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J Clin Exp Neuropsychol. Author manuscript; available in PMC 2012 Aug 1.

Published in concluding edited form every bit:

PMCID: PMC3154384

NIHMSID: NIHMS276951

Demographically Corrected Norms for African Americans and Caucasians on the Hopkins Exact Learning Exam-Revised, Brief Visuospatial Memory Test-Revised, Stroop Color and Word Test, and Wisconsin Card Sorting Test 64-Card Version

Marc A. Norman,¹ David J. Moore,¹ Michael Taylor,^{1, 3} Donald Franklin, Jr.,¹ Lucette Cysique,^{1, 2} Chris Ake,¹ Deborah Lazarretto,^ane Florin Vaida,^ane Robert K. Heaton,^ane and the HNRC Grouping

Marc A. Norman

¹ Academy of California, San Diego, Department of Psychiatry

David J. Moore

^ane Academy of California, San Diego, Department of Psychiatry

Michael Taylor

¹ University of California, San Diego, Department of Psychiatry

³ San Diego Country Academy, Department of Psychology

Donald Franklin, Jr.

¹ University of California, San Diego, Section of Psychiatry

Lucette Cysique

¹ University of California, San Diego, Department of Psychiatry

² University of New S Wales, Encephalon Sciences, Australia

Chris Ake

¹ University of California, San Diego, Department of Psychiatry

Deborah Lazarretto

¹ Academy of California, San Diego, Department of Psychiatry

Florin Vaida

¹ University of California, San Diego, Department of Psychiatry

Robert K. Heaton

^ane University of California, San Diego, Department of Psychiatry

Abstruse

Memory and executive operation are ii of import components of clinical neuropsychological (NP) practice and research. Multiple demographic factors are known to affect performance differentially on nearly NP tests, but adequate normative corrections, inclusive of race/ethnicity, are not available for many widely used instruments. This written report compared demographic contributions for widely used tests of exact and visual learning and retentivity (Brief Visual Memory Test-Revised, Hopkins Verbal Memory Test-Revised), and executive functioning (Stroop Colour and Discussion Exam, Wisconsin Card Sorting Test-64) in groups of salubrious Caucasians (northward = 143) and African-Americans (northward = 103). Demographic factors of age, education, gender, and race/ethnicity were found to be significant factors on some indices of all iv tests. The magnitude of demographic contributions (especially historic period) was greater for African-Americans than Caucasians on most measures. New, demographically corrected T-score formulas were calculated for each race/ethnicity. The rates of NP impairment using previously published normative standards significantly overestimated NP harm in African-Americans. Utilizing the new demographic corrections developed and presented herein, NP harm rates were comparable between the two race/ethnicities and unrelated to the other demographic characteristics (age, education, gender) in either race/ethnicity group. Findings back up the need to consider extended demographic contributions to neuropsychological exam performance in clinical and research settings.

Introduction

Learning, memory and executive functioning are core components of comprehensive neuropsychological (NP) assessment batteries. Accurate classification of NP impairment in these domains is especially of import for the differential diagnosis of many neurologic conditions. Unfortunately, some of the about widely used neuropsychological tests do not have bachelor norms that are corrected for race/ethnicity differences, despite research showing that differential ethnicity backgrounds affect NP operation, along with other demographic variables such as age, education, and gender (Heaton, Miller, Taylor, & Grant, 2004). Inadequate normative sampling and standards may lead to neuropsychological misclassification and may particularly contribute to misdiagnosis of African-Americans.

Researchers have begun examining demographic influences on learning and memory performance in an effort to produce normative standards among minority groups such as in African-Americans (e.g., California Verbal Learning Test -CVLT)[Norman et al., 2000]; and the Third Edition of the Wechsler Memory Calibration, WMS-III [Heaton, Taylor, & Manly, 2003],and in Spanish-speaking Hispanics (Hopkins Verbal Learning Examination-Revised—HVLT-Rand Brief Visuospatial Memory Examination-Revised—BVMT-R) (Cherner, et al., 2007). However, data on a wider range of neuropsychological tasks are lacking. Regardless of the domain or racial group under study, race/ethnicity typically influences scores on NP measures (Manly, Schupf, Tang, & Stern, 2005).

Several studies have demonstrated lower NP performance among African-Americans as compared to Caucasians on a broad variety of NP measures (Diehr, et al., 2003; Diehr, Heaton, Miller, & Grant, 1998; Gladsjo, et al., 1999; Heaton, et al., 2004; Heaton, et al., 2003; Norman, 2000; Rilling, et al., 2005). Importantly, information technology has been shown that these differences persists even when groups were matched for other demographic factors, including age, gender, education, and reading power to a bottom extent (Manly, et al., 2005).

Accurate nomenclature of the level of NP impairment in diverse racial groups has pragmatic clinical relevance to neuropsychologists. Without race/ethnicity-corrected scores in the clinical setting, a substantial number of normal African-Americans patients might be incorrectly classified as neuropsychologically impaired, and misdiagnosed. For instance, Norman et al. (2000) demonstrated that 46%of African-Americans were classified as NP dumb (i.eastward., NP test T-score< 40) on the California Verbal Learning Test (CVLT) Trials one–5 using the original Delis et al.(Delis, Kramer, Kaplan, & Ober, 1987) norms, which were based upon a predominantly Caucasian standardization sample. When CVLT norms were corrected for age, gender, and education, just non race,36% of African-Americans were still classified equally NP impaired. Once race was sufficiently accounted for in the equation, only 17.eight% of African Americans were classified as neuropsychologically impaired demonstrating that the new CVLT norms clearly improved the proportion of individuals scoring greater than one standard deviation below the mean. Misdiagnoses for neurodegenerative disorders or other atmospheric condition that affect encephalon functions take serious implications in terms of public health consequences also as social and healthcare consequences for the patients and their families. Accurate classification of NP-impairment amidst African Americans is equally of import in research settings for similar reasons.

