Developing a prototype for federated analysis to enhance privacy and enable trustworthy access to COVID-19 research data

Solmaz Eradat Oskoui; Matthew Retford; Eoghan Forde; Rodrigo Barnes; Karen J Hunter; Anne Wozencraft; Simon Thompson; Chris Orton; David Ford; Sharon Heys; Julie Kennedy; Cynthia McNerney; Jeffrey Peng; Hamed Ghanbariadolat; Sarah Rees; Rachel H Mulholland; Aziz Sheikh; David Burgner; Meredith Brockway; Meghan B Azad; Natalie Rodriguez; Helga Zoega; Sarah J Stock; Clara Calvert; Jessica E Miller; Nicole Fiorentino; Amy Racine; Jonas Haggstrom; Neil Postlethwaite

doi:10.1016/j.ijmedinf.2024.105708

Developing a prototype for federated analysis to enhance privacy and enable trustworthy access to COVID-19 research data

Int J Med Inform. 2024 Nov 20:195:105708. doi: 10.1016/j.ijmedinf.2024.105708. Online ahead of print.

Authors

Solmaz Eradat Oskoui¹, Matthew Retford², Eoghan Forde³, Rodrigo Barnes³, Karen J Hunter⁴, Anne Wozencraft⁴, Simon Thompson⁵, Chris Orton⁵, David Ford⁵, Sharon Heys⁵, Julie Kennedy⁵, Cynthia McNerney⁵, Jeffrey Peng⁵, Hamed Ghanbariadolat⁵, Sarah Rees⁵, Rachel H Mulholland⁶, Aziz Sheikh⁶, David Burgner⁷, Meredith Brockway⁸, Meghan B Azad⁹, Natalie Rodriguez⁹, Helga Zoega¹⁰, Sarah J Stock⁶, Clara Calvert¹¹, Jessica E Miller¹², Nicole Fiorentino¹³, Amy Racine¹⁴, Jonas Haggstrom¹⁴, Neil Postlethwaite⁴

Affiliations

¹ Aridhia Informatics, UK; Usher Institute, University of Edinburgh, Edinburgh, UK.
² Health Data Research UK (HDR UK) , UK. Electronic address: matthew.retford@hdruk.ac.uk.
³ Aridhia Informatics, UK.
⁴ Health Data Research UK (HDR UK) , UK.
⁵ Swansea University Medical School, UK.
⁶ Usher Institute, University of Edinburgh, Edinburgh, UK.
⁷ Infection, Immunity and Global Health Theme, Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia; Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia.
⁸ Faculty of Nursing, University of Calgary, Canada; Alberta Children's Hospital Research Institute, Calgary, AB, Canada.
⁹ Pediatrics and Child Health, University of Manitoba, Winnipeg, Canada; Children's Hospital Research Institute of Manitoba, Winnipeg, Canada.
¹⁰ Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland.
¹¹ Usher Institute, University of Edinburgh, Edinburgh, UK; Department of Population Health, London School of Hygiene and Tropical Medicine, UK.
¹² Infection, Immunity and Global Health Theme, Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia.
¹³ Pediatrics and Child Health, University of Manitoba, Winnipeg, Canada.
¹⁴ Cytel, USA.

PMID: 39642590
DOI: 10.1016/j.ijmedinf.2024.105708

Abstract

Background: The use of federated networks can reduce the risk of disclosure for sensitive datasets by removing the requirement to physically transfer data. Federated networks support federated analytics, a type of privacy-enhancing technology, enabling trustworthy data analysis without the movement of source data.

Objectives: To set out the methodology used by the International COVID-19 Data Alliance (ICODA) and its partners, the Secure Anonymised Information Linkage (SAIL) Databank and Aridhia Informatics in piloting a federated network infrastructure and consequently testing federated analytics using test data provided from an ICODA project, the International Perinatal Outcome in the Pandemic (iPOP) Study. To share the challenges and benefits of using a federated network infrastructure to enable trustworthy analysis of health-related data from multiple countries and sources.

Results: This project successfully developed a federated network between the SAIL Databank and the ICODA Workbench and piloted the use of federated analysis using aggregate-level model outputs as test data from the iPOP Study, a one-year, multi-country COVID-19 research project. This integration is a first step in implementing the necessary technical, governance and user experiences for future research studies to build upon, including those using individual-level datasets from multiple data nodes.

Conclusions: Creating federated networks requires extensive investment from a data governance, technology, training, resources, timing and funding perspective. For future initiatives, the establishment of a federated network should be built into medium to long term plans to provide researchers with a secure and robust data analysis platform to perform joint multi-site collaboration. Federated networks can unlock the enormous potential of national and international health datasets through enabling collaborative research that addresses critical public health challenges, whilst maintaining privacy and trustworthiness by preventing direct access to the source data.

Keywords: COVID-19; Data Re-use; Federated Analytics; Federated Networks; Health Data Research; Privacy-Preserving; Secondary Data.