PO 8490 Promoting good data management practices in clinical research in resource-poor settings. (24th April 2019)

• Record Type: Journal Article
• Authors: Loen, Harry Van; Arango, Diana; Landuyt, Hanne; Burm, Christophe; Herrewege, Yven Van
• Is Part Of: BMJ global health. Volume 4(2019)Supplement 3
• Page Start: A45
• Page End: A45
• Publication Date: 2019-04-24
• Subjects: World health -- Periodicals; 362.105
• DOI: 10.1136/bmjgh-2019-EDC.118
• Languages: English
• ISSNs: 2059-7908

Abstract

Abstract : Background: Accurate and timely data management (DM) is of key importance in clinical research to generate high-quality and GCP-compliant data for analysis and/or sharing. Our objective is to strengthen the capacity for DM in clinical research in resource-poor settings by organising several teaching initiatives. Methods: Our teaching initiatives have a twofold approach. First, a generic and comprehensive approach with capacity building on various thematical modules. These include a research component (overviewing the research data management procedures) and a technological component (introducing databases and software). In addition, a component on legislation, guidelines and standards specific towards DM is discussed, as well as a project management component on how to organise DM efficiently and timely. Second, we apply a more focused and study-specific approach which details roles and responsibilities in data management, milestones and documentation practices. Both approaches are based upon successful implementation in EDCTP-funded clinical trials, such as the 4ABC, PREGACT and Microbicide Safety Biomarkers studies, as well as the FP7 sponsored NIDIAG project. The target audience comprises various study stakeholders such as data managers, IT administrators, clinicians, laboratory researchers and statisticians, coming from sub-Saharan Africa, South-East Asia and Latin America. Results: A teaching model for promoting Good Data Management Practices has been developed with theory- and practice-based modules. This model is used at face-to-face workshops in remote settings and has been re-used by colleagues and implemented by other research institutions to promote further capacity building and sustainable development in the South. In addition, it has led to mutual learning and enhanced institutional and personal North-South collaborations. Conclusion: There is a clear case for promoting DM and providing guidelines for Good Data Management Practices. Our twofold approach has enabled the successful conduct of GCP compliant non-commercial clinical trials in the South.