A global repository of novel antimicrobial emergence events.
Emma MendelsohnNoam RossAllison M WhiteKarissa WhitingCale BasarabaBrooke Watson MadubuonwuErica JohnsonMushtaq DualehZach MatsonSonia DattarayNchedochukwu EzeokoliMelanie Kirshenbaum LiebermanJacob KotcherSamantha MaherCarlos Zambrana-TorrelioPeter DaszakPublished in: F1000Research (2020)
Despite considerable global surveillance of antimicrobial resistance (AMR), data on the global emergence of new resistance genotypes in bacteria has not been systematically compiled. We conducted a study of English-language scientific literature (2006-2017) and ProMED-mail disease surveillance reports (1994-2017) to identify global events of novel AMR emergence (first clinical reports of unique drug-bacteria resistance combinations). We screened 24,966 abstracts and reports, ultimately identifying 1,757 novel AMR emergence events from 268 peer-reviewed studies and 26 disease surveillance reports (294 total). Events were reported in 66 countries, with most events in the United States (152), China (128), and India (127). The most common bacteria demonstrating new resistance were Klebsiella pneumoniae (344) and Escherichia coli (218). Resistance was most common against antibiotic drugs imipenem (89 events), ciprofloxacin (84) and ceftazidime (83). We provide an open-access database of emergence events with standardized fields for bacterial species, drugs, location, and date. We discuss the impact of reporting and surveillance bias on database coverage, and we suggest guidelines for data analysis. This database may be broadly useful for understanding rates and patterns of AMR evolution, identifying global drivers and correlates, and targeting surveillance and interventions.
Keyphrases
- adverse drug
- antimicrobial resistance
- escherichia coli
- public health
- klebsiella pneumoniae
- data analysis
- systematic review
- electronic health record
- healthcare
- emergency department
- multidrug resistant
- staphylococcus aureus
- autism spectrum disorder
- machine learning
- cystic fibrosis
- pseudomonas aeruginosa
- physical activity
- big data
- clinical practice
- deep learning
- cancer therapy
- candida albicans