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Data management plan for a community-level study of the hidden burden of cutaneous leishmaniasis in Colombia

Authors
  • Oviedo Sarmiento, Oscar Javier1, 2
  • Castro, María del Mar1
  • Lerma, Yenifer Orobio1, 2
  • Bernal, Leonardo Vargas2
  • Navarro, Andrés2
  • Alexander, Neal D. E.1, 2
  • 1 CIDEIM, Calle 18 #122-135 Campus de la Universidad Icesi (Edificio O-CIDEIM), Cali, 760031, Colombia , Cali (Colombia)
  • 2 Universidad Icesi Grupo de investigación en informática y telecomunicaciones i2t, Calle 18 #122-135, Cali, 760031, Colombia , Cali (Colombia)
Type
Published Article
Journal
BMC Research Notes
Publisher
Springer (Biomed Central Ltd.)
Publication Date
May 31, 2021
Volume
14
Issue
1
Identifiers
DOI: 10.1186/s13104-021-05618-4
Source
Springer Nature
Keywords
License
Green

Abstract

ObjectivesCutaneous leishmaniasis is a vector-borne parasitic disease whose lasting scars can cause stigmatization and depressive symptoms. It is endemic in remote rural areas and its incidence is under-reported, while the effectiveness, as opposed to efficacy, of its treatments is largely unknown. Here we present the data management plan (DMP) of a project which includes mHealth tools to address these knowledge gaps in Colombia. The objectives of the DMP are to specify the tools and procedures for data collection, data transfer, data entry, creation of analysis dataset, monitoring and archiving.ResultsThe DMP includes data from two mobile apps: one implements a clinical prediction rule, and the other is for follow-up and treatment of confirmed cases. A desktop interface integrates these data and facilitates their linkage with other sources which include routine surveillance as well as paper and electronic case report forms. Multiple user and programming interfaces are used, as well as multiple relational and non-relational database engines. This DMP describes the successful integration of heterogeneous data sources and technologies. However the complexity of the project meant that the DMP took longer to develop than expected. We describe lessons learned which could be useful for future mHealth projects.

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