Association between history of HBV vaccine response and anti-SARS-CoV-2 spike antibody response to the BioNTech/Pfizer's BNT162b2 mRNA SARS-CoV-2 vaccine among healthcare workers in Japan: A prospective observational study.

INTRODUCTION: Inadequate vaccine response is a common concern among healthcare workers at the frontlines of the COVID-19 pandemic. We aimed to investigate if healthcare workers with history of weak immune response to HBV vaccination are more likely to have weak responses against the BioNTech/Pfizer's BNT162b2 mRNA SARS-CoV-2 vaccine.
METHODS: We prospectively tested 954 healthcare workers for the Anti-SARS-CoV-2 spike (S) protein antibody titers prior to the first and second BNT162b2 vaccination doses and after four weeks after the second dose using Roche's Elecsys® assay. We calculated the percentage of patients who seroconverted after the first and second doses. We estimated the relative risk of non-seroconversion after the first BNT162b2 vaccine (defined as anti-SARS-CoV-2-S titer <15 U/mL) among HBV vaccine non-responders (HBs-Ab titer <10 mIU/mL) and weak responders (≥10 and <100 mIU/mL) compared to normal responders (≥100 mIU/mL).
RESULTS: Among 954 healthcare workers recruited between March 9 and March 24, 2021 at Osaka Medical and Pharmaceutical University, weak and normal HBV vaccine responders had comparable S-protein titers after the first BNT162b2 dose (51.4 [95% confidence interval 25.2-137.0] versus 59.7 [29.8-138.0] U/mL, respectively). HBV vaccine non-responders were more likely than normal responders to not seroconvert after a single dose (age and sex-adjusted relative risk 1.85 95% confidence interval [1.10-3.13]) although nearly all participants seroconverted after the second dose. After limiting the analysis to 382 patients with baseline comorbidity data, the comorbidity-adjusted relative risk of non-seroconversion among HBV vaccine non-responders to normal responders was 1.32 (95% confidence interval [0.59-2.98]). DISCUSSION: Long term follow-up studies are needed to understand if protective immunity against SARS-CoV-2 wanes faster among those with history of HBV vaccine non-response and when booster doses are warranted for these healthcare workers.

Introduction

BioNTech/Pfizer’s BNT162b2 mRNA SARS-CoV-2 (COVID-19) vaccine has shown high clinical efficacy and excellent antibody response in both clinical trials and real-world settings [1-3]. More recent studies investigating the characteristics of patients with reduced humoral response to COVID-19 vaccines have shown elderly and immunosuppressed patients (i.e. cancer patients on chemotherapies, transplant recipients on immunosuppressants, hemodialysis, and high-dose glucocorticoids) to generally yield lower antibody titer responses, promoting the medical community to recommend booster doses for high-risk populations [4-7].

Understanding the duration and strength of protective immunity against COVID-19 after vaccination and being able to identify who is at risk of reduced humoral response is of paramount importance among healthcare workers who are at the frontlines of the pandemic. One understudied potential risk factor is history of reduced immune response to other vaccines such as the Hepatitis B Virus (HBV) vaccine. Vaccine non-response is well studied in HBV and is one of the few vaccines which has recommendations for serological response testing due to primary vaccine failure. Data from HBV vaccination studies show that roughly 5% of individuals are “non-responders” to HBV vaccination, meaning their immune systems do not elicit protective levels of humoral response (defined as HBs antibody titers of ≥10 mIU/mL) after receiving a full vaccination course [8]. Weak antibody response (HBs antibody titer between 10 and 100 mIU/mL) is also common, and both have been associated with older age, obesity, smoking, male gender, and immunosuppressed states [9, 10]. The mechanism for non-response is unclear, but genetic predisposition, including certain HLA allele types, and immunosenescence are thought to play a key role [11, 12].

Studies show certain immunocompromised patient groups such as hemodialysis patients and transplant recipients to have weak antibody responses after COVID-19 vaccines [7]. However, there is a dearth of studies investigated potential risk factors for diminished antibody response among generally healthy cohorts. Of particular interest among healthcare workers is knowing if robust immune response is achieved after COVID-19 vaccination for those with history of weak immune response to HBV vaccines. We therefore aimed to investigate if healthcare workers with history of non-response or weak response to HBV vaccination tend to also be weak responders to the BNT162b2 vaccine.

Methods

Healthcare workers from Osaka Medical and Pharmaceutical University Hospital (Osaka, Japan) scheduled to receive BioNTech/Pfizer’s BNT162b2 vaccine were recruited consecutively between March 9, 2021 and March 24, 2021 according to the University’s vaccination prioritization schedule. Vaccination priority was given to frontline healthcare works (mostly nurses, physicians, pharmacists, technicians) and administrative workers with greater patient exposure. Among 1,051 recruited, 1,032 provided written consent to participate in the study.

We obtained baseline serum blood samples and surveys from participants immediately prior to their first vaccination dose and repeated the blood test and survey questionnaire immediately prior to and after four weeks of their second vaccination dose. We tested the blood samples using two platforms–Elecsys® Anti-SARS-CoV-2, a qualitative assay that measures the antibody responses against nucleocapsid (N) protein, and the Anti-SARS-CoV-2-S immunoassay (Roche Diagnostics International Ltd, Rotkreuz, Switzerland), a semi-quantitative assay that measures the adaptive humoral response to the SARS-CoV-2 spike (S) protein receptor binding domain. Both assays were tested on the Cobas e801 platform at our University Hospital’s central laboratory. Results from Elecsys®Anti-SARS-CoV-2 (N-protein antibody) were considered positive if the cut-off index (COI) was greater than or equal to 1.0, and negative if the COI was less than 1.0. The Elecsys®Anti-SARS-CoV-2-S assay results range from 0.4 to 250 U/mL and the test was defined as “positive” if titers were 0.8 U/mL or above, and “negative” if under 0.8 U/mL [13]. The cut-off of 15 U/mL was used to define seroconversion after the BNT162b2 vaccine (“seroconverted” if titer was ≥15 U/mL) according to manufacturer analysis which demonstrated an inhibition cut-off of 20% on the cPass SARS-CoV-2 Neutralisation Antibody Detection Kit (Genscript, Netherlands) with a positive percent agreement of 88.9% [95% confidence interval (CI) 85.8–91.5], negative percent agreement of 90.0% [95% CI 76.3–97.2], and positive predictive value of 99.1% [95% CI 97.7–99.6] [14]. Consequently, S-protein titers between 0.8 and 15 U/mL were categorized as “weak responses.”

