The effects of health histories on stochastic process models of aging and mortality.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Yashin, AI; Manton, KG; Woodbury, MA; Stallard, E

Published Date

  • January 1995

Published In

Volume / Issue

  • 34 / 1

Start / End Page

  • 1 - 16

PubMed ID

  • 8568421

Pubmed Central ID

  • 8568421

Electronic International Standard Serial Number (EISSN)

  • 1432-1416

International Standard Serial Number (ISSN)

  • 0303-6812

Digital Object Identifier (DOI)

  • 10.1007/bf00180134

Language

  • eng