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Xia H, Horn J, Piotrowska MJ, Sakowski K, Karch A, Kretzschmar M, Mikolajczyk R. Regional patient transfer patterns matter for the spread of hospital-acquired pathogens. Sci Rep 2024; 14:929. [PMID: 38195669 PMCID: PMC10776674 DOI: 10.1038/s41598-023-50873-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 12/27/2023] [Indexed: 01/11/2024] Open
Abstract
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.
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Affiliation(s)
- Hanjue Xia
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Centre for Health Sciences, Medical School of the Martin Luther University Halle-Wittenberg, 06108, Halle, Saale, Germany.
| | - Johannes Horn
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Centre for Health Sciences, Medical School of the Martin Luther University Halle-Wittenberg, 06108, Halle, Saale, Germany
| | - Monika J Piotrowska
- Institute of Applied Mathematics and Mechanics, University of Warsaw, 02-097, Warsaw, Poland
| | - Konrad Sakowski
- Institute of Applied Mathematics and Mechanics, University of Warsaw, 02-097, Warsaw, Poland
| | - André Karch
- Institute for Epidemiology and Social Medicine, University of Münster, 48149, Münster, Germany
| | - Mirjam Kretzschmar
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, 3584 CG, Utrecht, The Netherlands
| | - Rafael Mikolajczyk
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Centre for Health Sciences, Medical School of the Martin Luther University Halle-Wittenberg, 06108, Halle, Saale, Germany
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Westra D, Makai P, Kemp R. Return to sender: Unraveling the role of structural and social network ties in patient sharing networks. Soc Sci Med 2024; 340:116351. [PMID: 38043439 DOI: 10.1016/j.socscimed.2023.116351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 09/22/2023] [Accepted: 10/22/2023] [Indexed: 12/05/2023]
Abstract
Healthcare is increasingly delivered through networks of organizations. Well-structured patient sharing networks are known to have positive associations with the quality of delivered services. However, the drivers of patient sharing relations are rarely studied explicitly. In line with recent developments in network and integration theorizing, we hypothesize that structural and social network ties between organizations are uniquely associated with a higher number of shared patients. We test these hypotheses using a Bayesian zero-dispersed Poisson regression model within the Additive and Multiplicative Effects Framework based on administrative claims data from 732,122 dermatological patients from the Netherlands in 2017. Our results indicate that 2.6% of all dermatological patients are shared and that the amount of shared patients is significantly associated with structural (i.e. emergency contracts) and social (i.e. shared physicians) ties between organizations, confirming our hypotheses. We also find some evidence that patients are shared with more capable organizations. Our findings highlight the role of relational ties in the way health services are delivered. At the same time, they also raise some potential anti-trust concerns.
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Affiliation(s)
- Daan Westra
- Department of Health Services Research, Care and Public Health Research Institute (CAPHRI), Faculty of Health Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands.
| | - Peter Makai
- Healthcare Department, Netherlands Authority for Consumers and Markets (ACM), The Hague, the Netherlands; Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Ron Kemp
- Healthcare Department, Netherlands Authority for Consumers and Markets (ACM), The Hague, the Netherlands; Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, the Netherlands
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Brachaczek P, Lonc A, Kretzschmar ME, Mikolajczyk R, Horn J, Karch A, Sakowski K, Piotrowska MJ. Transmission of drug-resistant bacteria in a hospital-community model stratified by patient risk. Sci Rep 2023; 13:18593. [PMID: 37903799 PMCID: PMC10616222 DOI: 10.1038/s41598-023-45248-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 10/17/2023] [Indexed: 11/01/2023] Open
Abstract
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.
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Affiliation(s)
- Paweł Brachaczek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland
| | - Agata Lonc
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland
| | - Mirjam E Kretzschmar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Rafael Mikolajczyk
- Institute for Medical Epidemiology, Biometry, and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin Luther University Halle Wittenberg, Halle (Saale), Germany
| | - Johannes Horn
- Institute for Medical Epidemiology, Biometry, and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin Luther University Halle Wittenberg, Halle (Saale), Germany
| | - Andre Karch
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Konrad Sakowski
- Institute of Applied Mathematics and Mechanics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland.
| | - Monika J Piotrowska
- Institute of Applied Mathematics and Mechanics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland
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Aruhomukama D, Nakabuye H. Investigating the evolution and predicting the future outlook of antimicrobial resistance in sub-saharan Africa using phenotypic data for Klebsiella pneumoniae: a 12-year analysis. BMC Microbiol 2023; 23:214. [PMID: 37553587 PMCID: PMC10408162 DOI: 10.1186/s12866-023-02966-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 08/01/2023] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND Antimicrobial resistance (AMR) is a major public health challenge, particularly in sub-Saharan Africa (SSA). This study aimed to investigate the evolution and predict the future outlook of AMR in SSA over a 12-year period. By analysing the trends and patterns of AMR, the study sought to enhance our understanding of this pressing issue in the region and provide valuable insights for effective interventions and control measures to mitigate the impact of AMR on public health in SSA. RESULTS The study found that general medicine patients had the highest proportion of samples with AMR. Different types of samples showed varying levels of AMR. Across the studied locations, the highest resistance was consistently observed against ceftaroline (ranging from 68 to 84%), while the lowest resistance was consistently observed against ceftazidime avibactam, imipenem, meropenem, and meropenem vaborbactam (ranging from 92 to 93%). Notably, the predictive analysis showed a significant increasing trend in resistance to amoxicillin-clavulanate, cefepime, ceftazidime, ceftaroline, imipenem, meropenem, piperacillin-tazobactam, and aztreonam over time. CONCLUSIONS These findings suggest the need for coordinated efforts and interventions to control and prevent the spread of AMR in SSA. Targeted surveillance based on local resistance patterns, sample types, and patient populations is crucial for effective monitoring and control of AMR. The study also highlights the urgent need for action, including judicious use of antibiotics and the development of alternative treatment options to combat the growing problem of AMR in SSA.
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Affiliation(s)
- Dickson Aruhomukama
- Department of Medical Microbiology, College of Health Sciences, Makerere University, Kampala, Uganda.
| | - Hellen Nakabuye
- Department of Medical Microbiology, College of Health Sciences, Makerere University, Kampala, Uganda
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Domegan L, Brehony C, Fitzpatrick F, O'Connell K, Dinesh B, Cafferkey J, Burns K. Social network and genomic analysis of an OXA-48 carbapenemase-producing Enterobacterales hospital ward outbreak in Ireland, 2018-2019. Infect Prev Pract 2023; 5:100282. [PMID: 37168234 PMCID: PMC10164899 DOI: 10.1016/j.infpip.2023.100282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 03/28/2023] [Indexed: 05/13/2023] Open
Abstract
Background Nosocomial transmission and outbreaks of carbapenemase-producing Enterobacterales (CPE) represent a challenge to healthcare systems. In July 2018, a CPE hospital ward outbreak was declared. Our aim was to investigate transmission patterns, using social network analysis and genomics in a nosocomial CPE outbreak. Methods A retrospective descriptive analysis of all patients (cases and contacts) admitted to a ward experiencing a CPE outbreak (2018-2019) was undertaken. A case had a negative CPE admission screen, and subsequent positive test. A contact shared a multi-bed area and/or facility with a case (>4 hours). Social networks, including genomics data and ward locations, were constructed. Network metrics were analysed. Findings Forty-five cases and 844 contacts were analysed. The median age of cases was 78 years (IQR 67-83), 58% (n=26) were male and 100% had co-morbidities. The median outbreak ward length-of-stay (LOS) was 17 days (IQR 10-34). OXA-48 CPE was confirmed in all cases and from 26 environmental samples. Social networks identified clusters by time, gender and species/sequence type/plasmid. Network metrics indicated potential superspreading involving a subset of patients with behavioural issues. Conclusion Social networks elucidated high resolution transmission patterns involving two related OXA-48 plasmids, multiple species/genotypes and potential super-spreading. Interventions prevented intra-hospital spread. An older patient cohort, extended hospital LOS and frequent intra-ward bed transfers, coupled with suboptimal ward infrastructure, likely prolonged this outbreak. We recommend social network analysis contemporaneously with genomics (on case and environmental samples) for complex nosocomial outbreaks and bespoke care plans for patients with behavioural issues on outbreak wards.
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Affiliation(s)
- Lisa Domegan
- Health Service Executive, Health Protection Surveillance Centre, Dublin, Ireland
- European Programme for Intervention Epidemiology Training (EPIET), European Centre for Disease Prevention and Control, (ECDC), Stockholm, Sweden
- Corresponding author. Address: Health Service Executive, Health Protection Surveillance Centre, Dublin, Ireland.
| | - Carina Brehony
- Health Service Executive, Health Protection Surveillance Centre, Dublin, Ireland
- European Public Health Microbiology Training (EUPHEM), European Centre for Disease Prevention and Control, (ECDC), Stockholm, Sweden
| | - Fidelma Fitzpatrick
- Department of Clinical Microbiology, Infection Prevention & Control, Beaumont Hospital, Dublin, Ireland
- Department of Clinical Microbiology, Royal College of Surgeons in Ireland
| | - Karina O'Connell
- Department of Clinical Microbiology, Infection Prevention & Control, Beaumont Hospital, Dublin, Ireland
- Department of Clinical Microbiology, Royal College of Surgeons in Ireland
| | - Binu Dinesh
- Department of Clinical Microbiology, Infection Prevention & Control, Beaumont Hospital, Dublin, Ireland
- Department of Clinical Microbiology, Royal College of Surgeons in Ireland
| | - Jacqueline Cafferkey
- Department of Clinical Microbiology, Infection Prevention & Control, Beaumont Hospital, Dublin, Ireland
| | - Karen Burns
- Department of Clinical Microbiology, Infection Prevention & Control, Beaumont Hospital, Dublin, Ireland
- Department of Clinical Microbiology, Royal College of Surgeons in Ireland
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Leclerc Q, Clements A, Dunn H, Hatcher J, Lindsay JA, Grandjean L, Knight GM. Quantifying patient- and hospital-level antimicrobial resistance dynamics in Staphylococcus aureus from routinely collected data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.15.23285946. [PMID: 36824943 PMCID: PMC9949191 DOI: 10.1101/2023.02.15.23285946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Antimicrobial resistance (AMR) to all antibiotic classes has been found in the pathogen Staphylococcus aureus . The reported prevalence of these resistances vary, driven by within-host AMR evolution at the patient level, and between-host transmission at the hospital level. Without dense longitudinal sampling, pragmatic analysis of AMR dynamics at multiple levels using routine surveillance data is essential to inform control measures. We explored S. aureus AMR diversity in 70,000 isolates from a UK paediatric hospital between 2000-2020, using electronic datasets containing multiple routinely collected isolates per patient with phenotypic antibiograms, hospitalisation information, and antibiotic consumption. At the hospital-level, the proportion of isolates that were meticillin-resistant (MRSA) increased between 2014-2020 from 25 to 50%, before sharply decreasing to 30%, likely due to a change in inpatient demographics. Temporal trends in the proportion of isolates resistant to different antibiotics were often correlated in MRSA, but independent in meticillin-susceptible S. aureus . Ciprofloxacin resistance in MRSA decreased from 70% to 40% of tested isolates between 2007-2020, likely linked to a national policy to reduce fluoroquinolone usage in 2007. At the patient level, we identified frequent AMR diversity, with 4% of patients ever positive for S. aureus simultaneously carrying, at some point, multiple isolates with different resistances. We detected changes over time in AMR diversity in 3% of patients ever positive for S. aureus . These changes equally represented gain and loss of resistance. Within this routinely collected dataset, we found that 65% of changes in resistance within a patient’s S. aureus population could not be explained by antibiotic exposure or between-patient transmission of bacteria, suggesting that within-host evolution via frequent gain and loss of AMR genes may be responsible for these changing AMR profiles. Our study highlights the value of exploring existing routine surveillance data to determine underlying mechanisms of AMR. These insights may substantially improve our understanding of the importance of antibiotic exposure variation, and the success of single S. aureus clones.