History of HBV vaccination and anti-HBs antibody titer results were obtained from employee health records recorded by the University’s occupational health program, which tests the HBV antibody titer levels of all its employees at the time of recruitment and follows their vaccination history and follow-up HBs antibody tests if they have no documented history of full vaccination. We defined HBV vaccine “non-responders” as those who had HBs antibody levels less than 10 mIU/mL after their HBV vaccination course and “weak responders” as those with titers between 10 mIU/mL and 100 mIU/mL [8, 9]. Individuals with anti-HBs titers of 100 mIU/mL or greater were defined as “normal” responders. Participants whose anti-HBs titers reached 10 mIU/mL or greater only after their second vaccine series, were categorized as a “weak responder.” All participants in the study had their three-dose HBV vaccination series completed before December 2020.

We used the two-tailed Mann-Whitney test for the group comparison of antibody titer levels and the Kruskal-Wallis test for three or more group comparisons. Confidence intervals (CI) for vaccine response and other binomial proportions were calculated using the Clopper-Pearson method. Chi-square tests were used for group comparisons. Log transformation and non-parametric tests were used for non-normal data. We used relative risk (RR) to estimate the odds of not seroconverting among HBV vaccine non-responders and weak responders compared to normal responders. For this analysis, we excluded eleven patients with positive N-protein antibody test results at baseline as to exclude those with previous SARS-CoV-2 infection and calculated the age and sex-adjusted relative risk (aRR) and 95% CIs. We obtained data on smoking status, alcohol use, and comorbid conditions (history of stroke, history of cardiovascular diseases, and presence of arrhythmia, valvular heart diseases, dyslipidemia, diabetes, hypertension, cancer, and collagen disorders) from the baseline survey to identify potential confounders. Among the subset of participants whose comorbidity data could be linked, we calculated the comorbidity adjusted relative risk of non-seroconversion by HBV vaccine response. An alpha of 0.05 was used throughout and all statistical analyses were performed using STATA (15.1, StataCorp LLC, College Station, TX) and graphics were created using R (v4.1.0). The study was approved by the University’s Ethics Committee (IRB approval number 2020163).

Results

Among 1,032 consenting healthcare workers, we excluded 71 participants from the study who had missing HBs antibody titer results and seven participants who consented but did not provide blood samples for the study. Of the 954 participants included in the analysis, the median age was 28 [IQR 34.5–45.0] years and 56% (n = 533/954) were female (Table 1). Three percent (n = 31/954) were non-responders to the HBV vaccine and 32% (n = 302/954) were weak HBV vaccine responders. At baseline, only 1% (n = 11/954) of the participants had positive antibodies against the SARS-CoV-2 N protein. Among the eleven participants with positive anti-N protein antibody results (indicating previous infection), six were unaware of their previous infection status. Of these eleven, ten also had positive test results for anti‑SARS‑CoV‑2-S. Thirty-eight participants did not return for their SARS-CoV2-S antibody testing three weeks after their first dose of BNT162b2.

Table 1

Characteristics and SARS-CoV-2 N and S protein antibody status before and after receiving the BNT162b2 mRNA-1273 COVID-19 vaccine among healthcare workers in the study (n = 954).

Characteristics		Total (n = 954)		Normal responder (n = 621)		Weak responder (n = 302)		Non-responder (n = 31)		p-values
Age (median years, IQR)		28.0	(34.5–45.0)	28.0	(35.0–44.0)	27.0	(33.5–45.0)	39.5	(49.0–54.5)	0.0001
Female sex, (n, %)		533/954	(56)	361/621	(58)	152/302	(50)	20/31	(65)	0.050
N-protein antibody (positive n, %)
	Baseline	11/954	(1)	8/621	(1)	3/302	(1)	0/31	(0)	1.000
	Three weeks after 1st dose	11/954	(1)	7/621	(1)	4/302	(1)	0/31	(0)	0.831
	Four weeks after 2nd dose	10/916	(1)	7/594	(1)	3/294	(1)	0/28	(0)	1.000
Positive S-protein antibody (0.8 U/mL or above, n, %)
	Baseline	13/954	(1)	9/621	(1)	4/302	(1)	0/31	(0)	1.000
	Three weeks after 1st dose	951/954	(99.7)	620/621	(99.8)	301/302	(99.7)	30/31	(96.8)	0.085
	Four weeks after 2nd dose	916/916	(100)	594/594	(100)	294/294	(100)	28/28	(100)	N/A
Seroconversion S-protein antibody 15 U/mL or above, n, %)
	Baseline	5/954	(0.5)	4/621	(0,6)	1/302	(0.3)	0/31	(0)	1.000
	Three weeks after 1st dose	817/954	(86)	541/621	(87)	256/302	(85)	20/31	(65)	0.004*
	Four weeks after 2nd dose	916/916	(100)	594/594	(100)	294/294	(100)	28/28	(100)	N/A
S-protein antibody quantitative response (median titer U/mL, IQR)
	Baseline	0.07	(0.07–0.08)	0.07	(0.07–0.08)	0.07	(0.07–0.08)	0.08	(0.07–0.08)	0.0044*
	Three weeks after 1st dose	56.1	(27.9–137.0)	59.7	(29.8–138.0)	51.4	(25.2–137.0)	36.9	(9.7–96.7)	0.0263*
	Four weeks after 2nd dose	250	(250–250)	250	(250–250)	250	(250–250)	250	(250–250)	0.0734

Numbers indicate median (IQR: interquartile range), n/N (%, 95% CI: confidence intervals) for outcomes. p-values for two-sided test of significance using Kruskal-Wallis H test for continuous variables and Fisher’s exact and Chi-squared tests for categorical variables. Normal HBV vaccine responders are those with HBs antibody titers ≥100 mIU/mL. Weak HBV vaccine responders are those with HBs antibody titers between 10 and 100 mIU/mL. HBV vaccine non-responders are those with less than 10 mIU/mL HBs antibody response. Positive Anti-SARS-CoV-2 N-protein antibody results if COI (cut-off index) was ≥1.0.

*Denotes statistical significance at alpha = 0.05. N = nucleocapsid. S = spike.

