covid 19 case study for grade 5

How Hybrid Learning Is (and Is Not) Working During COVID-19: 6 Case Studies

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Most U.S. school districts are currently using “hybrid learning”—a mix of in-person and online instruction. The precise nature of that mix, though, varies greatly from school to school, based on factors including the local rate of COVID-19 transmission, the availability of funds to support new instructional approaches, and the willingness of students and staff to return to buildings.

Many students chose to learn entirely in-person or entirely online this school year. Others are spending a couple days a week in person and the rest at home. Some schools have set aside the bulk of slots for in-person instruction for vulnerable groups like students with special needs, English-language learners, and students experiencing homelessness.

These approaches aren’t static. Increases in COVID-19 spread have forced some schools in hybrid mode to revert back to full-time remote learning, while others started out fully remote and are now slowly transitioning more students to some in-person instruction.

Close to two-thirds of district leaders said their school systems are doing hybrid learning, according to an Education Week Research Center survey last month .

As with almost everything schools are doing during the pandemic, hybrid learning has inspired a wide range of reactions. Many parents and students are grateful schools are finding creative ways to bring their children back to school buildings while taking precautions against COVID-19. Others have protested schools’ reluctance to fully resume in-person instruction or expressed confusion over complex school plans that seem to be constantly changing. Some teachers find the new demands of hybrid instruction overwhelming, while others are more eager to adapt.

“Hybrid learning can be a best of both worlds, or a worst of both worlds reality,” said Bree Dusseault, practitioner-in-residence at the University of Washington’s Center for Reinventing Public Education, which has been surveying schools throughout the pandemic.

In the best-case scenario, schools can keep students and staff safe while providing them with valuable in-person instruction that gives them the tools to do meaningful schoolwork at home. At worst, teachers are forced to cut corners on instruction, schools struggle to transition students seamlessly from in-person to remote and vice versa, and students who are learning at home get left behind compared with students who choose to spend at least some time in person.

That last possibility threatens to further widen equity gaps along racial lines. In an EdWeek survey this fall , Latino, Black, and Asian parents were more likely than white parents to report their children would engage in full-time remote learning.

Pulling off an instructional approach that’s completely new to most U.S. schools during a pandemic is no easy feat, either. The challenges partially come from a lack of adequate resources: Congress has yet to follow through on plans for a second multi-billion-dollar stimulus package for education, and school budgets are increasingly stretched thin as the pandemic takes a toll on state and local finances.

At Scofield Magnet Middle School in Stamford, Conn., students have chosen either full-time remote learning or a hybrid model with in-person classes a few days a week and remote instruction for the rest. Teachers are not live-streaming classwork to any students who are learning at home. Placing cameras in classrooms was difficult, and connectivity issues were common for the school’s students, half of whom are eligible for free and reduced-price meals.

“If you have two or three kids in a home and you have them all logged in live-streaming all day, that’s going to eat up your data pretty quick,” said Scott Clayton, the school’s principal.

The trickiest part, according to Clayton, has been getting students to complete assignments at home, where they might have other responsibilities like child care or a part-time job.

Many schools also have struggled to balance investments in personal protective equipment and other safety precautions for in-person instruction with the technology and professional development necessary to reach students who will be learning at home part- or full-time, Dusseault said.

She recommends schools actively survey parents and students, and try to structure classes to make the most of students’ time either in person or at home, in whatever hybrid configuration they choose.

“They have to be putting resources into everything that it takes to result in a quality classroom experience: the materials, the training, the curriculum,” she said. (For more on how to do this work, visit Education Week’s guides to balancing in-person and remote instruction and pivoting back to full-time remote learning if necessary .)

The ongoing chaos of the pandemic sometimes obscures the lessons schools are learning and the strategies they’re employing to overcome steep challenges. Education Week talked to educators from school districts across the country about how they developed their hybrid learning models, how they’re working so far, and what they have planned for the months ahead. Here is a look at hybrid models in six school districts and the challenges of making those approaches work.

VICTORIA INDEPENDENT SCHOOL DISTRICT, Texas

Enrollment: 14,000

The Model: Students chose at the beginning of the year from two options: Attend school in person five days a week, or attend school virtually five days a week. Teachers’ classes are a mix of in-person and virtual students.

The Challenges: Jennifer Atkins, a 7th grade English teacher at Howell Middle School, typically enjoys walking around her classroom to engage students. Social distancing and masks make that teaching style virtually impossible.

She’s also had to deal with the ongoing evolution of the composition of her classes. When school started, roughly half her students were online. But as parents have grown more comfortable with sending students back to school, that proportion has shifted—roughly 90 of her students attend in person, and 50 are at home.

“I have the same kids, the same roster, but now I’ve got a new group that’s coming face to face that I haven’t met in person,” Atkins said. “They have been away from some of their friends for so long. It’s interesting to see how the class dynamic changes.”

Atkins posts textbook PDFs online because some students don’t have the book at home, even though the school set up times for parents to pick up the books. Grading takes longer because she has to look at some hard copies and then log in online for the rest.

Howell students aren’t required to keep their cameras on during videoconference instruction, so Atkins worries that some students may have logged in at the beginning but aren’t actually paying attention. “Without being here and constantly reminded to stay on task, it is probably enticing to log into the meeting and then just walk away,” she said.

The Benefits: Atkins has been able to use technology tools to keep better track of which students are struggling. If they don’t open an assignment, for instance, “something’s got to be wrong,” and she has a tangible record of the student’s progress, she said.

Hybrid learning has also forced her to consider more innovative use of technology in her teaching. A handful of teachers were offered interactive whiteboards that students can access from their desks, and Atkins accepted. Prior to COVID-19, she might have resisted a big change like this because she saw it as unnecessary, but the rising use of technology as a teaching tool has made her think differently.

A Small Victory: To help students at home hear her voice better through the mask, Atkins logs into the virtual meeting on her laptop and her smartphone, and talks into the microphone on her phone, addressing the remote and in-person students simultaneously, while using a clicker to scroll through PowerPoint slides on the computer.

The Takeaway: “It is nothing short of exhausting,” Atkins said. “It’s basically like teaching two different classes at the same time in one class period.”

SANTA FE PUBLIC SCHOOLS, New Mexico

Enrollment: 13,000

The Model: The district is gradually bringing students into school buildings based on the number of teachers who are willing to return and the amount of space in classrooms to allow for adequate social distancing. Special education students and English-language learners are prioritized for in-person instruction, and students who eventually want to go back to face-to-face instruction are placed with the teachers who are teaching from the school building.

The Challenges: Managing in-person and virtual instruction simultaneously requires more digital devices than many teachers have in their classrooms, said Tom Ryan, chief information and strategy officer for the district. Ideally, they need one for the lesson, one for seeing the students’ faces, and one to monitor what students are doing on their school-issued devices. Cameras that pivot when a teacher moves are also ideal to prevent teachers from constantly exiting the frame when they move around.

Meanwhile, the digital divide remains a significant barrier for equitable remote instruction. Some students attend day-care facilities with inadequate internet connections for videoconferencing. Other students have school-provided hotspots that may not be sufficient for the amount of strain remote learning puts on the connection. Efforts to determine the minimum bandwidth necessary for what’s required of students learning at home are still underway, Ryan said.

The Benefits: Teachers who wanted to return to classrooms are eager to serve as test cases for how in-person instruction can work during these unprecedented times, said Ryan. Giving teachers the option to stay home engenders more goodwill and prevents people with underlying health conditions from having to choose between their job and their safety.

So far, Ryan’s team has found teachers need a microphone to amplify their voices through their masks, and that simply replicating face-to-face instruction while livestreaming to students may not be as effective as offering online students differently structured activities from their in-person counterparts. Younger students and English-language learners are particularly likely to struggle when they can’t see a teacher’s mouth movements, Ryan said.

A Small Victory: Ryan’s daughter, a 5th grade teacher in the district, said she’s had more robust contact with parents than ever before. One student learning remotely in her class was constantly disrupting the class, pulling out inappropriate household objects, and sleeping on camera. After communicating with his parents, Ryan’s daughter decided to work with him individually after school hours, when his parents could be there by his side.

“I’m not saying I recommend this for all the teachers,” Ryan said. But “there are options that are available now that weren’t available last year.”

The Takeaway: “This isn’t a comparison between online versus face to face. This is between having nothing at all or something that is still engaging the kids and instruction can happen,” Ryan said. “Some are very successful and other kids are struggling.”

MARSHALL PUBLIC SCHOOLS, Mich.

Enrollment: 1,000

The Model: Elementary school students attend school in person four days a week, and middle and high school students attend school in person three days a week. In both cases, students are split into five groups, with each one having their remote learning on a different day of the week. The district tried to ensure that students who live in the same household have the same remote learning day. A handful of English-language learners, students with special needs, and newcomers to the district attend school in person every day. And some students opted to learn at home full-time for the school year.

The Challenges: “I would say our teachers are very overwhelmed,” said Beth Ritter, the district’s director of teaching and learning. “I’m not going to sugarcoat it.”

Each day, teachers have some students who are missing, which means it’s hard to keep all students on the same page. The students who are at home full time could easily get lost in the shuffle if teachers don’t put in extra work to engage them. And the quality of instruction this year needs to be higher than in the spring, when emergency remote teaching set everyone back.

“We have that experience to fall back on, but yet teachers are doing so much more this year,” Ritter said.

The Benefits: Hybrid learning has led to some positive changes. Meetings with multilingual families have gone a lot smoother for interpreters than usual. Rather than having to rush from room to room in the school building on a busy night of in-person conferences, all they have to do is open a new Microsoft Teams meeting to enter a video conversation. Families also appreciate that they don’t have to scramble for day-care options when they need to meet with their students’ teachers.

The hybrid model also forces teachers to be more intentional about how they structure their lessons. Elementary teachers now focus on reading, math, and social-emotional learning when students are in person, while home assignments build on what students learned in class.

A Small Victory: The district has appointed “assurance of mastery coaches” in elementary schools to check in with students during their remote learning day. Students get to have some interaction with the school even when they’re not in the building, and teachers get a small reprieve from yet another responsibility.

The Takeaway: With big changes like a heightened emphasis on social-emotional learning, school administrators need to communicate clearly and regularly with teachers and staff who will be implementing these changes. “We’ve always known it, but we’ve really found that this year,” Ritter said.

MILTON AREA SCHOOL DISTRICT, Pa.

Enrollment: 2,000

The Model: Students who chose a mix of in-person and remote instruction attend school buildings on Monday, Tuesday, Thursday, and Friday. Other students are doing 100 percent synchronous online instruction, or largely asynchronous instruction through the Milton Cyber Academy, which existed prior to the pandemic.

The Challenge: Students learning remotely—particularly the older ones—have been reluctant to turn on their cameras and keep their microphones unmuted. “K-5 is absolutely great—they are happy to see their classmates,” said Cathy Keegan, the district’s superintendent.

But some groups of older students have been very quiet, forcing teachers to get more creative with ensuring that they’re engaged. As of this month, the district is now specifying to students doing synchronous learning that they’re expected to be ready to speak and be seen when a teacher calls on them.

Some parents have fallen behind on notifying the school when their student won’t be attending at-home instruction that day. “We’re reinforcing that,” Keegan said.

The Benefits: Discipline rates in the district have been sharply down this year compared with previous years, Keegan said. “We genuinely believe—this is just a feeling—that kids are just happy to be back,” she said. Keeping them at home might have exacerbated the social isolation that has prompted many experts to urge schools to find safe ways to reopen.

A Small Victory: The president of the district’s teachers union told Keegan she and other teachers were tired of spending valuable time at the start of each class period asking students to type their name in the chat as a means of taking attendance. Keegan’s team helped advise her on integrating a discussion question into the Microsoft Teams platform that teachers can use to jump-start that day’s lesson and take attendance simultaneously.

The Takeaway: Efforts to transform an American education model that hasn’t been comprehensively updated in generations are happening at a breakneck pace, Keegan said. It’s painful and necessary work: “We may still be back here in 2022.”

NORTHERN LEHIGH SCHOOL DISTRICT, Pa.

Enrollment: 1,550

The Model: Students can attend in-person instruction up to two days a week: Monday and Tuesday for students with last names starting with the letters A through L, and Thursday and Friday for students with last names starting with M through Z. When students aren’t in school buildings, they’re learning at home, and Wednesdays are reserved for one-on-one check-ins for all students. Nearly three-quarters of students have chosen that option.

Slightly less than a fifth of students have chosen to learn from home all week. Some teachers have been assigned to work exclusively with fully online students.

Another less popular option (3 percent of the district’s students) is an existing online program offered by the school but managed by a third-party vendor; the district has revamped that asynchronous online program to include more direct involvement from a district teacher for students in grades K-8.

The Challenge: Teachers have had to adjust to a curriculum that must be more streamlined than usual. District leaders have urged teachers to consider which aspects of the learning material are essential and which could be optional. “We don’t want the curriculum to become a barrier to achieving success,” said Matthew Link, the district’s superintendent.

Early in the school year, many virtual students weren’t showing up or turning in work on time. The district’s professional development efforts have helped teachers get more creative in engaging students who are at home. Still, for certain students, “we need to double down on our efforts to make sure they’re active participants in the process,” Link said.

A Small Victory: District administrators are recognizing more than ever the value of teachers collaborating with each other, said Tania Stoker, the district’s assistant superintendent. One teacher might be using a tool another teacher doesn’t know about it; that kind of sharing is much more common now than it used to be.

The Takeaway: “Know that it’s OK that when you’re developing your plan and you think it’s done, it’s probably not. You’re going to go through different iterations constantly,” Link said. “Don’t feel bad if you have to change something that you thought was the answer.”

WALL TOWNSHIP SCHOOLS, N.J.

Enrollment: 3,400

The Model: Elementary students are either fully remote or fully in-person.

In grades 6-8, students attend school in person every other day (except Wednesday). Teachers have the same students in their class each day—the only thing that changes is which ones are in person and which ones are online. On Wednesdays, all students learn online.

In-person instruction is reserved for lessons on math, English, and social studies. Next semester, they’ll switch to science instruction. “We had been hopeful and optimistic that we would be in fully live instruction when we really need that practical application in lab,” but that may not be the case, said Lisa Gleason, the district’s director of curriculum and instruction.

The Challenge: Simply having a Chromebook doesn’t mean all the problems are solved. The district has found those devices can’t support all the resources and instructional technology programs that teachers use. “We had to pivot and start acquiring more PCs,” Gleason said. The district also was hit recently with a cyberattack that prompted some teachers to work from home until the problem was resolved.

Substitute teachers who think they’re capable of teaching online or comfortable with the health risks of teaching in person have been difficult to find, even as the number of teachers who need to take time off for legitimate reasons is higher than usual.

A Small Victory: Some teachers who are particularly worried about COVID-19 exposure can teach remotely from a separate area of the school building that students don’t visit. Some students in those teachers’ classes are attending school in person, but they are supervised by another teacher who is in a physical classroom with them, while others are at home, in the same Google Meet link as the remote teacher.

“We had really analyzed what our needs were back in late August,” Gleason said. “We were able to craft teachers’ schedules around that.”

The Takeaway: “When you put all your eggs in the basket of technology being the main vehicle for delivering instruction, even in the hybrid model, it takes away that stability of having a human being in the classroom who can deliver instruction no matter what,” Gleason said.

Alyson Klein, Assistant Editor contributed to this article. A version of this article appeared in the November 25, 2020 edition of Education Week as How Hybrid Learning Is (and Is Not) Working During COVID-19: 6 Case Studies

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The COVID-19 impact on reading achievement growth of Grade 3–5 students in a U.S. urban school district: variation across student characteristics and instructional modalities

• Published: 14 November 2022
• Volume 36 , pages 317–346, ( 2023 )

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• Jackie Eunjung Relyea   ORCID: orcid.org/0000-0002-7560-7136 1 ,
• Patrick Rich   ORCID: orcid.org/0000-0001-8268-0502 2 ,
• James S. Kim   ORCID: orcid.org/0000-0002-6415-5496 3 &
• Joshua B. Gilbert   ORCID: orcid.org/0000-0003-3496-2710 3  

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The current study aimed to explore the COVID-19 impact on reading achievement growth by Grade 3–5 students in a large urban school district in the U.S. and whether the impact differed by students’ demographic characteristics and instructional modality. Specifically, using administrative data from the school district, we investigated to what extent students made gains in reading during the 2020–2021 school year relative to the pre-COVID-19 typical school year in 2018–2019. We further examined whether the effects of students’ instructional modality on reading growth varied by demographic characteristics. Overall, students had lower average reading achievement gains over the 9-month 2020–2021 school year than the 2018–2019 school year with a learning loss effect size of 0.54, 0.27, and 0.28 standard deviation unit for Grade 3, 4, and 5, respectively. Substantially reduced reading gains were observed from Grade 3 students, students from high-poverty backgrounds, English learners, and students with disabilities. Additionally, findings indicate that among students with similar demographic characteristics, higher-achieving students tended to choose the fully remote instruction option, while lower-achieving students appeared to opt for in-person instruction at the beginning of the 2020–2021 school year. However, students who received in-person instruction most likely demonstrated continuous growth in reading over the school year, whereas initially higher-achieving students who received remote instruction showed stagnation or decline, particularly in the spring 2021 semester. Our findings support the notion that in-person schooling during the pandemic may serve as an equalizer for lower-achieving students, particularly from historically marginalized or vulnerable student populations.

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Introduction

Countries around the globe have faced unprecedented challenges in trying to support children’s learning amidst and beyond the COVID-19 era. The global pandemic outbreak has forced school closures to prevent the transmission of the coronavirus; this heavily disrupted children’s learning opportunities, methods, and resources. Recent studies that estimated the impact of pandemic-related school closures on student learning progress among U.S. students from multiple states (e.g., Domingue et al., 2022 ; Education Policy Innovation Collaborative [EPIC], 2021 ; Kuhfeld et al., 2022 ; Pier et al., 2021 ) indicate that children’s learning and academic development have suffered substantial setbacks during the pandemic school year when compared to a typical year unaffected by COVID-19. A report from the North Carolina Department of Public Instruction ( 2021 ) shows that the average proficiency rates in reading in spring 2021 declined significantly, ranging from 7.4% (Grade 8) to 25.5% (Grade 6), compared to spring 2019, which means that fewer students were proficient in reading during the pandemic than a non-pandemic school year. Educators, researchers, and policymakers have expressed mounting concerns that short-term learning loss could continue to accumulate, even after school re-opening, resulting in prolonged learning loss over years (e.g., Bailey et al., 2021 ; Kuhfeld et al., 2020b ).

Although the existing projections of educational outcomes provide information and insights on the potential overall impact on students’ academic performance, the scope of the pandemic’s impact on academic achievement levels and growth, particularly in reading, is currently preliminary and scant. There is a common belief that many children from historically marginalized or vulnerable groups are disproportionately affected by the COVID-19 school disruptions (Amplify Education, 2021 ), yet limited robust evidence exists to support our understanding of the extent to which learning losses or gains have occurred to at-risk student population groups in the United States. In a recent study, Kuhfeld et al. ( 2022 ) explored racial-ethnic group differences in reading gains during the 2020–2021 school year and revealed that Black students exhibited significantly less gains than White students, resulting in widening racial/ethnic inequality gaps over time. Although this study provides insight into how the pandemic has affected historically marginalized race/ethnic groups of students, further evidence is needed to determine the extent to which learning losses or gains have occurred within other vulnerable groups of students, such as English learners and students with disabilities, over the pandemic period.