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Piotrowska MJ, Sakowski K, Horn J, Mikolajczyk R, Karch A. The effect of re-directed patient flow in combination with targeted infection control measures on the spread of multi-drug-resistant Enterobacteriaceae in the German health-care system: a mathematical modelling approach. Clin Microbiol Infect 2023; 29:109.e1-109.e7. [PMID: 35970445 DOI: 10.1016/j.cmi.2022.08.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 07/27/2022] [Accepted: 08/03/2022] [Indexed: 12/27/2022]
Abstract
OBJECTIVE The introduction of multi-drug-resistant Enterobacteriaceae (MDR-E) by colonized patients transferred from high-prevalence countries has led to several large outbreaks of MDR-E in low-prevalence countries, with the risk of propagated spread to the community. The goal of this study was to derive a strategy to counteract the spread of MDR-E at the regional health-care network level. METHODS We used a hybrid ordinary differential equation and network model built based on German health insurance data to evaluate whether the re-direction of patient flow in combination with targeted infection control measures can counteract the spread of MDR-E in the German health-care system. We applied pragmatic re-direction strategies focusing on a reduced choice of hospitals for subsequent stays after initial hospitalization but not manipulating direct transfers because these are most likely determined by medical needs. RESULTS The re-direction strategies alone did not reduce the system-wide spread of MDR-E (system-wide prevalence of MDR-E is 18.7% vs. 25.7%/29.9%). In contrast, targeted hospital-based infection control measures restricted to institutions with the highest institutional basic reproduction numbers in the network were identified as an effective tool for reducing system-wide prevalence (system-wide prevalence of MDR-E is 18.7% vs. 9.3%). If these measures were applied to the top one-third of hospitals, the system-wide prevalence could be reduced by approximately 80% (system-wide prevalence of 18.7% vs. 3.5% for one-third of patients subjected to interventions). A combination of this hospital-based intervention and patient re-direction strategies could not improve the effectiveness of the hospital-based approach (system-wide prevalence of MDR-E is 9.3% vs. 14.2%/14.3%). CONCLUSIONS The pragmatic patient re-direction strategies were not capable of restricting the spread of MDR-E in a simulation of the German health-care system; in contrast, hospital-based interventions focusing on institutions identified based on network transmission patterns seem to be a promising approach for sustainable reduction of the spread of MDR-E through the German population.
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Affiliation(s)
- Monika J Piotrowska
- Institute of Applied Mathematics and Mechanics, University of Warsaw, Poland
| | - Konrad Sakowski
- Institute of Applied Mathematics and Mechanics, University of Warsaw, Poland.
| | - Johannes Horn
- Institute for Medical Epidemiology, Biometrics, and Informatics, Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Rafael Mikolajczyk
- Institute for Medical Epidemiology, Biometrics, and Informatics, Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - André Karch
- Institute of Epidemiology and Social Medicine, University of Münster, Germany
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Zhou C, Hao Y, Lan Y, Li W. To introduce or not? Strategic analysis of hospital operations with telemedicine. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023; 304:292-307. [PMID: 34955589 PMCID: PMC8683093 DOI: 10.1016/j.ejor.2021.12.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 12/12/2021] [Indexed: 06/02/2023]
Abstract
Despite its efficiency in reducing the impact of pandemics (e.g., the COVID-19), whether to introduce telemedicine as an additional way to serve chronically ill patients remains controversial for hospitals in many countries. This paper builds a stylized model to investigate a hospital's telemedicine strategy and the corresponding impacts on its operations regarding outpatient management of chronic diseases. We implement our analysis from three key concerns of the hospital in the presence of a pandemic: the differences in medical consumption and reimbursement between in-person and telemedicine modalities and the effort cost of infection reduction resulting from the pandemic. We find that in the absence of the pandemic, the hospital prefers to introduce telemedicine when the differences in medical consumption and reimbursement are both small. In the presence of the pandemic, we find that the introduction of telemedicine does not always benefit the hospital and that it is better not to introduce telemedicine in some cases since it may exacerbate the negative influence of the pandemic on the hospital's total costs. Furthermore, we surprisingly find that the hospital may set greater in-person capacity but less telemedicine capacity in response to the outbreak of the pandemic under certain conditions, which contradicts public beliefs. Finally, we show that social welfare can be improved by introducing telemedicine when the effort cost of infection reduction and the difference in reimbursement are both of moderate size. The condition under which social welfare is improved tightens with a greater difference in medical consumption.
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Affiliation(s)
- Cuihua Zhou
- College of Management & Economics, Tianjin University, Tianjin 300072, China
| | - Yifei Hao
- School of Business Administration, Chongqing Technology & Business University, Chongqing 400067, China
| | - Yanfei Lan
- College of Management & Economics, Tianjin University, Tianjin 300072, China
| | - Weifeng Li
- Academy of Medical Engineering & Translation Medicine, Tianjin University, Tianjin 300072, China
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Carrignon S, Bentley RA, Silk M, Fefferman NH. How social learning shapes the efficacy of preventative health behaviors in an outbreak. PLoS One 2022; 17:e0262505. [PMID: 35015794 PMCID: PMC8752029 DOI: 10.1371/journal.pone.0262505] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 12/27/2021] [Indexed: 12/31/2022] Open
Abstract
The global pandemic of COVID-19 revealed the dynamic heterogeneity in how individuals respond to infection risks, government orders, and community-specific social norms. Here we demonstrate how both individual observation and social learning are likely to shape behavioral, and therefore epidemiological, dynamics over time. Efforts to delay and reduce infections can compromise their own success, especially when disease risk and social learning interact within sub-populations, as when people observe others who are (a) infected and/or (b) socially distancing to protect themselves from infection. Simulating socially-learning agents who observe effects of a contagious virus, our modelling results are consistent with with 2020 data on mask-wearing in the U.S. and also concur with general observations of cohort induced differences in reactions to public health recommendations. We show how shifting reliance on types of learning affect the course of an outbreak, and could therefore factor into policy-based interventions incorporating age-based cohort differences in response behavior.
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Affiliation(s)
- Simon Carrignon
- Department of Anthropology and Center for the Dynamics of Social Complexity (DySoC), University of Tennessee, Knoxville, TN, United States of America
| | - R. Alexander Bentley
- Department of Anthropology and Center for the Dynamics of Social Complexity (DySoC), University of Tennessee, Knoxville, TN, United States of America
| | - Matthew Silk
- Centre for Ecology and Conservation, University of Exeter, Exeter, United Kingdom
| | - Nina H. Fefferman
- Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, United States of America
- Department of Mathematics, University of Tennessee, Knoxville, TN, United States of America
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Hu H, Yang Y, Zhang C, Huang C, Guan X, Shi L. Review of social networks of professionals in healthcare settings-where are we and what else is needed? Global Health 2021; 17:139. [PMID: 34863221 PMCID: PMC8642762 DOI: 10.1186/s12992-021-00772-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 09/28/2021] [Indexed: 01/08/2023] Open
Abstract
Background Social Network Analysis (SNA) demonstrates great potential in exploring health professional relationships and improving care delivery, but there is no comprehensive overview of its utilization in healthcare settings. This review aims to provide an overview of the current state of knowledge regarding the use of SNA in understanding health professional relationships in different countries. Methods We conducted an umbrella review by searching eight academic databases and grey literature up to April 30, 2021, enhanced by citation searches. We completed study selection, data extraction and quality assessment using predetermined criteria. The information abstracted from the reviews was synthesized quantitatively, qualitatively and narratively. Results Thirteen reviews were included in this review, yielding 330 empirical studies. The degree of overlaps of empirical studies across included reviews was low (4.3 %), indicating a high diversity of included reviews and the necessity of this umbrella review. Evidence from low- and middle-income countries (LMIC), particularly Asian countries, was limited. The earliest review was published in 2010 and the latest in 2019. Six reviews focused on the construction or description of professional networks and seven reviews reported factors or influences of professional networks. We synthesized existing literature on social networks of health care professionals in the light of (i) theoretical frameworks, (ii) study design and data collection, (iii) network nodes, measures and analysis, and (iv) factors of professional networks and related outcomes. From the perspective of methodology, evidence lies mainly in cross-sectional study design and electronic data, especially administrative data showing “patient-sharing” relationships, which has become the dominant data collection method. The results about the impact of health professional networks on health-related consequences were often contradicting and not truly comparable. Conclusions Methodological limitations, inconsistent findings, and lack of evidence from LMIC imply an urgent need for further investigations. The potential for broader utilization of SNA among providers remains largely untapped and the findings of this review may contain important value for building optimal healthcare delivery networks. PROSPERO registration number The protocol was published and registered with PROSPERO, the International Prospective Register of Systematic Reviews (CRD42020205996). Supplementary Information The online version contains supplementary material available at 10.1186/s12992-021-00772-7.
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Affiliation(s)
- Huajie Hu
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, 100191, Beijing, China
| | - Yu Yang
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, 100191, Beijing, China
| | - Chi Zhang
- Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Cong Huang
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, 100191, Beijing, China
| | - Xiaodong Guan
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, 100191, Beijing, China. .,International Research Center for Medicinal Administration, Peking University, Beijing, China.
| | - Luwen Shi
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, 100191, Beijing, China.,International Research Center for Medicinal Administration, Peking University, Beijing, China
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11
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Understanding MRSA clonal competition within a UK hospital; the possible importance of density dependence. Epidemics 2021; 37:100511. [PMID: 34662751 DOI: 10.1016/j.epidem.2021.100511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 06/18/2021] [Accepted: 10/06/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Methicillin resistant Staphylococcus aureus (MRSA) bacteria cause serious, often healthcare-associated infections and are frequently highly resistant to diverse antibiotics. Multiple MRSA clonal complexes (CCs) have evolved independently and countries have different prevalent CCs. It is unclear when and why the dominant CC in a region may switch. METHODS We developed a mathematical deterministic model of MRSA CC competing for limited resource. The model distinguishes 'standard MRSA' and multidrug resistant sub-populations within each CC, allowing for resistance loss and transfer between same CC bacteria. We first analysed how dynamics of this system depend on growth-rate and resistance-potential differences between CCs, and on their resistance gene accumulation. We then fit the model to capture the longitudinal CC dynamics observed at a single UK hospital, which exemplified the UK-wide switch from mainly CC30 to mainly CC22. RESULTS We find that within a CC, gain and loss of resistance can allow for co-existence of sensitive and resistant sub-populations. Due to more efficient transfer of resistance at higher CC density, more drug resistance can accumulate in the population of a more prevalent CC. We show how this process of density dependent competition, together with prevalence disruption, could explain the relatively sudden switch from mainly CC30 to mainly CC22 in the UK hospital setting. Alternatively, the observed hospital dynamics could be reproduced by assuming that multidrug resistant CC22 evolved only around 2004. CONCLUSIONS We showed how higher prevalence may advantage a CC by allowing it to acquire antimicrobial resistances more easily. Due to this density dependence in competition, dominance in an area can depend on historic contingencies; the MRSA CC that happened to be first could stay dominant because of its high prevalence advantage. This then could help explain the stability, despite frequent stochastic introductions across borders, of geographic differences in MRSA CC.
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Stewart S, Robertson C, Kennedy S, Kavanagh K, Haahr L, Manoukian S, Mason H, Dancer S, Cook B, Reilly J. Personalized infection prevention and control: identifying patients at risk of healthcare-associated infection. J Hosp Infect 2021; 114:32-42. [PMID: 34301394 DOI: 10.1016/j.jhin.2021.03.032] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/22/2021] [Accepted: 03/25/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND Few healthcare-associated infection (HAI) studies focus on risk of HAI at the point of admission. Understanding this will enable planning and management of care with infection prevention at the heart of the patient journey from the point of admission. AIM To determine intrinsic characteristics of patients at hospital admission and extrinsic events, during the two years preceding admission, that increase risk of developing HAI. METHODS An incidence survey of adults within two hospitals in NHS Scotland was undertaken for one year in 2018/19 as part of the Evaluation of Cost of Nosocomial Infection (ECONI) study. The primary outcome measure was developing any HAI using recognized case definitions. The cohort was derived from routine hospital episode data and linkage to community dispensed prescribing data. FINDINGS The risk factors present on admission observed as being the most significant for the acquisition of HAI were: being treated in a teaching hospital, increasing age, comorbidities of cancer, cardiovascular disease, chronic renal failure and diabetes; and emergency admission. Relative risk of developing HAI increased with intensive care unit, high-dependency unit, and surgical specialties, and surgery <30 days before admission and a total length of stay of >30 days in the two years to admission. CONCLUSION Targeting patients at risk of HAI from the point of admission maximizes the potential for prevention, especially when extrinsic risk factors are known and managed. This study proposes a new approach to infection prevention and control (IPC), identifying those patients at greatest risk of developing a particular type of HAI who might be potential candidates for personalized IPC interventions.
