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Creating a Research-Ready Data Asset version of primary care data for Wales and investigating the impact of COVID-19 on utilisation of primary care services
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Stuart Bedston,
Ashley Akbari
PLOS One, Volume: 20, Issue: 12, Start page: e0338652
Swansea University Authors:
Hoda Abbasizanjani , Stuart Bedston, Ashley Akbari
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© 2025 Abbasizanjani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.
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DOI (Published version): 10.1371/journal.pone.0338652
Abstract
Objectives: We developed an efficient Research-Ready Data Asset (RRDA) for the Welsh Longitudinal General Practice (WLGP) data within the Secure Anonymised Information Linkage Databank to standardise curation, enhance reproducibility, and facilitate research on primary care trends. Using this, we in...
| Published in: | PLOS One |
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| ISSN: | 1932-6203 |
| Published: |
Public Library of Science (PLoS)
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71128 |
| Abstract: |
Objectives: We developed an efficient Research-Ready Data Asset (RRDA) for the Welsh Longitudinal General Practice (WLGP) data within the Secure Anonymised Information Linkage Databank to standardise curation, enhance reproducibility, and facilitate research on primary care trends. Using this, we investigated primary care activity trends during and after the COVID-19 pandemic. Methods: The RRDA involves cleaning, curation using GP-registration history, and transforming data into a structured, normalised format to support efficient large-scale queries. A comprehensive clinical code look-up was developed, incorporating official, local, and supplementary categories to enhance event classification. To enable patient-practice interaction analysis, a four-layer approach was developed to capture healthcare providers, access mode, interaction type, and event details. We assessed RRDA coverage, defined as the proportion of residents with shared primary care records, stratified by demographic and geographic factors, using longitudinal binomial Generalised Additive Mixed Models (GAMMs). We categorised GP events into key activity types and summarised averaged daily rates per month per 100,000 people (2000–2024), with trends analysed using negative binomial GAMMs. Results: Curating 4.6 billion records for 5.1 million people (1990–2024) revealed significant improvements in data quality and completeness over time, with data retention increased from 40% to 94%, and patient inclusion from 43% to 98%. Use of SNOMED-CT and local codes increased after Read-V2 discontinuation in 2018, while invalid codes declined—reflecting evolving coding practices and improved data quality. WLGP RRDA coverage rose from 35% in 1990 to 86% in 2024, with regional variation but modest demographic differences. From 2000 to 2024, consultation rates rose by 1.9 times, with post-COVID-19 pandemic levels 8% above 2019. Prescription-only activity doubled with little variation associated with the pandemic. Vaccination rates spiked during the pandemic, and remain 1.8 times above pre-pandemic levels. Other less frequent activities were significantly disrupted during the COVID-19 pandemic but recovered to 2019 levels. Conclusions: The WLGP RRDA improves the usability of primary care data, supporting timely, scalable analysis of healthcare delivery and system-level trends. |
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| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
This work was supported by the ADR Wales programme of work. ADR Wales, part of the ADR UK investment, unites research expertise from Swansea University Medical School and WISERD (Wales Institute of Social and Economic Research and Data) at Cardiff University with analysts from Welsh Government. ADR UK is funded by the Economic and Social Research Council (ESRC), part of UK Research and Innovation. This research was supported by ESRC funding, including Administrative Data Research Wales (ES/W012227/1). |
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12 |
| Start Page: |
e0338652 |