Quantifying interhospital patient sharing as a mechanism for infectious disease spread

Carbapenemase-producing enterobacterales colonisation status does not lead to more frequent admissions: a linked patient study

BACKGROUND

Hospitals in any given region can be considered as part of a network, where facilities are connected to one another - and hospital pathogens potentially spread - through the movement of patients between them. We sought to describe the hospital admission patterns of patients known to be colonised with carbapenemase-producing Enterobacterales (CPE), and compare them with CPE-negative patient cohorts, matched on comorbidity information.

METHODS

We performed a linkage study in Victoria, Australia, including datasets with notifiable diseases (CPE notifications) and hospital admissions (admission dates and diagnostic codes) for the period 2011 to 2020. Where the CPE notification date occurred during a hospital admission for the same patient, we identified this as the 'index admission'. We determined the number of distinct health services each patient was admitted to, and time to first admission to a different health service. We compared CPE-positive patients with four cohorts of CPE-negative patients, sampled based on different matching criteria.

RESULTS

Of 528 unique patients who had CPE detected during a hospital admission, 222 (42%) were subsequently admitted to a different health service during the study period. Among these patients, CPE diagnosis tended to occur during admission to a metropolitan public hospital (86%, 190/222), whereas there was a greater number of metropolitan private (23%, 52/222) and rural public (18%, 39/222) hospitals for the subsequent admission. Median time to next admission was 4 days (IQR, 0-75 days). Admission patterns for CPE-positive patients was similar to the cohort of CPE-negative patients matched on index admission, time period, and age-adjusted Charlson comorbidity index.

CONCLUSIONS

Movement of CPE-positive patients between health services is not a rare event. While the most common movement is from one public metropolitan health service to another, there is also a trend for movement from metropolitan public hospitals into private and rural hospitals. After accounting for clinical comorbidities, CPE colonisation status does not appear to impact on hospital admission frequency or timing. These findings support the potential utility of a centralised notification and outbreak management system for CPE positive patients.

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.

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.

Regional transmission patterns of carbapenemase-producing Enterobacterales: A healthcare network analysis

OBJECTIVE

Carbapenem-resistant Enterobacterales (CRE) pose a serious public health threat and spread rapidly between healthcare facilities (HCFs) during interfacility patient movement. We examined patterns of transmission of CRE associated with network clustering and positions during patient interfacility transfer.

METHODS

A retrospective cohort study was conducted in the Greater Houston region ofTexas, , and social network analysis was performed by constructing facility-to-facility patient transfer network using CRE surveillance data. The network method (community detection algorithm) was used to detect clustering patterns of CRE in the network. In addition, network measures of centrality and local connectivity (clustering coefficient) were computed for each healthcare facility. Zero-inflated negative binomial regression analysis was applied to test the association between network measures and facility-specific incidence rate of CRE.

RESULTS

A network of 268 healthcare facilities was identified, in which 10 acute-care hospitals (ACHs) alone accounted for 63% of identified CRE cases. Transmission of New Delhi metallo-β-lactamase-producing CRE occurred in 3 clusters, yet all cases were traced to patients who had had medical care abroad. The incidence rate of CRE attributed to ACHs was >4-fold (adjusted rate ratio, 4.5; 95% confidence interval [CI], 3.02-6.72) higher than that of long-term care facilities. Each additional patient shared with another HCF conferred a 3% (95% CI, 2%-4%) increase in the incidence rate of CRE at that HCF.

CONCLUSIONS

The incidence rates of CRE at a given HCF was predicted by the healthcare network metrics. Increased surveillance and selective targeting of high-risk facilities are warranted.

Antibiotic Susceptibility Patterns for Carbapenem-Resistant Enterobacteriaceae

Carbapenem is a broad-spectrum beta-lactam antibiotic considered the last choice for the treatment of antibiotic-resistant Gram-negative bacteria. Thus, the increasing rate of carbapenem resistance (CR) in Enterobacteriaceae is an urgent public health threat. This study aimed to evaluate the antibiotic susceptibility pattern of carbapenem-resistant Enterobacteriaceae (CRE) to new and old antibiotics. In this study, Klebsiella pneumoniae, E. coli, and Enterobacter spp. were collected from 10 hospitals in Iran for one year. CRE is recognized by resistance to meropenem and/or imipenem disk after identification of the collected bacteria. Antibiotic susceptibility of CRE against fosfomycin, rifampin, metronidazole, tigecycline, and aztreonam was detected by disk diffusion method and colistin by MIC. In this study, 1222 E. coli, 696 K. pneumoniae, and 621 Enterobacter spp. were collected from 10 hospitals in Iran in one year. Fifty-four E. coli (4.4%), 84 K. pneumoniae (12%), and 51 Enterobacter spp. (8.2%) were CRE. All CRE strains were resistant to metronidazole and rifampicin. Tigecycline has the highest sensitivity on CRE and levofloxacin for Enterobacter spp. Tigecycline showed an acceptable effectiveness rate of sensitivity on the CRE strain. Therefore, we suggest that clinicians consider this valuable antibiotic to treat CRE.

Clinical Epidemiology of Carbapenem-Resistant Enterobacterales in the Greater Houston Region of Texas: A 6-year Trend and Surveillance Analysis

Objectives:

Carbapenem-resistant Enterobacterales (CRE) remain an urgent public health priority in the United States. CRE poses a major threat to patients in healthcare and a potential risk to the community. This study examined the epidemiological trends, clinical, and microbiological data of CRE in the Greater Houston region of Texas.

Methods:

A multi-institutional retrospective observational study was conducted using surveillance data collected from 2015 to 2020. Predictors of incidence rates of CRE were determined by a negative binomial regression fit using a generalized estimation equation.

Results:

Over a 6-year period, 4236 CRE cases were reported, of which Klebsiella pneumoniae accounted for 84.8%. The results show a steady increase in CRE cases, with a sharp rise since 2018. The majority of carbapenemase-producing Enterobacterales were Klebsiella pneumoniae carbapenemase (KPC)-producing (77.2%), followed by other rare carbapenemases, which includes OXA-48, NDM, IMP, VIM, coproduction of KPC with OXA-48, KPC with NDM, and NDM with OXA-48. Acute care hospitals (ACH) accounted for 68.5% of the source of CRE cases. The incidence rate of CRE cases reported from ACH and long-term acute care (LTAC) facilities was 1.16 times that of long-term care facilities (adjusted rate ratio [ARR] = 1.16, 95% confidence interval [CI]:1.04–1.30). The incidence rate of CRE among patients with indwelling devices was 15% (ARR = 0.85, 95% CI: 0.79–0.92) lower than that of patients without indwelling devices.

