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Articles
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629
Global burden of bacterial antimicrobial resistance in 2019:
a systematic analysis
Antimicrobial Resistance Collaborators*
Summary
Background Antimicrobial resistance (AMR) poses a major threat to human health around the world. Previous
publications have estimated the eect 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.
Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY
4.0 license.
Lancet 2022; 399: 629–55
Published Online
January 20, 2022
https://doi.org/10.1016/
S0140-6736(21)02724-0
See Comment page 606
*Collaborators are listed at the
end of the paper
Correspondence to:
Dr Mohsen Naghavi, Institute for
Health Metrics and Evaluation,
University of Washington,
Seattle, WA 98195, USA
nagham@uw.edu
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Introduction
Bacterial antimicrobial resistance (AMR)—which occurs
when changes in bacteria cause the drugs used to treat
infections to become less eective—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 2050.1,2 Although these
forecasts have been criticised by some,3,4 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.5–8 Information about
the current magnitude of the burden of bacterial AMR,
trends in dierent 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 eects of AMR
on incidence, deaths, hospital length of stay, and health-
care costs for select pathogen–drug combinations in
specific locations,1,2,6,9–12 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
while Cassini and colleagues10 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 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
Research in context
Evidence before this study
To identify previous estimates of antimicrobial resistance
(AMR) burden before this study, we did a systematic review and
consulted with content experts. We searched the evidence
available in PubMed for published works that evaluate exposure
to antimicrobial resistant organisms (bacteria only) and
evaluated all human-focused publications with more than ten
cases, all genders, and all age groups. From these findings, we
extracted study type, pathogen–drug combinations,
counterfactuals, locations, methods, outcomes, and
population. Extensive literature exists estimating incidence,
deaths, hospital length of stay, and health-care costs associated
with AMR from a small number of drug-resistant infections in
select locations. There is widespread agreement that AMR poses
a serious potential threat to human health around the world.
The Review on Antimicrobial Resistance, published in 2016,
estimated that as many as 10 million people could die annually
from AMR by 2050. Recent estimates of the burden of drug-
resistant infections covering several pathogens have also been
published for the USA, Thailand, the EU and European
Economic Area, and several other locations, as well as estimates
for several pathogen–drug combinations for a wider range of
locations. To our knowledge, however, there have been no
comprehensive estimates covering all locations and a broad
range of pathogens and pathogen–drug combinations.
Added value of this study
This study is the most comprehensive analysis of the burden of
AMR to date, producing estimates for 204 countries and
territories, 23 bacterial pathogens, and 88 pathogen–drug
combinations, in 2019. This study uses major methodological
innovations to provide important new insights into the AMR
burden. Additionally, since this analysis builds on estimates of
disease incidence, prevalence, and mortality from the Global
Burden of Diseases, Injuries, and Risk Factors Study 2019, our
findings on the burden of bacterial AMR can be compared with
other causes of death, offering crucial context on the
magnitude of the burden of this important health issue.
Improvements to the input data and models compared with
previous publications make our AMR estimates the most robust
of any to date. Finally, this study is the first to quantify the
burden of AMR using two different AMR counterfactual
scenarios.
Implications of all the available evidence
Our estimates indicate that bacterial AMR is a health problem
whose magnitude is at least as large as major diseases such as
HIV and malaria, and potentially much larger. Bacterial AMR is a
problem in all regions; we estimated that, in 2019, the highest
rates of AMR burden were in sub-Saharan Africa. Six pathogens
accounted for 73·4% (95% uncertainty interval 66·9–78·8) of
deaths attributable to bacterial AMR. Seven pathogen–drug
combinations each caused more than 50 000 deaths,
highlighting the importance of developing policies that
specifically target the deadliest pathogen–drug combinations,
particularly through expansion of infection prevention and
control programmes, improving access to essential second-line
antibiotics where needed, and through vaccine and antibiotic
development. Additionally, our comprehensive data collection
effort shows that high-quality data on infectious disease,
pathogens, and AMR are only sparsely available in many
low-income settings. Both preventing bacterial AMR and
increasing microbiological laboratory and data collection
capacity to improve scientific understanding of this health
threat should be a very high priority for global health policy
makers.
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Articles
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631
Although these publications are important contribu-
tions to the body of work on AMR, they are insucient to
understand the global burden of AMR and identify and
target the highest priority pathogens in dierent
locations. Additionally, existing studies have generally
considered only one measure of AMR burden.13 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
Overview
We developed an approach for estimating the burden of
AMR that makes use of all available data and builds on
death and incidence estimates for dierent underlying
conditions from the Global Burden of Diseases, Injuries,
and Risk Factors Study (GBD) 2019, which provides
age-specific and sex-specific estimates of disease burden
for 369 diseases and injuries in 204 countries and
territories in 1990–2019.14 Our approach can be divided
into ten estimation steps that occur within five broad
modelling components (a flowchart of the estimation
steps is given in the appendix p 123). First, we obtained
data from multiple data sources, including from
published studies (eg, microbiology data, inpatient data,
data on multiple causes of death, and pharmaceutical
sales data) and directly from collaborators on the Global
Research on Antimicrobial Resistance project,15 members
of the GBD Collaborator Network, and other data
providers.
We estimated the disease burdens associated with and
attributable to AMR for 12 major infectious syndromes
(lower respiratory infections and all related infections in
the thorax; bloodstream infections; peritoneal and intra-
abdominal infections; meningitis and other bacterial
CNS infections; typhoid, paratyphoid, and invasive non-
typhoidal Salmonella spp; urinary tract infections and
pyelonephritis; diarrhoea; tuberculosis [not including
tuberculosis associated with HIV]; bacterial infections of
the skin and subcutaneous systems; endocarditis and
other cardiac infections; infections of bones, joints,
and related organs; and gonorrhoea and chlamydia) and
one residual category, 23 bacterial pathogens, 18 drug
categories or combinations of drugs for which there
is resistance, and 88 pathogen–drug combinations
(appendix pp 45–46). We modelled all-age and age-specific
deaths and disability-adjusted life-years (DALYs) for
204 countries and territories, and we present aggregated
estimates for 21 GBD regions, seven GBD super-regions,
and globally in 2019 (a full list of GBD locations by region
is available in the appendix pp 100–05).16
For the first counterfactual scenario—where all drug-
resistant infections are replaced by susceptible infections—
we estimated only deaths and DALYs directly attributable
to resistance. For the second counterfactual scenario—
where all drug-resistant infections are replaced by no
infection—we estimated all deaths and DALYs associated
with resistant infection. Estimates of AMR burden based
on each counterfactual are useful in dierent ways for
informing the development of potential intervention
strategies to control AMR.13,17,18
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 Each component
of the estimation process had dierent data requirements
and, as such, the input data used for each modelling
component diered. 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.
All data inputs for the models were empirical data, not
modelled estimates, except for a custom meta-analysis of
See Online for appendix
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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 2019,20,21
or those that were modelled by Browne and colleagues.22
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.
