Thesis title: Autoimmune neurological adverse event following immunization (AEFI), comparison between pharmacovigilance databases (EudraVigilance, VAERS): general analysis and sex differences.
Background
Autoimmune neurological adverse events following immunisation (AEFI) are rare but clinically significant conditions that may raise safety concerns and contribute to vaccine hesitancy. Comparative evaluations across pharmacovigilance systems remain limited. This study aimed to analyse reporting patterns of autoimmune neurological AEFI in two major spontaneous reporting databases, the EU EudraVigilance (EV) and the US Vaccine Adverse Event Reporting System (VAERS), with particular attention to sex- and age-related differences and vaccine-specific disproportionality.
Methods
A retrospective analysis of Individual Case Safety Reports (ICSRs) recorded between 2003 and 2024 was conducted using publicly available EV and VAERS data. Cases were identified through MedDRA terms mapped within the High-Level Term “Nervous system autoimmune disorders” (NSAD). Selected central nervous system disorders (ADEM, AE, MS, TM, NMO) and peripheral nervous system disorders (GBS, CIDP, DP, NeA) were analysed. Descriptive statistics, crude Reporting Odds Ratios (RORs), sex- and age-stratified analyses, sensitivity analyses using alternative reference groups, and multivariable logistic regression models to estimate adjusted RORs (aRORs) were performed.
Results
Overall reporting patterns were broadly consistent between EV and VAERS. Central nervous system autoimmune disorders, particularly multiple sclerosis, showed a predominance of female reports, whereas peripheral disorders such as Guillain–Barré syndrome displayed a male-predominant distribution, broadly consistent with background epidemiological patterns described in the general population. Using the NSAD subset as primary reference, statistically significant disproportionality associations were identified for selected vaccine–event pairs in both databases. Sensitivity analyses using alternative comparators yielded higher ROR magnitudes but similar directional patterns. Multivariable adjustment attenuated several crude associations, indicating the influence of demographic and reporting-related factors. In some instances, adjustment strengthened associations, suggesting negative confounding in crude estimates.
Conclusions
Autoimmune neurological AEFI reporting patterns were largely concordant across two independent pharmacovigilance systems and were broadly consistent with known background epidemiology. Multivariable modelling improved interpretability by accounting for demographic structure and reporting characteristics. Harmonised cross-database analyses may support more robust pharmacovigilance evaluation and improved contextualisation of rare neurological events.