Skip to main content
PMC home page
PLOS ONE logoLink to PLOS ONE
. 2021 Jun 10;16(6):e0243676. doi: 10.1371/journal.pone.0243676

Seroprevalence of anti-SARS-CoV-2 IgG antibodies in the staff of a public school system in the midwestern United States

Lilah Lopez 1,#, Thao Nguyen 1,#, Graham Weber 1,#, Katlyn Kleimola 2, Megan Bereda 3, Yiling Liu 4,5, Emma K Accorsi 6, Steven J Skates 4,5, John P Santa Maria Jr 7, Kendal R Smith 1,‡,*, Mark Kalinich 5,‡,*
Editor: Yury E Khudyakov8
PMCID: PMC8191884  PMID: 34111144

Abstract

Since March 2020, the United States has lost over 580,000 lives to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. A growing body of literature describes population-level SARS-CoV-2 exposure, but studies of antibody seroprevalence within school systems are critically lacking, hampering evidence-based discussions on school reopenings. The Lake Central School Corporation (LCSC), a public school system in suburban Indiana, USA, assessed SARS-CoV-2 seroprevalence in its staff and identified correlations between seropositivity and subjective histories and demographics. This study is a cross-sectional, population-based analysis of the seroprevalence of SARS-CoV-2 in LCSC staff measured in July 2020. We tested for seroprevalence with the Abbott Alinity™ SARS-CoV-2 IgG antibody test. The primary outcome was the total seroprevalence of SARS-CoV-2, and secondary outcomes included trends of antibody presence in relation to baseline attributes. 753 participants representative of the staff at large were enrolled. 22 participants (2.9%, 95% CI: 1.8% - 4.4%) tested positive for SARS-CoV-2 antibodies. Correcting for test performance parameters, the seroprevalence is estimated at 1.7% (90% Credible Interval: 0.27% - 3.3%). Multivariable logistic regression including mask wearing, travel history, symptom history, and contact history revealed a 48-fold increase in the odds of seropositivity if an individual previously tested positive for COVID-19 (OR: 48, 95% CI: 4–600). Amongst individuals with no previous positive test, exposure to a person diagnosed with COVID-19 increased the odds of seropositivity by 7-fold (OR: 7.2, 95% CI: 2.6–19). Assuming the presence of antibodies is associated with immunity against SARS-CoV-2 infection, these results demonstrate a broad lack of herd immunity amongst the school corporation’s staff irrespective of employment role or location. Protective measures like contact tracing, face coverings, and social distancing are therefore vital to maintaining the safety of both students and staff as the school year progresses.

Introduction

Over 3 million people around the world have lost their lives from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, or COVID-19) as of May 2021. The United States alone has reported over 580,000 deaths [1]. A critical bottleneck in containing the virus is understanding its transmission dynamics. Although much effort has been expended on population-level seroprevalence surveys, scant data exist to understand COVID-19’s potential effect on the US public school system’s students and staff. Existing data are sobering: ten days into its reopening, an Israeli high school experienced a major outbreak [2]. Work on the pediatric transmission of COVID-19 are conflicting; in multiple studies conducted in Southwest Germany, Ireland, and Northern France, children under 10 were found to have had little effect on the spread of the virus, while a study in Chile claimed that elementary students were more likely to contract the virus relative to secondary students [3–6]. Here, we determine the seroprevalence of COVID-19 in the Lake Central School Corporation (LCSC), a public school system located in suburban Indiana, US. The LCSC consists of 1 high school (grades 9–12; 3,261 students), 3 middle schools (grades 5–8; 2,859 students), and 6 elementary schools (grades kindergarten-4, 3,467 students), and transitioned to fully virtual instruction on March 16th 2020 [7]. Although there is enormous heterogeneity within the US public school system, LCSC staff’s demographics are broadly representative of national statistics. The LCSC has 16.6 students per teacher, similar to the United States average of 16 students per teacher [8, 9]. The median age of public school employees in the United States is 41, while the median age of the Lake Central employee population lies at 48 [10, 11]. Notably, previous reports have established that advanced age is highly associated with COVID-19 hospitalization, further underscoring the threat of morbidity and mortality within this community relative to other school districts [12]. Given the lack of specificity of COVID-19 symptoms and that mild and asymptomatic cases of COVID-19 may go undocumented, antibody-based seroprevalance studies are required to estimate population-level exposure to SARS-CoV-2, although it should be noted that immunity to SARS-CoV-2 via such antibodies has yet to be firmly established [13].

Materials and methods

Study design and participants

This cross-sectional, population-based analysis of the seroprevalence of anti-SARS-CoV-2 IgG enrolled participants over a five day period in July of 2020 as part of the LCSC staff’s annual wellness check. Individuals were eligible to participate if they were 18 years of age or older and were employees of the Lake Central school corporation during the 2018–2019 or 2019–2020 school years.