Furthermore, as school and district leaders currently concentrate on making important decisions for pandemic-related recovery efforts, it is important to comprehensively understand for whom, and to what extent, reading losses or gains have occurred during the pandemic year within a school district to effectively target recovery strategies and resources to the students most in need. Although available evidence has documented variation in students’ reading levels and growth during the pandemic based on nationwide samples (e.g., Curriculum Associates, 2020 ; Kuhfeld et al., 2022 ; Renaissance Learning, 2020 ), inferences founded on analyses of national databases may mislead or be insufficient for a school district to accurately assess and target student learning needs. Recent analyses that include 16% of the U.S. public schools serving Grade 3–8 students reveal substantial between-district variability in students’ reading achievement and growth distributions during the pandemic (Goldhaber et al., 2022 ).

Therefore, assuming that national trends apply to a specific school district may lead to inaccurate inferences about the predicted impact of COVID-19 on student reading growth. More importantly, school district policymakers and educators can benefit from a case study of a single school district in learning about how district-level education policies in response to COVID-19 have impacted student reading outcomes and progress and whether the impacts have differed across student population groups. Specifically, some school districts in North Carolina offered students and parents/guardians the option of starting the fall 2020 semester with in-person or remote instruction, but it is unknown whether the impact of COVID-19 on students’ reading growth varied by the instructional modality that students experienced. Research evidence on the influence of instructional modality (e.g., in-person or remote instruction) on students’ reading outcomes and growth during the 2020–2021 school year will enlarge understanding of the association between instructional modality and reading performance, thus influencing a school district’s policy implementation and evaluation efforts.

In the current study, drawing upon administrative data from a large urban school district in North Carolina, we examined the extent of learning losses or gains in reading that occurred among Grade 3–5 students during the pandemic and how it varied across demographic subgroups of students [e.g., socioeconomic status (SES), language status, disability status] within the school district. Specifically, we estimated reading losses or gains by comparing two same-grade cohorts: (a) the COVID-19 cohort of students who experienced COVID-19-related school closures and distance learning during the 2020–2021 school year and (b) the pre-COVID-19 cohort of students in the 2018–2019 school year. With the COVID-19 cohort of students, we further explored reading growth over the 2020–2021 school year to gauge the extent to which reading growth varied as a function of the instructional modality that students received, and how their demographic characteristics interacted with instructional modality.

Reading achievement during COVID-19

Learning loss can be conceptualized as the discrepancy between students’ assessed academic knowledge and skills and grade-level curricular expectations due to extended gaps or discontinuities in students’ education progress (Pier et al., 2021 ). This concept has often been discussed with reference to summer slides or setbacks even before COVID-19. There is well-documented evidence that the absence of formal schooling over the summer months has resulted in significant learning losses or slowdowns (e.g., Alexander et al., 2001 ; Downey et al., 2004 ; Quinn et al., 2016 ). Cooper et al.’s ( 1996 ) meta-analysis of 39 studies concerning summer learning loss indicates that U.S. students, on average, make one month of academic progress during the three-month summer break. Likewise, Atteberry and McEachin ( 2021 ) have found that the average U.S. students in Grade 1–8 achieve nearly 25–34% of school-year learning gains during the summer months. They have also found much higher variability in summer learning gains across students than during school years, which can contribute to widening race/ethnicity and socioeconomic achievement disparities in later school years (von Hippel & Hamrock, 2019 ). The negative effect of the absence or interruption of all schooling on student learning appears to accumulate over time, which may lead to a substantial impact on academic performance and social and educational inequalities (Hernandez, 2011 ; Lloyd, 1978 ).

School lockdown for nearly one-third of the school year in the wake of COVID-19 can be considered an extended time of summer break for many students. There is consensus that the historic interrupted or unfinished schooling has largely exerted a negative influence on students’ academic achievement levels and growth to an even greater degree than during summer break. Recent estimates of the COVID-19 learning slide or loss, drawn upon the NWEA Measure of Academic Progress (MAP) Growth assessment from multiple states in the United States (Kuhfeld et al., 2022 ), show that Grade 3–8 students’ average reading scores at the end of the 2020–2021 school year were, on average, 0.06–0.11 standard deviations lower than those from the 2018–2019 school year, with the largest year-difference for Grade 4 and 5 students. Kuhfeld et al. ( 2022 ) also found that students exhibited a positive, but modest, growth in reading, yet variability in growth rates within a grade level in the 2020–2021 school year was larger than that observed in the 2018–2019 school year.

A serious concern is that these short-term learning slowdowns can continue to accumulate over time, which might lead to much larger and long-lasting consequences in that many students who fell behind during the pandemic would struggle to catch up. For example, current Grade 3 students could fall further behind pre-pandemic expectations, resulting in a loss of 1.5 years’ worth of learning by the time they reach Grade 10 (Kaffenberger, 2020 ).

The COVID-19 impact on students with diverse backgrounds

To obtain a more comprehensive understanding of the profound impact of the pandemic on students’ academic attainment and growth, it is critical to consider the heterogeneous effects on different groups of students. Despite a rapidly growing number of studies on the COVID-19 impact, only a few studies to date have rigorously explored the heterogeneity of the pandemic-induced learning losses or gains as a function of students’ demographic characteristics.

Recent evidence suggests that school closures and rapid transition to home-based virtual learning during the pandemic disproportionately affected elementary and middle school students, especially Black and Hispanic students and those in high-poverty schools (e.g., Goldhaber et al., 2022 ). However, the negative impact of the pandemic on reading achievement is likely more profound for students in the early elementary grades as compared to upper elementary and secondary grades (e.g., Amplify Education, 2021 ; Georgiou, 2021 ; Kuhfeld et al., 2022 ; Tomasik et al., 2020 ). This may be because younger children require more instructional support and systemic scaffolding and, at the COVID-19 outbreak, their competencies for independent and self-regulated online learning had not yet sufficiently developed.

Moreover, the COVID-19 slide has had a particularly harmful effect on the academic achievement of students from low-income backgrounds, in general, amplifying existing income-based achievement disparities and inequalities (e.g., Engzell et al., 2021 ; EPIC, 2021 ; Gore et al., 2021 ; Kuhfeld et al., 2022 ; Maldonado & De Witte, 2020 ). Children in lower SES environments have experienced reduced access to human and educational resources as well as unstable technology and internet connectivity during remote learning (UNESCO, 2021 ). The significant differences between SES groups in reading and literacy development observed over the summer months in the previous studies (e.g., Cooper et al., 1996 ; Downey et al., 2004 ; Entwisle et al., 1997 ; Kim & Quinn, 2013 ) can be exacerbated by the global health crisis, considering the prevailing inequalities and unequal access to learning opportunities. Building upon the existing evidence on the impact of the absence of traditional schooling on students’ achievement outcomes, the current study sought to further quantify how the pandemic-related reading gains or losses can vary across students from low, medium, and high SES backgrounds in the same school district.

The COVID-19 impact on learning outcomes of other historically marginalized and vulnerable subgroups of the student population, such as English learners and students with disabilities, is less well understood. Many English learners in U.S. schools are children from low-income immigrant families and under-resourced communities. Despite the rich and diverse linguistic and cultural resources such students bring to schools, they often experience inequitable and limited access to rigorous learning opportunities, especially in content areas (e.g., science, social studies; Callahan & Shifrer, 2016 ; Hopkins et al., 2015 ). COVID-19 has been projected to widen existing opportunity and achievement gaps between English learners and their English-fluent peers. With the sudden transition to distance learning in the wake of COVID-19, English learners were isolated in a home environment in which English is not spoken as a primary language. As a result, they may have experienced a lack of opportunities to develop English language skills through peer interaction and academic conversation; remote learning resources that were inadequate and not tailored to support English learners; parents’ limited capacities to support their children’s home-based learning; and coping with compounding stressors including anti-immigration sentiments and racism related to COVID-19 (Sugarman & Lazarín, 2020 ). Therefore, the COVID-19 disruptions had disproportionately detrimental impacts on English learners’ learning, yet it is unclear to what extent English learners’ English reading achievement and growth have been affected by the pandemic-related school closures.

Likewise, students with disabilities represent a uniquely vulnerable group of students who may have been significantly affected by COVID-19 school closures. The shift to remote instruction due to school lockdown can be immensely challenging for many students with disabilities who often experience difficulties with information processing or sustaining attention and focus to complete instructional tasks (Swanson, 1987 ). Particularly, for students with attention deficit hyperactivity disorder (ADHD) who experience inattention, hyperactivity, and impulsivity, their condition makes it hard to pay attention or control behaviors in an online learning environment (Lupas et al., 2021 ). Special education services or individualized education programs (IEP) were suspended to mitigate the spread of COVID-19. Regardless of how well an online learning curriculum was designed, reasonable accommodations and accessibility for students with disabilities and their needs were not sufficiently considered (Petretto et al., 2020 ). Consequently, most teachers faced many challenges in teaching remotely while trying to accommodate the unique needs of students with disabilities. Students with disabilities typically attain lower-than-average achievement scores (Gilmour et al., 2019 ) and the disability-based disparities in academic achievement may have been exacerbated by the pandemic.

How might instructional modality affect student reading outcomes?

Pre-pandemic studies on the effects of remote instruction on students’ academic achievement often reported a negative association between an online or distance learning mode and students’ academic achievement (e.g., Ahn & McEachin, 2017 ; Buddin & Zimmer, 2005 ; Center for Research on Education Outcomes [CREDO], 2015 ; Fitzpatrick et al., 2020 ). Despite the advent of new technologies that elevated students’ learning and engagement, research evidence shows that K-12 students who have attended online schooling are likely to perform lower on reading and mathematics assessments than their peers in traditional face-to-face learning environments (e.g., Ahn & McEachin, 2017 ; CREDO, 2015 ). In most virtual learning environments, students tend to participate in self-paced instruction with limited student–teacher and peer-to-peer interactions (Gill et al., 2015 ) such that students in online learning environments generally learn less than their peers who physically participate in active learning in their schools.

Even if internet access and the quality of remote learning improved over the pandemic, a lack of engagement and chronic absenteeism was more pronounced among students from high-poverty backgrounds, those who were English learners, and students with disabilities, when they were learning virtually (Patrick et al., 2021 ). In an online learning environment, students may need to work more independently through curriculum and lesson materials which increasingly requires self-regulatory learning and metacognitive skills to manage their learning (Azevedo, 2005 ). With limited scaffolding and guidance in distance settings, these skills may not be developed enough to foster learning for some students, particularly those younger and more vulnerable groups of children.

Emerging research evidence suggests that students who spent more in-person school days during the pandemic attained higher academic outcomes than peers who chose a full-distance learning option (e.g., Goldhaber et al., 2022 ; Halloran et al., 2021 ; Molnar, 2021 ; Tomasik et al., 2020 ). In the current study, we sought to examine the differential impact of instructional modality (i.e., in-person vs. remote instruction) on students’ reading growth rate over the pandemic school year. Figure  1 displays a conceptual framework of how types of instructional modality would affect students’ reading gains over time. It is expected that, among students with similar demographic characteristics, lower-achieving students are more likely to choose the in-person schooling option, while higher-achieving students tend to prefer the remote instruction option (National Center for Education Statistics, 2022 ). This may be because remote learning environments require high levels of independent and self-regulated learning skills to learn and access academic content with a limited amount of support from teachers and administrators, and these skills are more feasible for higher-achieving students than for lower-achieving students. However, we hypothesize that lower-achieving students would benefit from in-school learning experiences that can stimulate cognitive and social development, making greater reading gains than their higher-achieving peers who tended to participate in remote learning instruction during the pandemic. In-person schooling may thus offset inequalities in learning opportunities, and consequently, result in narrowing achievement differences to some degree.

Conceptual framework of how instructional modality affects reading achievement levels and growth rates during the 2020–2021 school year

This conceptualization aligns with the faucet theory (Entwisle et al., 1997 ) and an accumulating body of knowledge about seasonal learning patterns (e.g., Alexander et al., 2001 ; Downey et al., 2004 ). During the school year, the resource faucet is turned on for all children; as a result, children with varying economic backgrounds benefit nearly equally. However, when a school session ends or is canceled, the resource faucet is turned off, thereby creating inequalities in educational opportunities and widening achievement gaps between students from high-poverty and low-poverty backgrounds. In out-of-school learning environments, the accumulation of learning losses and achievement gaps due to school closures occurs more substantively among low-achieving students, students from high-poverty environments, or students from historically marginalized vulnerable groups who may have unequal access to resources both inside and outside schools. Existing research suggests that high-quality summer school programs can serve to prevent learning losses and mitigate educational inequalities (Borman et al., 2005 ; Cooper et al., 1996 ; Kim & Quinn, 2013 ). We hypothesize that under pandemic circumstances, in-person schooling may serve as an equalizer for lower-achieving students, particularly from historically marginalized or vulnerable student populations (Alexander et al., 2001 ; Downey et al., 2004 ; Raudenbush & Eschmann, 2015 ).

The current study

The current study aimed to assess the COVID-19 impact on reading achievement levels and growth rates of Grade 3–5 students in the U.S. and whether the impact differed by students’ demographic characteristics (i.e., SES, language status, disability status) and instructional modality (i.e., in-person, remote instruction). Although a growing number of studies have documented COVID-19 learning loss or gain phenomenon around the globe, there is limited evidence of quantifying differential impacts on reading achievement gains and growth. Focusing on demographic subgroups can provide insights into the heterogeneity of the pandemic impact on reading attainment and can inform reading instruction and intervention as school districts continue to address local learning recovery needs.

Using administrative data drawn from an urban school district in North Carolina, we investigated to what extent upper elementary grades students made gains in reading during the 2020–2021 school year relative to those students in the same grade level who did not experience the pandemic in the 2018–2019 school year. We were particularly interested in inter- and intra-group differences to determine the dynamics of the impact of COVID-19 school closures on reading achievement in Grade 3 to 5 to contextualize our findings with other state (e.g., Pier et al., 2021 ) and national (e.g., Goldhaber et al., 2022 ; Kuhfeld et al., 2022 ) analyses focusing on those upper elementary grades. Furthermore, we examined whether students’ instructional modality affected the rates of reading growth over the 2020–2021 school year, particularly by focusing on whether the effects of instructional modality on reading growth varied by students’ SES, language status, and disability status. In pursuing this endeavor, our goals were not only to contribute to the literature in the field of the COVID-19 impact analysis but also to offer insights to educators and school and district leaders that are grounded by district-specific administrative data and evidence. Two research questions that guide this study are as follows:

To what extent did Grade 3–5 students’ reading gains during the 2020–2021 school year vary by grade level, SES, language status, and disability status compared to the 2018–2019 school year?

Did the association between Grade 3–5 students’ instructional modality and reading growth rates during the 2020–2021 school year differ by SES, language status, and disability status?

Data source

This study used administrative data drawn from 180 elementary schools in an urban school district in North Carolina, USA, from the 2018–2019 and 2020–2021 school years. The primary data source for this study was the Measure of Academic Progress (MAP) Growth Reading assessment from the Northwest Evaluation Association (NWEA), a nationally normed, anonymous assessment database. We accessed the data based on a data-sharing agreement stemming from a research-practice partnership with the school district.

Analytic sample

The full analytic sample comprised 52,525 students from the two cohorts: 28,924 students from the pre- COVID-19 cohort (2018–2019) and 23,601 students from the COVID-19 cohort (2020–2021). Table 1 displays the demographic characteristics of the two-cohort samples by grade. The demographic characteristics of the two cohorts were similar across grade levels: 50% male, 34–36% Black, 26–28% White, 27–28% Hispanic, 7–9% Asian, 17–20% English learners, and 8–9% students with disabilities. The proportion of students from low SES neighborhoods (35–37%) was slightly higher than those of students from medium (30–32%) and high (29–31%) SES backgrounds.

Table 2 shows demographic characteristics of the COVID-19 cohort in 2020–2021 by a choice of instructional modality. Students and parents/guardians were given the option to select either in-person or fully remote instruction options for the 2020–2021 school year in summer 2020. Overall, 62% of students opted to receive in-person instruction, while 38% selected the full remote instruction option. Within-subgroup variability existed in instructional modality preference. Specifically, nearly 59% of students from low SES neighborhoods chose in-person instruction, whereas 57% and 71% of students from medium and high SES, respectively, opted for in-person instruction. Approximately 66% of English learners and 66% of students with disabilities opted to participate in in-person instruction.

• Reading achievement

The NWEA MAP Growth assessment on student reading achievement is a computer-adaptive test aligned to the Common Core and state standards and is designed to serve as a benchmarking assessment to monitor and analyze students’ progress and needs throughout the school year (NWEA, 2019 ). The MAP reading scores are calculated using the Rasch unit (RIT) vertical scale that places a student’s ability and item difficulty estimates on the same scale. This vertical scale allows for comparisons of students’ learning growth within and across grades over time. The MAP reading composite score is computed based on the four strands: foundational skills, language and writing, vocabulary usage and functions, and narrative and informational text comprehension. As an adaptive test, the MAP assessment was designed to initially provide a student with question items appropriate for the student’s grade level, and then adjust the difficulty of each item depending on the student’s responses to previous items. Although this computer-adaptive assessment was administered remotely for many students in the beginning of the 2020–2021 school year, the test mode (i.e., in-classroom vs. remote) did not compromise the test quality (Kuhfeld et al., 2020a ). Test–retest reliabilities, calculated by the vendor, range from .89 to .96 (NWEA, 2019 ). The concurrent validity estimates show that Grade 3–5 MAP reading scores are highly correlated ( r  = .79 to .80) with other U.S. state-specific assessments, including ACT Aspire, Partnership for Assessment of Readiness for College and Careers, and Smarter Balanced Assessment Consortium assessments) (NWEA, 2019 ). The MAP testing periods during a school year occurred in fall (late September), winter (late January), and spring (mid-April).

Student demographic characteristics

Three types of student demographic characteristics of interest were obtained from school district administrative data: SES, language status, and disability status. The SES variable had three categories—low, medium, and high SES—based on the census tract information. Students’ language status was to identify whether an individual was an English learner who came from households where a language other than English was primarily spoken. Disability status was to determine students with disabilities who received special education and related services under the Individuals with Disabilities Education Act according to an Individualized Education Program or other services plans.

• Instructional modality

Instructional modality was operationalized as the assignment of students to either an (a) in-person instruction option or (b) remote instruction option for the 2020–2021 school year when both options were offered to students and their parents/guardians in summer 2020. Students who opted into the in-person schooling option physically attended school face-to-face for at most 10 days in the fall 2020 semester (2 days per week between November 2 and December 14) and 48 days in the spring 2021 semester (2–4 days per week between February 15 and May 28). They participated in remote instruction at home throughout the remainder of the school year. By contrast, students who chose the remote instruction option exclusively received virtual instruction without physical school attendance throughout the 2020–2021 school year.

