Let’s break barriers to fight Covid-19 together

Sharing standardized data to stop diseases like COVID-19

Effectively fighting an epidemic means making urgent and swift changes to policy and infrastructure. One of the best examples of this was the 2003 SARS outbreak, which ushered in new protocols and increased disaster preparedness amongst healthcare leaders. It even changed the way new hospitals are designed and how patients are screened at triage stations. 

Currently, the world is facing a new health crisis. On 30 January 2020, WHO declared the new airborne contagious coronavirus (SARS-CoV-2 a.k.a 2019-nCoV), that caused the disease dubbed COVID-19, a Public Health Emergency of International Concern. Like the SARS outbreak before it, this new global health crisis may spark systemic change – a data revolution – that could save thousands of lives.

Lessons from the Past

During an outbreak, data is the most valuable asset in the race to effective containment and finding a cure or vaccine. This was made clear by WHO in their statement on the second meeting of the International Health Regulations (2005) Emergency Committee regarding the outbreak of novel coronavirus (SARS-CoV-2): 

“As this is a new coronavirus, and it has been previously shown that similar coronaviruses required substantial efforts to enable regular information sharing and research, the global community should continue to demonstrate solidarity and cooperation, in compliance with Article 44 of the IHR (2005), in supporting each other on the identification of the source of this new virus, its full potential for human-to-human transmission, preparedness for potential importation of cases, and research for developing necessary treatment.”

Sadly, the need for “regular information sharing and research” was highlighted during the disastrous handling of data during the 2013-2016 Western African Ebola virus epidemic. In hindsight, it’s clear that there were many contributing factors to the difficulties encountered during that time.

Firstly, large pools of existing data from previous Ebola studies, a disease first discovered in 1976, had not been fully disseminated. Research had been conducted, but much of it was never published. When the recent severe outbreak triggered a rush to a cure, incomplete data led researchers down erroneous paths, wasting valuable time. During the outbreak itself, data was haphazardly collected and not standardized. There were large communication failures between affected countries and an unwillingness to share information. Finally, data that was actually collected during the outbreak was not always standardized and was often shared inefficiently, with some researchers hesitating to share any data before it was ready for publication in an academic journal. 

The full impact of the delays in gathering and sharing data during the Ebola outbreak may never be quantifiable. What we do know for certain is that we must rise to the challenge of effective data collection and sharing during current and future outbreaks.

The Need for Standardized Data

Standardizing data means using internationally recognized concepts like SNOMED or LOINC to annotate data, or in the very least, capture data in an agreed upon data model so data from these projects can be pooled and analyzed in unison. Therefore, data is most valuable when it’s standardized. During the current outbreak, WHO is providing technical guidance on how to conduct useful early investigations. The goal is to assemble large amounts of accurate and usable data as quickly as possible.

One of the ways this can be accomplished is through the Global 2019-nCoV Clinical Characterization Case Record Form, which is “intended to provide member states with a standardized approach to collect clinical data in order to better understand the natural history of disease and describe clinical phenotypes and treatment interventions (i.e. clinical characterization).” The use of this standardized clinical data tool should allow for clinical data from around the world to be aggregated. The hope is that this approach will accelerate the work of researchers, including large scale clinical trials. 

Castor is contributing to this cause by providing free access to Castor’s research data platform and eCRFs that were built according to the WHO CRF standard. These eCRFs enable researchers to start their study or registry in less than an hour. As of 27 March 2020, Castor is supporting more than 70 COVID-19 research projects. Click here for a video tutorial on how to capture data in eCRFs created in Castor’s EDC system for a COVID-19 study.

For a global data collection effort to be truly successful, we need to go beyond standardized CRFs. The threshold for capturing these datasets should be as low as possible, to ensure anyone is willing and able to contribute valuable data.

The Urgency of Data Sharing 

In the post-mortem analysis of the Ebola outbreak response, failure to provide and share relevant data in a timely way has been identified as one of the main hindrances to mounting an effective response. Although the outbreak was eventually contained, lack of data sharing and communication breakdowns delayed acknowledgement of the outbreak’s severity and a coordinated response. 

WHO has now declared data sharing a priority during public health emergencies to help identify the causative agents; investigate and predict disease spread; define diagnostic criteria; and evaluate treatments and methods to contain further spread.

In order to accomplish this, WHO is working with its own networks of researchers and other experts to coordinate global work on surveillance, epidemiology, modelling, diagnostics, clinical care and treatment of COVID-19. It is also launching a Global 2019-nCoV Clinical Data Platform. This will allow Member States to contribute anonymized clinical data, widening the breadth and depth of data collected. Sharing valuable data in real-time may expedite the identification, management, and containment of the virus.

Epidemics and pandemics spread fast, they do not wait for clinical trials or academic journals to publish results. In order to contain the virus, our scientific communities must leverage the power data through standardized datasets. With Coronavirus, we have an opportunity to get it right and accelerate the discovery of cures through cooperation and collaboration. The best way to save lives is to share meaningful data in real-time.

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