Estimation steps one and two: deaths in which
infection played a role by infectious syndrome
First, to define the number of deaths where infection
plays a role, we used GBD 2019 cause of death estimates14
to determine the number of deaths by age, sex, and
location for which either the underlying cause of death
Figure 1: Data inputs by source type
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).
Source type Number
of
study-
location-
years
Sample size Sample
size units
Estimation step
1:
sepsis
2:
infectious
syndrome
3:
case-
fatality
ratio
4:
pathogen
distribution
5:
antibiotic
use
6:
prevalence
of
resistance
7:
resistance
profiles
8:
relative
risk of
death
9:
relative
length
of stay
Multiple cause
of death 2980 120
871
372 Deaths
Hospital
discharge 391 192
533
415 Discharges
Microbial or
laboratory data
with outcome
1102 3
060
802 Isolates
Microbial or
laboratory data
without
outcome
2302 145
067
113 Isolates
Literature
studies 607 701
356
Cases,
isolates, or
pathogen–
drug
susceptibility
tests
Single drug
resistance
profiles
158 8
648
390
Pathogen–
drug
susceptibility
tests
Pharmaceutical
sales 1536 1536
Study-
country-
years
Antibiotic use
among children
younger than
5 years with
reported illness
203 151
455 Households
surveyed
7
870 Deaths
Linkage
(mortality only)
38 264
010 Deaths
Grand total 9324 471
300
319
Mortality
surveillance
(minimally
invasive tissue
sampling from
Child Health
and Mortality
Prevention
Surveillance)
For the data input citations see
http://ghdx.healthdata.org/
record/ihme-data/global-
bacterial-antimicrobial-
resistance-burden-
estimates-2019
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633
was infectious or—for non-communicable, maternal,
neonatal, nutritional, and injury deaths—for which the
pathway to death was through sepsis. Sepsis is defined as
a life-threatening organ dysfunction due to a dysregulated
host response to infection.23 The methods used to
estimate infectious underlying causes of death and sepsis
deaths have been published previously14,24 and are
summarised in the appendix (pp 17–18).
In estimation step one, we used data for multiple
causes of death covering 121 million deaths, 5·54 million
hospital discharges with discharge status of death, and
264 000 records of multiple causes of death linked to
hospital records from ten countries and territories, as
well as 870 deaths from Child Health and Mortality
Prevention Surveillance (CHAMPS) sites across six
countries (appendix pp 17–18), to develop random eects
logistic regression models to predict the fraction of
sepsis occurring in each communicable, maternal,
neonatal, and nutritional underlying cause of death;
non-communicable underlying cause of death; and
injury underlying cause of death. This approach follows
the methods validated by many researchers in sepsis
epidemiology25–28 and used by Rudd and colleagues.24
We then multiplied the fraction of sepsis predicted
from the logistic regression models onto GBD cause-
specific mortality estimates to determine the mortality
envelope for our analysis. Our mortality envelope
consisted of all deaths in which infection played a
role, which included all sepsis deaths with non-
infectious underlying causes, plus all deaths with an
infectious underlying cause in GBD 2019 (appendix
pp 21–23).
Component
1: sepsis and
infectious
syndrome
models*
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†
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.
Table 1: Data included in each modelling component by region and the fraction of countries represented in each region
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In estimation step two, we used details on the
pathways of disease provided in multiple causes of
death and hospital discharge data in a second stage of
random eects logistic regression models to further
subdivide deaths in which infection played a role into
12 major infectious syndromes and one residual
category. These regressions predicted the proportion of
sepsis-related deaths that were caused by a given
infectious syndrome separately for each communicable,
maternal, neonatal, and nutritional underlying cause of
death; non-communicable underlying cause of death;
and injury underlying cause of death. We used this
fraction to subdivide sepsis deaths with non-infectious
underlying causes into specific infectious syndromes.
For underlying causes of death that are themselves
infectious, all deaths were assigned to their single
corresponding infectious syndrome (eg, the GBD cause
“lower respiratory infections” was assigned to the
infectious syndrome “lower respiratory infections and
all related infections in the thorax”; appendix pp 21–23).
Due to the pathogen distributions varying substantially
for hospital-acquired and community-acquired infections
in two infectious syndromes—lower respiratory and
thorax infections and urinary tract infections—we
further estimated the subdivision of these syndromes
into community-acquired and hospital-acquired
infections (appendix pp 17–30; table with community-
acquired and hospital-acquired subdivisions presented
on pp 24–25).
Incidence of infectious syndromes disaggregated by
age, sex, and location
For the nine infectious syndromes in this study that were
estimated as one or more causes of death and disability
in GBD 2019 (lower respiratory and thorax infections;
CNS infections; typhoid, paratyphoid, and invasive non-
typhoidal Salmonella spp; urinary tract infections;
diarrhoea; tuberculosis; bacterial skin infections; cardiac
infections; and gonorrhoea and chlamydia), we used
GBD 2019 incidence estimates as a baseline for infectious
syndrome incidence (appendix p 16).14 To this baseline,
we added the number of incident cases of each infectious
syndrome that co-occurred with underlying non-
communicable diseases (NCDs); maternal, neonatal, and
nutritional diseases (MNNDs); and injuries, which we
calculated by dividing the number of infectious syndrome
deaths that occurred with underlying NCDs, MNNDs,
and injuries (by age, sex, location, and GBD cause) by
syndrome-specific and pathogen-specific case-fatality
ratios (CFRs; estimation described in the following
subsection). Bloodstream infections, bone and joint
infections, and intra-abdominal infections are not
estimated in GBD, so for these infectious syndromes, we
exclusively used the number of incident cases of each
infectious syndrome that co-occurred with underlying
NCDs, MNNDs, and injuries to estimate incidence
(appendix pp 56–60).
Estimation steps three and four: pathogen distribution
for deaths and incident cases
To estimate the pathogen distribution of each infectious
syndrome separately for deaths and incident cases for
each age, sex, and location, we made use of multiple data
sources. For estimation step three, we took data that
linked pathogen-specific disease incidence to deaths to
develop models for pathogen-specific CFRs that varied
by age, location, and syndrome. We used the Bayesian
meta-regression tool MR-BRT29 to estimate CFRs as a
function of the Healthcare Access and Quality Index and
various bias covariates (appendix pp 31–34).21 These
CFRs allowed us to integrate sources that reported
pathogen distribution only for deaths and those that
reported only incidence by mapping the reported deaths
by pathogen into implied cases by pathogen. After
mapping, we had 157 million isolates and cases from
118 countries and territories to estimate the pathogen
distribution of each infectious syndrome (estimation
step four), with each dataset including a unique
spectrum of pathogens and groups of pathogens. To
incorporate all these heterogeneous data, we used a new
modelling environment, termed multinomial estimation
with partial and composite observations. This modelling
environment allows for the inclusion of covariates in the
network analysis29 and for Bayesian prior probability
distributions to be incorporated. To model the infectious
syndrome pathogen distribution comprehensively, we
estimated, where applicable, the incidence and death
proportions attributable to viral, fungal, parasitic, and
bacterial pathogens; however, AMR burden was
calculated only for selected bacteria for which resistance
is clinically relevant and sucient data are available.
