and consisted of a structured questionnaire that included demographic and socioeconomic information, self-reported health behaviors, chronic conditions, perceived stress, personality, memory, and well-being. Additionally, assessments of blood pressure and average peak expiratory flow (APEF) were obtained following the survey. Participants were enrolled between 1999 and 2003. All participants provided informed consent and the study was approved by the Institutional Review Board at the University of North Carolina Chapel Hill and Pennsylvania State University. Of the 116 CAATSA participants who were included in this study, 41.4% were men. The average age of the CAATSA participants was 69.2 ± 7.2 years. Additional information regarding the CAATSA study design can be found elsewhere (10).

BSBA-PCA was designed to examine patterns and individual factors that contribute to individual differences in cognitive function in older African Americans (11). The sample consisted of 602 community-dwelling African American men and women between the ages of 48 and 92 at the study’s inception. These participants were recruited from 29 senior apartment complexes in the city of Baltimore, Maryland. Data collection lasted 18 months and took place between 2006 and 2008. The interviews lasted 2.5 hours on average and consisted of a face-to-face interview in which there were three blood pressure measurements, three lung function measurements, a battery of cognitive tests and information collected on physical and mental health. All participants signed a written informed consent agreement approved by the institutional review board at Duke University and received monetary compensation for their participation. For this study, 393 BSBA-PCA participants were included in this study. The average age of the participants was 73.7 ± 7.4 years and 21.4% of the BSBA-PCA participants were men.

SOLSAA was designed to examine similarity and differences in stress among siblings and between parents and their children to obtain information about factors that contribute to longevity in older African Americans. At the time of this manuscript, data collection was nearly half complete with the goal of interviewing 750 participants. The study is designed to collect data on siblings whose parents had passed away (short-lived) and those sibling pairs who have at least one living parent and who was willing to participate (long-lived). There were 59 SOLSAA participants who were included in this study. The average age of the SOLSAA participants was 77.4 ± 12.3 years and 28.8% of the SOLSAA participants were men.

Only participants who were either 60–79 or 80–99 were included in the analysis. This resulted in 568 participants with an average age of 73.2 (SD = 8.3) and 26.2% were men.

Measures

Stress

The Perceived Stress Scale (PSS) (12) is a global measure of perceived stress designed to quantify the degree to which situations in one’s life are appraised as stressful. The PSS consists of 14 items that use a 5-point Likert-scale for responses about the amount of stress the individual experienced during the previous month. Participants scores ranged from 0 to 60 (12).

Depressive symptomatology

Depressive symptomatology was assessed using the Center for Epidemiologic Studies-Depression (CES-D) scale, the 11-item version, which is designed to assess both frequency and severity of depressive symptoms during the previous week (13). Scores can range from 0 (reporting no depressive symptoms) to 33 (reporting more depressive symptoms).

Blood pressure

Blood pressure was taken by using an oscillometric automated device (A & D model UA-767; Milpitas California). Three measurements were taken in a sitting position, from the same arm, using a cuff of appropriate size for the participant’s arm (14). The average systolic blood pressure (SBP) and diastolic blood pressure (DBP) values were used in the analysis.

Average peak expiratory flow

Lung function or APEF status was measured using the Mini-Wright peak flow meter, which assessed participants’ peak expiratory flow (15). Participants stood and covered the end of the tube of the peak flow meter with their lips and blew as hard as possible after taking a deep breath for 1 second. The APEF was calculated as the average of three trials. There was at least a 30-second interval between each measurement.

Health status

Health status was based on self-report of chronic health conditions. Chronic health conditions were based on participants’ report of physician diagnoses of the following: arthritis, cancer, diabetes, stroke, heart attack, or high blood pressure. Each of the chronic conditions was coded as binary variables (1 = present; 0 = absent). All six conditions were summed to create a variable representing the total number of chronic health conditions.

Body mass index

Height and weight were assessed by interviewers from subjects dressed in lightweight clothes with their shoes removed. Body mass index was calculated as weight (kg) divided by height squared (m²).

Cognitive speed

The Digit Symbol Test (16) is a measure of cognitive speed that requires participants to reproduce, within 120 seconds (60 seconds per trial), as many coded symbols as possible in blank boxes beneath randomly generated digits, according to a coding scheme for pairing digits with symbols. The number correct and incorrect are recorded and used as variables.

Memory

The Alpha Span (17) is a task that measures short-term memory. Participants are read a list of words (from two to eight words). After each list is read, participants are asked to repeat the list in alphabetical order. Responses are recorded as pass or fail. If a subject fails two consecutive attempts, the test is ended.

Covariates

Covariates included age, gender, and education. Age was the main independent variable and although age was assessed as a continuous variable, a dichotomous variable was created to identify individuals who were between the ages of 60 and 79, and 80 to 99 years of age. Gender was included as a dichotomous variable where men were indicated as 0 and women as 1. Education level was based on the number of years of education completed.

