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Cohort profile: The SAIL long-term conditions e-cohort (SLTC cohort) investigating area-level changes in healthcare resource use in Wales

Timothy Osborne, Rowena Bailey, Amy Mizen Orcid Logo, Rich Fry Orcid Logo, Ronan Lyons

International Journal of Population Data Science, Volume: 10, Issue: 1

Swansea University Authors: Timothy Osborne, Rowena Bailey, Amy Mizen Orcid Logo, Rich Fry Orcid Logo, Ronan Lyons

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DOI (Published version): 10.23889/ijpds.v10i1.2465

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IntroductionThe prioritisation of acute cases of coronavirus during the pandemic caused significant disruption to non-urgent healthcare services, creating a backlog of undiagnosed and untreated individuals with long-term conditions. Previous research has explored the impact of the pandemic on long-t...

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Published in: International Journal of Population Data Science
ISSN: 2399-4908
Published: Swansea University 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa71035
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Previous research has explored the impact of the pandemic on long-term conditions in Wales, but not the geographic variation or underlying area-level characteristics associated with these changes.ObjectivesWe created the SAIL long-term conditions e-cohort (SLTC cohort) within the Secure Anonymised Information Linkage (SAIL) Databank to describe changes in healthcare service use of individuals living with long-term conditions during the COVID-19 pandemic, and to facilitate future investigations into the underlying reasons for these changes.MethodsIndividuals were included in the cohort if they interacted with health services with a long-term condition between January 2017 and December 2022. Interactions were identified using primary and secondary care datasets within the SAIL Databank. We linked this interaction level data with individual, residence, and area-level demographic data. We calculated area-level age-sex-standardised rates of interactions, based on an individual's address at the time of interaction, for the 3 years pre-COVID-19 (2017-2019) and during-COVID-19 (2020-2022). Percentage changes in rates between these time periods were calculated, and we investigated the underlying area-level characteristics associated with these differences.ResultsThe SLTC cohort contains 1,277,532 individuals. Age-sex standardised interaction rates varied by Welsh Index of Multiple Deprivation (WIMD) quintiles and Rural-Urban Classification. Areas in the most deprived WIMD quintile had the greatest median percentage decrease (23.5%) in primary care rates of interactions from pre- to during-COVID-19, and the least deprived overall WIMD quintile had the smallest (16.9%). Areas classified as 'Urban city &amp; town in a sparse setting' had the greatest decrease in primary care interactions (29.7%), and `Rural village' areas had the smallest decrease (17.1%). Secondary care rates of interactions showed less variation in rates of interactions between the two time periods.ConclusionWe have created a cohort that links area-level characteristics and measures of healthcare resource use, in a study period that covers pre- and during-COVID-19, which will allow researchers to investigate geographic variation of changes in healthcare resource use over this time period and the underlying influences. This cohort can also be further linked to other area-level characteristics of interest, such as travel times to general practices, or access to green space measures.</abstract><type>Journal Article</type><journal>International Journal of Population Data Science</journal><volume>10</volume><journalNumber>1</journalNumber><paginationStart/><paginationEnd/><publisher>Swansea University</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2399-4908</issnElectronic><keywords>long-term conditions; healthcare utilisation; COVID-19; data linkage; geographic variation</keywords><publishedDay>17</publishedDay><publishedMonth>6</publishedMonth><publishedYear>2025</publishedYear><publishedDate>2025-06-17</publishedDate><doi>10.23889/ijpds.v10i1.2465</doi><url/><notes/><college>COLLEGE NANME</college><department>Medical School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MEDS</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>This work was supported by Health and Care Research Wales[HRG-20-1755(P)]</funders><projectreference/><lastEdited>2026-01-16T11:09:22.2709094</lastEdited><Created>2025-12-01T13:36:05.7457555</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Health Data Science</level></path><authors><author><firstname>Timothy</firstname><surname>Osborne</surname><order>1</order></author><author><firstname>Rowena</firstname><surname>Bailey</surname><order>2</order></author><author><firstname>Amy</firstname><surname>Mizen</surname><orcid>0000-0001-7516-6767</orcid><order>3</order></author><author><firstname>Rich</firstname><surname>Fry</surname><orcid>0000-0002-7968-6679</orcid><order>4</order></author><author><firstname>Ronan</firstname><surname>Lyons</surname><order>5</order></author></authors><documents><document><filename>71035__36021__9179db03ac97445380d36bf2957e12c5.pdf</filename><originalFilename>71035.VoR.pdf</originalFilename><uploaded>2026-01-16T10:53:35.6921303</uploaded><type>Output</type><contentLength>964299</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>2025 &#xA9; The Authors. 