Dataset Information

Dynamic contact networks of patients and MRSA spread in hospitals.

ABSTRACT: 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.

SUBMITTER: Rocha LEC

PROVIDER: S-EPMC7283340 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Dynamic contact networks of patients and MRSA spread in hospitals.

Rocha Luis E C LEC Singh Vikramjit V Esch Markus M Lenaerts Tom T Liljeros Fredrik F Thorson Anna A

Scientific reports 20200609 1

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 ...[more]

PMID: 32518310

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Project description:ImportancePerson-to-person contact is important for the transmission of health care-associated pathogens. Quantifying these contact patterns is crucial for modeling disease transmission and understanding routes of potential transmission.ObjectiveTo generate and analyze the mixing matrices of hospital patients based on their contacts within hospital units.Design, setting, and participantsIn this quality improvement study, mixing matrices were created using a weighted contact network of connected hospital patients, in which contact was defined as occupying the same hospital unit for 1 day. Participants included hospitalized patients at 299 hospital units in 24 hospitals in the Southeastern United States that were part of the Duke Antimicrobial Stewardship Outreach Network between January 2015 and December 2017. Analysis was conducted between October 2021 and February 2022.Main outcomes and measuresThe mixing matrices of patients for each hospital unit were assessed using age, Elixhauser Score, and a measure of antibiotic exposure.ResultsAmong 1 549 413 hospitalized patients (median [IQR] age, 44 [26-63] years; 883 580 [56.3%] women) in 299 hospital units, some units had highly similar patterns across multiple hospitals, although the number of patients varied to a great extent. For most of the adult inpatient units, frequent mixing was observed for older adult groups, while outpatient units (eg, emergency departments and behavioral health units) showed mixing between different age groups. Most units mixing patterns followed the marginal distribution of age; however, patients aged 90 years or older with longer lengths of stay created a secondary peak in some medical wards. From the mixing matrices by Elixhauser Score, mixing between patients with relatively higher comorbidity index was observed in intensive care units. Mixing matrices by antibiotic spectrum, a 4-point scale based on priority for antibiotic stewardship programs, resulted in 6 major distinct patterns owing to the variation of the type of antibiotics used in different units, namely those dominated by a single antibiotic spectrum (narrow, broad, or extended), 1 pattern spanning all antibiotic spectrum types and 2 forms of narrow- and extended-spectrum dominant exposure patterns (an emergency room where patients were exposed to one type of antibiotic or the other and a pediatric ward where patients were exposed to both types).Conclusions and relevanceThis quality improvement study found that the mixing patterns of patients both within and between hospitals followed broadly expected patterns, although with a considerable amount of heterogeneity. These patterns could be used to inform mathematical models of health care-associated infections, assess the appropriateness of both models and policies for smaller community hospitals, and provide baseline information for the design of interventions that rely on altering patient contact patterns, such as practices for transferring patients within hospitals.

Project description:BackgroundInfluenza is a viral infection that primarily spreads via fluid droplets from an infected person's coughs and sneezes to others nearby. Social contact networks and the way people interact within them are thus important to its spread. We developed a method to characterize the social contact network for the potential transmission of influenza and then applied the method to school aged children and teenagers.MethodsSurveys were administered to students in an elementary, middle and high-school in the United States. The social contact network of a person was conceptualized as a set of groups to which they belong (e.g., households, classes, clubs) each composed of a sub-network of primary links representing the individuals within each group that they contact. The size of the group, number of primary links, time spent in the group, and level of contact along each primary link (near, talking, touching, or kissing) were characterized. Public activities done by groups venturing into the community where random contacts occur (e.g., friends viewing a movie) also were characterized.ResultsStudents, groups and public activities were highly heterogeneous. Groups with high potential for the transmission of influenza were households, school classes, friends, and sports; households decreased and friends and sports increased in importance with grade level. Individual public activity events (such as dances) were also important but lost their importance when averaged over time. Random contacts, primarily in school passing periods, were numerous but had much lower transmission potential compared to those with primary links within groups. Students are highly assortative, interacting mainly within age class. A small number of individual students are identified as likely "super-spreaders".ConclusionHigh-school students may form the local transmission backbone of the next pandemic. Closing schools and keeping students at home during a pandemic would remove the transmission potential within these ages and could be effective at thwarting its spread within a community. Social contact networks characterized as groups and public activities with the time, level of contact and primary links within each, yields a comprehensive view, which if extended to all ages, would allow design of effective community containment for pandemic influenza.

Dataset Information

Dynamic contact networks of patients and MRSA spread in hospitals.

Publications

Dynamic contact networks of patients and MRSA spread in hospitals.

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