After approval from the Community Healthcare System Central Institutional Review Board (CHS CIRB #07–02), participants were contacted through their respective LCSC email account as well as by voice message from the LCSC superintendent informing them of the opportunity to participate. These communications provided information regarding the study and a link in the email allowed participants to schedule their testing date. A second email containing a video promoting the study was sent to staff and shared on LCSC social media accounts. There was no cost to participate in testing. Once registered, each participant completed a data questionnaire containing questions about sociodemographic characteristics including self-identified gender, employment factors, and activities that can increase the risk of having COVID-19, as well as informed written consent (S1 Text).

During the five days of data collection, the study team was present at the testing site to assist with distributing additional consent forms and data questionnaires. Participants were required to wear a mask upon entering the building and hand sanitizer was available at each station. Stations were six feet apart, and a member of the LCSC staff used cleaning wipes to disinfect each station between participants. An independent third party vendor, Franciscan WorkingWell, performed venipuncture using standard procedure for the antibody tests, and obtained a 5 mL blood sample for the antibody test and a 6 mL blood sample for blood typing.

The primary outcome was the total seroprevalence of anti-SARS-CoV-2 IgG antibodies within staff. The secondary outcomes were the changes in odds of seropositivity associated with baseline demographics and COVID-19 related factors including mask use, a self-reported history of contact with a known COVID-19 positive person or persons, and a previous positive COVID-19 test (PCR or antibody-based).

Laboratory analysis

Seroprevalence was determined utilizing the commercially available Abbott Alinity™ SARS-CoV-2 IgG antibody test, with a reported 100% sensitivity (34/34, 95% CI: 89.7%-100%) and 99% specificity (99/100, 95% CI: 94.5%-100%) in detecting anti-SARS-CoV-2 IgG antibodies [14]. This test provides a binary, present/not present result.

Statistical analysis

Seroprevalence among LCSC staff was calculated as the proportion of staff members who received a positive antibody test out of the total staff tested. Confidence intervals were first estimated by generating a binomial confidence interval with the statsmodel package in Python v3.7. The seroprevalence was then corrected for uncertainties in the test sensitivity and specificity using previously established statistical approaches [15]. Relative risks (risk ratios) were calculated to identify associations between seropositivity and gender, BMI, blood type, contact history, symptom history, travel history, role in school corporation, school where employed, extracurricular role (coach, club sponsor), mask history, previous positive COVID-19 test, and age on 7/20/20, the last day of sample collection. Employment role was separated into two groups: those departments that worked in the school during the Indiana stay-at-home order (maintenance, technology, and administration) and those that did not (all others). Continuous variables (BMI, age) were binarized by segmenting the variable by its median value. Bonferroni correction was used to correct for multiple testing on the univariate analyses. To determine if having a history of mask-wearing reduced seropositivity, we constructed a causal diagram based on existing literature and first-hand knowledge of the Lake Central school system and a priori identified gender, age, travel history (as a proxy for risk taking behavior), school type, and role at LCSC as potential confounders to control for in the multivariable logistic regression (S1 Fig) [6, 16–19]. Finally, backward-stepwise feature elimination was used to identify the features most predictive of seropositivity in a multivariable logistic regression starting with the thirteen categories queried in the survey (threshold p-value of 0.05). After the model was identified for which all factors were statistically significant, three factors (mask history, travel history, and symptom history) which are understood based on external studies to have an impact on infectivity were reintroduced to this reduced model [14–16].

One factor, having a previous positive COVID-19 test in four individuals, was overwhelmingly predictive for seropositivity. The regression was then re-fitted on the vast majority of individuals without a previous positive test to estimate associations of features with seropositivity within this group. For continuous variables such as age and BMI, missing data were either removed (univariate analyses) or replaced with the median of existing data for that variable (multivariable analysis). Missing data for categorical variables were either removed (univariate analyses) or replaced with the mode of that variable (multivariable analysis). A sensitivity analysis which removed incomplete observations from the multivariable analyses (rather than replacing the missing values) was also performed. Logistic regressions were calculated using the scikit-learn Python package.

Results and discussion

Of the eligible 1261 staff members, 753 (60%) participated in the study. The LCSC staff comprises 1060 (84%) women; the participation group had 635 (85%) women; age demographics for the study population compared to that of the total staff were similarly representative (Table 1).

Table 1. Characteristics of study population and total staff population.

Characteristic Study Population Total Staff Population
n % n %
Gender*
 Female 635 84.7 1060 84.1
Age**
 18–35 153 20.3 261 20.7
 36–50 287 38.2 483 38.3
 51–65 282 37.5 455 36.1
 66+ 30 4.0 62 4.9
Open in a new tab

Note: Three participants did not share their gender and one did not share their age.

*Chi-square value 0.0025, p-value 0.960.

**Chi-square value 1.308, p-value 0.727.

22 individuals tested positive for anti-SARS-CoV-2 IgG antibodies, representing 2.9% seroprevalence (95% CI: 1.8% - 4.4%). After correcting for the reported sensitivity and specificity of the IgG antibody test, the seroprevalence is estimated as 1.7% (90% Credible Interval: 0.27% - 3.3%) [20].

