Department of Infectious Disease Epidemiology & Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.
DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.
Int J Epidemiol. 2022 Feb 18;51(1):265-278. doi: 10.1093/ije/dyab172.
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.
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.
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.
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.
传染病暴发给疫苗评估的研究设计带来了独特的挑战。测试阴性设计(TND)研究以前曾被用于估计疫苗的有效性,并被提议用于埃博拉病毒病(EVD)疫苗。然而,在暴发和新型病原体大流行期间,病例和对照的招募方式存在关键差异,这对使用该设计估计有效性的可靠性有影响。
我们使用建模方法来量化在不同研究和流行病学情况下,针对预防性疫苗的 TND 偏倚。我们的模型考虑了疫苗分布的异质性,以及两种潜在的检测和招募研究的途径:自我报告和接触者追踪。我们为该模型推导了常规和混合 TND 估计量,并提出了将公共卫生应对数据转化为模型参数的方法。
使用常规 TND 研究,我们的模型发现疫苗有效性估计存在偏差。偏差源于自我报告和接触者追踪的不同招募,以及疫苗接种的聚类。我们估计了在没有招募途径的情况下的偏差程度,并提出了一种研究设计,如果记录了招募途径,则可以消除偏差。
如果这些努力收集个人的检测途径,混合 TND 研究可以解决常规 TND 研究应用于暴发和大流行应对检测数据的设计偏差。如果没有检测途径,将需要其他流行病学数据来估计常规 TND 研究中潜在偏差的程度。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取这些数据,并且暴发和大流行应对研究的通用协议应强调记录检测途径的必要性。