Abstract
It is well understood that race/ethnic disparities in health, broadly defined, exist throughout the human life span (e.g., Haas, Krueger, & Rohlfsen, 2012). Cross-sectional studies suggest that there exist disadvantages in performance levels for older African Americans relative to Whites on a wide variety of cognitive screening and intelligence measures (e.g., Escobar, 1986; Fillenbaum, Heyman, Williams, Prosnitz, & Burchett, 1990; Heaton, Ryan, Grant, & Matthews, 1996; Kaufman, McLean, & Reynolds, 1988; Manly et al., 1998). More dynamically, however, there has been relatively little research that addresses similarity and differences in cognitive change rates for African American and White elders.
As older adults move into the later decades of life (e.g., 80s and 90s), growing evidence suggests that there is normative and accelerated cognitive decline (Ghisletta, Rabbitt, Lunn, & Lindenberger, 2012; Giambra, Arenberg, Zonderman, Kawas, & Costa, 1995; Lindenberger & Baltes, 1997; Schaie, 1996; T. Singer, Verhaeghen, Ghisletta, Lindenberger, & Baltes, 2003), particularly in areas of cognition that are considered to be more fluid (Horn & Cattell, 1967) such as processing speed, working memory, attention, and even declarative memory, and executive functioning (Baltes, 1993; Grady & Craik, 2000; Park et al., 2002). A question then arises whether this accelerated decline exists equally for African American and White individuals, or whether the lifelong cognitive performance disadvantages reported for African Americans manifest themselves also in greater rates of decline.
It has been argued that cognitive differences between race groups reflect cumulative disadvantage and not differential rates of decline. For example, Byrd et al. (2006) found that early environmental factors (collected retrospectively) were significantly related to performance on neuropsychological tests. Less favorable early environments were correlated with poorer cognitive performance, even after adjusting for education. Similarly, in an AHEAD-based longitudinal study examining demographic and socioeconomic predictors of cognitive decline, non-Hispanic Whites and non-Hispanic Blacks evinced level differences (i.e., differences in average or initial level of performance) in cognition at a baseline assessment. Examining cognitive change, even after demographic and socioeconomic factors were considered, the rate of decline was less steep for non-Hispanic Blacks than Whites, thereby resulting in diminished between-ethnicity differences to diminish with increasing age (Karlamangla et al., 2009).
Nonetheless, race differences in cognitive performance levels do manifest themselves in prevalence and incidence statistics for cognitive impairment. Diagnoses of Alzheimer’s disease, other types of dementia, and cognitive impairment are typically given when an individual’s cognitive performance is extremely low in comparison to a normative reference group.
Correspondingly, African Americans receive earlier and more frequent diagnoses of cognitive impairment or dementia (Inouye, Albert, Mohs, Sun, & Berkman, 1993; Manly et al., 1998; Whitfield, Weidner, Clark, & Anderson, 2002). Overall, older African Americans have not only been shown to have a greater prevalence of cognitive impairment and Alzheimer’s disease (Schwartz et al., 2004; Tang et al., 2001) but also been shown to have greater physical disability (Bowen, 2009; Kelley-Moore & Ferraro, 2004; Mendes de Leon, Barnes, Bienias, Skarupski, & Evans, 2005) and higher rates of mortality resulting from a variety of health conditions as well as shorter mean life expectancies (Hummer, 1996). What is not clear is whether these greater rates of impairment represent faster rates of decline, or simply that older adults enter late life at a lower level of functioning (i.e., closer to a threshold of impairment) because of cumulative lifelong disadvantages in cognitive performance. This latter interpretation would be consistent with other studies that suggest that persons with lower education do not decline at a faster rate but simply enter old age at lower cognitive levels (Zahodne et al., 2011).
Because of the late-life disadvantages of older African Americans, relative to Whites, likely reflecting health and educational disparities, it has been proposed that level differences in cognition between African American and White elders should be attenuated after adjusting for lifetime disadvantage indicators such as socioeconomic status, health status, education, and gender (e.g., Aiken Morgan, Marsiske, & Whitfield, 2008; Jones, 2003; Manly et al., 1998; Manly, Jacobs, Touradji, Small, & Stern, 2002). Importantly, work done by Aiken Morgan et al. (2010) with the ACTIVE sample has shown that race-related test bias (i.e., differential test functioning for different race groups) was not a significant factor in the lower mean scores found in African Americans.
