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Meta-Analysis
. 2022 Dec 27;24(12):e40035.
doi: 10.2196/40035.

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

Emily Jefferson  1 , Christian Cole  1 , Shahzad Mumtaz  1 , Samuel Cox  2 , Thomas Charles Giles  2 , Sam Adejumo  2 , Esmond Urwin  2 , Daniel Lea  2 , Calum Macdonald  3 , Joseph Best  2   4 , Erum Masood  1 , Gordon Milligan  1 , Jenny Johnston  1 , Scott Horban  1 , Ipek Birced  1 , Christopher Hall  1 , Aaron S Jackson  1 , Clare Collins  2 , Sam Rising  2 , Charlotte Dodsley  2 , Jill Hampton  1 , Andrew Hadfield  2 , Roberto Santos  2 , Simon Tarr  2 , Vasiliki Panagi  2 , Joseph Lavagna  2 , Tracy Jackson  3 , Antony Chuter  5 , Jillian Beggs  1 , Magdalena Martinez-Queipo  6 , Helen Ward  7 , Julie von Ziegenweidt  8   9 , Frances Burns  10 , Joanne Martin  11 , Neil Sebire  12 , Carole Morris  13 , Declan Bradley  14   15 , Rob Baxter  16 , Anni Ahonen-Bishopp  17 , Paul Smith  17 , Amelia Shoemark  18 , Ana M Valdes  19 , Benjamin Ollivere  19 , Charlotte Manisty  20 , David Eyre  21 , Stephanie Gallant  18 , George Joy  22 , Andrew McAuley  23 , David Connell  24 , Kate Northstone  25 , Katie Jeffery  26   27 , Emanuele Di Angelantonio  28   29   30   31   32 , Amy McMahon  28   30 , Mat Walker  28   30 , Malcolm Gracie Semple  33   34 , Jessica Mai Sims  35 , Emma Lawrence  36 , Bethan Davies  7 , John Kenneth Baillie  37 , Ming Tang  38 , Gary Leeming  39 , Linda Power  40 , Thomas Breeze  41 , Duncan Murray  42   43 , Chris Orton  44 , Iain Pierce  45   46 , Ian Hall  47 , Shamez Ladhani  48 , Natalie Gillson  49 , Matthew Whitaker  7 , Laura Shallcross  50 , David Seymour  4 , Susheel Varma  4 , Gerry Reilly  4 , Andrew Morris  4 , Susan Hopkins  40 , Aziz Sheikh  51 , Philip Quinlan  2   19
Affiliations
Meta-Analysis

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

Emily Jefferson et al. J Med Internet Res. .

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.

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.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: A Sheikh is a member of the Scottish Government Chief Medical Officer’s COVID-19 Advisory Group and its Standing Committee on Pandemics. PQ was previously on a paid secondment to BC Platforms and now resides on their Scientific Advisory Board as a paid consultant. AA-B and PS work for BC Platforms, whose solution CO-CONNECT utilized.

Figures

Figure 1
Figure 1
The CO-CONNECT federated architecture. A data partner (dark box) has potentially identifiable data (A) from which an extraction is made and pseudonymized (B). A metadata extraction is performed with WhiteRabbit (within the identifiable Data Zone, red box) and sent to the CO-CONNECT infrastructure (green box). A mapping script to the OMOP CDM is created using the CO-CONNECT data mapping tool (CaRROT-Mapper). The pseudonymized data are securely transferred (B) into a secure virtual machine hosted by the data partner (Federated Node, dashed dark box), mapped to OMOP (CaRROT-CDM), and connected to the federation software (C and D). From there, the data are queryable by the Innovation Gateway (E). Only aggregated fully anonymous data discovery and meta-analysis results are returned to the Gateway (D). CDM: Common Data Model; OMOP: Observational Medical Outcomes Partnership.
Figure 2
Figure 2
An example phenome-wide association study plot across 4 test data sets comparing females with pneumonia against a background population of female-only samples. The most overrepresented classes include fever (OMOP:437663), disease caused by 2019-nCoV (OMOP:37311061), dysphenia (OMOP:312437), and cough (OMOP:254761). OMOP: Observational Medical Outcomes Partnership.
Figure 3
Figure 3
The HDR UK Cohort Discovery Tool. The interface enables the user to define their cohort search criteria and displays aggregate results across different data sets. The available cohort search criteria (A) are used to create selected cohort criteria (a drag and drop feature, B). Results matching the cohort search criteria across different data sets are presented in the output once the federated queries are completed (C). HDR: Health Data Research.
Figure 4
Figure 4
An example of the static metadata found in the data catalogue of the HDR Innovation Gateway (MATCH data set). (A) Summary of the MATCH data set. (B) Technical details – a list of tables with their field names and data types. HDR: Health Data Research.
See this image and copyright information in PMC

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