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

Objective

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.

Field of science

  • /medical and health sciences/health sciences/public and environmental health
  • /medical and health sciences/basic medicine/pharmacology and pharmacy/drug resistance/antibiotic resistance
  • /natural sciences/computer and information sciences/artificial intelligence/machine learning

Call for proposal

ERC-2018-STG
See other projects for this call

Funding Scheme

ERC-STG - Starting Grant

Host institution

DEUTSCHES INSTITUT FUR WIRTSCHAFTSFORSCHUNG DIW (INSTITUT FUR KONJUNKTURFORSCHUNG) EV
Address
Mohrenstrasse 58
10117 Berlin
Germany
Activity type
Research Organisations
EU contribution
€ 903 996,25

Beneficiaries (2)

DEUTSCHES INSTITUT FUR WIRTSCHAFTSFORSCHUNG DIW (INSTITUT FUR KONJUNKTURFORSCHUNG) EV
Germany
EU contribution
€ 903 996,25
Address
Mohrenstrasse 58
10117 Berlin
Activity type
Research Organisations
KOBENHAVNS UNIVERSITET
Denmark
EU contribution
€ 594 923,75
Address
Norregade 10
1165 Kobenhavn
Activity type
Higher or Secondary Education Establishments