Why do people expect antibiotics when they should not?

抗生素耐药性是现代最大的全球健康风险之一。初级保健中临床上不适当的抗生素处方是抗生素耐药性的关键驱动因素之一。过去的研究发现，患者的期望是临床医生决定开抗生素的最强有力的预测因素之一(Macfarlane，Holmes，Macfarlane&Britten，1997；Sirota，圆形，Samaranayaka&Kostopoulou，2017)。患者的期望和过度开药之间的联系得到了很好的支持，然而这些期望背后的机制仍然不清楚。因此，我提议的研究旨在回答为什么人们期望抗生素在临床上不合适的问题。我将测试一个旨在解释这一点的心理学理论。具体地说，我将使用基于效用的信号检测理论(Lynn&Barrett，2014)来衡量人们期望抗生素的最佳时间，并通过操纵模型定义的三个环境参数-收益、基本比率和相似性-根据他们对抗生素的期望来改变最优标准。例如，该模型预测，当人们在诊断不确定的情况下低估了与抗生素耐药性相关的成本时，对他们来说，期待抗生素是最好的。让人们意识到这一成本，改变了最优标准，从而降低了对抗生素的期望(Sirota，Thorpe和Juancich，在准备中)。这种方法已经成功地应用于这种情况(Sirota等人，在准备中)，但缺乏对定量模型预测的测试。在这三个参数的基础上，我们将根据适当的数学函数和模型指定的高级计算，计算每个可能的标准位置的预期效用。其目的是确定哪些参数以及它们对最佳标准位置的影响程度，以及何时将不适当的抗生素期望降至最低。在该模型的框架下，我将进一步测试如何以可理解和有说服力的方式最好地传达与抗生素过度使用(即抗生素耐药性)有关的风险，以减少人们的不适当期望。我将操纵风险表示的不同因素(例如，统计证据与叙事案例、时间和空间接近程度、个人与社会影响)，同时使用Vignette方法，主要目标是找到抗生素耐药性的最佳框架格式，以减少不适当的预期。数据将使用标准频率法(例如方差分析)进行分析，信号检测框架的建模方法和贝叶斯分析将补充这些分析。这项拟议的研究对这一理论具有影响，并具有相当大的社会影响潜力。它将为人们对抗生素的期望提供准确和可测试的机制，这将为我们理解人们期望临床上不合适的抗生素的心理原因做出重大贡献。它将提供以证据为基础的战略，以制定人们将接受的关于抗生素耐药性的信息。该提案可以帮助各国努力减少抗生素预期，从而减少英国的抗生素过量处方，并有助于当前旨在减少抗生素耐药性未来传播的全球努力。

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.