Abstract
With aging populations worldwide, there is growing interest in links between cognitive decline and elevated mortality risk—and, by extension, analytic approaches to further clarify these associations. Toward this end, some researchers have compared cognitive trajectories of survivors vs. decedents while others have examined longitudinal changes in cognition as predictive of mortality risk. A two-stage modeling framework is typically used in this latter approach; however, several recent studies have used joint longitudinal-survival modeling (i.e., estimating longitudinal change in cognition conditionally on mortality risk, and vice versa). Methodological differences inherent to these approaches may influence estimates of cognitive decline and cognition-mortality associations. These effects may vary across cognitive domains insofar as changes in broad fluid and crystallized abilities are differentially sensitive to aging and mortality risk. We compared these analytic approaches as applied to data from a large-sample, repeated-measures study of older adults (N = 5,954; ages 50–87 years at assessment; 4,453 deceased at last census). Cognitive trajectories indicated worse performance in decedents and when estimated jointly with mortality risk, but this was attenuated after adjustment for health-related covariates. Better cognitive performance predicted lower mortality risk, and, importantly, cognition-mortality associations were more pronounced when estimated in joint models. Associations between mortality risk and crystallized abilities only emerged under joint estimation. This may have important implications for cognitive reserve, which posits that knowledge and skills considered well-preserved in later life (i.e., crystallized abilities) may compensate for declines in abilities more prone to neurodegeneration, such as recall memory and problem solving. Joint longitudinal-survival models thus appear to be important (and currently underutilized) for research in cognitive epidemiology.
Highlights
The boundaries which divide Life from Death are at best shadowy and vague
Analyses were conducted separately by biological sex because we previously demonstrated non-proportional mortality hazards for women and men (Aichele et al, 2015), and we wanted the standalone multilevel models (MLM) to be consistent with the joint longitudinal-survival
Diagnostic plots for models re-estimated with MCMC indicated that 20,000 iterations were largely sufficient for obtaining stable parameter estimates
Summary
The boundaries which divide Life from Death are at best shadowy and vague. Who shall say where the one ends, and where the other begins?—Edgar Allen Poe, The Premature Burial Cognitive abilities (e.g., verbal skill, abstract reasoning) decline at different rates across the adult lifespan and with differential sensitivity to biological and health-related influences (Baltes et al, 2006). Increased knowledge of cognition-mortality associations can inform strategies to support mental wellness in later adulthood, provide caregivers with insight about the scope and timeframe of end-of-life mental declines, and may be useful for diagnostic purposes: E.g., processing speed declines as indicative of elevated vascular risk (Batterham et al, 2012; Aichele et al, 2016). Several studies have used joint longitudinal-survival models (Rizopoulos et al, 2014) to estimate, concurrently, intraindividual cognitive change conditionally on interindividual differences in mortality risk, and vice versa This latter approach may provide increased statistical efficiency and lessen statistical bias (i.e., because longitudinal performance may inform death-related attrition, and mortality-related processes may influence longitudinal performance). We turn to cognitive ability as predictive of mortality risk (cognitive epidemiology), the focal application for the study
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