The IDDO Chagas disease data platform is a collaboration with our key partner Drugs for Neglected Diseases Initiative (DNDi) that brings together researchers in disease-endemic regions, leading global research institutions, policy specialists and funders. The ultimate aim of the platform is to strengthen evidence for the diagnosis and treatment of Chagas disease, and create a framework to inform future data collection, making research more efficient. 

Family group photo
Credit: Ana Ferreira, DNDi

The WorldWide Antimalarial Resistance Network (WWARN), now part of IDDO, has been collating and standardising malaria trial data since 2009, demonstrating that it is possible to produce policy-changing evidence from existing data. DNDi is a not-for-profit research and development organisation that has run numerous clinical trials for better treatments for Chagas disease in Latin America and Europe.

Need for a Chagas Disease data platform

Working with DNDi, the IDDO Chagas disease theme aims to extend WWARN's successful approach to Chagas disease. 

Chagas disease has unique challenges: the time between acute infection and severe disease is usually decades, and in chronic Chagas disease it is difficult to assess cure, because parasite densities in blood are very low and seroconversion can take years. 

This means that data are lost over time, but a sophisticated and reliable platform can bring this information together. 

The IDDO individual patient data repository aims to:

  1. Provide an accessible, comprehensive and up-to-date archive of Chagas clinical trials, describing the protocols, methods, patient populations and outcomes of each trial. This resource would be invaluable to clinicians, drug developers and healthcare policy makers, helping to improve the efficacy of Chagas treatments and regimens in specific regions or populations.
  2. Promote understanding of existing data to clearly identify gaps in research, as well as unnecessary data replications. Pooled data also allows an assessment of quality data quality, identify improvements, and ways to standardise data. Using this information to  guide future trial design will improve consistency in trial outcomes.
  3. Enable detailed pooled analyses, to compare treatment outcomes in specific regions and sub-groups of patients. These analyses could provide a better understanding of the determinants of treatment efficacy and identify sub-populations at particular risk of treatment failure due to factors such as age, geographic origin, or coinfection.

If you have any questions, do email chagas@iddo.org