Publication: Exploring spatial patterns and hotspots of diarrhea in Chiang Mai, Thailand
Issued Date
2009-06-24
Resource Type
ISSN
1476072X
Other identifier(s)
2-s2.0-67650739720
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Mahidol University
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SCOPUS
Bibliographic Citation
International Journal of Health Geographics. Vol.8, No.1 (2009)
Suggested Citation
Nakarin Chaikaew, Nitin K. Tripathi, Marc Souris Exploring spatial patterns and hotspots of diarrhea in Chiang Mai, Thailand. International Journal of Health Geographics. Vol.8, No.1 (2009). doi:10.1186/1476-072X-8-36 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/27358
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Title
Exploring spatial patterns and hotspots of diarrhea in Chiang Mai, Thailand
Author(s)
Abstract
Background: Diarrhea is a major public health problem in Thailand. The Ministry of Public Health, Thailand, has been trying to monitor and control this disease for many years. The methodology and the results from this study could be useful for public health officers to develop a system to monitor and prevent diarrhea outbreaks. Methods: The objective of this study was to analyse the epidemic outbreak patterns of diarrhea in Chiang Mai province, Northern Thailand, in terms of their geographical distributions and hotspot identification. The data of patients with diarrhea at village level and the 2001-2006 population censuses were collected to achieve the objective. Spatial analysis, using geographic information systems (GIS) and other methods, was used to uncover the hidden phenomena from the data. In the data analysis section, spatial statistics such as quadrant analysis (QA), nearest neighbour analysis (NNA), and spatial autocorrelation analysis (SAA), were used to identify the spatial patterns of diarrhea in Chiang Mai province. In addition, local indicators of spatial association (LISA) and kernel density (KD) estimation were used to detect diarrhea hotspots using data at village level. Results: The hotspot maps produced by the LISA and KD techniques showed spatial trend patterns of diarrhea diffusion. Villages in the middle and northern regions revealed higher incidences. Also, the spatial patterns of diarrhea during the years 2001 and 2006 were found to represent spatially clustered patterns, both at global and local scales. Conclusion: Spatial analysis methods in GIS revealed the spatial patterns and hotspots of diarrhea in Chiang Mai province from the year 2001 to 2006. To implement specific and geographically appropriate public health risk-reduction programs, the use of such spatial analysis tools may become an integral component in the epidemiologic description, analysis, and risk assessment of diarrhea. © 2009 Chaikaew et al; licensee BioMed Central Ltd.