Limiting the spread of highly resistant hospital-acquired microorganisms via critical care transfers: a simulation study

Considerations for Social Networks and Health Data Sharing: An Overview

The use of network analysis as a tool has increased exponentially as more clinical researchers see the benefits of network data for modeling of infectious disease transmission or translational activities in a variety of areas, including patient-caregiving teams, provider networks, patient-support networks, and adoption of health behaviors or treatments, to name a few. Yet, relational data such as network data carry a higher risk of deductive disclosure. Cases of reidentification have occurred and this is expected to become more common as computational ability increases. Recent data sharing policies aim to promote reproducibility, support replicability, and protect federal investment in the effort to collect these research data by making them available for secondary analyses. However, typical practices to protect individual-level clinical research data may not be sufficiently protective of participant privacy in the case of network data, nor in some cases do they permit secondary data analysis. When sharing data, researchers must balance security, accessibility, reproducibility, and adaptability (suitability for secondary analyses). Here, we provide background about applying network analysis to health and clinical research, describe the pros and cons of applying typical practices for sharing clinical data to network data, and provide recommendations for sharing network data.

Spatial-temporal simulation for hospital infection spread and outbreaks of Clostridioides difficile

Validated and curated datasets are essential for studying the spread and control of infectious diseases in hospital settings, requiring clinical information on patients' evolution and their location. The literature shows that approaches based on Artificial Intelligence (AI) in the development of clinical-support systems have benefits that are increasingly recognized. However, there is a lack of available high-volume data, necessary for trusting such AI models. One effective method in this situation involves the simulation of realistic data. Existing simulators primarily focus on implementing compartmental epidemiological models and contact networks to validate epidemiological hypotheses. Nevertheless, other practical aspects such as the hospital building distribution, shifts or safety policies on infections has received minimal attention. In this paper, we propose a novel approach for a simulator of nosocomial infection spread, combining agent-based patient description, spatial-temporal constraints of the hospital settings, and microorganism behavior driven by epidemiological models. The predictive validity of the model was analyzed considering micro and macro-face validation, parameter calibration based on literature review, model alignment, and sensitive analysis with an expert. This simulation model is useful in monitoring infections and in the decision-making process in a hospital, by helping to detect spatial-temporal patterns and predict statistical data about the disease.

Comparing optimization criteria in antibiotic allocation protocols

Clinicians prescribing antibiotics in a hospital context follow one of several possible 'treatment protocols'-heuristic rules designed to balance the immediate needs of patients against the long-term threat posed by the evolution of antibiotic resistance and multi-resistant bacteria. Several criteria have been proposed for assessing these protocols; unfortunately, these criteria frequently conflict with one another, each providing a different recommendation as to which treatment protocol is best. Here, we review and compare these optimization criteria. We are able to demonstrate that criteria focused primarily on slowing evolution of resistance are directly antagonistic to patient health both in the short and long term. We provide a new optimization criteria of our own, intended to more meaningfully balance the needs of the future and present. Asymptotic methods allow us to evaluate this criteria and provide insights not readily available through the numerical methods used previously in the literature. When cycling antibiotics, we find an antibiotic switching time which proves close to optimal across a wide range of modelling assumptions.

A 'time bomb' in the human intestine-the multiple emergence and spread of antibiotic-resistant bacteria

Antibiotics have a strong killing effect on bacteria and are the first choice for the prevention and treatment of bacterial infectious diseases. Therefore, they have been widely used in the medical field, animal husbandry and planting industry. However, with the massive use of antibiotics, more and more antibiotic-resistant bacteria (ARB) have emerged. Because human intestines are rich in nutrients, have suitable temperature, and are high in bacterial abundance, they can easily become a hotbed for the spread of ARB and antibiotic-resistant genes (ARGs). When opportunistic pathogenic bacteria in the intestine acquire ARGs, the infectious diseases caused by such opportunistic pathogens will become more difficult to treat, or even impossible to cure. Therefore, ARB in the human intestine are like a 'time bomb'. In this review, we discuss the sources of intestinal ARB and the transmission routes of ARGs in the human intestine from the perspective of One Health. Further, we describe various methods to prevent the emergence of ARB and inhibit the spread of ARGs in the human intestine. Finally, we may be able to overcome ARB in the human intestine using an interdisciplinary 'One Health' approach.

