Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis

Antimicrobial Resistance Collaborators

Research output: Contribution to journalArticlepeer-review

1972 Citations (Scopus)


Background: Antimicrobial resistance (AMR) poses a major threat to human health around the world. Previous publications have estimated the effect of AMR on incidence, deaths, hospital length of stay, and health-care costs for specific pathogen–drug combinations in select locations. To our knowledge, this study presents the most comprehensive estimates of AMR burden to date. Methods: We estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 23 pathogens and 88 pathogen–drug combinations in 204 countries and territories in 2019. We obtained data from systematic literature reviews, hospital systems, surveillance systems, and other sources, covering 471 million individual records or isolates and 7585 study-location-years. We used predictive statistical modelling to produce estimates of AMR burden for all locations, including for locations with no data. Our approach can be divided into five broad components: number of deaths where infection played a role, proportion of infectious deaths attributable to a given infectious syndrome, proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antibiotic of interest, and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden based on two counterfactuals: deaths attributable to AMR (based on an alternative scenario in which all drug-resistant infections were replaced by drug-susceptible infections), and deaths associated with AMR (based on an alternative scenario in which all drug-resistant infections were replaced by no infection). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. We present final estimates aggregated to the global and regional level. Findings: On the basis of our predictive statistical models, there were an estimated 4·95 million (3·62–6·57) deaths associated with bacterial AMR in 2019, including 1·27 million (95% UI 0·911–1·71) deaths attributable to bacterial AMR. At the regional level, we estimated the all-age death rate attributable to resistance to be highest in western sub-Saharan Africa, at 27·3 deaths per 100 000 (20·9–35·3), and lowest in Australasia, at 6·5 deaths (4·3–9·4) per 100 000. Lower respiratory infections accounted for more than 1·5 million deaths associated with resistance in 2019, making it the most burdensome infectious syndrome. The six leading pathogens for deaths associated with resistance (Escherichia coli, followed by Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa) were responsible for 929 000 (660 000–1 270 000) deaths attributable to AMR and 3·57 million (2·62–4·78) deaths associated with AMR in 2019. One pathogen–drug combination, meticillin-resistant S aureus, caused more than 100 000 deaths attributable to AMR in 2019, while six more each caused 50 000–100 000 deaths: multidrug-resistant excluding extensively drug-resistant tuberculosis, third-generation cephalosporin-resistant E coli, carbapenem-resistant A baumannii, fluoroquinolone-resistant E coli, carbapenem-resistant K pneumoniae, and third-generation cephalosporin-resistant K pneumoniae. Interpretation: To our knowledge, this study provides the first comprehensive assessment of the global burden of AMR, as well as an evaluation of the availability of data. AMR is a leading cause of death around the world, with the highest burdens in low-resource settings. Understanding the burden of AMR and the leading pathogen–drug combinations contributing to it is crucial to making informed and location-specific policy decisions, particularly about infection prevention and control programmes, access to essential antibiotics, and research and development of new vaccines and antibiotics. There are serious data gaps in many low-income settings, emphasising the need to expand microbiology laboratory capacity and data collection systems to improve our understanding of this important human health threat. Funding: Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.

Original languageEnglish
Pages (from-to)629-655
Number of pages27
JournalThe Lancet
Issue number10325
Publication statusPublished - 12 Feb 2022

