Seroprevalence and longevity of SARS-CoV-2 nucleocapsid antigen-IgG among health care workers in a large COVID-19 public hospital in Saudi Arabia: A prospective cohort study.

Seroprevalence of SARS-CoV-2 IgG among health care workers (HCWs) is crucial to inform infection control programs. Conflicting reports have emerged on the longevity of SARS-CoV-2 IgG. Our objective is to describe the prevalence of SARS-CoV-2 IgG in HCWs and perform 8 months longitudinal follow-up (FU) to assess the duration of detectable IgG. In addition, we aim to explore the risk factors associated with positive SARS-CoV-2 IgG. The study was conducted at a large COVID-19 public hospital in Riyadh, Saudi Arabia. All HCWs were recruited by social media platform. The SARS-CoV-2 IgG assay against SARS-CoV-2 nucleocapsid antigen was used. Multivariable logistic regression was used to examine association between IgG seropositive status and clinical and epidemiological factors. A total of 2528 (33% of the 7737 eligible HCWs) participated in the survey and 2523 underwent baseline serological testing in June 2020. The largest occupation groups sampled were nurses [n = 1351(18%)], physicians [n = 456 (6%)], administrators [n = 277 (3.6%)], allied HCWs [n = 205(3%)], pharmacists [n = 95(1.2%)], respiratory therapists [n = 40(0.5%)], infection control staff [n = 21(0.27%], and others [n = 83 (1%)]. The total cohort median age was 36 (31-43) years and 66.3% were females. 273 were IgG seropositive at baseline with a seroprevalence of 10.8% 95% CI (9.6%-12.1%). 165/185 and 44/112 were persistently IgG positive, at 2-3 months and 6 months FU respectively. The median (25th- 75th percentile) IgG level at the 3 different time points was 5.86 (3.57-7.04), 3.91 (2.46-5.38), 2.52 (1.80-3.99) respectively. Respiratory therapists OR 2.38, (P = 0.035), and those with hypertension OR = 1.86, (P = 0.009) were more likely to be seropositive. A high proportion of seropositive staff had prior symptoms 214/273(78%), prior anosmia was associated with the presence of antibodies, with an odds ratio of 9.25 (P<0.001), as well as fever and cough. Being a non-smoker, non-Saudi, and previously diagnosed with COVID-19 infection by PCR were statistically significantly different by seroprevalence status. We found that the seroprevalence of IgG against SARS-CoV-2 nucleocapsid antigen was 10.8% in HCWs at the peak of the pandemic in Saudi Arabia. We also observed a decreasing temporal trend of IgG seropositivity over 8 months follow up period.

Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic. The first case of SARS-CoV-2 infection in Saudi Arabia was reported in Eastern province January 20,2020, with 545829 infections and 8610 deaths as of 11 September 2021 [1].

Health care workers (HCWs) are at an increased risk of becoming infected with SARS-CoV-2. Understanding the prevalence of SARS-CoV-2 carriage amongst HCWs is crucial to help monitor transmission dynamics and inform the development of screening programs. Access to COVID-19 molecular testing during early time of pandemic was mainly confined to symptomatic individuals, and therefore the rates of infection in asymptomatic or minimally symptomatic HCWs have been difficult to determine.

Serological testing can detect prior SARS-CoV-2 infection for which nasopharyngeal sampling resulted in false negatives or for which reverse transcription-PCR testing was not performed. It requires high sensitivity and specificity, especially when seroprevalence is low, in order to have an acceptable positive predictive value [2].

Several seroprevalence studies of HCWs from different countries in the first phase of the pandemic revealed a wide range of seropositivity [3-6]. The reasons for such variation may reflect the underlying community transmission rate in addition to an increased risk in certain hospital.

Conflicting reports have been published on the longevity of SARS-CoV-2 antibodies. For instance, an Iceland study showed IgG antibody levels to Nucleocapsid (NC) and the S1 component of spike were relatively stable in 1215 individuals for 100–125 days [7]. Similarly, data from 121 individuals suggest that IgG response to trimerized spike were sustained for 110 days post symptoms onset [8]. However, other reports have observed declines in IgG antibody levels over similar time periods [9-11].

Few studies have looked at HCWs across a healthcare system that included individuals with both direct patient care and non-clinical functions. In this study, we invited all HCWs to participate in a serologic survey from June 2020 to February 2021, after the first wave of SARS-CoV-2 infection which occurred from March through May 2020. We aimed to investigate the seroprevalence and its predictors and evaluate temporal trends in the levels of IgG antibody over an 8-month follow up period.

Methods

Setting and participants

King Fahad Medical City (KFMC) is a large referral facility that has a total of 7737 HCWs: 1021 medical staff, 3004 nursing staff, 1906 allied health personnel, and 1806 administrative personnel. It was assigned by the Ministry of Health as a public COVID-19 center.

All HCWs who could provide written informed consent were deemed eligible to be included in this study. There were no exclusion criteria except for actively symptomatic employees. HCWs were recruited via social media directed to the entire employee workforce. There was no predefined sample size. Participants self-reported for enrolment. Staff were asked to complete a survey on sociodemographic and clinical characteristics, job duties and location, COVID-19 symptoms, a self-reported polymerase chain reaction (PCR) test history with test date if available, travel record since January 2020, and exposure risks (patient, coworker, and household contact).

We planned to follow the IgG positive cohort over 1 year at regular time intervals. First time point was on June-July, 2020, and then on September, 2020 and finally on January-February 2021 to have approximately three months’ interval between each test. This study was approved by the local institutional review board IRB log # 20–382, and written informed consent was obtained from all participants.

Laboratory methods

Serum IgG to SARS-CoV-2 NC was measured using chemiluminescent microparticle immunoassay performed on an automated high throughput chemistry immunoanalyzer on the ARCHITECT i System. The resulting reaction is measured as a relative light unit (RLU). There is a direct relationship between the amount of IgG antibodies and the RLU.

Results are reported in RLU index; a value greater than or equal to 1.4 RLU is considered a positive antibody response. Values of more than 1.4 and < 3.99 were categorized as low values, and > 3.99 as high values. Though not a direct titer, higher index values highly correlate to neutralization titers [12].

The reported assay sensitivity is 100% with a specificity of 99% at greater than 14 days’ post symptom onset, and at 5% prevalence, the positive predictive value is 93.4% and negative predictive value 100% [13].