Three weeks after a single dose of BNT162b2 COVID-19 vaccine, 99.7% [95% CI 99.0–99.9] had positive SARS-CoV-2-S protein test results (titer of 0.8 U/mL and above), 85.6% [95% CI 83.3–87.7] seroconverted (titer of 15 U/mL and above), and 10.7% [95% CI 8.9–12.8] had titer levels beyond the assay range of 250 U/mL (Table 1). The median antibody titer after the first does was 56.1 (IQR 27.9–137.0) U/mL. The distribution of SARS-CoV-2 spike protein antibody titers are demonstrated in Fig 1A at baseline and after the first and second BNT162b2 COVID-19 vaccine dose. Four weeks after the second vaccination dose, all participants (n = 916/916) had positive S-protein antibodies regardless of their history for HBV vaccine response and seroconverted, and 99.3% (95% CI 98.5–99.7, n = 910/916) had titer levels above the assay range of 250 U/mL. None of the participants developed positive Anti-SARS-CoV-2 test results (positive N-protein antibody) during follow-up and no one reported being infected with SARS-CoV-2 during the study.

Fig 1

Spike protein antibody levels of patients in the study.

(A) Distribution of SARS-CoV-2 spike protein antibody titers (logarithmic scale) at baseline, after one dose of BNT162b2 mRNA-1273 COVID-19 vaccine, and after two doses (n = 954). (B) Distribution of SARS-CoV-2 spike protein antibody titers (logarithmic scale) after a single dose of BNT162b2 mRNA-1273 COVID-19 vaccine, stratified by age groups (n = 954). (C) Distribution of SARS-CoV-2 spike protein antibody titers (logarithmic scale) after a single dose of BNT162b2 mRNA-1273 COVID-19 vaccine, stratified by history of HBV vaccine response (n = 954).

Increasing age was significantly associated with lower S-protein antibody titer levels after the first dose of BNT162b2 (Fig 1B, Kruskal-Wallis H test p = 0.0001). Participants under 30 years of age had a median S-protein antibody titer of 74.1 [IQR 34.6–156.0] U/mL (n = 303) and those above 50 and older had a median titer of 37.8 [IQR 15.7–86.4] U/mL (n = 160).

After a single vaccine dose, HBV non-responders were significantly less likely to seroconverge compared to normal and weak HBV vaccine responders. Among 621 normal HBV vaccine responders, 87.1% [95% CI 84.2–89.5] seroconverted after the first dose. Among 302 weak HBV vaccine responders, 84.7% [95% CI, 80.2–88.4] seroconverted after the first dose. Only 64.5% [95% CI 45.5–79.9] seroconverted after a single dose among 31 HBV vaccine non-responders (Table 1).

We found quantitative differences in the anti-SARS-CoV-2 S-protein titers across the three HBV vaccine response groups (Fig 1C, Kruskal-Wallis H test p = 0.026). More specifically, differences in the median S-protein titers were negligible between HBV vaccine responders compared to normal responders (51.4 versus 59.7 U/mL, respectively: Mann-Whitney U test, p = 0.295), but significantly lower among non-responders compared normal responders (36.9 versus 59.7 U/mL, respectively: Mann-Whitney U test, p = 0.01).

After a single vaccine dose, HBV vaccine non-responders were at greater risk of not seroconverting (unadjusted RR 2.75, [95% CI 1.64–4.62], p<0.001) after adjusting for age and sex (aRR 1.88, [95% CI 1.13–3.18], p = 0.018, Table 2).

Table 2

Log-binomial regression for the unadjusted and adjusted relative risk non-seroconversion (anti-SARS-CoV-2 spike protein antibody level of <15 U/mL) after a single dose of BNT162b2 mRNA-1273 COVID-19 vaccine by history of HBV vaccine response among participants with negative SARS-CoV-2 N antibody at baseline (n = 943).

Response to HBV vaccine	S-protein titer of <15U/mL		Unadjusted relative risk		Adjusted relative risk
	n/N (%, [95% CI])		Estimate, 95% CI	p-value	Estimate, 95% CI	p-value
Normal responder	80/621	12.9% (10.5–15.8)	1	–	1	–
Weak responder	46/302	15.2% (11.6–19.8)	1.18 (0.85–1.65)	0.328	1.17 (0.84–1.63)	0.357
Non-responder	11/31	35.5% (20.1–54.5)	2.75 (1.64–4.62)	<0.001*	1.88 (1.11–3.18)	0.018*

Weak HBV vaccine responders were those with HBs antibody titers between 10 and 100 mIU/mL. Non-responders were those with less than 10 mIU/mL HBs antibody response. Adjusted relative risk adjusted for age and sex.

*Denotes statistical significance.

Among a subset of 943 participants with negative SARS-CoV-2 N antibody at baseline, we identified 382 participants whose relative risk calculation could be adjusted for by their comorbidity status obtained from the baseline survey. Among 943 participants, 188 failed to respond or responded anonymously to the survey and 373 additional were excluded who had partially or completely missing responses regarding their comorbidity status. Using the data from the remaining 382 patients and adjusting for age, sex, and past or current history of any of the following: stroke, cardiovascular diseases, arrythmia, hypertension, dyslipidemia, and diabetes, we did not find HBV vaccine response to be associated with non-seroconversion risk after a single dose of BNT162b2 mRNA-1273 COVID-19 vaccine (Table 3). We did not find smoking status, alcohol status, history of cancer, collagen disorders, chronic kidney disease, respiratory disorders, and psychiatric diseases to be a potential confounder (no association to the exposure or outcome).

Table 3

Log-binomial regression for the unadjusted and adjusted relative risk non-seroconversion (anti-SARS-CoV-2 spike protein antibody level of <15 U/mL) after a single dose of BNT162b2 mRNA-1273 COVID-19 vaccine by history of HBV vaccine response among a subset of patients with baseline comorbidity data (n = 382).

Response to HBV vaccine	S-protein titer of <15U/mL		Unadjusted relative risk		Adjusted relative risk
	n/N (%, [95% CI])		Estimate, 95% CI	p-value	Estimate, 95% CI	p-value
Normal responder	36/246	14.6% (10.7–19.7)	1	–	1	–
Weak responder	21/118	17.8% (11.8–25.9)	1.23 (0.86–1.77)	0.257	1.13 (0.70–1.83)	0.608
Non-responder	5/18	27.8% (11.2–53.9)	2.66 (1.54–4.60)	<0.001*	1.32 (0.59–2.98)	0.496

Weak HBV vaccine responders were those with HBs antibody titers between 10 and 100 mIU/mL. Non-responders were those with less than 10 mIU/mL HBs antibody response. Adjusted relative risk adjusted for age, sex, and comorbidity status (past or current history of any of the following: stroke, cardiovascular diseases, arrythmia, hypertension, dyslipidemia, and diabetes).

