Abstract:Objective To analyze the features of infectious disease automatic early warning signals in Shanghai from 2008 to 2016, and to provide a basis for optimizing the infectious diseases automated-alert and response system. Methods We downloaded the data regarding infectious disease automatic early warning signals from the China infectious diseases automated-alert and response system (CIDARS), then described and analyzed according to the factors like year, type of early warning signal, type of disease and early warning signal response. Results A total of 59,603 signals were issued by the CIDARS in Shanghai from 2008 to 2016, involving 32 kinds of infectious diseases. The signals occupied the first five places were chicken pox, measles, other infectious diarrhea, mumps and hand-food-mouth disease. The types of early warnings of different diseases were different, and the early warning signals of different diseases varied with the years. 1,952 signals were verified as suspected outbreaks. The effective rate of early warning signals was 3.28%, of which the efficiency of single-case early warning was the highest (12.50%), followed by the time warning (1.73%) and the space-time warning (1.01%), with a statistically significant difference (P<0.001). The timely processing rate of early warning signals was 85.69% (51,071/59,603), of which the timely processing rate of single-case early warning signals was the highest (91.47%), followed by the time warning (86.34%) and the space-time warning (80.69%), with a statistically significant difference (P<0.001). Conclusions The overall running status of the CIDARS in Shanghai during 2008-2016 was good, but there were still problems like high proportion of false-positive early warning signals and delayed response. It is necessary to further improve functions and parameter setting of the CIDARS and promote medical workers’ disposal ability and reporting consciousness.
[1] Loonsk JW. BioSense-a national initiative for early detection and quantification of public health emergencies[J]. MMWR Suppl, 2004,53:53-55. [2] Wagner MM, Espino J, Tsui FC, et al. Syndrome and outbreak detection using chief-complaint data-experience of the Real-Time Outbreak and Disease Surveillance project s[J]. MMWR Suppl, 2004,53:28-31. [3] Faensen D, Claus H, Benzler J, et al. SurvNet@RKI-a multistate electronic reporting system for communicable diseases s[J]. EuroSurveill, 2006,11(4):100-103. [4] Wang L, Wang Y, Jin S, et al. Emergence and control of infectious diseases in China [J]. Lancet, 2008, 372(9649):1598-1605. [5] 杨维中, 兰亚佳, 李中杰, 等. 国家传染病自动预警系统的设计与应用[J].中华流行病学杂志, 2010, 31(11):1240-1244. [6] 鲁琴宝,徐旭卿,林君芬,等.2013年浙江省传染病自动预警系统预警信号响应情况分析[J].中华预防医学杂志, 2014, 48(4):328-329. [7] 李中杰,马家奇,赖圣杰,等.2011-2013年国家传染病自动预警系统运行结果分析[J].中华预防医学杂志, 2014, 48(4):252-258. [8] 陈碧云,高立冬,陈长,等.我国传染病预警研究及工作现况[J].实用预防医学, 2014, 21(12):1537-1539. [9]王瑞平,春雅丽,毕安华,等.上海市松江区流行性感冒控制图法预警界值优选研究[J].中国初级卫生保健, 2010,24(7):77-78. [10] 王瑞平,春雅丽,郭晓芹,等.上海市松江区手足口病控制图法预警界值优选研究[J].中国卫生统计, 2011, 28(3):264-265. [11] 王瑞平,姜永根,郭晓芹,等.基于传染病自动预警信息系统的“流行标准”最优化选择分析[J].中国卫生统计, 2017, 34(2):214-217,221. [12] 朱婷婷,孙利文,常姗姗,等.比数图法在北京市怀柔区重点传染病预警中的方法探讨[J].实用预防医学, 2017, 24(10):1270-1273.