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Health and social factors key to understanding attrition in longitudinal aging research

Published online by Cambridge University Press:  09 June 2021

Judith Godin  and
Olga Theou
Judith Godin
Affiliation:
Geriatric Medicine, Dalhousie University and Nova Scotia Health, Halifax, NS, Canada
Olga Theou*
Affiliation:
Geriatric Medicine, Dalhousie University and Nova Scotia Health, Halifax, NS, Canada Department of Physiotherapy, Dalhousie University, Halifax, NS, Canada
*
Email: olga.theou@dal.ca
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Abstract

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Type
Commentary
Information
International Psychogeriatrics , Volume 33 , Special Issue 8: Issue Theme: Longitudinal Studies of Aging , August 2021 , pp. 743 - 746
Copyright
© International Psychogeriatric Association 2021

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References

Andrew, M. K., Mitnitski, A. B., Kirkland, S. A. and Rockwood, K. (2012). The impact of social vulnerability on the survival of the fittest older adults. Age and Ageing, 41, 161–165. https://doi.org/10.1093/ageing/afr176 CrossRefGoogle ScholarPubMed
Andrew, M. K., Mitnitski, A. B. and Rockwood, K. (2008). Social vulnerability, frailty and mortality in elderly people. PloS One, 3, e2232. https://doi.org/10.1371/journal.pone.0002232 CrossRefGoogle ScholarPubMed
Andrew, M. K. and Rockwood, K. (2010). Social vulnerability predicts cognitive decline in a prospective cohort of older Canadians. Alzheimer’s & Dementia, 6, 319–325. https://doi.org/10.1016/j.jalz.2009.11.001 CrossRefGoogle Scholar
Brilleman, S.L., Pachana, N. A. and Dobson, A. J. (2010). The impact of attrition on the representativeness of cohort studies of older people. BMC Medical Research Methodology, 10. https://doi.org/10.1186/1471-2288-10-71CrossRefGoogle Scholar
Burke, S. L. et al. (2019). Factors influencing attrition in 35 Alzheimer’s Disease Centers across the USA: a longitudinal examination of the National Alzheimer’s Coordinating Center’s Uniform Data Set. Aging Clinical and Experimental Research, 31, 1283–1297. https://doi.org/10.1007/s40520-018-1087-6 CrossRefGoogle ScholarPubMed
Burton, A. and Altman, D. G. (2004). Missing covariate data within cancer prognostic studies: a review of current reporting and proposed guidelines. British Journal of Cancer, 91, 4–8. https://doi.org/10.1038/sj.bjc.6601907 CrossRefGoogle ScholarPubMed
Chatfield, M. D., Brayne, C. E. and Matthews, F. E. (2005). A systematic literature review of attrition between waves in longitudinal studies in the elderly shows a consistent pattern of dropout between differing studies. Journal of Clinical Epidemiology, 58(1), 13–19. https://doi.org/10.1016/j.jclinepi.2004.05.006 CrossRefGoogle Scholar
Eekhout, I. et al. (2012). Brief report: missing data: a systematic review of how they are reported and handled. Epidemiology, 23, 729–732. https://doi.org/10.1097/EDE.0b013e3182576cdb CrossRefGoogle Scholar
Geva, D., Shahar, D., Harris, T., Tepper, S., Molenberghs, G. and Friger, M. (2013). Snapshot of statistical methods used in geriatric cohort studies: how do we treat missing data in publications? International Journal of Statistics in Medical Research, 2, 289–296. https://doi.org/10.6000/1929-6029.2013.02.04.5 Google Scholar
Godin, J. and Andrew, M. (2019). Frailty and Social Vulnerability. In: Gu, D. and Dupre, M. E. (Eds.), Encylcopedia of Gerontology and Population Aging. Cham, Switzerland: Springer Nature.Google Scholar
Godin, J., Armstrong, J. J., Rockwood, K. and Andrew, M. K. (2017). Dynamics of frailty and cognition after age 50: why it matters that cognitive decline is mostly seen in old age. Journal of Alzheimer’s Disease, 58, 231–242.CrossRefGoogle ScholarPubMed
