Abstract
Dementia is a complex, progressive syndrome characterized by cognitive decline and disability. Gold-standard dementia diagnosis requires several hours of cognitive and clinical assessment and review by a panel of clinicians and is infeasible in large population-based cohort studies. Alternatively, algorithmic dementia classification methods, which use models that take measures of cognition and functional limitations into account or cognitive and functional limitation score cutoffs, have been developed to predict dementia status for participants in large studies. Developing accurate dementia classification algorithms is crucial for high-quality studies of the distribution and determinants of dementia. The article by Nichols et al. (Am J Epidemiol. XXXX;XXX(XX):XXXX–XXXX) assesses differences in associations of measures of cognition and functional limitations with prevalent versus incident dementia and discusses implications for algorithmic demen tia classification in research studies. This work highlights important opportunities for tailoring measures of cognition and functional limitations to study goals by selecting optimal measures and developing and validating algorithms specific to study needs. Combining efficient, high-quality assessments of cognition and functional limitations with innovative study designs will facilitate collection of higher quality measures in larger samples and support future development of accurate dementia classifications, ultimately leading to more impactful epidemiologic studies.
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