Most existing normative data that are published in test manuals, lack data about race/ethnicity influences on test performance. The Hopkins Verbal Learning Test-Revised (HVLT-R) is a widely used task of exact learning and memory (Brandt & Benedict, 2001), using 12 words belonging to three semantic categories. Half dozen alternate forms facilitate reducing practice effects on repeated administrations. The standardization group's age range was from 15 to 92 (M = 59.0, SD = 18.6) and education ranged from two to 20 years (Grand = 13.4, SD = ii.ix); 79% were women. The normative sample for the HVLT-R included 1,179 adults; however, racial/ethnicity demographics were not provided. In the HVLT-R manual, stepwise multiple regression examined the effects of age, education, and gender for HVLT-R Total Call up, Delayed Recall, Percent Retained, and Recognition Discrimination. Age accounted for a considerable amount of variance, merely didactics and gender were not establish to significantly contribute to test functioning. Cherner et al. (Cherner, et al., 2007) contend that a limitation of the original norms was that the reference group was highly educated and had suboptimal representation of depression levels of education. Because of this reference group limitation, the rate of NP impairment may be erroneously elevated among lower educated persons.

The Brief Visual Retentivity Test – Revised (BVMT-R) is a short job of visual memory (Benedict, 1997). As with the HVLT-R, in that location are half-dozen different versions that let for repeat testing with reduced practice furnishings. Similar to the HVLT-R, the manual describes a standardization grouping of 588 healthy English language-speaking adults (171 college students and 471 community-dwelling participants) between the ages of eighteen and 79 (M = 38.6, SD = 18.0) and with a mean education of thirteen.4 years (SD = ane.viii). African-Americans accounted for 14.5% of the standardization sample; however, the authors did not provide information concerning whether and how race/ethnicity related to BVMT-R functioning. The BVMT-R and HVLT-R produce indices of Total Recall, Delayed Call back, Per centum Retained, and a Recognition Discrimination Alphabetize.

The Stroop Colour and Give-and-take Test consists of speeded trials of Word Reading, Color Naming, and Color-Word Interference. Numerous versions of the Stroop be, and the version used in the current study assigns a score for each trial based on the number of words read or colors named in xl-five seconds (Gilded, 1978). The normative sample mentioned in the 2002 manual (Gold & Freshwater, 2002) includes the previous normative grouping (n = 100) from the original manual (Gold, 1978) as well equally 300 additional cases collected betwixt 1977 and 1997 (Gilded & Freshwater, 2002). Age and instruction showed significant associations with Stroop scores and every bit such, the manual includes predicted scores for each trial based on these ii demographic characteristics. Gender furnishings on the Stroop accept been examined, simply have been plant to be inconsistent and confounded by sampling concerns; however, the racial characteristics of the original or full normative samples were not described.

The Wisconsin Bill of fare Sorting Exam – 64 Card Version (WCST-64) is a computerized test of executive office that requires strategic planning and the ability to use ecology feedback to shift cognitive set (Kongs, Thompson, Iverson, & Heaton, 1993). The normative sample for the WCST-64 consisted of 445 adults ages 18–89 (M = 49.83 SD = 17.92). Education ranged from 6 to 20 years (M = 14.95, SD = 2.97) and 23% of the sample was female. Unfortunately, information nigh race/ethnicity was not routinely collected and therefore was not available for analysis. The manual states that hierarchical polynomial regressions were used to examine the effects of historic period, gender, and education. Age demonstrated a meaning quadratic relationship with WSCT-64 scores and accounted for one.4% to 18.9% of the variance in scores. Didactics deemed 1.3% to 7.7% of the variance in scores after adjusting for historic period. There were no significant gender effects after bookkeeping for historic period and pedagogy.

This current written report was designed to provide improved, demographically corrected normative standards among healthy samples of African-Americans and Caucasians on the HVLT-R, BVMT-R, Stroop Color and Word Exam, and Wisconsin Card Sorting Test – 64 Carte du jour Version. The project has two specific aims: 1) To clarify the effects of demographic variables, including race/ethnicity (i.eastward., African-American and Caucasian) on test performance and classification accurateness (normal vs. aberrant), and 2) To develop normative equations that correct for all relevant demographic characteristics (age, educational activity, gender, and race/ethnicity) to provide a more than accurate classification of NP performance. We predict that Caucasian vs. African-American race/ethnicity will significantly contribute to NP performance, and that these differences volition support the assertion that exact and visual learning and memory as well as executive function measures require race/ethnicity corrections in order to correctly categorize NP impairment amongst African Americans (Manly & Echemendia, 2007).

Methods

Participants

The sample consisted of 246 salubrious individuals recruited as comparison participants (HIV uninfected controls) in a longitudinal study of HIV infected participants at the University of California, San Diego (UCSD)HIV Neurobehavioral Research Centre (HNRC). 1 hundred forty 3 participants self identified equally Caucasian and 103 cocky identified equally African-American. Trained research associates used structured interviews and administered screening questionnaires to potential participants to assess inclusion/exclusion criteria prior to written report enrollment. Exclusionary criteria for all subjects included whatsoever history of neurological disorders, current substance use disorders, and other conditions (e.g., psychiatric disorder with psychotic features, medications with CNS effects) known to affect neurocognitive operation. The UCSD Human Enquiry Protections Programme approved the protocol. Demographic information is presented in Table one. The cantankerous-sectional, stratified sample ranged in age from twenty to 65. The 2 samples (African American and Caucasian) did non differ significantly in terms of age or education, merely the Caucasian group independent a smaller proportion of females (31% vs. 50%).

Table 1A

Demographic information in the two groups (Mean, SD)

Mean (SD) [Range]	Caucasian (n=143)	African American (n=103)	p
Historic period	37.half dozen (12.3) [twenty–66]	40.6 (12.three) [20–69]	0.06
Didactics	14.ane (2.4) [8–20]	xiii.viii(two.1) [viii–19]	0.37
Sexual practice (% Female)	31%	50%	0.003

Participants were asked to self identify their own race/ethnicity and this identification was used to define the African American and Caucasian groups used in this study. Years of instruction were determined using a previously defined and standardized procedure where education level ranges from 0–20 based on number of years of schooling completed (Heaton, et al., 2004). For instance, a high school graduate receives 12 years of educational activity and a person with a bachelor's degree receives sixteen years of educational activity.

