Data Management in Tropical Medicine: a call for uniformity
Abstract accepted as poster on the 7th European Congress of Tropical Medicine and International Health, Barcelona (3-6oct2011)
Yves Claeys(1), Sayouba Ouedraogo(2), Anish Battarai(3), Hercule Kalonji(4), Robert Meester(5), David Mwakazanga(6), James Smedley(7), Sok Sopheak(8), Mary Thiongo(9), Arouna Woukeu(10), Greta Gondol(1), Raffaella Ravinetto(1), Harry van Loen(1)
(1)Institute of Tropical Medicine (ITM) Prince Leopold, Belgium
(2)IRSS/Centre Muraz, Burkina Faso)
(3)BP Koirala Institute of Health Sciences, Nepal
(4)University of Kinshasa, Democratic Republic of Congo
(5)Amsterdam Medical Center, The Netherlands
(6)Tropical Disease Research Centre, Zambia
(7)Liverpool School of Tropical Medicine, UK
(8)Sinahouk Hospital Centre of Hope, Cambodia
(9)International Centre for Reproductive Health, Kenya
(10)London School of Hygiene and Tropical Medicine, UK.
Booming applications of informatics and telecommunication had a major impact on society over the last decades. This Information Age is creating a new frame in which all sectors, including Tropical Medicine, can collect data at high speed and in large volumes. Research projects become more ambitious but must achieve appropriate standards on data quality, leaving a number of challenges for Data Management (DM).
In December 2010, a network of clinical data managers and database developers from Belgium, Burkina Faso, Cambodia, the Democratic Republic of Congo, Kenya, Nepal, The Netherlands, UK and Zambia met at the Institute of Tropical Medicine in Antwerp, with the aim of setting up a platform for knowledge sharing and support on DM activities
Problems commonly met by clinical data managers include underestimation of the workload by study coordinators and donors; short timelines, which have an impact on the quality of deliverables; late involvement in projects, making streamlining of DM with project purposes difficult; low position in hierarchy of research groups, causing communication problems during projects. In addition, in the absence of colleagues and broader networks, a data manager is often left alone to choose appropriate technical solutions (e.g., software or validation methods adapted to the research constraints), without consulting fellow colleagues.
The above shortcomings illustrate the growing pains of DM, which is not always recognized within the research team. We recommend that clinical data managers working in tropical medicine gather in formal networks, where they can introduce common tools and working methods and create uniform templates and SOPs .This will facilitate communication, knowledge sharing and collaboration both in and among institutions in similar resource settings. In the long term each network member will upgrade his/her level significantly, with a better involvement in research projects and a positive influence on quality of research