The burden of bacterial antimicrobial resistance in the WHO European region in 2019: a cross-country systematic analysis

Background Antimicrobial resistance (AMR) represents one of the most crucial threats to public health and modern health care. Previous studies have identified challenges with estimating the magnitude of the problem and its downstream effect on human health and mortality. To our knowledge, this study presents the most comprehensive set of regional and country-level estimates of AMR burden in the WHO European region to date. Methods We estimated deaths and disability-adjusted life-years attributable to and associated with AMR for 23 bacterial pathogens and 88 pathogen–drug combinations for the WHO European region and its countries in 2019. Our methodological approach consisted of five broad components: the number of deaths in which infection had a role, the proportion of infectious deaths attributable to a given infectious syndrome, the proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antimicrobial drug of interest, and the excess risk of mortality (or duration of an infection) associated with this resistance. These components were then used to estimate the disease burden by using two counterfactual scenarios: deaths attributable to AMR (considering an alternative scenario where infections with resistant pathogens are replaced with susceptible ones) and deaths associated with AMR (considering an alternative scenario where drug-resistant infections would not occur at all). Data were solicited from a wide array of international stakeholders; these included research hospitals, surveillance networks, and infection databases maintained by private laboratories and medical technology companies. 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. Findings We estimated 541 000 deaths (95% UI 370 000–763 000) associated with bacterial AMR and 133 000 deaths (90 100–188 000) attributable to bacterial AMR in the whole WHO European region in 2019. The largest fatal burden of AMR in the region came from bloodstream infections, with 195 000 deaths (104 000–333 000) associated with resistance, followed by intra-abdominal infections (127 000 deaths [81 900–185 000]) and respiratory infections (120 000 deaths [94 500–154 000]). Seven leading pathogens were responsible for about 457 000 deaths associated with resistance in 53 countries of this region; these pathogens were, in descending order of mortality, Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, Pseudomonas aeruginosa, Enterococcus faecium, Streptococcus pneumoniae, and Acinetobacter baumannii. Methicillin-resistant S aureus was shown to be the leading pathogen–drug combination in 27 countries for deaths attributable to AMR, while aminopenicillin-resistant E coli predominated in 47 countries for deaths associated with AMR. Interpretation The high levels of resistance for several important bacterial pathogens and pathogen–drug combinations, together with the high mortality rates associated with these pathogens, show that AMR is a serious threat to public health in the WHO European region. Our regional and cross-country analyses open the door for strategies that can be tailored to leading pathogen–drug combinations and the available resources in a specific location. These results underscore that the most effective way to tackle AMR in this region will require targeted efforts and investments in conjunction with continuous outcome-based research endeavours. Funding Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.

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). Coauthors affiliated with this organisation provided feedback on the initial maps and drafts of this manuscript. MA acknowledges partial support by the Romanian National Authority for Scientific Research and Innovation, under the UEFISCDI PN-III-P4-ID-PCCF-2016-0084 research grant. VBG and VKG acknowledge funding support from the National Health and Medical Research Council Australia. CH is partially supported by a grant from the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084, and by a grant from the Romanian Ministry of Research Innovation and Digitalization, MCID, project number ID-585-CTR-42-PFE-2021. SH was supported by the operational programme Research, Development and Education, Postdoc2MUNI (CZ.02.2.69/0.0/0.0/18_053/0016952). GL was supported by national funds through the Fundação para a Ciência e Tecnologia (FCT) under the Scientific Employment Stimulus–Individual Call (CEECIND/01768/2021). AGM was supported by the National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Centre and by an NIHR Clinical Lectureship in Respiratory Medicine. AP is partially supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. JP was supported by FCT through the Scientific Employment Stimulus–Individual Call (CEECIND/00394/2017 and UID/DTP/04138/2019). AS acknowledges support from Health Data Research UK. LRS was supported by project CENTRO-04-3559-FSE-000162, Fundo Social Europeu. SBZ acknowledges receiving a scholarship from the Australian Government Research Training Program in support of his academic career.