EcoHealth Alliance, New York, NY, 10018, USA.
Current Address: Office of U.S. Foreign Disaster Assistance, USAID, District of Columbia, USA.
F1000Res. 2020 Nov 12;9:1320. doi: 10.12688/f1000research.26870.2. eCollection 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 (344) and (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.
尽管全球对抗微生物药物耐药性(AMR)进行了大量监测,但尚未系统地汇编有关细菌中新出现的耐药基因型的全球数据。我们研究了英语科学文献(2006-2017 年)和 ProMED-mail 疾病监测报告(1994-2017 年),以确定新出现的 AMR 全球事件(独特的药物-细菌耐药组合的首次临床报告)。我们筛选了 24966 篇摘要和报告,最终从 268 篇同行评议研究和 26 份疾病监测报告中确定了 1757 例新出现的 AMR 事件(总计 294 例)。这些事件发生在 66 个国家/地区,其中美国(152 例)、中国(128 例)和印度(127 例)的事件最多。最常见的表现出新的耐药性的细菌是 (344 例)和 (218 例)。最常见的耐药性是针对抗生素药物亚胺培南(89 例)、环丙沙星(84 例)和头孢他啶(83 例)。我们提供了一个新出现的事件数据库,其中包含标准化的细菌种类、药物、地点和日期字段。我们讨论了报告和监测偏差对数据库覆盖范围的影响,并提出了数据分析指南。该数据库可能广泛用于了解 AMR 进化的速度和模式,识别全球驱动因素和相关性,并针对监测和干预措施。