key: cord-0811995-1qkhc5ch
authors: Wang, Xuan; Jiang, Xiaobing; Huang, Qimin; Wang, Han; Gurarie, David; Ndeffo-Mbah, Martial; Fan, Fei; Fu, Peng; Horn, Mary Ann; Mondal, Anirban; King, Charles; Xu, Shuai; Zhao, Hongyang; Bai, Yansen
title: Risk factors of SARS-CoV-2 infection in healthcare workers: A retrospective study of a nosocomial outbreak
date: 2020-10-14
journal: Sleep medicine: X
DOI: 10.1016/j.sleepx.2020.100028
sha: 4a04069e4c52e5350d8213ebe756a7d2f54b6629
doc_id: 811995
cord_uid: 1qkhc5ch

Background Healthcare workers (HCWs) are at the forefront of fighting against the COVID-19 pandemic. However, they are at high risk of acquiring the pathogen from infected patients and transmitting to other HCWs. We aimed to investigate risk factors for nosocomial COVID-19 infection among HCWs in a non-COVID-19 hospital yard. Methods Retrospective data collection on demographics, lifestyles, contact status with infected subjects for 118 HCWs (including 12 COVID-19 HCWs) at Union Hospital of Wuhan, China. Sleep quality and working pressure were evaluated by the Pittsburgh Sleep Quality Index (PSQI) and The Nurse Stress Index (NSI), respectively. The follow-up duration was from Dec 25, 2019, to Feb 15, 2020. Results A high proportion of COVID-19 HCWs had engaged in night shift-work (75.0% vs. 40.6%) and felt working under pressure (66.7% vs. 32.1%) than uninfected HCWs. SARS-CoV-2 infected HCWs had significantly higher scores of PSQI and NSI than uninfected HCWs (P<0.001). Specifically, scores of 5 factors (sleep quality, time, efficiency, disorder, and daytime dysfunction) in PSQI were higher among infected HCWs. For NSI, its 5 subscales (nursing profession and work, workload and time allocation, working environment and resources, patient care, management, and interpersonal relations) were all higher in infected than uninfected HCWs. Furthermore, total scores of PSQI (HR=50.99, 95%CI=4.13-630.15; P=0.002) and NSI (HR=55.42, 95%CI=2.39-1285.99; P=0.012) were both positively associated with the risk of SARS-CoV-2 infection. Conclusion Our analysis shows that poor sleep quality and higher working pressure may increase the risk of nosocomial SARS-CoV-2 infection among HCWs.

In December 2019, pneumonia with previously unknown etiology began to spread in Wuhan, Hubei province in China. The causative virus of this disease was soon identified as a novel coronavirus, and it was preliminarily named as the 2019 novel coronavirus (2019-nCoV). This virus was later renamed as SARS-CoV-2, and pneumonia it causes was named 2019 novel coronavirus diseases (COVID-19) by the World Health Organization (WHO). As with other infectious disease outbreaks, healthcare workers (HCWs) have been at the front line of the fight against COVID-19. However, hospitals are vulnerable to infectious disease spread through rapid patients-HCWs and HCWs-HCWs cross-infection, especially when dealing with a disease of unknown or not well-known etiology as it was the case during the early phase of the COVID-19 outbreak 1-3 .

A recent study from the Chinese Center for Disease Control and Prevention showed that a total of 1,716 HCWs had been diagnosed with COVID-19, including 5 deaths by Feb 11, 2020 , with a crude case fatality rate of 0.3% 4 . This situation resulted in a shortage of HCWs and the collapse of the medical system, even though Wuhan had high-quality medical resources 5 . Thousands of HCWs from various provinces in China were needed to ease the strain on the Wuhan medical system and support the response to the epidemic. Since then, similar scenarios have been observed in various countries. For example, reports from Italy indicated that 20% of responding HCWs were infected with COVID-19 6 , and in Spain, HCWs infected with COVID-19 accounts for around 12% of all confirmed cases 7 . Therefore, the establishment of protection guidelines for HCWs is an important step to fight against COVID-19, and is the most important bridge that prevents the collapse of the medical system and reduces social panic.

