Seroprevalence of Antibodies against SARS-CoV-2 Infection among Healthcare Workers at a Tertiary Hospital in Saudi Arabia

doi:10.21203/rs.3.rs-1102731/v1

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Research Article

Seroprevalence of Antibodies against SARS-CoV-2 Infection among Healthcare Workers at a Tertiary Hospital in Saudi Arabia

https://doi.org/10.21203/rs.3.rs-1102731/v1

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Background

The nature of the healthcare workers' jobs standing at the frontline against the coronavirus disease 2019 (COVID-19) puts them at a higher risk of unknowingly contracting the disease and potentially contributing to the spread. This study aims to assess the overall positive seroconversion prevalence of SARS-CoV-2.

Methods

This is a longitudinal cohort study of healthcare workers at a tertiary hospital serving patients in several districts in the Eastern Province of Saudi Arabia. Participants were recruited between June - December 2020. Each participant had a serology blood test and completed the World Health Organization's risk factors assessment questionnaire.

Results

This study included 682 participants working in any capacity at a tertiary hospital, representing 15.7% of our population. Only 87 participants tested positive for SARS-CoV-2 antibodies, a prevalence of 12.7% of all participants. Of the 87 participants, 17 participants never tested positive for COVID-19 rt-PCR before the study, a prevalence of 2.9%. Moreover, the improper technique of using alcohol-based hand rub or soap and water after the risk of body fluid exposure and wearing personal protective equipment when indicated were found to be statistically significant to having positive SARS-CoV-2 IgG assay with P=0.02, P=0.03, and CI: 95% respectively.

Conclusion

Positive seroconversion rate was considerably low during the first wave of COVID-19 amongst our healthcare workers and similar to other healthcare organizations in Saudi Arabia. Seropositivity correlated significantly with adherence to infection prevention and control recommendations.

Infectious Diseases

Virology

Immunology

COVID-19

HCW

Healthcare workers

Prevalence

SARS-CoV-2

Since the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the entire world had been threatened¹. As of September 2021, nearly 229 million cases of coronavirus disease 2019 (COVID-19) have been reported with more than 4.7 million deaths worldwide². The pandemic has affected the healthcare systems and resources severely in many countries, especially with the healthcare workers (HCWs) are in the highest-risk group due to the nature of their job and increased exposure while closely interacting with infected individuals³.

There is a level of uncertainty of epidemiological, clinical, and virological characteristics associated with a novel virus and its severity and ability to spread amongst humans. With SARS-CoV-2 high transmission rate, the disease caused a tremendous burden on the healthcare systems and frontline HCWs¹. As a result of the disease's nature of transmission and the HCWs high risk of infection, they may have unknowingly contracted the disease and potentially contributed to the spread⁴. Several studies have reported that the evidence of COVID-19 infection among HCWs is growing with a current rate of (2.3%) in Saudi Arabia⁵ and up to (17.4%) globally⁴. A number of these studies also reported that (38%-48%) of the HCWs with positive seroconversion never experienced any symptoms^6,7.

Questions have been risen about using reverse transcription-polymerase chain reaction (rt-PCR) solely for infection screening as there is still little to be known about the SARS-CoV-2 virus and the knowledge of asymptomatic infections among HCWs is also limited⁸. Therefore, serological testing can provide information on the proportion of individuals who have been infected with COVID-19. It is crucial to monitor the prevalence of infection in critical subgroups of the population such as HCWs and nursing homes employees⁹. This is beneficial for many vital reasons, for one, it will help determine risks and levels of exposure among staff and identify high-risk departments/ wards. Moreover, information about current and previous infections can be useful for healthcare resource planning as well as avoiding unnecessary quarantines for the staff⁹.

Healthcare workers are considered our shield against COVID-19, the nature of their job has placed them on the frontline to fight this pandemic globally placing them at the highest risk to contract an infection which might lead to a heavier burden on healthcare organizations¹⁰. A better understanding of the pattern and prevalence of the SARS-CoV-2 infection can guide HCWs for better protection as well as assistance to policymakers to develop policies and regulations for infection prevention and control in the clinical setting¹⁰. Therefore, it is crucial to understand and determine the correlation between the positive seroconversion and duration of immunity as well as the possibilities of reinfection amongst HCWs⁹.

