key: cord-0976429-v1lvzzi4
authors: Zhou, Qi; Gao, Yelei; Wang, Xingmei; Liu, Rui; Du, Peipei; Wang, Xiaoqing; Zhang, Xianzhuo; Lu, Shuya; Wang, Zijun; Shi, Qianling; Li, Weiguo; Ma, Yanfang; Luo, Xufei; Fukuoka, Toshio; Ahn, Hyeong Sik; Lee, Myeong Soo; Liu, Enmei; Chen, Yaolong; Luo, Zhengxiu; Yang, Kehu
title: Nosocomial Infections Among Patients with COVID-19, SARS and MERS: A Rapid Review and Meta-Analysis
date: 2020-04-17
journal: nan
DOI: 10.1101/2020.04.14.20065730
sha: 5cbab240083e769b36a042308007c8c088283007
doc_id: 976429
cord_uid: v1lvzzi4

Background: COVID-19, a disease caused by SARS-CoV-2 coronavirus, has now spread to most countries and regions of the world. As patients potentially infected by SARS-CoV-2 need to visit hospitals, the incidence of nosocomial infection can be expected to be high. Therefore, a comprehensive and objective understanding of nosocomial infection is needed to guide the prevention and control of the epidemic. Methods: We searched major international and Chinese databases Medicine, Web of science, Embase, Cochrane, CBM(China Biology Medicine disc), CNKI (China National Knowledge Infrastructure) and Wanfang database)) for case series or case reports on nosocomial infections of COVID-19, SARS(Severe Acute Respiratory Syndromes) and MERS(Middle East Respiratory Syndrome) from their inception to March 31st, 2020. We conducted a meta-analysis of the proportion of nosocomial infection patients in the diagnosed patients, occupational distribution of nosocomial infection medical staff and other indicators. Results: We included 40 studies. Among the confirmed patients, the proportions of nosocomial infections were 44.0%, 36.0% and 56.0% for COVID-19, SARS and MERS, respectively. Of the confirmed patients, the medical staff and other hospital-acquired infections accounted for 33.0% and 2.0% of COVID-19 cases, 37.0% and 24.0% of SARS cases, and 19.0% and 36.0% of MERS cases, respectively. Nurses and doctors were the most affected among the infected medical staff. The mean numbers of secondary cases caused by one index patient were 29.3 and 6.3 for SARS and MERS, respectively. Conclusions: The proportion of nosocomial infection in patients with COVID-19 was 44%. Patients attending hospitals should take personal protection. Medical staff should be awareness of the disease to protect themselves and the patients. Keywords: COVID-19; meta-analysis; nosocomial infection; rapid review.

Background COVID-19 is a respiratory infectious disease caused by a novel coronavirus, SARS-CoV-2. The first batch of COVID-19 patients were found in China in December 2019 (1) . The disease is mainly transmitted through respiratory droplets and close contact, and all people are susceptible to it(2). SARS-CoV-2 is highly contagious (3) , and has quickly spread to most countries and regions of the world. COVID-19 has become a global pandemic and has received great attention from all over the world (4, 5) . As of April 7, 2020, 1,214,466 confirmed cases of COVID-19 have been found in 211 countries and regions, causing 67,767 deaths (6) .

The main clinical manifestations of COVID-19 are cough, fever and complications such as acute respiratory distress syndrome (1) . Disease clusters and nosocomial infections have been reported(7,8). The proportion of nosocomial infections is high among diagnosed infections, and medical staff are at high risk of infection(8). One study on 44,672 patients showed that health workers accounted for 3.8% of the COVID-19 cases and five health workers died as a result of the infection(9). There is still no specific medicine for COVID-19, so preventing nosocomial infections is crucial.

This study compares the incidence of nosocomial infections during the COVID-19, SARS and MERS epidemics and analyzes the characteristics of the nosocomial infection, to enhance the understanding of nosocomial infection among medical and non-medical staff.