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spelling 2026-01-16T11:09:22.2709094 v2 71035 2025-12-01 Cohort profile: The SAIL long-term conditions e-cohort (SLTC cohort) investigating area-level changes in healthcare resource use in Wales 28f92ffb3c0d67444a64d9666aa58918 Timothy Osborne Timothy Osborne true false 455e2c1e6193448f6269b9e72acaf865 Rowena Bailey Rowena Bailey true false 9e9db8229784e27fcd79a14ee097e10b 0000-0001-7516-6767 Amy Mizen Amy Mizen true false d499b898d447b62c81b2c122598870e0 0000-0002-7968-6679 Rich Fry Rich Fry true false 83efcf2a9dfcf8b55586999d3d152ac6 Ronan Lyons Ronan Lyons true false 2025-12-01 MEDS IntroductionThe prioritisation of acute cases of coronavirus during the pandemic caused significant disruption to non-urgent healthcare services, creating a backlog of undiagnosed and untreated individuals with long-term conditions. Previous research has explored the impact of the pandemic on long-term conditions in Wales, but not the geographic variation or underlying area-level characteristics associated with these changes.ObjectivesWe created the SAIL long-term conditions e-cohort (SLTC cohort) within the Secure Anonymised Information Linkage (SAIL) Databank to describe changes in healthcare service use of individuals living with long-term conditions during the COVID-19 pandemic, and to facilitate future investigations into the underlying reasons for these changes.MethodsIndividuals were included in the cohort if they interacted with health services with a long-term condition between January 2017 and December 2022. Interactions were identified using primary and secondary care datasets within the SAIL Databank. We linked this interaction level data with individual, residence, and area-level demographic data. We calculated area-level age-sex-standardised rates of interactions, based on an individual's address at the time of interaction, for the 3 years pre-COVID-19 (2017-2019) and during-COVID-19 (2020-2022). Percentage changes in rates between these time periods were calculated, and we investigated the underlying area-level characteristics associated with these differences.ResultsThe SLTC cohort contains 1,277,532 individuals. Age-sex standardised interaction rates varied by Welsh Index of Multiple Deprivation (WIMD) quintiles and Rural-Urban Classification. Areas in the most deprived WIMD quintile had the greatest median percentage decrease (23.5%) in primary care rates of interactions from pre- to during-COVID-19, and the least deprived overall WIMD quintile had the smallest (16.9%). Areas classified as 'Urban city & town in a sparse setting' had the greatest decrease in primary care interactions (29.7%), and `Rural village' areas had the smallest decrease (17.1%). Secondary care rates of interactions showed less variation in rates of interactions between the two time periods.ConclusionWe have created a cohort that links area-level characteristics and measures of healthcare resource use, in a study period that covers pre- and during-COVID-19, which will allow researchers to investigate geographic variation of changes in healthcare resource use over this time period and the underlying influences. This cohort can also be further linked to other area-level characteristics of interest, such as travel times to general practices, or access to green space measures. Journal Article International Journal of Population Data Science 10 1 Swansea University 2399-4908 long-term conditions; healthcare utilisation; COVID-19; data linkage; geographic variation 17 6 2025 2025-06-17 10.23889/ijpds.v10i1.2465 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University This work was supported by Health and Care Research Wales[HRG-20-1755(P)] 2026-01-16T11:09:22.2709094 2025-12-01T13:36:05.7457555 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Timothy Osborne 1 Rowena Bailey 2 Amy Mizen 0000-0001-7516-6767 3 Rich Fry 0000-0002-7968-6679 4 Ronan Lyons 5 71035__36021__9179db03ac97445380d36bf2957e12c5.pdf 71035.VoR.pdf 2026-01-16T10:53:35.6921303 Output 964299 application/pdf Version of Record true 2025 © The Authors. Open Access under CC BY 4.0. true eng https://creativecommons.org/licenses/by/4.0/deed.en
