Using signal detection theory to understand people’s antibiotic expectations

Antibiotic resistance is currently one of the biggest global health threats. Patients’ antibiotic expectations have been found among the strongest predictors of clinicians’ decisions to prescribe antibiotics. However, the factors underlying these expectations still remain unclear. To better understand the drivers behind people’s antibiotic expectations, we used a utility-based signal detection theory framework to provide causal evidence and exact cognitive and computationally testable model predictions behind people’s antibiotic expectations by disentangling the two distinct aspects underlying behaviour: sensitivity and bias. In a series of six experiments (N = 1,360), we designed different decision environments by manipulating and eliciting three important drivers of people’s antibiotic expectations that map into the main environmental model parameters - payoffs, diagnostic uncertainty, and disease base rate - and presented participants with hypothetical medical scenarios for symptoms of respiratory tract infections. We found that the public’s high inappropriate antibiotic expectations can be seen as manifestations of diagnostic uncertainty in environments with high base rates of viral infections, such as the real-world environment we all live in. The findings provide novel causal and computationally testable evidence for the effects of uncertainty and base rate on people’s antibiotic expectations and advance our understanding of the factors that drive people to expect antibiotics. The findings also have significant practical implications as they can help tailor effective communication interventions for reducing diagnostic uncertainty and people’s antibiotic expectations, and consequently the spread of antibiotic resistance.