Summarize the problems that have led to the worldwide problem of managing antimicrobial drugs.

Summary

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.

Introduction

Bacterial antimicrobial resistance (AMR)—which occurs when changes in bacteria cause the drugs used to treat infections to become less effective—has emerged as one of the leading public health threats of the 21st century. The Review on Antimicrobial Resistance, commissioned by the UK Government, argued that AMR could kill 10 million people per year by 2050.

1

• O'Neill J

Tackling drug-resistant infections globally: final report and recommendations.

• Google Scholar

, 

2

• O'Neill J

Antimicrobial resistance: tackling a crisis for the health and wealth of nations.

• Google Scholar

Although these forecasts have been criticised by some,

3

• de Kraker MEA
• Stewardson AJ
• Harbarth S

Will 10 million people die a year due to antimicrobial resistance by 2050?.

• Google Scholar

, 

4

National Office for Animal Health
NOAH response to final O'Neill AMR review report July 2016.

• Google Scholar

WHO and numerous other groups and researchers agree that the spread of AMR is an urgent issue requiring a global, coordinated action plan to address.

, 

6

US Centers for Disease Control and Prevention
Antibiotic resistance threats in the United States, 2019.

• Google Scholar

, 

7

• Prestinaci F
• Pezzotti P
• Pantosti A

Antimicrobial resistance: a global multifaceted phenomenon.

• Google Scholar

, 

Information about the current magnitude of the burden of bacterial AMR, trends in different parts of the world, and the leading pathogen–drug combinations contributing to bacterial AMR burden is crucial. If left unchecked, the spread of AMR could make many bacterial pathogens much more lethal in the future than they are today.

One major challenge to tackling AMR is understanding the true burden of resistance, particularly in locations where surveillance is minimal and data are sparse. Extensive literature exists estimating the effects of AMR on incidence, deaths, hospital length of stay, and health-care costs for select pathogen–drug combinations in specific locations,

1

• O'Neill J

Tackling drug-resistant infections globally: final report and recommendations.

• Google Scholar

, 

2

• O'Neill J

Antimicrobial resistance: tackling a crisis for the health and wealth of nations.

• Google Scholar

, 

6

US Centers for Disease Control and Prevention
Antibiotic resistance threats in the United States, 2019.

• Google Scholar

, 

9

• Naylor NR
• Atun R
• Zhu N
• et al.

Estimating the burden of antimicrobial resistance: a systematic literature review.

• Google Scholar

, 

10

• Cassini A
• Högberg LD
• Plachouras D
• et al.

Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis.

• Google Scholar

, 

11

• Lim C
• Takahashi E
• Hongsuwan M
• et al.

Epidemiology and burden of multidrug-resistant bacterial infection in a developing country.

• Google Scholar

, 

12

• Temkin E
• Fallach N
• Almagor J
• Gladstone BP
• Tacconelli E
• Carmeli Y

Estimating the number of infections caused by antibiotic-resistant Escherichia coli and Klebsiella pneumoniae in 2014: a modelling study.

• Google Scholar

but, to our knowledge, no comprehensive estimates covering all locations and a broad range of pathogens and pathogen–drug combinations have ever been published. For instance, the US Centers for Disease Control and Prevention (CDC) published a 2019 report on AMR infections and deaths in the USA for 18 AMR threats using surveillance data,

6

US Centers for Disease Control and Prevention
Antibiotic resistance threats in the United States, 2019.

• Google Scholar

while Cassini and colleagues

10

• Cassini A
• Högberg LD
• Plachouras D
• et al.

Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis.

• Google Scholar

estimated the burden of eight bacterial pathogens and 16 pathogen–drug combinations in the EU and European Economic Area for 2007–15. Likewise, Lim and colleagues estimated the burden of multidrug resistance in six bacterial pathogens in Thailand in 2010,

11

• Lim C
• Takahashi E
• Hongsuwan M
• et al.

Epidemiology and burden of multidrug-resistant bacterial infection in a developing country.

• Google Scholar

and Temkin and colleagues estimated the incidence of Escherichia coli and Klebsiella pneumoniae resistant to third-generation cephalosporins and carbapenems in 193 countries in 2014.

12

• Temkin E
• Fallach N
• Almagor J
• Gladstone BP
• Tacconelli E
• Carmeli Y

Estimating the number of infections caused by antibiotic-resistant Escherichia coli and Klebsiella pneumoniae in 2014: a modelling study.

• Google Scholar

Although these publications are important contributions to the body of work on AMR, they are insufficient to understand the global burden of AMR and identify and target the highest priority pathogens in different locations. Additionally, existing studies have generally considered only one measure of AMR burden.

13

• de Kraker MEA
• Lipsitch M

Burden of antimicrobial resistance: compared to what?.

• Google Scholar

Because we do not know the extent to which drug-resistant infections would be replaced by susceptible infections or by no infection in a scenario in which all drug resistance was eliminated, it is important to quantify the burden on the basis of both these counterfactual scenarios.

In this study, we present the first global estimates of the burden of bacterial AMR covering an extensive set of pathogens and pathogen–drug combinations using consistent methods for both counterfactual scenarios.

Methods

Input data

We used several data collection strategies. Through our large collaborator networks, we obtained datasets not previously available for AMR research, including hospital and laboratory data, as well as datasets published previously and those outlined in research articles.

19

• Ashley EA
• Recht J
• Chua A
• et al.

An inventory of supranational antimicrobial resistance surveillance networks involving low- and middle-income countries since 2000.

• Google Scholar

Each component of the estimation process had different data requirements and, as such, the input data used for each modelling component differed. The diverse data sought included the following sources: pharmaceutical companies that run surveillance networks, diagnostic laboratories, and clinical trial data; high-quality data from researchers including large multisite research collaborations, smaller studies, clinical trials, and well established research institutes based in low-income and middle-income countries (LMICs); data from public and private hospitals and public health institutes providing diagnostic testing; global surveillance networks; enhanced surveillance systems; national surveillance systems; and surveillance systems for specific organisms such as Mycobacterium tuberculosis and Neisseria gonorrhoeae (all sources are listed by data type in the appendix pp 8–15).

Figure 1 shows a summary of the distinct data types gathered and for which estimation step each data type was used. Also shown in figure 1 is the number of unique study-location-years and individual records or isolates available for each data type. Location-years of data refer to unique GBD locations and years for which we have records or isolates. In total, 471 million individual records or isolates covering 7585 study-location-years were used as input data to the estimation process. Table 1 shows the number of individual records or isolates used and number of countries covered in each of the five broad modelling components separately by GBD region. Two of five components included data from every GBD region and two of five included data from 19 of 21 GBD regions. Our models of sepsis and infectious syndrome were the most geographically sparse, covering 16 countries from ten regions; the input data for these models were highly detailed microdata that are only sparsely available. However, our framework for estimating the total envelope of infectious syndrome mortality used GBD cause-specific mortality estimates to minimise reliance on these sparse data.

Figure 1Data inputs by source type

Show full caption

Total sample size for each source type, regardless of specific inclusion criteria for a given estimation step. Individual isolates that were tested multiple times for resistance to different antibiotics are listed only once here whenever isolates were identified uniquely in the data. For datasets where isolates could not be uniquely identified across pathogen–drug combinations, such as some antimicrobial resistance surveillance systems, some isolates might be double counted. Yellow boxes indicate that the source type was used in that estimation step. A full list of data sources included in this study, organised by data type, is included in the appendix (pp 8–15).

• View Large Image
• Figure Viewer
• Download Hi-res image
• Download (PPT)

Table 1Data included in each modelling component by region and the fraction of countries represented in each region

