Abstract

Use of normative data stratified by education may result in misclassification of African American older adults because reading ability, an estimate of educational attainment, is lower than reported years of education for some African American elders. This study examined the contribution of reading ability versus education to neuropsychological test performance in 86 community-dwelling African American elders ages 56–91 with 8–18 years of education. Hierarchical multiple regression analyses revealed that reading ability, but not education, was significantly associated with performances on the Trail Making Test, Controlled Oral Word Association Test, Animal Naming, Digit Span, and the Stroop test. Reading ability was not significantly related to performances on measures of memory. Medium to large effect sizes (Cohen's d = 0.58–1.41) were found when comparing mean performances on neuropsychological measures in groups with low versus high reading scores. Results indicate that reading ability contributes beyond educational attainment to performances on some neuropsychological measures among African American elders. These findings have implications for reducing misclassification among minority populations through the use of appropriate normative data.

Introduction

Performance on neuropsychological measures is influenced by several factors, including education, ethnicity, and age (Manly, Jacobs, Touradji, Small, & Stern, 2002; Rohit et al., 2007; Schafer Johnson, Ficker, & Lichtenberg, 2006); however, these variables are not able to account for the, on average, lower performances of African American adults on neuropsychological measures compared with Caucasians (Heaton, Ryan, Grant, & Matthews, 1996; Manly et al., 1998, 2002). Factors such as acculturation (Manly, Byrd, Touradji, & Stern, 2004), test validity (Cosentino, Manly, & Mungas, 2007), and differences in neurological status (Luchsinger et al., 2007) have been proposed to explain this discrepancy in test performance. Among these factors, differences in reading ability have been shown to account for test score discrepancies among minority older adults (Manly, Schupf, Tang, & Stern, 2005).

Because educational experiences are impacted by many variables, including availability of materials and one's ability to attend school, years of education may not accurately represent the actual educational experiences among various ethnic groups. African American elders historically have not had equal opportunities to education resulting in differences in what years of education represents (Lucas et al., 2005; Manly et al., 2002). Estimations of educational attainment based on word reading are significantly lower than reported years of education in samples of middle-aged and older African American adults (Albert & Teresi, 1999; Manly et al., 2002; Rohit et al., 2007). For example, 29% of the Mayo Older African Americans Normative Study (MOAANS) sample obtained reading estimates that were 3 or more years below their reported years of education (O'Bryant et al., 2007), and in a sample of African American elders from the Detroit area, 73.2% read at least one grade level below their stated educational attainment (Schafer Johnson et al., 2006). Due to these discrepancies, estimating expected neuropsychological performances based on years of education alone can result in misclassification of African American older adults. Rather than using self-reported education, measures of educational quality or achievement may be a better proxy. Measures of reading ability, such as the Wide Range Achievement Test-3rd edition (WRAT-3), have been identified as indicators of educational quality because they correlate with educational experiences (Hedges, Laine, & Greenwald, 1994). These measures also remain relatively stable across time for both healthy adults and those with dementia (Ashendorf, Jefferson, Green, & Stern, 2009; McGurn et al., 2004), making them a good proxy for both educational attainment and quality across a wide range of ages.

Consideration of reading ability attenuates the discrepancy between African American and white adults' performances on measures routinely used for dementia screening, such as the Mini-Mental Status Exam (MMSE) and the Executive Clock-Drawing Task (CLOX1; Albert & Teresi, 1999; Crowe, Clay, Sawyer, Crowther, & Allman, 2007). Reading ability accounts for more variance than demographics on neuropsychological measures of learning/memory, reasoning, language, and visuospatial ability (Manly, Byrd, Touradji, & Stern, 2004; Schafer Johnson et al., 2006). After controlling for reading ability, Morgan, Marsiske, and Whitfield (2008) found no significant differences in test performance between groups of African American and Caucasian older adults on measures of episodic memory, general cognition (MMSE), language, and everyday problem solving. Despite such research demonstrating the significant contributions of reading ability to neuropsychological test performance, few normative sets have been published that are stratified by reading ability. Dotson, Kitner-Triolo, Evans, and Zonderman (2008) recently provided literacy-based norms for middle-aged (i.e., 30–64 years) African American adults; however, to date, no published norms for African American elders (i.e., >65) stratified by reading ability are known.

