Trajectories of Cognitive Ageing among Thai Later-Life Adults: The Role of Education Using the Characteristics Approach

Main Article Content

Paolo Miguel Manalang Vicerra
Wiraporn Pothisiri


People are living longer and functioning better than at the same age in prior decades, and those aged 60 years and older are often assumed to have similar levels of health and functioning to each other. This study analyzed health and social data from the 2016 Survey of Population Change and Well-being in the Context of Aging Society using the characteristics approach. This method determines variation in the speed of cognitive ageing - assessed through a measurement integrating memory and numeracy, in relation to education levels. A higher education was found to be statistically significantly associated with better cognitive ageing. Great disparities existed in cognitive functioning between those with a below-primary level of education and those with higher education levels. Men tended to have higher scores than women in cognitive function at 60 years of age, but women had a much slower trajectory of cognitive decline associated with ageing. The characteristics approach provides a quantitative perspective on how social gradients can affect people at older ages.


Download data is not yet available.

Article Details

How to Cite
Vicerra, P. M. M., & Pothisiri, W. (2020). Trajectories of Cognitive Ageing among Thai Later-Life Adults: The Role of Education Using the Characteristics Approach. Journal of Population and Social Studies [JPSS], 28(4), 276 - 286. Retrieved from
Author Biography

Paolo Miguel Manalang Vicerra, College of Population Studies, Chulalongkorn University

