Stochastic Process Models of Mortality and Aging

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

Chapter Chapter 12 in Biodemography of Aging, 2016, pp 263-284 from Springer

Abstract: Abstract A better understanding of relationships among human aging, health, and longevity requires integrative statistical methods capable of taking into account relevant knowledge accumulated in the field when extracting useful information from the data. In this chapter, we describe an approach to statistical analyses of longitudinal data based on the use of stochastic process models of human aging, health, and longevity. An important advantage of this approach is the possibility of incorporating state of the art advances in aging research into the model structure and then use this model in statistical estimation procedures. Specifically, to describe changes due to aging, the model incorporates variables characterizing resistance to stresses, adaptive capacity, and “optimal” (normal) physiological states. To capture the effects of exposure to persistent external disturbances, variables describing effects of allostatic adaptation and allostatic load are also introduced into the model. These variables facilitate the description of linkages between aging-related changes in physiological indices and morbidity and mortality risks. The model is tested in simulation experiments and applied to the analyses of Framingham Heart Study data. The results of these analyses provide researchers with a convenient conceptual framework for studying dynamic aspects of aging, and with an appropriate tool for systematically organizing and analyzing information about aging and its connection with health and longevity.

Keywords: Adaptive Capacity; Ordinary Differential Equation; Allostatic Load; Physiological Index; Mortality Curve (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssdmcp:978-94-017-7587-8_12

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DOI: 10.1007/978-94-017-7587-8_12

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