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Affiliation(s)
- S Stewart
- Safeguarding Health through Infection Prevention Research Group, Research Centre for Health (ReaCH), Glasgow Caledonian University, Glasgow, UK.
| | - C Robertson
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK
| | | | - K Kavanagh
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK
| | - L Haahr
- Safeguarding Health through Infection Prevention Research Group, Research Centre for Health (ReaCH), Glasgow Caledonian University, Glasgow, UK
| | - S Manoukian
- Yunus Centre for Social Business and Health, Glasgow Caledonian University, Glasgow, UK
| | - H Mason
- Yunus Centre for Social Business and Health, Glasgow Caledonian University, Glasgow, UK
| | - S Dancer
- Department of Microbiology, Hairmyres Hospital, NHS Lanarkshire, UK; School of Applied Science, Edinburgh Napier University, Edinburgh, UK
| | - B Cook
- Departments of Anaesthesia and Critical Care, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - J Reilly
- Safeguarding Health through Infection Prevention Research Group, Research Centre for Health (ReaCH), Glasgow Caledonian University, Glasgow, UK; National Services Scotland (NSS), UK
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Masuda N, Miller JC, Holme P. Concurrency measures in the era of temporal network epidemiology: a review. J R Soc Interface 2021; 18:20210019. [PMID: 34062106 PMCID: PMC8169215 DOI: 10.1098/rsif.2021.0019] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 05/11/2021] [Indexed: 01/19/2023] Open
Abstract
Diseases spread over temporal networks of interaction events between individuals. Structures of these temporal networks hold the keys to understanding epidemic propagation. One early concept of the literature to aid in discussing these structures is concurrency-quantifying individuals' tendency to form time-overlapping 'partnerships'. Although conflicting evaluations and an overabundance of operational definitions have marred the history of concurrency, it remains important, especially in the area of sexually transmitted infections. Today, much of theoretical epidemiology uses more direct models of contact patterns, and there is an emerging body of literature trying to connect methods to the concurrency literature. In this review, we will cover the development of the concept of concurrency and these new approaches.
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Affiliation(s)
- Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, New York, NY, USA
- Computational and Data-Enabled Science and Engineering Program, State University of New York at Buffalo, New York, NY, USA
| | - Joel C. Miller
- School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, Australia
| | - Petter Holme
- Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan
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14
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Xia H, Horn J, Piotrowska MJ, Sakowski K, Karch A, Tahir H, Kretzschmar M, Mikolajczyk R. Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections. PLoS Comput Biol 2021; 17:e1008941. [PMID: 33956787 PMCID: PMC8130968 DOI: 10.1371/journal.pcbi.1008941] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 05/18/2021] [Accepted: 04/06/2021] [Indexed: 11/25/2022] Open
Abstract
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.
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Affiliation(s)
- Hanjue Xia
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, Halle, Saxony-Anhalt, Germany
| | - Johannes Horn
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, Halle, Saxony-Anhalt, Germany
| | - Monika J. Piotrowska
- Institute of Applied Mathematics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Konrad Sakowski
- Institute of Applied Mathematics and Mechanics, University of Warsaw, Warsaw, Poland
- Institute of High Pressure Physics, Polish Academy of Sciences, Warsaw, Poland
| | - André Karch
- Institute for Epidemiology and Social Medicine, University of Münster, Münster, North Rhine-Westphalia, Germany
| | - Hannan Tahir
- Julius Center for Health Sciences & Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Mirjam Kretzschmar
- Julius Center for Health Sciences & Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Rafael Mikolajczyk
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, Halle, Saxony-Anhalt, Germany
- * E-mail:
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15
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Relevance of intra-hospital patient movements for the spread of healthcare-associated infections within hospitals - a mathematical modeling study. PLoS Comput Biol 2021; 17:e1008600. [PMID: 33534784 PMCID: PMC7857595 DOI: 10.1371/journal.pcbi.1008600] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 12/02/2020] [Indexed: 11/23/2022] Open
Abstract
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.
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16
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Arif S, Sadeeqa S, Saleem Z, Latif S, Sharif M. The burden of healthcare-associated infections among pediatrics: a repeated point prevalence survey from Pakistan. Hosp Pract (1995) 2021; 49:34-40. [PMID: 32990488 DOI: 10.1080/21548331.2020.1826783] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 09/18/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Healthcare-associated infections (HAIs) are considered a major threat to public health resulting in significant morbidity, mortality, and additional costs. The present study aimed to assess the current patterns and risk factors of HAIs among hospitalized children. MATERIALS AND METHODS Three repeated point prevalence surveys were conducted in the pediatric inpatients of four hospitals by using the methodology developed by the European Center for Disease Prevention and Control. All patients present in the ward at 8:00 AM on the survey day and not discharged from the hospital on the same day were included. A standardized data collection form containing information on the presence of HAIs and the associated risk factors was completed for the patients. FINDINGS Out of 888 hospitalized patients, 116 (13.1%) had the symptoms of HAIs. Most common infections were bloodstream infections (BSIs) (32.8%), pneumonia (21.0%), ear, eyes, nose and throat infections (11.8%), and skin and soft tissue infections (SSTs) (19.0%). Factors significantly associated with infections were the length of hospital stay (p = 0.000), admission to the medicine ward (p = 0.034), and male gender (p = 0.010). BSIs were most common in children belonging to the age group of less than one month (78.9%), who were admitted to intensive care units (73.7%). SSTs including surgical site infections were more prevalent in surgery wards (78.3%). CONCLUSIONS A high rate of HAIs among pediatrics was found in Pakistan. Infection control and prevention strategies are needed with a major focus on interventions to prevent the spread of most prevalent HAIs.
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Affiliation(s)
- Sara Arif
- Institute of Pharmacy, Faculty of Pharmaceutical and Allied Health Sciences, Lahore College for Women University , Lahore, Pakistan
| | - Saleha Sadeeqa
- Institute of Pharmacy, Faculty of Pharmaceutical and Allied Health Sciences, Lahore College for Women University , Lahore, Pakistan
| | - Zikria Saleem
- Department of Pharmacy Practice, Faculty of Pharmacy, The University of Lahore , Lahore, Pakistan
| | - Sumaira Latif
- Institute of Pharmacy, Faculty of Pharmaceutical and Allied Health Sciences, Lahore College for Women University , Lahore, Pakistan
| | - Muhammad Sharif
- Department of Paediatric Surgery, King Edward Medical University , Lahore, Pakistan
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17
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Suwono B, Eckmanns T, Kaspar H, Merle R, Zacher B, Kollas C, Weiser AA, Noll I, Feig M, Tenhagen BA. Cluster analysis of resistance combinations in Escherichia coli from different human and animal populations in Germany 2014-2017. PLoS One 2021; 16:e0244413. [PMID: 33471826 PMCID: PMC7817003 DOI: 10.1371/journal.pone.0244413] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 12/09/2020] [Indexed: 11/18/2022] Open
Abstract
Recent findings on Antibiotic Resistance (AR) have brought renewed attention to the comparison of data on AR from human and animal sectors. This is however a major challenge since the data is not harmonized. This study performs a comparative analysis of data on resistance combinations in Escherichia coli (E. coli) from different routine surveillance and monitoring systems for human and different animal populations in Germany. Data on E. coli isolates were collected between 2014 and 2017 from human clinical isolates, non-clinical animal isolates from food-producing animals and food, and clinical animal isolates from food-producing and companion animals from national routine surveillance and monitoring for AR in Germany. Sixteen possible resistance combinations to four antibiotics—ampicillin, cefotaxime, ciprofloxacin and gentamicin–for these populations were used for hierarchical clustering (Euclidian and average distance). All analyses were performed with the software R 3.5.1 (Rstudio 1.1.442). Data of 333,496 E. coli isolates and forty-one different human and animal populations were included in the cluster analysis. Three main clusters were detected. Within these three clusters, all human populations (intensive care unit (ICU), general ward and outpatient care) showed similar relative frequencies of the resistance combinations and clustered together. They demonstrated similarities with clinical isolates from different animal populations and most isolates from pigs from both non-clinical and clinical isolates. Isolates from healthy poultry demonstrated similarities in relative frequencies of resistance combinations and clustered together. However, they clustered separately from the human isolates. All isolates from different animal populations with low relative frequencies of resistance combinations clustered together. They also clustered separately from the human populations. Cluster analysis has been able to demonstrate the linkage among human isolates and isolates from various animal populations based on the resistance combinations. Further analyses based on these findings might support a better one-health approach for AR in Germany.
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Affiliation(s)
- Beneditta Suwono
- Department Biological Safety, Unit Epidemiology, Zoonoses and Antimicrobial Resistance, German Federal Institute for Risk Assessment, Berlin, Germany
- Department Infectious Disease Epidemiology, Unit Healthcare-associated Infections, Surveillance for Antibiotic Resistance and Consumption, Robert Koch Institute, Berlin, Germany
| | - Tim Eckmanns
- Department Infectious Disease Epidemiology, Unit Healthcare-associated Infections, Surveillance for Antibiotic Resistance and Consumption, Robert Koch Institute, Berlin, Germany
| | - Heike Kaspar
- Unit Antibiotic Resistance Monitoring, Federal Office of Consumer Protection and Food Safety, Berlin, Germany
| | - Roswitha Merle
- Department of Veterinary Medicine, Institute for Veterinary Epidemiology and Biostatistics, Working Group Applied Epidemiology, Free University Berlin, Berlin, Germany
| | - Benedikt Zacher
- Department Infectious Disease Epidemiology, Unit Healthcare-associated Infections, Surveillance for Antibiotic Resistance and Consumption, Robert Koch Institute, Berlin, Germany
| | - Chris Kollas
- Department Biological Safety, Unit Epidemiology, Zoonoses and Antimicrobial Resistance, German Federal Institute for Risk Assessment, Berlin, Germany
| | - Armin A. Weiser
- Department Biological Safety, Unit Epidemiology, Zoonoses and Antimicrobial Resistance, German Federal Institute for Risk Assessment, Berlin, Germany
| | - Ines Noll
- Department Infectious Disease Epidemiology, Unit Healthcare-associated Infections, Surveillance for Antibiotic Resistance and Consumption, Robert Koch Institute, Berlin, Germany
| | - Marcel Feig
- Department Infectious Disease Epidemiology, Unit Healthcare-associated Infections, Surveillance for Antibiotic Resistance and Consumption, Robert Koch Institute, Berlin, Germany
| | - Bernd-Alois Tenhagen
- Department Biological Safety, Unit Epidemiology, Zoonoses and Antimicrobial Resistance, German Federal Institute for Risk Assessment, Berlin, Germany
- * E-mail:
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18
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Modelling pathogen spread in a healthcare network: Indirect patient movements. PLoS Comput Biol 2020; 16:e1008442. [PMID: 33253154 PMCID: PMC7728397 DOI: 10.1371/journal.pcbi.1008442] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 12/10/2020] [Accepted: 10/16/2020] [Indexed: 11/28/2022] Open
Abstract
Inter-hospital patient transfers (direct transfers) between healthcare facilities have been shown to contribute to the spread of pathogens in a healthcare network. However, the impact of indirect transfers (patients re-admitted from the community to the same or different hospital) is not well studied. This work aims to study the contribution of indirect transfers to the spread of pathogens in a healthcare network. To address this aim, a hybrid network–deterministic model to simulate the spread of multiresistant pathogens in a healthcare system was developed for the region of Lower Saxony (Germany). The model accounts for both, direct and indirect transfers of patients. Intra-hospital pathogen transmission is governed by a SIS model expressed by a system of ordinary differential equations. Our results show that the proposed model reproduces the basic properties of healthcare-associated pathogen spread. They also show the importance of indirect transfers: restricting the pathogen spread to direct transfers only leads to 4.2% system wide prevalence. However, adding indirect transfers leads to an increase in the overall prevalence by a factor of 4 (18%). In addition, we demonstrated that the final prevalence in the individual healthcare facilities depends on average length of stay in a way described by a non-linear concave function. Moreover, we demonstrate that the network parameters of the model may be derived from administrative admission/discharge records. In particular, they are sufficient to obtain inter-hospital transfer probabilities, and to express the patients’ transfers as a Markov process. Using the proposed model, we show that indirect transfers of patients are equally or even more important as direct transfers for the spread of pathogens in a healthcare network. Direct patient transfers between hospitals have been shown to play an important role in the spread of pathogens in a healthcare network. However, readmission of patients from the community (indirect transfers) to the same or a different hospital is not well studied, and its role for the spread of pathogens in a healthcare network is not quantified. In this work, we developed a network model of a healthcare system to study the impact of indirect transfers on the prevalence in the individual hospitals as well as in the overall healthcare system. The model includes both, direct and indirect transfers of patients between the healthcare facilities due to transferring as well as readmission of infectious (colonized or infected) patients. Our results show that the readmission of patients (indirect transfers), either to the same or different facility, is an important potential channel of pathogen transmission. Such indirect transfers are of no less importance than direct patient transfers in controlling the spread of pathogens in a healthcare network.
