Dispersal of antibiotic-resistant high-risk clones by hospital networks: changing the patient direction can make all the difference

Regional patient transfer patterns matter for the spread of hospital-acquired pathogens

Pathogens typically responsible for hospital-acquired infections (HAIs) constitute a major threat to healthcare systems worldwide. They spread via hospital (or hospital-community) networks by readmissions or patient transfers. Therefore, knowledge of these networks is essential to develop and test strategies to mitigate and control the HAI spread. Until now, no methods for comparing healthcare networks across different systems were proposed. Based on healthcare insurance data from four German federal states (Bavaria, Lower Saxony, Saxony and Thuringia), we constructed hospital networks and compared them in a systematic approach regarding population, hospital characteristics, and patient transfer patterns. Direct patient transfers between hospitals had only a limited impact on HAI spread. Whereas, with low colonization clearance rates, readmissions to the same hospitals posed the biggest transmission risk of all inter-hospital transfers. We then generated hospital-community networks, in which patients either stay in communities or in hospitals. We found that network characteristics affect the final prevalence and the time to reach it. However, depending on the characteristics of the pathogen (colonization clearance rate and transmission rate or even the relationship between transmission rate in hospitals and in the community), the studied networks performed differently. The differences were not large, but justify further studies.

The effect of re-directed patient flow in combination with targeted infection control measures on the spread of multi-drug-resistant Enterobacteriaceae in the German health-care system: a mathematical modelling approach

OBJECTIVE

The introduction of multi-drug-resistant Enterobacteriaceae (MDR-E) by colonized patients transferred from high-prevalence countries has led to several large outbreaks of MDR-E in low-prevalence countries, with the risk of propagated spread to the community. The goal of this study was to derive a strategy to counteract the spread of MDR-E at the regional health-care network level.

METHODS

We used a hybrid ordinary differential equation and network model built based on German health insurance data to evaluate whether the re-direction of patient flow in combination with targeted infection control measures can counteract the spread of MDR-E in the German health-care system. We applied pragmatic re-direction strategies focusing on a reduced choice of hospitals for subsequent stays after initial hospitalization but not manipulating direct transfers because these are most likely determined by medical needs.

RESULTS

The re-direction strategies alone did not reduce the system-wide spread of MDR-E (system-wide prevalence of MDR-E is 18.7% vs. 25.7%/29.9%). In contrast, targeted hospital-based infection control measures restricted to institutions with the highest institutional basic reproduction numbers in the network were identified as an effective tool for reducing system-wide prevalence (system-wide prevalence of MDR-E is 18.7% vs. 9.3%). If these measures were applied to the top one-third of hospitals, the system-wide prevalence could be reduced by approximately 80% (system-wide prevalence of 18.7% vs. 3.5% for one-third of patients subjected to interventions). A combination of this hospital-based intervention and patient re-direction strategies could not improve the effectiveness of the hospital-based approach (system-wide prevalence of MDR-E is 9.3% vs. 14.2%/14.3%).

CONCLUSIONS

The pragmatic patient re-direction strategies were not capable of restricting the spread of MDR-E in a simulation of the German health-care system; in contrast, hospital-based interventions focusing on institutions identified based on network transmission patterns seem to be a promising approach for sustainable reduction of the spread of MDR-E through the German population.

National surveillance pilot study unveils a multicenter, clonal outbreak of VIM-2-producing Pseudomonas aeruginosa ST111 in the Netherlands between 2015 and 2017