Neuropsychological Assessment

Participants completed an NP exam battery of which a subset of two memory and two executive function tests were examined for this study, considering these tests lacked race/ethnicity corrections as compared to other tests in the battery. Trained psychometrists following instructions from the respective manuals completed administration and scoring. Analyzed measures included Form A of the Hopkins Verbal Learning Exam-Revised (Brandt & Bridegroom, 2001), Display A from the Brief Visual Memory Test-Revised (Benedict, 1997), the Stroop Color-Word Interference Test (Aureate, 1978), and the Wisconsin Menu Sorting Test-64 Computer Version (Heaton, Chelune, Talley, Kay, & Curtiss, 1993). We evaluated Total Recall across three learning trials and Delayed Remember for the HVLT-R and BVMT-R. Additionally, full numbers of correct items identified with the 45-2d trials were analyzed for StroopWord Reading, Color Naming, and Color-Discussion formats. For the WCST, scores analyzed included Total Errors, Perseverative Errors, and Conceptual Level Responses.

Data Analysis

The distributions of all scores were examined. Although distributions of test raw scores were non-normal, parametric statistics were confirmed with not-parametric versions of the same statistical comparisons, and tails of distributions were similar between racial groups bold symmetry in impairment rates. Effect sizes were measured with the unbiased Cohen'southward d (Hedges & Olkin, 1985). This study was powered to detect a small-scale consequence size.

In the first step, African-American and Caucasian group scores were compared to clarify the effects of race/ethnicity on examination performance. Next, linear regression was used to examine the effects of age, education, and gender; this was done separately for African American and Caucasian groups because it was determined that they had somewhat dissimilar age effects and (to a lesser caste) education effects. Fractional regressions were then run to examine the independent contribution of historic period, education, and gender on measures in each group (Caucasian and African-American).

HVLT-R, BVMT-R, Stroop, and WCST-64 raw scores for the full subject group were converted into quantiles and mapped into the corresponding quantiles of a standard normal distribution. These scores were and then converted into normalized scaled scores with a mean of x and standard deviation of iii. We used a subset of individuals (n=208) from the present study and some additional normal subjects from other ethnicities to create demography-matched subset of individuals to generate the scaled scores every bit described beneath, only results from other ethnicities were not used in subsequent analyses that focused on African Americans and Caucasians. The rationale for calculation these additional individuals for raw score to scaled score conversions was to reflect in the scaled scores the major ethnic group limerick reported in the 2000 US census. These individuals met the same screening procedures as the study population. The resulting census matched proportions of race/ethnicity categories were 68.7% Caucasian, 13.5% African-American, thirteen.0% Hispanic, and 4.8% other race/ethnicities. Scaled score conversion tables for all variables are presented in Tables 2–4.

Table 2

Raw-to-Scaled Score conversions for the BVMT-R and HVLT-R

	BVMT-R		HVLT-R
Scaled	Full Recall Raw	Delayed Remember Raw	Total Recall Raw	Delayed Remember Raw
17	36		36
16
15	34–35		35
xiv	33	12	34
13	32		33	12
12	30–31		31–32
11	28–29	xi	30	11
10	26–27		29
9	24–25	x	27–28	10
8	21–23	ix	26
7	19–20	8	24–25	ix
6	16–18	7	22–23	8
5	fourteen–15	5–half-dozen	21	7
4	10–13	4	20	5–6
3	0–9	3	xvi–19
2		0–2	0–15	4
1				0–3

Table 4

Raw-to-Scaled Score conversions for the WCST-64

Scaled	Total Errors	Perseverative Errors	Conceptual Level Responses
xviii		0–three
17	0–half-dozen		≥58
16	seven		57
15	eight		56
fourteen		4	54–55
13	9–10		53
12	11	v	51–52
11	12	6	49–50
10	13–15	vii	45–48
9	xvi–19	eight	39–44
8	twenty–22	9–x	34–38
seven	23–28	11–thirteen	28–33
6	29–32	14–fifteen	20–27
five	33–35	sixteen–xviii	16–19
four	36–39	xix–26	thirteen–15
3	40–48	26–41	6–12
2	≥49	≥42	≤5
1

In the next step, partial polynomial multiple regression was employed to develop demographically-corrected prediction equations on the Caucasian and African American samples (respective n's = 143 and 103) for each NP test scaled score using the methods outlined by Royston and Altman (Royston & Altman, 1994; also see Heaton, et al.,2004, and Cherner et al., 2007). Separate regressions were run for each race/ethnicity, and the predictors included age, education, and sexual practice. The partial polynomial method developed by Royston and Altman (1994) uses an interactive algorithm to evaluate the influence of combinations of predictors with predetermined exponents (−two, −1, −0.5, 0, 0.5, ane, 2, 3) (the coefficient of 0 stands for the natural logarithm transformation). The algorithm compares all sets of predictors using these transformations to generate the concluding optimal fit. The residuals from the optimal regression equations were converted to T-scores with a hateful of l and a standard deviation of 10. As designed, the resultant T-scores are not correlated with historic period, sex, or education for either racial group.

Results

In the first footstep, African American and Caucasian raw scores on each of the neuropsychological measures examined in this study were compared to analyze the effects of race/ethnicity. Table 5 demonstrates significant Caucasian and African American differences on all measures, such that Caucasians performed better in each instance. Table v too depicts medium to large effect sizes on most learning, memory and executive performance indices; the only exceptions were small to medium result sizes on HVLT-Delayed Think and Stroop Color Naming and Word Reading.

