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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 
,
Rich Fry ,
Ronan Lyons
,
Rich Fry ,
Ronan Lyons
International Journal of Population Data Science, Volume: 10, Issue: 1
Swansea University Authors:
Timothy Osborne, Rowena Bailey, Amy Mizen , Rich Fry , Ronan Lyons
DOI (Published version): 10.23889/ijpds.v10i1.2465
Abstract
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...
| Published in: | International Journal of Population Data Science |
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| ISSN: | 2399-4908 |
| Published: |
Swansea University
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71035 |
| Abstract: |
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. |
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| Keywords: |
long-term conditions; healthcare utilisation; COVID-19; data linkage; geographic variation |
| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
This work was supported by Health and Care Research Wales[HRG-20-1755(P)] |
| Issue: |
1 |