Disability trajectories at the end of life: A "countdown" model

Douglas A. Wolf, Vicki A. Freedman, Jan I. Ondrich, Christopher L. Seplaki, Brenda C. Spillman

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


Objectives. Studies of late-life disablement typically address the role of advancing age as a factor in developing disability, and in some cases have pointed out the importance of time to death (TTD) in understanding changes in functioning. However, few studies have addressed both factors simultaneously, and none have dealt satisfactorily with the problem of missing data on TTD in panel studies. Methods. We fit latent-class trajectory models of disablement using data from the Health and Retirement Study. Among survivors (~20% of the sample), TTD is unknown, producing a missingdata problem. We use an auxiliary regression equation to impute TTD and employ multiple imputation techniques to obtain final parameter estimates and standard errors. Results. Our best-fitting model has 3 latent classes. In all 3 classes, the probability of having a disability increases with nearness to death; however, in only 2 of the 3 classes is age associated with disability. We find gender, race, and educational differences in class-membership probabilities. Discussion. The model reveals a complex pattern of age-And time-dependent heterogeneity in late-life disablement. The techniques developed here could be applied to other phenomena known to depend on TTD, such as cognitive change, weight loss, and health care spending.

Original languageEnglish (US)
Pages (from-to)745-752
Number of pages8
JournalJournals of Gerontology - Series B Psychological Sciences and Social Sciences
Issue number5
StatePublished - Sep 2015


  • Disability
  • Latent classes
  • Time to death
  • Trajectories

ASJC Scopus subject areas

  • Health(social science)
  • Sociology and Political Science
  • Life-span and Life-course Studies


Dive into the research topics of 'Disability trajectories at the end of life: A "countdown" model'. Together they form a unique fingerprint.

Cite this