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Article

At-Home Testing to Characterize SARS-CoV-2 Seroprevalence Among Children and Adolescents

by
Amina Ahmed
1,2,*,
Michael E. DeWitt
3,4,
Keerti L. Dantuluri
1,2,
Asare Buahin
5,
Paola Castri
2,
DeAnna Friedman-Klabanoff
6,
Michael Gibbs
7,
William H. Lagarde
8,
Roberto P. Santos
9,
Hazel Tapp
10,
Diane Uschner
5 and
on behalf of the COVID-19 Community Research Partnership
†
1
Department of Pediatrics, Division of Pediatric Infectious Disease, Levine Children’s Hospital, Atrium Health, Charlotte, NC 28203, USA
2
Department of Pediatrics, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA
3
Department of Medicine, Section on Infectious Disease, Wake Forest University School of Medicine, Winston-Salem, NC 27102, USA
4
Department of Biology, Wake Forest University, Winston-Salem, NC 27109, USA
5
Department of Biostatistics and Bioinformatics at the Biostatistics Center, George Washington University, Washington, DC 20052, USA
6
Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA
7
Department of Emergency Medicine, Atrium Health, Charlotte, NC 28203, USA
8
Department of Pediatrics, WakeMed Health and Hospitals, Raleigh, NC 27610, USA
9
Department of Pediatrics, Division of Pediatric Infectious Disease, University of Mississippi Medical Center, Jackson, MS 39216, USA
10
Department of Family Medicine, Atrium Health, Charlotte, NC 28203, USA
*
Author to whom correspondence should be addressed.
†
Contributors to the COVID-19 Community Research Partnership are listed in the Acknowledgments.
COVID 2025, 5(5), 68; https://doi.org/10.3390/covid5050068
Submission received: 25 March 2025 / Revised: 25 April 2025 / Accepted: 25 April 2025 / Published: 3 May 2025
(This article belongs to the Section COVID Public Health and Epidemiology)
Browse Figures
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Abstract

:
During the COVID-19 pandemic, longitudinal serological surveillance was critical to accurately assess the cumulative incidence of COVID-19 and monitor population-level immunity among children. We characterized the epidemiology of COVID-19 among North and South Carolina children using remote electronic symptom surveillance and serial at-home antibody testing in 2–17-year-old children from April to December 2021. We estimated accumulation of infection-induced antibodies, defined as seropositivity before vaccination. Reported novel symptoms were characterized for seropositive and seronegative participants. A total of 1058 children (median age 10 years (IQR 7–13)) participated in symptom surveillance. Estimated cumulative prevalence of infection-induced antibody increased from 0% (0–60%) to 85.7% (31.6–97%), 3.1% (0–9.0%) to 61.7% (46.1–72.7%) and 5.6% (0–15.6%) to 75% (59.9–84.4%), respectively, among those aged 2–4, 5–11 and 12–17 years. Determinants of seropositivity included Black race (OR 2.06 (1.19 to 3.47)) and having >5 household members (OR 3.05 (1.21 to 7.45)). Participants seroconverting reported mostly respiratory or systemic symptoms, but 52% (28/52) were asymptomatic. We demonstrate the role of at-home testing in trending SARS-CoV-2 seroprevalence in a community cohort of children. Such regional serological surveillance complements national data for a more complete assessment of SARS-CoV-2 burden and transmission. Future applications of the remote surveillance platform may be leveraged for characterizing the epidemiology of novel pathogens.

1. Introduction

Early in the COVID-19 pandemic, children were thought to account for <5% of SARS-CoV-2 infections in the United States [1]. As virological surveillance expanded, it became evident that children in fact contribute much more substantially. By January 2022, children accounted for >25% of COVID-19 cases [2]. Virological surveillance, however, still underestimated the extent of the pandemic, especially in children, who are more likely to be asymptomatic [3]. Multiple studies highlight the undercount of SARS-CoV-2 infections in children using viral testing alone, with approximately 50% of serologically identified infections being asymptomatic [4,5].
Serological surveillance, by capturing missed infections, provides a more accurate assessment of the burden of COVID-19 in children. The Nationwide Laboratory Survey estimates that the prevalence of SARS-CoV-2 infection-induced antibodies among children has exceeded that in adults since November 2020, reaching 74% by February 2022 [6,7]. These prior studies, however, were retrospective, relying on residual clinical specimens with incomplete demographic data, thereby limiting our understanding of timing of infections or identification of at-risk populations who may benefit from additional mitigation measures.
As the pandemic evolves, prospective longitudinal serosurveillance is needed to reliably estimate the cumulative incidence of COVID-19, identify disproportionately affected groups, and monitor immunologic exposure among children. Such surveillance could provide insight into the kinetics of infection-induced and vaccine-induced antibody responses during and after spread of infection to inform public health response [8]. Capturing such data requires increased test capacity, which presents logistical challenges. These barriers may be addressed in part by at-home antibody testing, with its benefits of convenience and reduced need for healthcare resources. The platform has been described in adults [9,10,11] but remains largely unexplored in children.
We characterized local COVID-19 epidemiology using remote syndromic surveillance and serial at-home serological testing among children and adolescents in North and South Carolina. Similar serosurveillance will be critical for understanding the dynamics between population-level immunity and emerging variants that will dictate the future course of the pandemic.

2. Materials and Methods

2.1. Study Population

The COVID-19 Community Research Partnership (CCRP) is a multisite, prospective cohort study combining electronic symptom surveillance with at-home serological testing in adults and children [12]. We enrolled children 2–17 years old in three healthcare systems in North Carolina (NC): Atrium Health (AH) Levine Children’s Hospital (Charlotte, NC, USA), AH Wake Forest Baptist (Winston-Salem, NC, USA) and WakeMed (Raleigh, NC, USA).
Participants were recruited April 2 to June 24, 2021, in English and Spanish, through email, patient portal systems, advertisement and in person. The healthcare centers served as central hubs for recruitment; invitations to participate were emailed broadly to parents/guardians of children who were established patients at the centers. Eligibility was broad with the only inclusion criteria being age 2–17 years and having a parent/guardian with a valid email address. Parents/guardians of participants consented electronically to symptom surveillance alone or symptom surveillance and testing; adolescents 13 years and older provided assent. The study was approved by the Wake Forest School of Medicine Institutional Review Board.

