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- Springer Journals
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- Copyright © The Author(s) 2023
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- 2047-2994
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- 10.1186/s13756-023-01247-7
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Abstract
Background Healthcare facilities have been challenged by the risk of SARS-CoV-2 transmission between healthcare workers (HCW ) and patients. During the first wave of the COVID-19 pandemic, infections among HCW were observed, questioning infection prevention and control (IPC) measures implemented at that time. Aim This study aimed to identify nosocomial transmission routes of SARS-CoV-2 between HCW and patients in a tertiary care hospital. Methods All SARS-CoV-2 PCR positive HCW and patients identified between 1 March and 19 May 2020, were included in the analysis. Epidemiological data were collected from patient files and HCW contact tracing interviews. Whole genome sequences of SARS-CoV-2 were generated using Nanopore sequencing ( WGS). Epidemiological clusters were identified, whereafter WGS and epidemiological data were combined for re-evaluation of epidemiologi- cal clusters and identification of potential transmission clusters. HCW infections were further classified into categories based on the likelihood that the infection was acquired via nosocomial transmission. Secondary cases were defined as COVID-19 cases in our hospital, part of a transmission cluster, of which the index case was either a patient or HCW from our hospital. Findings The study population consisted of 293 HCW and 245 patients. Epidemiological data revealed 36 poten- tial epidemiological clusters, with an estimated 222 (75.7%) HCW as secondary cases. WGS results were available for 195 HCW (88.2%) and 20 patients (12.8%) who belonged to an epidemiological cluster. Re-evaluation of the Cynthia P. Haanappel and Bas B. Oude Munnink contributed equally to this manuscript as first authors. Marion P. G. Koopmans and Juliëtte A. Severin contributed equally to this manuscript as senior authors. *Correspondence: Juliëtte A. Severin j.severin@erasmusmc.nl Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Haanappel et al. Antimicrobial Resistance & Infection Control (2023) 12:46 Page 2 of 12 epidemiological clusters, with the available WGS data identified 31 transmission clusters with 65 (29.4%) HCW as secondary cases. Transmission clusters were all part of 18 (50.0%) previously determined epidemiological clusters, demonstrating that several larger outbreaks actually consisted, of several smaller transmission clusters. A total of 21 (7.2%) HCW infections were classified as from confirmed nosocomial, of which 18 were acquired from another HCW and 3 from a patient. Conclusion The majority of SARS-CoV-2 infections among HCW could be attributed to community-acquired infec- tion. Infections among HCW that could be classified as due to nosocomial transmission, were mainly caused by HCW- to-HCW transmission rather than patient-to-HCW transmission. It is important to recognize the uncertainties of cluster analyses based solely on epidemiological data. Keywords COVID-19, SARS-CoV-2, Epidemiology, Nosocomial transmission, Hospital, Pandemic preparedness, Whole genome sequencing, Cluster analysis Background Methods The coronavirus disease 2019 (COVID-19) pandemic Setting caused by severe acute respiratory syndrome corona- The Erasmus MC University Medical Center (Erasmus virus-2 (SARS-CoV-2) generated a significant burden MC) is a large tertiary care hospital in Rotterdam, The on healthcare facilities worldwide [1]. Besides the large Netherlands, with a total of 1100 beds and 39 operat- influx of COVID-19 patients, the nosocomial transmis - ing rooms, including the Sophia Children’s Hospital. sion of SARS-CoV-2 between patients and healthcare There are approximately 32,000 clinical admissions per workers (HCW) has been a major concern. year and 14,000 HCW employed (including physicians, Many studies have investigated SARS-CoV-2 out- registered nurses and researchers) [6]. The adult clinic breaks in healthcare facilities. However, many studies primarily consists of single-occupancy rooms with pri- reporting on the nosocomial transmission of SARS- vate bathrooms, whereas the pediatric clinic mainly has CoV-2 were in the context of either department-spe- multiple-occupancy rooms with shared bathrooms [7]. cific outbreaks or a select set of samples of