Methicillin-resistant Staphylococcus aureus in hospitals and the community: stealth dynamics and control catastrophes

The contribution of community transmission to the burden of hospital-associated pathogens: A systematic scoping review of epidemiological models

Healthcare-associated infections (HAI), particularly those involving multi-drug resistant organisms (MDRO), pose a significant public health threat. Understanding the transmission of these pathogens in short-term acute care hospitals (STACH) is crucial for effective control. Mathematical and computational models play a key role in studying transmission but often overlook the influence of long-term care facilities (LTCFs) and the broader community on transmission. In a systematic scoping review of 4,733 unique studies from 2016 to 2022, we explored the modeling landscape of the hospital-community interface in HAI-causing pathogen transmission. Among the 29 eligible studies, 28 % (n = 8) exclusively modeled LTCFs, 45 % (n = 13) focused on non-healthcare-related community settings, and 31 % (n = 9) considered both settings. Studies emphasizing screening and contact precautions were more likely to include LTCFs but tended to neglect the wider community. This review emphasizes the crucial need for comprehensive modeling that incorporates the community's impact on both clinical and public health outcomes.

Estimating nosocomial transmission of micro-organisms in hospital settings using patient records and culture data

Pathogenic bacteria are a major threat to patient health in hospitals. Here we leverage electronic health records from a major New York City hospital system collected during 2020-2021 to support simulation inference of nosocomial transmission and pathogenic bacteria detection using an agent-based model (ABM). The ABM uses these data to inform simulation of importation from the community, nosocomial transmission, and patient spontaneous decolonization of bacteria. We additionally use patient clinical culture results to inform an observational model of detection of the pathogenic bacteria. The model is coupled with a Bayesian inference algorithm, an iterated ensemble adjustment Kalman filter, to estimate the likelihood of detection upon testing and nosocomial transmission rates. We evaluate parameter identifiability for this model-inference system and find that the system is able to estimate modelled nosocomial transmission and effective sensitivity upon clinical culture testing. We apply the framework to estimate both quantities for seven prevalent bacterial pathogens: Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus (both sensitive, MSSA, and resistant, MRSA, phenotypes), Enterococcus faecium and Enterococcus faecalis. We estimate that nosocomial transmission for E. coli is negligible. While bacterial pathogens have different importation rates, nosocomial transmission rates were similar among organisms, except E. coli. We also find that estimated likelihoods of detection are similar for all pathogens. This work highlights how fine-scale patient data can support inference of the epidemiological properties of micro-organisms and how hospital traffic and patient contact determine epidemiological features. Evaluation of the transmission potential for different pathogens could ultimately support the development of hospital control measures, as well as the design of surveillance strategies.

Different sampling strategies for optimal detection of the overall genetic diversity of methicillin-resistant Staphylococcus aureus

Surveillance schemes for methicillin-resistant Staphylococcus aureus (MRSA) are widely established at the national and international levels. Due to the simple standardization of the protocol, mainly isolates from bloodstream infections are used. However, the limitations of this simple surveillance system are well described. We conducted a comprehensive analysis of MRSA isolates in a large Slovenian region over 5 years to identify the optimal sample group for assessing the overall MRSA diversity. At the same time, this study provides to date non-available molecular characterization of Slovenian MRSA isolates. A total of 306 MRSA isolates from various sources were sequenced and phenotypically tested for resistance. The isolates exhibited significant molecular diversity, encompassing 30 multi locus sequence type (MLST) sequence types (STs), 39 ST-SCCmec genetic lineages, 49 spa types, and 29 antibiotic resistance profiles. Furthermore, the isolate pool comprised 57 resistance genes, representing 22 resistance mechanisms, and 96 virulence genes. While bloodstream isolates, commonly used in surveillance, provided insights into frequently detected clones, they overlooked majority of clones and important virulence and resistance genes. Blood culture isolates detected 21.3% spa types, 24.1% resistance phenotypes, and 28.2% MLST-SCCmec profiles. In contrast, strains from soft tissues demonstrated superior genomic diversity capture, with 65.3% spa types, 58.6% resistance phenotypes, and 71.8% MLST-SCCmec profiles. These strains also encompassed 100.0% of virulence and 82.5% of resistance genes, making them better candidates for inclusion in surveillance programs. This study highlights the limitations of relying solely on bloodstream isolates in MRSA surveillance and suggests incorporating strains from soft tissues to obtain a more comprehensive understanding of the epidemiology of MRSA.IMPORTANCEIn this study, we investigated the diversity of methicillin-resistant Staphylococcus aureus (MRSA), a bacterium that can cause infections that are difficult to treat due to its resistance to antimicrobial agents. Currently, surveillance programs for MRSA mainly rely on isolates from bloodstream infections, employing a standardized protocol. However, this study highlights the limitations of this approach and introduces a more comprehensive method. The main goal was to determine which group of samples is best suited to understand the overall diversity of MRSA and to provide, for the first time, molecular characterization of Slovenian MRSA isolates. Our results suggest that including MRSA strains from soft tissue infections rather than just blood infections provides a more accurate and comprehensive view of bacterial diversity and characteristics. This insight is valuable for improving the effectiveness of surveillance programs and for developing strategies to better manage MRSA infections.

System identifiability in a time-evolving agent-based model

Mathematical models are a valuable tool for studying and predicting the spread of infectious agents. The accuracy of model simulations and predictions invariably depends on the specification of model parameters. Estimation of these parameters is therefore extremely important; however, while some parameters can be derived from observational studies, the values of others are difficult to measure. Instead, models can be coupled with inference algorithms (i.e., data assimilation methods, or statistical filters), which fit model simulations to existing observations and estimate unobserved model state variables and parameters. Ideally, these inference algorithms should find the best fitting solution for a given model and set of observations; however, as those estimated quantities are unobserved, it is typically uncertain whether the correct parameters have been identified. Further, it is unclear what 'correct' really means for abstract parameters defined based on specific model forms. In this work, we explored the problem of non-identifiability in a stochastic system which, when overlooked, can significantly impede model prediction. We used a network, agent-based model to simulate the transmission of Methicillin-resistant staphylococcus aureus (MRSA) within hospital settings and attempted to infer key model parameters using the Ensemble Adjustment Kalman Filter, an efficient Bayesian inference algorithm. We show that even though the inference method converged and that simulations using the estimated parameters produced an agreement with observations, the true parameters are not fully identifiable. While the model-inference system can exclude a substantial area of parameter space that is unlikely to contain the true parameters, the estimated parameter range still included multiple parameter combinations that can fit observations equally well. We show that analyzing synthetic trajectories can support or contradict claims of identifiability. While we perform this on a specific model system, this approach can be generalized for a variety of stochastic representations of partially observable systems. We also suggest data manipulations intended to improve identifiability that might be applicable in many systems of interest.

Transmission of drug-resistant bacteria in a hospital-community model stratified by patient risk

A susceptible-infectious-susceptible (SIS) model for simulating healthcare-acquired infection spread within a hospital and associated community is proposed. The model accounts for the stratification of in-patients into two susceptibility-based risk groups. The model is formulated as a system of first-order ordinary differential equations (ODEs) with appropriate initial conditions. The mathematical analysis of this system is demonstrated. It is shown that the system has unique global solutions, which are bounded and non-negative. The basic reproduction number ([Formula: see text]) for the considered model is derived. The existence and the stability of the stationary solutions are analysed. The disease-free stationary solution is always present and is globally asymptotically stable for [Formula: see text], while for [Formula: see text] it is unstable. The presence of an endemic stationary solution depends on the model parameters and when it exists, it is globally asymptotically stable. The endemic state encompasses both risk groups. The endemic state within only one group only is not possible. In addition, for [Formula: see text] a forward bifurcation takes place. Numerical simulations, based on the anonymised insurance data, are also presented to illustrate theoretical results.

