Dynamic contact networks of patients and MRSA spread in hospitals

Effect of UV-C disinfection and copper plating on healthcare-associated infections in a NICU with high ESBL infections

INTRODUCTION

Healthcare-associated infections (HCAIs) in neonates are frequent and highly lethal, in particular those caused by extended spectrum beta-lactamase (ESBL) producing bacteria. We evaluated the beneficial effects of ultraviolet C (UV-C) disinfection and copper adhesive plating on HCAIs in the Neonatal Intensive Care Unit (NICU) of a third level paediatric hospital in Mexico City, both in combination of hand-hygiene (HH) and prevention bundles.

METHODS

All NICU patients were included. There were 4 periods (P): P1: HH monitoring and prevention bundles; P2: P1+UV-C disinfection; P3: P2+Copper adhesive plating on frequent-contact surfaces and P4: Monitoring of P3 actions.

RESULTS

552 neonates were monitored during 15,467 patient days (PD). HCAI rates decreased from 11.03/1000 PD in P1 to 5.35/1000 PD in P4 (p=0.006). HCAIs with bacterial isolates dropped from 5.39/1000 PD in PI to 1.79/1000 PD in P4 (p=0.011). UV-C and copper were associated with significant HCAI prevention (RR 0.49, CI95% 0.30-0.81, p=0.005) and with lesser HCAIs with bacterial isolates (RR 0.33, CI95% 0.14-0.77, p=0.011).

CONCLUSIONS

Copper adhesive plating combined with UV-C disinfection were associated with a drop in HCAI rates and with the elimination of ESBL-caused HCAIs. Hence, we propose that these strategies be considered in MDRO proliferation preventions.

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.

Using spectral characterization to identify healthcare-associated infection (HAI) patients for clinical contact precaution

Healthcare-associated infections (HAIs) are a major problem in hospital infection control. Although HAIs can be suppressed using contact precautions, such precautions are expensive, and we can only apply them to a small fraction of patients (i.e., a limited budget). In this work, we focus on two clinical problems arising from the limited budget: (a) choosing the best patients to be placed under precaution given a limited budget to minimize the spread (the isolation problem), and (b) choosing the best patients to release when limited budget requires some of the patients to be cleared from precaution (the clearance problem). A critical challenge in addressing them is that HAIs have multiple transmission pathways such that locations can also accumulate 'load' and spread the disease. One of the most common practices when placing patients under contact precautions is the regular clearance of pathogen loads. However, standard propagation models like independent cascade (IC)/susceptible-infectious-susceptible (SIS) cannot capture such mechanisms directly. Hence to account for this challenge, using non-linear system theory, we develop a novel spectral characterization of a recently proposed pathogen load based model, 2-MODE-SIS model, on people/location networks to capture spread dynamics of HAIs. We formulate the two clinical problems using this spectral characterization and develop effective and efficient algorithms for them. Our experiments show that our methods outperform several natural structural and clinical approaches on real-world hospital testbeds and pick meaningful solutions.

The use of networks in spatial and temporal computational models for outbreak spread in epidemiology: A systematic review

OBJECTIVES

To examine recent literature in order to present a comprehensive overview of the current trends as regards the computational models used to represent the propagation of an infectious outbreak in a population, paying particular attention to those that represent network-based transmission.

METHODS

a systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Papers published in English between 2010 and September 2021 were sought in the ACM Digital Library, IEEE Xplore, PubMed and Scopus databases.

RESULTS

Upon considering their titles and abstracts, 832 papers were obtained, of which 192 were selected for a full content-body check. Of these, 112 studies were eventually deemed suitable for quantitative and qualitative analysis. Emphasis was placed on the spatial and temporal scales studied, the use of networks or graphs, and the granularity of the data used to evaluate the models. The models principally used to represent the spreading of outbreaks have been stochastic (55.36%), while the type of networks most frequently used are relationship networks (32.14%). The most common spatial dimension used is a region (19.64%) and the most used unit of time is a day (28.57%). Synthetic data as opposed to an external source were used in 51.79% of the papers. With regard to the granularity of the data sources, aggregated data such as censuses or transportation surveys are the most common.

CONCLUSION

We identified a growing interest in the use of networks to represent disease transmission. We detected that research is focused on only certain combinations of the computational model, type of network (in both the expressive and the structural sense) and spatial scale, while the search for other interesting combinations has been left for the future.