Conclusion:

The rise in the rate of CRE cases despite aggressive infection prevention and control strategies in the region is alarming. Evaluating and improving the current infection control strategies may be warranted.

Modeling Interventions to Reduce the Spread of Multidrug-Resistant Organisms Between Health Care Facilities in a Region

IMPORTANCE

Multidrug-resistant organisms (MDROs) can spread across health care facilities in a region. Because of limited resources, certain interventions can be implemented in only some facilities; thus, decision-makers need to evaluate which interventions may be best to implement.

OBJECTIVE

To identify a group of target facilities and assess which MDRO intervention would be best to implement in the Shared Healthcare Intervention to Eliminate Life-threatening Dissemination of MDROs in Orange County, a large regional public health collaborative in Orange County, California.

DESIGN, SETTING, AND PARTICIPANTS

An agent-based model of health care facilities was developed in 2016 to simulate the spread of methicillin-resistant Staphylococcus aureus (MRSA) and carbapenem-resistant Enterobacteriaceae (CRE) for 10 years starting in 2010 and to simulate the use of various MDRO interventions for 3 years starting in 2017. All health care facilities (23 hospitals, 5 long-term acute care hospitals, and 74 nursing homes) serving adult inpatients in Orange County, California, were included, and 42 target facilities were identified via network analyses.

EXPOSURES

Increasing contact precaution effectiveness, increasing interfacility communication about patients' MDRO status, and performing decolonization using antiseptic bathing soap and a nasal product in a specific group of target facilities.

MAIN OUTCOMES AND MEASURES

MRSA and CRE prevalence and number of new carriers (ie, transmission events).

RESULTS

Compared with continuing infection control measures used in Orange County as of 2017, increasing contact precaution effectiveness from 40% to 64% in 42 target facilities yielded relative reductions of 0.8% (range, 0.5%-1.1%) in MRSA prevalence and 2.4% (range, 0.8%-4.6%) in CRE prevalence in health care facilities countywide after 3 years, averting 761 new MRSA transmission events (95% CI, 756-765 events) and 166 new CRE transmission events (95% CI, 158-174 events). Increasing interfacility communication of patients' MDRO status to 80% in these target facilities produced no changes in the prevalence or transmission of MRDOs. Implementing decolonization procedures (clearance probability: 39% in hospitals, 27% in long-term acute care facilities, and 3% in nursing homes) yielded a relative reduction of 23.7% (range, 23.5%-23.9%) in MRSA prevalence, averting 3515 new transmission events (95% CI, 3509-3521 events). Increasing the effectiveness of antiseptic bathing soap to 48% yielded a relative reduction of 39.9% (range, 38.5%-41.5%) in CRE prevalence, averting 1435 new transmission events (95% CI, 1427-1442 events).

CONCLUSIONS AND RELEVANCE

The findings of this study highlight the ways in which modeling can inform design of regional interventions and suggested that decolonization would be the best strategy for the Shared Healthcare Intervention to Eliminate Life-threatening Dissemination of MDROs in Orange County.

Evaluation of carbapenem-resistant Enterobacteriaceae (CRE) guideline implementation in the Veterans Affairs Medical Centers using the consolidated framework for implementation research

Background

Infections caused by carbapenem-resistant Enterobacteriaceae (CRE) and carbapenemase-producing (CP) CRE are difficult to treat, resulting in high mortality in healthcare settings every year. The Veterans Health Administration (VHA) disseminated guidelines in 2015 and an updated directive in 2017 for control of CRE focused on laboratory testing, prevention, and management. The Consolidated Framework for Implementation Research (CFIR) framework was used to analyze qualitative interview data to identify contextual factors and best practices influencing implementation of the 2015 guidelines/2017 directive in VA Medical Centers (VAMCs). The overall goals were to determine CFIR constructs to target to improve CRE guideline/directive implementation and understand how CFIR, as a multi-level conceptual model, can be used to inform guideline implementation.

Methods

Semi-structured interviews were conducted at 29 VAMCs with staff involved in implementing CRE guidelines at their facility. Survey and VHA administrative data were used to identify geographically representative large and small VAMCs with varying levels of CRE incidence. Interviews addressed perceptions of guideline dissemination, laboratory testing, staff attitudes and training, patient education, and technology support. Participant responses were coded using a consensus-based mixed deductive-inductive approach guided by CFIR. A quantitative analysis comparing qualitative CFIR constructs and emergent codes to sites actively screening for CRE (vs. non-screening) and any (vs. no) CRE-positive cultures was conducted using Fisher’s exact test.

Results

Forty-three semi-structured interviews were conducted between October 2017 and August 2018 with laboratory staff (47%), Multi-Drug-Resistant Organism Program Coordinators (MPCs, 35%), infection preventionists (12%), and physicians (6%). Participants requested more standardized tools to promote effective communication (e.g., electronic screening). Participants also indicated that CRE-specific educational materials were needed for staff, patient, and family members. Quantitative analysis identified CRE screening or presence of CRE as being significantly associated with the following qualitative CFIR constructs: leadership engagement, relative priority, available resources, team communication, and access to knowledge and information.

Conclusions

Effective CRE identification, prevention, and treatment require ongoing collaboration between clinical, microbiology, infection prevention, antimicrobial stewardship, and infectious diseases specialists. Our results emphasize the importance of leadership’s role in promoting positive facility culture, including access to resources, improving communication, and facilitating successful implementation of the CRE guidelines.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43058-021-00170-5.

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.

Regional Emergence of Candida auris in Chicago and Lessons Learned From Intensive Follow-up at 1 Ventilator-Capable Skilled Nursing Facility

BACKGROUND

Since the identification of the first 2 Candida auris cases in Chicago, Illinois, in 2016, ongoing spread has been documented in the Chicago area. We describe C. auris emergence in high-acuity, long-term healthcare facilities and present a case study of public health response to C. auris and carbapenemase-producing organisms (CPOs) at one ventilator-capable skilled nursing facility (vSNF-A).

METHODS

We performed point prevalence surveys (PPSs) to identify patients colonized with C. auris and infection-control (IC) assessments and provided ongoing support for IC improvements in Illinois acute- and long-term care facilities during August 2016-December 2018. During 2018, we initiated a focused effort at vSNF-A and conducted 7 C. auris PPSs; during 4 PPSs, we also performed CPO screening and environmental sampling.

