Throughout the COVID-19 pandemic, schools were tasked with making decisions on the implementation of COVID-19 prevention and surveillance strategies. Early in the pandemic, the Centers for Disease and Control and Prevention (CDC) provided guidance on COVID-19 prevention in schools that underscored the importance of early detection and control of COVID-19 outbreaks in schools through school-based surveillance of COVID-19 cases.¹ However, the pandemic evolved with Delta and Omicron variant surges, overwhelming the monitoring capacity of both schools and health departments. Given changing population immunity due to vaccines and infections, the availability of COVID-19 therapeutics, and increasing availability of at-home COVID-19 tests, many health departments and schools shifted away from universal case investigation and contact-tracing models and adjusted case reporting based on local need and resources.²

Even before the COVID-19 pandemic, the capacity for school-based surveillance of infectious diseases had been tested, often during outbreaks of infectious diseases such as norovirus.³ Beyond traditional measures of infectious disease burden, such as cases and hospitalizations, use of school-based syndromic surveillance measures (eg, absenteeism, illness-related absenteeism) as potential signals of disease burden has increased, often under the direction of local health departments.^4–16 Yet, from a national perspective, we have a limited understanding of school-based data collection and monitoring of infectious disease outcomes (eg, case and absenteeism data) because such efforts may be constrained due to lack of partnerships, expertise, financial resources, and infrastructure; staff shortages; and competing priorities experienced by health departments and schools. In particular, health departments’ capacity to lead infectious disease surveillance in schools may be especially constrained when other congregate settings require more intensive efforts (eg, nursing homes). Understanding the availability—or lack thereof—of school-based infectious disease data during different time points in the COVID-19 pandemic can provide insight into the feasibility of school-based surveillance, as well as factors associated with capturing such data.

The CDC initiated the National School COVID-19 Prevention Study (NSCPS), a mixed-method study to evaluate the use and effectiveness of COVID-19 prevention strategies by kindergarten through 12th grade (K-12) public schools and monitor school-based COVID-19 trends during the pandemic.¹⁷ As part of the NSCPS, we recruited a nationally representative sample of public K-12 schools and their corresponding health departments to compile school-level COVID-19 case, quarantine, and student absenteeism data. In the present study, we wanted to determine the availability of school-based infectious disease surveillance data (eg, cases, quarantines, absences) during the 2021–2022 school year, document the varied approaches to collecting such data, and elucidate factors associated with missingness. Additionally, we aimed to provide insight into the feasibility of rapidly organizing a national data collection system to collect school-level COVID-19 case, quarantine, and student absenteeism data to inform federal decision-making during the pandemic and provide considerations for future data collections to respond effectively during public health emergencies.

Of the 1602 schools invited to participate in the NSCPS, 437 participated in wave 2 (27.3% response rate), 423 in wave 3 (26.4% response rate), 420 in wave 4 (26.2% response rate), and 412 in wave 5 (25.7% response rate). Most of the invited schools were elementary schools (n = 833; 52.0%), followed by middle (n = 411; 25.7%) and high (n = 358; 22.3%) schools (Table 2). Response rates did not vary substantially by examined school characteristics. Nonresponse to any NSCPS survey in waves 2–5 did not vary by school level, region, and school poverty level (Table 3). City schools had higher odds of not participating in any NSCPS survey in waves 2–5 compared with town schools (odds ratio [OR] = 1.46; 95% CI, 1.00–2.13).

Among the invited sample of schools, we did not have data for school-level COVID-19 cases, quarantines, and student absenteeism from most schools (missingness for outcomes ranged from 81.1% to 96.1%; Table 4). Almost one-third of participating schools had missing data on COVID-19 cases among students (31.6%) and COVID-19 cases among staff (30.7%) at wave 2 (Table 4). Almost 50% of participating schools had missing data on COVID-19 quarantines (48.1%), and more than 50% had missing data on all-cause absenteeism (61.6%) and illness-related absenteeism (78.0%) at wave 2. Missingness for all examined outcomes increased with each survey wave.

Among both the invited and participating samples, middle schools were more likely to have missing data for several outcomes compared with elementary and high schools (Tables 5 and 6). Schools in the South in the invited sample had higher odds of missingness for staff COVID-19 cases compared with schools in the Midwest (OR = 1.45; 95% CI, 1.04–2.01) and for all-cause absenteeism compared with schools in the Northeast (OR = 1.76; 95% CI, 1.07–2.89). Within the participating sample, schools in the Northeast had lower odds of missingness for all-cause absenteeism than schools in the West (OR = 0.45; 95% CI, 0.26–0.77) and schools in the Midwest (OR = 0.54; 95% CI, 0.33–0.90). Among participating schools, small schools had lower odds of missingness for COVID-19 cases among students and staff and higher odds of missingness for illness-related absenteeism compared with schools that were not small. Furthermore, the odds of missingness were greater for waves 3, 4, and 5 compared with wave 2 for all outcomes, although not all comparisons were statistically significant.