the total This analysis used data collected from 1 March 2020 SARS-CoV-2 positive population in a healthcare facility, until 19 May 2020 during the first wave of the COVID- whereby HCW data was not always available or analysed 19 pandemic. The end study date was chosen because [2–5]. HCW experience community as well as occupa- no HCW tested positive for SARS-CoV-2 in the six tional exposure to SARS-CoV-2, therefore HCW can weeks hereafter. At that time diagnostic test availability play an important role in hospital outbreaks. It is impor- for SARS-CoV-2 was limited in The Netherlands; test - tant to determine what extent of COVID-19 among ing was only available to clinically suspected patients HCW is community- or hospital-acquired and how from COVID-19 risk groups, and hospital HCW. Until much they contribute to in-hospital transmission. Com- 19 May 2020 a total of 44,010 SARS-CoV-2 positive bining epidemiological and whole-genome sequencing persons were registered in The Netherlands and 5,691 (WGS) data can help elucidate the dynamics of SARS- COVID-19 related deaths [8]. In the region of the hos- CoV-2 hospital outbreaks, hereby allowing real-time pital, Rotterdam-Rijnmond, a total of 4,252 COVID-19 adjustment of targeted infection prevention and control cases were identified and 532 deaths in a population of (IPC) measures. However, WGS is a technique not read- 1.3 million inhabitants [8]. ily available for many healthcare facilities, especially not with a fast turn-around time. Consequently, many healthcare facilities rely on epidemiological data for Study design and data collection their initial outbreak response. Therefore, it is necessary All SARS-CoV-2 positive HCW and admitted patients, to investigate the over- or underestimation of outbreak patients visiting the outpatient clinic, and patients visit- clusters when using solely epidemiological data. ing the emergency department who were tested between Here, we describe the transmission of SARS-CoV-2 1 March 2020 and 19 May 2020 were included. COVID- between HCW and patients within a large tertiary 19 patients were either patients who tested SARS-CoV-2 hospital in The Netherlands during the first months of positive upon admission (often referred by the general the COVID-19 pandemic. Secondly, we determine the practitioner or transferred from other Dutch hospitals), added value of WGS in addition to epidemiological at their hospital visit, or patients who tested positive dur- investigations with regard to outbreak investigation and ing hospitalization. HCW and patients were tested for source and contact tracing. SARS-CoV-2 by reverse transcriptase-polymerase chain Haanappel et al. Antimicrobial Resistance & Infection Control (2023) 12:46 Page 3 of 12 reaction (RT-PCR) on nasopharyngeal and throat swabs adjusted according to new insights from experience and [9]. newly available literature. HCW did, however, always Epidemiological data collected from SARS-CoV-2 posi- use personal protective equipment (PPE) when provid- tive HCW included the date of positive SARS-CoV-2 test, ing care for (suspected) COVID-19 patients. The rec - age at the date of first positive SARS-CoV-2 PCR, date ommended use of PPE changed over time, according to of symptom onset, symptom description, work location/ updated versions of national guidelines and new insights department, job description, and self-reported source of on transmissibility at the time. The Erasmus MC did infection. These data were prospectively collected as part not experience any shortage of PPE supply during the of routine occupational health activity when the posi- first COVID-19 wave. Details on the implemented IPC tive test result was shared with the HCW. Patient data measures in the Erasmus MC during the study period extracted from electronic health records (EHR) included are described in Fig. 1. For HCW, an occupational health the date of positive SARS-CoV-2 PCR test, age at the date facility for sampling and RT-PCR testing was available of the first positive SARS-CoV-2 test, hospital admis - from 1 March 2020. Based on the routine occupational sion date, admission location, and previous contact with health information provided it was decided whether SARS-CoV-2 positive persons. a source and contact investigation (CI) was necessary. These investigations were performed for each HCW that Infection prevention and control measures had been working with symptoms and for each patient During the first months of the COVID-19 pandemic, that had not been cared for in