Superspreaders drive the largest outbreaks of hospital onset COVID-19 infection

Illingworth, Christopher J. R.; Hamilton, William L.; Warne, Ben; Routledge, Matthew; Popay, A.I.; Jackson, Christopher; Fieldman, Tom; Meredith, Luke W.; Houldcroft, Charlotte J.; Hosmillo, Myra; Jahun, Aminu S.; Caller, Laura G; Caddy, Sarah; Yakovleva, Anna; Hall, Grant; Khokhar, Fahad; Feltwell, Theresa; Pinckert, Malte L.; Georgana, Iliana; Chaudhry, Yasmin; Curran, Martin D.; Parmar, Surendra; Sparkes, Dominic; Rivett, Lucy; Jones, Nick K; Sridhar, Sushmita; Forrest, Sally; Dymond, Tom; Grainger, Kayleigh; Workman, Christopher T.; Gkrania‐Klotsas, Effrossyni; Brown, Nicholas; Baker, Stephen J.; Weekes, Michael P.; Peacock, Sharon J.; Goodfellow, Ian; Gouliouris, Theodore; Angelis, Daniela De; Török, Estée

doi:10.31219/osf.io/wmkn3

Cited by 9 publications

(9 citation statements)

References 34 publications

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“…This study demonstrates that retrospective analyses of genomic data is useful in some circumstances to guide future IPC practice, with results consistent with similar studies in the UK [7][8][9][10]. It remains to be seen whether the additional costs of generating and analysing this genomic data near real-time (<48hrs from sample to dissemination of results) are justified by additional IPC gains, or whether the rapid and rigorous application of gold standard epidemiological methods in response to fast accumulation of nosocomial PCR-based diagnoses is the key intervention.…”

Section: Discussionsupporting

confidence: 86%

“…Individuals infected with identical or near-identical (≤1 SNP) viruses, are more likely to be linked in a transmission chain than those with more distantly related viruses, as demonstrated by previous retrospective studies that have utilised WGS to identify nosocomial infections and outbreaks. [7][8][9][10][11][12] We investigated whether sequencing could enhance epidemiological investigation of healthcare-associated SARS-CoV-2 acquisition in two areas: i) confirming/excluding nosocomial acquisition and ii) understanding the role of outbreaks in nosocomial acquisition. We highlight the benefits and pitfalls of this approach, to help guide local practice in individual centres.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing epidemiological investigation of nosocomial SARS-CoV-2 infection with whole genome sequencing: A retrospective cohort study across four hospitals in the UK

Lumley

Constantinides

Sanderson

et al. 2021

Preprint

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BackgroundDespite robust efforts, patients and staff acquire SARS-CoV-2 infection in hospitals. In this retrospective cohort study, we investigated whether whole-genome sequencing (WGS) could enhance the epidemiological investigation of healthcare-associated SARS-CoV-2 acquisition.Methods and findingsFrom 17-November-2020 to 5-January-2021, 803 inpatients and 329 staff were diagnosed with SARS-CoV-2 infection across four teaching hospitals in Oxfordshire, UK. We classified cases according to epidemiological definitions, sought epidemiological evidence of a potential source for each nosocomial infection, and evaluated if epidemiologically-linked cases had genomic evidence supporting transmission. We compared epidemiological and genomic outbreak identification.Using national epidemiological definitions, 109/803 (14%) inpatient infections were classified as definite/probable nosocomial, 615 (77%) as community-acquired and 79 (10%) as indeterminate. There was strong epidemiological evidence to support definite/probable cases as nosocomial: 107/109 (98%) had a prior-negative PCR in the same hospital stay before testing positive, and 101(93%) shared time and space with known infected patients/staff. Many indeterminate cases were likely infected in hospital: 53/79 (67%) had a prior-negative PCR and 75 (95%) contact with a potential source. 89/615 (11% of all 803 patients) with apparent community-onset had a recent hospital exposure.WGS highlighted SARS-CoV-2 is mainly imported into hospitals: within 764 samples sequenced 607 genomic clusters were identified (>1 SNP distinct). Only 43/607 (7%) clusters contained evidence of onward transmission (subsequent cases within ≤1 SNP). 20/21 epidemiologically-identified outbreaks contained multiple genomic introductions. Most (80%) nosocomial acquisition occurred in rapid super-spreading events in settings with a mix of COVID-19 and non-COVID-19 patients. Hospitals not routinely admitting COVID-19 patients had low rates of transmission. Undiagnosed/unsequenced individuals prevent genomic data from excluding nosocomial acquisition.ConclusionsOur findings suggest current surveillance definitions underestimate nosocomial acquisition and reveal most nosocomial transmission occurs from a relatively limited number of highly infectious individuals.