In the univariate analyses, having a previous positive COVID-19 test (RR 29.5, 95% CI: 14.3–60.4, p<0.0001) or contact with a COVID-19 case (RR 6.86, 95% CI: 3.04–15.5), p<0.0001) were found to be statistically significantly associated with seropositivity, after Bonferroni correction (Table 2).

Table 2. Univariate analysis.

Characteristic Relative Risk 95% Confidence Interval P
Covid Status (Self-reported Previous Positive Test) 29.5 (14.3, 60.4) <0.0001
Contact History (Yes) 6.86 (3.04, 15.5) <0.0001
Club Sponsor 3.10 (1.22, 7.84) 0.0258
Symptom History (Yes) 2.31 (0.990, 5.42) 0.0577
BMI (Binary) 2.17 (0.884, 5.25) 0.0867
Was Working at school (in the summer) 0.196 (0.027, 1.45) 0.0979
Sports Coach 1.96 (0.591, 6.49) 0.225
Gender (Female) 0.616 (0.232, 1.64) 0.362
Mask Wearing (Yes) 0.721 (0.172, 3.03) 0.654
Age (Binary) 1.28 (0.559, 2.92) 0.667
Travel History (Yes) 0.744 (0.255, 2.17) 0.798
Elementary School ref -
Middle School 2.25 (0.813, 6.20) 0.117
High School 1.15 (0.357, 3.73) 1.00
Other School 1.10 (0.226, 5.35) 1.00
Blood Type (O+) ref -
Blood Type (B-) 3.28 (0.475, 22.6) 0.286
Blood Type (B+) 0.336 (0.044, 2.58) 0.468
Blood Type (A+) 0.758 (0.293, 1.96) 0.628
Blood Type (A-) 0.444 (0.058, 3.40) 0.696
Open in a new tab

AB+, AB-, and O- had no positive cases and therefore have no RR estimate.

No causal relationship between self-reported mask-wearing history and seropositivity could be identified, after controlling for six confounders available in our data set, identified using a causal diagram (S1 Table) (RR 0.63, 95% CI 0.16–4.3). This result was maintained when missing data were replaced, rather than excluded (S2 Table). In the multivariable model employing backward-stepwise feature elimination, a previous positive COVID-19 test (OR 48 95% CI 3.9–600, p = 0.003) or contact with a COVID-19 case (OR 5.6, 95% CI 2.1–15.1, p = 0.001) were found to be the most predictive factors for seropositivity (S3 Table); similar results were observed when missing data were replaced, rather than excluded (S4 Table). To interrogate the specific interaction between contact history and seropositivity, the dataset was subsetted to the vast majority of individuals without a previous positive COVID-19 test and complete data (n = 727). In this population, a logistic regression analysis including the predictors mask wearing, travel history, and symptom history, a positive contact history conferred a 7-fold increase in the odds of seropositivity (p<0.0001) (Table 3). The odds increased from 1:54 without such a contact to 1:7 with contact—or the probability of seropositivity increased from 1.8% to 12%. Similar results were observed when replacing, rather than excluding, missing data (S5 Table).

Table 3. Multivariable model.

Effect Odds Ratio 95% CI p
LL UL
Intercept 0.025 0.004 0.092 <0.001
 Contact History 7.2 2.6 19. <0.001
 Symptom History 2.0 0.72 5.1 0.16
 Travel History 0.47 0.11 1.5 0.25
 Mask History 0.68 0.18 4.5 0.63
Open in a new tab

Note: CI = confidence interval; LL = CI lower limit; UL = CI upper limit.

This study provides the necessary baseline for future longitudinal monitoring of COVID-19 transmission through a representative US public school system. With a demographically representative 60% staff participation rate, these data offer an unprecedented view into the seroprevalence of the target population of a Midwest US public school system. The estimated 1.7% seroprevalence of COVID-19 antibodies among LCSC staff is far below that reported in metropolitan areas throughout the US at the time of testing, underscoring the large and continued risk of COVID-19 infection within this community upon exposure [20]. As a point of comparison, Lake County, Indiana, the county within which the LCSC resides, had 4,985 reported cases of COVID-19, representing 1.0% of the county’s population [21].

Although the effect of mask wearing on seropositivity, utilizing a causal diagram to identify potential confounders, did not achieve statistical significance, this result should not be interpreted as a lack of efficacy in the use of masks given the overwhelming quality and quantity of data supporting their continued use, and is likely due to lack of power [22]. Both univariate and multivariable analyses demonstrate that a previous positive COVID-19 test or a history of contact with a COVID-19 patient were associated with seropositivity. The 7-fold increase in odds of seropositivity for individuals with a positive contact history is especially noteworthy amongst the vast majority (n = 727) of the individuals without a previous positive COVID-19 test and complete data. Although these relationships are associational, the combination of low staff seroprevalence and the strong positive association of seropositivity with contact history highlights a high risk group for which the importance of aggressive contact tracing would be efficient and which would minimize the transmission of COVID-19, as well as continued protective procedures during school hours such as mask and face shield wearing, and social distancing [23].

