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Hands-on Tutorial on a Modeling Framework for Projections of Climate Change Impacts on Health
Reliable estimates of future health impacts due to climate change are needed to inform and contribute to the design of efficient adaptation and mitigation strategies. However, projecting health burdens associated to specific environmental stressors is a challenging task because of the complex risk p...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6533172/ https://www.ncbi.nlm.nih.gov/pubmed/30829832 http://dx.doi.org/10.1097/EDE.0000000000000982 |
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author | Vicedo-Cabrera, Ana M. Sera, Francesco Gasparrini, Antonio |
author_facet | Vicedo-Cabrera, Ana M. Sera, Francesco Gasparrini, Antonio |
author_sort | Vicedo-Cabrera, Ana M. |
collection | PubMed |
description | Reliable estimates of future health impacts due to climate change are needed to inform and contribute to the design of efficient adaptation and mitigation strategies. However, projecting health burdens associated to specific environmental stressors is a challenging task because of the complex risk patterns and inherent uncertainty of future climate scenarios. These assessments involve multidisciplinary knowledge, requiring expertise in epidemiology, statistics, and climate science, among other subjects. Here, we present a methodologic framework to estimate future health impacts under climate change scenarios based on a defined set of assumptions and advanced statistical techniques developed in time-series analysis in environmental epidemiology. The proposed methodology is illustrated through a step-by-step hands-on tutorial structured in well-defined sections that cover the main methodological steps and essential elements. Each section provides a thorough description of each step, along with a discussion on available analytical options and the rationale on the choices made in the proposed framework. The illustration is complemented with a practical example of study using real-world data and a series of R scripts included as Supplementary Digital Content; http://links.lww.com/EDE/B504, which facilitates its replication and extension on other environmental stressors, outcomes, study settings, and projection scenarios. Users should critically assess the potential modeling alternatives and modify the framework and R code to adapt them to their research on health impact projections. |
format | Online Article Text |
id | pubmed-6533172 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-65331722020-05-01 Hands-on Tutorial on a Modeling Framework for Projections of Climate Change Impacts on Health Vicedo-Cabrera, Ana M. Sera, Francesco Gasparrini, Antonio Epidemiology Environmental Epidemiology Reliable estimates of future health impacts due to climate change are needed to inform and contribute to the design of efficient adaptation and mitigation strategies. However, projecting health burdens associated to specific environmental stressors is a challenging task because of the complex risk patterns and inherent uncertainty of future climate scenarios. These assessments involve multidisciplinary knowledge, requiring expertise in epidemiology, statistics, and climate science, among other subjects. Here, we present a methodologic framework to estimate future health impacts under climate change scenarios based on a defined set of assumptions and advanced statistical techniques developed in time-series analysis in environmental epidemiology. The proposed methodology is illustrated through a step-by-step hands-on tutorial structured in well-defined sections that cover the main methodological steps and essential elements. Each section provides a thorough description of each step, along with a discussion on available analytical options and the rationale on the choices made in the proposed framework. The illustration is complemented with a practical example of study using real-world data and a series of R scripts included as Supplementary Digital Content; http://links.lww.com/EDE/B504, which facilitates its replication and extension on other environmental stressors, outcomes, study settings, and projection scenarios. Users should critically assess the potential modeling alternatives and modify the framework and R code to adapt them to their research on health impact projections. Lippincott Williams & Wilkins 2019-05 2019-04-08 /pmc/articles/PMC6533172/ /pubmed/30829832 http://dx.doi.org/10.1097/EDE.0000000000000982 Text en Copyright © 2019 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC) (http://creativecommons.org/licenses/by-nc/4.0/) , where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. |
spellingShingle | Environmental Epidemiology Vicedo-Cabrera, Ana M. Sera, Francesco Gasparrini, Antonio Hands-on Tutorial on a Modeling Framework for Projections of Climate Change Impacts on Health |
title | Hands-on Tutorial on a Modeling Framework for Projections of Climate Change Impacts on Health |
title_full | Hands-on Tutorial on a Modeling Framework for Projections of Climate Change Impacts on Health |
title_fullStr | Hands-on Tutorial on a Modeling Framework for Projections of Climate Change Impacts on Health |
title_full_unstemmed | Hands-on Tutorial on a Modeling Framework for Projections of Climate Change Impacts on Health |
title_short | Hands-on Tutorial on a Modeling Framework for Projections of Climate Change Impacts on Health |
title_sort | hands-on tutorial on a modeling framework for projections of climate change impacts on health |
topic | Environmental Epidemiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6533172/ https://www.ncbi.nlm.nih.gov/pubmed/30829832 http://dx.doi.org/10.1097/EDE.0000000000000982 |
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