Abstract

Background

Infectious disease outbreaks present unique challenges to study designs for vaccine evaluation. Test-negative design (TND) studies have previously been used to estimate vaccine effectiveness and have been proposed for Ebola virus disease (EVD) vaccines. However, there are key differences in how cases and controls are recruited during outbreaks and pandemics of novel pathogens, whcih have implications for the reliability of effectiveness estimates using this design.

Methods

We use a modelling approach to quantify TND bias for a prophylactic vaccine under varying study and epidemiological scenarios. Our model accounts for heterogeneity in vaccine distribution and for two potential routes to testing and recruitment into the study: self-reporting and contact-tracing. We derive conventional and hybrid TND estimators for this model and suggest ways to translate public health response data into the parameters of the model.

Results

Using a conventional TND study, our model finds biases in vaccine effectiveness estimates. Bias arises due to differential recruitment from self-reporting and contact-tracing, and due to clustering of vaccination. We estimate the degree of bias when recruitment route is not available, and propose a study design to eliminate the bias if recruitment route is recorded.

Conclusions

Hybrid TND studies can resolve the design bias with conventional TND studies applied to outbreak and pandemic response testing data, if those efforts collect individuals’ routes to testing. Without route to testing, other epidemiological data will be required to estimate the magnitude of potential bias in a conventional TND study. Since these studies may need to be conducted retrospectively, public health responses should obtain these data, and generic protocols for outbreak and pandemic response studies should emphasize the need to record routes to testing.

中文翻译：

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暴发环境中潜在的试验阴性设计研究偏差：在刚果民主共和国埃博拉疫苗接种中的应用

摘要

背景

传染病爆发对疫苗评估的研究设计提出了独特的挑战。试验阴性设计 (TND) 研究以前曾用于估计疫苗有效性，并已被提议用于埃博拉病毒病 (EVD) 疫苗。然而，在新病原体的爆发和大流行期间如何招募病例和对照存在关键差异，这对使用这种设计的有效性估计的可靠性有影响。

方法

我们使用建模方法来量化不同研究和流行病学情景下预防性疫苗的 TND 偏差。我们的模型考虑了疫苗分布的异质性以及测试和招募研究的两种潜在途径：自我报告和接触者追踪。我们为该模型推导出传统和混合 TND 估计量，并提出将公共卫生响应数据转化为模型参数的方法。

结果

使用传统的 TND 研究，我们的模型发现疫苗有效性估计存在偏差。偏见的产生是由于自我报告和接触者追踪的不同招募，以及疫苗接种的聚集。我们估计了招聘途径不可用时的偏差程度，并提出了一种研究设计，以在记录招聘途径的情况下消除偏差。

结论

混合 TND 研究可以解决设计偏差，将传统 TND 研究应用于爆发和大流行应对测试数据，如果这些努力收集个人的测试路线。在没有检测途径的情况下，将需要其他流行病学数据来估计传统 TND 研究中潜在偏倚的程度。由于这些研究可能需要回顾性进行，公共卫生应对措施应获得这些数据，而暴发和大流行应对研究的通用方案应强调记录检测途径的必要性。