Corresponding author


Adler, N., Boyce, T., Chesney, M., Cohen, S., Folkman, S., Kahn, R., & Syme, S.L. (1994). Socioeconomic status and health: The challenge of the gradient. American Psychologist, 49(1), 15–24.
Andrade, F.C.D., Corona, L.P., Lebrão, M.L., & Duarte, Y.A. de O. (2014). Life expectancy with and without cognitive impairment among Brazilian older adults. Archives of Gerontology and Geriatrics, 58(2), 219–225.
Bordone, V., Scherbov, S., & Steiber, N. (2015). Smarter every day: The deceleration of population ageing in terms of cognition. Intelligence, 52, 90–96. doi:
College of Population Studies. (2018). A report on the 2016 population change and well-being in the context of ageing society. Bangkok: Chulalongkorn University.
Fiocco, A.J., & Yaffe, K. (2010). Defining successful aging: The importance of including cognitive function over time. Archives of Neurology, 67(7), 876–880.
Fujiwara, Y., Suzuki, H., Yasunaga, M., Sugiyama, M., Ijuin, M., Sakuma, N., … Shinkai, S. (2010). Brief screening tool for mild cognitive impairment in older Japanese: Validation of the Japanese version of the Montreal. Geriatrics & Gerontology International, 10(3), 225–232. doi:
Gómez, F., Zunzunegui, M., Lord, C., Alvarado, B., & García, A. (2013). Applicability of the MoCA-S test in populations with little education in Colombia. International Journal of Geriatric Psychiatry, 28(8), 813–820. doi:
Hayden, K.M., Reed, B.R., Manly, J.J., Tommet, D., Pietrzak, R.H., Chelune, G.J., … Jones, R.N. (2011). Cognitive decline in the elderly: An analysis of population heterogeneity. Age and Ageing, 40, 684–689. doi:
Hinton, P.R., McMurray, I., & Brownlow, C. (2014). SPSS explained (2nd ed.). London: Routledge.
Hirschman, C., Tan, J., Chamratrithirong, A., Guest, P., Hirschman, B.C., Tan, J., … Guest, P. (1994). The path to below replacement-level fertility in Thailand. International Family Planning Perspectives, 20(3), 82–87. doi:
Ichimura, H., Shimizutani, S., & Hashimoto, H. (2009). JSTAR first results 2009 report (IRETE Discussion Paper Series No. 09-E-04).
Kamnuansilpa, P., Chamratrithirong, A., & Knodel, J. (1982). Thailand’s reproductive revolution: An update. International Family Planning Perspectives, 8(2), 51–56.
Karcharnubarn, R., Rees, P., & Gould, M. (2013). Healthy life expectancy changes in Thailand, 2002-2007. Health & Place, 24, 1–10. Retrieved from
Kim, J.S., Won, C.W., Kim, B.S., & Choi, H.R. (2013). Predictability of various serial subtractions on global deterioration scale according to education level. Korean Journal of Family Medicine, 34(5), 327–333.
Knodel, J., & Chayovan, N. (2008). Population ageing and the well-being of older persons in Thailand: Past trends, current situation and future challenges (Papers in Population Ageing No. 5). Bangkok.
Kye, B. (2016). An alternative index of population aging: Accounting for education and elderly health in the case of Korea. Development and Society, 45(3), 563–589.
Maurer, J. (2011). Education and male-female differences in later-life cognition: International evidence from Latin America and the Caribbean. Demography, 48, 915–930.
Mielke, M.M., Vemuri, P., & Rocca, W.A. (2014). Clinical epidemiology of Alzheimer’s disease: Assessing sex and gender differences. Clinical Epidemiology, 6, 37–48. doi:
Petersen, R.C., Roberts, R.O., Knopman, D.S., Geda, Y.E., Cha, R.H., Pankratz, V.S., … Rocca, W.A. (2010). Prevalence of mild cognitive impairment is higher in men: The mayo clinic study of aging. Neurology, 75(10), 889–897. doi:
Quashie, N.T., & Pothisiri, W. (2018). Parental status and psychological distress among older Thais. Asian Social Work and Policy Review, 1–14.
Reed, B.R., Dowling, M., Tomaszewski Farias, S., Sonnen, J., Strauss, M., Schneider, J.A., … Mungas, D. (2011). Cognitive activities during adulthood are more important than education in building reserve. Journal of the International Neuropsychological Society, 17(4), 615–624. doi:
Ross, C.E., & Mirowsky, J. (2010). Gender and the health benefits of education. Sociological Quarterly, 51(1), 1–19. doi:
Sanderson, W.C., & Scherbov, S. (2007). A new perspective on population aging. Demographic Research, 16, 27–58. doi:
Sanderson, W.C., & Scherbov, S. (2008). Rethinking age and aging. Population Bulletin (Vol. 63). Retrieved from
Sanderson, W.C., & Scherbov, S. (2010). Remeasuring aging. Science, 329(5997), 1287–1288.
Sanderson, W.C., & Scherbov, S. (2013). The characteristics approach to the measurement of population aging. Population and Development Review, 39(4), 673–685.
Sanderson, W.C., & Scherbov, S. (2016). A unifying framework for the study of population aging. Vienna Yearbook of Population Research, 14, 7–39. doi:
Sanderson, W.C., Scherbov, S., Weber, D., & Bordone, V. (2016). Combined measures of upper and lower body strength and subgroup differences in subsequent survival among the older population of England. Journal of Aging and Health, 28(7), 1178–1193.
Singh-Manoux, A., Marmot, M.G., Glymour, M., Sabia, S., Kivimäki, M., & Dugravot, A. (2011). Does cognitive reserve shape cognitive decline? Annals of Neurology, 70(2), 296–304. doi:
Skirbekk, V., Loichinger, E., & Weber, D. (2012). Variation in cognitive functioning as a refined approach to comparing aging across countries. Proceedings of the National Academy of Sciences of the United States of America, 109(3), 770–774. doi:
Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve concept. Journal of International Neuropsychological Society, 8, 448–460.
Suttajit, S., Punpuing, S., Jirapramukpitak, T., Tangchonlatip, K., Darawuttimaprakorn, N., Stewart, R., … Abas, M.A. (2010). Impairment, disability, social support and depression among older parents in rural Thailand. Psychological Medicine, 40(10), 1711–1721. doi:
United Nations. (2019). 2019 Revision of world population prospects. Retrieved December 6, 2019, from
Weber, D. (2016). Differences in physical aging measured by walking speed: Evidence from the English longitudinal study of ageing. BMC Geriatrics, 16(1), 31. doi:
Welmerink, D.B., Longstreth, W.T., Lyles, M.F., & Fitzpatrick, A.L. (2010). Cognition and the risk of hospitalization for serious falls in the elderly: Results from the cardiovascular health study. Journals of Gerontology, Series A, Biological Sciences and Medical Sciences, 65(11), 1242–1249.
Zahodne, L.B., Stern, Y., & Manly, J.J. (2015). Differing effects of education on cognitive decline in diverse elders with low versus high educational attainment. Neuropsychology, 29(4), 649–657.