The goals of this study are two-fold: (1) to examine the contribution of reading ability to performance on neuropsychological measures after accounting for age, gender, and years of education and (2) to provide mean scores stratified by reading ability for performances on several neuropsychological measures in a sample of community-dwelling African American older adults. It was predicted that reading ability would significantly contribute to performance on all neuropsychological measures after accounting for age, gender, and education.

Methods

Sample

Participants were drawn from the Health, Disability, and Cognitive Function in Urban Black Older Adults data set, which includes 130 community-dwelling Black adults between the ages of 55 and 95. This project received approval from the Institutional Review Board, Human Investigation Committee. Prior to participation, all participants provided signed consent. Subjects were recruited from independent living centers, community centers, and senior apartments through presentations within the community given by the PI (Brooke Schneider). Fliers stating that the aim of the study was to understand “health and cognitive functioning in older African American adults” were given to potential participants. Potential participants were excluded if they (1) were unable to speak English fluently; (2) had major hearing or vision loss; and/or (3) were aged <55. In addition, a total of 36 participants were excluded from this study due to a self-reported history of neurological disease, head injury with a loss of consciousness, current depression (defined as a Geriatric Depression Scale score ≥5), or a previous diagnosis of dementia.

A summary of participant demographic and self-reported health information for the final sample is presented in Table 1. Participants ranged in age from 56 to 91 (mean = 72.37, SD = 7.59) and formal education ranged from 8 to 18 years (mean = 12.62; SD = 2.30). The sample was comprised almost entirely of women (91.9%).

Table 1.

Demographic and health characteristics of sample (n = 86)

 Mean SD Range 
Age 72.37 7.59 56–91 
Education 12.62 2.30 8–18 
 Frequencies Percentage  
Gender 79 (women) 91.9  
Medical conditions 
 Hypertension 59 68.6  
 Myocardial Infarct 8.1  
 Hypercholesterolemia 40 46.5  
 Peripheral Vascular Disease 5.8  
 Diabetes 17 19.8  
 Thyroid Disease 11 12.8  
 Cancer 13 15.1  
 Alcoholic drinks per week 
  Do not drink at all 82 95.3  
  Two 3.5  
  Three or more 1.2  
 Mean SD Range 
Age 72.37 7.59 56–91 
Education 12.62 2.30 8–18 
 Frequencies Percentage  
Gender 79 (women) 91.9  
Medical conditions 
 Hypertension 59 68.6  
 Myocardial Infarct 8.1  
 Hypercholesterolemia 40 46.5  
 Peripheral Vascular Disease 5.8  
 Diabetes 17 19.8  
 Thyroid Disease 11 12.8  
 Cancer 13 15.1  
 Alcoholic drinks per week 
  Do not drink at all 82 95.3  
  Two 3.5  
  Three or more 1.2  

A summary of participant demographics and the number of participants with self-reported medical and psychological conditions known to affect cognitive functioning are presented in Table 1. Due to the small sample size, based on previously used methodology (Dotson et al., 2008), rather than exclude these individuals from all analyses, they were removed from the analysis of individual tests only if their health condition was shown to have a significant effect (p < .05) on the test score when entered into standard regression analyses. In these analyses, the test of interest served as the dependent variable while health condition and demographic variables (age, gender, education, and reading ability) predicted each test score. These analyses revealed that alcohol consumption (n = 4) was significantly related to Fuld Object Memory Evaluation (FOME) total score, peripheral vascular disease (n = 5) was significantly related to performance on the Controlled Oral Word Association Test (COWAT), and diabetes (n = 17) was related to performance on the Rey Auditory Verbal Learning Test (RAVLT) total score and RAVLT Long-Delay score (Table 2). Participants with these conditions were removed only for the regression analysis in which the health condition was shown to affect test performance. Hypertension (n = 59) was significantly related to performance on the Trail Making Test, Part A (TMT-A), though these participants were not removed from analyses because it is a health condition commonly found in African American older adults (Wyatt et al., 2008). Removing participants based on this condition may result in an unrepresentative sample.

Table 2.

