GIS-Based Spatial Statistical Analysis of Risk Areas for Liver Flukes in Surin Province of Thailand

Rujirakul, Ratana; Ueng-arporn, Naporn; Kaewpitoon, Soraya; Loyd, Ryan J; Kaewthani, Sarochinee; Kaewpitoon, Natthawut

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자료유형: 학술저널

저자정보: Rujirakul, Ratana (Parasitic Infectious Disease Unit, Institute of Medicine, Suranaree University of Technology) Ueng-arporn, Naporn (Parasitic Infectious Disease Unit, Institute of Medicine, Suranaree University of Technology) Kaewpitoon, Soraya (Parasitic Infectious Disease Unit, Institute of Medicine, Suranaree University of Technology) Loyd, Ryan J (Parasitic Infectious Disease Unit, Institute of Medicine, Suranaree University of Technology) Kaewthani, Sarochinee (Faculty of Science and Technology, Nakhon Ratchasima Rajabhat University) Kaewpitoon, Natthawut (Faculty of Public Health, Vongchavalitkul University)

저널정보: 아시아태평양암예방학회 Asian Pacific journal of cancer prevention : APJCP Asian Pacific journal of cancer prevention : APJCP 제16권 제6호

발행연도: 2015.1

수록면: 2,323 - 2,326 (4page)

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It is urgently necessary to be aware of the distribution and risk areas of liver fluke, Opisthorchis viverrini, for proper allocation of prevention and control measures. This study aimed to investigate the human behavior, and environmental factors influencing the distribution in Surin Province of Thailand, and to build a model using stepwise multiple regression analysis with a geographic information system (GIS) on environment and climate data. The relationship between the human behavior, attitudes (<50%; $X_{111}$), environmental factors like population density ($148-169pop/km^2$; $X_{73}$), and land use as wetland ($X_{64}$), were correlated with the liver fluke disease distribution at 0.000, 0.034, and 0.006 levels, respectively. Multiple regression analysis, by equations OV= -0.599 + 0.005(population density ($148-169pop/km^2$); $X_{73}$) + 0.040 (human attitude (<50%); $X_{111}$) +0.022 (land used (wetland; X64), was used to predict the distribution of liver fluke. OV is the patients of liver fluke infection, R Square= 0.878, and, Adjust R Square= 0.849. By GIS analysis, we found Si Narong, Sangkha, Phanom Dong Rak, Mueang Surin, Non Narai, Samrong Thap, Chumphon Buri, and Rattanaburi to have the highest distributions in Surin province. In conclusion, the combination of GIS and statistical analysis can help simulate the spatial distribution and risk areas of liver fluke, and thus may be an important tool for future planning of prevention and control measures.

#Risk areas #liver fluke #Surin province #geographic information system #Thailand

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