Table v

Neuropsychological exam operation (raw scores) in Caucasians and African-Americans (Mean, SD)

	Caucasian (n=143)	African-American (n=103)	p	Cohen'due south d
BVMT-R Total Recollect	26.five (five.9)	22.7 (6.viii)	.0003	−0.60
BVMT-R Delayed Recall	10.2 (i.7)	8.7 (2.four)	<.0001	−0.74
HVLT-R Total Recall	29.two (3.9)	26.8 (4.nine)	.0002	−0.55
HVLT-R Delayed Recall	10.4 (one.ix)	nine.4 (2.three)	.0016	−0.48
Stroop Word Reading	101.nine (14.4)	96.2 (16.9)	.007	−0.37
Stroop Color Naming	76.4 (10.8)	70.eight (13.0)	.0008	−0.47
StroopColor-Word	45.0 (ix.5)	38.2 (10.two)	<.0001	−0.69
WCST-64 Total Errors	15.vi (vii.viii)	22.1 (10.2)	<.0001	0.73
WCST-64 Perseverative Errors	7.6 (3.ix)	xi.0 (four.2)	.0002	0.63
WCST-64Conceptual Level Responses	44.3 (11.three)	35.v (14.iii)	<.0001	−0.69

For each of the 10 test scores, stepwise linear regressions were then conducted separately for each group (African American & Caucasian) to determine the proportion of variance deemed for past historic period, education, and gender (Tables half dozen & 7). None of the fractional polynomials were significant predictors.

Table half dozen

Full model R^two and partial R² for the upshot of age, instruction and gender in each grouping on memory measures.

	Caucasian (due north=143)			African American (due north=103)
	R2	Fractional R2	95% CI	R2	PartialR2	95% CI
BVMT-R Total Recall	.11**		.02, .20	.28***		.14, .42
Age		.05**	.00, .12		.21***	.07, .35
Education		.05**	.00, .12		.05**	.00, .13
Sex		.02	.00, .06		.03*	.00, .09
BVMT-R Delayed Recall	.06*		.00, .13	.29***		.15, .43
Age		.03*	.00, .08		.24***	.10, .38
Teaching		.04*	.00, .10		.03*	.00, .09
Sex activity		.00	.00, .02		.03*	.00, .09
HVLT-R Full Recall	.xix***		.07, .30	.15***		.03, .27
Age		.00	.00, .02		.07**	.00, .16
Education		.18***	.06, .29		.05*	.00, .13
Sex		.03*	.00, .08		.03*	.00, .09
HVLT-R Delayed Think	.18***		.06, .29	.fourteen***		.02, .26
Age		.01	.00, .04		.05*	.00, .13
Education		.14***	.04, .24		.08**	.00, .18
Sex		.05**	.00, .12		.01	.00, .05

Table 7

Total model Rⁱⁱ and partial R² for the effect of age, education and gender in each group on executive measures.

	Caucasian (n=143)			African-American (n=103)
	R2	Fractional Rtwo	95% CI	R2	Fractional R2	95% CI
Stroop Measures
Word Reading	.01		.00, .04	.xx***		07, .33
Age		.00	.00, .02		.12***	.00, .24
Education		.00	.00, .02		.06*	.00, .xv
Sexual activity		.00	.00, .02		.03	.00, .09
Color Naming	.01		.00, .04	.26***		.12, xl
Age		.00	.00, .02		.16***	.03,.29
Education		.01	.00, .04		.04*	.00, .11
Sex activity		.00	.00, .02		.05**	.00, .xiii
Color-Word	.15***		.04, .25	.39***		.25, .53
Age		.11***	.01, .20		.28***	.14 .42
Education		.04*	.00, .ten		.03*	.00, .09
Sex		.00	.00, .02		.10***	.00, .21
WCST-64 Measures
Total Errors	.22***		.10, .34	.19**		.06, .32
Age		.17***	.06, .28		.13**	.01, .25
Teaching		.07***	.00, .xv		.06**	.00, .xv
Sex activity		.00	.00, .02		.00	.00, .03
Perseverative Errors	.20***		.09, .31	.14**		.02, .26
Age		.17***	.06, .28		.09**	.00, .xix
Instruction		.05**	.00, .12		.05*	.00, .13
Sex		.00	.00, .02		.00	.00, .03
Conceptual Level Responses	.20***		.09, .31	.20***		.07, .33
Age		.15***	.04, .26		.thirteen***	.01, .25
Education		.07***	.00, .xv		.07**	.00, .xvi
Sex		.00	.00, .02		.00	.00, .03

Retentivity

Table 6 shows information related to the demographic influences on learning and memory performance in Caucasians and African Americans independently. When considering the partial R² results, but the African-American group showed a significant effect of age, and this was truthful for all measures (especially robust for BVMT-R measures). Total demographic effects (R²s) were college for African Americans due to greater historic period effects on the BVMT-R, whereas more comparable effects were seen for the HVLT-R. Although education was a pregnant contained predictor of memory test performance for all measures in both groups, the education furnishings on the verbal (HVLT-R) measures were especially robust for the Caucasian group. Gender effects were absent or modest for both groups on most measures, with women performing ameliorate, and there were no systematic differences for the Caucasians versus African Americans.

Executive Performance

Comparable results for Stroop and WCST-64 measures are presented in Tabular array vii. Every bit was the instance for measures of visual learning and retentiveness (BVMT-R), merely the African American groups showed very large contained effects of age on all of the Stroop indices (Word Reading, Color Naming, and Color-Discussion). Only the African American group besides showed significant gender effects on Stroop Color-Give-and-take (Interference condition) and Color Naming, with women performing faster. On the WCST-64 measures, both race/ethnicity groups demonstrated medium sized age effects (typically somewhat larger for Caucasians), and ordinarily small to medium education furnishings. Neither racial/ethnicity group showed gender effect on this test.

Normative T-Score Derivation

As described in the Methods section, partial polynomial regression analyses were conducted to derive normative scores that would right for the observed demographic effects on normal test performance. This process began with the conversion of raw scores to normalized scaled scores (mean = 10, SD = iii) on all test measures (see Tables 2–4 for these conversions).

