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    Vaccine surveillance programs are crucial for the analysis of the vaccine's safety profile and the guidance of health policies. The Epidemiological Observatory of the Italian Apulia Region carried out an active surveillance program of adverse effects following immunization (AEFI) after the first dose of the measles-mumps-rubella-varicella (MMRV) vaccine, finding 462 AEFIs per 1000 doses, with 11% rated serious. Applying the World Health Organization (WHO) causality assessment algorithm, 38 serious AEFIs/1000 enrolled were classified as 'consistent causal associations' with MMRV immunization. Severe hyperpyrexia, neurological symptoms and gastrointestinal diseases occurred in 38, 20 and 15 cases/1000 enrolled, respectively. A projection of such AEFIs in an Italian birth cohort would give tens of thousands of serious AEFIs. These incidence data are much greater than the incidence of serious AEFIs reported by the Italian Medicines Agency (AIFA) for years 2017 and 2018, mainly based on passive (or mixed) pharmacovigilance. In a previous epidemiological study in the same Italian Region, during an eight year passive surveillance, the reporting rate of serious AEFI was 0.06/1000 doses, and no cases of febrile seizures were detected applying the WHO algorithm. Taken together, the data suggest that passive pharmacovigilance is utterly inadequate to document the real incidence of serious AEFIs and that current methods of assessing causality may be questioned. Active surveillance programs are required in representative population samples, with results presented separately from those of spontaneous reporting, and causality assessment should be performed carefully and using a correct technique for AEFIs presenting as complex and multifactorial diseases, like those with serious neurologic disorders. Copyright: © 2021 Bellavite P and Donzelli A.

    Citation

    Paolo Bellavite, Alberto Donzelli. Adverse events following measles-mumps-rubella-varicella vaccine: an independent perspective on Italian pharmacovigilance data. F1000Research. 2020;9:1176

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    PMID: 33335717

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