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Shapiro JT, Leboucher G, Myard-Dury AF, Girardo P, Luzzati A, Mary M, Sauzon JF, Lafay B, Dauwalder O, Laurent F, Lina G, Chidiac C, Couray-Targe S, Vandenesch F, Flandrois JP, Rasigade JP. Metapopulation ecology links antibiotic resistance, consumption, and patient transfers in a network of hospital wards. eLife 2020; 9:54795. [PMID: 33106223 PMCID: PMC7690951 DOI: 10.7554/elife.54795] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 10/12/2020] [Indexed: 02/07/2023] Open
Abstract
Antimicrobial resistance (AMR) is a global threat. A better understanding of how antibiotic use and between-ward patient transfers (or connectivity) impact population-level AMR in hospital networks can help optimize antibiotic stewardship and infection control strategies. Here, we used a metapopulation framework to explain variations in the incidence of infections caused by seven major bacterial species and their drug-resistant variants in a network of 357 hospital wards. We found that ward-level antibiotic consumption volume had a stronger influence on the incidence of the more resistant pathogens, while connectivity had the most influence on hospital-endemic species and carbapenem-resistant pathogens. Piperacillin-tazobactam consumption was the strongest predictor of the cumulative incidence of infections resistant to empirical sepsis therapy. Our data provide evidence that both antibiotic use and connectivity measurably influence hospital AMR. Finally, we provide a ranking of key antibiotics by their estimated population-level impact on AMR that might help inform antimicrobial stewardship strategies.
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Affiliation(s)
- Julie Teresa Shapiro
- CIRI, Centre International de Recherche en Infectiologie, Université de Lyon, Inserm U1111, Ecole Normale Supérieure de Lyon, Université Lyon 1, CNRS, UMR5308, Lyon, France
| | | | - Anne-Florence Myard-Dury
- Pôle de Santé Publique, Département d'Information Médicale, Hospices Civils de Lyon, Lyon, France
| | - Pascale Girardo
- Institut des Agents Infectieux, Hospices Civils de Lyon, Lyon, France
| | - Anatole Luzzati
- Institut des Agents Infectieux, Hospices Civils de Lyon, Lyon, France
| | - Mélissa Mary
- Institut des Agents Infectieux, Hospices Civils de Lyon, Lyon, France
| | | | - Bénédicte Lafay
- Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 5558, University of Lyon, Lyon, France
| | - Olivier Dauwalder
- Institut des Agents Infectieux, Hospices Civils de Lyon, Lyon, France
| | - Frédéric Laurent
- CIRI, Centre International de Recherche en Infectiologie, Université de Lyon, Inserm U1111, Ecole Normale Supérieure de Lyon, Université Lyon 1, CNRS, UMR5308, Lyon, France.,Institut des Agents Infectieux, Hospices Civils de Lyon, Lyon, France
| | - Gerard Lina
- CIRI, Centre International de Recherche en Infectiologie, Université de Lyon, Inserm U1111, Ecole Normale Supérieure de Lyon, Université Lyon 1, CNRS, UMR5308, Lyon, France.,Institut des Agents Infectieux, Hospices Civils de Lyon, Lyon, France
| | - Christian Chidiac
- Service des Maladies Infectieuses et Tropicales, Hospices Civils de Lyon, Lyon, France
| | - Sandrine Couray-Targe
- Pôle de Santé Publique, Département d'Information Médicale, Hospices Civils de Lyon, Lyon, France
| | - François Vandenesch
- CIRI, Centre International de Recherche en Infectiologie, Université de Lyon, Inserm U1111, Ecole Normale Supérieure de Lyon, Université Lyon 1, CNRS, UMR5308, Lyon, France.,Institut des Agents Infectieux, Hospices Civils de Lyon, Lyon, France
| | - Jean-Pierre Flandrois
- Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 5558, University of Lyon, Lyon, France
| | - Jean-Philippe Rasigade
- CIRI, Centre International de Recherche en Infectiologie, Université de Lyon, Inserm U1111, Ecole Normale Supérieure de Lyon, Université Lyon 1, CNRS, UMR5308, Lyon, France.,Institut des Agents Infectieux, Hospices Civils de Lyon, Lyon, France
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20
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Piotrowska MJ, Sakowski K, Lonc A, Tahir H, Kretzschmar ME. Impact of inter-hospital transfers on the prevalence of resistant pathogens in a hospital-community system. Epidemics 2020; 33:100408. [PMID: 33128935 DOI: 10.1016/j.epidem.2020.100408] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 08/21/2020] [Accepted: 10/07/2020] [Indexed: 10/23/2022] Open
Abstract
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 RA 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 RA 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.
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Affiliation(s)
- M J Piotrowska
- Institute of Applied Mathematics and Mechanics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland
| | - K Sakowski
- Institute of Applied Mathematics and Mechanics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland; Institute of High Pressure Physics, Polish Academy of Sciences, Sokolowska 29/37, 01-142 Warsaw, Poland; Research Institute for Applied Mechanics, Kyushu University, Kasuga, Fukuoka 816-8580, Japan.
| | - A Lonc
- Institute of Applied Mathematics and Mechanics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland
| | - H Tahir
- Julius Center for Health Sciences & Primary Care, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - M E Kretzschmar
- Julius Center for Health Sciences & Primary Care, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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21
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Nguyen LKN, Megiddo I, Howick S. Simulation models for transmission of health care-associated infection: A systematic review. Am J Infect Control 2020; 48:810-821. [PMID: 31862167 PMCID: PMC7161411 DOI: 10.1016/j.ajic.2019.11.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 11/01/2019] [Accepted: 11/03/2019] [Indexed: 01/08/2023]
Abstract
BACKGROUND Health care-associated infections (HAIs) are a global health burden because of their significant impact on patient health and health care systems. Mechanistic simulation modeling that captures the dynamics between patients, pathogens, and the environment is increasingly being used to improve understanding of epidemiological patterns of HAIs and to facilitate decisions on infection prevention and control (IPC). The purpose of this review is to present a systematic review to establish (1) how simulation models have been used to investigate HAIs and their mitigation and (2) how these models have evolved over time, as well as identify (3) gaps in their adoption and (4) useful directions for their future development. METHODS The review involved a systematic search and identification of studies using system dynamics, discrete event simulation, and agent-based model to study HAIs. RESULTS The complexity of simulation models developed for HAIs significantly increased but heavily concentrated on transmission dynamics of methicillin-resistant Staphylococcus aureus in the hospitals of high-income countries. Neither HAIs in other health care settings, the influence of contact networks within a health care facility, nor patient sharing and referring networks across health care settings were sufficiently understood. CONCLUSIONS This systematic review provides a broader overview of existing simulation models in HAIs to identify the gaps and to direct and facilitate further development of appropriate models in this emerging field.
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Rocha LEC, Singh V, Esch M, Lenaerts T, Liljeros F, Thorson A. Dynamic contact networks of patients and MRSA spread in hospitals. Sci Rep 2020; 10:9336. [PMID: 32518310 PMCID: PMC7283340 DOI: 10.1038/s41598-020-66270-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 05/14/2020] [Indexed: 11/09/2022] Open
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.
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Affiliation(s)
- Luis E C Rocha
- Department of Economics, Ghent University, Ghent, Belgium. .,Department of Physics and Astronomy, Ghent University, Ghent, Belgium.
| | | | - Markus Esch
- Department of Engineering, Saarland University of Applied Sciences, Saarbrücken, Germany
| | - Tom Lenaerts
- MLG, Université Libre de Bruxelles, Brussels, Belgium.,AI-lab, Vrije Universteit Brussel, Brussels, Belgium.,Interuniversity Institute for Bioinformatics, Brussels, Belgium
| | - Fredrik Liljeros
- Department of Sociology, Stockholm University, Stockholm, Sweden
| | - Anna Thorson
- Department of Public Health Sciences, Karolinska Institute, Stockholm, Sweden.,World Health Organisation, Geneva, Switzerland
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23
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A Systematic Review of Network Studies Based on Administrative Health Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17072568. [PMID: 32283623 PMCID: PMC7177895 DOI: 10.3390/ijerph17072568] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 04/05/2020] [Accepted: 04/06/2020] [Indexed: 11/17/2022]
Abstract
Effective and efficient delivery of healthcare services requires comprehensive collaboration and coordination between healthcare entities and their complex inter-reliant activities. This inter-relation and coordination lead to different networks among diverse healthcare stakeholders. It is important to understand the varied dynamics of these networks to measure the efficiency of healthcare delivery services. To date, however, a work that systematically reviews these networks outlined in different studies is missing. This article provides a comprehensive summary of studies that have focused on networks and administrative health data. By summarizing different aspects including research objectives, key research questions, adopted methods, strengths and weaknesses, this research provides insights into the inherently complex and interlinked networks present in healthcare services. The outcome of this research is important to healthcare management and may guide further research in this area.
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24
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Are Invasive Procedures and a Longer Hospital Stay Increasing the Risk of Healthcare-Associated Infections among the Admitted Patients at Hiwot Fana Specialized University Hospital, Eastern Ethiopia? Adv Prev Med 2020; 2020:6875463. [PMID: 32292604 PMCID: PMC7150733 DOI: 10.1155/2020/6875463] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 11/17/2019] [Accepted: 01/06/2020] [Indexed: 11/23/2022] Open
Abstract
Background Healthcare-associated infection is a major public health problem, in terms of mortality, morbidity, and costs. Majorities of the cause of these infections were preventable. Understanding the potential risk factors is important to reduce the impact of these avoidable infections. The study was aimed to identify factors associated with healthcare-associated infections among patients admitted at Hiwot Fana Specialized University Hospital, Harar, Eastern Ethiopia. Methods A cross-sectional study was carried out among 433 patients over a period of five months at Hiwot Fana Specialized University Hospital. Sociodemographic and clinical data were obtained from a patient admitted for 48 hours and above in the four wards (surgical, medical, obstetrics/gynecology, and pediatrics) using a structured questionnaire. A multivariate logistic regression model was applied to identify predictors of healthcare-associated infections. A p value <0.05 was considered statistically significant. Results Fifty-four (13.7%) patients had a history of a previous admission. The median length of hospital stay was 6.1 days. Forty-six (11.7%) participants reported comorbid conditions. Ninety-six (24.4%) participants underwent surgical procedures. The overall prevalence of healthcare-associated infection was 29 (7.4%, 95% CI: 5.2–10.6). Cigarette smoking (AOR: 5.18, 95% CI: 2.15–20.47), staying in the hospital for more than 4 days (AOR: 4.29, 95% CI: 2.31–6.15), and undergoing invasive procedures (AOR: 3.58, 95% CI: 1.11–7.52) increase the odds of acquiring healthcare-associated infections. Conclusion The cumulative prevalence of healthcare-associated infections in this study was comparable with similar studies conducted in developing countries. Cigarette smoking, staying in the hospital for more than 4 days, and undergoing invasive procedures increase the odds of healthcare-associated infections. These factors should be considered in the infection prevention and control program of the hospital.
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25
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Sewell DK. Analysis of network interventions with an application to hospital-acquired infections. Stat Med 2019; 38:5376-5390. [PMID: 31631371 DOI: 10.1002/sim.8373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 08/20/2019] [Accepted: 08/24/2019] [Indexed: 11/06/2022]
Abstract
Regional interventions to prevent the spread of hospital-acquired infections, vaccination campaigns, and information dissemination strategies are examples of treatment interventions applied to members of a network with the intent of effecting a network-wide change. In designing clinical trials or determining policy changes, it may not be cost effective or otherwise possible to treat all actors of a network. There is a notable lack of study designs and statistical frameworks with which to plan a network-wide intervention in this context and analyze the resulting data. This paper builds off of the network autocorrelation model in order to provide such a framework for a pre-post study design. We derive key quantitative measures of the network-wide treatment effect, exact formulas for power analyses of these measures, and extensions for the context in which the network is unknown. As the treatment assignation is part of the network-wide treatment, we provide methods for determining the assignation which optimizes the overall treatment effect over all members of the network subject to any arbitrary set of implementation costs and cost constraint. We implement these methods on Clostridioides difficile data for the state of California, where the hospitals are linked through patient sharing.
