Quantifying where human acquisition of antibiotic resistance occurs: a mathematical modelling study

Insights into the Microbiome and Antibiotic Resistance Genes from Hospital Environmental Surfaces: A Prime Source of Antimicrobial Resistance

Hospital environmental surfaces are potential reservoirs for transmitting hospital-associated pathogens. This study aimed to profile microbiomes and antibiotic resistance genes (ARGs) from hospital environmental surfaces using 16S rRNA amplicon and metagenomic sequencing at a tertiary teaching hospital in Malaysia. Samples were collected from patient sinks and healthcare staff counters at surgery and orthopaedic wards. The samples' DNA were subjected to 16S rRNA amplicon and shotgun sequencing to identify bacterial taxonomic profiles, antibiotic resistance genes, and virulence factor pathways. The bacterial richness was more diverse in the samples collected from patient sinks than those collected from staff counters. Proteobacteria and Verrucomicrobia dominated at the phylum level, while Bacillus, Staphylococcus, Pseudomonas, and Acinetobacter dominated at the genus level. Staphylococcus epidermidis and Staphylococcus aureus were prevalent on sinks while Bacillus cereus dominated the counter samples. The highest counts of ARGs to beta-lactam were detected, followed by ARGs against fosfomycin and cephalosporin. We report the detection of mcr-10.1 that confers resistance to colistin at a hospital setting in Malaysia. The virulence gene pathways that aid in antibiotic resistance gene transfer between bacteria were identified. Environmental surfaces serve as potential reservoirs for nosocomial infections and require mitigation strategies to control the spread of antibiotic resistance bacteria.

Fate and transport modelling for evaluating antibiotic resistance in aquatic environments: Current knowledge and research priorities

Antibiotics have revolutionised medicine in the last century and enabled the prevention of bacterial infections that were previously deemed untreatable. However, in parallel, bacteria have increasingly developed resistance to antibiotics through various mechanisms. When resistant bacteria find their way into terrestrial and aquatic environments, animal and human exposures increase, e.g., via polluted soil, food, and water, and health risks multiply. Understanding the fate and transport of antibiotic resistant bacteria (ARB) and the transfer mechanisms of antibiotic resistance genes (ARGs) in aquatic environments is critical for evaluating and mitigating the risks of resistant-induced infections. The conceptual understanding of sources and pathways of antibiotics, ARB, and ARGs from society to the water environments is essential for setting the scene and developing an appropriate framework for modelling. Various factors and processes associated with hydrology, ecology, and climate change can significantly affect the fate and transport of ARB and ARGs in natural environments. This article reviews current knowledge, research gaps, and priorities for developing water quality models to assess the fate and transport of ARB and ARGs. The paper also provides inputs on future research needs, especially the need for new predictive models to guide risk assessment on AR transmission and spread in aquatic environments.

Distinctive hospital and community resistomes in Scottish urban wastewater: Metagenomics of a paired wastewater sampling design

The wastewater microbiome contains a multitude of resistant bacteria of human origin, presenting an opportunity for surveillance of resistance in the general population. However, wastewater microbial communities are also influenced by clinical sources, such as hospitals. Identifying signatures of the community and hospital resistome in wastewater is needed for interpretation and risk analysis. In this study, we compare the resistome and microbiome of hospital, community, and mixed municipal wastewater to investigate how and why the composition of these different sites differ. We conducted shotgun metagenomic analysis on wastewater samples from eight wastewater treatment plants (WWTPs), four hospitals, and four community sites in Scotland, using a paired sampling design. Cluster analysis and source attribution random forest models demonstrated that the hospital resistome was distinct from community and WWTP resistomes. Hospital wastewater had a higher abundance and diversity of resistance genes, in keeping with evidence that hospitals act as a reservoir and enricher of resistance. However, this distinctive 'hospital' signature appeared to be weak in the resistome of downstream WWTPs, likely due to dilution. We conclude that hospital and community wastewater resistomes differ, with the hospital wastewater representing a reservoir of patient- and hospital environment-associated bacteria. However, this 'hospital' signature is transient and does not overwhelm the community signature in the resistome of the downstream WWTP influent.