To examine the diagnostic ("normal" versus "abnormal") classification accuracy of the new T-score conversions with more complete demographic corrections, we compared the harm rates in both samples with those using previously published normative data (Benedict, 1997; Brandt & Benedict, 2001; Gold & Freshwater, 2002; Kongs, et al., 1993) that did non correct for race/ethnicity. The formulas used to generate the results for the new T-scores are included in Appendix A. Subjects were considered impaired if their T-Score was less than forty(Heaton, et al., 2004; Taylor & Heaton, 2001).

Figure i shows the results for the African American group. When applying previously published normative corrections to this sample, 24–49% of normal individuals were classified as NP impaired depending on the test score examined. Using our newly generated normative data the impairment rates significantly improved and ranged from 13–16%. The damage rates for the African American sample with the previously published norms are significantly greater than what would be expected from the normal distribution with the selected one SD cutoff (Golden, 1978). All comparisons of impairment rates amongst African Americans using previously published normative corrections equally compared to the newly generated normative corrections were statistically significant with the exception of the HVLT-R Delayed Recall measure that approached significance (p=0.08).

Percent of normal African American sample classified as "dumb" (1 SD cutoff) past published norms versus new, demographically corrected norms.

*p≤.05

**p≤ .01

***p≤ .001

The new normative correction formulas improved the consistency of impairment rates across examination scores for the Caucasian sample as well (come across Figure ii). Impairment rates for these norms ranged from 12 to 17% equally compared to 8 to 26% using previously published normative information. The newly developed WCST-64 norms produced impairment rates more aligned with the expected impairment rates and were significantly lower than impairment rates with previously published norms on all WCST-64 indices.

Percentage of normal Caucasian sample classified every bit "impaired" (1 SD cutoff) by published norms versus new, demographically corrected norms.

*p≤ .05

**p≤ .01

***p≤ .001

Give-and-take

This study complements previous literature on demographic corrections for neuropsychological test norms by examining a broader range of memory and executive functioning measures and specifically examining the upshot of African American versus Caucasian race/ethnicity on exam performance. These findings strongly support the use of separate norms for African-American and Caucasian examinees on the tests used hither and, when combined with previously published results in the same ability domains, on learning, memory and executive functioning measures more by and large. Consistent with prior findings on the Wechsler Intelligence and Memory Scales (Heaton, et al., 2003) and expanded Halstead-Reitan Battery (Heaton, et al., 2004), we found, in our sample of 103 African-Americans and 143 Caucasians, that African-American participants obtained lower raw scores on visual and exact learning and memory and executive operation measures.

In that location are multiple background differences between African American and Caucasian adults within U.Due south. club today that may place African Americans at a disadvantage on standardized NP testing. The observed raw NP score differences may be consequent with disparities in quality of formal and informal educational experiences; however, other factors may too contribute to these discrepancies. It is considered unlikely that race has a directly causal upshot on differences in adult noesis, so race/ethnicity is viewed as a proxy for other factors, much like has been discussed about education (Manly, Byrd, Touradji, & Stern, 2004). Factors potentially contributing to raw NP score differences betwixt African American and Caucasian groups may include bookish exposure, education quality, academic resources, acculturation, socioeconomic condition, social exposure, "test wiseness", societal discrimination (Byrd, Sanchez, & Manly, 2005; Manly, et al., 2004) and lifelong experiences contributing to low group and self-expectations (Steele & Aronson, 1995).

There are few opportunities in the literature to compare our raw score results with those reported by other investigators. Whereas this study institute about a 2-point (raw score) deviation on HVLT-R Total Recall performances between Caucasians and African-Americans, Morgan et al. (Morgan, Marsiske, & Whitfield, 2008) found a iv-indicate difference and less variability. The current report demonstrated moderate to large race/ethnicity effect sizes, but the raw score differences between Caucasians and African Americans do not seem to be particularly large (east.m., an average of only ane.5 points on BVMT-R Delayed Think is associated with a medium to large issue size; run into Table 5). All the same, these differences were sufficiently robust to cause unacceptably large "impairment" classification rates in the African American sample (Figure 1).

Although concerns might be raised that the method of raced-based norming could "overcorrect" performances of neurologically impaired African Americans (making them less sensitive to affliction), this could be said too for norms that right for older historic period, lower education levels, or whatsoever demographic feature that is associated with lower test operation in normal people. In our view, the most important part of norms is to maintain an acceptable and consistent level of diagnostic specificity (accuracy in classifying normal people as normal) for people regardless of their demographic characteristics. Our data suggest that the norms presented here issue in rates of impairment that are comparable, and are within statistical expectations for a healthy population, for both our Caucasian and African American participants.

As addressed by Byrd et al. (Byrd, et al., 2005) and others, the term "race" is an arbitrary stardom and difficult to operationalize. Oft race is based on pare color and self-identification. Equally Gasquoine (2009) notes, race is a social definition rather than a scientific classification and race is not homogeneous. Devising ways to understand the influences of ethnicity and race on NP tests will get increasingly complex as rates of self-identified multiracial individuals rise.

Given the unclear relationship of "race" on knowledge, some suggest recording, quantifying, and modeling the effects of all background factors that can influence cognitive development and test performance. Gasquoine (Gasquoine, 2009) and others have advocated that an alternative approach to race/ethnicity-based norms is to estimate preexisting neuropsychological condition based on a case-by-instance basis from regular normative tables. On the other hand, Gasquoine acknowledges that there is little empirical support for this technique, and there is no agreed upon method for establishing NP status on a example-past-case basis. Furthermore, authentic retrospective drove of such complex information across the lifespan is very difficult (Byrd, et al., 2005).

