Approaches for incorporating environmental mixtures as mediators in mediation analysis

Mediation analysis offers an essential and rapidly expanding tool in environmental health studies to investigate the contribution of environmental factors towards observed associations between risk factors and health outcomes. When evaluating environmental factors, there may be particular interest i...

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Main Authors: Andrea Bellavia, Tamarra James-Todd, Paige L. Williams
Format: Article
Language:English
Published: Elsevier 2019-02-01
Series:Environment International
Online Access:http://www.sciencedirect.com/science/article/pii/S016041201832021X
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spelling doaj-bf76c80c542a438eb5ca5488fa8281d62020-11-24T21:45:12ZengElsevierEnvironment International0160-41202019-02-01123368374Approaches for incorporating environmental mixtures as mediators in mediation analysisAndrea Bellavia0Tamarra James-Todd1Paige L. Williams2Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States of America; Corresponding author at: Department of Environmental Health, Harvard T.H. Chan School of Public Health, 665 Huntington Ave., Bldg. 1, 14th Floor, Boston, MA 02120, United States of America.Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States of America; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States of America; Division of Women's Health, Department of Medicine, Connors Center for Women's Health and Gender Biology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02120, United States of AmericaDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States of America; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States of AmericaMediation analysis offers an essential and rapidly expanding tool in environmental health studies to investigate the contribution of environmental factors towards observed associations between risk factors and health outcomes. When evaluating environmental factors, there may be particular interest in quantifying the impact of exposure to environmental mixtures on human health. In this context, evaluating the joint effect of multiple chemicals or pollutants, rather than individual examination, allows accurate identification of risk factors, assessment of interactions, and ultimately development of more targeted public health interventions. While mediation analysis has been extended to incorporate several methodological complexities specific to environmental factors, little attention has been given to integrating the analysis of environmental mixtures.The aim of this review is to present some of the available methods for environmental mixtures, and discuss how these methods can be integrated within a mediation analysis framework. By incorporating these methods into a mediation framework, investigators will be able to evaluate the contribution of environmental mixtures as mediators of exposure-outcome associations, based on methodologies that are currently available.While standard regression-based methods for multiple mediators can be used, these can easily become unstable as the number of mixture components increases. Summary and classification methods, or hierarchical modeling, can reduce the number of mediators by creating scores or possibly uncorrelated subgroups. This approach allows retrieving indirect effects due to the mixture or to a specific subgroup, but makes identification of component-specific effects and interactions complicated. Finally, one can use various approaches for analyzing mixtures in a two-stage fashion, selecting relevant mediators to be included in the final model.We focused this review on techniques that have been presented to the environmental health community and that can be conducted with major statistical software. We encourage researchers to move beyond the evaluation of one environmental factor at a time to the assessment of the joint effects of environmental mixtures when a mediation model is of interest. Available methods target different aspects related to environmental mixtures and the choice of the suitable approach will depend on data structures and the research question of interest. Keywords: Environmental mixtures, Environmental epidemiology, Methods, Mediation analysishttp://www.sciencedirect.com/science/article/pii/S016041201832021X
collection DOAJ
language English
format Article
sources DOAJ
author Andrea Bellavia
Tamarra James-Todd
Paige L. Williams
spellingShingle Andrea Bellavia
Tamarra James-Todd
Paige L. Williams
Approaches for incorporating environmental mixtures as mediators in mediation analysis
Environment International
author_facet Andrea Bellavia
Tamarra James-Todd
Paige L. Williams
author_sort Andrea Bellavia
title Approaches for incorporating environmental mixtures as mediators in mediation analysis
title_short Approaches for incorporating environmental mixtures as mediators in mediation analysis
title_full Approaches for incorporating environmental mixtures as mediators in mediation analysis
title_fullStr Approaches for incorporating environmental mixtures as mediators in mediation analysis
title_full_unstemmed Approaches for incorporating environmental mixtures as mediators in mediation analysis
title_sort approaches for incorporating environmental mixtures as mediators in mediation analysis
publisher Elsevier
series Environment International
issn 0160-4120
publishDate 2019-02-01
description Mediation analysis offers an essential and rapidly expanding tool in environmental health studies to investigate the contribution of environmental factors towards observed associations between risk factors and health outcomes. When evaluating environmental factors, there may be particular interest in quantifying the impact of exposure to environmental mixtures on human health. In this context, evaluating the joint effect of multiple chemicals or pollutants, rather than individual examination, allows accurate identification of risk factors, assessment of interactions, and ultimately development of more targeted public health interventions. While mediation analysis has been extended to incorporate several methodological complexities specific to environmental factors, little attention has been given to integrating the analysis of environmental mixtures.The aim of this review is to present some of the available methods for environmental mixtures, and discuss how these methods can be integrated within a mediation analysis framework. By incorporating these methods into a mediation framework, investigators will be able to evaluate the contribution of environmental mixtures as mediators of exposure-outcome associations, based on methodologies that are currently available.While standard regression-based methods for multiple mediators can be used, these can easily become unstable as the number of mixture components increases. Summary and classification methods, or hierarchical modeling, can reduce the number of mediators by creating scores or possibly uncorrelated subgroups. This approach allows retrieving indirect effects due to the mixture or to a specific subgroup, but makes identification of component-specific effects and interactions complicated. Finally, one can use various approaches for analyzing mixtures in a two-stage fashion, selecting relevant mediators to be included in the final model.We focused this review on techniques that have been presented to the environmental health community and that can be conducted with major statistical software. We encourage researchers to move beyond the evaluation of one environmental factor at a time to the assessment of the joint effects of environmental mixtures when a mediation model is of interest. Available methods target different aspects related to environmental mixtures and the choice of the suitable approach will depend on data structures and the research question of interest. Keywords: Environmental mixtures, Environmental epidemiology, Methods, Mediation analysis
url http://www.sciencedirect.com/science/article/pii/S016041201832021X
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