A stochastic dynamical model for nosocomial infections with co-circulation of sensitive and resistant bacterial strains

Nosocomial infections (hospital-acquired) has been an important public health problem, which may make those patients with infections or involved visitors and hospital personnel at higher risk of worse clinical outcomes or infection, and then consume more healthcare resources. Taking into account the stochasticity of the death and discharge rate of patients staying in hospitals, in this paper, we propose a stochastic dynamical model describing the transmission of nosocomial pathogens among patients admitted for hospital stays. The stochastic terms of the model are incorporated to capture the randomness arising from death and discharge processes of patients. Firstly, a sufficient condition is established for the stochastic extinction of disease. It shows that introducing randomness in the model will result in lower potential of nosocomial outbreaks. Further, we establish a threshold criterion on the existence of stationary distribution and ergodicity for any positive solution of the model. Particularly, the spectral radius form of stochastic threshold value is calculated in the special case. Moreover, the numerical simulations are conducted to both validate the theoretical results and investigate the effect of prevention and control strategies on the prevalence of nosocomial infection. We show that enhancing hygiene, targeting colonized and infected patients, improving antibiotic treatment accuracy, shortening treatment periods are all crucial factors to contain nosocomial infections.

Ensemble inference of unobserved infections in networks using partial observations

Undetected infections fuel the dissemination of many infectious agents. However, identification of unobserved infectious individuals remains challenging due to limited observations of infections and imperfect knowledge of key transmission parameters. Here, we use an ensemble Bayesian inference method to infer unobserved infections using partial observations. The ensemble inference method can represent uncertainty in model parameters and update model states using all ensemble members collectively. We perform extensive experiments in both model-generated and real-world networks in which individuals have differential but unknown transmission rates. The ensemble method outperforms several alternative approaches for a variety of network structures and observation rates, despite that the model is mis-specified. Additionally, the computational complexity of this algorithm scales almost linearly with the number of nodes in the network and the number of observations, respectively, exhibiting the potential to apply to large-scale networks. The inference method may support decision-making under uncertainty and be adapted for use for other dynamical models in networks.

Challenges in Forecasting Antimicrobial Resistance

Antimicrobial resistance is a major threat to human health. Since the 2000s, computational tools for predicting infectious diseases have been greatly advanced; however, efforts to develop real-time forecasting models for antimicrobial-resistant organisms (AMROs) have been absent. In this perspective, we discuss the utility of AMRO forecasting at different scales, highlight the challenges in this field, and suggest future research priorities. We also discuss challenges in scientific understanding, access to high-quality data, model calibration, and implementation and evaluation of forecasting models. We further highlight the need to initiate research on AMRO forecasting using currently available data and resources to galvanize the research community and address initial practical questions.

Quantifying patient- and hospital-level antimicrobial resistance dynamics in Staphylococcus aureus from routinely collected data

Antimicrobial resistance (AMR) to all antibiotic classes has been found in the pathogen Staphylococcus aureus . The reported prevalence of these resistances vary, driven by within-host AMR evolution at the patient level, and between-host transmission at the hospital level. Without dense longitudinal sampling, pragmatic analysis of AMR dynamics at multiple levels using routine surveillance data is essential to inform control measures. We explored S. aureus AMR diversity in 70,000 isolates from a UK paediatric hospital between 2000-2020, using electronic datasets containing multiple routinely collected isolates per patient with phenotypic antibiograms, hospitalisation information, and antibiotic consumption. At the hospital-level, the proportion of isolates that were meticillin-resistant (MRSA) increased between 2014-2020 from 25 to 50%, before sharply decreasing to 30%, likely due to a change in inpatient demographics. Temporal trends in the proportion of isolates resistant to different antibiotics were often correlated in MRSA, but independent in meticillin-susceptible S. aureus . Ciprofloxacin resistance in MRSA decreased from 70% to 40% of tested isolates between 2007-2020, likely linked to a national policy to reduce fluoroquinolone usage in 2007. At the patient level, we identified frequent AMR diversity, with 4% of patients ever positive for S. aureus simultaneously carrying, at some point, multiple isolates with different resistances. We detected changes over time in AMR diversity in 3% of patients ever positive for S. aureus . These changes equally represented gain and loss of resistance. Within this routinely collected dataset, we found that 65% of changes in resistance within a patient’s S. aureus population could not be explained by antibiotic exposure or between-patient transmission of bacteria, suggesting that within-host evolution via frequent gain and loss of AMR genes may be responsible for these changing AMR profiles. Our study highlights the value of exploring existing routine surveillance data to determine underlying mechanisms of AMR. These insights may substantially improve our understanding of the importance of antibiotic exposure variation, and the success of single S. aureus clones.

Check your assumptions: Further scrutiny of basic model frameworks of antimicrobial resistance

Since the mid-1990s, growing concerns over antimicrobial resistant (AMR) organisms has led to an increase in the use of mathematical models to explore the inter-host transmission of such infections. Previous work reviewing such models categorised them into generic frameworks based on their underlying assumptions. These assumptions dictated the coexistence between AMR and antimicrobial sensitive strains. We add to this work performing stability analyses of the frameworks, along with simulating them deterministically and stochastically. Stability analyses found that many of these assumptions lead to models having the same equilibria, but showed differences in the equilibria's stability between models. Deterministic simulations reveal that assuming replacement of one infecting strain by another leads to an unusual antimicrobial treatment threshold. Increasing beyond this threshold causes a discontinuous increase in disease burden. The cost of AMR to pathogen fitness (lowered transmission) dictates both the threshold of treatment that causes the discontinuous increase in disease burden and the size of that increase. It was also shown that Superinfection states can be biased against resident strains and so favour coexistence of both strains. Stochastic simulations demonstrated that differing scenario starting conditions can guide models to converge upon equilibria that they may not have under deterministic simulation. These findings highlight the importance of checking assumptions when modelling AMR and strain competition more widely.

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.

Impact of the WHO Integrated Stewardship Policy on the Control of Methicillin-Resistant Staphyloccus aureus and Third-Generation Cephalosporin-Resistant Escherichia coli: Using a Mathematical Modeling Approach

Methicillin-resistant Staphylococcus aureus (MRSA) and third-generation cephalosporin-resistant Escherichia coli (3GCREc) are community and hospital-associated pathogens causing serious infections among populations by infiltrating into hospitals and surrounding environment. These main multi-drug resistant or antimicrobial resistance (AMR) bacterial pathogens are threats to human health if not properly tackled and controlled. Tackling antimicrobial resistance (AMR) is one of the issues for the World Health Organization (WHO) to design a comprehensive set of interventions which also helps to achieve the end results of the developing indicators proposed by the same organization. A deterministic mathematical model is developed and studied to investigate the impact of the WHO policy on integrated antimicrobial stewardship activities to use effective protection measures to control the spread of AMR diseases such as MRSA and 3GCREc in hospital settings by incorporating the contribution of the healthcare workers in a hospital and the environment in the transmission dynamics of the diseases. The model also takes into account the parameters describing various intervention measures and is used to quantify their contribution in containing the diseases. The impact of combinations of various possible control measures on the overall dynamics of the disease under study is investigated. The model analysis suggests that the contribution of the interventions: screening and isolating the newly admitted patients, improving the hygiene in hospital settings, decolonizing the pathogen carriers, and increasing the frequency of disinfecting the hospital environment are effective tools to contain the disease from invading the population. The study revealed that without any intervention, the diseases will continue to be a major cause of morbidity and mortality in the affected communities. In addition, the study indicates that a coordinated implementation of the integrated control measures suggested by WHO is more effective in curtailing the spread of the diseases than piecemeal strategies. Numerical experiments are provided to support the theoretical analysis.

