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This paper develops a continuous-time multi-state Markov model to analyze health and disability transitions in later life using data from the Health and Retirement Study (HRS). We define five distinct states combining health and functional status, allowing for 12 possible transitions including recovery and death. Transition intensities are estimated via a proportional hazards framework, incorporating individual-specific and time-dependent covariates. Model selection is guided by likelihood-based criteria, and results highlight significant heterogeneity by age, gender, and baseline condition. We derive one-year transition probabilities and compute dynamic life expectancies by initial state. Our findings reveal persistent disparities in ageing trajectories and underscore the importance of distinguishing between health and disability dimensions. The model offers practical insights for projecting future health burdens and designing long-term care strategies.

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Modeling Health and Disability Trajectories in Later Life: A Multi-state Approach Using HRS Data

  • 会议论文

  • 发表日期：

    2025年

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  • Domenico De Giovanni,
  • Massimiliano Menzietti,
  • Marco Pirra,
  • Fabio Viviano

摘要

This paper develops a continuous-time multi-state Markov model to analyze health and disability transitions in later life using data from the Health and Retirement Study (HRS). We define five distinct states combining health and functional status, allowing for 12 possible transitions including recovery and death. Transition intensities are estimated via a proportional hazards framework, incorporating individual-specific and time-dependent covariates. Model selection is guided by likelihood-based criteria, and results highlight significant heterogeneity by age, gender, and baseline condition. We derive one-year transition probabilities and compute dynamic life expectancies by initial state. Our findings reveal persistent disparities in ageing trajectories and underscore the importance of distinguishing between health and disability dimensions. The model offers practical insights for projecting future health burdens and designing long-term care strategies.