2.2. Procedures

After consent completion, parents/guardians registered for daily symptom surveillance by electronically submitting sociodemographic information, health history, prior history of SARS CoV-2 infection and household and employment information. Participant history of SARS-CoV-2 infection was not qualified further as to whether it was confirmed by testing. Daily electronic surveys administered by Oracle Corporation (Redwood, CA, USA) solicited COVID-19-like illness symptoms and reports of infection with or exposure to SARS-CoV-2 (Figure S1). Parents/guardians submitted surveys for and conducted at-home tests on their children; adolescents 13 years and older could opt to respond to surveys themselves. Reports of SARS-CoV-2 infection were solicited by prompting participants for new COVID-19 test results since the last survey; an affirmative response generated questions on the type, date and result of the test (Figure S1). Participants also reported receipt (including date) of COVID-19 vaccines since the last submitted survey. Participants were considered fully vaccinated ≥ 14 days after completion of a primary series (2 doses) of a Food and Drug Administration (FDA)-authorized COVID-19 vaccine, partially vaccinated if <14 days since completion of a primary series or having not completed the primary series and unvaccinated if no vaccine was received. Symptom surveillance continued through 31 December 2021.
Participants were shipped at least 4 serology test kits to complete once monthly with instructions in English and Spanish. They downloaded a smartphone application (Scanwell Health, Anaheim, CA, USA) for video instructions on use of an immunoglobulin (IgM/IgG) rapid lateral flow immunoassay (Innovita Biological Technology, Beijing, China) detecting (but not differentiating between) antibodies to both nucleocapsid and spike proteins. Blood collected by finger prick was placed into a test cassette; an application-captured image of the result was uploaded for review. Sensitivity and specificity for IgG were 84.5% and 99.0%, respectively. Results were invalid if the internal control line was absent, and participants were instructed to complete another test. Results interpretation (positive, negative or invalid) was sent to participants via email and smartphone application within 24–48 h. Serological testing continued through 31 October 2021.
Testing participation was supported by weekly email reminders prompting completion of sequential tests. Participants had email and telephone access to a centralized call center for troubleshooting, including any explanation of results. A study website was established to update participants on enrollment accrual and study publications. Monthly emails thanking families for participating in the study provided similar updates. We further encouraged retention by inviting participants to virtual town halls held monthly from July to October 2021 to share data and receive feedback. Following each town hall, we collated and emailed to participants a summary of questions and answers addressed during the session.

2.3. Statistical Methods

Means (continuous variables) and frequencies (categorical variables) were calculated for baseline demographic and clinical characteristics. We assessed differences between those who consented to symptom surveillance and testing but never completed any tests and those who both enrolled and completed tests using Pearson’s chi-square tests for categorical variables and a Wilcoxon rank sum test for continuous variables. For participants submitting at least one test, we conducted chi-square tests and Fisher’s exact tests to assess the differences in the distribution of the number of completed tests by demographic.
Seropositivity was defined as having a positive immunoglobulin G result (IgG). Because the assay did not differentiate between antibodies to nucleocapsid and spike protein, infection-induced antibody was defined as a positive IgG result in the absence of any vaccination at the time of test completion. Adolescents ≥ 12 years of age partially or fully vaccinated at enrollment (n = 261) were not included in the time to seroconversion analysis. We defined cumulative incidence as the proportion of children ever seropositive at a given timepoint.
Univariate logistic regression models were employed to calculate the odds ratio associated with seroconversion associated with evidence of infection-induced antibodies compared to those participants without evidence of infection-induced antibodies by the end of the study period. We utilized a Kaplan–Meier approach to estimate the cumulative incidence of infection-induced antibodies overall and by age strata. Participants entered analysis on the date of their first serology result. An event was defined as a positive IgG assay prior to the receipt of any vaccine. Participants were censored at the end of the study or upon reporting of receipt of vaccine. We also used a Kaplan–Meier approach to assess seroconversion from vaccination among participants who had at least one prior negative serology result. The event of interest was the first positive IgG result after vaccination. Participants were censored at the end of the study.
We analyzed the association between seropositivity and symptoms by evaluating symptoms reported within 30 days preceding a serology result. New symptoms were defined as those reported in the thirty-day window which had not been reported in the seven days prior. We calculated rates of symptomatic infections by dividing the number of individuals with at least one new symptom by the total number of participants with evidence of infection-induced antibodies. Participants with infection-induced antibodies were included only if their first serology test result was not positive to better bound the infection in the study period. We calculated the Pearson correlation coefficients to assess the correlation among symptoms and symptom complexes for participants who reported new symptoms prior to their reported serology among those who had evidence of infection-induced antibodies and separately for those who did not have evidence of positive serology but attested to symptoms. We calculated the differences in proportions by unique symptoms by taking the difference in the point estimates and used Bayesian confidence intervals for difference of proportions using the simulation method with uninformative beta priors of one for both shape and scale. We further grouped unique symptoms into symptom complexes representing respiratory (congestion or runny nose, sore throat, cough or shortness of breath), gastrointestinal (vomiting or diarrhea) and systemic (fever, fatigue, muscle or body aches or poor appetite or feeding) symptoms. Logistic regression models were then fit to assess the odds of seroconversion associated with symptoms reported in each symptom complex.

3. Results

3.1. Study Population

Of 2612 participants consenting to both symptom surveillance and testing, 1058 (39.8%) completed at least one serology test and comprised the study population (Figure S2). These participants resided in 35 and 6 counties in North and South Carolina, respectively (Figure S3); 748 (71%) enrolled at AH Levine Children’s Hospital, 261 (25%) at AH Wake Forest Baptist and 49 (5%) at WakeMed. We previously used the same cohort of 1058 participants to estimate infection-induced seroprevalence among unvaccinated North Carolina children and adolescents to derive infection-to-case ratios, as described below [13].
Among participants consenting to testing, children 2–4 years old and those residing in rural settings were significantly less likely to ever complete a test (Table 1). The likelihood of completing a test did not differ based on race or ethnicity. Participants with two or more comorbidities and those vaccinated were more likely to participate in testing.
The median age (IQR) of the study population was 10 years (IQR 7–13). Most participants were non-Hispanic White (Table 2). Underlying medical conditions were uncommon; the most frequently reported condition was asthma (12%). Only 93 (8.9%) of participants reported a prior COVID-19 diagnosis at enrollment. Of 244 participants responding, 87 (36%) reported a healthcare worker in the household.
At peak, 701 (67%) of participants submitted surveys on the same day, with a 7-day rolling average maximum of 669 surveys (Figure 1). More than 90% of participants submitted surveys monthly from June through September, decreasing to 86% and 50% in October and November, respectively. Only 21 (2%) participants withdrew from the study.
Participants completed a median of 2 (IQR 1–4) tests a median of 32 (IQR 27–38) days apart. Only 30 test results were invalid; all but 4 were repeated. Among participants completing at least one serology test, the proportion who were 2–4 years of age was significantly lower than the proportion who were 5–11 or 12–17 years of age (Table 3). However, the proportions completing multiple tests were not significantly different than those completing only one, regardless of age group, race or ethnicity.

3.2. Serological Surveillance

Among children unvaccinated by November 1, 2021, we estimated 70.5% (95% CI 60.2–78.1%) developed infection-induced antibodies an average of 74.5 days after enrollment (188 events). The cumulative prevalence of infection-induced antibodies increased from 3.6% in May to 70.5% by the end of October (Figure 2). By age group, seroprevalence increased from an estimated 0% (0–0%) to 85.7% (31.6–97%), 3.1% (0–9.0%) to 61.7% (46.1–72.7%) and 5.6% (0–15.6%) to 75% (59.9–84.4%), respectively, among those aged 2–4, 5–11 and 12–17 years.
Black children were twice as likely to have infection-induced antibodies compared with White children (p = 0.008, Table 1). Living with ≥5 persons and prior COVID-19 diagnosis at enrollment were also significantly associated with the presence of infection-induced antibodies. COVID-19 diagnosis by viral testing during the study was reported by 10 participants, all of whom tested positive for infection-induced antibody. Among those with infection-induced antibodies, only 10 (n = 169, 6%) reported a known exposure to COVID-19. Ethnicity, rurality of residence and living with a healthcare worker were not associated with likelihood of having infection-induced antibody.

