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A synthetic population dataset for estimating small area health and socio-economic outcomes in Great Britain
In order to understand the health outcomes for distinct sub-groups of the population or across different geographies, it is advantageous to be able to build bespoke groupings from individual level data. Individuals possess distinct characteristics, exhibit distinct behaviours and accumulate their ow...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8776798/ https://www.ncbi.nlm.nih.gov/pubmed/35058471 http://dx.doi.org/10.1038/s41597-022-01124-9 |
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author | Wu, Guoqiang Heppenstall, Alison Meier, Petra Purshouse, Robin Lomax, Nik |
author_facet | Wu, Guoqiang Heppenstall, Alison Meier, Petra Purshouse, Robin Lomax, Nik |
author_sort | Wu, Guoqiang |
collection | PubMed |
description | In order to understand the health outcomes for distinct sub-groups of the population or across different geographies, it is advantageous to be able to build bespoke groupings from individual level data. Individuals possess distinct characteristics, exhibit distinct behaviours and accumulate their own unique history of exposure or experiences. However, in most disciplines, not least public health, there is a lack of individual level data available outside of secure settings, especially covering large portions of the population. This paper provides detail on the creation of a synthetic micro dataset for individuals in Great Britain who have detailed attributes which can be used to model a wide range of health and other outcomes. These attributes are constructed from a range of sources including the United Kingdom Census, survey and administrative datasets. It provides a rationale for the need for this synthetic population, discusses methods for creating this dataset and provides some example results of different attribute distributions for distinct sub-population groups and over different geographical areas. |
format | Online Article Text |
id | pubmed-8776798 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-87767982022-02-04 A synthetic population dataset for estimating small area health and socio-economic outcomes in Great Britain Wu, Guoqiang Heppenstall, Alison Meier, Petra Purshouse, Robin Lomax, Nik Sci Data Data Descriptor In order to understand the health outcomes for distinct sub-groups of the population or across different geographies, it is advantageous to be able to build bespoke groupings from individual level data. Individuals possess distinct characteristics, exhibit distinct behaviours and accumulate their own unique history of exposure or experiences. However, in most disciplines, not least public health, there is a lack of individual level data available outside of secure settings, especially covering large portions of the population. This paper provides detail on the creation of a synthetic micro dataset for individuals in Great Britain who have detailed attributes which can be used to model a wide range of health and other outcomes. These attributes are constructed from a range of sources including the United Kingdom Census, survey and administrative datasets. It provides a rationale for the need for this synthetic population, discusses methods for creating this dataset and provides some example results of different attribute distributions for distinct sub-population groups and over different geographical areas. Nature Publishing Group UK 2022-01-20 /pmc/articles/PMC8776798/ /pubmed/35058471 http://dx.doi.org/10.1038/s41597-022-01124-9 Text en © The Author(s) 2022 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, 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 metadata files associated with this article. |
spellingShingle | Data Descriptor Wu, Guoqiang Heppenstall, Alison Meier, Petra Purshouse, Robin Lomax, Nik A synthetic population dataset for estimating small area health and socio-economic outcomes in Great Britain |
title | A synthetic population dataset for estimating small area health and socio-economic outcomes in Great Britain |
title_full | A synthetic population dataset for estimating small area health and socio-economic outcomes in Great Britain |
title_fullStr | A synthetic population dataset for estimating small area health and socio-economic outcomes in Great Britain |
title_full_unstemmed | A synthetic population dataset for estimating small area health and socio-economic outcomes in Great Britain |
title_short | A synthetic population dataset for estimating small area health and socio-economic outcomes in Great Britain |
title_sort | synthetic population dataset for estimating small area health and socio-economic outcomes in great britain |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8776798/ https://www.ncbi.nlm.nih.gov/pubmed/35058471 http://dx.doi.org/10.1038/s41597-022-01124-9 |
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