Transmission routes of antibiotic resistant bacteria: a systematic review

Background

Quantification of acquisition routes of antibiotic resistant bacteria (ARB) is pivotal for understanding transmission dynamics and designing cost-effective interventions. Different methods have been used to quantify the importance of transmission routes, such as relative risks, odds ratios (OR), genomic comparisons and basic reproduction numbers. We systematically reviewed reported estimates on acquisition routes’ contributions of ARB in humans, animals, water and the environment and assessed the methods used to quantify the importance of transmission routes.

Methods

PubMed and EMBASE were searched, resulting in 6054 articles published up until January 1st, 2019. Full text screening was performed on 525 articles and 277 are included.

Results

We extracted 718 estimates with S. aureus (n = 273), E. coli (n = 157) and Enterobacteriaceae (n = 99) being studied most frequently. Most estimates were derived from statistical methods (n = 560), mainly expressed as risks (n = 246) and ORs (n = 239), followed by genetic comparisons (n = 85), modelling (n = 62) and dosage of ARB ingested (n = 17). Transmission routes analysed most frequently were occupational exposure (n = 157), travelling (n = 110) and contacts with carriers (n = 83). Studies were mostly performed in the United States (n = 142), the Netherlands (n = 87) and Germany (n = 60). Comparison of methods was not possible as studies using different methods to estimate the same route were lacking. Due to study heterogeneity not all estimates by the same method could be pooled.

Conclusion

Despite an abundance of published data the relative importance of transmission routes of ARB has not been accurately quantified. Links between exposure and acquisition are often present, but the frequency of exposure is missing, which disables estimation of transmission routes’ importance. To create effective policies reducing ARB, estimates of transmission should be weighed by the frequency of exposure occurrence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07360-z.

Does plasmid-based beta-lactam resistance increase E. coli infections: Modelling addition and replacement mechanisms

Infections caused by antibiotic-resistant bacteria have become more prevalent during past decades. Yet, it is unknown whether such infections occur in addition to infections with antibiotic-susceptible bacteria, thereby increasing the incidence of infections, or whether they replace such infections, leaving the total incidence unaffected. Observational longitudinal studies cannot separate both mechanisms. Using plasmid-based beta-lactam resistant E. coli as example we applied mathematical modelling to investigate whether seven biological mechanisms would lead to replacement or addition of infections. We use a mathematical neutral null model of individuals colonized with susceptible and/or resistant E. coli, with two mechanisms implying a fitness cost, i.e., increased clearance and decreased growth of resistant strains, and five mechanisms benefitting resistance, i.e., 1) increased virulence, 2) increased transmission, 3) decreased clearance of resistant strains, 4) increased rate of horizontal plasmid transfer, and 5) increased clearance of susceptible E. coli due to antibiotics. Each mechanism is modelled separately to estimate addition to or replacement of antibiotic-susceptible infections. Fitness costs cause resistant strains to die out if other strain characteristics are maintained equal. Under the assumptions tested, increased virulence is the only mechanism that increases the total number of infections. Other benefits of resistance lead to replacement of susceptible infections without changing the total number of infections. As there is no biological evidence that plasmid-based beta-lactam resistance increases virulence, these findings suggest that the burden of disease is determined by attributable effects of resistance rather than by an increase in the number of infections.

Improving mathematical modeling of interventions to prevent healthcare-associated infections by interrupting transmission or pathogens: How common modeling assumptions about colonized individuals impact intervention effectiveness estimates

Mathematical models are used to gauge the impact of interventions for healthcare-associated infections. As with any analytic method, such models require many assumptions. Two common assumptions are that asymptomatically colonized individuals are more likely to be hospitalized and that they spend longer in the hospital per admission because of their colonization status. These assumptions have no biological basis and could impact the estimated effects of interventions in unintended ways. Therefore, we developed a model of methicillin-resistant Staphylococcus aureus transmission to explicitly evaluate the impact of these assumptions. We found that assuming that asymptomatically colonized individuals were more likely to be admitted to the hospital or spend longer in the hospital than uncolonized individuals biased results compared to a more realistic model that did not make either assumption. Results were heavily biased when estimating the impact of an intervention that directly reduced transmission in a hospital. In contrast, results were moderately biased when estimating the impact of an intervention that decolonized hospital patients. Our findings can inform choices modelers face when constructing models of healthcare-associated infection interventions and thereby improve their validity.

Precautionary breaks: Planned, limited duration circuit breaks to control the prevalence of SARS-CoV2 and the burden of COVID-19 disease

COVID-19 in the UK has been characterised by periods of exponential growth and decline, as different non-pharmaceutical interventions (NPIs) are brought into play. During the early uncontrolled phase of the outbreak (March 2020) there was a period of prolonged exponential growth with epidemiological observations such as hospitalisation doubling every 3-4 days. The enforcement of strict lockdown measures led to a noticeable decline in all epidemic quantities that slowed during the summer as control measures were relaxed. From August 2020, infections, hospitalisations and deaths began rising once more and various NPIs were applied locally throughout the UK in response. Controlling any rise in infection is a compromise between public health and societal costs, with more stringent NPIs reducing cases but damaging the economy and restricting freedoms. Typically, NPI imposition is made in response to the epidemiological state, are of indefinite length and are often imposed at short notice, greatly increasing the negative impact. An alternative approach is to consider planned, limited duration periods of strict NPIs aiming to purposefully reduce prevalence before such emergency NPIs are required. These "precautionary breaks" may offer a means of keeping control of the epidemic, while their fixed duration and the forewarning may limit their societal impact. Here, using simple analysis and age-structured models matched to the UK SARS-CoV-2 epidemic, we investigate the action of precautionary breaks. In particular we consider their impact on the prevalence of SARS-CoV-2 infection, as well as the total number of predicted hospitalisations and deaths caused by COVID-19 disease. We find that precautionary breaks provide the biggest gains when the growth rate is low, but offer a much needed brake on increasing infection when the growth rate is higher, potentially allowing other measures to regain control.

Biohybrid microswimmers against bacterial infections

Biohybrid microswimmers exploit the natural abilities of motile microorganisms e.g. in releasing cargo on-demand. However, using such engineered swarms to release antibiotics addressing bacterial infections has not yet been realized. Herein, a design strategy for biohybrid microswimmers is reported, which features the covalent attachment of antibiotics with a photo-cleavable linker to the algae Chlamydomonas reinhardtii via two synthetic steps. This surface engineering does not rely on genetic manipulations, proceeds with high efficiency, and retains the viability or phototaxis of microalgae. Two different antibiotics have been separately utilized, which result in activity against both gram-positive and gram-negative strains. Guiding the biohybrid microswimmers by an external beacon, and on-demand delivery of the drugs by light with high spatial and temporal control, allowed for strong inhibition of bacterial growth. This efficient strategy could potentially allow for the selective treatment of bacterial infections by engineered algal microrobots with high precision in space and time. STATEMENT OF SIGNIFICANCE: Biological swimmers with innate sensing and actuation capabilities and integrated components have been widely investigated to create autonomous microsystems. The use of natural swimmers as cargo delivery systems presents an alternative strategy to transport therapeutics to the required locations with the difficult access by traditional strategies. Although the transfer of various therapeutic cargo has shown promising results, the utilization of microswimmers for the delivery of antimicrobials was barely covered. Therefore, we present biohybrid microalga-powered swimmers designed and engineered to carry antibiotic cargo against both Gram-positive and Gram-negative bacteria. Guided by an external beacon, these microhybrids deliver the antibiotic payload to the site of bacterial infection, with high spatial and temporal precision, released on-demand by an external trigger to inhibit bacterial growth.