3.3. Symptom Surveillance

Among 1058 participants, 648 responded to symptom surveys prior to seroconversion, with a median of 23 (IQR 10–36) responses per individual during the testing window days (30 + 7-day window). At least one new symptom was reported by 60% (352/594) of seronegative and 48% (26/54) of seropositive participants. The likelihood of seropositivity being associated with symptoms was highest for respiratory and systemic symptoms (Figure 3, Tables S1 and S2). The most common symptoms reported were cough (54%) and fatigue (39%). Among seropositive participants, 28/54 (52%) reported no symptoms. Adolescents 12–17 years old were significantly more likely to be asymptomatic than those in younger age groups.

3.4. Vaccination

The FDA expanded the emergency use authorization for the Pfizer–BioNTech COVID-19 vaccine to adolescents aged 12–15 years on May 10 and for children aged 5–11 years on 29 October 2021 [14,15]. By study completion on December 31, of 12–17-year-olds, 3 (0.7%) had received one vaccine dose and 284 (68%) had received two; of 5–11-year-olds, 41 (8.3%) had received one dose and 290 (59%) had received two.
Among 284 participants for whom at least one serology result was available after at least one dose of COVID-19 vaccine, 275 (97%) were seropositive. We observed similar rates of detectable SARS-CoV-2 antibody after full vaccination (97.7% seropositive among 257 participants). Seroconversion rates after full vaccination were similar across 12–17-year-olds (n = 167, 99%) and 5–11-year-olds (n = 24, 100%) ≥ 14 days after the second dose (Figure S4).

4. Discussion

In this prospective study of a community cohort of children and adolescents in North and South Carolina during SARS-CoV-2 Delta variant circulation, prevalence of infection-induced antibodies increased significantly in all age groups. These findings parallel and expound on national seroprevalence trends [6,7]. Determinants of SARS-CoV-2 seropositivity included Black race and residence in a household with five or more persons, highlighting populations who may benefit from targeted mitigation measures. A high proportion (52%) of seropositive children were asymptomatic, underscoring the critical role of serosurveillance in the accurate assessment of COVID-19 incidence in this population. The consistency of our findings with prior research demonstrates the potential role of at-home serological testing as a critical adjunct to virological surveillance for understanding the epidemiology of SARS-CoV-2 infections in children.
Several studies described regional trends in SARS-CoV-2 seroprevalence among US children during the pandemic. In Mississippi, seroprevalence increased from 2.5% in May to 16.3% in September 2020 [16], while in Missouri seroprevalence increased from 5.2% in July 2020 and 21.2% in January 2021 [17]. The National Commercial Laboratory Survey has monitored infection-induced SARS-CoV-2 seroprevalence in all age groups in the US since August 2020. From December 2021 to February 2022, seroprevalence increased from 33% to 68% among children aged 1–4 years, from 47% to 77% among those aged 5–11 years, and from 46% to 74% among adolescents aged 12–17 years [6,7]. Using the same data, Couture demonstrated variability in seroprevalence across states, ranging from 26% in North Carolina to 44% in New Jersey in May 2021 [18]. Although these studies provided important data, they were limited by use of convenience samples of commercial laboratory residual sera, with overrepresentation of children with access to or need for healthcare. The analyses also lacked demographic variables such as race and ethnicity known to be significantly associated with COVID-19 case rates [6,18]. Our study is unique in its prospective design, granularity of individual-level data and generalizability. Similar surveys can be used to trend regional seroprevalence by population demographics to expound on national data, characterize outbreaks and inform meaningful local public health interventions. The Ciao Corona study, for example, found minimal clustering of seropositive COVID-19 cases within Swiss schools during a period of high community transmission, supporting continued in-person learning [19]. Although this study relied on personnel for specimen collection, self-administered antibody testing was used by school staff and students in Chile to provide valuable information for school re-openings [8].
The COVID-19 pandemic highlighted well-recognized health disparities, with minority and socioeconomically disadvantaged populations most severely impacted. In one study, Canadian children whose parents self-identified as a racial or ethnic minority were more likely to be seropositive compared with children of White parents [20]. Similarly, in Northern Virginia, Hispanic ethnicity, public or absent insurance and multifamily dwelling were significantly associated with SARS-CoV-2 seropositivity [4]. Our findings, associating seropositivity with Black race and more crowded housing, further contribute to evidence of COVID-19 inequities. In TestBoston, a cohort study with monthly viral and antibody self-testing, minority race and Hispanic ethnicity were associated with seropositivity or a positive SARS-CoV-2 molecular test [10]. While there was a 2-fold greater positivity rate based on serology compared with viral testing for White individuals, the difference was 3.5-fold and 4-fold for Blacks and Asians, suggesting greater access to viral testing outside of the study for White participants. These disparities would have been significantly underestimated based on viral testing alone, highlighting the role of serosurveillance in assessment of distribution of infection in at-risk populations in a community.
By coupling syndromic surveillance with serosurveillance, we captured a comprehensive range of SARS-CoV-2 symptoms. Participants who seroconverted reported mostly respiratory and systemic symptoms. Interestingly, seronegative participants also reported respiratory and systemic symptoms (86% and 40%, respectively) suggesting circulation of other viruses with similar presentations. While our analysis did not extend to the Omicron period, an adult CCRP study captured a variant-specific shift in clinical presentation. DeWitt et al found increased rates of cough and sore throat, decreased rates of loss of taste and smell and shorter symptom duration during the Omicron compared with Delta variant period [21]. As COVID-19 becomes endemic and the immune landscape changes through vaccination and infection, syndromic surveillance may provide early indications of variant emergence and help differentiate SARS-CoV-2 from other viral infections.
The high proportion of asymptomatic seropositive children in our study echoes earlier observations and highlights the underestimation of cases based on viral testing [3,22,23]. A meta-analysis of 350 publications found approximately one third of infections are asymptomatic, with almost 50% of children being asymptomatic compared with 30% of adults [3]. The mild clinical presentation of COVID-19 in children, with implications for silent spread, may be a hidden driver of the pandemic given the higher frequency and proximity of social contacts of children, including adults. Relying on symptom-based viral screening alone may fail to identify a substantial portion of childhood SARS-CoV-2 infections, impeding containment. Unlike these previous reports, we found the highest proportion of asymptomatic cases among adolescents, again highlighting the value of regional surveys in characterizing local epidemiology given geographic differences in infection patterns.
The undercount of reported SARS-CoV-2 infections has decreased over time but remains higher in children compared with adults [7,24]. Among Mississippi children, ratios of estimated SARS-CoV-2 infections based on seropositivity to reported COVID-19 cases decreased from 68.2 in May to 12.7 in September 2020 [16]. Using more than 6500 residual serological specimens, Couture estimated ratios of infection to reported cases among North Carolina children of 7.7 in August 2020, declining to 4.7 in May 2021 [18]. To follow up on these findings, we used approximately 2700 specimens from the same cohort of 1058 participants described in the current study to estimate seroprevalence among North Carolina children and adolescents using multilevel regression with poststratification. In comparing seroprevalence to cumulative reported cases in North Carolina, we estimated an infection-to-case ratio as high as 6.1 in August 2021, declining to 4.8 in October 2021 [13]. By prospectively following a relatively small but generalizable cohort, we were able to demonstrate continued underestimation of cases in North Carolina late into the second year of the pandemic.
A major strength of our study is the successful implementation of a remote platform with serial at-home testing to collect prospective, real-world pediatric epidemiological, clinical and serological data. To our knowledge, only two other studies have described at-home antibody testing in children, both collecting only a single specimen per participant [8,20]. With unprecedented logistical barriers created by the pandemic, researchers developed innovative partially or fully remote methodologies which, aside from accommodating infection prevention practices, are equitable and scalable, enabling recruitment of underrepresented populations [9,10,25]. TestBoston, for example, enrolled a large adult cohort, providing disadvantaged communities access to testing [10]. At-home testing offers convenience, decreased costs, reduced healthcare burden and lower infection control risks [9]. For resource-poor settings, with limited access to molecular testing, rapid point-of-care serological testing is recommended to monitor the extent of population infection and inform control strategies [26]; at-home testing would facilitate scaling up such an effort at the community level. Prior studies using remote platforms have largely targeted adults and typically focused on viral testing [27]. Barriers identified for implementing at-home testing in children include caregivers’ adherence to and concerns about sample collection [28]. Despite this, our cohort demonstrated consistent participation, with few invalid results and minimal attrition. The high retention rate may in part be attributable to the study’s multifaceted participant support, including call center access and scheduled emails. The study website and town halls promoted dissemination of findings and knowledge, a key principle of community-based participatory research [29]. Although younger children were less likely to participate in testing, once engaged, the proportion submitting multiple test results was similar to the proportion submitting only one, regardless of age. For future serological surveys in children relying on at-home testing, saliva may be considered as an alternate or additional specimen type for SARS-CoV-2 antibody detection. Recent studies demonstrate SARS-CoV-2 antibody levels in saliva correlate with serum levels, although assay sensitivity is variable and data in children are limited [30,31]. Sampling of saliva is non-invasive and better tolerated than blood sampling; as the performance of these assays improve, this specimen type may prove valuable in facilitating at-home testing for serological surveys for children or adults.
Our study has several limitations. As with other CCRP studies, minority populations were underrepresented, possibly due to the study requirement of mobile technology access [12]. This may limit the generalizability of the findings, particularly given recognized disparities in COVID-19 outcomes across racial and ethnic groups. However, once engaged, no disparities in testing participation based on race or ethnicity existed [32]. Further, the remote platform is adaptive and can guide adjustments to recruitment strategies for more equitable representation [12]. Seroprevalence estimates and vaccination rates may inherently differ in children participating in symptom surveillance compared with the general population. Self-reported symptoms, SARS-CoV-2 infections and vaccination history are subject to recall bias, potentially leading to underreporting and inflated infection-induced seroprevalence rates. However, correlation between self-reporting and electronic health record documentation of vaccines for the adult CCRP cohorts has been high, suggesting that underreporting is unlikely [33]. The antibody test used did not distinguish between vaccine induced- and infection-induced antibodies, but our analysis focused on unvaccinated individuals. Because seroconversion requires 10–14 days, recent infections could have been missed, and irregular sampling prevented precise timing of infections. With a sensitivity of 84.5%, our point-of-care antibody assay may have underestimated infection-induced seroprevalence. Although laboratory-based tests such as enzyme-linked immunosorbent assay (ELISA) are more sensitive, a systematic review found only small differences, with sensitivities averaging 72.4–92.4% for laboratory-based assays compared with 69.9–86.9% for lateral flow assays [34]. Laboratory-based tests, however, typically require venipuncture, which is a barrier to sequential testing in children. Despite the slightly lower sensitivity, the point-of-care assay allowed for remote collection of longitudinal serological data in children, which, as above, has not been previously accomplished. Future studies using at-home antibody testing may employ more sensitive tests that can distinguish between infection- and vaccination-induced immunity, providing insights into the durability of immune responses and timing of breakthrough infections to better understand transmission.
The future course of the COVID-19 pandemic depends on the interplay between population immunity and emerging variants. Questions remain regarding whether widespread immunity will lead to transient declines in incidence or complete eradication. Moving forward, understanding the long-term kinetics and durability of immune response against SARS-CoV-2 is vital for optimizing vaccination strategies. Longitudinal serological surveillance as achieved by our remote platform offers an opportunity to explore changes in vaccination-induced and infection-induced immunity to determine the trajectory of COVID-19 among children. Similar platforms may prove valuable in understanding the epidemiology of additional novel pathogens.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/covid5050068/s1, Figure S1: Daily symptom survey questions with screenshots of electronic survey administered via Oracle Corporation (Redwood, CA, USA); Figure S2: Flow diagram of selection of participants for analysis; Figure S3: Study enrollment catchment areas in North and South Carolina. (Counties with at least one participant in the study are shaded.); Figure S4: Cumulative incidence of seroconversion among participants who were vaccinated and reported at least one negative serology result prior to vaccination. (Children 2-4 years old included to account for age transition to 5 years of age during the study.); Table S1: Absolute rates of new symptoms. (Refer to Figure 3.); Table S2: Absolute rates of symptom complexes. (Refer to Figure 3.).

