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Hidden multistate models to study multimorbidity trajectories
Valentina Manzoni1, Francesca Ieva1,2, Amaia Calderón-Larrañaga3,4
1Department of Mathematics, Politecnico di Milano, MOX-modeling and Scientific Computing Laboratory, Milan, Italy.
Scientific Reports
|July 31, 2026
Summary
New continuous-time hidden multistate models accurately track complex multimorbidity in older adults. This approach improves understanding of disease progression and mortality risk, aiding targeted interventions for aging populations.
Area of Science:
- Gerontology
- Biostatistics
- Epidemiology
Background:
- Multimorbidity is prevalent, dynamic, and linked to disability and healthcare use in older adults.
- Current methods for studying multimorbidity trajectories have limitations, especially with irregular data and censoring.
- There is a need for advanced statistical frameworks to model complex multimorbidity patterns.
Purpose of the Study:
- To develop and validate a continuous-time hidden multistate modeling framework for analyzing multimorbidity dynamics.
- To compare the performance of this new framework against traditional models using simulations.
- To apply the framework to real-world longitudinal data to identify risk factors and outcomes.
Main Methods:
- Development of a continuous-time hidden multistate model accounting for interval censoring and misclassification.
- Simulation studies to assess model performance under various conditions (sample size, follow-up).
- Application of the best model specification to the Swedish National study on Aging and Care-Kungsholmen (SNAC-K) cohort.
Main Results:
- Hidden multistate models significantly reduced bias in transition hazard estimates compared to non-hidden models.
- Fully time-inhomogeneous models demonstrated superior performance over piecewise approximations in simulations.
- The framework successfully identified risk factors for progressing multimorbidity and linked patterns to mortality gradients in the SNAC-K data.
Conclusions:
- Continuous-time hidden multistate models offer a robust and flexible approach to studying multimorbidity trajectories.
- This framework enhances the ability to predict individualized outcomes and inform targeted interventions.
- The findings support improved secondary prevention strategies for aging populations experiencing multimorbidity.
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