More details on this approach are provided in the
appendix (pp 34–44).
Estimation steps five to seven: prevalence of resistance
by pathogen
We used data from 52·8 million isolates to analyse the
proportion of phenotypic AMR for each pathogen—the
proportion of infections that were drug resistant, hereafter
referred to as prevalence of resistance—for 88 pathogen–
drug combinations. We chose these 88 combinations by
first creating an exhaustive list of all clinically relevant
combinations for which we had any data and then
eliminating combinations that did not meet minimum
data availability and computational feasibility requirements
for accurate statistical modelling (appendix pp 59–60).
For the pathogen–drug combinations in the 2014 WHO
AMR global report on surveillance,30 as well as
fluoroquinolone and multidrug resistance in Salmonella
enterica serotypes Typhi and Paratyphi, we supplemented
microbial datasets from collaborators and surveillance
networks with aggregate microbiology data from sys-
tematic reviews and published surveillance reports. The
number of positive isolates identified for each pathogen–
drug combination is shown in the appendix (pp 90–91).
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635
Clinical and Laboratory Standard Institute (CLSI)
guidelines were used to define minimum inhibitory
concentration breakpoints when these minimums were
provided. When only a phenotypic disk interpretation was
available, we used the interpretation as provided. We
used two categories of susceptibility: susceptible and
non-susceptible. The non-susceptible group includes
isolates reported as “non-susceptible”, “intermediate”, and
“resistant”. To account for bias in resistance data provided
by tertiary care facilities, we adjusted tertiary rates of
resistance by crosswalking them to data from non-tertiary
and mixed facilities using MR-BRT as described in the
appendix (pp 45–48).31
We used a two-stage spatiotemporal modelling
framework to estimate the prevalence of resistance in
each pathogen–drug combination by location for 2018.
Given the many challenges to data collection and
reporting caused by the COVID-19 pandemic,32,33 as well
as our collaborators’ process of data collation and
cleaning, we were unable to collect more contemporary
data; we assumed no change in prevalence of resistance
for 2019. First, we fitted a stacked ensemble model
between the input data and selected covariates from the
list of plausible and health-related covariates available in
GBD 2019 (appendix pp 48–49, 92–93); the estimates
from the stacked ensemble model were then inputted
into a spatiotemporal Gaussian process regression
model31 to smooth the estimates in space and time. The
exceptions to this modelling approach were multidrug-
resistant (MDR) excluding extensively drug-resistant
(XDR) tuberculosis and XDR tuberculosis, for which
published GBD 2019 estimates were already available.14
Given the strong relationship between antibiotic
consumption levels and the proliferation of resistance,
we modelled antibiotic consumption at the national
level to use as a covariate in the stacked ensemble
model of prevalence of resistance. We analysed data
from 65 Demographic and Health Surveys and
138 Multiple Indicator Cluster Surveys using model-
based geostatistics to quantify antibiotic usage in
LMICs. These LMIC-specific estimates of antibiotic
usage were combined with pharmaceutical sales data
from IQVIA, WHO, and the European Centre for
Disease Prevention and Control (ECDC) by use of an
ensemble spatiotemporal Gaussian process regression
model to produce a location-year covariate on antibiotic
consumption for all 204 countries and territories
included in this study.22 Additional details on our
estimation method for prevalence of resistance are
available in the appendix (pp 44–53).
To account for multidrug resistance, we used line-
level microbiology data that tested multiple antibiotics
for the same isolate to produce Pearson correlation
coecients of the co-occurrence of resistance to
dierent antibiotics. With these Pearson correlations
and our prevalence of resistance estimates, we used an
optimisation-based approach to solve for multivariate
binomial distributions that define the prevalence of
resistance of every combination of resistance to the
antibiotics analysed. Every such distribution was
characterised by a contingency table specifying
probabilities of all combinations of resistance and
susceptibility among the antibiotics analysed. The
observed prevalence of each drug overall and Pearson
correlations between drugs provided noisy partial
observations of combinations of these entries. We
optimised over the space of such contingency tables to
find the nearest feasible distribution given the data,
producing, for each pathogen, a set of resistance
profiles: the proportions of bacteria with each com-
bination of resistance and susceptibility among all the
antibiotics analysed (appendix pp 48–49).
Estimation steps eight and nine: relative risk of death
for drug-resistant infection compared with drug-
sensitive infections
Using data from 164 sources representing 511 870 patients
with known outcome and resistance information, we
estimated the relative risk of death for each pathogen–
drug combination for a resistant infection compared
with that of a drug-sensitive infection using MR-BRT.
Because of data sparsity, we assumed the relative risk
was the same for every syndrome, location, and age
group; the assumptions on location and age group risk
are consistent with those in the estimation process
previously used by Cassini and colleagues.10 We used a
two-stage nested mixed eects meta-regression model to
estimate relative risk of death for each pathogen–drug
combination that was adjusted for age, admission
diagnosis, hospital-acquired versus community-acquired
infection, and site of infection (appendix pp 54–56). For
the non-fatal excess risk, we estimated the relative
increase in length of stay associated with a resistant
infection compared with that of a drug-sensitive
infection, adjusted for length of stay prior to culture
being drawn. Data on length of stay were available from
59 sources representing 455 906 admissions. We used
the same modelling framework for excess length of stay
as we used for relative risk of death. Due to data sparsity
on the excess risk of death associated with drug-resistant
N gonorrhoeae, we did not produce a fatal estimate for
this pathogen.
To produce burden estimates of multiple pathogen–
drug combinations that were mutually exclusive within
a given pathogen (and thus could be added), we
produced a population-attributable fraction (PAF) for
each resistance profile with resistance to at least one
drug (appendix pp 56–60). The PAF represents the
proportional reduction in deaths or years lived with
disability (YLDs) that would occur if all infections
with the resistance profile of interest were instead
susceptible to all antibiotics included in the analysis.
When two or more antibiotics were resistant in a single
profile, we used the relative risk for the antibiotic class
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that was the largest as the relative risk for calculating
the PAF:
Where R is prevalence of resistance, RR is relative risk, K
is a pathogen with d=1, …, n resistance profiles with
resistance to at least one antibiotic class, and D is the
antibiotic class in profile d with the highest relative risk
(appendix pp 56–60).
Estimation step ten: computing burden attributable to
drug resistance and burden associated with drug-
resistant infections
We computed two counterfactuals to estimate the drug-
resistant burden: the burden attributable to bacterial
AMR based on the counterfactual of drug-sensitive
infection and the burden associated with bacterial AMR
based on the counterfactual of no infection (appendix
pp 56–60). Briefly, to estimate the burden attributable to
AMR, we first calculated the deaths attributable to
resistance by taking the product of deaths for each
underlying cause, the proportion of these deaths in
which infection played a role, the proportion of infectious
deaths attributable to each infectious syndrome, the
proportion of infectious syndrome deaths attributable to
each pathogen, and the mortality PAF for each resistance
profile. We used previously described GBD methods14 to
convert age-specific deaths into years of life lost (YLLs)
using the standard counterfactual life expectancy at each
age.34 To calculate attributable YLDs, we took the product
of the infectious syndrome incidence, the proportion of
infectious syndrome incident cases attributable to each
pathogen, YLDs per incident case, and the non-fatal PAF.
