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van Veen A, de Goeij I, Damen M, Huijskens EGW, Paltansing S, van Rijn M, Bentvelsen RG, Veenemans J, van der Linden M, Vos MC, Severin JA. Regional variation in the interpretation of contact precautions for multi-drug-resistant Gram-negative bacteria: a cross-sectional survey. J Hosp Infect 2024; 152:1-12. [PMID: 39069006 DOI: 10.1016/j.jhin.2024.06.020] [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: 03/22/2024] [Revised: 06/12/2024] [Accepted: 06/15/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Contact precautions are recommended when caring for patients with carbapenemase-producing Enterobacterales (CPE), carbapenemase-producing Pseudomonas aeruginosa (CPPA), and extended-spectrum β-lactamase-producing Enterobacterales (ESBL-E). AIM Our aim was to determine the interpretation of contact precautions and associated infection prevention and control (IPC) measures in the non-ICU hospital setting for patients with CPE, CPPA or ESBL-E in 11 hospitals in the Southwest of the Netherlands. METHODS A cross-sectional survey was developed to collect information on all implemented IPC measures, including use of personal protective equipment, IPC measures for visitors, cleaning and disinfection, precautions during outpatient care and follow-up strategies. All 11 hospitals were invited to participate between November 2020 and April 2021. FINDINGS The survey was filled together with each hospital. All hospitals installed isolation precautions for patients with CPE and CPPA during inpatient care and day admissions, whereas 10 hospitals (90.9%) applied isolation precautions for patients with ESBL-E. Gloves and gowns were always used during physical contact with the patient in isolation. Large variations were identified in IPC measures for visitors, cleaning and disinfection products used, and precautions during outpatient care. Four hospitals (36.4%) actively followed up on CPE or CPPA patients with the aim of declaring them CPE- or CPPA-negative as timely as possible, and two hospitals (20.0%) actively followed up on ESBL-E patients. CONCLUSION Contact precautions are interpreted differently between hospitals, leading to regional differences in IPC measures applied in clinical settings. Harmonizing infection-control policies between the hospitals could facilitate patient transfers and benefit collective efforts of preventing transmission of multi-drug-resistant Gram-negative bacteria.
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Affiliation(s)
- A van Veen
- Department of Medical Microbiology and Infectious Diseases, Erasmus MC University Medical Centre, Rotterdam, The Netherlands
| | - I de Goeij
- Department of Medical Microbiology and Infectious Diseases, Erasmus MC University Medical Centre, Rotterdam, The Netherlands
| | - M Damen
- Department of Medical Microbiology, Maasstad General Hospital, Rotterdam, The Netherlands
| | - E G W Huijskens
- Department of Medical Microbiology, Albert Schweitzer Hospital, Dordrecht, The Netherlands
| | - S Paltansing
- Department of Medical Microbiology and Infection Prevention, Franciscus Gasthuis & Vlietland, Rotterdam, The Netherlands
| | - M van Rijn
- Department of Medical Microbiology and Infectious Diseases, Ikazia Hospital, Rotterdam, The Netherlands
| | - R G Bentvelsen
- Department of Infection Prevention, ZorgSaam Hospital, Terneuzen, The Netherlands; Microvida Laboratory for Microbiology, Amphia Hospital, Breda, The Netherlands
| | - J Veenemans
- Department of Medical Microbiology, Albert Schweitzer Hospital, Dordrecht, The Netherlands; Department of Infection Prevention, Admiraal de Ruyter Hospital, Goes, The Netherlands
| | - M van der Linden
- Department of Infection Prevention, IJsselland Hospital, Capelle aan den IJssel, The Netherlands
| | - M C Vos
- Department of Medical Microbiology and Infectious Diseases, Erasmus MC University Medical Centre, Rotterdam, The Netherlands
| | - J A Severin
- Department of Medical Microbiology and Infectious Diseases, Erasmus MC University Medical Centre, Rotterdam, The Netherlands.
