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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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/ |
work_keys_str_mv |
AT franswillekens interdisciplinaryresearchonhealthyagingintroduction AT jamesrcarey interdisciplinaryresearchonhealthyagingintroduction AT qiangli interdisciplinaryresearchonhealthyagingintroduction |
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