This study aims to assess the immune status of HCWs at a tertiary hospital. We aim to assess the overall positive seroconversion prevalence to SARS-CoV-2 as well as the prevalence of asymptomatic infections. Moreover, we aim to assess the risk factors of seroconversion amongst HCWs using the World Health Organization's (WHO) protocol for assessment of potential risk factors for 2019-novel coronavirus (2019-nCoV) infection among healthcare workers in a healthcare setting as one of their Early Investigations protocols¹¹.

Study design

The study received the hospital's Institutional Review Board's approval (IRB # 20-09) on the 13th of May 2020. This is a longitudinal cohort study of healthcare workers at a tertiary hospital located in Dhahran serving patients in several districts in the Eastern Province of Saudi Arabia. The study took place from June 2020 until the end of April 2021.

Each participant was assigned a study ID and written informed consent was obtained as well as a hard copy of the WHO's risk factors assessment questionnaire.

Study Population and Sample

Initially, the participants were randomly selected from the hospital's employees' database and invited to participate in the study via emails. However, due to the slow recruitment, an open invitation was sent to all staff members through staff announcement to their emails. Recruitment was completed by December 2020 and follow-up appointments were carried on until April 2021. Participants were asked to visit the research team in 2 months period for a follow-up test. Participants with positive SARS-CoV-2 IgG assay were asked to return to the research team for a third follow-up visit. As of December 2020, our hospital had 3939 staff members and 383 housekeepers working in all departments. This study included 682 participants working in different departments at the hospital from front-liners dealing with COVID-19 patients on daily basis to administrative whom had minimal contact with any patient.

Data Collection

Blood samples were collected from participants by a phlebotomist at the local hospital laboratory. The SARS-CoV-2 IgG assay results were then sent to the research team as positive or negative and their index which then entered into the study database.

The WHO Questionnaires extracted from their protocol for assessment of potential risk factors for 2019-novel coronavirus (2019-nCoV) were administered to all participants upon consenting to the study and before their blood collection appointments. All completed questionnaires were entered into the study database by two nurses and validated by the study's principal investigator.

ARCHITECT SARS-CoV-2 IgG Assay

ARCHITECT SARS-CoV-2 IgG Assay was used throughout the whole study. The assay is an automated, two-step immunoassay for the qualitative detection of IgG antibodies to SARS-CoV-2 in human serum using chemiluminescent microparticle immunoassay (CMIA) technology.

The index reference range interpretation for this qualitative IgG test is positive if the index was 1.4 or above and negative if the index was below 1.4.

Validation of SARS-CoV-2 IgG Testing

The validation study of the SARS-CoV-2 IgG test was carried on Architect i2000SR System. Precision study with in-run and total is not applicable as this is a qualitative method. Positive and negative controls for SARS-CoV-2 IgG Assay were run daily for fifteen days and it has been indicated acceptable result. A total of 23 patient serum specimens was tested on the Architect i2000SR System, and at the same time on the Architect i2000SR in King Fahad Specialist Hospital for SARS-CoV-2 IgG test, results compared for parallel testing. The comparison study showed that the results of the SARS-CoV-2 IgG on the DH-Architect i2000SR System and KFSH-Architect i2000SR System were compared and correlated. A comparison study indicated 100% specificity and sensitivity. The validation study concluded that the SARS-CoV-2 IgG test performed on Architect i2000SR System is acceptable for patient testing.

Summary and explanation of the test

The assay is designed to detect IgG antibodies to the nucleocapsid protein of SARS-CoV-2 in serum from patients with signs and symptoms of infection who are suspected of coronavirus disease (COVID-19) or in serum of subjects that may have been infected by SARS-CoV-2. The SARS-CoV-2 IgG assay can be used as an indication of a possible recent infection. However, negative results do not rule out previous infections, especially for those who have been in contact with positive cases.

