ABSTRACT
Thrombosis and Thrombocytopenia Syndrome (TTS) has been associated with the AstraZencea (AZ) COVID-19 vaccine (Vaxzevria). Australia has reported low TTS incidence of < 3/100,000 after the first dose, with case fatality rate (CFR) of 5-6%. Risk-benefit analysis of vaccination has been challenging because of rapidly evolving data, changing levels of transmission, and variation in rates of TTS, COVID-19, and CFR between age groups. We aim to optimise risk-benefit analysis by developing a model that enables inputs to be updated rapidly as evidence evolves. A Bayesian network was used to integrate local and international data, government reports, published literature and expert opinion. The model estimates probabilities of outcomes under different scenarios of age, sex, low/medium/high transmission (0.05%/0.45%/5.76% of population infected over 6 months), SARS-CoV-2 variant, vaccine doses, and vaccine effectiveness. We used the model to compare estimated deaths from AZ vaccine-associated TTS with i) COVID-19 deaths prevented under different scenarios, and ii) deaths from COVID-19 related atypical severe blood clots (cerebral venous sinus thrombosis & portal vein thrombosis). For a million people aged ≥ 70 years where 70% received first dose and 35% received two doses, our model estimated < 1 death from TTS, 25 deaths prevented under low transmission, and > 3000 deaths prevented under high transmission. Risks versus benefits varied significantly between age groups and transmission levels. Under high transmission, deaths prevented by AZ vaccine far exceed deaths from TTS (by 8 to > 4500 times depending on age). Probability of dying from COVID-related atypical severe blood clots was 58-126 times higher (depending on age and sex) than dying from TTS. To our knowledge, this is the first example of the use of Bayesian networks for risk-benefit analysis for a COVID-19 vaccine. The model can be rapidly updated to incorporate new data, adapted for other countries, extended to other outcomes (e.g., severe disease), or used for other vaccines.
Subject(s)
COVID-19 , SARS-CoV-2 , Bayes Theorem , COVID-19 Vaccines , Humans , Infant, Newborn , Vaccine EfficacyABSTRACT
Australia and New Zealand have achieved excellent community control of COVID-19 infection. In light of the imminent COVID-19 vaccination roll out in both countries, representatives from the Haematology Society of Australia and New Zealand and infectious diseases specialists have collaborated on this consensus position statement regarding COVID-19 vaccination in patients with haematological disorders. It is our recommendation that patients with haematological malignancies, and some benign haematological disorders, should have expedited access to high-efficacy COVID-19 vaccines, given that these patients are at high risk of morbidity and mortality from COVID-19 infection. Vaccination should not replace other public health measures in these patients, given that the effectiveness of COVID-19 vaccination, specifically in patients with haematological malignancies, is not known. Given the limited available data, prospective collection of safety and efficacy data of COVID-19 vaccination in this patient group is a priority.