Verona Integron-encoded Metallo-beta-lactamase (VIM) is the most frequently-encountered carbapenemase in the healthcare-related pathogen Pseudomonas aeruginosa. In the Netherlands, a low-endemic country for antibiotic-resistant bacteria, no national surveillance data on the prevalence of carbapenemase-producing P. aeruginosa (CPPA) was available. Therefore, in 2016, a national surveillance pilot study was initiated to investigate the occurrence, molecular epidemiology, genetic characterization, and resistomes of CPPA among P. aeruginosa isolates submitted by medical microbiology laboratories (MMLs) throughout the country. From 1221 isolates included in the study, 124 (10%) produced carbapenemase (CIM-positive); of these, the majority (95, 77%) were positive for the bla_VIM gene using PCR. Sequencing was performed on 112 CIM-positive and 56 CIM-negative isolates (n = 168), and genetic clustering revealed that 75/168 (45%) isolates were highly similar. This genetic cluster, designated Group 1, comprised isolates that belonged to high-risk sequence type ST111/serotype O12, had similar resistomes, and all but two carried the bla_VIM-2 allele on an identical class 1 integron. Additionally, Group 1 isolates originated from around the country (i.e. seven provinces) and from multiple MMLs. In conclusion, the Netherlands had experienced a nationwide, inter-institutional, clonal outbreak of VIM-2-producing P. aeruginosa for at least three years, which this pilot study was crucial in identifying. A structured, national surveillance program is strongly advised to monitor the spread of Group 1 CPPA, to identify emerging clones/carbapenemase genes, and to detect transmission in and especially between hospitals in order to control current and future outbreaks.

Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections

In the year 2020, there were 105 different statutory insurance companies in Germany with heterogeneous regional coverage. Obtaining data from all insurance companies is challenging, so that it is likely that projects will have to rely on data not covering the whole population. Consequently, the study of epidemic spread in hospital referral networks using data-driven models may be biased. We studied this bias using data from three German regional insurance companies covering four federal states: AOK (historically “general local health insurance company”, but currently only the abbreviation is used) Lower Saxony (in Federal State of Lower Saxony), AOK Bavaria (in Bavaria), and AOK PLUS (in Thuringia and Saxony). To understand how incomplete data influence network characteristics and related epidemic simulations, we created sampled datasets by randomly dropping a proportion of patients from the full datasets and replacing them with random copies of the remaining patients to obtain scale-up datasets to the original size. For the sampled and scale-up datasets, we calculated several commonly used network measures, and compared them to those derived from the original data. We found that the network measures (degree, strength and closeness) were rather sensitive to incompleteness. Infection prevalence as an outcome from the applied susceptible-infectious-susceptible (SIS) model was fairly robust against incompleteness. At incompleteness levels as high as 90% of the original datasets the prevalence estimation bias was below 5% in scale-up datasets. Consequently, a coverage as low as 10% of the local population of the federal state population was sufficient to maintain the relative bias in prevalence below 10% for a wide range of transmission parameters as encountered in clinical settings. Our findings are reassuring that despite incomplete coverage of the population, German health insurance data can be used to study effects of patient traffic between institutions on the spread of pathogens within healthcare networks.

Patterns of patients’ transfer between different hospitals contribute crucially to the risk of hospital-acquired infections (HAIs) in the health care system. To quantify this risk, network models can be applied. The estimated risk can be inaccurate in the case of incomplete data on hospital admissions, which can be a consequence of the multiplicity of insurance companies as it is the case in Germany. To develop a better understanding of how incompleteness of data affects network measures and the simulated spread of HAI, we compared those measures derived from sampled, scale-up and original data, based on hospitalization data from three AOK insurance companies. We found that common network measures were affected by incompleteness, but the simulated prevalence as a measure of epidemic spread in the network was robust over a large range of incompleteness proportions. Epidemics and the transition of the infectious diseases may be modelled on hospital data with a coverage as low as 10% of the local population, whilst maintaining accuracy to within 10% of the true population prevalence.