Understanding MRSA clonal competition within a UK hospital; the possible importance of density dependence

BACKGROUND

Methicillin resistant Staphylococcus aureus (MRSA) bacteria cause serious, often healthcare-associated infections and are frequently highly resistant to diverse antibiotics. Multiple MRSA clonal complexes (CCs) have evolved independently and countries have different prevalent CCs. It is unclear when and why the dominant CC in a region may switch.

METHODS

We developed a mathematical deterministic model of MRSA CC competing for limited resource. The model distinguishes 'standard MRSA' and multidrug resistant sub-populations within each CC, allowing for resistance loss and transfer between same CC bacteria. We first analysed how dynamics of this system depend on growth-rate and resistance-potential differences between CCs, and on their resistance gene accumulation. We then fit the model to capture the longitudinal CC dynamics observed at a single UK hospital, which exemplified the UK-wide switch from mainly CC30 to mainly CC22.

RESULTS

We find that within a CC, gain and loss of resistance can allow for co-existence of sensitive and resistant sub-populations. Due to more efficient transfer of resistance at higher CC density, more drug resistance can accumulate in the population of a more prevalent CC. We show how this process of density dependent competition, together with prevalence disruption, could explain the relatively sudden switch from mainly CC30 to mainly CC22 in the UK hospital setting. Alternatively, the observed hospital dynamics could be reproduced by assuming that multidrug resistant CC22 evolved only around 2004.

CONCLUSIONS

We showed how higher prevalence may advantage a CC by allowing it to acquire antimicrobial resistances more easily. Due to this density dependence in competition, dominance in an area can depend on historic contingencies; the MRSA CC that happened to be first could stay dominant because of its high prevalence advantage. This then could help explain the stability, despite frequent stochastic introductions across borders, of geographic differences in MRSA CC.

Identifying asymptomatic spreaders of antimicrobial-resistant pathogens in hospital settings

Antimicrobial-resistant organisms (AMROs) can colonize people without symptoms for long periods of time, during which these agents can spread unnoticed to other patients in healthcare systems. The accurate identification of asymptomatic spreaders of AMRO in hospital settings is essential for supporting the design of interventions against healthcare-associated infections (HAIs). However, this task remains challenging because of limited observations of colonization and the complicated transmission dynamics occurring within hospitals and the broader community. Here, we study the transmission of methicillin-resistant Staphylococcus aureus (MRSA), a prevalent AMRO, in 66 Swedish hospitals and healthcare facilities with inpatients using a data-driven, agent-based model informed by deidentified real-world hospitalization records. Combining the transmission model, patient-to-patient contact networks, and sparse observations of colonization, we develop and validate an individual-level inference approach that estimates the colonization probability of individual hospitalized patients. For both model-simulated and historical outbreaks, the proposed method supports the more accurate identification of asymptomatic MRSA carriers than other traditional approaches. In addition, in silica control experiments indicate that interventions targeted to inpatients with a high-colonization probability outperform heuristic strategies informed by hospitalization history and contact tracing.

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.

Estimated Methicillin-Resistant Staphylococcus aureus Decolonization in Intensive Care Units Associated With Single-Application Chlorhexidine Gluconate or Mupirocin

IMPORTANCE

Chlorhexidine gluconate (CHG) and mupirocin are widely used to decolonize patients with methicillin-resistant Staphylococcus aureus (MRSA) and reduce risks associated with infection in hospitalized populations. Quantifying the association of an application of CHG alone or in combination with mupirocin with risk of MRSA infection is important for studies evaluating alternative decolonization strategies or schedules and for identifying whether there is room for improved decolonizing agents.

OBJECTIVE

To estimate the proportion of patients with MRSA decolonized per application of CHG and mupirocin from existing population-level studies.

DESIGN, SETTING, AND PARTICIPANTS

A stochastic mathematical model of an 18-bed intensive care unit (ICU) in an academic medical center operating over 1 year was used to estimate parameters for the proportion of simulated patients with MRSA decolonized per application of CHG and mupirocin. The model was conducted using approximate bayesian computation with data from an existing meta-analysis of studies conducted from February 2005 through January 2015. Data were analyzed from January 2018 through November 2019.

EXPOSURE

A universal decolonization protocol for colonized patients in the ICU using CHG or CHG and mupirocin in combination was simulated.

MAIN OUTCOMES AND MEASURES

The proportion of patients with MRSA decolonized per application of CHG and mupirocin was estimated.

RESULTS

The estimated proportion of patients with MRSA decolonized per application of CHG was 0.15 (95% credible interval, 0.01-0.42), and the estimated proportion per application of mupirocin in conjunction with CHG was 0.15 (95% credible interval, 0.01-0.54). A lag in colonization detection was associated with decreases in the CHG estimate (0.11; 95% credible interval, 0.01-0.30) and mupirocin estimate (0.10; 95% credible interval, 0.00-0.34), which were sensitive to the value of the modeled contact rate between nurses and patients. A 1% increase in the value of this parameter was associated with a 0.73% increase in the estimated combined outcomes associated with CHG and mupirocin (95% CI: 0.71, 0.75). Gaps longer than 24 hours in the administration of decolonizing agents were associated with a decrease of within-ICU MRSA transmission. Compared with a mean (SD) of 1.23 (0.27) acquisitions per 1000 patient-days in scenarios with no decolonizing bathing, a bathing protocol administering CHG and mupirocin every 120 hours was associated with a mean (SD) acquisition rate of 1.03 (0.24) acquisitions per 1000 patient days, a 16.3% decrease (95% CI, 14.7%-18.0%; P > .001).

CONCLUSIONS AND RELEVANCE

These findings suggest that there may be room for significant improvement in anti-MRSA disinfectants, including the compounds themselves and their delivery mechanisms. Despite the decolonization estimates found in this study, these agents are associated with robust outcomes after delays in administration, which may help in alleviating concerns over patient comfort and toxic effects.

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.

Impact of inter-hospital transfers on the prevalence of resistant pathogens in a hospital-community system

The spread of resistant bacteria in hospitals is an increasing problem worldwide. Transfers of patients, who may be colonized with resistant bacteria, are considered to be an important driver of promoting resistance. Even though transmission rates within a hospital are often low, readmissions of patients who were colonized during an earlier hospital stay lead to repeated introductions of resistant bacteria into hospitals. We developed a mathematical model that combines a deterministic model for within-hospital spread of pathogens, discharge to the community and readmission, with a hospital-community network simulation of patient transfers between hospitals. Model parameters used to create the hospital-community network are obtained from two health insurance datasets from Germany. For parameter values representing transmission of resistant Enterobacteriaceae, we compute estimates for the single admission reproduction numbers R_A and the basic reproduction numbers R₀ per hospital-community pair. We simulate the spread of colonization through the network of hospitals, and investigate how increasing connectedness of hospitals through the network influences the prevalence in the hospital-community pairs. We find that the prevalence in hospitals is determined by their R_A and R₀ values. Increasing transfer rates between network nodes tend to lower the overall prevalence in the network by diluting the high prevalence of hospitals with high R₀ to hospitals where persistent spread is not possible. We conclude that hospitals with high reproduction numbers represent a continuous source of risk for importing resistant pathogens for hospitals with otherwise low levels of transmission. Moreover, high risk hospital-community nodes act as reservoirs of pathogens in a densely connected network.

