Introduction COMPARISON BETWEEN CIRCLE BASED SCAN

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Chapter 3 COMPARISON BETWEEN CIRCLE BASED SCAN

STATISTICS AND UPPER LEVEL SET SCAN STATISTICS BASED ON SIMULATION STUDY

3.1 Introduction

“Hotspot” is an area that has an elevated response compared with the rest area. Detection or identification this area is important in many fields of study. Spatial epidemiology is a field that needs hotspot detection method most to help identifying environmental factors, associated with disease or many other indicators related to a measurement of a concept in a region. There are some hotspot detection methods developed by experts, some of which are: 1 a spatial scan statistic with SaTScan software Kulldorff 1997, 2 Upper Level Set scan statistic Patil and Taillie 2004, 3 generalized additive models GAM Hastie and Tibshirani 1991, and 4 Bayesian disease mapping BYM Besag et al. 1991. The hotspot detection methods 1 and 2 have the same basic theory where the difference is in the way they do the scanning process. Circle-based Scan Statistic is a hotspot detection method based on circle shape detection area. This method builds circle continuously wider to detect hotspot area. Whereas, Upper Level Set scan statistic builds adjacent cells with highest intensity to find hotspot area. This chapter focuses on these two hotspot detection methods. The objective of this chapter is to compare two hotspot detection methods suitable for detecting local spatial clusters, i.e. Circle-based Scan Statistic SS and Upper Level Set scan statistic ULS with the observed incidence is assumed as Poisson distribution. For the next explanation, the terms SS and ULS are used for Circle-based Scan Statistic and Upper Level Set scan statistic, respectively. A simulation is carried out to compare these two methods. A geographic cluster is chosen as high-risk areas. For each simulation, the performance of the methods is assessed in terms of the sensitivity, specificity, and percentage correctly classified for each cluster. As the detail, performance is measured through 14 criteria. 34 The best method, as the result of comparison between the two methods will be used to detect bad nutrition cases in some districts. Explanation about the application of this method will be discussed in section 3.5 and 3.6. Furthermore, the result of this hotspot detection will be used as an explanatory variable in modeling at Chapter 4.

3.2 Theoretical Study