Extrapolating from physical/functional aging studies, one possible reason for the accelerated cognitive decline for African Americans might in part be due to higher cardiovascular and comorbidity burden (Cooper et al., 2000). However, even when adjusting for socioeconomic status (SES), there is a lingering effect of race on some health outcomes (e.g., hypertension, diabetes, and arthritis; Whitfield et al., 2002; Williams, 1996). Zsembik and Peek (2001) hypothesized that because African Americans had greater prevalence of biological risk factors than Whites, race would operate on cognitive functioning indirectly through biological factors, particularly vascular diseases. Nonetheless, results indicated that race had a direct effect on cognition, after accounting for social and biological correlates. It is important to note that this direct race effect was smaller once the social and biological factors were added to the model, thereby indicating that these factors account for some of the level differences (i.e., mean differences) seen on cognitive scores between African American and White older adults. It was concluded that the remaining race differences are most likely a result of further background variables such as quality of education and early life inequalities that were simply not accounted for in their study (Zsembik & Peek, 2001).
Education has been strongly implicated in the observed cognitive level differences observed in African American and White elders. For current cohorts of older adults, African Americans obtained fewer years of education (e.g., Snyder, 1993; Williams, 1999) and likely also experienced poorer quality (shorter years, impoverished study materials) of education in the early 20th century (Bullock, 1967; Jones, 2003; Whitfield, 1996; Williams, 1999). There is somewhat conflicting literature about difference in health and physical functioning benefits seen between races for increasing years of education. Farmer and Ferraro (2005) showed an interaction between race and education and race and employment status for self-rated health measures in older adults. This suggested that with increasing education levels, African Americans did show the same improvement on self-reported health measures as the White sample. However, in a recent study by Barnes et al. (2011), Blacks appeared to show greater benefits than Whites in functional health for greater that 12 years of education, thereby indicating that with each year of education beyond 12 years, there was significantly greater gain on the functional health outcomes for Blacks than for Whites .
The current study examined a 5-year cognitive change on a broad battery of psychometric measures in a subset of untrained African American and White participants from the ACTIVE study. The availability of the five years of longitudinal data, on multiple cognitive measures, for African American and White elders represents a major advantage of the ACTIVE dataset over many previous studies. The ACTIVE no-training control group (N = 690) was used, because intervention effects might alter the naturally occurring rates of change. Further detail on the control group, in the context of the larger study, is provided in the Overview paper of this Special Issue of the Journal of Aging and Health. Only African American and White participants were included because representatives of other races were included in very low numbers (8 participants). ACTIVE provided a strong dataset with which to study African American and White longitudinal trajectories because of the study’s overall commitment to substantially represent African Americans, who comprised 27.5% of the sample. Because the larger ACTIVE clinical trial excluded persons of low cognitive status (described below), the educational distributions of the two race groups were more similar to one another than they would be in the American population as a whole. Consequently, ACTIVE permitted a comparison of differences in mean-level and rates of change in race groups that were selected to be relatively more educationally and cognitively similar than they might be in the population, as a function of the clinical trial inclusion/exclusion criteria. Nonetheless, factors such as educational level and health differences were included in all analyses, as were gender and age. In ACTIVE, African American participants tended to be slightly younger at enrollment and slightly more female. Analyses also controlled for retest/practice effects and participant dropout effects (Lindenberger, Singer, & Baltes, 2002; Rabbitt, Diggle, Holland, & McInnes, 2004; Salthouse, 2010; Schaie, Labouvie, & Barrett, 1973; Willis & Schaie, 1986)
Method
Participants
This article used the African American and White no-training control group participants in ACTIVE. The control sample in ACTIVE (N = 690, M = 182; F = 508) has been extensively described elsewhere (e.g., Ball et al., 2002; Jobe et al., 2001). Table 1 shows the distribution of age, education, gender, and physical functioning by race/ethnicity categories. At time of enrollment in 1998-2000, participants were, on average, 74.05 years of age and had an average of 13.37 years of education. With regard to the racial/ethnic distribution, 500 participants (72.46%) were White, 190 (27.54%) were African American. This study excluded the less than 1% of the total ACTIVE sample who reported themselves as members of other races or bi-/multi-racial. As Table 1 also shows, there were significant racial/ethnic group differences in age, education, physical functioning, and proportion of subsample that was female, with African American participants tending to be younger (p < .05), less educated, have lower physical functioning, and contain a larger proportion of women (p < .001) than Whites; effect sizes for the race differences tended to be small-to-medium (Cohen, 1998).