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Grant R, Rubin M, Abbas M, Pittet D, Srinivasan A, Jernigan JA, Bell M, Samore M, Harbarth S, Slayton RB. Expanding the use of mathematical modeling in healthcare epidemiology and infection prevention and control. Infect Control Hosp Epidemiol 2024:1-6. [PMID: 39228083 DOI: 10.1017/ice.2024.97] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
During the coronavirus disease 2019 pandemic, mathematical modeling has been widely used to understand epidemiological burden, trends, and transmission dynamics, to facilitate policy decisions, and, to a lesser extent, to evaluate infection prevention and control (IPC) measures. This review highlights the added value of using conventional epidemiology and modeling approaches to address the complexity of healthcare-associated infections (HAI) and antimicrobial resistance. It demonstrates how epidemiological surveillance data and modeling can be used to infer transmission dynamics in healthcare settings and to forecast healthcare impact, how modeling can be used to improve the validity of interpretation of epidemiological surveillance data, how modeling can be used to estimate the impact of IPC interventions, and how modeling can be used to guide IPC and antimicrobial treatment and stewardship decision-making. There are several priority areas for expanding the use of modeling in healthcare epidemiology and IPC. Importantly, modeling should be viewed as complementary to conventional healthcare epidemiological approaches, and this requires collaboration and active coordination between IPC, healthcare epidemiology, and mathematical modeling groups.
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Affiliation(s)
- Rebecca Grant
- Infection Control Programme and WHO Collaborating Centre for Infection Prevention and Control and Antimicrobial Resistance, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Michael Rubin
- Division of Epidemiology, University of Utah School Medicine, Salt Lake City, UT, USA
| | - Mohamed Abbas
- Infection Control Programme and WHO Collaborating Centre for Infection Prevention and Control and Antimicrobial Resistance, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Didier Pittet
- Infection Control Programme and WHO Collaborating Centre for Infection Prevention and Control and Antimicrobial Resistance, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Arjun Srinivasan
- Division of Healthcare Quality Promotion, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - John A Jernigan
- Division of Healthcare Quality Promotion, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Michael Bell
- Division of Healthcare Quality Promotion, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Matthew Samore
- Division of Epidemiology, University of Utah School Medicine, Salt Lake City, UT, USA
| | - Stephan Harbarth
- Infection Control Programme and WHO Collaborating Centre for Infection Prevention and Control and Antimicrobial Resistance, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Rachel B Slayton
- Division of Healthcare Quality Promotion, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA
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Cui J, Heavey J, Lin L, Klein EY, Madden GR, Sifri CD, Lewis B, Vullikanti AK, Prakash BA. Modeling relaxed policies for discontinuation of methicillin-resistant Staphylococcus aureus contact precautions. Infect Control Hosp Epidemiol 2024; 45:833-838. [PMID: 38404133 PMCID: PMC11439595 DOI: 10.1017/ice.2024.23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/15/2023] [Accepted: 01/05/2024] [Indexed: 02/27/2024]
Abstract
OBJECTIVE To evaluate the economic costs of reducing the University of Virginia Hospital's present "3-negative" policy, which continues methicillin-resistant Staphylococcus aureus (MRSA) contact precautions until patients receive 3 consecutive negative test results, to either 2 or 1 negative. DESIGN Cost-effective analysis. SETTINGS The University of Virginia Hospital. PATIENTS The study included data from 41,216 patients from 2015 to 2019. METHODS We developed a model for MRSA transmission in the University of Virginia Hospital, accounting for both environmental contamination and interactions between patients and providers, which were derived from electronic health record (EHR) data. The model was fit to MRSA incidence over the study period under the current 3-negative clearance policy. A counterfactual simulation was used to estimate outcomes and costs for 2- and 1-negative policies compared with the current 3-negative policy. RESULTS Our findings suggest that 2-negative and 1-negative policies would have led to 6 (95% CI, -30 to 44; P < .001) and 17 (95% CI, -23 to 59; -10.1% to 25.8%; P < .001) more MRSA cases, respectively, at the hospital over the study period. Overall, the 1-negative policy has statistically significantly lower costs ($628,452; 95% CI, $513,592-$752,148) annually (P < .001) in US dollars, inflation-adjusted for 2023) than the 2-negative policy ($687,946; 95% CI, $562,522-$812,662) and 3-negative ($702,823; 95% CI, $577,277-$846,605). CONCLUSIONS A single negative MRSA nares PCR test may provide sufficient evidence to discontinue MRSA contact precautions, and it may be the most cost-effective option.
