Interdisciplinary Research on Healthy Aging: Introduction

<b>Background</b>: This is an introduction to a Special Collection of Demographic Research on Interdisciplinary Research on Healthy Aging. The collection is an outcome of an international conference in China on biodemography and multistate modeling in healthy aging research. Causal analy...

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Main Authors: Frans Willekens, James R. Carey, Qiang Li
Format: Article
Language:English
Published: Max Planck Institute for Demographic Research 2018-01-01
Series:Demographic Research
Subjects:
Online Access:https://www.demographic-research.org/volumes/vol38/10/
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spelling doaj-536c9a262b7841f2a32df8f996b200d32020-11-24T20:41:21ZengMax Planck Institute for Demographic ResearchDemographic Research1435-98712018-01-01381010.4054/DemRes.2018.38.103685Interdisciplinary Research on Healthy Aging: IntroductionFrans Willekens0James R. Carey1Qiang Li2Nederlands Interdisciplinair Demografisch Instituut (NIDI)University of California, DavisEast China Normal University<b>Background</b>: This is an introduction to a Special Collection of Demographic Research on Interdisciplinary Research on Healthy Aging. The collection is an outcome of an international conference in China on biodemography and multistate modeling in healthy aging research. Causal analysis is the common theme of the papers. Healthy aging is an outcome of pathways of causally related distant and proximate determinants and intervening factors that mediate the effects of the determinants. <b>Objective</b>: The objective is to introduce the papers in this SC and to highlight the place of multistate modeling in causal analysis. <b>Methods</b>: We adopt the common distinction between structural causal modeling and dynamic causal modeling. The papers in the SC concentrate on structural causal modeling. Multistate models (and, more particularly, the continuous-time Markov process model) are oriented more toward dynamic causal modeling. In dynamic causal modeling the causal dependencies are defined in terms of events (outcomes), exposure time, and transition rates that relate exposures to events. <b>Results</b>: The contributions to the SC illustrate the progress made in structural causal modeling in the study of healthy aging. Dynamic causal analysis, however, has progressed comparatively slowly. <b>Contribution</b>: The papers in the SC and the brief introduction to multistate modeling in causal analysis pave the way to enhanced causal analysis in the study of healthy aging and in demography.https://www.demographic-research.org/volumes/vol38/10/causal analysisdemographyhealthy agingmultistate models
collection DOAJ
language English
format Article
sources DOAJ
author Frans Willekens
James R. Carey
Qiang Li
spellingShingle Frans Willekens
James R. Carey
Qiang Li
Interdisciplinary Research on Healthy Aging: Introduction
Demographic Research
causal analysis
demography
healthy aging
multistate models
author_facet Frans Willekens
James R. Carey
Qiang Li
author_sort Frans Willekens
title Interdisciplinary Research on Healthy Aging: Introduction
title_short Interdisciplinary Research on Healthy Aging: Introduction
title_full Interdisciplinary Research on Healthy Aging: Introduction
title_fullStr Interdisciplinary Research on Healthy Aging: Introduction
title_full_unstemmed Interdisciplinary Research on Healthy Aging: Introduction
title_sort interdisciplinary research on healthy aging: introduction
publisher Max Planck Institute for Demographic Research
series Demographic Research
issn 1435-9871
publishDate 2018-01-01
description <b>Background</b>: This is an introduction to a Special Collection of Demographic Research on Interdisciplinary Research on Healthy Aging. The collection is an outcome of an international conference in China on biodemography and multistate modeling in healthy aging research. Causal analysis is the common theme of the papers. Healthy aging is an outcome of pathways of causally related distant and proximate determinants and intervening factors that mediate the effects of the determinants. <b>Objective</b>: The objective is to introduce the papers in this SC and to highlight the place of multistate modeling in causal analysis. <b>Methods</b>: We adopt the common distinction between structural causal modeling and dynamic causal modeling. The papers in the SC concentrate on structural causal modeling. Multistate models (and, more particularly, the continuous-time Markov process model) are oriented more toward dynamic causal modeling. In dynamic causal modeling the causal dependencies are defined in terms of events (outcomes), exposure time, and transition rates that relate exposures to events. <b>Results</b>: The contributions to the SC illustrate the progress made in structural causal modeling in the study of healthy aging. Dynamic causal analysis, however, has progressed comparatively slowly. <b>Contribution</b>: The papers in the SC and the brief introduction to multistate modeling in causal analysis pave the way to enhanced causal analysis in the study of healthy aging and in demography.
topic causal analysis
demography
healthy aging
multistate models
url https://www.demographic-research.org/volumes/vol38/10/
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AT jamesrcarey interdisciplinaryresearchonhealthyagingintroduction
AT qiangli interdisciplinaryresearchonhealthyagingintroduction
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