adequate isolation condi- national and Erasmus MC guidelines related to SARS- tions. Contacts were registered starting from the day of CoV-2 IPC measures were still being developed and symptom onset. Fig. 1 Timeline of infection prevention and control measures implemented in our hospital, per category. AGP = Aerosol generating procedures, ARDS = Acute respiratory distress syndrome, CI = Contact investigation, FFP = filtering face piece, GP = general practitioner, HCW = Healthcare workers, ICU = Intensive care unit, PPE = Personal protective equipment, RTI = Respiratory tract infection, m = meter. (a) Noord-Brabant is a neighboring province with a relatively high COVID-19 prevalence during the first wave. (b) Change from FFP-2 to FFP-1 masks in accordance to the national guideline at the time. (c) HCW who contacted their general practitioner for SARS-CoV-2 testing were referred to the municipal public health authorities. (d) Specific symptoms were: fever, coughing, shortness of breath, sore throat, and loss of sense of smell or taste. (e) Non-specific symptoms were: general malaise, fatigue, muscle ache, joint pain, gastrointestinal complaints and, pain behind the eyes. (f ) Healthcare worker with a household member or partner with confirmed COVID-19 or with fever and respiratory symptoms. (g) China, Singapore, South Korea, Iran, Italy, Taiwan, Japan, Malaysia, Thailand, UAE, and Vietnam. From 10 March 2020 also the province of Noord-Brabant in The Netherlands Haanappel et al. Antimicrobial Resistance & Infection Control (2023) 12:46 Page 4 of 12 Whole genome sequencing and cluster identification clusters. Regional clusters were defined as sequence WGS was performed on all SARS-CoV-2 PCR posi- clusters which were identified in ≥ 5 primary COVID-19 tive HCW and patient nasopharyngeal swab samples patients, whereby patients with a positive SARS-CoV-2 with a cycle threshold (Ct) value of below 32. Nanopore test upon admission were regarded as primary COVID- sequencing was performed on these samples as described 19 patients. We assumed primary COVID-19 patients previously [10]. Successful sequencing was defined as were a good reflection of clusters circulating in the com - having more than 90% genome coverage. The generated munity. All other clusters were defined as non-regional. sequences and all other publicly available sequences from The Netherlands collected before 3 July 2020 were used Data analyses for downstream analyses. Phylogenetic analysis was per- Epidemiological data were analyzed with SPSS version formed using IQ-TREE and trees were visualized using 28.0 (IBM, Armonk, NY, USA). Continuous variables FigTree v.1.4.4 [11, 12]. Sequence clusters were identi- were summarized as medians with range, and categori- fied as sequences from the same epidemiological cluster, cal variables were expressed as median numbers and per- same department and having a maximum of two nucleo- centages. For our retrospective cluster analysis, we first tide differences and sampled within two weeks [13]. Clus - identified epidemiological clusters from all patients and ter definition for clusters between different departments HCW with a SARS-CoV-2 positive PCR. In a second was set on having a maximum of 1 nucleotide difference. analysis, the number and size of epidemiological clusters were determined only for patients and HCW with avail- Definitions able WGS results. Thirdly, transmission clusters were Epidemiological clusters were defined as two or more established by combining epidemiological clusters and SARS-CoV-2 positive HCW or patients with a spatiotem- sequence clusters. poral link, either by unprotected contact < 14 days before The number of secondary cases among HCW resulting symptom onset at the same hospital department, or from identified clusters was determined by subtracting known contact determined by source and contact investi- the total number of index SARS-CoV-2 positive HCW gations. Contact between patients and HCW whereby all from the total number of HCW in epidemiological or required PPE and appropriate IPC measures were used transmission clusters. were not regarded as transmission moments for the clus- ter analysis based on epidemiological data alone. When Results the date of symptom onset was not available the date of Population characteristics: healthcare workers the first positive PCR test was used. Between 1 March and 19 May 2020, 4362 HCW Transmission clusters were identified by re-evaluation were tested for SARS-CoV-2 by RT-PCR at the Erasmus of epidemiological