Author Contributions

Conceptualization: A.A., K.L.D., D.F.-K., P.C., M.G., W.H.L., R.P.S. and H.T.; Methodology: A.A., D.F.-K., P.C., M.G., R.P.S., H.T. and D.U.; Formal analysis: M.E.D., A.B. and D.U.; Investigation: A.A., M.G., D.F.-K., P.C., W.H.L. and H.T.; Data curation: M.E.D., A.B. and D.U.; Writing—original draft preparation: A.A.; Writing—review and editing: M.E.D., K.L.D., A.B., D.F.-K., M.G., W.H.L., R.P.S., H.T. and D.U.; Supervision: A.A.; Project administration: A.A. and D.U.; Funding acquisition: A.A. and M.G. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Coronavirus Aid, Relief, and Economic Security Act of the U.S. Department of Health and Human Services (HHS; H.R.748-116th Congress (2019–2020): CARES Act|Congress.gov|Library of Congress) (Contract # North Carolina Department of Health and Human Services #49927; NC DHHS: North Carolina Department of Health and Human Services). Funding was received by the institutions of Amina Ahmed, Keerti L. Dantuluri, Michael E. Dewitt, Asare Buahin, Paola Castri, Michael Gibbs, William H. Lagarde, Hazel Tapp, Diane Uschner. No funding was received by DeAnna Friedman-Klabanoff, Roberto P. Santos or their institutions. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Institutional Review Board of Wake Forest School of Medicine for studies involving humans (IRB00064912) on 21 December 2020. The study is registered at ClinicalTrials.gov (NCT04342884, 8 April 2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study as described in the Materials and Methods.

Data Availability Statement

The original data supporting reported results have been placed in a repository (Zenodo) and are openly available at https://doi.org/10.5281/zenodo.11222028, accessed on 20 May 2024.

