Long-term Distributional Prediction of Cognitive Function
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This study examines the long-term effects of diverse risk factors on the distribution of cognitive function measures, paying special attention to potential heterogeneities across different levels of cognitive function scores. It employs quantile regression techniques on a 10-year panel dataset from the Korean Longitudinal Study of Aging to assess the predictability of risk factors on cognitive decline. Findings indicate that factors such as age, education level, social interactions with close friends, and health status have more pronounced effects on cognitive function at lower quantiles of the Mini-Mental State Examination (MMSE) scores than at higher quantiles. This study also reveals that social interactions with parents, spouses, or close friends significantly predict cognitive function beyond age and education level, which are established nonmodifiable risk factors. It also identifies gender-specific predictors of cognitive function, namely, parental living status, marital status, and satisfaction with health and life for men and income and handgrip strength for women. The differential impact of these risk factors on MMSE score distribution suggests that interventions tailored according to the assessed cognitive function levels could be effective in identifying the cognitive decline risk group and implementing preventive measures.
Abstract
This study examines the long-term effects of diverse risk factors on the distribution of cognitive function measures, paying special attention to potential heterogeneities across different levels of cognitive function scores. It employs quantile regression techniques on a 10-year panel dataset from the Korean Longitudinal Study of Aging to assess the predictability of risk factors on cognitive decline. Findings indicate that factors such as age, education level, social interactions with close friends, and health status have more pronounced effects on cognitive function at lower quantiles of the Mini-Mental State Examination (MMSE) scores than at higher quantiles. This study also reveals that social interactions with parents, spouses, or close friends significantly predict cognitive function beyond age and education level, which are established nonmodifiable risk factors. It also identifies gender-specific predictors of cognitive function, namely, parental living status, marital status, and satisfaction with health and life for men and income and handgrip strength for women. The differential impact of these risk factors on MMSE score distribution suggests that interventions tailored according to the assessed cognitive function levels could be effective in identifying the cognitive decline risk group and implementing preventive measures.
참고문헌 (27)
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