Health and study dropout: health aspects differentially predict attrition

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.

(Healthy) Aging Patterns in Europe: A Multistate Health Transition Approach

“…Furthermore, SHARE survey is not free of attrition, a common limitation in panel surveys. However, the profile of SHARE dropouts has shown a high level of heterogeneity without a defined pattern (Beller et al, 2022 ). Indeed, attrition has not been shown to alter the composition of the final sample with respect to the real one in the different countries that participate in the SHARE Survey (Friedel & Birkenbach, 2020 ).…”

The Seeking Mental Health Care model: prediction of help-seeking for depressive symptoms by stigma and mental illness representations

“…Total drop-out rate was 28.14% between baseline assessment and both follow-ups together (i.e., participation in either three- or six-month follow up). To examine potential reasons for attrition we conducted logistic regressions in which dropout is defined as not providing any follow-up data, the variable is dummy-coded as 0 = “no missing variable” and 1 = “missing value due to drop-out” [ 65 ]. Predictors were self-identification, general health condition, depression severity, treatment experience, and various sociodemographic variables chosen broadly to accurately analyse possible reasons for attrition.…”

Cross-sectional estimates of population health from the Survey of Health and Retirement in Europe (SHARE) are biased due to health-related sample attrition

“…However, it needs to be tailored to the specifics of a study, i.e., which cross-sectional wave and a health dimension are used. For example, as demonstrated by Beller, Geyer, and Epping (2022) for the German Aging Survey, the effect of decreased health on the risk of attrition differs between health dimensions. This requires the development of an R-package or software that follows the steps of this study and derives appropriate weights to correct for the attrition bias in the panel sample for a specific health variable, wave, sex, and country of interest.…”