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Updated: Oct 19, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Examining racial disparities in the diagnosis of mild cognitive impairment
Andrew M Kiselica1, Ellen Johnson1,2, Kaleea R Lewis3,4
1Department of Health Psychology, University of Missouri, Columbia, MO, USA.
Abstract:
Black individuals are less likely to receive an accurate diagnosis of mild cognitive impairment (MCI) than their White counterparts, possibly because diagnoses are typically made by a physician, often without reference to objective neuropsychological test data. We examined racial differences in actuarial MCI diagnoses among individuals diagnosed with MCI via semi-structured clinical interview (the Clinical Dementia Rating) to examine for possible biases in the diagnostic process. Participants were drawn from the National Alzheimer's Coordinating Center Uniform Data Set and included 491 individuals self-identifying as Black and 2,818 individuals self-identifying as White. Chi-square tests were used to examine racial differences in rates of low scores for each cognitive test (domains assessed included attention, processing speed/executive functioning, memory, language, and visual skills). Next, we tested for racial differences in probability of meeting actuarial criteria for MCI by race. Compared to Black participants diagnosed with MCI via clinical interview, White individuals diagnosed with MCI via clinical interview demonstrated significantly higher rates of low demographically-adjusted z-scores on tests of memory, attention, processing speed, and verbal fluency. Furthermore, White individuals were significantly more likely to meet actuarial criteria for MCI (71.60%) than Black individuals (57.90%). Results suggest there may be bias in MCI classification based on semi-structured interview, leading to over diagnosis among Black individuals and/or under diagnosis among White individuals. Examination of neuropsychological test data and use of actuarial approaches may reduce racial disparities in the diagnosis of MCI. Nonetheless, issues related to race-based norming and differential symptom presentations complicate interpretation of results.
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