An experienced librarian searched the following databases from their inception to March 31, 2020 in the following electronic databases(10): the Cochrane library, MEDLINE (via PubMed), EMBASE, Web of Science, CBM (China Biology Medicine disc), CNKI (China National Knowledge Infrastructure), and Wanfang Data. We made no restrictions on language or publication status. We used the following search formula is as follow: ("Novel coronavirus" OR "2019-novel coronavirus" OR "Novel CoV" OR "2019-nCoV" OR "Wuhan-Cov" OR "2019-CoV" OR "Wuhan Coronavirus" OR "Wuhan seafood All rights reserved. No reuse allowed without permission. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.14.20065730 doi: medRxiv preprint market pneumonia virus" OR "COVID-19" OR "SARS-CoV-2" OR "Middle East Respiratory Syndrome" OR "MERS" OR "MERS-CoV" OR "Severe Acute Respiratory Syndrome" OR "SARS" OR "SARS-CoV" OR "SARS-Related" OR "SARS-Associated" ) AND ("Cross Infection" OR "Cross Infections" OR "Healthcare Associated Infections" OR "Healthcare Associated Infection" OR "Health Care Associated Infection " OR "Health Care Associated Infections" OR "Hospital Infection" OR "Nosocomial Infection" OR "Nosocomial Infections" OR "Hospital Infections" OR "hospital-related infection" OR "hospital-acquired infection"). We also searched clinical trial registry platforms (the World Health Organization Clinical Trials Registry Platform (http://www.who.int/ictrp/en/), US National Institutes of Health Trials Register (https://clinicaltrials.gov/)), Google Scholar (https://scholar.google.nl/), preprint platform (medRxiv (https://www.medrxiv.org/), bioRxiv (https://www.biorxiv.org/) and SSRN (https://www.ssrn.com/index.cfm/en/)) and reference lists of the included reviews to find unpublished or further potential studies. Finally, we contacted experts in the field to identify relevant trials. The search strategy was also reviewed by another information specialist. The details of the search strategy can be found in the Supplementary Material 1.

We included case series studies and case reports about the proportion of cases of COVID-19, SARS and MERS who were infected in health facilities, about infections among medical staff and outbreaks in hospitals. Abstract, letter, new, guideline, articles for which we could not access all relevant data or full text were excluded. All rights reserved. No reuse allowed without permission. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.14.20065730 doi: medRxiv preprint

After eliminating duplicates, two reviewers(Y Gao and X Wang) independently selected the relevant studies in two steps with the help of the EndNote software. Discrepancies were settled by discussion or consulting a third reviewer(Qi Zhou). In the first step, all titles and abstracts were screened using pre-defined criteria. In the second step, full-texts of the potentially eligible and unclear studies were reviewed to decide about final inclusion. All reasons for exclusion of ineligible studies were recorded. The process of study selection was documented using a PRISMA flow diagram (11).

Two reviewers(R Liu and X Wang) extracted the data independently using a standardized data collection table. Any differences were resolved by consensus, and a third auditor checked the consistency and accuracy of the data. The following data were extracted: 1) basic information: title, first author, country, year of publication, and type of study; 2) population baseline characteristics: age and sex distribution, and sample size; and 3) the proportion of nosocomial infections, the proportion of patients with occupation of medical staff, and for studies on hospital outbreaks, the number of index cases and total infections.

Two researchers (Z Wang and Q Shi) independently assessed the potential bias in each included study. The included studies were evaluated using appropriate assessment scales depending on the study type: for case control studies, the Newcastle-Ottawa Scale (NOS)(12), for cross-sectional studies and epidemiological surveys, the methodology evaluation tool recommended by the Agency All rights reserved. No reuse allowed without permission. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.14.20065730 doi: medRxiv preprint for Healthcare Research and Quality (AHRQ)(13), and for case reports and case series, we used a methodology evaluation tool recommended by National Institute for Health and Care Excellence (NICE) (14).

We performed a meta-analysis of proportions for dichotomous outcomes (nosocomial infection among the confirmed cases, and infections among the health care workers), reporting the effect size (ES) with 95% confidence intervals (CI) by using random-effects models. Two-sided P values < 0.05 were considered statistically significant. Heterogeneity was defined as P<0.10 and I 2 >50%. All analyses were performed in STATA version 14.

Two reviewers(Z Wang and Q Shi) assessed the quality of evidence independently using the Grading of Recommendations Assessment, Development and Evaluation (GRADE)(15-16). We produced a "Summary of Findings" table using the GRADEpro software. This table includes overall grading of evidence body for each prespecified outcome that is accounted in a meta-analysis. The overall quality can be downgraded for five considerations (study limitations, consistency of effect, imprecision, indirectness, and publication bias) and upgraded for three considerations (large magnitude of effect, dose-response relation and plausible confounders or biases). The overall quality of evidence will be classified as high, moderate, low or very low, which reflecting to what extent that we can be confident the effect estimates are correct.