Modelling pathogen spread in a healthcare network: Indirect patient movements

Inter-hospital patient transfers (direct transfers) between healthcare facilities have been shown to contribute to the spread of pathogens in a healthcare network. However, the impact of indirect transfers (patients re-admitted from the community to the same or different hospital) is not well studied. This work aims to study the contribution of indirect transfers to the spread of pathogens in a healthcare network. To address this aim, a hybrid network–deterministic model to simulate the spread of multiresistant pathogens in a healthcare system was developed for the region of Lower Saxony (Germany). The model accounts for both, direct and indirect transfers of patients. Intra-hospital pathogen transmission is governed by a SIS model expressed by a system of ordinary differential equations. Our results show that the proposed model reproduces the basic properties of healthcare-associated pathogen spread. They also show the importance of indirect transfers: restricting the pathogen spread to direct transfers only leads to 4.2% system wide prevalence. However, adding indirect transfers leads to an increase in the overall prevalence by a factor of 4 (18%). In addition, we demonstrated that the final prevalence in the individual healthcare facilities depends on average length of stay in a way described by a non-linear concave function. Moreover, we demonstrate that the network parameters of the model may be derived from administrative admission/discharge records. In particular, they are sufficient to obtain inter-hospital transfer probabilities, and to express the patients’ transfers as a Markov process. Using the proposed model, we show that indirect transfers of patients are equally or even more important as direct transfers for the spread of pathogens in a healthcare network.

Direct patient transfers between hospitals have been shown to play an important role in the spread of pathogens in a healthcare network. However, readmission of patients from the community (indirect transfers) to the same or a different hospital is not well studied, and its role for the spread of pathogens in a healthcare network is not quantified. In this work, we developed a network model of a healthcare system to study the impact of indirect transfers on the prevalence in the individual hospitals as well as in the overall healthcare system. The model includes both, direct and indirect transfers of patients between the healthcare facilities due to transferring as well as readmission of infectious (colonized or infected) patients. Our results show that the readmission of patients (indirect transfers), either to the same or different facility, is an important potential channel of pathogen transmission. Such indirect transfers are of no less importance than direct patient transfers in controlling the spread of pathogens in a healthcare network.

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.

Relocating a pediatric hospital: Does antimicrobial resistance change?

Objective

Analyze the changes in antimicrobial drug resistance patterns due to hospital relocation. To this end, we conducted a retrospective analysis of microbiological results, especially minimum inhibitory concentrations (MIC) of all isolates from blood, urine and bronchial secretions, in our pediatric university hospital before and after moving to a new building.

Results

While the number of tests done did not change, the fraction of those positive increased, more MICs were determined and certain microbes (A. baumannii, E. faecalis, Klebsiella spp. and P. mirabilis) were detected more frequently. Most changes in MICs occurred in E. faecium (increases in 8 antimicrobials, decreases only in linezolid and gentamicin). For imipenem and aminopenicillins the MICs commonly rose after relocation, the opposite is true for gentamicin and trimethoprim/sulfamethoxazole. The other factors that alter by moving a hospital such as changes in medical personnel or case severity cannot be corrected for, but using MICs we are able to provide insights into changes down to the individual antimicrobial drug and even small changes usually undetectable to the common categorical reporting of resistance.

Tracing and Preventing Infections

A total of 10–20% of somatic patients experience hospital infections during/after hospitalization. Pneumonia, sepsis, surgical site infections and urinary tract infections are most often associated with patient-related use of medical devices for approximately 65% of cases, while nontechnical equipment may be linked to 35% of cases. It is resource-intensive to detect the cause of infection outbreaks and even more expensive not to take action. Unexplained causes of outbreaks may lead to uncertainty and reduced activity at the hospital. To trace and prevent hospital outbreaks, joint efforts from hospital management, microbiology and infection control are needed. This chapter is focused on practical measures to trace and prevent hospital outbreaks.

Evaluation of the Inter- and Intrahospital Spread of Multidrug Resistant Gram-Negative Bacteria in Lithuanian Hospitals

