Why do people expect antibiotics when they should not?

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Antibiotic resistance is one of the greatest global health risks of modern times. Clinically inappropriate prescribing of antibiotics in primary care is one of the key drivers of antibiotic resistance. Past research has found that patients' expectations are among the strongest predictors of clinicians' decisions to prescribe antibiotics (Macfarlane, Holmes, Macfarlane & Britten, 1997; Sirota, Round, Samaranayaka & Kostopoulou, 2017). The link between patients' expectations and over-prescribing is well supported, however the mechanisms underlying these expectations still remain unclear. My proposed research, therefore, aims to answer the question of why people expect antibiotics when it is not clinically appropriate. I will test a psychological theory that aims to explain this. Specifically, I will use the utility based signal detection theory (Lynn & Barrett, 2014) to measure when it is optimal for people to expect antibiotics and by manipulating the three environmental parameters defined by the model-payoff, base rate, similarity-change the optimal criterion in terms of their expectations for antibiotics. For instance, the model predicts that when people underestimate the costs linked with the antibiotic resistance in situations of diagnostic uncertainty then it is optimal for them to expect antibiotics. Making people aware of this cost, changes the optimal criterion and hence decreases the expectation of antibiotics (Sirota, Thorpe & Juanchich, in prep.). This approach has been successfully applied to this context (Sirota et al., in prep.), but tests of quantitative model-predictions are missing. Based on the three parameters, we will calculate the expected utility for every possible criterion location, according to the appropriate mathematical functions and advanced computations as specified by the model. The aim is to identify which parameters and to what extent they affect the optimal criterion location, and when inappropriate antibiotic expectations will be minimised. Drawing on the framework of the model, I will further test how the risks linked with antibiotic overuse (i.e., antibiotic resistance) can best be communicated in an understandable and persuasive manner to reduce people's inappropriate expectations. I will manipulate different factors of risk representation (e.g. statistical evidence vs narrative cases, temporal and spatial proximity, personal vs societal impact), while using vignette approaches, with the main goal to find the optimal framing format of antibiotic resistance that will reduce inappropriate expectations. Data will be analysed using standard frequentist methods (e.g. ANOVA), modelling approaches of the signal detection framework and the analyses will be complemented by Bayesian analyses. The proposed research has implications for the theory and considerable potential for societal impact. It will provide exact and testable mechanisms behind people's antibiotic expectations, which will provide substantial contribution to our understanding of the psychological reasons why people expect antibiotics that are not clinically appropriate. It will provide evidence-based strategy to formulate messages about antibiotic resistance to which people will be receptive. The proposal can help inform national efforts to reduce antibiotic expectations and consequently antibiotic over-prescription in the UK and contribute to current global efforts aimed at reducing future spread of antibiotic resistance.