A Metadata Driven Module for Managing and Interpreting HDSS Verbal Autopsy Datasets using Interva-4 Model
Background: The World Health Organisation provides a standardised survey-based questionnaire for collecting cause of death data. This standardised tool is undergoing iterative changes almost every 3 to 5 years resulting in the redesign of in-use re-adapted questionnaires and their database schemes. Given the size of this questionnaire, its redesign process requiring a lot of time and resources does not allow research centres to update their questionnaire. In addition, the heaviness and the
... viness and the expensive cost of the Physician Certified Verbal Autopsy method used for collected data interpretation, led the emergence of new methods with which data are usually managed in ad hoc fashion by using spreadsheets and Comma Separated Value files. Therefore, these tools not allow preservation of the contextual metadata and also not support recovery and building relationship among data object. While the absence of data object relationships does not facilitate the use of relational database management systems and data preservation over time in longitudinal studies contexts such Health and Demographic surveillance systems.Results: This research used Microsoft Visual studio based on model-driven and metadata architectures associated with R.NET Package,R InterVA function, Google Maps API, eXtensible Mark-up Language and Microsoft SQL Server 2012 to develop a Verbal Autopsy data management platform. This platform assists INDEPTH Network HDSS fields sites to quickly follow the iterative changes of WHO questionnaire through questionnaire generation, data collection and entry, and a mapping layer that translates verbal autopsy CRF to ODK XML data dictionary enabling cause of death data collection in offline mode using handheld devices. In addition, being a R InterVA function aided tool for the interpretation of cause of death data, this web application has an interface for visualising cause of death patterns using Google Maps API.Conclusions: Verbal autopsy data management interpretation over time in the longitudinal studies context such Health and Demographic Surveillance System is feasible. Thereby, possibilities given by metadata-driven to build reliable software architecture for verbal autopsy data collection, interpretation and cause of death patterns visualisation and particularly compliance with regulatory relational database management requirements are achievable.