COVID-19 Vaccines and the Misclassification of Adverse Events

Abstract

In order to evaluate the impact of outcome misclassification on the possible causal association between Adverse Events of Special Interest (AESIs) and COVID-19 vaccination, we conducted a literature review and a simulation study. The literature study aimed to obtain a plausible range of outcome misclassification indices of the International Classification of Diseases (ICD) coding systems used in electronic healthcare databases. We used logistic regression to contrast a naïve estimator that disregards misclassification with a misclassification-integrating Maximum Likelihood Estimation (MLE) model to explore the relationship between vaccine exposure and the occurrence of AESIs. The MLE model employed marginal probability from a Bernoulli distribution to account for misclassification, facilitating a comparison of log of odds ratios and relative risks between the models. In our simulation study, we generated data which incorporated a fixed vaccination rate, varying sample sizes, regression coefficients, and misclassification rates to evaluate bias and mean squared error (MSE) of the log of odds ratio and the relative risk. The analyses showed that the MLE model exhibited reduced bias under high prevalence and specificity conditions when examining the relative risk bias. Despite its great variability, the MLE model outperformed the naïve estimator in specific simulations with increased association strengths, supporting our hypothesis. Our findings reveal a need for rigorous methodologies to address misclassification in vaccine safety assessments and indicate that the degree of misclassification may be significant, depending on the specific ICD code. Our research highlighted the importance of thoroughly recognising misclassified data and outlined critical areas for future research in epidemiology. Our observations demonstrated that the traditional approach, which disregards the subsequent bias brought on by misclassification – is not fundamentally flawed, emphasising the need to investigate how and when these errors can be significant.