Acquisition of MDR-GNB in hospital settings: a systematic review and meta-analysis focusing on ESBL-E

INTRODUCTION

Extended-spectrum beta-lactamase-producing Enterobacterales (ESBL-E) and other multi-drug-resistant Gram-negative bacteria (MDR-GNB) have disseminated globally since their discovery in the late 20^th century. Various infection prevention and control measures are in place to prevent nosocomial transmission of these organisms, but their efficacy remains disputed. New literature has emerged in recent years providing further evidence which can be used to formulate effective strategies to tackle this issue in the future.

METHODS

A systematic review was performed to characterize the prevalence of colonization of multi-drug-resistant organisms and subsequent acquisition of these organisms within hospital settings. A meta-analysis was performed to characterize the prevalence and acquisition of ESBL-E in Europe and North America.

RESULTS

Twenty-eight studies fulfilled the inclusion criteria. Escherichia coli formed the main burden of MDR-GNB colonization worldwide. Patient-to-patient transmission of ESBL-E was found to be rare, but increased transmissibility of Klebsiella pneumoniae was described over E. coli. Within European and North American healthcare settings, a meta-analysis of eight studies identified a pooled prevalence of ESBL-E on admission to hospital of 7.91% and an acquisition rate of 3.73%.

DISCUSSION

Low prevalence at the point of hospital admission and insufficient evidence of patient-to-patient transmission suggests that infection prevention and control measures such as universal surveillance screening and single-room isolation are unlikely to be practical or effective interventions in reducing the overall burden of ESBL-E in hospitals, in line with current European guidelines. Instead, it is argued that efforts should be placed on controlling the spread of these organisms and other MDR-GNB in the community, predominantly long-term care facilities.

Photosensitive properties, synergistic antibacterial abilities of intelligent response-type self-assembled nanoparticle TiO2@V2O5

Representative pathogenic bacteria such as Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) are widespread in nature and pose a threat to human health. To control the propagation of these pathogens from the source, the key is to design broad-spectrum antibacterial materials to reduce the serious damage of pathogenic bacteria. At present, more and more nanoparticles are widely researched and applied due to their multi-pathway antibacterial properties, such as regulating physiology, biochemistry and physical chemistry. In this work, we synthesized a uniformly dispersed and stable spherical nanoparticle (TiO₂@V₂O₅) synthesized by self-assembly of tianium dioxide and vanadium pentoxide. Based on its excellent photosensitive properties, TiO₂@V₂O₅ nanoparticles have showed excellent antibacterial properties under the light irradiation due to the production of hydroxyl radicals in antibacterial and mechanism tests. In addtion, related cell and plant experiments have showed that TiO₂@V₂O₅ nanoparticles are excellent biocompatible materials, it could be widely used in environmental pollution control, limiting the serious damage caused by pathogens.

Population structure across scales facilitates coexistence and spatial heterogeneity of antibiotic-resistant infections

Antibiotic-resistant infections are a growing threat to human health, but basic features of the eco-evolutionary dynamics remain unexplained. Most prominently, there is no clear mechanism for the long-term coexistence of both drug-sensitive and resistant strains at intermediate levels, a ubiquitous pattern seen in surveillance data. Here we show that accounting for structured or spatially-heterogeneous host populations and variability in antibiotic consumption can lead to persistent coexistence over a wide range of treatment coverages, drug efficacies, costs of resistance, and mixing patterns. Moreover, this mechanism can explain other puzzling spatiotemporal features of drug-resistance epidemiology that have received less attention, such as large differences in the prevalence of resistance between geographical regions with similar antibiotic consumption or that neighbor one another. We find that the same amount of antibiotic use can lead to very different levels of resistance depending on how treatment is distributed in a transmission network. We also identify parameter regimes in which population structure alone cannot support coexistence, suggesting the need for other mechanisms to explain the epidemiology of antibiotic resistance. Our analysis identifies key features of host population structure that can be used to assess resistance risk and highlights the need to include spatial or demographic heterogeneity in models to guide resistance management.

Dynamic contact networks of patients and MRSA spread in hospitals

Methicillin-resistant Staphylococcus aureus (MRSA) is a difficult-to-treat infection. Increasing efforts have been taken to mitigate the epidemics and to avoid potential outbreaks in low endemic settings. Understanding the population dynamics of MRSA is essential to identify the causal mechanisms driving the epidemics and to generalise conclusions to different contexts. Previous studies neglected the temporal structure of contacts between patients and assumed homogeneous behaviour. We developed a high-resolution data-driven contact network model of interactions between 743,182 patients in 485 hospitals during 3,059 days to reproduce the exact contact sequences of the hospital population. Our model captures the exact spatial and temporal human contact behaviour and the dynamics of referrals within and between wards and hospitals at a large scale, revealing highly heterogeneous contact and mobility patterns of individual patients. A simulation exercise of epidemic spread shows that heterogeneous contacts cause the emergence of super-spreader patients, slower than exponential polynomial growth of the prevalence, and fast epidemic spread between wards and hospitals. In our simulated scenarios, screening upon hospital admittance is potentially more effective than reducing infection probability to reduce the final outbreak size. Our findings are useful to understand not only MRSA spread but also other hospital-acquired infections.

Transmission dynamics and control of multidrug-resistant Klebsiella pneumoniae in neonates in a developing country

Multidrug-resistant Klebsiella pneumoniae is an increasing cause of infant mortality in developing countries. We aimed to develop a quantitative understanding of the drivers of this epidemic by estimating the effects of antibiotics on nosocomial transmission risk, comparing competing hypotheses about mechanisms of spread, and quantifying the impact of potential interventions. Using a sequence of dynamic models, we analysed data from a one-year prospective carriage study in a Cambodian neonatal intensive care unit with hyperendemic third-generation cephalosporin-resistant K. pneumoniae. All widely-used antibiotics except imipenem were associated with an increased daily acquisition risk, with an odds ratio for the most common combination (ampicillin + gentamicin) of 1.96 (95% CrI 1.18, 3.36). Models incorporating genomic data found that colonisation pressure was associated with a higher transmission risk, indicated sequence type heterogeneity in transmissibility, and showed that within-ward transmission was insufficient to maintain endemicity. Simulations indicated that increasing the nurse-patient ratio could be an effective intervention.

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.

The Transmissibility of Antibiotic-Resistant Enterobacteriaceae in Intensive Care Units

Background

The global emergence of infections caused by Enterobacteriaceae resistant to expanded-spectrum cephalosporins (ESCs) in intensive care units (ICUs) is, at least partly, driven by cross-transmission. Yet, individual transmission capacities of bacterial species have not been quantified.

Methods

In this post hoc analysis of a multicenter study in 13 European ICUs, prospective surveillance data and a mathematical model were used to estimate transmission capacities and single-admission reproduction numbers (R_A) of Escherichia coli and non–E. coli Enterobacteriaceae (non-EcE), all being ESC resistant. Surveillance was based on a chromogenic selective medium for ESC-resistant Enterobacteriaceae, allowing identification of E. coli and of Klebsiella, Enterobacter, Serratia, and Citrobacter species, grouped as non-EcE.

Results

Among 11420 patients included, the admission prevalence was 3.8% for non-EcE (74% being Klebsiella pneumoniae) and 3.3% for E. coli. Acquisition rates were 7.4 and 2.6 per 100 admissions at risk for non-EcE and E. coli, respectively. The estimated transmission capacity of non-EcE was 3.7 (95% credibility interval [CrI], 1.4–11.3) times higher than that of E. coli, yielding single-admission reproduction numbers (R_A) of 0.17 (95% CrI, .094–.29) for non-EcE and 0.047 (95% CrI, .018–.098) for E. coli.

