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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Prevalence of dementia and mild cognitive impairment in the Mexican population: evidence from Mex-Cog, a harmonized
Silvia Mejia-Arango1, Miguel Arce Rentería2, Joseph L Saenz3
1Institute of Neuroscience, School of Medicine, University of Texas Rio Grande Valley, Harlingen, Texas, United States.
Objectives:
This study aimed to estimate the prevalence of dementia and mild cognitive impairment (MCI) in Mexico and establish sociodemographic, contextual, and health predictors leveraging the first wave of the Mexican Cognitive Aging Ancillary Study (Mex-Cog), a subsample of the Mexican Health and Aging Study.
Methods:
Data came from the 2016 wave of Mex-Cog. The analytic sample included 1,629 participants aged 60 and older who completed a comprehensive cognitive battery and informant questionnaire. We employed a robust normative approach to classify cognitive impairment and developed an algorithmic classification of MCI/dementia based on standard research criteria. Prevalence rates used nationally-representative sampling weights.
Results:
We estimated the prevalence of dementia at 6.7% [95% confidence interval (CI): 5.4-8.0] and MCI at 10% (95% CI: 8.7-11.7). Older age, lower education, living in rural areas, having a history of stroke, and heart attack were associated with higher odds of dementia. Mild cognitive impairment was associated with older age, female sex, lower education, rural residence, and lack of health insurance.
Discussion:
Leveraging the nationally-representative and comprehensive cognitive data in Mex-Cog, an algorithm-derived classification of dementia and MCI provided the prevalence of dementia and MCI in Mexico. Besides the association with cerebrovascular and cardiovascular disease, the effect of education, rural residence, and lack of health insurance suggests an unequal burden of MCI and dementia among the aging Mexican population.
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