Global mortality associated with 33 bacterial pathogens in 2019: a systematic analysis for the Global Burden of Disease Study 2019

Research in context

Evidence before this study

Communicable diseases have long been recognised as a cause of substantial health loss globally, but few studies to date have concentrated on pathogen-specific mortality caused by common bacterial pathogens. Many estimates exist for pathogens like Mycobacterium tuberculosis, Plasmodium spp, and HIV but estimates of the burden of bacterial infections have been restricted to either a small number of locations, specific populations (such as invasive pneumococcal disease in children younger than 5 years), or a small number of bacteria in the context of the scope of infectious syndromes (eg, Streptococcus pneumoniae and Neisseria meningitidis as a cause of meningitis). The US Centers for Disease Control and Prevention (CDC) Active Bacterial Core surveillance and Emerging Infections Program, and the European CDC's European Antimicrobial Resistance Surveillance Network have provided crucial estimates of selected invasive bacterial infections in high-income countries. These estimates are important first steps in building our understanding of the burden of specific bacterial infections but they provide an incomplete picture: within the locations with the greatest infectious burden, the mortality associated with these pathogens remains unknown, making it difficult to set global public health priorities.

Added value of this study

To our knowledge, this is the first study to produce global estimates of mortality associated with 33 clinically significant bacterial pathogens (including those susceptible to antibacterial compounds) across 11 infectious syndromes, and to provide these estimates for all ages and for males and females across 204 countries and territories in 2019. This analysis is intended to provide an audit of the mortality associated with common bacterial pathogens. We estimated the number of deaths associated with each of these bacterial pathogens using three modelling steps: deaths where infection had a role, the fraction of deaths due to infection attributable to a given infectious syndrome, and the fraction of deaths due to infectious syndromes attributable to a given pathogen. Deaths in which infection had a role were estimated using the number of deaths for which either the underlying cause of death was infectious or the pathway of death was through sepsis. The fraction of deaths due to infection attributable to a given infectious syndrome was estimated using data to determine the infectious syndrome responsible for sepsis by underlying cause of death, age, sex, and geographical location. The fraction of deaths due to an infectious syndrome attributable to a given pathogen was estimated by integrating estimates of pathogen-specific and syndrome-specific case-fatality ratios with modelled pathogen distributions for each infectious syndrome that varied by age and geographical location.

Implications of all the available evidence

Our findings show that more than half of all global bacterial deaths in 2019 were due to five bacterial pathogens: Staphylococcus aureus, Escherichia coli, Streptococcus pneumoniae, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The substantial burden of health loss associated with these five pathogens requires increased attention from the global health community and collaborative intervention approaches. Understanding the leading infectious syndromes and pathogens for each region is of the utmost importance so that targeted prevention efforts can be implemented. This study can be used to guide strategies for reducing the burden of bacterial infectious diseases, including infection prevention and control measures, vaccine development and implementation, and the availability of basic acute care services.

Introduction

Communicable diseases have long been highlighted as a global public health priority and are recognised as a leading cause of health loss globally.

UN
Sustainable Development Goals.

UN
Millennium Development Goals.

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

A recent study estimated that there were more than 10 million sepsis-related deaths in 2017, indicating that infections were involved in more than 20% of deaths globally for that year.

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Rudd KE
Johnson SC
Agesa KM
et al.

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

Reducing the number of deaths due to infections is a foundational principle in moving towards health equity

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Valentine N
Solar O
Irwin A
Nolen L
Prasad A

because there is a disproportionate infectious burden in low-income and middle-income countries (LMICs).

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Rudd KE
Johnson SC
Agesa KM
et al.

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

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GBD 2016 Causes of Death Collaborators
Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016.

Preventing and effectively treating infections is also essential to achieving Sustainable Development Goal (SDG) 3: ensure healthy lives and promote wellbeing for all at all ages.

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UN
Department of Economic and Social Affairs. The 17 goals.

Although the contribution of non-bacterial causes (eg, fungal infections, malaria, and HIV) to the overall infection burden must be acknowledged, reducing the number of cases and health impact of bacterial infectious diseases is a priority area that necessitates a multipronged approach with infection prevention and control measures;

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Luby SP
Agboatwalla M
Feikin DR
et al.

Effect of handwashing on child health: a randomised controlled trial.

vaccine development, deployment, and uptake;

⁹

Poolman JT

Expanding the role of bacterial vaccines into life-course vaccination strategies and prevention of antimicrobial-resistant infections.