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Affiliation(s)
- Jiaming Cui
- College of Computing, Georgia Institute of Technology, Atlanta, Georgia
| | - Jack Heavey
- Department of Computer Science, University of Virginia, Charlottesville, Virginia
| | - Leo Lin
- Department of Computer Science, University of Virginia, Charlottesville, Virginia
| | - Eili Y. Klein
- Center for Disease Dynamics, Economics & Policy, Washington, DC
- Department of Emergency Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Gregory R. Madden
- Division of Infectious Diseases & International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Costi D. Sifri
- Division of Infectious Diseases & International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia
- Office of Hospital Epidemiology/Infection Prevention & Control, UVA Health, Charlottesville, Virginia
| | - Bryan Lewis
- Biocomplexity Institute, University of Virginia, Charlottesville, Virginia
| | - Anil K. Vullikanti
- Department of Computer Science, University of Virginia, Charlottesville, Virginia
- Biocomplexity Institute, University of Virginia, Charlottesville, Virginia
| | - B. Aditya Prakash
- College of Computing, Georgia Institute of Technology, Atlanta, Georgia
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Lee BY, Pavilonis B, John DC, Heneghan J, Bartsch SM, Kavouras I. The Need to Focus More on Climate Change Communication and Incorporate More Systems Approaches. JOURNAL OF HEALTH COMMUNICATION 2024; 29:1-10. [PMID: 38831666 DOI: 10.1080/10810730.2024.2361566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Society is at an inflection point-both in terms of climate change and the amount of data and computational resources currently available. Climate change has been a catastrophe in slow motion with relationships between human activity, climate change, and the resulting effects forming a complex system. However, to date, there has been a general lack of urgent responses from leaders and the general public, despite urgent warnings from the scientific community about the consequences of climate change and what can be done to mitigate it. Further, misinformation and disinformation about climate change abound. A major problem is that there has not been enough focus on communication in the climate change field. Since communication itself involves complex systems (e.g. information users, information itself, communications channels), there is a need for more systems approaches to communication about climate change. Utilizing systems approaches to really understand and anticipate how information may be distributed and received before communication has even occurred and adjust accordingly can lead to more proactive precision climate change communication. The time has come to identify and develop more effective, tailored, and precise communication for climate change.
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Affiliation(s)
- Bruce Y Lee
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, New York, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York, USA
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York, USA
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York, USA
| | - Brian Pavilonis
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, New York, USA
| | - Danielle C John
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, New York, USA
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York, USA
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York, USA
| | - Jessie Heneghan
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York, USA
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York, USA
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York, USA
| | - Sarah M Bartsch
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York, USA
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York, USA
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York, USA
| | - Ilias Kavouras
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, New York, USA
