Prevalence of mild cognitive impairment by multiple classifications: The Monongahela-Youghiogheny Healthy Aging Team (MYHAT) project
- PMID: 20220597
- PMCID: PMC2906673
- DOI: 10.1097/JGP.0b013e3181cdee4f
Prevalence of mild cognitive impairment by multiple classifications: The Monongahela-Youghiogheny Healthy Aging Team (MYHAT) project
Abstract
Objectives: To estimate and compare the frequency and prevalence of mild cognitive impairment (MCI) and related entities using different classification approaches at the population level.
Design: Cross-sectional epidemiologic study of population-based cohort recruited by age-stratified random sampling from electoral rolls.
Setting: Small-town communities in western Pennsylvania.
Participants: Of 2,036 individuals aged 65 years and older, 1,982 participants with normal or mildly impaired cognition (age-education-corrected Mini-Mental State scores ≥ 21).
Measurements: Demographics, neuropsychological assessment expressed as cognitive domains, functional ability, and subjective reports of cognitive difficulties; based on these measurements, operational criteria for the Clinical Dementia Rating (CDR) scale, the 1999 criteria for amnestic MCI, the 2004 Expanded criteria for MCI, and new, purely cognitive criteria for MCI.
Results: A CDR rating of 0.5 (uncertain/very mild dementia) was obtained by 27.6% of participants, whereas 1.2% had CDR ≥ 1 (mild or moderate dementia). Among those with CDR <1, 2.27% had amnestic MCI and 17.66% had expanded MCI, whereas 35.17% had MCI by purely cognitive classification. Isolated executive function impairment was the least common, whereas impairment in multiple domains including executive function was the most common. Prevalence estimates weighted against the U.S. Census are also provided.
Conclusions: The manner in which criteria for MCI are operationalized determines the proportion of individuals who are thus classified and the degree of overlap with other criteria. Prospective follow-up is needed to determine progression from MCI to dementia and thus empirically develop improved MCI criteria with good predictive value.
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