Statistical methods

Descriptive statistics (mean ± SD or Median (25th– 75th percentiles) were used to summarize patient characteristics. Bivariate testing was carried out using appropriate statistical tools based on variable type and distribution (e.g., chi-square test, Fisher’s exact test, Wilcoxon’s rank sum test and univariate linear or logistic regression analysis). Multivariable logistic regression was used to find association between serology positivity and different patient characteristics and COVID-19 symptoms. A p value of less than or equal to 0.05 was set as the threshold for statistical significance. All statistical analyses were performed using Stata 16.1 (Stata Corp, Texas).

Results

Of the 7737 eligible HCWs, 2528 (33%) participated in the survey and 2523 underwent baseline serological testing. The largest occupation groups sampled were nurses [n = 1351(18%)], physicians [n = 456 (6%)], administrators [n = 277 (3.6%)], allied HCWs [n = 205(3%)], pharmacists [n = 95(1.2%)], respiratory therapists [n = 40(0.5%)], infection control staff [n = 21(0.27%], and others [n = 83 (1%)]. The median (25th– 75th percentile) age was 36 years (31–43), and 66% were female. The other sociodemographic and clinical characteristics of the cohort are shown in Table 1 and Fig 1.

Table 1

Demographics and clinical characteristics of study participants.

Characteristics	All participants, n (%)	Seropositive, n (%)	Seronegative, n (%)	Seroprevalence, (%)	P value
N	2528	273 (10.80%)	2250 (89.00%)	10.8%
Age	36 (19–71)	37 (24–63)	36 (19–71)		0.073
Serology test 1	0.03 (0.02–0.07)	5.86 (3.57–7.04)	0.03 (0.02–0.05)
Serology test 2	3.73 (2.22–5.24)	3.91 (2.46–5.38)	1.10 (0.86–1.29)
Serology test 3	1.12 (0.56–2.07)	2.52 (1.80–3.99)	0.67 (0.37–0.93)
Gender					0.479
Female	1675 (66.3)	186 (11.1%)	1484 (88.9%)	11.1%
Male	852 (33.7)	87 (10.2%)	765 (89.8%)	10.2%
Nationality					0.028
Non-Saudi	1654 (65.4)	195 (11.8%)	1456 (88.2%)	11.8%
Saudi	874 (34.6)	78 (8.9%)	794 (91.1%)	8.9%
Smoking Status					<0.001
Smokers	355 (14.1%)	19 (5.4%)	336 (94.7%)	5.4%
Non-smokers	2168 (85.9%)	254 (11.7%)	1914 (88.3%)	11.7%
Flu Vaccine					0.402
Yes	791 (31.3%)	59 (7.5%)	731 (92.5%)	7.5%
No	331 (13.1%)	20 (6.1%)	310 (93.9%)	6.1%
Positive PCR Test					<0.001
Yes	290 (11.5%)	208 (72.0%)	81 (28.0%)	72.0%
No/ or not done	2238 (88.5%)	656 (2.9%)	2169(94.0%)	2.9%
Occupation					0.080
Doctor	456 (18.0%)	45 (9.9%)	410 (90.1%)	9.9%
Nurse	1351 (53.0%)	153 (11.3%)	1196 (88.7%)	11.3%
Non-clinical Staff	277 (11.0%)	37 (13.4%)	240 (86.6%)	13.4%
Infection Control Specialist	21 (0.8%)	2 (9.5%)	19 (90.5%)	9.5%
Allied Healthcare	205 (8.1%)	11 (5.4%)	192 (90.6%)	5.4%
Pharmacist	95 (3.8%)	9 (9.5%)	86 (90.5%)	9.5%
Respiratory Therapist	40 (1.6%)	8 (20.0%)	32 (80.0%)	20.0%
Others	83 (3.3%)	8 (9.6%)	75 (90.4%)	9.6%
Blood Group					0.784
A+	614 (24.3%)	75 (12.3%)	537 (87.8%)	12.3%
A-	37 (1.5%)	3 (8.1%)	34 (91.9%)	8.1%
AB+	147 (5.8%)	15 (10.2%)	132 (89.8%)	10.2%
AB-	12 (0.5%)	1 (8.3%)	11 (91.7%)	8.3%
B+	524 (20.7%)	56 (10.7%)	466 (89.3%)	10.7%
B-	24 (1.0%)	4 (16.7%)	20 (83.3%)	16.7%
O+	1031 (40.8%)	111 (10.8%)	919 (89.2%)	10.8%
O-	77 (3.1%)	5 (6.5%)	72 (93.5%)	6.5%
Previous medications
ACE inhibitors	92 (3.6%)	12 (13.0%)	80 (87.0%)	13.0%	0.447
Statins	113 (4.5%)	15 (13.3%)	98 (86.7%)	13.3%	0.360
Immuno-modular Agent	16 (0.6%)	0 (0.0%)	16 (100.0%)	0.0%
Steroids	49 (2.0%)	9 (18.4%)	40 (81.6%)	18.4%	0.080
Medical conditions
Cancer	21 (0.8%)	0 (0%)	21 (100%)	0%	0.109
Chronic Lung Disease	51 (2.0%)	7 (13.7%)	44 (86.3%)	13.7%	0.500
Diabetes	150 (5.9%)	24 (16.0%)	126 (84.0%)	16.0%	0.035
Hypertension	244 (9.7%)	43 (17.6%)	201 (82.4%)	17.6%	<0.001
Pregnancy	42 (1.7%)	6 (14.3%)	36 (85.7%)	14.3%	0.466

*some people’s gender info missing.

Fig 1

Sociodemographic and clinical characteristics of the study population.

(A) Demography of sample. (B) Distribution of Participants by Occupation. (C) Serological Status by Gender. (D) Serological status per Nationality. E) Serological Status by Smoking. (F) Serological Status by Occupation.

Overall, 273 HCWs had detectable IgG antibodies for SARS-CoV-2 with seroprevalence rate of 10.8% 95% CI (9.6%-12.1%). We observed highest prevalence rate among respiratory therapist (20%), non-clinical staff had prevalence rate of 13.4%. Nurses and physicians had seropositive rates of 11.3 and 9.9% respectively.

