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.

翻译：老年人多病共存现象普遍存在、高度异质且动态变化，与失能风险增加和医疗资源使用密切相关。然而，现有研究多病共存轨迹的方法多局限于描述性分析或离散时间模型，难以处理不规则的观测间隔和右删失数据。本研究开发了一种连续时间隐式多状态建模框架，用于捕捉潜在多病共存模式间的转移过程，同时处理区间删失和误分类问题。通过模拟研究比较了不同样本量和随访方案下的模型设定，并将最优模型应用于瑞典国家老龄化与护理研究-孔斯霍尔默队列（SNAC-K）的纵向数据，该队列包含2,716名多病共存参与者，随访时间长达18年。模拟结果表明：相较于非隐式模型，隐式多状态模型能显著降低转移风险估计的偏差，且完全时间非齐次模型优于分段近似模型。SNAC-K数据的实证应用验证了该框架的可行性与实用价值，既能识别加速进展为复杂多病状态的风险因素，又揭示了不同模式间死亡风险的梯度差异。连续时间隐式多状态模型为传统方法提供了稳健的替代方案，有助于实现个体化预测，并为老年多病共存人群的靶向干预和二级预防策略提供依据。