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中国广州白纹伊蚊幼虫指数阈值在登革热传播中的识别

Identification of Aedes albopictus larval index thresholds in the transmission of dengue in Guangzhou, China.

作者信息

Luo Lei, Li Xiaoning, Xiao Xincai, Xu Ya, Huang Miaoling, Yang Zhicong

机构信息

Guangzhou Center for Disease Control and Prevention, Guangdong Province, China.

Guangdong Pharmaceutical University, Guangdong Province, China.

出版信息

J Vector Ecol. 2015 Dec;40(2):240-6. doi: 10.1111/jvec.12160.

Abstract

Entomological indices have been used to quantitatively express vector density, but the threshold of larval indices of Aedes albopictus in dengue epidemics is still undefined. We conducted a case-control study to identify the thresholds of Aedes albopictus larval indices in dengue epidemics. Two unit levels of analysis were used: district and street. The discriminative power of the indices was assessed by receiver operating characteristic (ROC) curves. The association between the entomologic indices and dengue transmission was further explored by a logistic regression model. At the district level, there was no significant difference in the Breteau index (BI) between districts that reported cases and those did not (t=0.164, p>0.05), but the Container index (CI) did show a significant difference (t=2.028, p<0.01). The AUC (Area Under the Curve) of BI, CI, and prediction value were 0.540, 0.630, and 0.533, respectively. Predicting at the street level, the AUC of BI, CI, and prediction values were 0.684, 0.660, and 0.685, respectively, and 0.861, 0.827, and 0.867 for outbreaks. BI=5.1, CI=5.4, or prediction value =0.491were suggested to control the epidemic efficiently with the fewest resources, where BI=4.0, CI=5.1, or PRE =0.483 were suggested to achieve effectiveness.

摘要

昆虫学指标已被用于定量表达病媒密度,但白纹伊蚊幼虫指标在登革热流行中的阈值仍未明确。我们进行了一项病例对照研究,以确定登革热流行中白纹伊蚊幼虫指标的阈值。采用了两个分析单位水平:区和街道。通过受试者工作特征(ROC)曲线评估指标的判别能力。通过逻辑回归模型进一步探讨昆虫学指标与登革热传播之间的关联。在区层面,报告病例的区与未报告病例的区之间布雷图指数(BI)无显著差异(t = 0.164,p>0.05),但容器指数(CI)确实显示出显著差异(t = 2.028,p<0.01)。BI、CI和预测值的曲线下面积(AUC)分别为0.540、0.630和0.533。在街道层面进行预测时,BI、CI和预测值的AUC分别为0.684、0.660和0.685,疫情爆发时分别为0.861、0.827和0.867。建议BI = 5.1、CI = 5.4或预测值 = 0.491,以最少资源有效控制疫情,其中建议BI = 4.0、CI = 5.1或PRE = 0.483以实现防控效果。

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