For resistance profiles that had resistance to more than
one antibiotic class, we redistributed burden to the
Associated with resistance Attributable to resistance
Deaths YLLs DALY s YLDs Deaths YLLs DALY s YLDs
Counts, thousands
Global 4950
(3620–6570)
189 000
(145 000–245 000)
192 000
(146 000–248 000)
2290
(1520–3450)
1270
(911–1710)
47 600
(35 000–63 400)
47 900
(35 300–63 700)
275
(161–439)
Central Europe, eastern
Europe, and central Asia
283
(190–403)
7530
(5240–10 500)
7630
(5320–10 600)
102
(69–140)
73·7
(48·7–105)
1980
(1350–2790)
1990
(1360–2800)
9·95
(4·79–16·8)
High income 604
(434–824)
10 100
(6960–14 200)
10 300
(7040–14 400)
123
(79·7–183)
141
(98·6–197)
2390
(1620–3400)
2410
(1640–3420)
20·2
(12·7–31·2)
Latin America and Caribbean 338
(243–453)
9550
(6770–12 900)
9640
(6830–13 100)
97·2
(63·2–146)
84·3
(60·3–117)
2370
(1660–3310)
2380
(1680–3330)
16
(9·79–24·9)
North Africa and Middle East 256
(174–362)
9970
(6880–13 900)
10 100
(6970–14 000)
116
(73·4–176)
68·3
(45·6–99)
2590
(1770–3700)
2610
(1790–3720)
20·7
(12–33·5)
South Asia 1390
(1030–1830)
58 900
(44 800–76 300)
59 900
(45 700–77 500)
1000
(638–1550)
389
(273–538)
16 000
(11 500–21 600)
16 100
(11 600–21 700)
111
(58·5–188)
Southeast Asia, east Asia,
and Oceania
1020
(678–1460)
27 500
(18 700–38 600)
27 900
(19 100–39 100)
437
(256–776)
254
(167–369)
6830
(4620–9840)
6870
(4670–9890)
45·6
(25–80·1)
Sub-Saharan Africa 1070
(847–1340)
65 800
(51 400–83 600)
66 200
(51 800–84 000)
416
(270–599)
255
(196–331)
15 400
(11 700–19 900)
15 500
(11 800–20 000)
51·1
(30·2–81·8)
Rates, per 100 000
Global 64·0
(46·8–84·9)
2448·1
(1868·9–3170·3)
2477·7
(1889·9–3199·1)
29·6
(19·7–44·5)
16·4
(11·8–22·0)
615·1
(452·4–819·1)
618·7
(455·7–823·2)
3·6
(2·1–5·7)
Central Europe, eastern
Europe, and central Asia
67·7
(45·4–96·6)
1802·5
(1253·9–2515·1)
1826·9
(1274·5–2545·4)
24·4
(16·5–33·6)
17·6
(11·7–25·3)
474·3
(323·0–667·3)
476·7
(325·2–671·0)
2·4
(1·1–4·0)
High income 55·7
(40·1–76·0)
935·3
(641·9–1310·1)
946·7
(649·8–1327·2)
11·3
(7·3–16·9)
13·0
(9·1–18·2)
220·4
(149·9–314·0)
222·3
(151·5–315·9)
1·9
(1·2–2·9)
Latin America and Caribbean 57·9
(41·6–77·6)
1633·8
(1158·7–2215·9)
1650·5
(1169·0–2236·6)
16·6
(10·8–25·0)
14·4
(10·3–20·0)
405·3
(284·8–566·6)
408·1
(286·9–570·0)
2·7
(1·7–4·3)
North Africa and Middle East 42·0
(28·7–59·5)
1637·5
(1130·4–2283·2)
1656·6
(1145·2–2300·9)
19·1
(12·1–28·9)
11·2
(7·5–16·3)
425·6
(291·2–608·4)
429·0
(293·7–611·5)
3·4
(2·0–5·5)
South Asia 76·8
(57·2–101·2)
3262·6
(2482·4–4228·2)
3318·1
(2532·9–4291·7)
55·4
(35·4–86·0)
21·5
(15·1–29·8)
885·8
(636·3–1194·6)
892·0
(643·1–1200·2)
6·2
(3·2–10·4)
Southeast Asia, east Asia, and
Oceania
47·1
(31·4–67·7)
1272·6
(866·8–1789·0)
1292·8
(884·7–1811·4)
20·2
(11·8–35·9)
11·7
(7·8–17·1)
316·1
(213·9–455·7)
318·2
(216·1–458·0)
2·1
(1·2–3·7)
Sub-Saharan Africa 98·9
(78·6–124·2)
6105·3
(4770·2–7749·1)
6143·9
(4802·8–7792·2)
38·6
(25·1–55·6)
23·7
(18·2–30·7)
1432·0
(1084·6–1848·1)
1436·7
(1090·0–1853·5)
4·7
(2·8–7·6)
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.
Table 2: Deaths, 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
PAF=RKd(RRKD–1)
1+Σn
RKd (RRKD–1)
d=1
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637
individual antibiotic classes proportionally on the basis
of excess risk, providing a mutually exclusive burden for
each pathogen–drug combination (appendix pp 56–60).
To calculate DALYs, we took the sum of YLLs and YLDs.
To estimate the overall AMR burden of the drug-sensitive
counterfactual, we added the burden estimates of all the
pathogen–drug combinations.
The approach for calculating the fatal burden
associated with AMR was identical to that for fatal
burden attributable to AMR, except we replaced the
mortality PAF for each resistance profile with the
prevalence of resistance in deaths. For the number of
incident infections associated with resistance, we took
the product of infectious syndrome incidence, the
proportion of infectious incident cases attributable to
each pathogen, and the prevalence of resistance in
incident cases. On the basis of these death and incidence
estimates, we then computed YLLs, YLDs, and DALYs
associated with drug-resistant infections. We calculated
YLLs using the same methods used to calculate YLLs
attributable to AMR. We converted incidence into YLDs
using a YLDs per incident case ratio for each infectious
syndrome based on a proxy GBD cause (a simplified
YLD calculation compared with the standard sequelae-
based method; appendix pp 56–60). Finally, we calculated
DALYs by summing YLLs and YLDs. To estimate the
overall AMR burden of this counterfactual, we repeated
the described calculations with the prevalence of
resistance to one or more antibiotics estimated and
summed across all pathogens.
