MJM, Vol 70 Supplement 1 September 2015
Risk cluster mapping at point level for dengue disease
Medical Research Resource Centre, Institute for Medical Research, Jalan Pahang, 50588 Kuala Lumpur
Introduction: Dengue disease is a global disease burden and particular endemic in Malaysia. As vaccine is still under-developed, the current interventions focus on vector control. To efficiently plan vector control, one of the fundamental task is to identify and validate the high risk disease clustering area. To date, there is still lack of studies on the disease cluster mapping at point level. The objective of this study was to compare and validate the risk cluster mapping at point level, in particular for infectious disease such as dengue.
Method: The study area was Selangor and Kuala Lumpur that reported high number of dengue cases in Malaysia. Dengue cases of the year 2010 were obtained from Disease Control Division of Ministry of Health Malaysia. The dengue cases were geocoded according to the residency address using R software. Two cluster detection approaches, Software for the spatial and space-time scan statistics (SatTScan) and Kernel Density were used to compare the disease clustering mapping results.
Results: The validation results showed that SaTScan was sensitive at showing more clusters than Kernel Density. However, Kernel Density exhibited better smoothing function than SaTScan. Clusters of dengue cases were mainly concentrated at the central part of the study area.
Conclusion: Crime hot-spot analysis and disease hot-spot analysis based on case-control were well-established. However, particularly in infectious disease, cluster analysis at point level was still lacking. For frequentist approach, SaTScan performed better than Kernel Density in term of space and time. The high-risk clusters implied that vector control effort should be focused on the hot-spot area. The identification of high-risk disease cluster area assists better dengue control interventions. SaTScan is recommended to be adopted in the current surveillance system.
Keywords: hot-spot, dengue, cluster analysis, point level