Hospital-Acquired COVID-19 Infection – The Magnitude of the Problem

Marago, Italo

doi:10.2139/ssrn.3622387

Cited by 11 publications

(11 citation statements)

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“…A comparison of epidemiological and genome data on HAI is provided in appendix two. In our of COVID-19 we report numbers of HAI that are comparable to other centres 3,4 . Interestingly, our study spans the rst three waves of the COVID-19 pandemic in Belgium; the number and percentages of HAI in our centre were stable across the rst two waves, and increased towards the third wave, despite more control measures in place by that time of the pandemic.…”

Section: Part 2 Description Of Clusters and Validation Of The Ecdc Source De Nitions For Haisupporting

confidence: 76%

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Healthcare-associated COVID-19 across 3 pandemic waves: patient characterisation and validation of clinical definitions using genome sequencing

Demuyser

Seyler

Buttiens

et al. 2022

Preprint

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BackgroundWorldwide, healthcare-associated SARS-CoV-2 infections are a major problem: they are associated with increased morbidity, mortality, and hospitalization costs. In-depth studies across the pandemic are crucial to understand and prevent transmission in hospital settings. The principal aims of this study were to characterise patients and validate ECDC definitions of healthcare-associated COVID-19 infections.MethodsWe set up a retrospective observational study spanning the first three waves of the COVID-19 pandemic in a Belgian university hospital: it describes the characteristics of COVID-19 patients admitted, with either healthcare- or community-associated infections. We performed a cluster analysis through epidemiological and viral genome analyses of the healthcare-associated infections, in order to validate the ECDC definitions of healthcare-associated COVID-19 infections.ResultsBetween week 10 of 2020 and week 22 of 2021, 168 patients were hospitalized with healthcare-associated COVID-19. The following factors were found more often in symptomatic healthcare- than in community-associated hospitalized patients: older age, increased frailty, smoking habits, and comorbidities. The genome-based cluster analyses showed that different viral lineages predominated in different timeframes. We observed a good correlation of epidemiological data with genome sequencing results in at least 12 different outbreaks in our hospital, thus validating the ECDC definitions. ConclusionsThis in-depth characterization sheds new light on the problem of healthcare-associated COVID-19 infections, in particular on patients’ characteristics, epidemiology, and cluster dynamics. Even though epidemiological evaluation of nosocomial infections is vital, management of nosocomial outbreaks can undoubtedly benefit from genome sequencing analyses to reinforce their strategy.

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Section: Part 2 Description Of Clusters and Validation Of The Ecdc Source De Nitions For Haisupporting

confidence: 76%

“…During the rst wave of the pandemic, approximately 10 to 15% of hospitalized COVID-19 cases were HAI 3,4 . Front-line healthcare workers also have an increased risk of acquiring COVID-19 by a factor of 3.4, compared to the general population 5 .…”

Section: Introductionmentioning

confidence: 99%

Healthcare-associated COVID-19 across 3 pandemic waves: patient characterisation and validation of clinical definitions using genome sequencing

Demuyser

Seyler

Buttiens

et al. 2022

Preprint

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“…The percentage of hospital-acquired infections is highly dependent on the location and varies widely. Hospital-acquired cases reached 16.2% in England [60]; however, in a study in US hospitals, such as the one by Rhee et al [61], the incidence of hospital-acquired COVID-19 is low and negligible. Rhee et al studied all patients admitted to Brigham and Women's Hospital (Boston, Massachusetts) between March 7, 2020 and May 30, 2020.…”

Section: Assumptions and Limitations Of The Modelmentioning

confidence: 92%

An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City

et al. 2021

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Epidemiological models can provide the dynamic evolution of a pandemic but they are based on many assumptions and parameters that have to be adjusted over the time when the pandemic lasts. However, often the available data are not sufficient to identify the model parameters and hence infer the unobserved dynamics. Here, we develop a general framework for building a trustworthy data-driven epidemiological model, consisting of a workflow that integrates data acquisition and event timeline, model development, identifiability analysis, sensitivity analysis, model calibration, model robustness analysis, and projection with uncertainties in different scenarios. In particular, we apply this framework to propose a modified susceptible–exposed–infectious–recovered (SEIR) model, including new compartments and model vaccination in order to project the transmission dynamics of COVID-19 in New York City (NYC). We find that we can uniquely estimate the model parameters and accurately project the daily new infection cases, hospitalizations, and deaths, in agreement with the available data from NYC’s government’s website. In addition, we employ the calibrated data-driven model to study the effects of vaccination and timing of reopening indoor dining in NYC.

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“…Our estimates of the proportion of hospital cases that are due to hospital-acquired infection are higher than those from an England wide study 8 and those from single hospital settings in the UK. 3,9,[26][27][28] , as we estimate all hospital-acquired infections whether identified or not. Our estimates of all infections are similar to previous modelling work using an SEIR model which estimates that nosocomial transmission was responsible for 20% (IQR 14•4, 27•1%) of infections in inpatients.…”

Section: Discussionmentioning

confidence: 99%

The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020

Knight

Pham

Stimson

et al. 2021

Preprint

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Background SARS-CoV-2 spreads in hospitals, but the contribution of these settings to the overall COVID-19 burden at a national level is unknown. Methods We used comprehensive national English datasets and simulation modelling to determine the total burden (identified and unidentified) of symptomatic hospital-acquired infections. Those unidentified would either be 1) discharged before symptom onset ("missed"), or 2) have symptom onset 7 days or fewer from admission ("misclassified"). We estimated the contribution of "misclassified" cases and transmission from "missed" symptomatic infections to the English epidemic before 31st July 2020. Findings In our dataset of hospitalised COVID-19 patients in acute English Trusts with a recorded symptom onset date (n = 65,028), 7% were classified as hospital-acquired (with symptom onset 8 or more days after admission and before discharge). We estimated that only 30% (range across weeks and 200 simulations: 20-41%) of symptomatic hospital-acquired infections would be identified. Misclassified cases and onward transmission from missed infections could account for 15% (mean, 95% range over 200 simulations: 14.1%-15.8%) of cases currently classified as community-acquired COVID-19. From this, we estimated that 26,600 (25,900 to 27,700) individuals acquired a symptomatic SARS-CoV-2 infection in an acute Trust in England before 31st July 2020, resulting in 15,900 (15,200-16,400) or 20.1% (19.2%-20.7%) of all identified hospitalised COVID-19 cases. Conclusion Transmission of SARS-CoV-2 to hospitalised patients likely caused approximately a fifth of identified cases of hospitalised COVID-19 in the "first wave", but fewer than 1% of all SARS-CoV-2 infections in England. Using symptom onset as a detection method for hospital-acquired SARS-CoV-2 likely misses a substantial proportion (>60%) of hospital-acquired infections.

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Hospital-Acquired COVID-19 Infection – The Magnitude of the Problem

Cited by 11 publications

References 0 publications

Healthcare-associated COVID-19 across 3 pandemic waves: patient characterisation and validation of clinical definitions using genome sequencing

Healthcare-associated COVID-19 across 3 pandemic waves: patient characterisation and validation of clinical definitions using genome sequencing

An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City

The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020

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