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; Christian Cole; Shahzad Mumtaz; Sam Cox; Tom Giles; Samuel Adejumo; Esmond Urwin; Daniel Lea; Calum McDonald; Joseph Best; Erum Masood; Gordon Milligan; Jenny Johnston; Scott Horban; Ipek Birced; Christopher Hall; Aaron 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; Jo Martin; Neil Sebire; Carole Morris; Declan Bradley; Rob Baxter; Anni Ahonen-Bishop; Amelia Shoemark; Ana Valdes; Benjamin J Ollivere; Charlotte Manisty; David William Eyre; Stephanie Gallant; George Joy; Andrew McAuley; David W Connell; Kate Northstone; Katie Jm Jeffery; Emanuele Di Angelantonio; Amy McMahon; Matthew Walker; Malcolm Gracie Semple; Jessica Mai Sims; Emma Lawrence; Bethan Davies; J Kenneth Baillie; Ming Tang; Gary Leeming; Linda Power; Thomas Breeze; Natalie Gilson; Duncan J Murray; Chris Orton; Iain Pierce; Ian Hall; Shamez Ladhani; Matthew Whitaker; Laura Shallcross; David Seymour; Susheel Varma; Gerry Reilly; Andrew Morris; Susan Hopkins; Aziz Sheikh; Philip Quinlan

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, Christian Cole, Shahzad Mumtaz, Sam Cox, Tom Giles, Samuel Adejumo, Esmond Urwin, Daniel Lea, Calum McDonald, Joseph Best, Erum Masood, Gordon Milligan, Jenny Johnston, Scott Horban, Ipek Birced, Christopher Hall, Aaron Jackson, Clare Collins, Sam Rising, Charlotte DodsleyJill 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, Jo Martin, Neil Sebire, Carole Morris, Declan Bradley, Rob Baxter, Anni Ahonen-Bishop, Amelia Shoemark, Ana Valdes, Benjamin J Ollivere, Charlotte Manisty, David William Eyre, Stephanie Gallant, George Joy, Andrew McAuley, David W Connell, Kate Northstone, Katie Jm Jeffery, Emanuele Di Angelantonio, Amy McMahon, Matthew Walker, Malcolm Gracie Semple, Jessica Mai Sims, Emma Lawrence, Bethan Davies, J Kenneth Baillie, Ming Tang, Gary Leeming, Linda Power, Thomas Breeze, Natalie Gilson, Duncan J Murray, Chris Orton, Iain Pierce, Ian Hall, Shamez Ladhani, Matthew Whitaker, Laura Shallcross, David Seymour, Susheel Varma, Gerry Reilly, Andrew Morris, Susan Hopkins, Aziz Sheikh, Philip Quinlan

Research output: Contribution to journal › Article › peer-review

Abstract / Description of output

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.

Original language	English
Pages (from-to)	e40035
Journal	Journal of medical Internet research
Volume	24
Issue number	12
DOIs	https://doi.org/10.2196/40035
Publication status	Published - 27 Dec 2022

Keywords / Materials (for Non-textual outputs)

COVID-19/epidemiology
Humans
Pandemics
United Kingdom/epidemiology

Access to Document

10.2196/40035Licence: Creative Commons: Attribution (CC-BY)

preprint-40035-acceptedAccepted author manuscript, 607 KBLicence: Creative Commons: Attribution (CC-BY)

Cite this

Jefferson, E., Cole, C., Mumtaz, S., Cox, S., Giles, T., Adejumo, S., Urwin, E., Lea, D., McDonald, C., Best, J., Masood, E., Milligan, G., Johnston, J., Horban, S., Birced, I., Hall, C., Jackson, A., Collins, C., Rising, S., ... 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. Journal of medical Internet research, 24(12), e40035. https://doi.org/10.2196/40035

@article{44a818acd5fe49f3b420dcfe6b2b5284,

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",

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/epidemiology, Humans, Pandemics, United Kingdom/epidemiology",

author = "Emily Jefferson and Christian Cole and Shahzad Mumtaz and Sam Cox and Tom Giles and Samuel Adejumo and Esmond Urwin and Daniel Lea and Calum McDonald and Joseph Best and Erum Masood and Gordon Milligan and Jenny Johnston and Scott Horban and Ipek Birced and Christopher Hall and Aaron Jackson and Clare Collins and Sam Rising and Charlotte Dodsley and Jill Hampton and Andrew Hadfield and Roberto Santos and Simon Tarr and Vasiliki Panagi and Joseph Lavagna and Tracy Jackson and Antony Chuter and Jillian Beggs and Magdalena Martinez-Queipo and Helen Ward and {von Ziegenweidt}, Julie and Frances Burns and Jo Martin and Neil Sebire and Carole Morris and Declan Bradley and Rob Baxter and Anni Ahonen-Bishop and Amelia Shoemark and Ana Valdes and Ollivere, {Benjamin J} and Charlotte Manisty and Eyre, {David William} and Stephanie Gallant and George Joy and Andrew McAuley and Connell, {David W} and Kate Northstone and Jeffery, {Katie Jm} and {Di Angelantonio}, Emanuele and Amy McMahon and Matthew Walker and Semple, {Malcolm Gracie} and Sims, {Jessica Mai} and Emma Lawrence and Bethan Davies and Baillie, {J Kenneth} and Ming Tang and Gary Leeming and Linda Power and Thomas Breeze and Natalie Gilson and Murray, {Duncan J} and Chris Orton and Iain Pierce and Ian Hall and Shamez Ladhani and Matthew Whitaker and Laura Shallcross and David Seymour and Susheel Varma and Gerry Reilly and Andrew Morris and Susan Hopkins and Aziz Sheikh and Philip Quinlan",