However, the specific reasons for the infection of HCWs and the failure of protection still need to be fully investigated 8 .

Before public health interventions were implemented by the Chinese government on Jan 23, 2020, the COVID-19 had already spread to HCWs unknowingly treating patients infected with the virus. Little J o u r n a l P r e -p r o o f is known of risk factors for nosocomial COVID-19 infection among HCWs prior to this date, as there is no existing peer-reviewed literature quantifying the transmissibility of SARS-CoV-2 among HCWs during that period. Besides, the dynamics of COVID-19 spread among HCWs largely remained unknown.

In the present study, we conducted a retrospective study of a COVID-19 outbreak among HCWs in the Department of Neurosurgery, which is not a COVID-19 hospitalization yard in Union Hospital in Wuhan.

Their information before the phase of the big outbreak of COVID-19, including epidemiological, demographical, and lifestyles were collected. We investigate the risk factors that play roles in the susceptibility of HCWs to COVID-19.

We carried out this single-center respective cohort study in the Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. The nucleic acid testing for SARS-CoV-2 by RT-PCR tests showed 14 of 171 HCWs (an infection rate of 8.19% ) in this single-center were infected with SARS-CoV-2 by a hospitalized patient who was later diagnosed with COVID-19 and defined as the index case. Out of the 14 COVID-19 HCWs, 12 participants with complete questionnaire data were enrolled in this study. The participants reported they do not have a history of contacting other infected cases and also their family members have not been previously infected. To acquire the information of the history of HCWs' contact with other infected cases, they were asked about the following question in the questionnaire: (1) Have you ever contacted with other infected cases inside or outside the hospital? (2) Were your close colleagues or family members infected?

If "yes", they were further asked whether they were diagnosed or developed clinical manifestations (e.g., fever, nonproductive cough, dyspnea, fatigue, and radiographic evidence of pneumonia) earlier than their contacts. This study was approved by the institutional ethics board of Union Hospital, Tongji Medical

College, Huazhong University of Science and Technology (No. 20200029). All participants provided J o u r n a l P r e -p r o o f informed consent using an online form, and they were assured of the only academic use of the collected data.

An online electronic questionnaire was sent to all 171 HCWs in the Department of Neurosurgery of Union Hospital of Wuhan, and 118 valid questionnaires were finally collected, including the questionnaires from 12 COVID-19 HCWs (including 4 doctors and 8 nurses), and 106 uninfected HCWs.

Baseline demographics (age, gender, height, weight, education level), lifestyle factors (physical activity, smoking status, and alcohol drinking status, diet), medical post, and chronic medical diseases were gathered. For all HCWs, their data on sleep quality were assessed by the Pittsburgh Sleep Quality Index (PSQI) 9 , and for nurses, their feeling of working under pressure was further evaluated by The Nurse Stress Index (NSI) 10 . The contact status with the identified COVID-19 cases was also collected. A detailed description of these data as described in the Supplementary Materials. We used several methods to minimized recall bias. First, the online questionnaires were filled out as soon as HCWs completed their nucleic acid testing to minimize the time-interval between memory acquisition and exposure. Second, the data of the exposure to the index cases and infected colleagues, as well as their night duty information were matched to their daily work records in the department. Third, 8 of the 118 valid questionnaires were re-filled out (within a two-week interval) by the same participants, which showed good consistency (Kappa=0.871).

The data on the use of personal protective equipment (PPE) (e.g., surgical masks, non-surgical masks, disposable gloves, safety glasses, and protective clothing) in the early stages before an outbreak were also collected. But this data was not shown in the following analysis due to the low utilization rate of PPE in daily work in a non-infectious ward. 

Continuous variables were described as mean ± SD, or median and interquartile range (IQR), and values between COVID-19 HCWs and uninfected groups were compared using independent Student's ttest or Mann-Whitney U test when data were normally or skewed distributed, respectively. Categorical variables were described using counts (%) and were compared using the χ 2 test, or Fisher's exact test.

