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Including household effects in Big Data research: the experience of building a longitudinal residence algorithm using linked administrative data in Wales

Karen Susan Tingay, Matthew Roberts, Charles Musselwhite Orcid Logo

International Journal of Population Data Science, Volume: 3, Issue: 1

Swansea University Author: Charles Musselwhite Orcid Logo

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DOI (Published version): 10.23889/ijpds.v3i1.452

Abstract

The effect of the wider social-environment on physical and emotional health has long been an area of study. Extrapolating the impact of the individual’s immediate environment, such as living with a smoker or caring for a chronically-ill child, would potentially reduce confounding effects in health-r...

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Published in: International Journal of Population Data Science
ISSN: 2399-4908
Published: 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa45996
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first_indexed 2018-11-20T14:20:41Z
last_indexed 2020-10-22T02:58:00Z
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spelling 2020-10-21T11:53:34.7469357 v2 45996 2018-11-20 Including household effects in Big Data research: the experience of building a longitudinal residence algorithm using linked administrative data in Wales c9a49f25a5adb54c55612ae49560100c 0000-0002-4831-2092 Charles Musselwhite Charles Musselwhite true false 2018-11-20 PHAC The effect of the wider social-environment on physical and emotional health has long been an area of study. Extrapolating the impact of the individual’s immediate environment, such as living with a smoker or caring for a chronically-ill child, would potentially reduce confounding effects in health-related research. Surveys, including the UK Census, are beginning to collect data on household composition. However, these surveys are expensive, time consuming, and, as such, are only completed by a subsection of the population. Large-scale, linked databanks, such as the SAIL Databank at Swansea University, which hold routinely collected secondary use clinical and administrative datasets, are broader in scope, both in terms of the nature of the data held, and the population. The SAIL databank includes demographic data and a geographic indicator that makes it possible to identify groups of people that share accommodation, and in some cases the familial relationships among them. This paper describes a method for creating households, including considerations for how that information can be securely shared for research purposes. This approach has broad implications in Wales and beyond, opening up possibilities for more detailed population level research that includes consideration of residential social interactions. Journal Article International Journal of Population Data Science 3 1 2399-4908 Big data, household, family, health, dataset, methodology, protocol 20 11 2018 2018-11-20 10.23889/ijpds.v3i1.452 https://ijpds.org/article/view/452 COLLEGE NANME Public Health COLLEGE CODE PHAC Swansea University 2020-10-21T11:53:34.7469357 2018-11-20T11:26:21.3665544 Faculty of Medicine, Health and Life Sciences The Centre for Innovative Ageing Karen Susan Tingay 1 Matthew Roberts 2 Charles Musselwhite 0000-0002-4831-2092 3 45996__11948__a023239e302e4906ab3f3a53d5a77dd4.pdf Tingay,RobertsMusselwhite2018.pdf 2018-11-20T11:29:44.8270000 Output 469934 application/pdf Version of Record true 2018-11-20T00:00:00.0000000 This work is licensed under a Creative Commons Attribution 4.0 International License. true eng
title Including household effects in Big Data research: the experience of building a longitudinal residence algorithm using linked administrative data in Wales
spellingShingle Including household effects in Big Data research: the experience of building a longitudinal residence algorithm using linked administrative data in Wales
Charles Musselwhite
title_short Including household effects in Big Data research: the experience of building a longitudinal residence algorithm using linked administrative data in Wales
title_full Including household effects in Big Data research: the experience of building a longitudinal residence algorithm using linked administrative data in Wales
title_fullStr Including household effects in Big Data research: the experience of building a longitudinal residence algorithm using linked administrative data in Wales
title_full_unstemmed Including household effects in Big Data research: the experience of building a longitudinal residence algorithm using linked administrative data in Wales
title_sort Including household effects in Big Data research: the experience of building a longitudinal residence algorithm using linked administrative data in Wales
author_id_str_mv c9a49f25a5adb54c55612ae49560100c
author_id_fullname_str_mv c9a49f25a5adb54c55612ae49560100c_***_Charles Musselwhite
author Charles Musselwhite
author2 Karen Susan Tingay
Matthew Roberts
Charles Musselwhite
format Journal article
container_title International Journal of Population Data Science
container_volume 3
container_issue 1
publishDate 2018
institution Swansea University
issn 2399-4908
doi_str_mv 10.23889/ijpds.v3i1.452
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str The Centre for Innovative Ageing{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}The Centre for Innovative Ageing
url https://ijpds.org/article/view/452
document_store_str 1
active_str 0
description The effect of the wider social-environment on physical and emotional health has long been an area of study. Extrapolating the impact of the individual’s immediate environment, such as living with a smoker or caring for a chronically-ill child, would potentially reduce confounding effects in health-related research. Surveys, including the UK Census, are beginning to collect data on household composition. However, these surveys are expensive, time consuming, and, as such, are only completed by a subsection of the population. Large-scale, linked databanks, such as the SAIL Databank at Swansea University, which hold routinely collected secondary use clinical and administrative datasets, are broader in scope, both in terms of the nature of the data held, and the population. The SAIL databank includes demographic data and a geographic indicator that makes it possible to identify groups of people that share accommodation, and in some cases the familial relationships among them. This paper describes a method for creating households, including considerations for how that information can be securely shared for research purposes. This approach has broad implications in Wales and beyond, opening up possibilities for more detailed population level research that includes consideration of residential social interactions.
published_date 2018-11-20T03:57:41Z
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score 11.024221

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