clusters with the addition of WGS MC, of whom 293 HCW (6.7%) tested positive (Fig. 2). data. Hereby, transmission clusters were defined as a The median age was 36 years (range 18–65) and 73% group of ≥ 2 SARS-CoV-2 positive HCW or patients was female. Of positive HCW, 197 (67.2%) were clini- with a link in time and place, confirmed by WGS data i.e. cal staff (e.g., nurses and physicians), of whom 11 (3.7%) belonging to the same sequence cluster. This was done HCW worked on a COVID-19 ward. The other 96 regardless of required PPE and appropriate IPC meas- (32.8%) HCW did not work in direct patient care (e.g., ures. Sequence clusters without epidemiological links administrative workers and analysts). The median time were not regarded as transmission clusters. between symptom-onset and SARS-CoV-2 testing was For further classification of COVID-19 among HCW, 3 days (range 0–24 days). we established definitions for the likelihood that nosoco - Regarding self-reported sources of infection, 62 of 293 mial transmission had taken place (Table 1). Definitions (21.2%) HCW reported a colleague, 33 (11.3%) HCW a were developed in a multidisciplinary group of epidemi- patient, 28 (9.6%) HCW reported a family member, 21 ologists, medical microbiologists and occupational health (7.2%) HCW recent travel and 151 (51.5%) HCW did physicians, and were based upon literature and own not report any possible source. Fourteen (4.8%) HCW experience [14]. For identification of an index case, it was reported multiple potential sources of infection. assumed a minimum of 2 days and a maximum of 14 days Of HCW who tested positive, 162 out of 293 (55.3%) between symptom onset of the index case and the sec- reported to have worked while symptomatic, whereby ondary case was necessary to be able to appoint a defi - contact tracing was required for 103 (35.2%) HCW. For nite index case. During instances where multiple indices 56 HCW (19.1%) contact tracing among both HCW and of both HCW and patients were plausible, the index was patients was necessary, for 47 HCW (16.0%) only contact classified as indeterminate. For classification a distinc - tracing among HCW was needed. tion was also made between regional and non-regional Haanappel et al. Antimicrobial Resistance & Infection Control (2023) 12:46 Page 5 of 12 Table 1 Definitions for the likelihood that healthcare worker infections were the result of nosocomial transmission a b b,c Likelihood WGS result availableWGS cluster HCW and/or patient exposureCommunity exposure Confirmed transmission A SARS-CoV-2 PCR positive HCW with Part of a non-regional sequence cluster With exposure to other SARS-CoV-2 And without known exposure in the WGS result positive HCW and/or patients < 14 days community before symptom onset Probable transmission A SARS-CoV-2 PCR positive HCW with Part of a regional sequence cluster With exposure to other SARS-CoV-2 And without known exposure in the WGS result positive HCW and/or patients < 14 days community before symptom onset B SARS-CoV-2 PCR positive HCW with Part of a non-regional sequence cluster With exposure to other SARS-CoV-2 And with known exposure in the WGS result positive HCW and/or patients < 14 days community of symptoms onset Possible transmission A SARS-CoV-2 PCR positive HCW with- With exposure to other SARS-CoV-2 And without known exposure in the out WGS result positive HCW and/or patients, who community were part of a WGS confirmed non-regional transmission cluster with ≥ 3 transmissions, < 14 days before symptom onset Transmission not confirmed A SARS-CoV-2 PCR positive HCW with Part of a non-regional sequence cluster But is the index case of the epidemio- And without known community WGS result logical cluster based on first date of exposure symptom onset B SARS-CoV-2 PCR positive HCW with Part of a regional sequence cluster With exposure to other SARS-CoV-2 And with known exposure in the WGS result positive HCW and/or patients < 14 days community before symptom onset C SARS-CoV-2 PCR positive HCW with Part of a regional or non-regional With exposure to other SARS-CoV-2 And without known exposure in the WGS result sequence cluster or a unique viral positive HCW and/or patients, without community strain WGS result, < 14 days before symp- tom onset D SARS-CoV-2 PCR positive HCW with- With known exposure to other out WGS result SARS-CoV-2 positive HCW and/or patients < 14 days before symptom onset Haanappel et al. Antimicrobial Resistance & Infection Control (2023) 12:46 Page 6 of 12 Table 1 (continued) a