This study has several limitations. Although data were collected from staff employed at 11 different sites (10 schools and the transportation facility), only the LCSC was involved in the study, limiting the generalizability of this work. Neither ethnicity nor income data were collected, precluding analysis of these variables’ previously demonstrated associations with COVID-19 positivity [24]. We rely on study participants to self-report variables, excluding antibody status and blood type, via questionnaire. Some questions may be insufficiently granular, such as the binary mask wearing variable, and participants may also make errors filling out the questionnaire. Insufficient granularity is likely to make groups defined by the variable more similar to each other, while reporting errors are likely to be random and unrelated to the outcome, antibody status. Thereby both will be expected to create bias towards the null, meaning that the results reported in this study are conservative. The low baseline seroprevalence (22 positive tests in the 753-person cohort) prevents the identification of more subtle, but potentially real, associations among the collected variables and seropositivity. Additionally, the lack of county-level seroprevalence data prevents comparison to the broader population outside of the school district. Given that not all staff elected to participate in the study, some level of volunteer bias is possible. The high participation rate (60.0%), highly representative sample population (Table 1) and the wide availability of COVID-19 testing outside of this study, however, all limit the potential for volunteer bias to drive the observed results [25]. Finally, data was not collected on students in the corporation, preventing the investigation of a potential link between staff or student positivity and transmission within or between these two co-exposed groups.

The reopening of US schools poses a potential risk of COVID-19 transmission to both staff and students. The low pre-opening seroprevalence, in combination with the advanced age of a significant fraction of the LCSC staff, may increase the number and severity of cases should an outbreak occur within this school system, or other school systems sharing its demographic characteristics. Teachers should consider deploying novel teaching strategies that limit the amount of non-distanced interactions. Administrators and legislators should allocate the required resources to implement and maintain robust personal protective measures as recommended by the CDC, including contact-tracing, masks, face shields, and social distancing to protect the lives of both the children of the school district and those charged with educating them [26].

Supporting information

S1 Dataset. De-identified LCSC COVID dataset.

(CSV)

Click here for additional data file. (29KB, csv)
S1 Fig. Construction of a causal diagram to identify confounders of the relationship between mask wearing and seropositivity.

(DOCX)

Click here for additional data file. (31KB, docx)
S1 Table. Logistic regression results for the relationship between mask wearing history and seropositivity, adjusting for potential confounders (missing data excluded).

(DOCX)

Click here for additional data file. (14.7KB, docx)
S2 Table. Logistic regression results for the relationship between mask wearing history and seropositivity, adjusting for potential confounders (missing data replaced).

(DOCX)

Click here for additional data file. (14.3KB, docx)
S3 Table. Stepwise backwards feature elimination regression results (missing data excluded).

(DOCX)

Click here for additional data file. (13.8KB, docx)
S4 Table. Stepwise backwards feature elimination regression results (missing data replaced).

(DOCX)

Click here for additional data file. (14KB, docx)
S5 Table. Stepwise backwards feature elimination regression results, previously COVID+ patients excluded (missing data replaced).

(DOCX)

Click here for additional data file. (13.4KB, docx)
S1 Text. Data collection for seroprevalence study of COVID 19 in Lake Central Staff.

(DOCX)

Click here for additional data file. (17.5KB, docx)

Acknowledgments

We thank all of the LCSC staff members who participated in this study. We also thank Jana L. Lacera, Director, IRB/Bio-Ethics, who graciously volunteered both her and her organization’s resources; the nurses from Franciscan WorkingWell for collecting participant samples; Dr. Marc Lipsitch for his invaluable guidance, feedback and encouragement; and the LCSC for enabling this work.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

K.K. was supported by the City of Evanston. Y.L. and S.S. were supported by the MGH Biostatistics Center and Harvard Medical School. E.K.A. was supported by the Harvard T.H. Chan School of Public Health. J.S.M was supported by the Novartis Institute for Biomedical Research. K.R.S. was supported by the Lake Central School Corporation. M.K. was supported by Harvard Medical School and NIH grants T32GM007753 and F30 CA224588. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

Decision Letter 0

Yury E Khudyakov

30 Mar 2021

PONE-D-20-38006

Seroprevalence of anti-SARS-CoV-2 IgG Antibodies in the Staff of a Public School System in the Midwestern United States

PLOS ONE

Dear Dr. Kalinich,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Your manuscript was reviewed by one expert in the field. Unfortunately, many potential reviewers did not accept the review invitation. Nevertheless, this reviewer identified all major points that require your attention. Please carefully consider the attached comments and provide thorough responses.

Please submit your revised manuscript by May 13 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • 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: http://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,

Yury E Khudyakov, PhD

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

3. Thank you for stating the following in the Financial Disclosure section:

'The author(s) received no specific funding for this work. The Lake Central School Corporation funded the seroprevalence testing. '

We note that one or more of the authors are employed by commercial companies: Novartis, Independent Researcher.

a. Please provide an amended Funding Statement declaring these commercial affiliations, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

b. Please also provide an updated Competing Interests Statement declaring these commercial affiliations along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.  

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to  PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and  there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

c. Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.

Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests

4. Please include your tables as part of your main manuscript and remove the individual files. Please note that supplementary tables should remain as separate "supporting information" files.

5. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information

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

Reviewers' comments:

Reviewer's Responses to Questions

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: No

**********

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

Reviewer #1: No

**********

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

**********

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

**********

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 article examines the seroprevalence among a staff of a public school in the Midwestern in the USA. The aim is to get an idea of ​​the seroprevalence and the associated risk factors to determine whether it makes sense to reopen the schools.

The question is indeed interesting and interests a much wider audience than the United States alone. However, the paper is sorely lacking in precision and rigor at all levels of its structure:

1. Introduction

We do not know what is the "student" population concerned by this study, is it a primary school, a high school ...? Knowing that there are disparities in the circulation of SARS-CoV-2 according to the age of the children, it is absolutely necessary to mention it, to discuss it at a minimum if it cannot be taken into account.

2. Methods

a. Regarding this question of the school's student population, it would have been interesting to note the seroprevalence among them as well, to determine whether or not it was higher than among the staff. An adapted questionnaire would have made it possible to highlight the relevance of the barrier measures put in place.

b. It is announced that the variable "contact with a positive person" will be analyzed, without specifying whether this contact concerns a colleague or a relative. It would be desirable to specify and take into account these two modes of contact.

c. You announce that you have replaced the missing data with the median or the mode depending on the variable. You thus create a huge bias in your data, especially when you know that they relate, for example, to age and BMI, which have a major influence on the attack by SARS-CoV-2. Missing data should be excluded.

d. Where is the methodological part concerning the DAG? There are a huge number of methods to build a DAG, which one have you used? With which package and which assumptions ?

3. Results

a. You describe a fragmented participation in your study. What about the biases associated with this participation rate?

b. Where is the DAG produced after all? We only see the DAG of the tested hypotheses. Again, what about the package used?

4. Discussion

To rule on the reopening of schools, it would have been necessary to determine whether the school staff was more affected than the general population, taking into account the participation bias. This issue of bias is not addressed.

To judge the relevance of maintaining the barrier measures within the school, it would have been necessary to compare the seroprevalence of the staff both with that of the general population under these conditions of bias but also with the seroprevalence of the student population of the school and assess the adherence of these populations to barrier measures. Without these comparisons no conclusion can be given on these points.

**********

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

[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.

PLoS One. 2021 Jun 10;16(6):e0243676. doi: 10.1371/journal.pone.0243676.r002

Author response to Decision Letter 0

12 May 2021

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

We have modified our draft to conform to the above guidance.

2. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

We agree that we should share as much of the data as is feasible for the broader scientific community’s research efforts in combating COVID-19. We have attached the IRB determination after sharing the above request, which has permitted us to share the following variables:

● Age by decile

● Gender

● BMI

● Symptom History

● Travel History

● COVID Status

● Mask History

● School type (elementary, middle, high school)

● Contact History

● Antibody test result

● Blood type

These variables will enable the broader scientific community to recapitulate our key findings while maintaining the anonymity of our participants, and are included as S1 Dataset: De-Identified LCSC COVID Dataset.

3.Thank you for stating the following in the Financial Disclosure section:

'The author(s) received no specific funding for this work. The Lake Central School Corporation funded the seroprevalence testing. '

We note that one or more of the authors are employed by commercial companies: Novartis, Independent Researcher.

a. Please provide an amended Funding Statement declaring these commercial affiliations, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

We thank the editor for suggesting this clarification of the role of these funding sources and have amended the funding statement to the following:

“K.K. was supported by the City of Evanston. Y.L. and S.S. were supported by the MGH Biostatistics Center and Harvard Medical School. E.K.A. was supported by the Harvard T.H. Chan School of Public Health. J.S.M was supported by the Novartis Institute for Biomedical Research. K.R.S. was supported by the Lake Central School Corporation. M.K. was supported by Harvard Medical School and NIH grants T32GM007753 and F30 CA224588. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

At the time of writing, author MB was unemployed; how would you recommend we refer to her affiliation (currently listed as ‘Independent Researcher’?

b. Please also provide an updated Competing Interests Statement declaring these commercial affiliations along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Given that the funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript, nor at any time did anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals, we do not believe any author has a competing interest to declare, outside of their existing commercial affiliations. We have modified the competing interest statement to the following:

“One author was a full-time employee of Novartis Institutes for Biomedical Research (NIBR) during the preparation of this work (John P. Santa Maria Jr.). NIBR had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials.”

We confirm that these commercial affiliations do not alter our adherence to PLOS ONE policies. Any restrictions on data sharing stem from the IRB’s determination as above.

c. Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.

We have included both an updated Funding Statement and Competing Interest Statement in the body of the cover letter.

Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests

4. Please include your tables as part of your main manuscript and remove the individual files. Please note that supplementary tables should remain as separate "supporting information" files.

We have added our tables as part of the main manuscript and removed the individual files.

5. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information

We have added captions for the supporting information files at the end of our manuscript file, and have updated the in-text citations to match accordingly.

Comments to the Author

Reviewer #1: This article examines the seroprevalence among a staff of a public school in the Midwestern in the USA. The aim is to get an idea of the seroprevalence and the associated risk factors to determine whether it makes sense to reopen the schools.