title Cohort profile: The SAIL long-term conditions e-cohort (SLTC cohort) investigating area-level changes in healthcare resource use in Wales
spellingShingle Cohort profile: The SAIL long-term conditions e-cohort (SLTC cohort) investigating area-level changes in healthcare resource use in Wales
Timothy Osborne
Rowena Bailey
Amy Mizen
Rich Fry
Ronan Lyons
title_short Cohort profile: The SAIL long-term conditions e-cohort (SLTC cohort) investigating area-level changes in healthcare resource use in Wales
title_full Cohort profile: The SAIL long-term conditions e-cohort (SLTC cohort) investigating area-level changes in healthcare resource use in Wales
title_fullStr Cohort profile: The SAIL long-term conditions e-cohort (SLTC cohort) investigating area-level changes in healthcare resource use in Wales
title_full_unstemmed Cohort profile: The SAIL long-term conditions e-cohort (SLTC cohort) investigating area-level changes in healthcare resource use in Wales
title_sort Cohort profile: The SAIL long-term conditions e-cohort (SLTC cohort) investigating area-level changes in healthcare resource use in Wales
author_id_str_mv 28f92ffb3c0d67444a64d9666aa58918
455e2c1e6193448f6269b9e72acaf865
9e9db8229784e27fcd79a14ee097e10b
d499b898d447b62c81b2c122598870e0
83efcf2a9dfcf8b55586999d3d152ac6
author_id_fullname_str_mv 28f92ffb3c0d67444a64d9666aa58918_***_Timothy Osborne
455e2c1e6193448f6269b9e72acaf865_***_Rowena Bailey
9e9db8229784e27fcd79a14ee097e10b_***_Amy Mizen
d499b898d447b62c81b2c122598870e0_***_Rich Fry
83efcf2a9dfcf8b55586999d3d152ac6_***_Ronan Lyons
author Timothy Osborne
Rowena Bailey
Amy Mizen
Rich Fry
Ronan Lyons
author2 Timothy Osborne
Rowena Bailey
Amy Mizen
Rich Fry
Ronan Lyons
format Journal article
container_title International Journal of Population Data Science
container_volume 10
container_issue 1
publishDate 2025
institution Swansea University
issn 2399-4908
doi_str_mv 10.23889/ijpds.v10i1.2465
publisher Swansea University
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 Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
document_store_str 1
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description IntroductionThe prioritisation of acute cases of coronavirus during the pandemic caused significant disruption to non-urgent healthcare services, creating a backlog of undiagnosed and untreated individuals with long-term conditions. Previous research has explored the impact of the pandemic on long-term conditions in Wales, but not the geographic variation or underlying area-level characteristics associated with these changes.ObjectivesWe created the SAIL long-term conditions e-cohort (SLTC cohort) within the Secure Anonymised Information Linkage (SAIL) Databank to describe changes in healthcare service use of individuals living with long-term conditions during the COVID-19 pandemic, and to facilitate future investigations into the underlying reasons for these changes.MethodsIndividuals were included in the cohort if they interacted with health services with a long-term condition between January 2017 and December 2022. Interactions were identified using primary and secondary care datasets within the SAIL Databank. We linked this interaction level data with individual, residence, and area-level demographic data. We calculated area-level age-sex-standardised rates of interactions, based on an individual's address at the time of interaction, for the 3 years pre-COVID-19 (2017-2019) and during-COVID-19 (2020-2022). Percentage changes in rates between these time periods were calculated, and we investigated the underlying area-level characteristics associated with these differences.ResultsThe SLTC cohort contains 1,277,532 individuals. Age-sex standardised interaction rates varied by Welsh Index of Multiple Deprivation (WIMD) quintiles and Rural-Urban Classification. Areas in the most deprived WIMD quintile had the greatest median percentage decrease (23.5%) in primary care rates of interactions from pre- to during-COVID-19, and the least deprived overall WIMD quintile had the smallest (16.9%). Areas classified as 'Urban city & town in a sparse setting' had the greatest decrease in primary care interactions (29.7%), and `Rural village' areas had the smallest decrease (17.1%). Secondary care rates of interactions showed less variation in rates of interactions between the two time periods.ConclusionWe have created a cohort that links area-level characteristics and measures of healthcare resource use, in a study period that covers pre- and during-COVID-19, which will allow researchers to investigate geographic variation of changes in healthcare resource use over this time period and the underlying influences. This cohort can also be further linked to other area-level characteristics of interest, such as travel times to general practices, or access to green space measures.
published_date 2025-06-17T05:34:24Z
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