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病例混乱方法学是否是用于调查疫情爆发的传统病例对照研究的合适替代方法?

Is case-chaos methodology an appropriate alternative to conventional case-control studies for investigating outbreaks?

作者信息

Edelstein Michael, Wallensten Anders, Kühlmann-Berenzon Sharon

出版信息

Am J Epidemiol. 2014 Aug 15;180(4):406-11. doi: 10.1093/aje/kwu123. Epub 2014 Jul 2.

Abstract

Case-chaos methodology is a proposed alternative to case-control studies that simulates controls by randomly reshuffling the exposures of cases. We evaluated the method using data on outbreaks in Sweden. We identified 5 case-control studies from foodborne illness outbreaks that occurred between 2005 and 2012. Using case-chaos methodology, we calculated odds ratios 1,000 times for each exposure. We used the median as the point estimate and the 2.5th and 97.5th percentiles as the confidence interval. We compared case-chaos matched odds ratios with their respective case-control odds ratios in terms of statistical significance. Using Spearman's correlation, we estimated the correlation between matched odds ratios and the proportion of cases exposed to each exposure and quantified the relationship between the 2 using a normal linear mixed model. Each case-control study identified an outbreak vehicle (odds ratios = 4.9-45). Case-chaos methodology identified the outbreak vehicle 3 out of 5 times. It identified significant associations in 22 of 113 exposures that were not associated with outcome and 5 of 18 exposures that were significantly associated with outcome. Log matched odds ratios correlated with their respective proportion of cases exposed (Spearman ρ = 0.91) and increased significantly with the proportion of cases exposed (b = 0.054). Case-chaos methodology missed the outbreak source 2 of 5 times and identified spurious associations between a number of exposures and outcome. Measures of association correlated with the proportion of cases exposed. We recommended against using case-chaos analysis during outbreak investigations.

摘要

病例-混乱方法是一种提议用于替代病例对照研究的方法,该方法通过随机重新排列病例的暴露情况来模拟对照。我们使用瑞典疫情的数据对该方法进行了评估。我们从2005年至2012年发生的食源性疾病暴发中识别出5项病例对照研究。使用病例-混乱方法,我们对每种暴露情况计算了1000次比值比。我们将中位数用作点估计值,将第2.5百分位数和第97.5百分位数用作置信区间。我们在统计学显著性方面比较了病例-混乱匹配比值比与其各自的病例对照比值比。使用斯皮尔曼相关性,我们估计了匹配比值比与暴露于每种暴露情况的病例比例之间的相关性,并使用正态线性混合模型对两者之间的关系进行了量化。每项病例对照研究都确定了一个暴发载体(比值比 = 4.9 - 45)。病例-混乱方法在5次中有3次确定了暴发载体。它在113种与结局无关的暴露中有22种以及在18种与结局显著相关的暴露中有5种确定了显著关联。对数匹配比值比与其各自的暴露病例比例相关(斯皮尔曼ρ = 0.91),并随着暴露病例比例的增加而显著增加(b = 0.054)。病例-混乱方法在5次中有2次未找出暴发源头,并确定了一些暴露与结局之间的虚假关联。关联度量与暴露病例比例相关。我们建议在暴发调查期间不要使用病例-混乱分析。

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