Space-Time Cluster Analysis of Malaria in Fars Province-Iran

A. Rezaianzadeh, M. Zare, M. Aliakbarpoor, H. Faramarzi, M. Ebrahimi
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引用次数: 3

Abstract

Background: Malaria, a mosquito-borne infection, is caused by protozoan parasites invading the red blood cells of both humans and animals. Iran is an endemic site for malaria with 1 - 10 cases per one million dwellers, in which 90% of cases occur in southern parts of Iran. Objectives: to detect space-time clusters of Malaria in Fars province-Iran during 1/1/2011 and 31/12/2015. Methods: 357 malaria cases were recorded from 19 cities of Fars province. Permutation scan modeling was applied retrospectively to detect the outbreaks of malaria during 1/1/2011 and 31/12/2015. SPSS V. 22, ITSM V. 2002, ArcGIS10, and SaTScan9.4.4 software tools were used. The significance level was considered 0.05. Results: Based on the results of the current study, 5 space-time clusters were discovered for malaria, indicating that malaria followed a time-space trend in the area (P value < 0.05). The Most Likely Cluster (MLC) contained almost 50% (6/12) of all cases during 1/7/2015 and 31/7/2015 in Firoozabad (P < 0.05); however, non-statistically significant clusters were detected which had clinically important information on the canons of the outbreaks. Conclusions: Malaria did not occur uniformly in Fars province during 1/1/2011 and 31/12/2015. Detected space-time clusters could help Public health managers and policymakers dedicating clinical staff and facilities to more needy areas and consequently reduce the rate of disease in the area.
伊朗法尔斯省疟疾时空聚类分析
背景:疟疾是一种蚊媒感染,由原虫寄生虫侵入人类和动物的红细胞引起。伊朗是疟疾流行地,每100万居民中有1 - 10例疟疾病例,其中90%的病例发生在伊朗南部地区。目的:检测2011年1月1日至2015年12月31日伊朗法尔斯省疟疾时空聚类。方法:对法尔斯省19个城市357例疟疾病例进行调查。应用排列扫描模型对2011年1月1日至2015年12月31日期间的疟疾疫情进行回顾性检测。采用SPSS V. 22、ITSM V. 2002、ArcGIS10、SaTScan9.4.4软件工具。认为显著性水平为0.05。结果:在本研究的基础上,发现了疟疾的5个时空聚类,表明该地区疟疾具有一定的时空趋势(P值< 0.05)。2015年1月7日至2015年7月31日期间,Firoozabad最可能聚集群(MLC)占所有病例的近50% (6/12)(P < 0.05);然而,检测到的非统计显着的聚集性具有有关暴发的临床重要信息。结论:2011年1月1日和2015年12月31日,法尔斯省的疟疾发病情况并不均匀。探测到的时空集群可以帮助公共卫生管理人员和政策制定者将临床工作人员和设施投入到更需要的地区,从而降低该地区的发病率。
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