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ABRSEIST

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Cerrado

Antibiotic Resistance Socio Economic Determinants and the Role of Information a...

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 bact... ver más

31/12/2025

GERMAN INSTITUTE F...

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

DEUTSCHES INSTITUT FUR WIRTSCHAFTSFORSCHUNG D... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 3 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2018-11-23

ERC-2018-STG: ERC Starting Grant

I+D

Cerrada hace 7 años

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3 Participantes

GERMAN INSTITUTE FOR ECONOMI...

904.00K€ | Lider

PROXIMA SYSTEMS

151.99K€ | Participante

UCPH

594.92K€ | Participante

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Duración del proyecto: 85 meses Fecha Inicio: 2018-11-23
Fecha Fin: 2025-12-31

Líder del proyecto

DEUTSCHES INSTITUT FUR WIRTSCHAFTSFORSCHUNG D... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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