GIS on Weekend: SIG EPI dan SaTScan
The 4th meeting at the “GIS on Weekend” on last Saturday (23/08/08) was presenting topic of SlG Epi and SaTScan utilization for epidemiology. Those applications can perform health spatial data analysis.
SIG Epi is current GIS application developed by Health Analysis Information and System-Pan American Heatlh Organization (HAIS–PAHO) for spatial analysis in epidemiology. PAHO has cooperated with ESRI to develop SIG Epi so that only shapefile (SHP) format can be run by this application. The output is useful for people who concern on epidemiology and public health, also for managerial decision maker. It has several features, such as descriptive statistic, correlation, health service, etc. The strength point of this apllication is package of data analysis including in the application (on menu of epi analysis) so that it does not need additional application to do analysis.
The SIG Epi can be downloaded at http://ais.paho.org:80/sigepi/dwld/SIGEpi_En.zip, It is free trial for the 90 days and we can buy the licensed one for the full version. It can be run by using Microsoft Windows 95/98/Me/NT/2000/Xp. It needs processor of 486, pentium, or higher and memory of 128 MB with 40MB free spaces.
SaTScan is a free software that analyzes spatial, temporal and space-time data using the spatial, temporal, or space-time scan statistics. It is designed for any of the following interrelated purposes:
- Perform geographical surveillance of disease, to detect spatial or space-time disease clusters, and to see if they are statistically significant.
- Test whether a disease is randomly distributed over space, over time or over space and time.
- Evaluate the statistical significance of disease cluster alarms.
- Perform repeated time-periodic disease surveillance for early detection of disease outbreaks.
To perform the SaTScan output in map, we need application of Epi Info or ArcView-GIS. So you can install those two applications together with the SaTScan to produce the spatial analysis map.
At last, this GIS on weekend training has been ended for this August. Hopefully, we will arrange the similar training after the Ramadhan. For anyone who is interested in being the GIS expert, don’t worry! You still have a chance to make it real.
Detection of Dengue Haemorhagic Fever (DHF) Endemicity at Grogol Subdistrict, Sukoharjo District by Using Geographic Information Systems (GIS)
ABSTRACT
Sunardi, Hari Kusnanto
Background : Grogol Subdistrict is one of subditricts in Sukoharjo which has the highest of DHF cases, there are 11 endemic villages from all 14 vilages at Grogol. Consider to the high number of DHF cases, it is required a study to find out the factors which are related to detection of DHF endemicity for determining infection risk of DHF and risk management. Detection of DHF endemic factors used proportional data of residential area usage, larva free rate, and density. Data processing use geographical information system (GIS).
Objective: This study was aimed to find out the relationship between factors of DHF endemicity detection and clustering of DHF in the Grogol Subdistrict, Sukoharjo.
Methods: This research was cross sectional survey. The research population was area population, that was region segments which included number of research units (entire villages at the Grogol subdistrict map) and all DHF cases (352 cases) at Grogol Subdistrict within 2004-2006 and woluld be determined its coordinat. All population would be examined (total population). Spatial anlyses of SatScanTM was used to find out clusters of DHF, and then spatially weighted regression analyses of GeoDaTM was used for to measure the relationship between density, larva free rate, width of residential area, and DHF endemicity.
Result: The results of the spatially weighted regression analyses (spatiai error model) demonstrated that the level of DHF endemicity was not associated with density (z = 0,785 p = 0.42 (p>0.05)), larva free rate (z = 0.785, p = 0.168 (p>0.05)); and the width of residential land (z value = 0.702 p = 0.482 (p>0.05)). The DHF endemicity followed a certain spatial distribution pattern (p=0.004 (p 0.05)). Based on Space Time Permutation Model (Likehood Ratio Test), there was clustering of DHF at The Grogol Subdistrict significantly. Cluster 1 happened within January 1, 2004 – January 31, 2004, centered at the coordinat of (-7.623250 s, 110.820450 E) and radius of 0.00 km. The Most Likely Cluster happened during August 1, 2005 – September 30, 2005, centered at the coordinat of (-7.586030 s, 110.794590 E) and radius of 0.79 km. Clustering of DHF cases followed the trend of high density, low larvas free rate, and wide propotion of residential area.
Conclusion : The spread of DHF followed a certain spatial distribution pattern. The DHF distribution was not associated with density, larvas free rate, and the width of residential area. There were significant DHF clusters at the Grogol subdistrict.
Keywords : Endemicity, Dengue Haemorhagic Fever, Geographic Information Systems.