RESULTS

During August 2016-December 2018 in Illinois, 490 individuals were found to be colonized or infected with C. auris. PPSs identified the highest prevalence of C. auris colonization in vSNF settings (prevalence, 23-71%). IC assessments in multiple vSNFs identified common challenges in core IC practices. Repeat PPSs at vSNF-A in 2018 identified increasing C. auris prevalence from 43% to 71%. Most residents screened during multiple PPSs remained persistently colonized with C. auris. Among 191 environmental samples collected, 39% were positive for C. auris, including samples from bedrails, windowsills, and shared patient-care items.

CONCLUSIONS

High burden in vSNFs along with persistent colonization of residents and environmental contamination point to the need for prioritizing IC interventions to control the spread of C. auris and CPOs.

Interfacility patient sharing and Clostridioides difficile infection incidence in the Ontario hospital system: A 13-year cohort study

OBJECTIVE

Interfacility patient movement plays an important role in the dissemination of antimicrobial-resistant organisms throughout healthcare systems. We evaluated how 3 alternative measures of interfacility patient sharing were associated with C. difficile infection incidence in Ontario acute-care facilities.

DESIGN

The cohort included adult acute-care facility stays of ≥3 days between April 2003 and March 2016. We measured 3 facility-level metrics of patient sharing: general patient importation, incidence-weighted patient importation, and C. difficile case importation. Each of the 3 patient-sharing metrics were examined against the incidence of C. difficile infection in the facility per 1,000 stays, using Poisson regression models.

RESULTS

The analyzed cohort included 6.70 million stays at risk of C. difficile infection across 120 facilities. Over the 13-year period, we included 62,189 new cases of healthcare-associated CDI (incidence, 9.3 per 1,000 stays). After adjustment for facility characteristics, general importation was not strongly associated with C. difficile infection incidence (risk ratio [RR] per doubling, 1.10; 95% confidence interval [CI], 0.97-1.24; proportional change in variance [PCV], -2.0%). Incidence-weighted (RR per doubling, 1.18; 95% CI, 1.06-1.30; PCV, -8.4%) and C. difficile case importation (RR per doubling, 1.43; 95% CI, 1.29-1.58; PCV, -30.1%) were strongly associated with C. difficile infection incidence.

CONCLUSIONS

In this 13-year study of acute-care facilities in Ontario, interfacility variation in C. difficile infection incidence was associated with importation of patients from other high-incidence acute-care facilities or specifically of patients with a recent history of C. difficile infection. Regional infection control strategies should consider the potential impact of importation of patients at high risk of C. difficile shedding from outside facilities.

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.

Creation of a Geospatially Explicit, Agent-based Model of a Regional Healthcare Network with Application to Clostridioides difficile Infection

Agent-based models (ABMs) describe and simulate complex systems comprising unique agents, or individuals, while accounting for geospatial and temporal variability among dynamic processes. ABMs are increasingly used to study healthcare-associated infections (ie, infections acquired during admission to a healthcare facility), including Clostridioides difficile infection, currently the most common healthcare-associated infection in the United States. The overall burden and transmission dynamics of healthcare-associated infections, including C difficile infection, may be influenced by community sources and movement of people among healthcare facilities and communities. These complex dynamics warrant geospatially explicit ABMs that extend beyond single healthcare facilities to include entire systems (eg, hospitals, nursing homes and extended care facilities, the community). The agents in ABMs can be built on a synthetic population, a model-generated representation of the actual population with associated spatial (eg, home residence), temporal (eg, change in location over time), and nonspatial (eg, sociodemographic features) attributes. We describe our methods to create a geospatially explicit ABM of a major regional healthcare network using a synthetic population as microdata input. We illustrate agent movement in the healthcare network and the community, informed by patient-level medical records, aggregate hospital discharge data, healthcare facility licensing data, and published literature. We apply the ABM output to visualize agent movement in the healthcare network and the community served by the network. We provide an application example of the ABM to C difficile infection using a natural history submodel. We discuss the ABM's potential to detect network areas where disease risk is high; simulate and evaluate interventions to protect public health; adapt to other geographic locations and healthcare-associated infections, including emerging pathogens; and meaningfully translate results to public health practitioners, healthcare providers, and policymakers.

Knowing More of the Iceberg: How Detecting a Greater Proportion of Carbapenem-Resistant Enterobacteriaceae Carriers Influences Transmission

BACKGROUND

Clinical testing detects a fraction of carbapenem-resistant Enterobacteriaceae (CRE) carriers. Detecting a greater proportion could lead to increased use of infection prevention and control measures but requires resources. Therefore, it is important to understand the impact of detecting increasing proportions of CRE carriers.

METHODS

We used our Regional Healthcare Ecosystem Analyst-generated agent-based model of adult inpatient healthcare facilities in Orange County, California, to explore the impact that detecting greater proportions of carriers has on the spread of CRE.

RESULTS

Detecting and placing 1 in 9 carriers on contact precautions increased the prevalence of CRE from 0% to 8.0% countywide over 10 years. Increasing the proportion of detected carriers from 1 in 9 up to 1 in 5 yielded linear reductions in transmission; at proportions >1 in 5, reductions were greater than linear. Transmission reductions did not occur for 1, 4, or 5 years, varying by facility type. With a contact precautions effectiveness of ≤70%, the detection level yielding nonlinear reductions remained unchanged; with an effectiveness of >80%, detecting only 1 in 5 carriers garnered large reductions in the number of new CRE carriers. Trends held when CRE was already present in the region.

CONCLUSION

Although detection of all carriers provided the most benefits for preventing new CRE carriers, if this is not feasible, it may be worthwhile to aim for detecting >1 in 5 carriers.

Carbapenem and Cephalosporin Resistance among Enterobacteriaceae in Healthcare-Associated Infections, California, USA1

We analyzed antimicrobial susceptibility test results reported in healthcare-associated infections by California hospitals during 2014-2017. Approximately 3.2% of Enterobacteriaceae reported in healthcare-associated infections were resistant to carbapenems and 26.9% were resistant to cephalosporins. The proportion of cephalosporin-resistant Escherichia coli increased 7% (risk ratio 1.07, 95% CI 1.04-1.11) per year during 2014-2017.

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.

Reduction in Clostridium difficile infection rates following a multifacility prevention initiative in Orange County, California: A controlled interrupted time series evaluation

OBJECTIVE

To evaluate the Orange County Clostridium difficile infection (CDI) prevention collaborative's effect on rates of CDI in acute-care hospitals (ACHs) in Orange County, California.

