Measles Cluster Detection Using Ordinal Scan Statistic Model

INTRODUCTION: Measles a very contagious disease which responsible for the thousand’s mortality in the world, including Indonesia. Even though vaccination has been claimed victorious to reduce the transmission, but it does not mean that the world is free from Measles. GIS is offering a powerful method to support the decision maker in generating the Measles program. AIM: This research aimed to investigate Measles clustering in Bantul, Yogyakarta, Indonesia by considering population density and income level. This study was essential to support decision maker to develop a proper intervention for preventing Measles. MATERIAL AND METHODS: Quantitative approach was used in this study. Secondary data that consisted of measles cases, population density and income level were collected from the district health office and related government office in Bantul District. Ordinal Scan Statistic Model by using SaTScan v9.6 was applied to detect the cluster and to test the association between the cases and the variables. RESULTS: This research revealed that population density and income level are the two predictors of Measles hotspot cluster. People who live in the very high-income level district will have 4.8 higher possibility to be exposed with Measles. People who live in the district with medium and high population density predicted to have 4.5 fewer risks to be infected with Measles. CONCLUSION: There is a correlation between income level and Measles cases. Geographic Information System (GIS) can contribute to a decision support system for disease prevention such as on Measles.