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

Abstract

Attrition is a particularly salient problem for longitudinal research on aging. The extent that participants who are lost to follow-up for any reason are different from full study participants can bias the results of the study and reduce generalizability. This issue is compounded because older participants and participants with lower cognitive scores are more likely to be lost to follow-up (Chatfield et al., 2005). In their study, “Predictors of attrition in a longitudinal population-based study of aging,” Jacobsen et al. (2020) investigated differences between participants who completed the study versus those who were lost to follow-up for any reason. Jacobsen and colleagues considered a single outcome that represents all-cause attrition. That is, participants who did not leave the study were compared to participants who left the study for any reason including death, refusal, too sick, relocation, and unreachable for follow-up. They considered a broad range of variables as predictors of attrition. In the fully adjusted model, a number of variables were significantly associated with attrition: being older, male, physically inactive, dependent in at least one instrumental activity of daily living (IADL), leaving the house less than once a week, and having a clinical dementia rating>0were associated with higher odds of attrition, while living with a partner who was also in the study, higher cognitive scores (but more subjective memory complaints), having a hobby or interest, and various forms of social participation were associated with lower odds of attrition. The logistic regression and the four machine learning approaches yielded very similar areas under the curve (AUC= 0.62 to 0.68) for predicting attrition. The researchers presented both unadjusted results and the results from a multivariable logistic regression. Both sets of results are informative, although the researchers focused their interpretation on the adjusted results. For instance, lower education was associated with higher odds of attrition in the unadjusted model, consistent with some research (Jacomb et al., 2002; Van Beijsterveldt et al., 2002; Young et al., 2006), but was not statistically significant in the full model. Included covariates vary across studies and this could explain some of the inconsistency. Jacobsen et al. considered a fairly large number of social variables compared to other studies on attrition and these, along with the variables assessing cognition, may have sufficiently overlapped with education to render it not statistically significant. However, education likely influences social factors and cognition so, even though it was not significant in the full model, it may still be a useful variable in understanding attrition. Another difference between the unadjusted and multivariable regression results was for subjective memory complaints. As expected, in the unadjusted results more subjective memory complaints were associated with higher odds of attrition; however, the association was in the opposite direction in the fully adjusted model. Lower objective cognitive scores were, as expected, associated with higher odds of attrition even in the full model. This suggests that once actual cognition is controlled, those with more subjective complaints are more likely to participate in future waves of the study. This finding goes against the theory that lower cognitive scores may be associated with attrition because people with cognitive difficulties may be afraid to return for fear of a dementia diagnosis or noticeable decline (Levin et al., 2000). In fact, this finding suggests that among people with the same cognitive level, the ones who perceive more memory complaints may have more motivation and desire to continue with the study. A meta-analysis examining preventable nonresponse (i.e., not death) found patterns that were fairly consistent with Jacobsen et al.’s study (Chatfield et al., 2005). Older age, poor functioning, cognitive impairment, living alone, and not being married were associated with more dropout. However, the authors of the meta-analysis also noted that the association between social factors and attrition has not been as thoroughly investigated. Jacobsen et al.’s study included a number of social factors that can be considered components of social vulnerability (Godin and Andrew, 2019). Social vulnerability is a construct that comprehensively considers a broad range of social factors that can leave individuals vulnerable to physical andmental health issues and it has been found to be associated with both cognition (Andrew and Rockwood, 2010) and mortality (Andrew et al., 2012). If the results of longitudinal studies on aging do not generalize to those who are most socially vulnerable, policies based on these International Psychogeriatrics (2021), 33:8, 743–746 © International Psychogeriatric Association 2021