Hazard ratios (HRs) and 95% confidence intervals (CIs) for the risk of COVID-19 that are associated with the sleep quality and working pressure were calculated by Cox proportional hazards regression models, with adjustment for age, gender, and medical post (if necessary) in model 1, while HCWs' exposure status to the COVID-19 index patient or HCWs diagnosed with COVID-19 were additionally adjusted in model 2. All the statistical hypothesis tests were two-sided with p-value < 0.05 as the level to reject the null hypothesis, and these analyses were performed with the SAS program (version 9.4; SAS Institute, Carry, NC). Detailed descriptions for the calculation of basic reproduction number (R 0 ) were shown in Supplementary Materials.

The overview of the transmission of SARS-CoV-2 from the index case to HCWs was shown in had symptoms on Jan 14, 2020. HCW4 worked in the operating room when contacted the index case on Jan 6, 2020, and developed symptoms on Jan 13, 2020. HCW3 contacted the index case in the process of surgical nursing, and then she had lunch and dinner with HCW6 between Jan 6 and Jan 8, 2020. In the meantime, HCW6 worked together with HCW9 and HCW7. These four HCWs successively developed symptoms from Jan 12 to Jan 16, 2020. HCW2 participated in the symptomatic treatment of the index case on Jan 7, 2020, and she also had a history of contact with HCW10, then both of them showed symptoms onset on Jan 8 and Jan 17, 2020, respectively. HCW11 and HCW12 contacted the index case before the environmental disinfection was conducted on Jan 11, 2020, and they developed symptoms on Jan 21, 2020, and Jan 23, 2020, respectively. Detailed dates of these 12 HCWs with the onset of symptoms, isolation, and diagnosis of COVID-19 were shown in Table S1 .

The mean age of COVID-19 HCWs was 36.6 (SD=7.4) years old, which was significantly higher than uninfected HCWs (mean=30.5, SD=5.3) (P= 0.006) ( Table 1 ). The proportion of COVID-19 HCWs was significantly higher for those who had a master's degree or above (50.0% vs. 18.9%), engaged in (Table S1 and Figure S1 ), the resulting R 0 was estimated to be 1.03.

Given the findings that a higher proportion of COVID-19 HCWs worked the night shift and felt they were working under pressure than uninfected HCWs, we further evaluated their sleep quality and workrelated stress by computing and analyzing their scores of PSQI and NSI. The analyses showed that COVID-19 HCWs had a significantly higher PSQI score than uninfected HCWs (P<0.001, Figure 2A ).

Specifically, for the 7 factors of the PSQI test, COVID-19 HCWs had significantly high scores for 5 factors (sleep quality, sleep time, sleep efficiency, sleep disorder, and daytime dysfunction), while the remaining 2 factors (sleep duration, and use of the hypnotic drug) were not significantly different. For the NSI, the scores of its 5 subscales (nursing profession and work, workload and time allocation, working environment and resources, patient care, management, and interpersonal relations) were all significantly higher in infected than uninfected nurses ( Figure 2B ).

We further investigated the associations of sleep quality and working pressure with the risk of COVID-19 using two models. In both models, we found that the total scores of PSQI and NSI were positively associated with the risk of COVID-19 (Table 2) 

Given the false-negative rate of the viral nucleic acid test for SARS-CoV-2, we also consider the positive findings from chest computed tomography (CT) scan, which showed bilateral ground-glass opacity among 28 HCWs (including acid test diagnosed 12 infected HCWs). When these 28 HCWs were defined as the infected HCWs, the associations of the total scores PSQI and NSI with the risk of COVID-19 were essentially unchanged (Table S2 ).

As the pandemic (COVID-19) accelerates, millions of people are recommended to work from home (social distancing) to minimize the transmission of COVID-19, HCWs have to do the exact oppositegoing to hospitals, clinics, and putting themselves at high risk from COVID-2019 6 . Even as non-COVID-before the measures of infection control were widely conducted, which ensured the data was under the natural transmission of COVID-19.