¹⁰

Lynch 3rd, JP
Zhanel GG

Streptococcus pneumoniae: epidemiology, risk factors, and strategies for prevention.

and early and effective case management.

¹¹

Evans L
Rhodes A
Alhazzani W
et al.

Executive summary: surviving sepsis campaign: international guidelines for the management of sepsis and septic shock 2021.

¹²

Seymour CW
Gesten F
Prescott HC
et al.

Time to treatment and mortality during mandated emergency care for sepsis.

Detailed estimates of the number of deaths related to bacterial infections and their causes are an important step in tracking progress towards global health goals and are essential to inform priorities for vaccine and drug development.

To date, no global burden estimates exist for many common bacterial pathogens, making establishment of public health priorities difficult. The few estimates that do exist are often constrained to specific pathogens,

¹³

O'Brien KL
Wolfson LJ
Watt JP
et al.

Burden of disease caused by Streptococcus pneumoniae in children younger than 5 years: global estimates.

infectious syndromes,

¹⁴

Thigpen MC
Whitney CG
Messonnier NE
et al.

Bacterial meningitis in the United States, 1998–2007.

or high-income countries.

¹⁵

US Centers for Disease Control and Prevention
GBS Surveillance Report 2019. Centers for Disease Control and Prevention, 2019.

For example, global estimates of the burden of Streptococcus pneumoniae are available; however, these estimates are mostly restricted to children younger than 5 years

¹³

O'Brien KL
Wolfson LJ
Watt JP
et al.

Burden of disease caused by Streptococcus pneumoniae in children younger than 5 years: global estimates.

or as a cause of pneumonia or meningitis

and do not reflect the total burden across all populations and all infectious syndromes.

¹⁶

Wahl B
O'Brien KL
Greenbaum A
et al.

Burden of Streptococcus pneumoniae and Haemophilus influenzae type b disease in children in the era of conjugate vaccines: global, regional, and national estimates for 2000–15.

Estimates of selected invasive bacterial infections exist in high-income countries that use passive surveillance systems, such as the US Centers for Disease Control (CDC) Active Bacterial Core surveillance and Emerging Infections Program

¹⁷

Centers for Disease Control and Prevention
Active Bacterial Core surveillance (ABCs). July 19, 2021.

and the European CDC's European Antimicrobial Resistance Surveillance Network.

¹⁸

European Centre for Disease Prevention and Control
European Antimicrobial Resistance Surveillance Network (EARS-Net).

Although such estimates offer important insights, no comprehensive estimates exist covering all locations for a broad range of bacteria across major infectious syndromes. Notably absent are country-level estimates for LMICs, which have the greatest burden of infectious diseases,

⁴

Rudd KE
Johnson SC
Agesa KM
et al.

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

as also emphasised by the recent Global Burden of Antimicrobial Resistance 2019 study.

¹⁹

Antimicrobial Resistance Collaborators
Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis.

For this reason, there has been profound neglect of these pathogens, and relevant infectious syndromes, in global advocacy campaigns aiming to maximise life-saving interventions.

In this study, we present, to our knowledge, the first global estimates of deaths associated with 33 clinically significant bacterial pathogens (both susceptible and resistant to antimicrobials), across 11 infectious syndromes in 2019. We used data obtained from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019

and the Global Burden of Antimicrobial Resistance 2019 study

¹⁹

Antimicrobial Resistance Collaborators
Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis.

to estimate global, regional, and national mortality and years of life lost (YLLs) associated with these 33 bacterial pathogens across 204 countries and territories and 286 underlying causes of death, by age and sex, in 2019. This manuscript was produced as part of the GBD Collaborator Network and in accordance with the GBD Protocol.

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Institute for Health Metrics and Evaluation
Protocol for the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), version 4.0. March, 2020.

Methods

Overview

In this study, we estimated the fatal burden associated with infection caused by 33 bacterial species or genera across 11 infectious syndromes using methods and data from the GBD 2019 and Global Burden of Antimicrobial Resistance studies.

¹⁹

Antimicrobial Resistance Collaborators
Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis.

Detailed methods have been published elsewhere.

¹⁹

Antimicrobial Resistance Collaborators
Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis.

Briefly, using 343 million individual records or isolates covering 11 361 study-location-years, we implemented three modelling steps to estimate the number of deaths associated with each bacterial pathogen across 204 countries and territories for 2019. First, we estimated the overall number of deaths in which infection had a role using methods described in the Global Burden of Antimicrobial Resistance study.

¹⁹

Antimicrobial Resistance Collaborators
Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis.