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Affiliation(s)
- Daniel K Sewell
- Department of Biostatistics, University of Iowa, Iowa City, Iowa
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26
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Predicting hospital-onset Clostridium difficile using patient mobility data: A network approach. Infect Control Hosp Epidemiol 2019; 40:1380-1386. [PMID: 31656216 DOI: 10.1017/ice.2019.288] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE To examine the relationship between unit-wide Clostridium difficile infection (CDI) susceptibility and inpatient mobility and to create contagion centrality as a new predictive measure of CDI. DESIGN Retrospective cohort study. METHODS A mobility network was constructed using 2 years of patient electronic health record data for a 739-bed hospital (n = 72,636 admissions). Network centrality measures were calculated for each hospital unit (node) providing clinical context for each in terms of patient transfers between units (ie, edges). Daily unit-wide CDI susceptibility scores were calculated using logistic regression and were compared to network centrality measures to determine the relationship between unit CDI susceptibility and patient mobility. RESULTS Closeness centrality was a statistically significant measure associated with unit susceptibility (P < .05), highlighting the importance of incoming patient mobility in CDI prevention at the unit level. Contagion centrality (CC) was calculated using inpatient transfer rates, unit-wide susceptibility of CDI, and current hospital CDI infections. The contagion centrality measure was statistically significant (P < .05) with our outcome of hospital-onset CDI cases, and it captured the additional opportunities for transmission associated with inpatient transfers. We have used this analysis to create easily interpretable clinical tools showing this relationship as well as the risk of hospital-onset CDI in real time, and these tools can be implemented in hospital EHR systems. CONCLUSIONS Quantifying and visualizing the combination of inpatient transfers, unit-wide risk, and current infections help identify hospital units at risk of developing a CDI outbreak and, thus, provide clinicians and infection prevention staff with advanced warning and specific location data to inform prevention efforts.
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27
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Tosas Auguet O, Stabler RA, Betley J, Preston MD, Dhaliwal M, Gaunt M, Ioannou A, Desai N, Karadag T, Batra R, Otter JA, Marbach H, Clark TG, Edgeworth JD. Frequent Undetected Ward-Based Methicillin-Resistant Staphylococcus aureus Transmission Linked to Patient Sharing Between Hospitals. Clin Infect Dis 2019; 66:840-848. [PMID: 29095965 PMCID: PMC5850096 DOI: 10.1093/cid/cix901] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 11/16/2017] [Indexed: 12/04/2022] Open
Abstract
Background Recent evidence suggests that hospital transmission of methicillin-resistant Staphylococcus aureus (MRSA) is uncommon in UK centers that have implemented sustained infection control programs. We investigated whether a healthcare-network analysis could shed light on transmission paths currently sustaining MRSA levels in UK hospitals. Methods A cross-sectional observational study was performed in 2 National Health Service hospital groups and a general district hospital in Southeast London. All MRSA patients identified at inpatient, outpatient, and community settings between 1 November 2011 and 29 February 2012 were included. We identified genetically defined MRSA transmission clusters in individual hospitals and across the healthcare network, and examined genetic differentiation of sequence type (ST) 22 MRSA isolates within and between hospitals and inpatient or outpatient and community settings, as informed by average and median pairwise single-nucleotide polymorphisms (SNPs) and SNP-based proportions of nearly identical isolates. Results Two hundred forty-eight of 610 (40.7%) MRSA patients were linked in 90 transmission clusters, of which 27 spanned multiple hospitals. Analysis of a large 32 patient ST22-MRSA cluster showed that 26 of 32 patients (81.3%) had multiple contacts with one another during ward stays at any hospital. No residential, outpatient, or significant community healthcare contacts were identified. Genetic differentiation between ST22 MRSA inpatient isolates from different hospitals was less than between inpatient isolates from the same hospitals (P ≤ .01). Conclusions There is evidence of frequent ward-based transmission of MRSA brought about by frequent patient admissions to multiple hospitals. Limiting in-ward transmission requires sharing of MRSA status data between hospitals.
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Affiliation(s)
- Olga Tosas Auguet
- Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, King's College London and Guy's and St Thomas' NHS Foundation Trust.,Oxford Health Systems Collaboration, Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford
| | - Richard A Stabler
- Department of Pathogen Molecular Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine
| | - Jason Betley
- Illumina, Cambridge Ltd, Chesterford Research Park, Little Chesterford, Essex
| | - Mark D Preston
- Department of Pathogen Molecular Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine
| | - Mandeep Dhaliwal
- Department of Pathogen Molecular Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine
| | - Michael Gaunt
- Department of Pathogen Molecular Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine
| | - Avgousta Ioannou
- Illumina, Cambridge Ltd, Chesterford Research Park, Little Chesterford, Essex
| | - Nergish Desai
- Department of Medical Microbiology, King's College Hospital NHS Foundation Trust
| | - Tacim Karadag
- Department of Microbiology, University Hospital Lewisham, Lewisham and Greenwich NHS Trust
| | - Rahul Batra
- Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, King's College London and Guy's and St Thomas' NHS Foundation Trust
| | - Jonathan A Otter
- Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, King's College London and Guy's and St Thomas' NHS Foundation Trust.,National Institute for Health Research Health Protection Research Unit in Healthcare-Associated Infections and Antimicrobial Resistance at Imperial College London, and Imperial College Healthcare NHS Trust, Infection Prevention and Control
| | - Helene Marbach
- Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, King's College London and Guy's and St Thomas' NHS Foundation Trust
| | - Taane G Clark
- Department of Pathogen Molecular Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine.,Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Jonathan D Edgeworth
- Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, King's College London and Guy's and St Thomas' NHS Foundation Trust
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28
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Whole-Genome Sequencing To Identify Drivers of Carbapenem-Resistant Klebsiella pneumoniae Transmission within and between Regional Long-Term Acute-Care Hospitals. Antimicrob Agents Chemother 2019; 63:AAC.01622-19. [PMID: 31451495 DOI: 10.1128/aac.01622-19] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 08/16/2019] [Indexed: 12/17/2022] Open
Abstract
Carbapenem-resistant Klebsiella pneumoniae (CRKP) is an antibiotic resistance threat of the highest priority. Given the limited treatment options for this multidrug-resistant organism (MDRO), there is an urgent need for targeted strategies to prevent transmission. Here, we applied whole-genome sequencing to a comprehensive collection of clinical isolates to reconstruct regional transmission pathways and analyzed this transmission network in the context of statewide patient transfer data and patient-level clinical data to identify drivers of regional transmission. We found that high regional CRKP burdens were due to a small number of regional introductions, with subsequent regional proliferation occurring via patient transfers among health care facilities. While CRKP was predicted to have been imported into each facility multiple times, there was substantial variation in the ratio of intrafacility transmission events per importation, indicating that amplification occurs unevenly across regional facilities. While myriad factors likely influence intrafacility transmission rates, an understudied one is the potential for clinical characteristics of colonized and infected patients to influence their propensity for transmission. Supporting the contribution of high-risk patients to elevated transmission rates, we observed that patients colonized and infected with CRKP in high-transmission facilities had higher rates of carbapenem use, malnutrition, and dialysis and were older. This report highlights the potential for regional infection prevention efforts that are grounded in genomic epidemiology to identify the patients and facilities that make the greatest contribution to regional MDRO prevalence, thereby facilitating the design of precision interventions of maximal impact.
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29
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Donker T, Smieszek T, Henderson KL, Walker TM, Hope R, Johnson AP, Woodford N, Crook DW, Peto TEA, Walker AS, Robotham JV. Using hospital network-based surveillance for antimicrobial resistance as a more robust alternative to self-reporting. PLoS One 2019; 14:e0219994. [PMID: 31344075 PMCID: PMC6657867 DOI: 10.1371/journal.pone.0219994] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 07/05/2019] [Indexed: 11/28/2022] Open
Abstract
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.
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Affiliation(s)
- Tjibbe Donker
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom.,Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.,National Infection Service, Public Health England, Colindale, London, United Kingdom
| | - Timo Smieszek
- National Infection Service, Public Health England, Colindale, London, United Kingdom.,MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Katherine L Henderson
- National Infection Service, Public Health England, Colindale, London, United Kingdom
| | - Timothy M Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Russell Hope
- National Infection Service, Public Health England, Colindale, London, United Kingdom
| | - Alan P Johnson
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom.,National Infection Service, Public Health England, Colindale, London, United Kingdom
| | - Neil Woodford
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom.,National Infection Service, Public Health England, Colindale, London, United Kingdom
| | - Derrick W Crook
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom.,Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.,National Infection Service, Public Health England, Colindale, London, United Kingdom.,NIHR Biomedical Research Centre, Oxford, United Kingdom
| | - Tim E A Peto
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom.,Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.,NIHR Biomedical Research Centre, Oxford, United Kingdom
| | - A Sarah Walker
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom.,Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.,NIHR Biomedical Research Centre, Oxford, United Kingdom
| | - Julie V Robotham
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom.,National Infection Service, Public Health England, Colindale, London, United Kingdom
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30
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Niewiadomska AM, Jayabalasingham B, Seidman JC, Willem L, Grenfell B, Spiro D, Viboud C. Population-level mathematical modeling of antimicrobial resistance: a systematic review. BMC Med 2019; 17:81. [PMID: 31014341 PMCID: PMC6480522 DOI: 10.1186/s12916-019-1314-9] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Accepted: 03/25/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Mathematical transmission models are increasingly used to guide public health interventions for infectious diseases, particularly in the context of emerging pathogens; however, the contribution of modeling to the growing issue of antimicrobial resistance (AMR) remains unclear. Here, we systematically evaluate publications on population-level transmission models of AMR over a recent period (2006-2016) to gauge the state of research and identify gaps warranting further work. METHODS We performed a systematic literature search of relevant databases to identify transmission studies of AMR in viral, bacterial, and parasitic disease systems. We analyzed the temporal, geographic, and subject matter trends, described the predominant medical and behavioral interventions studied, and identified central findings relating to key pathogens. RESULTS We identified 273 modeling studies; the majority of which (> 70%) focused on 5 infectious diseases (human immunodeficiency virus (HIV), influenza virus, Plasmodium falciparum (malaria), Mycobacterium tuberculosis (TB), and methicillin-resistant Staphylococcus aureus (MRSA)). AMR studies of influenza and nosocomial pathogens were mainly set in industrialized nations, while HIV, TB, and malaria studies were heavily skewed towards developing countries. The majority of articles focused on AMR exclusively in humans (89%), either in community (58%) or healthcare (27%) settings. Model systems were largely compartmental (76%) and deterministic (66%). Only 43% of models were calibrated against epidemiological data, and few were validated against out-of-sample datasets (14%). The interventions considered were primarily the impact of different drug regimens, hygiene and infection control measures, screening, and diagnostics, while few studies addressed de novo resistance, vaccination strategies, economic, or behavioral changes to reduce antibiotic use in humans and animals. CONCLUSIONS The AMR modeling literature concentrates on disease systems where resistance has been long-established, while few studies pro-actively address recent rise in resistance in new pathogens or explore upstream strategies to reduce overall antibiotic consumption. Notable gaps include research on emerging resistance in Enterobacteriaceae and Neisseria gonorrhoeae; AMR transmission at the animal-human interface, particularly in agricultural and veterinary settings; transmission between hospitals and the community; the role of environmental factors in AMR transmission; and the potential of vaccines to combat AMR.
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Affiliation(s)
- Anna Maria Niewiadomska
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA
| | - Bamini Jayabalasingham
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.,Present Address: Elsevier Inc., 230 Park Ave, Suite B00, New York, NY, 10169, USA
| | - Jessica C Seidman
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA
| | | | - Bryan Grenfell
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.,Princeton University, Princeton, NJ, USA
| | - David Spiro
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA
| | - Cecile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.