How can lessons from the COVID-19 pandemic enhance antimicrobial resistance surveillance and stewardship?

COVID-19 demanded urgent and immediate global attention, during which other public health crises such as antimicrobial resistance (AMR) increased silently, undermining patient safety and the life-saving ability of several antimicrobials. In 2019, WHO declared AMR a top ten global public health threat facing humanity, with misuse and overuse of antimicrobials as the main drivers in the development of antimicrobial-resistant pathogens. AMR is steadily on the rise, especially in low-income and middle-income countries across south Asia, South America, and Africa. Extraordinary circumstances often demand an extraordinary response as did the COVID-19 pandemic, underscoring the fragility of health systems across the world and forcing governments and global agencies to think creatively. The key strategies that helped to contain the increasing SARS-CoV-2 infections included a focus on centralised governance with localised implementation, evidence-based risk communication and community engagement, use of technological methods for tracking and accountability, extensive expansion of access to diagnostics, and a global adult vaccination programme. The extensive and indiscriminate use of antimicrobials to treat patients, particularly in the early phase of the pandemic, have adversely affected AMR stewardship practices. However, there were important lessons learnt during the pandemic, which can be leveraged to strengthen surveillance and stewardship, and revitalise efforts to address the AMR crisis.

Colonization dynamics of extended-spectrum beta-lactamase-producing Enterobacterales in the gut of Malawian adults

Drug-resistant bacteria of the order Enterobacterales which produce extended-spectrum beta-lactamase enzymes (ESBL-Enterobacterales, ESBL-E) are global priority pathogens. Antimicrobial stewardship interventions proposed to curb their spread include shorter courses of antimicrobials to reduce selection pressure but individual-level acquisition and selection dynamics are poorly understood. We sampled stool of 425 adults (aged 16-76 years) in Blantyre, Malawi, over 6 months and used multistate modelling and whole-genome sequencing to understand colonization dynamics of ESBL-E. Models suggest a prolonged effect of antimicrobials such that truncating an antimicrobial course at 2 days has a limited effect in reducing colonization. Genomic analysis shows largely indistinguishable diversity of healthcare-associated and community-acquired isolates, hence some apparent acquisition of ESBL-E during hospitalization may instead represent selection from a patient's microbiota by antimicrobial exposure. Our approach could help guide stewardship protocols; interventions that aim to review and truncate courses of unneeded antimicrobials may be of limited use in preventing ESBL-E colonization.

Transmission of gram-negative antibiotic-resistant bacteria following differing exposure to antibiotic-resistance reservoirs in a rural community: a modelling study for bloodstream infections

Exposure to community reservoirs of gram-negative antibiotic-resistant bacteria (GN-ARB) genes poses substantial health risks to individuals, complicating potential infections. Transmission networks and population dynamics remain unclear, particularly in resource-poor communities. We use a dynamic compartment model to assess GN-ARB transmission quantitatively, including the susceptible, colonised, infected, and removed populations at the community-hospital interface. We used two side streams to distinguish between individuals at high- and low-risk exposure to community ARB reservoirs. The model was calibrated using data from a cross-sectional cohort study (N = 357) in Chile and supplemented by existing literature. Most individuals acquired ARB from the community reservoirs (98%) rather than the hospital. High exposure to GN-ARB reservoirs was associated with 17% and 16% greater prevalence for GN-ARB carriage in the hospital and community settings, respectively. The higher exposure has led to 16% more infections and attributed mortality. Our results highlight the need for early-stage identification and testing capability of bloodstream infections caused by GN-ARB through a faster response at the community level, where most GN-ARB are likely to be acquired. Increasing treatment rates for individuals colonised or infected by GN-ARB and controlling the exposure to antibiotic consumption and GN-ARB reservoirs, is crucial to curve GN-ABR transmission.

Ethics and antibiotic resistance

INTRODUCTION OR BACKGROUND

Antibiotic resistance raises ethical issues due to the severe and inequitably distributed consequences caused by individual actions and policies.