Second, we determined the infectious syndrome responsible for each death due to an infection. Finally, for each infectious syndrome we estimated the distribution of pathogens responsible. With the use of these components, we estimated the number of deaths associated with each of the 33 bacterial pathogens of interest in this study. A summarising flowchart and detailed approach description for each step of the estimation process are in appendix 1 (section 10). All estimates were produced by age, for males and females, and for 204 countries and territories.

We followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines

²¹

Page MJ
McKenzie JE
Bossuyt PM
et al.

The PRISMA 2020 statement: an updated guideline for reporting systematic reviews.

throughout the study (detailed in appendix 1 [section 7]). This study complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) recommendations.

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Stevens GA
Alkema L
Black RE
et al.

Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement.

The complete GATHER checklist is provided in appendix 1 (section 8).

Input data

We used a subset of the input data described in the Global Burden of Antimicrobial Resistance study to estimate mortality burden by pathogen.

¹⁹

Antimicrobial Resistance Collaborators
Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis.

We selected data inputs only if they were based on a representative sampling framework that would not bias the aetiology estimation towards a specific pathogen (eg, we did not deliberately sample 100 cases of every pathogen). The input data source types that met these criteria were: multiple-cause-of-death and vital registration data; hospital discharge data; linkage data sources; mortality surveillance in the Child Health and Mortality Prevention Surveillance (CHAMPS) study; literature reviews of the microbial cause of meningitis, neonatal sepsis, lower respiratory infections, urinary tract infections, skin infections, peritonitis, and bone and joint infections; and laboratory-based passive surveillance data. We used multiple-cause-of-death and vital registration data, hospital discharge data, CHAMPS, and linkage data sources to estimate the number of deaths for which infection had a role and the distribution of infectious syndromes (appendix 1 [section 4]). We used data from CHAMPS, literature reviews, and laboratory-based passive surveillance systems to estimate the pathogen distribution for each infectious syndrome (appendix 1 [section 6]). The number of individual records or isolates used in each step for each of the GBD regions is shown in appendix 1 (p 62).

Deaths in which infection played a role

Detailed methods on how the number of deaths in which infection played a role were estimated have been published previously.

⁴

Rudd KE
Johnson SC
Agesa KM
et al.

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

Briefly, we estimated the number of deaths for which either the underlying cause of death was infectious (using GBD 2019 estimates) or for which a contributing factor in the death was sepsis and the underlying cause was non-infectious. For the identification of sepsis in non-infectious underlying causes of death, we used the following data inputs: 121 million cause-of-death records with multiple-cause-of-death data from eight countries and territories; 192 million hospital records with patient discharge status from seven countries and territories; 264 000 multiple-cause-of-death records linked to hospital records from ten countries and territories; and 849 deaths from CHAMPS sites across six countries. We developed a random-effects logistic regression model to predict the fraction of deaths involving sepsis for each underlying cause of death, age, sex, and geographical location using methods described previously.

⁴

Rudd KE
Johnson SC
Agesa KM
et al.

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

¹⁹

Antimicrobial Resistance Collaborators
Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis.

Using this cause-fraction, we estimated the number of deaths for which the underlying cause was non-infectious and sepsis occurred. We then added this to the number of deaths in which the underlying cause was infectious from GBD 2019 to estimate the number of deaths in which infection had a role.

Infectious syndrome estimates

Detailed methods on the estimation process for infectious syndromes have been published previously

¹⁹

Antimicrobial Resistance Collaborators
Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis.

and are in appendix 1 (section 4). Briefly, we used the available data described in the Input data section (multiple cause of death, hospital data with patient discharge status, linkage data, and CHAMPS) to determine the infectious syndrome responsible for sepsis by underlying cause, age, sex, and geographical location. Within our modelling framework, an infectious syndrome is the infection directly responsible for sepsis and serves as the bridge between the underlying cause of death and sepsis. We estimated 11 infectious syndromes: meningitis and other bacterial CNS infections; cardiac infections; peritoneal and intra-abdominal infections; lower respiratory infections and all related infections in the thorax; bacterial infections of the skin and subcutaneous systems; infections of bones, joints, and related organs; typhoid, paratyphoid, and invasive non-typhoidal Salmonella; diarrhoea; urinary tract infections and pyelonephritis; bloodstream infections; and gonorrhoea and chlamydia. We then used syndrome-and-age-specific mixed effects logistic regression models (using sex, Healthcare Access and Quality Index, and syndrome-specific bias covariates and a nested random effect on underlying cause) to estimate the fraction of sepsis-related deaths that were caused by each infectious syndrome for each underlying cause of death, age, sex, and geographical location. Applying this fraction to the estimate of number of infection-related deaths from the previous step, we determined the number of deaths that occurred due to a given infectious syndrome by underlying cause of death, age, sex, and geographical location. We estimated deaths with an infectious syndrome as the sum of deaths with the syndrome as an underlying cause of death (ie, for those syndromes considered to be underlying causes) plus deaths with a non-infectious underlying cause where the syndrome was estimated to occur (eg, all deaths where the underlying cause was meningitis plus all road traffic injury deaths in which meningitis occurred). Bloodstream infections; infections of bones, joints, and related organs; and peritoneal and intra-abdominal infections are not estimated in GBD, so for these three infectious syndromes, we assumed they had a non-infectious underlying cause to estimate deaths.

