A1166
Title: Uncovering hidden mental health patterns via a dynamic mixture of latent trait analyzers
Authors: Francesca Martella - La Sapienza University of Rome (Italy) [presenting]
Dalila Failli - University degli Studi di Perugia (Italy)
Maria Francesca Marino - University of Florence (Italy)
Abstract: An extension of the mixture of latent trait analyzers (MLTA) model is proposed for clustering longitudinal data on mental health. In detail, the focus is on a data set from SHARE (survey of health, aging, and retirement in Europe), composed of several indicators of mental health, emotional well-being, cognitive function, and behavioral symptoms experienced over the past month by various individuals over four years. Specifically, it is moved from a mixture of latent trait analyzers (MLTA) to a latent Markov model (LMM) framework with the aim to (i) enable dynamic clustering of individuals based on their mental health status over time, allowing for time-varying cluster memberships; (ii) account for possible unobserved factors related to psychological well-being. The proposed model can capture both time-constant and time-varying sources of unobserved heterogeneity, which are typical in longitudinal data settings. For parameter estimation, the Baum-Welch algorithm is extended, typically used with LMMs, to handle the presence of a multidimensional continuous latent trait. Since the model involves multidimensional integrals that lack closed-form solutions, suitable approximation methods are required. The obtained results demonstrate the model's effectiveness in identifying latent states that clearly reflect an individual's propensity for poor mental health. Further details and an in-depth discussion of the empirical findings will be provided.