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Gussin GM, McKinnell JA, Singh RD, Miller LG, Kleinman K, Saavedra R, Tjoa T, Gohil SK, Catuna TD, Heim LT, Chang J, Estevez M, He J, O’Donnell K, Zahn M, Lee E, Berman C, Nguyen J, Agrawal S, Ashbaugh I, Nedelcu C, Robinson PA, Tam S, Park S, Evans KD, Shimabukuro JA, Lee BY, Fonda E, Jernigan JA, Slayton RB, Stone ND, Janssen L, Weinstein RA, Hayden MK, Lin MY, Peterson EM, Bittencourt CE, Huang SS. Reducing Hospitalizations and Multidrug-Resistant Organisms via Regional Decolonization in Hospitals and Nursing Homes. JAMA 2024; 331:1544-1557. [PMID: 38557703 PMCID: PMC10985619 DOI: 10.1001/jama.2024.2759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 02/16/2024] [Indexed: 04/04/2024]
Abstract
Importance Infections due to multidrug-resistant organisms (MDROs) are associated with increased morbidity, mortality, length of hospitalization, and health care costs. Regional interventions may be advantageous in mitigating MDROs and associated infections. Objective To evaluate whether implementation of a decolonization collaborative is associated with reduced regional MDRO prevalence, incident clinical cultures, infection-related hospitalizations, costs, and deaths. Design, Setting, and Participants This quality improvement study was conducted from July 1, 2017, to July 31, 2019, across 35 health care facilities in Orange County, California. Exposures Chlorhexidine bathing and nasal iodophor antisepsis for residents in long-term care and hospitalized patients in contact precautions (CP). Main Outcomes and Measures Baseline and end of intervention MDRO point prevalence among participating facilities; incident MDRO (nonscreening) clinical cultures among participating and nonparticipating facilities; and infection-related hospitalizations and associated costs and deaths among residents in participating and nonparticipating nursing homes (NHs). Results Thirty-five facilities (16 hospitals, 16 NHs, 3 long-term acute care hospitals [LTACHs]) adopted the intervention. Comparing decolonization with baseline periods among participating facilities, the mean (SD) MDRO prevalence decreased from 63.9% (12.2%) to 49.9% (11.3%) among NHs, from 80.0% (7.2%) to 53.3% (13.3%) among LTACHs (odds ratio [OR] for NHs and LTACHs, 0.48; 95% CI, 0.40-0.57), and from 64.1% (8.5%) to 55.4% (13.8%) (OR, 0.75; 95% CI, 0.60-0.93) among hospitalized patients in CP. When comparing decolonization with baseline among NHs, the mean (SD) monthly incident MDRO clinical cultures changed from 2.7 (1.9) to 1.7 (1.1) among participating NHs, from 1.7 (1.4) to 1.5 (1.1) among nonparticipating NHs (group × period interaction reduction, 30.4%; 95% CI, 16.4%-42.1%), from 25.5 (18.6) to 25.0 (15.9) among participating hospitals, from 12.5 (10.1) to 14.3 (10.2) among nonparticipating hospitals (group × period interaction reduction, 12.9%; 95% CI, 3.3%-21.5%), and from 14.8 (8.6) to 8.2 (6.1) among LTACHs (all facilities participating; 22.5% reduction; 95% CI, 4.4%-37.1%). For NHs, the rate of infection-related hospitalizations per 1000 resident-days changed from 2.31 during baseline to 1.94 during intervention among participating NHs, and from 1.90 to 2.03 among nonparticipating NHs (group × period interaction reduction, 26.7%; 95% CI, 19.0%-34.5%). Associated hospitalization costs per 1000 resident-days changed from $64 651 to $55 149 among participating NHs and from $55 151 to $59 327 among nonparticipating NHs (group × period interaction reduction, 26.8%; 95% CI, 26.7%-26.9%). Associated hospitalization deaths per 1000 resident-days changed from 0.29 to 0.25 among participating NHs and from 0.23 to 0.24 among nonparticipating NHs (group × period interaction reduction, 23.7%; 95% CI, 4.5%-43.0%). Conclusions and Relevance A regional collaborative involving universal decolonization in long-term care facilities and targeted decolonization among hospital patients in CP was associated with lower MDRO carriage, infections, hospitalizations, costs, and deaths.