The incubation period of COVID-19 ranges between 1 and 14 days. The host immune system reacts to the infection by SARS-CoV-2 by producing specific antibodies. These antibodies have been reported to appear in serum or plasma of infected individuals after the detection of viral ribonucleic acid (RNA) in swabs and a few days to 2 weeks after the onset of symptoms. Specific IgG antibodies to SARS-CoV-2 are detectable during the symptomatic phase of the disease after RNA is no longer detectable.

The sensitivity of combining RNA with antibody results has been reported as above 99%. The persistence of IgG antibodies allows identification of people who have been infected in the past, recovered from the illness, and possibly become immune.

Statistical Analysis

The distribution of quantitative values of variables for the subjects has been examined with descriptive statistics (such as Mean, and Standard deviation). The distribution of all-qualitative (i.e. close-ended) values of variables for both demographic and research variables of the study samples have been examined with frequency tables. The association tables have been calculated by using the chi-square test with cross-tabulation. A P-value < 0.05 will be considered for statistical significance. Binary logistic regression has been used to predict the odds of the cases based on the independent variables which were the reported symptoms with respect to the dependent variables such as previous infection of COVID-19 and positive seroconversion. The statistical analysis has been done using SPSS (Statistical Package for Social Sciences) Package with version 25.

A total of 682 HCWs consented to participate in the study. The overall participation rate was (15.7%) from all the hospital staff. Out of the 682 participants, (52.8%) were females, (39%) were Saudi nationals, (88.2%) were non-smoker, and (56.8%) of the participants were clinicians (physicians and nurses), following that 26.3% are from supporting services departments (such as patient safety and quality improvement, maintenance, population health, and housekeeping), and finally 16.9% were from the applied medicine departments (such as rehabilitation, laboratory, and pharmacy). The mean age of the participants was 44.5 (±10) and the mean years of professional experience 16 (±10) years (Table 1).

One hundred and twelve (15.2%) participants had a positive SARS-CoV-2 rt-PCR before taking part in the study. However, only 87 tested positive for SARS-CoV-2 antibodies, a prevalence of (12.7%) of all participants. Out of the 87 positives for SARS-CoV-2 antibodies, 17 participants never tested positive for COVID-19 rt-PCR, a prevalence of (2.9%) of all participants who never tested positive for COVID-19 rt-PCR (Table 1).

Of the initial participants, 665 completed the WHO's assessment questionnaire of potential risk factors for COVID-19 infection among healthcare workers in a healthcare setting. The most prevalent comorbidity was obesity (15.6%), then Diabetes Mellitus (7.3%), asthma (5.9%), heart diseases (2.9%), and cancer (1.6%) (Table 1).

Two factors were found statistically significant in the infection prevention and control measures to having positive SARS-CoV-2 IgG assay. The first factor was using alcohol-based hand rub or soap and water after the risk of body fluid exposure with (P=0.02, CI: 95%), and the second factor was wearing personal protective equipment (PPE) when indicated with (P=0.03, CI: 95%) (Table 2). In addition, exposure to the community such as delivery personnel, and/or family visits had no statistical significance difference between positive and negative SARS-CoV-2 IgG assay (P> 0.05, CI: 95%), however, exposure to co-workers had a statistical significance to testing positive for COVID-19 rt-PCR with (P= 0.01, CI: 95%).

The 87 participants with positive SARS-CoV-2 IgG assay have experienced symptoms that were statistically significant compared to HCWs with negative serology. The most reported symptoms before taking part in the study were fever 38 (43.6%), chills 32 (36.7%), muscle aches 47 (54%), fatigue 47 (54%), joint ache 33 (37.9%), loss of appetite 33 (37.9%), headache 44 (50.5%), general malaise 34 (39%), diarrhea 25 (28.7%), shortness of breath 22 (25.2%), cough 38 (43.6%), runny nose 26 (29.8%), and sore throat 32 (36.7%) (Table 3). Moreover, Obesity as the highest comorbidity reported, was also significantly linked to all symptoms related to COVID-19 with (P< 0.05, CI: 95%), while another comorbidity like diabetes was statistically insignificant to the exhibited symptoms (Table 4).