Characteristics of ACTIVE Control Participants at Baseline.
Note. T-tests show significance of the comparison between African American and White participants; for Gender, a corresponding chi-square test was used. No covariate adjustment was employed.
Degrees of freedom and t-statistic were adjusted for nonhomogeneity of variance.
The ACTIVE sample was drawn from older adults without dementia who were living independently in the community. Persons were excluded from participation if they had Mini Mental Status Examination (MMSE; Folstein, Folstein, & McHugh, 1975) scores less than 23, vision (Rubin & Salive, 1995) worse than 20/70, or health conditions such as history of stroke or low-survival cancers, or had experienced substantial functional impairment with dressing, personal hygiene, or bathing. Based on initial screening of comparisons between those randomized and not-randomized in the total sample, 379 of 2,454 Whites screened were excluded (15.4%) and 239 of 976 African Americans screened were excluded (24.5%). This represented a significantly higher exclusion rate for African American participants: χ2(df = 1, N = 3430) = 38.66, Cramer’s V = 0.11, p < .001. These comparisons could not be restricted to the current study sample only, because exclusion occurred prior to randomization.
Of the 690 elders included at baseline, there were 447 survivors and 246 dropouts at the fifth annual follow-up. T-test comparisons of baseline characteristics of these two groups of participants revealed that, relative to dropouts, those assessed at Year 5 were younger (73.6 vs. 74.9 years), reported higher levels of education (13.6 vs. 13.0 years), had higher MMSE scores (27.5 versus 26.8), and included a higher percentage of females (68.3% vs. 66.3%) and White participants (75.7% vs. 63.0%; all significant at p < .05). Correspondingly, results that follow adjust for these covariates and use a missing data pattern mixture approach (described below) to produce estimates combined across missing data mixture groups.
Procedure
Data at each occasion were collected over three sessions (as described in Jobe et al., 2001 and Ball et al., 2002). Two 2-hour sessions were conducted individually, and a third 3-hour session was included in a group format. Assignment of measures to sessions was governed largely by prior practices and practical considerations. For example, some neuropsychological measures, such as the digit-symbol substitution task, that are more commonly given in one-on-one testing situations to maximize quality control were administered in single participant sessions. To reduce respondent burden, a goal was to distribute cognitive measures across several sessions to minimize fatigue in any single session. Some sessions had to be individual in nature (e.g., those that included physical performance measures needed for other aspects of the ACTIVE study), and so cognitive tasks that were fit into those sessions also happened to be administered individually. Following baseline assessment, participants were reevaluated in a similar manner as during baseline at 1-year follow-up, 2-year follow-up, 3-year follow-up, and 5-year follow-up. All available data from the selected participants were used in the subsequent analyses.
Measures
This study reports performance in five cognitive domains used in ACTIVE. These domains used traditional, well-studied laboratory tasks of cognitive and intellectual functioning. In the ACTIVE study, composites made up of multiple measures of memory, reasoning, and visual processing speed (Useful Field of View [UFOV]; Ball, Owsley, Sloane, Roenker, & Bruni, 1993) were considered proximal outcomes, because their chief function was to permit detection of memory, reasoning, and speed-training effects. In addition, measures of vocabulary (Ekstrom, French, Harman, & Derman, 1976) and digit-symbol substitution (Wechsler, 1981) were administered to permit the assessment of training effect breadth and could be used to assess age-related change in widely studied cognitive measures thought to represent crystallized intelligence and perceptual/motor speed, respectively. Table 2 presents an overview of measures used. Further psychometric details about these instruments are given in other sources (e.g., Jobe et al., 2001; Ball et al., 2002). For the composite of memory, reasoning, and speed, all constituent measures were Blom-normalized and standardized (mean = 0, standard deviation = 1) across all occasions and were then summed into unit weighted composites. As noted above, covariates of self-reported age, education, gender, and health (a general health rating scale from the SF-36 inventory; Ware & Sherbourne, 1992) were included in all conditional models.