Conclusions

In ICUs, non-EcE, mainly K. pneumoniae, are 3.7 times more transmissible than E. coli. Estimated R_A values of these bacteria were below the critical threshold of 1, suggesting that in these ICUs outbreaks typically remain small with current infection control policies.

Influence of primary care antibiotic prescribing on incidence rates of multidrug-resistant Gram-negative bacteria in hospitalised patients

PURPOSE

Use of antibiotics can give rise to the selection of resistant bacteria. It remains unclear whether antibiotic use in primary care can influence bacterial resistance incidence in patients when hospitalised. The aim of this study is to explore the impact of prior community antibiotic usage on hospital-detected multidrug-resistant Gram-negative (MRGN) incidence rate.

METHODS

This pharmacoepidemiological study was case-control in design, and was carried out in the Antrim Area Hospital (N. Ireland) in two phases. In phase 1, the controls were matched according to: age, gender, admission ward, date of admission, and age-adjusted Charlson co-morbidity index score. During the second phase, controls were selected randomly from the total population of admissions to the hospital over the 2-year study period.

RESULTS

In phase 1, multivariate analysis revealed that prior exposure to the second- and third-generation cephalosporins (p = 0.004) and fluoroquinolones (p = 0.023) in primary care was associated with an increased likelihood of MRGN detection in inpatients. In phase 2, an independent relationship between an increased risk of identification of MRGN, while hospitalised was associated with: prolonged hospitalisation (p < 0.001), being elderly (p < 0.001), being female (p = 0.007), and having genitourinary disease (p < 0.001).

CONCLUSION

This study provides clear evidence which supports the need to optimise antibiotic use in primary care to help reduce MRGN incidence in hospitalised patients.

Impact of an Antimicrobial Stewardship Intervention on Within- and Between-Patient Daptomycin Resistance Evolution in Vancomycin-Resistant Enterococcus faecium

Vancomycin-resistant Enterococcus (VRE) is a leading cause of hospital-acquired infection, with limited treatment options. Resistance to one of the few remaining drugs, daptomycin, is a growing clinical problem and has previously been described in this hospital. In response to increasing resistance, an antimicrobial stewardship intervention was implemented to reduce hospital-wide use of daptomycin. To assess the impact of the intervention, daptomycin prescribing patterns and clinically reported culture results from vancomycin-resistant Enterococcus faecium (VREfm) bloodstream infections (BSIs) from 2011 through 2017 were retrospectively extracted and the impact of the intervention was estimated using interrupted time series analysis (ITS). We corrected for a change in MIC determination methodology by retesting 262 isolates using Etest and broth microdilution. Hospital-wide and within-patient resistance patterns of corrected daptomycin MICs are reported. Our data show that daptomycin prescriptions decreased from an average of 287 days of therapy/month preintervention to 151 days of therapy/month postintervention. Concurrently, the proportion of patients experiencing an increase in daptomycin MIC during an infection declined from 14.6% (7/48 patients) in 2014 to 1.9% (1/54 patients) in 2017. Hospital-wide resistance to daptomycin also decreased in the postintervention period, but this was not maintained. This study shows that an antimicrobial stewardship-guided intervention reduced daptomycin use and improved individual level outcomes but had only transient impact on the hospital-level trend.

Incentivizing hospital infection control

Healthcare-associated infections (HAIs) pose a significant burden to patient safety. Institutions can implement hospital infection control (HIC) measures to reduce the impact of HAIs. Since patients can carry pathogens between institutions, there is an economic incentive for hospitals to free ride on the HIC investments of other facilities. Subsidies for infection control by public health authorities could encourage regional spending on HIC. We develop coupled mathematical models of epidemiology and hospital behavior in a game-theoretic framework to investigate how hospitals may change spending behavior in response to subsidies. We demonstrate that under a limited budget, a dollar-for-dollar matching grant outperforms both a fixed-amount subsidy and a subsidy on uninfected patients in reducing the number of HAIs in a single institution. Additionally, when multiple hospitals serve a community, funding priority should go to the hospital with a lower transmission rate. Overall, subsidies incentivize HIC spending and reduce the overall prevalence of HAIs.

Evolutionary epidemiology models to predict the dynamics of antibiotic resistance

The evolution of resistance to antibiotics is a major public health problem and an example of rapid adaptation under natural selection by antibiotics. The dynamics of antibiotic resistance within and between hosts can be understood in the light of mathematical models that describe the epidemiology and evolution of the bacterial population. "Between-host" models describe the spread of resistance in the host community, and in more specific settings such as hospitalized hosts (treated by antibiotics at a high rate), or farm animals. These models make predictions on the best strategies to limit the spread of resistance, such as reducing transmission or adapting the prescription of several antibiotics. Models can be fitted to epidemiological data in the context of intensive care units or hospitals to predict the impact of interventions on resistance. It has proven harder to explain the dynamics of resistance in the community at large, in particular because models often do not reproduce the observed coexistence of drug-sensitive and drug-resistant strains. "Within-host" models describe the evolution of resistance within the treated host. They show that the risk of resistance emergence is maximal at an intermediate antibiotic dose, and some models successfully explain experimental data. New models that include the complex host population structure, the interaction between resistance-determining loci and other loci, or integrating the within- and between-host levels will allow better interpretation of epidemiological and genomic data from common pathogens and better prediction of the evolution of resistance.

Nanoparticle-Mediated Capture and Electrochemical Detection of Methicillin-Resistant Staphylococcus aureus

The spread of antibiotic-resistant bacteria poses a global threat to public health. Conventional bacterial detection and identification methods often require pre-enrichment and/or sample preprocessing and purification steps that can prolong diagnosis by days. Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most widespread antibiotic-resistant bacteria and is the leading cause of hospital-acquired infections. Here, we have developed a method to specifically capture and detect MRSA directly from patient nasal swabs with no prior culture and minimal processing steps using a microfluidic device and antibody-functionalized magnetic nanoparticles. Bacteria are captured based on antibody recognition of a membrane-bound protein marker that confers β-lactam antibiotic resistance. MRSA identification is then achieved by the use of a strain-specific antibody functionalized with alkaline phosphatase for electrochemical detection. This approach ensures that only those bacteria of the target strain and resistance profile are measured. The method has a limit of detection of 845 CFU/mL and excellent discrimination against high concentrations of common nontarget nasal flora with a turnaround time of under 4.5 h. This detection method was successfully validated using clinical nasal swab specimens ( n = 30) and has the potential to be tailored to various bacterial targets.

Methicillin-Resistant Staphylococcus aureus (MRSA): Prevalence and Antimicrobial Sensitivity Pattern among Patients-A Multicenter Study in Asmara, Eritrea

Background

Methicillin-resistant Staphylococcus aureus (MRSA) is a well-recognized public health problem throughout the world. The evolution of new genetically distinct community-acquired and livestock-acquired MRSA and extended resistance to other non-β-lactams including vancomycin has only amplified the crisis. This paper presents data on the prevalence of MRSA and resistance pattern to other antibiotics on the selected specimen from two referral hospitals in Asmara, Eritrea.

Method

A cross-sectional study was conducted among 130 participants recruited from two referral hospitals in Asmara, Eritrea. Isolation of S. aureus was based on culture and biochemical profiles. Standard antimicrobial disks representing multiple drug classes were subsequently set for oxacillin, gentamicin, erythromycin, and vancomycin. Data were analyzed using SPSS version 20 software.