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Affiliation(s)
- Gabrielle M. Gussin
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - James A. McKinnell
- Division of Infectious Diseases, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California
| | - Raveena D. Singh
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Loren G. Miller
- Division of Infectious Diseases, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California
| | - Ken Kleinman
- Program in Biostatistics, University of Massachusetts Amherst School of Public Health and Health Sciences, Amherst
| | - Raheeb Saavedra
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Thomas Tjoa
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Shruti K. Gohil
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Tabitha D. Catuna
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Lauren T. Heim
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Justin Chang
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Marlene Estevez
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Jiayi He
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Kathleen O’Donnell
- Healthcare-Associated Infections Program, Center for Healthcare Quality, California Department of Public Health, Richmond
| | - Matthew Zahn
- Epidemiology and Assessment, Orange County Health Care Agency, Santa Ana, California
| | - Eunjung Lee
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
- Division of Infectious Diseases, Department of Internal Medicine, Soonchunhyang University Seoul Hospital, Seoul, Republic of Korea
| | - Chase Berman
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Jenny Nguyen
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Shalini Agrawal
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Isabel Ashbaugh
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Christine Nedelcu
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Philip A. Robinson
- Division of Infectious Diseases, Hoag Hospital, Newport Beach, California
| | - Steven Tam
- Division of Geriatric Medicine and Gerontology, University of California Irvine Health, Orange
| | - Steven Park
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
| | - Kaye D. Evans
- Clinical Microbiology Laboratory, University of California Irvine Health, Orange
| | - Julie A. Shimabukuro
- Clinical Microbiology Laboratory, University of California Irvine Health, Orange
| | - Bruce Y. Lee
- PHICOR (Public Health Informatics Computational Operations Research), Department of Health Policy and Management, City University of New York Graduate School of Public Health, New York
| | | | - John A. Jernigan
- Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Rachel B. Slayton
- Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Nimalie D. Stone
- Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Lynn Janssen
- Healthcare-Associated Infections Program, Center for Healthcare Quality, California Department of Public Health, Richmond
| | - Robert A. Weinstein
- Division of Infectious Diseases, Department of Medicine, Rush University Medical Center, Chicago, Illinois
- Department of Medicine, Cook County Health and Hospitals System, Chicago, Illinois
| | - Mary K. Hayden
- Division of Infectious Diseases, Department of Medicine, Rush University Medical Center, Chicago, Illinois
| | - Michael Y. Lin
- Division of Infectious Diseases, Department of Medicine, Rush University Medical Center, Chicago, Illinois
| | - Ellena M. Peterson
- Department of Pathology and Laboratory Medicine, University of California Irvine Health, Orange
| | - Cassiana E. Bittencourt
- Department of Pathology and Laboratory Medicine, University of California Irvine Health, Orange
| | - Susan S. Huang
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine
- Department of Epidemiology and Infection Prevention, University of California Irvine Health, Orange
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Crnich CJ. Controlling Multidrug-Resistant Organisms Across Patient-Sharing Networks. JAMA 2024; 331:1532-1533. [PMID: 38557704 DOI: 10.1001/jama.2024.0267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Affiliation(s)
- Christopher J Crnich
- Division of Infectious Diseases, University of Wisconsin School of Medicine and Public Health, Madison
- William S. Middleton Memorial VA Hospital, Madison, Wisconsin
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Cui J, Cho S, Kamruzzaman M, Bielskas M, Vullikanti A, Prakash BA. Using spectral characterization to identify healthcare-associated infection (HAI) patients for clinical contact precaution. Sci Rep 2023; 13:16197. [PMID: 37758756 PMCID: PMC10533902 DOI: 10.1038/s41598-023-41852-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Healthcare-associated infections (HAIs) are a major problem in hospital infection control. Although HAIs can be suppressed using contact precautions, such precautions are expensive, and we can only apply them to a small fraction of patients (i.e., a limited budget). In this work, we focus on two clinical problems arising from the limited budget: (a) choosing the best patients to be placed under precaution given a limited budget to minimize the spread (the isolation problem), and (b) choosing the best patients to release when limited budget requires some of the patients to be cleared from precaution (the clearance problem). A critical challenge in addressing them is that HAIs have multiple transmission pathways such that locations can also accumulate 'load' and spread the disease. One of the most common practices when placing patients under contact precautions is the regular clearance of pathogen loads. However, standard propagation models like independent cascade (IC)/susceptible-infectious-susceptible (SIS) cannot capture such mechanisms directly. Hence to account for this challenge, using non-linear system theory, we develop a novel spectral characterization of a recently proposed pathogen load based model, 2-MODE-SIS model, on people/location networks to capture spread dynamics of HAIs. We formulate the two clinical problems using this spectral characterization and develop effective and efficient algorithms for them. Our experiments show that our methods outperform several natural structural and clinical approaches on real-world hospital testbeds and pick meaningful solutions.
