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Updated: Sep 19, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
The impact of census-tract level mortgage discrimination on cognitive function: accounting for measurement
Yueying Hu1, Michael R Elliott1,2, Helen C S Meier2
1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, United States.
Abstract:
Racial disparities in cognitive health reflect entrenched structural inequalities. This study investigates the association between census-tract level mortgage discrimination, operationalized as the mortgage density index ratio (MDIR), and cognitive outcomes among racially diverse older adults. Using data from the Michigan Cognitive Aging Project, a cohort of 644 participants was analyzed across 6 cognitive measures. Hypersegregation, driven in part by historical redlining and contemporary racial discrimination in housing and lending, introduces instability in ratio indices like MDIR. To address this, we employed a joint modeling approach that simultaneously estimates cognitive outcomes and latent mortgage rates for Black and White households. This method identified a significant association between MDIR and processing speed only among non-Hispanic Black participants, with a 1-unit MDIR increase corresponding to a 0.48 standard deviation improvement in processing speed (95% confidence interval, 0.05-0.93) while controlling for individual demographics. Contrarily, traditional regression methods failed to detect such effects. Simulations further demonstrated the superiority of joint modeling in managing measurement instability, showing notably lower bias and greater robustness in small- to moderate-sized census tracts. These findings underscore the importance of advanced statistical methods in quantifying structural racism and highlight the disproportionate effects of mortgage discrimination on cognitive outcomes among Black adults.
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