Godin, J., Armstrong, J. J., Wallace, L., Rockwood, K. and Andrew, M. K. (2019). The impact of frailty and cognitive impairment on quality of life: employment and social context matter. International Psychogeriatrics, 31, 789–797. https://doi.org/10.1017/S1041610218001710 CrossRefGoogle ScholarPubMed
Helliwell, B., Aylesworth, R., McDowell, I., Baumgarten, M. and Sykes, E. (2001). Correlates of nonparticipation in the Canadian Study of Health and Aging. International Psychogeriatrics, 13, 49–56. https://doi.org/10.1017/S1041610202007986 CrossRefGoogle ScholarPubMed
Jacobsen, E., Ran, X., Liu, A., Chang, C. and Ganguli, M. (2020). Predictors of attrition in a longitudinal population-based study of aging. International Psychogeriatrics, 1–12. doi:10.1017/S1041610220000447 CrossRefGoogle Scholar
Jacomb, P. A., Jorm, A. F., Korten, A. E., Christensen, H. and Henderson, A. S. (2002). Predictors of refusal to participate: a longitudinal health survey of theelderly in Australia. BMC Public Health, 2, 1–6. https://doi.org/10.1186/1471-2458-2-4 CrossRefGoogle Scholar
Levin, B. E., Katzen, H. L., Klein, B. and Llabre, M. L. (2000). Cognitive decline affects subject attrition in longitudinal research. Journal of Clinical and Experimental Neuropsychology, 22, 580–586. https://doi.org/10.1076/1380-3395(200010)22:5;1-9;FT580 CrossRefGoogle ScholarPubMed
Mitnitski, A. and Rockwood, K. (2016). The rate of aging: the rate of deficit accumulation does not change over the adult life span. Biogerontology, 17, 199–204. https://doi.org/10.1007/s10522-015-9583-y CrossRefGoogle Scholar
Salthouse, T. A. (2019). Attrition in longitudinal data is primarily selective with respect to level rather than rate of change. Journal of the International Neuropsychological Society, 25, 618–623. https://doi.org/10.1017/S135561771900016X CrossRefGoogle ScholarPubMed
Searle, S. D., Mitnitski, A. B., Gahbauer, E. A., Gill, T. M. and Rockwood, K. (2008). A standard procedure for creating a fraily index. BMC Geriatrics, 8, 24. https://doi.org/10.1186/1471-2318-8-24 CrossRefGoogle Scholar
Slymen, D. J., Drew, J. A., Elder, J. P. and Williams, S. J. (1996). Determinants of non-compliance and attrition in the elderly. International Journal of Epidemiology, 25, 411–419. https://doi.org/10.1093/ije/25.2.411 CrossRefGoogle ScholarPubMed
St John, P. D., Tyas, S. L., Griffith, L. E. and Menec, V. (2017). The cumulative effect of frailty and cognition on mortality - Results of a prospective cohort study. International Psychogeriatrics, 29, 535–542. https://doi.org/10.1017/S1041610216002088 CrossRefGoogle ScholarPubMed
Stolz, E., Mayerl, H., Rásky, V. and Freidl, W. (2018). Does sample attrition affect the assessment of frailty trajectories among older adults? A joint model approach. Gerontology, 64, 430–439. https://doi.org/10.1159/000489335 CrossRefGoogle ScholarPubMed
Van Beijsterveldt, C. E. M., van Boxtel, M. P. J., Bosma, H., Houx, P. J., Buntinx, F. and Jolles, J. (2002). Predictors of attrition in a longitudinal cognitive aging study: The Maastricht Aging Study (MAAS). Journal of Clinical Epidemiology, 55, 216–223. https://doi.org/10.1016/S0895-4356(01)00473-5 CrossRefGoogle Scholar
Wallace, L. M. K., Theou, O., Kirkland, S. A., Rockwood, M. R. H., Davidson, K. W., Shimbo, D. and Rockwood, K. (2014). Accumulation of non-traditional risk factors for coronary heart disease is associated with incident coronary heart disease hospitalization and death. PLoS ONE, 9. https://doi.org/10.1371/journal.pone.0090475 CrossRefGoogle Scholar
Young, A. F., Powers, J. R. and Bell, S. L. (2006). Attrition in longitudinal studies: Who do you lose? Australian and New Zealand Journal of Public Health, 30, 353–361. https://doi.org/10.1111/j.1467-842X.2006.tb00849.x CrossRefGoogle Scholar