Acknowledgments

Contributors to the COVID-19 Community Research Partnership include: Wake Forest School of Medicine: Thomas F Wierzba, John Walton Sanders, David Herrington, Mark A. Espeland, John Williamson, Morgana Mongraw-Chaffin, Alain Bertoni, Martha A. Alexander-Miller, Allison Mathews, Iqra Munawar, Austin Lyles Seals, Brian Ostasiewski, Christine Ann Pittman Ballard, Metin Gurcan, Alexander Ivanov, Giselle Melendez Zapata, Marlena Westcott, Karen Blinson, Laura Blinson, Mark Mistysyn, Donna Davis, Lynda Doomy, Perrin Henderson, Alicia Jessup, Kimberly Lane, Beverly Levine, Jessica McCanless, Sharon McDaniel, Kathryn Melius, Christine O’Neill, Angelina Pack, Ritu Rathee, Scott Rushing, Jennifer Sheets, Sandra Soots, Michele Wall, Samantha Wheeler, John White, Lisa Wilkerson, Rebekah Wilson, Kenneth Wilson, Deb Burcombe, Georgia Saylor, Megan Lunn, Karina Ordonez, Ashley O’Steen, Leigh Wagner. Atrium Health: Michael S. Runyon, Lewis H. McCurdy, Yhenneko J. Taylor, Lydia Calamari, Hazel Tapp, Michael Brennan, Lindsay Munn, Timothy Hetherington, Lauren C. Lu, Connell Dunn, Melanie Hogg, Andrea Price, Marina Leonidas, Melinda Manning, Frank X. Gohs, Anna Harris, Jennifer S. Priem, Pilar Tochiki, Nicole Wellinsky, Crystal Silva, Tom Ludden, Jackeline Hernandez, Kennisha Spencer, Laura McAlister. Wake Med Health and Hospitals: LaMonica Daniel. George Washington University Data Coordinating Center: Sharon L. Edelstein, Michele Santacatterina, Greg Strylewicz, Brian Burke, Mihili Gunaratne, Meghan Turney, Shirley Qin Zhou, Ashley H Tjaden, Lida Fette, Asare Buahin, Matthew Bott, Sophia Graziani, Ashvi Soni, Guoqing Diao, Jone Renteria, George Washington University Mores Lab: Christopher Mores, Abigail Porzucek. Oracle Corporation: Rebecca Laborde, Pranav Acharya. Vysnova Partners: Anne McKeague, Johnathan Ward, Diana P. Naranjo, Nana Darko, Kimberly Castellon, Ryan Brink, Haris Shehzad, Derek Kuprianov, Douglas McGlasson, Devin Hayes, Sierra Edwards, Stephane Daphnis, Britnee Todd.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision to publish the results.