Evaluating Movement of Patients With Carbapenem-resistant Enterobacteriaceae Infections in the Greater Atlanta Metropolitan Area Using Social Network Analysis

BACKGROUND

Carbapenem-resistant Enterobacteriaceae (CRE) are an urgent threat with potential for rapid spread. We evaluated the role of Medicare patient movement between facilities to model the spread of CRE within a region.

METHODS

Through population-based CRE surveillance in the 8-county Atlanta (GA) metropolitan area, all Escherichia coli, Enterobacter spp., or Klebsiella spp. resistant to ≥1 carbapenem were reported from residents. CRE was attributed to a facility based on timing of culture and facility exposures. Centrality metrics were calculated from 2016 Medicare data and compared to CRE-transfer derived centrality metrics by Spearman correlation.

RESULTS

During 2016, 283 incident CRE cases with concurrent or prior year facility stays were identified; cases were attributed mostly to acute care hospitals (ACHs; 141, 50%) and skilled nursing facilities (SNFs; 113, 40%), and less frequently to long-term acute care hospitals (LTACHs; 29, 10%). Attribution was widespread, originating at 17 of 20 ACHs (85%), 7 of 8 (88%) LTACHs, but only 35 of 65 (54%) SNFs. Betweenness of Medicare patient transfers strongly correlated with betweenness of CRE case-transfer data in ACHs (r = 0.75; P < .01) and LTACHs (r = 0.77; P = .03), but not in SNFs (r = 0.02; P = 0.85). We noted 6 SNFs with high CRE-derived betweenness but low Medicare-derived betweenness.

CONCLUSIONS

CRE infections originate from almost all ACHs and half of SNFs. We identified a subset of SNFs central to the CRE transfer network but not the Medicare transfer network; other factors may explain CRE patient movement in these facilities.

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.

Regional Spread of an Outbreak of Carbapenem-Resistant Enterobacteriaceae Through an Ego Network of Healthcare Facilities

Background

In 2013, New Delhi metallo-β-lactamase (NDM)-producing Escherichia coli, a type of carbapenem-resistant Enterobacteriaceae uncommon in the United States, was identified in a tertiary care hospital (hospital A) in northeastern Illinois. The outbreak was traced to a contaminated duodenoscope. Patient-sharing patterns can be described through social network analysis and ego networks, which could be used to identify hospitals most likely to accept patients from a hospital with an outbreak.

Methods

Using Illinois' hospital discharge data and the Illinois extensively drug-resistant organism (XDRO) registry, we constructed an ego network around hospital A. We identified which facilities NDM outbreak patients subsequently visited and whether the facilities reported NDM cases.

Results

Of the 31 outbreak cases entered into the XDRO registry who visited hospital A, 19 (61%) were subsequently admitted to 13 other hospitals during the following 12 months. Of the 13 hospitals, the majority (n = 9; 69%) were in our defined ego network, and 5 of those 9 hospitals consequently reported at least 1 additional NDM case. Ego network facilities were more likely to identify cases compared to a geographically defined group of facilities (9/22 vs 10/66; P = .01); only 1 reported case fell outside of the ego network.

Conclusions

The outbreak hospital's ego network accurately predicted which hospitals the outbreak patients would visit. Many of these hospitals reported additional NDM cases. Prior knowledge of this ego network could have efficiently focused public health resources on these high-risk facilities.