In the regression analysis, fever was the only common symptom with statistical significance to be reported by both participants who had tested positive for COVID-19 rt-PCR and SARS-CoV-2 IgG assay. Moreover, it was also found that it is (33%) likely that participants with positive COVID-19 rt-PCR would suffer from cough, (24%) are likely to report headaches, (22%) are likely to feel fatigued, and it is also most likely that 5 out of 10 would suffer from conjunctivitis (Table 5). However, no significance other than fever was found in the regression between positive SARS-CoV-2 IgG assay and symptoms presentation.

Table 1

**Clinical and non-clinical characteristics of the study participants**
	Frequency	Percentage	Mean (SD)
Age			44.5(10.2)
Gender
Male	360	47.2
Female	322	52.8
Nationality
Saudi	248	39
Non-Saudi	388	61
Smoker
Yes	87	11.8
No	648	88.2
Occupation
Physician	181	28
Nurse	186	28.8
Applied medicines	109	16.9
Supporting services	170	26.3
Years of Experience			16(10.3)
Diagnosed with COVID-19 before serology testing
Yes	112	15.2
No	623	84.8
SARS-COV-2 ANTIBODY
Positive	87	12.7
Negative	595	87.3
Underlying medical conditions
Obesity	115	15.6
Cancer	12	1.6
Diabetes	54	7.3
HIV/other immune deficiency	5	0.7
Heart disease	21	2.9
Asthma	43	5.9
Chronic lung disease	7	1.0
Chronic liver disease	3	0.4
Chronic hematological disorder	9	1.2
Chronic kidney disease	4	0.5
Chronic neurological impairment/ disease	5	0.7
Organ or bone marrow recipient	3	0.4
Pregnancy	6	0.8

Table 2

Cross Tabulation between positive SARS-CoV-2 IgG assay and Infection Prevention and Control Measures
Infection Prevention and Control (IPC) Measures		COVID -19 Serology		Chi square	P-value
Infection Prevention and Control (IPC) Measures		Positive	Negative	Chi square	P-value
Did you attend any IPC training within the hospital?	Yes	73 (15.2)	407 (84.8)	0.07	0.79
Did you attend any IPC training within the hospital?	No	27 (16.1)	141 (83.9)	0.07	0.79
How much cumulative IPC training (standard precautions, additional precautions) have you had at the hospital?	Less than 2 hours	32 (14.7)	185 (85.3)	1.17	0.68
	More than 2 hours	65 (14.3)	399 (85.7)	1.17	0.68
Do you follow the recommended hand hygiene practice?	Always as recommended	92 (15.9)	486 (84.1)	1.95	0.58
	Most of the time	14 (19.4)	58 (80.6)
	Occasionally	0	3 (100)
	N/A	0	4 (100)
Do you use alcohol-based hand rub or soap and water before and after touching a patient?	Always as recommended	83 (17.1)	401 (82.9)	4.12	0.39
	Most of the time	8 (19.5)	33 (80.5)
	Occasionally	1 (33.3)	2 (66.7)
	Rarely	0	1 (100)
	N/A	14 (10.9)	114 (89.1)
Do you use alcohol-based hand rub or soap and water before cleaning/aseptic procedures?	Always as recommended	74 (16.4)	157 (86.3)	2.74	0.43
	Most of the time	6 (21.4)	408 (87)
	Occasionally	1 (50)	1 (50)
	N/A	23 (14.2)	139 (85.8)
Do you use alcohol-based hand rub or soap and water after (risk of) body fluid exposure?	Always as recommended	81 (16.5)	410 (83.5)	12.28	0.02*
	Most of the time	6 (37.5)	10 (62.5)
	Occasionally	1 (100)	0
	Rarely	0	2 (100)
	N/A	17 (12.5)	119 (87.5)
Do you use alcohol-based hand rub or soap and water after touching a patient’s surroundings?	Always as recommended	85 (71.3)	407 (82.7)	9.13	0.06
	Most of the time	11 (20)	39 (78)
	Occasionally	0	2 (100)
	Rarely	1 (50)	1 (50)
	N/A	8 (7.8)	95 (92.2)
Do you follow IPC standard precautions when in contact with any patient?	Always as recommended	91 (16.3)	466 (83.7)	1.22	0.94
	Most of the time	6 (16.7)	30 (83.3)
	Occasionally	1 (33.3)	2 (66.7)
	Rarely	1 (20)	4 (80)
	N/A	2 (10.5)	17 (89.5)
	I don’t know what IPC standard are	3 (18.8)	13 (81.3)
Do you wear Personal protective Equipment (PPE) when indicated?	Always as recommended	90 (16.2)	465 (83.8)	11.02	0.03*
	Most of the time	7 (12.7)	51 (87.9)
	Occasionally	3 (50)	3 (50)
	Rarely	3 (37.5)	5 (62.5)
	N/A	0	13 (100)