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Affiliation(s)
- Jiaming Cui
- College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
| | - Sungjun Cho
- College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Methun Kamruzzaman
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA
| | - Matthew Bielskas
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA
- Department of Computer Science, University of Virginia, Charlottesville, VA, 22904, USA
| | - Anil Vullikanti
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA
- Department of Computer Science, University of Virginia, Charlottesville, VA, 22904, USA
| | - B Aditya Prakash
- College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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Khairullah AR, Kurniawan SC, Effendi MH, Sudjarwo SA, Ramandinianto SC, Widodo A, Riwu KHP, Silaen OSM, Rehman S. A review of new emerging livestock-associated methicillin-resistant Staphylococcus aureus from pig farms. Vet World 2023; 16:46-58. [PMID: 36855358 PMCID: PMC9967705 DOI: 10.14202/vetworld.2023.46-58] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 11/22/2022] [Indexed: 01/12/2023] Open
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is a S. aureus strain resistant to β-lactam antibiotics and is often associated with livestock, known as livestock-associated (LA)-MRSA. Using molecular typing with multi-locus sequence typing, MRSA clones have been classified in pigs, including clonal complex 398. Livestock-associated-methicillin-resistant S. aureus was first discovered in pigs in the Netherlands in 2005. Since then, it has been widely detected in pigs in other countries. Livestock-associated-methicillin-resistant S. aureus can be transmitted from pigs to pigs, pigs to humans (zoonosis), and humans to humans. This transmission is enabled by several risk factors involved in the pig trade, including the use of antibiotics and zinc, the size and type of the herd, and the pig pen management system. Although LA-MRSA has little impact on the pigs' health, it can be transmitted from pig to pig or from pig to human. This is a serious concern as people in direct contact with pigs are highly predisposed to acquiring LA-MRSA infection. The measures to control LA-MRSA spread in pig farms include conducting periodic LA-MRSA screening tests on pigs and avoiding certain antibiotics in pigs. This study aimed to review the emerging LA-MRSA strains in pig farms.
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Affiliation(s)
- Aswin Rafif Khairullah
- Doctoral Program in Veterinary Science, Faculty of Veterinary Medicine, Universitas Airlangga. Jl. Dr. Ir. H. Soekarno, Kampus C Mulyorejo, Surabaya 60115, East Java, Indonesia
| | - Shendy Canadya Kurniawan
- Master Program of Animal Sciences, Department of Animal Sciences, Specialisation in Molecule, Cell and Organ Functioning, Wageningen University and Research, Wageningen 6708 PB, Netherlands
| | - Mustofa Helmi Effendi
- Department of Veterinary Public Health, Faculty of Veterinary Medicine, Universitas Airlangga. Jl. Dr. Ir. H. Soekarno, Kampus C Mulyorejo, Surabaya 60115, East Java, Indonesia,Corresponding author: Mustofa Helmi Effendi, e-mail: Co-authors: ARK: , SCK: , SAS: , SCR: , AW: , KHPR: , OSMS: , SR:
| | - Sri Agus Sudjarwo
- Department of Veterinary Pharmacology, Faculty of Veterinary Medicine, Universitas Airlangga. Jl. Dr. Ir. H. Soekarno, Kampus C Mulyorejo, Surabaya 60115, East Java, Indonesia
| | | | - Agus Widodo
- Doctoral Program in Veterinary Science, Faculty of Veterinary Medicine, Universitas Airlangga. Jl. Dr. Ir. H. Soekarno, Kampus C Mulyorejo, Surabaya 60115, East Java, Indonesia
| | - Katty Hendriana Priscilia Riwu
- Doctoral Program in Veterinary Science, Faculty of Veterinary Medicine, Universitas Airlangga. Jl. Dr. Ir. H. Soekarno, Kampus C Mulyorejo, Surabaya 60115, East Java, Indonesia
| | - Otto Sahat Martua Silaen
- Doctoral Program in Biomedical Science, Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya No. 6 Senen, Jakarta 10430, Indonesia
| | - Saifur Rehman
- Doctoral Program in Veterinary Science, Faculty of Veterinary Medicine, Universitas Airlangga. Jl. Dr. Ir. H. Soekarno, Kampus C Mulyorejo, Surabaya 60115, East Java, Indonesia
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Garcia R, Barnes S, Boukidjian R, Goss LK, Spencer M, Septimus EJ, Wright MO, Munro S, Reese SM, Fakih MG, Edmiston CE, Levesque M. Recommendations for change in infection prevention programs and practice. Am J Infect Control 2022; 50:1281-1295. [PMID: 35525498 PMCID: PMC9065600 DOI: 10.1016/j.ajic.2022.04.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/18/2022] [Accepted: 04/19/2022] [Indexed: 01/25/2023]
