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Early Warning Systems for Emerging Profiles of Antimicrobial Resistance in Italy: A National Survey.

Jessica IeraChiara SeghieriLara TavoschiClaudia IsonneValentina BaccoliniDaniele PetroneAntonella AgodiMartina BarchittaLuca ArnoldoRoberta CretiSilvia ForniAnnibale RaglioEnrico RicchizziLorenzo BandiniAdriano GrossiFortunato D'Ancona
Published in: International journal of environmental research and public health (2023)
Antimicrobial resistance (AMR) national surveillance systems in Italy lack alert systems for timely detection of emerging profiles of AMR with potential relevance to public health. Furthermore, the existence of early warning systems (EWS) at subnational level is unclear. This study aims at mapping and characterizing EWS for microbiological threats available at regional level in Italy, focusing on emerging AMR, and at outlining potential barriers and facilitators to their development/implementation. To this end, a three-section, web-based survey was developed and administered to all Italian regional AMR representatives from June to August 2022. Twenty out of twenty-one regions and autonomous provinces (95.2%) responded to the survey. Among these, nine (45%) reported the implementation of EWS for microbiological threats at regional level, three (15%) reported that EWS are in the process of being developed, and eight (40%) reported that EWS are not currently available. EWS characteristics varied widely among the identified systems concerning both AMR profiles reported and data flow: the microorganisms most frequently included were extensively drug-resistant (XDR) Enterobacterales , with the lack of a dedicated regional IT platform reported in most cases. The results of this study depict a highly heterogeneous scenario and suggest that more efforts aimed at strengthening national AMR surveillance systems are needed.
Keyphrases
  • antimicrobial resistance
  • drug resistant
  • public health
  • multidrug resistant
  • quality improvement
  • primary care
  • healthcare
  • acinetobacter baumannii
  • machine learning
  • high throughput
  • human health
  • risk assessment