	Component 1: sepsis and infectious syndrome models * The data points listed in the sepsis and infectious syndrome models include only sources used to determine the fraction of sepsis in non-communicable diseases; maternal, neonatal, and nutritional diseases; and injuries, as well as the distribution of infectious syndromes; final estimates of the number of deaths in each infectious syndrome were generated by multiplying the fractions of sepsis and infection syndromes on GBD 2019 death estimates; GBD 2019 death estimates include 7417 sources with 28 106 location-years of data for under-5 mortality and 7355 sources with over 7322 location-years of data.	Fraction of countries represented in component 1	Component 2: case-fatality ratio	Fraction of countries represented in component 2	Component 3: pathogen distribution	Fraction of countries represented in component 3	Component 4: fraction of resistance † For sources in the fraction of resistance modelling component, de-duplication across antibiotic resistance tests was not possible, leading to potential double counting, as seen in the high-income Asia Pacific region.	Fraction of countries represented in component 4	Component 5: relative risk	Fraction of countries represented in component 5
Andean Latin America	0	0/3	1784	2/3	12 010	2/3	538 644	3/3	4338	2/3
Australasia	320 909	1/2	94 818	1/2	6 294 677	2/2	4 653 832	2/2	5211	2/2
Caribbean	0	0/19	2858	5/19	6225	5/19	68 078	10/19	529	1/19
Central Asia	0	0/9	43 852	2/9	2785	1/9	304 341	9/9	6065	1/9
Central Europe	0	0/13	371 112	10/13	627 844	11/13	3 148 864	13/13	397 885	10/13
Central Latin America	8 130 066	2/9	3 932 601	9/9	11 641 626	8/9	829 686	9/9	20 210	5/9
Central sub-Saharan Africa	0	0/6	0	0/6	770	2/6	40 243	6/6	0	0/6
East Asia	1 189 309	1/3	385 443	2/3	257 522	2/3	2 501 536	3/3	185 980	2/3
Eastern Europe	0	0/7	118 754	4/7	64 212	5/7	968 565	7/7	102 904	4/7
Eastern sub-Saharan Africa	292	3/15	6388	4/15	68 791	9/15	474 280	14/15	3436	2/15
High-income Asia Pacific	0	0/4	135 907	3/4	99 042	3/4	18 909 332	4/4	7577	3/4
High-income North America	84 520 574	2/3	7 184 424	3/3	7 255 147	2/3	32 205 001	3/3	14 071 025	2/3
North Africa and Middle East	0	0/21	209 479	13/21	53 833	16/21	531 120	21/21	90 079	10/21
Oceania	0	0/18	0	0/18	20	1/18	4297	12/18	0	0/18
South Asia	54	1/5	77 811	4/5	51 810	4/5	1 413 840	5/5	97 131	4/5
Southeast Asia	0	0/13	195 087	9/13	91 259	8/13	3 128 014	12/13	172 947	8/13
Southern Latin America	0	0/3	200 665	3/3	73 512	2/3	740 385	3/3	5000	1/3
Southern sub-Saharan Africa	4 696 789	1/6	80 717	2/6	4 699 304	2/6	910 509	6/6	1051	1/6
Tropical Latin America	17 224 511	1/2	3 988 611	1/2	20 956 932	2/2	286 450	2/2	6443	1/2
Western Europe	10 599 906	2/24	94 506 554	20/24	105 183 184	21/24	18 909 732	21/24	932 016	21/24
Western sub-Saharan Africa	83	2/19	26 985	9/19	21 896	10/19	369 482	18/19	14 880	2/19

Total sample size and fraction of countries covered for each modelling component by GBD region. The units for sample size are deaths for sepsis and infectious syndrome models; cases for case-fatality ratios; cases, deaths, or isolates for pathogen distribution; pathogen–drug tests for fraction of resistance; and pathogen–drug tests for relative risk. Sample sizes reflect model-specific selection criteria, resulting in lower totals for the sepsis, infectious syndrome, case-fatality ratio, and pathogen distribution models in this table than those in figure 1. Totals for fraction of resistance and relative risk are higher in this table than in figure 1 because of the difference in units for certain source types, such as microbial data (isolates in figure 1, pathogen–drug tests here). Several data sources inform multiple components; therefore, data points should not be summed across a row as that will lead to duplication. More information on the data types used and the components that they inform is presented in the appendix (pp 8–15). GBD=Global Burden of Diseases, Injuries, and Risk Factors Study.

* The data points listed in the sepsis and infectious syndrome models include only sources used to determine the fraction of sepsis in non-communicable diseases; maternal, neonatal, and nutritional diseases; and injuries, as well as the distribution of infectious syndromes; final estimates of the number of deaths in each infectious syndrome were generated by multiplying the fractions of sepsis and infection syndromes on GBD 2019 death estimates; GBD 2019 death estimates include 7417 sources with 28 106 location-years of data for under-5 mortality and 7355 sources with over 7322 location-years of data.

† For sources in the fraction of resistance modelling component, de-duplication across antibiotic resistance tests was not possible, leading to potential double counting, as seen in the high-income Asia Pacific region.

• Open table in a new tab

All data inputs for the models were empirical data, not modelled estimates, except for a custom meta-analysis of vaccine probe data that we did to estimate the fraction of pneumonia caused by Streptococcus pneumoniae (appendix pp 37–38). All study-level covariates for models, such as age and sex, were extracted from empirical data. All country-level covariates were modelled estimates that were produced previously for GBD 2019,

20

Institute for Health Metrics and Evaluation
Global Burden of Disease Study 2019 (GBD 2019) data input sources tool.

• Google Scholar

, 

21

• Fullman N
• Yearwood J
• Abay SM
• et al.

Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: a systematic analysis from the Global Burden of Disease Study 2016.

• Google Scholar

or those that were modelled by Browne and colleagues.

22

• Browne A
• Chipeta M
• Haines-Woodhouse G
• et al.

Global antibiotic consumption in humans, 2000 to 2018: a spatial modelling study.

• Google Scholar

We describe data inputs for each of ten estimation steps in greater detail in the following subsections and in the appendix (pp 17–18, 31, 34–35, 44, 54). Data input citations are available online.

Results

We estimated that, in 2019, 1·27 million deaths (95% uncertainty interval [UI] 0·911–1·71) were directly attributable to resistance (ie, based on the counterfactual scenario that drug-resistant infections were instead drug susceptible) in the 88 pathogen–drug combinations evaluated in this study. On the basis of a counterfactual scenario of no infection, we estimated that 4·95 million deaths (3·62–6·57) were associated with bacterial AMR globally in 2019 (including those directly attributable to AMR). Table 2 provides estimates of deaths, YLLs, and DALYs from AMR for each counterfactual.

Table 2Deaths, YLLs, YLDs, and DALYs (in counts and all-age rates) associated with and attributable to bacterial antimicrobial resistance, globally and by GBD super-region, 2019

DALYs=disability-adjusted life-years. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. YLDs=years lived with disability. YLLs=years of life lost.

• Open table in a new tab

We estimated that among the 21 GBD regions, Australasia had the lowest AMR burden in 2019, with 6·5 deaths per 100 000 (95% UI 4·3–9·4) attributable to AMR and 28·0 deaths per 100 000 (18·8–39·9) associated with AMR in 2019 (figure 2). Western sub-Saharan Africa had the highest burden, with 27·3 deaths per 100 000 (20·9–35·3) attributable to AMR and 114·8 deaths per 100 000 (90·4–145·3) associated with AMR. Five regions had all-age death rates associated with bacterial AMR higher than 75 per 100 000: all four regions of sub-Saharan Africa and south Asia. Although sub-Saharan Africa had the highest all-age death rate attributable to and associated with AMR, the percentage of all infectious deaths attributable to AMR was lowest in this super-region (appendix p 97).

Figure 2All-age rate of deaths attributable to and associated with bacterial antimicrobial resistance by GBD region, 2019

Show full caption

Estimates were aggregated across drugs, accounting for the co-occurrence of resistance to multiple drugs. Error bars show 95% uncertainty intervals. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study.

• View Large Image
• Figure Viewer
• Download Hi-res image
• Download (PPT)

Three infectious syndromes dominated the global burdens attributable to and associated with AMR in 2019: lower respiratory and thorax infections, bloodstream infections, and intra-abdominal infections (figure 3). Combined, these three syndromes accounted for 78·8% (95% UI 70·8–85·2) of deaths attributable to AMR in 2019; lower respiratory infections alone accounted for more than 400 000 attributable deaths and 1·5 million associated deaths (figure 3).

Figure 3Global deaths (counts) attributable to and associated with bacterial antimicrobial resistance by infectious syndrome, 2019

Show full caption

Estimates were aggregated across drugs, accounting for the co-occurrence of resistance to multiple drugs. Error bars show 95% uncertainty intervals. Does not include gonorrhoea and chlamydia because we did not estimate the fatal burden of this infectious syndrome. Bone+=infections of bones, joints, and related organs. BSI=bloodstream infections. Cardiac=endocarditis and other cardiac infections. CNS=meningitis and other bacterial CNS infections. Intra-abdominal=peritoneal and intra-abdominal infections. LRI+=lower respiratory infections and all related infections in the thorax. Skin=bacterial infections of the skin and subcutaneous systems. TF–PF–iNTS= typhoid fever, paratyphoid fever, and invasive non-typhoidal Salmonella spp. UTI=urinary tract infections and pyelonephritis.

• View Large Image
• Figure Viewer
• Download Hi-res image
• Download (PPT)

In 2019, six pathogens were each responsible for more than 250 000 deaths associated with AMR (figure 4): E coli, Staphylococcus aureus, K pneumoniae, S pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa, by order of number of deaths. Together, these six pathogens were responsible for 929 000 (95% UI 660 000–1 270 000) of 1·27 million deaths (0·911–1·71) attributable to AMR and 3·57 million (2·62–4·78) of 4·95 million deaths (3·62–6·57) associated with AMR globally in 2019. Six more pathogens were each responsible for between 100 000 and 250 000 deaths associated with AMR: M tuberculosis, Enterococcus faecium, Enterobacter spp, Streptococcus agalactiae (group B Streptococcus), S Typhi, and Enterococcus faecalis. For deaths attributable to AMR, E coli was responsible for the most deaths in 2019, followed by K pneumoniae, S aureus, A baumannii, S pneumoniae, and M tuberculosis.

The share of AMR burden caused by each of the six leading pathogens differed substantially across GBD super-regions. In the high-income super-region, approximately half of the fatal AMR burden was linked to two pathogens: S aureus (constituting 26·1% [95% UI 17·4–34·1] of deaths attributable to AMR and 25·4% [24·1–27·0] of deaths associated with AMR) and E coli (constituting 23·4% [19·5–28·2] of deaths attributable to AMR and 24·3% [22·9–25·8] of deaths associated with AMR; figure 5). By contrast, in sub-Saharan Africa, the leading pathogens were distinct from those of the high-income super-region, and each represented a smaller share of the AMR burden; S pneumoniae contributed to 15·9% (11·4–21·0) of the deaths attributable to AMR and 19·0% (17·1–21·1) of the deaths associated with AMR, whereas K pneumoniae contributed to 19·9% (15·1–25·4) of the deaths attributable to AMR and 17·5% (16·3–18·7) of the deaths associated with AMR.