Results

S. aureus isolation rate from the 130 samples was 82 (63.1%). Patients <18 years of age were more likely to be colonized by S. aureus compared to patients above 61 years. The proportion of MRSA among the isolates was 59 (72%), methicillin-intermediate S. aureus (MISA) was 7 (8.5%), and methicillin-sensitive S. aureus (MSSA) was 15 (19.5%). The isolates were mostly from the pus specimen in burn, diabetic, and surgical wound patients. Antimicrobial susceptibility test showed that 13 (15.9%) of the isolates were resistant to vancomycin, 9 (11.0%) to erythromycin, and 1 (1.2%) to gentamicin. Coresistance of MRSA isolates to some commonly used antibiotics was also noted: oxacillin/erythromycin 5 (6.1%) and oxacillin/vancomycin 9 (11%). A few isolates were resistant to oxacillin/vancomycin/erythromycin 2 (2.4%) and oxacillin/gentamicin and erythromycin 1 (1.2%).

Conclusion

This study reports a relatively high prevalence of MRSA. Isolates that are resistant to other tested antibiotics including vancomycin are also reported. The data have important implication for quality of patients care in the two settings: antibiotic selection and infection control practices, and the need for additional studies.

Inference and control of the nosocomial transmission of methicillin-resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus (MRSA) is a continued threat to human health in both community and healthcare settings. In hospitals, control efforts would benefit from accurate estimation of asymptomatic colonization and infection importation rates from the community. However, developing such estimates remains challenging due to limited observation of colonization and complicated transmission dynamics within hospitals and the community. Here, we develop an inference framework that can estimate these key quantities by combining statistical filtering techniques, an agent-based model, and real-world patient-to-patient contact networks, and use this framework to infer nosocomial transmission and infection importation over an outbreak spanning 6 years in 66 Swedish hospitals. In particular, we identify a small number of patients with disproportionately high risk of colonization. In retrospective control experiments, interventions targeted to these individuals yield a substantial improvement over heuristic strategies informed by number of contacts, length of stay and contact tracing.

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

BACKGROUND

Antibiotic-resistant bacteria (ARB) are selected by the use of antibiotics. The rational design of interventions to reduce levels of antibiotic resistance requires a greater understanding of how and where ARB are acquired. Our aim was to determine whether acquisition of ARB occurs more often in the community or hospital setting.

METHODS

We used a mathematical model of the natural history of ARB to estimate how many ARB were acquired in each of these two environments, as well as to determine key parameters for further investigation. To do this, we explored a range of realistic parameter combinations and considered a case study of parameters for an important subset of resistant strains in England.

RESULTS

If we consider all people with ARB in the total population (community and hospital), the majority, under most clinically derived parameter combinations, acquired their resistance in the community, despite higher levels of antibiotic use and transmission of ARB in the hospital. However, if we focus on just the hospital population, under most parameter combinations a greater proportion of this population acquired ARB in the hospital.

CONCLUSIONS

It is likely that the majority of ARB are being acquired in the community, suggesting that efforts to reduce overall ARB carriage should focus on reducing antibiotic usage and transmission in the community setting. However, our framework highlights the need for better pathogen-specific data on antibiotic exposure, ARB clearance and transmission parameters, as well as the link between carriage of ARB and health impact. This is important to determine whether interventions should target total ARB carriage or hospital-acquired ARB carriage, as the latter often dominated in hospital populations.

On the Antibacterial Activity of Azacarboxylate Ligands: Lowered Metal Ion Affinities for Bis-amide Derivatives of EDTA do not mean Reduced Activity

EDTA is widely used as an inhibitor of bacterial growth, affecting the uptake and control of metal ions by microorganisms. We describe the synthesis and characterisation of two symmetrical bis-amide derivatives of EDTA, featuring glycyl or pyridyl substituents: AmGly₂ and AmPy₂ . Metal ion affinities (logK) have been evaluated for a range of metals (Mg²⁺ , Ca²⁺ , Fe³⁺ , Mn²⁺ , Zn²⁺ ), revealing less avid binding compared to EDTA. The solid-state structures of AmGly₂ and of its Mg²⁺ complex have been determined crystallographically. The latter shows an unusual 7-coordinate, capped octahedral Mg²⁺ centre. The antibacterial activities of the two ligands and of EDTA have been evaluated against a range of health-relevant bacterial species, three Gram negative (Escherichia coli, Pseudomonas aeruginosa and Klebsiella pneumoniae) and a Gram positive (Staphylococcus aureus). The AmPy₂ ligand is the only one that displays a significant inhibitory effect against K. pneumoniae, but is less effective against the other organisms. AmGly₂ exhibits a more powerful inhibitory effect against E. coli at lower concentrations than EDTA (<3 mm) or AmPy₂ , but loses its efficacy at higher concentrations. The growth inhibition of EDTA and AmGly₂ on mutant E. coli strains with defects in outer-membrane lipopolysaccharide (LPS) structures has been assessed to provide insight into the unexpected behaviour. Taken together, the results contradict the assumption of a simple link between metal ion affinity and antimicrobial efficacy.

Application of Nanoparticle Technologies in the Combat against Anti-Microbial Resistance

Anti-microbial resistance is a growing problem that has impacted the world and brought about the beginning of the end for the old generation of antibiotics. Increasingly, more antibiotics are being prescribed unnecessarily and this reckless practice has resulted in increased resistance towards these drugs, rendering them useless against infection. Nanotechnology presents a potential answer to anti-microbial resistance, which could stimulate innovation and create a new generation of antibiotic treatments for future medicines. Preserving existing antibiotic activity through novel formulation into or onto nanotechnologies can increase clinical longevity of action against infection. Additionally, the unique physiochemical properties of nanoparticles can provide new anti-bacterial modes of action which can also be explored. Simply concentrating on antibiotic prescribing habits will not resolve the issue but rather mitigate it. Thus, new scientific approaches through the development of novel antibiotics and formulations is required in order to employ a new generation of therapies to combat anti-microbial resistance.

Why sensitive bacteria are resistant to hospital infection control

Background: Large reductions in the incidence of antibiotic-resistant strains of Staphylococcus aureus and Clostridium difficile have been observed in response to multifaceted hospital-based interventions. Reductions in antibiotic-sensitive strains have been smaller or non-existent. It has been argued that since infection control measures, such as hand hygiene, should affect resistant and sensitive strains equally, observed changes must have largely resulted from other factors, including changes in antibiotic use. We used a mathematical model to test the validity of this reasoning. Methods: We developed a mechanistic model of resistant and sensitive strains in a hospital and its catchment area. We assumed the resistant strain had a competitive advantage in the hospital and the sensitive strain an advantage in the community. We simulated a hospital hand hygiene intervention that directly affected resistant and sensitive strains equally. The annual incidence rate ratio ( IRR) associated with the intervention was calculated for hospital- and community-acquired infections of both strains. Results: For the resistant strain, there were large reductions in hospital-acquired infections (0.1 ≤ IRR ≤ 0.6) and smaller reductions in community-acquired infections (0.2 ≤ IRR ≤  0.9). These reductions increased in line with increasing importance of nosocomial transmission of the strain. For the sensitive strain, reductions in hospital acquisitions were much smaller (0.6 ≤ IRR ≤ 0.9), while communityacquisitions could increase or decrease (0.9 ≤ IRR ≤ 1.2). The greater the importance of the community environment for the transmission of the sensitive strain, the smaller the reductions. Conclusions: Counter-intuitively, infection control interventions, including hand hygiene, can have strikingly discordant effects on resistant and sensitive strains even though they target them equally, following differences in their adaptation to hospital and community-based transmission. Observed lack of effectiveness of control measures for sensitive strains does not provide evidence that infection control interventions have been ineffective in reducing resistant strains.

