The network positions of methicillin resistant Staphylococcus aureus affected units in a regional healthcare system

Ohst, Jan Peter; Liljeros, Fredrik; Stenhem, Mikael; Holme, Petter

doi:10.1140/epjds/s13688-014-0029-6

Cited by 7 publications

(4 citation statements)

References 18 publications

(6 reference statements)

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“…This study builds on prior research examining physician networks 19 , 20 and facility networks by incorporating nationwide data on hospital transfers together with epidemiologic data on healthcare associated infections by C. difficile 21 Using methods from network analysis, we find that a facility’s rate of C. difficile cases is significantly correlated with those of its transfer neighbors. There are two possible explanations for this phenomenon: first, that transfers can serve as a substrate for the spread of C. difficile , correlating the infections rates of connected hospitals, or second, that community factors driving C. difficile infection rates influence nearby hospitals.…”

Section: Discussionmentioning

confidence: 93%

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

Fernández-Gracia

Onnela

Barnett

et al. 2017

Sci Rep

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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.

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Section: Discussionmentioning

confidence: 93%

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

Fernández-Gracia

Onnela

Barnett

et al. 2017

Sci Rep

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“…Although the transmission between a patient and a HCW is more likely in certain wards (e.g. burns or transplant unit 8 ), the mobility of patients between wards or hospitals 15,16 creates the missing links sustaining the spread of HA-MRSA across the hospitalised population. Therefore, identifying which contact patterns (or network structures 17 ) regulate the propagation of the infection is the first step to better understand the spread potential of MRSA, and then to develop efficient strategies to reduce the incidence in endemic areas and to avoid potential outbreaks in low-prevalence contexts 18 .…”

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confidence: 99%

Dynamic contact networks of patients and MRSA spread in hospitals

Rocha

Singh²,

Esch

et al. 2020

Sci Rep

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

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“…One important avenue of hospitalized patient-to-patient MRSA transmission is thought to be through contamination of hospital room surfaces and equipment [29]. In addition, patients may be more or less susceptible to acquiring MRSA given individual factors [40], and MRSA transmission rates may vary according to particular hospital wards (or hospital units) [32]. The MRSA dataset contains observational data including patient covariates, room-sharing information, and MRSA test record from a real-world hospital.…”

Section: Case Study On Real-world Hospital Datamentioning

confidence: 99%

A Look into Causal Effects under Entangled Treatment in Graphs: Investigating the Impact of Contact on MRSA Infection

Chen

Vullikanti

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Methicillin-resistant Staphylococcus aureus (MRSA) is a type of bacteria resistant to certain antibiotics, making it difficult to prevent MRSA infections. Among decades of efforts to conquer infectious diseases caused by MRSA, many studies have been proposed to estimate the causal effects of close contact (treatment) on MRSA infection (outcome) from observational data. In this problem, the treatment assignment mechanism plays a key role as it determines the patterns of missing counterfactuals -the fundamental challenge of causal effect estimation. Most existing observational studies for causal effect learning assume that the treatment is assigned individually for each unit. However, on many occasions, the treatments are pairwisely assigned for units that are connected in graphs, i.e., the treatments of different units are entangled. Neglecting the entangled treatments can impede the causal effect estimation. In this paper, we study the problem of causal effect estimation with treatment entangled in a graph. Despite a few explorations for entangled treatments, this problem still remains challenging due to the following challenges: (1) the entanglement brings difficulties in modeling and leveraging the unknown treatment assignment mechanism; (2) there may exist hidden confounders which lead to confounding biases in causal effect estimation; (3) the observational data is often time-varying. To tackle these challenges, we propose a novel method NEAT, which explicitly leverages the graph structure to model the treatment assignment mechanism, and mitigates confounding biases based on the treatment assignment modeling. We also extend our method into a dynamic setting to handle timevarying observational data. Experiments on both synthetic datasets and a real-world MRSA dataset validate the effectiveness of the proposed method, and provide insights for future applications. CCS CONCEPTS• Applied computing → Law, social and behavioral sciences;• Mathematics of computing → Causal networks.

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The network positions of methicillin resistant Staphylococcus aureus affected units in a regional healthcare system

Cited by 7 publications

References 18 publications

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

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

Dynamic contact networks of patients and MRSA spread in hospitals

A Look into Causal Effects under Entangled Treatment in Graphs: Investigating the Impact of Contact on MRSA Infection

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