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Updated: Sep 15, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Prevalence and Determinants of Cognitive Impairment: A Study from Punjab, North India
Neha Yadav1, Vineet Chaudhary, Kallur Nava Saraswathy
1Department of Anthropology, University of Delhi, Delhi, India.
Background:
Due to the rapid aging of the global population, the prevalence of cognitive impairment (CI) has increased several-fold over the years. Studies have revealed that sociodemographic and lifestyle factors play a crucial role in the etiology of CI. While most studies have focused on urban populations, there is limited knowledge about the prevalence and determinants of CI in rural areas.
Objectives:
This study was carried out to estimate the prevalence and determinants of CI among rural adults in Mansa, Punjab, North India.
Methods:
A total of 956 rural adults of either sex were screened for CI using the Mini-Mental State Examination (MMSE). The prevalence of CI was calculated using the standard cutoff (MMSE score <24) as well as cutoffs derived from age and education-stratified norms. Mean, standard deviation, median, and percentiles of MMSE score were used to determine age and education-stratified norms for CI. The association of CI with sociodemographic and lifestyle variables was examined by adjusted binary logistic regression analysis.
Results:
The prevalence (95% confidence interval) of CI using age and education-stratified norms was found to be 8.8% (7.1%-10.8%). Adjusted logistic regression models revealed older age and illiteracy to be associated with an increased risk for CI. Additionally, unemployment and smoking were also identified as risk factors for CI.
Conclusion:
The present study suggests a substantial prevalence rate of CI in the studied population. Older age, illiteracy, smoking, and unemployment were independently associated with CI risk.
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