Pathogen distribution

Detailed methods on the estimation process for pathogen distribution have been published previously

¹⁹

Antimicrobial Resistance Collaborators
Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis.

and are in appendix 1 (sections 5 and 6), including exceptions and special handling decisions. Briefly, we used data from 343 million isolates covering 11 361 study-location-years to estimate pathogen distributions for each infectious syndrome that varied by age and location, with a subset of this data adapted to calculate pathogen-specific and syndrome-specific case-fatality ratios (CFRs). We selected a set of pathogens to be explicitly estimated as part of the cause of each infectious syndrome. This selection was based on several factors. First, selection was influenced by the prevalence of each pathogen in the raw data, because the amount of available data restricts the number of pathogens that can be estimated successfully. Second, we aimed to produce estimates for the combination of pathogens that, collectively, represented at least 85% of the aetiological causes of a given infectious syndrome. We included three residual categories: (1) other bacteria and (2) polymicrobial—for bacteria that did not meet these criteria or had two or more bacteria isolated from a single isolate—and (3) non-bacterial pathogen, for pathogens that were not bacteria (ie, viruses, fungi, or parasites).

Much of the input data on pathogen distribution were only reported on a subset of pathogens, such that they did not have a complete denominator for all possible pathogens. For example, many surveillance systems for meningitis only monitor S pneumoniae and Neisseria meningitidis as the causative pathogen. To account for this partial distribution, we used a network meta-analysis, which allowed us to include any dataset reporting on two or more pathogens. We implemented this network meta-analysis using the multinomial estimation with partial and composite observations (MEPCO) modelling environment. This approach allowed us to include covariates in the network analysis, incorporate Bayesian priors (ie, prior probability distributions), and use data that compared one pathogen with all other pathogens. Input data for the MEPCO process consisted of ratios of sums of cases within a study (with each sum representing a specific pathogen or combination of pathogens). The model was fit by minimising the sum of the residuals between log-transformed observed ratios and predictions using a non-linear likelihood minimisation problem optimised using the Gauss-Newton method

²³

Nocedal J
Wright SJ

(appendix 1 [section 6.3.1]). The resultant MEPCO estimate was the non-fatal pathogen distribution for each infectious syndrome.

To estimate the fatal pathogen distribution, we calculated syndrome-specific and pathogen-specific CFRs using data that linked pathogen-specific disease incidence to deaths and the meta-regression–Bayesian regularised, trimmed (MR-BRT) tool. We estimated CFRs as a function of age, Healthcare Access and Quality Index, and various bias covariates that were specific to the nuances of the data for each infectious syndrome (appendix 1 [section 5]). We then used the pathogen-specific and syndrome-specific CFRs to produce a pathogen distribution of number of deaths estimated for each infectious syndrome by age and location. Our modelling framework accounted for both data-rich and data-sparse pathogens (appendix 1 [section 5.3]). In this analysis we do not report estimates for Mycobacterium tuberculosis because this specific pathogen is already part of a global strategy with well delineated surveillance and data-driven control plans, and the motivation for the current study was to provide insight into the public health burden of less well studied pathogens.

Estimating mortality and YLLs

To estimate the number of deaths due to the pathogens of interest, we multiplied the number of deaths for each underlying cause, age, sex, and location by the fraction of deaths in which infection had a role, the fatal infectious syndrome fraction, and the pathogen fraction, and summed across all underlying causes of death and infectious syndromes to estimate the number of deaths due to a given pathogen by age, sex, and location. We estimated YLLs associated with each pathogen using previously published methods

that convert age-specific deaths into YLLs using the standard counterfactual life expectancy at each age.

Uncertainty and validity analysis

Following standard GBD methods,

¹⁶

Wahl B
O'Brien KL
Greenbaum A
et al.

