An Innovative Database for Epidemiological Field Studies of Neglected Tropical Diseases

Abstract

The neglected tropical diseases (NTDs) are of major public health importance, accounting for 56.6 million disability-adjusted life years (DALYs), which places them sixth out of the ten leading causes of life years lost to disability and premature death [1]. These diseases are prominent in the developing world where there is low income, poor hygiene, and inadequate sanitation [1],[2]. Recent targeting of these diseases for large-scale control programs by the World Health Organization [3] is likely to increase the number of epidemiological field studies requiring valid and reliable data, in order to determine the most appropriate strategies for control. In order to ensure a control strategy is effective and appropriate, the data need to be of a high standard, and as a result, epidemiological field studies require a rigorous and systematic approach to data management. Recent publications by Ali et al. [4] and Roberts et al. [5] stress that the importance of data management is often underestimated in such studies, with greater emphasis instead placed on the study design, data collection, and data analysis [4],[5]. This can result in an ad hoc approach to data management that ultimately affects the reliability and validity of the data collected and increases the workload involved in data cleaning. There are additional difficulties in developing countries in the collection, entry, management, and analysis of high-quality data, mainly due to limited infrastructure and capacity [4]-[7], which can exacerbate the problems associated with ensuring effective and reliable data management. We undertook an epidemiological study of the transmission dynamics of Schistosoma japonicum in China [8] that necessitated a rigorous approach to the collection and management of an extensive dataset. Some technical and conceptual constraints were encountered as the data management protocols in place were designed for the monitoring and control of schistosomiasis, rather than for the evaluation of a complex epidemiological study, requiring expertise in the principles and practice of data management. Language barriers provided additional challenges in implementing an efficient data management system. Accordingly, we present details of the innovative database we developed, which allowed us to produce data that were protected against data entry errors and therefore more likely to be of high quality and reliability. Furthermore, it also provided us with evidence of protection. This database can also serve as a template for other epidemiological studies of NTDs in the future.