Diversity and Persistence of MRSA and VRE in Skilled Nursing Facilities: Environmental Screening, Whole Genome Sequencing, Development of a Dispersion Index

BACKGROUND

Environmental contamination with methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE) in skilled nursing facilities (SNFs) may contribute to patient acquisition. We assessed diversity and association of MRSA and VRE isolates in a SNF wing and developed a mathematical index to define each strain's tendency to persist in rooms and spread horizontally.

METHODS

Longitudinal study of MRSA and VRE colonization and contamination among successive patient occupancies in a cluster of nine SNF private rooms during eight months characterized by microbiological testing and whole genome isolate typing. 'Dispersion index" of a strain is defined as the number of rooms it was found in (including the patient), divided by the average of times it was found consecutively in the same room.

FINDINGS

MRSA (ten strain types) and VRE (seven types) were recovered from room or patient in 16.4% and 35.6% of the occupancies, respectively. MRSA showed moderate horizontal spread and several episodes of same-room persistence (three distinct strain types) (overall dispersion index: 1.08). VRE showed high tendency towards horizontal spread /new introductions (overall dispersion index: 3.25), and only one confirmed persistence episode.

INTERPRETATION

The emerging picture of high diversity among contaminating strains and high likelihood of room persistence despite terminal cleaning (MRSA) and horizontal spread between rooms (VRE) in this setting calls for improved cleaning practices, heightened contact precautions, and most of all to establish individually tailored facility screening programs to enable informed choices based on local, measurable and actionable epidemiologic parameters.

FUNDING

University of Michigan OAIC REC Scholarship to M.C. National Institutes of Health K24 AG050685 to L.M.

Multilayer networks of plasmid genetic similarity reveal potential pathways of gene transmission

Antimicrobial resistance (AMR) is a significant threat to public health. Plasmids are principal vectors of AMR genes, significantly contributing to their spread and mobility across hosts. Nevertheless, little is known about the dynamics of plasmid genetic exchange across animal hosts. Here, we use theory and methodology from network and disease ecology to investigate the potential of gene transmission between plasmids using a data set of 21 plasmidomes from a single dairy cow population. We constructed a multilayer network based on pairwise plasmid genetic similarity. Genetic similarity is a signature of past genetic exchange that can aid in identifying potential routes and mechanisms of gene transmission within and between cows. Links between cows dominated the transmission network, and plasmids containing mobility genes were more connected. Modularity analysis revealed a network cluster where all plasmids contained a mobM gene, and one where all plasmids contained a beta-lactamase gene. Cows that contain both clusters also share transmission pathways with many other cows, making them candidates for super-spreading. In support, we found signatures of gene super-spreading in which a few plasmids and cows are responsible for most gene exchange. An agent-based transmission model showed that a new gene invading the cow population will likely reach all cows. Finally, we showed that edge weights contain a non-random signature for the mechanisms of gene transmission, allowing us to differentiate between dispersal and genetic exchange. These results provide insights into how genes, including those providing AMR, spread across animal hosts.

Aligning staff schedules, testing, and isolation reduces the risk of COVID-19 outbreaks in carceral and other congregate settings: A simulation study

COVID-19 outbreaks in congregate settings remain a serious threat to the health of disproportionately affected populations such as people experiencing incarceration or homelessness, the elderly, and essential workers. An individual-based model accounting for individual infectiousness over time, staff work schedules, and testing and isolation schedules was developed to simulate community transmission of SARS-CoV-2 to staff in a congregate facility and subsequent transmission within the facility that could cause an outbreak. Systematic testing strategies in which staff are tested on the first day of their workweek were found to prevent up to 16% more infections than testing strategies unrelated to staff schedules. Testing staff at the beginning of their workweek, implementing timely isolation following testing, limiting test turnaround time, and increasing test frequency in high transmission scenarios can supplement additional mitigation measures to aid outbreak prevention in congregate settings.

Examining the impact of ICU population interaction structure on modeled colonization dynamics of Staphylococcus aureus

Background

Complex transmission models of healthcare-associated infections provide insight for hospital epidemiology and infection control efforts, but they are difficult to implement and come at high computational costs. Structuring more simplified models to incorporate the heterogeneity of the intensive care unit (ICU) patient-provider interactions, we explore how methicillin-resistant Staphylococcus aureus (MRSA) dynamics and acquisitions may be better represented and approximated.