References

  1. Kao, C.M.; Orenstein, W.A.; Anderson, E.J. The Importance of Advancing Severe Acute Respiratory Syndrome Coronavirus 2 Vaccines in Children. Clin. Infect. Dis. Off. Publ. Infect. Dis. Soc. Am. 2021, 72, 515–518. [Google Scholar] [CrossRef]
  2. American Academy of Pediatrics. Children and COVID-19: State-Level Data Report [Internet]. Available online: https://www.aap.org/en/pages/2019-novel-coronavirus-covid-19-infections/children-and-covid-19-state-level-data-report/ (accessed on 19 September 2023).
  3. Sah, P.; Fitzpatrick, M.C.; Zimmer, C.F.; Abdollahi, E.; Juden-Kelly, L.; Moghadas, S.M.; Singer, B.H.; Galvani, A.P. Asymptomatic SARS-CoV-2 infection: A systematic review and meta-analysis. Proc. Natl. Acad. Sci. USA 2021, 118, e2109229118. [Google Scholar] [CrossRef] [PubMed]
  4. Levorson, R.E.; Christian, E.; Hunter, B.; Sayal, J.; Sun, J.; Bruce, S.A.; Garofalo, S.; Southerland, M.; Ho, S.; Levy, S.; et al. A cross-sectional investigation of SARS-CoV-2 seroprevalence and associated risk factors in children and adolescents in the United States. PLoS ONE 2021, 16, e0259823. [Google Scholar] [CrossRef] [PubMed]
  5. Lugon, P.; Fuller, T.; Damasceno, L.; Calvet, G.; Resende, P.C.; Matos, A.R.; Fumian, T.M.; Maltaa, F.C.; Salgado, A.D.; Fernandes, F.C.M.; et al. SARS-CoV-2 Infection Dynamics in Children and Household Contacts in a Slum in Rio de Janeiro. Pediatrics 2021, 148, e2021050182. [Google Scholar] [CrossRef] [PubMed]
  6. Clarke, K.E.N.; Jones, J.M.; Deng, Y.; Nycz, E.; Lee, A.; Iachan, R.; Gundlapalli, A.V.; Hall, A.J.; MacNeil, A. Seroprevalence of Infection-Induced SARS-CoV-2 Antibodies—United States, September 2021-February 2022. MMWR Morb. Mortal. Wkly. Rep. 2022, 71, 606–608. [Google Scholar] [CrossRef]
  7. Clarke, K.E.N.; Kim, Y.; Jones, J.; Lee, A.; Deng, Y.; Nycz, E.; Iachan, R.; Gundlapalli, A.; Macneil, A.; Hall, A.J. Pediatric Infection-Induced SARS-CoV-2 Seroprevalence Estimation Using Commercial Laboratory Specimens: How Representative Is It of the General U.S. Pediatric Population? [Internet]. Rochester, NY; 2022. Available online: https://papers.ssrn.com/abstract=4092074 (accessed on 25 October 2022).
  8. Torres, J.P.; Piñera, C.; De La Maza, V.; Lagomarcino, A.J.; Simian, D.; Torres, B.; Urquidi, C.; Valenzuela, M.T.; O’ryan, M. Severe Acute Respiratory Syndrome Coronavirus 2 Antibody Prevalence in Blood in a Large School Community Subject to a Coronavirus Disease 2019 Outbreak: A Cross-sectional Study. Clin. Infect. Dis. Off. Publ. Infect. Dis. Soc. Am. 2021, 73, e458–e465. [Google Scholar] [CrossRef]
  9. Naz-McLean, S.; Kim, A.; Zimmer, A.; Laibinis, H.; Lapan, J.; Tyman, P.; Hung, J.; Kelly, C.; Nagireddy, H.; Narayanan-Pandit, S.; et al. Feasibility and lessons learned on remote trial implementation from TestBoston, a fully remote, longitudinal, large-scale COVID-19 surveillance study. PLoS ONE 2022, 17, e0269127. [Google Scholar] [CrossRef]
  10. Woolley, A.E.; Dryden-Peterson, S.; Kim, A.; Naz-McLean, S.; Kelly, C.; Laibinis, H.H.; Bagnall, J.; Livny, J.; Ma, P.; Orzechowski, M.; et al. At-home Testing and Risk Factors for Acquisition of SARS-CoV-2 Infection in a Major US Metropolitan Area. Open Forum Infect. Dis. 2022, 9, ofac505. [Google Scholar] [CrossRef]
  11. Racey, C.S.; Booth, A.; Albert, A.; Smith, L.W.; Krajden, M.; Murray, M.C.M.; Côté, H.C.F.; Gottschlich, A.; Goldfarb, D.M.; Sadarangani, M.; et al. Seropositivity of SARS-CoV-2 in an unvaccinated cohort in British Columbia, Canada: A cross-sectional survey with dried blood spot samples. BMJ Open 2022, 12, e062567. [Google Scholar] [CrossRef]
  12. The COVID-19 Community Research Partnership. The COVID-19 Community Research Partnership: A multistate surveillance platform for characterizing the epidemiology of the SARS-CoV-2 pandemic. Biol. Methods Protoc. 2022, 7, bpac033. [Google Scholar]
  13. Ahmed, A.; DeWitt, M.E.; Dantuluri, K.L.; Castri, P.; Buahin, A.; LaGarde, W.H.; Weintraub, W.S.; Rossman, W.; Santos, R.P.; Gibbs, M.; et al. Characterisation of infection-induced SARS-CoV-2 seroprevalence amongst children and adolescents in North Carolina. Epidemiol. Infect. 2023, 151, e63. [Google Scholar] [CrossRef]
  14. United States Food & Drug Administration. FDA. FDA; 2021. FDA Authorizes Pfizer-BioNTech COVID-19 Vaccine for Emergency Use in Children 5 Through 11 Years of Age. Available online: https://www.fda.gov/news-events/press-announcements/fda-authorizes-pfizer-biontech-covid-19-vaccine-emergency-use-children-5-through-11-years-age (accessed on 20 September 2023).
  15. United States Food & Drug Administration. FDA. FDA; 2021. Coronavirus (COVID-19) Update: FDA Authorizes Pfizer-BioNTech COVID-19 Vaccine for Emergency Use in Adolescents in Another Important Action in Fight Against Pandemic. Available online: https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-authorizes-pfizer-biontech-covid-19-vaccine-emergency-use (accessed on 20 September 2023).
  16. Hobbs, C.V.; Drobeniuc, J.; Kittle, T.; Williams, J.; Byers, P.; Satheshkumar, P.S.; Inagaki, K.; Stephenson, M.; Kim, S.S.; Patel, M.M.; et al. Estimated SARS-CoV-2 Seroprevalence Among Persons Aged <18 Years—Mississippi, May–September 2020. MMWR Morb. Mortal. Wkly. Rep. 2021, 70, 312–315. [Google Scholar] [PubMed]
  17. Smith, B.K.; Janowski, A.B.; Fremont, A.C.; Adams, L.J.; Dai, Y.N.; Farnsworth, C.W.; Gronowski, A.M.; Roper, S.M.; Wang, D.; Fremont, D.H. Progression of SARS-CoV-2 Seroprevalence in St. Louis, Missouri, through January 2021. mSphere 2021, 6, e0045021. [Google Scholar] [CrossRef]
  18. Couture, A.; Lyons, B.C.; Mehrotra, M.L.; Sosa, L.; Ezike, N.; Ahmed, F.S.; Brown, C.M.; Yendell, S.; A Azzam, I.; Katić, B.J.; et al. Severe Acute Respiratory Syndrome Coronavirus 2 Seroprevalence and Reported Coronavirus Disease 2019 Cases in US Children, August 2020–May 2021. Open Forum Infect. Dis. 2022, 9, ofac044. [Google Scholar] [CrossRef] [PubMed]
  19. Ulyte, A.; Radtke, T.; Abela, I.A.; Haile, S.R.; Berger, C.; Huber, M.; Schanz, M.; Schwarzmueller, M.; Trkola, A.; Fehr, J.; et al. Clustering and longitudinal change in SARS-CoV-2 seroprevalence in school children in the canton of Zurich, Switzerland: Prospective cohort study of 55 schools. BMJ 2021, 372, n616. [Google Scholar] [CrossRef]
  20. Zinszer, K.; McKinnon, B.; Bourque, N.; Pierce, L.; Saucier, A.; Otis, A.; Cheriet, I.; Papenburg, J.; Hamelin, M.; Charland, K.; et al. Seroprevalence of SARS-CoV-2 Antibodies Among Children in School and Day Care in Montreal, Canada. JAMA Netw. Open 2021, 4, e2135975. [Google Scholar] [CrossRef]
  21. DeWitt, M.E.; Tjaden, A.H.; Herrington, D.; Schieffelin, J.; Gibbs, M.; Weintraub, W.S.; Cheriet, I.; Papenburg, J.; Hamelin, M.; Charland, K.; et al. COVID-19 Symptoms by Variant Period in the North Carolina COVID-19 Community Research Partnership, North Carolina, USA. Emerg. Infect. Dis. 2023, 29, 207–211. [Google Scholar] [CrossRef]
  22. Machado, M.B.; Fajardo, T.C.G.; de Oliveira, L.B.; de Quadros, A.C., Jr.; Catalan, D.T.; Piovesan, K.C.; Garcia, M.E.D.D.; da Silva, M.F.; Dezena, R.d.C.d.A.B.; Passos, S.D. Mild and Asymptomatic Coronavirus Disease in Children, Adolescents, and Household Contacts and Prolonged Viral Excretion. Int. J. Microbiol. 2022, 2022, e5625104. [Google Scholar] [CrossRef] [PubMed]
  23. Allan-Blitz, L.T.; Hertlein, F.; Klausner, J.D. Prevalence of Asymptomatic Severe Acute Respiratory Syndrome Coronavirus 2 Infection Among Youth. Pediatr. Infect. Dis. J. 2021, 40, e132–e133. [Google Scholar] [CrossRef]
  24. Kugeler, K.J.; Podewils, L.J.; Alden, N.B.; Burket, T.L.; Kawasaki, B.; Biggerstaff, B.J.; Biggs, H.M.; Zacks, R.; Foster, M.A.; Lim, T.; et al. Assessment of SARS-CoV-2 Seroprevalence by Community Survey and Residual Specimens, Denver, Colorado, July–August 2020. Public Health Rep. 2022, 137, 128–136. [Google Scholar] [CrossRef]
  25. Northstone, K.; Smith, D.; Bowring, C.; Hill, A.; Hobbs, R.; Wells, N.; Biggs, H.M.; Zacks, R.; Foster, M.A.; Lim, T.; et al. The Avon Longitudinal Study of Parents and Children—A resource for COVID-19 research: Home-based antibody testing results, October 2020. An emphasis on self-screening at a population level. Wellcome Open Res. 2021, 6, 34. [Google Scholar] [CrossRef]
  26. Peeling, R.W.; Wedderburn, C.J.; Garcia, P.J.; Boeras, D.; Fongwen, N.; Nkengasong, J.; Sall, A.; Tanuri, A.; Heymann, D.L. Serology testing in the COVID-19 pandemic response. Lancet Infect. Dis. 2020, 20, e245–e249. [Google Scholar] [CrossRef] [PubMed]
  27. Ciccone, E.J.; Conserve, D.F.; Dave, G.; Hornik, C.P.; Kuhn, M.L.; Herling, J.L.; Song, M.; Alston, S.; Singler, L.; Schmidt, M.D.; et al. At-home testing to mitigate community transmission of SARS-CoV-2: Protocol for a public health intervention with a nested prospective cohort study. BMC Public Health 2021, 21, 2209. [Google Scholar] [CrossRef] [PubMed]
  28. Barton, H.J.; Werner, N.E.; Morgen, M.; DeMuri, G.P.; Kelly, M.M.; Wald, E.R.; Warner, G.; Katz, B.; Coller, R.J. Task Analysis of In-Home SARS-CoV-2 Rapid Antigen Testing by Families. Pediatrics 2022, 150, e2022056681. [Google Scholar] [CrossRef]
  29. Israel, B.A.; Schulz, A.J.; Parker, E.A.; Becker, A.B. Review of Community-Based Research: Assessing Partnership Approaches to Improve Public Health. Annu. Rev. Public Health 1998, 19, 173–202. [Google Scholar] [CrossRef]
  30. Hoschler, K.; Ijaz, S.; Andrews, N.; Ho, S.; Dicks, S.; Jegatheesan, K.; Poh, J.; Warrener, L.; Kankeyan, T.; Baawuah, F.; et al. SARS Antibody Testing in Children: Development of Oral Fluid Assays for IgG Measurements. Microbiol. Spectr. 2022, 10, e0078621. [Google Scholar] [CrossRef]
  31. Heinzel, C.; Pinilla, Y.T.; Elsner, K.; Friessinger, E.; Mordmüller, B.; Kremsner, P.G.; Held, J.; Fendel, R.; Kreidenweiss, A. Non-Invasive Antibody Assessment in Saliva to Determine SARS-CoV-2 Exposure in Young Children. Front. Immunol. 2021, 12, 753435. [Google Scholar] [CrossRef]
