BACKGROUND 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. METHODS 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. RESULTS 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. CONCLUSIONS 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.

中文翻译：

---

漏报感染对来自回顾性队列研究的疫苗有效性估计的影响。


背景技术监测数据和疫苗接种登记被广泛用于提供实时疫苗有效性（VE）估计，该估计可能由于报告不足（即，确定不足和通知不足）感染而产生偏差。在这里，我们研究了回顾性队列研究中这种偏差来源的大小和方向在不同情况下有何不同，包括不同程度的漏报、接种疫苗和未接种疫苗的漏报异质性以及不同水平的病原体传播。方法我们开发了一个基于个体的随机模型，模拟呼吸道病毒的传播动力学和大规模疫苗接种活动。考虑到年攻击率 22.5% 和报告率 30% 的基准情景，我们探索了 14 种替代情景，每种情景都修改了一个或多个基准假设。使用综合个体水平监测数据和模型生成的疫苗接种登记，我们以未接种疫苗或最近接种疫苗的个体（接种后 14 天内）为参考，估计了针对记录感染的 VE。通过将估计值与模型中假定的已知 VE 进行比较来量化偏差。结果 当假设报告比率相同时，即使在低水平 (10%) 和中等攻击率 (<50%) 的情况下，VE 估计也是准确的。由于同质报告和攻击率超过 50%，估计出现了大幅向下偏差。接种疫苗和未接种疫苗的报告比率存在轻微异质性，VE 估计值存在很大偏差，如果接种疫苗的病例更有可能报告，则 VE 估计值会向下，否则向上，特别是在将未接种疫苗的个体作为参考时。 结论 在观察性研究中，高发病率或接种疫苗和未接种疫苗之间的漏报差异可能会导致 VE 估计值出现偏差。这项研究强调了监测数据质量和理解观察性研究中的偏差的至关重要性，以便更充分地为公共卫生决策提供信息。