1
|
Bruyneel A, Miesse I, Mathieu D, Djuidjé Yuemo C, Simon A. Prevalence and factors associated with methicillin-resistant Staphylococcus aureus colonization on admission to geriatric care units: impact on screening practices. J Hosp Infect 2024; 146:109-115. [PMID: 38309666 DOI: 10.1016/j.jhin.2024.01.014] [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: 10/27/2023] [Revised: 01/04/2024] [Accepted: 01/14/2024] [Indexed: 02/05/2024]
Abstract
OBJECTIVES Universal screening for methicillin-resistant Staphylococcus aureus (MRSA) entails additional costs, and there is no consensus for targeted screening for high-risk units. The aims of this study were to determine the prevalence of MRSA in geriatric care units, and to identify the factors associated with MRSA colonization on admission. METHODS This retrospective case-control study (1:1) in the geriatric care unit of six Belgian hospitals covered the period from 1st January 2021 to 31st December 2022. Cases were patients with a positive MRSA screening result within 48 h of admission to the geriatric care unit, and controls were patients with a negative screening result. RESULTS In total, 556 patients were included in this study (278 in each group). Prevalence per 100 admissions for the total sample was 2.3 [95% confidence interval (CI) 2.2-2.6]. Significant multi-variate factors associated with MRSA carriage on admission were: history of MRSA, nursing home origin, and chronic skin lesions. Applying these three factors would give an area under the receiver operating characteristic (ROC) curve of 0.73 (95% CI 0.71-0.77), and would allow screening to be carried out in only 55.4% of cases (95% CI 51.2-59.6%). CONCLUSIONS Using these factors as screening criteria in geriatric care units could significantly reduce the number of patients screened for MRSA, while maintaining satisfactory sensitivity and specificity.
Collapse
Affiliation(s)
- A Bruyneel
- Hospital Outbreak Support Team, Réseau Hospitalier Universitaire Cœur de Wallonie, Belgium; Health Economics, Hospital Management and Nursing Research Department, School of Public Health, Université Libre de Bruxelles, Belgium.
| | - I Miesse
- Hospital Outbreak Support Team, Réseau Hospitalier Universitaire Cœur de Wallonie, Belgium
| | - D Mathieu
- Hospital Outbreak Support Team, Réseau Hospitalier Universitaire Cœur de Wallonie, Belgium; Infectiology - Infection Prevention and Control Department, CHU Tivoli, La Louviere, Belgium
| | | | - A Simon
- Infection Control Team, CHU HELORA, Belgium
| |
Collapse
|
2
|
Blane B, Raven KE, Brown NM, Harrison EM, Coll F, Thaxter R, Enoch DA, Gouliouris T, Leek D, Girgis ST, Akram A, Matuszewska M, Rhodes P, Parkhill J, Peacock SJ. Evaluating the impact of genomic epidemiology of methicillin-resistant Staphylococcus aureus (MRSA) on hospital infection prevention and control decisions. Microb Genom 2024; 10. [PMID: 38630616 DOI: 10.1099/mgen.0.001235] [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] [Indexed: 04/19/2024] Open
Abstract
Genomic epidemiology enhances the ability to detect and refute methicillin-resistant Staphylococcus aureus (MRSA) outbreaks in healthcare settings, but its routine introduction requires further evidence of benefits for patients and resource utilization. We performed a 12 month prospective study at Cambridge University Hospitals NHS Foundation Trust in the UK to capture its impact on hospital infection prevention and control (IPC) decisions. MRSA-positive samples were identified via the hospital microbiology laboratory between November 2018 and November 2019. We included samples from in-patients, clinic out-patients, people reviewed in the Emergency Department and healthcare workers screened by Occupational Health. We sequenced the first MRSA isolate from 823 consecutive individuals, defined their pairwise genetic relatedness, and sought epidemiological links in the hospital and community. Genomic analysis of 823 MRSA isolates identified 72 genetic clusters of two or more isolates containing 339/823 (41 %) of the cases. Epidemiological links were identified between two or more cases for 190 (23 %) individuals in 34/72 clusters. Weekly genomic epidemiology updates were shared with the IPC team, culminating in 49 face-to-face meetings and 21 written communications. Seventeen clusters were identified that were consistent with hospital MRSA transmission, discussion of which led to additional IPC actions in 14 of these. Two outbreaks were also identified where transmission had occurred in the community prior to hospital presentation; these were escalated to relevant IPC teams. We identified 38 instances where two or more in-patients shared a ward location on overlapping dates but carried unrelated MRSA isolates (pseudo-outbreaks); research data led to de-escalation of investigations in six of these. Our findings provide further support for the routine use of genomic epidemiology to enhance and target IPC resources.
Collapse
Affiliation(s)
- Beth Blane
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
| | - Kathy E Raven
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
| | - Nicholas M Brown
- Clinical Microbiology and Public Health Laboratory, UK Health Security Agency, Addenbrooke's Hospital, Cambridge, UK
| | - Ewan M Harrison
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
- Wellcome Sanger Institute, Hinxton, Cambridge, UK
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Francesc Coll
- Wellcome Sanger Institute, Hinxton, Cambridge, UK
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Rachel Thaxter
- Clinical Microbiology and Public Health Laboratory, UK Health Security Agency, Addenbrooke's Hospital, Cambridge, UK
| | - David A Enoch
- Clinical Microbiology and Public Health Laboratory, UK Health Security Agency, Addenbrooke's Hospital, Cambridge, UK
| | - Theodore Gouliouris
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
- Clinical Microbiology and Public Health Laboratory, UK Health Security Agency, Addenbrooke's Hospital, Cambridge, UK
| | - Danielle Leek
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
| | - Sophia T Girgis
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
| | - Asha Akram
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
| | - Marta Matuszewska
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
- Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - Paul Rhodes
- Next Gen Diagnostics, LLC, (NGD) Mountain View, CA, USA
- Broers Building, 21 JJ Thomson Ave., Cambridge, UK
| | - Julian Parkhill
- Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge, UK
| | - Sharon J Peacock
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
| |
Collapse
|
3
|
Allel K, Hernández-Leal MJ, Naylor NR, Undurraga EA, Abou Jaoude GJ, Bhandari P, Flanagan E, Haghparast-Bidgoli H, Pouwels KB, Yakob L. Costs-effectiveness and cost components of pharmaceutical and non-pharmaceutical interventions affecting antibiotic resistance outcomes in hospital patients: a systematic literature review. BMJ Glob Health 2024; 9:e013205. [PMID: 38423548 PMCID: PMC10910705 DOI: 10.1136/bmjgh-2023-013205] [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: 06/22/2023] [Accepted: 01/26/2024] [Indexed: 03/02/2024] Open
Abstract
INTRODUCTION Limited information on costs and the cost-effectiveness of hospital interventions to reduce antibiotic resistance (ABR) hinder efficient resource allocation. METHODS We conducted a systematic literature review for studies evaluating the costs and cost-effectiveness of pharmaceutical and non-pharmaceutical interventions aimed at reducing, monitoring and controlling ABR in patients. Articles published until 12 December 2023 were explored using EconLit, EMBASE and PubMed. We focused on critical or high-priority bacteria, as defined by the WHO, and intervention costs and incremental cost-effectiveness ratio (ICER). Following Preferred Reporting Items for Systematic review and Meta-Analysis guidelines, we extracted unit costs, ICERs and essential study information including country, intervention, bacteria-drug combination, discount rates, type of model and outcomes. Costs were reported in 2022 US dollars ($), adopting the healthcare system perspective. Country willingness-to-pay (WTP) thresholds from Woods et al 2016 guided cost-effectiveness assessments. We assessed the studies reporting checklist using Drummond's method. RESULTS Among 20 958 articles, 59 (32 pharmaceutical and 27 non-pharmaceutical interventions) met the inclusion criteria. Non-pharmaceutical interventions, such as hygiene measures, had unit costs as low as $1 per patient, contrasting with generally higher pharmaceutical intervention costs. Several studies found that linezolid-based treatments for methicillin-resistant Staphylococcus aureus were cost-effective compared with vancomycin (ICER up to $21 488 per treatment success, all 16 studies' ICERs CONCLUSION Robust information on ABR interventions is critical for efficient resource allocation. We highlight cost-effective strategies for mitigating ABR in hospitals, emphasising substantial knowledge gaps, especially in low-income and middle-income countries. Our study serves as a resource for guiding future cost-effectiveness study design and analyses.PROSPERO registration number CRD42020341827 and CRD42022340064.