SOURCES OF DATA

Synthesis of ethical, scientific and clinical literature.

AREAS OF AGREEMENT

Ethical analyses have focused on the moral responsibilities of patients to complete antibiotic courses, resistance as a tragedy of the commons and attempts to limit use through antibiotic stewardship.

AREAS OF CONTROVERSY

Each of these analyses has significant limitations and can result in self-defeating or overly narrow implications for policy.

GROWING POINTS

More complex analyses focus on ethical implications of ubiquitous asymptomatic carriage of resistant bacteria, non-linear outcomes within and between patients over time and global variation in resistant disease burdens.

AREAS TIMELY FOR DEVELOPING RESEARCH

Neglected topics include the harms of antibiotic use, including off-target effects on the human microbiome, and the lack of evidence guiding most antibiotic prescription decisions.

Analysis of multiple bacterial species and antibiotic classes reveals large variation in the association between seasonal antibiotic use and resistance

Understanding how antibiotic use drives resistance is crucial for guiding effective strategies to limit the spread of resistance, but the use-resistance relationship across pathogens and antibiotics remains unclear. We applied sinusoidal models to evaluate the seasonal use-resistance relationship across 3 species (Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae) and 5 antibiotic classes (penicillins, macrolides, quinolones, tetracyclines, and nitrofurans) in Boston, Massachusetts. Outpatient use of all 5 classes and resistance in inpatient and outpatient isolates in 9 of 15 species-antibiotic combinations showed statistically significant amplitudes of seasonality (false discovery rate (FDR) < 0.05). While seasonal peaks in use varied by class, resistance in all 9 species-antibiotic combinations peaked in the winter and spring. The correlations between seasonal use and resistance thus varied widely, with resistance to all antibiotic classes being most positively correlated with use of the winter peaking classes (penicillins and macrolides). These findings challenge the simple model of antibiotic use independently selecting for resistance and suggest that stewardship strategies will not be equally effective across all species and antibiotics. Rather, seasonal selection for resistance across multiple antibiotic classes may be dominated by use of the most highly prescribed antibiotic classes, penicillins and macrolides.

Microbiome-pathogen interactions drive epidemiological dynamics of antibiotic resistance: A modeling study applied to nosocomial pathogen control

The human microbiome can protect against colonization with pathogenic antibiotic-resistant bacteria (ARB), but its impacts on the spread of antibiotic resistance are poorly understood. We propose a mathematical modeling framework for ARB epidemiology formalizing within-host ARB-microbiome competition, and impacts of antibiotic consumption on microbiome function. Applied to the healthcare setting, we demonstrate a trade-off whereby antibiotics simultaneously clear bacterial pathogens and increase host susceptibility to their colonization, and compare this framework with a traditional strain-based approach. At the population level, microbiome interactions drive ARB incidence, but not resistance rates, reflecting distinct epidemiological relevance of different forces of competition. Simulating a range of public health interventions (contact precautions, antibiotic stewardship, microbiome recovery therapy) and pathogens (Clostridioides difficile, methicillin-resistant Staphylococcus aureus, multidrug-resistant Enterobacteriaceae) highlights how species-specific within-host ecological interactions drive intervention efficacy. We find limited impact of contact precautions for Enterobacteriaceae prevention, and a promising role for microbiome-targeted interventions to limit ARB spread.

Antibiotic-Dispensing Practice in Community Pharmacies: Results of a Cross-Sectional Study in Italy