Table 3

Cross Tabulation between positive SARS-CoV-2 IgG assay and Symptoms
Symptoms		SARS-COV-2 Antibody		Chi square	P-value
		Positive	Negative
Fever	Yes	38 (49.4)	39 (50.6)	100.73	0.000
	No	49 (8.3)	539 (91.7)
Sore Throat	Yes	32 (25.8)	92 (74.2)	21.70	0.000
	No	55 (10.2)	486 (89.8)
Cough	Yes	38 (36.2)	67 (63.8)	58.55	0.000
	No	49 (8.8)	511 (91.3)
Runny Nose	Yes	26 (27.4)	69 (72.6)	19.89	0.000
	No	61 (10.7)	509 (89.3)
Shortness of Breath	Yes	22 (32.4)	46 (67.6)	24.74	0.000
	No	65 (10.9)	532 (89.1)
Chills	Yes	32 (45.7)	38 (54.3)	73.26	0.000
	No	55 (9.2)	540 (90.8)
Vomiting	Yes	9 (37.5)	15 (62.5)	13.06	0.000
	No	78 (12.2)	563 (87.8)
Nausea	Yes	21 (42)	29 (58)	39.76	0.000
	No	66 (10.7)	549 (89.3)
Diarrhea	Yes	25 (32.5)	52 (67.5)	28.78	0.000
	No	62 (10.5)	526 (89.5)
Headache	Yes	44 (34.9)	82 (65.1)	65.20	0.000
	No	43 (8.0)	496 (92.0)
Rash	Yes	5 (26.3)	14 (73.7)	3.01	0.08
	No	82 (12.7)	564 (87.3)
Conjunctivitis	Yes	4 (17.4)	19 (82.6)	0.39	0.53
	No	83 (12.9)	559 (87.1)
Muscle Aches	Yes	47 (45.2)	57 (54.8)	111.78	0.000
	No	40 (7.1)	521 (92.9)
Joint Aches	Yes	33 (47.1)	37 (52.9)	79.82	0.000
	No	54 (9.1)	541 (90.9)