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Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Enhancing epidemiological investigation of nosocomial SARS-CoV-2 infection with whole genome sequencing: A retrospective cohort study across four hospitals in the UK

Lumley

Constantinides

Sanderson

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Our findings are different from those in a smaller study focusing on a few large clusters in another UK hospital, where 20% of individuals caused 80% of transmission events. [11] Importantly, we find that hospitalacquired SARS-CoV-2 cases give rise to a greater number of secondary cases than community-onset community-associated cases. Cases admitted from the community already suspected as having COVID-19 will have been isolated in single cubicles or COVID-19 cohort areas more rapidly, thus limiting opportunities for onward transmission.…”

Section: Discussionmentioning

confidence: 62%

“…[10] In the time scale of an outbreak, a large proportion of individuals are infected by viruses too genetically similar to each other to distinguish genuine transmission events from unrelated infections. Furthermore, data from HCWs have rarely been included in previous analyses [11] and the relative role that patients and HCWs have played in fuelling hospital outbreaks in the UK remains largely unknown.…”

Section: Introductionmentioning

confidence: 99%

Characterising within-hospital SARS-CoV-2 transmission events: a retrospective analysis integrating epidemiological and viral genomic data from a UK tertiary care setting across two pandemic waves

Lindsey

Villabona-Arenas

Campbell

et al. 2021

Preprint

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Objectives - To characterise within-hospital SARS-CoV-2 transmission across two waves of the COVID-19 pandemic. Design - A retrospective Bayesian modelling study to reconstruct transmission chains amongst 2181 patients and healthcare workers using combined viral genomic and epidemiological data. Setting - A large UK NHS Trust with over 1400 beds and employing approximately 17,000 staff. Participants - 780 patients and 522 staff testing SARS-CoV-2 positive between 1st March 2020 and 25th July 2020 (Wave 1); and 580 patients and 299 staff testing SARS-CoV-2 positive between 30th November 2020 and 24th January 2021 (Wave 2). Main outcome measures - Transmission pairs including who-infected-whom; location of transmission events in hospital; number of secondary cases from each individual, including differences in onward transmission from community and hospital onset patient cases. Results - Staff-to-staff transmission was estimated to be the most frequent transmission type during Wave 1 (31.6% of observed hospital-acquired infections; 95% CI 26.9 to 35.8%), decreasing to 12.9% (95% CI 9.5 to 15.9%) in Wave 2. Patient-to-patient transmissions increased from 27.1% in Wave 1 (95% CI 23.3 to 31.4%) to 52.1% (95% CI 48.0 to 57.1%) in Wave 2, to become the predominant transmission type. Over 50% of hospital-acquired infections were concentrated in 8/120 locations in Wave 1 and 10/93 locations in Wave 2. Approximately 40% to 50% of hospital-onset patient cases resulted in onward transmission compared to less than 4% of definite community-acquired cases. Conclusions - Prevention and control measures that evolved during the COVID-19 pandemic may have had a significant impact on reducing infections between healthcare workers, but were insufficient during the second wave to prevent a high number of patient-to-patient transmissions. As hospital-acquired cases appeared to drive most onward transmissions, more frequent and rapid identification and isolation of these cases will be required to break hospital transmission chains in subsequent pandemic waves

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“…[5] Prior to the introduction of FFP3, HCW infection rates on "red" wards were greater than those on "green" wards, and also demonstrated no correlation with community case rates, suggesting infections resulted from direct patient care -in line with genomic evidence that a large proportion of HCW infections are transmitted from patients. [6] Some studies have suggested that staff infection rates merely reflect transmission in the community such that staff rates increase as community rates rise and are therefore inevitable. [7,8] This study highlights that this is only true for non-COVID-19 facing staff, with exposure to infected patients being the driving factor for infections in COVID-19 facing staff.…”

mentioning

confidence: 99%