Inappropriate use of antibiotics in the community contributes to the development of antibiotic resistance (ABR), one of the most concerning issues in modern medicine. The objectives of the study were to investigate the knowledge and attitudes regarding ABR and dispensing antibiotics without prescription (DAwP) and to assess the extent of the practice of DAwP among Italian community pharmacists (CPs). A nationwide cross-sectional study using an anonymous, structured, validated, and pilot-tested questionnaire was conducted. The five sections gathered data on demographic and professional characteristics, knowledge and attitudes toward ABR and DAwP, practices regarding dispensing antibiotics with or without prescription and their reasons, counselling on the potential antibiotic side effects and the importance of adherence to medication regimen, and the information sources used to update the knowledge about ABR. About 4 in 10 CPs (37.1%) reported being involved in DAwP, although 93.7% knew that it is illegal in Italy. The vast majority affirmed to have always/often asked clients about their drug allergies (95.5%) and about their medication history (82.5%). Two-thirds (66.2%) warned their clients about the potential side effects of the drugs, and 55% informed them about the importance of completing the full course of antibiotics. Complacency with clients who found it difficult to consult the physician was the most significant predictor of DAwP. A considerable proportion of DAwP was described, so it could be easy for patients to misuse these drugs. Future policies need to enhance the enforcement of existing prescription-only regulations and to develop monitoring strategies to ensure their establishment in real-life practices.

Investigating the impact of COVID-19 on primary care antibiotic prescribing in North West London across two epidemic waves

OBJECTIVES

We investigated the impact of COVID-19 and national pandemic response on primary care antibiotic prescribing in London.

METHODS

Individual prescribing records between 2015 and 2020 for 2 million residents in north west London were analysed. Prescribing records were linked to SARS-CoV-2 test results. Prescribing volumes, in total, and stratified by patient characteristics, antibiotic class and AWaRe classification, were investigated. Interrupted time series analysis was performed to detect measurable change in the trend of prescribing volume since the national lockdown in March 2020, immediately before the first COVID-19 peak in London.

RESULTS

Records covering 366 059 patients, 730 001 antibiotic items and 848 201 SARS-CoV-2 tests between January and November 2020 were analysed. Before March 2020, there was a background downward trend (decreasing by 584 items/month) in primary care antibiotic prescribing. This reduction rate accelerated to 3504 items/month from March 2020. This rate of decrease was sustained beyond the initial peak, continuing into winter and the second peak. Despite an overall reduction in prescribing volume, co-amoxiclav, a broad-spectrum "Access" antibiotic, prescribing rose by 70.1% in patients aged 50 and older from February to April. Commonly prescribed antibiotics within 14 days of a positive SARS-CoV-2 test were amoxicillin (863/2474, 34.9%) and doxycycline (678/2474, 27.4%). This aligned with national guidelines on management of community pneumonia of unclear cause. The proportion of "Watch" antibiotics used decreased during the peak in COVID-19.

DISCUSSION

A sustained reduction in community antibiotic prescribing has been observed since the first lockdown. Investigation of community-onset infectious diseases and potential unintended consequences of reduced prescribing is urgently needed.

Antimicrobial resistance and COVID-19: Intersections and implications

Before the coronavirus 2019 (COVID-19) pandemic began, antimicrobial resistance (AMR) was among the top priorities for global public health. Already a complex challenge, AMR now needs to be addressed in a changing healthcare landscape. Here, we analyse how changes due to COVID-19 in terms of antimicrobial usage, infection prevention, and health systems affect the emergence, transmission, and burden of AMR. Increased hand hygiene, decreased international travel, and decreased elective hospital procedures may reduce AMR pathogen selection and spread in the short term. However, the opposite effects may be seen if antibiotics are more widely used as standard healthcare pathways break down. Over 6 months into the COVID-19 pandemic, the dynamics of AMR remain uncertain. We call for the AMR community to keep a global perspective while designing finely tuned surveillance and research to continue to improve our preparedness and response to these intersecting public health challenges.

One Health drivers of antibacterial resistance: Quantifying the relative impacts of human, animal and environmental use and transmission

Objectives

Antibacterial resistance (ABR) is a major global health security threat, with a disproportionate burden on lower-and middle-income countries (LMICs). It is not understood how 'One Health', where human health is co-dependent on animal health and the environment, might impact the burden of ABR in LMICs. Thailand's 2017 "National Strategic Plan on Antimicrobial Resistance" (NSP-AMR) aims to reduce AMR morbidity by 50% through 20% reductions in human and 30% in animal antibacterial use (ABU). There is a need to understand the implications of such a plan within a One Health perspective.