Also, a subjective interpretation of cerebral arrears will nearly likely have the result of wide variations in the impairment classifications of minorities betwixt different clinical neuropsychologists. Instead, the utilise of the more full general race/ethnicity proxy (with all its shortcomings) in normative corrections should at least enhance consistency/reliability and may greatly reduce the probability of incorrectly attributing cognitive and mayhap central nervous organization abnormalities to normal African Americans.

Of course, clinical interpretation of neuropsychological information should non strictly rely upon employ of norms, simply also consider the appropriateness of available norms in relation to each person's background, including social, educational and medial history, and other factors (i.e., psychiatric, substance use, etc.). In item, diagnostic sensitivity and specificity are likely to vary when norms are applied to people whose backgrounds differ significantly from those represented in the normative sample populations (Heaton et al., 2004).

In addition, information technology is important to note that demographically corrected norms are intended to reflect the divergence betwixt electric current functioning and a best estimate of the person's expected "normal" performance (i.e., in the absence of CNS aberration). Such norms are less appropriate, at all-time, when the goal is to determine the person's absolute level of ability (east.g., in relation to requirements of specific everyday tasks and activities).

Following the derivation of split normative equations and confirming adequate normative distributions, nosotros found that our new demographically corrected formulas provided significantly improved impairment estimates. These data advise that scores that have not been corrected for race/ethnicity classify 31–32% of the African-American sample with visual learning and retentiveness harm, 25–26% as having verbal learning and memory harm, and 24–49% every bit having executive dysfunction. These percentages are essentially higher than expected values in any normative population. In contrast, when the African-American scores were corrected for race/ethnicity, the boilerplate impairment frequencies dropped to expected levels. The over interpretation of impairment with existing normative information can pb to misclassification and/or misdiagnosis of African American individuals and can have serious negative consequences for the patients and their families. Misdiagnosis and misclassification is problematic in clinical, forensic, and research applications of neuropsychology; however, few NP norms account for these demographic variables.

The nowadays study likewise demonstrated that the demographic contributions of age, instruction and gender to NP examination performances were somewhat different for African Americans as compared to Caucasians. The contribution of age tended to be stronger for African-American participants on the BVMT-R, HVLT-R, and Stroop tests, but less pronounced on the WCST-64. The current study was not designed to explore why demographic factors exhibit stronger influences among African Americans, although big age effects for African Americans every bit compared to Caucasians have been observed in other large U.S. samples and on other neuropsychological tests (Heaton et al., 2004). Because these differential effects of demographics are non well understood, they require additional careful investigation (especially taking into account age related medical atmospheric condition and associated treatments that could differ across ethnicity groups).

The electric current study is express, as are others of the aforementioned type, in terms of failing to provide insights into the factors that contribute to racial differences on these memory and executive function measures. Equally discussed earlier, Manly (1998) and others suggest that educational quality, exposure, and other factors might play a role in the poorer observed performance of African Americans on these neuropsychological tests. The amount of education may be less important than the quality of ane's instruction, every bit measured past reading scores. Dotson et al. (Dotson, Kitner-Triolo, Evans, & Zonderman, 2008) and Manly et al. (Manly, et al., 1999; Manly, Jacobs, Touradji, Minor, & Stern, 2002) establish that literacy was a ameliorate predication of cognitive scores than education. In an African-American sample, Dotson and colleagues (2008) used memory, naming, fluency, visuospatial, attention, and psychomotor scores and regressed them on sex, age, literacy, and instruction scores. They did not discover a unique contribution of education subsequently literacy was added to the model; however, this study only included African-American participants. The present study did not measure literacy, merely as with previous studies, pedagogy was found to be a significant predictor to cognitive scores. The measurement of these factors remains elusive, however, as effects of educational opportunities and importance within the cultural experience, and other potentially important factors are complex and difficult to determine retrospectively (east.g., asking an adult about parental influences and early school experiences; Byrd et al., 2005). On the other paw, current attempts at understanding these factors are starting to emerge and multifactorial models involving psychological factors, stress factors, social and cerebral factors have been proposed (Mays et al., 2007). An additional complexity is that information technology is probable that some or all of the factors influencing NP test performance accept changed over generations and continue to do so. For example, it is probable that the educational quality for thirty year-erstwhile and 60 year-quondam African Americans has been quite different (probably more than so for than for Caucasians in the U.S.).

An additional limitation in this research is the ambiguity in classifying race or ethnicity. While "race" and "ethnicity" are oftentimes interchangeably used in this expanse of research, they are not equivalent terms. Given that there are no biological race/ethnicity markers, group identification has been pragmatically based on cocky-identification – and this is the arroyo that was used on the current study. Race is more than just skin colour and there may be multiple ethnic groups within a race. Some argue that the inability to specifically identify and characterize race/ethnicity should preclude demographic corrections; nonetheless, even with this limitation, the electric current data demonstrate excessive rates of diagnostic error if clinicians use norms that are not corrected for race/ethnicity. In particular, our findings with the new T-score conversions suggest greater and more equal specificity, with regard to race/ethnicity, within the salubrious population than was achieved by the published norms. Although we accept no data concerning sensitivity of the new norms to CNS compromise, sensitivity also is likely to be more than equivalent amongst demographic groups (eastward.chiliad., (Heaton, Ryan, & Grant, 2009). Despite limitations, we believe that the current quantitative standards provide a substantial improvement for the classification of neurocognitive impairment status in self identified African-Americans.