Table 4

Cross tabulation between Symptoms and Comorbidities
Symptoms		Obesity				Diabetes
Symptoms		Yes	No	Chi square	P-value	Yes	No	Chi square	P-value
Fever	Yes	23 (29.5)	55 (70.5)	12.67	0.000	10 (12.8)	68 (87.2)	3.84	0.05
Fever	No	92 (14.0)	565 (86.0)	12.67	0.000	44 (6.7)	613 (93.3)	3.84	0.05
Sore Throat	Yes	26 (20.6)	100 (79.4)	2.87	0.09	12 (9.5)	114 (90.5)	1.06	0.30
Sore Throat	No	89 (14.6)	520 (85.4)	2.87	0.09	42 (6.9)	567 (93.1)	1.06	0.30
Cough	Yes	24 (22.9)	81 (77.1)	4.83	0.03	8 (7.6)	97 (92.4)	0.01	0.91
Cough	No	91 (14.4)	539 (85.6)	4.83	0.03	46 (7.3)	584 (92.7)	0.01	0.91
Runny Nose	Yes	24 (25)	72 (75)	7.32	0.01	7 (7.3)	89 (92.7)	0.000	0.98
Runny Nose	No	91 (14.2)	548 (85.8)	7.32	0.01	47 (7.4)	592 (92.6)	0.000	0.98
Shortness of Breath	Yes	19 (27.9)	49 (72.1)	8.58	0.00	9 (13.2)	59 (86.8)	3.82	0.05
Shortness of Breath	No	96 (14.4)	571 (85.6)	8.58	0.00	45 (6.7)	622 (93.3)	3.82	0.05
Chills	Yes	19 (26.8)	52 (73.2)	7.36	0.007	9 (12.7)	62 (87.3)	3.28	0.07
Chills	No	96 (14.5)	568 (85.5)	7.36	0.007	45 (6.8)	619 (93.2)	3.28	0.07
Vomiting	Yes	8 (32)	17 (68)	5.24	0.02	4 (16)	21 (84)	2.85	0.09
Vomiting	No	107 (15.1)	603 (84.9)	5.24	0.02	50 (7)	650 (93)	2.85	0.09
Nausea	Yes	13 (25.5)	38 (74.5)	4.02	0.04	5 (9.8)	46 (90.2)	0.49	0.49
Nausea	No	102 (14.9)	582 (85.1)	4.02	0.04	49 (7.2)	635 (92.8)	0.49	0.49
Diarrhea	Yes	22 (28.2)	56 (71.8)	10.43	0.001	5 (6.4)	73 (93.6)	0.11	0.74
Diarrhea	No	93 (14.2)	564 (85.8)	10.43	0.001	49 (7.5)	608 (92.5)	0.11	0.74
Head Ache	Yes	33 (25.8)	95 (74.2)	12.06	0.001	12 (9.4)	116 (90.6)	0.94	0.33
Head Ache	No	82 (13.5)	525 (86.5)	12.06	0.001	42 (6.9)	565 (93.1)	0.94	0.33
Rash	Yes	7 (36.8)	12 (63.2)	6.64	0.01	3 (15.8)	16 (84.2)	2.04	0.15
Rash	No	108 (15.1)	608 (84.9)	6.64	0.01	51 (7.1)	665 (92.9)	2.04	0.15
Conjunctivitis	Yes	8 (34.8)	15 (65.2)	6.59	0.01	4 (17.4)	19 (82.6)	3.52	0.06
Conjunctivitis	No	107 (15)	605 (85)	6.59	0.01	50 (7)	662 (93)	3.52	0.06
Muscle Aches	Yes	25 (24)	79 (76)	6.46	0.011	10 (9.6)	94 (90.4)	0.92	0.34
Muscle Aches	No	90 (14.3)	541 (85.7)	6.46	0.011	44 (7)	587 (93)	0.92	0.34
Joint Ache	Yes	18 (25.7)	52 (85.4)	5.94	0.02	7 (10)	63 (90)	0.80	0.37
	No	97 (14.6)	568 (85.4)			47 (7.1)	618 (92.9)
Loss of Appetite	Yes	18 (25)	54 (75)	5.29	0.02	9 (12.5)	63 (87.5)	3.11	0.08
Loss of Appetite	No	97 (14.6)	566 (85.4)			45 (6.8)	618 (93.2)	3.11	0.08
Nose Bleed	Yes	6 (40)	9 (60)	6.88	0.01	3 (20)	12 (80)	3.60	0.06
Nose Bleed	No	109 (15.1)	611 (84.9)			51 (7.1)	669 (92.9)	3.60	0.06
Fatigue	Yes	27 (24.1)	85 (75.9)	7.17	0.01	12 (10.7)	100 (89.3)	2.20	0.14
Fatigue	No	88 (14.1)	535 (85.9)			42 (6.7)	581 (93.3)	2.20	0.14
General Malaise	Yes	23 (27.4)	61 (72.6)	9.90	0.002	10 (11.9)	74 (88.1)	2.89	0.09
General Malaise	No	92 (14.1)	559 (85.9)			4 (6.8)	607 (93.2)	2.89	0.09

