Detailed Contact Data and the Dissemination of Staphylococcus aureus in Hospitals

Obadia, Thomas; Silhol, Romain; Opatowski, Lulla; Temime, Laura; Legrand, Judith; Thiébaut, Anne C. M.; Herrmann, Jean-Louis; Fleury, Éric; Guillemot, Didier; Boëlle, Pierre‐Yves

doi:10.1371/journal.pcbi.1004170

Cited by 70 publications

(108 citation statements)

References 41 publications

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“…Another particularly important application of understanding close proximity contacts is the characterization of pathogen transmissions in hospitals settings [669,[716][717][718][719]. Indeed, nosocomial infections are a major problem for the safety and health of patients and induce high economical costs [720].…”

Section: Measuring and Understanding Close Proximity Interactionsmentioning

confidence: 99%

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Statistical physics of vaccination

et al. 2016

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Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

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Section: Measuring and Understanding Close Proximity Interactionsmentioning

confidence: 99%

“…Wearables provide an unique opportunity in this context. To the present day, RFID tags have been used to identify or estimate pathways of transmission [716][717][718] and to help the test and develop prevention as well as containment measures [669,719].…”

Section: Measuring and Understanding Close Proximity Interactionsmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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“…20 However, on the basis of a preliminary analysis, accounting for spa-typing data would not drastically increase the number of incident cases.…”

Section: Discussion Of Methods and Limitsmentioning

confidence: 99%

“…The age, sex, and job type distributions of the HCWs according to hospital departments are summarized in Table 1. On average, 9.6 swab samples were taken per HCW (range, [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] during the study period.…”

Section: Participant Characteristicsmentioning

confidence: 99%

Occupational Determinants of Methicillin-Resistant Staphylococcus aureus Colonization Among Healthcare Workers: A Longitudinal Study in a Rehabilitation Center

Legrand¹,

Temime²,

Lawrence³

et al. 2015

Infect. Control Hosp. Epidemiol.

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BACKGROUND Staphylococcus aureus carriage among healthcare workers (HCWs) is a concern in hospital settings, where it may provide a reservoir for later infections in both patients and staff. Earlier studies have shown that the prevalence of methicillin-resistant Staphylococcus aureus (MRSA) carriage in HCWs is highly variable, depending notably on location, hospital department type, MRSA prevalence among patients, and type of contacts with patients. However, MRSA incidence in HCWs and its occupational determinants have seldom been studied. METHODS A prospective, observational cohort study was conducted between May and October 2009 in a French rehabilitation center hospital. HCWs and patients were screened weekly for S. aureus nasal carriage. Methicillin-susceptible S. aureus and MRSA prevalence and incidence were estimated and factors associated with MRSA acquisition were identified using generalized estimating equation regression methods. RESULTS Among 343 HCWs included in the analysis, the average prevalence was 27% (95% CI, 24%-29%) for methicillin-susceptible S. aureus and 10% (8%-11%) for MRSA. We observed 129 MRSA colonization events. According to the multivariable analysis, high MRSA prevalence level among patients and HCW occupation were significantly associated with MRSA acquisition in HCWs, with assistant nurses being more at risk than nurses (odds ratio, 2.2; 95% CI, 1.4-3.6). CONCLUSIONS Our findings may help further our understanding of the transmission dynamics of MRSA carriage acquisition in HCWs, suggesting that it is notably driven by carriage among patients and by the type of contact with patients.

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“…For instance, Jarynowski and Liljeros made a dataset based on the registry of visits in the hospital, and it was used to specify the interactions between hosts [27]. Also contact networks for disease spreading in hospitals have been studied [28], where the sequence of contacts was aggregated in a daily scale.…”

Section: Introductionmentioning

confidence: 99%

Risk of Coinfection Outbreaks in Temporal Networks: A Case Study of a Hospital Contact Network

2017

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We study the spreading of cooperative infections in an empirical temporal network of contacts between people, including health care workers and patients, in a hospital. The system exhibits a phase transition leading to one or several endemic branches, depending on the connectivity pattern and the temporal correlations. There are two endemic branches in the original setting and the non-cooperative case. However, the cooperative interaction between infections reinforces the upper branch, leading to a smaller epidemic threshold and a higher probability for having a big outbreak. We show the microscopic mechanisms leading to these differences, characterize three different risks, and use the influenza features as an example for this dynamics.

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Detailed Contact Data and the Dissemination of Staphylococcus aureus in Hospitals

Cited by 70 publications

References 41 publications

Statistical physics of vaccination

Statistical physics of vaccination

Occupational Determinants of Methicillin-Resistant Staphylococcus aureus Colonization Among Healthcare Workers: A Longitudinal Study in a Rehabilitation Center

Risk of Coinfection Outbreaks in Temporal Networks: A Case Study of a Hospital Contact Network

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