Uncertainty analysis and out-of-sample validation
Following previously described GBD methods,14 we
propagated uncertainty from each step of the analysis into
the final estimates of deaths and infections attributable to
and associated with drug resistance by taking the 25th and
975th of 1000 draws from the posterior distribution of each
quantity of interest. Out-of-sample validity estimates are
provided in the appendix for our models of sepsis
(pp 25–30), infectious syndrome distribution (pp 25–30),
pathogen distribution (pp 43–44), prevalence of resistance
(pp 51–53), and relative risk (pp 55–56).
Role of the funding source
The funders of the study had no role in study design,
data collection, data analysis, data interpretation, or the
writing of the report.
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.
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).
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).
Western sub-Saharan Africa
Eastern sub-Saharan Africa
Central sub-Saharan Africa
Southern sub-Saharan Africa
South Asia
Eastern Europe
Southern Latin America
Oceania
High-income Asia Pacific
Central Europe
Caribbean
Andean Latin America
Tropical Latin America
Southeast Asia
Central Asia
Western Europe
High-income North America
Central Latin America
East Asia
North Africa and Middle East
Australasia
GBD region
Deaths (rate per 100000 population)
150
100
50
0
Central Europe, eastern Europe, and central Asia
High income
Latin America and Caribbean
North Africa and Middle East
South Asia
Southeast Asia, east Asia, and Oceania
Sub-Saharan Africa
Associated with resistance
Attributable to resistance
GBD super-regio
nR
esistance
Figure 2: All-age rate of deaths attributable to and associated with bacterial antimicrobial resistance by GBD
region, 2019
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.
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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 diered 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-genera-
tion 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.
In the appendix, we present the equivalent AMR
findings for DALYs instead of deaths (pp 124–29), as well
2000 000
1500 000
1000 000
Deaths (count)
500000
0
Associated with resistance
Attributable to resistance
Resistance
LRI+ BSI Intra-
abdominal
UTI Tuberculosis Skin
Infectious syndrome
CNS TF–PF–iNTS Diarrhoea Cardiac Bone+
Figure 3: Global deaths (counts) attributable to and associated with bacterial antimicrobial resistance by infectious syndrome, 2019
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.
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639
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 dier from the
input data, figure 7 also shows the raw, unadjusted
prevalence of resistance for each of the seven leading
pathogen–drug combinations.
Discussion
The global burden associated with drug-resistant
infections assessed across 88 pathogen–drug combina-
tions 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 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 By any
metric, bacterial AMR is a leading global health issue.12
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 WHO34 and
AMR has been highlighted in the political arena through
the Global Action Plan on AMR,8 the UN Interagency
0
300000
600000
900000
Escherichia coli
Staphylococcus aureus
Klebsiella pneumoniae
Streptococcus pneumoniae
Acinetobacter baumannii
Pseudomonas aeruginosa
Mycobacterium tuberculosis
Enterococcus faecium
Enterobacter spp
Group B Streptococcus
Salmonella enterica serotype Typhi
Enterococcus faecalis
Proteus spp
Other enterococci
Serratia spp
Group A Streptococcus
Citrobacter spp
Haemophilus influenzae
Shigella spp
Non-typhoidal Salmonella
Salmonella enterica serotype Paratyphi
Morganella spp
Pathogen
Deaths (count)
Resistance
Associated with resistance
Attributable to resistance
Figure 4: Global deaths (counts) attributable to and associated with bacterial antimicrobial resistance by pathogen, 2019
Estimates were aggregated across drugs, accounting for the co-occurrence of resistance to multiple drugs. Error bars show 95% uncertainty intervals.
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Coordination Group,35 the One Health Global Leaders
Group,36 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 vacci nation.37 Furthermore, the
first Sustainable Development Goal38 indicator for
antimicrobial resistance was only proposed in 2019, and
this indicator (3.d.2) is very limited in scope.39,40 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 infections41—accounted for more than
70% of deaths attributable to AMR across pathogens.
Central Europe,
eastern Europe, and
central Asia
High income Latin America
and Caribbean
North Africa
and Middle East
South Asia Southeast Asia,
east Asia, and Oceania
Sub-Saharan Africa
0
0·10
0·20
B
Pathogen-attributable fraction of AMR deaths associated with resistance
0
0·10
0·20
0·30
A
Pathogen-attributable fraction of AMR deaths attributable to resistance
Pathogen
Acinetobacter baumannii
Escherichia coli
Klebsiella pneumoniae
Pseudomonas aeruginosa
Staphylococcus aureus
Streptococcus pneumoniae
Figure 5: Pathogen-attributable fraction of deaths attributable to (A) and associated with (B) bacterial AMR for the six leading pathogens by GBD super-
region, 2019
Error bars show 95% uncertainty intervals. AMR=antimicrobial resistance. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study.
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641
In 2017, WHO published a priority list for developing
new and eective 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
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 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.42,43 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 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 Vaccination
programmes are an important strategy for preventing
S pneumoniae,46 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
Acinetobacter baumannii
Citrobacter spp
Enterobacter spp
Enterococcus faecalis
Enterococcus faecium
Other enterococci
Escherichia coli
Group A Streptococcus
Group B Streptococcus
Haemophilus influenzae
Klebsiella pneumoniae
Morganella spp
Mycobacterium tuberculosis
Proteus spp
Pseudomonas aeruginosa
S Paratyphi
S Typhi
Non-typhoidal Salmonella
Serratia spp
Shigella spp
Staphylococcus aureus
Streptococcus pneumonia
All pathogens
Resistance to 1+
3GC
4GC
Aminoglycosides
Aminopenicillin
Anti−pseudomonal
BL−BLI
Carbapenems
Fluoroquinolones
Macrolide
MDR excluding XDR in tuberculosis
MDR in S Typhi and S Paratyphi
Meticillin
Mono INH
Mono RIF
Penicillin
TMP-SMX
Vancomycin
XDR in tuberculosis
132 000
10 600
46 100
30 200
51 500
14 500
219 000
3630
25 800
6760
193 000
749
84 800
11 500
84 600
4110
23 700
5620
10 700
5990
178 000
122 000
1 270 000
6860
1840
59 900
2470
50 100
168
4730
10 400
1100
3330
141 000
3280
1340
5320
154
4370
2610
17 100
10 400
411
3070
11 700
26 300
887
3010
953
56 800
13 300
2170
9950
10 300
2480
38 200
811
21 300
7930
2040
32 100
57 700
2300
15 300
29 500
55 700
38 100
2450
41 900
243 000
4650
30 200
23 500
1620
18 700
38 700
117 000
3420
14 300
2220
3120
23 100
5210
5210
40 000
2510
7800
26 800
37 200
12 200
56 000
11 500
29 000
427
2970
18 300
4040
17 200
5620
1080
5990
15 900
11 200
306 000
799
12 400
13 200
3630
13 500
19 600
12 500
49 300
64 600
64 600
64
6460
6530
121 000
121 000
11 600
11 600
3350
3350
10 500
4290
1,330
16 100
≥100 75 to <100 50 to <75 25 to <50 10 to <25 5 to <10 <5 NA
Count (thousands)
Figure 6: Global deaths (counts) attributable to bacterial antimicrobial resistance by pathogen–drug combination, 2019
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.
