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Updated: May 28, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Population norms for the mini-mental state examination in Ethiopia
Seid Ali Gugssa1, Gail Davey, Atalay Alem Ejigu
1Department of Neurology, School of Medicine, College of Health Sciences, Addis Ababa University, Ethiopia.
Background:
Mini-mental state Exam (MMSE) is the most frequently used instrument to test cognitive function in Ethiopia. But there is little or no attempt to interpret the MMSE score in accordance to age and educational level of the individual. The cut off scores developed and used elsewhere may result in falsely high number of screen positives in our setup.
Objective:
To determine the percentile distribution of MMSE scores and to examine the association between the MMSE score and demographic variables of interest.
Methods:
Cross sectional survey was made in household inhabitants selected by systematic random sampling at Butajira town, Ethiopia. The MMSE was administered to 1560 subjects through a structured questionnaire. Chi-square test and Spearman's correlation coefficient were used to see the association between the MMSE score and demographic variables of interest. Multinomial logistic regression analysis was done to determine variables that were independent predictors of the MMSE score. The weighted percentile distribution was calculated for those variables that were found to be independent predictors of MMSE sore. The percentile distribution was further customized by age and educational level
Results:
The MMSE score showed association with educational level, gender, proficiency in the test language and marital status but only educational status, gender and proficiency in the test language were the independent predictors ofMMSE score on the regression model.
Conclusion:
In the Ethiopian setup considerations should be given to the individuals' educational level, gender and proficiency in the test language before interpreting MMSE Score.
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