Hidden multistate models to study multimorbidity trajectories

2026-02-20

AI Summary

Existing approaches struggle to effectively handle irregular observation times, right censoring, and misclassification of disease states when modeling the dynamic trajectories of multimorbidity in older adults. This work proposes a continuous-time hidden multi-state model that, for the first time, incorporates a fully time-varying non-homogeneous Markov process into multimorbidity research to accurately characterize transition mechanisms among latent disease states while simultaneously correcting for interval censoring and misclassification bias. Empirical analysis using the SNAC-K cohort successfully identifies key risk factors driving multimorbidity progression and reveals distinct mortality gradients across states, substantially reducing bias in transition risk estimation and providing a robust methodological foundation for personalized prediction and precision intervention.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Multimodal Learning
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• multimorbidity
• trajectories
• irregular observation intervals
• right-censoring
• hidden multistate models

Innovation

• hidden multistate models
• continuous-time modeling
• multimorbidity trajectories
• interval censoring
• time-inhomogeneous transitions