Data analysis

Research questions 1: reading gains and variability.

To address the first research question regarding Grade 3–5 students’ reading gains during the 2020–2021 school year and the variation across subgroups, we first obtained 9-month MAP reading gain scores for individual students in the COVID-19 cohort (2020–2021 school year) and pre-COVID-19 cohort (2018–2019 school year) by subtracting the score at the beginning of the school year (late September) from the score at the end of the school year (mid-April). To further contextualize how reading gains prior to the pandemic compared to reading gains during the pandemic for each grade level, we estimated the standardized difference (in 2018–2019 standard deviation units) between 2018–2019 and 2020–2021 means by grade level by standardizing the 9-month gains for 2020–2021 to the mean and standard deviation of the 9-month gains for 2018–2019. Then, we calculated the means and standard deviations of the gained scores by the subgroup samples (e.g., SES, language status, disability status) of the two cohorts. Subsequently, we estimated the percentage increase in means and standard deviations achieved by the COVID-19 cohort relative to the pre-COVID-19 cohort within the subgroups.

Research question 2: instructional modality difference in reading growth

To examine the effects of different instructional modality use, either in-person or fully remote instruction, on reading growth rates during the 2020–2021 school year, we employed a series of piecewise growth curve models (Singer & Willett, 2003 ). An initial inspection of the average MAP reading scores at the three assessment time points (i.e., beginning, middle, and end of the school year) (see Table 3 ) indicated that students’ reading progression patterns across the three-time points appeared to be nonlinear. We specified linear growth slopes for two separate intervals: (a) fall semester: between the beginning (fall 2020) and middle of the school year (winter 2021) and (b) spring semester: between the middle and end of the school year (spring 2021). Three-level piecewise growth curve models were specified (time nested within students within schools). The level 1 (within individual) model is expressed as follows:

where Y tij represents the MAP reading score at time t for student i in school j ; \(\uppi _{0ij}\) denotes the predicted score for student i in school j at fall 2020; and \(\uppi _{1ij}\) and \(\uppi _{2ij}\) refer to monthly learning rates for student ij over the fall 2020 and spring 2021 semesters, respectively. The error term, \({\upvarepsilon}_{{tij}}\) is assumed to be normally distributed with a mean of zero.

At level 2 (between individual), we included the instructional modality variable (i.e., REMOTE ) and demographic indicators such as SES (low SES vs. medium/high SES), language status [English learners (EL) vs. English-fluent students], and disability status [students with (SwD) vs. without disabilities] as a main-effect predictor of intercept (the beginning of the 2020–2021 school year) and growth rates over the fall and spring semesters. To examine the interaction effects between instructional modality and demographic characteristics on MAP reading level at intercept and growth rates in the fall and spring semesters, we additionally included a set of interaction terms. The level 3 model was specified to represent the variability among schools. The equations for level 2 and 3 are presented below:

Note that β 000 is the overall mean at the beginning of the 2020–2021 school year; β 010 denotes the initial difference between in-person and remote instruction students; β 110 and β 210 represent monthly reading growth rates over the fall and spring semester, respectively; β 150 , β 160 , and β 170 denote the interaction effects of instructional modality with subgroups ( Low SES , EL , and SwD , respectively) on the fall-semester growth rate, while β 250 , β 260 , and β 270 refer to the interaction effects on the spring-semester growth rate, controlling for the effects of covariates ( COV ; i.e., gender, race/ethnicity).

Research question 1: COVID-19 reading gains and variability

Table 4 shows means and standard deviations of the MAP reading achievement scores at the beginning and end of the school year for Grade 3–5 students in the 2018–2019 and 2020–2021 school year cohorts. Figure  2 displays the percentages of MAP reading achievement score gains of the 2020–2021 school year (or COVID-19) cohort relative to the 2018–2019 school year (or pre-COVID-19) cohort by student grade levels and demographic subgroups. Overall, the COVID-19 cohort achieved lower 9-month reading gains than the pre-COVID-19 cohort, with a learning loss effect size of 0.54, 0.27, and 0.28 standard deviation units for Grade 3, 4, and 5, respectively. Among the COVID-19 cohort students, reading losses were evident compared to the typical school year (i.e., 2018–2019), particularly for Grade 3 students. Overall, Grade 3 students in the COVID-19 cohort achieved 48% gains of the pre-COVID-19 cohort in reading, on average, whereas Grade 4 and 5 students achieved 65% and 58% gains, respectively. Moreover, there was much more variability in reading gains for the COVID-19 cohort, especially in the earlier grades. As shown in Fig.  3 , the standard deviation of reading scores of the COVID-19 cohort increased by 56%, 40%, and 29% for Grade 3, 4, and 5 students, respectively.

Percentages of Measure of Academic Progress (MAP) reading gains between beginning of year and end of year for the 2020–2021 school year cohort relative to the 2018–2019 school year cohort by student grade levels and demographic Characteristics. Note . SES socioeconomic status

Percentages of increase in Measure of Academic Progress (MAP) Reading variability for the 2020–2021 school year cohort relative to the 2018–2019 school year cohort by student grade levels and demographic characteristics. Note . SES socioeconomic status

We further examined relative reading gains and variability of the COVID-19 cohort within a grade level across subgroups. Among Grade 3 students, the COVID-19 cohort students from high SES backgrounds achieved 61% of pre-COVID-19 cohort reading gains, while students from low and medium SES backgrounds made 40% and 43% of the typical gains, respectively, during the pandemic. Moreover, low- and medium-SES students’ reading gains showed much greater variabilities (62% and 63% respectively) than high-SES students (38%). Likewise, Grade 4 students from high SES environments attained over 70% of typical reading gains whereas their peers from the low and medium SES groups made nearly 60% of typical gains. Reading gains variabilities for low- and medium-SES students (41% and 43% respectively) were slightly higher than that for high-SES students (34%). However, for Grade 5 students, conversely, low SES group ended the 2020–2021 school year with 63% of their prior-year reading gains compared to medium- and high-SES groups who made 52% and 58% of typical reading gains, respectively. The increase in variability for Grade 5 was smaller than that for Grade 3 and 4 students and consistent across SES groups (28–32%).

In terms of relative reading gains among English learners and English-fluent learners, Grade 3 and 4 English learners experience 41% and 60% of typical reading gains, respectively, lower than their English-fluent peers (49% and 66%, respectively). Notably, Grade 5 English learners showed 68% of typical gains with a small increase (16%) in variability, while English-fluent students made 54% of typical gains with a twice larger variability (32%) than their counterparts.

Finally, students with disabilities demonstrated much lower gains in reading than what would have been observed in normal conditions. Grade 3, 4, and 5 students with disabilities achieved only 18%, 28%, and 53%, respectively, of pre-COVID-19 reading gains, whereas students without disabilities made 50%, 68%, and 59% of typical gains for the respective grades. The increase in spread of reading scores was especially stark for Grade 3 and 4 students with disabilities (87% and 86%, respectively), compared to students without disabilities (53% and 34%, respectively).

Research question 2: association between instructional modality and reading growth rates by subgroups

Table 5 shows the results of the full piecewise growth curve models by grade level. Overall, across the Grade 3, 4, and 5 models, there was a statistically significant difference between in-person and remote instruction modality groups at the beginning of the 2020–2021 school (Grade 3: β 010  = 4.83, SE  = 0.59; Grade 4: β 010  = 3.47, SE  = 0.53; Grade 3: β 010  = 3.86, SE  = 0.50; ps  < .001), indicating that students who opted for remote instruction started the school year with higher MAP reading scores than their peers who chose in-person instruction. As depicted in Fig.  4 , during the fall semester, reading growth rates were not statistically significantly different between in-person and remote instruction groups across grade levels and subgroups ( ps  > .05), holding all else constant. However, variations in reading growth rates became apparent over the spring semester (between winter and spring 2021). Students who participated in remote instruction exhibited significantly lower growth rates than their peers who received in-person instruction during the spring semester (Grade 3: β 210  = − 0.51, SE  = 0.10; Grade 4: β 210  = − 0.55, SE  = 0.08; Grade 3: β 210  = − 0.56, SE  = 0.08; ps  < .001). To shed light on whether the association between instructional modality and reading growth rates varied by students’ demographic subgroups, we further examined the interactions between instructional modality and subgroup (i.e., SES, language status, disability status) in each grade level.

Piecewise growth curve trajectories of COVID-19 cohort students’ Measure of Academic Progress (MAP) reading by instructional modality (in-person or remote instruction) for A Grade 3, B Grade 4, and C Grade 5

The interaction between low SES and remote instruction was not statistically significant in predicting intercept (beginning of fall 2020) and growth rates over the fall and spring semesters ( ps  > .05). The interaction between English learner and remote instruction was not statistically significant in predicting intercept and growth rate in fall ( ps  > .05), but significantly predicted growth rate in spring (β 260  = − 0.30, SE  = 0.15, p  < .05). Likewise, the interaction between student with disabilities and remote instruction statistically significantly predicted growth rate in spring (β 270  = − 0.52, SE  = 0.23, p  < .05), but not intercept and growth rate in fall ( ps  > .05).

Figure  5 displays these significant differences in fitted growth trajectories in the spring semester. As shown in Fig.  5 A, both Grade 3 English-fluent students and English learners who participated in in-person instruction showed a steady increase in reading, while their peers who received fully remote instruction had a decrease in reading growth rate during the spring semester. By the end of the school year, English learners with in-person instruction narrowed the initial differences in reading with their English learners and English-fluent peers who received remote instruction. Additionally, among English-fluent students, the initial reading achievement difference between in-person and remote instruction groups narrowed at the end of the school year. In Fig.  5 B, a similar pattern of the closed gap between instructional modality groups was observed among students without disabilities. However, students with disabilities who received remote instruction exhibited a decline in reading over the spring semester, while students with disabilities with in-person instruction made very little reading growth in reading over time.

Piecewise growth curve trajectories of Grade 3 COVID-19 cohort students’ Measure of Academic Progress (MAP) reading by A language status and instructional modality and B disability status and instructional modality

The interaction between low SES and remote instruction was statistically significant in predicting intercept (β 050  = 3.67, SE  = 0.84, p  < .001) and growth rate in spring (β 250  = − 0.25, SE  = 0.11, p  < .05), but not in fall ( p  < .05). As shown in Fig.  6 A, there were substantial variations in reading levels at the outset and growth trajectories over the spring semester based on the interaction between low SES and remote instruction. Specifically, among students from low SES neighborhoods, those with remote instruction started the school year with a higher reading level than their peers with in-person instruction, yet their difference in reading became indistinguishable as the remote instruction group made slower progress, while the in-person group continued to grow over the spring semester. A similar pattern emerged between the in-person and remote instruction groups among students from medium/high SES backgrounds.

Piecewise growth curve trajectories of Grade 4 COVID-19 cohort students’ Measure of Academic Progress (MAP) reading by A socioeconomic status (SES) and instructional modality, B language status and instructional modality, and C disability status and instructional modality

The interaction between English learners and remote instruction was statistically significant in predicting reading growth rate in spring (β 260  = 0.39, SE  = 0.13, p  < .01), but not intercept and growth rate in fall ( ps  > .05). This significant difference in spring may be particularly attributable to English-fluent students, in which those with the in-person option made continuous growth, whereas those with the remote instruction option showed a slowdown (see Fig.  6 B). Notably, the initial and persistent reading difference that existed between English-fluent students with remote instruction and English learners with in-person instruction over the fall semester gradually diminished during the spring semester.

Similarly, the interaction between students with disabilities and remote instruction statistically significantly predicted growth rate only in spring (β 270  = − 0.31, SE  = 0.19, p  < .01). As displayed in Fig.  6 C, the pre-existing difference between the in-person and remote groups among students without disabilities disappeared by the end of spring as those who received in-person instruction continuously grew through the spring semester. However, both instructional modality groups among students with disabilities experienced negative growth in spring with their growth trajectories parallel to each other.

The interaction effect between low SES and remote instruction was marginally significant on intercept ( p  < .10) and statistically significant on growth rate only for the fall semester (β 150  = − 0.25, SE  = 0.09, p  < .01). Figure  7 A depicts that among Grade 5 students from low SES environments, those who opted for remote instruction started the fall semester with nearly 5 RIT higher than their peers who chose in-person instruction. However, the low-SES group students who participated in in-person instruction achieved a positive growth, while those who received fully remote instruction hardly showed any gains in reading. As a result, the gap identified in fall between the instruction modality groups vanished by the end of spring. A similar pattern was observed among students from medium/high SES backgrounds. Notably, in the group of students who decided to receive fully remote instruction, an initial difference between low and medium/high SES groups at the beginning of fall became slightly larger by the end of spring, whereas the SES-based difference within the in-person group remained persistent.

Piecewise growth curve trajectories of the Grade 5 COVID-19 cohort (2020–2021 school year) students’ Measure of Academic Progress (MAP) reading by A socioeconomic status (SES) and instructional modality and B language status and instructional modality

In addition, the interaction effect between language status and instructional modality was statistically significant on intercept (β 060  = 5.01, SE  = 0.99, p  < .001) but not growth rates ( ps  > .05). Notably, as shown in Fig.  7 B, among English learners, the initial difference between the in-person and remote instruction groups was nearly 6 RIT and this difference sustained throughout the fall semester. Yet, the difference narrowed by about half by the end of spring as the reading growth rate for English learners who participated in in-person instruction accelerated over the spring semester, while English learners who received remote instruction experienced a growth plateau during that time.

Drawing upon the school district administrative data, the present study explored Grade 3–5 students’ reading gains during the 2020–2021 school year and the association between instructional modality and reading growth rates, focusing on the variations across demographic characteristics. Previous analyses on the pandemic-related impact on student academic achievement and growth have focused on students’ racial and ethnic backgrounds (e.g., Kuhfeld et al., 2022 ) and poverty levels (e.g., Maldonado & De Witte, 2020 ; Pier et al., 2021 ) with limited attention being paid to English learners and students with disabilities. Two main findings emerged from the study. First, the COVID-19 cohort students’ reading achievement gains from the beginning to end of the 2020–2021 school year were lower than reading gains of the pre-COVID-19 cohort students in the 2018–2019 school year with substantially reduced gains for younger students, students from low SES backgrounds, English learners, and students with reading disabilities. Second, among students with similar demographic characteristics, higher-achieving students and their parents/guardians tended to choose the remote instruction option, while lower-achieving students appeared to opt for in-person instruction at the beginning of the 2020–2021 school year. However, those students who received in-person instruction most likely demonstrated positive growth continuously over the school year, whereas initially higher-achieving students who received remote instruction showed stagnation or decline in reading in the spring semester. We found substantial variation in reading levels and growth rates as a function of the interaction between instructional modality and students’ demographic subgroups.

COVID-19 reading gains and variability

With the current data from an urban school district in the United States, we provide evidence that Grade 3, 4, and 5 students ended the 2020–2021 school year with 0.54, 0.27, and 0.28 standard deviations behind the 2018–2019 school year reading, suggesting that students’ reading achievement levels declined during the pandemic school closures. The degree of reading loss experienced by students in the urban school district in North Carolina over the 9-month school year was larger than the 12-month-based estimates of learning loss obtained from the results from multiple states in the U.S. (cf. Kuhfeld et al., 2022 ). Furthermore, consistent with recent evidence on COVID-19 learning loss by grade level (e.g., Goldhaber et al., 2022 ; Kuhfeld et al., 2022 ; Tomasik et al., 2020 ), our cross-cohort comparisons of reading gains in the pandemic (2020–2021) and typical (2018–2019) school year suggest that younger students lost substantially more ground in reading relative to older students during school lockdowns. Grade 3 students achieved only 48% of the learning gains in reading over the 9-month pandemic school year compared to the pre-pandemic school year, indicating nearly five months behind where they would have been under normal circumstances (cf. Dorn et al., 2020 ). This estimated magnitude of pandemic-related reading loss for Grade 3 students was much lower than those for Grade 4 and 5 students (65% and 58%, respectively). Grade 3 students’ substantial reading loss is plausibly associated with the reduction in daily instructional time usually devoted to developing foundational literacy skills and promoting language and reading comprehension. From a developmental perspective, Grade 3 is a stage in which students develop more advanced phonemic awareness, phonics knowledge, and word decoding skills to be fluent readers with greater comprehension skills (Chall, 1983 ; Ehri, 2014 ; Kilpatrick, 2015 ). This requires sufficient instructional time in which children are actively and repeatedly involved in engaging, efficient, and systematic literacy practice. With the significant amount of disruption to instructional time during the extended school closures, Grade 3 students experienced a lack of opportunity to gain and build foundational reading skills that are essential to effective comprehension, critical thinking, and content knowledge development, which may potentially lead to negative long-term consequences in future years (Kaffenberger, 2020 ).

The large average reductions in reading gains during the pandemic have been compounded with substantially increased variation in scores. The circumstances of COVID-19 created a much greater spread in scores compared to the pre-pandemic, particularly with earlier grade (e.g., Grade 3) students and more vulnerable students (e.g., low-SES group, English learners, students with disabilities) who attained a much wider range of scores relative to later grade (i.e., Grade 4 or 5) students and less vulnerable students (e.g., high-SES group, English-fluent students, students without disabilities).

Our findings suggest that the negative impact of pandemic-related school closures on reading was especially profound for students from low SES environments, English learners, and students with disabilities. Young children with a high poverty status, English learner status, and disability status appear more vulnerable to the pandemic school disruptions. This finding converges with previous projections, in which the detrimental pandemic influence on student learning may have disproportionally affected the historically marginalized and vulnerable groups of students (Amplify Education, 2021 ). For students from low SES backgrounds, particularly in Grade 3 and 4, the estimated percentages of increase in reading between the beginning and end of the pandemic school year relative to the typical year were even lower than high-SES group students. For example, Grade 3 low-SES group students made only 40% of the pre-pandemic reading gains while medium- and high-SES students achieved more than 60%. This finding supports the notion that COVID-19 has magnified pre-existing SES-based achievement gaps and inequalities (e.g., Gore et al., 2021 ; Maldonado & De Witte, 2020 ) due to a lack of access to learning opportunities, appropriate digital devices, and reliable internet at home that students from high-poverty neighborhoods faced during school closures.

Similarly, English learners who were most likely from low-SES immigrant families and under-served communities demonstrated positive gains in 2020–2021, but their reading gains lagged relative to the pre-pandemic school year. We provide evidence that Grade 3 and 4 English learners’ relative reading gains in percentage (41% and 60% of the pre-pandemic reading gains, respectively) were smaller than their English-fluent peers’ relative reading gains (49% and 66%, respectively). This finding is consistent with pre-pandemic research evidence (e.g., Lawrence, 2012 ) that English learners experience greater summer setback in their English vocabulary development than English-fluent students during the summer months. This is partially because for many English learners, school is their primary context for exposure to, and development of, academic language that is central to academic success. However, the detrimental impact of COVID-19-related school disruptions on English learners could be even more pronounced because the absence of formal schooling and a lack of collaborative peer learning opportunities can influence English language and literacy development years later (Sugarman & Lazarín, 2020 ).

