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Disease Attribution to Multiple Exposures Using Aggregate Data

BACKGROUND: Identifying which exposures cause disease and quantifying their impacts is essential in promoting and monitoring public health. When multiple exposures are involved, measuring individual contributions becomes challenging. METHODS: The authors propose a disease attribution method based on...

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Detalles Bibliográficos
Autores principales: Lee, Wen-Chung, Wu, Yun-Chun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Japan Epidemiological Association 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10319529/
https://www.ncbi.nlm.nih.gov/pubmed/35283399
http://dx.doi.org/10.2188/jea.JE20210084
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author Lee, Wen-Chung
Wu, Yun-Chun
author_facet Lee, Wen-Chung
Wu, Yun-Chun
author_sort Lee, Wen-Chung
collection PubMed
description BACKGROUND: Identifying which exposures cause disease and quantifying their impacts is essential in promoting and monitoring public health. When multiple exposures are involved, measuring individual contributions becomes challenging. METHODS: The authors propose a disease attribution method based on aggregate data or summary statistics of individual-level data, possibly from multiple data sources. RESULTS: Using the proposed method, the burden of disease is apportioned to the independent and interaction effects of each of its major risk factors and all the other factors as a whole. This scheme guarantees that 100% is the total share of the burden. CONCLUSION: The calculation is simple and straightforward; therefore, it is recommended for use in studies on disease burden.
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spelling pubmed-103195292023-08-05 Disease Attribution to Multiple Exposures Using Aggregate Data Lee, Wen-Chung Wu, Yun-Chun J Epidemiol Original Article BACKGROUND: Identifying which exposures cause disease and quantifying their impacts is essential in promoting and monitoring public health. When multiple exposures are involved, measuring individual contributions becomes challenging. METHODS: The authors propose a disease attribution method based on aggregate data or summary statistics of individual-level data, possibly from multiple data sources. RESULTS: Using the proposed method, the burden of disease is apportioned to the independent and interaction effects of each of its major risk factors and all the other factors as a whole. This scheme guarantees that 100% is the total share of the burden. CONCLUSION: The calculation is simple and straightforward; therefore, it is recommended for use in studies on disease burden. Japan Epidemiological Association 2023-08-05 /pmc/articles/PMC10319529/ /pubmed/35283399 http://dx.doi.org/10.2188/jea.JE20210084 Text en © 2022 Wen-Chung Lee et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Original Article
Lee, Wen-Chung
Wu, Yun-Chun
Disease Attribution to Multiple Exposures Using Aggregate Data
title Disease Attribution to Multiple Exposures Using Aggregate Data
title_full Disease Attribution to Multiple Exposures Using Aggregate Data
title_fullStr Disease Attribution to Multiple Exposures Using Aggregate Data
title_full_unstemmed Disease Attribution to Multiple Exposures Using Aggregate Data
title_short Disease Attribution to Multiple Exposures Using Aggregate Data
title_sort disease attribution to multiple exposures using aggregate data
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10319529/
https://www.ncbi.nlm.nih.gov/pubmed/35283399
http://dx.doi.org/10.2188/jea.JE20210084
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