DESIGN

Controlled interrupted time series.

METHODS

We convened a CDI prevention collaborative with healthcare facilities in Orange County to reduce CDI incidence in the region. Collaborative participants received onsite infection control and antimicrobial stewardship assessments, interactive learning and discussion sessions, and an interfacility transfer communication improvement initiative during June 2015-June 2016. We used segmented regression to evaluate changes in monthly hospital-onset (HO) and community-onset (CO) CDI rates for ACHs. The baseline period comprised 17 months (January 2014-June 2015) and the follow-up period comprised 28 months (September 2015-December 2017). All 25 Orange County ACHs were included in the CO-CDI model to account for direct and indirect effects of the collaborative. For comparison, we assessed HO-CDI and CO-CDI rates among 27 ACHs in 3 San Francisco Bay Area counties.

RESULTS

HO-CDI rates in the 15 participating Orange County ACHs decreased 4% per month (incidence rate ratio [IRR], 0.96; 95% CI, 0.95-0.97; P < .0001) during the follow-up period compared with the baseline period and 3% (IRR, 0.97; 95% CI, 0.95-0.99; P = .002) per month compared to the San Francisco Bay Area nonparticipant ACHs. Orange County CO-CDI rates declined 2% per month (IRR, 0.98; 95% CI, 0.96-1.00; P = .03) between the baseline and follow-up periods. This decline was not statistically different from the San Francisco Bay Area ACHs (IRR, 0.97; 95% CI, 0.95-1.00; P = .09).

CONCLUSIONS

Our analysis of ACHs in Orange County provides evidence that coordinated, regional multifacility initiatives can reduce CDI incidence.

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.

Emergence of carbapenem-resistant Enterobacteriaceae in Orange County, California, and support for early regional strategies to limit spread

BACKGROUND

The east-to-west spread of carbapenem-resistant Enterobacteriaceae (CRE) represents an opportunity to explore strategies to limit spread in nonendemic areas. We evaluated CRE emergence and regional support for containment strategies.

METHODS

A 17-question cross-sectional survey was administered to infection prevention programs in Orange County, CA (31 hospitals serving 3 million residents), between January and September 2014. Questions addressed newly detected hospital- and community-onset CRE cultures (2008-2013), current CRE control strategies, and support for prevention strategies for a hypothetical regional intervention.

RESULTS

Among 31 hospitals, 21 (68%, representing 17 infection prevention programs) completed the survey. CRE was scarcely detected between 2009-2010; within 4 years, 90% of hospitals reported CRE, with 2.5-fold higher community-onset than hospital-onset CRE. Between 2011 and 2013, annual CRE incidence increased 4.7-fold (1.4-6.3 cases/10,000 admissions). Support for a regional CRE prevention bundle was unanimous. Although 22% bathed patients positive for CRE with chlorhexidine gluconate and 11% actively screened for CRE, 86% and 57%, respectively, would consider these strategies in a regional intervention.

CONCLUSIONS

CRE epidemiology in Orange County parallels early progression previously seen in now-endemic areas, representing an opportunity to consider interventions to prevent endemic spread. Many facilities would consider proactive strategies, such as chlorhexidine bathing, in the setting of a regional collaborative.

Failure to Communicate: Transmission of Extensively Drug-Resistant bla OXA-237-Containing Acinetobacter baumannii-Multiple Facilities in Oregon, 2012-2014

OBJECTIVE To determine the scope, source, and mode of transmission of a multifacility outbreak of extensively drug-resistant (XDR) Acinetobacter baumannii. DESIGN Outbreak investigation. SETTING AND PARTICIPANTS Residents and patients in skilled nursing facilities, long-term acute-care hospital, and acute-care hospitals. METHODS A case was defined as the incident isolate from clinical or surveillance cultures of XDR Acinetobacter baumannii resistant to imipenem or meropenem and nonsusceptible to all but 1 or 2 antibiotic classes in a patient in an Oregon healthcare facility during January 2012-December 2014. We queried clinical laboratories, reviewed medical records, oversaw patient and environmental surveillance surveys at 2 facilities, and recommended interventions. Pulsed-field gel electrophoresis (PFGE) and molecular analysis were performed. RESULTS We identified 21 cases, highly related by PFGE or healthcare facility exposure. Overall, 17 patients (81%) were admitted to either long-term acute-care hospital A (n=8), or skilled nursing facility A (n=8), or both (n=1) prior to XDR A. baumannii isolation. Interfacility communication of patient or resident XDR status was not performed during transfer between facilities. The rare plasmid-encoded carbapenemase gene bla OXA-237 was present in 16 outbreak isolates. Contact precautions, chlorhexidine baths, enhanced environmental cleaning, and interfacility communication were implemented for cases to halt transmission. CONCLUSIONS Interfacility transmission of XDR A. baumannii carrying the rare blaOXA-237 was facilitated by transfer of affected patients without communication to receiving facilities. Infect Control Hosp Epidemiol 2017;38:1335-1341.

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.

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.

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.

Risk factors associated with interfacility transfers among patients with Clostridium difficile infection

BACKGROUND

Preventing the transmission of Clostridium difficile infection (CDI) over the continuum of care presents an important challenge for infection control.

METHODS

A prospective case-control study was conducted on patients admitted with CDI to a tertiary care hospital in Detroit between August 2012 and September 2013. Patients were then followed for 1 year by telephone interviews and the hospital administrative database. Cases, patients with interfacility transfers (IFTs), were patients admitted to our facility from another health care facility and discharged to long-term care (LTC) facilities. Controls were patients admitted from and discharged to home.

RESULTS

There were 143 patients included in the study. Thirty-six (30%) cases were compared with 84 (70%) controls. Independent risk factors of CDI patients with IFTs (compared with CDI patients without IFTs) included Charlson Comorbidity Index score ≥6 (odds ratio [OR], 5.30; P = .016) and hospital-acquired CDI (OR, 4.92; P = .023). Patients with IFTs were more likely to be readmitted within 90 days of discharge than patients without IFTs (OR, 2.24; P = .046). One-year mortality rate was significantly higher among patients with IFTs than among patients without IFTs (OR, 4.33; P = .01).

CONCLUSIONS

With the growing number of alternate health care centers, it is highly critical to establish better collaboration between acute care and LTC facilities to tackle the increasing burden of CDI across the health care system.

Nascent regional system for alerting infection preventionists about patients with multidrug-resistant gram-negative bacteria: implementation and initial results

OBJECTIVE

To build and to begin evaluating a regional automated system to notify infection preventionists (IPs) when a patient with a history of gram-negative rod multidrug-resistant organism (GNRMDRO) is admitted to an emergency department (ED) or inpatient setting.