Finally, it is important to acknowledge that our current sample size was relatively small, and we were unable to cross-validate the normative distribution with an independent sample. Nosotros recommend caution when using these normative data with individuals over age threescore or with other groups non well represented in our normative sample. At that place were relatively few individuals with less than 10 years of education enrolled in this study and therefore circumspection should be used when applying these normative corrections to persons with such depression levels of teaching. In addition, all participants in this study were from the San Diego area and participants were carefully screened to exclude anyone with neuromedical or developmental histories suggesting any increased take chances for CNS compromise. Equally such, generalizability of these results and associated normative standards to other, ostensibly normal, African American and Caucasian groups cannot be assumed. To partially address this question, we applied the demographically corrected norms in the WAIS-Iii/WMS-III/WIAT-II Scoring Assistant programme (The Psychological Corporation, 1999; Heaton Taylor and Manly, 2003) to the current samples' results on three WAIS-Three subtests (Letter of the alphabet-Number Sequencing, Digit Symbol Coding, and Symbol Search). These latter norms were based upon a large, national standardization sample from all U.South. regions, and correct for all demographic variables that were examined in the current study (age, educational activity, gender and African American versus Caucasian race/ethnicity). We reasoned that application of these norms to the current samples' WAIS-III results would provide some indication of their representativeness of the much larger national sample. Ideally, the mean (SD) T-scores would approach 50 (10) and would not differ for the two race/ethnicity groups in the study.

For Letter of the alphabet-Number Sequencing, the mean (SD) T-scores were 53.0 (9.8) for our African American Group and 51.8 (ix.iv) for our Caucasian group (p-value for grouping difference = 0.35). On the WAIS-III Processing Speed Index (which combines Digit Symbol and Symbol Search), the respective scores were 54.half dozen (10.7) for our African American group and 52.vi (9.nine) for our Caucasian grouping (p=.14). The fact that both of our race/ethnicity groups performed slightly ameliorate than the national standardization samples on these WAIS-III tests may reflect our (arguably) more stringent neuromedical screening procedures and/or slight regional differences. Also, even so, these results indicate that, relative to normal expectations for African American and Caucasians in the U.S., our race/ethnicity groups performed comparably. This suggests that our groups' findings on the retentiveness and executive function tests are unlikely to be overestimating the race/ethnicity bias in the previously published norms.

Our results for the HVLT-R and BVMT-R are limited to Form A of these measures, and future studies will focus on assessing the need for specific corrections for all the multiple forms of these measures. Additionally, it is important to assess whether or non the demographic corrections tin be validated in a clinical sample, showing equivalent results beyond the various demographic categories.

​

Table 1B

Caucasian

Education	Age Range
	<xxx	30–39	xl–49	l–59	60+
< ten	0	1	1	0	0
ten–11	iv	2	2	1	0
12	v	six	five	5	2
thirteen–15	12	4	xiii	seven	0
16	7	5	4	v	0
>16	2	6	six	half dozen	0

Tabular array 1C

African American

Educational activity	Historic period Range
	<30	30–39	40–49	l–59	lx+
< 10	0	1	0	0	0
ten–11	one	ane	4	1	1
12	6	4	6	3	i
13–15	viii	9	10	8	4
16	4	4	5	ane	1
>xvi	0	3	one	2	2

Table 3

Raw-to-Scaled Score conversions for the Stroop Colour and Word Examination

	Stroop Colour and Discussion Exam
Scaled	Give-and-take Reading Raw	Color Naming Raw	Color-Give-and-take Raw
18	≥145	≥107
17	134–144	100–106	≥65
xvi	128–133	97–99	63–64
15	123–127	93–96	59–62
xiv	118–122	89–92	56–58
13	114–117	85–88	53–55
12	109–113	80–84	49–52
11	106–108	76–79	46–48
10	101–105	74–75	42–45
ix	97–100	70–73	39–41
viii	89–96	66–69	36–38
seven	83–88	62–65	32–35
6	77–82	58–61	29–31
v	71–76	49–57	25–28
4	67–lxx	43–48	22–24
3	66	forty–42	0–21
2	<66	0–39

Acknowledgments

The HIV Neurobehavioral Research Center (HNRC) is supported by Center accolade MH 62512 from NIMH.

Appendix A: Normative Formulas for Caucasians and African Americans

Caucasian T-score formulas

BVMT Total Think:

[(Total learning scaled score − (0.2589 ∗ (edu − fourteen.eleven) + (−0.0515) ∗ (age − 37.62) + 0.9276 ∗ sex activity + 10.0712))/two.8912] ∗ 10 + fifty

BVMT Delayed Recall

[(Delayed think scaled score − (0.2084 ∗ (edu − 14.11) + (−0.0286) ∗ (age − 37.62) +  + 10.3007))/two.6989] ∗ 10 + 50

HVLT Total Recall

[(Total learning scaled score − (0.5225 ∗ (edu − 14.eleven) + i.0035 ∗ gender + 10.1738))/two.5927] ∗ ten + l

HVLT Delayed Recall

[(Delayed call back scaled score − (0.5414 ∗ (edu − 14.11) + 1.7324 ∗ genter + 9.9285))/2.6989] ∗ ten + 50

Stroop Word Reading

[(Word reading scaled score − (0.0819 ∗ (edu − 13.92) + 0.0038 ∗ (age − 36.17) + (−0.4022) ∗ gender + 10.2102)−(−0.00003))/2.9435] ∗ 10 + 50

Stroop Colour Naming

[(Color naming scaled score − (0.0941 ∗ (edu − xiii.92) + 10.4444))/2.8101] ∗ ten + 50

Stroop Color-Give-and-take

[(Color − word scaled score − (0.2479 ∗ (edu − xiii.92) + (−0.0828) ∗ (age − 36.22) +  + 10.3968))/ii.6002] ∗ ten + 50

Stroop Interference

[(Interference scaled score − (0.2346 ∗ (edu − 13.92) + (−0.0762) ∗ (age − 36.22) + 0.7465 ∗ sexual practice + 9.9952))/2.6342] ∗ x + 50

WCST-64 Total Errors

[(Total Errors Scaled score − (0.3187 ∗ (edu − 14.13) + (−0.01) ∗ (historic period − 37.37) + 0.1608 ∗ gender + ten.4049)−(−0.0017))/2.674] ∗ 10 + 50

WCST-64 Perseverative Errors

[(Perseverative Errors Scaled Score − (0.2357 ∗ (edu − 14.thirteen) + (−0.0941) ∗ (age − 37.37) + 0.0341 ∗ gender + 10.33)−(−0.0012))/2.5506] ∗ ten + fifty

