Description du projet
Une nouvelle politique pour réduire la résistance aux antibiotiques
Les antibiotiques représentent une avancée considérable dans le traitement des maladies humaines, mais leur usage intensif favorise le développement de bactéries résistantes. Selon l’Organisation mondiale de la santé, la résistance aux antibiotiques représente une menace mondiale de taille, associée à 700 000 décès par an dus à des infections non traitables. Il est donc indispensable de concevoir de nouvelles politiques relatives à l’offre et à la demande de médicaments existants et nouveaux. Le projet ABRSEIST, financé par l’UE, vise à identifier et à évaluer des interventions politiques réalisables et efficaces au niveau de la demande qui s’adressent aux médecins et aux patients. Le projet utilisera un large ensemble d’outils logiciels économétriques pour détecter les mécanismes qui lient la résistance aux antibiotiques et la consommation. ABRSEIST apportera, à l’aide de méthodes d’apprentissage automatique et d’analyses économétriques, des arguments solides en faveur de la conception d’interventions efficaces, améliorant ainsi notre compréhension de la prescription, de la résistance et de l’utilisation des antibiotiques.
Objectif
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.
Champ scientifique
- social scienceseconomics and businesseconomicseconometrics
- medical and health scienceshealth sciencespublic health
- medical and health sciencesbasic medicinepharmacology and pharmacypharmaceutical drugsantibiotics
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- medical and health sciencesbasic medicinepharmacology and pharmacydrug resistanceantibiotic resistance
Programme(s)
Thème(s)
Régime de financement
ERC-STG - Starting GrantInstitution d’accueil
10117 Berlin
Allemagne