
The effects of health histories on stochastic process models of aging and mortality.
A model of human health history and aging, based on a multivariate stochastic process with both continuous diffusion and discrete jump components, is presented. Discrete changes generate non-Gaussian diffusion with time varying continuous state distributions. An approach to calculating transition rates in dynamically heterogeneous populations, which generalizes the conditional averaging of hazard rates done in "fixed frailty" population models, is presented to describe health processes with multiple jumps. Conditional semi-invariants are used to approximate the conditional p.d.f. of the unobserved health history components. This is useful in analyzing the age dependence of mortality and health changes at advanced age (e.g., 95+) where homeostatic controls weaken, and physiological dynamics and survival manifest nonlinear behavior.
Duke Scholars
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Related Subject Headings
- Stochastic Processes
- Population Dynamics
- Normal Distribution
- Mortality
- Models, Theoretical
- Medical History Taking
- Mathematics
- Humans
- Health Status
- Bioinformatics
Citation

Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Stochastic Processes
- Population Dynamics
- Normal Distribution
- Mortality
- Models, Theoretical
- Medical History Taking
- Mathematics
- Humans
- Health Status
- Bioinformatics