Bibliographical note

Funding Information:
Funding was provided by the Bill & Melinda Gates Foundation (OPP1176062), the Wellcome Trust (A126042), and the UK Department of Health and Social Care using UK aid funding managed by the Fleming Fund (R52354 CN001). E Ashley acknowledges that Lao-Oxford-Mahosot Hospital–Wellcome Trust Research Unit receives core funding from Wellcome (20211/Z/20/Z). N Feasey acknowledges that the Malawi Liverpool Wellcome Trust Clinical Research Programme diagnostic microbiology service is funded by a Wellcome Asia and Africa Programme grant. S Dunachie acknowledges funding from NIHR Global Research Professorship (NIHR300791). F Krapp was supported by Framework Agreement Belgian Directorate of Development Cooperation-Institute of Tropical Medicine in Antwerp. M Khorana and S Boonkasidecha would like to acknowledge GARDP. A Peleg acknowledges the support from an Australian National Health and Medical Research Council Practitioner Fellowship. A Stewardson is supported by an Australian National Health and Medical Research Council Early Career Fellowship (GNT1141398). P Turner acknowledges that the Cambodia Oxford Medical Research Unit is part of the Mahidol-Oxford Tropical Medicine Research Unit Tropical Health Network and is core funded by Wellcome (220211/Z/20/Z). T Wangrangsimakul acknowledges funding from the Wellcome Trust, as part of the MORU Tropical Health Network institutional funding support. The Medical Research Council Unit—The Gambia at the LSHTM acknowledges all the staff in the microbiology clinical laboratory for their support. We acknowledge The Australian Group for Antimicrobial Resistance, and The Australian Commission on Safety and Quality in Healthcare, Sydney, Australia. We acknowledge John Murray, Becton, Dickinson and Company. We give thanks to the late Rattanaphone Phetsouvanh, the Lao Ministry of Health, and the Directorate of Mahosot Hospital who enabled the collection and sharing of Lao data, Vientiane, Lao People's Democratic Republic. We thank Sabrina Bacci, Liselotte Diaz Högberg, Marlena Kaczmarek, Maria Keramarou, Favelle Lamb, Dominique L Monnet, Gianfranco Spiteri, Carl Suetens, Therese Westrell and Klaus Weist at the ECDC, Solna, Sweden, for providing information on databases and discussions on data interpretation. We acknowledge Jennifer R Verani and team, CDC, Nairobi, Kenya; Allan Audi and team, Centre for Global Health Research, KEMRI, Kisumu, Kenya. We acknowledge Jephté Kaleb and Giscard Wilfried Koyaweda, National Laboratory of Clinical Biology and Public Health, Bangui, Central African Republic. We acknowledge Tien Viet Dung Vu and Nguyen Minh Trang Nghiem, Oxford University Clinical Research Unit, Wellcome Africa Asia Programme, National Hospital for Tropical Diseases, Hanoi, Vietnam; and the VINARES Consortium. We acknowledge the Department of Pathology and Laboratory Medicine, and Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan. We acknowledge Samuel Akech, Ednah Ooko, James Bukosia, Neema Mturi, J Anthony G Scott, Philip Bejon, Lynette Isabella Oyier, Salim Mwarumba, Esther Muthumbi, Christina Obiero, Robert Musyimi, Shebe Mohammed, Caroline Ogwang, Christopher Maronga, Ambrose Agweyu, KEMRI Wellcome Trust Research Programme, Kilifi, Kenya. We acknowledge scientific contributions to this work from the Pan American Health Organization. We would like to acknowledge the scientific contributions made from the GRAM advisory committee, specifically Neil Ferguson and Sharon Peacock. We would like to acknowledge Tomislav Mestrovic for his significant contributions to this manuscript and the overall research enterprise. We thank the Kenya Medical Research Institute, United States Army Medical Research Directorate-Africa, Kenya, Nairobi, Kenya; we acknowledge the International Nosocomial Infection Control Consortium, Buenos Aires, Argentina; we would like to thank the CHILDS Trust Medical Research Foundation, Chennai, India; we acknowledge the Childhood Acute Illness & Nutrition Network investigators; we thank Institut Pasteur and Laboratoire National de Biologie Clinique et de Santé Publique in Bangui, Central African Republic; we would like to acknowledge the Global Tuberculosis Programme of WHO, Geneva, Switzerland; we thank the SENTRY Antimicrobial Surveillance Program, JMI Laboratories, North Liberty, Iowa, USA; we acknowledge the Sihanouk Hospital Center of Hope, Phnom Penh, Cambodia.

Publisher Copyright:
© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license


Dive into the research topics of 'Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis'. Together they form a unique fingerprint.

Cite this