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Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
AMeticillin-resistant Staphylococcus aureus
<5%
5 to <10%
10 to <20%
20 to <30%
30 to <40%
40 to <50%
50 to <60%
60 to <70%
70 to <80%
≥80%
Percentage of isolates with resistance
Raw data
Modelled estimates
(Figure 7 continues on next page)
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643
Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
BIsoniazid and rifampicin co-resistant (excluding XDR) Mycobacterium tuberculosis
<5%
5 to <10%
10 to <20%
20 to <30%
30 to <40%
40 to <50%
50 to <60%
60 to <70%
70 to <80%
≥80%
Percentage of isolates with resistance
Raw data
Modelled estimates
(Figure 7 continues on next page)
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Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
CThird-generation cephalosporin-resistant Escherichia coli
<5%
5 to <10%
10 to <20%
20 to <30%
30 to <40%
40 to <50%
50 to <60%
60 to <70%
70 to <80%
≥80%
Percentage of isolates with resistance
Raw data
Modelled estimates
(Figure 7 continues on next page)
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645
Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
DCarbapenem-resistant Acinetobacter baumannii
<5%
5 to <10%
10 to <20%
20 to <30%
30 to <40%
40 to <50%
50 to <60%
60 to <70%
70 to <80%
≥80%
Percentage of isolates with resistance
Raw data
Modelled estimates
(Figure 7 continues on next page)
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Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
EFluoroquinolone-resistant Escherichia coli
<5%
5 to <10%
10 to <20%
20 to <30%
30 to <40%
40 to <50%
50 to <60%
60 to <70%
70 to <80%
≥80%
Percentage of isolates with resistance
Raw data
Modelled estimates
(Figure 7 continues on next page)
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647
Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
FCarbapenem-resistant Klebsiella pneumoniae
<5%
5 to <10%
10 to <20%
20 to <30%
30 to <40%
40 to <50%
50 to <60%
60 to <70%
70 to <80%
≥80%
Percentage of isolates with resistance
Raw data
Modelled estimates
(Figure 7 continues on next page)
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Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
GThird-generation cephalosporin-resistant Klebsiella pneumoniae
<5%
5 to <10%
10 to <20%
20 to <30%
30 to <40%
40 to <50%
50 to <60%
60 to <70%
70 to <80%
≥80%
Percentage of isolates with resistance
Raw data
Modelled estimates
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649
reduction in antibiotic prescribing and can reduce AMR
emergence even for pathogens without vaccines.45
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,2,47–49 although the direct causal link remains
controversial.50,51
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 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.53,54
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 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 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,56 the inappropriate use of antibiotics driven
by insucient regulations and ease of acquisition,57
inadequate access to second-line and third-line
antibiotics, counterfeit or substandard antibiotics that
can drive resistance,52,58,59 and poor sanitation and
hygiene.60–62
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 dierent pathogens and
pathogen–drug combinations dominating in dierent
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 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 At the global level,
the dierence is nearly four-times that attributable to
AMR. We estimated both measures of burden because
there is insucient 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,63,64 but this finding
might not generalise to all other pathogens and other
mechanisms of resistance.
Both measures are informative in dierent 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 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 which did not provide death estimates by
Figure 7: Raw data and modelled estimates for the percentage of pathogen
isolates that are resistant by country and territory, 2019
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.
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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 colleagues10 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 approx-
imately 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 oers 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.6,10 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 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 insucient 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 eective 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.
For the pathogen–drug
combinations estimates see
http://ghdx.healthdata.org/
record/ihme-data/global-
bacterial-antimicrobial-
resistance-burden-
estimates-2019
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651
Our analysis echoes that of another paper in
highlighting critical AMR data gaps in several regions.65
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
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 insucient
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 sucient detail to
allow for retrospective reanalysis of the data.67
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 oer eective
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 fluoro-
quinolones. Eorts 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 eects of
AMR, such as the eect of AMR on perioperative
prophylaxis or prophylaxis of infections in transplant
recipients, the eects 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.
Antimicrobial Resistance Collaborators
Christopher J L Murray, Kevin Shunji Ikuta, Fablina Sharara,
Lucien Swetschinski, Gisela Robles Aguilar, Authia Gray, Chieh Han,
Catherine Bisignano, Puja Rao, Eve Wool, Sarah C Johnson,
Annie J Browne, Michael Give Chipeta, Frederick Fell, Sean Hackett,
Georgina Haines-Woodhouse, Bahar H Kashef Hamadani,
Emmanuelle A P Kumaran, Barney McManigal, Ramesh Agarwal,
Samuel Akech, Samuel Albertson, John Amuasi, Jason Andrews,
Aleskandr Aravkin, Elizabeth Ashley, Freddie Bailey, Stephen Baker,
Buddha Basnyat, Adrie Bekker, Rose Bender, Adhisivam Bethou,
Julia Bielicki, Suppawat Boonkasidecha, James Bukosia,
Cristina Carvalheiro, Carlos Castañeda-Orjuela, Vilada Chansamouth,
Suman Chaurasia, Sara Chiurchiù, Fazle Chowdhury, Aislinn J Cook,
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Ben Cooper, Tim R Cressey, Elia Criollo-Mora, Matthew Cunningham,
Saatou Darboe, Nicholas P J Day, Maia De Luca, Klara Dokova,
Angela Dramowski, Susanna J Dunachie, Tim Eckmanns,
Daniel Eibach, Amir Emami, Nicholas Feasey, Natasha Fisher-Pearson,
Karen Forrest, Denise Garrett, Petra Gastmeier, Ababi Zergaw Giref,
Rachel Claire Greer, Vikas Gupta, Sebastian Haller, Andrea Haselbeck,
Simon I Hay, Marianne Holm, Susan Hopkins, Kenneth C Iregbu,
Jan Jacobs, Daniel Jarovsky, Fatemeh Javanmardi, Meera Khorana,
Niranjan Kissoon, Elsa Kobeissi, Tomislav Kostyanev, Fiorella Krapp,
Ralf Krumkamp, Ajay Kumar, Hmwe H Kyu, Cherry Lim,
Direk Limmathurotsakul, Michael James Loftus, Miles Lunn,
Jianing Ma, Neema Mturi, Tatiana Munera-Huertas, Patrick Musicha,
Marisa Marcia Mussi-Pinhata, Tomoka Nakamura, Ruchi Nanavati,
Sushma Nangia, Paul Newton, Chanpheaktra Ngoun, Amanda Novotney,
Davis Nwakanma, Christina W Obiero, Antonio Olivas-Martinez,
Piero Olliaro, Ednah Ooko, Edgar Ortiz-Brizuela, Anton Yariv Peleg,
Carlo Perrone, Nishad Plakkal, Alfredo Ponce-de-Leon, Mathieu Raad,
Tanusha Ramdin, Amy Riddell, Tamalee Roberts, Julie Victoria
Robotham, Anna Roca, Kristina E Rudd, Neal Russell, Jesse Schnall,
John Anthony Gerard Scott, Madhusudhan Shivamallappa,
Jose Sifuentes-Osornio, Nicolas Steenkeste, Andrew James Stewardson,
Temenuga Stoeva, Nidanuch Tasak, Areerat Thaiprakong, Guy Thwaites,
Claudia Turner, Paul Turner, H Rogier van Doorn, Sithembiso Velaphi,
Avina Vongpradith, Huong Vu, Timothy Walsh, Seymour Waner,
Tri Wangrangsimakul, Teresa Wozniak, Peng Zheng, Benn Sartorius,
Alan D Lopez, Andy Stergachis, Catrin Moore*, Christiane Dolecek*,
Mohsen Naghavi.