WCST-64 Conceptual Level Responses

[(Conceptual Level Responses scaled score − (0.3223 ∗ (edu − 14.13) + (−0.0941) ∗ (age − 37.37) + 0.0577 ∗ gender + 10.4292)−(−0.0016))/two.568] ∗ 10 + l

African-American T-score formulas

BVMT Total Remember:

[(Total learning scaled score − (0.2834 ∗ (edu − 13.86) + (−0.1125) ∗ (age − 40.63) + ane.0394 ∗ sex + 8.0679))/2.5701] ∗ 10 + l

BVMT Delayed Recall

[(Delayed recall scaled score − (0.2267 ∗ (edu − 13.86) + (−0.12.62) ∗ (age − 40.63) + 0.8593 ∗ sex + 7.691))/2.5197] ∗ x + 50

HVLT Total Think

[(Total learning scaled score − (0.2917 ∗ (edu − thirteen.86) + (−0.0644) ∗ (age − 40.63) + one.1462 ∗ sex + 8.3063))/2.8333] ∗ 10 + 50

HVLT Delayed Recall

[(Delayed recall scaled score − (0.3986 ∗ (edu − xiii.86) + (−0.0733) ∗ (historic period − xl.63) + .9145 ∗ sexual practice + viii.2753))/3.1354] ∗ 10 + fifty

Stroop Word Reading

[(Give-and-take reading scaled score − (0.3557 ∗ (edu − 13.92) + (−0.0866 ∗ (age − 40.67) + 1.2315 ∗ sex + viii.3263)−(0.00095))/2.8127] ∗ 10 + fifty

Stroop Color Naming

[(Color namimg scaled score − (0.3102 ∗ (edu − thirteen.92) + (−0.1006) ∗ (age − 40.67) + i.4915 ∗ sex + 8.2672))/2.6643] ∗ 10 + 50

Stroop Colour-Word.

[(Color − Give-and-take scaled score − (0.2363 ∗ (edu − 13.94) + (−0.1219) ∗ (age − 40.67) + 1.9479 ∗ sex + 7.449))/2.2658] ∗ 10 + fifty

Stroop Interference.

[(Interference scaled score − ((−0.0303) ∗ (age − forty.67) + (1.4688 ∗ sex activity + 8.1343))/2.663] ∗ ten + fifty

WCST-64 Total Errors

[(Total Errors Scaled score − (0.3321 ∗ (edu − thirteen.97) + (−0.0838) ∗ (historic period − forty.7) + 0.3215 ∗ sex + viii.1621)−(−0.0006))/2.7911] ∗ 10 + 50

WCST-64 Perseverative Errors

[(Perseverative Errors Scaled Score − (0.3599 ∗ (edu − 13.97) + (−0.0776) ∗ (age − 40.seven) + (−0.1093) ∗ sexual practice + eight.5524)−(0.0006))/3.0124] ∗ ten + 50

WCST-64 Conceptual Level Responses

[(Conceptual Level Response scaled score − (0.4002 ∗ (edu − 13.97) + (−0.0874) ∗ (age − twoscore.vii) + 0.2546 ∗ gender + 8.2248)−(0.0006))/2.5887] ∗ ten + fifty

Notes

Gender

Male = 0

Female person = 1

Education

Years of didactics were determined using a previously defined and standardized procedure where pedagogy level ranges from 1–twenty based on number of years of schooling completed (Heaton, et al., 2004).

Footnotes

^*The San Diego HIV Neurobehavioral Research Center [HNRC] grouping is affiliated with the University of California, San Diego, the Naval Medial Eye, San Diego, and the Veterans Affairs San Diego Healthcare Organisation, and includes: Director: Igor Grant, M.D.; Co-Directors: J. Hampton Atkinson, Thou.D., Ronald J. Ellis, M.D., Ph.D., and J. Allen McCutchan, M.D.; Heart Managing director: Thomas D. Marcotte, Ph.D.; Jennifer Marquie-Beck, M.P.H.; Melanie Sherman; Neuromedical Component: Ronald J. Ellis, Thousand.D., Ph.D. (P.I.), J. Allen McCutchan, One thousand.D., Scott Letendre, K.D., Edmund Capparelli, Pharm.D., Rachel Schrier, Ph.D., Terry Alexander, R.North., Debra Rosario, M.P.H., Shannon LeBlanc; Neurobehavioral Component: Robert K. Heaton, Ph.D. (P.I.), Steven Paul Forest, Psy.D., Mariana Cherner, Ph.D., David J. Moore, Ph.D.; Matthew Dawson; Neuroimaging Component: Terry Jernigan, Ph.D. (P.I.), Christine Fennema-Notestine, Ph.D., Sarah 50. Archibald, Grand.A., John Hesselink, Grand.D., Jacopo Annese, Ph.D., Michael J. Taylor, Ph.D.; Neurobiology Component: Eliezer Masliah, Yard.D. (P.I.), Ian Everall, FRCPsych., FRCPath., Ph.D., Cristian Achim, Grand.D., Ph.D.; Neurovirology Component: Douglas Richman, M.D., (P.I.), David Thousand. Smith, M.D.; International Component: J. Allen McCutchan, Chiliad.D., (P.I.); Developmental Component: Ian Everall, FRCPsych., FRCPath., Ph.D. (P.I.), Stuart Lipton, M.D., Ph.D.; Participant Accrual and Retention Unit: J. Hampton Atkinson, Thousand.D. (P.I.), Rodney von Jaeger, G.P.H.; Data Direction Unit: Anthony C. Gamst, Ph.D. (P.I.), Clint Cushman (Data Systems Manager); Statistics Unit: Ian Abramson, Ph.D. (P.I.), Florin Vaida, Ph.D., Reena Deutsch, Ph.D., Tanya Wolfson, M.A.

The views expressed in this commodity are those of the authors and practice non reverberate the official policy or position of the Department of the Navy, Department of Defense force, nor the Usa Government.

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