  32. Dantuluri, K.L.; Rossman, W.; Lu, L.C.; Dunn, C.O.; Harris, A.M.; Hetherington, T.C.; Priem, J.S.; Ahmed, A.; The COVID-19 Community Research Partnership. Improving Minority Representation Through COVID-19 Community Research Partnership. Infect Dis Diagn Treat [Internet]. 12 January 2023. Available online: https://www.gavinpublishers.com/article/view/improving-minority-representation-through-covid-19-community-research-partnership (accessed on 14 June 2023).
  33. Tjaden, A.H.; Edelstein, S.L.; Ahmed, N.; Calamari, L.; Dantuluri, K.L.; Gibbs, M.; Hinkelman, A.; Mongraw-Chaffin, M.; Sanders, J.W.; Saydah, S.; et al. Association Between COVID-19 and Consistent Mask Wearing During Contact with Others Outside the Household—A Nested Case–Control Analysis, November 2020–October 2021. Influenza Other Respir Viruses 2023, 17, e13080. Available online: https://onlinelibrary.wiley.com/doi/10.1111/irv.13080 (accessed on 10 June 2023). [CrossRef]
  34. Fox, T.; Geppert, J.; Dinnes, J.; Scandrett, K.; Bigio, J.; Sulis, G.; Hettiarachchi, D.; Mathangasinghe, Y.; Weeratunga, P.; Wickramasinghe, D.; et al. Antibody Tests for Identification of Current and Past Infection with SARS-CoV-2—Fox, T—2022|Cochrane Library. Available online: https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD013652.pub2/full (accessed on 24 April 2025).
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Figure 1. Study participation and retention rates of daily symptom survey. Participation represented by the seven-day rolling average number of completed surveys (indicated by the black line; left y-axis) and serology test completion by result (indicated by the stacked bar chart; right y-axis).
Figure 1. Study participation and retention rates of daily symptom survey. Participation represented by the seven-day rolling average number of completed surveys (indicated by the black line; left y-axis) and serology test completion by result (indicated by the stacked bar chart; right y-axis).
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Figure 2. Cumulative incidence of infection-induced SARS-CoV-2 antibodies by age group.
Figure 2. Cumulative incidence of infection-induced SARS-CoV-2 antibodies by age group.
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Figure 3. Differences in symptoms among participants with evidence of infection-induced SARS-CoV-2 antibodies compared with those without evidence of SARS-CoV-2 antibodies by symptom reported, 95% credible intervals (A). Odds of having infection-induced antibodies (seropositive) by symptom group (B). Proportion of participants with evidence of infection-induced antibodies by age group who reported symptoms in the 30 days prior to seroconversion (C).
Figure 3. Differences in symptoms among participants with evidence of infection-induced SARS-CoV-2 antibodies compared with those without evidence of SARS-CoV-2 antibodies by symptom reported, 95% credible intervals (A). Odds of having infection-induced antibodies (seropositive) by symptom group (B). Proportion of participants with evidence of infection-induced antibodies by age group who reported symptoms in the 30 days prior to seroconversion (C).
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Table 1. Baseline demographic and study engagement characteristics of participants consenting to but not completing any serology testing compared to those of participants completing at least one serology test.
Table 1. Baseline demographic and study engagement characteristics of participants consenting to but not completing any serology testing compared to those of participants completing at least one serology test.
CharacteristicDid Not Complete Serology Tests,
n = 1568 1		Completed at Least One Valid Serology Test,
n = 1058 1		p-Value 2
Age (Years) <0.001
2–4304 (19%)146 (14%)
5–11703 (45%)495 (47%)
12–17561 (36%)417 (39%)
Sex 0.5
Female619 (50%)537 (51%)
Male628 (50%)516 (49%)
Race 0.6
Asian48 (4.2%)34 (3.3%)
Black or African American86 (7.5%)71 (6.9%)
Other48 (4.2%)48 (4.7%)
White964 (84%)873 (85%)
Ethnicity 0.6
Hispanic or Latino75 (6.4%)62 (5.9%)
Not Hispanic/Latino1060 (90%)956 (91%)
Unknown39 (3.3%)28 (2.7%)
Number of Comorbidities 0.044
None825 (76%)760 (74%)
One220 (20%)211 (21%)
Two or more36 (3.3%)57 (5.5%)
Site 0.8
Atrium Health1088 (69%)748 (71%)
Wake Forest Baptist Health402 (26%)261 (25%)
Wake Med Health78 (5.0%)49 (4.6%)
Rurality 0.046
Rural226 (14%)119 (11%)
Suburban527 (34%)384 (36%)
Urban815 (52%)555 (52%)
Participated in daily surveys1141 (73%)1047 (99%)<0.001
Received at least 1 dose of vaccine468 (30%)644 (61%)<0.001
Received 2 doses of vaccine411 (26%)593 (56%)<0.001
1 n (%); Median (IQR); 2 Pearson’s Chi-squared test, Wilcoxon rank sum test.
Table 2. Comparison of children with evidence of infection-induced antibodies to SARS-CoV-2 with those without infection-induced antibodies.
Table 2. Comparison of children with evidence of infection-induced antibodies to SARS-CoV-2 with those without infection-induced antibodies.
IncidenceOdds
CharacteristicOverall,
n = 1058 1		Infection Induced Antibodies,
n = 195		No Infection induced Antibodies,
n = 863		OR (95% CI) 2p-Value
Age (Years), n (%)
2–4146 (14)25 (13)121 (14)—
5–11495 (47)76 (39)419 (49)0.88 (0.54 to 1.46)0.61
12–17417 (39)94 (48)323 (37)1.41 (0.88 to 2.33)0.17
Sex (Female), n (%)537 (51)103 (53)434 (51)1.11 (0.81 to 1.52)0.52
Race, n (%)
White873 (83)156 (80)717 (83)—
Black or African American71 (6.7)22 (11)49 (5.7)2.06 (1.19 to 3.47)0.008
Other114 (11)17 (8.7)97 (11)0.81 (0.45 to 1.35)0.44
Ethnicity, n (%)
Not Hispanic/Latino956 (90)172 (88)784 (91)—
Hispanic or Latino62 (5.9)15 (7.7)47 (5.4)1.45 (0.77 to 2.60)0.22
Not Specified or unknown40 (3.8)8 (4.1)32 (3.7)1.14 (0.48 to 2.40)0.75
Asthma (Yes), n (%)124 (12)17 (8.9)107 (13)0.67 (0.38 to 1.12)0.15
Other condition (Yes), n (%) 3176 (17)30 (15)146 (17)0.89 (0.57 to 1.35)0.60
No health conditions, n (%)763 (74)149 (78)614 (73)1.33 (0.92 to 1.95)0.14
Healthcare worker (Yes), n (%)87 (36)15 (38)72 (35)1.15 (0.56 to 2.32)0.69
Rural–urban classification, n (%)
Urban555 (52)90 (46)465 (54)—
Suburban384 (36)77 (39)307 (36)1.30 (0.92 to 1.81)0.13
Rural119 (11)28 (14)91 (11)1.59 (0.97 to 2.54)0.058
Person in the household, n (%)
<3138 (57)16 (41)122 (60)—
3–471 (29)13 (33)58 (28)1.71 (0.76 to 3.79)0.19
5 +35 (14)10 (26)25 (12)3.05 (1.21 to 7.45)0.015
Dwelling type, n (%)
Apartment6 (2.5)2 (5.1)4 (2.0)—
House238 (98)37 (95)201 (98)0.37 (0.07 to 2.72)0.26
Attended in-person class (Yes), n (%)185 (76)28 (72)157 (77)0.78 (0.37 to 1.74)0.52
School type, n (%)
Private85 (17)15 (14)70 (18)0.77 (0.41 to 1.38)0.40
Public414 (83)90 (86)324 (82)—
Attended daycare (Yes), n (%)69 (9.4)16 (11)53 (9.1)1.18 (0.64 to 2.09)0.58
Attended after school program (Yes), n (%)61 (8.3)10 (6.6)51 (8.8)0.74 (0.35 to 1.43)0.40
Previous COVID-19 diagnosis (Yes), n (%)  493 (8.9)44 (23)49 (5.7)4.85 (3.11 to 7.55)<0.001
Ever vaccinated (Yes), n (%)593 (56)46 (24)547 (63)
Self-reported infection within 30 days of recorded seroconversion, n (%)  510 (17)10 (29)0 (0)
1 n (%), percentage calculated based on total number of respondents for the characteristic; 2 OR = Odds Ratio, CI = Confidence Interval; 3 Other conditions included: chronic lung disease, congenital heart disease, connective tissue or autoimmune diseases, diabetes, epilepsy or seizures, HIV, high blood pressure, kidney disease, kidney disease requiring dialysis or transplant, leukemia, liver disease, neurological disease, prematurity (born before 37 weeks’ gestation), sickle cell, solid tumor (cancer), transplant (liver, kidney, heart, small bowel or gut), bone marrow transplant and “other health problem” with open-ended response allowed; 4 Self-reported history of COVID-19 provided at enrollment without specifics of testing; 5 Among those children who had a new seroconversion during the study, how many self-reported a COVID-19 case (with specifics of testing provided), where new seroconversion indicates a previous seronegative result prior to a seropositive result.
Table 3. Frequencies of SARS-CoV-2 serology tests completed.
Table 3. Frequencies of SARS-CoV-2 serology tests completed.
CharacteristicTotal Completed Serology Tests
n = 1058 a		Completed 1 Serology Tests
n = 373 a		Completed 2 Serology Tests
n = 216 a		Completed 3 Serology Tests
n = 172 a		Completed 4+ Serology Tests,
n = 297 a		p-Value b
Age group (years), n (%) 0.21
2–4146 (14%)58 (16%)32 (15%)21 (12%)35 (12%)
5–11495 (47%)186 (50%)89 (41%)78 (45%)142 (48%)
12–17417 (39%)129 (35%)95 (44%)73 (42%)120 (40%)
Sex, n (%) 0.20
Female537 (51%)176 (47%)109 (50%)99 (58%)153 (52%)
Male516 (49%)193 (52%)107 (50%)73 (42%)143 (48%)
Unknown5 (0.5%)4 (1.1%)0 (0%)0 (0%)1 (0.3%)
Race, n (%)
Black or African American71 (6.7%)32 (8.6%)15 (6.9%)11 (6.4%)13 (4.4%)0.22 c
White873 (83%)290 (78%)179 (83%)144 (84%)260 (88%)
Other102 (9.6%)41 (11%)21 (9.7%)17 (9.9%)23 (7.7%)
Unknown12 (1.1%)10 (2.7%)1 (0.5%)0 (0%)1 (0.3%)
Ethnicity, n (%) 0.36
Hispanic or Latino62 (5.9%)22 (5.9%)11 (5.1%)12 (7.0%)17 (5.7%)
Not Hispanic/Latino956 (90%)330 (88%)200 (93%)156 (91%)270 (91%)
Unknown40 (3.8%)21 (5.6%)5 (2.3%)4 (2.3%)10 (3.4%)
Rurality, n (%) 0.10
Rural119 (11%)38 (10%)25 (12%)26 (15%)30 (10%)
Suburban384 (36%)134 (36%)64 (30%)66 (38%)120 (40%)
Urban555 (52%)201 (54%)127 (59%)80 (47%)147 (49%)
Site, n (%) <0.001
Atrium Peds748 (71%)248 (66%)140 (65%)114 (66%)246 (83%)
Wake Med Peds49 (4.6%)14 (3.8%)12 (5.6%)12 (7.0%)11 (3.7%)
WFBH Peds261 (25%)111 (30%)64 (30%)46 (27%)40 (13%)
a n (%); b Pearson’s Chi-squared test, Fisher’s exact test; c Does not include those with an unknown characteristic.
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Ahmed, A.; DeWitt, M.E.; Dantuluri, K.L.; Buahin, A.; Castri, P.; Friedman-Klabanoff, D.; Gibbs, M.; Lagarde, W.H.; Santos, R.P.; Tapp, H.; et al. At-Home Testing to Characterize SARS-CoV-2 Seroprevalence Among Children and Adolescents. COVID 2025, 5, 68. https://doi.org/10.3390/covid5050068