Cognitive Measures in ACTIVE.
Analyses
Conditional growth models, one each for reasoning, memory, UFOV, digit symbol, and vocabulary, were parameterized to control for the effects of background covariate variables (i.e., age, education, health, gender) and being African American on level and rate-of-change in cognitive functioning across the 5-year study period. This was done through an implementation of a multilevel model (MLM) for change (Bryk & Raudenbush, 1992; J. D. Singer & Willett, 2003).
To examine any influence of missing data and participant attrition, a pattern mixture model (PMM) approach (Hedeker & Gibbons, 1997) was also implemented. PMM allows for the examination of performance as a function of different patterns of missing data, although the current study presents final parameter estimates aggregated across missing and nonmissing data patterns.
For each of the five cognitive outcomes (memory, reasoning, visual processing speed/useful field of view, digit-symbol substitution, and vocabulary) a 6-step hierarchical model building approach was adopted for each cognitive outcome variable. The steps involved estimating an unconditional null model (needed for later incremental fit and proportional variance explained calculations), followed by an unconditional growth model (estimating the linear and quadratic effects of time), followed by the addition of background covariates, a variable representing being African American, and finally time by African American residual-centered product terms (Little, Bovaird, & Widaman, 2006) to assess whether being African American moderated the slope of change, detailed in the appendix, which also shows the model fit results (including -2LL, AIC, within-person r2, and between-person r2) for each added predictor block and cognitive domain assessed. Following the penultimate product-term step, the model was reestimated using missing data PMM. This step involved the introduction of a dummy-coded predictor variable indicating whether an individual was present at the final measurement occasion. Product terms between this variable and the above described time functions and being African American were also estimated. Throughout the models, where product term interaction terms were included, they were residual-centered (partialing out constituent main effects) to eliminate multicollinearity effects (Little et al., 2006).
All models were estimated under the simplest assumptions about the repeated error structure over time (i.e., homoscedasticity and independence of errors) and diagonal random error structure (i.e., heteroscedasticity and independence of observations). The models were also estimated using the maximum likelihood (ML) method. The ability of a model to predict cognitive performance better than the baseline model (i.e., deviance) was used as an index of Goodness of Fit. Improvements in predictability were determined by the amount of reduction of within-person residual variances and between-person intercept variances compared with the baseline model (Bryk & Raudenbush, 1992). Decreases in residual and intercept variances represent a proportional reduction of the prediction error, which is analogous to R2, and was used as an estimate of within-person and between-person effect sizes. The results summary that follows comes from the final model (Step 6) following the implementation of pattern mixture model aggregation across missing and nonmissing mixture groups.
Results
Investigation of Proposed Models
Prior to the proposed models, initial preliminary models (not detailed here) examined the unique effect of being African American and whether being African American moderated the age effects without an inclusion of covariates. This step (included at the suggestion of an anonymous reviewer of the previous draft) provided an initial estimate of the raw bivariate association between being African American and the cognitive outcomes. The standardized beta for the effect of being African American was, for Digit Symbol: β = −0.30, for Memory: β = −0.35, for Reasoning: β = −0.39, for UFOV: β = −0.21, and for Vocabulary: β = −0.50 (standard error = .05 for all outcomes; p < .001 for all). Being African American did not moderate either the linear or quadratic rates of change in any of these models. In these bivariate models, being African American uniquely explained between 4% (UFOV) and 25% (Vocabulary) of the variance. In subsequent proposed models, the effect of being African American was reexamined after controlling for covariates; the unique effect of being African American was much attenuated.
The final models, following implementation of pattern mixture model aggregation across missing and nonmissing mixture groups, for each cognitive domain assessed are summarized in Table 3. In general, each model step yielded a significant improvement in model fit and an increase in explained variance, with the exception of the final race-moderation step, which showed that being African American only significantly interacted with the rate of change (quadratic) for the reasoning composite. In practical terms, being African American added relatively little to the models. Being African American uniquely accounted for between 2% and 7% of the between-person differences in cognition and, as noted, race groups did not evince significant differences in rates of linear or quadratic change. Despite the relatively unimportant role of race in these models, the models in general explained between 43% and 50% of the between-person variability in cognition and linear/quadratic change and retest effects explained between 8% and 21% of the within-person variability.