Spread of multidrug-resistant pathogenic bacteria became one of the greatest threats in healthcare worldwide. It is generally accepted that both inter- and intrahospital transmissions of these bacteria contribute significantly to this problem. The purpose of the current study was the evaluation of the inter- and intrahospital spread of multidrug resistant Gram-negative pathogenic bacteria in Lithuania. Clinical isolates of Acinetobacter sp., Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa were subjected for the screening for extended spectrum β-lactamase, carbapenemase, as well as plasmid-mediated AmpC β-lactamase genes. BOX-PCR genotyping was used for the genotyping of these isolates. Our results show that all four pathogens are involved in the intra- and/or interhospital dissemination between the Lithuanian healthcare institutions. The level of transmissions differed between pathogens, and the worst situation was detected for Acinetobacter sp. followed by E. coli. In almost all cases, transmissible strains had at least one gene conferring β-lactam resistance, thereby contributing to the dissemination of the resistance determinants in and between Lithuanian hospitals. Our study clearly demonstrated that immediate actions, more effective strategy, and surveillance are needed to confine and prevent further spread of multidrug resistant Gram-negative pathogenic bacteria in Lithuanian healthcare institutions.

Applications of network analysis to routinely collected health care data: a systematic review

Objective

To survey network analyses of datasets collected in the course of routine operations in health care settings and identify driving questions, methods, needs, and potential for future research.

Materials and Methods

A search strategy was designed to find studies that applied network analysis to routinely collected health care datasets and was adapted to 3 bibliographic databases. The results were grouped according to a thematic analysis of their settings, objectives, data, and methods. Each group received a methodological synthesis.

Results

The search found 189 distinct studies reported before August 2016. We manually partitioned the sample into 4 groups, which investigated institutional exchange, physician collaboration, clinical co-occurrence, and workplace interaction networks. Several robust and ongoing research programs were discerned within (and sometimes across) the groups. Little interaction was observed between these programs, despite conceptual and methodological similarities.

Discussion

We use the literature sample to inform a discussion of good practice at this methodological interface, including the concordance of motivations, study design, data, and tools and the validation and standardization of techniques. We then highlight instances of positive feedback between methodological development and knowledge domains and assess the overall cohesion of the sample.

Mathematical models of infection transmission in healthcare settings: recent advances from the use of network structured data

PURPOSE OF REVIEW

Mathematical modeling approaches have brought important contributions to the study of pathogen spread in healthcare settings over the last 20 years. Here, we conduct a comprehensive systematic review of mathematical models of disease transmission in healthcare settings and assess the application of contact and patient transfer network data over time and their impact on our understanding of transmission dynamics of infections.

RECENT FINDINGS

Recently, with the increasing availability of data on the structure of interindividual and interinstitution networks, models incorporating this type of information have been proposed, with the aim of providing more realistic predictions of disease transmission in healthcare settings. Models incorporating realistic data on individual or facility networks often remain limited to a few settings and a few pathogens (mostly methicillin-resistant Staphylococcus aureus).

SUMMARY

To respond to the objectives of creating improved infection prevention and control measures and better understanding of healthcare-associated infections transmission dynamics, further innovations in data collection and parameter estimation in modeling is required.

Spread of hospital-acquired infections: A comparison of healthcare networks

Hospital-acquired infections (HAIs), including emerging multi-drug resistant organisms, threaten healthcare systems worldwide. Efficient containment measures of HAIs must mobilize the entire healthcare network. Thus, to best understand how to reduce the potential scale of HAI epidemic spread, we explore patient transfer patterns in the French healthcare system. Using an exhaustive database of all hospital discharge summaries in France in 2014, we construct and analyze three patient networks based on the following: transfers of patients with HAI (HAI-specific network); patients with suspected HAI (suspected-HAI network); and all patients (general network). All three networks have heterogeneous patient flow and demonstrate small-world and scale-free characteristics. Patient populations that comprise these networks are also heterogeneous in their movement patterns. Ranking of hospitals by centrality measures and comparing community clustering using community detection algorithms shows that despite the differences in patient population, the HAI-specific and suspected-HAI networks rely on the same underlying structure as that of the general network. As a result, the general network may be more reliable in studying potential spread of HAIs. Finally, we identify transfer patterns at both the French regional and departmental (county) levels that are important in the identification of key hospital centers, patient flow trajectories, and regional clusters that may serve as a basis for novel wide-scale infection control strategies.