Methods

Using a stochastic compartmental model of an 18-bed ICU, we compared the rates of MRSA acquisition across three ICU population interaction structures: a model with nurses and physicians as a single staff type (SST), a model with separate staff types for nurses and physicians (Nurse-MD model), and a Metapopulation model where each nurse was assigned a group of patients. The proportion of time spent with the assigned patient group (γ) within the Metapopulation model was also varied.

Results

The SST, Nurse-MD, and Metapopulation models had a mean of 40.6, 32.2 and 19.6 annual MRSA acquisitions respectively. All models were sensitive to the same parameters in the same direction, although the Metapopulation model was less sensitive. The number of acquisitions varied non-linearly by values of γ, with values below 0.40 resembling the Nurse-MD model, while values above that converged toward the Metapopulation structure.

Discussion

Inclusion of complex population interactions within a modeled hospital ICU has considerable impact on model results, with the SST model having more than double the acquisition rate of the more structured metapopulation model. While the direction of parameter sensitivity remained the same, the magnitude of these differences varied, producing different colonization rates across relatively similar populations. The non-linearity of the model’s response to differing values of a parameter gamma (γ) suggests simple model approximations are appropriate in only a narrow space of relatively dispersed nursing assignments.

Conclusion

Simplifying assumptions around how a hospital population is modeled, especially assuming random mixing, may overestimate infection rates and the impact of interventions. In many, if not most, cases more complex models that represent population mixing with higher granularity are justified.

The prevalence of virulence determinants in methicillin-resistant Staphylococcus aureus isolated from different infections in hospitalized patients in Poland

Methicillin-resistant Staphylococcus aureus (MRSA) is responsible for hard-to-treat infections. The presence of 19 virulence genes in 120 MRSA isolates obtained from hospitalized patients and genetic relationships of these isolates were investigated. The eno (100%) and ebps (93.3%) genes encoding laminin- and elastin binding proteins, respectively, were ubiquitous. Other adhesion genes: fib (77.5%), fnbB (41.6%), bbp (40.8%), cna (30.8%) encoding proteins binding fibrinogen, fibronectin, bone sialoprotein and collagen, respectively, and map/eap (62.5%), encoding Eap, were also frequent. The etB and etD genes, encoding exfoliative toxins, were present in 15.6% and 12.5% isolates, respectively. The splA, splE and sspA, encoding serine protease were detected in 100%, 70.8% and 94.2% isolates, respectively. The tst gene, encoding toxic shock syndrome toxin-1 was found in 75% isolates. The cna, map/eap and tst genes were the most common in wound isolates and much less common in blood isolates. We identified 45 different spa types, t003 (21.7%) and t008 (18.8%) being the most common. The t003 was the most frequent among isolates from the respiratory tract (35.5%), while t008 in blood isolates (40%). Identification of virulence factors of MRSA is important for evaluation of pathogen transmission rate and disease development.

Identifying asymptomatic spreaders of antimicrobial-resistant pathogens in hospital settings

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

Modeling transmission of pathogens in healthcare settings

PURPOSE OF REVIEW

Mathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcare-associated infections. To contextualize recent findings, we offer a summative review of recent literature focused on modeling transmission of pathogens in healthcare settings.

RECENT FINDINGS

The COVID-19 pandemic has led to a dramatic shift in the modeling landscape as the healthcare community has raced to characterize the transmission dynamics of SARS-CoV-2 and develop effective interventions. Inequities in COVID-19 outcomes have inspired new efforts to quantify how structural bias impacts both health outcomes and model parameterization. Meanwhile, developments in the modeling of methicillin-resistant Staphylococcus aureus, Clostridioides difficile, and other nosocomial infections continue to advance. Machine learning continues to be applied in novel ways, and genomic data is being increasingly incorporated into modeling efforts.

SUMMARY

As the type and amount of data continues to grow, mathematical, statistical, and computational modeling will play an increasing role in healthcare epidemiology. Gaps remain in producing models that are generalizable to a variety of time periods, geographic locations, and populations. However, with effective communication of findings and interdisciplinary collaboration, opportunities for implementing models for clinical decision-making and public health decision-making are bound to increase.