In analyses adjusted for school level, NCES locale, and school poverty, schools that had a system to report if a child has been exposed to COVID-19 had 0.59 (95% CI, 0.39–0.89) times the odds, and schools that had a system to report COVID-19 diagnoses had 0.57 (95% CI, 0.33–0.99) times the odds of having missing data on COVID-19 cases among staff (Table 7). Schools that reported tracking the COVID-19 vaccination status of staff had 1.66 (95% CI, 1.03–2.65) times the odds of having missing data for illness-related absenteeism. Schools that had a system to report positive at-home COVID-19 tests had 72% lower odds of having missing data on COVID-19 cases among students (OR = 0.28; 95% CI, 0.14–0.58), 74% lower odds of having missing data on COVID-19 cases among staff (OR = 0.26; 95% CI, 0.12–0.54), and 68% lower odds of having missing data on COVID-19 quarantines (OR = 0.32; 95% CI, 0.15–0.70). Schools that experienced teacher staff shortages had increased odds of having missingness for 4 of 5 examined outcomes, with adjusted ORs ranging from 1.43 to 1.61. Schools that experienced staff shortages for administrative staff had increased odds of having missingness for illness-related absenteeism (OR = 2.07; 95% CI, 1.00–4.26).

One local health department and 27 state health departments provided school-level COVID-19 case data for 729 of 1602 schools in the NSCPS sample for at least 1 month during the 2021–2022 school year. Among the full invited sample of NSCPS schools, we obtained school-level COVID-19 case data for less than 50% of schools for any given month from August 2021 to June 2022 (Figure 1). Among schools for which health departments provided COVID-19 case data during the 2021–2022 school year for at least 1 month, the availability of COVID-19 case data ranged from 37% to 67% for each month for all 3 populations—students, staff, and combined students and staff.

More than half (61%) of the health departments reported these data were obtained by linking COVID-19 case data to specific schools through case investigation and contact-tracing protocols, and 39% reported the data were obtained through collation of case data that schools obtained themselves through their own reporting systems (Table 8). Other mechanisms whereby school-level COVID-19 case data were obtained by health departments included linking school enrollment data with health department case data; outbreak investigations at schools; and COVID-19 screening testing programs at schools. Half of the health departments reported the school-level COVID-19 case data did not include at-home positive COVID-19 tests, and 36% of health departments reported they did include at-home positive COVID-19 tests; 14% of health departments were unsure. Almost one-third of health departments reported there were changes to school-level COVID-19 case data during the 2021–2022 school year, with changes including deprioritizing collection of school COVID-19 case data to prioritize settings at higher risk of severe COVID-19 outcomes and no longer requiring schools to report cases to health departments.

We found high levels of missingness for school-level COVID-19 case, quarantine, and student absenteeism data assessed in a national sample of public K-12 schools. Furthermore, missingness increased over time and varied by school characteristics. Missingness of these indicators differed by varying school characteristics depending on if we examined the full invited sample or the responding sample of schools, suggesting distinct predictors of survey nonresponse and item nonresponse. For the same sample of schools, based on data requests to state or local health departments in their jurisdiction, we found similarly high levels of missingness of school-level COVID-19 case data, as well as a myriad of approaches used by health departments to obtain school case data. Together, these findings provide insight into the feasibility of school-based infectious disease surveillance and help identify priorities for technical assistance and resources to improve such surveillance during public health emergencies and routine operations.