Using hospital network-based surveillance for antimicrobial resistance as a more robust alternative to self-reporting

Hospital performance is often measured using self-reported statistics, such as the incidence of hospital-transmitted micro-organisms or those exhibiting antimicrobial resistance (AMR), encouraging hospitals with high levels to improve their performance. However, hospitals that increase screening efforts will appear to have a higher incidence and perform poorly, undermining comparison between hospitals and disincentivising testing, thus hampering infection control. We propose a surveillance system in which hospitals test patients previously discharged from other hospitals and report observed cases. Using English National Health Service (NHS) Hospital Episode Statistics data, we analysed patient movements across England and assessed the number of hospitals required to participate in such a reporting scheme to deliver robust estimates of incidence. With over 1.2 million admissions to English hospitals previously discharged from other hospitals annually, even when only a fraction of hospitals (41/155) participate (each screening at least 1000 of these admissions), the proposed surveillance system can estimate incidence across all hospitals. By reporting on other hospitals, the reporting of incidence is separated from the task of improving own performance. Therefore the incentives for increasing performance can be aligned to increase (rather than decrease) screening efforts, thus delivering both more comparable figures on the AMR problems across hospitals and improving infection control efforts.

Hospitalized Pets as a Source of Carbapenem-Resistance

The massive and irrational use of antibiotics in livestock productions has fostered the occurrence and spread of resistance to “old class antimicrobials.” To cope with that phenomenon, some regulations have been already enforced in the member states of the European Union. However, a role of livestock animals in the relatively recent alerts on the rapid worldwide increase of resistance to last-choice antimicrobials as carbapenems is very unlikely. Conversely, these antimicrobials are increasingly administered in veterinary hospitals whose role in spreading bacteria or mobile genetic elements has not adequately been addressed so far. A cross-sectional study was carried out on 105 hospitalized and 100 non-hospitalized pets with the aim of measuring the prevalence of carbapenem-resistant Gram-negative bacteria (GNB) colonizing dogs and cats, either hospitalized or not hospitalized and estimating the relative odds. Stool samples were inoculated on MacConkey agar plates containing 1 mg/L imipenem which were then incubated aerobically at 37°C ± 1 for 48 h. Isolated bacteria were identified first by Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and were confirmed by 16S rRNA sequencing. The genetic basis of resistance was investigated using PCR methods, gene or whole genome sequencing (WGS). The prevalence of pets harboring carbapenem-resistant bacteria was 11.4 and 1.0% in hospitalized and not-hospitalized animals, respectively, with an odds ratio of 12.8 (p < 0.01). One pet carried two diverse isolates. Overall, 14 gram-negative non-fermenting bacteria, specifically, one Acinetobacter radioresistens, five Acinetobacter baumannii, six Pseudomonas aeruginosa and two Stenotrophomonas maltophilia were isolated. The Acinetobacter species carried acquired carbapenemases genes encoded by bla_NDM-1 and bla_OXA-23. In contrast, Pseudomonas phenotypic resistance was associated with the presence of mutations in the oprD gene. Notably, inherent carbapenem-resistant isolates of S. maltophilia were also resistant to the first-line recommended chemotherapeutic trimethoprim/sulfamethoxazole. This study estimates the risk of colonization by carbapenem-resistant non-fermenting GNB in pets hospitalized in veterinary tertiary care centers and highlights their potential role in spreading resistance genes among the animal and human community. Public health authorities should consider extending surveillance systems and putting the release of critical antibiotics under more strict control in order to manage the infection/colonization of pets in veterinary settings.

