Orkis L T, Peterson E R, Brooks M M, Mertz K J, Harrison L H, Stout J E, Greene S K
Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, USA.
Allegheny County Health Department, Bureau of Assessment, Statistics, and Epidemiology, Pittsburgh, Pennsylvania, USA.
Epidemiol Infect. 2018 Oct 18;147:e29. doi: 10.1017/S0950268818002789.
Legionnaires' disease (LD) incidence in the USA has quadrupled since 2000. Health departments must detect LD outbreaks quickly to identify and remediate sources. We tested the performance of a system to prospectively detect simulated LD outbreaks in Allegheny County, Pennsylvania, USA. We generated three simulated LD outbreaks based on published outbreaks. After verifying no significant clusters existed in surveillance data during 2014-2016, we embedded simulated outbreak-associated cases into 2016, assigning simulated residences and report dates. We mimicked daily analyses in 2016 using the prospective space-time permutation scan statistic to detect clusters of ⩽30 and ⩽180 days using 365-day and 730-day baseline periods, respectively. We used recurrence interval (RI) thresholds of ⩾20, ⩾100 and ⩾365 days to define significant signals. We calculated sensitivity, specificity and positive and negative predictive values for daily analyses, separately for each embedded outbreak. Two large, simulated cooling tower-associated outbreaks were detected. As the RI threshold was increased, sensitivity and negative predictive value decreased, while positive predictive value and specificity increased. A small, simulated potable water-associated outbreak was not detected. Use of a RI threshold of ⩾100 days minimised time-to-detection while maximizing positive predictive value. Health departments should consider using this system to detect community-acquired LD outbreaks.
自2000年以来，美国退伍军人病（LD）的发病率增长了两倍。卫生部门必须迅速发现LD疫情，以确定并整治源头。我们测试了一个系统在美国宾夕法尼亚州阿勒格尼县前瞻性检测模拟LD疫情的性能。我们根据已发表的疫情生成了三起模拟LD疫情。在核实2014年至2016年监测数据中不存在显著聚集性病例后，我们将模拟疫情相关病例嵌入到2016年的数据中，并指定模拟居住地址和报告日期。我们模拟了2016年的日常分析，分别使用前瞻性时空置换扫描统计量，以365天和730天为基线期，检测持续时间小于等于30天和小于等于180天的聚集性病例。我们使用复发间隔（RI）阈值大于等于20天、大于等于100天和大于等于365天来定义显著信号。我们分别针对每起嵌入的疫情，计算了日常分析的灵敏度、特异度以及阳性和阴性预测值。检测到了两起与大型模拟冷却塔相关的疫情。随着RI阈值的提高，灵敏度和阴性预测值降低，而阳性预测值和特异度增加。未检测到一起与小型模拟饮用水相关的疫情。使用大于等于100天的RI阈值可在最大限度提高阳性预测值的同时，将检测时间减至最短。卫生部门应考虑使用该系统来检测社区获得性LD疫情。