b b,c Likelihood WGS result availableWGS cluster HCW and/or patient exposureCommunity exposure No transmission A SARS-CoV-2 PCR positive HCW with Part of a non-regional sequence cluster But is the index case of the epidemio- And with known exposure in the WGS result logical cluster based on first date of community symptom onset B SARS-CoV-2 PCR positive HCW with Part of a regional sequence cluster But is the index case of the epidemio- WGS result logical cluster based on first date of symptom onset C SARS-CoV-2 PCR positive HCW with Part of a non-regional sequence cluster But with household member (HCW) WGS result in the same epidemiological and genetic cluster D SARS-CoV-2 PCR positive HCW with With a unique viral strain And without known exposure to WGS result SARS-CoV-2 positive HCW and/or patients E SARS-CoV-2 PCR positive HCW with- And without known exposure to out WGS result SARS-CoV-2 positive HCW and/or patients Regional clusters were defined as sequence clusters which were identified in ≥ 5 primary COVID-19 patients, whereby patients with a positive SARS-CoV-2 test upon admission were regarded as primary COVID-19 patients. All other clusters were defined as non-regional Exposure to SARS-CoV-2 positive persons is defined as exposure < 14 days before symptom onset Community exposure is referred to as known exposure to SARS-CoV-2 infected individuals via household contacts, family, friends, events, and/or travels HCW = healthcare worker, WGS = whole genome sequencing Haanappel et al. Antimicrobial Resistance & Infection Control (2023) 12:46 Page 7 of 12 Fig. 2 The number of newly positive SARS-CoV-2 tests per day. The number of newly SARS-CoV-2 positive patients and healthcare workers and the number of positive SARS-CoV-2 PCR tests in the hospital region per day. Blue = Healthcare workers; Orange = Patients; Grey = Region Rotterdam-Rijnmond Population characteristics: patients sequences available from The Netherlands during the During the study period, 245 patients tested positive. time of the study were used for phylogenetic analysis and Patients had a median age of 62 years (range 3–94) and cluster determination (Fig. 4). 35.7% were women. Out of 245 patients, 16 patients A re-evaluation of the epidemiological clusters (6.5%) only had an outpatient visit while the other 229 based on WGS data identified 31 transmission clusters patients (93.5%) were admitted as inpatients. Patients (Fig. 3C). Five epidemiological clusters could not be fur- were admitted to the hospital for a median of 12.6 days ther analyzed as there was only one person with avail- (range 1–79 days). Contact tracing was required for 24 able WGS data. The WGS determined 31 transmission patients (9.8%). clusters were part of 18 (50.0%) of the previously deter- mined epidemiological clusters, demonstrating that sev- Epidemiological cluster analysis eral larger outbreaks actually consisted, of several smaller In total, 257 out of 293 HCW (87.7%) and 24 out of 245 transmission clusters. These clusters consisted of 17 patients (9.7%) were potentially part of an epidemiologi- patients and 92 HCW. One hundred and thirty of 221 cal cluster. Epidemiological data revealed 36 potential (58.9%) HCW did not belong to a cluster as they had a epidemiological clusters among the SARS-CoV-2 posi- unique viral strain, indicating acquisition of the infection tive HCW and patients. Cluster size ranged from 2 to outside of the hospital. Eleven clusters consisted of both 31 cases and contained a median of 5 cases. Epidemio- HCW and patients. Combining sequence and epidemio- logical clusters identified were found in 11 non-clinical logical clusters resulted in a total number of 65 HCW departments, 15 inpatient departments, 7 outpatient (29.4%) as secondary cases. departments, and 3 clusters in the operation room com- plex. Eight epidemiological clusters consisted of both Likelihood of nosocomial transmission among HCW patients and HCW, while the remaining 28 consisted of When SARS-CoV-2 positive HCW in transmission only HCW (Fig. 3A). Out of the 36 epidemiological clus- clusters were classified based on the likelihood of noso - ters, only one cluster (cluster 3) had a patient as the index comial SARS-CoV-2 transmission, the following was case, in all other clusters a HCW was the index case. Epi- found: for 21 HCW (7.2% of all cases) there was con- demiological cluster investigations resulted in the identi- firmed nosocomial transmission, of which 18 acquired fication of 222 (75.7%) secondary cases out of 293 HCW. the