*Denotes statistical significance.

Discussion

In our healthy and young cohort of 954 healthcare workers, 86% seroconverted and 99.7% had positive anti‑SARS‑CoV‑2-S test results after a single dose of BNT162b2 vaccine. Seroconversion after a single vaccination dose was less common among those with history of non-response to the HBV vaccine (65%) compared to those with history of normal and weak HBV vaccine response (87% and 85%, respectively). The age and sex adjusted relative risk of having <15U/mL SARS-CoV-2-S antibody result after a single dose of BNT162b2 compared to normal HBV vaccine responders was 1.85 [95% CI 1.10–3.13]. The difference became negligible when limiting the analysis to 382 patients with available comorbidity information (past or current history of any of the following: stroke, cardiovascular diseases, arrythmia, hypertension, dyslipidemia, and diabetes). Furthermore, after the second dose, all participants (n = 916) seroconverted, and nearly all (99%) acquired SARS-CoV-2-S antibody titers above 250 U/mL.

Our findings yielded similar findings to previous real-world reports of S-protein antibody response after BNT162b2 vaccines. Shrotri et al., reported 96.3% positive anti‑SARS‑CoV‑2‑S response of ≥0.8 U/mL three to four weeks after one dose of BNT162b2 among 3,099 participants and 99.1% two weeks after the second dose among 537 participants [15]. Eyre reported 98.9% seroconversion among 3610 healthcare workers two weeks after the first dose and 99.5% among 2720 prior after the second dose [16]. Seroconversion rate was higher in our study (100%, n = 916), most likely due to the healthy worker effect as our healthcare cohort were younger than the participants in these previous studies.

Despite its observational design, one of the strengths of this study is the uniqueness of the occupational health environment in Japan where many institutions offer its employees screening for HBV at the time of recruitment, and additional dose series and follow-up antibody tests are given to ensure antibody levels reach the 10 mIU/mL cut-off. This allowed us to investigate the potential association between history of HBV vaccine non-response and response to COVID-19 vaccine which has never been studied before.

One of the limitations of this study was in the retrospective collection of occupational health records which were missing in 6.9% (n = 71/1,032) of the participants who initially consented but were later dropped from the study. These tended to be older employees (median age of 47). Misclassification may also have occurred during the study due to the retrospective nature of the occupational health data collected. HBs antibody screening is conducted for all employees at the time of recruitment and a full vaccination series is offered if their titers are below the cut-off even if they have been vaccinated. Therefore, those who developed positive HBs antibody titer only after two or more series may have been categorized as a normal HBV vaccine responder if records of previous vaccinations were not entered into the hospital’s occupational health database. However, we accepted this bias as it should theoretically lead to weaken the strength of the association towards the null.

Another limitation was the use of the cut-off value of 15 U/mL for the definition of “seroconversion” in our study. The clinical significance of “seroconversion” and the choice of the 15 U/mL threshold is arbitrary as vaccine efficacy changes over time depending on the circulating COVID-19 variant.

For our comorbidity-adjusted relative risk calculation, we dropped roughly 60% of the participants due to missing or unlinkable information. Comorbidity data was collected from survey response and not based on screening tests. The definition of each comorbidity was also not clearly defined in the survey. For example, participants were asked if they have previously been diagnosed with or are currently being treated for diabetes, but no clear definition of diabetes such as their HbA1c value was provided. Therefore, the reliability of the comorbid data in our study may be limited.

Overall, having a history of weak or non-response to the HBV vaccine did not appear to impact seroconversion of anti-SARS-CoV-2-S antibody after the second BNT162b2 vaccine dose. HBV vaccine non-responders were at greater risk of not seroconverting after a single vaccine dose compared to normal vaccine responders. After adjusting for comorbid conditions, we found the strength of the association to disappear. Our findings lead us to question if HBV vaccine non-responders have some intrinsic immunological ineptness to respond fully to COVID-19 mRNA vaccines, but insight from previous study of non-responders to HBV vaccines and tick-borne encephalitis vaccines by Garner-Spitzer et al. suggest otherwise [17]. The authors suggest that in immunocompetent individuals, non-responsiveness to a certain vaccine is most likely an antigen/ vaccine specific phenomenon and not an individual intrinsic tendency [17, 18]. Recent studies have shown comorbid conditions such as poor glycaemic control, hypercholesterolaemia, and immunosuppressed state to impact immune response to COVID-19 vaccines [19], which are also factors associated with diminished immune response to HBV vaccines. At this point, our study can only propose a potential association.

In HBV vaccination, protection is generally achieved if vaccination is done at an early age. Even if antibody titers wane over time even for those who once had very high antibody titer levels, this is not indicative of loss of protection as the host immune system is able to respond in time after exposure to HBV through the activation of memory immune cells before an infection is established [20]. As such, international guidelines no longer recommend the need for booster doses once protection is achieved after the first vaccine course [21]. For COVID-19, the exact immunological mechanism of protection from COVID-19 infection after mRNA vaccination is still being investigated. We know from recent studies that SARS-CoV-2 antibody levels and vaccine efficacy decline over time both after infection and vaccination, but that some level of immunity is sustained through both memory B cell and T cells [22-25]. Although higher anti-S protein antibody titers have been suggested as important precursors for the strength and duration of immunity [26], real-world vaccine efficacy depends also on innate immunity and external factors such as the type of variants that are circulating and people’s behavior patterns and their level of daily exposure to COVID-19. However, emerging evidence clearly demonstrate declining efficacy over time [27].

Questions remain regarding what clinical significance having initial lower titer response after the first BNT162b2 dose may have, especially when all participants eventually seroconvert after the second dose. Other antibody measurement studies conducted on immunosuppressed patients also follow the same pattern–significantly lower anti-SARS-CoV-2-S antibody levels after the first dose compared to healthy cohorts, but with great majority reaching protection after the second dose [28]. Immunological research suggest different mechanisms of protection are playing a key role in neutralization after a single or two doses [21]. Seroconversion does not necessarily mean neutralizing or immunity from SARS-CoV-2. We will need to better understand how immunity is maintained after mRNA vaccination. Longer follow-up is needed to determine if anti-SARS-CoV-2-S antibody titers also wane faster among weak or non-responders to the HBV vaccine and if that would also indicate waning protection against COVID-19 infection.

