Designing an evidence-based Bayesian network for estimating the risk versus benefits of AstraZeneca COVID-19 vaccine. Issue 22 (11th May 2022)

• Record Type: Journal Article
• Authors: Mayfield, Helen J.; Lau, Colleen L.; Sinclair, Jane E.; Brown, Samuel J.; Baird, Andrew; Litt, John; Vuorinen, Aapeli; Short, Kirsty R.; Waller, Michael; Mengersen, Kerrie
• Is Part Of: Vaccine. Volume 40:Issue 22(2022)
• Page Start: 3072
• Page End: 3084
• Publication Date: 2022-05-11
• Subjects: COVID-19 -- Bayesian network -- Vaccination -- Adverse events -- Informed decision-making; Vaccines -- Periodicals; 615.372
• DOI: 10.1016/j.vaccine.2022.04.004
• Languages: English
• ISSNs: 0264-410X

Abstract

Abstract: Uncertainty surrounding the risk of developing and dying from Thrombosis and Thrombocytopenia Syndrome (TTS) associated with the AstraZeneca (AZ) COVID-19 vaccine may contribute to vaccine hesitancy. A model is urgently needed to combine and effectively communicate evidence on the risks versus benefits of the AZ vaccine. We developed a Bayesian network to consolidate evidence on risks and benefits of the AZ vaccine, and parameterised the model using data from a range of empirical studies, government reports, and expert advisory groups. Expert judgement was used to interpret the available evidence and determine the model structure, relevant variables, data for inclusion, and how these data were used to inform the model. The model can be used as a decision-support tool to generate scenarios based on age, sex, virus variant and community transmission rates, making it useful for individuals, clinicians, and researchers to assess the chances of different health outcomes. Model outputs include the risk of dying from TTS following the AZ COVID-19 vaccine, the risk of dying from COVID-19 or COVID-19-associated atypical severe blood clots under different scenarios. Although the model is focused on Australia, it can be adapted to international settings by re-parameterising it with local data. This paper provides detailed description of the model-building methodology, which can be used to expand the scope of the model to include other COVID-19 vaccines, booster doses, comorbidities and other health outcomes (e.g., long COVID) to ensure the model remains relevant in the face of constantly changing discussion on risks versus benefits of COVID-19 vaccination.