Organizational aspects and implementation of data systems in large-scale epidemiological studies in less developed countries

Background: In the conduct of epidemiological studies in less developed countries, while great emphasis is placed on study design, data collection, and analysis, often little attention is paid to data management. As a consequence, investigators working in these countries frequently face challenges in cleaning, analyzing and interpreting data. In most research settings, the data management team is formed with temporary and unskilled persons. A proper working environment and training or guidance in constructing a reliable database is rarely available. There is little information available that describes data management problems and solutions to those problems. Usually a line or two can be obtained in the methods section of research papers stating that the data are doubly-entered and that outliers and inconsistencies were removed from the data. Such information provides little assurance that the data are reliable. There are several issues in data management that if not properly practiced may create an unreliable database, and outcomes of this database will be spurious. Results: We have outlined the data management practices for epidemiological studies that we have modeled for our research sites in seven Asian countries and one African country. Conclusion: Information from this model data management structure may help others construct reliable databases for large-scale epidemiological studies in less developed countries.