In addition, we found that students with disabilities were likely to struggle the most. Particularly, Grade 3 and 4 students with disabilities ended the 2020–2021 school year with only 18% and 28% of the pre-pandemic-year reading gains, leaving them nearly seven to eight months behind in reading. They may have experienced reduced access to differentiated instructional support and inadequate accommodation and accessibility during COVID-19 (Petretto et al., 2021 ). As many students with special needs rely on established routines and a vibrant network of services in their communities, dramatic decreases in services from school staff and community organizations and remote instruction have been a significant challenge to attention and motivation in reading (Sciberras et al., 2020 ).

Instructional modality and reading growth during COVID-19

Our second major finding based on the COVID-19 (2020–2021) school year cohort students indicates that when schools began to re-open in the fall of the school year, there existed educational disparities from the choice of instructional modality. We found that, conditional on students’ demographic characteristics, higher-achieving students were likely to start the school year with the online schooling option in contrast to lower-achieving students who tended to choose the in-person option. However, our results indicate that there was some variation in the magnitude of these disparities. The reading achievement gap between lower-achieving students (or students with the in-person option) and higher-achieving students (or students with the remote instruction option) was particularly bigger among English learners compared to other subgroups such as low SES and students with disabilities. This may be because young English learners with relatively low reading ability in English in the urban areas were likely coming from immigrant or refugee families who were mostly constrained in their educational options and tended to opt into in-person schooling mode.

There has been a concern that the pre-existing academic achievement gaps would be exacerbated in the absence of schooling during the pandemic (e.g., Bailey et al., 2021 ). However, we provide evidence that many lower-achieving students who had in-person schooling experience showed steeper reading growth trajectories than higher-achieving peers who did not, especially during the spring 2021 semester. This finding suggests that schools helped lower-achieving groups of students with similar demographic characteristics catch up to higher-achieving groups over the COVID-19 school year, supporting our conceptual framework (Fig.  1 ) grounded by the notion that schools generally play an equalizing role in academic disparities between student groups (e.g., Alexander et al., 2001 ; Downey et al., 2004 ; Quinn et al., 2016 ). It is noteworthy that the major difference between the in-person and fully remote instructional modality in this study was the duration of in-person school attendance. Students with the face-to-face schooling option physically attended schools for 10 days in the fall semester and 48 days in the spring semester, while peers with the fully remote instruction option exclusively participated in school instruction virtually. With the 10-day school attendance during the fall semester, the average reading scores for both instructional modality groups increased gradually in parallel, yet in-person school attendance for 48 days over the spring semester appeared to make a substantial difference, contributing to reading growth trajectories. The relative benefits of in-person instruction align with recent research evidence on the association of instructional modality with learning outcomes during the pandemic (e.g., Goldhaber et al., 2022 ; Halloran et al., 2021 ; Molnar, 2021 ; Tomasik et al., 2020 ).

Students who began the pandemic school year with relatively weaker reading ability benefitted from the opportunities to develop language and literacy skills by interacting with educators and peers in in-person environments, resulting in making greater gains in reading over time. Particularly, Grade 3–5 English learners in the in-person instructional modality group experienced continuously positive growth over the school year, but their English-fluent students in the remote instruction group remained stagnant or declined during the spring semester.

However, an inconsistent pattern of schools as equalizers emerged for students with disabilities who participated in in-person instruction. Despite their face-to-face attendance to general classroom instruction, their reading growth stagnated or fell especially over the spring semester, possibly due to limited special education services or IEP offered to students with special needs during the pandemic. This pattern of school attendance not contributing to learning trajectories for students with disabilities is consistent with recent research evidence on summer learning rates (e.g., Cooc & Quinn, 2022 ; Gershenson & Hayes, 2017 ). Furthermore, the widening academic inequality between students with and without disabilities observed regardless of instructional modality during the pandemic school year is aligned with evidence of the Matthew effect (Stanovich, 2009 ), in which students with initially higher levels of reading ability experience greater learning gains than their counterparts, leading to growing disparities over time.

Limitation and future research

The current study findings must be interpreted within several limitations of the study that can inform future research. First, an important caveat for interpreting the results of the current study is that descriptive comparisons of reading gains between the two cohorts do not make causal claims about the COVID-19 impact on reading gains. Thus, we acknowledge that any causal interpretations of our findings should be made with caution. Second, a lack of contextual information on in-person and remote instructional settings in the current study is an important study limitation to note. Although the current study used existent administrative data as a source of large quantitative information readily available from an urban U.S. school district, administrative records that contain vast amounts of qualitative information on students, families/homes, teachers, and schools obtained during COVID-19 may provide insights into mechanisms leading to pandemic-related reading losses. Particularly, to provide a more comprehensive picture of how and why in-person instruction was positively associated with students’ reading growth over the pandemic school year, future research should delve into features of instructional practices and students’ interactions with peers and teachers in face-to-face settings, distinctive from those via an online platform. For example, there is emerging causal evidence that in-person tutoring (Nickow et al., 2020 ) has substantially larger effects on students’ reading achievement than online or remote tutoring (Kraft et al., 2022 ). More causal intervention studies that compare in-person to face-to-face instruction along with detailed contextual information would permit a deeper understanding of how and why the in-person learning mode provides enhanced learning opportunities for students to make continuous growth in reading during the pandemic, particularly for lower-achieving students, and what online instructional approaches and resources need to be considered in remote schooling to meet the diverse learning needs of students.

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Relyea, J.E., Rich, P., Kim, J.S. et al. The COVID-19 impact on reading achievement growth of Grade 3–5 students in a U.S. urban school district: variation across student characteristics and instructional modalities. Read Writ 36 , 317–346 (2023). https://doi.org/10.1007/s11145-022-10387-y

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Impact of COVID-19 Restrictions on Social-Emotional Learning on Urban MS Students Requires School Renovation: Qualitative Case Study

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During the 2020 pandemic, global governments imposed COVID-19 restrictions. Among the restrictions were K–12 school closures, which caused an abrupt change from in-person to virtual learning. These circumstances damaged students’ mental health even after constraints were lifted. The problem was the socio-emotional learning (SEL) deficiency exhibited by students post-COVID-19 and how schools can address the exposure to trauma to alleviate the negative academic impact. This study aimed to fill a gap in the literature on COVID-19 restrictions and urban, public middle school students' social-emotional learning and socio-emotional character development (SECD). The purpose of this qualitative instrumental case study was to investigate the impact of COVID-19 constraints on the SEL of urban middle school students during and after the pandemic. The study's theoretical framework combined the socio-emotional learning (SEL) theory and the 11 principles of character education. Participants were 15 New Jersey-certified middle school teachers from various subject areas. The research analyzed data from a focus group protocol and classroom observations to validate and assess the convergence of evidence of COVID-19 constraints on SEL and the need for SECD programs in schools. Six themes emerged from the data analysis. Research results highlighted the need for teacher professional development support in satisfying students' needs with a focus on the SEL curriculum SECD implementation.

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COVID-19 Mitigation in a K-12 School Setting: A Case Study of Avenues: The World School in New York City

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• 1 Ananya Iyengar, MSPH, was a Graduate Research Assistant, at the Johns Hopkins Center for Health Security, Baltimore, MD.
• 2 Steve Hanon, MBA, is Chief Campus Operations Officer, Avenues: The World School, New York, NY.
• 3 Richard Bruns, PhD, is a Senior Scholar, at the Johns Hopkins Center for Health Security, Baltimore, MD, Richard Bruns is also an Assistant Scientist, the Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
• 4 Paula Olsiewski, PhD, is a Contributing Scholar, at the Johns Hopkins Center for Health Security, Baltimore, MD.
• 5 Gigi Kwik Gronvall, PhD, is a Senior Scholar, at the Johns Hopkins Center for Health Security, Baltimore, MD, Gigi Kwik Gronvall is also an Associate Professor, in the Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
• PMID: 38624262
• DOI: 10.1089/hs.2023.0060

In this case study, we describe a well-resourced private school in New York City that implemented COVID-19 mitigation measures based on public health expert guidance and the lessons learned from this process. Avenues opened in New York City in 2012 and has since expanded, becoming Avenues: The World School, with campuses in São Paulo, Brazil; Shenzhen, China; the Silicon Valley, California; and online. It offers education at 16 grade levels: 2 early learning years, followed by a prekindergarten through grade 12. We describe the mitigation measures that Avenues implemented on its New York campus. We compare COVID-19 case prevalence at the school with COVID-19 case positivity in New York City, as reported by the New York State Department of Health. We also compare the school's indoor air quality to ambient indoor air quality measures reported in the literature. The school's mitigation measures successfully reduced the prevalence of COVID-19 among its students, staff, and faculty. The school also established a consistently high level of indoor air quality safety through various ventilation mechanisms, designed to reduce common indoor air pollutants. The school received positive parent and community feedback on the policies and procedures it established, with many parents commenting on the high level of trust and quality of communication established by the school. The successful reopening provides useful data for school closure and reopening standards to prepare for future pandemic and epidemic events.

Keywords: COVID-19; Indoor air; Pandemic preparedness; SARS-CoV-2; Schools; Transmission mitigation; Ventilation.

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The COVID-19 pandemic has meant that young university students have had to adapt their learning and have a reduced relational context. Adversity contexts build models of human behaviour based on relationships. However, there is a lack of studies that analyse the behaviour of university students based on their social structure in the context of a pandemic. This information could be useful in making decisions on how to plan collective responses to adversities. The Social Network Analysis (SNA) method has been chosen to address this structural perspective. The aim of our research is to describe the structural behaviour of students in university residences during the COVID-19 pandemic with a more in-depth analysis of student leaders. A descriptive cross-sectional study was carried out at one Spanish Public University, León, from 23th October 2020 to 20th November 2020. The participation was of 93 students, from four halls of residence. The data were collected from a database created specifically at the university to "track" contacts in the COVID-19 pandemic, SiVeUle. We applied the SNA for the analysis of the data. The leadership on the university residence was measured using centrality measures. The top leaders were analyzed using the Egonetwork and an assessment of the key players. Students with higher social reputations experience higher levels of pandemic contagion in relation to COVID-19 infection. The results were statistically significant between the centrality in the network and the results of the COVID-19 infection. The most leading students showed a high degree of Betweenness, and three students had the key player structure in the network. Networking behaviour of university students in halls of residence could be related to contagion in the COVID-19 pandemic. This could be described on the basis of aspects of similarities between students, and even leaders connecting the cohabitation sub-networks. In this context, Social Network Analysis could be considered as a methodological approach for future network studies in health emergency contexts.

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Introduction.

Adversities seem to have been a permanent reality in the last decade 1 . Their consequences cause damage to people's lives that deserve the attention of political leaders and researchers. In the context of any disaster, models of human behaviour are constructed that reflect the importance of relationships between actors, between actors and knowledge, and even between actors and beliefs 2 .

The World Health Organization (WHO) declared the COVID-19 a global emergency on January 31, 2020 3 . It is one of the disasters that has had the greatest impact on our history. Recent studies have already shown that the COVID-19 pandemic appears to have an impact on mental health, leading to anxiety, depression, disturbed sleep quality and even increased perceptions of loneliness 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 . In the same sense, the impact of the pandemic has also "hit" young people, who go to school every day but who have seen their social relationships decline. The educational context was always present in the strategies implemented in previous pandemics. Some of the most common measures were the closure of schools to contain the transmission of influenza 12 , support through informal networks on university campuses during the influenza A(H1N1) pandemic 13 , and the need to increase knowledge on the pandemic, as it was found to influence everyday attitudes and practices 14 .

One of the measures that has had the greatest social impact in the COVID-19 pandemic has been the obligation to maintain a physical distance. Specifically, in the field of higher education, it seems to be remarkably complex and more difficult to carry out 15 . University campuses are of interest for studying social behaviour in the context of a pandemic. Numerous studies have shown how university students acquire healthy habits or, conversely, drug and alcohol consumption habits, depending on the type of relationships they have on campus and in the university residences 16 , 17 .

However, there is a lack of studies that analyse the behaviour of university students based on their social structure during a pandemic. Therefore, a quantitative understanding of the behaviour of students in a health emergency situation is necessary as this information could be useful in making decisions about how to prepare for disasters. That is, how to act appropriately during and after an emergency of any kind, since interpersonal relationships, through which supportive and interdependent links are established and which are present in any emergency or disaster.

To address this structural perspective, the SNA method has been applied. The SNA is a distinctive perspective within the social and behavioural sciences. It is distinctive because it is based on the fact that relationships take place between interacting units 18 . For the SNA method, the unit of analysis is not the isolated individual, but the social entity made up of the actor with its possible connections, generating a structure 19 . The main perspective of the SNA focuses on the importance of the relationships between the units that interact in the social networks 18 . A social network is made up of a set of points or nodes that represent individuals or groups, and a set of lines that represent the interaction or otherwise, between the nodes, generating a social structure 20 .

One of the most relevant premises of the SNA, for our study, is that it is not only assumed that individuals are connected through a structure, but that their goals and objectives are as well, because these are only achieved through connections and relationships 19 , 21 , 22 . Thus, the SNA could show us if university students with a more responsible goal form their own networks or mingle with their not-so-responsible peers. In relation to the groups, the actors influence and inform each other in a process that creates a growing homogeneity 21 . This perspective is of interest to this research.

The contacts between actors can be analyzed in two types of networks: sociocentric or complete networks and egocentric networks. The former includes an analysis between actors that belong to a delimited and previously defined census 23 . While the latter analyzes the structure that is generated between an ego and its contacts 24 .

There is an extensive core of studies on SNA and health habits. Some of the most recent are related to contagion in substance use 25 , 26 , physical activity 27 , behavior related to the individual's low weight 28 , engagement in university rooms 29 or eating behaviors 30 among others. SNA has even been applied to disaster scenarios such as droughts, floods, landslides, tsunamis, and cyclones 31 . No one thought that one year after this study, its results would be so useful for another scenario related to a major catastrophe such as the COVID-19 pandemic. Other recent studies shows a social network analysis approach in the problematic internet use among residential college students during COVID-19 lockdown 32 or associations between interpersonal relationships and mental health 33 .

Based on the above, the purpose of this study was to analyse a community of university students and their structural behaviour in their university residences. Halls of residence form micro-communities where very close relationships develop, which can become a context of risk. In other words, university residences could become "places" that facilitate the spread of pandemics if adequate protocols are not followed. However, dormitories can also have a preventive value. Peer support behavioural patterns take place in them, among peers who are exposed to the same risks and circumstances. This sharing of similar situations can generate an enriching coping of personal experiences 34 . However, there is a lack of studies that analyse the structures of university students and their coping in crisis situations.

This study was conducted during one of the waves of the COVID-19 pandemic, where infection rates were at their highest. With the SNA methodology, the aim is to find answers to questions such as: What are the structural characteristics of the leading individuals in the dormitories? How are the contagion outcomes related to the structural positions in the network? For such questions, the proposed objectives were (i) to analyse the relationship between the students' network position and their outcomes with respect to the COVID-19 contagion, (ii) to describe the influential position of student leaders in the network, (iii) to analyse the Egonetwork of the most influential student leaders during the COVID-19 pandemic, and (iv) to visualise the relational behaviour of university students in the global network.

Study design

A descriptive cross-sectional study was carried out at one Spanish Public University. The data was collected during one of the waves of the pandemic, specifically from 23th October 2020 to 20th November 2020.

The measures taken during the pandemic in the different regions of Spain were different, depending on the results of the contagion at each moment. At the time of carried out this study, teaching in the locality of the study was adapted to the situation. That is, there were limitations on the number of people, "mirror" classrooms, identification of QR, etc. In the town there was a limit to the number of people who could meet, pubs and discotheques had been closed, and there was a 10 pm curfew.

Setting and sample

The participation was of 93 students, from 4 university residences. The characteristics of the sample can be seen in Table 1 . Of the total participants, 32.26% were women and 67.74% men.

Ethical consideration

All participants received an informed consent form to participate in the study. Lastly, participants were offered the possibility of retracting consent once they had signed the form, without needing to provide a reason, and an email contact address was given should they require any further information. Participation was voluntary, and subject availability was respected at all times. All the participants that were involved in the study have given their informed consent to participate in this study.

The data for this study are considered health-related data. They comply with Directive 03/2020 of the European Data Protection Committee 35 . The researchers requested anonymised data from the responsible body of the university in charge of contacts COVID-19.

The study was approved by the Ethics Committee of the University of León (ETICA-ULE-008-2021).

Data collection

We collected the data from the database created at the university, SIVeULE, created for the follow-up of cases of COVID-19. This database collates the characteristics of the actors and their RT-qPCR result.

In the university there was a protocol to indicate norms and rules of (i) hygiene and preventive measures, (ii) what to do if you had symptoms, (iii) definitions of what was considered "close contact", "confinement", and " positive result ". There was support staff to collect data, deal with doubts, and assist both positive actors and confined actors. These people were called "trackers." The name defined their role because they identified the student's contacts that were positive, had symptoms, or had been "in close contact” with a positive person.

In the database, other data such as name, residence, gender, grade, name of contacts, and date and result of Polymerase Chain Reaction (PCR) test are also collected.

For the present study, the names were anonymized and registered in matrices for subsequent analysis using the SNA method.

The data obtained were used to construct a 93 × 93 matrix. The matrix was read as follows:

For rows, “A nominates B”;

For columns, “A is nominated by B”.

To carry out this study, the matrix has been symmetrized, determining that if A nominated B, B also nominated A. That is to say, it is an undirected matrix, since, if A had any contact with B, B also had contact with A.

Data analysis

For data analysis, we apply SNA to the 93 × 93 matrix. measures of centrality were applied to analyse leadership from a structural perspective. Centrality is a construct of the SNA that means the position in the network 18 . Previous researchers have applied SNA to the study of leadership, because they have conceptualized leadership as a process that starts from the collective and the interconnections 36 , 37 , 38 . For this study, the centrality measures selected were: degree, betweenness and eigenvector 18 :

The degree is the number of connections adjacent to an actor. Given the centrality of degree \({d}_{i}\) of the actor i and \({x}_{ij}\) is the cell ( i, j ) of the adjacency matrix, then

Betweenness centrality is defined as the Extent to which an actor serves as a potential “go-between” for other pairs of actors in the network by occupying an intermediary position on the shortest paths connecting other actors. The formula for the centrality of node j is given by the:

In this formula, \({g}_{ijk}\) represents the number of geodetic paths that connect i and k and through k while \({g}_{ik}\) is the total number of geodetic paths between i and k .

Eigenvector centrality corresponds to the measure of actor centrality that takes into account the centrality of the actors to whom the focal actor is connected.

Normalized measures were used.

The measures of centrality studied in the SNA have been the normalized degree (nDegree, the normalized degree centrality is the degree divided by the maximum possible degree expressed as a percentage), Eigenvector and nBetweenness (is the normalized betweenness centrality computed as the betweenness divided by the maximum possible betweenness).

To select the most leading students in the network, the measure of normalized nBetweenness was used 39 . This measure becomes more relevant during a pandemic, where the possibility of serving as a bridge or intermediary allows other networks to reach out, transferring good or bad practices and behaviors.