In 2019, meticillin-resistant S aureus was the one pathogen–drug combination in our analysis with more than 100 000 deaths and 3·5 million DALYs attributable to resistance (figure 6; appendix pp 121–22, 129). Six more pathogen–drug combinations each caused between 50 000 and 100 000 resistance-attributable deaths in 2019: MDR excluding XDR 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 (figure 6). In the next tier of pathogen–drug combinations, ten combinations each caused between 25 000 and 50 000 deaths attributable to AMR. Four of these ten combinations included fluoroquinolone resistance, three included carbapenem resistance, and two had trimethoprim-sulfamethoxazole resistance.

Figure 6Global deaths (counts) attributable to bacterial antimicrobial resistance by pathogen–drug combination, 2019

Show full caption

For this figure, only deaths attributable to resistance, not deaths associated with resistance, are shown due to the very high levels of correlation for resistance patterns between some drugs. 3GC=third-generation cephalosporins. 4GC=fourth-generation cephalosporins. Anti-pseudomonal=anti-pseudomonal penicillin or beta-lactamase inhibitors. BL-BLI=β-lactam or β-lactamase inhibitors. MDR=multidrug resistance. Mono INH=isoniazid mono-resistance. Mono RIF=rifampicin mono-resistance. NA=not applicable. Resistance to 1+=resistance to one or more drug. S Paratyphi=Salmonella enterica serotype Paratyphi. S Typhi=S enterica serotype Typhi. TMP-SMX=trimethoprim-sulfamethoxazole. XDR=extensive drug resistance.

• View Large Image
• Figure Viewer
• Download Hi-res image
• Download (PPT)

In the appendix, we present the equivalent AMR findings for DALYs instead of deaths (pp 124–29), as well as the burden attributable to and associated with specific pathogen–drug combinations by age group (neonatal, post-neonatal, age 1–4 years, and age 5 years or older) and super-region (pp 106–18).

Among the seven leading pathogen–drug combinations for deaths attributable to resistance, the proportion of isolates estimated to be resistant varied substantially by country and territory (figure 7A–G). For meticillin-resistant S aureus, resistance was generally highest (60% to less than 80%) in countries in north Africa and the Middle East (eg, Iraq and Kuwait) and lowest (less than 5%) in several countries in Europe and sub-Saharan Africa (figure 7A). For isoniazid and rifampicin co-resistant (MDR excluding XDR) M tuberculosis, isolate resistance was highest (primarily 10% to less than 30%) in eastern Europe and under 5% in many countries around the world (figure 7B). To show where data are available and how the modelled estimates differ from the input data, figure 7 also shows the raw, unadjusted prevalence of resistance for each of the seven leading pathogen–drug combinations.

Figure 7Raw data and modelled estimates for the percentage of pathogen isolates that are resistant by country and territory, 2019

Show full caption

Meticillin-resistant Staphylococcus aureus (A), isoniazid and rifampicin co-resistant (excluding XDR) Mycobacterium tuberculosis (B), third-generation cephalosporin-resistant Escherichia coli (C), carbapenem-resistant Acinetobacter baumannii (D), fluoroquinolone-resistant E coli (E), carbapenem-resistant Klebsiella pneumoniae (F), and third-generation cephalosporin-resistant K pneumoniae (G). Locations with no data or modelled estimates are presented in white. XDR=extensively drug resistant.

• View Large Image
• Figure Viewer
• Download Hi-res image
• Download (PPT)

Figure 7Raw data and modelled estimates for the percentage of pathogen isolates that are resistant by country and territory, 2019

Show full caption

Meticillin-resistant Staphylococcus aureus (A), isoniazid and rifampicin co-resistant (excluding XDR) Mycobacterium tuberculosis (B), third-generation cephalosporin-resistant Escherichia coli (C), carbapenem-resistant Acinetobacter baumannii (D), fluoroquinolone-resistant E coli (E), carbapenem-resistant Klebsiella pneumoniae (F), and third-generation cephalosporin-resistant K pneumoniae (G). Locations with no data or modelled estimates are presented in white. XDR=extensively drug resistant.

• View Large Image
• Figure Viewer
• Download Hi-res image
• Download (PPT)

Figure 7Raw data and modelled estimates for the percentage of pathogen isolates that are resistant by country and territory, 2019

Show full caption

Meticillin-resistant Staphylococcus aureus (A), isoniazid and rifampicin co-resistant (excluding XDR) Mycobacterium tuberculosis (B), third-generation cephalosporin-resistant Escherichia coli (C), carbapenem-resistant Acinetobacter baumannii (D), fluoroquinolone-resistant E coli (E), carbapenem-resistant Klebsiella pneumoniae (F), and third-generation cephalosporin-resistant K pneumoniae (G). Locations with no data or modelled estimates are presented in white. XDR=extensively drug resistant.

• View Large Image
• Figure Viewer
• Download Hi-res image
• Download (PPT)

Figure 7Raw data and modelled estimates for the percentage of pathogen isolates that are resistant by country and territory, 2019

Show full caption

Meticillin-resistant Staphylococcus aureus (A), isoniazid and rifampicin co-resistant (excluding XDR) Mycobacterium tuberculosis (B), third-generation cephalosporin-resistant Escherichia coli (C), carbapenem-resistant Acinetobacter baumannii (D), fluoroquinolone-resistant E coli (E), carbapenem-resistant Klebsiella pneumoniae (F), and third-generation cephalosporin-resistant K pneumoniae (G). Locations with no data or modelled estimates are presented in white. XDR=extensively drug resistant.

• View Large Image
• Figure Viewer
• Download Hi-res image
• Download (PPT)

Figure 7Raw data and modelled estimates for the percentage of pathogen isolates that are resistant by country and territory, 2019

Show full caption

Meticillin-resistant Staphylococcus aureus (A), isoniazid and rifampicin co-resistant (excluding XDR) Mycobacterium tuberculosis (B), third-generation cephalosporin-resistant Escherichia coli (C), carbapenem-resistant Acinetobacter baumannii (D), fluoroquinolone-resistant E coli (E), carbapenem-resistant Klebsiella pneumoniae (F), and third-generation cephalosporin-resistant K pneumoniae (G). Locations with no data or modelled estimates are presented in white. XDR=extensively drug resistant.

• View Large Image
• Figure Viewer
• Download Hi-res image
• Download (PPT)

Figure 7Raw data and modelled estimates for the percentage of pathogen isolates that are resistant by country and territory, 2019

Show full caption

Meticillin-resistant Staphylococcus aureus (A), isoniazid and rifampicin co-resistant (excluding XDR) Mycobacterium tuberculosis (B), third-generation cephalosporin-resistant Escherichia coli (C), carbapenem-resistant Acinetobacter baumannii (D), fluoroquinolone-resistant E coli (E), carbapenem-resistant Klebsiella pneumoniae (F), and third-generation cephalosporin-resistant K pneumoniae (G). Locations with no data or modelled estimates are presented in white. XDR=extensively drug resistant.

• View Large Image
• Figure Viewer
• Download Hi-res image
• Download (PPT)

Figure 7Raw data and modelled estimates for the percentage of pathogen isolates that are resistant by country and territory, 2019

Show full caption

Meticillin-resistant Staphylococcus aureus (A), isoniazid and rifampicin co-resistant (excluding XDR) Mycobacterium tuberculosis (B), third-generation cephalosporin-resistant Escherichia coli (C), carbapenem-resistant Acinetobacter baumannii (D), fluoroquinolone-resistant E coli (E), carbapenem-resistant Klebsiella pneumoniae (F), and third-generation cephalosporin-resistant K pneumoniae (G). Locations with no data or modelled estimates are presented in white. XDR=extensively drug resistant.

• View Large Image
• Figure Viewer
• Download Hi-res image
• Download (PPT)

Discussion

The global burden associated with drug-resistant infections assessed across 88 pathogen–drug combinations in 2019 was an estimated 4·95 million (95% UI 3·62–6·57) deaths, of which 1·27 million (0·911–1·71) deaths were directly attributable to drug resistance. In other words, if all drug-resistant infections were replaced by no infection, 4·95 million deaths could have been prevented in 2019, whereas if all drug-resistant infections were replaced by drug-susceptible infections, 1·27 million deaths could have been prevented. Compared with all underlying causes of death in GBD 2019, AMR would have been the third leading GBD Level 3 cause of death in 2019, on the basis of the counterfactual of no infection; only ischaemic heart disease and stroke accounted for more deaths that year.

14

• Vos T
• Lim SS
• Abbafati C
• et al.

Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019.

• Google Scholar

Using the counterfactual of susceptible infection, AMR would have been the 12th leading GBD Level 3 cause of death globally, ahead of both HIV and malaria (more information on GBD causes by level presented in the appendix pp 18, 67–75).

14

• Vos T
• Lim SS
• Abbafati C
• et al.

Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019.

• Google Scholar

By any metric, bacterial AMR is a leading global health issue.

12

• Temkin E
• Fallach N
• Almagor J
• Gladstone BP
• Tacconelli E
• Carmeli Y

Estimating the number of infections caused by antibiotic-resistant Escherichia coli and Klebsiella pneumoniae in 2014: a modelling study.

• Google Scholar

Additionally, our analysis showed that AMR all-age death rates were highest in some LMICs, making AMR not only a major health problem globally but a particularly serious problem for some of the poorest countries in the world.

All six of the leading pathogens contributing to the burden of AMR in 2019 (E coli, S aureus, K pneumoniae, S pneumoniae, A baumannii, and P aeruginosa) have been identified as priority pathogens by WHO

34

WHO
Global priority list of antibiotic-resistant bacteria to guide research, discovery, and development of new antibiotics.

• Google Scholar

and AMR has been highlighted in the political arena through the Global Action Plan on AMR,

the UN Interagency Coordination Group,

the One Health Global Leaders Group,

and several others. However, only one of these pathogens has been the focus of a major global health intervention programme—S pneumoniae, primarily through pneumococcal vaccination.

37

WHO
Pneumococcal conjugate vaccines in infants and children under 5 years of age: WHO position paper—February 2019.

• Google Scholar

Furthermore, the first Sustainable Development Goal

38

UN
SDG Indicators—global indicator framework for the Sustainable Development Goals and targets of the 2030 Agenda for Sustainable Development.

• Google Scholar

indicator for antimicrobial resistance was only proposed in 2019, and this indicator (3.d.2) is very limited in scope.

39

WHO
Indicator 3.d.2: percentage of bloodstream infections due to selected antimicrobial-resistant organisms.

• Google Scholar

, 

40

UN
Report of the Inter-Agency and Expert Group on Sustainable Development Goal indicators.

• Google Scholar

Our findings, which—to our knowledge—are the most comprehensive estimates of the burden of bacterial AMR to date, clearly show that drug resistance in each of these leading pathogens is a major global health threat that warrants more attention, funding, capacity building, research and development, and pathogen-specific priority setting from the broader global health community.

Resistance to fluoroquinolones and β-lactam antibiotics (ie, carbapenems, cephalosporins, and penicillins)—antibiotics often considered first line for empirical therapy of severe infections

41

WHO
The selection and use of essential medicines: report of the WHO Expert Committee, 2017 (including the 20th WHO Model List of Essential Medicines and the 6th WHO Model List of Essential Medicines for Children).

• Google Scholar

—accounted for more than 70% of deaths attributable to AMR across pathogens. In 2017, WHO published a priority list for developing new and effective antibiotic treatments. The list was intended to inform research and development priorities related to new antibiotics and put the most emphasis on pathogens with multidrug resistance that cause severe and often deadly infections in health-care and nursing home settings. Although the intention of this list was to set new antibiotic research and development priorities rather than identify the most burdensome pathogen–drug combinations, its utility in dictating priorities has still been limited by the absence of a global assessment of the burden of bacterial AMR. Only five of the seven pathogen–drug combinations that we estimated to have caused the most deaths attributable to bacterial AMR in 2019 are currently on the list; MDR tuberculosis and fluoroquinolone-resistant E coli are not included.

34

WHO
Global priority list of antibiotic-resistant bacteria to guide research, discovery, and development of new antibiotics.

• Google Scholar

Additionally, meticillin-resistant S aureus—the leading pathogen–drug combination in our analysis for attributable deaths in 2019—is listed as “high” but not “critical” priority.

34

WHO
Global priority list of antibiotic-resistant bacteria to guide research, discovery, and development of new antibiotics.

• Google Scholar

WHO has explained that the absence of MDR tuberculosis from its priority list is because it has already been established globally as a top priority for innovative treatments, but this exclusion remains a source of considerable debate.

, 

43

• Castro J

The WHO made a big mistake on tuberculosis. It must fix it. STAT.

• Google Scholar

Although many factors were considered in producing the WHO priority list, these new estimates of the global burden of specific pathogen–drug combinations can inform future work on WHO priority pathogen–drug combinations.

Intervention strategies for addressing the challenge of bacterial AMR fall into five main categories. First, the principles of infection prevention and control remain a foundation for preventing infections broadly and a cornerstone in combating the spread of AMR.

44

Organisation for Economic Co-operation and DevelopmentEuropean Centre for Disease Prevention and Control
Antimicrobial resistance: tackling the burden in the European Union. Briefing note for EU/EEA countries.

• Google Scholar

These include both hospital-based infection prevention and control programmes focused on preventing health-care-acquired infections, and community-based programmes focused on water, sanitation, and hygiene. Community-based programmes are particularly important in LMICs where the AMR burden is highest and clean water and sanitation infrastructure is weak; sustained support for these programmes is an essential element of combating AMR.

Second, preventing infections through vaccinations is paramount for reducing the need for antibiotics. Vaccines are available for only one of the six leading pathogens (S pneumoniae), although new vaccine programmes are underway for S aureus, E coli, and others.

45

• Jansen KU
• Knirsch C
• Anderson AS

The role of vaccines in preventing bacterial antimicrobial resistance.

• Google Scholar

Vaccination programmes are an important strategy for preventing S pneumoniae,

46

• Klugman KP
• Black S

Impact of existing vaccines in reducing antibiotic resistance: primary and secondary effects.

• Google Scholar

and vaccine development is crucial for pathogens that currently have no vaccine. Other vaccines, such as the influenza or rotavirus vaccines, also play a role in preventing febrile illness, which can lead to a reduction in antibiotic prescribing and can reduce AMR emergence even for pathogens without vaccines.

45

• Jansen KU
• Knirsch C
• Anderson AS

The role of vaccines in preventing bacterial antimicrobial resistance.

• Google Scholar

Third, reducing exposure to antibiotics unrelated to treating human disease is an important potential way to reduce risk. Increased use of antibiotics in farming has been identified as a potential contributor to AMR in humans,

2

• O'Neill J

Antimicrobial resistance: tackling a crisis for the health and wealth of nations.

• Google Scholar

, 

47

• Tang KL
• Caffrey NP
• Nóbrega DB
• et al.

Restricting the use of antibiotics in food-producing animals and its associations with antibiotic resistance in food-producing animals and human beings: a systematic review and meta-analysis.

• Google Scholar

, 

48

• Van Boeckel TP
• Pires J
• Silvester R
• et al.

Global trends in antimicrobial resistance in animals in low- and middle-income countries.

• Google Scholar

, 

49

• Ter Kuile BH
• Kraupner N
• Brul S

The risk of low concentrations of antibiotics in agriculture for resistance in human health care.

• Google Scholar

although the direct causal link remains controversial.

50

• Wu G
• Day MJ
• Mafura MT
• et al.

Comparative analysis of ESBL-positive Escherichia coli isolates from animals and humans from the UK, the Netherlands and Germany.

• Google Scholar

, 

51

• Mather AE
• Reid SWJ
• Maskell DJ
• et al.

Distinguishable epidemics of multidrug-resistant Salmonella Typhimurium DT104 in different hosts.

• Google Scholar

Fourth, minimising the use of antibiotics when they are not necessary to improve human health—such as treating viral infections—should be prioritised. To this end, building infrastructure that allows clinicians to diagnose infection accurately and rapidly is crucial so that antimicrobial use can be narrowed or stopped when appropriate.

52

• Holmes AH
• Moore LSP
• Sundsfjord A
• et al.

Understanding the mechanisms and drivers of antimicrobial resistance.

• Google Scholar

The notion of antibiotic stewardship remains a core strategy in most national and international AMR management plans, although barriers to implementing stewardship programmes in LMICs should be addressed.

53

• Cox JA
• Vlieghe E
• Mendelson M
• et al.

Antibiotic stewardship in low- and middle-income countries: the same but different?.

• Google Scholar

, 

54

• Rolfe Jr, R
• Kwobah C
• Muro F
• et al.

Barriers to implementing antimicrobial stewardship programs in three low- and middle-income country tertiary care settings: findings from a multi-site qualitative study.

• Google Scholar

Fifth, maintaining investment in the development pipeline for new antibiotics—and access to second-line antibiotics in locations without widespread access—is essential. In the past few decades, investments have been small compared with those in other public health issues with similar or less impact.

55

WHO
2019 antibacterial agents in clinical development: an analysis of the antibacterial clinical development pipeline.

• Google Scholar

Given the global importance of bacterial AMR, more assessment of which policies have worked, and where, is urgently needed.

Many might expect that with higher antibiotic consumption in high-resource settings, the burden of bacterial AMR would be correspondingly higher in those settings. We found, however, that the highest rates of death were in sub-Saharan Africa and south Asia. High bacterial AMR burdens are a function of both the prevalence of resistance and the underlying frequency of critical infections such as lower respiratory infections, bloodstream infections, and intra-abdominal infections, which are higher in these regions.

14

• Vos T
• Lim SS
• Abbafati C
• et al.

Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019.

• Google Scholar

Other drivers of the observed higher burden in LMICs include the scarcity of laboratory infrastructure making microbiological testing unavailable to inform treatment to stop or narrow antibiotics,

the inappropriate use of antibiotics driven by insufficient regulations and ease of acquisition,

57

• Morgan DJ
• Okeke IN
• Laxminarayan R
• Perencevich EN
• Weisenberg S

Non-prescription antimicrobial use worldwide: a systematic review.