Why sensitive bacteria are resistant to hospital infection control

Background: Large reductions in the incidence of antibiotic-resistant strains of Staphylococcus aureus and Clostridium difficile have been observed in response to multifaceted hospital-based interventions. Reductions in antibiotic-sensitive strains have been smaller or non-existent. It has been argued that since infection control measures, such as hand hygiene, should affect resistant and sensitive strains equally, observed changes must have largely resulted from other factors, including changes in antibiotic use. We used a mathematical model to test the validity of this reasoning.

Methods: We developed a mechanistic model of resistant and sensitive strains in a hospital and its catchment area. We assumed the resistant strain had a competitive advantage in the hospital and the sensitive strain an advantage in the community. We simulated a hospital hand hygiene intervention that directly affected resistant and sensitive strains equally. The annual incidence rate ratio ( IRR) associated with the intervention was calculated for hospital- and community-acquired infections of both strains.

Results: For the resistant strain, there were large reductions in hospital-acquired infections (0.1 ≤ IRR ≤ 0.6) and smaller reductions in community-acquired infections (0.2 ≤ IRR ≤  0.9). These reductions increased in line with increasing importance of nosocomial transmission of the strain. For the sensitive strain, reductions in hospital acquisitions were much smaller (0.6 ≤ IRR ≤ 0.9), while communityacquisitions could increase or decrease (0.9 ≤ IRR ≤ 1.2). The greater the importance of the community environment for the transmission of the sensitive strain, the smaller the reductions.

Conclusions: Counter-intuitively, infection control interventions, including hand hygiene, can have strikingly discordant effects on resistant and sensitive strains even though they target them equally, following differences in their adaptation to hospital and community-based transmission. Observed lack of effectiveness of control measures for sensitive strains does not provide evidence that infection control interventions have been ineffective in reducing resistant strains.

Measuring distance through dense weighted networks: The case of hospital-associated pathogens

Hospital networks, formed by patients visiting multiple hospitals, affect the spread of hospital-associated infections, resulting in differences in risks for hospitals depending on their network position. These networks are increasingly used to inform strategies to prevent and control the spread of hospital-associated pathogens. However, many studies only consider patients that are received directly from the initial hospital, without considering the effect of indirect trajectories through the network. We determine the optimal way to measure the distance between hospitals within the network, by reconstructing the English hospital network based on shared patients in 2014–2015, and simulating the spread of a hospital-associated pathogen between hospitals, taking into consideration that each intermediate hospital conveys a delay in the further spread of the pathogen. While the risk of transferring a hospital-associated pathogen between directly neighbouring hospitals is a direct reflection of the number of shared patients, the distance between two hospitals far-away in the network is determined largely by the number of intermediate hospitals in the network. Because the network is dense, most long distance transmission chains in fact involve only few intermediate steps, spreading along the many weak links. The dense connectivity of hospital networks, together with a strong regional structure, causes hospital-associated pathogens to spread from the initial outbreak in a two-step process: first, the directly surrounding hospitals are affected through the strong connections, second all other hospitals receive introductions through the multitude of weaker links. Although the strong connections matter for local spread, weak links in the network can offer ideal routes for hospital-associated pathogens to travel further faster. This hold important implications for infection prevention and control efforts: if a local outbreak is not controlled in time, colonised patients will appear in other regions, irrespective of the distance to the initial outbreak, making import screening ever more difficult.

Shared patients can spread hospital-associated pathogens between hospitals, together forming a large network in which all hospitals are connected. We set out to measure the distance between hospitals in such a network, best reflecting the risk of a hospital-associated pathogen spreading from one to the other. The central problem is that this risk may not be a directly reflected by the weight of the direct connections between hospitals, because the pathogen could arrive through a longer indirect route, first causing a problem in an intermediate hospital. We determined the optimal balance between connection weights and path length, by testing different weighting factors between them against simulated spread of a pathogen. We found that while strong connections are important risk factor for a hospital’s direct neighbours, weak connections offer ideal indirect routes for hospital-associated pathogens to travel further faster. These routes should not be underestimated when designing control strategies.

Impact of Hepatitis C Treatment as Prevention for People Who Inject Drugs is sensitive to contact network structure

Treatment as Prevention (TasP) using directly-acting antivirals has been advocated for Hepatitis C Virus (HCV) in people who inject drugs (PWID), but treatment is expensive and TasP’s effectiveness is uncertain. Previous modelling has assumed a homogeneously-mixed population or a static network lacking turnover in the population and injecting partnerships. We developed a transmission-dynamic model on a dynamic network of injecting partnerships using data from survey of injecting behaviour carried out in London, UK. We studied transmission on a novel exponential-clustered network, as well as on two simpler networks for comparison, an exponential unclustered and a random network, and found that TasP’s effectiveness differs markedly. With respect to an exponential-clustered network, the random network (and homogeneously-mixed population) overestimate TasP’s effectiveness, whereas the exponential-unclustered network underestimates it. For all network types TasP’s effectiveness depends on whether treated patients change risk behaviour, and on treatment coverage: higher coverage requires fewer total treatments for the same health gain. Whilst TasP can greatly reduce HCV prevalence, incidence of infection, and incidence of reinfection in PWID, assessment of TasP’s effectiveness needs to take account of the injecting-partnership network structure and post-treatment behaviour change, and further empirical study is required.

Modeling Nosocomial Infections of Methicillin-Resistant Staphylococcus aureus with Environment Contamination<sup/>

In this work, we investigate the role of environmental contamination on the clinical epidemiology of antibiotic-resistant bacteria in hospitals. Methicillin-resistant Staphylococcus aureus (MRSA) is a bacterium that causes infections in different parts of the body. It is tougher to treat than most strains of Staphylococcus aureus or staph, because it is resistant to some commonly used antibiotics. Both deterministic and stochastic models are constructed to describe the transmission characteristics of MRSA in hospital setting. The deterministic epidemic model includes five compartments: colonized and uncolonized patients, contaminated and uncontaminated health care workers (HCWs), and bacterial load in environment. The basic reproduction number R ₀ is calculated, and its numerical and sensitivity analysis has been performed to study the asymptotic behavior of the model, and to help identify factors responsible for observed patterns of infections. A stochastic epidemic model with stochastic simulations is also presented to supply a comprehensive analysis of its behavior. Data collected from Beijing Tongren Hospital will be used in the numerical simulations of our model. The results can be used to provide theoretical guidance for designing efficient control measures, such as increasing the hand hygiene compliance of HCWs and disinfection rate of environment, and decreasing the transmission rate between environment and patients and HCWs.

A unified inter-host and in-host model of antibiotic resistance and infection spread in a hospital ward

As the battle continues against hospital-acquired infections and the concurrent rise in antibiotic resistance among many of the major causative pathogens, there is a dire need to conduct controlled experiments, in order to compare proposed control strategies. However, cost, time, and ethical considerations make this evaluation strategy either impractical or impossible to implement with living patients. This paper presents a multi-scale model that offers promise as the basis for a tool to simulate these (and other) controlled experiments. This is a "unified" model in two important ways: (i) It combines inter-host and in-host dynamics into a single model, and (ii) it links two very different modeling approaches - agent-based modeling and differential equations - into a single model. The potential of this model as an instrument to combat antibiotic resistance in hospitals is demonstrated with numerical examples.

Optimal control applied to community-acquired methicillin-resistant Staphylococcus aureus in hospitals

Optimal control methods are applied to a deterministic mathematical model to characterize the factors contributing to the replacement of hospital-acquired methicillin-resistant Staphylococcus aureus (HA-MRSA) with community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA), and quantify the effectiveness of three interventions aimed at limiting the spread of CA-MRSA in healthcare settings. Characterizations of the optimal control strategies are established, and numerical simulations are provided to illustrate the results.