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31
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Methicillin-Resistant Staphylococcus aureus (MRSA): Prevalence and Antimicrobial Sensitivity Pattern among Patients-A Multicenter Study in Asmara, Eritrea. CANADIAN JOURNAL OF INFECTIOUS DISEASES & MEDICAL MICROBIOLOGY 2019; 2019:8321834. [PMID: 30881532 PMCID: PMC6381584 DOI: 10.1155/2019/8321834] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 10/08/2018] [Accepted: 12/17/2018] [Indexed: 12/04/2022]
Abstract
Background Methicillin-resistant Staphylococcus aureus (MRSA) is a well-recognized public health problem throughout the world. The evolution of new genetically distinct community-acquired and livestock-acquired MRSA and extended resistance to other non-β-lactams including vancomycin has only amplified the crisis. This paper presents data on the prevalence of MRSA and resistance pattern to other antibiotics on the selected specimen from two referral hospitals in Asmara, Eritrea. Method A cross-sectional study was conducted among 130 participants recruited from two referral hospitals in Asmara, Eritrea. Isolation of S. aureus was based on culture and biochemical profiles. Standard antimicrobial disks representing multiple drug classes were subsequently set for oxacillin, gentamicin, erythromycin, and vancomycin. Data were analyzed using SPSS version 20 software. Results S. aureus isolation rate from the 130 samples was 82 (63.1%). Patients <18 years of age were more likely to be colonized by S. aureus compared to patients above 61 years. The proportion of MRSA among the isolates was 59 (72%), methicillin-intermediate S. aureus (MISA) was 7 (8.5%), and methicillin-sensitive S. aureus (MSSA) was 15 (19.5%). The isolates were mostly from the pus specimen in burn, diabetic, and surgical wound patients. Antimicrobial susceptibility test showed that 13 (15.9%) of the isolates were resistant to vancomycin, 9 (11.0%) to erythromycin, and 1 (1.2%) to gentamicin. Coresistance of MRSA isolates to some commonly used antibiotics was also noted: oxacillin/erythromycin 5 (6.1%) and oxacillin/vancomycin 9 (11%). A few isolates were resistant to oxacillin/vancomycin/erythromycin 2 (2.4%) and oxacillin/gentamicin and erythromycin 1 (1.2%). Conclusion This study reports a relatively high prevalence of MRSA. Isolates that are resistant to other tested antibiotics including vancomycin are also reported. The data have important implication for quality of patients care in the two settings: antibiotic selection and infection control practices, and the need for additional studies.
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Pei S, Morone F, Liljeros F, Makse H, Shaman JL. Inference and control of the nosocomial transmission of methicillin-resistant Staphylococcus aureus. eLife 2018; 7:e40977. [PMID: 30560786 PMCID: PMC6298769 DOI: 10.7554/elife.40977] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 11/16/2018] [Indexed: 12/19/2022] Open
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is a continued threat to human health in both community and healthcare settings. In hospitals, control efforts would benefit from accurate estimation of asymptomatic colonization and infection importation rates from the community. However, developing such estimates remains challenging due to limited observation of colonization and complicated transmission dynamics within hospitals and the community. Here, we develop an inference framework that can estimate these key quantities by combining statistical filtering techniques, an agent-based model, and real-world patient-to-patient contact networks, and use this framework to infer nosocomial transmission and infection importation over an outbreak spanning 6 years in 66 Swedish hospitals. In particular, we identify a small number of patients with disproportionately high risk of colonization. In retrospective control experiments, interventions targeted to these individuals yield a substantial improvement over heuristic strategies informed by number of contacts, length of stay and contact tracing.
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Affiliation(s)
- Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public HealthColumbia UniversityNew YorkUnited States
| | - Flaviano Morone
- Levich Institute and Physics DepartmentCity College of New YorkNew YorkUnited States
| | | | - Hernán Makse
- Levich Institute and Physics DepartmentCity College of New YorkNew YorkUnited States
| | - Jeffrey L Shaman
- Department of Environmental Health Sciences, Mailman School of Public HealthColumbia UniversityNew YorkUnited States
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Snitkin ES, Won S, Pirani A, Lapp Z, Weinstein RA, Lolans K, Hayden MK. Integrated genomic and interfacility patient-transfer data reveal the transmission pathways of multidrug-resistant Klebsiella pneumoniae in a regional outbreak. Sci Transl Med 2018; 9:9/417/eaan0093. [PMID: 29167391 DOI: 10.1126/scitranslmed.aan0093] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 06/16/2017] [Accepted: 08/18/2017] [Indexed: 12/15/2022]
Abstract
Development of effective strategies to limit the proliferation of multidrug-resistant organisms requires a thorough understanding of how such organisms spread among health care facilities. We sought to uncover the chains of transmission underlying a 2008 U.S. regional outbreak of carbapenem-resistant Klebsiella pneumoniae by performing an integrated analysis of genomic and interfacility patient-transfer data. Genomic analysis yielded a high-resolution transmission network that assigned directionality to regional transmission events and discriminated between intra- and interfacility transmission when epidemiologic data were ambiguous or misleading. Examining the genomic transmission network in the context of interfacility patient transfers (patient-sharing networks) supported the role of patient transfers in driving the outbreak, with genomic analysis revealing that a small subset of patient-transfer events was sufficient to explain regional spread. Further integration of the genomic and patient-sharing networks identified one nursing home as an important bridge facility early in the outbreak-a role that was not apparent from analysis of genomic or patient-transfer data alone. Last, we found that when simulating a real-time regional outbreak, our methodology was able to accurately infer the facility at which patients acquired their infections. This approach has the potential to identify facilities with high rates of intra- or interfacility transmission, data that will be useful for triggering targeted interventions to prevent further spread of multidrug-resistant organisms.
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Affiliation(s)
- Evan S Snitkin
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA. .,Division of Infectious Diseases, Department of Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sarah Won
- Division of Infectious Diseases, Department of Internal Medicine, Rush University Medical Center, Chicago, IL 60612, USA
| | - Ali Pirani
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA.,Division of Infectious Diseases, Department of Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Zena Lapp
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Robert A Weinstein
- Division of Infectious Diseases, Department of Internal Medicine, Rush University Medical Center, Chicago, IL 60612, USA.,Division of Infectious Diseases, Department of Medicine, Cook County Health and Hospitals System, Chicago, IL 60612, USA
| | - Karen Lolans
- Division of Clinical Microbiology, Department of Pathology, Rush University Medical Center, Chicago, IL 60612, USA
| | - Mary K Hayden
- Division of Infectious Diseases, Department of Internal Medicine, Rush University Medical Center, Chicago, IL 60612, USA. .,Division of Clinical Microbiology, Department of Pathology, Rush University Medical Center, Chicago, IL 60612, USA
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An C, O’Malley AJ, Rockmore DN. Referral paths in the U.S. physician network. APPLIED NETWORK SCIENCE 2018; 3:20. [PMID: 30839747 PMCID: PMC6214314 DOI: 10.1007/s41109-018-0081-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Accepted: 07/11/2018] [Indexed: 06/09/2023]
Abstract
In this paper, we analyze the millions of referral paths of patients' interactions with the healthcare system for each year in the 2006-2011 time period and relate them to U.S. cardiovascular treatment records. For a patient, a "referral path" records the chronological sequence of physicians encountered by a patient (subject to certain constraints on the times between encounters). It provides a basic unit of analysis in a broader referral network that encodes the flow of patients and information between physicians in a healthcare system. We consider referral networks defined over a range of interactions as well as the characteristics of referral paths, producing a characterization of the various networks as well as the physicians they comprise. We further relate these metrics and findings to outcomes in the specific area of cardiovascular care. In particular, we match a referral path to occurrences of Acute Myocardial Infarction (AMI) and use the summary measures of the referral path to predict the treatment a patient receives and medical outcomes following treatment. Some referral path features are more significant with respect to their ability to boost a tree-based predictive model, and have stronger correlations with numerical treatment outcome variables. The patterns of referral paths and the derived informative features illustrate the potential for using network science to optimize patient referrals in healthcare systems for improved treatment outcomes and more efficient utilization of medical resources.
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Affiliation(s)
- Chuankai An
- Department of Computer Science, Dartmouth College, Hanover, 03755 NH USA
| | - A. James O’Malley
- Department of Biomedical Data Science and the Dartmouth Institute of Health Policy and Clinical Practice in the Geisel School of Medicine at Dartmouth College, Lebanon, 03784 NH USA
| | - Daniel N. Rockmore
- Department of Computer Science, Dartmouth College, Hanover, 03755 NH USA
- Department of Mathematics, Dartmouth College, Hanover, 03755 NH USA
- External Faculty, The Santa Fe Institute, Santa Fe, 87501 NM USA
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DuGoff EH, Fernandes-Taylor S, Weissman GE, Huntley JH, Pollack CE. A scoping review of patient-sharing network studies using administrative data. Transl Behav Med 2018; 8:598-625. [PMID: 30016521 PMCID: PMC6086089 DOI: 10.1093/tbm/ibx015] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
There is a robust literature examining social networks and health, which draws on the network traditions in sociology and statistics. However, the application of social network approaches to understand the organization of health care is less well understood. The objective of this work was to examine approaches to conceptualizing, measuring, and analyzing provider patient-sharing networks. These networks are constructed using administrative data in which pairs of physicians are considered connected if they both deliver care to the same patient. A scoping review of English language peer-reviewed articles in PubMed and Embase was conducted from inception to June 2017. Two reviewers evaluated article eligibility based upon inclusion criteria and abstracted relevant data into a database. The literature search identified 10,855 titles, of which 63 full-text articles were examined. Nine additional papers identified by reviewing article references and authors were examined. Of the 49 papers that met criteria for study inclusion, 39 used a cross-sectional study design, 6 used a cohort design, and 4 were longitudinal. We found that studies most commonly theorized that networks reflected aspects of collaboration or coordination. Less commonly, studies drew on the strength of weak ties or diffusion of innovation frameworks. A total of 180 social network measures were used to describe the networks of individual providers, provider pairs and triads, the network as a whole, and patients. The literature on patient-sharing relationships between providers is marked by a diversity of measures and approaches. We highlight key considerations in network identification including the definition of network ties, setting geographic boundaries, and identifying clusters of providers, and discuss gaps for future study.
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Affiliation(s)
- Eva H DuGoff
- Department of Health Services Administration, University of Maryland School of Public Health, College Park, MD, USA
| | - Sara Fernandes-Taylor
- Department of Surgery, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Gary E Weissman
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
- Hospital of the University of Pennsylvania, Pulmonary, Allergy, and Critical Care Division, Philadelphia, PA, USA
| | - Joseph H Huntley
- Department of Medicine, Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Craig Evan Pollack
- Department of Medicine, Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Health Policy & Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Kwok KO, Read JM, Tang A, Chen H, Riley S, Kam KM. A systematic review of transmission dynamic studies of methicillin-resistant Staphylococcus aureus in non-hospital residential facilities. BMC Infect Dis 2018; 18:188. [PMID: 29669512 PMCID: PMC5907171 DOI: 10.1186/s12879-018-3060-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Accepted: 03/25/2018] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Non-hospital residential facilities are important reservoirs for MRSA transmission. However, conclusions and public health implications drawn from the many mathematical models depicting nosocomial MRSA transmission may not be applicable to these settings. Therefore, we reviewed the MRSA transmission dynamics studies in defined non-hospital residential facilities to: (1) provide an overview of basic epidemiology which has been addressed; (2) identify future research direction; and (3) improve future model implementation. METHODS A review was conducted by searching related keywords in PUBMED without time restriction as well as internet searches via Google search engine. We included only articles describing the epidemiological transmission pathways of MRSA/community-associated MRSA within and between defined non-hospital residential settings. RESULTS Among the 10 included articles, nursing homes (NHs) and correctional facilities (CFs) were two settings considered most frequently. Importation of colonized residents was a plausible reason for MRSA outbreaks in NHs, where MRSA was endemic without strict infection control interventions. The importance of NHs over hospitals in increasing nosocomial MRSA prevalence was highlighted. Suggested interventions in NHs included: appropriate staffing level, screening and decolonizing, and hand hygiene. On the other hand, the small population amongst inmates in CFs has no effect on MRSA community transmission. Included models ranged from system-level compartmental models to agent-based models. There was no consensus over the course of disease progression in these models, which were mainly featured with NH residents /CF inmates/ hospital patients as transmission pathways. Some parameters used by these models were outdated or unfit. CONCLUSIONS Importance of NHs has been highlighted from these current studies addressing scattered aspects of MRSA epidemiology. However, the wide variety of non-hospital residential settings suggest that more work is needed before robust conclusions can be drawn. Learning from existing work for hospitals, we identified critical future research direction in this area from infection control, ecological and economic perspectives. From current model deficiencies, we suggest more transmission pathways be specified to depict MRSA transmission, and further empirical studies be stressed to support evidence-based mathematical models of MRSA in non-hospital facilities. Future models should be ready to cope with the aging population structure.
