Using group‐based latent class transition models to analyze chronic disability data from the National Long‐Term Care Survey 1984–2004

Abstract

Latent class transition models track how individuals move among latent classes through time, traditionally assuming a complete set of observations for each individual. In this paper, we develop group-based latent class transition models that allow for staggered entry and exit, common in surveys with rolling enrollment designs. Such models are conceptually similar to, but structurally distinct from, pattern mixture models of the missing data literature. We employ group-based latent class transition modeling to conduct an in-depth data analysis of recent trends in chronic disability among the U.S. elderly population. Using activities of daily living data from the National Long-Term Care Survey (NLTCS), 1982-2004, we estimate model parameters using the expectation-maximization algorithm, implemented in SAS PROC IML. Our findings indicate that declines in chronic disability prevalence, observed in the 1980s and 1990s, did not continue in the early 2000s as previous NLTCS cross-sectional analyses have indicated.

Longitudinal Mixed Membership trajectory models for disability survey data

“…Furthermore, it is so in a positive sense: not only do younger generations acquire disabilities differently; they acquire them later. These findings are consistent with previous evidence showing a decline in disability obtained from purely cross-sectional analyses (Manton et al, 1997, 2006), from wave to wave latent class transition analysis (White and Erosheva, 2013), and from latent trajectory analysis (Connor, 2006). …”

“…So far declines in disability have been analyzed mostly from wave to wave, either from uncorrelated cross-sectional samples as changes in prevalence [Manton, Corder and Stallard (1997), Manton, Gu and Lamb (2006), Manton, Lamb and Gu (2007)] or from longitudinal analysis as transitions between states [Stallard (2005), White and Erosheva (2013)]. Our approach, in contrast, is not rooted on survey waves, nor does it directly assess changes in prevalence of disability.…”

“…In essence, these models correspond to what Kurland and Heagerty (2005) and Kurland et al (2009) call an "immortal cohort." This is of particular importance in the present application because patterns of disability are usually tied to patterns of mortality [Ferrucci et al (1996), Connor (2006), White and Erosheva (2013)]: progression into more severe disability goes together with an increased probability of death. One way of integrating mortality into this framework is to extend the definition of extreme profiles to characterize not only patterns of disability acquisition, but also of survival.…”

“…Most researchers have instead analyzed the NLTCS as a series of uncorrelated cross-sectional samples [see, e.g., Manton, Corder and Stallard (1997), Manton, Gu and Lamb (2006), Manton, Lamb and Gu (2007)]. Recent attempts to deal with the longitudinal nature of the NLTCS have been undertaken by Stallard (2005), Connor (2006) and White and Erosheva (2013).…”

“…Manton et al, 1997, 2006, 2007). Recent attempts to deal with the longitudinal nature of the NLTCS have been undertaken by Stallard (2005), Connor (2006) and White and Erosheva (2013).…”