Salimi Mojtaba, Jesri Nahid, Javanbakht Mohammad, Farahani Leyli Zanjirani, Shirzadi Mohammad Reza, Saghafipour Abedin
1Research Center for Environmental Determinants of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran.
2Research Center for Environmental Pollutants, Qom University of Medical Sciences, Qom, Iran.
J Parasit Dis. 2018 Dec;42(4):570-576. doi: 10.1007/s12639-018-1036-5. Epub 2018 Oct 13.
Geographic information system (GIS) nowadays is one of the most helpful epidemiological tools for identifying the high risk areas of cutaneous leishmaniasis (CL). This study was conducted to determine the spatio-temporal distribution of CL in Qom province during 2009-2017. In a cross-sectional study, for the survey of spatial dispersion of CL in the study region, the incidence rate of disease was calculated in all of 23 villages during 2009-2017. Then, spatial analysis of the infection was performed using two methods: spatial autocorrelation (Moran's I) in order to determine the special distribution pattern of disease and Kriging method to reveal high risk areas for disease. The incidence of CL in Qom province has been decreasing as of 2009-2015 and increasing in 2015-2017. The highest incidence was stated in 2009 (36.5 per 100,000) and the least was reported in 2015 (13.3 per 100,000). The Moran autocorrelation index revealed that the study area has a cluster pattern. The temporal distribution of disease incidence showed that northeast, southwest and northwest parts of Qom province involved highest incidence of CL in 90% significant level. Leishmaniasis incidence is a function of spatial and geographical trends, thus spatial variations of the infection incidence illustrate that the incidence rate does not increase or decrease from one region to another intensively. The results of this study indicate that marking high risk areas using spatial analysis can be helpful as a main tool in CL control and prevention.
地理信息系统(GIS)如今是识别皮肤利什曼病(CL)高风险区域最有用的流行病学工具之一。本研究旨在确定2009 - 2017年期间库姆省CL的时空分布。在一项横断面研究中,为了调查研究区域内CL的空间分布情况,计算了2009 - 2017年期间所有23个村庄的疾病发病率。然后,使用两种方法对感染情况进行空间分析:空间自相关(莫兰指数I)以确定疾病的特殊分布模式,以及克里金法以揭示疾病的高风险区域。自2009 - 2015年以来,库姆省CL的发病率一直在下降,而在2015 - 2017年有所上升。发病率最高的是2009年(每10万人中36.5例),最低的是2015年(每10万人中13.3例)。莫兰自相关指数显示研究区域具有聚集模式。疾病发病率的时间分布表明,在90%的显著水平下,库姆省的东北部、西南部和西北部CL发病率最高。利什曼病发病率是空间和地理趋势的函数,因此感染发病率的空间变化表明发病率不会从一个区域到另一个区域急剧增加或减少。本研究结果表明,利用空间分析标记高风险区域可作为CL控制和预防的主要工具。