Of the 273 positive HCWs, 80 (29%) had an IgG value of ≥ 1.4 to ≤ 3.99 and 193 (71%) had a value > 3.99. Thus, we conclude that the vast majority of positive individuals have moderate-to-high titers of NC antibodies.

Age and sex were not statistically significantly different among staff with or without antibodies (median age, 37 (31–44) vs 36 (31–43) years; 87/273 [32%] vs 765/2249 [34%] male). Table 2. Being a non-smoker, non-Saudi, previously diagnosed with COVID-19 infection by PCR, and having diabetes and hypertension were statistically significantly different by seroprevalence status Table 1.

Table 2

Demographics and clinical characteristics of study participants by gender.

Characteristics	All participants, n (%)	Gender		P value
n	2528	Female	Male
		1669 (66.2%)	851 (33.8%)
Age	36 (31–43)	36 (31–42)	37 (31–43)	0.042
Serology test 1	0.03 (0.02–0.07)	0.03 (0.02–0.07)	0.03 (0.02–0.07)	0.355
Serology test 2	3.73 (2.22–5.24)	3.91 (3.32–5.56)	3.01 (1.85–4.89)	0.072
Serology test 3	1.12 (0.56–2.07)	1.14 (0.57–2.06)	1.17 (0.60–2.67)	0.548
Nationality				<0.001
Non-Saudi	1654 (65.4)	1265 (76.8%)	382 (23.19%)
Saudi	874 (34.6)	404 (46.3%)	469 (53.7%)
Smoking Status				<0.001
Smokers	355 (14.1%)	69 (19.4%)	286 (80.6%)
Non-smokers	2168 (85.9%)	1600 (73.9%)	565 (26.1%)
Flu Vaccine				0.530
Yes	791 (31.3%)	468 (59.3%)	321 (40.7%)
No	331 (13.1%)	203 (61.3%)	128 (38.7%)
Positive PCR Test				0.554
Yes	290 (11.5%)	183 (64.7%)	100 (35.3%)
No/ or not done	2238 (88.5%)	1486 (66.4%)	751 (33.6%)
Occupation				<0.001
Doctor	456 (18.0%)	105 (23.0%)	351 (77.0%)
Nurse	1351 (53.0%)	1190 (88.5%)	155 (11.5%)
Non-clinical Staff	277 (11.0%)	156 (56.3%)	121 (43.7%)
Infection Control Specialist	21 (0.8%)	17 (81.0%)	4 (19.0%)
Allied Healthcare	205 (8.1%)	87 (42.7%)	117 (57.3%)
Pharmacist	95 (3.8%)	52 (54.7%)	43 (45.3%)
Respiratory Therapist	40 (1.6%)	10 (25.6%)	29 (74.4%)
Others	83 (3.3%)	52 (62.7%)	31 (37.3%)
Blood Group				0.118
A+	614* (24.3%)	397 (64.9%)	215 (35.1%)
A-	37 (1.5%)	23 (62.2%)	14 (37.8%)
AB+	147* (5.8%)	98 (67.1%)	48 (32.9%)
AB-	12 (0.5%)	9 (75.0%)	3 (25.0%)
B+	524* (20.7%)	370 (70.9%)	152 (29.1%)
B-	24* (1.0%)	14 (60.9%)	9 (39.1%)
O+	1031* (40.8%)	675 (65.5%)	355 (34.5%)
O-	77 (3.1%)	42 (54.6%)	35 (45.4%)
Previous medications
ACE inhibitors	92 (3.6%)	54 (58.7%)	38 (41.3%)	0.110
Statins	113 (4.5%)	62 (54.9%)	51 (45.3%)	0.009
Immuno-modular Agent	16 (0.6%)	11 (68.8%)	5 (31.2%)	0.864
Steroids	49 (2.0%)	38 (77.6%)	11 (22.4%)	0.111
Medical conditions
Cancer	21 (0.8%)	18 (85.7%)	3 (14.3%)	0.058
Chronic Lung Disease	51 (2.0%)	34 (66.7%)	17 (33.3%)	0.947
Diabetes	150* (5.9%)	100 (67.1%)	49 (32.9%)	0.814
Hypertension	244 (9.7%)	172 (71.1%)	70 (28.9%)	0.094
Pregnancy	42 (1.7%)	40 (95.2%)	2 (4.8%)	<0.001

*some people’s gender info missing.

In this cohort, 456/2523(18%) had at least 1 prior symptom, (3 employees did not answer this question). A high proportion of staff with antibodies had prior symptoms 214/273(78%), The proportion of asymptomatic staff with positive serology was 59/273(22%). Most symptoms were significantly associated with positive serology except sore throat.

Prior anosmia was associated with the presence of antibodies, with an odds ratio of 9.25 (P<0.001), as well as fever and cough. When considering comorbidities, positive serology was significantly associated with a lower prevalence in smokers (OR, 0.48; P = 0.003) and a higher prevalence with hypertension (OR, 1.86; P = 0.009). For occupation, Positive serology was significantly associated with being respiratory therapist (OR, 2.38; P = 0.035), allied health care workers were found protected against SARS-CoV-2 infection (OR, 0.43; P = 0.016) (Tables 3 and 4).

Table 3

Risk factors (symptoms) associated with positive SARS-CoV-2 IgG.

Symptom	Unadjusted		Adjusted
	OR (95% CI)	P value	OR (95% CI)	P value
Fever	5.14 (3.43–7.71)	<0.001	2.02 (1.16–3.55)	0.013
Sore throat	1.05 (0.73–1.52)	0.779	0.58 (0.35–0.95)	0.032
Vomiting	6.47 (2.19–19.18)	0.001	3.76 (0.93–15.21)	0.063
Diarrhoea	2.97 (1.87–4.72)	<0.001	1.19 (0.63–2.25)	0.584
Chills	5.55 (3.17–9.73)	<0.001	1.73 (0.82–3.65)	0.151
Muscle ache	2.95 (2.01–4.33)	<0.001	0.96 (0.55–1.70)	0.894
Cough	3.81 (2.57–5.63)	<0.11	2.09 (1.24–3.51)	0.005
Loss of smell	14.91 (9.01–24.69)	<0.001	9.25 (5.01–17.05)	<0.001
Fatigue	3.45 (2.34–5.08)	<0.001	1.45 (0.82–2.57)	0.201
Loss of appetite	6.37 (3.73–10.88)	<0.001	1.14 (0.54–2.38)	0.733
Nausea	2.60 (1.55–4.35)	<0.001	0.48 (0.22–1.05)	0.065
Shortness of breath	2.42 (1.47–3.97)	<0.001	0.53 (0.25–1.12)	0.095
Headache	2.51 (1.69–3.73)	<0.001	1.08 (0.64–1.83)	0.783

Table 4

Risk factors associated with positive SARS-CoV-2 IgG.