Descriptive statistics of sample

Measure N Mean SD Range 
WRAT-3 86 41.91 6.44 24–55 
RAVLT Total 69 36.60 8.61 20–56 
RAVLT LD 69 5.80 2.96 0–14 
FOME 82 39.94 5.11 25–50 
TMT-A 86 56.99 24.71 20–118 
TMT-B 86 173.86 74.40 49–300 
Stroop Word 86 77.95 18.46 36–138 
Stroop Color 86 53.90 14.58 23–112 
Stroop Color/Word 86 24.52 9.66 2–49 
COWAT 81 29.11 11.67 5–66 
Animal Naming 86 13.72 4.42 4–29 
Digits Forward 86 8.63 1.81 5–13 
Digits Backward 86 5.07 1.99 0–10 
Measure N Mean SD Range 
WRAT-3 86 41.91 6.44 24–55 
RAVLT Total 69 36.60 8.61 20–56 
RAVLT LD 69 5.80 2.96 0–14 
FOME 82 39.94 5.11 25–50 
TMT-A 86 56.99 24.71 20–118 
TMT-B 86 173.86 74.40 49–300 
Stroop Word 86 77.95 18.46 36–138 
Stroop Color 86 53.90 14.58 23–112 
Stroop Color/Word 86 24.52 9.66 2–49 
COWAT 81 29.11 11.67 5–66 
Animal Naming 86 13.72 4.42 4–29 
Digits Forward 86 8.63 1.81 5–13 
Digits Backward 86 5.07 1.99 0–10 

Notes: WRAT-3 = Wide Range Achievement Test-3rd edition; RAVLT Total = Rey Auditory Verbal Learning Test total score; RAVLT LD = RAVLT Long-Delay Free Recall; FOME = Fuld Object Memory Evaluation; TMT-A = Trail Making Test, Part A; TMT-B = Trail Making Test, Part B; COWAT = Controlled Oral Word Association Test.

Measures and Procedure

The neuropsychological measures were administered as part of a larger evaluation that involved data collection on demographics, physical health, cognition, health behaviors, and mental health in urban Black elders for a dissertation project. Data were collected in an individual interview session format by three graduate student interviewers who were supervised by a research psychologist.

Controlled Oral Word Association test

The COWAT (Ruff, Light, Parker, & Levin, 1996) consists of four trials in which participants are asked to generate as many words as possible beginning with the letters F, A, and S as well as names of animals in 1 min intervals. The total number of correct responses was calculated.

Fuld Object Memory Evaluation

The FOME (Fuld, Masur, Blau, Crystal, & Aronson, 1990) is a measure of verbal memory that involves recall of 10 common objects. Recall trials are separated by distraction task to minimize the effects of short-term memory.

Rey Auditory Verbal Learning Test

The RAVLT (Rey, 1964) is a measure of immediate and long-term memory in which a 15-word list is read at a rate of one word per second. Immediately after each of the five learning trials, recall of the words in any order is requested. After a 20 min delay, recall of the first list is requested. The total number of words recalled across the five learning trials and the total recalled after the delay were recorded.

Stroop

The Stroop (Golden, 1978) test includes three conditions. The baseline task asks participants to read a list of color names. The second subtask requires participants to name the color of ink in which six “X's” are printed. The final task, the interference task, requires the participant to name the color of ink a color name is printed in. For example, saying “green” when the word red is printed in green ink. The interference trial required inhibition and mental flexibility. Time to completion was recorded for each trial.

Trail Making Test

The TMT (Reitan & Wolfson, 1985) is a timed test of complex sequencing abilities and set-shifting. The test is comprised of two parts. In the first, Part A, participants are asked to connect numbers in numerical order (1-2-3 etc.) as quickly as possible. In the second section, Part B, participants connect letters and numbers in an alternating pattern (i.e., 1-A-2-B etc.) as quickly as possible. Scores are based on the time to completion.

Wide Range Achievement Test-3rd edition, reading subtest

The WRAT-3 (Wilkinson, 1993) reading subtest is a test of word familiarity and sight reading ability involving the pronunciation of a series of 15 letters of the alphabet and 42 increasingly difficult words. The total number of correctly read words was recorded.

Statistical Methods

Statistical analyses were performed using PASW Statistics 18. Participants who were missing data on variables of interest (n = 13) were excluded from the analyses. All variables were examined to ensure that they met the assumptions of multivariate normality. All variables except FOME and Stroop Color were within acceptable ranges; a logarithmic transformation was performed on these variables. The transformed variable was used in all analyses.

