Development and application of a geo-medical information decision support system (geo medinfo) for malaria surveillance and risk modelling in Nyanza Province, Kenya
Mogere, Nyaribo Stephen
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In sub-Saharan Africa, malaria is a leading cause of morbidity and mortality. Detailed knowledge of spatial variation of malaria epidemiology and associated risk factors is important for planning and evaluating malaria-control measures. This study therefore investigated an approach in the development and application of a GIS-based healthcare management system with abilities to incorporate climate-based risk predicators of malaria transmission in Nyanza Province. Two sites, Siaya district and Kisii Highlands were selected to implement this study. The PMIS was designed with capabilities to carry out both micro- and macro-levels spatial epidemiologic analyses of malarial transmission. Using Universal Modeling Language (UML) and Microsoft Visio 2003 health data classes, relationships, attributes and data types were modeled which formed the basis for customizing and design of the Patient Management Information System (PMIS). A tailor-made PMIS was then implemented to capture malarial data alongside patient care in Siaya District Hospital, a rural health facility in Nyanza Province, Kenya. A total of 822 malarial case households were tracked and mapped using the Global Positioning System (GPS) and entered into the PMIS. In addition, malaria monthly cases from a total of 127 health facilities in Kisii Highlands were obtained for the period between 1996 and 2005 alongside data on rainfall, Normalised Difference Vegetation Index (NDVI), temperature and Digital Elevation Model (DEM) as possible predicators of malarial risk in the study area. Spatial analyses results revealed that the average distance traveled by study participants to Siaya District Hospital (SDH) was 6km while the longest distance was about 13.15km. There was a significant positive correlation between distances of malaria case households to the health facility, proximity to water bodies and malarial outcomes at 0.05 level of significance (P<0.005). However, no significant differences (p<0.005) were found between malarial case households and controls with regard to proximity to local road network. Regression modelling of malarial transmission in the Kisii Highlands revealed associations between rainfall, NDVI, temperature and DEM and malaria cases in the three administrative districts of Nyamira, Kisii and Gucha. These factors had varied influence on malaria risk transmission with the DEM found to explain most of the malaria case variations in the study area. Geospatial risk models developed for malaria transmission predictions were validated using F-test. The study recommends further testing and validation of both PMIS and the spatial predictive malaria risk model in other parts of the country. The study concludes that it is feasible to develop GeoMedlnfo in broader health information sharing nationally, designed as a tool for improved diagnostics, planning and management programming of malarial surveillance system.