Multilevel Growth Models Examining 5-Year Cognitive Change.
Note. AA = African American (reference group is White); UFOV = Useful Field of View.
Variance too small to be estimated—the final Hessian matrix was not positive definite although all convergence criteria were satisfied. In addition, all models included study site and an indicator of when in time data collection occurred (replicate). These covariates are not shown for presentation simplification. There were no hypotheses about site and replicate effects, but they were a substantial identifiable source of participant variation. These effects, along with degree of freedom and standard error information for each effect, are available on request.
p < .05. **p < .01.
Looking across cognitive domains, in general, there was relatively modest change over the 5-year period. Linear change was significant and negative only for the memory composite, although there was significant random variance (i.e., individual differences) in the linear slope for all abilities except for vocabulary. There was a negative quadratic time trend (accelerated decline in later years) for memory, visual processing speed/UFOV, and the digit-symbol substitution test. There was also a significant positive retest effect for the reasoning and visual processing speed/UFOV measures. Whereas the vocabulary measure had no fixed association with time, there was significant random variance in the quadratic trend, thereby indicating that some individuals may have experienced relatively more decline than others. The model-implied linear and quadratic trajectories of the five cognitive domains, plotted separately for African American and White participants, are shown in Figure 1.
Model estimated growth curves for 5-year cognitive change by racial group.
All covariates included to adjust for group differences in level showed some significant associations with the cognitive outcomes. Age was significantly and negatively associated with all cognitive measures except for vocabulary; women showed significantly better performance on the memory composite and the digit-symbol substitution measures. Education and health were positively related to performance on all measures, whereas African American participants performed more poorly on all five cognitive outcomes.
As noted above, being African American did not moderate the linear or quadratic trends for any of the outcomes except for the reasoning composite, where African Americans evinced slightly more negative quadratic age change trajectories than Whites.
African American Status as a Moderator of Rate of Change
As a follow-up to the main analyses, several post-hoc multilevel growth models were parameterized and examined. These models examined the effects of age, race, and age by race interactions on 5-year cognitive change to answer the question of whether race might moderate the rate of change differentially based on age (e.g., might race differences emerge in rate of change for the oldest participants?). Across all dependent variables, only the Age × Linear Time interaction was significant: Digit Symbol, β = −0.003, t(505.27) = −3.62, p < .001; Memory: Age × Linear Time, β = −0.004, t(509.61) = −3.70, p < .001; Reasoning: Age × Linear Time, β = −0.008, t(492.21) = −3.69, p < .001; UFOV: Age × Linear Time, β = −0.012, t(542.22) = −3.73, p < .001; Vocabulary: Age ×Linear Time, β = −0.002, t(480.69) = −2.771, p < .01. In all models, none of the previously reported results were substantially changed by the inclusion of these additional predictors (i.e., all fixed and random effects remained significant).
Discussion
In a sample of African American and White older adults selected for the ACTIVE clinical trial, race differences in the level of performance on a variety of cognitive outcomes were relatively small in magnitude. With regard to the central question motivating the current article, there was little evidence of race differences in the rates of change over the 5-year period studied. Being African American accounted for only 2% to 7% of the individual differences in the level of cognitive performance. The general trend of cognitive change over the 5-year period was negative quadratic change (initial increase followed by accelerated decline) for memory, visual processing speed/UFOV, and digit-symbol substitution, but these trends were not moderated by being African American. In the sole exception, being African American appeared to slightly moderate individual differences in the rates of change for reasoning, in the form of more accelerated quadratic decline for African American participants, even though the overall 5-year trajectory for reasoning was flat. Follow-up analyses showed that higher age was associated with more negative linear change for all abilities studied, but this did not interact with being African American.
One criticism of the current findings likely relates to the positively selected nature of the sample—that is, to meet the inclusion criteria of the larger ACTIVE clinical trial, participants were required to perform at or above 23 on the MMSE. This had the effect of truncating the lower part of the cognitive status distribution, thereby serving to homogenize differences between race groups at enrollment. In addition, participants who reported extreme difficulty with two or more activities of daily living were also excluded. These selection factors clearly limit the generalizability of the obtained 5-year changes to the larger population of older adults. An open question is whether these selection actors also account for the relatively modest 5-year changes observed in this study. The inclusion/exclusion criteria for the ACTIVE study may not necessarily have eliminated all education and health effects, but they may have helped to minimize the confounding effects of education and health, because the groups were relatively equated at baseline (Aiken Morgan et al., 2010).