*Contributed equally.
Affiliations
Institute for Health Metrics and Evaluation (Prof C J L Murray DPhil,
K S Ikuta MD, F Sharara MS, L Swetschinski MSc, A Gray BS,
C Han BA, C Bisignano MPH, P Rao MPH, E Wool MPH,
S C Johnson MSc, S Albertson BS, A Aravkin PhD, R Bender BS,
M Cunningham MSc, Prof S I Hay FMedSci, H H Kyu PhD, J Ma MS,
A Novotney MPH, A Vongpradith BA, P Zheng PhD, A Stergachis PhD),
Department of Health Metrics Sciences, School of Medicine
(Prof C J L Murray, A Aravkin, Prof S I Hay, B Sartorius PhD,
Prof M Naghavi PhD), Department of Applied Mathematics (A Aravkin),
Department of Global Health (A Stergachis, Prof M Naghavi),
Department of Pharmacy, School of Pharmacy (A Stergachis), University
of Washington, Seattle, WA, USA; Department of Infectious Diseases
(K S Ikuta), Veterans Aairs Greater Los Angeles Healthcare System,
Los Angeles, CA, USA; Department of Infectious Diseases (K S Ikuta),
University of California, Los Angeles, Los Angeles, CA, USA; Nueld
Department of Medicine, Big Data Institute (F Bailey MBChB,
A Browne MPH, M Chipeta PhD, F Fell MSc, N Fisher-Pearson BA,
S Hackett PhD, G Haines-Woodhouse MRes, E Kobeissi MPH,
E Kumaran MSc, M Lunn BSc, B McManigal PhD, C E Moore DPhil,
P Olliaro PhD, G Robles Aguilar DPhil), Nueld Department of
Medicine, Centre for Tropical Medicine and Global Health
(E Ashley FRCPath, V Chansamouth MSc, B Cooper PhD,
Prof C Dolecek FRCP, S Dunachie PhD, B Kashef Hamadani MPH,
C Lim MSc, Prof D Limmathurotsakul PhD, P Newton FRCP,
C Perrone MD, G Thwaites FMedSci, P Turner FRCPath,
B Sartorius PhD, N Day DM, R Greer MRCGP, H van Doorn PhD,
T Wangrangsimakul FRCPath), Ineos Oxford Institute of Antimicrobial
Research (Prof T Walsh DSc), University of Oxford, Oxford, UK;
Department of Pediatrics (R Agarwal DM), All India Institute of Medical
Sciences, New Delhi, India; Health Services Research Unit
(S Akech PhD), Nairobi Programme (J Bukosia MSc), Kenya Medical
Research Institute (KEMRI)—Wellcome Trust Research Programme,
Nairobi, Kenya; Department of Global Health (J Amuasi PhD), Kwame
Nkrumah University of Science and Technology, Kumasi, Ghana; Global
Health and Infectious Diseases (J Amuasi), Kumasi Centre for
Collaborative Research in Tropical Medicine, Kumasi, Ghana;
Department of Medicine (J Andrews MD), Stanford University, Stanford,
CA, USA; Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit
(E Ashley, V Chansamouth, P Newton, T Roberts PhD), Mahosot
Hospital, Vientiane, Laos; Department of Medicine (S Baker PhD),
University of Cambridge, Cambridge, UK; Oxford University Clinical
Research Unit-Nepal (B Basnyat FRCPE), Oxford University,
Kathmandu, Nepal; Department of Paediatrics and Child Health
(A Bekker PhD, A Dramowski PhD), Stellenbosch University,
Cape Town, South Africa; Department of Neonatology (A Bethou PhD,
N Plakkal MD), Jawaharlal Institute of Postgraduate Medical Education
& Research, Puducherry, India; Paediatric Infectious Disease
Department (J Bielicki PhD), University of Basel Children’s Hospital,
Basel, Switzerland; Paediatric Infectious Diseases Research Group
(J Bielicki, A J Cook MSc, A Riddell PhD, N Russell MBBS), Institute for
Infection and Immunity (T Munera-Huertas PhD), St George’s
University of London, London, UK; Department of Pediatrics
(S Boonkasidecha MD, M Khorana MD), Queen Sirikit National
Institute of Child Health, Bangkok, Thailand; Department of Pediatrics
(C Carvalheiro PhD), University of Sao Paulo, Ribeirao Preto, Brazil;
Colombian National Health Observatory (C Castañeda-Orjuela MD),
Instituto Nacional de Salud, Bogota, Colombia; Epidemiology and Public
Health Evaluation Group (C Castañeda-Orjuela), Universidad Nacional
de Colombia, Bogota, Colombia; Department of Neonatology
(S Chaurasia PhD), All India Institute of Medical Sciences, Rishikesh,
India; Immunology and Infectious Disease Unit Academic Department
of Pediatrics (S Chiurchiù MD), Academic Hospital Pediatric
Department (M De Luca MD), Bambino Gesù Children’s Hospital,
Rome, Italy; Internal Medicine (F Chowdhury PhD), Bangabandhu
Sheikh Mujib Medical University, Dhaka, Bangladesh; Mahidol-Oxford
Tropical Medicine Research Unit (F Chowdhury, Prof C Dolecek),
Faculty of Tropical Medicine (N P J Day, C Perrone), Mahidol University,
Bangkok, Thailand; Nueld Department of Medicine (B Cooper PhD),
University of Oxford, Oxford, UK; PHPT-AMS Research Unit
(T R Cressey PhD), Chiang Mai University, Chiang Mai, Thailand;
Department of Molecular & Clinical Pharmacology (T R Cressey),
University of Liverpool, Liverpool, UK; Department of Pharmacy
(E Criollo-Mora BSc), Department of Medicine (A Olivas-Martinez MD,
E Ortiz-Brizuela MSc), Department of Infectious Diseases
(A Ponce-de-Leon MD), Instituto Nacional de Ciencias Medicas y
Nutricion Salvador Zubiran, Mexico City, Mexico; Disease Control and
Elimination Department (S Darboe MSc, A Roca PhD), Clinical Services
Department (K Forrest FRCP), Laboratory Services Department
(D Nwakanma PhD), Medical Research Council Unit The Gambia at the
London School of Hygiene & Tropical Medicine, Banjul, The Gambia;
Department of Social Medicine and Health Care Organization
(K Dokova PhD), Department of Microbiology and Virology
(T Stoeva PhD), Medical University of Varna, Varna, Bulgaria; Infectious
Disease Epidemiology (T Eckmanns PhD, S Haller MPH), Robert Koch
Institute, Berlin, Germany; Infectious Disease Epidemiology
(D Eibach MD, R Krumkamp DrPH), Bernhard Nocht Institute for
Tropical Medicine, Hamburg, Germany; Microbiology Department
(A Emami PhD), Shiraz University of Medical Sciences, Shiraz, Iran;
Clinical Sciences (N Feasey PhD), Liverpool School of Tropical Medicine,
Liverpool, UK; Malawi Liverpool Wellcome Trust Clinical Research
Programme, Blantyre, Malawi (N Feasey); Applied Epidemiology
Programs (D Garrett MD), Sabin Vaccine Institute, Washington, DC,
USA; Institute of Hygiene (Prof P Gastmeier MD), Charité University
Medicine Berlin, Berlin, Germany; Department of Health Policy and
