Influenza infection rates, measurement errors and the interpretation of paired serology.
Influenza infection rates, measurement errors and the interpretation of paired serology.
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DOI：
10.1371/journal.ppat.1003061
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发表时间：
2012
期刊：
影响因子：
6.7
通讯作者：
Ferguson NM
中科院分区：
文献类型：
--
作者：
Cauchemez S;Horby P;Fox A;Mai le Q;Thanh le T;Thai PQ;Hoa le NM;Hien NT;Ferguson NM
血清学研究是估计人群中流感感染发病率 (AR) 的金标准方法。在一个共同的方案中，在流行病爆发之前和之后收集一组个体的血液样本；流行期间血凝抑制（HI）抗体滴度的升高被认为是感染的标志。由于固有的测量误差，2 倍的升高通常被认为是感染的证据不足，因此血清转化通常被定义为 4 倍或以上的升高。在这里，我们重新审视这个被广泛接受的 70 年前的标准。我们开发了马尔可夫链蒙特卡罗数据增强模型，以量化测量误差并重建越南 3 年血清学队列中潜在真实血清学状态的分布，其中可以进行重复测量。我们估计，当抗体滴度低于 10 时，2 倍误差的单边概率为 9.3%（95% 可信区间，CI：3.3%、17.6%），否则为 20.2%（95% CI：15.9%、24.0%）。在校正测量误差后，我们发现抗体滴度上升2倍的个体比例太大，无法仅用测量误差来解释。 AR 的估计值会有很大差异，具体取决于这些人是否包含在感染人群的定义中。模拟研究表明我们的方法是无偏的。当针对个别病例进行特定诊断时，4 倍上升病例定义是相关的，但当目标是估计 AR 时，其合理性就不那么明显了。特别是，它可能会导致 AR 的大幅低估。确定哪种生物现象对抗体滴度增加 2 倍贡献最大，对于评估传统病例定义的偏差并提供改进的流感 AR 估计至关重要。每年，季节性流感会导致全球约三到五百万人罹患严重疾病，并导致约 25 万到 50 万人死亡。为了评估疾病负担并指导控制政策，量化每年感染流感病毒的人数比例非常重要。由于感染通常会在感染者的血液中留下“特征”（即抗体升高），因此标准方案包括收集一组受试者的血液样本并确定经历这种升高的人的比例。然而，由于固有的测量误差，标准的 4 倍上升情况定义中仅考虑了较大的上升。在这里，我们重新审视这个已有 70 年历史并被广泛接受和应用的标准。我们提出创新的统计技术，以更好地捕捉测量误差的影响并改进我们对数据的解释。我们的分析表明，每年感染流感病毒的人数可能比之前想象的要多得多，这对于我们了解流感的传播和演变以及感染的性质具有重要意义。
Serological studies are the gold standard method to estimate influenza infection attack rates (ARs) in human populations. In a common protocol, blood samples are collected before and after the epidemic in a cohort of individuals; and a rise in haemagglutination-inhibition (HI) antibody titers during the epidemic is considered as a marker of infection. Because of inherent measurement errors, a 2-fold rise is usually considered as insufficient evidence for infection and seroconversion is therefore typically defined as a 4-fold rise or more. Here, we revisit this widely accepted 70-year old criterion. We develop a Markov chain Monte Carlo data augmentation model to quantify measurement errors and reconstruct the distribution of latent true serological status in a Vietnamese 3-year serological cohort, in which replicate measurements were available. We estimate that the 1-sided probability of a 2-fold error is 9.3% (95% Credible Interval, CI: 3.3%, 17.6%) when antibody titer is below 10 but is 20.2% (95% CI: 15.9%, 24.0%) otherwise. After correction for measurement errors, we find that the proportion of individuals with 2-fold rises in antibody titers was too large to be explained by measurement errors alone. Estimates of ARs vary greatly depending on whether those individuals are included in the definition of the infected population. A simulation study shows that our method is unbiased. The 4-fold rise case definition is relevant when aiming at a specific diagnostic for individual cases, but the justification is less obvious when the objective is to estimate ARs. In particular, it may lead to large underestimates of ARs. Determining which biological phenomenon contributes most to 2-fold rises in antibody titers is essential to assess bias with the traditional case definition and offer improved estimates of influenza ARs. Each year, seasonal influenza is responsible for about three to five million severe illnesses and about 250,000 to 500,000 deaths worldwide. In order to assess the burden of disease and guide control policies, it is important to quantify the proportion of people infected by an influenza virus each year. Since infection usually leaves a “signature” in the blood of infected individuals (namely a rise in antibodies), a standard protocol consists in collecting blood samples in a cohort of subjects and determining the proportion of those who experienced such rise. However, because of inherent measurement errors, only large rises are accounted for in the standard 4-fold rise case definition. Here, we revisit this 70 year old and widely accepted and applied criterion. We present innovative statistical techniques to better capture the impact of measurement errors and improve our interpretation of the data. Our analysis suggests that the number of people infected by an influenza virus each year might be substantially larger than previously thought, with important implications for our understanding of the transmission and evolution of influenza – and the nature of infection.
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