Burden of Streptococcus pneumoniae and Haemophilus influenzae type b disease in children in the era of conjugate vaccines: global, regional, and national estimates for 2000–15.

we propagated uncertainty from each step of the analysis into the final estimates of deaths associated with each pathogen by taking the 2·5th and 97·5th percentiles of 1000 draws from the posterior distribution of each quantity of interest. To assess model validity, we calculated the root mean square error and coefficient of determination (R²) for each pathogen distribution model in proportion space for both in-sample and out-of-sample predictions (appendix 1 [section 6.5]).

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

In 2019, there were an estimated 13·7 million (95% UI 10·9–17·1) infection-related deaths globally, with 7·7 million (5·7–10·2) deaths associated with the 33 bacterial pathogens we investigated. These bacteria altogether were associated with 13·6% (10·2–18·1) of all global deaths in 2019 and 56·2% (52·1–60·1) of all infection-related deaths for that year. The all-age mortality rate was 99·6 deaths (74·2–132) per 100 000 population collectively for these pathogens. Only one organism, Staphylococcus aureus, was associated with more than 1 million deaths in 2019 (1 105 000 deaths [816 000–1 470 000]; table). Four additional pathogens were associated with more than 500 000 deaths each in 2019; these were Escherichia coli, S pneumoniae, Klebsiella pneumoniae, and Pseudomonas aeruginosa (table, figure 1A). These five leading pathogens were associated with 30·9% (28·6–33·1) of all infection-related deaths and were responsible for 54·9% (52·9–56·9) of all deaths among the investigated bacterial pathogens. Of the bacteria estimated, Morganella spp, Providencia spp, and Neisseria gonorrhoeae had the fewest associated deaths (table). There were 304 million (234–392) YLLs associated with the 33 bacterial pathogens globally in 2019, representing 18·1% (14·1–22·8) of the global YLLs for the year. The leading five organisms by YLL burden were similar to the mortality estimates but the order changed: S pneumoniae was associated with the greatest YLL burden with 40·3 million (32·8–50·0) YLLs, followed by S aureus with 34·3 million (25·5–45·3), K pneumoniae with 31·4 million (23·2–41·5), E coli with 30·4 million (22·7–40·2), and P aeruginosa with 18·9 million (13·6–25·7; figure 1B; appendix 1 [section 10]).

TableGlobal number of deaths and age-standardised mortality rate per 100 000 population by bacterial pathogen and infectious syndrome, 2019

95% uncertainty intervals are shown in parentheses. Death counts are shown to three significant figures and age-standardised mortality rates are shown to one decimal place. iNTS=invasive non-typhoidal Salmonella. Salmonella Typhi=Salmonella enterica serotype Typhi. Salmonella Paratyphi=Salmonella enterica serotype Paratyphi.

Figure thumbnail gr1 — Figure 1Global number of deaths (A) and YLLs (B), by pathogen and infectious syndrome, 2019
Show full caption
Columns show total number of deaths for each pathogen, with error bars showing 95% uncertainty intervals, with the bars split into infectious syndromes. LRI=lower respiratory infection. iNTS=invasive non-typhoidal *Salmonella. Salmonella* Typhi=*Salmonella enterica* serotype Typhi. *Salmonella* Paratyphi=*Salmonella enterica* serotype Paratyphi. UTI=urinary tract infection. YLLs=years of life lost.

The age-standardised mortality rate associated with these 33 bacterial pathogens varied by super-region in 2019 but was highest in sub-Saharan Africa, at 230 deaths (95% UI 185–285) per 100 000 population, and lowest in the high-income super-region, at 52·2 deaths (37·4–71·5) per 100 000 population. Central African Republic was the country with the highest age-standardised mortality rate associated with these 33 bacterial pathogens, with 394 deaths (297–518) per 100 000 population, while Iceland had the lowest rate, with 35·7 deaths (25·1–49·3) per 100 000 population in 2019 (figure 2; appendix 1 [section 10]). The pathogens linked to the most deaths varied across locations. S aureus was the leading bacterial cause of death in 135 countries, followed by E coli (leading cause in 37 countries), S pneumoniae (leading cause in 24 countries), and K Pneumoniae and Acinetobacter baumannii (leading causes in four countries each; figure 3A; appendix 2). S aureus, E coli, K pneumoniae, and S pneumoniae were among the five leading pathogens associated with the greatest death count and the greatest YLL burden in every super-region (figure 4). S aureus was also the pathogen with the high...

Neglected Tropical Diseases