Age and education effects and norms on a cognitive test battery from a population-based cohort: the Monongahela-Youghiogheny Healthy Aging Team.
OBJECTIVE
Performance on cognitive tests can be affected by age, education, and also selection bias. We examined the distribution of scores on several cognitive screening tests by age and educational levels in a population-based cohort.


METHODS
An age-stratified random sample of individuals aged 65+ years was drawn from the electoral rolls of an urban US community. Those obtaining age and education-corrected scores > or = 21/30 on the Mini-Mental State Examination (MMSE) were designated as cognitively normal or only mildly impaired, and underwent a full assessment including a battery of neuropsychological tests. Participants were also rated on the Clinical Dementia Rating (CDR) scale. The distribution of neuropsychological test scores within demographic strata, among those receiving a CDR of 0 (no dementia), are reported here as cognitive test norms. After combining individual test scores into cognitive domain composite scores, multiple linear regression models were used to examine associations of cognitive test performance with age and education.


RESULTS
In this cognitively normal sample of older adults, younger age and higher education were associated with better performance in all cognitive domains. Age and education together explained 22% of the variation of memory, and less of executive function, language, attention, and visuospatial function.


CONCLUSIONS
Older age and lesser education are differentially associated with worse neuropsychological test performance in cognitively normal older adult representatives of the community at large. The distribution of scores in these participants can serve as population-based norms for these tests, and can be especially useful to clinicians and researchers assessing older adults outside specialty clinic settings.
Ganguli
Mary
M
Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA. gangulim@upmc.edu
Snitz
Beth E
BE
Lee
Ching-Wen
CW
Vanderbilt
Joni
J
Saxton
Judith A
JA
Chang
Chung-Chou H
CC
D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D003072:Cognition Disorders / Q000175:diagnosis* / Q000453:epidemiology; D015331:Cohort Studies; D004522:Educational Status; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D009483:Neuropsychological Tests / Q000706:statistics & numerical data; D010414:Pennsylvania / Q000453:epidemiology