Methods

A model of ABU, gut colonisation with extended-spectrum beta-lactamase (ESBL)-producing bacteria and transmission was calibrated using estimates of the prevalence of ESBL-producing bacteria in Thailand. This model was used to project the reduction in human ABR over 20 years (2020-2040) for each One Health driver, including individual transmission rates between humans, animals and the environment, and to estimate the long-term impact of the NSP-AMR intervention.

Results

The model predicts that human ABU was the most important factor in reducing the colonisation of humans with resistant bacteria (maximum 65.7-99.7% reduction). The NSP-AMR is projected to reduce human colonisation by 6.0-18.8%, with more ambitious targets (30% reductions in human ABU) increasing this to 8.5-24.9%.

Conclusions

Our model provides a simple framework to explain the mechanisms underpinning ABR, suggesting that future interventions targeting the simultaneous reduction of transmission and ABU would help to control ABR more effectively in Thailand.

The role of "spillover" in antibiotic resistance

Antibiotic use is a key driver of antibiotic resistance. Understanding the quantitative association between antibiotic use and resulting resistance is important for predicting future rates of antibiotic resistance and for designing antibiotic stewardship policy. However, the use-resistance association is complicated by "spillover," in which one population's level of antibiotic use affects another population's level of resistance via the transmission of bacteria between those populations. Spillover is known to have effects at the level of families and hospitals, but it is unclear if spillover is relevant at larger scales. We used mathematical modeling and analysis of observational data to address this question. First, we used dynamical models of antibiotic resistance to predict the effects of spillover. Whereas populations completely isolated from one another do not experience any spillover, we found that if even 1% of interactions are between populations, then spillover may have large consequences: The effect of a change in antibiotic use in one population on antibiotic resistance in that population could be reduced by as much as 50%. Then, we quantified spillover in observational antibiotic use and resistance data from US states and European countries for three pathogen-antibiotic combinations, finding that increased interactions between populations were associated with smaller differences in antibiotic resistance between those populations. Thus, spillover may have an important impact at the level of states and countries, which has ramifications for predicting the future of antibiotic resistance, designing antibiotic resistance stewardship policy, and interpreting stewardship interventions.

Leveraging Computational Modeling to Understand Infectious Diseases

Purpose of Review

Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation.

Recent Findings

Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps.

Summary

Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.

Predicting the future distribution of antibiotic resistance using time series forecasting and geospatial modelling

Background: Increasing antibiotic resistance in a location may be mitigated by changes in treatment policy, or interventions to limit transmission of resistant bacteria. Therefore, accurate forecasting of the distribution of antibiotic resistance could be advantageous. Two previously published studies addressed this, but neither study compared alternative forecasting algorithms or considered spatial patterns of resistance spread. Methods: We analysed data describing the annual prevalence of antibiotic resistance per country in Europe from 2012 – 2016, and the quarterly prevalence of antibiotic resistance per clinical commissioning group in England from 2015 – 2018. We combined these with data on rates of possible covariates of resistance. These data were used to compare the previously published forecasting models, with other commonly used forecasting models, including one geospatial model. Covariates were incorporated into the geospatial model to assess their relationship with antibiotic resistance. Results: For the European data, which was recorded on a coarse spatiotemporal scale, a naïve forecasting model was consistently the most accurate of any of the forecasting models tested. The geospatial model did not improve on this accuracy. However, it did provide some evidence that antibiotic consumption can partially explain the distribution of resistance. The English data were aggregated at a finer scale, and expected-trend-seasonal (ETS) forecasts were the most accurate. The geospatial model did not significantly improve upon the median accuracy of the ETS model, but it appeared to be less sensitive to noise in the data, and provided evidence that rates of antibiotic prescription and bacteraemia are correlated with resistance. Conclusion: Annual, national-level surveillance data appears to be insufficient for fitting accurate antibiotic resistance forecasting models, but there is evidence that data collected at a finer spatiotemporal scale could be used to improve forecast accuracy. Additionally, incorporating antibiotic prescription or consumption data into the model could improve the predictive accuracy.