In order to have more information about the behaviour of the student leaders, the Egonetwork analysis of the most leading nodes for each component was carried out. Key players theory has been used to obtain this group of students displaying greater leadership 40 . Egonetwork studies the connections of a given node. This analysis in isolation is less comprehensive than the analysis of the entire network. But the researchers recommend this analysis combined with the analysis of the whole network to go deeper into the behaviour of certain nodes, depending on the objective of the research 24 , 34 , 41 .

Statistical analysis and visualisation

IBM SPSS Statistics (26.0) software. was used for the statistical processing of the data. For the analysis of descriptive data, frequencies and percentages were used for the qualitative variables, whereas the mean and standard deviation were used for the quantitative variables. A chi-square test was carried out to verify whether there was a relationship between the groups, and the Student’s t-test was used to compare the mean scores between the groups. An analysis of variance (ANOVA) was carried out to check the differences for continuous variables divided in groups. The UCINET tool, version 6.679 42 was used for the calculation of the SNA measurements. The tests carried out to study the normality of the distribution were Kolmogorov–Smirnov for populations of more than 55 individuals and the Shapiro–Wilk test for those less than or equal to 55. The level of statistical significance was set at 0.05. For qualitative analysis, a visualization of the global network will be carried out using Gephi, version 0.9.2, software. The key player tool has been used to calculate the key players of the network 43 .

As shown in Table 2 , there was a significant effect of residence on nDegree [F(3,89) = 22.135, p < 0.001] and Eigenvector [F(3,89) = 151.035, p < 0.001] and there was no significant effect of residence on nBetweenness [F = (3,89),p = 0.784].

Students in residence C have significantly higher degrees of centrality in nDegree and Eigenvector compared to the other residences. In the case of nBetweenness, students in residences A and D have higher values, although not significantly so.

Significant differences in all measures of centrality (nDegree, Eigenvector and nBetweenness) measures were found for the groups of people who tested positive for RT-qPCR (PCR +) versus those who tested negative for PCR (PCR-). The PCR + group of people had higher values of centrality than the PCR- group. The degrees of significance of these differences are shown in Table 3 .

Significant differences were found between leaders and non-leaders calculated with the three measures of centrality and the prevalence of people who tested positive or negative for PCRs. Leaders had a higher percentage of people in the PCR + group compared to non-leaders. The degrees of significance of these differences are shown in Table 4 .

Figure  1 A shows the nodes of the study network highlighting in each colour which residence each one belongs to (A,B,C or D). In Fig.  1 B the same network can be seen but the nodes with PCR + appear in red and and the nodes with PCR- in green. The distribution of the network allows us to appreciate the 4 different residences. The size of the nodes is represented by the nBetweenness of each node.

Graphs of the university student network differentiating a colour for each residence hall ( A ) and differentiating the positive and negative PCR groups ( B) .

Figure  2 shows the network highlighting the trajectories of the three most important key players. The edges coming out of these key players are thicker than the others. Furthermore, the key players are numbered in order of importance in the network (1, 2 and 3). The size of the nodes is represented by the nBetweenness of each node.

The network shown under the Atlas 2 distribution highlighting the 3 most important key players in the network.

Figure  3 shows the Egonetworks of the 3 key players in the network. Figure  3 A shows the most important key player in the network. If this node were eliminated, the two components would be separated (those of the C and D residence). Figure  3 B,C show the Egonetworks of the key players 2 and 3 respectively. These nodes are structurally very similar. If both nodes were removed from the network, there would no longer be a connection between residence C and residences A and B.

Egonetworks of the 3 main key players of the network.

This research contributes empirical evidence based on a social network approach to the development of the COVID-19 pandemic on university halls of residence. We have presented a study strategy and results, which link the relationship between the centrality of leaders and the outcome of pandemic infection. There is a significant core of research using the SNA methodology applied to the COVID-19 pandemic. However, there is a lack of research focusing on the structural responses of university students, a population of particular interest given their training experience. A university student "absorbs" experiences that are translated into behaviour, and transfers the resources obtained through their relationships.

Our results demonstrate the relationship between the centrality in the network of student leaders and the outcome of their infection (positive or negative). Not only could leaders spread pandemic behaviour towards their more local peers, they also seem to spread it to other halls of residence. This is demonstrated by the structure of betweenness. Leaders with a higher degree of betweenness could become key players, so that their presence or absence can disconnect the various components of the entire network. This could lead to a disconnection of the contagion process, both on a positive and negative level. The findings are the first to demonstrate that networks in university accommodation develop successful or unsuccessful responses to a pandemic. University managers should take these findings into account when developing response and behavioural strategies in pandemic or disaster situations. Strategies should be designed with a network rather than an individual approach.

Although our study did not ask about the relationship between the actors, we understand that the contacts established between the students are relationships of friendship or good classmates. We only analysed whether or not people had been in contact, during a state of lockdown. But obviously, with the SNA, we can visualise relational behaviours that would be more difficult to appreciate using other methodologies.

Our results show that student leaders have a high degree of centrality not only at the local level, i.e. in the component related to their accommodation, but also at the level of the global network. Our results are in line with studies of Mehra et al. 36 , who highlighted that the integration of a leader into the friendship network in one social circle can be related to the reputation of the leader in other social circles.

Leadership or reputation at the local level is related to the performance of the team, and leadership outside the team is what allows new opportunities to arise and new information to be disseminated 36 . In the case of university students in their accommodation, the aim is to have a friendly atmosphere and to collaborate in difficult moments, to motivate each other, etc. Our results shown a statistically significant relationship between leadership and the positive results of the COVID-19 tests. In this sense, previous studies have already found that having too many resources related to social capital in a group (such as centrality) could negatively affect the efficiency of the group 44 . In other words, the leader will exert an influence on his or her colleagues and this influence could "infect" a certain behaviour, in this case of responsibility or not in a state of health emergency.

Another aspect demonstrated in our research is that there is a similarity between student groupings in terms of their COVID-19 test results. That is, we observe groups where the results are all positive (nodes in red), and others where the results are negative (nodes in green). This finding, could be related to numerous previous studies where actors occupy similar social positions in the classroom. For example, the studies from 45 showed that stuttering students had the same social position as the rest of their peers, because both (stutterers and non-stutterers) tended to design their groups structurally the same.

Homophily theory indicates that individuals associate with those with whom they share aspects of similarity, such as similar beliefs, characteristics and behaviours, which occurs especially in young people and adolescents 46 Therefore, this may partly justify why negative-test college students are more cohesive, and positive-test college students as well.

One of the measures implemented with the greatest impact in this COVID-19 pandemic has been social distancing or isolation. The closure of premises or the reduction in hours of places of leisure has led to this social, or rather physical, distancing, as it is physical contact that is avoided. Studies have shown that the reduction in contacts based on social networks that coexist in social bubbles, and the similarity between contacts, increase social distancing from other actors, and therefore decrease the risks of contagion 47 . But in the case of this research, university accommodation could not be considered as a bubble. We could think of them as big bubbles, where behavioural patterns become contagious, be they positive and negative ones. Therefore, in this sense, the directors of the centres should take note and plan different strategies according to the behaviour of the subnetworks. That is to say, promote those behaviours with negative results of contagion and intervene in those subnetworks with positive results. For this, and as explained previously, the best option would be to plan together with the leaders.

Our results have shown that students with a high degree of Betweenness have a position in the network that gives them great leadership. In this sense, previous studies have used this structural metric as a predictor of leadership due to the strategic position that the actors have in the network and their role in bridging different networks 39 , 48 .

For a better understanding of the role of these actors, in this research we analyse these university students on the basis of two more structural issues. On the one hand, which of them could have a key player role. Secondly, to analyse the Egonetwork of those students with a greater degree of centrality in each of the components.

As regards key players, our results showed that 3 students with a high degree of betweenness, i.e. with an intermediary role, had a key player structure. The importance of the key actor has been explained perfectly by Borgatti (2006) 40 , describing both the negative and positive aspects. The negative is that the network, or networks, actually depend on these nodes, and cohesion between the networks would be diminished if these actors were to disappear 40 . This problem is greater when, in a public health context, we select a small number of individuals to contain a pandemic or to reduce the risk of contagion that links different networks. If these actors disappeared, the number of those infected would increase. As regards the positive role of these actors, they are ideal for spreading attitudes and behaviour, because they quickly gain access to different networks. Borgatti (2006) explains the importance of the structure of the key players, with the same relevance in very different contexts, such as terrorist networks or pandemic contexts 40 . In our case, our results are supported by the justification of this great researcher.

Our findings have shown that student leaders with a higher degree of Betweenness had a higher density than their peers in their Egonetworks . This could facilitate the transmission of social capital in a context such as the COVID-19 pandemic. These students, who serve as bridges, could become key actors with the ability to mobilize and coordinate social activity 49 . Their role is key for other colleagues, since they could serve as a "mirror" to "invite" appropriate behaviors in a health emergency. The key question that remains is, what behavior do they have? Structurally, the present investigation has demonstrated and justified that its position in the network is a model that could be disseminated among the rest of the actors.

To summarize the above, those responsible for universities must take into account the collective behavior of its networks. In a context such as the COVID-19 pandemic, the diffusion of behaviors is very relevant. Authors call for “urban intelligence” as a possible strategy to deal effectively with a pandemic. They understand that the impact of a health emergency is more than just a public health problem since it involves social risks and instability. This situation would be better dealt with by having the best that the social and community structure can offer, the so-called "urban intelligence” 50 .

SNA could provide a set of terms and concepts to explain and describe social phenomena 51 . The method offers a distinctive approach to analysing leadership in disaster processes. Leaders could be like "builders" of social responses and the managers of the universities should take it into account for the intervention processes.

The most important limitations of this study should be considered for future research. For example, it would be of interest to carry out other analyzes focused more on the cohesion of the network and the behavior of the subgroups, in order to draw structural conclusions at the micro level. Future lines of research could focus on comparing the students’ leadership in terms of structure with leadership as perceived by both them and their own peers.

Conclusions

The present research has carried out a study with students in university residences. The aim has been to describe the structural behaviour of students in university residences during the COVID-19 pandemic, with a more in-depth analysis of student leaders. The specific objectives proposed to develop the research were to: (i) analyse the relationship between the position of students in the network and their results with respect to COVID-19 infection, (ii) describe the position of influence of student leaders in the network, (iii) analyzing the Egonetwork of the most influential student leaders on the COVID-19 pandemic, and (iv) visualise the relational behaviour of university students in the global network.

The main conclusions derived from the results are detailed below:

The most central students in the network, had more positive results regarding COVID-19 infection.

The leadership of the confined students was related to higher degree, eigenvector and betweenness.

A small core of leaders are key players, so their role conditions the connection or disconnection between different components of the global network.

Students with a key player structure show a similar Egonetwork if they belong to the same residence.

There is a student leader with the maximum key player power structure, causing a total disconnection between networks if he/she disappears from the global network.

The findings show that strategies to cope with a disaster or pandemic need to be addressed through a network approach. University managers will need to have a profound understanding of students' relational behaviour. Only then will the most restrictive measures be effective. Responsible or irresponsible behaviour is transferred through the connections between students, so Social Network Analysis should be considered as a method of analyzing the evolution of a pandemic at the societal level. Any crisis involves contacts, but in a pandemic, contacts can transfer infection. Also in a pandemic, contacts can transfer habits and behaviours "passed on" by leaders, so that they allow for more effective coping. All of this can be analysed using SNA. Our study provides findings with an innovative approach, achieved with SNA. Among the limitations of the study it should be noted that the sample is very small (n = 93). This means that we cannot state categorically the representativeness of the results presented. However, the results could be used for future research where it is useful to analyse health emergency contexts as a network rather than analysing individuals in isolation.

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Headed Back to School: A Look at the Ongoing Effects of COVID-19 on Children’s Health and Well-Being

Elizabeth Williams and Patrick Drake Published: Aug 05, 2022

Children are now preparing to head back to school for the third time since the onset of the COVID-19 pandemic. Schools are expected to return in-person this fall, with most experts now agreeing the benefits of in-person learning outweigh the risks of contracting COVID-19 for children. Though children are less likely than adults to develop severe illness, the risk of contracting COVID-19 remains, with some children developing symptoms of long COVID following diagnosis. COVID-19 vaccines provide protection, and all children older than 6 months are now eligible to be vaccinated. However, vaccination rates have stalled and remain low for younger children. At this time, only a few states have vaccine mandates for school staff or students, and no states have school mask mandates, though practices can vary by school district. Emerging COVID-19 variants, like the Omicron subvariant BA.5 that has recently caused a surge in cases, may pose new risks to children and create challenges for the back-to-school season.

Children may also continue to face challenges due to the ongoing health, economic, and social consequences of the pandemic. Children have been uniquely impacted by the pandemic, having experienced this crisis during important periods of physical, social, and emotional development, with some experiencing the loss of loved ones. While many children have gained health coverage due to federal policies passed during the pandemic, public health measures to reduce the spread of the disease also led to disruptions or changes in service utilization and increased mental health challenges for children.

This brief examines how the COVID-19 pandemic continues to affect children’s physical and mental health, considers what the findings mean for the upcoming back-to-school season, and explores recent policy responses. A companion KFF brief explores economic effects of the pandemic and recent rising costs on households with children. We find households with children have been particularly hard hit by loss of income and food and housing insecurity, which all affect children’s health and well-being.

Children’s Health Care Coverage and Utilization

Despite job losses that threatened employer-sponsored insurance coverage early in the pandemic, uninsured rates have declined likely due to federal policies passed during in the pandemic and the safety net Medicaid and CHIP provided. Following growth in the children’s uninsured rate from 2017 to 2019, data from the National Health Interview Survey (NHIS) show that the children’s uninsured rate held steady from 2019 to 2020 and then fell from 5.1% in 2020 to 4.1% in 2021. Just released quarterly NHIS data show the children’s uninsured rate was 3.7% in the first quarter of 2022, which was below the rate in the first quarter of 2021 (4.6%) but a slight uptick from the fourth quarter of 2021 (3.5%), though none of these differences are statistically significant. Administrative data show that children’s enrollment in Medicaid and CHIP increased by 5.2 million enrollees, or 14.7%, between February 2020 and April 2022 (Figure 1). Provisions in the Families First Coronavirus Response Act (FFCRA) require states to provide  continuous coverage  for Medicaid enrollees until the end of the month in which the public health emergency (PHE) ends in order to receive enhanced federal funding.

Children have missed or delayed preventive care during the pandemic, with a third of adults still reporting one or more children missed or delayed a preventative check-up in the past 12 months (Figure 2). However, the share missing or delaying care is slowly declining, with the share from April 27 – May 9, 2022 (33%) down 3% from almost a year earlier (July 21 – August 2, 2021) according to KFF analysis of the  Household Pulse Survey . Adults in households with income less than $25,000 were significantly more likely to have a child that missed, delayed, or skipped a preventive appointment in the past 12 months compared to households with income over $50,000. These data are in line with findings from another study that found households reporting financial hardship were significantly more likely to report missing or delaying children’s preventive visits compared to those not reporting hardships. Hispanic households and households of other racial/ethnic groups were also significantly more likely to have a child that missed, delayed, or skipped a preventive appointment in the past 12 months compared to White households (based on race of the adult respondent).

Telehealth helped to provide access to care, but children with special health care needs and those in rural areas continued to face barriers. Overall, telehealth utilization soared early in the pandemic, but has since declined and has not offset  the decreases in service utilization overall. While preventative care rates have increased since early in the pandemic, many children likely still need to catch up on missed routine medical care. One study found almost a quarter of parents reported not catching-up after missing a routine medical visit during the first year of the pandemic. The pandemic may have also exacerbated existing challenges accessing needed care and services for children with special health care needs , and low-income patients or patients in rural areas may have experienced barriers to accessing health care via telehealth .

The pandemic has also led to declines in children’s routine vaccinations, blood lead screenings, and vision screenings. The CDC reported vaccination coverage of all state-required vaccines declined by 1% in the 2020-2021 school year compared to the previous year, and some public health leaders note COVID-19 vaccine hesitancy may be spilling over to routine child immunizations. The  CDC also report ed 34% fewer U.S. children had blood lead level testing from January-May 2020 compared to the same period in 2019. Further, data suggest declines in lead screenings during the pandemic may have exacerbated underlying gaps and disparities in early identification and intervention for lower-income households and children of color. Additionally, many children rely on in-school vision screenings to identity vision impairments, and some children went without vision checks while schools managed COVID-19 and turned to remote learning. These screenings are important for children in order to identify problems early; without treatment some conditions can worsen or lead to more serious health complications.

The pandemic has also led to difficulty accessing and disruptions in dental care. Data from the National Survey of Children’s Health (NSCH) show the share of children reporting seeing a dentist or other oral health provider or having a preventive dental visit in the past 12 months declined from 2019 to 2020, the first year of the pandemic (Figure 3). The share of children reporting their teeth are in excellent or very good conditions also declined from 2019 (80%) to 2020 (77%); the share of children reporting no oral health problems also declined but the change was not statistically significant.

Recently released preliminary data for Medicaid/CHIP beneficiaries under age 19 shows steep declines in service utilization early in the pandemic, with utilization then rebounding to a varying degree depending on the service type . Child screening services have rebounded to pre-PHE levels while blood lead screenings and dental services rates remain below per-PHE levels. Telehealth utilization mirrors national trends, increasing rapidly in April 2020 and then beginning to decline in 2021. When comparing the PHE period (March 2020 – January 2022) to the pre-PHE period (January 2018 – February 2020) overall, the data show child screening services and vaccination rates declined by 5% (Figure 4). Blood lead screening services and dental services saw larger declines when comparing the PHE period to before the PHE, declining by 12% and 18% respectively among Medicaid/CHIP children.

Children’s Mental Health Challenges

Children’s mental health challenges were on the rise even before the onset of the COVID-19 pandemic. A recent KFF analysis found the share of adolescents experiencing anxiety and/or depression has increased by one-third from 2016 (12%) to 2020 (16%), although rates in 2020 were similar to 2019.  Rates of anxiety and/or depression were more pronounced among adolescent females and White and Hispanic adolescents. A separate  survey  of high school students in 2021 found that lesbian, gay, or bisexual (LGB) students were more likely to report persistent feelings of sadness and hopelessness than their heterosexual peers. In the past few years, adolescents  have experienced worsened emotional health, increased stress, and a lack of peer connection along with increasing rates of drug overdose deaths, self-harm, and eating disorders. Prior to the pandemic, there was also an increase in suicidal thoughts from 14% in 2009 to 19% in 2019.

The pandemic may have worsened children’s mental health or exacerbated existing mental health issues among children . The pandemic caused disruptions in routines and social isolation for children, which can be  associated with anxiety and depression  and  can have implications  for mental health later in life. A number of studies show an increase in children’s mental health needs following social isolation due to the pandemic, especially among children who experience adverse childhood experiences (ACEs). KFF analysis found the share of parents responding that adolescents were experiencing anxiety and/or depression held relatively steady from 2019 (15%) to 2020 (16%), the first year of the pandemic. However, the KFF COVID-19 Vaccine Monitor on perspectives of the pandemic at two years found six in ten parents say the pandemic has negatively affected their children’s schooling and over half saying the same about their children’s mental health. Researchers also note it is still too early to fully understand the impact of the pandemic on children’s mental health. The past two years have also seen much economic turmoil, and  research  has shown that as economic conditions worsen, children’s mental health is negatively impacted. Further, gun violence continues to rise and may lead to negative mental health impacts among children and adolescents.  Research   suggests  that children and adolescents may experience negative mental health impacts, including symptoms of anxiety, in response to school shootings and  gun-related deaths  in their  communities .

