Unravelling data for rapid evidence-based response to COVID-19: a summary of the unCoVer protocol

Abstract

IntroductionunCoVer—Unravelling data for rapid evidence-based response to COVID-19—is a Horizon 2020-funded network of 29 partners from 18 countries capable of collecting and using real-world data (RWD) derived from the response and provision of care to patients with COVID-19 by health systems across Europe and elsewhere. unCoVer aims to exploit the full potential of this information to rapidly address clinical and epidemiological research questions arising from the evolving pandemic.Methods and analysisFrom the onset of the COVID-19 pandemic, partners are gathering RWD from electronic health records currently including information from over 22 000 hospitalised patients with COVID-19, and national surveillance and screening data, and registries with over 1 900 000 COVID-19 cases across Europe, with continuous updates. These heterogeneous datasets will be described, harmonised and integrated into a multi-user data repository operated through Opal-DataSHIELD, an interoperable open-source server application. Federated data analyses, without sharing or disclosing any individual-level data, will be performed with the objective to reveal patients’ baseline characteristics, biomarkers, determinants of COVID-19 prognosis, safety and effectiveness of treatments, and potential strategies against COVID-19, as well as epidemiological patterns. These analyses will complement evidence from efficacy/safety clinical trials, where vulnerable, more complex/heterogeneous populations and those most at risk of severe COVID-19 are often excluded.Ethics and disseminationAfter strict ethical considerations, databases will be available through a federated data analysis platform that allows processing of available COVID-19 RWD without disclosing identification information to analysts and limiting output to data aggregates. Dissemination of unCoVer’s activities will be related to the access and use of dissimilar RWD, as well as the results generated by the pooled analyses. Dissemination will include training and educational activities, scientific publications and conference communications.

Burden of Disease of COVID-19: Strengthening the Collaboration for National Studies

“…The data within the network comprise mostly information from electronic medical records from hospitals, but also national surveillance data, and registries, and is reached through a federated data infrastructure that ensures data protection and ethical and legal compliance. Thus far, they integrate information from over 20 databases and a sizeable number of COVID-19 patients, which is anticipated to increase as databases are being continuously updated ( 38 ). These data may inform future burden of disease assessments, by gaining a deeper understanding on the disease model and variations among heterogeneous groups of patients, including COVID-19 manifestations in vulnerable population subgroups, and shedding light into post-acute COVID-19 conditions that may add to the YLD component of the DALY.…”

Challenges of data sharing in European Covid-19 projects: A learning opportunity for advancing pandemic preparedness and response

“…This federated infrastructure offers the most efficient and secure approach to handling highly sensitive patient information derived from EHR, information which has not been collected for research purposes and demanding a particularly secured environment as well as close monitoring of data protection compliance. 40 It should be noted that unCoVer also faced significant barriers from the ethics perspective in the initial project start-up phase when setting up the federated infrastructure and therefore delays in the planned analyses that could have helped alleviated pandemic outcomes. 40 …”

“… 40 It should be noted that unCoVer also faced significant barriers from the ethics perspective in the initial project start-up phase when setting up the federated infrastructure and therefore delays in the planned analyses that could have helped alleviated pandemic outcomes. 40 …”

Accessibility of covariance information creates vulnerability in Federated Learning frameworks

“…First we provide an example implementation in R DataSHIELD (version 6.2.0) framework and its base package dsBaseClient [19]. This tool is well-established and used in various biomedical applications [3,[21][22][23][24] in which data sharing is limited, e.g. to ensure compliance with privacy regulations, such as the General Data Protection Regulation (GDPR).…”