The role of intra and inter-hospital patient transfer in the dissemination of heathcare-associated multidrug-resistant pathogens

Healthcare-associated infections cause significant patient morbidity and mortality, and contribute to growing healthcare costs, whose effects may be felt most strongly in developing countries. Active surveillance systems, hospital staff compliance, including hand hygiene, and a rational use of antimicrobials are among the important measures to mitigate the spread of healthcare-associated infection within and between hospitals. Klebsiella pneumoniae is an important human pathogen that can spread in hospital settings, with some forms exhibiting drug resistance, including resistance to the carbapenem class of antibiotics, the drugs of last resort for such infections. Focusing on the role of patient movement within and between hospitals on the transmission and incidence of enterobacteria producing the K. pneumoniae Carbapenemase (KPC, an enzyme that inactivates several antimicrobials), we developed a metapopulation model where the connections among hospitals are made using a theoretical hospital network based on Brazilian hospital sizes and locations. The pathogen reproductive number, R₀ that measures the average number of new infections caused by a single infectious individual, was calculated in different scenarios defined by both the links between hospital environments (regular wards and intensive care units) and between different hospitals (patient transfer). Numerical simulation was used to illustrate the infection dynamics in this set of scenarios. The sensitivity of R₀ to model input parameters, such as hospital connectivity and patient-hospital staff contact rates was also established, highlighting the differential importance of factors amenable to change on pathogen transmission and control.

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.

Prevalence and risk factors for bloodstream infection present on hospital admission

BACKGROUND

Bloodstream infection present on hospital admission (BSI-POA) is a major cause of morbidity and mortality. The purpose of this study was to measure prevalence and describe the risk factors of patients with BSI-POA and to determine the prevalence of resistance in isolates by admission source.

METHODS

We conducted a retrospective cohort study of patients discharged from three hospitals in New York City between 2006 and 2014. BSI-POA was defined as BSI diagnosed within 48 h of hospitalisation.

RESULTS

The prevalence for BSI-POA was 5307/315,010 discharges (1.7%). The odds of being admitted with BSI-POA were greatest among patients admitted with renal failure, chronic dermatitis, malignancies and prior hospitalisation. Odds ratios and 95% confidence intervals (CI) were 2.72 (95% CI = 2.56-2.88), 2.15 (95% CI = 1.97-2.34), 1.76 (95% CI = 1.64-1.88) and 1.59 (95% CI = 1.50-1.69), respectively. The largest proportion of BSI-POA presented with Staphylococcus aureus (48.4%), followed by Enterococcus faecalis/faecium (20.3%), Klebsiella pneumoniae (16.2%), Streptococcus pneumoniae (8.7%), Pseudomonas aeruginosa (4.2%) and Acinetobacter baumannii (2.2%). Overall, 44% of those admitted from nursing homes presented with antibiotic resistant strains versus 34% from other hospitals and 31% from private homes (P = 0.002).

CONCLUSION

Understanding the risk factors of patients who present to the hospital with BSI could enable timely interventions and better patient outcomes.

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.

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.

Influence of a patient transfer network of US inpatient facilities on the incidence of nosocomial infections

Antibiotic-resistant bacterial infections are a substantial source of morbidity and mortality and have a common reservoir in inpatient settings. Transferring patients between facilities could be a mechanism for the spread of these infections. We wanted to assess whether a network of hospitals, linked by inpatient transfers, contributes to the spread of nosocomial infections and investigate how network structure may be leveraged to design efficient surveillance systems. We construct a network defined by the transfer of Medicare patients across US inpatient facilities using a 100% sample of inpatient discharge claims from 2006-2007. We show the association between network structure and C. difficile incidence, with a 1% increase in a facility's C. difficile incidence being associated with a 0.53% increase in C. difficile incidence of neighboring facilities. Finally, we used network science methods to determine the facilities to monitor to maximize surveillance efficiency. An optimal surveillance strategy for selecting "sensor" hospitals, based on their network position, detects 80% of the C. difficile infections using only 2% of hospitals as sensors. Selecting a small fraction of facilities as "sensors" could be a cost-effective mechanism to monitor emerging nosocomial infections.

The relative importance of large problems far away versus small problems closer to home: insights into limiting the spread of antimicrobial resistance in England

BACKGROUND

To combat the spread of antimicrobial resistance (AMR), hospitals are advised to screen high-risk patients for carriage of antibiotic-resistant bacteria on admission. This often includes patients previously admitted to hospitals with a high AMR prevalence. However, the ability of such a strategy to identify introductions (and hence prevent onward transmission) is unclear, as it depends on AMR prevalence in each hospital, the number of patients moving between hospitals, and the number of hospitals considered 'high risk'.