The question is indeed interesting and interests a much wider audience than the United States alone. However, the paper is sorely lacking in precision and rigor at all levels of its structure:

We greatly appreciate the reviewer’s time and input on the study, and their interest in this important subject area. As the reviewer pointed out, our study aims to fill a large gap in the current COVID-19 literature by identifying risk factors for seropositivity within a school system in order to better understand the role of schools in SARS-CoV-2 transmission.

1. Introduction

We do not know what is the "student" population concerned by this study, is it a primary school, a high school ...? Knowing that there are disparities in the circulation of SARS-CoV-2 according to the age of the children, it is absolutely necessary to mention it, to discuss it at a minimum if it cannot be taken into account.

We thank the reviewer for pointing out the lack of an LCSC student population profile. We have added the following text to the introduction:

“The LCSC consists of 1 high school (grades 9-12; 3,261 students), 3 middle schools (grades 5-8; 2,859 students), and 6 elementary schools (grades kindergarten-4, 3,467 students), and transitioned to fully virtual instruction on March 16th 2020.”

2. Methods

a.Regarding this question of the school's student population, it would have been interesting to note the seroprevalence among them as well, to determine whether or not it was higher than among the staff. An adapted questionnaire would have made it possible to highlight the relevance of the barrier measures put in place.

We thank the reviewer for this thoughtful comment, and agree that such data could be of great scientific value. With respect to barrier measures: starting on March 16th, 2020, the LCSC transitioned entirely to virtual teaching. Barrier measures were therefore not implemented at LCSC between the start of the pandemic and the time of testing. We have added the following text to the manuscript to clarify this point (new text bolded):

“The LCSC consists of 1 high school (grades 9-12; 3,261 students), 3 middle schools (grades 5-8; 2,859 students), and 6 elementary schools (grades kindergarten-4, 3,467 students), and transitioned to fully virtual instruction on March 16th 2020.”

With respect to the seroprevalence of the LCSC student population: unfortunately, we were unable to collect these data for multiple reasons. Funding was unavailable for surveilling the student population from the LCSC, as the staff’s insurer paid for the cost of the test. Additionally, our IRB had ethical concerns about LCSC students analyzing fellow students’ data (a majority pediatric population) at the level of granularity that was to be performed even if we were to successfully raise funding. We have modified the text to reflect that this analysis was a component of the LCSC’s annual staff annual wellness check, as below (new text bolded):

“This cross-sectional, population-based analysis of the seroprevalence of anti-SARS-CoV-2 IgG enrolled participants over a five day period in July of 2020 as part of the LCSC staff’s annual wellness check.”

b.It is announced that the variable "contact with a positive person" will be analyzed, without specifying whether this contact concerns a colleague or a relative. It would be desirable to specify and take into account these two modes of contact.

We agree with the reviewer that if school had been in session physically, this information would be crucial to assisting in deconvolving home versus school exposure, and have incorporated this feedback into a subsequent survey sent to the staff for a separate project. Given that the LCSC transitioned to fully-virtual instruction on 3/16/2020, the staff (with the exception of the administrative and janitorial staff), exposure from colleagues was negligible. Additionally, the LCSC had no method or policy of informing staff of exposure to a positive staff member until after the time of the LCSC annual wellness check. We have updated the manuscript Methods section (pertinent section bolded) to provide clarification:

“The primary outcome was the total seroprevalence of anti-SARS-CoV-2 IgG antibodies within staff. The secondary outcomes were the changes in odds of seropositivity associated with baseline demographics and COVID-19 related factors including mask use, a self-reported history of contact with a known COVID-19 positive person or persons, and a previous positive COVID-19 test (PCR or antibody-based).”

c. You announce that you have replaced the missing data with the median or the mode depending on the variable. You thus create a huge bias in your data, especially when you know that they relate, for example, to age and BMI, which have a major influence on the attack by SARS-CoV-2. Missing data should be excluded.

We thank the reviewer for this excellent feedback, and agree that a sensitivity analysis that excludes, rather than replaces, missing data for the multivariable analyses is appropriate (the univariable analyses used excluded data in the initial submission). We now report the results for the excluded data throughout the main manuscript, and provide the results from replaced data as supplementary tables.

Reassuringly, both of the resulting multivariable analyses where missing data were excluded maintained qualitatively identical results to our initial results. The causal diagram with excluded data found no statistically significant link between mask-wearing and COVID seropositivity (RR 0.63, 95% CI 0.16-4.3) relative to the replaced data (RR 0.83, 95% CI 0.18 - 3.8). We have modified the text to read:

No causal relationship between self-reported mask-wearing history and seropositivity could be identified, after controlling for six confounders available in our data set, identified using a causal diagram (S1 Table) (RR 0.63, 95% CI 0.16-4.3). This result was maintained when missing data were replaced, rather than excluded (S2 Table).

