Integrated Genomic and Social Network Analyses of Severe Acute Respiratory Syndrome Coronavirus 2 Transmission in the Healthcare Setting
© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissionsoup.com..
BACKGROUND: Infection prevention (IP) measures are designed to mitigate the transmission of pathogens in healthcare. Using large-scale viral genomic and social network analyses, we determined if IP measures used during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were adequate in protecting healthcare workers (HCWs) and patients from acquiring SARS-CoV-2.
METHODS: We performed retrospective cross-sectional analyses of viral genomics from all available SARS-CoV-2 viral samples collected at UC San Diego Health and social network analysis using the electronic medical record to derive temporospatial overlap of infections among related viromes and supplemented with contact tracing data. The outcome measure was any instance of healthcare transmission, defined as cases with closely related viral genomes and epidemiological connection within the healthcare setting during the infection window. Between November 2020 through January 2022, 12 933 viral genomes were obtained from 35 666 patients and HCWs.
RESULTS: Among 5112 SARS-CoV-2 viral samples sequenced from the second and third waves of SARS-CoV-2 (pre-Omicron), 291 pairs were derived from persons with a plausible healthcare overlap. Of these, 34 pairs (12%) were phylogenetically linked: 19 attributable to household and 14 to healthcare transmission. During the Omicron wave, 2106 contact pairs among 7821 sequences resulted in 120 (6%) related pairs among 32 clusters, of which 10 were consistent with healthcare transmission. Transmission was more likely to occur in shared spaces in the older hospital compared with the newer hospital (2.54 vs 0.63 transmission events per 1000 admissions, P < .001).
CONCLUSIONS: IP strategies were effective at identifying and preventing healthcare SARS-CoV-2 transmission.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America - (2024) vom: 16. Jan. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Keehner, Jocelyn [VerfasserIn] |
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| Links: |
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| Themen: |
Healthcare transmission of SARS-CoV-2 |
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| Anmerkungen: |
Date Revised 02.04.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1093/cid/ciad738 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a © The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissionsoup.com. | ||
| 520 | |a BACKGROUND: Infection prevention (IP) measures are designed to mitigate the transmission of pathogens in healthcare. Using large-scale viral genomic and social network analyses, we determined if IP measures used during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were adequate in protecting healthcare workers (HCWs) and patients from acquiring SARS-CoV-2 | ||
| 520 | |a METHODS: We performed retrospective cross-sectional analyses of viral genomics from all available SARS-CoV-2 viral samples collected at UC San Diego Health and social network analysis using the electronic medical record to derive temporospatial overlap of infections among related viromes and supplemented with contact tracing data. The outcome measure was any instance of healthcare transmission, defined as cases with closely related viral genomes and epidemiological connection within the healthcare setting during the infection window. Between November 2020 through January 2022, 12 933 viral genomes were obtained from 35 666 patients and HCWs | ||
| 520 | |a RESULTS: Among 5112 SARS-CoV-2 viral samples sequenced from the second and third waves of SARS-CoV-2 (pre-Omicron), 291 pairs were derived from persons with a plausible healthcare overlap. Of these, 34 pairs (12%) were phylogenetically linked: 19 attributable to household and 14 to healthcare transmission. During the Omicron wave, 2106 contact pairs among 7821 sequences resulted in 120 (6%) related pairs among 32 clusters, of which 10 were consistent with healthcare transmission. Transmission was more likely to occur in shared spaces in the older hospital compared with the newer hospital (2.54 vs 0.63 transmission events per 1000 admissions, P < .001) | ||
| 520 | |a CONCLUSIONS: IP strategies were effective at identifying and preventing healthcare SARS-CoV-2 transmission | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a SARS-CoV-2 pandemic | |
| 650 | 4 | |a healthcare transmission of SARS-CoV-2 | |
| 650 | 4 | |a infection prevention precautions | |
| 650 | 4 | |a social network analyses and contact tracing | |
| 650 | 4 | |a whole-genome sequencing | |
| 700 | 1 | |a Abeles, Shira R |e verfasserin |4 aut | |
| 700 | 1 | |a Longhurst, Christopher A |e verfasserin |4 aut | |
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| 700 | 1 | |a Torriani, Francesca J |e verfasserin |4 aut | |
| 700 | 1 | |a Aigner, Stefan |e investigator |4 oth | |
| 700 | 1 | |a Andersen, Kristian G |e investigator |4 oth | |
| 700 | 1 | |a Anderson, Catelyn |e investigator |4 oth | |
| 700 | 1 | |a Baer, Nathan A |e investigator |4 oth | |
| 700 | 1 | |a Barber, Tom |e investigator |4 oth | |
| 700 | 1 | |a Bauk, Marco |e investigator |4 oth | |
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| 700 | 1 | |a Belda-Ferre, Pedro |e investigator |4 oth | |
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| 700 | 1 | |a Castro-Martinez, Anelizze |e investigator |4 oth | |
| 700 | 1 | |a Cheung, Willi |e investigator |4 oth | |
| 700 | 1 | |a De Hoff, Peter |e investigator |4 oth | |
| 700 | 1 | |a Fisch, Kathleen M |e investigator |4 oth | |
| 700 | 1 | |a Gangavarapu, Karthik |e investigator |4 oth | |
| 700 | 1 | |a Gargano, Isabella |e investigator |4 oth | |
| 700 | 1 | |a Hakim, Abbas |e investigator |4 oth | |
| 700 | 1 | |a Harsono, Shania |e investigator |4 oth | |
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| 700 | 1 | |a Laurent, Louise C |e investigator |4 oth | |
| 700 | 1 | |a Marotz, Clarisse A |e investigator |4 oth | |
| 700 | 1 | |a Matteson, Nathaniel L |e investigator |4 oth | |
| 700 | 1 | |a Moshiri, Niema |e investigator |4 oth | |
| 700 | 1 | |a Ngo, Toan T |e investigator |4 oth | |
| 700 | 1 | |a Ostrander, Tyler R |e investigator |4 oth | |
| 700 | 1 | |a Perkins, Sarah |e investigator |4 oth | |
| 700 | 1 | |a Plascencia, Ashley |e investigator |4 oth | |
| 700 | 1 | |a Rivera, Andrea |e investigator |4 oth | |
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| 700 | 1 | |a Yeo, Gene W |e investigator |4 oth | |
| 700 | 1 | |a Zeller, Mark |e investigator |4 oth | |
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