Collapse
Affiliation(s)
- Kasim Allel
- Disease Control Department, London School of Hygiene & Tropical Medicine, London, UK
- Institute for Global Health, University College London, London, UK
- Department of Health and Community Sciences, University of Exeter, Exeter, UK
| | - María José Hernández-Leal
- Department of Community, Maternity and Paediatric Nursing, University of Navarra, Pamplona, Spain
- Millennium Nucleus on Sociomedicine, Santiago, Chile
| | - Nichola R Naylor
- Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK
- HCAI, Fungal, AMR, AMU & Sepsis Division, UK Health Security Agency, London, UK
| | - Eduardo A Undurraga
- Escuela de Gobierno, Pontificia Universidad Catolica de Chile, Santiago, Chile
- CIFAR Azrieli Global Scholars program, Canadian Institute for Advanced Research, Toronto, Ontario, Canada
| | | | - Priyanka Bhandari
- Disease Control Department, London School of Hygiene & Tropical Medicine, London, UK
| | - Ellen Flanagan
- Disease Control Department, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Koen B Pouwels
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - Laith Yakob
- Disease Control Department, London School of Hygiene & Tropical Medicine, London, UK
| |
Collapse
|
4
|
Tran M, Smurthwaite KS, Nghiem S, Cribb DM, Zahedi A, Ferdinand AD, Andersson P, Kirk MD, Glass K, Lancsar E. Economic evaluations of whole-genome sequencing for pathogen identification in public health surveillance and health-care-associated infections: a systematic review. THE LANCET. MICROBE 2023; 4:e953-e962. [PMID: 37683688 DOI: 10.1016/s2666-5247(23)00180-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 09/10/2023]
Abstract
Whole-genome sequencing (WGS) has resulted in improvements to pathogen characterisation for the rapid investigation and management of disease outbreaks and surveillance. We conducted a systematic review to synthesise the economic evidence of WGS implementation for pathogen identification and surveillance. Of the 2285 unique publications identified through online database searches, 19 studies met the inclusion criteria. The economic evidence to support the broader application of WGS as a front-line pathogen characterisation and surveillance tool is insufficient and of low quality. WGS has been evaluated in various clinical settings, but these evaluations are predominantly investigations of a single pathogen. There are also considerable variations in the evaluation approach. Economic evaluations of costs, effectiveness, and cost-effectiveness are needed to support the implementation of WGS in public health settings.
Collapse
Affiliation(s)
- My Tran
- National Centre for Epidemiology and Population Health, Australian National University, Canberra ACT, Australia.
| | - Kayla S Smurthwaite
- National Centre for Epidemiology and Population Health, Australian National University, Canberra ACT, Australia
| | - Son Nghiem
- National Centre for Epidemiology and Population Health, Australian National University, Canberra ACT, Australia
| | - Danielle M Cribb
- National Centre for Epidemiology and Population Health, Australian National University, Canberra ACT, Australia
| | - Alireza Zahedi
- Public Health Microbiology, Forensic and Scientific Services, Queensland Health, Brisbane QLD, Australia
| | - Angeline D Ferdinand
- Microbiological Diagnostic Unit, Peter Doherty Institute, University of Melbourne, Melbourne VIC, Australia
| | - Patiyan Andersson
- Microbiological Diagnostic Unit, Peter Doherty Institute, University of Melbourne, Melbourne VIC, Australia
| | - Martyn D Kirk
- National Centre for Epidemiology and Population Health, Australian National University, Canberra ACT, Australia
| | - Kathryn Glass
- National Centre for Epidemiology and Population Health, Australian National University, Canberra ACT, Australia
| | - Emily Lancsar
- National Centre for Epidemiology and Population Health, Australian National University, Canberra ACT, Australia
| |
Collapse
|
5
|
Panca M, Blackstone J, Stirrup O, Cutino-Moguel MT, Thomson E, Peters C, Snell LB, Nebbia G, Holmes A, Chawla A, Machin N, Taha Y, Mahungu T, Saluja T, de Silva TI, Saeed K, Pope C, Shin GY, Williams R, Darby A, Smith DL, Loose M, Robson SC, Laing K, Partridge DG, Price JR, Breuer J. Evaluating the cost implications of integrating SARS-CoV-2 genome sequencing for infection prevention and control investigation of nosocomial transmission within hospitals. J Hosp Infect 2023; 139:23-32. [PMID: 37308063 PMCID: PMC10257337 DOI: 10.1016/j.jhin.2023.06.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 05/30/2023] [Accepted: 06/01/2023] [Indexed: 06/14/2023]
Abstract
BACKGROUND The COG-UK hospital-onset COVID-19 infection (HOCI) trial evaluated the impact of SARS-CoV-2 whole-genome sequencing (WGS) on acute infection, prevention, and control (IPC) investigation of nosocomial transmission within hospitals. AIM To estimate the cost implications of using the information from the sequencing reporting tool (SRT), used to determine likelihood of nosocomial infection in IPC practice. METHODS A micro-costing approach for SARS-CoV-2 WGS was conducted. Data on IPC management resource use and costs were collected from interviews with IPC teams from 14 participating sites and used to assign cost estimates for IPC activities as collected in the trial. Activities included IPC-specific actions following a suspicion of healthcare-associated infection (HAI) or outbreak, as well as changes to practice following the return of data via SRT. FINDINGS The mean per-sample costs of SARS-CoV-2 sequencing were estimated at £77.10 for rapid and £66.94 for longer turnaround phases. Over the three-month interventional phases, the total management costs of IPC-defined HAIs and outbreak events across the sites were estimated at £225,070 and £416,447, respectively. The main cost drivers were bed-days lost due to ward closures because of outbreaks, followed by outbreak meetings and bed-days lost due to cohorting contacts. Actioning SRTs, the cost of HAIs increased by £5,178 due to unidentified cases and the cost of outbreaks decreased by £11,246 as SRTs excluded hospital outbreaks. CONCLUSION Although SARS-CoV-2 WGS adds to the total IPC management cost, additional information provided could balance out the additional cost, depending on identified design improvements and effective deployment.
Collapse
Affiliation(s)
- M Panca
- Comprehensive Clinical Trials Unit, Institute of Clinical Trials and Methodology, UCL, London, UK.
| | - J Blackstone
- Comprehensive Clinical Trials Unit, Institute of Clinical Trials and Methodology, UCL, London, UK
| | - O Stirrup
- Institute for Global Health, UCL, London, UK
| | | | - E Thomson
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - C Peters
- NHS Greater Glasgow and Clyde, Glasgow, UK
| | - L B Snell
- Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK
| | - G Nebbia
- Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK
| | - A Holmes
- Imperial College Healthcare NHS Trust, London, UK
| | - A Chawla
- Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - N Machin
- Manchester University NHS Foundation Trust, Manchester, UK
| | - Y Taha
- Departments of Virology and Infectious Diseases, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle, UK
| | - T Mahungu
- Royal Free NHS Foundation Trust, London, UK
| | - T Saluja
- Sandwell and West Birmingham NHS Trust, UK
| | - T I de Silva
- Department of Infection, Immunity and Cardiovascular Disease, Medical School, The University of Sheffield, Sheffield, UK
| | - K Saeed
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - C Pope
- St George's University Hospitals NHS Foundation Trust, London, UK; Institute for Infection and Immunity, St George's University of London, London, UK
| | - G Y Shin
- University College London Hospitals NHS Foundation Trust, London, UK
| | - R Williams
- Department of Genetics & Genomic Medicine, UCL Great Ormond Street Institute of Child Health, UCL, London, UK
| | - A Darby
- Centre for Genomic Research, University of Liverpool, Liverpool, UK
| | - D L Smith
- Department of Applied Sciences, Northumbria University, Newcastle, UK
| | - M Loose
- School of Life Sciences, University of Nottingham, Nottingham, UK
| | - S C Robson
- Centre for Enzyme Innovation & School of Pharmacy and Biomedical Science, University of Portsmouth, Portsmouth, UK
| | - K Laing
- Institute for Infection and Immunity, St George's University of London, London, UK
| | - D G Partridge
- Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - J R Price
- Imperial College Healthcare NHS Trust, London, UK
| | - J Breuer
- Department of Infection, Immunity and Inflammation, Great Ormond Street Institute of Child Health, UCL, London, UK
| |
Collapse
|
6
|
Fox JM, Saunders NJ, Jerwood SH. Economic and health impact modelling of a whole genome sequencing-led intervention strategy for bacterial healthcare-associated infections for England and for the USA. Microb Genom 2023; 9:mgen001087. [PMID: 37555752 PMCID: PMC10483413 DOI: 10.1099/mgen.0.001087] [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: 03/16/2023] [Accepted: 07/24/2023] [Indexed: 08/10/2023] Open
Abstract
Bacterial healthcare-associated infections (HAIs) are a substantial source of global morbidity and mortality. The estimated cost associated with HAIs ranges from $35 to $45 billion in the USA alone. The costs and accessibility of whole genome sequencing (WGS) of bacteria and the lack of sufficiently accurate, high-resolution, scalable and accessible analysis for strain identification are being addressed. Thus, it is timely to determine the economic viability and impact of routine diagnostic bacterial genomics. The aim of this study was to model the economic impact of a WGS surveillance system that proactively detects and directs interventions for nosocomial infections and outbreaks compared to the current standard of care, without WGS. Using a synthesis of published models, inputs from national statistics, and peer-reviewed articles, the economic impacts of conducting a WGS-led surveillance system addressing the 11 most common nosocomial pathogen groups in England and the USA were modelled. This was followed by a series of sensitivity analyses. England was used to establish the baseline model because of the greater availability of underpinning data, and this was then modified using USA-specific parameters where available. The model for the NHS in England shows bacterial HAIs currently cost the NHS around £3 billion. WGS-based surveillance delivery is predicted to cost £61.1 million associated with the prevention of 74 408 HAIs and 1257 deaths. The net cost saving was £478.3 million, of which £65.8 million were from directly incurred savings (antibiotics, consumables, etc.) and £412.5 million from opportunity cost savings due to re-allocation of hospital beds and healthcare professionals. The USA model indicates that the bacterial HAI care baseline costs are around $18.3 billion. WGS surveillance costs $169.2 million, and resulted in a net saving of ca.$3.2 billion, while preventing 169 260 HAIs and 4862 deaths. From a 'return on investment' perspective, the model predicts a return to the hospitals of £7.83 per £1 invested in diagnostic WGS in the UK, and US$18.74 per $1 in the USA. Sensitivity analyses show that substantial savings are retained when inputs to the model are varied within a wide range of upper and lower limits. Modelling a proactive WGS system addressing HAI pathogens shows significant improvement in morbidity and mortality while simultaneously achieving substantial savings to healthcare facilities that more than offset the cost of implementing diagnostic genomics surveillance.
Collapse
|
7
|
Cassone M, Wang J, Lansing BJ, Mantey J, Gibson KE, Gontjes KJ, Mody L. Diversity and Persistence of MRSA and VRE in Skilled Nursing Facilities: Environmental Screening, Whole Genome Sequencing, Development of a Dispersion Index. J Hosp Infect 2023:S0195-6701(23)00140-8. [PMID: 37160232 DOI: 10.1016/j.jhin.2023.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/28/2023] [Accepted: 04/30/2023] [Indexed: 05/11/2023]
Abstract
BACKGROUND Environmental contamination with methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE) in skilled nursing facilities (SNFs) may contribute to patient acquisition. We assessed diversity and association of MRSA and VRE isolates in a SNF wing and developed a mathematical index to define each strain's tendency to persist in rooms and spread horizontally. METHODS Longitudinal study of MRSA and VRE colonization and contamination among successive patient occupancies in a cluster of nine SNF private rooms during eight months characterized by microbiological testing and whole genome isolate typing. 'Dispersion index" of a strain is defined as the number of rooms it was found in (including the patient), divided by the average of times it was found consecutively in the same room. FINDINGS MRSA (ten strain types) and VRE (seven types) were recovered from room or patient in 16.4% and 35.6% of the occupancies, respectively. MRSA showed moderate horizontal spread and several episodes of same-room persistence (three distinct strain types) (overall dispersion index: 1.08). VRE showed high tendency towards horizontal spread /new introductions (overall dispersion index: 3.25), and only one confirmed persistence episode. INTERPRETATION The emerging picture of high diversity among contaminating strains and high likelihood of room persistence despite terminal cleaning (MRSA) and horizontal spread between rooms (VRE) in this setting calls for improved cleaning practices, heightened contact precautions, and most of all to establish individually tailored facility screening programs to enable informed choices based on local, measurable and actionable epidemiologic parameters. FUNDING University of Michigan OAIC REC Scholarship to M.C. National Institutes of Health K24 AG050685 to L.M.
Collapse
Affiliation(s)
- M Cassone
- Division of Geriatric & Palliative Medicine, Michigan Medicine.
| | - J Wang
- Department of Microbiology and Immunology, Michigan Medicine
| | - B J Lansing
- Division of Geriatric & Palliative Medicine, Michigan Medicine
| | - J Mantey
- Division of Geriatric & Palliative Medicine, Michigan Medicine
| | - K E Gibson
- Division of Geriatric & Palliative Medicine, Michigan Medicine
| | - K J Gontjes
- Division of Geriatric & Palliative Medicine, Michigan Medicine; Department of Epidemiology, University of Michigan School of Public Health
| | - L Mody
- Division of Geriatric & Palliative Medicine, Michigan Medicine; Geriatrics Research Education & Clinical Center, VA Ann Arbor Healthcare System
| |
Collapse
|
8
|
Price V, Ngwira LG, Lewis JM, Baker KS, Peacock SJ, Jauneikaite E, Feasey N. A systematic review of economic evaluations of whole-genome sequencing for the surveillance of bacterial pathogens. Microb Genom 2023; 9:mgen000947. [PMID: 36790430 PMCID: PMC9997737 DOI: 10.1099/mgen.0.000947] [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: 09/08/2022] [Accepted: 12/07/2022] [Indexed: 02/16/2023] Open
Abstract
Whole-genome sequencing (WGS) has unparalleled ability to distinguish between bacteria, with many public health applications. The generation and analysis of WGS data require significant financial investment. We describe a systematic review summarizing economic analyses of genomic surveillance of bacterial pathogens, reviewing the evidence for economic viability. The protocol was registered on PROSPERO (CRD42021289030). Six databases were searched on 8 November 2021 using terms related to 'WGS', 'population surveillance' and 'economic analysis'. Quality was assessed with the Drummond-Jefferson checklist. Following data extraction, a narrative synthesis approach was taken. Six hundred and eighty-one articles were identified, of which 49 proceeded to full-text screening, with 9 selected for inclusion. All had been published since 2019. Heterogeneity was high. Five studies assessed WGS for hospital surveillance and four analysed foodborne pathogens. Four were cost-benefit analyses, one was a cost-utility analysis, one was a cost-effectiveness analysis, one was a combined cost-effectiveness and cost-utility analysis, one combined cost-effectiveness and cost-benefit analyses and one was a partial analysis. All studies supported the use of WGS as a surveillance tool on economic grounds. The available evidence supports the use of WGS for pathogen surveillance but is limited by marked heterogeneity. Further work should include analysis relevant to low- and middle-income countries and should use real-world effectiveness data.
Collapse
Affiliation(s)
| | | | - Joseph M. Lewis
- University of Liverpool, Liverpool, UK
- Liverpool School of Tropical Medicine, Liverpool, UK
| | | | | | | | | |
Collapse
|
9
|
Prolonged silent carriage, genomic virulence potential and transmission between staff and patients characterize a neonatal intensive care unit (NICU) outbreak of methicillin-resistant Staphylococcus aureus (MRSA). Infect Control Hosp Epidemiol 2023; 44:40-46. [PMID: 35311638 DOI: 10.1017/ice.2022.48] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND Methicillin-resistant Staphylococcus aureus (MRSA) is an important pathogen in neonatal intensive care units (NICU) that confers significant morbidity and mortality. OBJECTIVE Improving our understanding of MRSA transmission dynamics, especially among high-risk patients, is an infection prevention priority. METHODS We investigated a cluster of clinical MRSA cases in the NICU using a combination of epidemiologic review and whole-genome sequencing (WGS) of isolates from clinical and surveillance cultures obtained from patients and healthcare personnel (HCP). RESULTS Phylogenetic analysis identified 2 genetically distinct phylogenetic clades and revealed multiple silent-transmission events between HCP and infants. The predominant outbreak strain harbored multiple virulence factors. Epidemiologic investigation and genomic analysis identified a HCP colonized with the dominant MRSA outbreak strain who cared for most NICU patients who were infected or colonized with the same strain, including 1 NICU patient with severe infection 7 months before the described outbreak. These results guided implementation of infection prevention interventions that prevented further transmission events. CONCLUSIONS Silent transmission of MRSA between HCP and NICU patients likely contributed to a NICU outbreak involving a virulent MRSA strain. WGS enabled data-driven decision making to inform implementation of infection control policies that mitigated the outbreak. Prospective WGS coupled with epidemiologic analysis can be used to detect transmission events and prompt early implementation of control strategies.
Collapse
|
10
|
Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics. Int J Mol Sci 2022; 23:ijms23179834. [PMID: 36077231 PMCID: PMC9456280 DOI: 10.3390/ijms23179834] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/24/2022] [Accepted: 08/26/2022] [Indexed: 12/21/2022] Open
Abstract
Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into the antimicrobial resistance and virulence potential of a single microbiological isolate during the diagnostic process. In contrast, metagenomic sequencing allows the analysis of DNA segments from multiple microorganisms within a community, either using an amplicon- or shotgun-based approach. However, WGS and shotgun metagenomic data are rarely combined, although such an approach may generate additive or synergistic information, critical for, e.g., patient management, infection control, and pathogen surveillance. To produce a combined workflow with actionable outputs, we need to understand the pre-to-post analytical process of both technologies. This will require specific databases storing interlinked sequencing and metadata, and also involves customized bioinformatic analytical pipelines. This review article will provide an overview of the critical steps and potential clinical application of combining WGS and metagenomics together for microbiological diagnosis.
Collapse
|
11
|
Lagos AC, Sundqvist M, Dyrkell F, Stegger M, Söderquist B, Mölling P. Evaluation of within-host evolution of methicillin-resistant Staphylococcus aureus (MRSA) by comparing cgMLST and SNP analysis approaches. Sci Rep 2022; 12:10541. [PMID: 35732699 PMCID: PMC9214674 DOI: 10.1038/s41598-022-14640-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 06/09/2022] [Indexed: 11/17/2022] Open
Abstract
Whole genome sequencing (WGS) of methicillin-resistant Staphylococcus aureus (MRSA) provides high-resolution typing, facilitating surveillance and outbreak investigations. The aim of this study was to evaluate the genomic variation rate in MRSA, by comparing commonly used core genome multilocus sequencing (cgMLST) against single nucleotide polymorphism (SNP) analyses. WGS was performed on 95 MRSA isolates, collected from 20 carriers during years 2003–2019. To assess variation and methodological-related differences, two different cgMLST schemes were obtained using Ridom SeqSphere+ and the cloud-based 1928 platform. In addition, two SNP methods, 1928 platform and Northern Arizona SNP Pipeline (NASP) were used. The cgMLST using Ridom SeqSphere+ and 1928 showed a median of 5.0 and 2.0 allele variants/year, respectively. In the SNP analysis, performed with two reference genomes COL and Newman, 1928 showed a median of 13 and 24 SNPs (including presumed recombination) and 3.8 respectively 4.0 SNPs (without recombination) per individual/year. Accordantly, NASP showed a median of 5.5 and 5.8 SNPs per individual/year. In conclusion, an estimated genomic variation rate of 2.0–5.8 genetic events per year (without recombination), is suggested as a general guideline to be used at clinical laboratories for surveillance and outbreak investigations independently of analysis approach used.
Collapse
Affiliation(s)
- Amaya Campillay Lagos
- Department of Laboratory Medicine, Clinical Microbiology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
| | - Martin Sundqvist
- Department of Laboratory Medicine, Clinical Microbiology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | | | - Marc Stegger
- Department of Laboratory Medicine, Clinical Microbiology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.,Department of Bacteria, Parasites and Fungi, Statens Serum Institut, Copenhagen, Denmark
| | - Bo Söderquist
- Department of Laboratory Medicine, Clinical Microbiology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Paula Mölling
- Department of Laboratory Medicine, Clinical Microbiology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| |
Collapse
|
12
|
Waddington C, Carey ME, Boinett CJ, Higginson E, Veeraraghavan B, Baker S. Exploiting genomics to mitigate the public health impact of antimicrobial resistance. Genome Med 2022; 14:15. [PMID: 35172877 PMCID: PMC8849018 DOI: 10.1186/s13073-022-01020-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 02/04/2022] [Indexed: 12/13/2022] Open
Abstract
Antimicrobial resistance (AMR) is a major global public health threat, which has been largely driven by the excessive use of antimicrobials. Control measures are urgently needed to slow the trajectory of AMR but are hampered by an incomplete understanding of the interplay between pathogens, AMR encoding genes, and mobile genetic elements at a microbial level. These factors, combined with the human, animal, and environmental interactions that underlie AMR dissemination at a population level, make for a highly complex landscape. Whole-genome sequencing (WGS) and, more recently, metagenomic analyses have greatly enhanced our understanding of these processes, and these approaches are informing mitigation strategies for how we better understand and control AMR. This review explores how WGS techniques have advanced global, national, and local AMR surveillance, and how this improved understanding is being applied to inform solutions, such as novel diagnostic methods that allow antimicrobial use to be optimised and vaccination strategies for better controlling AMR. We highlight some future opportunities for AMR control informed by genomic sequencing, along with the remaining challenges that must be overcome to fully realise the potential of WGS approaches for international AMR control.
Collapse
Affiliation(s)
- Claire Waddington
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0AW, UK.,Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, UK
| | - Megan E Carey
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0AW, UK.,Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, UK
| | | | - Ellen Higginson
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0AW, UK.,Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, UK
| | - Balaji Veeraraghavan
- Department of Microbiology, Christian Medical College, Vellore, Tamil Nadu, India
| | - Stephen Baker
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0AW, UK. .,Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, UK.
| |
Collapse
|
13
|
Eyre DW. Infection prevention and control insights from a decade of pathogen whole-genome sequencing. J Hosp Infect 2022; 122:180-186. [PMID: 35157991 PMCID: PMC8837474 DOI: 10.1016/j.jhin.2022.01.024] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 01/31/2022] [Indexed: 12/13/2022]
Abstract
Pathogen whole-genome sequencing has become an important tool for understanding the transmission and epidemiology of infectious diseases. It has improved our understanding of sources of infection and transmission routes for important healthcare-associated pathogens, including Clostridioides difficile and Staphylococcus aureus. Transmission from known infected or colonized patients in hospitals may explain fewer cases than previously thought and multiple introductions of these pathogens from the community may play a greater a role. The findings have had important implications for infection prevention and control. Sequencing has identified heterogeneity within pathogen species, with some subtypes transmitting and persisting in hospitals better than others. It has identified sources of infection in healthcare-associated outbreaks of food-borne pathogens, Candida auris and Mycobacterium chimera, as well as individuals or groups involved in transmission and historical sources of infection. SARS-CoV-2 sequencing has been central to tracking variants during the COVID-19 pandemic and has helped understand transmission to and from patients and healthcare workers despite prevention efforts. Metagenomic sequencing is an emerging technology for culture-independent diagnosis of infection and antimicrobial resistance. In future, sequencing is likely to become more accessible and widely available. Real-time use in hospitals may allow infection prevention and control teams to identify transmission and to target interventions. It may also provide surveillance and infection control benchmarking. Attention to ethical and wellbeing issues arising from sequencing identifying individuals involved in transmission is important. Pathogen whole-genome sequencing has provided an incredible new lens to understand the epidemiology of healthcare-associated infection and to better control and prevent these infections.
Collapse
Affiliation(s)
- D W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK; National Institiute for Health Research, Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK; Oxford University Hospitals, Oxford, UK.
| |
Collapse
|
14
|
Forde BM, De Oliveira DMP, Falconer C, Graves B, Harris PNA. Strengths and caveats of identifying resistance genes from whole genome sequencing data. Expert Rev Anti Infect Ther 2021; 20:533-547. [PMID: 34852720 DOI: 10.1080/14787210.2022.2013806] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Antimicrobial resistance (AMR) continues to present major challenges to modern healthcare. Recent advances in whole-genome sequencing (WGS) have made the rapid molecular characterization of AMR a realistic possibility for diagnostic laboratories; yet major barriers to clinical implementation exist. AREAS COVERED We describe and compare short- and long-read sequencing platforms, typical components of bioinformatics pipelines, tools for AMR gene detection and the relative merits of read- or assembly-based approaches. The challenges of characterizing mobile genetic elements from genomic data are outlined, as well as the complexities inherent to the prediction of phenotypic resistance from WGS. Practical obstacles to implementation in diagnostic laboratories, the critical role of quality control and external quality assurance, as well as standardized reporting standards are also discussed. Future directions, such as the application of machine-learning and artificial intelligence algorithms, linked to clinically meaningful outcomes, may offer a new paradigm for the clinical application of AMR prediction. EXPERT OPINION AMR prediction from WGS data presents an exciting opportunity to advance our capacity to comprehensively characterize infectious pathogens in a rapid manner, ultimately aiming to improve patient outcomes. Collaborative efforts between clinicians, scientists, regulatory bodies and healthcare administrators will be critical to achieve the full promise of this approach.
Collapse
Affiliation(s)
- Brian M Forde
- University of Queensland, Faculty of Medicine, Uq Centre for Clinical Research, Royal Brisbane and Woman's Hospital, Herston, Australia
| | - David M P De Oliveira
- University of Queensland, Faculty of Science, School of Chemistry and Molecular Biosciences, St Lucia, Australia
| | - Caitlin Falconer
- University of Queensland, Faculty of Medicine, Uq Centre for Clinical Research, Royal Brisbane and Woman's Hospital, Herston, Australia
| | - Bianca Graves
- Herston Infectious Disease Institute, Royal Brisbane & Women's Hospital, Herston, Australia
| | - Patrick N A Harris
- University of Queensland, Faculty of Medicine, Uq Centre for Clinical Research, Royal Brisbane and Woman's Hospital, Herston, Australia.,Herston Infectious Disease Institute, Royal Brisbane & Women's Hospital, Herston, Australia.,Central Microbiology, Pathology Queensland, Royal Brisbane & Women's Hospital, Herston, Australia
| |
Collapse
|
15
|
Abstract
PURPOSE OF REVIEW Mathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcare-associated infections. To contextualize recent findings, we offer a summative review of recent literature focused on modeling transmission of pathogens in healthcare settings. RECENT FINDINGS The COVID-19 pandemic has led to a dramatic shift in the modeling landscape as the healthcare community has raced to characterize the transmission dynamics of SARS-CoV-2 and develop effective interventions. Inequities in COVID-19 outcomes have inspired new efforts to quantify how structural bias impacts both health outcomes and model parameterization. Meanwhile, developments in the modeling of methicillin-resistant Staphylococcus aureus, Clostridioides difficile, and other nosocomial infections continue to advance. Machine learning continues to be applied in novel ways, and genomic data is being increasingly incorporated into modeling efforts. SUMMARY As the type and amount of data continues to grow, mathematical, statistical, and computational modeling will play an increasing role in healthcare epidemiology. Gaps remain in producing models that are generalizable to a variety of time periods, geographic locations, and populations. However, with effective communication of findings and interdisciplinary collaboration, opportunities for implementing models for clinical decision-making and public health decision-making are bound to increase.
Collapse
|
16
|
Kumar P, Sundermann AJ, Martin EM, Snyder GM, Marsh JW, Harrison LH, Roberts MS. Method for Economic Evaluation of Bacterial Whole Genome Sequencing Surveillance Compared to Standard of Care in Detecting Hospital Outbreaks. Clin Infect Dis 2021; 73:e9-e18. [PMID: 32367125 DOI: 10.1093/cid/ciaa512] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 04/29/2020] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Whole genome sequencing (WGS) surveillance and electronic health record data mining have the potential to greatly enhance the identification and control of hospital outbreaks. The objective was to develop methods for examining economic value of a WGS surveillance-based infection prevention (IP) program compared to standard of care (SoC). METHODS The economic value of a WGS surveillance-based IP program was assessed from a hospital's perspective using historical outbreaks from 2011-2016. We used transmission network of outbreaks to estimate incremental cost per transmission averted. The number of transmissions averted depended on the effectiveness of intervening against transmission routes, time from transmission to positive culture results and time taken to obtain WGS results and intervene on the transmission route identified. The total cost of an IP program included cost of staffing, WGS, and treating infections. RESULTS Approximately 41 out of 89 (46%) transmissions could have been averted under the WGS surveillance-based IP program, and it was found to be a less costly and more effective strategy than SoC. The results were most sensitive to the cost of performing WGS and the number of isolates sequenced per year under WGS surveillance. The probability of the WGS surveillance-based IP program being cost-effective was 80% if willingness to pay exceeded $2400 per transmission averted. CONCLUSIONS The proposed economic analysis is a useful tool to examine economic value of a WGS surveillance-based IP program. These methods will be applied to a prospective evaluation of WGS surveillance compared to SoC.
Collapse
Affiliation(s)
- Praveen Kumar
- Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Alexander J Sundermann
- The Microbial Genomic Epidemiology Laboratory, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania, USA
| | - Elise M Martin
- Department of Infection Prevention and Control, University of Pittsburgh Medical Center Presbyterian Hospital, Pittsburgh, Pennsylvania, USA
| | - Graham M Snyder
- Department of Infection Prevention and Control, University of Pittsburgh Medical Center Presbyterian Hospital, Pittsburgh, Pennsylvania, USA
| | - Jane W Marsh
- The Microbial Genomic Epidemiology Laboratory, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania, USA.,Department of Infection Prevention and Control, University of Pittsburgh Medical Center Presbyterian Hospital, Pittsburgh, Pennsylvania, USA
| | - Lee H Harrison
- The Microbial Genomic Epidemiology Laboratory, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania, USA
| | - Mark S Roberts
- Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| |
Collapse
|
17
|
Gordon LG, Elliott TM, Forde B, Mitchell B, Russo PL, Paterson DL, Harris PNA. Budget impact analysis of routinely using whole-genomic sequencing of six multidrug-resistant bacterial pathogens in Queensland, Australia. BMJ Open 2021; 11:e041968. [PMID: 33526501 PMCID: PMC7852923 DOI: 10.1136/bmjopen-2020-041968] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
OBJECTIVE To predict the cost and health effects of routine use of whole-genome sequencing (WGS) of bacterial pathogens compared with those of standard of care. DESIGN Budget impact analysis was performed over the following 5 years. Data were primarily from sequencing results on clusters of multidrug-resistant organisms across 27 hospitals. Model inputs were derived from hospitalisation and sequencing data, and epidemiological and costing reports, and included multidrug resistance rates and their trends. SETTING Queensland, Australia. PARTICIPANTS Hospitalised patients. INTERVENTIONS WGS surveillance of six common multidrug-resistant organisms (Staphylococcus aureus, Escherichia coli, Enterococcus faecium, Klebsiella pneumoniae, Enterobacter sp and Acinetobacter baumannii) compared with standard of care or routine microbiology testing. PRIMARY AND SECONDARY OUTCOMES Expected hospital costs, counts of patient infections and colonisations, and deaths from bloodstream infections. RESULTS In 2021, 97 539 patients in Queensland are expected to be infected or colonised with one of six multidrug-resistant organisms with standard of care testing. WGS surveillance strategy and earlier infection control measures could avoid 36 726 infected or colonised patients and avoid 650 deaths. The total cost under standard of care was $A170.8 million in 2021. WGS surveillance costs an additional $A26.8 million but was offset by fewer costs for cleaning, nursing, personal protective equipment, shorter hospital stays and antimicrobials to produce an overall cost savings of $30.9 million in 2021. Sensitivity analyses showed cost savings remained when input values were varied at 95% confidence limits. CONCLUSIONS Compared with standard of care, WGS surveillance at a state-wide level could prevent a substantial number of hospital patients infected with multidrug-resistant organisms and related deaths and save healthcare costs. Primary prevention through routine use of WGS is an investment priority for the control of serious hospital-associated infections.
Collapse
Affiliation(s)
- Louisa G Gordon
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- School of Nursing, Queensland University of Technology (QUT), Brisbane, Queensland, Australia
- School of Public Health, The University of Queensland, Brisbane, Queensland, Australia
| | - Thomas M Elliott
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Brian Forde
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Queensland, Australia
- The University of Queensland, Centre for Clinical Research, Brisbane, Queensland, Australia
| | - Brett Mitchell
- School of Nursing and Midwifery, The University of Newcastle, Newcastle, New South Wales, Australia
| | - Philip L Russo
- School of Nursing and Midwifery, Monash University, Melbourne, Victoria, Australia
| | - David L Paterson
- The University of Queensland, Centre for Clinical Research, Brisbane, Queensland, Australia
| | - Patrick N A Harris
- The University of Queensland, Centre for Clinical Research, Brisbane, Queensland, Australia
- Pathology Queensland, Queensland Health, Brisbane, Queensland, Australia
| |
Collapse
|
18
|
Hwang SM, Cho HW, Kim TY, Park JS, Jung J, Song KH, Lee H, Kim ES, Kim HB, Park KU. Whole-Genome Sequencing for Investigating a Health Care-Associated Outbreak of Carbapenem-Resistant Acinetobacter baumannii. Diagnostics (Basel) 2021; 11:diagnostics11020201. [PMID: 33573077 PMCID: PMC7910894 DOI: 10.3390/diagnostics11020201] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 01/25/2021] [Accepted: 01/27/2021] [Indexed: 12/16/2022] Open
Abstract
Carbapenem-resistant Acinetobacter baumannii (CRAB) outbreaks in hospital settings challenge the treatment of patients and infection control. Understanding the relatedness of clinical isolates is important in distinguishing outbreak isolates from sporadic cases. This study investigated 11 CRAB isolates from a hospital outbreak by whole-genome sequencing (WGS), utilizing various bioinformatics tools for outbreak analysis. The results of multilocus sequence typing (MLST), single nucleotide polymorphism (SNP) analysis, and phylogenetic tree analysis by WGS through web-based tools were compared, and repetitive element polymerase chain reaction (rep-PCR) typing was performed. Through the WGS of 11 A. baumannii isolates, three clonal lineages were identified from the outbreak. The coexistence of blaOXA-23, blaOXA-66, blaADC-25, and armA with additional aminoglycoside-inactivating enzymes, predicted to confer multidrug resistance, was identified in all isolates. The MLST Oxford scheme identified three types (ST191, ST369, and ST451), and, through whole-genome MLST and whole-genome SNP analyses, different clones were found to exist within the MLST types. wgSNP showed the highest discriminatory power with the lowest similarities among the isolates. Using the various bioinformatics tools for WGS, CRAB outbreak analysis was applicable and identified three discrete clusters differentiating the separate epidemiologic relationships among the isolates.
Collapse
Affiliation(s)
- Sang Mee Hwang
- Department of Laboratory Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea; (S.M.H.); (J.S.P.)
- College of Medicine, Seoul National University, Seoul 03080, Korea; (H.W.C.); (J.J.); (K.-H.S.); (H.L.); (E.S.K.); (H.B.K.)
| | - Hee Won Cho
- College of Medicine, Seoul National University, Seoul 03080, Korea; (H.W.C.); (J.J.); (K.-H.S.); (H.L.); (E.S.K.); (H.B.K.)
| | - Tae Yeul Kim
- Department of Laboratory Medicine and Genetics, Samsung Medical Center, Seoul 06351, Korea;
| | - Jeong Su Park
- Department of Laboratory Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea; (S.M.H.); (J.S.P.)
- College of Medicine, Seoul National University, Seoul 03080, Korea; (H.W.C.); (J.J.); (K.-H.S.); (H.L.); (E.S.K.); (H.B.K.)
| | - Jongtak Jung
- College of Medicine, Seoul National University, Seoul 03080, Korea; (H.W.C.); (J.J.); (K.-H.S.); (H.L.); (E.S.K.); (H.B.K.)
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea
| | - Kyoung-Ho Song
- College of Medicine, Seoul National University, Seoul 03080, Korea; (H.W.C.); (J.J.); (K.-H.S.); (H.L.); (E.S.K.); (H.B.K.)
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea
| | - Hyunju Lee
- College of Medicine, Seoul National University, Seoul 03080, Korea; (H.W.C.); (J.J.); (K.-H.S.); (H.L.); (E.S.K.); (H.B.K.)
- Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam 13620, Korea
| | - Eu Suk Kim
- College of Medicine, Seoul National University, Seoul 03080, Korea; (H.W.C.); (J.J.); (K.-H.S.); (H.L.); (E.S.K.); (H.B.K.)
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea
| | - Hong Bin Kim
- College of Medicine, Seoul National University, Seoul 03080, Korea; (H.W.C.); (J.J.); (K.-H.S.); (H.L.); (E.S.K.); (H.B.K.)
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea
| | - Kyoung Un Park
- Department of Laboratory Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea; (S.M.H.); (J.S.P.)
- College of Medicine, Seoul National University, Seoul 03080, Korea; (H.W.C.); (J.J.); (K.-H.S.); (H.L.); (E.S.K.); (H.B.K.)
- Correspondence: ; Tel.: +82-2740-8005
| |
Collapse
|
19
|
Cost-effectiveness analysis of whole-genome sequencing during an outbreak of carbapenem-resistant Acinetobacter baumannii. ANTIMICROBIAL STEWARDSHIP & HEALTHCARE EPIDEMIOLOGY 2021; 1:e62. [PMID: 36168472 PMCID: PMC9495627 DOI: 10.1017/ash.2021.233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/25/2021] [Accepted: 10/25/2021] [Indexed: 11/12/2022]
Abstract
Background: Whole-genome sequencing (WGS) shotgun metagenomics (metagenomics) attempts to sequence the entire genetic content straight from the sample. Diagnostic advantages lie in the ability to detect unsuspected, uncultivatable, or very slow-growing organisms. Objective: To evaluate the clinical and economic effects of using WGS and metagenomics for outbreak management in a large metropolitan hospital. Design: Cost-effectiveness study. Setting: Intensive care unit and burn unit of large metropolitan hospital. Patients: Simulated intensive care unit and burn unit patients. Methods: We built a complex simulation model to estimate pathogen transmission, associated hospital costs, and quality-adjusted life years (QALYs) during a 32-month outbreak of carbapenem-resistant Acinetobacter baumannii (CRAB). Model parameters were determined using microbiology surveillance data, genome sequencing results, hospital admission databases, and local clinical knowledge. The model was calibrated to the actual pathogen spread within the intensive care unit and burn unit (scenario 1) and compared with early use of WGS (scenario 2) and early use of WGS and metagenomics (scenario 3) to determine their respective cost-effectiveness. Sensitivity analyses were performed to address model uncertainty. Results: On average compared with scenario 1, scenario 2 resulted in 14 fewer patients with CRAB, 59 additional QALYs, and $75,099 cost savings. Scenario 3, compared with scenario 1, resulted in 18 fewer patients with CRAB, 74 additional QALYs, and $93,822 in hospital cost savings. The likelihoods that scenario 2 and scenario 3 were cost-effective were 57% and 60%, respectively. Conclusions: The use of WGS and metagenomics in infection control processes were predicted to produce favorable economic and clinical outcomes.
Collapse
|
20
|
Whole-genome sequencing as part of national and international surveillance programmes for antimicrobial resistance: a roadmap. BMJ Glob Health 2020; 5:e002244. [PMID: 33239336 PMCID: PMC7689591 DOI: 10.1136/bmjgh-2019-002244] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 03/18/2020] [Accepted: 03/27/2020] [Indexed: 12/26/2022] Open
Abstract
The global spread of antimicrobial resistance (AMR) and lack of novel alternative treatments have been declared a global public health emergency by WHO. The greatest impact of AMR is experienced in resource-poor settings, because of lack of access to alternative antibiotics and because the prevalence of multidrug-resistant bacterial strains may be higher in low-income and middle-income countries (LMICs). Intelligent surveillance of AMR infections is key to informed policy decisions and public health interventions to counter AMR. Molecular surveillance using whole-genome sequencing (WGS) can be a valuable addition to phenotypic surveillance of AMR. WGS provides insights into the genetic basis of resistance mechanisms, as well as pathogen evolution and population dynamics at different spatial and temporal scales. Due to its high cost and complexity, WGS is currently mainly carried out in high-income countries. However, given its potential to inform national and international action plans against AMR, establishing WGS as a surveillance tool in LMICs will be important in order to produce a truly global picture. Here, we describe a roadmap for incorporating WGS into existing AMR surveillance frameworks, including WHO Global Antimicrobial Resistance Surveillance System, informed by our ongoing, practical experiences developing WGS surveillance systems in national reference laboratories in Colombia, India, Nigeria and the Philippines. Challenges and barriers to WGS in LMICs will be discussed together with a roadmap to possible solutions.
Collapse
|
21
|
Argimón S, Masim MAL, Gayeta JM, Lagrada ML, Macaranas PKV, Cohen V, Limas MT, Espiritu HO, Palarca JC, Chilam J, Jamoralin MC, Villamin AS, Borlasa JB, Olorosa AM, Hernandez LFT, Boehme KD, Jeffrey B, Abudahab K, Hufano CM, Sia SB, Stelling J, Holden MTG, Aanensen DM, Carlos CC. Integrating whole-genome sequencing within the National Antimicrobial Resistance Surveillance Program in the Philippines. Nat Commun 2020; 11:2719. [PMID: 32483195 PMCID: PMC7264328 DOI: 10.1038/s41467-020-16322-5] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 04/25/2020] [Indexed: 12/15/2022] Open
Abstract
National networks of laboratory-based surveillance of antimicrobial resistance (AMR) monitor resistance trends and disseminate these data to AMR stakeholders. Whole-genome sequencing (WGS) can support surveillance by pinpointing resistance mechanisms and uncovering transmission patterns. However, genomic surveillance is rare in low- and middle-income countries. Here, we implement WGS within the established Antimicrobial Resistance Surveillance Program of the Philippines via a binational collaboration. In parallel, we characterize bacterial populations of key bug-drug combinations via a retrospective sequencing survey. By linking the resistance phenotypes to genomic data, we reveal the interplay of genetic lineages (strains), AMR mechanisms, and AMR vehicles underlying the expansion of specific resistance phenotypes that coincide with the growing carbapenem resistance rates observed since 2010. Our results enhance our understanding of the drivers of carbapenem resistance in the Philippines, while also serving as the genetic background to contextualize ongoing local prospective surveillance.
Collapse
Affiliation(s)
- Silvia Argimón
- Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus, Hinxton, UK
| | - Melissa A L Masim
- Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines
| | - June M Gayeta
- Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines
| | - Marietta L Lagrada
- Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines
| | - Polle K V Macaranas
- Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines
| | - Victoria Cohen
- Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus, Hinxton, UK
| | - Marilyn T Limas
- Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines
| | - Holly O Espiritu
- Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines
| | - Janziel C Palarca
- Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines
| | - Jeremiah Chilam
- Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines
| | - Manuel C Jamoralin
- Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines
| | - Alfred S Villamin
- Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines
| | - Janice B Borlasa
- Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines
| | - Agnettah M Olorosa
- Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines
| | - Lara F T Hernandez
- Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines
| | - Karis D Boehme
- Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines
| | - Benjamin Jeffrey
- Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus, Hinxton, UK
| | - Khalil Abudahab
- Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus, Hinxton, UK
| | - Charmian M Hufano
- Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines
- St. Luke's Medical Center-Global City, Taguig, Metro Manila, The Philippines
| | - Sonia B Sia
- Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines
| | | | | | - David M Aanensen
- Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus, Hinxton, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| | - Celia C Carlos
- Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines.
| |
Collapse
|
22
|
Marsh JW, Mustapha MM, Griffith MP, Evans DR, Ezeonwuka C, Pasculle AW, Shutt KA, Sundermann A, Ayres AM, Shields RK, Babiker A, Cooper VS, Van Tyne D, Harrison LH. Evolution of Outbreak-Causing Carbapenem-Resistant Klebsiella pneumoniae ST258 at a Tertiary Care Hospital over 8 Years. mBio 2019; 10:e01945-19. [PMID: 31481386 PMCID: PMC6722418 DOI: 10.1128/mbio.01945-19] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 08/09/2019] [Indexed: 12/21/2022] Open
Abstract
Carbapenem-resistant Klebsiella pneumoniae (CRKP) strains belonging to sequence type 258 (ST258) are frequent causes of hospital-associated outbreaks and are a major contributor to the spread of carbapenemases. This genetic lineage emerged several decades ago and remains a major global health care challenge. In this study, genomic epidemiology was used to investigate the emergence, evolution, and persistence of ST258 carbapenem-resistant K. pneumoniae outbreak-causing lineages at a large tertiary care hospital over 8 years. A time-based phylogenetic analysis of 136 ST258 isolates demonstrated the succession of multiple genetically distinct ST258 sublineages over the 8-year period. Ongoing genomic surveillance identified the emergence and persistence of several distinct clonal ST258 populations. Patterns of multidrug resistance determinants and plasmid replicons were consistent with continued evolution and persistence of these populations. Five ST258 outbreaks were documented, including three that were caused by the same clonal lineage. Mutations in genes encoding effectors of biofilm production and iron acquisition were identified among persistent clones. Two emergent lineages bearing K. pneumoniae integrative conjugative element 10 (ICEKp10) and harboring yersiniabactin and colibactin virulence factors were identified. The results show how distinct ST258 subpopulations have evolved and persisted within the same hospital over nearly a decade.IMPORTANCE The carbapenem class of antibiotics is invaluable for the treatment of selected multidrug-resistant Gram-negative pathogens. The continued transmission of carbapenem-resistant bacteria such as ST258 K. pneumoniae is of serious global public health concern, as treatment options for these infections are limited. This genomic epidemiologic investigation traced the natural history of ST258 K. pneumoniae in a single health care setting over nearly a decade. We found that distinct ST258 subpopulations have caused both device-associated and ward-associated outbreaks, and some of these populations remain endemic within our hospital to the present day. The finding of virulence determinants among emergent ST258 clones supports the idea of convergent evolution of drug-resistant and virulent CRKP strains and highlights the need for continued surveillance, prevention, and control efforts to address emergent and evolving ST258 populations in the health care setting.
Collapse
Affiliation(s)
- Jane W Marsh
- Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania, USA
| | - Mustapha M Mustapha
- Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania, USA
| | - Marissa P Griffith
- Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania, USA
| | - Daniel R Evans
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Chinelo Ezeonwuka
- Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania, USA
| | - A William Pasculle
- Division of Microbiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Kathleen A Shutt
- Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania, USA
| | - Alexander Sundermann
- Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania, USA
- Division of Hospital Epidemiology and Infection Control, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Ashley M Ayres
- Division of Hospital Epidemiology and Infection Control, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Ryan K Shields
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Ahmed Babiker
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Vaughn S Cooper
- Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Daria Van Tyne
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Lee H Harrison
- Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania, USA
| |
Collapse
|