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

Background: The number of infections caused by resistant organisms is largely unknown. We estimated the number of infections worldwide that are caused by the WHO priority pathogens third-generation cephalosporin-resistant and carbapenem-resistant Escherichia coli and Klebsiella pneumoniae. Methods: We calculated a uniform weighted mean incidence of serious infections caused by antibiotic-susceptible E coli and K pneumoniae using data from 17 countries. Using this uniform incidence, as well as population sizes and country-specific resistance levels, we estimated the number of infections caused by third-generation cephalosporin-resistant and carbapenem-resistant E coli and K pneumoniae in 193 countries in 2014. We also calculated interval estimates derived from changing the fixed incidence of susceptible infections to 1 SD below and above the weighted mean. We compared an additive model with combination models in which resistant infections were replaced by susceptible infections. We distinguished between higher-certainty regions (those with good-quality data sources for resistance levels and resistance ≤30%), moderate-certainty regions (those with good-quality data sources for resistance levels and including some countries with resistance >30%), and low-certainty regions (those in which good-quality data sources for resistance levels were unavailable for countries comprising at least 20% of the region's population, regardless of resistance level). Findings: Using the additive model, we estimated that third-generation cephalosporin-resistant E coli and K pneumoniae caused 6·4 million (interval estimate 3·5–9·2) bloodstream infections and 50·1 million (27·5–72·8) serious infections in 2014; estimates were 5·5 million (3·0–7·9) bloodstream infections and 43·1 million (23·6–62·2) serious infections in the 25% replacement model, 4·6 million (2·5–6·6) bloodstream infections and 36·0 million (19·7–52·2) serious infections in the 50% replacement model, and 3·7 million (2·0–5·3) bloodstream infections and 28·9 million (15·8–41·9) serious infections in the 75% replacement model. Carbapenem-resistant strains caused 0·5 million (0·3–0·7) bloodstream infections and 3·1 million (1·8–4·5) serious infections based on the additive model, 0·5 million (0·3–0·7) bloodstream infections and 3·0 million (1·7–4·3) serious infections based on the 25% replacement model, 0·4 million (0·2–0·6) bloodstream infections and 2·8 million (1·6–4·1) serious infections based on the 50% replacement model, and 0·4 million (0·2–0·6) bloodstream infections and 2·7 million (1·5–3·8) serious infections based on the 75% replacement model. Interpretation: To our knowledge, this study is the first to report estimates of the global number of infections caused by antibiotic-resistant priority pathogens. Uncertainty stems from scant data on resistance levels from low-income and middle-income countries and insufficient knowledge regarding resistance dynamics when resistance is high. Funding: Innovative Medicines Initiative

The impact of antibiotic use on transmission of resistant bacteria in hospitals: Insights from an agent-based model

Extensive antibiotic use over the years has led to the emergence and spread of antibiotic resistant bacteria (ARB). Antibiotic resistance poses a major threat to public health since for many infections antibiotic treatment is no longer effective. Hospitals are focal points for ARB spread. Antibiotic use in hospitals exerts selective pressure, accelerating the spread of ARB. We used an agent-based model to explore the impact of antibiotics on the transmission dynamics and to examine the potential of stewardship interventions in limiting ARB spread in a hospital. Agents in the model consist of patients and health care workers (HCW). The transmission of ARB occurs through contacts between patients and HCW and between adjacent patients. In the model, antibiotic use affects the risk of transmission by increasing the vulnerability of susceptible patients and the contagiousness of colonized patients who are treated with antibiotics. The model shows that increasing the proportion of patients receiving antibiotics increases the rate of acquisition non-linearly. The effect of antibiotics on the spread of resistance depends on characteristics of the antibiotic agent and the density of antibiotic use. Antibiotic's impact on the spread increases when the bacterial strain is more transmissible, and decreases as resistance prevalence rises. The individual risk for acquiring ARB increases in parallel with antibiotic density both for patients treated and not treated with antibiotics. Antibiotic treatment in the hospital setting plays an important role in determining the spread of resistance. Interventions to limit antibiotic use have the potential to reduce the spread of resistance, mainly by choosing an agent with a favorable profile in terms of its impact on patient's vulnerability and contagiousness. Methods to measure these impacts of antibiotics should be developed, standardized, and incorporated into drug development programs and approval packages

Baseline transmission probabilities, by ARB strain.