Analysis

Frequencies, means, and standard errors were calculated to describe the sample. Analysis of variance was conducted to determine whether there were differences between the two age groups relative to the health outcomes. Levene’s test for homogeneity of variance was conducted to determine whether variance was equal across the age groups (18,19). The Breusch-Pagan’s Test was used to examine the heteroskedasticity across age groups in our adjusted models (19). All analyses were conducted using STATA v.14 (College Station, TX) and p values less than .05 were considered statistically significant.

Results

Demographic and health-related characteristics by age cohort are given in Table 1. The average age of the 568 participants was 73.2 ± 0.3 years. Regarding demographic variables, approximately three-fourth of the participants were female and had on average 11.2 ± 0.1 years of education. As it relates to health characteristics, the participants exhibited the following averages: height 65.0 ± 0.1 inches, weight 184.4 ± 1.8 pounds, body mass index 30.6 ± 0.3, number of chronic conditions 2.2 ± 0.1, FEV 232.4 ± 4.1, systolic blood pressure 146.1 ± 1.0 mmHg, diastolic blood pressure 82.9 ± 0.5 mmHg, perceived stress score 19.5 ± 0.3, depressive symptoms score 6.5 ± 0.1, digital symbol substitution test 4.5 ± 0.1, and alpha span test 4.4 ± 0.1. When examining the participants by age cohort, in the younger cohort, there were fewer women and the participants had a higher educational attainment, were taller, were heavier, and had better lung function than participants in the older cohort. There were no observed significant differences between the age cohorts with regard to number of health conditions, systolic or diastolic blood pressure, perceived stress score, depressive symptoms score, digital symbol substitution test, or the alpha span test.

Table 1.

Demographic and Health-Related Characteristics for the Total Sample and by Age Cohort of Participants in CAATSA, BSBA-PCA, and SOLSAA

	Age Cohort
Characteristic	60–79 Years (N = 425)	80–99 Years (N = 143)	p Value	Levene’s Test p Valuea	Breusch-Pagan’s Test p Valueb	Breusch-Pagan’s Test p Valuec
Demographic
Age (years)	—	—	—
Female (%)	71.0	81.8	.011
Married (%)	22.4	12.5	.011
Education attainment (years)	11.4 ± 0.1	10.6 ± 0.2	.019
Health-related characteristics
Body mass index	31.3 ± 0.3	28.5 ± 0.5	<.001	.063	.140	.236
Number of health conditions	2.1 ± 0.1	2.3 ± 0.1	.072	.958	.701	.923
Mean APEF (mm/l)	246.8 ± 4.9	188.9 ± 5.9	<.001	<.001	<.001	.005
Mean systolic blood pressure (mmHg)	145.4 ± 1.1	148.1 ± 1.9	.255	.248	.550	.795
Mean diastolic blood pressure (mmHg)	83.4 ± 0.6	81.4 ± 1.0	.137	.255	.614	.946
Mean Perceived Stress Score	19.7 ± 0.3	19.0 ± 0.6	.330	.110	.231	—
Mean Depressive Symptoms Score	6.7 ± 0.1	6.1 ± 0.2	.136	.041	.179	—
Digital Symbol Substitution Test	4.6 ± 0.1	4.2 ± 0.1	.047	.998	.554	.617
Alpha Span Test	4.5 ± 0.1	4.2 ± 0.1	.098	.017	.026	.165

Notes. APEF = average peak expiratory flow; BSBA-PCA = Baltimore Study of Black Aging-Patterns of Cognitive Aging; CAATSA = Carolina African American Twin Study of Aging; SOLSAA = Study of Longevity and Stress in African American Families.

^aLevene’s test for the unadjusted test of homogeneity of variance across age cohorts. ^bThe Breusch-Pagan’s test is a test for homogeneity of variance across age cohorts adjusting for gender and education. ^cThe Breusch-Pagan’s test is a test for homogeneity of variance across age cohorts adjusting for gender, education, depressive symptoms, and perceived stress.

The Levene’s test and the Breusch-Pagan’s test for constant variance as it relates to age cohorts is also displayed in Table 1. There were differences in variances across the age cohorts for APEF (p = .041), depressive symptoms score (p = .017), and the Alpha Span test (p < .001) with the 80- to 99-year-old cohort having larger variances compared to the younger cohort. To adjust for possible demographic differences, the Breusch-Pagan’s test was used to adjust for gender and education. The results showed that the variance of the APEF (p < .001) and the Alpha Span test(p = .026) remained significant with larger variances for the older cohort.

Next, we used these findings to examine whether psychosocial factors (ie, perceived stress and depression) accounted for differences in variances between the two age groups. In addition to gender and education, we accounted for stress and depressive symptoms score. With respect to APEF, the differences in the variances in age group remained significant (p = .005) with the larger variances for the older cohort. However, the differences in the variances in age group for Alpha Span test were no longer significant (p = .165) after accounting for stress and depressive symptoms score.

