Ainslie Kylie E C, Haber Michael, Orenstein Walter A
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd., Atlanta, GA 30322, USA; MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK.
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd., Atlanta, GA 30322, USA.
Vaccine. 2019 Mar 28;37(14):1987-1993. doi: 10.1016/j.vaccine.2019.02.036. Epub 2019 Mar 2.
Test-negative (TN) studies have become the most widely used study design for the estimation of influenza vaccine effectiveness (VE) and are easily incorporated into existing influenza surveillance networks. We seek to determine the bias of TN-based VE estimates during a pandemic using a dynamic probability model. The model is used to evaluate and compare the bias of VE estimates under various sources of bias when vaccination occurs after the beginning of an outbreak, such as during a pandemic. The model includes two covariates (health status and health awareness), which may affect the probabilities of vaccination, developing influenza and non-influenza acute respiratory illness (ARI), and seeking medical care. Specifically, we evaluate the bias of VE estimates when (1) vaccination affects the probability of developing a non-influenza ARI; (2) vaccination affects the probability of seeking medical care; (3) a covariate (e.g. health status) is related to both the probabilities of vaccination and developing an ARI; and (4) a covariate (e.g. health awareness) is related to both the probabilities of vaccination and of seeking medical care. We considered two outcomes against which the vaccine is supposed to protect: symptomatic influenza and medically-attended influenza. When vaccination begins during an outbreak, we found that the effect of delayed onset of vaccination is unpredictable. VE estimates from TN studies were biased regardless of the source of bias present. However, if the core assumption of the TN design is satisfied, that is, if vaccination does not affect the probability of non-influenza ARI, then TN-based VE estimates against medically-attended influenza will only suffer from small (<0.05) to moderate bias (≥0.05 and <0.10). These results suggest that if sources of bias listed above are ruled out, TN studies are a valid study design for the estimation of VE during a pandemic.
检测阴性（TN）研究已成为估计流感疫苗效力（VE）时使用最广泛的研究设计，并且很容易纳入现有的流感监测网络。我们试图使用动态概率模型来确定大流行期间基于TN的VE估计值的偏差。该模型用于评估和比较在疫情爆发开始后（如在大流行期间）进行疫苗接种时，各种偏差来源下VE估计值的偏差。该模型包括两个协变量（健康状况和健康意识），它们可能会影响疫苗接种、患流感和非流感急性呼吸道疾病（ARI）以及寻求医疗护理的概率。具体而言，我们评估在以下情况下VE估计值的偏差：（1）疫苗接种影响患非流感ARI的概率；（2）疫苗接种影响寻求医疗护理的概率；（3）一个协变量（如健康状况）与疫苗接种和患ARI的概率均相关；（4）一个协变量（如健康意识）与疫苗接种和寻求医疗护理的概率均相关。我们考虑了疫苗应预防的两种结果：有症状流感和就医的流感。当在疫情爆发期间开始接种疫苗时，我们发现疫苗接种延迟开始的影响是不可预测的。无论存在何种偏差来源，TN研究的VE估计值都存在偏差。然而，如果满足TN设计的核心假设，即如果疫苗接种不影响非流感ARI的概率，那么针对就医流感的基于TN的VE估计值只会受到小偏差（<0.05）到中度偏差（≥0.05且<0.10）的影响。这些结果表明，如果排除上述偏差来源，TN研究是大流行期间估计VE的有效研究设计。