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Affiliation(s)
- Kin On Kwok
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, Hong Kong, Special Administrative Region of China
- Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Shatin, Hong Kong, Special Administrative Region of China
- Shenzhen Research Institute of the Chinese University of Hong Kong, Shenzhen, China
| | - Jonathan M. Read
- Centre for Health Informatics Computing and Statistics, Lancaster Medical School, Faculty of Health and Medicine, Lancaster University, Lancaster, UK
- Institute of Infection and Global Health, The Farr Institute@HeRC, University of Liverpool, Liverpool, UK
| | - Arthur Tang
- Department of Software, Sungkyunkwan University, Seoul, South Korea
| | - Hong Chen
- Centre for Health Protection, Hong Kong, Hong Kong, Special Administrative Region of China
| | - Steven Riley
- MRC Centre for Outbreak Analysis and Modelling, Department for Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Kai Man Kam
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, Hong Kong, Special Administrative Region of China
- Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Shatin, Hong Kong, Special Administrative Region of China
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Brunson JC, Laubenbacher RC. Applications of network analysis to routinely collected health care data: a systematic review. J Am Med Inform Assoc 2018; 25:210-221. [PMID: 29025116 PMCID: PMC6664849 DOI: 10.1093/jamia/ocx052] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 04/18/2017] [Accepted: 04/23/2017] [Indexed: 01/21/2023] Open
Abstract
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.
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Mathematical models of infection transmission in healthcare settings: recent advances from the use of network structured data. Curr Opin Infect Dis 2018; 30:410-418. [PMID: 28570284 DOI: 10.1097/qco.0000000000000390] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
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.
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Nekkab N, Astagneau P, Temime L, Crépey P. Spread of hospital-acquired infections: A comparison of healthcare networks. PLoS Comput Biol 2017; 13:e1005666. [PMID: 28837555 PMCID: PMC5570216 DOI: 10.1371/journal.pcbi.1005666] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Accepted: 07/03/2017] [Indexed: 11/20/2022] Open
Abstract
Hospital-acquired infections (HAIs), including emerging multi-drug resistant organisms, threaten healthcare systems worldwide. Efficient containment measures of HAIs must mobilize the entire healthcare network. Thus, to best understand how to reduce the potential scale of HAI epidemic spread, we explore patient transfer patterns in the French healthcare system. Using an exhaustive database of all hospital discharge summaries in France in 2014, we construct and analyze three patient networks based on the following: transfers of patients with HAI (HAI-specific network); patients with suspected HAI (suspected-HAI network); and all patients (general network). All three networks have heterogeneous patient flow and demonstrate small-world and scale-free characteristics. Patient populations that comprise these networks are also heterogeneous in their movement patterns. Ranking of hospitals by centrality measures and comparing community clustering using community detection algorithms shows that despite the differences in patient population, the HAI-specific and suspected-HAI networks rely on the same underlying structure as that of the general network. As a result, the general network may be more reliable in studying potential spread of HAIs. Finally, we identify transfer patterns at both the French regional and departmental (county) levels that are important in the identification of key hospital centers, patient flow trajectories, and regional clusters that may serve as a basis for novel wide-scale infection control strategies. Hospital-acquired infections (HAIs), including emerging multi-drug resistant organisms, threaten healthcare systems worldwide. Efficient containment measures of HAIs must mobilize the entire healthcare network. Thus, to best understand how to reduce the scale of potential HAI epidemic spread, we explore patient transfer patterns in the French healthcare system. We construct and compare the characteristics of three different patient transfer networks based on data on transfers of patients with diagnosed HAIs, suspected HAIs, or of all patients. Our analyses show that these healthcare networks, the patient populations that comprise them and the patient movement patterns are heterogeneous and centralized. Despite the differences in patient populations, the HAI-specific and suspected-HAI healthcare networks have the same underlying structure as that of the general healthcare network. We identify key hospital centers, patient flow trajectories, at both the regional and department (county) level that may serve as a basis for proposing novel wide-scale infection control strategies.
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Affiliation(s)
- Narimane Nekkab
- Laboratoire MESuRS, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, Paris, France
- Institut Pasteur, Cnam, Unité PACRI, 25–28, rue du Docteur Roux, Paris, France
- Ecole des Hautes Etudes en Santé Publique, Département d'Epidémiologie et de Biostatistiques, 15 Avenue du Professeur-Léon-Bernard, Rennes, France
- * E-mail:
| | - Pascal Astagneau
- Ecole des Hautes Etudes en Santé Publique, Département d'Epidémiologie et de Biostatistiques, 15 Avenue du Professeur-Léon-Bernard, Rennes, France
- Centre de prévention des infections associées aux soins (C-CLIN), APHP, Paris, France
- Faculté de médecine Pierre et Marie Curie, Sorbonne Universités, Paris, France
| | - Laura Temime
- Laboratoire MESuRS, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, Paris, France
- Institut Pasteur, Cnam, Unité PACRI, 25–28, rue du Docteur Roux, Paris, France
| | - Pascal Crépey
- Ecole des Hautes Etudes en Santé Publique, Département d'Epidémiologie et de Biostatistiques, 15 Avenue du Professeur-Léon-Bernard, Rennes, France
- UMR190, Emergence des Pathologies Virales, Marseille, France
- UPRES EA 7449 Reperes, Rennes, France
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Measuring distance through dense weighted networks: The case of hospital-associated pathogens. PLoS Comput Biol 2017; 13:e1005622. [PMID: 28771581 PMCID: PMC5542422 DOI: 10.1371/journal.pcbi.1005622] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 06/13/2017] [Indexed: 12/02/2022] Open
Abstract
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.
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Donker T, Reuter S, Scriberras J, Reynolds R, Brown NM, Török ME, James R, Network EOEMR, Aanensen DM, Bentley SD, Holden MTG, Parkhill J, Spratt BG, Peacock SJ, Feil EJ, Grundmann H. Population genetic structuring of methicillin-resistant Staphylococcus aureus clone EMRSA-15 within UK reflects patient referral patterns. Microb Genom 2017; 3:e000113. [PMID: 29026654 PMCID: PMC5605955 DOI: 10.1099/mgen.0.000113] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 04/07/2017] [Indexed: 12/21/2022] Open
Abstract
Antibiotic resistance forms a serious threat to the health of hospitalised patients, rendering otherwise treatable bacterial infections potentially life-threatening. A thorough understanding of the mechanisms by which resistance spreads between patients in different hospitals is required in order to design effective control strategies. We measured the differences between bacterial populations of 52 hospitals in the United Kingdom and Ireland, using whole-genome sequences from 1085 MRSA clonal complex 22 isolates collected between 1998 and 2012. The genetic differences between bacterial populations were compared with the number of patients transferred between hospitals and their regional structure. The MRSA populations within single hospitals, regions and countries were genetically distinct from the rest of the bacterial population at each of these levels. Hospitals from the same patient referral regions showed more similar MRSA populations, as did hospitals sharing many patients. Furthermore, the bacterial populations from different time-periods within the same hospital were generally more similar to each other than contemporaneous bacterial populations from different hospitals. We conclude that, while a large part of the dispersal and expansion of MRSA takes place among patients seeking care in single hospitals, inter-hospital spread of resistant bacteria is by no means a rare occurrence. Hospitals are exposed to constant introductions of MRSA on a number of levels: (1) most MRSA is received from hospitals that directly transfer large numbers of patients, while (2) fewer introductions happen between regions or (3) across national borders, reflecting lower numbers of transferred patients. A joint coordinated control effort between hospitals, is therefore paramount for the national control of MRSA, antibiotic-resistant bacteria and other hospital-associated pathogens.
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Affiliation(s)
- Tjibbe Donker
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
- Department of Medical Microbiology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Sandra Reuter
- Department of Medicine, University of Cambridge, Cambridge, UK
- Pathogen Genomics, Wellcome Trust Sanger Institute, Hinxton, UK
| | - James Scriberras
- The Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK
| | - Rosy Reynolds
- British Society for Antimicrobial Chemotherapy, UK
- North Bristol NHS Trust, Bristol, UK
| | - Nicholas M. Brown
- British Society for Antimicrobial Chemotherapy, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Public Health England, UK
| | - M. Estée Török
- Department of Medicine, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Public Health England, UK
| | - Richard James
- Department of Physics and Centre for Networks and Collective Behaviour, University of Bath, Bath, UK
| | | | - David M. Aanensen
- Faculty of Medicine, School of Public Health, Imperial College, London, UK
| | | | - Matthew T. G. Holden
- Pathogen Genomics, Wellcome Trust Sanger Institute, Hinxton, UK
- School of Medicine, University of St Andrews, St Andrews, UK
| | - Julian Parkhill
- Pathogen Genomics, Wellcome Trust Sanger Institute, Hinxton, UK
| | - Brian G. Spratt
- Faculty of Medicine, School of Public Health, Imperial College, London, UK
| | - Sharon J. Peacock
- Department of Medicine, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Public Health England, UK
| | - Edward J. Feil
- The Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK
| | - Hajo Grundmann
- Department of Medical Microbiology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
- Department of Infection Prevention and Hospital Hygiene, University Medical Centre Freiburg, Medical Faculty, University of Freiburg, Freiburg, Germany
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Donker T, Henderson KL, Hopkins KL, Dodgson AR, Thomas S, Crook DW, Peto TEA, Johnson AP, Woodford N, Walker AS, Robotham JV. The relative importance of large problems far away versus small problems closer to home: insights into limiting the spread of antimicrobial resistance in England. BMC Med 2017; 15:86. [PMID: 28446169 PMCID: PMC5406888 DOI: 10.1186/s12916-017-0844-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Accepted: 03/24/2017] [Indexed: 11/16/2022] Open
Abstract
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.
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Affiliation(s)
- Tjibbe Donker
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK. .,Nuffield Department of Medicine, University of Oxford, Oxford, UK. .,National Infection Service, Public Health England, Colindale, London, UK.
| | | | - Katie L Hopkins
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.,National Infection Service, Public Health England, Colindale, London, UK
| | - Andrew R Dodgson
- Public Health Laboratory, Public Health England, Manchester Royal Infirmary, Manchester, UK.,Department of Microbiology, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK
| | - Stephanie Thomas
- Microbiology Department, University Hospital South Manchester, Manchester, UK
| | - Derrick W Crook
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.,Nuffield Department of Medicine, University of Oxford, Oxford, UK.,National Infection Service, Public Health England, Colindale, London, UK.,NIHR Biomedical Research Centre, Oxford, UK
| | - Tim E A Peto
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.,Nuffield Department of Medicine, University of Oxford, Oxford, UK.,NIHR Biomedical Research Centre, Oxford, UK
| | - Alan P Johnson
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.,National Infection Service, Public Health England, Colindale, London, UK
| | - Neil Woodford
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.,National Infection Service, Public Health England, Colindale, London, UK
| | - A Sarah Walker
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.,Nuffield Department of Medicine, University of Oxford, Oxford, UK.,NIHR Biomedical Research Centre, Oxford, UK
| | - Julie V Robotham
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.,National Infection Service, Public Health England, Colindale, London, UK
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Staff exchange within and between nursing homes in The Netherlands and potential implications for MRSA transmission. Epidemiol Infect 2016; 145:739-745. [PMID: 27917736 PMCID: PMC5426333 DOI: 10.1017/s0950268816002831] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
A recent countrywide MRSA spa-type 1081 outbreak in The Netherlands predominantly affected nursing homes, generating questions on how infection spreads within and between nursing homes despite a low national prevalence. Since the transfer of residents between nursing homes is uncommon in The Netherlands, we hypothesized that staff exchange plays an important role in transmission. This exploratory study investigated the extent of former (last 2 years) and current staff exchange within and between nursing homes in The Netherlands. We relied on a questionnaire that was targeted towards nursing-home staff members who had contact with residents. We found that 17·9% and 12·4% of the nursing-home staff formerly (last 2 years) or currently worked in other healthcare institutes besides their job in the nursing home through which they were selected to participate in this study. Moreover, 39·7% of study participants worked on more than one ward. Our study shows that, in The Netherlands, nursing-home staff form a substantial number of links between wards within nursing homes and nursing homes are linked to a large network of healthcare institutes through their staff members potentially providing a pathway for MRSA transmission between nursing homes and throughout the country.
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Dik JWH, Sinha B, Lokate M, Lo-Ten-Foe JR, Dinkelacker AG, Postma MJ, Friedrich AW. Positive impact of infection prevention on the management of nosocomial outbreaks at an academic hospital. Future Microbiol 2016; 11:1249-1259. [DOI: 10.2217/fmb-2016-0030] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aim: Infection prevention (IP) measures are vital to prevent (nosocomial) outbreaks. Financial evaluations of these are scarce. An incremental cost analysis for an academic IP unit was performed. Material & methods: On a yearly basis, we evaluated: IP measures; costs thereof; numbers of patients at risk for causing nosocomial outbreaks; predicted outbreak patients; and actual outbreak patients. Results: IP costs rose on average yearly with €150,000; however, more IP actions were undertaken. Numbers of patients colonized with high-risk microorganisms increased. The trend of actual outbreak patients remained stable. Predicted prevented outbreak patients saved costs, leading to a positive return on investment of 1.94. Conclusion: This study shows that investments in IP can prevent outbreak cases, thereby saving enough money to earn back these investments.
