CORDIS - EU research results

Antibiotic Resistance: Socio-Economic Determinants and the Role of Information and Salience in Treatment Choice

Project description

New policy design to reduce antibiotic resistance

Antibiotics represent a huge step in human disease treatment, but their increased use promotes the development of resistant bacteria. According to the World Health Organization, resistance to antibiotics is a major global threat associated with 700 000 deaths per year due to untreatable infections. The design of new policies for the supply and demand of existing and new drugs is needed. The EU-funded ABRSEIST project intends to identify and assess feasible and efficient demand-side policy interventions that address physicians and patients. The project will use a broad econometric set of software tools to detect mechanisms connecting antibiotic resistance and consumption. Using machine learning methods and econometric analyses, ABRSEIST will provide strong evidence on effective intervention designs improving our understanding of prescribing, resistance and antibiotic use.


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. I propose new research to identify and evaluate feasible and effective demand-side policy interventions targeting the relevant decision makers: physicians and patients. ABRSEIST will make 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. Using machine learning methods adapted for causal inference, theory-driven structural econometric analysis, and randomization in the field it will 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. It will then estimate a structural model of general practitioners’ acquisition and use of information under uncertainty about resistance in prescription choice, allowing counterfactual analysis of information-improving policies such as mandatory diagnostic testing. The large-scale and structural econometric analyses allow flexible identification of physician heterogeneity, which ABRSEIST will exploit to design and evaluate targeted, randomized information nudges in the field. The result will be improved rational use and a toolset applicable in contexts of antibiotic prescribing.

Host institution

Net EU contribution
€ 903 996,25
10117 Berlin

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Berlin Berlin Berlin
Activity type
Research Organisations
Total cost
€ 903 996,25

Beneficiaries (2)