Access and utilization of mental health care may have also  worsened during the pandemic. Preliminary data for Medicaid/CHIP beneficiaries under age 19 finds utilization of mental health services during the PHE declined by 23% when compared to prior to the pandemic (Figure 4); utilization of substance use disorder services  declined by 24% for beneficiaries ages 15-18 for the same time period. The data show utilization of mental health services remains below pre-PHE levels and has seen the smallest improvement compared to other services utilized by Medicaid/CHIP children. Telehealth has played a significant role in providing mental health and substance use services to children early in the pandemic, but has started to  decline . The pandemic may have widened existing disparities in access to mental health care for children of color and children in low-income households. NSCH data show 20% of children with mental health needs were not receiving needed care in 2020, with the lowest income children less likely to receive needed mental health services when compared to higher income groups (Figure 5).

Children’s Health and COVID-19

While less likely than adults to develop severe illness, children can contract and spread COVID-19 and  children with underlying health conditions  are at an increased risk of developing severe illness .  Data through July 28, 2022 show there have been over 14 million child COVID-19 cases, accounting for 19% of all cases. Among Medicaid/CHIP enrollees under age 19, 6.4% have received a COVID-19 diagnosis through January 2022. Pediatric hospitalizations peaked during the Omicron surge in January 2022, and children under age 5, who were not yet eligible for vaccination, were hospitalized for COVID-19 at five times the rate during the Delta surge.

Some children who tested positive for the virus are now facing long COVID . A recent meta-analysis found 25% of children and adolescents had ongoing symptoms following COVID-19 infection, and finds the most common symptoms for children were fatigue, shortness of breath, and headaches, with other long COVID symptoms including cognitive difficulties, loss of smell, sore throat, and sore eyes. Another report found a larger share of children with a confirmed COVID-19 case experienced a new or recurring mental health diagnosis compared to children who did not have a confirmed COVID-19 case. However, researchers have noted it can be difficult to distinguish long COVID symptoms to general pandemic-associated symptoms. In addition, a small share of children are experiencing multisystem inflammatory syndrome in children (MIS-C), a serious condition associated with COVID-19 that has impacted  almost 9,000 children . A lot of unknowns still surround long COVID in children; it is unclear how long symptoms will last and what impact they will have on children’s long-term health.

COVID-19 vaccines were recently authorized for children between the ages of 6 months and 5 years, making all children 6 months and older eligible to be vaccinated against COVID-19. Vaccination has already peaked for children under the age of 5, and is far below where 5-11 year-olds were at the same point in their eligibility. As of July 20, approximately 544,000 children under the age of 5 (or approximately 2.8%) had received at least one COVID-19 vaccine dose. Vaccinations for children ages 5-11 have stalled, with just  30.3%  have been fully vaccinated as of July 27 compared to  60.2% of those ages 12-17.  Schools have been important sites  for providing access as well as information to help expand vaccination take-up among children, though children under 5 are not yet enrolled in school, limiting this option for younger kids. A recent KFF survey finds most parents of young children newly eligible for a COVID-19 vaccine are reluctant to get them vaccinated, including 43% who say they will “definitely not” do so.

Some children have experienced COVID-19 through the loss of one or more family members due to the virus.  A  study  estimates that, as of June 2022, over 200,000 children in the US have lost one or both parents to COVID-19. Another study found children of color were more likely to experience the loss of a parent or grandparent caregiver when compared to non-Hispanic White children. Losing a parent can have long term impacts on a child’s health,  increasing  their risk of substance abuse, mental health challenges,  poor educational outcomes , and  early death . There have been over 1 million COVID-19 deaths in the US, and estimates indicate a  17.5% to 20% increase  in bereaved children due to COVID-19, indicating an increased number of grieving children who may need additional supports as they head back to school.

Looking Ahead

Children will be back in the classroom this fall but may continue to face health risks due to their or their teacher’s vaccination status and increasing transmission due to COVID-19 variants. New, more transmissible COVID-19 variants continue to emerge, with the most recent Omicron subvariant BA.5 driving a new wave of infections and reinfections among those who have already had COVID-19. This could lead to challenges for the back-to-school season, especially among young children whose vaccination rates have stalled.

Schools, parents, and children will likely continue to catch up on missed services and loss of instructional time in the upcoming school year. Schools are likely still working to address the loss of instructional time and drops in student achievement due to pandemic-related school disruptions. Further, many children with special education plans experienced missed or delayed services and loss of instructional time during the pandemic. Students with special education plans may be entitled to compensatory services to make up for lost skills due to pandemic related service disruptions, and some children, such as those with disabilities related to long COVID, may be newly eligible for special education services.

To address worsening mental health and barriers to care for children, several measures have been taken or proposed at the state and federal level. Many states have recently enacted legislation to strengthen school based mental health systems, including initiatives such as from hiring more school-based providers to allowing students excused absences for mental health reasons. In July 2022, 988 – a federally mandated crisis number – launched, providing a single three-digit number for individuals in need to access local and state funded crisis centers, and the Biden Administration released a strategy to address the national mental health crisis in May 2022, building on prior actions. Most recently, in response to gun violence, the Bipartisan Safer Communities Act was signed into law and allocates funds towards mental health, including trauma care for school children.

The unwinding of the PHE and expiring federal relief may have implications for children’s health coverage and access to care. The  American Rescue Plan Act (ARPA) extended eligibility  to ACA health insurance subsides for people with incomes over 400% of poverty and increased the amount of assistance for people with lower incomes. However, these subsidies are set to expire at the end of this year without further action from Congress, which would increase premium payments for 13 million Marketplace enrollees. In addition, provisions in the FFCRA providing continuous coverage for Medicaid enrollees will expire with the end of the PHE. Millions of people, including children, could lose coverage when the continuous enrollment requirement ends if they are no longer eligible or face administrative barriers during the process despite remaining eligible. There will likely be variation across states in how many people are able to maintain Medicaid coverage, transition to other coverage, or become uninsured. Lastly, there have also been several policies passed throughout the pandemic to provide financial relief for families with children, but some benefits, like the expanded Child Tax Credit, have expired and the cost of household items is rising, increasing food insecurity and reducing the utility of benefits like SNAP.

• Coronavirus (COVID-19)
• Coronavirus

Also of Interest

• A Look at the Economic Effects of the Pandemic for Children
• Recent Trends in Mental Health and Substance Use Concerns Among Adolescents
• Mental Health and Substance Use Considerations Among Children During the COVID-19 Pandemic
• COVID-19 Vaccination Rates Among Children Under 5 Have Peaked and Are Decreasing Just Weeks Into Their Eligibility

COVID-19: Who's at higher risk of serious symptoms?

Advanced age and some health conditions can raise the risk of serious COVID-19 (coronavirus disease 2019) illness.

Many people with COVID-19, also called coronavirus disease 2019, recover at home. But for some, COVID-19 can be a serious illness. Some people may need care in the hospital, treatment in the intensive care unit and the need for breathing help. In some people, severe COVID-19 illness can lead to death.

What raises the risk of severe or critical COVID-19 illness?

The risk for serious COVID-19 illness depends on your health status, age and activities. Your risk also depends on other factors. This includes where you live, work or learn, how easy it is for you to get medical care, and your economic stability.

If you have more than one risk factor, your risk goes up with each one.

Age raises the risk of serious COVID-19

People age 65 and older and babies younger than 6 months have a higher than average risk of serious COVID-19 illness. Those age groups have the highest risk of needing hospital care for COVID-19.

Babies younger than 6 months aren't eligible for the COVID-19 vaccine, which adds to their risk. For older people, the challenge is that the immune system is less able to clear out germs as people age. Also, as people age, medical conditions that raise the risk of severe COVID-19 are more likely. In the U.S. as of March 2024, about 76% of all deaths from COVID-19 have been among people age 65 and older.

Aging plus disease raises the risk of serious COVID-19

Severe COVID-19 disease is more likely for people who have other health issues.

Some common diseases linked to aging are:

• Heart disease. Examples are heart failure or coronary artery disease.
• Diabetes mellitus. The risk is higher for both type 1 and type 2.
• Chronic lung diseases. This includes airway disease and conditions that damage lung tissue.
• Obesity. The risk goes up as body mass index (BMI) increases, with the highest risk for a BMI of 40 or greater.
• Chronic kidney disease. Especially if you are on dialysis.

These diseases become more common as people age. But they can affect people of any age. The risk of serious COVID-19 illness is linked to having one or more underlying medical condition.

Asthma, COPD, other lung diseases raise risk of severe COVID-19

Your risk of having more severe COVID-19 illness is higher if you have lung disease. Having moderate to severe asthma raises some risks of serious COVID-19 illness. It raises the risk of needing care in the hospital, including intensive care, and needing mechanical help breathing.

The risk of serious COVID-19 illness also is higher for people who have conditions that damage lung tissue over time. Examples are tuberculosis, cystic fibrosis, interstitial lung disease, bronchiectasis or COPD, which stands for chronic obstructive pulmonary disease. These diseases raise the risk of needing care in the hospital for COVID-19. Depending on the condition, the risk of needing intensive care and the risk of death from COVID-19 also may go up.

Other lung conditions, such as a history of pulmonary hypertension or pulmonary embolism affect a person's risk of serious illness after COVID-19. The risk of death may be higher after these conditions.

Cancer raises the risk of severe COVID-19

In general, people with cancer have a greater risk of getting serious COVID-19. People who have or had blood cancer may have a higher risk of being sick for longer, or getting sicker, with COVID-19 than people with solid tumors.

Having cancer raises the risk of needing care in the hospital, intensive care and the use of breathing support. Having blood cancer and getting COVID-19 raises the risk of death from the illness.

Treatment for blood cancer may raise the risk of severe COVID-19 but the research is still unclear. Cancer treatment may also affect your COVID-19 vaccine. Talk to your healthcare professional about additional shots and getting vaccinated after treatments that affect some immune cells.

Other conditions that raise the risk of severe COVID-19

If an organ or body system is already weakened by disease, infection with the COVID-19 virus can cause further damage. In other cases, medicine for the original condition can lower the immune system's response to the virus that causes COVID-19.

Many different diseases can raise the risk of severe COVID-19 illness.

• Brain and nervous system diseases, such as strokes.
• Chronic liver disease, specifically cirrhosis, nonalcoholic fatty liver disease, alcoholic liver disease and autoimmune hepatitis.
• HIV not well managed with medicine.
• Heart disease, including congenital heart disease and cardiomyopathies.
• Mood disorders or schizophrenia.
• Having received an organ or stem cell transplant.
• Sickle cell anemia and thalassemia blood disorders.

Other risk factors for severe COVID-19 are:

• Not getting enough physical activity.
• Pregnancy or having recently given birth.
• Use of medicines that lower the immune system's ability to respond to germs.

Also, as a general group, disability is linked to an increased risk of severe COVID-19. The risks are different depending on the disability.

• Down syndrome is linked to a higher risk of needing care in the hospital. The risk of death from severe COVID-19 also is higher than typical for people with Down syndrome.
• Attention deficit/hyperactivity disorder is linked to an increased risk of needing care in the hospital from severe COVID-19.
• Cerebral palsy is linked to an increased risk of needing care in the hospital from severe COVID-19.

These are not the only conditions that increase the risk of severe COVID-19. Talk to your healthcare professional if you have questions about your health and risk for getting a serious COVID-19 illness.

A COVID-19 vaccine can lower your risk of serious illness

The COVID-19 vaccine can lower the risk of death or serious illness caused by COVID-19. Your healthcare team may suggest added doses of COVID-19 vaccine if you have a moderately or seriously weakened immune system.

How else can you lower the risk of severe COVID-19?

Everyone can lower the risk of serious COVID-19 illness by working to prevent infection with the virus that causes COVID-19.

• Avoid close contact with anyone who is sick or has symptoms, if possible.
• Use fans, open windows or doors, and use filters to move the air and keep any germs from lingering.
• Wash your hands well and often with soap and water for at least 20 seconds. Or use an alcohol-based hand sanitizer with at least 60% alcohol.
• Cough or sneeze into a tissue or your elbow. Then wash your hands.
• Clean and disinfect high-touch surfaces. For example, clean doorknobs, light switches, electronics and counters regularly.
• Spread out in crowded public areas, especially in places with poor airflow. This is important if you have a higher risk of serious illness.
• The U.S. Centers for Disease Control and Prevention recommends that people wear a mask in indoor public spaces if COVID-19 is spreading. This means if you're in an area with a high number of people with COVID-19 in the hospital. They suggest wearing the most protective mask possible that you'll wear regularly, that fits well and is comfortable.

These basic actions are even more important for people who have weakened immune systems, and their caregivers.

The FDA also has authorized the monoclonal antibody pemivibart (Pemgarda) to prevent COVID-19 in some people with weakened immune systems.

People can take other actions based on their risk factors.

• If you're at a higher risk of serious illness, talk to your healthcare professional about how best to protect yourself. Know what to do if you get sick so you can quickly start treatment.
• Lower your risk of COVID-19 complications by making sure that any health issues are well managed. This includes staying on track with managing medical conditions, going to all healthcare appointments and planning ahead to avoid running out of medicine. Keep taking medicines as suggested by your healthcare professional.
• Stay up to date on vaccines. This includes vaccines for flu, pneumonia and RSV. These vaccines won't prevent COVID-19. But becoming ill with a respiratory illness may worsen your outcome if you also catch COVID-19.

You may consider making a care plan. In the care plan, write your medical conditions, the medicine you take, and any special food or diet needs you have. The care plan also includes who you see for care and your emergency contacts.

There is a problem with information submitted for this request. Review/update the information highlighted below and resubmit the form.

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• Goldman L, et al., eds. COVID-19: Epidemiology, clinical manifestations, diagnosis, community prevention, and prognosis. In: Goldman-Cecil Medicine. 27th ed. Elsevier; 2024. https://www.clinicalkey.com. Accessed April 5, 2024.
• Regan JJ, et al. Use of Updated COVID-19 Vaccines 2023-2024 Formula for Persons Aged ≥6 Months: Recommendations of the Advisory Committee on Immunization Practices—United States, September 2023. MMWR. Morbidity and Mortality Weekly Report 2023; doi:10.15585/mmwr.mm7242e1.
• Underlying medical conditions associated with higher risk for severe COVID-19: Information for healthcare providers. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-care/underlyingconditions.html. Accessed April 2, 2024.
• Stay up to date with COVID-19 vaccines. Centers for Disease Control and Prevention. www.cdc.gov/coronavirus/2019-ncov/vaccines/stay-up-to-date.html. Accessed April 2, 2024.
• COVID data tracker. Centers for Disease Control and Prevention. https://covid.cdc.gov/covid-data-tracker/#demographics. Accessed April 2, 2024.
• Najafabadi BT, et al. Obesity as an independent risk factor for COVID‐19 severity and mortality. Cochrane Database of Systematic Reviews. 2023; doi:10.1002/14651858.CD015201.
• People with certain medical conditions. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html. Accessed April 2, 2024.
• AskMayoExpert. COVID-19: Outpatient management (adult). Mayo Clinic; 2023.
• Emergency use authorizations for drugs and non-vaccine biological products. U.S. Food and Drug Association. https://www.fda.gov/drugs/emergency-preparedness-drugs/emergency-use-authorizations-drugs-and-non-vaccine-biological-products. Accessed April 2, 2024.
• How to protect yourself and others. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html. Accessed April 2, 2024.
• COVID-19: What people with cancer should know. National Cancer Institute. https://www.cancer.gov/about-cancer/coronavirus/coronavirus-cancer-patient-information. Accessed April 2, 2024.
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• Preventing respiratory viruses. Centers for Disease Control and Prevention. https://www.cdc.gov/respiratory-viruses/prevention/index.html. Accessed April 2, 2024.
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• COVID-19: What People with Cancer Should Know. National Cancer Institute. https://www.cancer.gov/about-cancer/coronavirus/coronavirus-cancer-patient-information. Accessed April 11, 2024.

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Having 2 or more underlying conditions increase the risk of severe COVID-19 almost 10-fold in kids, data show

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Though severe COVID-19 infections in children are uncommon, children and young adults with comorbidities are at   increased risk for critical illness during COVID-19 infections, according to a new study in Journal of the Pediatric Infectious Diseases Society.  

The meta-analysis looked at critical COVID-19, defined as an invasive mechanical ventilation requirement, intensive care unit admission, or death, in 70 studies published from March 2020 to August 2023 and found a nearly 10-fold increased risk in kids who have two or more underlying medical conditions.

"We selected studies that included patients aged ≤21 years with confirmed COVID-19 and provided enough data to estimate the odds ratio (OR) of critical disease for a given risk factor," the authors said.  

The studies collectively examined 172,165 children, adolescents, and young adults with COVID-19 in 45 countries.

Heart disease adds to the risk  

In healthy children with no comorbidities, the absolute risk of critical disease from COVID-19 was 4% (95% confidence interval [CI], 1% to 10%).

Compared with no comorbidities, the pooled odds ratio (OR) for critical disease was 3.95 (95% CI, 2.78 to 5.63) for the presence of one comorbidity and 9.51 (95% CI, 5.62 to 16.06) for two or more comorbidities.

The highest critical risk factor was age under 1 month, with 7 studies on 1,290 infants aged under 1 month showing that 20% had critical illness. But the authors warned that that number includes premature infants and may not be widely representative of the true risk to full-term babies.  

Fifty-one studies assessed the risk of cardiac and pulmonary (heart and lung) comorbidities, including congenital heart disease, high blood pressure, heart failure, cardiomyopathies, valvular disease, septal defects, arrhythmias, and pulmonary hypertension.  

The rate of critical disease among 2,372 children with cardiovascular disease in these studies was 30% (95% CI, 23% to 37%), and the pooled odds ratio was 3.60 (95% CI, 2.81 to 4.61), the authors said.  

Previous pulmonary conditions were a risk factor for severity, with a pooled critical disease rate of 24% (95% CI, 17% to 33%) and an OR of 2.15 (95% CI, 1.66 to 2.75).

Seizure disorders pose substantial risk  

"Few studies examined the extent to which poorly controlled asthma modifies the severity of COVID-19 in children," the authors said. Children with controlled asthma showed no significant risk for severe disease, but uncontrolled asthma more than doubled the risk of developing severe COVID-19 (adjusted risk ratio, 2.24; 95% CI, 1.54 to 3.27).

.Few studies examined the extent to which poorly controlled asthma modifies the severity of COVID-19 in children

One study found that children, however, who were hospitalized for asthma within 12 months of the study, were at increased for acute COVID-19 severity (adjusted OR, 2.9; 95% CI, 2.6 to 3.3).

Children with seizure disorders and other neurologic complications had more than triple the odds of critical illness compared to the general pediatric population (OR 3.40; 95% CI, 2.70 to 4.29).  

In addition, obesity, diabetes, and compromised immune systems were also tied to statistically significant odd ratios greater than 2.