To ascertain the relationships between predictor variables, Pearson's product-moment and point-biserial (i.e., gender) correlations were obtained. To examine the relationship between reading ability and cognition, hierarchical regression analyses were conducted. In Block 1, cognitive scores were regressed on age, gender, and education. To examine the incremental variance accounted for by reading ability, WRAT-3 reading was then entered in Block 2. Raw scores on each of the cognitive measures were dependent variables for each individual hierarchical regression. Squared semi-partial correlation coefficients for Block 2 are reported in Table 3 to demonstrate the independent and unique influence of these variables on test scores. To correct for multiple comparisons, p-values of <.01 were considered significant. For measures in which WRAT-3 reading made a significant contribution, mean scores for neuropsychological measures stratified by WRAT-3 reading score are reported. Cohen's d effect sizes are reported to compare group means.

Table 3.

Contributors of demographic variables to cognitive test performance

Measure Factor Squared semi-partial correlation t-value p-value 
RAVLT Total (Adj. R2 = .26) Age .11 −3.29 .00* 
Gender .06 2.32 .02 
Education .00 0.67 .51 
WRAT-3 reading .07 2.82 .02 
RAVLT LD (Adj. R2 = .15) Age .10 −2.84 .01* 
Gender .04 1.89 .07 
Education .00 0.21 .83 
WRAT-3 reading .02 1.43 .16 
FOME (Adj. R2 = .16) Age .17 4.02 .00* 
Gender .03 −0.445 .13 
Education .01 −0.89 .39 
WRAT-3 reading .00 −0.67 .80 
TMT-A (Adj. R2 = .24) Age .07 2.86 .02 
Gender .01 −1.22 .23 
Education .01 −0.90 .37 
WRAT-3 reading .11 −3.48 .00* 
TMT-B (Adj. R2 = .34) Age .01 1.33 .19 
Gender .03 −1.94 .06 
Education .00 −0.77 .44 
WRAT-3 reading .22 −5.32 .00* 
Stroop Word (Adj. R2 = .32) Age .02 −1.62 .11 
Gender .00 −0.01 .99 
Education .01 1.29 .20 
WRAT-3 reading .21 5.07 .00* 
Stroop Color (Adj. R2 = .19) Age .06 −2.52 .02 
Gender .01 1.24 .22 
Education .00 −0.74 .46 
WRAT-3 reading .14 3.78 .00* 
Stroop Color/Word (Adj. R2 = .16) Age .08 −2.78 .01* 
Gender .02 1.29 .20 
Education .00 0.35 .72 
WRAT-3 reading .07 2.63 .01* 
COWAT (Adj. R2 = .47) Age .02 −1.77 .05 
Gender .00 −0.40 .54 
Education .01 1.16 .28 
WRAT-3 reading .41 7.24 .00* 
Animal Naming (Adj. R2 = .22) Age .09 −3.18 .00* 
Gender .00 −0.17 .86 
Education .01 0.91 .37 
WRAT-3 reading .11 3.44 .00* 
Digit Span (Adj. R2 = .30) Age .03 −1.71 .09 
Gender .01 1.04 .30 
Education .01 0.99 .33 
WRAT-3 reading .20 34.90 .00* 
Measure Factor Squared semi-partial correlation t-value p-value 
RAVLT Total (Adj. R2 = .26) Age .11 −3.29 .00* 
Gender .06 2.32 .02 
Education .00 0.67 .51 
WRAT-3 reading .07 2.82 .02 
RAVLT LD (Adj. R2 = .15) Age .10 −2.84 .01* 
Gender .04 1.89 .07 
Education .00 0.21 .83 
WRAT-3 reading .02 1.43 .16 
FOME (Adj. R2 = .16) Age .17 4.02 .00* 
Gender .03 −0.445 .13 
Education .01 −0.89 .39 
WRAT-3 reading .00 −0.67 .80 
TMT-A (Adj. R2 = .24) Age .07 2.86 .02 
Gender .01 −1.22 .23 
Education .01 −0.90 .37 
WRAT-3 reading .11 −3.48 .00* 
TMT-B (Adj. R2 = .34) Age .01 1.33 .19 
Gender .03 −1.94 .06 
Education .00 −0.77 .44 
WRAT-3 reading .22 −5.32 .00* 
Stroop Word (Adj. R2 = .32) Age .02 −1.62 .11 
Gender .00 −0.01 .99 
Education .01 1.29 .20 
WRAT-3 reading .21 5.07 .00* 
Stroop Color (Adj. R2 = .19) Age .06 −2.52 .02 
Gender .01 1.24 .22 
Education .00 −0.74 .46 
WRAT-3 reading .14 3.78 .00* 
Stroop Color/Word (Adj. R2 = .16) Age .08 −2.78 .01* 
Gender .02 1.29 .20 
Education .00 0.35 .72 
WRAT-3 reading .07 2.63 .01* 
COWAT (Adj. R2 = .47) Age .02 −1.77 .05 
Gender .00 −0.40 .54 
Education .01 1.16 .28 
WRAT-3 reading .41 7.24 .00* 
Animal Naming (Adj. R2 = .22) Age .09 −3.18 .00* 
Gender .00 −0.17 .86 
Education .01 0.91 .37 
WRAT-3 reading .11 3.44 .00* 
Digit Span (Adj. R2 = .30) Age .03 −1.71 .09 
Gender .01 1.04 .30 
Education .01 0.99 .33 
WRAT-3 reading .20 34.90 .00* 

