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CC BY-NC-ND 4.0 · Methods Inf Med 2015; 54(04): 364-371
DOI: 10.3414/ME14-01-0133
Original Articles
Georg Thieme Verlag KG Stuttgart · New York

MOSAIC – A Modular Approach to Data Management in Epidemiological Studies

M. Bialke
1   Institute for Community Medicine, Section Epidemiology of Health Care and Community Health, University Medicine Greifswald, Greifswald, Germany
,
T. Bahls
1   Institute for Community Medicine, Section Epidemiology of Health Care and Community Health, University Medicine Greifswald, Greifswald, Germany
,
C. Havemann
1   Institute for Community Medicine, Section Epidemiology of Health Care and Community Health, University Medicine Greifswald, Greifswald, Germany
,
J. Piegsa
1   Institute for Community Medicine, Section Epidemiology of Health Care and Community Health, University Medicine Greifswald, Greifswald, Germany
,
K. Weitmann
1   Institute for Community Medicine, Section Epidemiology of Health Care and Community Health, University Medicine Greifswald, Greifswald, Germany
,
T. Wegner
2   Institute of Applied Microelectronics and Computer Engineering, University of Rostock, Rostock, Germany
,
W. Hoffmann
1   Institute for Community Medicine, Section Epidemiology of Health Care and Community Health, University Medicine Greifswald, Greifswald, Germany
› Author Affiliations
Further Information

Publication History

05 December 2014

03 June 2015

Publication Date:
22 January 2018 (online)

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Summary

Introduction: In the context of an increasing number of multi-centric studies providing data from different sites and sources the necessity for central data management (CDM) becomes undeniable. This is exacerbated by a multiplicity of featured data types, formats and interfaces. In relation to methodological medical research the definition of central data management needs to be broadened beyond the simple storage and archiving of research data.

Objectives: This paper highlights typical requirements of CDM for cohort studies and registries and illustrates how orientation for CDM can be provided by addressing selected data management challenges.

Methods: Therefore in the first part of this paper a short review summarises technical, organisational and legal challenges for CDM in cohort studies and registries. A deduced set of typical requirements of CDM in epidemiological research follows.

Results: In the second part the MOSAIC project is introduced (a modular systematic approach to implement CDM). The modular nature of MOSAIC contributes to manage both technical and organisational challenges efficiently by providing practical tools. A short presentation of a first set of tools, aiming for selected CDM requirements in cohort studies and registries, comprises a template for comprehensive documentation of data protection measures, an interactive reference portal for gaining insights and sharing experiences, supplemented by modular software tools for generation and management of generic pseudonyms, for participant management and for sophisticated consent management.

Conclusions: Altogether, work within MOSAIC addresses existing challenges in epidemiological research in the context of CDM and facilitates the standardized collection of data with pre-programmed modules and provided document templates. The necessary effort for in-house programming is reduced, which accelerates the start of data collection.

Keywords

Medical data management - data privacy protection - informed consent - pseudonyms - record linkage