Similarly close results were found between the multivariable models employing backward-stepwise feature elimination using replaced or excluded data. We have modified the text to read:

In the multivariable model employing backward-stepwise feature elimination, a previous positive COVID-19 test (OR 48 95% CI 3.9 - 600, p=0.003) or contact with a COVID-19 case (OR 5.6, 95% CI 2.1 - 15.1, p=0.001) were found to be the most predictive factors for seropositivity (S3 Table); similar results were observed when missing data were replaced, rather than excluded (S4 Table).

While performing the sensitivity analysis for the backward-stepwise feature elimination logistic regression, we realized that we had inadvertently included the results originating from excluded data, rather than replaced data, in our initial submission (which we had performed, but not included, in the initial manuscript). S3 Table and S4 Table have been appropriately updated.

After subsetting the data to only individuals without a previous positive COVID-19 test, models using either excluded or replaced data yielded similar results. Table 3 has been replaced with the results generated from the excluded data; the results from the replaced data are now in S5 Table. We have updated the text to read:

In this population, a logistic regression analysis including the predictors mask wearing, travel history, and symptom history, a positive contact history conferred a 7-fold increase in the odds of seropositivity (p<0.0001) (Table 3). The odds increased from 1:54 without such a contact to 1:7 with contact - or the probability of seropositivity increased from 1.8% to 12%. Similar results were observed when replacing, rather than excluding, missing data (S5 Table).

d. Where is the methodological part concerning the DAG? There are a huge number of methods to build a DAG, which one have you used? With which package and which assumptions?

We thank the reviewer for their feedback and for pointing out this opportunity to improve the explanation of our methods. In this paper, we took a causal inference approach to DAGs; we built the DAG using subject-matter knowledge to identify important variables that should be adjusted for in the analysis, which is different from other common statistical approaches to variable selection (Hernán et al., 2002). Therefore, we did not use any specific methods or packages to build the DAG. Based on subject-matter knowledge - including the existing literature and first-hand knowledge of the Lake Central school system - we identified gender, age, travel history (as a proxy for risk taking behavior), school type, and role in the school as variables that could likely affect both mask-wearing behavior and seropositivity, making them potential confounders that must be adjusted for in order to interpret our estimate for the effect of mask-wearing on seropositivity causally.

To make it more clear that we are referring to a causal inference perspective, we have replaced the word directed acyclic graph/DAG with “causal diagram” throughout the manuscript.

We have updated the main manuscript text to include relevant citations for the variables included on the DAG and to be more explicit that the DAG was generated a priori from subject-matter knowledge:

“To determine if having a history of mask-wearing reduced seropositivity, we constructed a causal diagram based on existing literature and first-hand knowledge of the Lake Central school system and a priori identified gender, age, travel history (as a proxy for risk taking behavior), school type, and role at LCSC as potential confounders to control for in the multivariable logistic regression (S1 Figure).”

We have also updated the S1 Figure caption and text to provide more details on our assumptions:

“S1 Figure: Construction of a Causal Diagram to Identify Confounders of the Relationship Between Mask Wearing and Seropositivity

This causal diagram is drawn under the null hypothesis of no effect of mask-wearing on seropositivity and shows the relationships between important variables hypothesized to affect both mask-wearing and seropositivity. We adjusted for these potential confounders to identify the effect of mask-wearing on seropositivity. To interpret this estimate causally, we assume that there are no unmeasured confounding variables and that our logistic model is correctly specified; however, it is possible that risk-taking personality may affect seroprevalence in other ways such as not social distancing, but those data weren’t collected and could not be adjusted for in this round of testing.”

3. Results

A. You describe a fragmented participation in your study. What about the biases associated with this participation rate?

We very much agree with the reviewer that biases related to participation could occur and should be discussed. In particular, we recently described potential recruitment-based biases present in serosurveys for SARS-CoV-2 (Accorsi et al., 2021). Firstly, ascertainment bias will occur if the people present for sampling are at lower or higher risk of COVID-19 than average due to the sampling location and time. Since all LCSC staff were approached to participate in this study, ascertainment bias is not an issue. However, not all staff elected to participate in the study and volunteer bias will occur if individuals are more (or less) likely to accept testing because they believe they’ve previously had COVID-19, resulting in estimates of seroprevalence that are too high (or low). We do not believe volunteer bias is driving the results found in this study because (1) we had an overall high participation rate for a SARS-CoV-2 serosurvey (60.0%), (2) the demographics of the study sample are highly representative of the target population (Table 1), (3) the study was performed during the summer of 2020 when COVID-19 test availability in general was much higher, reducing the need to seek testing through a study.

We have updated discussion of study limitations to include more information on biases arising from imperfect participation:

Given that not all staff elected to participate in the study, some level of volunteer bias is possible. The high participation rate (60.0%), highly representative sample population (Table 1) and the wide availability of COVID-19 testing outside of this study, however, all limit the potential for volunteer bias to drive the observed results.

B. Where is the DAG produced after all? We only see the DAG of the tested hypotheses. Again, what about the package used?