DESIGN

Observational, retrospective study.

SETTING

Twenty-seven hospitals, mostly in the Indianapolis metropolitan area, in a health information exchange (HIE).

PATIENTS

During testing of the new system: 80,180 patients with microbiology cultures between October 1, 2013, and December 31, 2013; 573 had a GNRMDRO. METHODS/INTERVENTION: A Health Level Seven (HL7) data feed from the HIE was obtained, corrected, enhanced, and used for decision support (secure e-mail notification to the IPs). Retrospective analysis of patients with microbiology data (October 1, 2013, through December 31, 2013) and subsequent healthcare encounters (through February 6, 2014).

RESULTS

The 573 patients (median age, 66 years; 68% women) had extended-spectrum β-lactamase-producing Enterobacteriaceae (78%), carbapenem-resistant Enterobacteriaceae (7%), Pseudomonas aeruginosa (9%), Acinetobacter baumannii (3%), or other GNR (3%). Body sources were urine (68%), sputum/trachea/bronchoalveolar lavage (13%), wound/skin (6%), blood (6%), or other/unidentified (7%). Between October 1, 2013, and February 6, 2014, 252 (44%) of 573 had an ED or inpatient encounter after the GNRMDRO culture, 47 (19% of 252) at an institution different from where the culture was drawn. During the first 7 weeks of actual alerts (January 29, 2014, through March 19, 2014), alerts were generated regarding 67 patients (19 of 67 admitted elsewhere from where the culture was drawn).

CONCLUSIONS

It proved challenging but ultimately feasible to create a regional microbiology-based alert system. Even in a few months, we observed substantial crossover between institutions. This system, if it contributes to timely isolation, may help reduce the spread of GNRMDROs.

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

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.

Beyond the Intensive Care Unit (ICU): Countywide Impact of Universal ICU Staphylococcus aureus Decolonization

A recent trial showed that universal decolonization in adult intensive care units (ICUs) resulted in greater reductions in all bloodstream infections and clinical isolates of methicillin-resistant Staphylococcus aureus (MRSA) than either targeted decolonization or screening and isolation. Since regional health-care facilities are highly interconnected through patient-sharing, focusing on individual ICUs may miss the broader impact of decolonization. Using our Regional Healthcare Ecosystem Analyst simulation model of all health-care facilities in Orange County, California, we evaluated the impact of chlorhexidine baths and mupirocin on all ICU admissions when universal decolonization was implemented for 25%, 50%, 75%, and 100% of ICU beds countywide (compared with screening and contact precautions). Direct benefits were substantial in ICUs implementing decolonization (a median 60% relative reduction in MRSA prevalence). When 100% of countywide ICU beds were decolonized, there were spillover effects in general wards, long-term acute-care facilities, and nursing homes resulting in median 8.0%, 3.0%, and 1.9% relative MRSA reductions at 1 year, respectively. MRSA prevalence decreased by a relative 3.2% countywide, with similar effects for methicillin-susceptible S. aureus. We showed that a large proportion of decolonization's benefits are missed when accounting only for ICU impact. Approximately 70% of the countywide cases of MRSA carriage averted after 1 year of universal ICU decolonization were outside the ICU.

Quantifying the Exposure to Antibiotic-Resistant Pathogens Among Patients Discharged From a Single Hospital Across All California Healthcare Facilities

OBJECTIVE

To assess the time-dependent exposure of California healthcare facilities to patients harboring methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), extended-spectrum β-lactamase (ESBL)-producing Escherichia coli and Klebsiella pneumoniae, and Clostridium difficile infection (CDI) upon discharge from 1 hospital.

METHODS

Retrospective multiple-cohort study of adults discharged from 1 hospital in 2005-2009, counting hospitals, nursing homes, cities, and counties in which carriers were readmitted, and comparing the number and length of stay of readmissions and the number of distinct readmission facilities among carriers versus noncarriers.

RESULTS

We evaluated 45,772 inpatients including those with MRSA (N=1,198), VRE (N=547), ESBL (N=121), and CDI (N=300). Within 1 year of discharge, MRSA, VRE, and ESBL carriers exposed 137, 117, and 45 hospitals and 103, 83, and 37 nursing homes, generating 58,804, 33,486, and 15,508 total exposure-days, respectively. Within 90 days of discharge, CDI patients exposed 36 hospitals and 35 nursing homes, generating 7,318 total exposure-days. Compared with noncarriers, carriers had more readmissions to hospitals (MRSA:1.8 vs 0.9/patient; VRE: 2.6 vs 0.9; ESBL: 2.3 vs 0.9; CDI: 0.8 vs 0.4; all P<.001) and nursing homes (MRSA: 0.4 vs 0.1/patient; VRE: 0.7 vs 0.1; ESBL: 0.7 vs 0.1; CDI: 0.3 vs 0.1; all P<.001) and longer hospital readmissions (MRSA: 8.9 vs 7.3 days; VRE: 8.9 vs 7.4; ESBL: 9.6 vs 7.5; CDI: 12.3 vs 8.2; all P<.01).

CONCLUSIONS

Patients harboring antibiotic-resistant pathogens rapidly expose numerous facilities during readmissions; regional containment strategies are needed.

Modeling spread of KPC-producing bacteria in long-term acute care hospitals in the Chicago region, USA

OBJECTIVE

Prevalence of bla KPC-encoding Enterobacteriaceae (KPC) in Chicago long-term acute care hospitals (LTACHs) rose rapidly after the first recognition in 2007. We studied the epidemiology and transmission capacity of KPC in LTACHs and the effect of patient cohorting.

METHODS

Data were available from 4 Chicago LTACHs from June 2012 to June 2013 during a period of bundled interventions. These consisted of screening for KPC rectal carriage, daily chlorhexidine bathing, medical staff education, and 3 cohort strategies: a pure cohort (all KPC-positive patients on 1 floor), single rooms for KPC-positive patients, and a mixed cohort (all KPC-positive patients on 1 floor, supplemented with KPC-negative patients). A data-augmented Markov chain Monte Carlo (MCMC) method was used to model the transmission process.

RESULTS

Average prevalence of KPC colonization was 29.3%. On admission, 18% of patients were colonized; the sensitivity of the screening process was 81%. The per admission reproduction number was 0.40. The number of acquisitions per 1,000 patient days was lowest in LTACHs with a pure cohort ward or single rooms for colonized patients compared with mixed-cohort wards, but 95% credible intervals overlapped.

