Jefferson, E;
Cole, C;
Mumtaz, S;
Cox, S;
Giles, TC;
Adejumo, S;
Urwin, E;
Lea, D;
Macdonald, C;
Best, J;
et al.
Jefferson, E; Cole, C; Mumtaz, S; Cox, S; Giles, TC; Adejumo, S; Urwin, E; Lea, D; Macdonald, C; Best, J; Masood, E; Milligan, G; Johnston, J; Horban, S; Birced, I; Hall, C; Jackson, AS; Collins, C; Rising, S; Dodsley, C; Hampton, J; Hadfield, A; Santos, R; Tarr, S; Panagi, V; Lavagna, J; Jackson, T; Chuter, A; Beggs, J; Martinez-Queipo, M; Ward, H; von Ziegenweidt, J; Burns, F; Martin, J; Sebire, N; Morris, C; Bradley, D; Baxter, R; Ahonen-Bishopp, A; Smith, P; Shoemark, A; Valdes, AM; Ollivere, B; Manisty, C; Eyre, D; Gallant, S; Joy, G; McAuley, A; Connell, D; Northstone, K; Jeffery, K; Di Angelantonio, E; McMahon, A; Walker, M; Semple, MG; Sims, JM; Lawrence, E; Davies, B; Baillie, JK; Tang, M; Leeming, G; Power, L; Breeze, T; Murray, D; Orton, C; Pierce, I; Hall, I; Ladhani, S; Gillson, N; Whitaker, M; Shallcross, L; Seymour, D; Varma, S; Reilly, G; Morris, A; Hopkins, S; Sheikh, A; Quinlan, P
(2022)
A Hybrid Architecture (CO-CONNECT) to Facilitate Rapid Discovery and Access to Data Across the United Kingdom in Response to the COVID-19 Pandemic: Development Study.
J Med Internet Res, 24 (12).
e40035.
ISSN 1438-8871
https://doi.org/10.2196/40035
SGUL Authors: Ladhani, Shamez Nizarali
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Abstract
BACKGROUND: COVID-19 data have been generated across the United Kingdom as a by-product of clinical care and public health provision, as well as numerous bespoke and repurposed research endeavors. Analysis of these data has underpinned the United Kingdom's response to the pandemic, and informed public health policies and clinical guidelines. However, these data are held by different organizations, and this fragmented landscape has presented challenges for public health agencies and researchers as they struggle to find relevant data to access and interrogate the data they need to inform the pandemic response at pace. OBJECTIVE: We aimed to transform UK COVID-19 diagnostic data sets to be findable, accessible, interoperable, and reusable (FAIR). METHODS: A federated infrastructure model (COVID - Curated and Open Analysis and Research Platform [CO-CONNECT]) was rapidly built to enable the automated and reproducible mapping of health data partners' pseudonymized data to the Observational Medical Outcomes Partnership Common Data Model without the need for any data to leave the data controllers' secure environments, and to support federated cohort discovery queries and meta-analysis. RESULTS: A total of 56 data sets from 19 organizations are being connected to the federated network. The data include research cohorts and COVID-19 data collected through routine health care provision linked to longitudinal health care records and demographics. The infrastructure is live, supporting aggregate-level querying of data across the United Kingdom. CONCLUSIONS: CO-CONNECT was developed by a multidisciplinary team. It enables rapid COVID-19 data discovery and instantaneous meta-analysis across data sources, and it is researching streamlined data extraction for use in a Trusted Research Environment for research and public health analysis. CO-CONNECT has the potential to make UK health data more interconnected and better able to answer national-level research questions while maintaining patient confidentiality and local governance procedures.
| Item Type: | Article |
|---|---|
| Additional Information: | ©Emily Jefferson, Christian Cole, Shahzad Mumtaz, Samuel Cox, Thomas Charles Giles, Sam Adejumo, Esmond Urwin, Daniel Lea, Calum Macdonald, Joseph Best, Erum Masood, Gordon Milligan, Jenny Johnston, Scott Horban, Ipek Birced, Christopher Hall, Aaron S Jackson, Clare Collins, Sam Rising, Charlotte Dodsley, Jill Hampton, Andrew Hadfield, Roberto Santos, Simon Tarr, Vasiliki Panagi, Joseph Lavagna, Tracy Jackson, Antony Chuter, Jillian Beggs, Magdalena Martinez-Queipo, Helen Ward, Julie von Ziegenweidt, Frances Burns, Joanne Martin, Neil Sebire, Carole Morris, Declan Bradley, Rob Baxter, Anni Ahonen-Bishopp, Paul Smith, Amelia Shoemark, Ana M Valdes, Benjamin Ollivere, Charlotte Manisty, David Eyre, Stephanie Gallant, George Joy, Andrew McAuley, David Connell, Kate Northstone, Katie Jeffery, Emanuele Di Angelantonio, Amy McMahon, Mat Walker, Malcolm Gracie Semple, Jessica Mai Sims, Emma Lawrence, Bethan Davies, John Kenneth Baillie, Ming Tang, Gary Leeming, Linda Power, Thomas Breeze, Duncan Murray, Chris Orton, Iain Pierce, Ian Hall, Shamez Ladhani, Natalie Gillson, Matthew Whitaker, Laura Shallcross, David Seymour, Susheel Varma, Gerry Reilly, Andrew Morris, Susan Hopkins, Aziz Sheikh, Philip Quinlan. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.12.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
| Keywords: | COVID-19, clinical care, data extraction, data governance, data privacy, federated network, health care, health care record, health data, infrastructure model, meta-analysis, public health, Humans, COVID-19, Pandemics, United Kingdom, Humans, Pandemics, United Kingdom, COVID-19, COVID-19, clinical care, public health, infrastructure model, health data, meta-analysis, federated network, health care record, data extraction, data privacy, data governance, health care, 08 Information and Computing Sciences, 11 Medical and Health Sciences, 17 Psychology and Cognitive Sciences, Medical Informatics |
| SGUL Research Institute / Research Centre: | Academic Structure > Infection and Immunity Research Institute (INII) |
| Journal or Publication Title: | J Med Internet Res |
| ISSN: | 1438-8871 |
| Language: | eng |
| Publisher License: | Creative Commons: Attribution 4.0 |
| Projects: | |
| PubMed ID: | 36322788 |
| Web of Science ID: | WOS:000976022600004 |
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Go to PubMed abstract |
| URI: | https://openaccess.sgul.ac.uk/id/eprint/117291 |
| Publisher's version: | https://doi.org/10.2196/40035 |
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