We thank the reviewer for their thorough review of the paper and for bringing up this lack of clarity related to the use of DAGs. As described above, the DAG was created a priori based on subject-matter knowledge, therefore we did not use any software packages to generate it and we only created one DAG (shown in S1 Figure) for the specific hypothesis we wanted to evaluate (i.e., the causal effect of mask-wearing on seropositivity in LCSC teachers). We used the causal diagram to identify possible sources of structural bias, such as confounding, that would need to be addressed in order to interpret the coefficient for mask wearing causally. As above, we have updated the text to improve clarity by instead using the term “causal diagram”, being more explicit that the DAG was generated a priori using subject-matter knowledge, and stating the assumptions required to interpret model estimates causally.

4. Discussion

To rule on the reopening of schools, it would have been necessary to determine whether the school staff was more affected than the general population, taking into account the participation bias. This issue of bias is not addressed.

We agree with the reviewer that determining the seropositivity of the general population would have been crucial for determining the effect of school re-opening on COVID transmission. The main purpose of our work was to understand the baseline seroprevalence of the LCSC staff, and therefore the fraction of the LCSC staff at risk for the sequelae of COVID-19 infection. We have changed the manuscript discussion section to clarify and support this purpose:

“This study provides the necessary baseline for future longitudinal monitoring of COVID-19 transmission through a representative US public school system.”

“The estimated 1.7% seroprevalence of COVID-19 antibodies among LCSC staff is far below that reported in metropolitan areas throughout the US at the time of testing, underscoring the large and continued risk of COVID-19 infection within this community upon exposure.16”

We thank the reviewer for correctly identifying the lack of discussion about participation bias. We have modified the text to address bias as described above.

To judge the relevance of maintaining the barrier measures within the school, it would have been necessary to compare the seroprevalence of the staff both with that of the general population under these conditions of bias but also with the seroprevalence of the student population of the school and assess the adherence of these populations to barrier measures. Without these comparisons no conclusion can be given on these points.

The LCSC was fully virtual as of 3/16/2020, and barrier protections were therefore not deployed. We have clarified in the discussion section that such recommendations are a reinforcement on CDC guidance concerning school re-openings:

“Administrators and legislators should allocate the required resources to implement and maintain robust personal protective measures as recommended by the CDC, including contact-tracing, masks, face shields, and social distancing to protect the lives of both the children of the school district and those charged with educating them.”

References

Accorsi, E. K., Qiu, X., Rumpler, E., Kennedy-Shaffer, L., Kahn, R., Joshi, K., Goldstein, E., Stensrud, M. J., Niehus, R., Cevik, M., & Lipsitch, M. (2021). How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19. European Journal of Epidemiology. https://doi.org/10.1007/s10654-021-00727-7

Hernán, M. A., Hernández-Díaz, S., Werler, M. M., & Mitchell, A. A. (2002). Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. American Journal of Epidemiology, 155(2), 176–184.

Attachment

Submitted filename: Response to Reviewer.docx

Click here for additional data file. (25.5KB, docx)

Decision Letter 1

Yury E Khudyakov

17 May 2021

Seroprevalence of anti-SARS-CoV-2 IgG Antibodies in the Staff of a Public School System in the Midwestern United States

PONE-D-20-38006R1

Dear Dr. Kalinich,

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.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. 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.

Kind regards,

Yury E Khudyakov, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Yury E Khudyakov

31 May 2021

PONE-D-20-38006R1

Seroprevalence of anti-SARS-CoV-2 IgG antibodies in the staff of a public school system in the midwestern United States

Dear Dr. Kalinich:

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. Yury E Khudyakov

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Dataset. De-identified LCSC COVID dataset.

    (CSV)

    Click here for additional data file. (29KB, csv)
    S1 Fig. Construction of a causal diagram to identify confounders of the relationship between mask wearing and seropositivity.

    (DOCX)

    Click here for additional data file. (31KB, docx)
    S1 Table. Logistic regression results for the relationship between mask wearing history and seropositivity, adjusting for potential confounders (missing data excluded).

    (DOCX)

    Click here for additional data file. (14.7KB, docx)
    S2 Table. Logistic regression results for the relationship between mask wearing history and seropositivity, adjusting for potential confounders (missing data replaced).

    (DOCX)

    Click here for additional data file. (14.3KB, docx)
    S3 Table. Stepwise backwards feature elimination regression results (missing data excluded).

    (DOCX)

    Click here for additional data file. (13.8KB, docx)
    S4 Table. Stepwise backwards feature elimination regression results (missing data replaced).

    (DOCX)

    Click here for additional data file. (14KB, docx)
    S5 Table. Stepwise backwards feature elimination regression results, previously COVID+ patients excluded (missing data replaced).

    (DOCX)

    Click here for additional data file. (13.4KB, docx)
    S1 Text. Data collection for seroprevalence study of COVID 19 in Lake Central Staff.

    (DOCX)

    Click here for additional data file. (17.5KB, docx)
    Attachment

    Submitted filename: Response to Reviewer.docx

    Click here for additional data file. (25.5KB, docx)

    Data Availability Statement

    All relevant data are within the manuscript and its Supporting Information files.

    Articles from PLoS ONE are provided here courtesy of PLOS

    RESOURCES