Hospital-acquired infections (HAIs), including emerging multi-drug resistant organisms, threaten healthcare systems worldwide. Efficient containment measures of HAIs must mobilize the entire healthcare network. Thus, to best understand how to reduce the scale of potential HAI epidemic spread, we explore patient transfer patterns in the French healthcare system. We construct and compare the characteristics of three different patient transfer networks based on data on transfers of patients with diagnosed HAIs, suspected HAIs, or of all patients. Our analyses show that these healthcare networks, the patient populations that comprise them and the patient movement patterns are heterogeneous and centralized. Despite the differences in patient populations, the HAI-specific and suspected-HAI healthcare networks have the same underlying structure as that of the general healthcare network. We identify key hospital centers, patient flow trajectories, at both the regional and department (county) level that may serve as a basis for proposing novel wide-scale infection control strategies.

Population genetic structuring of methicillin-resistant Staphylococcus aureus clone EMRSA-15 within UK reflects patient referral patterns

Antibiotic resistance forms a serious threat to the health of hospitalised patients, rendering otherwise treatable bacterial infections potentially life-threatening. A thorough understanding of the mechanisms by which resistance spreads between patients in different hospitals is required in order to design effective control strategies. We measured the differences between bacterial populations of 52 hospitals in the United Kingdom and Ireland, using whole-genome sequences from 1085 MRSA clonal complex 22 isolates collected between 1998 and 2012. The genetic differences between bacterial populations were compared with the number of patients transferred between hospitals and their regional structure. The MRSA populations within single hospitals, regions and countries were genetically distinct from the rest of the bacterial population at each of these levels. Hospitals from the same patient referral regions showed more similar MRSA populations, as did hospitals sharing many patients. Furthermore, the bacterial populations from different time-periods within the same hospital were generally more similar to each other than contemporaneous bacterial populations from different hospitals. We conclude that, while a large part of the dispersal and expansion of MRSA takes place among patients seeking care in single hospitals, inter-hospital spread of resistant bacteria is by no means a rare occurrence. Hospitals are exposed to constant introductions of MRSA on a number of levels: (1) most MRSA is received from hospitals that directly transfer large numbers of patients, while (2) fewer introductions happen between regions or (3) across national borders, reflecting lower numbers of transferred patients. A joint coordinated control effort between hospitals, is therefore paramount for the national control of MRSA, antibiotic-resistant bacteria and other hospital-associated pathogens.

Colonization of patients, healthcare workers, and the environment with healthcare-associated Staphylococcus epidermidis genotypes in an intensive care unit: a prospective observational cohort study

Background

During the last decades, healthcare-associated genotypes of methicillin-resistant Staphylococcus epidermidis (HA-MRSE) have been established as important opportunistic pathogens. However, data on potential reservoirs on HA-MRSE is limited. The aim of the present study was to investigate the dynamics and to which extent HA-MRSE genotypes colonize patients, healthcare workers (HCWs) and the environment in an intensive care unit (ICU).

Methods

Over 12 months in 2006–2007, swab samples were obtained from patients admitted directly from the community to the ICU and patients transferred from a referral hospital, as well as from HCWs, and the ICU environment. Patients were sampled every third day during hospitalization. Antibiotic susceptibility testing was performed according to EUCAST guidelines. Pulsed-field gel electrophoresis and multilocus sequence typing were used to determine the genetic relatedness of a subset of MRSE isolates.

Results

We identified 620 MRSE isolates from 570 cultures obtained from 37 HCWs, 14 patients, and 14 environmental surfaces in the ICU. HA-MRSE genotypes were identified at admission in only one of the nine patients admitted directly from the community, of which the majority subsequently were colonized by HA-MRSE genotypes within 3 days during hospitalization. Almost all (89%) of HCWs were nasal carriers of HA-MRSE genotypes. Similarly, a significant proportion of patients transferred from the referral hospital and fomites in the ICU were widely colonized with HA-MRSE genotypes.

Conclusions

Patients transferred from a referral hospital, HCWs, and the hospital environment serve as important reservoirs for HA-MRSE. These observations highlight the need for implementation of effective infection prevention and control measures aiming at reducing HA-MRSE transmission in the healthcare setting.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-2094-x) contains supplementary material, which is available to authorized users.