Abstract
Fifty years of evolution in infection prevention and control programs have involved significant accomplishments related to clinical practices, methodologies, and technology. However, regulatory mandates, and resource and research limitations, coupled with emerging infection threats such as the COVID-19 pandemic, present considerable challenges for infection preventionists. This article provides guidance and recommendations in 14 key areas. These interventions should be considered for implementation by United States health care facilities in the near future.
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Affiliation(s)
- Robert Garcia
- Department of Healthcare Epidemiology, State University of New York at Stony Brook, Stony Brook, NY.
| | - Sue Barnes
- Infection Preventionist (Retired), San Mateo, CA
| | | | - Linda Kaye Goss
- Department of Infection Prevention, The Queen's Health System, Honolulu, HI
| | | | - Edward J Septimus
- Department of Population Medicine, Harvard Medical School, Boston, MA
| | | | - Shannon Munro
- Department of Veterans Affairs Medical Center, Research and Development, Salem, VA
| | - Sara M Reese
- Quality and Patient Safety Department, SCL Health System Broomfield, CO
| | - Mohamad G Fakih
- Clinical & Network Services, Ascension Healthcare and Wayne State University School of Medicine, Grosse Pointe Woods, MI
| | | | - Martin Levesque
- System Infection Prevention and Control, Henry Ford Health, Detroit, MI
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10
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Kardaś-Słoma L, Fournier S, Dupont JC, Rochaix L, Birgand G, Zahar JR, Lescure FX, Kernéis S, Durand-Zaleski I, Lucet JC. Cost-effectiveness of strategies to control the spread of carbapenemase-producing Enterobacterales in hospitals: a modelling study. Antimicrob Resist Infect Control 2022; 11:117. [PMID: 36117231 PMCID: PMC9484055 DOI: 10.1186/s13756-022-01149-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 08/03/2022] [Indexed: 11/24/2022] Open
Abstract
Background Spread of resistant bacteria causes severe morbidity and mortality. Stringent control measures can be expensive and disrupt hospital organization. In the present study, we assessed the effectiveness and cost-effectiveness of control strategies to prevent the spread of Carbapenemase-producing Enterobacterales (CPE) in a general hospital ward (GW). Methods A dynamic, stochastic model simulated the transmission of CPE by the hands of healthcare workers (HCWs) and the environment in a hypothetical 25-bed GW. Input parameters were based on published data; we assumed the prevalence at admission of 0.1%. 12 strategies were compared to the baseline (no control) and combined different prevention and control interventions: targeted or universal screening at admission (TS or US), contact precautions (CP), isolation in a single room, dedicated nursing staff (DNS) for carriers and weekly screening of contact patients (WSC). Time horizon was one year. Outcomes were the number of CPE acquisitions, costs, and incremental cost-effectiveness ratios (ICER). A hospital perspective was adopted to estimate costs, which included laboratory costs, single room, contact precautions, staff time, i.e. infection control nurse and/or dedicated nursing staff, and lost bed-days due to prolonged hospital stay of identified carriers. The model was calibrated on actual datasets. Sensitivity analyses were performed. Results The baseline scenario resulted in 0.93 CPE acquisitions/1000 admissions and costs 32,050 €/1000 admissions. All control strategies increased costs and improved the outcome. The efficiency frontier was represented by: (1) TS with DNS at a 17,407 €/avoided CPE case, (2) TS + DNS + WSC at a 30,700 €/avoided CPE case and (3) US + DNS + WSC at 181,472 €/avoided CPE case. Other strategies were dominated. Sensitivity analyses showed that TS + CP might be cost-effective if CPE carriers are identified upon admission or if the cases have a short hospital stay. However, CP were effective only when high level of compliance with hand hygiene was obtained. Conclusions Targeted screening at admission combined with DNS for identified CPE carriers with or without weekly screening were the most cost-effective options to limit the spread of CPE. These results support current recommendations from several high-income countries. Supplementary Information The online version contains supplementary material available at 10.1186/s13756-022-01149-0.
