Predictors of two forms of attrition in a longitudinal health study involving ageing participants: An analysis based on the Whitehall II study

BMC Medical Research Methodology

Anthea Tinker⁴  Richard Ashcroft²  Clive Seale³  Robert L Grant¹  Suneeta Johal¹  Gill Mein¹ 

[1] Faculty of Health and Social Care Sciences, St. George's University of London and Kingston University, London, England, SW17 0RE, UK;School of Law, Queen Mary University of London, London, England, UK;Department of Sociology and Communications, Brunel University, Uxbridge Middlesex, England, UB8 3PH, UK;Institute of Gerontology, King's College London, London, England, UK

关键词: Whitehall II study;    Retention;    Longitudinal studies;    Attrition;    Older people;   

Others  :  1126530
DOI  :  10.1186/1471-2288-12-164

 received in 2012-02-28, accepted in 2012-10-22,  发布年份 2012

【 摘 要 】

Background

Longitudinal studies are crucial providers of information about the needs of an ageing population, but their external validity is affected if partipants drop out. Previous research has identified older age, impaired cognitive function, lower educational level, living alone, fewer social activities, and lower socio-economic status as predictors of attrition.

Methods

This project examined attrition in participants of the Whitehall II study aged between 51–71 years, using data from questionnaires participants have completed biennially since 1985 when the study began. We examine the possibility of two distinct forms of attrition – non-response and formally requesting to withdraw – and whether they have different predictors. Potential predictors were age, gender, marital status, occupational grade, retirement, home ownership, presence of longstanding illness, SF-36 quality of life scores, social participation and educational level comparing participants and those who had withdrawn from the study.

Results

The two forms of attrition share many predictors and are associated but remain distinct. Being older, male, having a lower job grade, not being a home owner, not having a long standing illness, having higher levels of education, and not having retired, were all associated with a greater probability of non-response; being married was associated with higher probability in women and lower in men. Being older, male, having a lower job grade, not being a home owner, having lower SF-36 scores, taking part in fewer social activities, and not having a long standing illness, were all associated with greater probability of withdrawal.

Conclusions

The results suggest a strong gender effect on both routes not previously considered in analyses of attrition. Investigators of longitudinal studies should take measures to retain older participants and lower level socio-economic participants, who are more likely to cease participating. Recognition should be given to the tendency for people with health problems to be more diligent participants in studies with a medical screening aspect, and for those with lower socio-economic status (including home ownership), quality of life and social participation, to be more likely to request withdrawal. Without taking these features into account, bias and loss of power could affect statistical analyses.

【 授权许可】

   
2012 Mein et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

【 参考文献 】

• [1]Academy of Medical Sciences: Rejuvenating ageing research. London: Academy of Medical Sciences; 2009.
• [2]European Commission: Meeting social needs in an ageing society. Luxembourg; 2008.
• [3]House of Lords Science and Technology Committee: Ageing: scientific aspects. London: The Stationery Office; 2005.
• [4]The Economist: Healthcare strategies for an ageing society. The Economist Intelligence Unit; 2009.
• [5]Miller R, Wright D: Detecting and correcting attrition bias in longitudinal family research. J Marriage Fam 1995, 57:921-929.
• [6]Miller R, Hollist C: Attrition bias. In Encyclopaedia of measurement and statistics. Edited by Salkind N. Thousand Oaks: Sage; 2007.
• [7]Van Beijsterveldt C, van Boxtel M, Bosma H, Houx P, Buntinx F, Jolles J: Predictors of attrition in a longitudinal cognitive aging study: the Maastricht Aging Study. J Clin Epidemiol 2001, 55:216-223.
• [8]Bhamra S, Tinker A, Mein G, Ashcroft R, Askham J: The retention of older people in longitudinal studies: A review of the literature. Quality in Ageing 2008, 9:27-35.
• [9]Chatfield M, Brayne C, Matthews F: A systematic review of attrition between waves in longitudinal studies in the elderly shows a consistent pattern of dropout between differing studies. J Clin Epidemiol 2005, 58:13-19.
• [10]Marmot M, Brunner E: Cohort profile: The Whitehall II study. Int J Epidemiol 2005, 34:251-256.
• [11]Deeg D: Attrition in longitudinal population studies: does it affect the generalizability of the findings? an introduction to the series. J Clin Epidemiol 2002, 55:213-215.
• [12]Ferrie J, Kivimäki M, Singh-Manoux A, Shortt A, Martikainen P, Head J, et al.: Non-response to baseline, non-response to follow-up and mortality in the Whitehall II cohort. Int J Epidemiol 2009, 38:831-837.
• [13]Rubin DB: Inference and missing data. Biometrika 1976, 63(3):581-592.
• [14]Rajan KB, Leurgans SE: Joint modelling of missing data due to non-participation and death in longitudinal aging studies. Stat Med 2010, 29:2260-2268.
• [15]Rabe-Hesketh S, Skrondal A, Pickles A: Reliable estimation of generalized linear mixed models using adaptive quadrature. Stata J 2002, 2(1):1-21.
• [16]Robinson GK: That BLUP is a good thing: the estimation of random effects. Stat Sci 1991, 6(1):15-51.
• [17]Greenland S, Pearl J, Robins JM: Causal diagrams for epidemiologic research. Epidemiology 1999, 10(1):37-48.
• [18]Ibrahim J, Chu H, Chen LM: Basic concepts and methods for joint models of longitudinal and survival time data. J Clin Oncol 2010, 28(16):2796-2801.