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A Novel Examination of Successful Aging Trajectories at the End of Life

Published online by Cambridge University Press:  25 October 2016

Theodore D. Cosco Open the ORCID record for Theodore D. Cosco [Opens in a new window] ,
Blossom C.M. Stephan ,
Graciela Muniz ,
Carol Brayne  and
the CC75C Study Collaboration
Theodore D. Cosco*
Affiliation:
Department of Public Health and Primary Care, University of Cambridge Medical Research Council Unit for Lifelong Health and Ageing at University College London UCL
Blossom C.M. Stephan
Affiliation:
Institute of Health and Society, Newcastle University
Graciela Muniz
Affiliation:
Medical Research Council Unit for Lifelong Health and Ageing at University College London UCL
Carol Brayne
Affiliation:
Department of Public Health and Primary Care, University of Cambridge
*
† La correspondance et les demandes de tire-à-part doivent être adressées à : / Correspondence and requests for offprints should be sent to: Theodore D. Cosco MRC Unit for lifelong Health and Ageing at UCL 33 Bedford Place London, United Kingdom WC1B 5JU (t.cosco@ucl.ac.uk)
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Abstract

A successful aging (SA) index was captured in a longitudinal population-based cohort study of individuals aged 75 and older and examined longitudinally using growth mixture modelling (GMM) to identify groups with similar trajectories using decedents’ (n = 1,015) last completed interview and up to four previous data collection waves before death. GMM identified a three-class model. Classes were high-functioning, no decline (HN); high-functioning, gradual decline (HG); and low-functioning, steep decline (LS). HN class individuals were significantly younger at death (p < 0.001) and at last interview (p < 0.001), consisted of more men (p < 0.001), and more likely to be married (p < 0.001) compared to HG and LS class individuals. These results demonstrate the different ways in which individuals can experience successful aging at the end of life. This study provides the framework for future research into life-course processes of aging, with important implications for policy and practice.

Résumé

Un indice de vieillissement réussi (SA) a été capturé dans une étude de cohorte longitudinale basée sur la population des personnes de 75 ans et plus, qui a été examiné longitudinalement en utilisant la modélisation d’un mélange de croissance (MMC) pour identifier les groupes ayant des trajectoires similaires utilisant la dernière interview complète de personnes décédées et jusqu’à quatre collections de données précédentes avant la mort. MMC a identifié un modèle avec trois classes. Les classes étaient : haut fonctionnement, pas de déclin (HPD); fonctionnement élevé, baisse progressive (HBP); et un faible fonctionnement, fort baisse (FB). Les individus de la classe HPD étaient significativement plus jeunes à la mort, et à la fin de l’examen, se composait de plus d’hommes, et plus susceptibles d’être mariées, comparativement aux individus HBP et FB. Ces résultats démontrent différentes façons dont les individus peuvent éprouver un vieillissement réussi à la fin de vie. Cette étude fournit le cadre pour la recherche future en ce qui concerne les processus du vieillissement pendant toute la vie, avec des implications importantes pour la politique et la pratique.

Keywords

agingend of lifegrowth mixture modellingsuccessful agingvieillissementfin de viemodélisation d’un mélange de croissancevieillissement réussi
Type
Research Notes / Notes de recherche
Information
Canadian Journal on Aging / La Revue canadienne du vieillissement , Volume 35 , Issue 4 , December 2016 , pp. 533 - 540
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Copyright
Copyright © Canadian Association on Gerontology 2016 