Variable	Unadjusted		Adjusted
	OR	P value	OR (95% CI)	P value
Male	0.91 (0.69–1.19)	0.479	-	-
Saudi	0.73 (0.56–0.97)	0.028	-	-
Smoker	0.43 (0.36–0.69)	0.001	0.48 (0.29–0.78)	0.003
Flu vaccine	1.25 (0.74–2.11)	0.402	-	-
Occupation
Nurse (reference)	-	-	-	-
Doctor	0.86 (0.60–1.22)	0.392	-	-
Non-clinical Staff	1.21 (0.82–1.77)	0.342	-	-
Infection Control Specialist	0.82 (0.19–3.57)	0.616	-	-
Allied Healthcare	0.45 (0.24–0.84)	0.013	0.43 (0.22–0.85)	0.016
Pharmacist	0.82 (0.40–1.66)	0.578	-	-
Respiratory Therapist	1.95 (0.88–4.32)	0.098	2.38 (1.10–5.34)	0.035
Others	0.83 (0.39–1.76)	0.634	-	-
Previous medicine
ACE inhibitors	1.27 (0.68–2.37)	0.448	-	-
Statins	1.30 (0.74–2.27)	0.361	-	-
Immunomodular Agent	-	-
Steroids	1.91 (0.91–3.98)	0.085	-	-
Medical conditions
Cancer	-	-
CLD	1.32 (0.59–2.96)	0.501	-	-
Hypertension	1.91 (1.33–2.72)	<0.001	1.86 (1.17–2.97)	0.009
Pregnancy	1.38 (0.58 - .331)	0.468	-	-

IgG levels trends over time

In comparing overall IgG levels, we observed a decline from the initial median (25th– 75th percentile) titer of 5.86 (3.57–7.04) to a median (25th– 75th percentile) of 3.91 (2.46–5.38) from the first to the second time point and another drop to a median (25th– 75th percentile) of 2.52 (1.80–3.99) for the last time point (Fig 2).

Fig 2

Trend in the titer of IgG over the study period as.

(A) Median. (B) Line plot (Lowess smoother).

Of the 185 participants who underwent a second serology test,165 were persistently IgG positive. Finally, 112 HCWs had a third serology test, 44 were still IgG positive (Fig 3).

Fig 3

Flow diagram of serology tests among study participants.

Perceived sources of COVID-19 exposure among study participants.

Among 290 PCR-confirmed SARSCoV-2 HCWs, exposure that led to SARS-CoV-2 infection could have occurred in the community or within the hospital setting (patient, or coworkers) and this study explored their perception between these potential sources of exposure. In general, we found colleagues (20.3%), patients (29.0%), community (10.3%), unknown source (36.6%) and no history of COVID-19 cases contact (3.8%) Table 5.

Table 5

Perceived sources of COVID-19 exposure among study participants (n = 290).

Sources	Colleagues (n = 59 (20.3%))	Patients (n = 84 (29.0%))	Community (n = 30 (10.3%))	Don’t know (n = 106 (36.6%))	No COVID-19 contact (n = 11 (3.8%))	P value
Nationality						0.068
Non-Saudi	30 (16.5%)	62 (34.1%)	20 (11.0%)	64 (35.2%)	6 (3.3%)
Saudi	29 (26.9%)	22 (20.4%)	10 (9.3%)	42 (38.9%)	5 (4.6%)
Occupation						a
Doctor	10 (17.9%)	20 (35.7%)	3 (5.4%)	22 (39.3%)	1 (1.8%)
Nurse	22 (14.2%)	55 (35.5%)	17 (11.0%)	56 (36.1%)	5 (3.2%)
Non-clinical Staff	12 (32.4%)	5 (13.5%)	4 (10.8%)	14 (37.8%)	2 (5.4%)
Infection Control Specialist	1 (33.3%)	0 (0%)	1 (33.3%)	1 (33.3%)	0 (0%)
Allied Healthcare	6 (33.3%)	3 (16.7%)	2 (11.1%)	5 (27.8%)	2 (11.1%)
Pharmacist	4 (50.0%)	0 (0%)	1 (12.5%)	3 (37.5%)	0 (0%)
Respiratory Therapist	3 (42.9%)	1 (14.3%)	0 (0%)	2 (28.6%)	1 (14.3%)
Others	1 (16.7%)	0 (0%)	2 (33.3%)	3 (50.0%)	0 (0%)

a Due to small numbers in many cells over several rows, exact test could not be performed.

Discussion

Antibody response against the surface S glycoprotein and NC can be detected in most infected individuals 10–15 days after the onset of COVID-19 symptoms [14].

Here, we evaluated the prevalence of SARS-CoV-2 IgG in a wide spectrum of HCWs consisting of frontline, support staff and administrators. We found a seroprevalence of 10.8% among our HCWs.

In a comprehensive review conducted by Galanis et al. [15] to estimate the seroprevalence of SARS-CoV-2 antibodies among 12748 HCWs, it was found an overall seroprevalence of 8.7%, ranging from 0% to 45.3% between studies. Seroprevalence in HCWs was higher in studies conducted in North America (12.7%) compared with those conducted in Europe (8.5%), Africa (8.2) and Asia (4%). The following factors were associated with seropositivity: male gender; Hispanic, Asian and Black HCWs; work in a COVID-19 area; front-line HCWs; patient-related work; healthcare assistants; shortage of personal protective equipment; previous positive PCR test; self-reported belief of prior COVID-19 infection and household contact with suspected or confirmed cases of COVID-19 [15].

These discrepancies may be explained by differences in the sensitivity, specificity, performance and design of the assays used, including the antigen targeted as well as differences in the study populations. Seroprevalence might be underestimated if infected individuals had not yet mounted an IgG response or if IgG titers had declined since infection.

