From Noise to Signal: The Age and Social Patterning of Intra-Individual Variability in Late-Life Health

Abstract

Objectives: Despite a long tradition of attending to issues of intra-individual variability in the gerontological literature, large-scale panel studies on late-life health disparities have primarily relied on average health trajectories, relegating intra-individual variability over time to random error terms, or "noise." This article reintegrates the systematic study of intra-individual variability back into standard growth curve modeling and investigates the age and social patterning of intra-individual variability in health trajectories. Method: Using panel data from the Health and Retirement Study, we estimate multilevel growth curves of functional limitations and cognitive impairment and examine whether intra-individual variability in these two health outcomes varies by age, gender, race/ethnicity, and socioeconomic status, using level-1 residuals extracted from the adjusted growth curve models. Results: For both outcomes, intra-individual variability increases with age. Racial/ethnic minorities and individuals with lower socioeconomic status tend to have greater intra-individual variability in health. Relying exclusively on average health trajectories may have masked important "signals" of life course health inequality. Discussion: The findings contribute to scientific understanding of the source of heterogeneity in late-life health and highlight the need to further investigate specific life course mechanisms that generate the social patterning of intra-individual variability in health status.

Associations between frailty trajectories and frailty status and adverse outcomes in community‐dwelling older adults

“…22 Nevertheless, although disabled rates increase with ageing, 30 their evolution could depend on intraindividual variability in mobility limitation. 31 This finding supports frailty as a multidimensional measurement of health status and a sign of a loss of homeostasis with overall system vulnerability to estimate adverse event risk and an opportunity to anticipate disability. 1,5,8 Our results show that those trajectories of physical and functional decline are more likely to lead to disability or to a progress in their disability levels.…”

“…Disablement process is not linear and could fluctuate in cycles of deterioration and recovery short time periods, and these fluctuations could be higher in late life 22 . Nevertheless, although disabled rates increase with ageing, 30 their evolution could depend on intraindividual variability in mobility limitation 31 . This finding supports frailty as a multidimensional measurement of health status and a sign of a loss of homeostasis with overall system vulnerability to estimate adverse event risk and an opportunity to anticipate disability 1,5,8 .…”

Re-engaging in Aging and Mobility Research in the COVID-19 Era: Early Lessons from Pivoting a Large-Scale, Interdisciplinary Study amidst a Pandemic

“…Although some may view this time period as an opportunity to investigate the impact of the pandemic on differential segments of the population, we must proceed with caution, particularly in studies involving older adults. Emerging evidence highlights the heterogeneity of the aging population and the importance of considering intra-individual differences, such as race and socio-economic status, which are known to influence health trajectories in later life (Lin & Kelley-Moore, 2017). Given this heterogeneity as well as the situational complexity imposed by the pandemic, the need for "cross-unit knowledge-sharing" (Hansen & von Oetinger, 2001) is even more critical in aging research.…”

A Longitudinal Study of Allostatic Load in Later Life: The Role of Sex, Birth Cohorts, and Risk Accumulation

“…The accumulation of risks by some and opportunities by others shape the life course according to a logic of cumulative (dis)advantage, with those who have initial advantage doing increasingly well and those with initial disadvantage doing worse (Dannefer, 1987; Ross & Wu, 1996). For instance, aging increases heterogeneity in disability (Campbell & Buchner, 1997; Gill, Allore, Hardy, & Guo, 2006; Lin & Kelley-Moore, 2017) and cognitive function (Hultsch, MacDonald, Hunter, Levy-Bencheton, & Strauss, 2000; Li, Aggen, Nesselroade, & Baltes, 2001; Lin & Kelley-Moore, 2017) because of the different levels of accumulated risks for different socioeconomic groups. This increasing heterogeneity is countered to some extent by selective mortality in later life of those who have accumulated most risks, so that age acts as a leveler (Beckett, 2000; House, Kessler, & Herzog, 1990; House et al, 1994).…”