Integrative Mortality Models with Parameters That Have Biological Interpretations

Anatoliy I. Yashin (), Igor Akushevich (), Konstantin G. Arbeev (), Alexander M. Kulminski () and Svetlana V. Ukraintseva ()
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Anatoliy I. Yashin: Duke Population Research Institute & Social Science Research Institute at Duke University, Biodemography of Aging Research Unit, Center for Population Health and Aging
Igor Akushevich: Duke Population Research Institute & Social Science Research Institute at Duke University, Biodemography of Aging Research Unit, Center for Population Health and Aging
Konstantin G. Arbeev: Duke Population Research Institute & Social Science Research Institute at Duke University, Biodemography of Aging Research Unit, Center for Population Health and Aging
Alexander M. Kulminski: Duke Population Research Institute & Social Science Research Institute at Duke University, Biodemography of Aging Research Unit, Center for Population Health and Aging
Svetlana V. Ukraintseva: Duke Population Research Institute & Social Science Research Institute at Duke University, Biodemography of Aging Research Unit, Center for Population Health and Aging

Chapter Chapter 15 in Biodemography of Aging, 2016, pp 321-330 from Springer

Abstract: Abstract Mortality rates are important characteristics of life span distributions that integrate the influences of many external and internal factors affecting individuals in the population during their life course. These include the ontogenetic program, individual aging processes, exposure to external (environmental) and internal (biological) factors, and changes in health status, as well as effects of compensatory adaptation to damages and changes induced by all these processes. Various parametric models of human mortality rates are used in the analyses of survival data in demographic and epidemiological applications, experimental studies of aging and longevity using laboratory animals, etc. The purpose of this chapter is to describe an approach to mortality modeling that represents mortality rates in terms of parameters of physiological changes and declining health status developing in aging human organisms. In contrast to traditional demographic and actuarial models dealing with mortality data, this model is appropriate for analyses of longitudinal data on aging, health, and longevity. We use diffusion-type continuous-time stochastic processes for describing the evolution of the physiological state over the life course, and a finite state continuous-time process for describing changes in health status during this period. We derive equations for the corresponding mortality models, and approximate changes in physiological states by a conditional Gaussian process, given the health state. Simulation experiments show that model parameters can be evaluated from longitudinal data. The application of this model to Framingham Heart Study data indicates important differences in physiological dynamics among healthy and sick individuals.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssdmcp:978-94-017-7587-8_15

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DOI: 10.1007/978-94-017-7587-8_15

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