Antibody-Enabled Antimicrobial Nanocapsules for Selective Elimination of Staphylococcus aureus

Targeted bactericide nanosystems hold significant promise to improve the efficacy of existing antimicrobials for treatment of severe bacterial infections, minimizing the side effects and lowering the risk of the development of antibiotic resistance. In this work, we developed antibody-functionalized nanocapsules (NCs) containing antibacterial essential oil (EO) for selective and effective eradication of Staphylococcus aureus. Antibacterial EO NCs were produced via self-assembly nanoencapsulation in the plant-derived protein zein. The obtained EO NCs were decorated with aminocellulose to provide more reactive surface groups for carboxyl-to-amine immobilization of a antibody that is specific against S. aureus. The antibody-enabled EO NCs (Ab@EO NCs) demonstrated 2-fold higher bactericidal efficacy against the targeted bacterium compared to the pristine EO NCs at the same concentrations. The improved antibacterial effect of the Ab@EO NCs toward S. aureus was also confirmed in a real-time assay by monitoring bacterial cells elimination using a quartz crystal microbalance. Furthermore, the Ab@EO NCs selectively decreased the load and changed the cell morphology of the targeted S. aureus in a mixed inoculum with nontargeted Pseudomonas aeruginosa. Applying the nanoformulated antibacterial actives to an in vitro coculture model of the bacteria and skin fibroblasts resulted in suppression of S. aureus growth while preserving the human cells viability. The novel antibody-enabled antibacterial NCs showed potential for improving the treatment efficacy of staphylococcal infections, minimally affecting the beneficial microbial and human cells.

Exposure Pathways to Antimicrobial Resistance at the Human-Animal Interface-A Qualitative Comparison of Swiss Expert and Consumer Opinions

Antimicrobial resistance (AMR) is an emerging global health concern, affecting both the animal and the human population. Transmission pathways of AMR are therefore abundant and complex, and ways to prevent or reduce transmission to consumers must be identified. The overall goal of this study was to define the content of an intervention study aimed at reducing the transmission of AMR from animal sources to humans. To identify the most relevant pathways, Swiss experts and consumers were interviewed about their opinions on the risks of transmission of AMR. Opinions of experts and consumers were then qualitatively compared and the main gaps identified. The results revealed that Swiss consumers had several misconceptions regarding the sources and transmission of AMR, and that they in particular underestimated the importance of poultry meat and pets as a potential source of AMR. Furthermore, high uncertainty was noted in experts regarding the prevalence of AMR in pets and the potential of transmission to their owners. Consequently, awareness of AMR transmission pathways should be increased among consumers to overcome common misconceptions, which will help reduce the risk of transmission. Further research is needed to better understand the pets' potential to harbor and transmit AMR to their owners, and to identify most effective methods to increase risk awareness in consumers as well as intervention strategies promoting consumer behaviors to mitigate AMR transmissions at the human-animal interface.

Quantifying antibiotic impact on within-patient dynamics of extended-spectrum beta-lactamase resistance

Antibiotic-induced perturbation of the human gut flora is expected to play an important role in mediating the relationship between antibiotic use and the population prevalence of antibiotic resistance in bacteria, but little is known about how antibiotics affect within-host resistance dynamics. Here we develop a data-driven model of the within-host dynamics of extended-spectrum beta-lactamase (ESBL) producing Enterobacteriaceae. We use blaCTX-M (the most widespread ESBL gene family) and 16S rRNA (a proxy for bacterial load) abundance data from 833 rectal swabs from 133 ESBL-positive patients followed up in a prospective cohort study in three European hospitals. We find that cefuroxime and ceftriaxone are associated with increased blaCTX-M abundance during treatment (21% and 10% daily increase, respectively), while treatment with meropenem, piperacillin-tazobactam, and oral ciprofloxacin is associated with decreased blaCTX-M (8% daily decrease for all). The model predicts that typical antibiotic exposures can have substantial long-term effects on blaCTX-M carriage duration.

