Modeling heterogeneity in health risk behaviors with a dynamic mixture model informed by textual occupational data

Health risk behaviors, including smoking, poor nutrition, alcohol misuse, and physical inactivity (SNAP), are leading contributors to chronic disease burden and healthcare costs worldwide. Their prevalence is shaped not only by individual demographic characteristics but also by contextual factors such as socioeconomic and occupational environments. We develop a Bayesian topic-informed dynamic mixture model to analyze SNAP behaviors using data from the Italian health and behavioral surveillance system PASSI. Free-text occupational descriptions are integrated through Structural Topic Modeling to define latent occupational groups that inform the mixture weights of a multivariate ordered probit model. Covariate effects are allowed to vary across occupational clusters and evolve over time. To enhance interpretability and facilitate variable selection, we incorporate non-local spike-and-slab priors on regression coefficients. We further develop a tailored online learning strategy based on sequential Monte Carlo, enabling efficient updating of inference as new surveillance data become available. The proposed approach provides a flexible and interpretable framework for monitoring how occupational contexts modulate the impact of socio-demographic factors on multiple health risk behaviors over time, supporting targeted public health interventions. More broadly, the methodology is applicable to other longitudinal health surveillance and clinical settings where complex contextual information is available in textual form.