Alluvial visualization and computation of probabilities of transitions in frailty index states over an 8-year period using multi-state Markov models

Abstract

The Frailty Index (FI) is an operationalization of frailty in older adults based on the accumulation of health deficits. FI cut-offs define the non-frail, prefrail and frail states. We described longitudinal transitions of FI states in The Irish Longitudinal Study on Ageing (TILDA). We included participants with information for a 31-deficit FI at TILDA wave 1 (2010), and follow-up over four subsequent longitudinal waves (2012, 2014, 2016, 2018). Next-wave transition probabilities were estimated using multi-state Markov models, and we investigated the effects of age, sex and education. 8174 wave 1 participants were included (54.2% female; mean age 63.8 years). Probabilities from non-frail to prefrail, and non-frail to frail were 18% and 2%, respectively. Prefrail had 19% probability of reversal to non-frail, and 15% risk of progression to frail. Frail had 21% probability of reversal to prefrail and 14% risk of death. Being older and female increased the risk of adverse FI state transitions, but being female reduced the risk of transition from frail to death. Higher level of education was associated with improvement from prefrail to non-frail. FI states are characterized by dynamic longitudinal transitions. Alluvial plots and Markov Models can help appreciate these dynamic state transitions in big data.