Not just a matter of size: a hospital-level risk factor analysis of MRSA bacteraemia in Scotland

Background

Worldwide, there is a wealth of literature examining patient-level risk factors for methicillin-resistant Staphylococcus aureus (MRSA) bacteraemia. At the hospital-level it is generally accepted that MRSA bacteraemia is more common in larger hospitals. In Scotland, size does not fully explain all the observed variation among hospitals. The aim of this study was to identify risk factors for the presence and rate of MRSA bacteraemia cases in Scottish mainland hospitals. Specific hypotheses regarding hospital size, type and connectivity were examined.

Methods

Data from 198 mainland Scottish hospitals (defined as having at least one inpatient per year) were analysed for financial year 2007-08 using logistic regression (Model 1: presence/absence of MRSA bacteraemia) and Poisson regression (Model 2: rate of MRSA bacteraemia). The significance of risk factors representing various measures of hospital size, type and connectivity were investigated.

Results

In Scotland, size was not the only significant risk factor identified for the presence and rate of MRSA bacteraemia. The probability of a hospital having at least one case of MRSA bacteraemia increased with hospital size only if the hospital exceeded a certain level of connectivity. Higher levels of MRSA bacteraemia were associated with the large, highly connected teaching hospitals with high ratios of patients to domestic staff.

Conclusions

A hospital’s level of connectedness within a network may be a better measure of a hospital’s risk of MRSA bacteraemia than size. This result could be used to identify high risk hospitals which would benefit from intensified infection control measures.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-1563-6) contains supplementary material, which is available to authorized users.

The Role of Nursing Homes in the Spread of Antimicrobial Resistance Over the Healthcare Network

OBJECTIVE

Recerntly, the role of the healthcare network, defined as a set of hospitals linked by patient transfers, has been increasingly considered in the control of antimicrobial resistance. Here, we investigate the potential impact of nursing homes on the spread of antimicrobial-resistant pathogens across the healthcare network and its importance for control strategies.

METHODS

Based on patient transfer data, we designed a network model representing the Dutch healthcare system of hospitals and nursing homes. We simulated the spread of an antimicrobial-resistant pathogen across the healthcare network, and we modeled transmission within institutions using a stochastic susceptible–infected–susceptible (SIS) epidemic model. Transmission between institutions followed transfers. We identified the contribution of nursing homes to the dispersal of the pathogen by comparing simulations of the network with and without nursing homes.

RESULTS

Our results strongly suggest that nursing homes in the Netherlands have the potential to drive and sustain epidemics across the healthcare network. Even when the daily probability of transmission in nursing homes is much lower than in hospitals, transmission of resistance can be more effective because of the much longer length of stay of patients in nursing homes.

CONCLUSIONS

If an antimicrobial-resistant pathogen emerges that spreads easily within nursing homes, control efforts aimed at hospitals may no longer be effective in preventing nationwide outbreaks. It is important to consider nursing homes in planning regional and national infection control and in implementing surveillance systems that monitor the spread of antimicrobial resistance.

Infect Control Hosp Epidemiol 2016;37:761–767

The Increasing Challenge of Multidrug-Resistant Gram-Negative Bacilli: Results of a 5-Year Active Surveillance Program in a Neonatal Intensive Care Unit

