"Antibiotic Prescribing under Uncertainty about Resistance"
By Michael Ribers, University of Zurich
The increasing level of antibiotic resistance constitutes a major worldwide health threat. Inefficient antibiotic prescribing is considered one of the main drivers of increasing resistance but no framework for evaluation of rational prescribing exists. We develop a dynamic structural model of antibiotic prescribing for forward-looking general practitioners (GP) in the presence of uncertainty concerning antibiotics’ effectiveness. Our model endogenizes information acquisition and features cross-patient learning from observed clinical microbiological testing. Reducing uncertainty is costly so that GPs have incentives to under-diagnose antibiotic resistance. Using patient-GP-level population data we estimate the structural parameters of our model and provide a framework for counterfactual evaluations of policy measures such as mandatory diagnostic testing, rapid resistance diagnostics, and the introduction of an antibiotic tax.
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