Table 5

**Association of Variables with Odds ratio and respective significance with confidence Intervals**
Symptoms	B	S.E.	Wald	df	P-value	Odds Ratio	95% C.I. for Odds
Symptoms	B	S.E.	Wald	df	P-value	Odds Ratio	Lower	Upper
Fever (≥37.8°C) or history of fever	-.800	.407	3.868	1	.049*	.449	.202	.997
Sore throat:	.704	.441	2.555	1	.110	2.022	.853	4.796
Cough:	-1.115	.426	6.866	1	.009*	.328	.142	.755
Runny Nose	.677	.435	2.423	1	.120	1.969	.839	4.620
Shortness of Breath	.483	.442	1.196	1	.274	1.621	.682	3.855
Chills:	.049	.486	.010	1	.920	1.050	.405	2.721
Vomiting	-1.269	.795	2.548	1	.110	.281	.059	1.335
Nausea	.859	.589	2.128	1	.145	2.360	.745	7.479
Diarrhea	-.781	.414	3.558	1	.059	.458	.204	1.031
Headache	-1.418	.367	14.940	1	.000*	.242	.118	.497
Rash	-.246	.698	.124	1	.724	.782	.199	3.072
Conjunctivitis	1.644	.751	4.795	1	.029*	5.177	1.188	22.549
Muscle aches	-.446	.496	.811	1	.368	.640	.242	1.690
Joint ache	-.404	.529	.584	1	.445	.668	.237	1.882
Loss of appetite	-.568	.481	1.398	1	.237	.567	.221	1.453
Nose bleed	1.272	.846	2.261	1	.133	3.569	.680	18.733
Fatigue	-1.500	.447	11.263	1	.001*	.223	.093	.536
General malaise	.092	.479	.037	1	.848	1.096	.429	2.803

In this study, we evaluated the seroprevalence of anti-SARS-CoV-2 antibodies among hospital staff with an overall prevalence of 2.9%⁵. In a previous study from Saudi Arabia, seroprevalence among HCWs was 2.36% with a statistical difference between hospitals who had COVID-19 cases with a prevalence of 2.9% vs. and 0.8% for hospitals that did not have COVID-19 cases⁵. Since our hospital admitted COVID-19 patients and had actively participated in the management of COVID-19 cases ^12,13,14, thus the prevalence in our study is consistent with that of the nationwide prevalence among HCWs. However, more targeted HCWs who worked in the operating room and intensive care units showed a seroprevalence of 12.2% in a study from KSA¹⁵. A third study conducted June to August 2020 in KSA showed a higher rate of seropositivity of 32.2% in referral hospitals and quarantine sites¹⁶. In a study from Spain, seroprevalence among HCWs was 16.6% ¹⁷ and a longitudinal study in the United States showed a prevalence of 2.8% at baseline and 4.8% in a follow-up after six months¹⁸. During the first wave in Italy, 2.8% of HCWs tested seropositive¹⁹. Information about community prevalence of anti-SARS-CoV-2 antibodies may be used as a gauge of community immunity before the introduction of vaccines²⁰. Such a study was done in KSA among blood donors and the seroprevalence was 1.4% ²¹ and is similar to those reported among HCWs in most of the KSA studies.

In our study, 112 (15.2%) participants had a positive SARS-CoV-2 rt-PCR before taking part in the study. The occurrence of SARS-CoV-2 infection among HCWs was found among 4.5% of 4462 patients in one hospital in KSA and 90.6% were community-acquired infection, and 61.3% of HCWs infection in Oman was also community-acquired ²². Another study described a hospital outbreak and thus 88% of infections in HCWs were hospital-acquired ²³. However, we did not study the source of infection among those HCWs. It is important to note that the current pandemic of COVID-19 had occurred mainly among the communities with limited healthcare-associated outbreaks. This is an important distinction of the occurrence of multiple outbreaks in healthcare settings in the previous coronavirus, mainly the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in the Kingdom of Saudi Arabia ²⁴.

We evaluated the occurrence of symptoms among the 87 participants with a positive SARS-CoV-2 IgG assay. Those individuals experienced symptoms that were statistically significant compared to HCWs with negative serology. The most reported symptoms were fever 38 (43.6%), chills 32 (36.7%), muscle aches 47 (54%), fatigue 47 (54%), joint ache 33 (37.9%), loss of appetite 33 (37.9%), headache 44 (50.5%), general malaise 34 (39%), diarrhea 25 (28.7%), shortness of breath 22 (25.2%), cough 38 (43.6%), runny nose 26 (29.8%), and sore throat 32 (36.7%). These symptoms are not specific for COVID-19 patients. However, the occurrence of such symptoms had been reported among patients with COVID-19 as well. One study from KSA showed that the most common symptoms were cough (53.6%), fever (36.2%), fatigue (26.4%), dyspnea (21.9%), and sore throat (21.9%) ²⁵ and similar symptomatology in other studies ^23,26,27,28.