Notes: WRAT-3 = Wide Range Achievement Test-3rd edition; RAVLT Total = Rey Auditory Verbal Learning Test total score; RAVLT LD = RAVLT Long-Delay Free Recall; FOME = Fuld Object Memory Evaluation; TMT-A = Trail Making Test, Part A; TMT-B = Trail Making Test, Part B; COWAT = Controlled Oral Word Association Test.

*p < .01.

Results

Initial analyses examining relationships among both demographic variables and WRAT-3 revealed significant bivariate relationships only between education and WRAT-3 reading (r = .43; p = .00). In Block 1 of the hierarchical regression analyses, education was significantly associated with TMT-B (p = .00), Digit Span (p = .00), Animal Naming (p = .01), COWAT (p = .00), and Stroop Word (p = .00), and age was associated with RAVLT Total (p = .00), RAVLT Long-Delay Recall (p = .00), Animal Naming (p = .01), FOME (p = .00), and Stroop Color/Word (p = .01) at the p < .01 criterion.

To test our main hypothesis that reading ability accounts for additional variance beyond age and education, WRAT-3 reading was entered in Block 2 of the hierarchical regression. WRAT-3 reading significantly predicted scores on TMT-A (p = .00), TMT-B (p = .00), Digit Span (p = .00), Stroop Color (p = .00), Stroop Word (p = .00), Stroop Color/Word (p = .01), Animal Naming (p = .00), and COWAT (p = .00), but did not significantly contribute to performances on measures of memory including RAVLT Total (p = .02), RAVLT Long-Delay Free Recall (p = .16), and the FOME (p = .80). Semi-partial correlations indicate that WRAT-3 reading accounted for between 7% and 41% of variance in neuropsychological tests. Squared semi-partial correlations and Adj. R2 values for Block 2 are reported in Table 3.

Mean scores stratified by WRAT-3 reading median split (i.e., WRAT-3 reading raw score of 42) for measures in which WRAT-3 reading was a significant predictor and effect sizes (Cohen's d) are reported in Table 4. Age was significantly associated with performance on two measures (Animal Naming and Stroop Color/Word); however, due to the sample size, stratification on more than one variable resulted in inappropriately small cell sizes.

Table 4.

Descriptive statistics of sample

Measure WRAT-3 ≤ 42
 
WRAT-3 > 42
 
Effect Size (Cohen's d
 N Mean (SDN Mean (SD 
TMT-A 42 66.21 (25.73) 44 48.18 (20.32) 0.77 
TMT-B 42 209.60 (67.78) 44 139.75 (64.21) 1.06 
Stroop Word 42 69.67 (13.28) 44 85.86 (19.35) 0.98 
Stroop Color 42 48.98 (11.19) 44 58.59 (15.97) 0.70 
Stroop C/W 42 20.71 (7.26) 44 28.16 (10.32) 0.83 
COWAT 40 22.30 (8.21) 41 35.76 (10.74) 1.41 
Animal Naming 42 12.45 (3.69) 44 14.93 (4.75) 0.58 
Digits Forward 42 7.81 (1.38) 44 9.41 (1.83) 0.99 
Digits Backward 42 4.21 (1.63) 44 5.89 (1.97) 0.93 
Digits Total 42 12.02 (2.38) 44 15.16 (3.27) 1.10 
Measure WRAT-3 ≤ 42
 