The relation between prescribing of different antibiotics and rates of mortality with sepsis in US adults

BACKGROUND

Antibiotic use contributes to the rates of sepsis and the associated mortality, particularly through lack of clearance of resistant infections following antibiotic treatment. At the same time, there is limited information on the effects of prescribing of some antibiotics vs. others on subsequent sepsis and sepsis-related mortality.

METHODS

We used a multivariable mixed-effects model to relate state-specific rates of outpatient prescribing overall for oral fluoroquinolones, penicillins, macrolides, and cephalosporins between 2014 and 2015 to state-specific rates of mortality with sepsis (ICD-10 codes A40-41 present as either underlying or contributing causes of death on a death certificate) in different age groups of US adults between 2014 and 2015, adjusting for additional covariates and random effects associated with the ten US Health and Human Services (HHS) regions.

RESULTS

Increase in the rate of prescribing of oral penicillins by 1 annual dose per 1000 state residents was associated with increases in annual rates of mortality with sepsis of 0.95 (95% CI (0.02,1.88)) per 100,000 persons aged 75-84y, and of 2.97 (0.72,5.22) per 100,000 persons aged 85 + y. Additionally, the percent of individuals aged 50-64y lacking health insurance, as well as the percent of individuals aged 65-84y who are African-American were associated with rates of mortality with sepsis in the corresponding age groups.

CONCLUSIONS

Our results suggest that prescribing of penicillins is associated with rates of mortality with sepsis in older US adults. Those results, as well as the related epidemiological data suggest that replacement of certain antibiotics, particularly penicillins in the treatment of different syndromes should be considered with the aim of reducing the rates of severe outcomes, including mortality related to bacterial infections.

Mathematical modelling for antibiotic resistance control policy: do we know enough?

BACKGROUND

Antibiotics remain the cornerstone of modern medicine. Yet there exists an inherent dilemma in their use: we are able to prevent harm by administering antibiotic treatment as necessary to both humans and animals, but we must be mindful of limiting the spread of resistance and safeguarding the efficacy of antibiotics for current and future generations. Policies that strike the right balance must be informed by a transparent rationale that relies on a robust evidence base.

MAIN TEXT

One way to generate the evidence base needed to inform policies for managing antibiotic resistance is by using mathematical models. These models can distil the key drivers of the dynamics of resistance transmission from complex infection and evolutionary processes, as well as predict likely responses to policy change in silico. Here, we ask whether we know enough about antibiotic resistance for mathematical modelling to robustly and effectively inform policy. We consider in turn the challenges associated with capturing antibiotic resistance evolution using mathematical models, and with translating mathematical modelling evidence into policy.

CONCLUSIONS

We suggest that in spite of promising advances, we lack a complete understanding of key principles. From this we advocate for priority areas of future empirical and theoretical research.

Individual and community predictors of urinary ceftriaxone-resistant Escherichia coli isolates, Victoria, Australia

Background

Ceftriaxone-resistant Enterobacteriaceae are priority pathogens of critical importance. Escherichia coli is the most commonly isolated Enterobacteriaceae. There are few data regarding non-invasive ceftriaxone-resistant E. coli (CR-EC) isolates in the Australian community. We aimed to describe the prevalence, phenotype, geographic variation, and sociodemographic predictors of ceftriaxone-resistance among E. coli isolates recovered from urine specimens.

Methods

In August 2017, we prospectively analysed E. coli isolates recovered from urine specimens submitted to Dorevitch Pathology (Victoria, Australia), a laboratory that services patients in the community and hospitals. In addition to patient-level predictors of ceftriaxone resistance, we mapped patient postcodes to community-level indicators including Index of Relative Socioeconomic Deprivation, remoteness, and proportion of residents born overseas. We used Poisson regression with log link and robust standard errors to quantify the association between ceftriaxone resistance and patient- and community-level factors.

