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Expected Rates of Select Adverse Events following Immunization for COVID-19 Vaccine Safety Monitoring (preprint)
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.08.31.21262919
ABSTRACT
BackgroundKnowledge of expected rates of potential adverse events of special interest (AESI) that may occur coincidentally following COVID-19 vaccination is essential for vaccine safety surveillance and assessment. We calculated the expected rates of 21 potential AESI following COVID-19 vaccination among vaccinated persons within 1 day, 7 days, and 42 days of vaccination. MethodsWe used meta-analytic methods to estimate background rates of 21 medical conditions considered potential AESI and calculated expected rates of each potential AESI within 1 day, 7 days, and 42 days of vaccination. ResultsBackground rates of three commonly monitored AESI, Guillain-Barre syndrome (GBS), myopericarditis, and all-cause deaths were 2.0 GBS cases/100,000 person-years, 1.3 myopericarditis cases/100,000 person-years, and 863.8 all-cause deaths/100,000 person-years, respectively. Based on these background rates, if 10,000,000 persons are vaccinated, we would expect 0.5, 3.7, and 22.5 GBS cases; 0.3, 2.4, and 14.3 myopericarditis cases; and 236.5, 1655.5, and 9932.8 all-cause deaths to occur in coincident temporal association (i.e., as a result of background incidence) within 1 day, 7 days, and 42 days of vaccination, respectively. ConclusionKnowledge of expected rates of potential AESI can help contextualize adverse health events associated temporally with immunization, aid in safety signal detection, guide COVID-19 vaccine public health communication, and inform benefit-risk assessments of COVID-19 vaccines.
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Full text: Available Collection: Preprints Database: medRxiv Main subject: Guillain-Barre Syndrome / Death / COVID-19 Language: English Year: 2021 Document Type: Preprint

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Full text: Available Collection: Preprints Database: medRxiv Main subject: Guillain-Barre Syndrome / Death / COVID-19 Language: English Year: 2021 Document Type: Preprint