CONCLUSIONS

Prevalence of KPC in LTACHs is high, primarily due to high admission prevalence and the resultant impact of high colonization pressure on cross transmission. In this setting, with an intervention in place, patient-to-patient transmission is insufficient to maintain endemicity. Inclusion of a pure cohort or single rooms for KPC-positive patients in an intervention bundle seemed to limit transmission compared to use of a mixed cohort.

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.

Healthcare-associated pathogens and nursing home policies and practices: results from a national survey

OBJECTIVE To examine the prevalence of healthcare-associated pathogens and the infection control policies and practices in a national sample of nursing homes (NHs). METHODS In 2012, we conducted a national survey about the extent to which NHs follow suggested infection control practices with regard to 3 common healthcare-associated pathogens: methicillin-resistant Staphylococcus aureus, Clostridium difficile, and extended-spectrum β-lactamase producers, and their prevalence in NHs. We adapted a previously used and validated NH infection control survey, including questions on prevalence, admission and screening policies, contact precautions, decolonization, and cleaning practices. RESULTS A total of 1,002 surveys were returned. Of the responding NHs, 14.2% were less likely to accept residents with methicillin-resistant Staphylococcus aureus, with the principal reason being lack of single or cohort rooms. NHs do not routinely perform admission screening (96.4%) because it is not required by regulation (56.2%) and would not change care provision (30.7%). Isolation strategies vary substantially, with gloves being most commonly used. Most NHs (75.1%) do not decolonize carriers of methicillin-resistant Staphylococcus aureus, but some (10.6%) decolonize more than 90% of residents. Despite no guidance on how resident rooms on contact precautions should be cleaned, 59.3% of NHs report enhanced cleaning for such rooms. CONCLUSION Overall, NHs tend to follow voluntary infection control guidelines only if doing so does not require substantial financial investment in new or dedicated staff or infrastructure.

The spread and control of norovirus outbreaks among hospitals in a region: a simulation model

BACKGROUND

Because hospitals in a region are connected via patient sharing, a norovirus outbreak in one hospital may spread to others.

METHODS

We utilized our Regional Healthcare Ecosystem Analyst software to generate an agent-based model of all the acute care facilities in Orange County (OC), California and simulated various norovirus outbreaks in different locations, both with and without contact precautions.

RESULTS

At the lower end of norovirus reproductive rate (R0) estimates (1.64), an outbreak tended to remain confined to the originating hospital (≤6.1% probability of spread). However, at the higher end of R0 (3.74), an outbreak spread 4.1%-17.5% of the time to almost all other OC hospitals within 30 days, regardless of the originating hospital. Implementing contact precautions for all symptomatic cases reduced the probability of spread to other hospitals within 30 days and the total number of cases countywide, but not the number of other hospitals seeing norovirus cases.

CONCLUSIONS

A single norovirus outbreak can continue to percolate throughout a system of different hospitals for several months and appear as a series of unrelated outbreaks, highlighting the need for hospitals within a region to more aggressively and cooperatively track and control an initial outbreak.

Quantifying sources of bias in National Healthcare Safety Network laboratory-identified Clostridium difficile infection rates

OBJECTIVE

To assess the effect of multiple sources of bias on state- and hospital-specific National Healthcare Safety Network (NHSN) laboratory-identified Clostridium difficile infection (CDI) rates.

DESIGN

Sensitivity analysis.

SETTING

A total of 124 New York hospitals in 2010.

METHODS

New York NHSN CDI events from audited hospitals were matched to New York hospital discharge billing records to obtain additional information on patient age, length of stay, and previous hospital discharges. "Corrected" hospital-onset (HO) CDI rates were calculated after (1) correcting inaccurate case reporting found during audits, (2) incorporating knowledge of laboratory results from outside hospitals, (3) excluding days when patients were not at risk from the denominator of the rates, and (4) adjusting for patient age. Data sets were simulated with each of these sources of bias reintroduced individually and combined. The simulated rates were compared with the corrected rates. Performance (ie, better, worse, or average compared with the state average) was categorized, and misclassification compared with the corrected data set was measured.

RESULTS

Counting days patients were not at risk in the denominator reduced the state HO rate by 45% and resulted in 8% misclassification. Age adjustment and reporting errors also shifted rates (7% and 6% misclassification, respectively).

CONCLUSIONS

Changing the NHSN protocol to require reporting of age-stratified patient-days and adjusting for patient-days at risk would improve comparability of rates across hospitals. Further research is needed to validate the risk-adjustment model before these data should be used as hospital performance measures.

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.

Modeling the regional spread and control of vancomycin-resistant enterococci

BACKGROUND

Because patients can remain colonized with vancomycin-resistant enterococci (VRE) for long periods of time, VRE may spread from one health care facility to another.

METHODS

Using the Regional Healthcare Ecosystem Analyst, an agent-based model of patient flow among all Orange County, California, hospitals and communities, we quantified the degree and speed at which changes in VRE colonization prevalence in a hospital may affect prevalence in other Orange County hospitals.

RESULTS

A sustained 10% increase in VRE colonization prevalence in any 1 hospital caused a 2.8% (none to 62%) average relative increase in VRE prevalence in all other hospitals. Effects took from 1.5 to >10 years to fully manifest. Larger hospitals tended to have greater affect on other hospitals.

CONCLUSIONS

When monitoring and controlling VRE, decision makers may want to account for regional effects. Knowing a hospital's connections with other health care facilities via patient sharing can help determine which hospitals to include in a surveillance or control program.

A regional informatics platform for coordinated antibiotic-resistant infection tracking, alerting, and prevention

BACKGROUND

We developed and assessed the impact of a patient registry and electronic admission notification system relating to regional antimicrobial resistance (AMR) on regional AMR infection rates over time. We conducted an observational cohort study of all patients identified as infected or colonized with methicillin-resistant Staphylococcus aureus (MRSA) and/or vancomycin-resistant enterococci (VRE) on at least 1 occasion by any of 5 healthcare systems between 2003 and 2010. The 5 healthcare systems included 17 hospitals and associated clinics in the Indianapolis, Indiana, region.

METHODS

We developed and standardized a registry of MRSA and VRE patients and created Web forms that infection preventionists (IPs) used to maintain the lists. We sent e-mail alerts to IPs whenever a patient previously infected or colonized with MRSA or VRE registered for admission to a study hospital from June 2007 through June 2010.