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Mietchen MS, Short CT, Samore M, Lofgren ET. Examining the impact of ICU population interaction structure on modeled colonization dynamics of Staphylococcus aureus. PLoS Comput Biol 2022; 18:e1010352. [PMID: 35877686 PMCID: PMC9352208 DOI: 10.1371/journal.pcbi.1010352] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 08/04/2022] [Accepted: 07/03/2022] [Indexed: 11/18/2022] Open
Abstract
Background
Complex transmission models of healthcare-associated infections provide insight for hospital epidemiology and infection control efforts, but they are difficult to implement and come at high computational costs. Structuring more simplified models to incorporate the heterogeneity of the intensive care unit (ICU) patient-provider interactions, we explore how methicillin-resistant Staphylococcus aureus (MRSA) dynamics and acquisitions may be better represented and approximated.
Methods
Using a stochastic compartmental model of an 18-bed ICU, we compared the rates of MRSA acquisition across three ICU population interaction structures: a model with nurses and physicians as a single staff type (SST), a model with separate staff types for nurses and physicians (Nurse-MD model), and a Metapopulation model where each nurse was assigned a group of patients. The proportion of time spent with the assigned patient group (γ) within the Metapopulation model was also varied.
Results
The SST, Nurse-MD, and Metapopulation models had a mean of 40.6, 32.2 and 19.6 annual MRSA acquisitions respectively. All models were sensitive to the same parameters in the same direction, although the Metapopulation model was less sensitive. The number of acquisitions varied non-linearly by values of γ, with values below 0.40 resembling the Nurse-MD model, while values above that converged toward the Metapopulation structure.
Discussion
Inclusion of complex population interactions within a modeled hospital ICU has considerable impact on model results, with the SST model having more than double the acquisition rate of the more structured metapopulation model. While the direction of parameter sensitivity remained the same, the magnitude of these differences varied, producing different colonization rates across relatively similar populations. The non-linearity of the model’s response to differing values of a parameter gamma (γ) suggests simple model approximations are appropriate in only a narrow space of relatively dispersed nursing assignments.
Conclusion
Simplifying assumptions around how a hospital population is modeled, especially assuming random mixing, may overestimate infection rates and the impact of interventions. In many, if not most, cases more complex models that represent population mixing with higher granularity are justified.
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Affiliation(s)
- Matthew S. Mietchen
- Paul G. Allen School for Global Health, College of Veterinary Medicine, Washington State University, Pullman, Washington, United States of America
| | - Christopher T. Short
- Paul G. Allen School for Global Health, College of Veterinary Medicine, Washington State University, Pullman, Washington, United States of America
| | - Matthew Samore
- Department of Internal Medicine, University of Utah School of Medicine, University of Utah, Salt Lake City, Utah, United States of America
- VA Salt Lake City Healthcare System, Salt Lake City, Utah
| | - Eric T. Lofgren
- Paul G. Allen School for Global Health, College of Veterinary Medicine, Washington State University, Pullman, Washington, United States of America
- * E-mail:
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