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Affiliation(s)
- Jan-Willem H Dik
- Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ, Groningen, The Netherlands
| | - Bhanu Sinha
- Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ, Groningen, The Netherlands
| | - Mariëtte Lokate
- Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ, Groningen, The Netherlands
| | - Jerome R Lo-Ten-Foe
- Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ, Groningen, The Netherlands
| | - Ariane G Dinkelacker
- Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ, Groningen, The Netherlands
- Department of Medical Microbiology, University Hospital Tübingen, Elfriede-Aulhorn-Straße 6, 72076, Tübingen, Germany
| | - Maarten J Postma
- Department of Pharmacy, Unit of PharmacoEpidemiology & PharmacoEconomics, University of Groningen, Antonius Deusinglaan 1, 9713AV, Groningen, The Netherlands
- Institute of Science in Healthy Aging & healthcaRE (SHARE), University Medical Center Groningen, Hanzeplein 1, 9713GZ, Groningen, The Netherlands
- Department of Epidemiology, University Medical Center Groningen, Hanzeplein 1, 9713GZ, Groningen, The Netherlands
| | - Alexander W Friedrich
- Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ, Groningen, The Netherlands
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Ray MJ, Lin MY, Weinstein RA, Trick WE. Spread of Carbapenem-Resistant Enterobacteriaceae Among Illinois Healthcare Facilities: The Role of Patient Sharing. Clin Infect Dis 2016; 63:889-93. [PMID: 27486116 DOI: 10.1093/cid/ciw461] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Accepted: 06/02/2016] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Carbapenem-resistant Enterobacteriaceae (CRE) spread regionally throughout healthcare facilities through patient transfer and cause difficult-to-treat infections. We developed a state-wide patient-sharing matrix and applied social network analyses to determine whether greater connectedness (centrality) to other healthcare facilities and greater patient sharing with long-term acute care hospitals (LTACHs) predicted higher facility CRE rates. METHODS We combined CRE case information from the Illinois extensively drug-resistant organism registry with measures of centrality calculated from a state-wide hospital discharge dataset to predict facility-level CRE rates, adjusting for hospital size and geographic characteristics. RESULTS Higher CRE rates were observed among facilities with greater patient sharing, as measured by degree centrality. Each additional hospital connection (unit of degree) conferred a 6% increase in CRE rate in rural facilities (relative risk [RR] = 1.056; 95% confidence interval [CI], 1.030-1.082) and a 3% increase among Chicagoland and non-Chicago urban facilities (RR = 1.027; 95% CI, 1.002-1.052 and RR = 1.025; 95% CI, 1.002-1.048, respectively). Sharing 4 or more patients with LTACHs was associated with higher CRE rates, but this association may have been due to chance (RR = 2.08; 95% CI, .85-5.08; P = .11). CONCLUSIONS Hospitals with greater connectedness to other hospitals in a statewide patient-sharing network had higher CRE burden. Centrality had a greater effect on CRE rates in rural counties, which do not have LTACHs. Social network analysis likely identifies hospitals at higher risk of CRE exposure, enabling focused clinical and public health interventions.
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Affiliation(s)
- Michael J Ray
- Division of Patient Safety and Quality, Illinois Department of Public Health
| | | | - Robert A Weinstein
- Rush University Medical Center Cook County Health and Hospitals System, Chicago, Illinois
| | - William E Trick
- Rush University Medical Center Cook County Health and Hospitals System, Chicago, Illinois
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Geraci DM, Giuffrè M, Bonura C, Graziano G, Saporito L, Insinga V, Rinaudo G, Aleo A, Vecchio D, Mammina C. A Snapshot on MRSA Epidemiology in a Neonatal Intensive Care Unit Network, Palermo, Italy. Front Microbiol 2016; 7:815. [PMID: 27303395 PMCID: PMC4882316 DOI: 10.3389/fmicb.2016.00815] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Accepted: 05/13/2016] [Indexed: 11/23/2022] Open
Abstract
Objectives: We performed a 1-year prospective surveillance study on MRSA colonization within the five NICUs of the metropolitan area of Palermo, Italy. The purpose of the study was to assess epidemiology of MRSA in NICU from a network perspective. Methods: Transfer of patients between NICUs during 2014 was traced based on the annual hospital discharge records. In the period February 2014–January 2015, in the NICU B, at the University teaching hospital, nasal swabs from all infants were collected weekly, whereas in the other four NICUs (A, C, D, E) at 4 week-intervals of time. MRSA isolates were submitted to antibiotic susceptibility testing, SCCmec typing, PCR to detect lukS-PV and lukF-PV (lukS/F-PV) genes and the gene encoding the toxic shock syndrome toxin (TSST-1), multilocus variable number tandem repeat fingerprinting (MLVF), and multilocus sequence typing (MLST). Results: In the period under study, 587 nasal swabs were obtained from NICU B, whereas 218, 180, 157, and 95 from NICUs A, C, D, and E, respectively. Two groups of NICUs at high prevalence and low prevalence of MRSA colonization were recognized. Overall, 113 isolates of MRSA were identified from 102 infants. Six MLVF types (A–F) were detected, with type C being subdivided into five subtypes. Five sequence types (STs) were found with ST22-IVa being the most frequent type in all NICUs. All the MRSA molecular subtypes, except for ST1-IVa, were identified in NICU B. Conclusions: Our findings support the need to approach surveillance and infection control in NICU in a network perspective, prioritizing referral healthcare facilities.
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Affiliation(s)
- Daniela M Geraci
- Department of Sciences for Health Promotion and Mother-Child Care "G. D'Alessandro," University of Palermo Palermo, Italy
| | - Mario Giuffrè
- Department of Sciences for Health Promotion and Mother-Child Care "G. D'Alessandro," University of Palermo Palermo, Italy
| | - Celestino Bonura
- Department of Sciences for Health Promotion and Mother-Child Care "G. D'Alessandro," University of Palermo Palermo, Italy
| | - Giorgio Graziano
- Post-graduate Residency School in Hygiene and Preventive Medicine, University of Palermo Palermo, Italy
| | - Laura Saporito
- Post-graduate Residency School in Hygiene and Preventive Medicine, University of Palermo Palermo, Italy
| | - Vincenzo Insinga
- Post-graduate Residency School in Pediatrics, University of Palermo Palermo, Italy
| | - Grazia Rinaudo
- Post-graduate Residency School in Pediatrics, University of Palermo Palermo, Italy
| | - Aurora Aleo
- Department of Sciences for Health Promotion and Mother-Child Care "G. D'Alessandro," University of Palermo Palermo, Italy
| | - Davide Vecchio
- Department of Sciences for Health Promotion and Mother-Child Care "G. D'Alessandro," University of Palermo Palermo, Italy
| | - Caterina Mammina
- Department of Sciences for Health Promotion and Mother-Child Care "G. D'Alessandro," University of Palermo Palermo, Italy
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Not just a matter of size: a hospital-level risk factor analysis of MRSA bacteraemia in Scotland. BMC Infect Dis 2016; 16:222. [PMID: 27209082 PMCID: PMC4875632 DOI: 10.1186/s12879-016-1563-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2015] [Accepted: 05/11/2016] [Indexed: 11/27/2022] Open
Abstract
Background Worldwide, there is a wealth of literature examining patient-level risk factors for methicillin-resistant Staphylococcus aureus (MRSA) bacteraemia. At the hospital-level it is generally accepted that MRSA bacteraemia is more common in larger hospitals. In Scotland, size does not fully explain all the observed variation among hospitals. The aim of this study was to identify risk factors for the presence and rate of MRSA bacteraemia cases in Scottish mainland hospitals. Specific hypotheses regarding hospital size, type and connectivity were examined. Methods Data from 198 mainland Scottish hospitals (defined as having at least one inpatient per year) were analysed for financial year 2007-08 using logistic regression (Model 1: presence/absence of MRSA bacteraemia) and Poisson regression (Model 2: rate of MRSA bacteraemia). The significance of risk factors representing various measures of hospital size, type and connectivity were investigated. Results In Scotland, size was not the only significant risk factor identified for the presence and rate of MRSA bacteraemia. The probability of a hospital having at least one case of MRSA bacteraemia increased with hospital size only if the hospital exceeded a certain level of connectivity. Higher levels of MRSA bacteraemia were associated with the large, highly connected teaching hospitals with high ratios of patients to domestic staff. Conclusions A hospital’s level of connectedness within a network may be a better measure of a hospital’s risk of MRSA bacteraemia than size. This result could be used to identify high risk hospitals which would benefit from intensified infection control measures. Electronic supplementary material The online version of this article (doi:10.1186/s12879-016-1563-6) contains supplementary material, which is available to authorized users.
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Dik JWH, Hendrix R, Poelman R, Niesters HG, Postma MJ, Sinha B, Friedrich AW. Measuring the impact of antimicrobial stewardship programs. Expert Rev Anti Infect Ther 2016; 14:569-75. [PMID: 27077229 DOI: 10.1080/14787210.2016.1178064] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Antimicrobial Stewardship Programs (ASPs) are being implemented worldwide to optimize antimicrobial therapy, and thereby improve patient safety and quality of care. Additionally, this should counteract resistance development. It is, however, vital that correct and timely diagnostics are performed in parallel, and that an institution runs a well-organized infection prevention program. Currently, there is no clear consensus on which interventions an ASP should comprise. Indeed this depends on the institution, the region, and the patient population that is served. Different interventions will lead to different effects. Therefore, adequate evaluations, both clinically and financially, are crucial. Here, we provide a general overview of, and perspective on different intervention strategies and methods to evaluate these ASP programs, covering before mentioned topics. This should lead to a more consistent approach in evaluating these programs, making it easier to compare different interventions and studies with each other and ultimately improve infection and patient management.
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Affiliation(s)
- Jan-Willem H Dik
- a Department of Medical Microbiology, University Medical Center Groningen , University of Groningen , Groningen , The Netherlands
| | - Ron Hendrix
- a Department of Medical Microbiology, University Medical Center Groningen , University of Groningen , Groningen , The Netherlands.,b Medical Microbiology , Certe Laboratory for Infectious Diseases , Groningen , The Netherlands
| | - Randy Poelman
- a Department of Medical Microbiology, University Medical Center Groningen , University of Groningen , Groningen , The Netherlands
| | - Hubert G Niesters
- a Department of Medical Microbiology, University Medical Center Groningen , University of Groningen , Groningen , The Netherlands
| | - Maarten J Postma
- c Unit of PharmacoEpidemiology & PharmacoEconomics (PE2), Department of Pharmacy , University of Groningen , Groningen , The Netherlands.,d Institute of Science in Healthy Aging & healthcaRE (SHARE), University Medical Center Groningen , University of Groningen , Groningen , The Netherlands.,e Department of Epidemiology, University Medical Center Groningen , University of Groningen , Groningen , The Netherlands
| | - Bhanu Sinha
- a Department of Medical Microbiology, University Medical Center Groningen , University of Groningen , Groningen , The Netherlands
| | - Alexander W Friedrich
- a Department of Medical Microbiology, University Medical Center Groningen , University of Groningen , Groningen , The Netherlands
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The Role of Nursing Homes in the Spread of Antimicrobial Resistance Over the Healthcare Network. Infect Control Hosp Epidemiol 2016; 37:761-7. [PMID: 27052880 PMCID: PMC4926272 DOI: 10.1017/ice.2016.59] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
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
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Donker T, Bosch T, Ypma RJF, Haenen APJ, van Ballegooijen WM, Heck MEOC, Schouls LM, Wallinga J, Grundmann H. Monitoring the spread of meticillin-resistant Staphylococcus aureus in The Netherlands from a reference laboratory perspective. J Hosp Infect 2016; 93:366-74. [PMID: 27105754 PMCID: PMC4964845 DOI: 10.1016/j.jhin.2016.02.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Accepted: 02/29/2016] [Indexed: 11/23/2022]
Abstract
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.
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Affiliation(s)
- T Donker
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands; National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.
| | - T Bosch
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - R J F Ypma
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - A P J Haenen
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - W M van Ballegooijen
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - M E O C Heck
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - L M Schouls
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - J Wallinga
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - H Grundmann
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands; National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
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