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Estimating risk factor attributable burden – challenges and potential solutions when using the comparative risk assessment methodology
BACKGROUND: Burden of disease analyses quantify population health and provide comprehensive overviews of the health status of countries or specific population groups. The comparative risk assessment (CRA) methodology is commonly used to estimate the share of the burden attributable to risk factors....
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9137119/ https://www.ncbi.nlm.nih.gov/pubmed/35624479 http://dx.doi.org/10.1186/s13690-022-00900-8 |
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author | Plass, Dietrich Hilderink, Henk Lehtomäki, Heli Øverland, Simon Eikemo, Terje A. Lai, Taavi Gorasso, Vanessa Devleesschauwer, Brecht |
author_facet | Plass, Dietrich Hilderink, Henk Lehtomäki, Heli Øverland, Simon Eikemo, Terje A. Lai, Taavi Gorasso, Vanessa Devleesschauwer, Brecht |
author_sort | Plass, Dietrich |
collection | PubMed |
description | BACKGROUND: Burden of disease analyses quantify population health and provide comprehensive overviews of the health status of countries or specific population groups. The comparative risk assessment (CRA) methodology is commonly used to estimate the share of the burden attributable to risk factors. The aim of this paper is to identify and address some selected important challenges associated with CRA, illustrated by examples, and to discuss ways to handle them. Further, the main challenges are addressed and finally, similarities and differences between CRA and health impact assessments (HIA) are discussed, as these concepts are sometimes referred to synonymously but have distinctly different applications. RESULTS: CRAs are very data demanding. One key element is the exposure-response relationship described e.g. by a mathematical function. Combining estimates to arrive at coherent functions is challenging due to the large variability in risk exposure definitions and data quality. Also, the uncertainty attached to this data is difficult to account for. Another key issue along the CRA-steps is to define a theoretical minimal risk exposure level for each risk factor. In some cases, this level is evident and self-explanatory (e.g., zero smoking), but often more difficult to define and justify (e.g., ideal consumption of whole grains). CRA combine all relevant information and allow to estimate population attributable fractions (PAFs) quantifying the proportion of disease burden attributable to exposure. Among many available formulae for PAFs, it is important to use the one that allows consistency between definitions, units of the exposure data, and the exposure response functions. When combined effects of different risk factors are of interest, the non-additive nature of PAFs and possible mediation effects need to be reflected. Further, as attributable burden is typically calculated based on current exposure and current health outcomes, the time dimensions of risk and outcomes may become inconsistent. Finally, the evidence of the association between exposure and outcome can be heterogeneous which needs to be considered when interpreting CRA results. CONCLUSIONS: The methodological challenges make transparent reporting of input and process data in CRA a necessary prerequisite. The evidence for causality between included risk-outcome pairs has to be well established to inform public health practice. |
format | Online Article Text |
id | pubmed-9137119 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-91371192022-05-28 Estimating risk factor attributable burden – challenges and potential solutions when using the comparative risk assessment methodology Plass, Dietrich Hilderink, Henk Lehtomäki, Heli Øverland, Simon Eikemo, Terje A. Lai, Taavi Gorasso, Vanessa Devleesschauwer, Brecht Arch Public Health Methodology BACKGROUND: Burden of disease analyses quantify population health and provide comprehensive overviews of the health status of countries or specific population groups. The comparative risk assessment (CRA) methodology is commonly used to estimate the share of the burden attributable to risk factors. The aim of this paper is to identify and address some selected important challenges associated with CRA, illustrated by examples, and to discuss ways to handle them. Further, the main challenges are addressed and finally, similarities and differences between CRA and health impact assessments (HIA) are discussed, as these concepts are sometimes referred to synonymously but have distinctly different applications. RESULTS: CRAs are very data demanding. One key element is the exposure-response relationship described e.g. by a mathematical function. Combining estimates to arrive at coherent functions is challenging due to the large variability in risk exposure definitions and data quality. Also, the uncertainty attached to this data is difficult to account for. Another key issue along the CRA-steps is to define a theoretical minimal risk exposure level for each risk factor. In some cases, this level is evident and self-explanatory (e.g., zero smoking), but often more difficult to define and justify (e.g., ideal consumption of whole grains). CRA combine all relevant information and allow to estimate population attributable fractions (PAFs) quantifying the proportion of disease burden attributable to exposure. Among many available formulae for PAFs, it is important to use the one that allows consistency between definitions, units of the exposure data, and the exposure response functions. When combined effects of different risk factors are of interest, the non-additive nature of PAFs and possible mediation effects need to be reflected. Further, as attributable burden is typically calculated based on current exposure and current health outcomes, the time dimensions of risk and outcomes may become inconsistent. Finally, the evidence of the association between exposure and outcome can be heterogeneous which needs to be considered when interpreting CRA results. CONCLUSIONS: The methodological challenges make transparent reporting of input and process data in CRA a necessary prerequisite. The evidence for causality between included risk-outcome pairs has to be well established to inform public health practice. BioMed Central 2022-05-27 /pmc/articles/PMC9137119/ /pubmed/35624479 http://dx.doi.org/10.1186/s13690-022-00900-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 | Methodology Plass, Dietrich Hilderink, Henk Lehtomäki, Heli Øverland, Simon Eikemo, Terje A. Lai, Taavi Gorasso, Vanessa Devleesschauwer, Brecht Estimating risk factor attributable burden – challenges and potential solutions when using the comparative risk assessment methodology |
title | Estimating risk factor attributable burden – challenges and potential solutions when using the comparative risk assessment methodology |
title_full | Estimating risk factor attributable burden – challenges and potential solutions when using the comparative risk assessment methodology |
title_fullStr | Estimating risk factor attributable burden – challenges and potential solutions when using the comparative risk assessment methodology |
title_full_unstemmed | Estimating risk factor attributable burden – challenges and potential solutions when using the comparative risk assessment methodology |
title_short | Estimating risk factor attributable burden – challenges and potential solutions when using the comparative risk assessment methodology |
title_sort | estimating risk factor attributable burden – challenges and potential solutions when using the comparative risk assessment methodology |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9137119/ https://www.ncbi.nlm.nih.gov/pubmed/35624479 http://dx.doi.org/10.1186/s13690-022-00900-8 |
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