Cargando…
Time-Based Data in Occupational Studies: The Whys, the Hows, and Some Remaining Challenges in Compositional Data Analysis (CoDA)
Data on the use of time in different exposures, behaviors, and work tasks are common in occupational research. Such data are most often expressed in hours, minutes, or percentage of work time. Thus, they are constrained or ‘compositional’, in that they add up to a finite sum (e.g. 8 h of work or 100...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7544002/ https://www.ncbi.nlm.nih.gov/pubmed/32607544 http://dx.doi.org/10.1093/annweh/wxaa056 |
_version_ | 1783591768705466368 |
---|---|
author | Gupta, Nidhi Rasmussen, Charlotte Lund Holtermann, Andreas Mathiassen, Svend Erik |
author_facet | Gupta, Nidhi Rasmussen, Charlotte Lund Holtermann, Andreas Mathiassen, Svend Erik |
author_sort | Gupta, Nidhi |
collection | PubMed |
description | Data on the use of time in different exposures, behaviors, and work tasks are common in occupational research. Such data are most often expressed in hours, minutes, or percentage of work time. Thus, they are constrained or ‘compositional’, in that they add up to a finite sum (e.g. 8 h of work or 100% work time). Due to their properties, compositional data need to be processed and analyzed using specifically adapted methods. Compositional data analysis (CoDA) has become a particularly established framework to handle such data in various scientific fields such as nutritional epidemiology, geology, and chemistry, but has only recently gained attention in public and occupational health sciences. In this paper, we introduce the reader to CoDA by explaining why CoDA should be used when dealing with compositional time-use data, showing how to perform CoDA, including a worked example, and pointing at some remaining challenges in CoDA. The paper concludes by emphasizing that CoDA in occupational research is still in its infancy, and stresses the need for further development and experience in the use of CoDA for time-based occupational exposures. We hope that the paper will encourage researchers to adopt and apply CoDA in studies of work exposures and health. |
format | Online Article Text |
id | pubmed-7544002 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-75440022020-10-15 Time-Based Data in Occupational Studies: The Whys, the Hows, and Some Remaining Challenges in Compositional Data Analysis (CoDA) Gupta, Nidhi Rasmussen, Charlotte Lund Holtermann, Andreas Mathiassen, Svend Erik Ann Work Expo Health Commentaries Data on the use of time in different exposures, behaviors, and work tasks are common in occupational research. Such data are most often expressed in hours, minutes, or percentage of work time. Thus, they are constrained or ‘compositional’, in that they add up to a finite sum (e.g. 8 h of work or 100% work time). Due to their properties, compositional data need to be processed and analyzed using specifically adapted methods. Compositional data analysis (CoDA) has become a particularly established framework to handle such data in various scientific fields such as nutritional epidemiology, geology, and chemistry, but has only recently gained attention in public and occupational health sciences. In this paper, we introduce the reader to CoDA by explaining why CoDA should be used when dealing with compositional time-use data, showing how to perform CoDA, including a worked example, and pointing at some remaining challenges in CoDA. The paper concludes by emphasizing that CoDA in occupational research is still in its infancy, and stresses the need for further development and experience in the use of CoDA for time-based occupational exposures. We hope that the paper will encourage researchers to adopt and apply CoDA in studies of work exposures and health. Oxford University Press 2020-07-01 /pmc/articles/PMC7544002/ /pubmed/32607544 http://dx.doi.org/10.1093/annweh/wxaa056 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of the British Occupational Hygiene Society. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Commentaries Gupta, Nidhi Rasmussen, Charlotte Lund Holtermann, Andreas Mathiassen, Svend Erik Time-Based Data in Occupational Studies: The Whys, the Hows, and Some Remaining Challenges in Compositional Data Analysis (CoDA) |
title | Time-Based Data in Occupational Studies: The Whys, the Hows, and Some Remaining Challenges in Compositional Data Analysis (CoDA) |
title_full | Time-Based Data in Occupational Studies: The Whys, the Hows, and Some Remaining Challenges in Compositional Data Analysis (CoDA) |
title_fullStr | Time-Based Data in Occupational Studies: The Whys, the Hows, and Some Remaining Challenges in Compositional Data Analysis (CoDA) |
title_full_unstemmed | Time-Based Data in Occupational Studies: The Whys, the Hows, and Some Remaining Challenges in Compositional Data Analysis (CoDA) |
title_short | Time-Based Data in Occupational Studies: The Whys, the Hows, and Some Remaining Challenges in Compositional Data Analysis (CoDA) |
title_sort | time-based data in occupational studies: the whys, the hows, and some remaining challenges in compositional data analysis (coda) |
topic | Commentaries |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7544002/ https://www.ncbi.nlm.nih.gov/pubmed/32607544 http://dx.doi.org/10.1093/annweh/wxaa056 |
work_keys_str_mv | AT guptanidhi timebaseddatainoccupationalstudiesthewhysthehowsandsomeremainingchallengesincompositionaldataanalysiscoda AT rasmussencharlottelund timebaseddatainoccupationalstudiesthewhysthehowsandsomeremainingchallengesincompositionaldataanalysiscoda AT holtermannandreas timebaseddatainoccupationalstudiesthewhysthehowsandsomeremainingchallengesincompositionaldataanalysiscoda AT mathiassensvenderik timebaseddatainoccupationalstudiesthewhysthehowsandsomeremainingchallengesincompositionaldataanalysiscoda |