<p>Baseline transmission probabilities, by ARB strain.</p

Individual risk for ARB acquisition.

<p>Probability of ARB-H and ARB-L acquisition during a 6-day hospital stay for patients treated and not treated with antibiotics (left axis) and the increase in incidence relative to the scenario without antibiotic use (right axis); (A) 1% of admissions are colonized with ARB-H (B) 10% of admissions are colonized with ARB-H (C) 1% of admissions are colonized with ARB-L (D) 10% of admissions are colonized with ARB-L. Antibiotic agent impact: V = 2, C = 2.</p

Hospital parameters used in the simulations.

<p>Hospital parameters used in the simulations.</p

Impact of antibiotic characteristics on acquisition of ARB.

<p>Increase in incidence of acquisition (left axis) of (A) strain ARB-L and (B) strain ARB-H relative to no antibiotic impact, and ARB acquisitions per 100,000 patient days (PD) (right axis) for various values of an antibiotic's impact on vulnerability (V) of susceptible patients and on contagiousness (C) of colonized patients. In the simulated scenario 40% of admissions receive antibiotics and 1% of patients are colonized on admission. Note that when V = 1 and C = 1, antibiotics have no effect on acquisition, or, alternatively, no patients are receiving antibiotics.</p

The effect of various antibiotic stewardship interventions on the daily number of ARB-H acquisitions.

<p>Prevalence of colonization among admitted patients is 1% and 10%. The note above each bar describes a scenario; % refers to the proportion of admitted patients treated with antibiotics; type is the antibiotic agent, where the characteristics of antibiotic type A are C = 4, V = 4 and of antibiotic type B are C = 2 and V = 2; days refer to duration of treatment. Percentage inside the bar indicates reduction in acquisition relative to the reference scenario.</p

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

Summary: Background: The number of infections caused by resistant organisms is largely unknown. We estimated the number of infections worldwide that are caused by the WHO priority pathogens third-generation cephalosporin-resistant and carbapenem-resistant Escherichia coli and Klebsiella pneumoniae. Methods: We calculated a uniform weighted mean incidence of serious infections caused by antibiotic-susceptible E coli and K pneumoniae using data from 17 countries. Using this uniform incidence, as well as population sizes and country-specific resistance levels, we estimated the number of infections caused by third-generation cephalosporin-resistant and carbapenem-resistant E coli and K pneumoniae in 193 countries in 2014. We also calculated interval estimates derived from changing the fixed incidence of susceptible infections to 1 SD below and above the weighted mean. We compared an additive model with combination models in which resistant infections were replaced by susceptible infections. We distinguished between higher-certainty regions (those with good-quality data sources for resistance levels and resistance ≤30%), moderate-certainty regions (those with good-quality data sources for resistance levels and including some countries with resistance >30%), and low-certainty regions (those in which good-quality data sources for resistance levels were unavailable for countries comprising at least 20% of the region's population, regardless of resistance level). Findings: Using the additive model, we estimated that third-generation cephalosporin-resistant E coli and K pneumoniae caused 6·4 million (interval estimate 3·5–9·2) bloodstream infections and 50·1 million (27·5–72·8) serious infections in 2014; estimates were 5·5 million (3·0–7·9) bloodstream infections and 43·1 million (23·6–62·2) serious infections in the 25% replacement model, 4·6 million (2·5–6·6) bloodstream infections and 36·0 million (19·7–52·2) serious infections in the 50% replacement model, and 3·7 million (2·0–5·3) bloodstream infections and 28·9 million (15·8–41·9) serious infections in the 75% replacement model. Carbapenem-resistant strains caused 0·5 million (0·3–0·7) bloodstream infections and 3·1 million (1·8–4·5) serious infections based on the additive model, 0·5 million (0·3–0·7) bloodstream infections and 3·0 million (1·7–4·3) serious infections based on the 25% replacement model, 0·4 million (0·2–0·6) bloodstream infections and 2·8 million (1·6–4·1) serious infections based on the 50% replacement model, and 0·4 million (0·2–0·6) bloodstream infections and 2·7 million (1·5–3·8) serious infections based on the 75% replacement model. Interpretation: To our knowledge, this study is the first to report estimates of the global number of infections caused by antibiotic-resistant priority pathogens. Uncertainty stems from scant data on resistance levels from low-income and middle-income countries and insufficient knowledge regarding resistance dynamics when resistance is high. Funding: Innovative Medicines Initiative