Descrizione del progetto
Una nuova progettazione delle politiche per ridurre la resistenza agli antibiotici
Gli antibiotici costituiscono un passo enorme per il trattamento delle patologie umane, tuttavia, il loro crescente utilizzo favorisce lo sviluppo di batteri resistenti. Secondo l’Organizzazione mondiale della sanità, la resistenza agli antibiotici rappresenta una grave minaccia di portata mondiale, correlata a 700 000 decessi l’anno a causa dell’insorgenza di infezioni incurabili. Occorre quindi progettare nuove politiche dedicate alla fornitura e alla domanda di medicinali esistenti e nuovi. Il progetto ABRSEIST, finanziato dall’UE, intende individuare e valutare interventi politici attuabili ed efficaci basati sulla domanda, rivolti a medici e pazienti. Il progetto si avvarrà di una vasta serie econometrica di strumenti software per il rilevamento dei meccanismi che collegano la resistenza agli antibiotici all’assunzione di tali farmaci. Tramite metodi di apprendimento automatico e analisi econometriche, il progetto ABRSEIST fornirà prove solide su modelli di intervento efficaci che miglioreranno la nostra comprensione in merito alla prescrizione, alla resistenza e all’impiego di antibiotici.
Obiettivo
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.
Campo scientifico
- 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
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-STG - Starting GrantIstituzione ospitante
10117 Berlin
Germania