Spatial Analysis of Malaria Cases During 2006 – 2007 in Lahewa Sub District, Nias District, Province of North Sumatera
ABSTRACT
Everoni Mendrofa, Sugeng Juwono, Dulbahri
Background : Nias District, Province of North Sumatera is one of malaria endemic areas which is influenced by the climate, heavy rainfall, mountainous topography, low socio economic status and education, most of the origin people work as farmers. Number of malaria cases in Lahewa Sub District were 1.432 clinical malaria cases (59,70%), 347 positive malaria cases (14,47%) in 2005, then they were 1.382 clinical malaria cases (44,13%) and 343 positive malaria cases (11,47%) in 2006. Therefore, it needed a geographical mapping for malaria cases in Lahewa Sub District.
Objective : This study aimed to map the spatial distribution of malaria cases and environment factors in Lahewa Sub District, Nias District in the year of 2007.
Method : This was a cross sectional study with analytical descriptive survey. Sample in this study were 84 patients with malaria symptoms, who lived in Lahewa, and had visited to the clinics during August 2007. Patients’ addresses were identified to determine coordinate points by using the Global Positioning System (GPS). The data were analyzed by using bivariate analysis with Chi Square test and spatial analysis with Sat Scan, Geode and Epi Info.
Results: This study resulted malaria cases associated with the use of mix garden (p = 0,0059) and humidity (p = 0,0309). However, malaria cases were not related to distance to health facilities (p = 0,084), the use of land as rice fields (p = 0,1405), the use of swamp area (p = 0,5442), the use of land as ponds (p = 0,6647), residential area (p = 0,0511), quantity of rainfall (p = 0,2379), and air temperatures ( p = 0,4513).
Conclusion: Malaria cases in Lahewa were not influenced by distance to health facilities and the use of land rice fields, ponds, residential area and swamp area, also the quantity of rainfall and air temperature. They were influenced significantly by the use of land as mix garden and humidity.
Keywords : GIS, spatial distribution, mapping, malaria cases.