PILOTING DHIS2 SYSTEM IN VISCERAL LEISHMANIASIS SURVEILLANCE. (12th February 2017)

• Record Type: Journal Article
• Authors: Okeyo, Seth; Omollo, Raymond; Kimutai, Robert; Ochieng, Michael; Egondi, Thaddaeus
• Is Part Of: BMJ global health. Volume 2(2017)Supplement 2
• Page Start: A64
• Page End: A65
• Publication Date: 2017-02-12
• Subjects: World health -- Periodicals; 362.105
• DOI: 10.1136/bmjgh-2016-000260.173
• Languages: English
• ISSNs: 2059-7908

Abstract

Abstract : Background: The District Health Information System 2 (DHIS2) is a tool for collection, validation, analysis and presentation of both individual (tracker) and aggregated data tailored to integrated health information management activities. DHIS2, developed by the Health Information Systems Programme (HISP) in collaboration with University of Oslo, is a modular web-based software package built with open source Java frameworks. Visceral leishmaniasis (VL) is not captured in the DHIS2 regional database, and therefore coming up with the modalities of aggregating available data from clinical trials and general patient records into the DHIS2 national database is crucial for surveillance. Methods: DHIS2 runs on Tomcat Server and PostgreSQL. We set up the VL surveillance program with different stages: enrolment and demographics, initial treatment outcome and follow-up visits. In this system, a patient is enrolled into the system and data is collected in individual data elements; data Indicators are built to help aggregate the data and thereafter used for report generation. It is programmed to visualise data and display reports in the system dashboard which can then be used to present data. Results: Piloting DHIS2 has enabled us to set up a system that uses the set indicators programmed to aggregate data, thus able to produce reports on the data and the user is also able to select the type of report in the form of pivot tables, charts and graphs and also in GIS mapped data. Conclusions: DHIS2 system is an open source that can be customised and expanded to capture detailed individual surveillance data and shared in reports. This data is useful for tracking neglected tropical diseases such as VL. Data can be handled in the following modalities: i) use of off-line data synchronisation. ii) remote data collection using mobile devices iii) data aggregation and organisation iv) data visualisation and presentation through charts, graphs and pivot tables.