Colonization and infection by multidrug-resistant gram-negative bacilli (MDR GNB) in neonatal intensive care units (NICUs) are increasingly reported.We conducted a 5-year prospective cohort surveillance study in a tertiary NICU of the hospital "Paolo Giaccone," Palermo, Italy. Our objectives were to describe incidence and trends of MDR GNB colonization and the characteristics of the most prevalent organisms and to identify the risk factors for colonization. Demographic, clinical, and microbiological data were prospectively collected. Active surveillance cultures (ASCs) were obtained weekly. Clusters of colonization by extended spectrum β-lactamase (ESBL) producing Escherichia coli and Klebsiella pneumoniae were analyzed by conventional and molecular epidemiological tools.During the study period, 1152 infants were enrolled in the study. Prevalences of colonization by MDR GNB, ESBL-producing GNB and multiple species/genera averaged, respectively, 28.8%, 11.7%, and 3.7%. Prevalence and incidence density of colonization by MDR GNB and ESBL-producing GNB showed an upward trend through the surveillance period. Rates of ESBL-producing E coli and K pneumoniae colonization showed wide fluctuations peaking over the last 2 years. The only independent variables associated with colonization by MDR GNB and ESBL-producing organisms and multiple colonization were, respectively, the days of NICU stay (odds ratio [OR] 1.041), the days of exposure to ampicillin-sulbactam (OR 1.040), and the days of formula feeding (OR 1.031). Most clusters of E coli and K pneumoniae colonization were associated with different lineages. Ten out of 12 clusters had an outborn infant as their index case.Our study confirms that MDR GNB are an increasing challenge to NICUs. The universal once-a-week approach allowed us to understand the epidemiology of MDR GNB, to timely detect new clones and institute contact precautions, and to assess risk factors. Collection of these data can be an important tool to optimize antimicrobials use and control the emergence and dissemination of resistances in NICU.

Use of whole-genome sequencing to trace, control and characterize the regional expansion of extended-spectrum β-lactamase producing ST15 Klebsiella pneumoniae

The study describes the transmission of a CTX-M-15-producing ST15 Klebsiella pneumoniae between patients treated in a single center and the subsequent inter-institutional spread by patient referral occurring between May 2012 and September 2013. A suspected epidemiological link between clinical K. pneumoniae isolates was supported by patient contact tracing and genomic phylogenetic analysis from May to November 2012. By May 2013, a patient treated in three institutions in two cities was involved in an expanding cluster caused by this high-risk clone (HiRiC) (local expansion, CTX-M-15 producing, and containing hypervirulence factors). A clone-specific multiplex PCR was developed for patient screening by which another patient was identified in September 2013. Genomic phylogenetic analysis including published ST15 genomes revealed a close homology with isolates previously found in the USA. Environmental contamination and lack of consistent patient screening were identified as being responsible for the clone dissemination. The investigation addresses the advantages of whole-genome sequencing in the early detection of HiRiC with a high propensity of nosocomial transmission and prolonged circulation in the regional patient population. Our study suggests the necessity for inter-institutional/regional collaboration for infection/outbreak management of K. pneumoniae HiRiCs.

Building a genomic framework for prospective MRSA surveillance in the United Kingdom and the Republic of Ireland

The correct interpretation of microbial sequencing data applied to surveillance and outbreak investigation depends on accessible genomic databases to provide vital genetic context. Our aim was to construct and describe a United Kingdom MRSA database containing over 1000 methicillin-resistant Staphylococcus aureus (MRSA) genomes drawn from England, Northern Ireland, Wales, Scotland, and the Republic of Ireland over a decade. We sequenced 1013 MRSA submitted to the British Society for Antimicrobial Chemotherapy by 46 laboratories between 2001 and 2010. Each isolate was assigned to a regional healthcare referral network in England and was otherwise grouped based on country of origin. Phylogenetic reconstructions were used to contextualize MRSA outbreak investigations and to detect the spread of resistance. The majority of isolates (n = 783, 77%) belonged to CC22, which contains the dominant United Kingdom epidemic clone (EMRSA-15). There was marked geographic structuring of EMRSA-15, consistent with widespread dissemination prior to the sampling decade followed by local diversification. The addition of MRSA genomes from two outbreaks and one pseudo-outbreak demonstrated the certainty with which outbreaks could be confirmed or refuted. We identified local and regional differences in antibiotic resistance profiles, with examples of local expansion, as well as widespread circulation of mobile genetic elements across the bacterial population. We have generated a resource for the future surveillance and outbreak investigation of MRSA in the United Kingdom and Ireland and have shown the value of this during outbreak investigation and tracking of antimicrobial resistance.