As COVID-19 is a public health emergency of international concern and the situation is All rights reserved. No reuse allowed without permission. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.14.20065730 doi: medRxiv preprint evolving rapidly, our study was not registered in order to speed up the process (17).

Our initial search revealed 2626 articles, of which 2598 were left after deleting the duplicates ( Figure 1) . After review the titles and abstracts, we screened the full texts of 66 articles, of which 40 were finally included ( 

The proportion of nosocomial infections was 44.0% (95% CI: 0.36 to 0.51; I 2 =0.00%) among COVID-19 patients, 36.0% (95% CI: 0.23 to 0.49; I 2 =97.8%) among SARS patients, and 56.0% (95% CI: 0.08 to 1.00; I 2 =99.9%) among MERS patients ( Figure 2) . Thirty-three percent(95% CI: 0.27 to 0.40; I 2 =0.00%) of patients with COVID-19 were medical staff, and 2.0% (95% CI: 0.01 to 0.03; 1 0 visitors). The corresponding proportions among SARS patients were 37.0% (95% CI: 0.25 to 0.49; I 2 =97.3%) and 24.0% (95% CI: 0.10 to 0.38; I 2 =86.6%), and 19.0% (95% CI: 0.04 to 0.35; I 2 =97.8%) and 36.0% (95% CI: 0.06 to 0.67; I 2 =99.3%) among MERS patients (Figures 3-4) .

Twenty studies metioned infection among the health workers, of which sixteen studies described the For MERS, for the corresponding proportions were 35.0% (95% CI:0.14 to 0.56; I 2 =0.00%), 50.0% (95% CI: 0.29 to 0.71; I 2 =0.00%) and 16.0% (95% CI: 0.00 to 0.32; I 2 =0.00%). For all three conditions combined, the proportion of doctors among infected hospital staff was 30.0%, 51.0% for the proportion of nurses, and 19.0% for the proportion of others ( Figure 5-7) .

Five studies described the protective measures of medical staff infected with SARS in hospital.

Sixty-three percent (95% CI: 0.35 to 0.92; I 2 =96.1%) of the infected staff did not wear protective clothing ), 58.0% (95% CI: 0.39 to 0.76; I 2 =0.00%) did not use gloves , 91.0% (95% CI: 0.80 to 1.00; 1 1 

Six studies described SARS outbreaks, and five studies MERS outbreaks that happened in hospitals.

The SARS studies reported on 23 patients, causing a total of 674 infections in hospitals, with an average of 29.3 infections per index patient. The MERS studies reported 24 patients causing 152 infections in hospitals, with an average of 6.3 infections per index patient ( Table 2) .

The results of GRADE on nosocomial infections showed that the quality of evidence were low or very low. The details can be found in the Supplementary Material 3.

Our rapid review identified a total of 40 studies. Low to very low-quality evidence indicated that the proportion of nosocomial infection among confirmed cases of COVID-19 was 44%, which is higher than for SARS but lower than for MERS. Most patients with COVID-19 and SARS infected in hospitals were medical staff, among whom nurses formed the largest group, followed by doctors.

Both SARS and MERS outbreaks have been reported in hospitals, but we found no evidence of a COVID-19 outbreak.

SARS-CoV-2, the infectious agent causing COVID-19, is highly contagious, mainly spread by droplets and close contact. So far, a number of familial disease clusters have been reported, and some of the confirmed patients had been infected in healthcare facilities. As health care workers are in contact with a large number of suspected patients on a daily basis, strict precautions need to be All rights reserved. No reuse allowed without permission. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.14. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.14.20065730 doi: medRxiv preprint possibility of the outbreak of nosocomial infection, and establish an early warning mechanism.

Emergency plans or measures should be developed to deal with nosocomial infections.

Our study included studies related to nosocomial infections among COVID-19, SARS and MERS patients. Our results can help the decision-making related to prevention, control and clinical management in hospitals. Some studies had missing data, and we used methods of meta-analyses of proportions to analyse those studies with available data, so the proportions estimated may not be accurate and similar to the actual data. Most of the results are based on low-quality research, so that the credibility of the results is low.