In a survey of a recovered patients in the Iceland population, IgG levels were higher in older patients and in those more severely affected by COVID-19. Females tend to become less sick than males and thus had lower IgG levels. IgG levels were lower in smokers, smoking increases the risk of severe Covid-19 illness among young patients [16], and smoking has been observed to increase the expression of ACE2 [17], the receptor for cellular entry of the SARS-CoV-2 virus [7].

Here, we observed that seropositivity was significantly associated with some symptoms with loss of smell having the strongest association with positivity and oddly sore throat was negatively associated with IgG positivity which is similar to a Belgium study and 2 different US studies [18-20]. A study conducted in France found similar association with five clinical symptoms were independently associated with positive serology including asthenia, fever, myalgia, ageusia and anosmia for which the higher odd ratio was observed (11.1 [7.4–16.6; 95% CI]) [21]. We noticed a high risk of infection associated with respiratory therapist job category which they are considered to be a front liner HCWs, opposite to allied health care workers whom we found that they were protected against SARS-CoV-2 infection which could be explained by low rate of direct patients contact. Table 6 outlines some of the IgG longitudinal studies conducted among HCWs [9, 22–26].

Table 6

Summary of some important longitudinal SARS-CoV-2 IG studies among Health Care Workers.

Country	Sample size	Clinical severity of the study population	Assay used With antigen target	Starting point	Duration
UK9	37	symptomatic and asymptomatic	S glycoprotein, RBD and N protein were measured by (ELISA)	POS	Decline within 94 days, varying with the initial peak response and diseases severity.
USA23	249	Asymptomatic-mild	A validated enzyme-linked immunosorbent assay against the prefusion-stabilized extracellular domain of the SARS-CoV-2 spike protein.	Baseline Positive serology	8/19 (42%) persist for 60 days
Belgium24	850	5 were asymptomatic, 75 had reported mild symptoms, and 1 hospitalized	Antibodies targeting S1 (spike subunit 1) protein with a commercial semi-quantitative enzyme-linked immunosorbent assay (ELISA) (Euroimmun IgG; Medizinische Labordiagnostika, Lübeck, Germany)	PSOa or positive PCRb for asymptomatic patients(day of first positive serological test minus 14 days).	74 (91%) who remained seropositive, median duration of antibody persistence
					168·5 (range 62–199) days. 71 (96%) of 74 HCWsc have already had antibodies for 90 days or more and 67 (91%) have had them for 120 days or more
UK25	3276	Asymptomatic and Symptomatic	Anti-trimeric-spike IgG levels were measured using an ELISA developed by the University of Oxford, Abbott Architect i2000 chemiluminescent microparticle immunoassay (CMIA; Abbott, Maidenhead, UK)	Positive serology	Median of 4 months from their maximum IgG titre.
USA26	3,458	Asymptomatic and mild symptoms	Anti-spike IgG antibodies—Ortho Clinical Diagnostics VITROS® XT 7600 platform	8 weeks after the first blood sample	all of our sero-positive HCWs have maintained antibody positivity for at least 8 weeks,
Spain27	578	Mild (a symptomatic and symptomatic)	Magnetic microspheres from Luminex Corporation (Austin, TX) against receptor-binding domain (RBD) of the spike glycoprotein of SARS-CoV-2	PSOa or positive PCRb	• (3.08%) seroconverted for IgG at 3 months follow up.
					• Decay rate 0.66 (95% CI, 0.53; 0.82)

aPSO: Post symptoms onset.

bPolymerase chain reaction.

cHCWs: Health Care Workers.

The differences observed in SARS-CoV-2 antibody trajectories may be assay and/or antigen dependent, e.g., waning of anti-NC IgG with stable anti-spike IgG using the same Abbott platform as seen in previous studies, but total anti-NC antibodies assayed using a Roche platform remained stable [12, 27]. Probably, these findings depend on assay cut-offs which can be tuned to priorities sensitivity or specificity, with inherently more specific assays having potential to also be set more sensitively, resulting in longer durations of detectable IgG antibodies. For anti-NC, it was observed a higher IgG titers with longer durability occurring after PCR confirmed infection, consistent with data from Long et al. where 40% of asymptomatic subjects and 13% of the symptomatic individuals became IgG negative in the early convalescent phase [24, 28].

Our results indicate that cross-sectional serosurveys to determine population level immunity may underestimate rates of previous infections. There will be epidemiological implication because if IgG levels fall below detection levels before they are measured, under ascertainment of past infection might occur. Thus, it is critical to understand immune responses to SARS-CoV-2 infection in order to define parameters in which antibody tests can provide meaningful data in the absence of PCR testing in population studies.

Strength

We included a larger number of subjects compared to most of the previous reported studies. Our study was conducted in multiple clinical sites and mobile teams were utilized, therefore, potentially more representative of the overall prevalence of SARS-CoV-2 infectivity amongst HCWs in the workplace with variable exposure to SARS-CoV-2 at their job.

Most published surveys are predominantly cross-sectional or at most include a longitudinal follow-up of short duration with few of them extend up to 6–8 months’ Here, a longitudinal data for IgG positive subjects were conducted for prolonged duration to determine the kinetics of SARS-CoV-2 antibodies with at least two time points per subject.

COVID-19 infections are predominantly mild or even asymptomatic. While the immunological responses to severe COVID-19 are relatively well described [29, 30] understanding the response in mild COVID-19 cases is required, since mild and asymptomatic cases constitute the majority of our cohort. It was crucial to understand the robustness of the antibody response in mild cases, including its longevity, so as to inform serosurveys, as well as to determine levels and duration of antibody titers.

We used a high-quality serological testing, in a head-to-head comparison of 12 different serology assays for detection of SARS-CoV-2 antibodies, our assay used here found to be among the tests with a highest clinical sensitivity and specificity [13]. Generally, majority of antibodies are directed against the most abundant protein, which is the NC. Therefore, tests that measure IgG to NC would be the most sensitive. However, the receptor-binding domain of S (RBD-S) protein is the host attachment protein, and antibodies against RBD-S are expected to be neutralizing and would be more specific. Therefore, using both antigens for detecting IgG would result in high sensitivity [31].

Limitation

The current study has some limitations. It is single-center design and testing of only 33% of HCWs, explained by the fact that at least one-third of those not tested were individuals not at work during the study period. Seroconversion may have been missed if testing was too early, especially without IgM results that might reflect more recent infection than IgG.

