ECDC Fellowship Programme, Field Epidemiology Path (EPIET), European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden.
Department of Infectious Diseases, Istituto Superiore di Sanità, Rome, Italy.
Int J Epidemiol. 2024 Apr 11;53(3). doi: 10.1093/ije/dyae077.
Surveillance data and vaccination registries are widely used to provide real-time vaccine effectiveness (VE) estimates, which can be biased due to underreported (i.e. under-ascertained and under-notified) infections. Here, we investigate how the magnitude and direction of this source of bias in retrospective cohort studies vary under different circumstances, including different levels of underreporting, heterogeneities in underreporting across vaccinated and unvaccinated, and different levels of pathogen circulation.
We developed a stochastic individual-based model simulating the transmission dynamics of a respiratory virus and a large-scale vaccination campaign. Considering a baseline scenario with 22.5% yearly attack rate and 30% reporting ratio, we explored fourteen alternative scenarios, each modifying one or more baseline assumptions. Using synthetic individual-level surveillance data and vaccination registries produced by the model, we estimated the VE against documented infection taking as reference either unvaccinated or recently vaccinated individuals (within 14 days post-administration). Bias was quantified by comparing estimates to the known VE assumed in the model.
VE estimates were accurate when assuming homogeneous reporting ratios, even at low levels (10%), and moderate attack rates (<50%). A substantial downward bias in the estimation arose with homogeneous reporting and attack rates exceeding 50%. Mild heterogeneities in reporting ratios between vaccinated and unvaccinated strongly biased VE estimates, downward if cases in vaccinated were more likely to be reported and upward otherwise, particularly when taking as reference unvaccinated individuals.
In observational studies, high attack rates or differences in underreporting between vaccinated and unvaccinated may result in biased VE estimates. This study underscores the critical importance of monitoring data quality and understanding biases in observational studies, to more adequately inform public health decisions.
监测数据和疫苗接种登记处被广泛用于提供实时疫苗效力（VE）估计，但由于报告不足（即未报告和未通知）的感染，这些估计可能存在偏差。在这里，我们研究了在不同情况下，包括报告不足的程度不同、疫苗接种者和未接种者之间报告不足的异质性以及病原体传播水平不同，这种偏倚源在回顾性队列研究中的幅度和方向如何变化。
我们开发了一个随机的基于个体的模型，模拟呼吸道病毒的传播动力学和大规模疫苗接种活动。考虑到每年发病率为 22.5%和报告率为 30%的基线情况，我们探索了十四个替代方案，每个方案都修改了一个或多个基线假设。使用模型生成的合成个体级监测数据和疫苗接种登记处，我们估计了针对记录感染的 VE，参考未接种者或最近接种者（接种后 14 天内）。通过将估计值与模型中假设的已知 VE 进行比较，来量化偏差。
当假设报告率均匀时，即使在低水平（10%）和中度发病率（<50%）下，VE 估计值也是准确的。当假设均匀的报告率和发病率超过 50%时，估计值会出现大幅向下偏差。在疫苗接种者和未接种者之间报告率存在轻度异质性时，VE 估计值会产生强烈的偏差，即如果接种者中的病例更有可能被报告，则向下偏差，否则向上偏差，尤其是参考未接种者时。
在观察性研究中，高发病率或疫苗接种者和未接种者之间报告不足的差异可能导致 VE 估计值存在偏差。本研究强调了监测数据质量和理解观察性研究中的偏差的重要性，以便更充分地为公共卫生决策提供信息。