Management (A Z Giref PhD), Addis Ababa University, Addis Ababa,
Ethiopia; National Data Management Center (A Z Giref), Ethiopian
Public Health Institute, Addis Ababa, Ethiopia; Chiangrai Clinical
Research Unit (R C Greer, T Wangrangsimakul), Department of
Microbiology (S Dunachie, C Lim, Prof D Limmathurotsakul,
N Tasak BNS, A Thaiprakong BS), Mahidol-Oxford Tropical Medicine
Research Unit, Bangkok, Thailand; MMS Medical Aairs
(V Gupta PharmD), Becton, Dickinson and Company, Franklin Lakes,
NJ, USA; Epidemiology & Public Health Research Department
(A Haselbeck Dr rer medic, M Holm PhD), International Vaccine
Institute, Seoul, South Korea; National Infection Service
(S Hopkins FRCP), Antimicrobrial Resistance Division
(J V Robotham PhD), Public Health England, London, UK; Department
of Medical Microbiology (K C Iregbu MD), National Hospital, Abuja,
Nigeria; Department of Medical Microbiology (K C Iregbu), University of
Abuja, Abuja, Nigeria; Department of Clinical Sciences
(Prof J Jacobs PhD), Institute of Tropical Medicine, Antwerp, Belgium;
Department of Microbiology, Immunology, and Transplantation
(Prof J Jacobs), KU Leuven, Leuven, Belgium; Pediatric Infectious
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653
Disease Department (D Jarovsky MD), Santa Casa de São Paulo,
São Paulo, Brazil; Microbiology Department (F Javanmardi PhDc),
Shiraz University of Medical sciences, Shiraz, Iran; Department of
Pediatrics (N Kissoon MBBS), University of British Columbia,
Vancouver, BC, Canada; Laboratory of Medical Microbiology
(T Kostyanev MD), University of Antwerp, Antwerp, Belgium; Instituto
de Medicina Tropical Alexander von Humboldt (F Krapp MSc),
Universidad Peruana Cayetano Heredia, Lima, Peru; Department of
Neonatology (A Kumar MD, S Nangia MD), Lady Hardinge Medical
College & Kalawati Saran’s Children’s Hospital, New Delhi, India;
Department of Infectious Diseases (M J Loftus MBBS, A Y Peleg PhD,
A J Stewardson PhD), Monash University, Melbourne, VIC, Australia;
Clinical Research Department (N Mturi MRCPCH, C W Obiero MPH),
KEMRI—Wellcome Trust Research Programme, Kilifi, Kenya
(E Ooko PhD); Parasites and Microbes Programme (P Musicha PhD),
Wellcome Sanger Institute, Cambridge, UK; Deparment of Pediatrics
(M M Mussi-Pinhata MD), University of São Paulo, Ribeirão Preto,
Brazil; Department of Immunization, Vaccines, and Biologicals
(T Nakamura MSPH), World Health Organization, Geneva, Switzerland;
Department of Infectious Disease Epidemiology (T Nakamura), London
School of Hygiene and Tropical Medicine, London, UK; Department of
Neonatology (R Nanavati MD), Seth GSMC & KEM Hospital, Mumbai,
India; Medical Department (C Ngoun MD), Executive Oce
(C Turner FRCPCH), Cambodia Oxford Medical Research Unit
(P Turner), Angkor Hospital for Children, Siem Reap, Cambodia;
Department of Global Health (C W Obiero), University of Amsterdam,
Amsterdam, Netherlands; Infectious Disease Department (A Y Peleg,
A J Stewardson), The Alfred Hospital, Melbourne, VIC, Australia;
Department of Medicine (A Ponce-de-Leon), Universidad Panamericana,
Mexico City, Mexico; International Operations Department
(M Raad MD), International Operations Direction (N Steenkeste PhD),
Fondation Mérieux, Lyon, France; Department of Paediatric and Child
Health (T Ramdin MBBCh), University of Witwatersrand, Parktown,
South Africa; Department of Critical Care Medicine (K E Rudd MD),
University of Pittsburgh, Pittsburgh, PA, USA; Doctors in Training
(J Schnall MBBS), Austin Health, Heidelberg, VIC, Australia;
Department of Infectious Disease Epidemiology
(Prof J A G Scott FMedSci), London School of Hygiene & Tropical
Medicine, London, UK; Department of Epidemiology & Demography
(Prof J A G Scott), KEMRI—Wellcome Trust Research Programme,
Kilifi, Kenya; Department of Neonatology (M Shivamallappa DM),
King Edward Memorial Hospital Mumbai, Mumbai, India; Department
of Medicine (J Sifuentes-Osornio MD), Instituto Nactional de Ciencias
Medicas, Mexico City, Mexico; Microbiology Laboratory (T Stoeva), Varna
University Hospital, Varna, Bulgaria; Oxford University Clinical
Research Unit Viet Nam (G Thwaites, H Vu PhD), University of Oxford,
Ho Chi Minh City, Vietnam; Cambodia Oxford Medical Research Unit,
Siem Reap, Cambodia (C Turner); Oxford University Clinical Research
Unit, Hanoi, Vietnam (H R van Doorn); School of Clinical Medicine,
Faculty of Health Sciences (S Velaphi PhD), University of the
Witwatersrand, Johannesburg, South Africa; Department of Paediatrics
(S Velaphi), Chris Hani Baragwanath Academic Hospital, Johannesburg,
South Africa; Department of Microbiology (S Waner MMed), Lancet
Laboratories, Johannesburg, South Africa; Department of Global
Tropical Health (T Wozniak PhD), Menzies School of Health Research,
Brisbane, QLD, Australia.
Contributors
Detailed information about individual author contributions to the
research are available in the appendix (pp 65–66). Members of the core
research team for this topic area had full access to the underlying data
used to generate estimates presented in this paper. All other authors had
access to, and reviewed, estimates as part of the research evaluation
process, which includes additional stages of formal review.
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). 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. 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. All other authors declare no competing
interests.
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.
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
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654
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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 sta 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.
Editorial note: the Lancet Group takes a neutral position with respect to
territorial claims in published maps and institutional aliations.
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