Certain patient groups such as solid-organ transplant recipients have already been identified as target groups of third booster doses [29]. Understanding who will require additional doses will not only allow us to identify healthy frontline healthcare workers at greater risk of immunological decay, but also protect immunocompromised patients in healthcare settings vulnerable to breakthrough infections that may occur among vaccinated healthcare professionals.

Conclusion

We found healthcare workers with history of non-response to HBV vaccination to be at greater risk of not seroconverting (<15U/mL anti-SARS-CoV-2-S antibody titer) after a single dose of BNT162b2 vaccine, although all achieved seroconversion levels after the second dose. Future studies are warranted to understand if the observed effect is due to comorbid conditions that predisposes people to weaker vaccine response and if immunity against COVID-19 decays faster among HBV vaccine non-responders.

2 Dec 2021

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Additional Editor Comments:

This is a study of COVID-19  and HBV vaccine responses in health care workers in Japan.

The study question is an important one and worthy of investigation.

The timing of HBV vaccination is unclear.  The authors should provide more information about when HBV vaccination and boosters were given relative to COVID-19 vaccinations.

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3. 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 #1: No

Reviewer #2: No

Reviewer #3: 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: Yes

Reviewer #2: Yes

Reviewer #3: 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: This manuscript summarizes a laboratory-based study assessing the immune response to the BioNTech/Pfizer’s BNT162b2 mRNA SARS-CoV-2 vaccine, comparing health care workers (HCW) who had a weak or non-response to the hepatitis B vaccine (HBV) to those who had a strong/normal response. HCWs who had a weak response to the HBV vaccine (i.e., HBs-Ab titer < 10 mlU/mL) were more likely to have a non-neutralizing S-protein antibody response to the first BNT162b2 mRNA SARS-CoV-2 vaccine compared to those who had a normal response to the HBV vaccine (i.e., HBs-Ab titer > 100 mIU/mL). This difference diminished to non-significance after the second vaccine. The researchers acknowledge the study is not novel, however the findings are useful to build knowledge on risk of non-response to the BNT162b2 mRNA SARS-CoV-2 vaccine. For example, with vaccine in short supply in some countries, decision-makers may utilize such study results to priority who obtains a second dose first. Additionally, as the authors stated, prior vaccine response may be informative on whose protective immunity against SARS-CoV-2 wanes faster.

The odds ratios overestimate the relative risks. Thus, relative risks need to be estimated directly.

Minor comments:

71 Consider changing “promoting the scientific” to “prompting the medical”

81 Consider adding the word “male” before “gender” given the factors are specific to higher risk.

86 Delete “to”

87 Instead of “no previous studies” maybe say there is a “dearth on studies” as no studies would be difficult to document

given the vast among of publications on SARS-CoV-2.

How many HCWs were invited into the study?

147 “protein antibodies and six were unaware of their previous COVID-19 infection” needs editing. This sentence is unclear.

167 The comma after “302” needs to be removed.

191 “in our study population was slightly higher in our study” needs to be edited.

214 “We were not able to adjust for these potential confounders in our analysis as 12% (n=117/968) responded anonymously to the survey…” While it is unlikely that it will change the final conclusions of the study, you could assess confounding based on a sub analysis of those HCWs whose surveys can be linked to laboratory data.

Table 1 is difficult to read. Consider landscape (that is, rotating the page) so the information in table cells do not wrap around. Also, column labels are needed.

Table 2 (Analysis): The odds ratios overestimate the relative risks. Thus, relative risks need to be estimated directly.

Unavailability of HCW data is acceptable due to privacy concerns. The authors state that analyses can be conducted to facilitate providing information from the study to researchers.

Reviewer #2: This manuscript reports on an interesting study aimed at finding a way to predict which individuals may be at-risk for a limited response to SARS-CoV-2 mRNA vaccination by using data related to HBV vaccination response history. At a stage in the pandemic where COVID-19 vaccine boosting is being considered and durability of vaccine response is uncertain, such an approach is intriguing. There is a major limitation to the analysis and interpretation of the results, however, as the threshold for determining “neutralizing” antibodies, which the authors discuss in great detail, is of unclear origin. This must be resolved, along with a response to the other comments noted below, before this reviewer would deem this manuscript acceptable for publication.

Major Comments:

1. In the referenced article by Rubio-Acero et al., the authors find that the Roche Elecsys Anti-SARS-CoV-2 S [Ro-RBD-Ig-quant]) had a different threshold than used in the manuscript under review. From the article’s conclusion, which is supported by the results (Table 4 in Rubio-Acero et al): “For example, raw values above 28.67 U/mL for Ro-RBD-Ig-quant and above 49.78 U/mL for EI-S1-IgGquant, respectively, predicted virus neutralization > 1:5 in 95% of cases. We may hypothesize that when the value of the quantitative tests is above the predictive value (e.g., 95%), there is little benefit in performing NT and that this could act as a surrogate marker for neutralizing titers, e.g., after mass vaccinations or post-infection.” This paper also uses a surrogate test for neutralization (GS-cPass), but the threshold for the Ro-RBD-Ig-quant corresponding to ≥ 20% is ≥ 6.99. Furthermore, in reviewing the other reference cited for this cutoff, the Elecsys package insert, the reviewer could not find any indication of an appropriate cutoff value correlating with neutralizing antibody levels. Instead, the package insert states: “The results of this semi quantitative test should not be interpreted as an indication or degree of immunity or protection from reinfection” and “The clinical applicability of semi quantitative results is currently unknown and cannot be interpreted as an indication or degree of immunity nor protection from reinfection, nor compared to other SARS CoV 2 antibody assays.” Thus, it is unclear why a cut-off of 15 U/mL was used to define neutralizing immunity within this study and it seems inappropriate to state “according to manufacturer information” (line 119) in reference to any threshold used to suggest neutralizing antibody response. Clarifying this threshold is critical for the interpretation of the results, particularly those reported in line 164-170, and for the overall conclusion of the manuscript. It is unclear if a larger proportion of the weak or normal HBV vaccine responders would have not achieved a “neutralizing” threshold if a higher cutoff was used.

2. It is important to note the limitations of the assay used for this analysis. Specifically, the manufacturer states that “The performance of this test has not been established in individuals that have received a COVID 19 vaccine. The clinical significance of a positive or negative antibody result following COVID 19 vaccination has not been established, and the result from this test should not be interpreted as an indication or degree of protection from infection after vaccination.” While the reviewer understands that EUA-platforms are often evaluated in-house for off-label usage, it is important for readers to be aware of this noted limitation within the text of the article.

