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Investigating years of life lost in Belgium, 2004–2019: A comprehensive analysis using a probabilistic redistribution approach

INTRODUCTION: Information on years of life lost (YLL) due to premature mortality is instrumental to assess the fatal impact of disease and necessary for the calculation of Belgian disability-adjusted life years (DALYs). This study presents a novel method to reallocate causes of death data. MATERIALS...

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Autores principales: Devleesschauwer, Brecht, Scohy, Aline, De Pauw, Robby, Gorasso, Vanessa, Kongs, Anne, Neirynck, Elias, Verduyckt, Peter, Wyper, Grant M. A., Van den Borre, Laura
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464430/
https://www.ncbi.nlm.nih.gov/pubmed/37626403
http://dx.doi.org/10.1186/s13690-023-01163-7
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author Devleesschauwer, Brecht
Scohy, Aline
De Pauw, Robby
Gorasso, Vanessa
Kongs, Anne
Neirynck, Elias
Verduyckt, Peter
Wyper, Grant M. A.
Van den Borre, Laura
author_facet Devleesschauwer, Brecht
Scohy, Aline
De Pauw, Robby
Gorasso, Vanessa
Kongs, Anne
Neirynck, Elias
Verduyckt, Peter
Wyper, Grant M. A.
Van den Borre, Laura
author_sort Devleesschauwer, Brecht
collection PubMed
description INTRODUCTION: Information on years of life lost (YLL) due to premature mortality is instrumental to assess the fatal impact of disease and necessary for the calculation of Belgian disability-adjusted life years (DALYs). This study presents a novel method to reallocate causes of death data. MATERIALS AND METHODS: Causes of death data are provided by Statistics Belgium (Statbel). First, the specific ICD-10 codes that define the underlying cause of death are mapped to the GBD cause list. Second, ill-defined deaths (IDDs) are redistributed to specific ICD-10 codes. A four-step probabilistic redistribution was developed to fit the Belgian context: redistribution using predefined ICD codes, redistribution using multiple causes of death data, internal redistribution, and redistribution to all causes. Finally, we used the GBD 2019 reference life table to calculate Standard Expected Years of Life Lost (SEYLL). RESULTS: In Belgium, between 2004 and 2019, IDDs increased from 31 to 34% of all deaths. The majority was redistributed using predefined ICD codes (14–15%), followed by the redistribution using multiple causes of death data (10–12%). The total number of SEYLL decreased from 1.83 to 1.73 million per year. In 2019, the top cause of SEYLL was lung cancer with a share of 8.5%, followed by ischemic heart disease (8.1%) and Alzheimer’s disease and other dementias (5.7%). All results are available in an online tool https://burden.sciensano.be/shiny/mortality2019/. CONCLUSION: The redistribution process assigned a specific cause of death to all deaths in Belgium, making it possible to investigate the full mortality burden for the first time. A large number of estimates were produced to estimate SEYLL by age, sex, and region for a large number of causes of death and every year between 2004 and 2019. These estimates are important stepping stones for future investigations on Disability-Adjusted Life Years (DALYs) in Belgium. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13690-023-01163-7.
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spelling pubmed-104644302023-08-30 Investigating years of life lost in Belgium, 2004–2019: A comprehensive analysis using a probabilistic redistribution approach Devleesschauwer, Brecht Scohy, Aline De Pauw, Robby Gorasso, Vanessa Kongs, Anne Neirynck, Elias Verduyckt, Peter Wyper, Grant M. A. Van den Borre, Laura Arch Public Health Research INTRODUCTION: Information on years of life lost (YLL) due to premature mortality is instrumental to assess the fatal impact of disease and necessary for the calculation of Belgian disability-adjusted life years (DALYs). This study presents a novel method to reallocate causes of death data. MATERIALS AND METHODS: Causes of death data are provided by Statistics Belgium (Statbel). First, the specific ICD-10 codes that define the underlying cause of death are mapped to the GBD cause list. Second, ill-defined deaths (IDDs) are redistributed to specific ICD-10 codes. A four-step probabilistic redistribution was developed to fit the Belgian context: redistribution using predefined ICD codes, redistribution using multiple causes of death data, internal redistribution, and redistribution to all causes. Finally, we used the GBD 2019 reference life table to calculate Standard Expected Years of Life Lost (SEYLL). RESULTS: In Belgium, between 2004 and 2019, IDDs increased from 31 to 34% of all deaths. The majority was redistributed using predefined ICD codes (14–15%), followed by the redistribution using multiple causes of death data (10–12%). The total number of SEYLL decreased from 1.83 to 1.73 million per year. In 2019, the top cause of SEYLL was lung cancer with a share of 8.5%, followed by ischemic heart disease (8.1%) and Alzheimer’s disease and other dementias (5.7%). All results are available in an online tool https://burden.sciensano.be/shiny/mortality2019/. CONCLUSION: The redistribution process assigned a specific cause of death to all deaths in Belgium, making it possible to investigate the full mortality burden for the first time. A large number of estimates were produced to estimate SEYLL by age, sex, and region for a large number of causes of death and every year between 2004 and 2019. These estimates are important stepping stones for future investigations on Disability-Adjusted Life Years (DALYs) in Belgium. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13690-023-01163-7. BioMed Central 2023-08-25 /pmc/articles/PMC10464430/ /pubmed/37626403 http://dx.doi.org/10.1186/s13690-023-01163-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Devleesschauwer, Brecht
Scohy, Aline
De Pauw, Robby
Gorasso, Vanessa
Kongs, Anne
Neirynck, Elias
Verduyckt, Peter
Wyper, Grant M. A.
Van den Borre, Laura
Investigating years of life lost in Belgium, 2004–2019: A comprehensive analysis using a probabilistic redistribution approach
title Investigating years of life lost in Belgium, 2004–2019: A comprehensive analysis using a probabilistic redistribution approach
title_full Investigating years of life lost in Belgium, 2004–2019: A comprehensive analysis using a probabilistic redistribution approach
title_fullStr Investigating years of life lost in Belgium, 2004–2019: A comprehensive analysis using a probabilistic redistribution approach
title_full_unstemmed Investigating years of life lost in Belgium, 2004–2019: A comprehensive analysis using a probabilistic redistribution approach
title_short Investigating years of life lost in Belgium, 2004–2019: A comprehensive analysis using a probabilistic redistribution approach
title_sort investigating years of life lost in belgium, 2004–2019: a comprehensive analysis using a probabilistic redistribution approach
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464430/
https://www.ncbi.nlm.nih.gov/pubmed/37626403
http://dx.doi.org/10.1186/s13690-023-01163-7
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