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References

Akaike, H. (1987). Factor-analysis and AIC. Psychometrika, 52, 317–332.CrossRefGoogle Scholar
Bauer, D., & Curran, P. (2003). Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes. Psychological methods, 8, 338–363.CrossRefGoogle ScholarPubMed
Celeux, G., & Soromenho, G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13, 195–212.CrossRefGoogle Scholar
Cosco, T. D., Prina, A. M., Perales, J., Stephan, B., & Brayne, C. (2013). Lay perspectives of successful ageing: A systematic review and meta-ethnography. BMJ Open, 3, e002710.CrossRefGoogle ScholarPubMed
Cosco, T. D., Prina, A. M., Perales, J., Stephan, B., & Brayne, C. (2014a). Operational definitions of successful aging: A systematic review. International Psychogeriatrics, 26, 373–381.CrossRefGoogle ScholarPubMed
Cosco, T. D., Prina, A. M., Perales, J., Stephan, B., & Brayne, C. (2014b). Whose “successful ageing”? Lay- and researcher-driven conceptualisations of ageing well. European Journal of Psychiatry, 28, 124–130.CrossRefGoogle Scholar
Cosco, T. D., Stephan, B., & Brayne, C. (2013). Deathless models of aging and the importance of acknowledging the dying process. Canadian Medical Association Journal, 185, 751–752.CrossRefGoogle ScholarPubMed
Cosco, T. D., Stephan, B., & Brayne, C. (2014). (Unsuccessful) binary modeling of successful aging in the oldest-old adults: A call for continuum-based measures. Journal of the American Geriatrics Society, 62, 1597–1598.CrossRefGoogle Scholar
Cosco, T. D., Stephan, B. C., & Brayne, C. (2015). Validation of an a priori, index model of successful aging in a population-based cohort study: The successful aging index. International Psychogeriatrics, 27, 1971–1977.CrossRefGoogle Scholar
Cumming, E., & Henry, W. (1961). Growing old. New York, NY: Basic Books.Google Scholar
Ding, J., Kritchevsky, S. B., Newman, A. B., Taaffe, D. R., Nicklas, B. J., Visser, M., … Health ABC Study. (2007). Effects of birth cohort and age on body composition in a sample of community-based elderly. American Journal of Clinical Nutrition, 85(2), 405–410.CrossRefGoogle Scholar
Fleming, J., Zhao, E., O’Connor, D., Pollitt, P. A., & Brayne, C. (2007). Cohort profile: The Cambridge City over-75s Cohort (CC75C). International Journal of Epidemiology, 36, 40–46.CrossRefGoogle ScholarPubMed
Folstein, M., Robins, L., & Helzer, J. (1983). The Mini-Mental State Examination. Archives of General Psychiatry, 40, 812.CrossRefGoogle ScholarPubMed
Havighurst, R. (1961). Successful aging. The Gerontologist, 1, 8–13.CrossRefGoogle Scholar
Jopp, D. S., Wozniak, D., Damarin, A. K., De Feo, M., Jung, S., & Jeswani, S. (2015). How could lay perspectives on successful aging complement scientific theory? Findings from a U.S. and a German life-span sample. The Gerontologist, 55(1): 91–106.CrossRefGoogle Scholar
Jung, T., & Wickrama, K. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2, 302–317.CrossRefGoogle Scholar
Katz, S. (1983). Assessing self-maintenance: Activities of daily living, mobility, and instrumental activities of daily living. Journal of the American Geriatrics Society, 31, 721–727.CrossRefGoogle ScholarPubMed
Kivimaki, M., & Ferrie, J. E. (2011). Epidemiology of healthy ageing and the idea of more refined outcome measures. International Journal of Epidemiology, 40, 845–847.CrossRefGoogle ScholarPubMed
Lawlor, D. A., Smith, G. D., & Ebrahim, S. (2004). Association between childhood socioeconomic status and coronary heart disease risk among postmenopausal women: Findings from the British Women’s Heart and Health Study. American Journal of Public Health, 94, 1386–1392.CrossRefGoogle ScholarPubMed
Lawton, M., & Brody, E. (1969). Assessment of older people: Self-maintaining and instrumental activities of daily living. The Gerontologist, 9, 179–186.CrossRefGoogle ScholarPubMed
Lo, Y., Mendell, N., & Rubin, D. (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767–778.CrossRefGoogle Scholar
Loehlin, C. (2009). Latent variable models: An introduction to factor, path, and structural equation analysis. New Jersey, NJ: Lawrence Erlbaum Associates.Google Scholar
Martinson, M., & Berridge, C. (2015). Successful aging and its discontents: A systematic review of the social gerontology literature. The Gerontologist, 55, 58–69.CrossRefGoogle ScholarPubMed
McCutcheon, A. (1987). Latent class analysis. Newbury Park, CA: Sage.CrossRefGoogle Scholar
Muniz-Terrera, G., Matthews, F., Dening, T., Huppert, F. A., & Brayne, C. (2009). Education and trajectories of cognitive decline over 9 years in very old people: Methods and risk analysis. Age and Ageing, 38, 277–282.CrossRefGoogle ScholarPubMed
Muniz-Terrera, G., Matthews, F. E., Stephan, B., Brayne, C., & Group, C. C. C. (2011). Are terminal decline and its potential indicators detectable in population studies of the oldest old? International Journal of Geriatric Psychiatry, 26, 584–592.CrossRefGoogle ScholarPubMed
Muthén, B., & Muthén, L. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism, Clinical and Experimental Research, 24, 882–891.CrossRefGoogle ScholarPubMed
Norton, S., Sacker, A., Young, A., & Done, J. (2011). Distinct psychological distress trajectories in rheumatoid arthritis: Findings from an inception cohort. Journal of Psychosomatic Research, 71, 290–295.CrossRefGoogle ScholarPubMed
Nylund, K. L., Asparoutiov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14, 535–569.CrossRefGoogle Scholar
Phelan, E., Anderson, L., LaCroix, A., & Larson, E. (2004). Older adults’ views of “successful aging” – How do they compare with researchers’ definitions? Journal of the American Geriatrics Society, 52, 211–216.CrossRefGoogle ScholarPubMed
Riley, M. W. (1998). Successful aging. The Gerontologist, 38, 151.CrossRefGoogle ScholarPubMed
Rowe, J., & Kahn, R. (1987). Human aging: Usual and successful. Science, 237, 143–149.CrossRefGoogle ScholarPubMed
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464.CrossRefGoogle Scholar
Searle, S., Mitnitski, A., Gahbauer, E., Gill, T. M., & Rockwood, K. (2008). A standard procedure for creating a frailty index. BMC Geriatrics, 8, 24.CrossRefGoogle ScholarPubMed