Results

We included 6732 non-duplicate E. coli isolates. Most (89.2%, 6008/6732) were obtained from female patients. Median age was 56 years (IQR, 32–74). Most patients (90.5%, 5789/6732) were neither referred from a hospital nor residing in a residential aged care facility (RACF). Among the 6732 isolates, 5.7% (382) were CR-EC, ranging from 3.5% (44/1268) in inner regional areas to 6.3% (330/5267) in major cities. Extended spectrum ß–lactamase (ESBL) -production was the most common mechanism for ceftriaxone resistance (89%, 341/382). Nitrofurantoin was the most active oral agent against CR-EC. Eight CR-EC isolates (2.4%) were susceptible only to amikacin, meropenem and nitrofurantoin. None were resistant to meropenem. On multivariable analysis, ceftriaxone resistance was associated with age, residence in a RACF (adjusted relative risk [aRR] 2.94, 95% confidence interval [CI] 2.10–4.13), specimen referral from hospital (aRR 2.05, 95% CI 1.45–2.9), and the proportion of residents born in North Africa and the Middle East (aRR 1.30 for each 5% absolute increase, 95% CI 1.09–1.54), South-East Asia (aRR 1.14, 95% CI 1.02–1.27), and Southern and Central Asia (aRR 1.16, 95% CI 1.04–1.28).

Conclusions

These results provide insights into sociodemographic variation in CR-EC in the community. A better understanding of this variation may inform empiric treatment guidelines and strategies to reduce community dissemination of CR-EC.

Microbiome predictors of dysbiosis and VRE decolonization in patients with recurrent C. difficile infections in a multi-center retrospective study

The gastrointestinal microbiome is intrinsically linked to the spread of antibiotic resistance. Antibiotic treatment puts patients at risk for colonization by opportunistic pathogens like vancomycin resistant Enterococcus and Clostridioides difficile by destroying the colonization resistance provided by the commensal microbiota. Once colonized, the host is at a much higher risk for infection by that pathogen. Furthermore, we know that microbiome community differences are associated with disease states, but we do not have a good understanding of how we can use these changes to classify different patient populations. To that end, we have performed a multicenter retrospective analysis on patients who received fecal microbiota transplants to treat recurrent Clostridioides difficile infection. We performed 16S rRNA gene sequencing on fecal samples collected as part of this study and used these data to develop a microbiome disruption index. Our microbiome disruption index is a simple index that is predictive across cohorts, indications, and batch effects. We are able to classify pre-fecal transplant vs post-fecal transplant samples in patients with recurrent C. difficile infection, and we are able to predict, using previously-published data from a cohort of patients receiving hematopoietic stem cell transplants, which patients would go on to develop bloodstream infections. Finally, we also identified patients in this cohort that were initially colonized with vancomycin resistant Enterococcus and that 92% (11/12) were decolonized after the transplant, but the microbiome disruption index was unable to predict such decolonization. We, however, were able to compare the relative abundance of different taxa between the two groups, and we found that increased abundance of Enterobacteriaceae predicts whether patients were colonized with vancomycin resistant Enterococcus. This work is an early step towards a better understanding of how microbiome predictors can be used to help improve patient care and patient outcomes.

The distribution of antibiotic use and its association with antibiotic resistance

Antibiotic use is a primary driver of antibiotic resistance. However, antibiotic use can be distributed in different ways in a population, and the association between the distribution of use and antibiotic resistance has not been explored. Here, we tested the hypothesis that repeated use of antibiotics has a stronger association with population-wide antibiotic resistance than broadly-distributed, low-intensity use. First, we characterized the distribution of outpatient antibiotic use across US states, finding that antibiotic use is uneven and that repeated use of antibiotics makes up a minority of antibiotic use. Second, we compared antibiotic use with resistance for 72 pathogen-antibiotic combinations across states. Finally, having partitioned total use into extensive and intensive margins, we found that intense use had a weaker association with resistance than extensive use. If the use-resistance relationship is causal, these results suggest that reducing total use and selection intensity will require reducing broadly distributed, low-intensity use.