infection from another HCW and 3 infections originated from a patient. For 37 HCW (12.6%) prob- Sequencing and phylogenetic analysis able transmission was found and for 3 HCW (1.0%) pos- WGS results were available for 195 HCW (88.2%) and sible transmission (Table 2). In five instances (1.7% of 20 patients (12.8%) who belonged to an epidemiological all cases) a patient was the most likely source for noso- cluster (Fig. 3B). Transmission clusters based on epide- comial transmission to a HCW. For the majority of the miologic data combined with WGS contained a median HCW (78.8% of all cases) the transmission could not be of 3 persons (range 2–24). A total of 158 (71.5%) HCW confirmed and/or were most probably infected outside were secondary cases. These sequences and all other the work setting. Haanappel et al. Antimicrobial Resistance & Infection Control (2023) 12:46 Page 8 of 12 Fig. 3 Identification of epidemiological clusters and transmission clusters. A Epidemiological clusters identified in the complete study population based only on epidemiological data. B Epidemiological clusters identified based only on epidemiological data, excluding cases without WGS results. C Transmission clusters identified in the study population confirmed by WGS. Epidemiological clusters portrayed in Fig. 3B were re-evaluated to form transmission clusters based on the combination of the epidemiological and sequence clusters. Different transmission cluster originating from the same epidemiological cluster are indicated with letters a/b/c/d. Blue = Healthcare workers; Orange = Patients shown to have a higher seroprevalence than the general Discussion population and a higher risk of severe COVID-19 [17, This comprehensive investigation of nosocomial SARS- 18]. Especially during the initial phase of the pandemic, CoV-2 transmission clusters revealed that the majority there were major concerns for nosocomial transmis- of SARS-CoV-2 infections among HCW could be attrib- sion of SARS-CoV-2 from patient-to-HCW. Because of uted to community-acquired infection. Infections among these concerns, HCW testing was prioritized over com- HCW that could be classified as due to nosocomial munity testing. Even though numerous IPC measures transmission, were mainly caused by HCW-to-HCW were in place, SARS-CoV-2 infections still occurred in transmission rather than patient-to-HCW transmis- HCW. Our analysis showed that only a very minor pro- sion. Furthermore, we demonstrated that analyses based portion of HCW infections (1.7%) were likely caused by on epidemiological data alone largely overestimated the patient-to-HCW transmission and limited nosocomial number of nosocomial transmissions, as well as the size transmission took place from HCW-to-HCW. These of nosocomial transmission clusters. results are in line with another Dutch study which iden- SARS-CoV-2 has been widely recognized as an occupa- tified multiple introductions of the virus among HCW tional health hazard for HCW [15, 16]. HCW have been Haanappel et al. Antimicrobial Resistance & Infection Control (2023) 12:46 Page 9 of 12 household members or contact with a potential case outside of work [17, 19, 20]. The findings of the study of Lindsey et al. similarly suggested that the majority of HCW were infected by another HCW [21]. In long-term care facilities, studies have shown that HCW posed a greater risk for patients rather than vice versa [22]. Con- trary to our findings, Lumley et al. suggest that noso - comial transmission is underestimated [23]. This study however, focused on nosocomial acquisition of SARS- CoV-2 by inpatients rather than HCW and did not clas- sify the likelihood of nosocomial transmission in HCW. Additionally, our setting with mainly single-occupancy rooms is different compared to settings with multiple occupancy rooms possibly resulting in different trans - mission dynamics. A couple of explanations for HCW-to-HCW trans- mission can be listed; more than half of HCW (55%) Fig. 4 Phylogenetic analysis of all available sequences from the reported working whilst symptomatic, reflected in the Netherlands on 3 July 2020. The different departments are depicted delay between the date of symptom onset and median in different clusters. Thirty-five sequence clusters were identified, test date three days later. Prior studies have noted that which made up 31 transmission clusters. The scale bar represents the sickness presenteeism behavior among HCW is com- number of nucleotide substitutions per site. Different colors represent