Discussion

The goal of this article was to examine similarities and differences in cognitive, psychosocial, and health indices in two age cohorts with the implied goal of gaining insights about changes in variability with advanced age. We hypothesized that variances would be larger in younger cohorts compared to older cohorts. If variances decrease, this would suggest that selection effects may be present as age increases resulting in a clustering of performance. Our results, however, showed that variances increased in the older age groups for a cognitive measure (alpha span) and a health indicator (average peak expiratory flow) relative to the younger age group. These finding emphasize the importance of both psychosocial factors and health indices as possible explanatory factors of exceptional survivorship among African Americans.

With respect to APEF, the differences in the variances by age group remained significant even after controlling for psychosocial factors with larger variances remaining for the older cohort. This finding is particularly interesting given the increased mortality that has been observed in chronic obstructive pulmonary disease in the last couple of decades (20). The differences in the variances by age group for Alpha Span test, however, were no longer significant after accounting for stress and depressive symptom score. The results suggest that psychosocial factors are important explanatory factors of individual variability in some cognitive tests with advancing age. We believe that these indicators may represent factors that are central to longevity and exceptional survival in African Americans. Given the significant health disparities observed in African Americans compared to Whites, these psychosocial factors are important indicators to understand the source and course of health disparities and how they may mollify other factors that produce poor health over the life course. This resistance to age-related variability may indicate that after African Americans surpass their life expectancy (in their 70s), the pressures that increase their likelihood of memory problems are due in part to stress and depressive symptoms. Stress and depressive symptoms on are well-known contributors to cognitive dysfunction and mental health problems by way of hormonal activity across the lifespan that impact the brain (21). It also implies that there are multiple ways African Americans preserve memory functioning into later life and that the threats to memory problems that might be experienced in earlier life have either been avoided and no longer pose a threat or reduce in their level of effect to cause age differences in memory functioning in very late life.

There are a few possible explanations for our findings. First, this work suggests that our analysis did not capture selection effects that might have occurred in the sample analyzed here. This may mean that the psychosocial and health variables we chose do not represent factors that produce exceptional aging in African Americans. These findings suggest that trajectories for cognitive and health factors show increasing variability with age and are not impacted by factors that cause early mortality, which might keep an individual from being in later aged cohorts. Another alternative explanation is the age groups we examined represent two distinct groups in relation to biological substrates. Following this rationale, we hypothesize that the younger group has not been impacted by physiological changes that create variability in cognitive and health indicators, which increase in their effects with advancing age. Thus, the older age group was more impacted by those factors than the young. We also considered that while the factors creating variability might be present in both groups, they create greater variability in the older group by way of a longer time to manifest and perhaps differentially impact the older group because they are more frail. Frail but still living. These are untested alternative hypotheses that require additional investigation.

The study has some limitations. One of the limitations was the type and size of the sample. While the size was not large compared to epidemiological studies, data on very old African Americans are not readily available. Even less available are longitudinal studies with large samples of older African Americans, particularly ones who could be considered exceptional survivors. The sample used here was collected from three different sources to get a sufficient sample size (BSBA, CAATSA, SOLSAA). Another limitation is the number of factors examined. This limit was due in part to obtaining data from three different studies. While we only had two to three indicators in each of the collections of measures (health, cognition, and psychosocial factors), the factors available resulted in significant findings. This suggests there may be more to learn from a larger and more diverse analysis of older African Americans of how variances change across age groups.

Nevertheless, this study has several strengths. The authors are unaware of any other study seeking to understand if there is variability in health and cognitive outcomes across age groups of older African Americans. While the examination of mean differences in age groups provides information about level effects it does not provide information about patterns of variability that may occur. Understanding this variability may provide insights about key factors about how selection effects may play a role in longevity among older African Americans. This study identified exceptional survivors for African Americans for the purpose of learning and writing from a positive perspective on this racial group.

The health and well-being of older African Americans is an often-understudied group due to the high rates of premature mortality for African Americans. Chaos theory applied to our results would suggest that as systems age, they become less regulated and increase in variability (22). Attempting to identify a discrete collection of causes for increased variability in exceptional survivors or for longevity is likely mired in improbability. The complexity of both psychosocial and biological underpinnings to exceptional survival will require additional complex biobehavioral approaches. This may be particularly true for African Americans who begin the first half of life with an increased probability for mortality and a second half of life with a far decreased probability for mortality when compared to other racial/ethnic groups. In this study, the investigators sought to determine the similarities and differences in age cohorts of older African Americans in cognitive, psychosocial, and health indices and longevity. Our findings underscore the importance of examining psychosocial factors as possible explanatory factors of exceptional survivorship. Future work should examine longitudinal data on individuals as well as including genetic markers as possible predictors or modifiers of the results found here. In addition, generational effects might also be studied using a within-family approach examine how historical events might be contributing to the variability observed in different age cohorts of African Americans. Future data from SOLSA study, which when completed will provide genetic, psychosocial, and health data on short- and long-lived families, will provide important additional insights to understand such effects.

Funding

This paper was published as part of a supplement sponsored and funded by AARP. The statements and opinions expressed herein by the authors are for information, debate, and discussion, and do not necessarily represent official policies of AARP

Conflict of Interest

None declared.