A synthetic population dataset for estimating small area health and socio-economic outcomes in Great Britain

Abstract

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.

Metadata

Item Type:	Article
Authors/Creators:	Wu, G. Heppenstall, A. Meier, P. Purshouse, R. https://orcid.org/0000-0001-5880-1925 Lomax, N.
Copyright, Publisher and Additional Information:	© 2022 The Author(s). 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/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.
Dates:	Published: 20 January 2022 Published (online): 20 January 2022 Accepted: 13 December 2021
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Funding Information:	Funder Grant number Medical Research Council MR/S037578/1
Depositing User:	Symplectic Sheffield
Date Deposited:	22 Feb 2022 15:19
Last Modified:	23 Feb 2022 09:36
Status:	Published
Publisher:	Springer Nature
Refereed:	Yes
Identification Number:	10.1038/s41597-022-01124-9
Related URLs:	PubMed URL
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:183924

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A synthetic population dataset for estimating small area health and socio-economic outcomes in Great Britain

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