• Google Scholar

inadequate access to second-line and third-line antibiotics, counterfeit or substandard antibiotics that can drive resistance,

52

• Holmes AH
• Moore LSP
• Sundsfjord A
• et al.

Understanding the mechanisms and drivers of antimicrobial resistance.

• Google Scholar

, 

58

• Laxminarayan R
• Duse A
• Wattal C
• et al.

Antibiotic resistance—the need for global solutions.

• Google Scholar

, 

59

• Kelesidis T
• Falagas ME

Substandard/counterfeit antimicrobial drugs.

• Google Scholar

and poor sanitation and hygiene.

60

• Collignon P
• Beggs JJ
• Walsh TR
• Gandra S
• Laxminarayan R

Anthropological and socioeconomic factors contributing to global antimicrobial resistance: a univariate and multivariable analysis.

• Google Scholar

, 

61

• Ramay BM
• Caudell MA
• Cordón-Rosales C
• et al.

Antibiotic use and hygiene interact to influence the distribution of antimicrobial-resistant bacteria in low-income communities in Guatemala.

• Google Scholar

, 

62

• Hendriksen RS
• Munk P
• Njage P
• et al.

Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage.

• Google Scholar

The higher burden in low-resource health systems highlights the importance—both for the management of individual patients and for the surveillance of AMR—of well developed national action plans and laboratory infrastructure in all regions and countries. The pattern of AMR varies geographically, with different pathogens and pathogen–drug combinations dominating in different locations. Our regional estimates could prove useful for tailoring local responses as a one size fits all approach might be inappropriate. Although antibiotic stewardship is a foundational aspect for preventing the spread of AMR, limiting access to antibiotics is not a suitable response to AMR in all settings. In fact, it could be argued that an increase in access to antibiotics would decrease the AMR burden in some locations where second-line antibiotics are unavailable and would be lifesaving; this might well be the case in western sub-Saharan Africa. By contrast, limiting access to antibiotics in south Asia through stewardship programmes might be the appropriate response for that region because antibiotic overuse or misuse is believed to be a major driver of AMR there.

58

• Laxminarayan R
• Duse A
• Wattal C
• et al.

Antibiotic resistance—the need for global solutions.

• Google Scholar

AMR is a global problem and one that requires both global action and nationally tailored responses.

This study evaluated both the burden of bacterial infections associated with drug resistance and the burden directly attributable to drug resistance.

13

• de Kraker MEA
• Lipsitch M

Burden of antimicrobial resistance: compared to what?.

• Google Scholar

At the global level, the difference is nearly four-times that attributable to AMR. We estimated both measures of burden because there is insufficient evidence to determine the extent to which drug-resistant infections would be replaced by no infection or susceptible infection if drug resistance was eliminated. Some evidence from the spread of meticillin-resistant S aureus and meticillin-susceptible S aureus suggests that drug-resistant infections do not simply replace drug-susceptible infections,

63

• Mera RM
• Suaya JA
• Amrine-Madsen H
• et al.

Increasing role of Staphylococcus aureus and community-acquired methicillin-resistant Staphylococcus aureus infections in the United States: a 10-year trend of replacement and expansion.

• Google Scholar

, 

64

• Delorme T
• Garcia A
• Nasr P

A longitudinal analysis of methicillin-resistant and sensitive Staphylococcus aureus incidence in respect to specimen source, patient location, and temperature variation.

• Google Scholar

but this finding might not generalise to all other pathogens and other mechanisms of resistance.

Both measures are informative in different ways. For instance, when considering the specific burden of each pathogen–drug combination, we believe that the burden attributable to resistance is more appropriate because very high levels of co-resistance among some drugs lead to many deaths being duplicated across drugs when considering burden associated with resistance. When thinking about the role of vaccination to combat AMR, the no-infection counterfactual is more appropriate because infections would be eliminated, whereas interventions based on antimicrobial stewardship might be better informed by the susceptible infection counterfactual because some resistant bacteria might be replaced by susceptible bacteria.

22

• Browne A
• Chipeta M
• Haines-Woodhouse G
• et al.

Global antibiotic consumption in humans, 2000 to 2018: a spatial modelling study.

• Google Scholar

In either case, the magnitude of the global bacterial AMR problem is very large and likely bounded by the two measures.

Our ability to compare our estimates with previous estimates is somewhat limited. The only global burden estimates for AMR are from the Review on Antimicrobial Resistance,

1

• O'Neill J

Tackling drug-resistant infections globally: final report and recommendations.

• Google Scholar

which did not provide death estimates by pathogen–drug combination, making direct comparison challenging. The Review on Antimicrobial Resistance estimated 700 000 deaths in 2014 attributable to resistance to six pathogens: HIV, tuberculosis, malaria, S aureus, E coli, and K pneumoniae. We produced estimates for four of those pathogens—tuberculosis, S aureus, E coli, and K pneumoniae—and estimated 670 000 deaths attributable to resistance to those pathogens in 2019.

Cassini and colleagues

10

• Cassini A
• Högberg LD
• Plachouras D
• et al.

Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis.

• Google Scholar

produced an estimate for the EU of 16 pathogen–antibiotic combinations in 2015. We produced estimates for 11 of these 16 combinations; we did not estimate colistin resistance in E coli, P aeruginosa, or A baumannii because of the paucity of data on colistin resistance in LMICs, or multidrug resistance in P aeruginosa or A baumannii because of our approach to MDR infections. For the 11 pathogen–drug combinations that overlap, Cassini and colleagues estimated approximately 30 000 deaths and 796 000 DALYs caused by resistance in the EU in 2015. For these same 11 pathogen–drug combinations, we estimated 23 100 deaths (95% UI 14 600–34 600) and 393 000 DALYs (246 000–595 000) attributable to bacterial AMR for western and central Europe combined. Cassini and colleagues used a mix of both counterfactuals to inform their estimates, so it is expected that their EU estimate is somewhat higher than ours for the susceptible counterfactual. This comparison is not perfect because there is not complete overlap in the locations included in western and central Europe and EU member countries (ie, Switzerland is included in our estimate and not in the EU designation, whereas Estonia is in the EU but is part of our eastern Europe region; appendix pp 100–05), but it offers some idea of how our estimates compare with those of previous publications.

Some of our estimates might be unexpected and deserve special attention, particularly the high burden in sub-Saharan Africa and the burden of carbapenem-resistant A baumannii. Although we estimated sub-Saharan Africa to be the super-region with the lowest percentage of infectious deaths attributable to AMR (appendix p 97), the rate of deaths in which infection plays a role was so much greater in sub-Saharan Africa than in other super-regions that it overcame a relatively low prevalence of resistance and was the super-region with the highest estimated AMR burden in 2019.

Regarding carbapenem-resistant A baumannii, we estimated that it was the fourth leading pathogen–drug combination globally for 2019, responsible for slightly fewer deaths than third-generation cephalosporin-resistant E coli. At first glance, this finding seems to contrast with other estimates such as those from Cassini and colleagues or the CDC, who have estimated the burden of carbapenem-resistant A baumannii to be substantially lower than that of third-generation cephalosporin-resistant E coli.

6

US Centers for Disease Control and Prevention
Antibiotic resistance threats in the United States, 2019.

• Google Scholar

, 

10

• Cassini A
• Högberg LD
• Plachouras D
• et al.

Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis.

• Google Scholar

When assessed by super-region, however, our results are much more consistent with the published literature: similar to the CDC and ECDC, we found the burden of third-generation cephalosporin-resistant E coli to exceed that of carbapenem-resistant A baumannii in high-income settings, whereas the inverse pattern was found in south Asia, where a higher relative burden of carbapenem-resistant A baumannii than that in high-income regions has been documented.

11

• Lim C
• Takahashi E
• Hongsuwan M
• et al.

Epidemiology and burden of multidrug-resistant bacterial infection in a developing country.

• Google Scholar

Our global burden was strongly influenced by this higher relative burden of carbapenem-resistant A baumannii in south Asia and other LMICs.

Our estimate for the burden of resistance is confined to the 88 pathogen–drug combinations we analysed. Expanding our resistance analysis to more pathogen–drug combinations—particularly adding viruses, parasites, and fungi—would increase our estimate of the burden and could alter some of the results reported, depending on the correlation structure of resistance between the newly added and original 88 pathogen–drug combinations. It would provide a more thorough account of the threat of AMR and improve the accuracy of our estimates for the combinations reported here that share a high degree of co-resistance with combinations not yet analysed.