different departments mon for influenza-like illness and that HCW are known to be vectors for infectious diseases [24, 25]. Further- more, the criteria for SARS-CoV-2 testing eligibility were Table 2 The likelihood that COVID-19 in a HCW was the result of quite stringent in our hospital in March 2020, partially nosocomial transmission due to the scarcity of tests and limited knowledge of the extent of COVID-19 symptoms. This could have contrib - HCW uted to the high number of HCW who remained work- N % ing while symptomatic. When HCW in patient care were not working in patient rooms, for instance during coffee Total 293 100 breaks or small meetings, masks were often not worn as Confirmed transmission 21 7.2 universal masking was not implemented at our hospital Index HCW 18 during the first wave. Physical distancing and universal Index patient 3 masking of HCW are IPC measures that can be imple- Index indeterminate 0 mented to assure fewer transmission events can take Probable transmission 37 12.6 place [26, 27]. Masking should also be accompanied by Index HCW 24 proper hand hygiene and adequate doffing and donning Index patient 2 [28]. Physical distancing was implemented in our hospi- Index indeterminate 11 tal at the end of March 2020, however, implementation in Possible transmission 3 1.0 practice took time. As HCW are essential workers, espe- Index HCW 2 cially during a pandemic response, preventive measures Index patient 0 for HCW-to-HCW transmission are important in addi- Index indeterminate 1 tion to measures during contact with patients. Transmission not confirmed 78 26.6 The comparison of cluster analyses demonstrated that No transmission 154 52.6 identification of secondary cases through epidemiologi - HCW = Healthcare worker. For all healthcare workers, including those without cal data alone can result in substantial overestimation. available whole genome sequencing data, the likelihood of nosocomial transmission was classified and the source of transmission was indicated These findings highlight once more the importance of investigating potential nosocomial transmission through a combination of detailed epidemiological investiga- through community-acquired infections [2]. Other stud- tion combined with WGS data [14, 21, 29]. Knowing ies reporting on hospital transmission dynamics have the extent of overestimation of nosocomial transmission also pinpointed many different sources of infection for will help us understand and put the findings of epide - HCW outside the hospital, such as SARS-CoV-2 positive miologic outbreak investigations into perspective. One of Haanappel et al. Antimicrobial Resistance & Infection Control (2023) 12:46 Page 10 of 12 the studies which clearly presented both epidemiological be taken into account. This is especially true in the clusters and clusters with combined epidemiological and beginning of a pandemic when testing and subsequent WGS data, is the study of Watt et al. [30]. Contrary to our sequencing of positive cases is biased towards high-risk findings, this study identified more nosocomial transmis - groups (i.e., HCW) and hospitalized patients, and com- sion compared to classical epidemiology using WGS data munity surveillance is not yet performed, possibly lead- [30]. This discrepancy may be due to the extent of epi - ing to an overestimation of the identified clusters. Our demiological data available for the initial epidemiological study only comprises data from the first COVID-19 wave cluster analysis, difference in genomic cluster definition in 2020, and different factors have changed during the and difference in the community prevalence of SARS- course of time such as SARS-CoV-2 variants, immune CoV-2 during the study period. status and differences in community prevalence. How - ever, the results of this study still highlight the challenges of pandemic preparedness and outbreak investigations Strengths and limitations when a new virus emerges. Results of our study were obtained through a retro- Moreover, information regarding SARS-CoV-2 posi- spective in-depth analysis combining epidemiologic tive visitors was not registered and asymptomatic HCW and WGS data. However, for IPC and outbreak man- and patients were not tested and therefore could not be agement WGS is often not readily available, requiring taken into account. Up to 33% of SARS-CoV-2 infec- decision-making of real-time outbreak interventions to tions in adults are estimated to be asymptomatic, there- rely on epidemiological data alone. While many studies