Normative data for 8 neuropsychological tests in older blacks and whites from the atherosclerosis risk in communities
Andrea L C Schneider1, Albert Richey Sharrett, Rebecca F Gottesman
1*Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health †Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD ‡Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston-Salem, NC §Department of Biostatistics, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC ∥Department of Neurology, Mayo Clinic, Rochester, MN ¶Department of Medicine, University of Mississippi Medical Center, Jackson, MS.
Abstract:
Accurate assessment of cognitive impairment requires comparison of cognitive performance in individuals to performance in a comparable healthy normative population. Few prior studies have included a large number of black participants and few have excluded participants from the normative sample with subclinical/latent neurological disease or dementia. This study provides age, race, and education-specific normative data for 8 cognitive tests derived from 320 black and 392 white participants aged 61 to 82 years (mean 71 y) in the Atherosclerosis Risk in Communities (ARIC) study without clinical or subclinical/latent neurological disease. Normative data are provided for the Delayed Word Recall Test, Logical Memory Parts I and II, the Word Fluency Test, Animal Naming, the Trail Making Test Parts A and B and the Digit Symbol Substitution Test. Age, race, and education-specific mean and -1.5 SD scores are given in tabular form and graphically, as well as regression-based equations to derive adjusted score cut-points. These robust normative data should enhance comparison across studies of cognitive aging, where these measures are widely used, and improve interpretation of performance on these tests for the diagnosis of cognitive impairment not only within the ARIC cohort, but also among older blacks and whites with similar demographics.
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