This study has several limitations, the most important being the sparsity of data from many LMICs on the distribution of pathogens by infectious syndrome, the prevalence of resistance for key pathogen–drug combinations, and the number of deaths involving infection; and the severe scarcity of data linking laboratory results to outcomes such as death. 19 of 204 countries and territories had no data available for any of our modelling components. Limited availability of data in some parts of the world was particularly consequential for the prevalence of resistance and relative risk modelling components; we assumed that the relative risk for each pathogen–drug combination, as well as the correlation structure of resistance between drugs, was the same in every location, age, and infectious syndrome. This might underestimate the AMR burden for LMICs, since the relative risk might be higher in locations where fewer second-line and third-line antibiotics are available. Assuming a single relative risk for all infectious syndromes is a potentially strong assumption; it is not immediately clear what direction this biases results, but it might lead to overestimation. Another substantial assumption we made due to insufficient linked data was that the relative risk of death or length of stay for infection from an MDR organism was assumed to be equal to the highest individual relative risk among the drugs assessed. This mostly likely underestimates the relative risk of MDR infections because fewer effective antibiotic options remain as resistance accumulates. In light of data sparsity, we made several additional methodological assumptions (appendix pp 17–60). Despite scarcity, our estimates are informed by data from all regions (figure 7, table 1). These figures, and the appendix (pp 26–30, 43–44, 52–53, and 56, which provides out-of-sample model validation), suggest that our modelled estimates fit the data, where available.

Our analysis echoes that of another paper in highlighting critical AMR data gaps in several regions.

65

• Oldenkamp R
• Schultsz C
• Mancini E
• Cappuccio A

Filling the gaps in the global prevalence map of clinical antimicrobial resistance.

• Google Scholar

There are many well described barriers to good-quality clinical bacteriology in LMICs, and proper quality assurance and quality-control measures are crucial for quality care and accurate laboratory-based surveillance.

66

• Ombelet S
• Ronat J-B
• Walsh T
• et al.

Clinical bacteriology in low-resource settings: today's solutions.

• Google Scholar

Many lab-based surveillance systems are not linked to patient diagnoses or outcomes, limiting the inferences that are possible to obtain from such data. Selection bias in how samples get incorporated into surveillance systems; scarcity of laboratory facilities to test for AMR and other challenges in identifying AMR;

17

• Dunachie SJ
• Day NP
• Dolecek C

The challenges of estimating the human global burden of disease of antimicrobial resistant bacteria.

• Google Scholar

insufficient data linking prevalence of resistance to infectious syndrome, underlying cause, and outcome; barriers to sharing data that have been collected; and other data-linking and data optimisation issues continue to complicate the assessment and interpretation of the results in many cases.

A second limitation of our study was the several potential sources of bias we noted when combining and standardising data from a wide variety of providers. Our estimates of the proportion of infections that were community acquired versus hospital acquired for lower respiratory and thorax infections and urinary tract infections were based on the coding of data from multiple causes of death and hospital discharge data. This approach could lead to misclassification, since the criteria used in this coding are not strictly related to community versus hospital acquisition. In future iterations of the project, we hope to improve on the identification of community-acquired and hospital-acquired infections.

Additionally, no universal laboratory standard exists to demarcate resistance versus susceptibility, and we often had to defer to laboratory interpretation to classify the isolates in our data, resulting in heterogeneous classification. Whenever possible, we classified resistance using the most recent CLSI guidelines based on the minimum inhibitory concentrations provided in the data; however, CLSI breakpoints have changed over time, and many datasets did not provide sufficient detail to allow for retrospective reanalysis of the data.

67

• Humphries RM
• Abbott AN
• Hindler JA

Understanding and addressing CLSI breakpoint revisions: a primer for clinical laboratories.

• Google Scholar

Finally, there is a possibility of selection bias in passive microbial surveillance data, particularly if cultures are not routinely drawn. It might be that, in certain locations, cultures are drawn only if a patient does not respond to initial antibiotic therapy, which might lead to an overestimate of the prevalence of resistance. Furthermore, in LMICs, hospital microbial data might skew towards more urban populations or more severe disease, which might not be representative of the broader population. We also received various data from tertiary care facilities; although we adjusted for bias in the prevalence of resistance data collected from these sources, much of our data came from mixed-classification or unclassifiable facilities, so it is possible that we did not fully adjust for all potential tertiary bias. Further limitations specific to each modelling component can be found in the appendix (pp 119–20).

Despite these limitations, this study is the most comprehensive analysis of bacterial AMR burden to date, reflecting the best and widest range of available data and the use of models that have been tested and iterated over years of GBD analysis to incorporate disparate data sources. Individually, these sources do not fully address the burden of AMR but, when used collectively, they provide a more complete estimate with robust geographical coverage. To our knowledge, our study is the first to report burden both attributable to and associated with AMR for an extensive list of pathogens and pathogen–drug combinations, with global and regional findings based on estimates for 204 countries and territories. In the future, these estimates could be used to better inform treatment guidelines. The dominant bacterial pathogens for a given infectious syndrome and the antibiotics that would offer effective treatment could be identified using the data for this study, which, along with estimates of pathogen–drug burden, could be used to inform empirical syndromic treatment guidelines tailored to a specific location.

Our analysis clearly shows that bacterial AMR is a major global health problem. It poses the largest threat to human health in sub-Saharan Africa and south Asia, but it is important in all regions. A diverse set of pathogens are involved, and resistance is high for multiple classes of essential agents, including beta-lactams and fluoroquinolones. Efforts to build laboratory infrastructure are paramount to addressing the large and universal burden of AMR, by improving the management of individual patients and the quality of data in local and global AMR surveillance and bolstering national AMR plans of action. Enhanced infrastructure would also expand AMR research in the future to evaluate the indirect effects of AMR, such as the effect of AMR on perioperative prophylaxis or prophylaxis of infections in transplant recipients, the effects of AMR on transmission, the impact and prevalence of specific variants evaluated through genotypic epidemiology, and more. Identifying strategies that can work to reduce the burden of bacterial AMR—either across a wide range of settings or those that are specifically tailored to the resources available and leading pathogen–drug combinations in a particular setting—is an urgent priority.

Data sharing

Citations for the data used in the study can be accessed from the Global Health Data Exchange AMR website. Access to the data are also provided as data use agreements permit.

Declaration of interests

E Ashley reports that Lao-Oxford-Mahosot Hospital—Wellcome Trust Research Unit received financial support from the Global Research on Antimicrobial Resistance Project (GRAM) to extract and prepare data for the present manuscript. J Bielicki reports grants from the European and Developing Countries Clinical Trials Partnership, Horizon 2020, and Swiss National Science Foundation, and a contract from the National Institute for Health Research (NIHR), outside of the submitted work; and consulting fees from Shionogi and Sandoz and speaking fees from Pfizer and Sandoz, outside the submitted work. C Carvalheiro reports financial support for the present manuscript from the Global Antibiotic Research and Development Partnership, who provided payments to Fundação de Apoio ao Ensino, Pesquisa e Assistência of the Clinical Hospital of the Faculty of Medicine of Ribeirão Preto, University of São Paulo, Brazil. S Dunachie reports financial support for the present manuscript from UL Flemming Fund at the Department of Health and Social Care, the Bill & Melinda Gates Foundation, and the Wellcome Trust; a paid membership role for the Wellcome Trust Vaccines Advisory Selection Panel Vaccines and AMR in November, 2019; and an unpaid role as an expert adviser to WHO's Global Antimicrobrial Resistance Surveillance System, from November, 2018 onwards, outside the submitted work. A Haselbeck reports support for the present manuscript from the Bill & Melinda Gates Foundation (OPP1205877). Y Hsia reports support for the present manuscript from National Institutes of Health for the Global Pediatric Point Prevalence Survey (GARPEC-PPS) study and Blood Stream Infections (GARPEC-BSI) study, paid directly to St George's University of London and grants or contract from the World Health Organisation to support the Global Pediatric Formulation Project. C Lim was supported by the Wellcome Trust Training Fellowship between September, 2017 and March 2020 (206736/Z/17/Z), outside the submitted work. M Mussi-Pinhata reports support for the present manuscript from research from grant funding from Fondazione PENTA—Onlus and the Clinical Trial Manager Global Antibiotic R&D Partnership (GARDP). P Newton reports support for the present manuscript from research grant funding from the Wellcome Trust. T J Ochoa reports support for the present manuscript from Pfizer research grant for invasive pneumococcal surveillance, paid directly to their institution. F Marks reports unpaid participation on the London School of Hygiene and Tropical Medicine data safety monitoring board for the Ebola vaccine study, outside the submitted work. J May reports support for the present manuscript from the Bill & Melinda Gates Foundation, German Federal Ministry of Research & Education, German Federal Ministry of Health, and German Research Association and support for attending meetings and/or travel from the German Federal Ministry of Health, outside the submitted work. J Robotham is a member of the UK Government Advisory Committee on Antimicrobial Prescribing Resistance and Healthcare Associated Infections, outside the submitted work. J Scott reports that the London School of Hygiene & Tropical Medicine (LSHTM) received financial support from Emory University to support CHAMPS projects in Ethiopia for the present manuscript; reports a paid fellowship from the Wellcome Trust, research grants from Gavi, the Vaccine Alliance, and NIHR paid to LSHTM, and an African research leader fellowship paid to LSHTM by the Medical Research Council, outside the submitted work; and reports being a member of the data safety and monitoring board for PATH Vaccines Solutions for SII PCV10 in The Gambia. J Sifuentes-Osornio reports financial support from Oxford University for the present manuscript; research grants from Oxford, CONACYT, Sanofi, and Novartis, outside of the study; consulting fees from Senosiain and speaker fees from Merck, outside of the study; and membership of the Sanofi advisory board of COVID-19 Vaccine Development, which is currently in progress, outside of the study. A J Stewardson reports grants or contracts from Merck, Sharp, & Dohme paid to Monash University, Melbourne, outside of the study. P Turner reports grants, consulting fees, and support for attending meetings or travel from Wellcome Trust, outside the study. H van Doorn reports grants or contracts from the University of Oxford and is the principal investigator for the Fleming Fund pilot grant; and he is a board member of Wellcome Trust's Surveillance and Epidemiology of Drug Resistant Infections. T Walsh reports financial support from the Bill & Melinda Gates Foundation for the BARNARDS (neonatal sepsis and mortality) study for the present manuscript. J L Walson reports grants or contracts from the Bill & Melinda Gates Foundation and National Institutes of Health, paid directly to their institution, outside of the submitted work. All other authors declare no competing interests.

