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Seoul Journal of Economics
[ Article ]
Seoul Journal of Economics - Vol. 37, No. 1, pp.75-98
ISSN: 1225-0279 (Print)
Print publication date 28 Feb 2024
Received 31 Jan 2024 Revised 16 Feb 2024 Accepted 16 Feb 2024
DOI: https://doi.org/10.22904/sje.2024.37.1.004

Long-term Distributional Prediction of Cognitive Function

Young-Joo Kim
Young-Joo Kim, Associate Professor, Department of Economics, Hongik University, 94 Wausan-ro, Mapo-gu, Seoul, 04066, Korea, Tel: 82-2-320-1759 y.j.kim@hongik.ac.kr

JEL Classification: I12, C55

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.

Keywords:

Cognitive function, Cognitive decline, Quantile regression, Prediction, Risk Factors

Acknowledgments

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2020S1A5A2A03046422).

References

  • Angrisani, M., and Lee, J., “Cognitive Decline and Household Financial Decisions at Older Ages.” The Journal of the Economics of Ageing 13(2019): 86-101. [https://doi.org/10.1016/j.jeoa.2018.03.003]
  • Atalay, K., Barrett, G. F., and Staneva, A., “The Effect of Retirement on Elderly Cognitive Functioning.” Journal of Health Economics 66(2019): 37-53. [https://doi.org/10.1016/j.jhealeco.2019.04.006]
  • Beehr, T. A., and Bennett, M. M., “Working After Retirement: Features of Bridge Employment and Research Directions.” Work, Aging and Retirement 1(No. 1 2015): 112-128. [https://doi.org/10.1093/workar/wau007]
  • Bennett, M.M., Beehr, T.A. and Lepisto, L.R., “A longitudinal study of work after retirement: examining predictors of bridge employment, continued career employment, and retirement.” The International Journal of Aging and Human Development 83(No. 3 2016): 228-255. [https://doi.org/10.1177/0091415016652403]
  • Bonsang, E., Adam, S., and Perelman, S., “Does retirement affect cognitive functioning?” Journal of Health Economics 31(No. 3 2012): 490-501. [https://doi.org/10.1016/j.jhealeco.2012.03.005]
  • Brown, C. L., Gibbons, L. E., Kennison, R. F., Robitaille, A., Lindwall, M., Mitchell, M. B., Shirk, S. D., Atri, A., Cimino, C. R., Benitez, A., and MacDonald, S. W., “Social activity and cognitive functioning over time: a coordinated analysis of four longitudinal studies.” Journal of Aging Research(2012). [https://doi.org/10.1155/2012/287438]
  • Cantarero-Prieto, D., Leon, P. L., Blazquez-Fernandez, C., Juan, P. S. and Cobo, C. S., “The economic cost of dementia: a systematic review.” Dementia 19(No. 8 2020): 2637-2657. [https://doi.org/10.1177/1471301219837776]
  • Crooks, V. C., Lubben, J., Petitti, D. B., Little, D., and Chiu, V., “Social network, cognitive function, and dementia incidence among elderly women.” American Journal of Public Health 98(No. 7 2008): 1221-1227. [https://doi.org/10.2105/AJPH.2007.115923]
  • Deaton, A. S. and Paxson, C. H., “Aging and Inequality in Income and Health.” The American Economic Review 88(No. 2 1998): 248-253.
  • Dong, L., Xiao, R., Cai, C., Xu, Z., Wang, S., Pan, L., and Yuan, L., “Diet, lifestyle and cognitive function in old Chinese adults.” Archives of Gerontology and Geriatrics 63(2016): 36-42. [https://doi.org/10.1016/j.archger.2015.12.003]
  • Jefferson, A. L., Gibbons, L. E., Rentz, D. M., Carvalho, J. O., Manly, J., Bennett, D. A., and Jones, R. N., “A life course model of cognitive activities, socioeconomic status, education, reading ability, and cognition.” Journal of the American Geriatrics Society 59(No. 8 2011): 1403-1411. [https://doi.org/10.1111/j.1532-5415.2011.03499.x]
