Disability Trajectories at the End of Life: A "Countdown" Model.

Abstract

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. 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 missing-data problem. We use an auxiliary regression equation to impute TTD and employ multiple imputation techniques to obtain final parameter estimates and standard errors. 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. 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.