Acknowledgments

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. Data provided for this manuscript was generated by work funded by the Armed Forces Health Surveillance Division, Global Emerging Infections Surveillance (GEIS) Branch PROMIS ID P0153 KY 2015-2019.

Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations.

Supplementary Material

References

1. 1.
   • O'Neill J

   Tackling drug-resistant infections globally: final report and recommendations.

   Review on Antimicrobial Resistance, London2016
2. 2.
   • O'Neill J

   Antimicrobial resistance: tackling a crisis for the health and wealth of nations.

   Review on Antimicrobial Resistance, London2014
3. 3.
   • de Kraker MEA
   • Stewardson AJ
   • Harbarth S

   Will 10 million people die a year due to antimicrobial resistance by 2050?.

   PLoS Med. 2016; 13e1002184
4. 4.
   • National Office for Animal Health

   NOAH response to final O'Neill AMR review report July 2016.

   National Office for Animal Health, Middlesex2016
5. 5.
   • WHO

   Antimicrobial resistance.

6. 6.
   • US Centers for Disease Control and Prevention

   Antibiotic resistance threats in the United States, 2019.

   US Department of Health and Human Services, Atlanta, GA2019
7. 7.
   • Prestinaci F
   • Pezzotti P
   • Pantosti A

   Antimicrobial resistance: a global multifaceted phenomenon.

   Pathog Glob Health. 2015; 109: 309-318
8. 8.
   • WHO

   Global action plan on antimicrobial resistance.

   World Health Organization, Geneva2015
9. 9.
   • Naylor NR
   • Atun R
   • Zhu N
   • et al.

   Estimating the burden of antimicrobial resistance: a systematic literature review.

   Antimicrob Resist Infect Control. 2018; 7: 58
10. 10.
    • Cassini A
    • Högberg LD
    • Plachouras D
    • et al.

    Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis.

    Lancet Infect Dis. 2019; 19: 56-66
11. 11.
    • Lim C
    • Takahashi E
    • Hongsuwan M
    • et al.

    Epidemiology and burden of multidrug-resistant bacterial infection in a developing country.

    eLife. 2016; 5e18082
12. 12.
    • Temkin E
    • Fallach N
    • Almagor J
    • Gladstone BP
    • Tacconelli E
    • Carmeli Y

    Estimating the number of infections caused by antibiotic-resistant Escherichia coli and Klebsiella pneumoniae in 2014: a modelling study.

    Lancet Glob Health. 2018; 6: e969-e979
13. 13.
    • de Kraker MEA
    • Lipsitch M

    Burden of antimicrobial resistance: compared to what?.

    Epidemiol Rev. 2021; ()
14. 14.
    • Vos T
    • Lim SS
    • Abbafati C
    • et al.

    Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019.

    Lancet. 2020; 396: 1204-1222
15. 15.
    • Hay SI
    • Rao PC
    • Dolecek C
    • et al.

    Measuring and mapping the global burden of antimicrobial resistance.

    BMC Med. 2018; 16: 78
16. 16.
    • Murray CJ
    • Ezzati M
    • Flaxman AD
    • et al.

    GBD 2010: design, definitions, and metrics.

    Lancet. 2012; 380: 2063-2066
17. 17.
    • Dunachie SJ
    • Day NP
    • Dolecek C

    The challenges of estimating the human global burden of disease of antimicrobial resistant bacteria.

    Curr Opin Microbiol. 2020; 57: 95-101
18. 18.
    • Limmathurotsakul D
    • Dunachie S
    • Fukuda K
    • et al.

    Improving the estimation of the global burden of antimicrobial resistant infections.

    Lancet Infect Dis. 2019; 19: e392-e398
19. 19.
    • Ashley EA
    • Recht J
    • Chua A
    • et al.

    An inventory of supranational antimicrobial resistance surveillance networks involving low- and middle-income countries since 2000.

    J Antimicrob Chemother. 2018; 73: 1737-1749
20. 20.
    • Institute for Health Metrics and Evaluation

    Global Burden of Disease Study 2019 (GBD 2019) data input sources tool.

21. 21.
    • Fullman N
    • Yearwood J
    • Abay SM
    • et al.

    Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: a systematic analysis from the Global Burden of Disease Study 2016.

    Lancet. 2018; 391: 2236-2271
22. 22.
    • Browne A
    • Chipeta M
    • Haines-Woodhouse G
    • et al.

    Global antibiotic consumption in humans, 2000 to 2018: a spatial modelling study.

    Lancet Planet Health. 2021; ()
23. 23.
    • Singer M
    • Deutschman CS
    • Seymour CW
    • et al.

    The third international consensus definitions for sepsis and septic shock (sepsis-3).

    JAMA. 2016; 315: 801-810
24. 24.
    • Rudd KE
    • Johnson SC
    • Agesa KM
    • et al.

    Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study.

    Lancet. 2020; 395: 200-211
25. 25.
    • WHO

    Global report on the epidemiology and burden of sepsis: current evidence, identifying gaps and future directions.

    World Health Organization, Geneva2020
26. 26.
    • Angus DC
    • Linde-Zwirble WT
    • Lidicker J
    • Clermont G
    • Carcillo J
    • Pinsky MR

    Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care.

    Crit Care Med. 2001; 29: 1303-1310
27. 27.
    • Martin GS
    • Mannino DM
    • Eaton S
    • Moss M

    The epidemiology of sepsis in the United States from 1979 through 2000.

    N Engl J Med. 2003; 348: 1546-1554
28. 28.
    • Rhee C
    • Dantes R
    • Epstein L
    • et al.

    Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009–2014.

    JAMA. 2017; 318: 1241-1249
29. 29.
    • Zheng P
    • Barber R
    • Sorensen RJD
    • Murray CJL
    • Aravkin AY

    Trimmed constrained mixed effects models: formulations and algorithms.

    J Comput Graph Stat. 2021; 0: 1-13
30. 30.
    • WHO

    Antimicrobial resistance global report on surveillance 2014.

    World Health Organization, Geneva2014
31. 31.
    • Murray CJL
    • Aravkin AY
    • Zheng P
    • et al.

    Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019.

    Lancet. 2020; 396: 1223-1249
32. 32.
    • New York Department of Health

    Hospital associated infection reporting persuant to Public Health Law section 2819.

33. 33.
    • Centers for Medicare and Medicaid Services

    COVID-19 emergency declaration blanket waivers for health care providers.

    Centers for Medicare and Medicaid Services, Baltimore, MD2021
34. 34.
    • WHO

    Global priority list of antibiotic-resistant bacteria to guide research, discovery, and development of new antibiotics.

35. 35.
    • UN

    Interagency Coordination Group on Antimicrobial Resistance.

    United Nations, 2017
36. 36.
    • WHO

    Global Leaders Group on antimicrobial resistance.

37. 37.
    • WHO

    Pneumococcal conjugate vaccines in infants and children under 5 years of age: WHO position paper—February 2019.

    World Health Organization, Geneva2019
38. 38.
    • UN

    SDG Indicators—global indicator framework for the Sustainable Development Goals and targets of the 2030 Agenda for Sustainable Development.

39. 39.
    • WHO

    Indicator 3.d.2: percentage of bloodstream infections due to selected antimicrobial-resistant organisms.

    World Health Organization, Geneva2020
40. 40.
    • UN

    Report of the Inter-Agency and Expert Group on Sustainable Development Goal indicators.

    United Nations Economic and Social Council, New York, NY2020
41. 41.
    • WHO

    The selection and use of essential medicines: report of the WHO Expert Committee, 2017 (including the 20th WHO Model List of Essential Medicines and the 6th WHO Model List of Essential Medicines for Children).

    World Health Organization, Geneva2017
42. 42.
    • Burki TK

    Tuberculosis missing from WHO bacteria list.

    Lancet Respir Med. 2017; 5: 252
43. 43.
    • Castro J

    The WHO made a big mistake on tuberculosis. It must fix it. STAT.

44. 44.
    • Organisation for Economic Co-operation and Development
    • European Centre for Disease Prevention and Control

    Antimicrobial resistance: tackling the burden in the European Union. Briefing note for EU/EEA countries.

    OECD Publications, Paris2019
45. 45.
    • Jansen KU
    • Knirsch C
    • Anderson AS

    The role of vaccines in preventing bacterial antimicrobial resistance.

    Nat Med. 2018; 24: 10-19
46. 46.
    • Klugman KP
    • Black S

    Impact of existing vaccines in reducing antibiotic resistance: primary and secondary effects.