In the context of the selection filter imposed by the clinical trial inclusion factors, as well as the inclusion of a number of covariates in all models, especially education and health, it is surprising that significant race differences still persisted. The persistent race effect here, though small, likely reflects unmeasured cultural factors that influence cognitive performance (Leveille et al., 1998; Zsembik & Peek, 2001). It is also likely that the persistent race effect might reflect unspecified effects due to biological influences (e.g., genetics), racism, or unobserved heterogeneity (Clark, Anderson, Clark, & Williams, 1999; J. S. Jackson et al., 1996). The years of education variable included in the current study also likely fails to capture the true diversity of education for the cohorts included in this study, where “separate and unequal” was often true of public education for African Americans and Whites (see Manly et al., 2002). Because ACTIVE was a clinical trial, and did not have as its initial goal the discovery and explanation of race disparities in cognition, some variables that have been found important in explaining race differences in cognition were not available here. For example, in work by Mehta et al. (2004), literacy and financial adequacy, along with age, sex, and education, accounted for 86% of the Black–White differences in mental status. This is consistent with the work of Manly et al. (1998, 2002) and our own prior work with the ACTIVE pilot sample (Aiken Morgan et al., 2008), which also found that literacy accounted for much of the race differences in cognition.
Another limitation of the study relates to the limited diversity of measures used to study cognitive change. Reasoning, memory, and speed were heavily sampled measurement domains, because these were the targets of the ACTIVE cognitive interventions (Ball et al., 2002; Jobe et al., 2001). More crystallized (Horn & Cattell, 1967) or knowledge-based domains were not well-represented in the study, with the exception of the vocabulary measure considered.
Nonetheless, the overarching finding with regard to race differences in longitudinal trends in the current study seems to be near-parallelism between the two racial groups. This is consistent with Allaire and Marsiske (1999), who reported that African Americans appeared to have parallel (linear) cross-sectional gradients to Whites for most intellectual abilities; an exception was for measures of knowledge (e.g., verbal ability, everyday “facts”), where cross-sectional trends were more steeply negative for Black participants. The current findings are also consistent with previous evidence that demographic variables that are associated with better performance in later life do not seem to confer advantages in terms of the rate of decline. For example, Baltes (1997) and Lindenberger and Baltes (1997) reported that the negative age gradient found for a general measure of intellectual performance in the Berlin Aging Study applied equally well in two groups stratified by educational and social history; similar recent findings were reported by Zahodne et al. (2011) in the Victoria Longitudinal Study.
It is unclear how much some form of the crossover effect (e.g., Elo & Preston, 1997), or the progressive narrowing of the mortality gap with age, might play a role in the small race effects observed in this study. Interpretively, this narrowing has been viewed as a reflection that, given ongoing health disparities and adverse living conditions for African Americans across the life span, only the fittest individuals are likely to reach old age (e.g., J. J. Jackson, 1980; Manton, Stallard, & Wing, 1991; Markides & Mindel, 1987) and age trends reflect this selective survivorship. Alternatively, it is also possible that, because recruiting and representing the oldest ages in a minority population are particularly difficult (Hogan & Robinson, 1993), the resulting sample becomes progressively more positively selected and biased as it relies on increasingly positively selected volunteers. These interpretations are only speculative in the current study (given study exclusion criteria, it was not possible to investigate the trajectories of the lowest functioning volunteers). It is clear, however, that ACTIVE enrolled a higher proportion of positively selected African Americans than Whites, given higher rates of exclusion for African Americans than for Whites (24.5% vs. 15.4%).