Relevance of intra-hospital patient movements for the spread of healthcare-associated infections within hospitals - a mathematical modeling study

The aim of this study is to analyze patient movement patterns between hospital departments to derive the underlying intra-hospital movement network, and to assess if movement patterns differ between patients at high or low risk of colonization. For that purpose, we analyzed patient electronic medical record data from five hospitals to extract information on risk stratification and patient intra-hospital movements. Movement patterns were visualized as networks, and network centrality measures were calculated. Next, using an agent-based model where agents represent patients and intra-hospital patient movements were explicitly modeled, we simulated the spread of multidrug resistant enterobacteriacae (MDR-E) inside a hospital. Risk stratification of patients according to certain ICD-10 codes revealed that length of stay, patient age, and mean number of movements per admission were higher in the high-risk groups. Movement networks in all hospitals displayed a high variability among departments concerning their network centrality and connectedness with a few highly connected departments and many weakly connected peripheral departments. Simulating the spread of a pathogen in one hospital network showed positive correlation between department prevalence and network centrality measures. This study highlights the importance of intra-hospital patient movements and their possible impact on pathogen spread. Targeting interventions to departments of higher (weighted) degree may help to control the spread of MDR-E. Moreover, when the colonization status of patients coming from different departments is unknown, a ranking system based on department centralities may be used to design more effective interventions that mitigate pathogen spread.

Pathogens including multidrug resistant enterobacteriacae (MDR-E) inside hospital settings are associated with higher morbidity, mortality, and healthcare costs. Better understanding of the transmission routes of these pathogens is required to develop targeted and efficient measures to contain the spread of MDR-E in a hospital. We analyzed datasets from five hospitals in different countries to explore patient movement patterns between departments of these hospitals (intra-hospital movements). We assessed whether movement patterns differ between patients at high or low risk of colonization. Our results show that in every intra-hospital network, there exist a few departments which are strongly connected and many peripheral departments which are loosely connected. High-risk patients stay on average longer in the hospital, and move more frequently between departments than low-risk patients. Targeting interventions to strongly connected departments may help to reduce pathogen spread inside the hospital. To explore this, we simulated the spread of MDR-E inside one hospital using an agent-based model that includes patient movements. In the simulations, we found positive correlations between departments’ prevalence and network characteristics such as degree and weighted degree, thus highlighting the importance of targeting interventions to departments of higher (weighted) degree to control the spread of MDR-E.

Prevalence of MRSA nasal carriage among pregnant women in Copenhagen

Background

Methicillin resistant Staphylococcus aureus (MRSA) frequently causes outbreaks in neonatal intensive care units (NICUs). It is believed that MRSA predominantly enters the NICU with MRSA colonized parents. In Denmark, 27 MRSA NICU outbreaks have been registered between 2008 and 2019.

Aim

The aim of this study was to determine the prevalence of MRSA nasal carriage in pregnant women in Copenhagen and to clarify if MRSA screening during pregnancy could add to the prevention of NICU outbreaks.

Methods

All pregnant women 18 years or older were offered MRSA nasal screening at their first midwife visit between 13 and 20 weeks of gestation.

Results

1778 pregnant women were included, two (0.11%) carried MRSA in the nose.

Conclusion

Infants of the two MRSA positive women were not admitted to a NICU and therefore the screening had no impact on NICU outbreaks. The low prevalence of MRSA found in this study does not justify MRSA screening of all pregnant women in Denmark.

Fast Response to Superspreading: Uncertainty and Complexity in the Context of COVID-19

Although the first coronavirus disease 2019 (COVID-19) wave has peaked with the second wave underway, the world is still struggling to manage potential systemic risks and unpredictability of the pandemic. A particular challenge is the "superspreading" of the virus, which starts abruptly, is difficult to predict, and can quickly escalate into medical and socio-economic emergencies that contribute to long-lasting crises challenging our current ways of life. In these uncertain times, organizations and societies worldwide are faced with the need to develop appropriate strategies and intervention portfolios that require fast understanding of the complex interdependencies in our world and rapid, flexible action to contain the spread of the virus as quickly as possible, thus preventing further disastrous consequences of the pandemic. We integrate perspectives from systems sciences, epidemiology, biology, social networks, and organizational research in the context of the superspreading phenomenon to understand the complex system of COVID-19 pandemic and develop suggestions for interventions aimed at rapid responses.