Journal of Cutaneous and Aesthetic Surgery
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ORIGINAL ARTICLE
Year : 2012  |  Volume : 5  |  Issue : 1  |  Page : 30-35

Spatial analysis of eco-environmental risk factors of cutaneous leishmaniasis in Southern Iran


1 Department of Epidemiology, Health Faculty, Shiraz University of Medical Sciences, Shiraz, Iran
2 Department of Public Health, Health Faculty, Qom University of Medical Sciences, Qom, Iran
3 Department of Parasitology, Medical Faculty, Shiraz University of Medical Sciences, Shiraz, Iran

Correspondence Address:
Abolfazl Mohammadbeigi
Department of Public Health, Health School, Qom
Iran
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0974-2077.94338

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Background: Despite the advances in the diagnosis and treatment of leishmaniasis, it is still considered as a severe public health problem particularly in developing countries and a great economic burden on the health resources. The present study was designed and conducted to determine the eco-environmental characteristics of the leishmaniasis disease by spatial analysis. Materials and Methods: In an ecological study, data were collected on eco-environmental factors of Fars province in Iran and on cutaneous leishmaniasis (CL) cases from 2002 to 2009. geographic weighted regression (GWR) was used to analyse the data and compare them with ordinary least square (OLS) regression model results. Moran's Index was applied for analysis of spatial autocorrelation in residual of OLS. P value less than 0.05 was considered as significant and adjusted R2 was used for model preferences. Results: There was a significant spatial autocorrelation in the residuals of OLS model (Z=2.45, P=0.014). GWR showed that rainy days, minimum temperature, wind velocity, maximum relative humidity and population density were the most important eco-environmental risk factors and explained 0.388 of the associated factors of CL. Conclusion: Spatial analysis can be a good tool for detection and prediction of CL disease. In autocorrelated and non-stationary data, GWR model yields a better fitness than OLS regression model. Also, population density can be used as a surrogate variable of acquired immunity and increase the adjusted R2.


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