METHODS

We tracked patient movements using data from the National Health Service of England Hospital Episode Statistics and estimated differences in regional AMR prevalences using, as an exemplar, data collected through the national reference laboratory service of Public Health England on carbapenemase-producing Enterobacteriaceae (CPE) from 2008 to 2014. Combining these datasets, we calculated expected CPE introductions into hospitals from across the hospital network to assess the effectiveness of admission screening based on defining high-prevalence hospitals as high risk.

RESULTS

Based on numbers of exchanged patients, the English hospital network can be divided into 14 referral regions. England saw a sharp increase in numbers of CPE isolates referred to the national reference laboratory over 7 years, from 26 isolates in 2008 to 1649 in 2014. Large regional differences in numbers of confirmed CPE isolates overlapped with regional structuring of patient movements between hospitals. However, despite these large differences in prevalence between regions, we estimated that hospitals received only a small proportion (1.8%) of CPE-colonised patients from hospitals outside their own region, which decreased over time.

CONCLUSIONS

In contrast to the focus on import screening based on assigning a few hospitals as 'high risk', patient transfers between hospitals with small AMR problems in the same region often pose a larger absolute threat than patient transfers from hospitals in other regions with large problems, even if the prevalence in other regions is orders of magnitude higher. Because the difference in numbers of exchanged patients, between and within regions, was mostly larger than the difference in CPE prevalence, it would be more effective for hospitals to focus on their own populations or region to inform control efforts rather than focussing on problems elsewhere.

Spread of Carbapenem-Resistant Enterobacteriaceae Among Illinois Healthcare Facilities: The Role of Patient Sharing

BACKGROUND

Carbapenem-resistant Enterobacteriaceae (CRE) spread regionally throughout healthcare facilities through patient transfer and cause difficult-to-treat infections. We developed a state-wide patient-sharing matrix and applied social network analyses to determine whether greater connectedness (centrality) to other healthcare facilities and greater patient sharing with long-term acute care hospitals (LTACHs) predicted higher facility CRE rates.

METHODS

We combined CRE case information from the Illinois extensively drug-resistant organism registry with measures of centrality calculated from a state-wide hospital discharge dataset to predict facility-level CRE rates, adjusting for hospital size and geographic characteristics.

RESULTS

Higher CRE rates were observed among facilities with greater patient sharing, as measured by degree centrality. Each additional hospital connection (unit of degree) conferred a 6% increase in CRE rate in rural facilities (relative risk [RR] = 1.056; 95% confidence interval [CI], 1.030-1.082) and a 3% increase among Chicagoland and non-Chicago urban facilities (RR = 1.027; 95% CI, 1.002-1.052 and RR = 1.025; 95% CI, 1.002-1.048, respectively). Sharing 4 or more patients with LTACHs was associated with higher CRE rates, but this association may have been due to chance (RR = 2.08; 95% CI, .85-5.08; P = .11).

CONCLUSIONS

Hospitals with greater connectedness to other hospitals in a statewide patient-sharing network had higher CRE burden. Centrality had a greater effect on CRE rates in rural counties, which do not have LTACHs. Social network analysis likely identifies hospitals at higher risk of CRE exposure, enabling focused clinical and public health interventions.

Monitoring the spread of meticillin-resistant Staphylococcus aureus in The Netherlands from a reference laboratory perspective

Background

In The Netherlands, efforts to control meticillin-resistant Staphylococcus aureus (MRSA) in hospitals have been largely successful due to stringent screening of patients on admission and isolation of those that fall into defined risk categories. However, Dutch hospitals are not free of MRSA, and a considerable number of cases are found that do not belong to any of the risk categories. Some of these may be due to undetected nosocomial transmission, whereas others may be introduced from unknown reservoirs.