Antibody responses were only analyzed using 1 antigen and other viral proteins may elicit different responses in different individuals [14], thus we could have slightly underestimated the overall seroprevalence of infection. Previous published studies indicated that NC-specific antibodies waned more quickly than did S-specific antibodies.

It estimates point prevalence of SARS-CoV-2 IgG in HCWs and was not designed to be conducted as periodic serosurveys, which allows monitoring of seroprevalence progression over the epidemic course. Prevalence among HCWs will be dynamic, and likely to be affected as the infection rate across the community changes. This snapshot study was not intended to capture such trends.

Selection bias might happen since the participants may have been more inclined to volunteer if they were concerned about COVID-19 infection. Indeed, it is conceivable that individuals who experienced COVID-19-like symptoms, or those that were less confined during lockdown were more likely to participate in the study, potentially leading to overestimation of our prevalence. HCWs with exposures or symptoms may have been less inclined to report these accurately (information bias), though reassurance about confidentiality will have at least in part mitigated this. The potential for exposure and symptoms recall bias about was present throughout. To reduce the effect of recall bias, all surveys were filled out by HCWs before receiving their serology results.

Conclusion

This survey found variation in the SARS-CoV-2 seroprevalence in different groups of HCWs. We identified increased risk of infection in frontline staff mainly respiratory therapist which could be explained by the nature of extensive direct patient contact, the lack of available personal protective equipment early on in the pandemic and participation in aerosolizing procedures which confer significant effect on seropositivity.

This strongly supports the notion that differential risk of SARS-CoV-2 exposure exists within the hospitals. Our findings raise concern that humoral immunity may not be long lasting in patients with mild illness, who represent the majority of Covid-19 patients. It is important to note that the loss of IgG positivity is not equivalent to loss of immunity. However, longitudinal reports are not in full agreement about the longevity of antibody titers, with some showing that IgG levels are waning rapidly by approximately several weeks after infection while others reporting stable levels detected over months, and whether protective immunity will be maintained with a lower antibody titer is unknown.

There are important questions that need to be answered with appropriately designed studies. Importantly, we need to define the specific antibody titers that correlate of protection. A combination of a detailed knowledge of specific antibody dynamic plus determining protective titers would help us to make predictions who is at a reduced risk of reinfection.

1 Mar 2022

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- https://pubmed.ncbi.nlm.nih.gov/33341124/

- https://discovery.ucl.ac.uk/id/eprint/10117965/2/Walker_The%20duration%2C%20dynamics%20and%20determinants%20of%20SARS-CoV-2%20antibody%20responses%20in%20individual%20healthcare%20workers_AAM.pdf

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We have reached a decision regarding the submitted manuscripts. Please revise your manuscript by adhering to the comment of the reviewers (and also to accomodate the reviewers' suggestion) as attached.

Best regards,

Academic Editor

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: My appreciation goes for the article. Here are some points to note:

1.For writing techniques, please refer to the publisher formats and articles that have been published. For example: the abstract should also contain the methods, the essential results, and conclusions in one complete paragraph; the second paragraph in line 93-95 one paragraph consists only 1 sentence.

2.In the abstract:

(i)Important results from statistical surveys, and results from multivariable logistic regression should be added.

(ii)The main results of the demographic statistics (Table 1) that specifically consider three factors: IgG seropositive status, clinical, and epidemiological respectively, should also be included.

3.In the methods:

(i)HCW should be associated with the location where they work. It must be mapped what are four hospitals in one city look like, such as the capacity, etc. (related with line 117)

(ii)Statictics methods should be explain more clearly particularly in multivariate logistic regression. Note: Is the multivariable logistic regression same with the multivariate logistic regression?

(iii)Alpha used is too large. It should be 0.01.

4. In the results:

(i)Please show the multivariable (multiple) logistic regression model, follows by the estimate parameters, chi-square table, etc. The example of mathematical model is as follows:

(ii)The descriptive statistics is more attractive and interesting if it is represented by figure/graph, for example:

B: Proportion of SARAS-Cov-2 samples across cities and regencies in West Java, Indonesia.

C: Distribution of SARS-CoV-2 sequenced samples across different cities and regencies in West Java Indonesia.

D: Demography of samples.

Source: Azzania, et al. (2021). Analysis of SARS-CoV-2 Genomes from West Java, Indonesia. Viruses (13), 2097.

(iii)Comparisons should suffice in unrelenting sentences. (What is the comparison table for?)

(iv)Demographics should be linked to where they live.

(v)Add an analysis of the effects of gender on that demographic, i. e. the effect of these results (3 factors: IgG seropositive status, clinical, and epidemiological) on each sex.

Reviewer #2: 1. The objective of the study should be clearly stated in the abstract.

2. Table 3 (line 186): All the p-values are for the test of significance of the variables not for the regression coefficients. For e.g., there should be a p-value for "occupation", not for certain types of occupation (allied, resp. therapist) - consult your statistician.

3. Line 193: Fig 1. Trend in mean titer of IgG over the study period. A boxplot is not plotting a mean but a median (Q1, Q2=median, Q3). It would be informative if the titer of IgG are visualized as a line plot per individuals with time and provided by an average line plot for all titer of IgG. A lowess smoothing plot can be used for the average line plot. Since the individuals are a lot, a soft tone color (grey color, for example) for all individual plot is used. The color for smoothing plot should be bold and strong (black color, for example). An appropriate time scale should be used for the plot, if the date of the serologic tests were available, use "the time since the PCR test" or "the time since the first serologic test" (Choose the best available starting point). Consult your statistician to do such plot.

4. There is no sound Conclusion (Line 309). There is no answer (at least a comment) to how occupation affect seropositive outcomes (the risk factors), for example. How long the duration of detectable IgG, and how it is as compare to other countries.

5. Table 1 (Line 163), Characteristics n: there were 5 subjects (2528-273-22650=5) unclassified (seropositive or seronegative). This should be reported either in the table or in the text.

6. Be consistent with using or not using a thousand separator (,), for e.g. in line 118: "... a total of 7737 HCWs: 1,021 medical staff.

**********

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Reviewer #1: No

Reviewer #2: No

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Submitted filename: Review Manuscript PLOS USP (2).docx

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15 Apr 2022

Academic editor Response

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. 1. DONE

In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.

4. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ

4. DONE

5. Thank you for submitting the above manuscript to PLOS ONE. During our internal evaluation of the manuscript, we found significant text overlap between your submission and the following previously published works, some of which you are an author.

- https://www.medrxiv.org/content/10.1101/2020.07.09.20150136v1- https://pubmed.ncbi.nlm.nih.gov/33341124/https://discovery.ucl.ac.uk/id/eprint/10117965/2/Walker_The%20duration%2C%20dynamics%20and%20determinants%20of%20SARS-CoV-2%20antibody%20responses%20in%20individual%20healthcare%20workers_AAM.pdf

We would like to make you aware that copying extracts from previous publications, especially outside the methods section, word-for-word is unacceptable. In addition, the reproduction of text from published reports has implications for the copyright that may apply to the publications.

Please revise the manuscript to rephrase the duplicated text, cite your sources, and provide details as to how the current manuscript advances on previous work. Please note that further consideration is dependent on the submission of a manuscript that addresses these concerns about the overlap in text with published work.

We will carefully review your manuscript upon resubmission, so please ensure that your revision is thorough. 5. DONE

Reviewer #1

My appreciation goes for the article. Here are some points to note:

1.For writing techniques, please refer to the publisher formats and articles that have been published. For example: the abstract should also contain the methods, the essential results, and conclusions in one complete paragraph; the second paragraph in line 93-95 one paragraph consists only 1 sentence. 1. DONE

2.In the abstract:

(i)Important results from statistical surveys, and results from multivariable logistic regression should be added.

(ii)The main results of the demographic statistics (Table 1) that specifically consider three factors: IgG seropositive status, clinical, and epidemiological respectively, should also be included. 2. (i) DONE

(ii) DONE

3.In the methods:

(i)HCW should be associated with the location where they work. It must be mapped what are four hospitals in one city look like, such as the capacity, etc. (related with line 117) 3. (i)Thank you for your comments, this information is not available. Line 117 has been modified

(ii)Statistics methods should be explain more clearly particularly in multivariate logistic regression. Note: Is the multivariable logistic regression same with the multivariate logistic regression?

(iii)Alpha used is too large. It should be 0.01. (ii)We did NOT use multivariate logistic regression, we used multivariable/multiple logistic regression. Logistic regression formula.

(iii)We applied the standard practice of using alpha 0.05.

4. In the results:

(i)Please show the multivariable (multiple) logistic regression model, follows by the estimate parameters, chi-square table, etc. The example of mathematical model is as follows:

(ii)The descriptive statistics is more attractive and interesting if it is represented by figure/graph, for example:

B: Proportion of SARAS-Cov-2 samples across cities and regencies in West Java, Indonesia.

C: Distribution of SARS-CoV-2 sequenced samples across different cities and regencies in West Java Indonesia.

D: Demography of samples.

Source: Azzania, et al. (2021). Analysis of SARS-CoV-2 Genomes from West Java, Indonesia. Viruses (13), 2097. (i) Below is the model used in the study and we did not see the need to add it in the manuscript.

Univariate logistic regression: The probability function is as below:

p=ⅇ^(β_0+β_1 x)/(1+ⅇ^(β_0+β_1 x) )

Multivariable or Multiple logistic regression: The probability function for multiple explanatory variables is as below:

p=ⅇ^(β_0+β_1 x_1+β_2 x_2+β_3 x_3+⋯.β_n x_n )/(1+e^(β_0+β_1 x_1+β_2 x_2+β_3 x_3+⋯.β_n x_n ) )

(ii) DONE, we added figures of descriptive statistics.

(iii)Comparisons should suffice in unrelenting sentences. (What is the comparison table for?)

(iv)Demographics should be linked to where they live. (iii) Thanks for this important comment, the written sentences are enough and the data in the table are more elaborative.

(iv) Thank you for your comment. We don't have location data in our dataset.

(v)Add an analysis of the effects of gender on that demographic, i. e. the effect of these results (3 factors: IgG seropositive status, clinical, and epidemiological) on each sex. (v) Table added

Reviewer #2:

1.The objective of the study should be clearly stated in the abstract. 1. The objective has been added.

2. Table 3 (line 186): All the p-values are for the test of significance of the variables not for the regression coefficients. For e.g., there should be a p-value for "occupation", not for certain types of occupation (allied, resp. therapist) - consult your statistician. 2. We have used occupation in a cross tabulation (chi-square test) in Table 1 but in univariable regression analysis we used dummy variables for each occupational type. Hence, they are statistically sound.

3. Line 193: Fig 1. Trend in mean titer of IgG over the study period. A boxplot is not plotting a mean but a median (Q1, Q2=median, Q3). It would be informative if the titer of IgG are visualized as a line plot per individuals with time and provided by an average line plot for all titer of IgG.

A lowess smoothing plot can be used for the average line plot. Since the individuals are a lot, a soft tone color (grey color, for example) for all individual plot is used. The color for smoothing plot should be bold and strong (black color, for example). An appropriate time scale should be used for the plot, if the date of the serologic tests were available, use "the time since the PCR test" or "the time since the first serologic test" (Choose the best available starting point). Consult your statistician to do such plot. 3. DONE (Figures added)

4. There is no sound Conclusion (Line 309). There is no answer (at least a comment) to how occupation affect seropositive outcomes (the risk factors), for example. How long the duration of detectable IgG, and how it is as compare to other countries. 4. Conclusion has been modified and table 6 in discussion demonstrated the differences among countries.

5. Table 1 (Line 163), Characteristics n: there were 5 subjects (2528-273-22650=5) unclassified (seropositive or seronegative). This should be reported either in the table or in the text. 5. DONE. Stated in the manuscript in abstract and result

6. Be consistent with using or not using a thousand separator (,), for e.g. in line 118: "... a total of 7737 HCWs: 1,021 medical staff.

6. DONE

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

12 Jun 2022

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Asep K. Supriatna, Ph.D

Academic Editor

PLOS ONE

Additional Editor Comments:

Dear authors,

Thank you for submiting the revised version of the manuscript. The revised manuscript has been evaluated by the reviewer and there are still some issues need to be addressed. Please make correction based on the comment of the reviewer.

Best regards,

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: Table 4 is still wrong (previously it was Table 3).

