Abstract
Observational studies on COVID-19 vaccine effectiveness (VE) have provided critical real-world data, informing public health policy globally. These studies, primarily using pre-existing data sources, have been indispensable in assessing VE across diverse populations and developing sustainable vaccination strategies. Cohort design is frequently employed in VE research. The rapid implementation of vaccination campaigns during the COVID-19 pandemic introduced differential vaccination influenced by sociodemographic disparities, public policies, perceived risks, health-promoting behaviors, and health status, potentially resulting in biases such as healthy user bias, healthy vaccinee effect, frailty bias, differential depletion of susceptibility bias, and confounding by indication. The overwhelming burden on healthcare systems has escalated the risk of data inaccuracies, leading to outcome misclassifications. Additionally, the extensive array of diagnostic tests used during the pandemic has also contributed to misclassification biases. The urgency to publish quickly may have further influenced these biases or led to their oversight, affecting the validity of the findings. These biases in studies vary considerably depending on the setting, data sources, and analytical methods and are likely more pronounced in low- and middle-income country (LMIC) settings due to inadequate data infrastructure. Addressing and mitigating these biases is essential for accurate VE estimates, guiding public health strategies, and sustaining public trust in vaccination programs. Transparent communication about these biases and rigorous improvement in the design of future observational studies are essential.