Health and study dropout: health aspects differentially predict attrition

Author:

Beller JohannesORCID,Geyer Siegfried,Epping Jelena

Abstract

Abstract Background Participant dropout poses significant problems in longitudinal survey studies. Although it is often assumed that a participant’s health predicts future study dropout, only a few studies have examined this topic, with conflicting findings. This study aims to contribute to the literature by clarifying the relationship between different aspects of health and study dropout. Methods The 2008 baseline sample of the German Aging Survey was used to predict study dropout (N = 4442). Indicators of health included physical health using the number of chronic conditions, physical functioning using the SF-36 Physical Functioning subscale, cognitive functioning using the digit symbol substitution test, and depression using the CESD-15. Results It was found that different aspects of health had differential associations with survey dropout: Worse physical functioning and in part worse cognitive functioning predicted increased dropout rates; contrarily, worse physical health predicted decreased dropout when controlling for other health aspects and covariates. Depression was not significantly related to study dropout. Conclusions Therefore, participants with chronic conditions, but minimal physical and cognitive disability were most likely to participate in the future. These findings suggest that health has a complex relationship with survey dropout and must be accounted for in longitudinal studies. Neglecting this systematic attrition due to health problems bears the risk of severely under- or overestimating health-related effects and trends.

Funder

Deutsche Forschungsgemeinschaft

Medizinische Hochschule Hannover (MHH)

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Epidemiology

Reference48 articles.

1. de Leeuw ED, Lugtig P. Dropouts in Longitudinal Surveys. In: Balakrishnan N, Colton T, Everitt B, Piegorsch W, Ruggeri F, Teugels JL, editors. Wiley StatsRef: Statistics Reference Online. Chichester: John Wiley & Sons, Ltd; 2015. p. 1–6.

2. Shadish WR, Cook TD, Campbell DT. Experimental and quasi-experimental designs for generalized causal inference. Belmont: Wadsworth, Cengage Learning; 2002.

3. Chatfield MD, Brayne CE, Matthews FE. A systematic literature review of attrition between waves in longitudinal studies in the elderly shows a consistent pattern of dropout between differing studies. J Clin Epidemiol. 2005;58:13–9.

4. Bell ML, Kenward MG, Fairclough DL, Horton NJ. Differential dropout and bias in randomised controlled trials: when it matters and when it may not. BMJ. 2013;346:e8668.

5. Banks J, Muriel A, Smith J. Attrition and health in ageing studies: evidence from ELSA and HRS. Long Life Course Stud. 2011;2:101–26.

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