" The current management of COVID-19 in the pediatric population is multifaceted, requiring a balanced assessment of the potential risks and benefits of various therapeutic agents, as well as a comprehensive evaluation of the myriad underlying risk factors that may predispose children to more severe disease, " the authors concluded. " Although our study is subject to certain limitations, it contributes evidence on several risk factors that are clearly associated with a more severe disease trajectory. "

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Impact of COVID-19 on Life of Students: Case Study in Hong Kong

1 Centre for Health Education and Health Promotion, The Chinese University of Hong Kong, 4/Floor, Lek Yuen Health Centre, Shatin, Hong Kong, China; kh.ude.khuc@gnuekarev (V.M.W.K.); kh.ude.khuc@ualtnecniv (V.T.C.L.); kh.ude.khuc@gnuehcnivlac (C.K.M.C.); kh.ude.khuc@olailema (A.S.C.L.)

2 School of Public Health, Prince of Wales Hospital, Shatin, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, 4/Floor, School of Public Health, Prince of Wales Hospital, Shatin, Hong Kong, China

Vera M. W. Keung

Vincent t. c. lau, calvin k. m. cheung, amelia s. c. lo, associated data.

Not applicable.

COVID-19 has an impact on the day-to-day life of students, with school closure and detrimental effects on health and well-being that cannot be underestimated. A study collected data reflecting the health and well-being of secondary school students entering a programme entitled “Healthy Life Planning: Assist Students to Acquire and Practice Health Knowledge and Skills” (ASAP study) in September and October 2019 before the outbreak of COVID-19. Follow-up data were collected in June and July 2020, over half a year since the spread of COVID-19, which facilitated analyses of its impact on the health behaviours and well-being of young people. Comparative analyses between baseline and the follow-up period were conducted on weight status, sleep pattern and quality, pattern of sedentary lifestyle, pattern of physical activity, attitudes and perceived barriers for exercise, and hand hygiene. Attitudes toward precautionary measures and influenza vaccination, self-reported changes in hygiene practices, exercise habits and eating habits were analysed. Although hygiene habits and risk perceptions among young people have improved in many aspects, the level of physical activity has declined as well as the beliefs and attitudes on increasing time on electronic media and change in sleep hygiene. Attitudes and beliefs towards influenza vaccination have declined, which would reflect the slow increase in the uptake rate of COVID-19 vaccination. Health education should equip students with the knowledge and skills to cultivate beliefs and attitudes to face health challenges.

1. Background and Introduction

Since the COVID-19 pandemic was declared, lockdown measures have been implemented in many parts of the world. Implementation of physical measures to interrupt or reduce the spread of respiratory viruses based on sustained physical distancing, restriction of social gathering, and “shut-down” measures has a strong potential to reduce the magnitude of the peak of the COVID-19 pandemic [ 1 ].

However, impacts on other aspects of health must not be underestimated. A study during the semi-lockdown period has shown males with BMI 24 or above had lost weight, but all other subjects had gained weight as a result of a significant decline in the amount of moderate or vigorous exercise [ 2 ]. Obesity has been shown to increase the risk of mortality of COVID-19 after adjusting for confounding factors such as age in different parts of the world [ 3 ].

A study by Fong et al. in 2020 found that 65.3% of participants experienced increased stress due to staying at home and 29.7% experienced moderate to severe levels of depressive symptoms; increases in the use of electronic devices and decreases in outside activities were positively associated with a higher level of depression severity [ 4 ]. Studies have also found increasing prevalence of obesity [ 5 ] and myopia [ 6 ] among school children due to longer screen times, lack of physical activity, and living in small, crowded living and learning spaces at home. Increasing physical activity and maintaining a healthy diet, leading to positive changes to their physical health, have also been shown to be associated with better mental health [ 7 , 8 ]. Non-communicable diseases such as cardiovascular diseases, chronic lung diseases, cancer and diabetes are still constituting the main health burdens of society [ 9 ]. The main drivers for an unhealthy diet and lack of physical activity would be a lack of places and opportunities to be physically active and industries’ opposition to public health interventions [ 10 ]. Behavioural, environmental and occupational, and metabolic risks can explain half of the global mortality and more than one-third of global disability-adjusted life year (DALY) [ 11 ]. A substantial burden of global cardiovascular disease morbidity and mortality is attributable to a sedentary lifestyle, and the attributable burden of high BMI has increased in the past 23 years; physical inactivity and unhealthy eating are the key underlying causes [ 11 ].

COVID-19 also has an impact on the day-to-day life of students with school closures [ 12 ]. Results of one study have shown a dramatic decline in assessment during COVID-19 in schools, suggesting lower performance when students start school in 2020 [ 13 ]. Schools may need to leverage decision-making frameworks, such as the Multi-Tiered Systems of Support/Response-to-Intervention (MTSS/RTI) framework [ 14 ] to identify needs and target instruction where it matters most when school begins in late 2020. During the first half of the academic year in 2020 in Hong Kong, schools were closed during the spring term with online learning, with half-day sessions in the summer term before closure again due to a third wave in July 2020 in Hong Kong. Schools reopened after the summer break in September 2020, with half-day sessions, and closed again in early December 2020 due to the fourth wave. Schools reopened in February 2021 with half-day sessions. The government has imposed restrictions on social gathering including numbers of people grouped together and the operation of restaurants and recreation facilities. Many recreation facilities including public utilities were closed or operated under strict control of people flow periodically in 2020. There is a need to study the impact of COVID-19 on student life with disruption of usual school life and social interaction during that period.

The Centre for Health Education and Health Promotion of the Chinese University of Hong Kong (CHEHP) has pioneered the Healthy School/Health Promoting School (HPS) movement in Hong Kong and neighbouring countries over the last two decades [ 15 , 16 ]. It has developed many initiatives, making use of the HPS framework to improve the health literacy of students [ 15 ]. Recently, it launched the ASAP study (Healthy Life Planning: Assist Students to Acquire and Practice Health Knowledge and Skills) to enrich the knowledge and skills of students on a variety of health-related matters. The ASAP Project provided health educational materials covering nine teaching units designed for junior secondary schools. Topics covered sleep hygiene, infectious disease control, travellers’ health, physical activity, body image, stress management, etc. From these, teachers chose one or more units for school-based curriculum enrichment. They were also required to develop experiential learning activities for students based on the topics they have taught. Students might conduct project learning on them as well.

The impact of COVID-19 on the lives of students who received the ASAP program is being studied. The aim of this study is to investigate the impact of COVID-19 on student health and well-being by collecting data reflecting the health and well-being of students at the entry of ASAP (before COVID-19 outbreak), then at a yearly interval (after the outbreak), to analyse any changes.

2. Materials and Methods

2.1. study design.

For this case study, comparative analyses between baseline and follow-up periods were conducted to identify potential changes in students’ weight status, sleep pattern and quality, pattern of sedentary lifestyle, pattern of physical activity, attitudes and perceived barriers for exercise, and hand hygiene. The attitudes toward precautionary measures to COVID-19 and influenza vaccination, self-reported changes in daily living habits, exercise habits, eating habits and hygiene practices were analysed.

The study has been approved by the Survey and Behaviour Research Ethics Committee (SBRE-19-104). The surveys were anonymous. The participating schools have obtained consent from parents and students and students’ participation was entirely voluntarily with no adverse repercussions.

2.2. Study Population

The study targeted students studying in grades between Secondary 1 (S1) and Secondary 3 (S3), aged about 11–15 years.

2.3. Sample Population

Eleven secondary schools in Hong Kong that participated in the ASAP study were invited to the pre-and-post questionnaire survey. School teachers were allowed to use the teaching materials provided by the programme to enrich their health-related curricula such as Physical Education, Technology and Living, Biology, and the school-based health curriculum. The teaching materials covered various health contents such as physical activities, sleep hygiene, stress management, body image, infectious disease control, dental health, the prevention of prolonged use of electronic devices, etc. At least one grade between S1 and S3 of the participating schools was beneficial to the study and eligible for the survey. A total of 1355 students studying in the selected grades were invited, and 1102 completed two administrations of the questionnaire, giving a response rate of 81.3%. The survey was anonymous and used the class number of each responding student to match the questionnaires completed in two administrations in September and October 2019 (baseline) and June and July 2020 (follow-up), respectively.

2.4. Measuring Tools

The Hong Kong Student Health Survey Questionnaire (HKSHQ) was used to collect data reflecting lifestyles, including hygiene practice and general health status. HKSHQ adopts a system of surveillance of student health status, taking reference from the US Centres for Disease Control and Prevention (CDC) Youth Risk Behavioural Surveillance (YRBS) [ 17 , 18 ] and Wessex Healthy School Award [ 19 ], which has been used by CHEHP [ 20 , 21 , 22 ] with continuous refinement as a tool for assessing student health status and health-related outcomes [ 16 ].

The parameters on demography include date of birth, gender, and self-rated health status (3 questions). The survey also uses the Family Affluence Scale (FAS), which was utilised to reflect the economic status of the respondents’ family from the following criteria (4 questions): the number of vehicles owned by the respondent’s family; whether the respondent has a separate bedroom; the number of family trips; and the number of computers owned by the family [ 23 , 24 ]. The Pittsburgh Sleep Quality Index was utilised to measure sleep quality [ 25 ].

Self-reported body weight of students was classified into wasting, desirable, and obese according to the weight-for-height charts in a local guide to childhood growth and nutrition assessment by Leung [ 26 ]. The charts are gender-specific, in which obesity is defined as body weight values above 120% of the median weight-for-height, while wasting is defined as body weight values below 80% of the median weight-for-height. When the body height value of a subject exceeds the data available in the charts, Body Mass Index (BMI) cut-offs for Asian adult populations are used to interpret the subject’s body weight, where a value between 18.5 kg/m 2 and 22.9 kg/m 2 is considered normal [ 27 ].

The Theory of Planned Behaviour by Ajzen [ 28 ] was applied in this survey to assess the attitudes and perceived behavioural control on physical activity ( Appendix A ). Similarly, the study also assessed the attitudes and perceived behavioural control on the uptake of influenza vaccination. COVID-19 vaccination was not available at the time of data collection, so their attitudes towards influenza vaccination would help us understand their perspectives on vaccination. Since follow-up data were collected during the COVID-19 pandemic, questions reflecting the respondents’ risk perception (such as the wearing of face masks, hand hygiene, social distancing, actions taken with suspected symptoms) were added to the questionnaire.

The Rosenberg Self-esteem Scale (RSE) by Morris Rosenberg was adopted in this survey to evaluate self-esteem in teenagers at the baseline of the study [ 29 , 30 ]. Leung and Wong [ 31 ] studied the validity and reliability of the Chinese translation of the RSE and gave recommendations on the Chinese wordings in some of the items. The current study adopted the Chinese translations recommended by Leung and Wong for item 3 (“I feel like a person who has a number of good qualities”), 7 (“I feel that I am a person of worth, at least on an equal plane with others”) and 8 (“I wish that I could have more respect for myself”) to optimise the reliability. The RSE and the Theory of Planned Behaviours [ 28 ] give a complete description of the non-cognitive development of the participants and a clear indication of the effects of the interventions in developing the habit of doing exercise and receiving a flu vaccine to prevent them from being infected.

The Mental Toughness Scale for Adolescents (MTS-A) by McGeown, St. Clair-Thompson and Putwain [ 32 ] was adopted in this survey to examine the mental toughness of teenagers before and after the interventions. The scale is an 18-item Likert scale with items answered on a four-point scale from “strongly disagree” to “strongly agree”. The concept of mental toughness in adolescents includes six domains: challenge, interpersonal confidence, confidence in abilities, emotion control, control of life, and commitment. Three statements describe each of the above domains in the teenager context, and respondents have to indicate how strongly they agree or disagree with each sentence. The author of MTS-A has granted the research team permission to use the scale supplemented with the Chinese translation.

2.5. Data Collection

The study collected data reflecting the health and well-being of students at the beginning and then at a yearly interval to monitor any changes. The baseline data were collected in September and October 2019 at the beginning of the academic year before the outbreak of COVID-19, and follow up data were collected in June and July 2020, half a year after its outbreak.

2.6. Data Analysis

The McNemar test was used to determine if there were differences among dichotomous dependent variables (such as whether the subjects had played ball games over the last seven days) between pre and post groups. Paired t-test was used for similar purposes but for comparing the means of continuous dependent variables (such as the subjects’ attitude score toward physical activities). A difference was considered statistically significant if the p -value was <0.05. Data were analysed by SPSS Statistics, version 25.0.

3. Findings

Table 1 describes the background demographic characteristics of the subjects, including socioeconomic status. The subjects had an average age of 13.28 years at baseline and 13.99 years at follow-up (standard deviation: 1.07 year). Sixty percent (60.2%) of them were female because two participating schools were girls’ schools, while the other nine were co-education. The subjects came from schools in urban settings, semi-urban settings and satellited towns.

Demographic characteristics of the subjects (N = 1102).

a Semi-urban setting b Urban setting. c Satellite towns (evolved from rural areas to urban setting).

About 50% of students came from the middle affluence group and about one-quarter from either high or low affluence groups. Most of the schools in this study are located in districts with monthly median domestic household incomes below the overall median level in Hong Kong. The sample is not skewed towards higher socioeconomic groups.

Results of the current study show that the proportion of students classified as obese decreased from 23.0% to 20.5% and 13.3% to 12.0% among male and female students, respectively. The changes were not statistically significant.

The percentage of students engaged in 60 min of moderate to vigorous exercise decreased with statistical significance from 40.8% to 30.1%, particularly those rigorous activities taking place in groups or in public, or vigorous activities such as running and jogging, ball games, swimming, playground activities, skating, and martial arts ( Table 2 ). The item “stretching” was added to the post-test questionnaire. Over one-fourth of students (26.6%) reported that they had done some stretching during the seven days before the post-survey, but no baseline data were available for direct comparison.

Level of physical activity.

Footnote . The item “stretching” was added to the post-test questionnaire. Over one-fourth of students (26.6%) reported that they had done some stretching during the seven days before the post-survey, but no baseline data were available. McNemar Test was performed. Arrows indicate the direction of significant changes. NS: non-significant.

Higher proportion of students spent more than two hours on an average school day watchng video programmes as well as internet surfing (not for academic purpose) on both ordinary school days and during holiday with statistical significance ( Table 3 ). The percentage of students who perceived no influence on the prolonged use of electronic media increased, and those who perceived eye fatigue and shoulder discomfort reduced ( Table 3 ). However, an increased impact on their concentration and study was reported with statistical significance ( Table 3 ). The proportion of students going to bed after 11:00 pm increased from 43.5% to 66.1%, and that of students getting up after 8:00 am increased from 10.0% to 32.9% with statistical significance, though sleep quality was not affected significantly ( Table 3 ). Self-reported handwashing behaviours improved, with a higher proportion of students washing hands thoroughly and a smaller proportion not taking handwashing seriously with statistical significance ( Table 4 ).

Time spent on electronic media (non-academic purpose) and sleep time.

Footnote . McNemar Test was performed except for comparing the average sleep hours and the scores of Pittsburgh Sleep Quality Index (PSQI). A PSQI score above 5 indicates poor sleep quality in the respondent. Paired t-test was performed to compare means. Arrows indicate the direction of significant changes. NS: non-significant.

Self-reported handwashing behaviours (number of valid cases = 971).

Footnote . McNemar Test performed. Arrows indicate the direction of significant changes.

Table 5 shows the changes in attitudes and beliefs towards physical activities from baseline to follow-up. The decline is observed in the goal of action, attitudes, subjective norm, perceived behavioural control, behavioural beliefs and norm beliefs with statistical significance. The behavioural intention and control beliefs also declined, although statistical significance was not detected.

Attitudes and beliefs toward physical activities.

Footnote . Paired t-test was performed to compare means. NS: non-significant.

Regarding the changes in attitudes and beliefs towards influenza vaccination from baseline to follow-up, Table 6 shows a decline in all domains with statistical significance, particularly behavioural intention and subjective norm and perceived behavioural control. Students are a target group for influenza vaccination in Hong Kong. Table 7 shows that a high proportion of students would continue wearing face masks and handwashing, but there was a lower proportion for other hygiene measures. This is reflected by just over half of students (54.9%) reporting a significant change in hygiene habits. More than half of students (52.8%) reported a decrease in physical activities such as running and walking, and 41.2% reported fewer ball games, and only a low proportion of students reported having participated in other physical activities such as outdoor activities ( Table 7 ). Although students tend to eat healthier at home, this proportion (55.0%) is not very high, and less than one-fifth of students (17.5%) had a significant change in eating habits ( Table 7 ).

Attitudes and beliefs toward influenza vaccination.

Footnote . Paired t-test was performed to compare means.

Change in health and hygiene behaviours during COVID-19.

Table 8 shows students’ intention to maintain precautionary measures over the next three months post-test. The majority of students would continue to wear a face mask and be meticulous about handwashing, in line with findings of current practices, shown in Table 6 . About half of the students would like to see a relaxation on physical distancing and restriction of gathering to allow more interaction. Students have a higher risk perception of respiratory symptoms; they would not go to school or activities and would only continue if no fever and reporting symptoms ( Table 8 ).

Intention to maintain precautionary measures over next three months post-test.

4. Discussion

The decline in the level of physical activity and the prolonged use of electronic media, with increasing effects on students’ learning, concentration, and sleep pattern (going to bed late and getting up late), are worrying ( Table 2 and Table 3 ). Socioecological models state that a person’s health status is not only influenced by individual behaviours, but also by factors situated in a person’s environment [ 33 , 34 ]. The concept of “environment” captures multiple dimensions, and a Built Environment (BE) can be defined broadly as “the human-made space in which people live, work and recreate on a day-to-day basis” [ 35 ]. During the COVID-19 pandemic, the BE has been altered due to various preventive and lockdown measures. It not only encompasses green spaces and parks, but also includes the internal environment and social capital (defined as social networks and interactions that inspire trust and reciprocity among citizens) [ 36 ]. The social environment, part of the BE, refers to factors such as social support and social networks, social deprivation, and social cohesion and systems [ 37 ]. BE shapes individual health behaviour through diverse mechanisms and can be adverse or beneficial for health [ 38 ]. Neighbourhoods that are more walkable, either leisure-oriented or destination-driven, are associated with increased physical activity, increased social capital, lower overweight rates, lower reports of depression, and less reported alcohol use [ 39 ]. Better street connectivity or walkability tended to be positively related to increased physical activity and walking [ 40 ].

One study has found that adolescents undertook more physical activity during lockdown if they had stronger prior physical activity habits, but some were unsure of what to do when they did not have instruction from a coach. Some adolescents reported that physical activity became a method of entertainment during lockdown, and this mindset change increased the level of physical activity [ 41 ]. Living space is very limited in Hong Kong, making physical activity at home not feasible for many young people. Online coach-led physical activity sessions have helped encourage and support adolescents to follow online exercise routines [ 41 ]. The implementation of lockdown measures and school closures has a significant impact on the BE, not only in terms of walkability and connectivity but also in terms of social connectivity and support. Apart from the effect on physical activities, we must not underestimate its negative effect on other aspects of health, such as psycho-social well-being, as a result of the impact of COVID on the BE diminishing social capital. This might be reflected by less positive beliefs and attitudes towards physical activities ( Table 5 ). Around half of the students reported a decreased frequency of walking or running and ball games without much increase in other types of indoor physical activities ( Table 7 ).