3. The use of the word “neutralizing” to describe the spike protein receptor binding domain (line 112) is inconsistent with the platform package insert and should be deleted, as it suggest the assay itself detects neutralizing antibodies

4. In line 117, and then later throughout when referring to the antibody results, the terms “positive” and “negative” should be used to be consistent with the manufacturer interpretation language found within the package insert

Minor Comments:

1. Test platform appears to be spelled incorrectly in some locations (line 114, 116)

2. “of” should be “or” in line 131… “three or more”

3. Why weren’t the 11 patients with reactive antibodies prior to vaccination excluded from the analysis? It seems that inclusion of these data in the analysis could confound the interpretation, specifically as it relates to predicting which individuals will or will not mount a strong response to vaccination based on HBV vaccine response (not based on HBV vaccine response + history of previous infection)

4. Maintain units when describing the semi-quantitative results, as use of the word “titer” along with a number without units may be confusing to readers (example: line 153 where only 56.1 is listed as the result)

5. Phrasing of lines 158-159 inconsistent with disease vs. pathogen (ie. “no one reported having COVID-19” or “no one reported being infected with SARS-CoV-2”)

6. For lines 170-175, please clarify what summary statistics is being used for the group comparison (median?)

7. As the authors note, the occupational health environment in Japan offers screening and additional dose series to ensure antibody levels reach the 10 mIU/mL cut-off. In what way may this bias the definition of “non-responders” compared to other settings? Is it possible to identify the cohort of individuals that did not initially respond to HBV vaccination, but ultimately achieved a weak response through additional dose series? This group may still be considered a “non-responder” in other healthcare settings where antibody level testing and additional dose series are not conducted. Some expanded discussion within lines 194-209 may help relate this study to other settings where the occupational health environment is different.

Reviewer #3: This is cohort analysis from Japan, where HBV vaccination is a strong part of public health. The investigators, through review of medical records of health workers getting COVID vaccine, identified an association between non-response to HBV vaccine and suboptimal response to first dose of COVID vaccine (the association didn’t hold after additional COVID vaccine doses). This is nice finding and is supported by what we know about vaccine efficacy. The implications of the finding could be more clearly stated to give the paper more impact.

Background:

• Scientific premise is good

• Could better explain why the focus on HBV vaccine response; people receive a wide range of vaccines. I think the reason is here is that its more common in clinical practice to check anti-HBs compared to antibodies to other vaccine-preventable infections (like measles, tetanus, etc.) . I suggest better justify the focus on HBV as part of background of the paper.

Methods:

• If the anti-HBs was low was HBV vaccine given together with COVID vaccine. I’m not aware of much data on co-administration of other vaccines with COVID. How was this handled in the program (the need to give COVID vaccine and HBV vaccine in weak/non reponders?

Results:

• Before presenting that non/weak responders to HBV vaccine were less likely to respond to COVID vaccine, please describe the # and % of non/weak HBV responders…so we have that context. Also, did they have a history of non/weak response in the past or was this based on repeat HBV vaccination (maybe I’m confused on the study design)..?

• This important line was very hard to read. Could you organize the sentence so that the % and the group are together “After a single vaccine dose, HBV non-responders were significantly less likely to reach neutralizing S-protein response compared to normal and weak HBV vaccine responders (87.1% [95% CI 84.2–166 89.5], 84.7% [95% CI, 80.2–88.4], 64.5% [95% CI 45.5–79.9] among 621 normal HBV vaccine 167 responders, 302, weak responders, and 31 non-responders, respectively [p=0.004]).”

Discussion:

• I think what is missing is more details on the implications of the paper on public health. Are you suggesting that occupational health programs should perhaps prioritize HCW with history of HBV non-response for COVID vaccines? Or booster doses? Should anti-HBs testing and HBV vaccine history assessment be expanded in places giving COVID vaccines to target outreach to certain individuals…to make sure they get 2nd, additional doses?

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Michael Vinikoor

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

23 Feb 2022

Additional Editor Comments:

The timing of HBV vaccination is unclear. The authors should provide more information about when HBV vaccination and boosters were given relative to COVID-19 vaccinations.

Thank you for the author comment. The classification of HBV vaccine response was based on HBV vaccination and their antibody titer response data that we have in the employee health records. The last vaccine HBV dose given among the participants in the study was December of 2020. The first COVID vaccine dose administered was March 9, 2021.

Reviewer #1:

This manuscript summarizes a laboratory-based study assessing the immune response to the BioNTech/Pfizer’s BNT162b2 mRNA SARS-CoV-2 vaccine, comparing health care workers (HCW) who had a weak or non-response to the hepatitis B vaccine (HBV) to those who had a strong/normal response. HCWs who had a weak response to the HBV vaccine (i.e., HBs-Ab titer < 10 mlU/mL) were more likely to have a non-neutralizing S-protein antibody response to the first BNT162b2 mRNA SARS-CoV-2 vaccine compared to those who had a normal response to the HBV vaccine (i.e., HBs-Ab titer > 100 mIU/mL). This difference diminished to non-significance after the second vaccine. The researchers acknowledge the study is not novel, however the findings are useful to build knowledge on risk of non-response to the BNT162b2 mRNA SARS-CoV-2 vaccine. For example, with vaccine in short supply in some countries, decision-makers may utilize such study results to priority who obtains a second dose first. Additionally, as the authors stated, prior vaccine response may be informative on whose protective immunity against SARS-CoV-2 wanes faster. The odds ratios overestimate the relative risks. Thus, relative risks need to be estimated directly.

Minor comments

71 Consider changing “promoting the scientific” to “prompting the medical”

Thank you for the author comment. We have edited the text accordingly.

81 Consider adding the word “male” before “gender” given the factors are specific to higher risk.

Thank you for the author comment. We have edited the text accordingly.

86 Delete “to”

Thank you for the author comment. We have rephrased the sentence for clarity.

87 Instead of “no previous studies” maybe say there is a “dearth on studies” as no studies would be difficult to document given the vast among of publications on SARS-CoV-2.

Thank you for pointing this out. We’ve corrected the overstatement.

How many HCWs were invited into the study?

We invited 1,051 HCWs into the study as indicated in the methods section.

147 “protein antibodies and six were unaware of their previous COVID-19 infection” needs editing. This sentence is unclear.

Thank you for this feedback. We’ve edited this section for better clarity.

