@Article{info:doi/10.2196/40035,
author="Jefferson, Emily
and Cole, Christian
and Mumtaz, Shahzad
and Cox, Samuel
and Giles, Thomas Charles
and Adejumo, Sam
and Urwin, Esmond
and Lea, Daniel
and Macdonald, Calum
and Best, Joseph
and Masood, Erum
and Milligan, Gordon
and Johnston, Jenny
and Horban, Scott
and Birced, Ipek
and Hall, Christopher
and Jackson, Aaron S
and Collins, Clare
and Rising, Sam
and Dodsley, Charlotte
and Hampton, Jill
and Hadfield, Andrew
and Santos, Roberto
and Tarr, Simon
and Panagi, Vasiliki
and Lavagna, Joseph
and Jackson, Tracy
and Chuter, Antony
and Beggs, Jillian
and Martinez-Queipo, Magdalena
and Ward, Helen
and von Ziegenweidt, Julie
and Burns, Frances
and Martin, Joanne
and Sebire, Neil
and Morris, Carole
and Bradley, Declan
and Baxter, Rob
and Ahonen-Bishopp, Anni
and Smith, Paul
and Shoemark, Amelia
and Valdes, Ana M
and Ollivere, Benjamin
and Manisty, Charlotte
and Eyre, David
and Gallant, Stephanie
and Joy, George
and McAuley, Andrew
and Connell, David
and Northstone, Kate
and Jeffery, Katie
and Di Angelantonio, Emanuele
and McMahon, Amy
and Walker, Mat
and Semple, Malcolm Gracie
and Sims, Jessica Mai
and Lawrence, Emma
and Davies, Bethan
and Baillie, John Kenneth
and Tang, Ming
and Leeming, Gary
and Power, Linda
and Breeze, Thomas
and Murray, Duncan
and Orton, Chris
and Pierce, Iain
and Hall, Ian
and Ladhani, Shamez
and Gillson, Natalie
and Whitaker, Matthew
and Shallcross, Laura
and Seymour, David
and Varma, Susheel
and Reilly, Gerry
and Morris, Andrew
and Hopkins, Susan
and Sheikh, Aziz
and Quinlan, Philip",
title="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",
journal="J Med Internet Res",
year="2022",
month="Dec",
day="27",
volume="24",
number="12",
pages="e40035",
keywords="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",
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. ",
issn="1438-8871",
doi="10.2196/40035",
url="https://www.jmir.org/2022/12/e40035",
url="https://doi.org/10.2196/40035",
url="http://www.ncbi.nlm.nih.gov/pubmed/36322788"
}