WRAT-3 > 42
 
Effect Size (Cohen's d
 N Mean (SDN Mean (SD 
TMT-A 42 66.21 (25.73) 44 48.18 (20.32) 0.77 
TMT-B 42 209.60 (67.78) 44 139.75 (64.21) 1.06 
Stroop Word 42 69.67 (13.28) 44 85.86 (19.35) 0.98 
Stroop Color 42 48.98 (11.19) 44 58.59 (15.97) 0.70 
Stroop C/W 42 20.71 (7.26) 44 28.16 (10.32) 0.83 
COWAT 40 22.30 (8.21) 41 35.76 (10.74) 1.41 
Animal Naming 42 12.45 (3.69) 44 14.93 (4.75) 0.58 
Digits Forward 42 7.81 (1.38) 44 9.41 (1.83) 0.99 
Digits Backward 42 4.21 (1.63) 44 5.89 (1.97) 0.93 
Digits Total 42 12.02 (2.38) 44 15.16 (3.27) 1.10 

Notes: WRAT-3 = Wide Range Achievement Test-3rd edition; TMT-A = Trail Making Test, Part A; TMT-B = Trail Making Test, Part B; Stroop C/W = Stroop Color/Word; COWAT = Controlled Oral Word Association Test.

Discussion

Partially confirming our hypothesis, results of this study revealed that after controlling for age, gender, and education, reading ability accounts for a significant proportion of variance on neuropsychological measures of executive function, attention, working memory, and verbal fluency in our sample of African American older adults. In contrast, measures of memory (RAVLT and FOME) were not related to either reading ability or education, but were significantly associated with age. When WRAT-3 reading was entered into a hierarchical regression analysis, the relationship between education and raw scores on five neuropsychological measures (TMT-B, Stroop Word, Animal Naming, COWAT, and Digit Span) became non-significant, whereas WRAT-3 reading was a significant predictor of performance on each of these measures. Additionally, reading ability was significantly associated with performances on TMT-A, Stroop Color/Word, and Stroop Word, though education was not. Impressively, examination of squared semi-partial correlations indicates that reading ability accounted for 7%–41% of the variance in raw test scores and effect size analyses revealed medium to large effects (Cohen, 1992) when comparing mean scores on neuropsychological measures for groups with low versus high scores on the WRAT-3.

A wide range of variables influences African American elders' performance on neuropsychological measures; however, normative data are generally stratified only by age, education, and, less frequently, race. Previous studies have demonstrated that years of education are often not an accurate proxy for actual educational experiences, putting African American adults at risk for misclassification, and that reading ability may be a better indicator of educational quality among African American adults (Dotson et al., 2008; Manly et al., 2002; Schafer Johnson et al., 2006.). This study sought to provide further evidence of the contribution of reading ability to test performance across a broad range of neuropsychological measures using a community-dwelling sample of African American elders.

The lack of relationship between performances on measures of memory and education or reading ability is counter to previously published work (Dotson et al., 2008; Morgan et al., 2008). The current findings are, however, partially consistent with Wall, Deshpande, MacNeill, and Lichtenberg (1998)  who found that Logical Memory scores from the Wechsler Memory Scale-Revised in a sample of urban African Americans were significantly related to reading levels, whereas scores on the FOME were not. In the current study, there was a trend (p = .02) for RAVLT Total score to be related to reading levels but even trend-level significance was not found for the FOME. The difference in the nature of the stimuli and their frequency of use (i.e., whether language is to be recalled vs. common objects) may account for the consistent findings of significant or near significant relationships between verbal memory tasks and reading ability versus the FOME. However, this is a finding that should be further investigated.

When interpreting these results, it may be argued that reading ability would be expected to contribute to measures that involve verbal skills. However, reading ability was also related to measures that weigh less heavily on verbal skills, such as the TMT-B and Digit Span, and was not related to test performance on measures that require components of verbal skills such as the RAVLT. Additionally, previous studies have reported that the WRAT-3 reading test accounted for significant variance in non-verbal tasks such as Colored Progressive Matrices (Schafer Johnson et al., 2006) and the Card Rotation Test (Dotson et al., 2008) in samples of African American adults.