“Among the eleven participants with positive N protein antibodies (indicating previous infection), six were unaware of their previous infection status. Of these eleven, ten also had reactive S-protein antibodies.”

167 The comma after “302” needs to be removed.

Thank you for this comment. Edits were made accordingly.

191 “in our study population was slightly higher in our study” needs to be edited.

Thank you for this comment. Edits have been to fix the redundant term.

214 “We were not able to adjust for these potential confounders in our analysis as 12% (n=117/968) responded anonymously to the survey…” While it is unlikely that it will change the final conclusions of the study, you could assess confounding based on a sub analysis of those HCWs whose surveys can be linked to laboratory data.

We identified 382 patients whose data on comorbidities could be linked to the baseline survey and presented the comorbidity adjusted relative risk in Table 3. After adjusting for the comorbidity status, the effect (relative risk) diminished to a non-significant level. However, to obtain this estimate, we dropped 561 participants from the analysis which may have significantly biased this finding. We added this concern in the discussion section. Thank you for this reviewer comment. We believe it added a much better insight into our findings.

Table 1 is difficult to read. Consider landscape (that is, rotating the page) so the information in table cells do not wrap around. Also, column labels are needed.

The table has been edited.

Table 2 (Analysis): The odds ratios overestimate the relative risks. Thus, relative risks need to be estimated directly.

We re-calculated the effects using relative risk.

Unavailability of HCW data is acceptable due to privacy concerns. The authors state that analyses can be conducted to facilitate providing information from the study to researchers.

Reviewer #2:

This manuscript reports on an interesting study aimed at finding a way to predict which individuals may be at-risk for a limited response to SARS-CoV-2 mRNA vaccination by using data related to HBV vaccination response history. At a stage in the pandemic where COVID-19 vaccine boosting is being considered and durability of vaccine response is uncertain, such an approach is intriguing. There is a major limitation to the analysis and interpretation of the results, however, as the threshold for determining “neutralizing” antibodies, which the authors discuss in great detail, is of unclear origin. This must be resolved, along with a response to the other comments noted below, before this reviewer would deem this manuscript acceptable for publication.

Major Comments:

1. In the referenced article by Rubio-Acero et al., the authors find that the Roche Elecsys Anti-SARS-CoV-2 S [Ro-RBD-Ig-quant]) had a different threshold than used in the manuscript under review. From the article’s conclusion, which is supported by the results (Table 4 in Rubio-Acero et al): “For example, raw values above 28.67 U/mL for Ro-RBD-Ig-quant and above 49.78 U/mL for EI-S1-IgGquant, respectively, predicted virus neutralization > 1:5 in 95% of cases. We may hypothesize that when the value of the quantitative tests is above the predictive value (e.g., 95%), there is little benefit in performing NT and that this could act as a surrogate marker for neutralizing titers, e.g., after mass vaccinations or post-infection.” This paper also uses a surrogate test for neutralization (GS-cPass), but the threshold for the Ro-RBD-Ig-quant corresponding to ≥ 20% is ≥ 6.99. Furthermore, in reviewing the other reference cited for this cutoff, the Elecsys package insert, the reviewer could not find any indication of an appropriate cutoff value correlating with neutralizing antibody levels. Instead, the package insert states: “The results of this semi quantitative test should not be interpreted as an indication or degree of immunity or protection from reinfection” and “The clinical applicability of semi quantitative results is currently unknown and cannot be interpreted as an indication or degree of immunity nor protection from reinfection, nor compared to other SARS CoV 2 antibody assays.” Thus, it is unclear why a cut-off of 15 U/mL was used to define neutralizing immunity within this study and it seems inappropriate to state “according to manufacturer information” (line 119) in reference to any threshold used to suggest neutralizing antibody response. Clarifying this threshold is critical for the interpretation of the results, particularly those reported in line 164-170, and for the overall conclusion of the manuscript. It is unclear if a larger proportion of the weak or normal HBV vaccine responders would have not achieved a “neutralizing” threshold if a higher cutoff was used.

We have inserted the appropriate reference that supports our choice of the 15U/mL cut-off. The above figures and tables have been taken from the supplementary material in the research article by Kennedy, et al. In this analysis of 534 patient samples, the 15U/mL cut-off yielded PPV of 99.1% on cPass at 20% inhibition. However, as pointed out by the reviewer, using the term “neutralizing” to express anti-SARS-CoV-2-S titer levels above this cut-off would be an overstatement, especially given the recent rise in new SARS-CoV-2 variants that are circulating around the globe. We therefore changed the term to “seroconversion,” and mentioned this point in the limitations.

Kennedy NA, Lin S, Goodhand JR, Chanchlani N, Hamilton B, Bewshea C, et al. Infliximab is associated with attenuated immunogenicity to BNT162b2 and ChAdOx1 nCoV-19 SARS-CoV-2 vaccines in patients with IBD. Gut. 2021 Oct;70(10):1884-1893. doi: 10.1136/gutjnl-2021-324789.

2. It is important to note the limitations of the assay used for this analysis. Specifically, the manufacturer states that “The performance of this test has not been established in individuals that have received a COVID 19 vaccine. The clinical significance of a positive or negative antibody result following COVID 19 vaccination has not been established, and the result from this test should not be interpreted as an indication or degree of protection from infection after vaccination.” While the reviewer understands that EUA-platforms are often evaluated in-house for off-label usage, it is important for readers to be aware of this noted limitation within the text of the article.

Thank you for the reviewer feedback. We have tried to edit the limitations section in the discussion to rectify this inaccuracy and overstatement.

3. The use of the word “neutralizing” to describe the spike protein receptor binding domain (line 112) is inconsistent with the platform package insert and should be deleted, as it suggest the assay itself detects neutralizing antibodies

Thank you for the reviewer feedback. We have decided not to use the term “neutralizing” in the study for better consistency with manufacturer information.

4. In line 117, and then later throughout when referring to the antibody results, the terms “positive” and “negative” should be used to be consistent with the manufacturer interpretation language found within the package insert

Phrases such as “reactive” and “non-reactive” were changed to “positive” and “negative” for consistency with manufacturer language use.

Minor Comments:

1. Test platform appears to be spelled incorrectly in some locations (line 114, 116)

Thank you for pointing this out. We’ve corrected this mistake (Elecsys was misspelled as Elecys).

2. “of” should be “or” in line 131… “three or more”

Thank you for pointing this out. We’ve corrected this mistake.

