Hospital-onset COVID-19 infection surveillance systems: a systematic review

Spatial Variations in the Nasal Microbiota of Staff Working in a Healthcare-Associated Research Core Facility

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.

Long term risk of death and readmission after hospital admission with covid-19 among older adults: retrospective cohort study

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.

Assessment of Hospital-Onset SARS-CoV-2 Infection Rates and Testing Practices in the US, 2020-2022

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.

Evaluating infection prevention and control structure of Indonesian COVID-19 referral hospitals

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.

Contributory conditions for unexpected COVID-19 cases and nosocomial COVID-19 infection cases identified from systematic investigation in a French University Hospital

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.

Risk factors for nosocomial COVID-19 in a French university hospital

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.

A Toolkit for Monitoring Immunoglobulin G Levels from Dried Blood Spots of Patients with Primary Immunodeficiencies

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.

Incidence of healthcare-associated coronavirus disease 2019 (COVID-19) in the state of Geneva

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.

Outbreak investigation in a COVID-19 designated hospital: The combination of phylogenetic analysis and field epidemiology study suggesting airborne transmission

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.

Hospital Strain and COVID-19 Fatality - England, April 2020-March 2022

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.

Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study

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.

Bacterial and fungal communities in indoor aerosols from two Kuwaiti hospitals

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.

SARS-CoV-2 in hospital air as revealed by comprehensive respiratory viral panel sequencing

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.

Reconstruction of transmission chains of SARS-CoV-2 amidst multiple outbreaks in a geriatric acute-care hospital: a combined retrospective epidemiological and genomic study

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: HCW_covid; HCWs working in non-Covid-19 wards where outbreaks occurred: HCW_outbreak; patients with nosocomial Covid-19: patient_noso) 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 HCW_covid, 36 HCW_outbreak) 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: patient_noso were almost twice as likely to be infected by other patient_noso than expected (observed:expected ratio 2.16, 95% CrI 1.17-4.20, p=0.006); similarly, HCW_outbreak were more than twice as likely to be infected by other HCW_outbreak than expected (2.72, 95% CrI 0.87-9.00, p=0.06). The proportion of infectors being HCW_covid was as expected as random. We found a trend towards a greater proportion of high transmitters (≥2 secondary cases) among HCW_outbreak than patient_noso 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 HCW_covid was more limited than transmission between patients and HCW_outbreak. 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).