Antibiotic Resistance: Socio-Economic Determinants and the Role of Information and Salience in Treatment Choice
Antibiotics have contributed to a tremendous increase in human well-being, saving many millions of lives. However, antibiotics become obsolete the more they are used as selection pressure promotes the development of resistant bacteria. The World Health Organization has proclaimed antibiotic resistance as a major global threat to public health. Today, 700,000 deaths per year are due to untreatable infections. To win the battle against antibiotic resistance, new policies affecting the supply and demand of existing and new drugs must be designed. In this project, we pursue new research to identify and evaluate feasible and effective demand-side policy interventions targeting the relevant decision makers: physicians and patients. ABRSEIST makes use of a broad econometric toolset to identify mechanisms linking antibiotic resistance and consumption exploiting a unique combination of physician-patient-level antibiotic resistance, treatment, and socio-economic data. Further, it aims to shed light on general practitioners’ acquisition and use of information under uncertainty about resistance in prescription choice, allowing counterfactual analysis of information-improving policies such as the provision of machine learning predictions in clinical practice and mandatory diagnostic testing. Using machine learning methods, theory-driven structural econometric analysis, and randomization in the field we work to provide rigorous evidence on effective intervention designs. This research will improve our understanding of how prescribing, resistance, and the effect of antibiotic use on resistance, are distributed in the general population which has important implications for the design of targeted interventions.
- Machine predictions and human decisions with variation in payoffs and skill (Michael Ribers and Hannes Ullrich)
- Battling Antibiotic Resistance: can machine learning improve prescribing? (Michael Ribers and Hannes Ullrich)
- Physician effects in antibiotic prescribing: evidence from physician exits (Shan Huang and Hannes Ullrich)
- The value of data for prediction policy problems: evidence from antibiotic prescribing (Shan Huang, Michael Ribers, and Hannes Ullrich)
- Selection bias in routine surveillance of antibiotic resistance (Michael Ribers and Hannes Ullrich)
- Information acquisition and treatment decisions: the case of antibiotic prescribing (Michael Ribers and Hannes Ullrich)
PhD Student, Berlin School of Economics, Department Firms and Markets, DIW Berlin
Research Assistant, Department of Economics, University of Copenhagen
This project is funded by the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 802450) for the period of 2019 – 2023.