RESULTS

Over a 3-year period, we delivered 12 748 e-mail alerts on 6270 unique patients to 24 IPs covering 17 hospitals. One in 5 (22%-23%) of all admission alerts was based on data from a healthcare system that was different from the admitting hospital; a few hospitals accounted for most of this crossover among facilities and systems.

CONCLUSIONS

Regional patient registries identify an important patient cohort with relevant prior antibiotic-resistant infection data from different healthcare institutions. Regional registries can identify trends and interinstitutional movement not otherwise apparent from single institution data. Importantly, electronic alerts can notify of the need to isolate early and to institute other measures to prevent transmission.

The Regional Healthcare Ecosystem Analyst (RHEA): a simulation modeling tool to assist infectious disease control in a health system

OBJECTIVE

As healthcare systems continue to expand and interconnect with each other through patient sharing, administrators, policy makers, infection control specialists, and other decision makers may have to take account of the entire healthcare 'ecosystem' in infection control.

MATERIALS AND METHODS

We developed a software tool, the Regional Healthcare Ecosystem Analyst (RHEA), that can accept user-inputted data to rapidly create a detailed agent-based simulation model (ABM) of the healthcare ecosystem (ie, all healthcare facilities, their adjoining community, and patient flow among the facilities) of any region to better understand the spread and control of infectious diseases.

RESULTS

To demonstrate RHEA's capabilities, we fed extensive data from Orange County, California, USA, into RHEA to create an ABM of a healthcare ecosystem and simulate the spread and control of methicillin-resistant Staphylococcus aureus. Various experiments explored the effects of changing different parameters (eg, degree of transmission, length of stay, and bed capacity).

DISCUSSION

Our model emphasizes how individual healthcare facilities are components of integrated and dynamic networks connected via patient movement and how occurrences in one healthcare facility may affect many other healthcare facilities.

CONCLUSIONS

A decision maker can utilize RHEA to generate a detailed ABM of any healthcare system of interest, which in turn can serve as a virtual laboratory to test different policies and interventions.

The importance of nursing homes in the spread of methicillin-resistant Staphylococcus aureus (MRSA) among hospitals

BACKGROUND

Hospital infection control strategies and programs may not consider control of methicillin-resistant Staphylococcus aureus (MRSA) in nursing homes in a county.

METHODS

Using our Regional Healthcare Ecosystem Analyst, we augmented our existing agent-based model of all hospitals in Orange County (OC), California, by adding all nursing homes and then simulated MRSA outbreaks in various health care facilities.

RESULTS

The addition of nursing homes substantially changed MRSA transmission dynamics throughout the county. The presence of nursing homes substantially potentiated the effects of hospital outbreaks on other hospitals, leading to an average 46.2% (range, 3.3%-156.1%) relative increase above and beyond the impact when only hospitals are included for an outbreak in OC's largest hospital. An outbreak in the largest hospital affected all other hospitals (average 2.1% relative prevalence increase) and the majority (~90%) of nursing homes (average 3.2% relative increase) after 6 months. An outbreak in the largest nursing home had effects on multiple OC hospitals, increasing MRSA prevalence in directly connected hospitals by an average 0.3% and in hospitals not directly connected through patient transfers by an average 0.1% after 6 months. A nursing home outbreak also had some effect on MRSA prevalence in other nursing homes.

CONCLUSIONS

Nursing homes, even those not connected by direct patient transfers, may be a vital component of a hospital's infection control strategy. To achieve effective control, a hospital may want to better understand how regional nursing homes and hospitals are connected through both direct and indirect (with intervening stays at home) patient sharing.

Simulation shows hospitals that cooperate on infection control obtain better results than hospitals acting alone

Efforts to control life-threatening infections, such as with methicillin-resistant Staphylococcus aureus (MRSA), can be complicated when patients are transferred from one hospital to another. Using a detailed computer simulation model of all hospitals in Orange County, California, we explored the effects when combinations of hospitals tested all patients at admission for MRSA and adopted procedures to limit transmission among patients who tested positive. Called "contact isolation," these procedures specify precautions for health care workers interacting with an infected patient, such as wearing gloves and gowns. Our simulation demonstrated that each hospital's decision to test for MRSA and implement contact isolation procedures could affect the MRSA prevalence in all other hospitals. Thus, our study makes the case that further cooperation among hospitals--which is already reflected in a few limited collaborative infection control efforts under way--could help individual hospitals achieve better infection control than they could achieve on their own.

Prevention of hospital-onset Clostridium difficile infection in the New York metropolitan region using a collaborative intervention model

The incidence, severity, and associated costs of Clostridium difficile (C. difficile) infection (CDI) have dramatically increased in hospitals over the past decade, indicating an urgent need for strategies to prevent transmission of C. difficile. This article describes a multifaceted collaborative approach to reduce hospital-onset CDI rates in 35 acute care hospitals in the New York metropolitan region. Hospitals participated in a comprehensive CDI reduction intervention and formed interdisciplinary teams to coordinate their efforts. Standardized clinical infection prevention and environmental cleaning protocols were implemented and monitored using checklists. Monthly data reports were provided to hospitals for facility-specific performance evaluation and comparison to aggregate data from all participants. Hospitals also participated in monthly teleconferences to review data and highlight successes, challenges, and strategies to reduce CDI. Incidence of hospital-onset CDI per 10,000 patient days was the primary outcome measure. Additionally, the incidence of nonhospital-associated, community-onset, hospital-associated, and recurrent CDIs were measured. The use of a collaborative model to implement a multifaceted infection prevention strategy was temporally associated with a significant reduction in hospital-onset CDI rates in participating New York metropolitan regional hospitals.

The potential regional impact of contact precaution use in nursing homes to control methicillin-resistant Staphylococcus aureus

OBJECTIVE

Implementation of contact precautions in nursing homes to prevent methicillin-resistant Staphylococcus aureus (MRSA) transmission could cost time and effort and may have wide-ranging effects throughout multiple health facilities. Computational modeling could forecast the potential effects and guide policy making.

DESIGN

Our multihospital computational agent-based model, Regional Healthcare Ecosystem Analyst (RHEA).

SETTING

All hospitals and nursing homes in Orange County, California.

METHODS

Our simulation model compared the following 3 contact precaution strategies: (1) no contact precautions applied to any nursing home residents, (2) contact precautions applied to those with clinically apparent MRSA infections, and (3) contact precautions applied to all known MRSA carriers as determined by MRSA screening performed by hospitals.