    Proc Natl Acad Sci USA. 2018; 115: 12896-12901
47. 47.
    • Tang KL
    • Caffrey NP
    • Nóbrega DB
    • et al.

    Restricting the use of antibiotics in food-producing animals and its associations with antibiotic resistance in food-producing animals and human beings: a systematic review and meta-analysis.

    Lancet Planet Health. 2017; 1: e316-e327
48. 48.
    • Van Boeckel TP
    • Pires J
    • Silvester R
    • et al.

    Global trends in antimicrobial resistance in animals in low- and middle-income countries.

    Science. 2019; 365eaaw1944
49. 49.
    • Ter Kuile BH
    • Kraupner N
    • Brul S

    The risk of low concentrations of antibiotics in agriculture for resistance in human health care.

    FEMS Microbiol Lett. 2016; 363fnw210
50. 50.
    • Wu G
    • Day MJ
    • Mafura MT
    • et al.

    Comparative analysis of ESBL-positive Escherichia coli isolates from animals and humans from the UK, the Netherlands and Germany.

    PLoS One. 2013; 8e75392
51. 51.
    • Mather AE
    • Reid SWJ
    • Maskell DJ
    • et al.

    Distinguishable epidemics of multidrug-resistant Salmonella Typhimurium DT104 in different hosts.

    Science. 2013; 341: 1514-1517
52. 52.
    • Holmes AH
    • Moore LSP
    • Sundsfjord A
    • et al.

    Understanding the mechanisms and drivers of antimicrobial resistance.

    Lancet. 2016; 387: 176-187
53. 53.
    • Cox JA
    • Vlieghe E
    • Mendelson M
    • et al.

    Antibiotic stewardship in low- and middle-income countries: the same but different?.

    Clin Microbiol Infect. 2017; 23: 812-818
54. 54.
    • Rolfe Jr, R
    • Kwobah C
    • Muro F
    • et al.

    Barriers to implementing antimicrobial stewardship programs in three low- and middle-income country tertiary care settings: findings from a multi-site qualitative study.

    Antimicrob Resist Infect Control. 2021; 10: 60
55. 55.
    • WHO

    2019 antibacterial agents in clinical development: an analysis of the antibacterial clinical development pipeline.

    World Health Organization, Geneva2019
56. 56.
    • WHO

    WHO sepsis technical expert meeting—meeting report.

    World Health Organization, Geneva2018
57. 57.
    • Morgan DJ
    • Okeke IN
    • Laxminarayan R
    • Perencevich EN
    • Weisenberg S

    Non-prescription antimicrobial use worldwide: a systematic review.

    Lancet Infect Dis. 2011; 11: 692-701
58. 58.
    • Laxminarayan R
    • Duse A
    • Wattal C
    • et al.

    Antibiotic resistance—the need for global solutions.

    Lancet Infect Dis. 2013; 13: 1057-1098
59. 59.
    • Kelesidis T
    • Falagas ME

    Substandard/counterfeit antimicrobial drugs.

    Clin Microbiol Rev. 2015; 28: 443-464
60. 60.
    • Collignon P
    • Beggs JJ
    • Walsh TR
    • Gandra S
    • Laxminarayan R

    Anthropological and socioeconomic factors contributing to global antimicrobial resistance: a univariate and multivariable analysis.

    Lancet Planet Health. 2018; 2: e398-e405
61. 61.
    • Ramay BM
    • Caudell MA
    • Cordón-Rosales C
    • et al.

    Antibiotic use and hygiene interact to influence the distribution of antimicrobial-resistant bacteria in low-income communities in Guatemala.

    Sci Rep. 2020; 1013767
62. 62.
    • Hendriksen RS
    • Munk P
    • Njage P
    • et al.

    Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage.

    Nat Commun. 2019; 101124
63. 63.
    • Mera RM
    • Suaya JA
    • Amrine-Madsen H
    • et al.

    Increasing role of Staphylococcus aureus and community-acquired methicillin-resistant Staphylococcus aureus infections in the United States: a 10-year trend of replacement and expansion.

    Microb Drug Resist. 2011; 17: 321-328
64. 64.
    • Delorme T
    • Garcia A
    • Nasr P

    A longitudinal analysis of methicillin-resistant and sensitive Staphylococcus aureus incidence in respect to specimen source, patient location, and temperature variation.

    Int J Infect Dis. 2017; 54: 50-57
65. 65.
    • Oldenkamp R
    • Schultsz C
    • Mancini E
    • Cappuccio A

    Filling the gaps in the global prevalence map of clinical antimicrobial resistance.

    Proc Natl Acad Sci USA. 2021; 118e2013515118
66. 66.
    • Ombelet S
    • Ronat J-B
    • Walsh T
    • et al.

    Clinical bacteriology in low-resource settings: today's solutions.

    Lancet Infect Dis. 2018; 18: e248-e258
67. 67.
    • Humphries RM
    • Abbott AN
    • Hindler JA

    Understanding and addressing CLSI breakpoint revisions: a primer for clinical laboratories.

    J Clin Microbiol. 2019; 57: e00203-e00219

Article Info

Linked Articles

• The overlooked pandemic of antimicrobial resistance

  • As COVID-19 rages on, the pandemic of antimicrobial resistance (AMR) continues in the shadows. The toll taken by AMR on patients and their families is largely invisible but is reflected in prolonged bacterial infections that extend hospital stays and cause needless deaths.1 Moreover, AMR disproportionately affects poor individuals who have little access to second-line, more expensive antibiotics that could work when first-line drugs fail.

  • Full-Text
  • PDF
• Department of Error

  • Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 2022; 399: 629–55—In this Article, the Antimicrobial Resistance Collaborators list and affiliations have been updated. These corrections have been made to the online version as of Sept 29, 2022.

  • Full-Text
  • PDF
  Open Access
• Global burden of antimicrobial resistance: essential pieces of a global puzzle

  • Christopher J L Murray and colleagues1 report on the dramatically high burden of antimicrobial resistance (AMR) worldwide, particularly in low-income and middle-income countries. The authors also emphasise the insufficient data on the prevalence of bacterial infections and AMR in low-resource settings. Although the figures presented are striking, they do not sufficiently depict the suffering of patients living in these locations and the frustration of clinicians unable to treat an infection that is typically easily curable elsewhere.

  • Full-Text
  • PDF
• Global burden of antimicrobial resistance: essential pieces of a global puzzle

  • Christopher J L Murray and colleagues1 evidence the global burden of antimicrobial resistance (AMR), which disproportionally affects low-income and middle-income countries (LMICs).

  • Full-Text
  • PDF
• Global burden of antimicrobial resistance: essential pieces of a global puzzle

  • Christopher J L Murray and colleagues1 provide a compelling contribution to the understanding of the burden of disease attributable to bacterial antimicrobial resistance (AMR) in 2019. Focusing on 23 bacterial pathogens, they used two-stage spatiotemporal modelling to estimate this burden to be larger than diseases such as HIV and malaria, with the highest rates in sub-Saharan Africa. This new information should raise this issue higher on the global health agenda. They also, however, highlight the scarcity of high-quality data for infectious diseases and antibiotic consumption, with the authors having to rely on antibiotics sales data for an indication of antibiotic consumption.

  • Full-Text
  • PDF
• Global burden of antimicrobial resistance: essential pieces of a global puzzle

  • Christopher J L Murray and colleagues1 highlight the global impact of the silent antimicrobial resistance (AMR) pandemic. The applied methodology is more thorough than previous efforts,2 but there are a number of important limitations.

  • Full-Text
  • PDF
• Global burden of antimicrobial resistance: essential pieces of a global puzzle – Authors' reply

  • We thank Esmita Charani and colleagues, Marlieke E A de Kraker and Stephan Harbarth, Timothy R Walsh and colleagues, Denis Mukwege and colleagues, and Diane Ashiru-Oredope and colleagues for their interest in our work1 and insightful comments that will improve the quality of our estimation process in the future. Estimating the global burden of antimicrobial resistance (AMR) is laden with challenges, but undertaking this enormous task is crucial for steadfast decisions on resource allocation from a national, regional, and global perspective, as well as for research prioritisation and intervention evaluation.

  • Full-Text
  • PDF
• Global burden of antimicrobial resistance: essential pieces of a global puzzle

  • Christopher J L Murray and colleagues1 provide a sobering analysis on the burden of common antimicrobial-resistant infections and a warning that, as a global community, we are rapidly surrendering any advantage we had on treating infections such as pneumonia and sepsis.2

  • Full-Text
  • PDF
• The problem of antimicrobial resistance in chronic liver disease

  • In 2019, an estimated 1·27 million individuals died due to bacterial antimicrobial resistance (AMR). Beyond the obvious threat to global public health, AMR also threatens global development—even the most optimistic scenario modelled by the World Bank in 2017 estimates that AMR will reduce annual global GDP by 1·1% by 2050, with the GDP shortfall exceeding $1 trillion every year after 2030. Against this backdrop, investment in the development of new antibiotics falls far short of that required. A 2020 report from WHO concluded that although 11 new antibacterial agents had received regulatory approval since 2017, many of the new agents had limited clinical benefit over existing treatment: nine were from existing classes for which resistance mechanisms are well established and for which rapid emergence of resistance can be foreseen.

  • Full-Text
  • PDF