Keywords: Antimicrobial resistance; Misprescription; Antibiotics; Indicators; crossculuturaladaptation; surveyresearch
AMR has emerged as a threat to public health worldwide. Italy has a high consumption rate of antibiotics and aggregated level data shows that there is a high variability of antibiotic prescription rates among different regions of the country and among different seasons of the year. For all these reasons it seems to be very likely that the majority of antibiotic prescriptions in Italy could be avoided. The 90% of antibiotic prescriptions is filled by GPs. Consequently, they could play an important role in tackling the phenomenon of antibiotic overuse and irrational prescription habits. Antibiotic prescribing is a complex behaviour, related to both intrinsic and extrinsic factors to the healthcare professional. Therefore, to inform stakeholders in the development of effective interventions, an understanding of the context specific determinants of antibiotic prescription is needed.
We aim to measure, with validated tools, knowledge and attitudes of GPs on AMR and antibiotic prescriptions in Italy and to evaluate the influence of knowledge and attitudes of GPs on their prescriptions of antibiotics.
variables related to the work environment, socio-demographic factors, knowledge and attitudes of GPs regarding AMR and antibiotic prescription (determinants) will be measured in a cross-sectional study through a questionnaire (ITA-KAAR-11) administered to all 1205 sardinia's GPs. Reliability and validity of ITA-KAAR-11 will be measured through Crohnbach’s alpha and exploratory factor analysis and assessed through known-group validation. Prescription data of GPs will be obtained from the Sardinian administrative database. Every GP will be deemed to have an adequate quality of prescriptions of antibiotics (AQPA) if the indicators proposed by the ESAC-net are better than the median of the region. The association between the dependent variable (AQPA=yes/no) and the determinants of prescription (=independent variables) will be calculated using a Poisson regression model and expressed as crude and adjusted prevalence risk ratios (PRR).
Points for discussion: