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Tartari E, Tomczyk S, Twyman A, Rehse APC, Gomaa M, Talaat M, Shah AS, Sobel H, Toledo JP, Allegranzi B. Evaluating national infection prevention and control minimum requirements: evidence from global cross-sectional surveys, 2017-22. Lancet Glob Health 2024; 12:e1620-e1628. [PMID: 39304235 PMCID: PMC11420467 DOI: 10.1016/s2214-109x(24)00277-8] [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: 11/20/2023] [Revised: 06/07/2024] [Accepted: 06/21/2024] [Indexed: 09/22/2024]
Abstract
BACKGROUND WHO infection prevention and control (IPC) minimum requirements provide standards to reduce the risk of infection during health-care delivery. We aimed to investigate the global implementation of these requirements at national levels and the progress of doing so across 2021-22 compared with 2017-18 to identify future directions for interventions. METHODS National IPC focal points were invited to complete an online survey measuring IPC minimum requirements from July 19, 2021, to Jan 31, 2022. The primary outcome was the proportion of countries meeting IPC minimum requirements. Country characteristics associated with this outcome were assessed with beta regression. Subset analyses were conducted to compare the 2021-22 indicators with a WHO IPC survey conducted in 2017-18 and to assess the correlation of the proportion of IPC minimum requirements met with the results of other WHO metrics. FINDINGS 106 countries (ie, 13 low income, 27 lower-middle income, 33 upper-middle income, and 33 high income) participated in the survey (56% response rate). Four (4%) of 106 met all IPC minimum requirements. The highest scoring IPC core component was multimodal improvement strategies and the lowest was IPC education and training. The odds of meeting IPC minimum requirements was higher among high-income countries compared with low-income countries (adjusted odds ratio 2·7, 95% CI 1·3-5·8; p=0·020). Compared with the 2017-18 survey, there was a significant increase in the proportion of countries reporting an active national IPC programme (65% to 82%, p=0·037) and a dedicated budget (26% to 44%, p=0·037). Evaluation of the IPC minimum requirements compared with other survey instruments revealed a low positive correlation. INTERPRETATION To build resilient health systems capable of withstanding future health threats, urgently scaling up adherence to WHO IPC minimum requirements is essential. FUNDING WHO. TRANSLATIONS For the French and Spanish translations of the abstract see Supplementary Materials section.
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Affiliation(s)
- Ermira Tartari
- Infection Prevention and Control Hub and Task Force, Department of Integrated Health Services, WHO, Geneva, Switzerland; Faculty of Health Sciences, University of Malta, Msida, Malta
| | - Sara Tomczyk
- Department for Infectious Disease Epidemiology, Robert Koch Institute, Berlin, Germany
| | - Anthony Twyman
- Infection Prevention and Control Hub and Task Force, Department of Integrated Health Services, WHO, Geneva, Switzerland
| | - Ana Paula Coutinho Rehse
- Infectious Hazard Management Programme, Health Emergencies Programme, WHO Regional Office for Europe, Copenhagen, Denmark
| | - Mohamed Gomaa
- WHO Regional Office for the Eastern Mediterranean, Cairo, Egypt
| | - Maha Talaat
- WHO Regional Office for the Eastern Mediterranean, Cairo, Egypt
| | - Aparna Singh Shah
- Health Surveillance, Disease Prevention and Control, WHO Regional Office for South-East Asia, New Delhi, India
| | - Howard Sobel
- Ministry of Health, WHO, Honiara, Solomon Islands
| | - Joao Paulo Toledo
- Infection Prevention and Control Hub and Task Force, Department of Integrated Health Services, WHO, Geneva, Switzerland; High Impact Epidemics, WHO Health Emergencies Programme, WHO, Geneva, Switzerland
| | - Benedetta Allegranzi
- Infection Prevention and Control Hub and Task Force, Department of Integrated Health Services, WHO, Geneva, Switzerland.
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Leong M, Picton R, Wratten M, Mahe A, Zimmerman PA. Baseline evaluation of the World Health Organization (WHO) infection prevention and control (IPC) core components in Pacific Island Countries and Territories (PICTs). Antimicrob Resist Infect Control 2024; 13:108. [PMID: 39334478 PMCID: PMC11437787 DOI: 10.1186/s13756-024-01447-9] [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: 02/14/2024] [Accepted: 07/31/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND Comprehensive infection prevention and control (IPC) programmes are proven to reduce the spread of healthcare-associated infections (HAIs) and antimicrobial resistance (AMR). However, published assessments of IPC programmes against the World Health Organization (WHO) IPC Core Components in Pacific Island Countries and Territories (PICTs) at the national and acute healthcare facility level are currently unavailable. METHODS From January 2022 to April 2023, a multi-country, cross-sectional study was conducted in PICTs. The self reporting survey was based on the WHO Infection Prevention Assessment Framework (IPCAF) that supports implementing the minimum requirements of the WHO eight core components of IPC programmes at both the national and facility level. The results were presented as a 'traffic light' (present, in progress, not present) matrix. Each PICT's overall status in achieving IPC core components was summarised using descriptive statistics. RESULTS Fifteen PICTs participated in this study. Ten (67%) PICTs had national IPC programmes, supported mainly by IPC focal points (87%, n = 13), updated national IPC guidelines (80%, n = 12), IPC monitoring and feedback mechanisms (80%, n = 12), and waste management plans (87%, n = 13). Significant gaps were identified in education and training (20%, n = 3). Despite being a defined component in 67% (n = 10) of national IPC programmes, HAI surveillance and monitoring was the lowest scoring core component (13%, n = 2). National and facility level IPC guidelines had been adapted and implemented in 67% (n = 10) PICTs; however, only 40% (n = 6) of PICTs had a dedicated IPC budget, 40% (n = 6) had multimodal strategies for IPC, and 33% (n = 5) had daily environmental cleaning records. CONCLUSIONS Identifying IPC strengths, gaps, and challenges across PICTs will inform future IPC programme priorities and contribute to regional efforts in strengthening IPC capacity. This will promote global public health through the prevention of HAIs and AMR.
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Affiliation(s)
| | - Rochelle Picton
- Griffith University, Parklands Drive, Southport, Brisbane, QLD, Australia
| | | | - Ana Mahe
- Vaiola Hospital, P.O.Box 59, Nuku'alofa, Tonga
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Reinoso Schiller N, Baier C, Dresselhaus I, Loderstädt U, Schlüter D, Eckmanns T, Scheithauer S. Proposed new definition for hospital-acquired SARS-CoV-2 infections: results of a confirmatory factor analysis. ANTIMICROBIAL STEWARDSHIP & HEALTHCARE EPIDEMIOLOGY : ASHE 2024; 4:e125. [PMID: 39257431 PMCID: PMC11384156 DOI: 10.1017/ash.2024.371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/28/2024] [Accepted: 05/23/2024] [Indexed: 09/12/2024]
Abstract
Objective The present study aims to develop and discuss an extension of hospital-acquired severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections (HA-SIs) definition which goes beyond the use of time parameters alone. Design A confirmatory factor analysis was carried out to test a suitable definition for HA-SI. Setting and Patients A two-center cohort study was carried out at two tertiary public hospitals in the German state of lower Saxony. The study involved a population of 366 laboratory-confirmed SARS-CoV-2-infected inpatients enrolled between March 2020 and August 2023. Results The proposed model shows adequate fit indices (CFI.scaled = 0.959, RMSEA = 0.049). A descriptive comparison with existing classifications revealed strong features of our model, particularly its adaptability to specific regional outbreaks. Conclusion The use of the regional incidence as a proxy variable to better define HA-SI cases represents a pragmatic and novel approach. The model aligns well with the latest scientific results in the literature. This work successfully unifies, within a single model, variables which the recent literature described as significant for the onset of HA-SI. Further potential improvements and adaptations of the model and its applications, such as automating the categorization process (in terms of hospital acquisition) or employing a comparable model for hospital-acquired influenza classification, are subjects open for discussion.
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Affiliation(s)
- Nicolás Reinoso Schiller
- Institute of Infection Control and Infectious Diseases, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
| | - Claas Baier
- Institute for Medical Microbiology and Hospital Epidemiology, Hannover Medical School, Hannover, Germany
| | - Isabella Dresselhaus
- Institute of Infection Control and Infectious Diseases, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
| | - Ulrike Loderstädt
- Institute of Infection Control and Infectious Diseases, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
| | - Dirk Schlüter
- Institute for Medical Microbiology and Hospital Epidemiology, Hannover Medical School, Hannover, Germany
| | | | - Simone Scheithauer
- Institute of Infection Control and Infectious Diseases, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
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Mustafa AS, Habibi N. Spatial Variations in the Nasal Microbiota of Staff Working in a Healthcare-Associated Research Core Facility. Med Princ Pract 2023; 33:66-73. [PMID: 38147830 PMCID: PMC10896616 DOI: 10.1159/000535983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 12/21/2023] [Indexed: 12/28/2023] Open
Abstract
OBJECTIVE Workers in the healthcare sector are exposed to a multitude of bacterial genera. The location of their work contributes significantly to shaping personal microbiomes. In this study, we investigated the role of the workspace on the nasal bacteriome of staff working in a healthcare-associated research facility. METHODS The anterior nares of 10 staff working in different laboratories on the ground and first floor of the research facility were aseptically swabbed. Genomic DNA from each sample was used to amplify the V3 and V4 regions of the 16S rRNA gene. The amplified products were sequenced using the MiSeq sequencer (Illumina). Operational taxonomic units were filtered through MG-RAST v.3.6. Taxonomic profiling and visualizations were performed in MicrobiomeAnalyst v2.0. RESULTS The Wilcoxson Sum test at median abundances (p < 0.05) indicated that seven taxa (Micromonosporaceae, Micromonospora, Lactobacillaceae, Lactobacillus, Betaproteobacteria, Burkholderiales, Pectobacterium) were significantly diverse between ground-floor and first-floor workers. The analysis of similarity coefficient was 0.412 (p < 0.03) between the ground and the first-floor workers. Random forest analysis predicted 15 features that were significantly different (p < 0.05) in individuals working in different laboratories. Species richness and evenness also differed according to the placement of individuals in respective laboratories. CONCLUSION These findings add to the knowledge that the healthcare support staff are at a speculated occupational risk. A slight shift in the abundances of bacterial genera and species might lead to unwanted consequences. Continual monitoring is thus warranted.
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Affiliation(s)
- Abu Salim Mustafa
- Department of Microbiology, College of Medicine, Kuwait University, Kuwait City, Kuwait
| | - Nazima Habibi
- OMICS Research Unit and Research Core Facility, College of Medicine, Health Sciences Centre, Kuwait University, Kuwait City, Kuwait
- Current address: Biotechnology Program, Environment and Life Science Research Centre, Kuwait Institute for Scientific Research, Kuwait City, Kuwait
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Oseran AS, Song Y, Xu J, Dahabreh IJ, Wadhera RK, de Lemos JA, Das SR, Sun T, Yeh RW, Kazi DS. Long term risk of death and readmission after hospital admission with covid-19 among older adults: retrospective cohort study. BMJ 2023; 382:e076222. [PMID: 37558240 PMCID: PMC10475839 DOI: 10.1136/bmj-2023-076222] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/07/2023] [Indexed: 08/11/2023]
Abstract
OBJECTIVES To characterize the long term risk of death and hospital readmission after an index admission with covid-19 among Medicare fee-for-service beneficiaries, and to compare these outcomes with historical control patients admitted to hospital with influenza. DESIGN Retrospective cohort study. SETTING United States. PARTICIPANTS 883 394 Medicare fee-for-service beneficiaries age ≥65 years discharged alive after an index hospital admission with covid-19 between 1 March 2020 and 31 August 2022, compared with 56 409 historical controls discharged alive after a hospital admission with influenza between 1 March 2018 and 31 August 2019. Weighting methods were used to account for differences in observed characteristics. MAIN OUTCOME MEASURES All cause death within 180 days of discharge. Secondary outcomes included first all cause readmission and a composite of death or readmission within 180 days. RESULTS The covid-19 cohort compared with the influenza cohort was younger (77.9 v 78.9 years, standardized mean difference -0.12) and had a lower proportion of women (51.7% v 57.3%, -0.11). Both groups had a similar proportion of black beneficiaries (10.3% v 8.1%, 0.07) and beneficiaries with dual Medicaid-Medicare eligibility status (20.1% v 19.2%; 0.02). The covid-19 cohort had a lower comorbidity burden, including atrial fibrillation (24.3% v 29.5%, -0.12), heart failure (43.4% v 49.9%, -0.13), and chronic obstructive pulmonary disease (39.2% v 52.9%, -0.27). After weighting, the covid-19 cohort had a higher risk (ie, cumulative incidence) of all cause death at 30 days (10.9% v 3.9%; standardized risk difference 7.0%, 95% confidence interval 6.8% to 7.2%), 90 days (15.5% v 7.1%; 8.4%, 8.2% to 8.7%), and 180 days (19.1% v 10.5%; 8.6%, 8.3% to 8.9%) compared with the influenza cohort. The covid-19 cohort also experienced a higher risk of hospital readmission at 30 days (16.0% v 11.2%; 4.9%, 4.6% to 5.1%) and 90 days (24.1% v 21.3%; 2.8%, 2.5% to 3.2%) but a similar risk at 180 days (30.6% v 30.6%;-0.1%, -0.5% to 0.3%). Over the study period, the 30 day risk of death for patients discharged after a covid-19 admission decreased from 17.9% to 7.2%. CONCLUSIONS Medicare beneficiaries who were discharged alive after a covid-19 hospital admission had a higher post-discharge risk of death compared with historical influenza controls; this difference, however, was concentrated in the early post-discharge period. The risk of death for patients discharged after a covid-19 related hospital admission substantially declined over the course of the pandemic.
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Affiliation(s)
- Andrew S Oseran
- Richard A. and Susan F. Smith Center for Outcomes Research, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
- Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Yang Song
- Richard A. and Susan F. Smith Center for Outcomes Research, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Jiaman Xu
- Richard A. and Susan F. Smith Center for Outcomes Research, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Issa J Dahabreh
- Richard A. and Susan F. Smith Center for Outcomes Research, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
- CAUSALab, Department of Epidemiology, and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Rishi K Wadhera
- Richard A. and Susan F. Smith Center for Outcomes Research, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
- Division of Cardiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - James A de Lemos
- Division of Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Sandeep R Das
- Division of Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Tianyu Sun
- Richard A. and Susan F. Smith Center for Outcomes Research, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Robert W Yeh
- Richard A. and Susan F. Smith Center for Outcomes Research, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
- Division of Cardiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Dhruv S Kazi
- Richard A. and Susan F. Smith Center for Outcomes Research, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
- Division of Cardiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
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Hatfield KM, Baggs J, Maillis A, Warner S, Jernigan JA, Kadri SS, Klompas M, Reddy SC. Assessment of Hospital-Onset SARS-CoV-2 Infection Rates and Testing Practices in the US, 2020-2022. JAMA Netw Open 2023; 6:e2329441. [PMID: 37639273 PMCID: PMC10463096 DOI: 10.1001/jamanetworkopen.2023.29441] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 07/04/2023] [Indexed: 08/29/2023] Open
Abstract
Importance Characterizing the scale and factors associated with hospital-onset SARS-CoV-2 infections could help inform hospital and public health policies regarding prevention and surveillance needs for these infections. Objective To evaluate associations of hospital-onset SARS-CoV-2 infection rates with different periods of the COVID-19 pandemic, hospital characteristics, and testing practices. Design, Setting, and Participants This cohort study of US hospitals reporting SARS-CoV-2 testing data in the PINC AI Healthcare Database COVID-19 special release files was conducted from July 2020 through June 2022. Data were collected from hospitals that reported at least 1 SARS-CoV-2 reverse transcription-polymerase chain reaction or antigen test during hospitalizations discharged that month. For each hospital-month where the hospital reported sufficient data, all hospitalizations discharged in that month were included in the cohort. SARS-CoV-2 viral tests and results reported in the microbiology files for all hospitalizations in the study period by discharge month were identified. Data analysis was conducted from September 2022 to March 2023. Exposure Hospitalizations discharged in an included hospital-month. Main Outcomes and Measures Multivariable generalized estimating equation negative-binomial regression models were used to assess associations of monthly rates of hospital-onset SARS-CoV-2 infections per 1000 patient-days (defined as a first positive SARS-CoV-2 test during after hospitalization day 7) with the phase of the pandemic (defined as the predominant SARS-CoV-2 variant in circulation), admission testing rates, and hospital characteristics (hospital bed size, teaching status, urban vs rural designation, Census region, and patient distribution variables). Results A total of 5687 hospital-months from 288 distinct hospitals were included, which contributed 4 421 268 hospitalization records. Among 171 564 hospitalizations with a positive SARS-CoV-2 test, 7591 (4.4%) were found to be hospital onset and 6455 (3.8%) were indeterminate onset. The mean monthly hospital-onset infection rate per 1000 patient-days was 0.27 (95 CI, 0.26-0.29). Hospital-onset infections occurred in 2217 of 5687 hospital-months (39.0%). The monthly percentage of discharged patients tested for SARS-CoV-2 at admission varied; 1673 hospital-months (29.4%) had less than 25% of hospitalizations tested at admission; 2199 hospital-months (38.7%) had 25% to 50% of all hospitalizations tested, and 1815 hospital months (31.9%) had more than 50% of all hospitalizations tested at admission. Postadmission testing rates and community-onset infection rates increased with admission testing rates. In multivariable models restricted to hospital-months testing at least 25% of hospitalizations at admission, a 10% increase in community-onset SARS-CoV-2 infection rate was associated with a 178% increase in the hospital-onset infection rate (rate ratio, 2.78; 95% CI, 2.52-3.07). Additionally, the phase of the COVID-19 pandemic, the admission testing rate, Census region, and bed size were all significantly associated with hospital-onset SARS-CoV-2 infection rates. Conclusions and Relevance In this cohort study of hospitals reporting SARS-CoV-2 infections, there was an increase of hospital-onset SARS-CoV-2 infections when community-onset infections were higher, indicating a need for ongoing and enhanced surveillance and prevention efforts to reduce in-hospital transmission of SARS-CoV-2 infections, particularly when community-incidence of SARS-CoV-2 infections is high.
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Affiliation(s)
- Kelly M. Hatfield
- Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - James Baggs
- Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Alexander Maillis
- Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Sarah Warner
- Critical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, Maryland
| | - John A. Jernigan
- Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Sameer S. Kadri
- Critical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, Maryland
| | - Michael Klompas
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, Massachusetts
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Sujan C. Reddy
- Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
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Listiowati E, Samsudin MA, Wulandari Y, Taritasari C, Mundakir M, Nurmansyah MI. Evaluating infection prevention and control structure of Indonesian COVID-19 referral hospitals. JAMBA (POTCHEFSTROOM, SOUTH AFRICA) 2023; 15:1466. [PMID: 37781445 PMCID: PMC10407457 DOI: 10.4102/jamba.v15i1.1466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 06/09/2023] [Indexed: 10/03/2023]
Abstract
Due to the emergence of COVID-19, hospitals are required to increase vigilance in providing care. However, their readiness for infection prevention and control (IPC) as a referral hospital in providing COVID-19 services has not been determined. This study aims to evaluate the IPC structure of 30 private non-profit Indonesian referral hospitals for COVID-19 based on the World Health Organization Infection Prevention and Control Assessment Framework (WHO IPCAF). A descriptive cross-sectional quantitative study was used, where 30 hospitals as the COVID-19 referral hospital were selected. The data collection was conducted by an online survey using the IPCAF questionnaire created by the WHO and was analysed with descriptive analysis. The majority of the hospitals' IPC level is at an advanced level (73.3%). All type B hospitals have an advanced IPC level, while only 64.7% of type C and 71.4% of type D have an advanced level. The highest average IPC score is on the IPC guidelines component (94.0), while the lowest value of 71.9 is on the Surveillance of HAIs component. In the minimum scores, there were hospitals with the lowest scores in HAI Surveillance and Multimodal strategies, namely 20.0 and 25.0, respectively. Preparing human resource capacities, establishing functional programmes, developing and implementing IPC guidelines, and providing adequate supplies are needed to improve hospital IPC structures. Contribution This study demonstrates the necessity to improve hospital IPC structures to increase the resilience of health services to natural hazards and public health emergencies.
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Affiliation(s)
- Ekorini Listiowati
- Department of Public Health, Faculty of Medicine and Health Sciences, Universitas Muhammadiyah Yogyakarta, Yogyakarta, Indonesia
| | - Mohammad A. Samsudin
- Department of Management, Faculty of Economics, Social and Humanities, Universitas Aisyiyah Yogyakarta, Indonesia
| | - Yuanita Wulandari
- Department of Nursing, Faculty of Health Sciences, Universitas Muhammadiyah Surabaya, Surabaya, Indonesia
| | | | - Mundakir Mundakir
- Department of Nursing, Faculty of Health Sciences, Universitas Muhammadiyah Surabaya, Surabaya, Indonesia
| | - Mochamad I. Nurmansyah
- Department of Public Health, Faculty of Health Sciences, Universitas Islam Negeri Syarif Hidayatullah, Jakarta, Indonesia
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Carré Y, Coppry M, Bataille C, Vivier L, Lasheras-Bauduin A, Rogues AM. Contributory conditions for unexpected COVID-19 cases and nosocomial COVID-19 infection cases identified from systematic investigation in a French University Hospital. Infect Dis Now 2023; 53:104648. [PMID: 36702305 PMCID: PMC9869609 DOI: 10.1016/j.idnow.2023.104648] [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: 08/30/2022] [Revised: 12/30/2022] [Accepted: 01/13/2023] [Indexed: 01/24/2023]
Abstract
INTRODUCTION Nosocomial case (NC) of COVID-19 infections is a challenge for hospitals. We report the results of a seven-month prospective cohort study investigating COVID-19 patients to assess unexpected cases (UC) (no COVID-19 precautionary measure application since admission) and NC. PATIENTS AND METHODS Investigation by an infection control team of 844 patients with COVID-19 infection hospitalized for more than 24 hours (cases). RESULTS A total of 301 UC were identified (31% after contact tracing) with a total of 129 contact patients, and 27 secondary cases for 59 of them. In geriatric wards, 50% of cases were UC. NC represented 18% of cases (37% in geriatric wards), mainly identified after contact tracing of wandering cases. CONCLUSION A rapid infection control response is essential to contain nosocomial transmission, along with detailed contact tracing and screening policy. Dealing with wandering elderly patients remain challenging for HCWs.
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Affiliation(s)
- Y Carré
- University Hospital of Bordeaux, Infection Prevention and Control Unit, F-33000 Bordeaux, France.
| | - M Coppry
- University Hospital of Bordeaux, Infection Prevention and Control Unit, F-33000 Bordeaux, France; University Hospital of Bordeaux, Inserm, Bordeaux Population Health Research Center, F-33000 Bordeaux, France
| | - C Bataille
- University Hospital of Bordeaux, Infection Prevention and Control Unit, F-33000 Bordeaux, France
| | - L Vivier
- University Hospital of Bordeaux, Infection Prevention and Control Unit, F-33000 Bordeaux, France
| | - A Lasheras-Bauduin
- University Hospital of Bordeaux, Infection Prevention and Control Unit, F-33000 Bordeaux, France
| | - A-M Rogues
- University Hospital of Bordeaux, Infection Prevention and Control Unit, F-33000 Bordeaux, France; University Hospital of Bordeaux, Inserm, Bordeaux Population Health Research Center, F-33000 Bordeaux, France
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Dinh C, Gallouche M, Terrisse H, Gam K, Giner C, Nemoz B, Larrat S, Giai J, Bosson JL, Landelle C. Risk factors for nosocomial COVID-19 in a French university hospital. Infect Dis Now 2023; 53:104695. [PMID: 36958692 PMCID: PMC10030266 DOI: 10.1016/j.idnow.2023.104695] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 02/09/2023] [Accepted: 03/15/2023] [Indexed: 03/24/2023]
Abstract
OBJECTIVES Prevention strategies implemented by hospitals to reduce nosocomial transmission of SARS-CoV-2 sometimes failed. Our aim was to determine the risk factors for nosocomial COVID-19. PATIENTS AND METHODS A case-control study was conducted (September 1, 2020-January 31, 2021) with adult patients hospitalized in medical or surgical units. Infants or patients hospitalized in ICU were excluded. Cases were patients with nosocomial COVID-19 (clinical symptoms and RT-PCR+ for SARS-CoV-2 or RT-PCR+ for SARS-CoV-2 with Ct ≤28 more than 5days after admission); controls were patients without infection (RT-PCR- for SARS-CoV-2 >5 days after admission). They were matched according to length of stay before diagnosis and period of admission. Analyses were performed with a conditional logistic regression. RESULTS A total of 281 cases and 441 controls were included. In the bivariate analysis, cases were older (OR per 10years: 1.22; 95%CI [1.10;1.36]), had more often shared a room (OR: 1.74; 95%CI [1.25;2.43]) or a risk factor for severe COVID-19 (OR: 1.94; 95%CI [1.09;3.45]), were more often hospitalized in medical units [OR: 1.59; 95%CI [1.12;2.25]), had higher exposure to contagious health care workers (HCW; OR per 1person-day: 1.12; 95%CI [1.08;1.17]) and patients (OR per 1 person-day: 1.11; 95%CI [1.08;1.14]) than controls. In an adjusted model, risk factors for nosocomial COVID-19 were exposure to contagious HCW (aOR per 1person-day: 1.08; 95%CI [1.03;1.14]) and to contagious patients (aOR per 1person-day: 1.10; 95%CI [1.07;1.13]). CONCLUSIONS Exposure to contagious professionals and patients are the main risk factors for nosocomial COVID-19.
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Affiliation(s)
- C Dinh
- Grenoble Alpes university/CNRS, Grenoble INP, MESP TIM-C UMR 5525, Grenoble, France
| | - M Gallouche
- Grenoble Alpes university/CNRS, Grenoble INP, MESP TIM-C UMR 5525, Grenoble, France; Infection Control Unit, Grenoble Alpes University Hospital, Grenoble, France
| | - H Terrisse
- Grenoble Alpes university/CNRS, Grenoble INP, MESP TIM-C UMR 5525, Grenoble, France
| | - K Gam
- Grenoble Alpes university/CNRS, Grenoble INP, MESP TIM-C UMR 5525, Grenoble, France
| | - C Giner
- Infection Control Unit, Grenoble Alpes University Hospital, Grenoble, France
| | - B Nemoz
- Virology Laboratory, Grenoble Alpes University Hospital, Grenoble, France; Antibodies and Infectious Diseases, Institut de Biologie Structurale (IBS), University Grenoble Alpes, CEA, CNRS, Grenoble, France
| | - S Larrat
- Virology Laboratory, Grenoble Alpes University Hospital, Grenoble, France
| | - J Giai
- Grenoble Alpes university/CNRS, Grenoble INP, MESP TIM-C UMR 5525, Grenoble, France; Public Health department, Grenoble Alpes University Hospital, Grenoble, France
| | - J L Bosson
- Grenoble Alpes university/CNRS, Grenoble INP, MESP TIM-C UMR 5525, Grenoble, France; Public Health department, Grenoble Alpes University Hospital, Grenoble, France
| | - C Landelle
- Grenoble Alpes university/CNRS, Grenoble INP, MESP TIM-C UMR 5525, Grenoble, France; Infection Control Unit, Grenoble Alpes University Hospital, Grenoble, France.
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10
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Haberstroh H, Hirsch A, Goldacker S, Zessack N, Warnatz K, Grimbacher B, Salzer U. A Toolkit for Monitoring Immunoglobulin G Levels from Dried Blood Spots of Patients with Primary Immunodeficiencies. J Clin Immunol 2023:10.1007/s10875-023-01464-0. [PMID: 36941491 PMCID: PMC10027597 DOI: 10.1007/s10875-023-01464-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 03/02/2023] [Indexed: 03/23/2023]
Abstract
PURPOSE This study assessed whether measuring immunoglobulin G (IgG) from dried blood spots (DBSs) using nephelometry is a suitable remote monitoring method for patients with primary immunodeficiencies (PID). METHODS Patients receiving immunoglobulin replacement therapy for PID were included in this non-interventional single-arm study (DRKS-ID: DRKS00020522) conducted in Germany from December 4, 2019, to December 22, 2020. Three blood samples, two capillary DBSs (one mail-transferred and the other direct-transferred to the laboratory), and one intravenous were collected from each patient. IgG levels were determined using nephelometry. IgG levels were summarized descriptively, and significant differences were assessed using Wilcoxon matched-pairs signed-rank tests. Correlation and agreement between IgG levels were assessed using Spearman correlation and Bland-Altman analyses, respectively. RESULTS Among 135 included patients, IgG levels measured from DBS samples were lower than those measured in serum (p < 0.0001). There was no significant difference between IgG levels in direct- and mail-transferred DBS samples. There was a high degree of correlation between IgG levels in serum samples and DBS samples (r = 0.94-0.95). Although there was a bias for higher levels of IgG in serum than in DBS samples, most samples were within the 95% interval of agreement. There was a high degree of correlation between IgG levels measured in direct- and mail-transferred DBS samples (r = 0.96) with no bias based on the shipment process and most samples within the 95% interval of agreement. CONCLUSION Monitoring IgG levels from DBS samples is a suitable alternative to the standard method, and results are not substantially affected by mailing DBS cards.
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Affiliation(s)
- Hanna Haberstroh
- Institute for Immunodeficiency, Center for Chronic Immunodeficiency (CCI), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- DZIF - German Center for Infection Research, Satellite Center, Freiburg, Germany
| | - Aleksandra Hirsch
- Department of Rheumatology and Clinical Immunology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Center for Chronic Immunodeficiency (CCI), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Sigune Goldacker
- Department of Rheumatology and Clinical Immunology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Center for Chronic Immunodeficiency (CCI), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | | | - Klaus Warnatz
- Department of Rheumatology and Clinical Immunology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Center for Chronic Immunodeficiency (CCI), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Bodo Grimbacher
- Institute for Immunodeficiency, Center for Chronic Immunodeficiency (CCI), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- DZIF - German Center for Infection Research, Satellite Center, Freiburg, Germany.
- Department of Rheumatology and Clinical Immunology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- Center for Chronic Immunodeficiency (CCI), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- CIBSS - Centre for Integrative Biological Signalling Studies, Albert-Ludwigs University, Freiburg, Germany.
- RESIST - Cluster of Excellence 2155 to Hanover Medical School, Satellite Center Freiburg, Freiburg, Germany.
| | - Ulrich Salzer
- Department of Rheumatology and Clinical Immunology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- Center for Chronic Immunodeficiency (CCI), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
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11
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Paulino MR, Moreira JAS, Correia MG, Abrahão Dos Santos LR, Duarte IP, Mucillo FB, Zappa B, Garrido RQ, Barbosa GIF, de Lorenzo A, Lamas CC. Impact of nosocomial acquisition of COVID-19 in hospitalized cardiac patients. J Hosp Infect 2023; 133:100-102. [PMID: 36566781 PMCID: PMC9780016 DOI: 10.1016/j.jhin.2022.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022]
Affiliation(s)
- M R Paulino
- Instituto Nacional de Cardiologia, Rio de Janeiro, Brazil
| | - J A S Moreira
- Instituto Nacional de Cardiologia, Rio de Janeiro, Brazil
| | - M G Correia
- Instituto Nacional de Cardiologia, Rio de Janeiro, Brazil
| | | | - I P Duarte
- Universidade do Grande Rio-Afya (UNIGRANRIO-Afya), Rio de Janeiro, Brazil
| | - F B Mucillo
- Instituto Nacional de Cardiologia, Rio de Janeiro, Brazil
| | - B Zappa
- Instituto Nacional de Cardiologia, Rio de Janeiro, Brazil
| | - R Q Garrido
- Instituto Nacional de Cardiologia, Rio de Janeiro, Brazil
| | - G I F Barbosa
- Instituto Nacional de Cardiologia, Rio de Janeiro, Brazil
| | - A de Lorenzo
- Instituto Nacional de Cardiologia, Rio de Janeiro, Brazil; Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - C C Lamas
- Instituto Nacional de Cardiologia, Rio de Janeiro, Brazil; Instituto Nacional de Infectologia Evandro Chagas, Fiocruz, Rio de Janeiro, Brazil.
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12
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Mongin D, Catho G, Iten A, Harbarth S, Courvoisier DS. Incidence of healthcare-associated coronavirus disease 2019 (COVID-19) in the state of Geneva. Infect Control Hosp Epidemiol 2023; 44:322-324. [PMID: 34689854 PMCID: PMC8593366 DOI: 10.1017/ice.2021.453] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 09/30/2021] [Accepted: 10/04/2021] [Indexed: 12/16/2022]
Abstract
An examination of all coronavirus disease 2019 (COVID-19) cases and patient movements in Geneva indicated important disease activity within the healthcare system since the beginning of the pandemic. We estimate that 4.3% of all COVID-19 cases were likely acquired within the healthcare system, contributing to 62% of the COVID-19-related deaths.
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Affiliation(s)
- Denis Mongin
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Gaud Catho
- Infection Control Program, Geneva University Hospitals, Geneva, Switzerland
| | - Anne Iten
- Infection Control Program, Geneva University Hospitals, Geneva, Switzerland
| | - Stephan Harbarth
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Infection Control Program, Geneva University Hospitals, Geneva, Switzerland
| | - Delphine S. Courvoisier
- Quality of Care Service, Department of Readaptation and Geriatrics, University of Geneva, Geneva, Switzerland
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13
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Outbreak investigation in a COVID-19 designated hospital: The combination of phylogenetic analysis and field epidemiology study suggesting airborne transmission. JOURNAL OF MICROBIOLOGY, IMMUNOLOGY, AND INFECTION = WEI MIAN YU GAN RAN ZA ZHI 2023:S1684-1182(23)00002-6. [PMID: 36690516 PMCID: PMC9841729 DOI: 10.1016/j.jmii.2023.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 12/06/2022] [Accepted: 01/09/2023] [Indexed: 01/19/2023]
Abstract
BACKGROUND Healthcare-associated COVID-19 infections caused by SARS-CoV-2 have increased morbidity and mortality. Hospitals and skilled nursing facilities (SNFs) have been challenged by infection control and management. METHODS This case study presents an outbreak investigation in a COVID-19-designated hospital and a hospital-based SNF. Real-time polymerase chain reaction (PCR) and other studies were performed on samples obtained from SNF residents, hospital patients, and healthcare workers (HCWs). The results of the laboratory tests and field epidemiological data were analyzed. Genome sequencing and phylogenetic analysis of SARS-CoV-2 were performed to identify the associations between cases. The tracer gas was released and recorded by a thermal imaging camera to investigate the spatial relations within clusters. RESULTS During the outbreak, 29 COVID-19 infections in 3 clusters were identified through hospital-wide, risk-guided, and symptom-driven PCR tests. This included 12 HCWs, 5 patients, and 12 SNF residents who had been hospitalized for at least 14 days. Serology tests did not identify any cases among the PCR-negative individuals. The phylogenetic analysis revealed that viral strains from the 3 clusters shared a common mutation of G3994T and were phylogenetically related, which suggested that this outbreak had a common source rather than multiple introductions from the community. Linked cases exhibited vertical spatial distribution, and the sulfur hexafluoride release test confirmed a potential airborne transmission. CONCLUSIONS This report addressed the advantage of a multi-disciplinary team in outbreak investigation. Identifying an airborne transmission within an outbreak highlighted the importance of regular maintenance of ventilation systems.
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14
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Lin T, Zhao Z, Yang Z, Li B, Wei C, Li F, Jiang Y, Liu D, Yang Z, Sha F, Tang J. Hospital Strain and COVID-19 Fatality - England, April 2020-March 2022. China CDC Wkly 2022; 4:1176-1180. [PMID: 36779170 PMCID: PMC9906047 DOI: 10.46234/ccdcw2022.236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 12/14/2022] [Indexed: 02/14/2023] Open
Abstract
What is already known about this topic? During the coronavirus disease 2019 (COVID-19) pandemic, tremendous efforts have been made in countries to suppress epidemic peaks and strengthen hospital services to avoid hospital strain and ultimately reduce the risk of death from COVID-19. However, there is limited empirical evidence that hospital strain increases COVID-19 deaths. What is added by this report? We found the risk of death from COVID-19 was linearly associated with the number of patients currently in hospitals, a measure of hospital strain, before the Omicron period. This risk could be increased by a maximum of 188.0%. What are the implications for public health practice? These findings suggest that any (additional) effort to reduce hospital strain would be beneficial during early large COVID-19 outbreaks and possibly also others alike. During an Omicron outbreak, vigilance remains necessary to prevent excess deaths caused by hospital strain as happened in Hong Kong Special Administrative Region, China.
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Affiliation(s)
- Tengfei Lin
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen City, Guangdong Province, China
| | - Ziyi Zhao
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen City, Guangdong Province, China
| | - Zhirong Yang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen City, Guangdong Province, China
| | - Bingli Li
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen City, Guangdong Province, China
| | - Chang Wei
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen City, Guangdong Province, China
| | - Fuxiao Li
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen City, Guangdong Province, China
| | - Yiwen Jiang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen City, Guangdong Province, China
| | - Di Liu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen City, Guangdong Province, China
| | - Zuyao Yang
- Division of Epidemiology, JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong Special Administrate Region, China,Zuyao Yang,
| | - Feng Sha
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen City, Guangdong Province, China,Feng Sha,
| | - Jinling Tang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen City, Guangdong Province, China,Department of Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou City, Guangdong Province, China
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15
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Talukder A, Roy A, Islam MN, Kabir Chowdhury MA, Sarker M, Chowdhury M, Chowdhury IA, Hasan M, Latif AHMM. Prevalence and correlates of knowledge and practices regarding infection prevention and control, and triage in primary healthcare settings: A cross-sectional study in Bangladesh. Infect Prev Pract 2022; 5:100258. [DOI: 10.1016/j.infpip.2022.100258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 11/07/2022] [Indexed: 11/19/2022] Open
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16
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Myall A, Price JR, Peach RL, Abbas M, Mookerjee S, Zhu N, Ahmad I, Ming D, Ramzan F, Teixeira D, Graf C, Weiße AY, Harbarth S, Holmes A, Barahona M. Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study. Lancet Digit Health 2022; 4:e573-e583. [PMID: 35868812 PMCID: PMC9296105 DOI: 10.1016/s2589-7500(22)00093-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 03/19/2022] [Accepted: 04/25/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level. METHODS We report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk. FINDINGS The framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88-0·90]) and similarly predictive using only contact-network variables (0·88 [0·86-0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0·80-0·84]) or patient clinical (0·64 [0·62-0·66]) variables. A model with only three variables (ie, network closeness, direct contacts with infectious patients [network derived], and hospital COVID-19 prevalence [hospital contextual]) achieved AUC-ROC 0·85 (95% CI 0·82-0·88). Incorporating contact-network variables improved performance across both validation datasets (AUC-ROC in the Geneva dataset increased from 0·84 [95% CI 0·82-0·86] to 0·88 [0·86-0·90]; AUC-ROC in the UK post-surge dataset increased from 0·49 [0·46-0·52] to 0·68 [0·64-0·70]). INTERPRETATION Dynamic contact networks are robust predictors of individual patient risk of HOCIs. Their integration in clinical care could enhance individualised infection prevention and early diagnosis of COVID-19 and other nosocomial infections. FUNDING Medical Research Foundation, WHO, Engineering and Physical Sciences Research Council, National Institute for Health Research (NIHR), Swiss National Science Foundation, and German Research Foundation.
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Affiliation(s)
- Ashleigh Myall
- Department of Infectious Disease, Imperial College London, London, UK; Department of Mathematics, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, UK.
| | - James R Price
- National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, UK; Imperial College Healthcare NHS Trust, Imperial College London, London, UK
| | - Robert L Peach
- Department of Mathematics, Imperial College London, London, UK; Department of Brain Sciences, Imperial College London, London, UK; Department of Neurology, University Hospital of Würzburg, Würzburg, Germany
| | - Mohamed Abbas
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK; Infection Control Programme, Geneva University Hospitals, Geneva, Switzerland
| | - Sid Mookerjee
- National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, UK; Imperial College Healthcare NHS Trust, Imperial College London, London, UK
| | - Nina Zhu
- Department of Infectious Disease, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, UK
| | - Isa Ahmad
- Department of Infectious Disease, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, UK
| | - Damien Ming
- Department of Infectious Disease, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, UK
| | - Farzan Ramzan
- Department of Infectious Disease, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, UK
| | - Daniel Teixeira
- Infection Control Programme, Geneva University Hospitals, Geneva, Switzerland
| | - Christophe Graf
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
| | - Andrea Y Weiße
- School of Biological Sciences and School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Stephan Harbarth
- Infection Control Programme, Geneva University Hospitals, Geneva, Switzerland
| | - Alison Holmes
- Department of Infectious Disease, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, UK
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Habibi N, Uddin S, Behbehani M, Al Salameen F, Razzack NA, Zakir F, Shajan A, Alam F. Bacterial and fungal communities in indoor aerosols from two Kuwaiti hospitals. Front Microbiol 2022; 13:955913. [PMID: 35966680 PMCID: PMC9366136 DOI: 10.3389/fmicb.2022.955913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 07/04/2022] [Indexed: 11/16/2022] Open
Abstract
The airborne transmission of COVID-19 has drawn immense attention to bioaerosols. The topic is highly relevant in the indoor hospital environment where vulnerable patients are treated and healthcare workers are exposed to various pathogenic and non-pathogenic microbes. Knowledge of the microbial communities in such settings will enable precautionary measures to prevent any hospital-mediated outbreak and better assess occupational exposure of the healthcare workers. This study presents a baseline of the bacterial and fungal population of two major hospitals in Kuwait dealing with COVID patients, and in a non-hospital setting through targeted amplicon sequencing. The predominant bacteria of bioaerosols were Variovorax (9.44%), Parvibaculum (8.27%), Pseudonocardia (8.04%), Taonella (5.74%), Arthrospira (4.58%), Comamonas (3.84%), Methylibium (3.13%), Sphingobium (4.46%), Zoogloea (2.20%), and Sphingopyxis (2.56%). ESKAPEE pathogens, such as Pseudomonas, Acinetobacter, Staphylococcus, Enterococcus, and Escherichia, were also found in lower abundances. The fungi were represented by Wilcoxinia rehmii (64.38%), Aspergillus ruber (9.11%), Penicillium desertorum (3.89%), Leptobacillium leptobactrum (3.20%), Humicola grisea (2.99%), Ganoderma sichuanense (1.42%), Malassezia restricta (0.74%), Heterophoma sylvatica (0.49%), Fusarium proliferatum (0.46%), and Saccharomyces cerevisiae (0.23%). Some common and unique operational taxonomic units (OTUs) of bacteria and fungi were also recorded at each site; this inter-site variability shows that exhaled air can be a source of this variation. The alpha-diversity indices suggested variance in species richness and abundance in hospitals than in non-hospital sites. The community structure of bacteria varied spatially (ANOSIM r 2 = 0.181-0.243; p < 0.05) between the hospital and non-hospital sites, whereas fungi were more or less homogenous. Key taxa specific to the hospitals were Defluvicoccales, fungi, Ganodermataceae, Heterophoma, and H. sylvatica compared to Actinobacteria, Leptobacillium, L. leptobacillium, and Cordycipitaceae at the non-hospital site (LefSe, FDR q ≤ 0.05). The hospital/non-hospital MD index > 1 indicated shifts in the microbial communities of indoor air in hospitals. These findings highlight the need for regular surveillance of indoor hospital environments to prevent future outbreaks.
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Affiliation(s)
| | - Saif Uddin
- Environment and Life Science Research Centre, Kuwait Institute for Scientific Research, Kuwait City, Kuwait
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18
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Abbas M, Cori A, Cordey S, Laubscher F, Robalo Nunes T, Myall A, Salamun J, Huber P, Zekry D, Prendki V, Iten A, Vieux L, Sauvan V, Graf CE, Harbarth S. Reconstruction of transmission chains of SARS-CoV-2 amidst multiple outbreaks in a geriatric acute-care hospital: a combined retrospective epidemiological and genomic study. eLife 2022; 11:e76854. [PMID: 35850933 PMCID: PMC9328768 DOI: 10.7554/elife.76854] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 07/03/2022] [Indexed: 12/02/2022] Open
Abstract
Background There is ongoing uncertainty regarding transmission chains and the respective roles of healthcare workers (HCWs) and elderly patients in nosocomial outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in geriatric settings. Methods We performed a retrospective cohort study including patients with nosocomial coronavirus disease 2019 (COVID-19) in four outbreak-affected wards, and all SARS-CoV-2 RT-PCR positive HCWs from a Swiss university-affiliated geriatric acute-care hospital that admitted both Covid-19 and non-Covid-19 patients during the first pandemic wave in Spring 2020. We combined epidemiological and genetic sequencing data using a Bayesian modelling framework, and reconstructed transmission dynamics of SARS-CoV-2 involving patients and HCWs, to determine who infected whom. We evaluated general transmission patterns according to case type (HCWs working in dedicated Covid-19 cohorting wards: HCWcovid; HCWs working in non-Covid-19 wards where outbreaks occurred: HCWoutbreak; patients with nosocomial Covid-19: patientnoso) by deriving the proportion of infections attributed to each case type across all posterior trees and comparing them to random expectations. Results During the study period (1 March to 7 May 2020), we included 180 SARS-CoV-2 positive cases: 127 HCWs (91 HCWcovid, 36 HCWoutbreak) and 53 patients. The attack rates ranged from 10% to 19% for patients, and 21% for HCWs. We estimated that 16 importation events occurred with high confidence (4 patients, 12 HCWs) that jointly led to up to 41 secondary cases; in six additional cases (5 HCWs, 1 patient), importation was possible with a posterior probability between 10% and 50%. Most patient-to-patient transmission events involved patients having shared a ward (95.2%, 95% credible interval [CrI] 84.2%-100%), in contrast to those having shared a room (19.7%, 95% CrI 6.7%-33.3%). Transmission events tended to cluster by case type: patientnoso were almost twice as likely to be infected by other patientnoso than expected (observed:expected ratio 2.16, 95% CrI 1.17-4.20, p=0.006); similarly, HCWoutbreak were more than twice as likely to be infected by other HCWoutbreak than expected (2.72, 95% CrI 0.87-9.00, p=0.06). The proportion of infectors being HCWcovid was as expected as random. We found a trend towards a greater proportion of high transmitters (≥2 secondary cases) among HCWoutbreak than patientnoso in the late phases (28.6% vs. 11.8%) of the outbreak, although this was not statistically significant. Conclusions Most importation events were linked to HCW. Unexpectedly, transmission between HCWcovid was more limited than transmission between patients and HCWoutbreak. This finding highlights gaps in infection control and suggests the possible areas of improvements to limit the extent of nosocomial transmission. Funding This study was supported by a grant from the Swiss National Science Foundation under the NRP78 funding scheme (Grant no. 4078P0_198363).
