Asian Journal of Microbiology, Biotechnology & Environmental Sciences Paper

Vol 20, Issue 1, 2018; Page No.(173-177)

MALARIA EVALUATION ANALYSIS BASED ON LAND COVER FACTOR IN OGAN KOMERING ULU REGENCY -SOUTH SUMATERA

P. ALAMSYAH, C. ANWAR, D. SETYAWAN AND L. HANUM

Abstract

Malaria is one of the most serious public health problems in the world. The study aimed to analyze the spatial pattern of malaria incidence distribution based on land cover type in Ogan Komering Ulu (OKU) Regency. The research was a descriptive epidemiological research using quantitative approach. The design of the study was an ecological study to compare the frequency of different disease in a population over the same group and period in the different areas. The study was conducted in OKU regency-South Sumatera. The population was all malaria incidence in the sub-district work area of OKU regency which recorded at Puskesmas in recording and reporting system of Malaria year 2013-2015. Furthermore, Land cover and administration were taken from the regional development planning Board of OKU regency and the Public Works Office of Cipta Karta. The presentation of data in maps was used for a spatial approach in the case of malaria with a distance of 2 km from the settlement. The spatial analysis was carried out using geographical approach. The processing of data was done using univariate data analysis by looking at the distribution of malaria cases based on the type of extent and land cover. The distribution of case was mapped by the region with the level of endemicity specified in the classification of each variable. Furthermore, the distribution of case was mapped with overlapping between endemic areas and potential areas for mosquito breeding. The malaria case data analysis was carried out using malaria incidence indicator using extraction and overlay method. The result showed the spatial pattern of malaria spreading using the ecological approach. In addition, the result also confirmed that OKU regency was categorized in the medium level of malaria endemicity with the highest endemicity occurring in Lubuk Batang districts and the lowest endemicity occurring in Lengkiti districts. The GIS approach facilitated the monitoring of malaria and program planning for more accurate control measures.

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