All the p-values should be significance test for the variables notfor the regression coefficients. For e.g., there should be a p-value for 'occupation., not for certain types of occupation ('allied', 'resp. therapist', etc.).

The p-value is the simultaneous test for the all dummy variables created by 'occupation' variable. Using this p-value you can evaluate the significance of occupation to the outcome (similar to that of the chi-square test but it is now adjusted for other variables). This is a typical mistake in using regression with dummy variables (with more than 2 categories). Do the same way for all variables with more than 2 categories ('previous medicine', 'medical condition', ... why not include the 'blood group' ...as in the chi-square test?). A sound conclusion then can be made from these p-values: what is the effect of "occupation", what is the effect of "previous medicine", etc.

I suggest you also performing variable selection (model building). In a software like R, there is available procedure like dropterm() to calculate the simultaneous test, stepAIC() for variable selection. It should be available also in STATA, perhaps the TEST syntax.

Fig 2.C

The horizzontal axis should be the time, and the vertical axis is the IgG results. If the date of the test available, it would be informative if the time is "the time since the first serologic test" (not just Test 1, Test 2, Test 3). The attached figure is an example of such a plot. The individual plots are for all 185 (?) participants.

The number of days since the first test may not be the same for all participants, even if the measurement time is planned every 3 months, therefore the horizontal axis is not just Test 1, Test 2, Test 3. (in the example, I made day 0 is the baseline and the Test 1 is participants who are underwent the next test (In your data, the baseline is the Test 1).

Fig 2.B and a plot as suggested in the attached file are informative enough.

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17 Jun 2022

To: Asep K. Supriatna, Ph.D

Academic Editor

PLOS ONE

Re: PONE-D-21-40113R1

Title: Seroprevalence and longevity of SARS-CoV-2 nucleocapsid antigen-IgG among health care workers in a large COVID-19 public hospital in Saudi Arabia: A prospective cohort study

We would like to thank the Editorial Office and the reviewers for their time and valuable comments. We appreciate your efforts in critically reviewing our manuscript and we believe these suggestions would add more depth to our manuscript. We hope we have addressed your concerns appropriately. We look forward to seeing our manuscript published in PLOS ONE. Please find our response to your queries below:

Academic Editor

4. Have the authors made all data underlying the findings in their manuscript fully available? Yes. Submitted in the link

Reviewer #2:

1. Table 4 is still wrong (previously it was Table 3).

All the p-values should be significance test for the variables not for the regression coefficients. For e.g., there should be a p-value for 'occupation., not for certain types of occupation ('allied', 'resp. therapist', etc.).

The p-value is the simultaneous test for the all dummy variables created by 'occupation' variable. Using this p-value you can evaluate the significance of occupation to the outcome (similar to that of the chi-square test but it is now adjusted for other variables). This is a typical mistake in using regression with dummy variables (with more than 2 categories). Do the same way for all variables with more than 2 categories ('previous medicine', 'medical condition', ... why not include the 'blood group' ...as in the chi-square test?). A sound conclusion then can be made from these p-values: what is the effect of "occupation", what is the effect of "previous medicine", etc.

I suggest you also performing variable selection (model building). In a software like R, there is available procedure like dropterm() to calculate the simultaneous test, stepAIC() for variable selection. It should be available also in STATA, perhaps the TEST syntax. 1. Thanks for your comment. We have assessed the association between occupation as a whole and serology test (outcome) in Table 1 using chi-square test and found it had a trend to significance (p = 0.08). That's why we tried to test using logistic regression to pinpoint whether any particular occupation is related to serology positivity compared to others. Unfortunately, in logistic regression we can't treat occupation as one single variable/category as it has 8 categories. If we do then Stata will treat it as a continuous variable. Hence, we must declare that it's a categorical variable and must declare which category will be the reference category (usually the category which holds most people (considered to be "normal"). That's why in Stata, we declared occupation to be a categorical variable and declared "Nurse" to be the :reference category" as this category held the highest number of people (n = 1351 (53.0%)) of all participants. Hence, we got OR and p value for all the other occupations compared to Nurses. We have also done model building by taking only those variables from simple logistic regression (unadjusted) that had p values less than 0.3 (either significant or potential to become significant). That's why you will see only a few variables in the multiple logistic regression (adjusted) section. As per your suggestion, we have done model building with variable selection. Please see new table-4 in the manuscript.

2. Fig 2.C

The horizontal axis should be the time, and the vertical axis is the IgG results. If the date of the test available, it would be informative if the time is "the time since the first serologic test" (not just Test 1, Test 2, Test 3). The attached figure is an example of such a plot. The individual plots are for all 185 (?) participants.

The number of days since the first test may not be the same for all participants, even if the measurement time is planned every 3 months, therefore the horizontal axis is not just Test 1, Test 2, Test 3. (in the example, I made day 0 is the baseline and the Test 1 is participants who are underwent the next test (In your data, the baseline is the Test 1).

Fig 2.B and a plot as suggested in the attached file are informative enough. We thank the reviewer for his valuable suggestion. We have created a new lowess smoothing graph (figure 2-B).

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

12 Jul 2022

22 Jul 2022

To: Asep K. Supriatna, Ph.D

Academic Editor

PLOS ONE

Re: PONE-D-21-40113R2

Title: Seroprevalence and longevity of SARS-CoV-2 nucleocapsid antigen-IgG among health care workers in a large COVID-19 public hospital in Saudi Arabia: A prospective cohort study

We would like to thank all the editors and reviewers for the valuable comments. We hope we have addressed all the comments appropriately. We look forward to seeing our manuscript published in PLOS ONE. Please find our response to the journal requirement:

Journal Requirement

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

1. Thanks for your comment. We have revised the reference list and completed it according to the journal requirement.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

27 Jul 2022

Seroprevalence and longevity of SARS-CoV-2 nucleocapsid antigen-IgG among health care workers in a large COVID-19 public hospital in Saudi Arabia: A prospective cohort study

PONE-D-21-40113R3

Dear Dr. Alasmari,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Asep K. Supriatna, Ph.D

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

2 Aug 2022

PONE-D-21-40113R3

Seroprevalence and longevity of SARS-CoV-2 nucleocapsid antigen-IgG among health care workers in a large COVID-19 public hospital in Saudi Arabia: A prospective cohort study

Dear Dr. Alasmari:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Asep K. Supriatna

Academic Editor

PLOS ONE