Geographical Satellite and Survey Data for Prediction of Dengue Cases in Sukoharjo, Indonesia

• Main Authors: Dyah Kusumawati, Adi Prayitno, Ruben Dharmawan
• Format: Article
• Language: English
• Published: Masters Program in Public Health, Universitas Sebelas Maret 2016-01-01
• Series: Journal of Epidemiology and Public Health
• Subjects: geographical data and survey data; prediction of dengue cases
• Online Access: http://www.jepublichealth.com/index.php?journal=jepublichealth&page=article&op=view&path%5B%5D=5&path%5B%5D=8

Background: Dengue fever is a disease based on environment and still a health problem. Problems related to the dengue fever vector distribution factor in terms of the spread of vector space with the use of geographic data and survey data in order to predict the incidence of dengue in the region. Subjects and Methods: This study used analytic observational with cross sectional approach using modeling Geographical Information Systems (GIS). The sampling technique in this research is saturated sampling of secondary data Sukoharjo District Health Profile in 2011-2014, population data and data Geographic, then all the data were analyzed using multiple linear regression. Results: There is a positive relationship between the area per Km2 with the number of new cases of dengue fever, although the relationship was not statistically significant. (B = <0:01; CI -0.01 - 0:02; p = 0.310). There is a positive relationship between population density per soul / Km2dengan number of new cases of dengue fever, a significant relationship between population density with DHF cases. (B = <0:01; CI <0:01 to 0:01; p = 0.013). There is a negative relationship between topography per masl by the number of new cases of dengue fever, although the relationship was not statistically significant. (B = <0:01; CI -0.02 - 0:01; p = 0.335). There is a positive correlation between rainfall per mm / yr with the number of new cases of dengue fever, although the relationship was not statistically significant. (B = <0:01; CI <0:01 to 0:01; p = 0101). There is a positive relationship between river flow per ha by the number of new cases of dengue fever, although the relationship was not statistically significant. (B = 0:02; CI -0.01 - 0:03; p = 0318). There is a negative correlation between% Non Flick figure by the number of new cases of dengue fever, although the relationship was not statistically significant. (B = <0:01; CI -0.02 - 0:01; p = 0764). Conclusions: The increase in land area, population density, rainfall, river flow is predicted to affect the increase in dengue cases, whereas the increase ABJ predicted topography and affecting the decline of dengue cases in the district of Sukoharjo in 2011-2014.