A large proportion of confirmed cases of COVID-19 were infected within healthcare facilities. Therefore, the patients who come to the hospital should do pay attention on personal protection. At the same time, medical institutions can reduce the spread of the virus through triage, and setting up separate fever clinic and isolation wards. Awareness of the disease needs to be improved among medical staff, so that they can protect themselves adequately and stop the spread of the virus within hospitals.

(I) Conception and design: Y Chen and E Liu; (II) Administrative support: Y Chen; (III) Provision of study materials or patients: Y Gao, R Liu and X Wang; (IV) Collection and assembly of data: R Liu, All rights reserved. No reuse allowed without permission.

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The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.14.20065730 doi: medRxiv preprint 1 4 X Wang, YL Gao, P DU, X Wang, X Zhang, S Lu and Z Wang; (V) Data analysis and interpretation: Q Zhou, Q Shi and Y Gao; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript:

All authors.

We thank Janne Estill, Institute of Global Health of University of Geneva for providing guidance and comments for our review. We thank all the authors for their wonderful collaboration. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint (which was not peer-reviewed) is the #2 TOPIC: "SARS-COV-2" #3 TOPIC: "Novel coronavirus"

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The copyright holder for this preprint (which was not peer-reviewed) is the Yes Yes 6 † †: According to the methodology evaluation tool recommended by National Institute for Health and Care Excellence. The risk of bias is evaluated according to eight criteria. The results were summarized by scoring method, for the "Yes" items, the score was 1, and for the "no" items, the score was 0. The maximum score is 8; the higher the score, the lower the risk of bias.

The numbers 1 to 8 refer to the items of the tool:1) Case series collected in more than one centre, i.e. multi-centre study; 2) Is the hypothesis/aim/objective of the study clearly described?; 3) Are the inclusion and exclusion criteria (case definition) clearly reported?; 4) Is there a clear definition of the outcomes reported?; 5) Were data collected prospectively?; 6) Is there an explicit statement that patients were recruited consecutively?; 7) Are the main findings of the study clearly described?; 8) Are outcomes stratified? (e.g., by disease stage, abnormal test results, patient characteristics) author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.14.20065730 doi: medRxiv preprint Figure 3 The proportion of nosocomial infections among confirm cases of COVID-19, SARS and MERS.

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The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.14.20065730 doi: medRxiv preprint Figure 4 Proportions of health care workers among confirmed cases of COVID-19, SARS and MERS All rights reserved. No reuse allowed without permission. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.14.20065730 doi: medRxiv preprint Figure 6 Proportion of doctors among hospital staff with COVID-19, SARS and MERS All rights reserved. No reuse allowed without permission. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.14.20065730 doi: medRxiv preprint 1 Figure 8 Proportion of staff other than doctors or nurses among hospital staff with COVID-19, SARS and MERS All rights reserved. No reuse allowed without permission. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.14.20065730 doi: medRxiv preprint Figure 9 Proportion of health care staff with SARS who did not take protective measures.

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Clinical features of patients infected with 2019 novel coronavirus in Wuhan

Diagnosis and treatment of pneumonia infected by novel coronavirus (trial version 7)

The reproductive number of COVID-19 is higher compared to SARS coronavirus

Novel coronavirus is putting the whole world on alert

World Health Organization; c2020

Coronavirus disease (COVID-19) outbreak situation Data as reported by national authorities by 7

World Health Organization; c2020

This tool assesses the quality of bias according to 11 criteria. And each criterion is answered by "Yes", "No" or "unsure". The results were summarized by scoring method, for the "Yes" items, the score was 1, and for the "no" items, the score was 0. The maximum score is 11; the higher the score, the lower the risk of bias. The numbers 1 to 11 refer to the items of the tool:1) Defining the source of information (survey, record review); 2) Listing the inclusion and exclusion criteria for exposed and unexposed subjects or referring to previous publications; 3) Indicate time period used for identifying patients; 4) Indicating whether the subjects were recruited consecutively (if not population-based); 5) Indicating if evaluators of subjective components of the study were masked from the participants; 6) Description of any assessments undertaken for quality assurance purposes (e.g., test/retest of primary outcome measurements); 7) Explaining any exclusions of patients from the analysis; 8) Description how confounding was assessed and/or controlled; 9) If applicable, explaining how missing data were handled in the analysis; 10) Summarizing patient response rates and completeness of data collection; 11) Clarification of the expected follow-up (if any), and the percentage of patients with incomplete data or follow-up.