There are several clinical implications to these findings. Results of this study as well as several prior studies indicate that reading ability is not congruent with reported years of education among African American elders. As such, judging an African American patient's performance on years of education alone may result in misclassification, especially on measures that are highly related to reading ability. As neuropsychologists become increasingly aware of the influence of educational experiences on test performance, providing normative data for some measures stratified in part by reading ability may improve diagnostic accuracy. Currently, studies investigating whether consideration of reading ability improves diagnostic accuracy are limited. Lucas and colleagues (2005) reported that controlling for reading ability in addition to age and education in the MOAANS sample only modestly improved negative predictive power and decreased positive predictive power in differentiation of normal African American elders from those with dementia. The authors indicate that these findings were not expected and posited that the findings may be explained partially by the lack of heterogeneity in the sample's educational background. As such, they assert that the “variability of reading scores within this ‘restricted’ sample may represent differences in true cognitive ability or cognitive reserve” rather than differences in educational quality. Further work is needed to identify whether there is diagnostic utility of controlling for reading ability in groups of African Americans with varying educational backgrounds.

Although WRAT-3 Reading is a screening tool of reading ability and does not replace a more in-depth evaluation or clinical interview regarding educational achievement and experiences, it is brief and offers clinicians a quick method by which to assess the literacy level. This measure could be easily used by researchers in studies for norm development as well as clinicians performing neuropsychological evaluations. One limitation of this approach is that as educational level increases beyond high school, educational quality becomes a less robust covariate (Ostrosky-Solis, Ardila, Rosselli, Lopez-Arango, & Uriel-Mendoza, 1998). Though reading ability accounted for variance in our sample beyond age and education, this should be examined in samples with greater mean levels of education. It may be that for those individuals who obtained advanced degrees or had educational experiences of high quality, years of education alone may be an accurate predictor of test performance. Additionally, for some clinical questions, comparing the patient to his or her real-world peers or using a specific cut-point may be best clinical practice.

Additionally, though a comparison across racial groups was not conducted as a part of this study, we do not believe that reading ability accounts fully for test performance discrepancies between African American and Caucasian older adults. There are certainly many other factors that contribute to differences in test performance including disparities in health status (Morgan et al., 2008), preferred language style (Manly et al., 1998), and cultural equivalency characteristics of tests.

There are several limitations to this study. First, data for health and psychological conditions were based on self-report. Though self-report is commonly used in many studies, it may be a weakness when gathering health-related information. This method is more likely to yield a conservative estimate of disease burden. It should also be noted that our sample size was limited to just 86 participants and was predominantly women (nearly 92%). Due to these factors, this study should be considered preliminary and our results may apply to only a select population of individuals matching the characteristics of our sample. Future studies using larger and more heterogenous samples of African American older adults would validate these findings. Finally, because the goal of this study was to examine community-dwelling African American older adults, participants were not excluded based on their performances on neuropsychological measures. The average educational attainment in this sample was 12 years; however, education ranged from 8 to 18 years of education. As demonstrated in previous work, namely Crum, Anthony, Bassett, and Folstein (1993), greater variability in scores on neurocognitive measures have been found in individuals with low versus high educational levels. As such, the range of scores on neuropsychological measures in this study reflects the lower educational attainment of some participants.

Taken together, this study has important implications for older African American adults. Mitrushina, Boone, Razani, and D'Elia (2005) state, “Issues of ethnic diversity pose difficult and serious challenges for the field of neuropsychology, particularly as clinicians are more frequently being requested to assess functioning in patients from varied ethnic, cultural, socioeconomic and linguistic backgrounds.” A continuing goal for neuropsychology is to improve the diagnostic accuracy among minority populations. Essential to this is an understanding of contributors to neuropsychological test performance across various populations. This study underscores the need to gather normative data that can be used for patients from various minority groups and suggests that stratification based on education alone is not appropriate for some older adults. Further work is needed to better understand the relationships between reading ability, education, and neuropsychological test performance in all minority groups.

Funding

This work was supported by the NIH/NIA Aging and Urban Health Pre-doctoral Research Training Grant (T32 AG00275-06).

Conflict of Interest

None declared.

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