fore this could have resulted in missing links and clusters have previously highlighted the added value of WGS for [34]. Only a fraction of all regional COVID-19 cases were in-depth cluster analysis, fewer studies have presented tested and sequenced. This might have resulted in an the disparity in results after adding WGS to the cluster underestimation of sequence diversity in the community analyses [30–32]. Factors which distinguish our study and thus, regional clusters. Additionally, regional clusters from others are our inclusion of the full (SARS-CoV-2 were defined as having ≥ 5 primary patients, which is an positive) hospital patient and HCW population and the arbitrary cut-off. provision of definitions on the likelihood of nosocomial transmission among HCW. Multiple outbreak investi- gations combining epidemiological and WGS data have Conclusion been described, however, the majority of studies focus on The findings of this study highlight the contribution of hospital-acquired COVID-19 among patients rather than SARS-CoV-2 community-acquired infections in HCW HCW. Therefore, studies often only describe definitions settings, the limited number of patient-to-HCW trans- for hospital-acquired COVID-19 for patients and exclude missions as well as the added value of WGS to epidemio- these definitions for HCW, making it unclear what pro - logical data. The COVID-19 pandemic has emphasized portion of SARS-CoV-2 positive HCW is attributable the importance of real-time outbreak management for to community transmission [26]. Studies that focus on pandemic preparedness. While epidemiological data SARS-CoV-2 transmission to HCW often describe risk such as source and contact tracing is important in hos- factors for a positive SARS-CoV-2 PCR among HCW, but pital outbreak management and investigation, it may do not classify the likelihood of nosocomial transmission not suffice in scenarios of high community prevalence. among HCW, nor pinpoint the actual number of SARS- Since WGS is not readily available for many healthcare CoV-2 positive HCW that can be attributed to nosoco- facilities it is important to recognize the uncertainties of mial transmission [33]. cluster analyses based solely on epidemiological data as Limitations of this study include missing WGS data well as to recognize the contribution of HCW-to-HCW due to low viral loads, which could have resulted in miss- transmission. The collaboration between the IPC team ing links. Another challenge in identifying and confirm - and occupational health services, together with the use of ing nosocomial transmission is the relatively low genetic complementary techniques like epidemiological cluster diversity of SARS-CoV-2 strains [22]. This can affect the analysis and WGS is essential to provide knowledge on cluster analysis in a way that separate community intro- nosocomial SARS-CoV-2 transmission dynamics. Dur- ductions or nosocomial transmission are indistinguish- ing this first wave of the pandemic, HCW testing was able based on WGS data. We regarded these cases as prioritized over community testing. Our study shows possible transmission and more information via detailed the importance of surveillance in the community in epidemiological data was crucial for the interpretation of order to understand sequence clusters. Our study popu- the WGS data in outbreak investigation. By classifying lation originated from a single tertiary care center with the likelihood of nosocomial transmission among HCW, single occupancy rooms, which could result in different this factor of uncertainty due to regional clusters should Haanappel et al. Antimicrobial Resistance & Infection Control (2023) 12:46 Page 11 of 12 Author details transmission dynamics compared to healthcare facilities Department of Medical Microbiology and Infectious Diseases, Erasmus MC with multiple occupancy rooms as more contact occurs University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands. between patients. Future studies should investigate this Department of Viroscience, Erasmus MC University Medical Center Rot- terdam, Rotterdam, The Netherlands. Department of Occupational Health difference. Services, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands. 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Journal
Antimicrobial Resistance and Infection Control – Springer Journals
Published: May 10, 2023
Keywords: COVID-19; SARS-CoV-2; Epidemiology; Nosocomial transmission; Hospital; Pandemic preparedness; Whole genome sequencing; Cluster analysis