In 2020, a hospital in China conducted a study of obesity and its association with COVID-19 severity ²⁹. The study concluded that patients who suffer from obesity have increased odds of developing and progressing to severe COVID-19 symptoms²⁹. In this study, our findings showed that obesity is significantly associated with many COVID-19 symptoms as well more than any other comorbidity such as diabetes.

Elucidating factors associated with positive SARS-CoV-2 serology revealed two associated factors with positivity in bivariate analysis. These are always using alcohol-based hand rub or soap and water after (risk of) body fluid exposure and always wearing PPE when indicated. The data showed that those staff who were positive were less likely to wear PPE and to perform proper hand hygiene. The practice of hand hygiene is of paramount activity to reduce infection³⁰. Multiple interventions were used to promote hand hygiene even before the current pandemic ^30,31,32. Staff compliance with the hospital's robust Infection Prevention and Control program and guidance helped in maintaining the risk of infection and prevalence of SARS-CoV-2 amongst HCWs considerably low compared to other national and international healthcare organizations.

During the pandemic, isolation of suspected patients, the use of facemasks, and intensified hand hygiene were important for the prevention of nosocomial transmission of COVID-19 ³³. One study showed that multiple services were contaminated and had positive SARS-CoV-2 RNA ³⁴. Another study from KSA showed high knowledge and practice scores in relation to hand hygiene and the use of masks ³⁵. In a case-control study, frequent handwashing, social distancing, and avoidance of close contact were independently associated with a lower risk for SARS-CoV-2 infection ³⁶.

One of the main limitations we exhibited in this study was the inability to categorize our participants into groups according to their level of exposure whether it was high or low to the virus / COVID-19 patients as this information was not comprehensively provided by the participants. In addition, due to the slow accrual, the study protocol was amended from randomized selection to open invitation which eventually resulted in achieving the targeted sample size (more than 10% of the hospital staff) at the expense of risk of selection bias. Finally, this study was conducted prospectively, participants depended fully on their memory and personal interpretation of their symptoms and association to the infection.

This study was carried out during the first wave of COVID-19 and before the availability of vaccination. Our findings showed that the positive seroconversion rate was considerably low amongst our healthcare workers and similar to other national and international healthcare organizations. Although the results of the study can be interpreted as a success in following the recommendations of the Intervention Prevention and Control Division, seropositivity correlated significantly with two factors of infection prevention which were appropriately alcohol-based hand rub or soap and water after the risk of body fluid exposure and wearing personal protective equipment when indicated

Acknowledgment

The authors of this study would like to thank the hospital's management and staff for their full support and volunteering to complete the study. The authors would also like to thank Ms. Suha Amoudi and Mr. Nizar Aridi for their support and facilitating the laboratory testing and Mr. Issam Abduljabar and Mr. Salam Abu Ghaida for their efforts in data entry.

Authors’ Contribution

AS, AA, MG, AB, and HM conceptualized the study. The work was completed by HM and supervised by AS, MG, and JT. The laboratory work was performed by AD and AO. The data analysis was performed by AZJ. The manuscript was written by HM and JT. The manuscript then was reviewed and edited by AA, SS, SQ, AS and MG.

Statements and Declarations

All authors of this study declare that this work is original and not being considered for publication elsewhere.

Competing Interests

All authors declare that there are no competing financial interest directly or indirectly to this work.

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No competing interests reported.

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Seroprevalence of Antibodies against SARS-CoV-2 Infection among Healthcare Workers at a Tertiary Hospital in Saudi Arabia

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ARCHITECT SARS-CoV-2 IgG Assay

Validation of SARS-CoV-2 IgG Testing

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