3. Why weren’t the 11 patients with reactive antibodies prior to vaccination excluded from the analysis? It seems that inclusion of these data in the analysis could confound the interpretation, specifically as it relates to predicting which individuals will or will not mount a strong response to vaccination based on HBV vaccine response (not based on HBV vaccine response + history of previous infection)

We thank the reviewer for this point. We excluded the eleven patients from our analysis (relative risk calculation).

4. Maintain units when describing the semi-quantitative results, as use of the word “titer” along with a number without units may be confusing to readers (example: line 153 where only 56.1 is listed as the result)

Thank you for this feedback. All units were corrected for consistency. We corrected the units in line 153.

5. Phrasing of lines 158-159 inconsistent with disease vs. pathogen (ie. “no one reported having COVID-19” or “no one reported being infected with SARS-CoV-2”)

Corrections have been made accordingly.

6. For lines 170-175, please clarify what summary statistics is being used for the group comparison (median?)

The word “median” has been added for clarity.

7. As the authors note, the occupational health environment in Japan offers screening and additional dose series to ensure antibody levels reach the 10 mIU/mL cut-off. In what way may this bias the definition of “non-responders” compared to other settings? Is it possible to identify the cohort of individuals that did not initially respond to HBV vaccination, but ultimately achieved a weak response through additional dose series? This group may still be considered a “non-responder” in other healthcare settings where antibody level testing and additional dose series are not conducted. Some expanded discussion within lines 194-209 may help relate this study to other settings where the occupational health environment is different.

Thank you for the reviewer comment. We tried to address this point in the discussion section.

Reviewer #3: This is cohort analysis from Japan, where HBV vaccination is a strong part of public health. The investigators, through review of medical records of health workers getting COVID vaccine, identified an association between non-response to HBV vaccine and suboptimal response to first dose of COVID vaccine (the association didn’t hold after additional COVID vaccine doses). This is nice finding and is supported by what we know about vaccine efficacy. The implications of the finding could be more clearly stated to give the paper more impact.

Background:

• Scientific premise is good

• Could better explain why the focus on HBV vaccine response; people receive a wide range of vaccines. I think the reason is here is that its more common in clinical practice to check anti-HBs compared to antibodies to other vaccine-preventable infections (like measles, tetanus, etc.) . I suggest better justify the focus on HBV as part of background of the paper.

Thank you for the reviewer comment. We briefly added our rationale for our investigation in the introduction.

Methods:

• If the anti-HBs was low was HBV vaccine given together with COVID vaccine. I’m not aware of much data on co-administration of other vaccines with COVID. How was this handled in the program (the need to give COVID vaccine and HBV vaccine in weak/non reponders?

We did not have any participants in the study who had co-administration of COVID and HBV vaccines.

The last vaccine HBV dose given among the participants in the study was December of 2020. The first COVID vaccine dose administered was March 9, 2021.

Results:

• Before presenting that non/weak responders to HBV vaccine were less likely to respond to COVID vaccine, please describe the # and % of non/weak HBV responders…so we have that context. Also, did they have a history of non/weak response in the past or was this based on repeat HBV vaccination (maybe I’m confused on the study design)..?

Thank you for the reviewer comment. We reported the relevant numbers in the results section. The classification of HBV vaccine response was based on HBV vaccination and their antibody titer response data that we have in the employee health records. As we mentioned in the limitations section, it is possible that some participants were vaccinated previously by their previous employer in addition to the vaccine series offered by our institution. These participants would be included as normal vaccine responders even if they initially did not respond to the first vaccine series. Their data would theoretically result in our effect estimate towards the null value.

• This important line was very hard to read. Could you organize the sentence so that the % and the group are together “After a single vaccine dose, HBV non-responders were significantly less likely to reach neutralizing S-protein response compared to normal and weak HBV vaccine responders (87.1% [95% CI 84.2–166 89.5], 84.7% [95% CI, 80.2–88.4], 64.5% [95% CI 45.5–79.9] among 621 normal HBV vaccine 167 responders, 302, weak responders, and 31 non-responders, respectively [p=0.004]).”

Thank you for the reviewer feedback. The sentence was rephrased accordingly.

Discussion:

• I think what is missing is more details on the implications of the paper on public health. Are you suggesting that occupational health programs should perhaps prioritize HCW with history of HBV non-response for COVID vaccines? Or booster doses? Should anti-HBs testing and HBV vaccine history assessment be expanded in places giving COVID vaccines to target outreach to certain individuals…to make sure they get 2nd, additional doses?

Thank you for the reviewer feedback. We’ve edited the discussion section.

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Submitted filename: ResponsetoReviewers_20210130.docx

Click here for additional data file.

3 May 2022

Association between history of HBV vaccine response and anti-SARS-CoV-2 spike antibody response to the BioNTech/Pfizer’s BNT162b2 mRNA SARS-CoV-2 vaccine among healthcare workers in Japan: A prospective observational study

PONE-D-21-33288R1

Dear Dr. Ukimura,

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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Academic Editor

PLOS ONE

Additional Editor Comments (optional):

None

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)

Reviewer #3: All comments have been addressed

**********

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: Yes

Reviewer #3: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: Yes

**********

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

Reviewer #3: Yes

**********

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

Reviewer #3: 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: The authors have made strong attempts to respond to all reviewer comments. The additional information regarding the threshold used to determine a significant response is very clarifying. Although this reviewer appreciates the attempt to improve accuracy by no longer using the phrase "neutralizing antibodies," the use of "seroconversion" may not be the most appropriate replacement. This reviewer thinks it may confuse readers to have a "positive" antibody response (<0.8 U/ml but < 15 U/ml) be different from "seroconversion" (> 15 U/ml). Perhaps using either "strong responder" (which contrasts nicely with "weak responder") would work, or use similar language to the Kennedy et al. supplemental figure that describes the 15 U/ml threshold as maximizing "neutralizing potential." Instead of saying either "seroconverted" or "had neutralizing responses", either saying "had strong responses" or "had neutralizing potential" may help achieve the balance and clarity required (as long as both "strong response" or "neutralizing potential" were well-defined using the Kennedy reference, as is currently done for "seroconversion" in the authors' text.

Reviewer #3: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Reviewer #3: Yes: Michael Vinikoor

6 May 2022

PONE-D-21-33288R1

Association between history of HBV vaccine response and anti-SARS-CoV-2 spike antibody response to the BioNTech/Pfizer’s BNT162b2 mRNA SARS-CoV-2 vaccine among healthcare workers in Japan: A prospective observational study

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