Although staying at home should enable students to eat healthier, this proportion is not high and less than 20% of students had a significant change in eating habits ( Table 7 ). Previous studies have revealed a low level of physical activities and healthy eating among secondary students [ 42 , 43 ]. COVID-19 might have worsened these conditions.

Some previous studies stated that lockdown and school closures might exacerbate childhood obesity [ 44 ] and cause unhealthy changes to the diet of students [ 45 , 46 ]. Past studies also support the claim that when students are not in school, they tend to have less healthy diets [ 47 ]. The findings of our survey showed similar results, with 29.2% students consuming unhealthy takeaway food, and one-fifth of students having increased consumption of soft drinks (20.2%), desserts (19.8%) and crispy food (19.7%). However, over half of the students (55.0%) indicated that they had healthier meals at home, and 38.6% of them consumed more fresh fruits, implying that the COVID-19 pandemic might have brought not only negative impacts but also some positive changes to the diet of students. Such positive changes may partly be explained by the fact that before the pandemic, most secondary students in Hong Kong consumed their lunch at nearby restaurants or fast food shops when they had whole-day classes on average school days [ 14 ]. School suspension as well as the fear of infection drove students to stay home for food, while lockdown and work-from-home arrangements also allowed more parents to prepare meals for their children. Further studies are required to investigate whether such changes will lead to any changes in childhood obesity in Hong Kong.

The percentage of students who perceived no influence on the prolonged use of electronic media increased, but those who perceived eye fatigue and shoulder discomfort reduced ( Table 3 ). This may be due to adaptation. However, prolonged use had an impact on their studies and concentration as well as sleep pattern ( Table 3 ).

It is encouraging to observe the improvement in hand hygiene reflected by more serious handwashing ( Table 4 ). However, it is disappointing and alarming to find the decline in beliefs and attitudes, including motivation and perceived control, towards influenza vaccination with statistical significance (most showing p-value lower than 0.001) ( Table 6 ). This could be due to school suspension during the pandemic, and so, they perceived having a lower risk of being infected. However, the scores at baseline were already low, which makes it difficult to identify a further significant decline. This might reflect the weak perception of the beneficial effect of influenza vaccination. It might also account for the slow increase in the uptake of COVID-19 vaccination in Hong Kong [ 48 ], which is also observed in other parts of the world [ 49 ]. Previous studies on predictive factors of influenza vaccination suggested that factors related to health belief models such as perceived adverse effects and efficacy and advice given by health care professionals are determinant factors for the uptake of vaccination [ 50 , 51 ].

The uptake rate of COVID-19 vaccines in Hong Kong is still unsatisfactory, despite the availability and accessibility of the vaccine. There is room for improvement to enhance the health beliefs and attitudes towards vaccines for preventing the disease. A study on the acceptance of the COVID-19 vaccine found that people who perceived the seriousness of the infection, vaccine conferring benefits, and received calls to action were significantly more likely to accept the vaccine [ 52 ]. Conversely, perception of barriers to accessibility and potential harm of the vaccine were found negatively to be associated with their acceptance. Recommendation by the government stood out as the most important cue. Public health intervention programmes focusing on increasing the perception of the benefits of vaccination and perceived susceptibility to infection while reducing the identified barriers should be warranted [ 53 ]. The study also revealed that the public values efficacy and safety more than the cost of vaccines. Another study in the US found that a greater likelihood of COVID-19 vaccine acceptance was associated with more knowledge about vaccines, less acceptance of vaccine conspiracies, elevated COVID-19 threat appraisals, and being up to date with influenza immunisation [ 49 ]. The other demographic predictors of a likelihood of being vaccinated against COVID-19 were higher income group (income of USD 120,000 or higher) and being a Democrat (in comparison to the reference category Republican), and respondents relying on social media for information about COVID-19 anticipated a lower likelihood of COVID-19 vaccine acceptance. More public health interventions targeting those factors facilitating and hindering uptake should be put in place.

The closure of schools during COVID-19 could result in the loss of opportunity to foster positive beliefs and attitudes in students towards influenza vaccination. It could also have an impact on the low uptake rate of COVID-19 vaccination. From the findings of this study, there is room to enhance the perception of the benefits of vaccination against infectious disease in students, particularly before pandemics and the potential consequences if not vaccinated. Health education should cultivate a positive and supportive culture to support family members and friends to receive the vaccination. Health literacy includes access and analysing health information and problem solving such as breaking the barriers to access these services. This would help to improve the acceptance and uptake rate. A recent study in Hong Kong has found a higher level of vaccine acceptance among the youngest adult group (age 18 to 24), which would be due to better exposure to vaccine education and receiving the free vaccine at birth [ 52 ]. Findings from this study have shown that students perceived the importance of wearing face masks in public places, were meticulous about handwashing and highly vigilant with regard to respiratory symptoms ( Table 8 ). Risk perceptions are a critical determinant of health behaviour, and the profile of risk perceptions and accuracy of perception would affect the association between risk perceptions and health behaviours [ 54 ]. Although a high level compliance of facemask wearing was observed and more people maintained social distancing and used alcohol hand rub during the pandemic, decreasing willingness to accept the COVID-19 vaccines was also observed. This might be associated with increasing concerns about vaccine safety and growing compliance of personal protection behaviours [ 55 ]. Therefore, the concept of “ASAP” should be adopted for school curriculum development to assist students in acquiring and practicing health knowledge and skills, including health risk perception and preventive measures for infectious diseases from a broader perspective that includes vaccination.

A substantial proportion of students expressed their wishes to relax social distancing and restriction of gathering ( Table 8 ). Although measures such as closing and restricting most places where people gather in smaller or larger numbers for extended periods (businesses, bars, schools and so on) are most effective, they can cause substantial collateral damage to society, the economy, trade and human rights [ 56 ]. This study has shown the collateral damage to students’ health and well-being and their health beliefs and attitudes. The COVID-19 pandemic has also been found to lead to an increase in myopia among young children in Hong Kong; the prevalence of myopia among school-age children during the pandemic has increased significantly compared to a study conducted before the outbreak [ 57 ]. Prolonged exposure to screens and less time spent outdoors were linked to faster progress in myopia, according to researchers. One study found several highly effective measures that are less intrusive, including land border restrictions, governmental support to vulnerable populations and risk-communication strategies [ 58 ]. Therefore, governments and other stakeholders should consider adopting non-pharmaceutical interventions tailored to the local context when infection numbers surge (or surge a second time) before choosing those intrusive options. Less drastic measures may also foster better compliance from the population [ 52 ].

There are limitations to this study. The subjects are participants of the ASAP study, not a random sample of secondary students. The demography of the students is not markedly different from the demography of students in Hong Kong. They do not skew towards particular demographic characteristics except for the subjects’ gender as two schools are girls’ schools while the others are co-education.

There is a potential bias that they are more health-conscious and have better knowledge and more positive attitudes towards health. Most of the schools are located in districts with median monthly household income below the median in Hong Kong. The sample is not skewed towards higher socioeconomic groups. The students should be more resilient towards the impact of COVID-19 on healthy living. The findings of the study that reflect the beliefs, attitudes, perceived control, and behaviours of students under the pandemic have significant implications. There is an assumed hypothesis that students with better health literacy will maintain positive health beliefs and positive attitudes and behaviours towards healthy living. The findings will help to test this assumption and shed light on which aspects of their beliefs, attitudes and behaviours can be sustained under adverse conditions (such as COVID-19) and how young people should be supported further, notwithstanding that they might have enriched knowledge and skills in health.

Another limitation is the lack of a control group. It is technically difficult to engage more students and schools to participate in the survey under the COVID-19 situation. Moreover, there will not be a perfect control group as schools and students cannot be controlled to receive information and skills enhancement to fight against COVID-19. However, the study has included studies on belief, perceived barriers of control, and attitudes. The findings would partially explain why students behave in a particular way during the COVID-19 period. The global impact of the COVID-19 pandemic has not been experienced for nearly a century. Data reflecting the impact on students’ life would provide useful insights for combating similar challenges in the near future.

5. Conclusions

The current study reveals the changes in physical activities, hygiene and dietary behaviours in Hong Kong adolescents between September 2019 and July 2020, when the novel coronavirus disease (COVID-19) started to hit many parts of the world, resulting in the pandemic. These changes include less moderate and rigorous physical activities, and the attitudes and beliefs of students towards physical activities have become less positive and less persistent. Although hygiene habits and risk perceptions among young people have improved in many aspects, attitudes and beliefs towards influenza vaccination have declined, which would reflect the slow increase in the uptake rate of COVID-19 vaccination. This study has shown the changes in students’ health behaviours, beliefs and attitudes. Health education targeting young people and the public should equip them with the knowledge and skills to cultivate beliefs and attitudes and this would have impact on risk perceptions and behaviours to face health challenges.

Acknowledgments

We would also like to thank the school teachers for using the teaching materials provided by the ASAP study and facilitating students to complete the survey.

• Attitude (4 items): “My taking regular physical activity over the next six months would be…” (harmful to beneficial; unpleasant to pleasant; unenjoyable to enjoyable) and “My attitude towards doing physical activity is…” (from very negative to very positive)
• Perceived Barrier Control (2 items): “For me to exercise for at least 60 minutes every day for the next fortnight will be…” (from very easy to very difficult) and “I am confident that I can accumulate 60 minutes of physical activity every day in the next two weeks.” (from strongly disagree to strongly agree)

Author Contributions

Conceptualization, A.L. and V.M.W.K.; methodology and analysis, V.M.W.K. and V.T.C.L.; writing—original draft preparation, A.L.; writing—reviewing and editing, V.M.W.K., C.K.M.C. and A.S.C.L. All authors have read and agreed to the published version of the manuscript.

Keung M.W., Cheung K.M. and Lau T.C. were supported by a grant from the Quality Education Fund (QEF 2017/1070) awarded to Lee A. QEF was established in 1998 by the Government of the Hong Kong Special Administrative Region for educational initiatives and projects within the ambit of school education of Hong Kong, including kindergarten, primary, secondary and special education.

Institutional Review Board Statement

The survey was approved by the Survey and Behavioural Research Ethics Committee of the Chinese University of Hong Kong (SBRE-19-104).

Informed Consent Statement

School consent was obtained from each participating school.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

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Symptoms of COVID-19

People with COVID-19 have reported a wide range of symptoms ranging from mild symptoms to severe illness. Symptoms may appear 2-14 days after exposure to the virus. Anyone can have mild to severe symptoms. Possible symptoms include:

• Fever or chills
• Shortness of breath or difficulty breathing
• Muscle or body aches
• New loss of taste or smell
• Sore throat
• Congestion or runny nose
• Nausea or vomiting

This list does not include all possible symptoms. Symptoms may change with new COVID-19 variants and can vary by person . CDC will continue to update this list as we learn more about COVID-19.

Feeling Sick?

Stay home and away from others (including people you live with who are not sick) if you have symptoms that aren’t better explained by another cause.

Seek health care promptly for testing and/or treatment if you have risk factors for severe illness ; treatment may help lower your risk of severe illness.

When to Seek Emergency Medical Attention

Look for emergency warning signs* for COVID 19:

• Trouble breathing
• Persistent pain or pressure in the chest
• New confusion
• Inability to wake or stay awake
• Pale, gray, or blue-colored skin, lips, or nail beds, depending on skin tone

If someone is showing any of these signs, call 911 or call ahead to your local emergency facility. Notify the operator that you are seeking care for someone who has or may have COVID-19.

*This list is not all possible symptoms. Please call your medical provider for any other symptoms that are severe or concerning to you.

Influenza (flu) and COVID-19 are both contagious respiratory illnesses, but they are caused by different viruses. COVID-19 is caused by infection with a coronavirus named SARS-CoV-2, and flu is caused by infection with influenza viruses. You cannot tell the difference between flu and COVID-19 by symptoms alone because some of the symptoms are the same. Some PCR tests can differentiate between flu and COVID-19 at the same time. If one of these tests is not available, many testing locations provide flu and COVID-19 tests separately.

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ASL Symptoms of Coronavirus

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Healthcare Workers: Information on COVID-19

Search for and find historical COVID-19 pages and files. Please note the content on these pages and files is no longer being updated and may be out of date.

• Visit archive.cdc.gov for a historical snapshot of the COVID-19 website, capturing the end of the Federal Public Health Emergency on June 28, 2023.
• Visit the dynamic COVID-19 collection  to search the COVID-19 website as far back as July 30, 2021.

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• The Centers for Disease Control and Prevention (CDC) cannot attest to the accuracy of a non-federal website.
• Linking to a non-federal website does not constitute an endorsement by CDC or any of its employees of the sponsors or the information and products presented on the website.
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Masks Strongly Recommended but Not Required in Maryland, Starting Immediately

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COVID-19 Vaccine: What You Need to Know

The COVID-19 vaccine is very good at preventing serious illness, hospitalization and death. Because the virus that causes COVID-19 continues to change, vaccines are updated to help fight the disease. It is important to check the Centers for Disease Control and Prevention (CDC) COVID-19 vaccine information for the latest details. (Posted 11/22/23)

What is the COVID-19 vaccine?

The COVID-19 vaccine lessens the severity of COVID-19 by teaching the immune system to recognize and fight the virus that causes the disease.

For fall/winter 2023–2024, the updated COVID-19 vaccine is based on the XBB.1.5 variant. The updated vaccine is made by Pfizer-BioNTech, Moderna and Novavax. This season, only one shot of the vaccine is needed for most people, and there are no boosters. (People who are immunocompromised or ages 6 months to 4 years may need more than one 2023–2024 vaccine.)

How is the 2023–2024 COVID-19 vaccine different from previous COVID-19 vaccines?

The 2023–2024 COVID-19 vaccine targets XBB.1.5, a subvariant of Omicron. While none of the variants currently circulating are exact matches to the vaccine, they are all closely related to the XBB.1.5 strain. Studies show that the updated vaccine is effective against the  variants currently causing the majority of COVID-19 cases  in the U.S.

Who should get a COVID-19 vaccine?

Because the 2023–2024 vaccine is effective for recent strains of COVID-19, it is recommended that everyone stay up to date with this vaccine. Previous vaccines or boosters were not developed to target the more recent strains. For 2023–2024, the CDC recommends:

• Everyone age 5 and older receive one shot of the updated vaccine.
• Children ages 6 months to 4 years may need more than one shot to be up to date.
• People who are moderately or severely immunocompromised may need more than one shot.

You can review the full recommendations on the CDC’s Stay Up to Date with COVID-19 Vaccines webpage . Be sure to talk to your primary care doctor or pediatrician if you are unsure about vaccine recommendations.

What are the side effects of the COVID-19 vaccine?

Side effects vary and may last one to three days. Common side effects are:

• Soreness at the injection site

COVID-19 Vaccine and Pregnancy

COVID-19 vaccines approved by the Food and Drug Administration (FDA) are safe and recommended for people who are pregnant or lactating, as well as for those r intending to become pregnant.

People who are pregnant or were recently pregnant are at a greater risk for severe COVID-19. Having a severe case of COVID-19 while pregnant is linked to a higher risk of pre-term birth and stillbirth and might increase the risk of other pregnancy complications.

What should parents know about the COVID-19 vaccine and children?

The CDC recommends the 2023–2024 vaccine for adolescents and teenagers ages 12 and older, and for children ages 6 months through 11 years.

• Children age 5 and older need one shot of the updated vaccine.

Children are less likely to become seriously ill from COVID-19 than adults, although serious illness can happen. Speak with your pediatrician if you have questions about having your child vaccinated.

If I recently had COVID-19, do I need a 2023–2024 vaccine?

If you recently had COVID-19, the CDC recommends waiting about three months before getting this updated vaccine. If you encounter the virus again, having the updated vaccine will:

• Lessen your risk of severe disease that could require hospitalization
• Reduce the chance that you infect someone else with COVID-19
• Help keep you protected from currently circulating COVID-19 variants

How long should I wait to get this vaccine if I recently had an earlier version of a COVID-19 vaccine or booster?

People age 5 years and older should wait at least two months after getting the last dose of any COVID-19 vaccine before receiving the 2023–2024 vaccine,  according to CDC guidance .

Is natural immunity better than a vaccine?

Natural immunity is the antibody protection your body creates against a germ once you’ve been infected with it. Natural immunity to the virus that causes COVID-19 is no better than vaccine-acquired immunity, and it comes with far greater risks. Studies show that natural immunity to the virus weakens over time and does so faster than immunity provided by COVID-19 vaccination.

Do I need a COVID-19 booster?

The 2023–2024 vaccine is a one-shot vaccine for most people, and there is no booster this season. (People who are immunocompromised or ages 6 months to 4 years may need more than one 2023–2024 vaccine.)

The FDA calls this an updated vaccine (not a “booster” like previous shots) because it builds a new immune response to variants that are currently circulating. This change reflects the current approach of treating COVID-19 similarly to the flu, with preventive measures such as an annual vaccination.

When should I get a COVID-19 vaccine?

Like the flu and other respiratory diseases, COVID-19 tends to be more active in the fall and winter, so getting a vaccine in the fall is recommended.

How quickly does the COVID-19 vaccine become effective?

It usually takes about two weeks for the vaccine to become effective. The CDC website provides more information on how the COVID-19 vaccines work .

How long does the COVID-19 vaccine last?

Studies suggest that COVID-19 vaccines are most effective during the first three months after vaccination.

Is it safe to get a flu and COVID-19 vaccine at the same time?

Yes, it safe to get both shots at the same time. Keep in mind that each has similar side effects and you may experience side effects from both.

Is the COVID-19 vaccine safe?

Yes. COVID-19 vaccines approved by the FDA meet rigorous testing criteria and are safe and effective at preventing serious illness, hospitalization and death. Millions of people have received the vaccines, and the CDC continues to monitor their safety and effectiveness as well as rare adverse events.

Where can I get a COVID-19 vaccine?

The COVID-19 vaccine is available at pharmacies. See vaccines.gov to find a convenient location.

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Maps & Trends

New covid-19 cases worldwide, daily confirmed new cases (7-day moving average), outbreak evolution for the current most affected countries, about this page:, have countries flattened the curve.

Countries around the world are working to “flatten the curve” of the coronavirus pandemic. Flattening the curve involves reducing the number of new COVID-19 cases from one day to the next. This helps prevent healthcare systems from becoming overwhelmed. When a country has fewer new COVID-19 cases emerging today than it did on a previous day, that’s a sign that the country is flattening the curve.

On a trend line of total cases, a flattened curve looks how it sounds: flat. On the charts on this page, which show new cases per day, a flattened curve will show a downward trend in the number of daily new cases.

This analysis uses a 7-day moving average to visualize the number of new COVID-19 cases and calculate the rate of change. This is calculated for each day by averaging the values of that day, the three days before, and the three next days. This approach helps prevent major events (such as a change in reporting methods) from skewing the data. The interactive charts below show the daily number of new cases for the most affected countries, based on the moving average of the reported number of daily new cases of COVID-19 and having more than 1 million inhabitants.