One hypothesis for the cognitive level differences observed between African American and White elders is the cumulative disadvantage model (Byrd et al., 2006). The reduction of the unique effect of being African American (from 4-25% to 2-7%) after variables such as education, age, health, and gender were included in the model suggests that these variables might be possible mediators of apparent late-life race differences in cognition. The ability to examine cumulative disadvantage directly is weakened in ACTIVE, because of the fact that one of the most salient variables, SES, was not collected in ACTIVE and data from earlier points in the life span was not collected. A related study from ACTIVE, however, was able to collect current neighborhood socioeconomic position (SEP) data for most study participants from the 2000 Census (Sisco & Marsiske, 2012). That study found that the current neighborhood SEP was uniquely associated with individual differences in cognitive level (especially verbal ability), even after controlling for education and verbal abilities as the indicators of cumulative advantage; moreover, SEP was strongly and negatively related to being African American.
One typical problem in longitudinal investigators of cognition is the issue of selective attrition. Correspondingly, in this study, participants who survived until the 5-year follow-up tended to be younger (by 1.3 years, on average), more educated (by 0.6 years, on average), and performed slightly better on the MMSE (by 0.7 points). Returning participants were also more likely to be female and White. To reduce the influence of these biasing features, we included these covariates in all conditional models and used a missing data pattern mixture approach (Hedeker & Gibbons, 1997). This approach modeled the main effect of attrition status, as well as the interaction of attrition with time, being African American, and their interaction. These analyses were then used to produce the pooled parameter estimates shown in Table 3.
Taken together, the results of this study and previous investigations on sociocultural/ethnic group differences suggest that there may be a dissociation of race differences in level and slope of cognitive performance that closely parallels what has been reported with regard to education (e.g., Zahodne et al., 2011). With regard to the level of performance, the lower performance of African American and lower educated elders in this study likely reflects the many potential sources of cumulative life diversity that tend to distinguish racial/ethnic minority members in American society (e.g., Whitfield et al., 2002). With regard to the rate of longitudinal decline, at least in our sample of African American and White elders who met identical inclusion criteria for our clinical trial, there was no evidence that being African American conferred any particular disadvantage.
Footnotes
Appendix
Modeling proceeded in six steps: (a) Null: Baseline model estimated only a fixed and random intercept for cognitive functioning and serves as a comparison for later models; (b) Time Added: Linear and quadratic effects of time (coded as time since baseline) control variables (data collection site, replicate, and a dummy-coded retest variable) were added. This model characterized the growth curves. The retest dummy code was set to 0 for first exposure (no previous experience) versus 1 for all subsequent exposures (following a procedure described in McArdle, Ferrer-Caja, Hamagami, & Woodcock, 2002), thereby reflecting the consistent finding that retest/practice-related gains are usually largest between the first and second measurement; (c) Background Covariates Added: Background covariate variables were added (age, education, health, and gender); (d) African American Added: African American (AA; reference group = Whites) was added. AA was entered last (in Steps 4 and 5) to show the unique effect of AA (if any) after preceding covariates had been controlled, so as not to inflate the effect of AA status; (e) Time × African American interaction added: Time × AA product terms were added to assess whether being African American moderated the slope of change.
Model fits at each hierarchical step are shown in the appendix table that follows:
Authors’ Note
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Nursing Research, National Institute on Aging, or the National Institutes of Health. Representatives of the funding agency have been involved in the review of the manuscript but not directly involved in the collection, management, analysis, or interpretation of the data.
Declaration of Conflicting Interests
The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Dr. Ball owns stock in the Visual Awareness Research Group and Posit Science, Inc., the companies that market the UFOV Test and speed of processing training software now called Insight, and she serves as a member of the Posit Science Scientific Advisory Board. Dr. Rebok is an investigator with Compact Disc Incorporated for the development of an electronic version of the ACTIVE memory intervention. Dr. Marsiske has received research support from Posit Science, Inc., in the form of site licenses for cognitive training programs for a different research project.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Mr. Dzierzewski and Ms. Thomas were supported by a grant from the National Institute of Aging (T32 AG020499), and Mr. Dzierzewski was also supported by a Ruth L. Kirchstein National Research Service Award (F31 AG032802). ACTIVE is supported by grants from the National Institute on Aging and the National Institute of Nursing Research to Hebrew Senior Life (U01NR04507), Indiana University School of Medicine (U01NR04508), Johns Hopkins University (U01AG14260), New England Research Institutes (U01AG14282), Pennsylvania State University (U01AG14263), University of Alabama at Birmingham (U01AG14289), University of Florida (U01AG14276).