AMA Style

Ahmed A, DeWitt ME, Dantuluri KL, Buahin A, Castri P, Friedman-Klabanoff D, Gibbs M, Lagarde WH, Santos RP, Tapp H, et al. At-Home Testing to Characterize SARS-CoV-2 Seroprevalence Among Children and Adolescents. COVID. 2025; 5(5):68. https://doi.org/10.3390/covid5050068

Chicago/Turabian Style

Ahmed, Amina, Michael E. DeWitt, Keerti L. Dantuluri, Asare Buahin, Paola Castri, DeAnna Friedman-Klabanoff, Michael Gibbs, William H. Lagarde, Roberto P. Santos, Hazel Tapp, and et al. 2025. "At-Home Testing to Characterize SARS-CoV-2 Seroprevalence Among Children and Adolescents" COVID 5, no. 5: 68. https://doi.org/10.3390/covid5050068

APA Style

Ahmed, A., DeWitt, M. E., Dantuluri, K. L., Buahin, A., Castri, P., Friedman-Klabanoff, D., Gibbs, M., Lagarde, W. H., Santos, R. P., Tapp, H., Uschner, D., & on behalf of the COVID-19 Community Research Partnership. (2025). At-Home Testing to Characterize SARS-CoV-2 Seroprevalence Among Children and Adolescents. COVID, 5(5), 68. https://doi.org/10.3390/covid5050068

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