  • Kleinberg, J., Ludwig, J., Mullainathan, S., and Obermeyer, Z., “Prediction Policy Problems.” American Economic Review 105(No. 5 2015): 491-95. [https://doi.org/10.1257/aer.p20151023]
  • Kang Y., Na, D., and Hahn, S., “A validity study on the Korean Mini-Mental State Examination (K-MMSE) in dementia patients.” Journal of Korean Neurological Association 15(No. 2 1997): 300-308.
  • Kim, Y. J., “Long-term Predictors of Cardiovascular Disease: A Machine Learning Approach.” Journal of Economic Theory and Econometrics 34 (No. 4 2023): 86-114.
  • Kivipelto, M., Mangialasche, F., Ngandu, T., “Lifestyle interventions to prevent cognitive impairment, dementia and Alzheimer disease.” Natural Reviews Neurology 14(No. 11 2018): 653-66. [https://doi.org/10.1038/s41582-018-0070-3]
  • Koenker, R., and Bassett, G., Jr., “Regression Quantiles.” Econometrica 46 (No. 1 1978): 33-50. [https://doi.org/10.2307/1913643]
  • Koenker, R., Quantile Regression, Econometric Society Monograph Series, Cambridge University Press. 2005. [https://doi.org/10.1017/CBO9780511754098]
  • Lee, K. S., Cheong, H. K., Oh, B. H., Hong, C. H., “Comparison of the validity of screening tests for dementia and mild cognitive impairment of the elderly in a community: K-MMSE, MMSE-K, MMSE-KC, and K-HDS.” Journal of Korean Neuropsychiatric Association 48 (No. 2 2009): 61-69.
  • Lehtisalo, J., Lindström, J., Ngandu, T., Kivipelto, M., Ahtiluoto, S., Ilanne-Parikka, P., et al., “Association of Long-Term Dietary Fat Intake, Exercise, and Weight with Later Cognitive Function in the Finnish Diabetes Prevention Study.” The Journal of Nutrition, Health, and Aging 20(NO. 2 2016): 146-54. [https://doi.org/10.1007/s12603-015-0565-1]
  • Lu, L., Wang, H., Elbeleidy, S., and Nie, F., “Predicting Cognitive Declines Using Longitudinally Enriched Representations for Imaging Biomarkers.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(2020). [https://doi.org/10.1109/CVPR42600.2020.00488]
  • Meng, X., and D’arcy, C., “Education and Dementia in the Context of the Cognitive Reserve Hypothesis: A Systematic Review with Meta-Analyses and Qualitative Analyses.” PloS One 7(No. 6 2012): e38268. [https://doi.org/10.1371/journal.pone.0038268]
  • Murdaca, G., et al., “Potential Predictors for Cognitive Decline in Vascular Dementia: A Machine Learning Analysis.” Processes 10(No. 10 2022): 2088. [https://doi.org/10.3390/pr10102088]
  • National Institute of Dementia, Annual report 2021, Seoul, https://www.nid.or.kr/info/dataroom_view.aspx?bid=239, , 2021. [accessed 14.11.22].
  • Nichols, E., Steinmetz, J. D., Vollset, S. E., Fukutaki, K., Chalek, J., Abd-Allah, F., et al., “Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019.” Lancet Public Health 7(No. 2 2022): e105-25. [https://doi.org/10.1002/alz.051496]
  • Seeman, T. E., Miller-Martinez, D. M., Stein Merkin, S., Lachman, M. E., Tun, P. A., and Karlamangla, A. S., “Histories of Social Engagement and Adult Cognition: Midlife in the US Study.” Journals of Gerontology Series B: Psychological Sciences and Social Sciences 66(Suppl_1 2011): i141-i152. [https://doi.org/10.1093/geronb/gbq091]
  • Qian, Xin, et al., “A Cardiovascular Disease Prediction Model Based on Routine Physical Examination Indicators Using Machine Learning Methods: A Cohort Study.” Frontiers in cardiovascular medicine 9(2022): 854287. [https://doi.org/10.3389/fcvm.2022.854287]
  • Qu, Y., Hu, H., Ou, Y., Shen, X., Xu, W., Wang, Z., et al., “Association of body mass index with risk of cognitive impairment and dementia: a systematic review and meta-analysis of prospective studies.” Neuroscience and Biobehaviroal Reviews 115(2020): 189-198. [https://doi.org/10.1016/j.neubiorev.2020.05.012]