Aim

Identifying multi-institutional clusters of MRSA isolates to estimate the contribution of potential unobserved reservoirs in The Netherlands.

Methods

We applied a clustering algorithm that combines time, place, and genetics to routine data available for all MRSA isolates submitted to the Dutch Staphylococcal Reference Laboratory between 2008 and 2011 in order to map the geo-temporal distribution of MRSA clonal lineages in The Netherlands.

Findings

Of the 2966 isolates lacking obvious risk factors, 579 were part of geo-temporal clusters, whereas 2387 were classified as MRSA of unknown origin (MUOs). We also observed marked differences in the proportion of isolates that belonged to geo-temporal clusters between specific multi-locus variable number of tandem repeat analysis (MLVA) clonal complexes, indicating lineage-specific transmissibility. The majority of clustered isolates (74%) were present in multi-institutional clusters.

Conclusion

The frequency of MRSA of unknown origin among patients lacking obvious risk factors is an indication of a largely undefined extra-institutional but genetically highly diverse reservoir. Efforts to understand the emergence and spread of high-risk clones require the pooling of routine epidemiological information and typing data into central databases.

Hospital Transfer Network Structure as a Risk Factor for Clostridium difficile Infection

OBJECTIVE

To determine the effect of interhospital patient sharing via transfers on the rate of Clostridium difficile infections in a hospital.

DESIGN

Retrospective cohort.

METHODS

Using data from the Healthcare Cost and Utilization Project California State Inpatient Database, 2005-2011, we identified 2,752,639 transfers. We then constructed a series of networks detailing the connections formed by hospitals. We computed 2 measures of connectivity, indegree and weighted indegree, measuring the number of hospitals from which transfers into a hospital arrive, and the total number of incoming transfers, respectively. Next, we estimated a multivariate model of C. difficile infection cases using the log-transformed network measures as well as covariates for hospital fixed effects, log median length of stay, log fraction of patients aged 65 or older, and quarter and year indicators as predictors.

RESULTS

We found an increase of 1 in the log indegree was associated with a 4.8% increase in incidence of C. difficile infection (95% CI, 2.3%-7.4%) and an increase of 1 in log weighted indegree was associated with a 3.3% increase in C. difficile infection incidence (1.5%-5.2%). Moreover, including measures of connectivity in our models greatly improved their fit.

CONCLUSIONS

Our results suggest infection control is not under the exclusive control of a given hospital but is also influenced by the connections and number of connections that hospitals have with other hospitals.

Efficient surveillance for healthcare-associated infections spreading between hospitals

Early detection of new or novel variants of nosocomial pathogens is a public health priority. We show that, for healthcare-associated infections that spread between hospitals as a result of patient movements, it is possible to design an effective surveillance system based on a relatively small number of sentinel hospitals. We apply recently developed mathematical models to patient admission data from the national healthcare systems of England and The Netherlands. Relatively short detection times are achieved once 10-20% hospitals are recruited as sentinels and only modest reductions are seen as more hospitals are recruited thereafter. Using a heuristic optimization approach to sentinel selection, the same expected time to detection can be achieved by recruiting approximately half as many hospitals. Our study provides a robust evidence base to underpin the design of an efficient sentinel hospital surveillance system for novel nosocomial pathogens, delivering early detection times for reduced expenditure and effort.

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

BACKGROUND

Patients who seek treatment in hospitals can introduce high-risk clones of hospital-acquired, antibiotic-resistant pathogens from previous admissions. In this manner, different healthcare institutions become linked epidemiologically. All links combined form the national patient referral network, through which high-risk clones can propagate.

AIM

To assess the influence of changes in referral patterns and network structure on the dispersal of these pathogens.

METHODS

Hospital admission data were mapped to reconstruct the English patient referral network, and 12 geographically distinct healthcare collectives were identified. The number of patients admitted and referred to hospitals outside their collective was measured. Simulation models were used to assess the influence of changing network structure on the spread of hospital-acquired pathogens.

