A History Embedded Accelerated Failure Time Model to Estimate Nursing Home Length of Stay

With its aging population, the United States is under increasing pressure to provide long-term care (LTC) coverage to its citizens and to manage their chronic health conditions. However, the research on LTC transition and utilization modeling remains in its infancy; needless to mention LTC resource allocation and transition pathway optimization. In this paper, we developed a parametric survival model to characterize nursing home (NH) length of stay (LOS), which incorporates information on transition history. In addition, the model addressed issues such as recurrent events and competing risks. To study the effect of covariates on LOS and to ensure the flexibility of the model in evaluating operational-level interventions, we elected to develop an accelerated failure time parametric survival model. We fit the model to care transition data collected from a large cohort of older adults receiving coordinated care in a Midwestern United States urban area. Through our study, we drew the following major conclusions: 1) transition history is a significant factor and a potential predictor of an individual's LOS in NH; 2) significance of frailty terms indicates that LOS estimates based on data with recurrent transition events can be significantly biased if not accounted for explicitly; and 3) the same clinical covariate can have opposite effects on NH LOS, depending on the destination care setting. Finally, we identified better-suited baseline hazard functions and frailty terms in each survival model from several representative candidates. Findings from our model can aid in operational-level NH care transition and utilization policy development. This paper also serves as the basis for extension into network-wide LTC transition models and utilization simulators.