Hidden multistate models to study multimorbidity trajectories

TL;DR

This paper introduces a continuous-time hidden multistate modeling framework for studying the complex, dynamic trajectories of multimorbidity in older adults, effectively handling irregular data and misclassification.

Contribution

The study develops and validates a novel continuous-time hidden multistate model that improves estimation accuracy and practical utility in analyzing multimorbidity trajectories.

Findings

Hidden multistate models reduce bias in transition hazard estimates.

Fully time-inhomogeneous models outperform piecewise models.

Application reveals risk factors and mortality gradients in multimorbidity patterns.

Abstract

Multimorbidity in older adults is common, heterogeneous, and highly dynamic, and it is strongly associated with disability and increased healthcare utilization. However, existing approaches to studying multimorbidity trajectories are largely descriptive or rely on discrete-time models, which struggle to handle irregular observation intervals and right-censoring. We developed a continuous-time hidden multistate modeling framework to capture transitions among latent multimorbidity patterns while accounting for interval censoring and misclassification. A simulation study compared alternative model specifications under varying sample sizes and follow-up schemes, and the best-performing specification was applied to longitudinal data from the Swedish National study on Aging and Care-Kungsholmen (SNAC-K), including 2,716 multimorbid participants followed for up to 18 years. Simulation results showed that hidden multistate models substantially reduced bias in transition hazard estimates compared to non-hidden models, with fully time-inhomogeneous models outperforming piecewise approximations. Application to SNAC-K confirmed the feasibility and practical utility of this framework, enabling identification of risk factors for accelerated progression toward complex multimorbidity and revealing a gradient of mortality risk across patterns. Continuous-time hidden multistate models provide a robust alternative to traditional approaches, supporting individualized predictions and informing targeted interventions and secondary prevention strategies for multimorbidity in aging populations.