Descripción del proyecto
Nueva formulación de políticas para reducir la resistencia a los antibióticos
Los antibióticos representan un gran paso en el tratamiento de enfermedades humanas, pero el aumento de su uso potencia el desarrollo de bacterias resistentes. Según la Organización Mundial de la Salud, la resistencia a los antibióticos constituye una importante amenaza mundial y está vinculada a 700 000 muertes al año debido a infecciones que no pueden tratarse. Es necesario formular políticas nuevas para la oferta y la demanda de fármacos nuevos y existentes. El proyecto financiado con fondos europeos ABRSEIST pretende identificar y evaluar intervenciones de política del lado de la demanda factibles y eficientes que se dirijan a médicos y enfermos. El proyecto utilizará un amplio conjunto econométrico de herramientas de «software» para detectar los mecanismos que conectan la resistencia a los antibióticos con su consumo. Mediante métodos de aprendizaje automático y análisis econométricos, ABRSEIST proporcionará pruebas sólidas sobre diseños de intervención efectivos que mejorarán nuestra comprensión de la prescripción, la resistencia y el uso de los antibióticos.
Objetivo
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.
Ámbito científico
- 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
Programa(s)
Régimen de financiación
ERC-STG - Starting GrantInstitución de acogida
10117 Berlin
Alemania