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Affiliation(s)
- Mohamed Abbas
- Infection Control Programme & WHO Collaborating Centre on Patient Safety, Geneva University HospitalsGenevaSwitzerland
- MRC Centre for Global Infectious Disease Analysis, Imperial College LondonLondonUnited Kingdom
- Faculty of Medicine, University of GenevaGenevaSwitzerland
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Imperial College LondonLondonUnited Kingdom
- Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College LondonLondonUnited Kingdom
| | - Samuel Cordey
- Faculty of Medicine, University of GenevaGenevaSwitzerland
- Laboratory of Virology, Department of Diagnostics, Geneva University HospitalsGenevaSwitzerland
| | - Florian Laubscher
- Laboratory of Virology, Department of Diagnostics, Geneva University HospitalsGenevaSwitzerland
| | - Tomás Robalo Nunes
- Infection Control Programme & WHO Collaborating Centre on Patient Safety, Geneva University HospitalsGenevaSwitzerland
- Serviço de Infecciologia, Hospital Garcia de Orta, EPEAlmadaPortugal
| | - Ashleigh Myall
- Department of Infectious Diseases, Imperial College LondonLondonUnited Kingdom
- Department of Mathematics, Imperial College LondonLondonUnited Kingdom
| | - Julien Salamun
- Department of Primary Care, Geneva University HospitalsGenevaSwitzerland
| | - Philippe Huber
- Department of Rehabilitation and Geriatrics, Geneva University HospitalsGenevaSwitzerland
| | - Dina Zekry
- Department of Rehabilitation and Geriatrics, Geneva University HospitalsGenevaSwitzerland
| | - Virginie Prendki
- Department of Rehabilitation and Geriatrics, Geneva University HospitalsGenevaSwitzerland
- Division of Infectious Diseases, Geneva University HospitalsGenevaSwitzerland
| | - Anne Iten
- Infection Control Programme & WHO Collaborating Centre on Patient Safety, Geneva University HospitalsGenevaSwitzerland
| | - Laure Vieux
- Occupational Health Service, Geneva University HospitalsGenevaSwitzerland
| | - Valérie Sauvan
- Infection Control Programme & WHO Collaborating Centre on Patient Safety, Geneva University HospitalsGenevaSwitzerland
| | - Christophe E Graf
- Department of Rehabilitation and Geriatrics, Geneva University HospitalsGenevaSwitzerland
| | - Stephan Harbarth
- Infection Control Programme & WHO Collaborating Centre on Patient Safety, Geneva University HospitalsGenevaSwitzerland
- Faculty of Medicine, University of GenevaGenevaSwitzerland
- Division of Infectious Diseases, Geneva University HospitalsGenevaSwitzerland
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Semenova Y, Trenina V, Pivina L, Glushkova N, Zhunussov Y, Ospanov E, Bjørklund G. The lessons of COVID-19, SARS, and MERS: Implications for preventive strategies. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2022. [DOI: 10.1080/20479700.2022.2051126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Yuliya Semenova
- Department of Neurology, Ophthalmology and Otolaryngology, Semey Medical University, Semey, Kazakhstan
- CONEM Kazakhstan Environmental Health and Safety Research Group, Semey Medical University, Semey, Kazakhstan
| | - Varvara Trenina
- Department of Neurology, Ophthalmology and Otolaryngology, Semey Medical University, Semey, Kazakhstan
| | - Lyudmila Pivina
- CONEM Kazakhstan Environmental Health and Safety Research Group, Semey Medical University, Semey, Kazakhstan
- Department of Emergency Medicine, Semey Medical University, Semey, Kazakhstan
| | - Natalya Glushkova
- Department of Epidemiology, Biostatistics & Evidence Based Medicine, Al-Farabi Kazakh National University, Almaty, Kazakhstan
| | | | - Erlan Ospanov
- Department of Neurology, Ophthalmology and Otolaryngology, Semey Medical University, Semey, Kazakhstan
| | - Geir Bjørklund
- Council for Nutritional and Environmental Medicine (CONEM), Mo i Rana, Norway
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Habibi N, Uddin S, Behbehani M, Abdul Razzack N, Zakir F, Shajan A. SARS-CoV-2 in hospital air as revealed by comprehensive respiratory viral panel sequencing. Infect Prev Pract 2022; 4:100199. [PMID: 34977533 PMCID: PMC8711137 DOI: 10.1016/j.infpip.2021.100199] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 12/16/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Nosocomially acquired severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection has become the most significant pandemic of our lifetime. Though its transmission was essentially attributed to droplets from an infected person, with recent advancements in knowledge, aerosol transmission seems to be a viable pathway, as well. Because of the lower biological load in ambient aerosol, detection of SARS-CoV-2 is challenging. A few recent attempts of sampling large aerosol volumes and using next-generation sequencing (NGS) to detect the presence of SARS-CoV-2 in the air at very low levels gave positive results. These results suggest the potential of using this technique to detect the presence of SARS-CoV-2 and use it as an early warning signal for possible outbreak or recurrence of coronavirus disease 2019 (COVID-19). AIM To assess efficacy of comprehensive respiratory viral panel (CRVP) sequencing and RT-PCR for low-level identification of SARS-CoV-2 and other respiratory viruses in indoor air. METHODS A large volume of indoor aerosol samples from three major hospitals involved in COVID-19 care in Kuwait was collected. Viral RNA was isolated and subjected to comprehensive respiratory viral panel sequencing (CRVP) as per the standard protocol to detect the SARS-CoV-2 and other respiratory viruses in the hospital aerosol and monitor variations within the sequences. RT-PCR was also employed to estimate the viral load of SARS-CoV-2. FINDINGS 13 of 15 (86.7%) samples exhibited SARS-CoV-2 with a relative abundance of 0.2-33.3%. The co-occurrence of human adenoviruses (type C1, C2, C5, C4), respiratory syncytial virus (RSV), influenza B, and non-SARS-CoV-229E were also recorded. Alignment of SARS-CoV-2 sequences against the reference strain of Wuhan China revealed variations in the form of single nucleotide polymorphisms (SNPs-17), insertions and deletions (indels-1). These variations were predicted to create missense (16), synonymous (15), frameshift (1) and stop-gained (1) mutations with a high (2), low (15), and moderate (16) impact. CONCLUSIONS Our results suggest that using CRVP on a large volume aerosol sample was a valuable tool for detecting SARS-CoV-2 in indoor aerosols of health care settings. Owing to its higher sensitivity, it can be employed as a surveillance strategy in the post COVID times to act as an early warning system to possibly control future outbreaks.
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Affiliation(s)
- Nazima Habibi
- Environment and Life Sciences Research Center, Kuwait Institute for Scientific Research, Kuwait
| | - Saif Uddin
- Environment and Life Sciences Research Center, Kuwait Institute for Scientific Research, Kuwait
| | - Montaha Behbehani
- Environment and Life Sciences Research Center, Kuwait Institute for Scientific Research, Kuwait
| | - Nasreem Abdul Razzack
- Environment and Life Sciences Research Center, Kuwait Institute for Scientific Research, Kuwait
| | - Farhana Zakir
- Environment and Life Sciences Research Center, Kuwait Institute for Scientific Research, Kuwait
| | - Anisha Shajan
- Environment and Life Sciences Research Center, Kuwait Institute for Scientific Research, Kuwait
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21
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Vuichard-Gysin D, Abbas M, Harbarth S. In-hospital COVID-19 outbreak investigation: A practical approach to root cause analysis. Intensive Crit Care Nurs 2021; 67:103132. [PMID: 34483027 PMCID: PMC8358106 DOI: 10.1016/j.iccn.2021.103132] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Affiliation(s)
- Danielle Vuichard-Gysin
- National Center for Infection Prevention, Swissnoso, Bern, Switzerland; Infectious Diseases & Hospital Hygiene, Thurgau Hospital Group, Muensterlingen and Frauenfeld, Switzerland.
| | - Mohamed Abbas
- Infection Control Programme, Geneva University Hospitals and Faculty of Medicine, WHO Collaborating Center for Patient Safety, Geneva, Switzerland; Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Stephan Harbarth
- National Center for Infection Prevention, Swissnoso, Bern, Switzerland; Infection Control Programme, Geneva University Hospitals and Faculty of Medicine, WHO Collaborating Center for Patient Safety, Geneva, Switzerland
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