FINDINGS

Simulation models showed that decreasing the number of between-collective referrals by redirecting, on average, just 1.5 patients/hospital/day had a strong effect on dispersal. By decreasing the number of between-collective referrals, the spread of high-risk clones through the network can be reduced by 36%. Conversely, by creating supra-regional specialist centres that provide specialist care at national level, the rate of dispersal can increase by 48%.

CONCLUSION

The structure of the patient referral network has a profound effect on the epidemic behaviour of high-risk clones. Any changes that affect the number of referrals between healthcare collectives, inevitably affect the national dispersal of these pathogens. These effects should be taken into account when creating national specialist centres, which may jeopardize control efforts.

Infection prevention in a connected world: the case for a regional approach

Results from microbiological and epidemiological investigations, as well as mathematical modelling, show that the transmission dynamics of nosocomial pathogens, especially of multiple antibiotic-resistant bacteria, is not exclusively amenable to single-hospital infection prevention measures. Crucially, their extent of spread depends on the structure of an underlying "healthcare network", as determined by inter-institutional referrals of patients. The current trend towards centralized healthcare systems favours the spread of hospital-associated pathogens, and must be addressed by coordinated regional or national approaches to infection prevention in order to maintain patient safety. Here we review recent advances that support this hypothesis, and propose a "next-generation" network-approach to hospital infection prevention and control.

The incomplete infrastructure for interhospital patient transfer

OBJECTIVE

Interhospital transfer of critically ill patients is a common part of their care. This article sought to review the data on the current patterns of use of interhospital transfer and identify systematic barriers to optimal integration of transfer as a mechanism for improving patient outcomes and value of care.

DATA SOURCE

Narrative review of medical and organizational literature.

SUMMARY

Interhospital transfer of patients is common, but not optimized to improve patient outcomes. Although there is a wide variability in quality among hospitals of nominally the same capability, patients are not consistently transferred to the highest quality nearby hospital. Instead, transfer destinations are selected by organizational routines or non-patient-centered organizational priorities. Accomplishing a transfer is often quite difficult for sending hospitals. But once a transfer destination is successfully found, the mechanics of interhospital transfer now appear quite safe.

CONCLUSION

Important technological advances now make it possible to identify nearby hospitals best able to help critically ill patients, and to successfully transfer patients to those hospitals. However, organizational structures have not yet developed to insure that patients are optimally routed, resulting in potentially significant excess mortality.

Hospital networks and the dispersal of hospital-acquired pathogens by patient transfer

Hospital-acquired infections (HAI) are often seen as preventable incidents that result from unsafe practices or poor hospital hygiene. This however ignores the fact that transmissibility is not only a property of the causative organisms but also of the hosts who can translocate bacteria when moving between hospitals. In an epidemiological sense, hospitals become connected through the patients they share. We here postulate that the degree of hospital connectedness crucially influences the rates of infections caused by hospital-acquired bacteria. To test this hypothesis, we mapped the movement of patients based on the UK-NHS Hospital Episode Statistics and observed that the proportion of patients admitted to a hospital after a recent episode in another hospital correlates with the hospital-specific incidence rate of MRSA bacteraemia as recorded by mandatory reporting. We observed a positive correlation between hospital connectedness and MRSA bacteraemia incidence rate that is significant for all financial years since 2001 except for 2008-09. All years combined, this correlation is positive and significantly different from zero (partial correlation coefficient r = 0.33 (0.28 to 0.38)). When comparing the referral pattern for English hospitals with referral patterns observed in the Netherlands, we predict that English hospitals more likely see a swifter and more sustained spread of HAIs. Our results indicate that hospitals cannot be viewed as individual units but rather should be viewed as connected elements of larger modular networks. Our findings stress the importance of cooperative effects that will have a bearing on the planning of health care systems, patient management and hospital infection control.