Sleep disturbance was highly prevalent among HCWs 11 .The data in the present study suggested that a high proportion of COVID-19 HCWs worked the night shift. Furthermore, the PSQI showed a higher total score, sleep quality score, and sleep time score among infected than uninfected HCWs, and these scores were positively associated with the risk of COVID-19. Although the underlying mechanism for these associations had not been explored, proper sleep is at the first line of defense against infections that had been reported 12 , since sleep deprivation may decrease the production protective cytokines that were released by the immune system. For example, sleep can show effects on plasma levels of cytokine IL-1, TNF, and IL-6 when sleep duration was restricted 13 . Besides, findings in this study showed that a high proportion of COVID-19 nurses felt they were working under pressure, especially the pressure of dealing with pneumonia of unknown etiology, such as COVID-19. Their self-reported working hours were almost 10 hours per day for at least 6 days per week before they received the assist from the medical support teams that were dispatched from other provinces in China. We further analyzed the pressure source by NSI, and these scores were positively associated with COVID-19 risk among nurses when the contact status with infected cases was adjusted. Similar results were also shown in a retrospective cohort study in Wuhan, China 14 . One possible reason is the prevalence of oxidative stress among nurses with higher job stress 15 , which can weaken the immune function 16 and adverse mental health outcomes 17 . Some studies had revealed that stress could alter the cytokine balance, such as Th1/Th2 with strong deviation toward the Th2 component which could increase susceptibility to certain infections 18 . Therefore, it can be inferred that poor sleep quality and high working pressure among HCWs are likely to lead to their increased susceptibility to COVID-19. However, due to the retrospective design of the present study, and the lack of bio-samples, the individual's immunity parameters were not monitored, and the hypothesis needs to be further validated. There are several limitations to this study. These include the possibility of unmeasured residual confounding effects of contact status with infected cases, although we had adjusted for some primary confounders. Besides, our relatively small sample size and the imbalance between the numbers in each group (uninfected and COVID-19 HCWs) may influence the statistical power of our analysis and results.

Furthermore, although several methods were used to minimize the recall bias, the influence of recall bias within the retrospective design could not be estimated and excluded. Further large prospective studies are needed to validate our findings.

The data before the outbreak of COVID-19 showed poor sleep quality, and high working pressure 

Influenza in the acute hospital setting

Severe acute respiratory syndrome (SARS): a year in review

Optimal control of environmental cleaning and antibiotic prescription in an epidemiological model of methicillin-resistant Staphylococcus aureus infections in hospitals

The COVID-19 Epidemiology Group of Emergency Response Mechanism of Chinese Center for

Disease Control and Prevention. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China

Nosocomial outbreak of 2019 novel coronavirus pneumonia in Wuhan, China

COVID-19: protecting health-care workers

COVID-19: Spain reports 462 more deaths in one day

Mental health care for medical staff in China during the COVID-19 outbreak

The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research

The Nursing Stress Scale: development of an instrument

Sleep disturbances among medical workers during the outbreak of COVID-2019

Association of insufficient sleep with respiratory infection among adults in the United States

The sleep-immune crosstalk in health and disease

Risk factors of healthcare workers with corona virus disease 2019: A retrospective cohort study in a designated hospital of Wuhan in China

A cross sectional study evaluating psychosocial job stress and health risk in emergency department nurses

How the redox state regulates immunity. Free Radic Biol Med

Covid-19: adverse mental health outcomes for healthcare workers

The adverse effects of psychological stress on immunoregulatory balance: applications to human inflammatory diseases

Medical masks vs N95 respirators for preventing COVID-19 in healthcare workers: A systematic review and meta-analysis of randomized trials. Influenza Other Respir Viruses

Psychological stress of ICU nurses in the time of COVID-19

The authors would like to appreciate all healthcare workers in this study. W.X., X.J., 

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