Towards an antimicrobial 'microglove'

A large proportion of hospital-related infections are acquired and spread due to the direct contacts between patients and healthcare workers. Accordingly, proper infection prevention measures, and especially hand hygiene, are key to limit the spread of infections in nosocomial settings. However, healthcare workers frequently experience difficulties in complying strictly to hand disinfection protocols. This study was therefore aimed at the development of a hand rub with antimicrobial activity that forms a protective film on the hand, a so-called microglove, in order to enhance hand hygiene. For this purpose, various co-polymer formulations consisting of different ratios of Polyvinylpyrrolidone (PVP) and a branched C20 derivatized maleate (M20) in combination with the known biocide benzalkonium chloride (BKC) were tested for their combined film-forming and antimicrobial activities. The results of a series of novel contamination and transmission assays show that a formulation of 80% PVP and 20% M20 co-polymer with 0.9% BKC fulfils the elementary requirements for an antimicrobial microglove.

Long-term persistence of a multi-resistant methicillin-susceptible Staphylococcus aureus (MR-MSSA) clone at a university hospital in southeast Sweden, without further transmission within the region

The objective of this study was to characterise isolates of methicillin-susceptible Staphylococcus aureus (MSSA) with resistance to clindamycin and/or tobramycin in southeast Sweden, including the previously described ECT-R clone (t002) found in Östergötland County, focusing on clonal relatedness, virulence determinants and existence of staphylococcal cassette chromosome (SCC) mec remnants. MSSA isolates with resistance to clindamycin and/or tobramycin were collected from the three county councils in southeast Sweden and investigated with spa typing, polymerase chain reaction (PCR) targeting the SCCmec right extremity junction (MREJ) and DNA microarray technology. The 98 isolates were divided into 40 spa types, and by microarray clustered in 17 multi-locus sequence typing (MLST) clonal complexes (MLST-CCs). All isolates with combined resistance to clindamycin and tobramycin (n = 12) from Östergötland County and two additional isolates (clindamycin-R) were designated as spa type t002, MREJ type ii and were clustered in CC5, together with a representative isolate of the ECT-R clone, indicating the clone's persistence. These isolates also carried several genes encoding exotoxins, Q9XB68-dcs and qacC. Of the isolates in CC15, 83% (25/30) were tobramycin-resistant and were designated spa type t084. Of these, 68% (17/25) were isolated from new-borns in all three counties. The persistence of the ECT-R clone in Östergötland County, although not found in any other county in the region, carrying certain virulence factors that possibly enhance its survival in the hospital environment, highlights the fact that basic hygiene guidelines must be maintained even when MRSA prevalence is low.

Controlling hospital-acquired infection: focus on the role of the environment and new technologies for decontamination

There is increasing interest in the role of cleaning for managing hospital-acquired infections (HAI). Pathogens such as vancomycin-resistant enterococci (VRE), methicillin-resistant Staphylococcus aureus (MRSA), multiresistant Gram-negative bacilli, norovirus, and Clostridium difficile persist in the health care environment for days. Both detergent- and disinfectant-based cleaning can help control these pathogens, although difficulties with measuring cleanliness have compromised the quality of published evidence. Traditional cleaning methods are notoriously inefficient for decontamination, and new approaches have been proposed, including disinfectants, steam, automated dispersal systems, and antimicrobial surfaces. These methods are difficult to evaluate for cost-effectiveness because environmental data are not usually modeled against patient outcome. Recent studies have reported the value of physically removing soil using detergent, compared with more expensive (and toxic) disinfectants. Simple cleaning methods should be evaluated against nonmanual disinfection using standardized sampling and surveillance. Given worldwide concern over escalating antimicrobial resistance, it is clear that more studies on health care decontamination are required. Cleaning schedules should be adapted to reflect clinical risk, location, type of site, and hand touch frequency and should be evaluated for cost versus benefit for both routine and outbreak situations. Forthcoming evidence on the role of antimicrobial surfaces could supplement infection prevention strategies for health care environments, including those targeting multidrug-resistant pathogens.