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Updated: Feb 23, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Increasing Burden of Complex Multimorbidity Across Gradients of Cognitive Impairment
Siran M Koroukian1,2, Nicholas K Schiltz1,2, David F Warner3
11 Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
Introduction:
This study evaluates the burden of multimorbidity (MM) across gradients of cognitive impairment (CI).
Methods:
Using data from the 2010 Health and Retirement Study, we identified individuals with no CI, mild CI, and moderate/severe CI. In addition, we adopted an expansive definition of complex MM by accounting for the occurrence and co-occurrence of chronic conditions, functional limitations, and geriatric syndromes.
Results:
In a sample of 18 913 participants (weighted n = 87.5 million), 1.93% and 1.84% presented with mild and moderate/severe CI, respectively. The prevalence of most conditions constituting complex MM increased markedly across the spectrum of CI. Further, the percentage of individuals presenting with 10 or more conditions was 19.9%, 39.3%, and 71.3% among those with no CI, mild CI, and moderate/severe CI, respectively.
Discussion:
Greater CI is strongly associated with increased burden of complex MM. Detailed characterization of MM across CI gradients will help identify opportunities for health care improvement.
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