note = "{\textcopyright}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.",

year = "2022",

month = dec,

day = "27",

doi = "10.2196/40035",

language = "English",

volume = "24",

pages = "e40035",

journal = "Journal of medical Internet research",

issn = "1438-8871",

publisher = "Journal of medical Internet Research",

number = "12",

}

Jefferson, E, Cole, C, Mumtaz, S, Cox, S, Giles, T, Adejumo, S, Urwin, E, Lea, D, McDonald, C, Best, J, Masood, E, Milligan, G, Johnston, J, Horban, S, Birced, I, Hall, C, Jackson, A, 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-Bishop, A, Shoemark, A, Valdes, A, Ollivere, BJ, Manisty, C, Eyre, DW, Gallant, S, Joy, G, McAuley, A, Connell, DW, Northstone, K, Jeffery, KJ, 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, Gilson, N, Murray, DJ, Orton, C, Pierce, I, Hall, I, Ladhani, S, 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', Journal of medical Internet research, vol. 24, no. 12, pp. e40035. https://doi.org/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. / Jefferson, Emily; Cole, Christian; Mumtaz, Shahzad et al.
In: Journal of medical Internet research, Vol. 24, No. 12, 27.12.2022, p. e40035.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - A Hybrid Architecture (CO-CONNECT) to Facilitate Rapid Discovery and Access to Data Across the United Kingdom in Response to the COVID-19 Pandemic

T2 - Development Study

AU - Jefferson, Emily

AU - Cole, Christian

AU - Mumtaz, Shahzad

AU - Cox, Sam

AU - Giles, Tom

AU - Adejumo, Samuel

AU - Urwin, Esmond

AU - Lea, Daniel

AU - McDonald, Calum

AU - Best, Joseph

AU - Masood, Erum

AU - Milligan, Gordon

AU - Johnston, Jenny

AU - Horban, Scott

AU - Birced, Ipek

AU - Hall, Christopher

AU - Jackson, Aaron

AU - Collins, Clare

AU - Rising, Sam

AU - Dodsley, Charlotte

AU - Hampton, Jill

AU - Hadfield, Andrew

AU - Santos, Roberto

AU - Tarr, Simon

AU - Panagi, Vasiliki

AU - Lavagna, Joseph

AU - Jackson, Tracy

AU - Chuter, Antony

AU - Beggs, Jillian

AU - Martinez-Queipo, Magdalena

AU - Ward, Helen

AU - von Ziegenweidt, Julie

AU - Burns, Frances

AU - Martin, Jo

AU - Sebire, Neil

AU - Morris, Carole

AU - Bradley, Declan

AU - Baxter, Rob

AU - Ahonen-Bishop, Anni

AU - Shoemark, Amelia

AU - Valdes, Ana

AU - Ollivere, Benjamin J

AU - Manisty, Charlotte

AU - Eyre, David William

AU - Gallant, Stephanie

AU - Joy, George

AU - McAuley, Andrew

AU - Connell, David W

AU - Northstone, Kate

AU - Jeffery, Katie Jm

AU - Di Angelantonio, Emanuele

AU - McMahon, Amy

AU - Walker, Matthew

AU - Semple, Malcolm Gracie

AU - Sims, Jessica Mai

AU - Lawrence, Emma

AU - Davies, Bethan

AU - Baillie, J Kenneth

AU - Tang, Ming

AU - Leeming, Gary

AU - Power, Linda

AU - Breeze, Thomas

AU - Gilson, Natalie

AU - Murray, Duncan J

AU - Orton, Chris

AU - Pierce, Iain

AU - Hall, Ian

AU - Ladhani, Shamez

AU - Whitaker, Matthew

AU - Shallcross, Laura

AU - Seymour, David

AU - Varma, Susheel

AU - Reilly, Gerry

AU - Morris, Andrew

AU - Hopkins, Susan

AU - Sheikh, Aziz

AU - Quinlan, Philip

N1 - ©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.

PY - 2022/12/27

Y1 - 2022/12/27

N2 - 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.

AB - 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.

KW - COVID-19/epidemiology

KW - Humans

KW - Pandemics

KW - United Kingdom/epidemiology

U2 - 10.2196/40035

DO - 10.2196/40035

M3 - Article

C2 - 36322788

SN - 1438-8871

VL - 24

SP - e40035

JO - Journal of medical Internet research

JF - Journal of medical Internet research

IS - 12

ER -

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

Abstract / Description of output

Keywords / Materials (for Non-textual outputs)

Access to Document

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Cite this