Using health literacy principles to improve understanding of evolving evidence in health emergencies: Optimisation and evaluation of a COVID-19 vaccination risk-benefit calculator.

BACKGROUND: Risk communication tools based on epidemiological models can help inform decision-making, but must be responsive to health literacy needs to be effective. To facilitate informed choice about risks and benefits of COVID-19 vaccination, an epidemiological model called the COVID-19 Risk Calculator (CoRiCal) tool was developed by a multi-disciplinary team. AIM: This paper demonstrates how to use health literacy principles to improve consumer understanding of COVID-19 and vaccine effects, using a range of methods that could be applied to any health emergency. METHODS: Stage 1: Health literacy optimisation and user testing to reduce improve understandability (n = 19). Stage 2: Experiments to explore the effect of risk communication formats on perceived understanding including probability, graphs, evaluative labels and comparison risks (n = 207). Stage 3: Randomised controlled trial (n = 2005) with 4 arms: 1) standard government information; 2) standard CoRiCal output based on bar graphs; 3) animation explaining bar graphs in "x per million" format; 4) animation explaining bar graphs in "1 in x chance" format. The primary outcome was knowledge about COVID-19 risk. RESULTS: Stage 1 reduced the complexity of the text and graphs. Stage 2 showed that different risk communication formats change perceived understanding, with a preference for evaluative labels across 2 experiments and some indication people with lower health literacy had a greater preference for bar graphs. Stage 3 showed both animations increased knowledge compared to standard government information. There was no difference between the probability formats, or by health literacy level. DISCUSSION: The results showed that simple explanations of complex epidemiological models improve knowledge about COVID-19 and vaccination. This demonstrates how health literacy design principles and short animations can be used to support informed decision making about health emergencies.

A Bayesian network analysis quantifying risks versus benefits of the Pfizer COVID-19 vaccine in Australia.

The Pfizer COVID-19 vaccine is associated with increased myocarditis incidence. Constantly evolving evidence regarding incidence and case fatality of COVID-19 and myocarditis related to infection or vaccination, creates challenges for risk-benefit analysis of vaccination. Challenges are complicated further by emerging evidence of waning vaccine effectiveness, and variable effectiveness against variants. Here, we build on previous work on the COVID-19 Risk Calculator (CoRiCal) by integrating Australian and international data to inform a Bayesian network that calculates probabilities of outcomes for the delta variant under different scenarios of Pfizer COVID-19 vaccine coverage, age groups (≥12 years), sex, community transmission intensity and vaccine effectiveness. The model estimates that in a population where 5% were unvaccinated, 5% had one dose, 60% had two doses and 30% had three doses, there was a substantially greater probability of developing (239-5847 times) and dying (1430-384,684 times) from COVID-19-related than vaccine-associated myocarditis (depending on age and sex). For one million people with this vaccine coverage, where transmission intensity was equivalent to 10% chance of infection over 2 months, 68,813 symptomatic COVID-19 cases and 981 deaths would be prevented, with 42 and 16 expected cases of vaccine-associated myocarditis in males and females, respectively. These results justify vaccination in all age groups as vaccine-associated myocarditis is generally mild in the young, and there is unequivocal evidence for reduced mortality from COVID-19 in older individuals. The model may be updated to include emerging best evidence, data pertinent to different countries or vaccines and other outcomes such as long COVID.

ASPREN surveillance system for influenza-like illness - A comparison with FluTracking and the National Notifiable Diseases Surveillance System.

BACKGROUND: General practitioners play an important role in the detection and clinical management of influenza. The Australian Sentinel Practice Research Network (ASPREN) has been collecting data from sentinel GPs on selected conditions, including influenza-like illness (ILI), since 1991 to inform public health authorities of communicable disease activity in the community. METHODS: Weekly incidence of ILI data reported by ASPREN GPs in 2007-2008 was compared with data from two separate surveillance systems: New South Wales data from FluTracking, an online self reporting ILI surveillance system; and national laboratory notifications of influenza reported to the National Notifiable Diseases Surveillance System between 2003 and 2008. RESULTS: ASPREN recorded peak ILI rates of 47 per 1000 consultations in week 30 (ending 29 July) 2007 and 34 per 1000 consultations in week 36 (ending 7 September) 2008. Similar trends in incidence were seen in FluTracking cough and fever rates, ASPREN data in New South Wales and National Notifiable Diseases Surveillance System laboratory notifications. DISCUSSION: Data captured by the three separate surveillance systems provide complementary information regarding influenza in the Australian population.