RESULTS

Our model demonstrated that contact precautions for patients with clinically apparent MRSA infections in nursing homes resulted in a median 0.4% (range, 0%-1.6%) relative decrease in MRSA prevalence in nursing homes (with 50% adherence) but had no effect on hospital MRSA prevalence, even 5 years after initiation. Implementation of contact precautions (with 50% adherence) in nursing homes for all known MRSA carriers was associated with a median 14.2% (range, 2.1%-21.8%) relative decrease in MRSA prevalence in nursing homes and a 2.3% decrease (range, 0%-7.1%) in hospitals 1 year after implementation. Benefits accrued over time and increased with increasing compliance.

CONCLUSIONS

Our modeling study demonstrated the substantial benefits of extending contact precautions in nursing homes from just those residents with clinically apparent infection to all MRSA carriers, which suggests the benefits of hospitals and nursing homes sharing and coordinating information on MRSA surveillance and carriage status.

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.

Patient sharing and population genetic structure of methicillin-resistant Staphylococcus aureus

Rates of hospital-acquired infections, specifically methicillin-resistant Staphylococcus aureus (MRSA), are increasingly being used as indicators for quality of hospital hygiene. There has been much effort on understanding the transmission process at the hospital level; however, interhospital population-based transmission remains poorly defined. We evaluated whether the proportion of shared patients between hospitals was correlated with genetic similarity of MRSA strains from those hospitals. Using data collected from 30 of 32 hospitals in Orange County, California, multivariate linear regression showed that for each twofold increase in the proportion of patients shared between 2 hospitals, there was a 7.7% reduction in genetic heterogeneity between the hospitals' MRSA populations (permutation P value = 0.0356). Pairs of hospitals that both served adults had more similar MRSA populations than pairs including a pediatric hospital. These findings suggest that concerted efforts among hospitals that share large numbers of patients may be synergistic to prevent MRSA transmission.

Clostridium difficile outbreak strain BI is highly endemic in Chicago area hospitals

OBJECTIVE

Describe the clinical and molecular epidemiology of incident Clostridium difficile infection (CDI) cases in Chicago area acute healthcare facilities (HCFs).

DESIGN AND SETTING

Laboratory, clinical, and epidemiologic information was collected for patients with incident CDI who were admitted to acute HCFs in February 2009. Stool cultures and restriction endonuclease analysis typing of the recovered C. difficile isolates was performed.

PATIENTS

Two hundred sixty-three patients from 25 acute HCFs.

RESULTS

Acute HCF rates ranged from 2 to 7 patients with CDI per 10,000 patient-days. The crude mortality rate was 8%, with 20 deaths occurring in patients with CDI. Forty-two (16%) patients had complications from CDI, including 4 patients who required partial, subtotal, or total colectomy, 3 of whom died. C. difficile was isolated and typed from 129 of 178 available stool specimens. The BI strain was identified in 79 (61%) isolates. Of patients discharged to long-term care who had their isolate typed, 36 (67%) had BI-associated CDI.

CONCLUSIONS

Severe disease was common and crude mortality was substantial among patients with CDI in Chicago area acute HCFs in February 2009. The outbreak-associated BI strain was the predominant endemic strain identified, accounting for nearly two-thirds of cases. Focal HCF outbreaks were not reported, despite the presence of the BI strain. Transfer of patients between acute and long-term HCFs may have contributed to the high incidence of BI cases in this investigation.

Long-term care facilities: important participants of the acute care facility social network?

BACKGROUND

Acute care facilities are connected via patient sharing, forming a network. However, patient sharing extends beyond this immediate network to include sharing with long-term care facilities. The extent of long-term care facility patient sharing on the acute care facility network is unknown. The objective of this study was to characterize and determine the extent and pattern of patient transfers to, from, and between long-term care facilities on the network of acute care facilities in a large metropolitan county.

METHODS/PRINCIPAL FINDINGS

We applied social network constructs principles, measures, and frameworks to all 2007 annual adult and pediatric patient transfers among the healthcare facilities in Orange County, California, using data from surveys and several datasets. We evaluated general network and centrality measures as well as individual ego measures and further constructed sociograms. Our results show that over the course of a year, 66 of 72 long-term care facilities directly sent and 67 directly received patients from other long-term care facilities. Long-term care facilities added 1,524 ties between the acute care facilities when ties represented at least one patient transfer. Geodesic distance did not closely correlate with the geographic distance among facilities.

CONCLUSIONS/SIGNIFICANCE

This study demonstrates the extent to which long-term care facilities are connected to the acute care facility patient sharing network. Many long-term care facilities were connected by patient transfers and further added many connections to the acute care facility network. This suggests that policy-makers and health officials should account for patient sharing with and among long-term care facilities as well as those among acute care facilities when evaluating policies and interventions.

Frequent hospital readmissions for Clostridium difficile infection and the impact on estimates of hospital-associated C. difficile burden

OBJECTIVE

Clostridium difficile infection (CDI) is associated with hospitalization and may cause readmission following admission for any reason. We aimed to measure the incidence of readmissions due to CDI.

DESIGN

Retrospective cohort study.

PATIENTS

Adult inpatients in Orange County, California, who presented with new-onset CDI within 12 weeks of discharge.

METHODS

We assessed mandatory 2000-2007 hospital discharge data for trends in hospital-associated CDI (HA-CDI) incidence, with and without inclusion of postdischarge CDI (PD-CDI) events resulting in rehospitalization within 12 weeks of discharge. We measured the effect of including PD-CDI events on hospital-specific CDI incidence, a mandatory reporting measure in California, and on relative hospital ranks by CDI incidence.

RESULTS

From 2000 to 2007, countywide hospital-onset CDI (HO-CDI) incidence increased from 15 per 10,000 to 22 per 10,000 admissions. When including PD-CDI events, HA-CDI incidence doubled (29 per 10,000 in 2000 and 52 per 10,000 in 2007). Overall, including PD-CDI events resulted in significantly higher hospital-specific CDI incidence, although hospitals had disproportionate amounts of HA-CDI occurring postdischarge. This resulted in substantial shifts in some hospitals' rankings by CDI incidence. In multivariate models, both HO and PD-CDI were associated with increasing age, higher length of stay, and select comorbidities. Race and Hispanic ethnicity were predictive of PD-CDI but not HO-CDI.

CONCLUSIONS

PD-CDI events associated with rehospitalization are increasingly common. The majority of HA-CDI cases may be occurring postdischarge, raising important questions about both accurate reporting and effective prevention strategies. Some risk factors for PD-CDI may be different than those for HO-CDI, allowing additional identification of high-risk groups before discharge.