Missingness was highest for absenteeism measures, followed by COVID-19 quarantines, then COVID-19 cases among students, and then staff. Although absenteeism data may be considered a routine part of school-based data collection, collecting student absenteeism data during the pandemic posed new challenges: a record number of schools experienced high levels of chronic absenteeism, requirements were lacking for capturing attendance in virtual learning platforms, and high numbers of student quarantines and isolations potentially complicated absenteeism data.^26–28 Based on NSCPS survey data, greater than 50% of schools classified instances wherein a student missed school due to a COVID-19 isolation or quarantine as an excused absence; in contrast, approximately 33% of schools did not classify these instances as absences at all as long as students kept up with their schoolwork (data not shown). Inconsistencies in defining absences—both across schools but also within the same schools over time—may compromise the use of absenteeism data as tracking indicators for infectious disease surveillance. A systematic review of the use of school attendance as syndromic surveillance of infectious diseases found that absenteeism related to influenza-like illness performed the best, followed by illness-related absenteeism; all-cause absenteeism performed the least well.¹⁶ Those findings, in the context of our study documenting that a minority of schools nationally were capturing and were able to provide us all-cause and illness-related absenteeism data, highlight potential challenges in expanding absenteeism-related syndrome surveillance approaches that have been tested in a more localized and controlled context. Other studies have examined the use of wastewater surveillance at specific schools to monitor the spread of infectious diseases, and future studies should consider documenting the feasibility of such approaches across US schools.^29,30 The National Outbreak Reporting System specifically captures foodborne and waterborne disease outbreaks, certain fungal disease, and enteric disease outbreaks by setting type, including schools.³¹

School-based COVID-19 case data collection is also subject to its own challenges. There were shifts away from school-based contact tracing during surges of COVID-19.^32,33 Likewise, we found that teacher shortages were associated with missingness of school-level COVID-19 case data. Effective COVID-19 surveillance may be of particular importance during surges because such data can provide information about individual cases and close contacts of cases requiring intervention (eg, isolation) and health services (eg, testing). Building workforce capacity at schools and health departments and building strong partnerships between schools and health departments are important for future emergency preparedness. In addition, collaboration between schools and other sectors that engage school systems and may play an important role in school emergency preparedness, such as emergency management organizations, public transportation systems, after-school programs, and local health systems and health care providers, is also important.

Notably, the approaches to collecting school-level COVID-19 case data varied substantially. Health departments indicated a myriad of approaches to collecting these data. These included collating school-level COVID-19 case data that schools obtained through their own reporting systems; linking cases to specific schools through contact tracing and case investigation; linking COVID-19 case data with attendance records; and obtaining case data through school-based COVID-19 screening testing programs or outbreak investigations. The Council of State and Territorial Epidemiologists, which conducted a National Data Call in early 2021 to compile various school-associated COVID-19 case and cluster definitions nationwide, found substantial variation.³⁴ Given the data-generating mechanisms and COVID-19 case definitions used, the types of COVID-19 case data collected are expected to vary substantially.

The data quality issues we observed indicate the challenges of organizing data collection systems for school-level infectious disease outcome data to inform federal decision-making during the pandemic. The NSCPS surveys provided essential data on ongoing challenges for schools during a pandemic, use of COVID-19 prevention strategies, their impact on priority populations, and disparities in strategies used.^{23,24,35–37} However, as this study demonstrated, collecting school-based data about cases, absences, and quarantines was subject to high levels of missingness and unstandardized approaches across schools and health departments. In turn, this limited our ability to aggregate data to obtain a national perspective on COVID-19 burden in schools. Developing nationally standardized case definitions and school-based surveillance systems early in an outbreak may be essential for producing actionable data. Additionally, the average response rate for NSCPS waves 2–5 was 26.4%, similar to response rates of other school-based data collections at this time, such as the School Pulse Panel.³⁸ Low response rates limit the representativeness of these data and may reflect school administrators’ time constraints during the pandemic. Updating school-based systems used during regular operations (eg, learning management systems) to regularly collect and share data (eg, absenteeism) with local, state, and federal agencies and to be easily adaptable during emergencies to capture pertinent data via low-burden methods may be worthwhile.

This study has limitations. First, we used a school’s or health department’s ability to provide us with specific outcome data as a proxy for data availability, but it is possible other factors precluded data sharing, such as time constraints, competing responsibilities, concerns about disclosure, and lack of trust in the data. More research is needed to understand the specific obstacles schools may experience in sharing data. Second, data pertain to the 2021–2022 school year of the COVID-19 pandemic, and findings may not be generalizable to other periods during the pandemic and beyond. Furthermore, low survey response rates may compromise the representativeness of these findings. Third, for the surveys delivered to schools, principals were the primary respondent; their awareness of protocols and data specific to COVID-19 may be limited, especially if data tend to be managed at the district, or higher, level.

Despite these limitations, we provide an overview of the availability of school-level COVID-19 case, quarantine, and student absenteeism data using a national sample of K-12 public schools. We also identified potential barriers and facilitators for maintaining these data, and we outlined the considerable variability in approaches to collecting school-level COVID-19 data. Findings may inform planning school-based data collections during outbreaks or public health emergencies and may underscore the importance of developing clear objectives, standardized approaches and definitions, and investing in developing schools’ infectious disease surveillance infrastructure.