introduction in research about absenteeism

Report | Children

Student absenteeism : Who misses school and how missing school matters for performance

Report • By Emma García and Elaine Weiss • September 25, 2018

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A broader understanding of the importance of student behaviors and school climate as drivers of academic performance and the wider acceptance that schools have a role in nurturing the “whole child” have increased attention to indicators that go beyond traditional metrics focused on proficiency in math and reading. The 2015 passage of the Every Student Succeeds Act (ESSA), which requires states to report a nontraditional measure of student progress, has codified this understanding.

The vast majority of U.S. states have chosen to comply with ESSA by using measures associated with student absenteeism—and particularly, chronic absenteeism. This report uses data on student absenteeism to answer several questions: How much school are students missing? Which groups of students are most likely to miss school? Have these patterns changed over time? And how much does missing school affect performance?

Data from the National Assessment of Educational Progress (NAEP) in 2015 show that about one in five students missed three days of school or more in the month before they took the NAEP mathematics assessment. Students who were diagnosed with a disability, students who were eligible for free lunch, Hispanic English language learners, and Native American students were the most likely to have missed school, while Asian students were rarely absent. On average, data show children in 2015 missing fewer days than children in 2003.

Our analysis also confirms prior research that missing school hurts academic performance: Among eighth-graders, those who missed school three or more days in the month before being tested scored between 0.3 and 0.6 standard deviations lower (depending on the number of days missed) on the 2015 NAEP mathematics test than those who did not miss any school days.

Introduction and key findings

Education research has long suggested that broader indicators of student behavior, student engagement, school climate, and student well-being are associated with academic performance, educational attainment, and with the risk of dropping out. 1

One such indicator—which has recently been getting a lot of attention in the wake of the passage of the Every Student Succeeds Act (ESSA) in 2015—is student absenteeism. Absenteeism—including chronic absenteeism—is emerging as states’ most popular metric to meet ESSA’s requirement to report a “nontraditional” 2 measure of student progress (a metric of “school quality or student success”). 3

Surprisingly, even though it is widely understood that absenteeism has a substantial impact on performance—and even though absenteeism has become a highly popular metric under ESSA—there is little guidance for how schools, districts, and states should use data about absenteeism. Few empirical sources allow researchers to describe the incidence, trends over time, and other characteristics of absenteeism that would be helpful to policymakers and educators. In particular, there is a lack of available evidence that allows researchers to examine absenteeism at an aggregate national level, or that offers a comparison across states and over time. And although most states were already gathering aggregate information on attendance (i.e., average attendance rate at the school or district level) prior to ESSA, few were looking closely into student-level attendance metrics, such as the number of days each student misses or if a student is chronically absent, and how they mattered. These limitations reduce policymakers’ ability to design interventions that might improve students’ performance on nontraditional indicators, and in turn, boost the positive influence of those indicators (or reduce their negative influence) on educational progress.

In this report, we aim to fill some of the gaps in the analysis of data surrounding absenteeism. We first summarize existing evidence on who misses school and how absenteeism matters for performance. We then analyze the National Assessment of Educational Progress (NAEP) data from 2003 (the first assessment with information available for every state) and 2015 (the most recent available microdata). As part of the NAEP assessment, fourth- and eighth-graders were asked about their attendance during the month prior to taking the NAEP mathematics test. (The NAEP assessment may be administered anytime between the last week of January and the end of the first week of March, so “last month” could mean any one-month period between the first week of January and the first week of March.) Students could report that they missed no days, 1–2 days, 3–4 days, 5–10 days, or more than 10 days.

We use this information to describe how much school children are missing, on average; which groups of children miss school most often; and whether there have been any changes in these patterns between 2003 and 2015. We provide national-level estimates of the influence of missing school on performance for all students, as well as for specific groups of students (broken out by gender, race/ethnicity and language status, poverty/income status, and disability status), to detect whether absenteeism is more problematic for any of these groups. We also present evidence that higher levels of absenteeism are associated with lower levels of student performance. We focus on the characteristics and outcomes of students who missed three days of school or more in the previous month (the aggregate of those missing 3–4, 5–10, and more than 10 school days), which is our proxy for chronic absenteeism. 4 We also discuss data associated with children who had perfect attendance the previous month and those who missed more than 10 days of school (our proxy for extreme chronic absenteeism).

Given that the majority of states (36 states and the District of Columbia) are using “chronic absenteeism” as a metric in their ESSA accountability plans, understanding the drivers and characteristics of absenteeism and, thus, the policy and practice implications, is more important than ever (Education Week 2017). Indeed, if absenteeism is to become a useful additional indicator of learning and help guide effective policy interventions, it is necessary to determine who experiences higher rates of absenteeism; why students miss school days; and how absenteeism affects student performance (after controlling for factors associated with absenteeism that also influence performance).

Major findings include:

One in five eighth-graders was chronically absent. Typically, in 2015, about one in five eighth-graders (19.2 percent) missed school three days or more in the month before the NAEP assessment and would be at risk of being chronically absent if that pattern were sustained over the school year.

• About 13 percent missed 3–4 days of school in 2015; about 5 percent missed 5–10 days of school (between a quarter and a half of the month); and a small minority, less than 2 percent, missed more than 10 days of school, or half or more of the school days that month.
• We find no significant differences in rates of absenteeism and chronic absenteeism by grade (similar shares of fourth-graders and eighth-graders were absent), and the patterns were relatively stable between 2003 and 2015.
• While, on average, there was no significant change in absenteeism levels between 2003 and 2015, there was a significant decrease over this period in the share of students missing more than 10 days of school.

Absenteeism varied substantially among the groups we analyzed. In our analysis, we look at absenteeism by gender, race/ethnicity and language status, FRPL (free or reduced-price lunch) eligibility (our proxy for poverty status), 5 and IEP (individualized education program) status (our proxy for disability status). 6 Some groups had much higher shares of students missing school than others.

• Twenty-six percent of IEP students missed three school days or more, compared with 18.3 percent of non-IEP students.
• Looking at poverty-status groups, 23.2 percent of students eligible for free lunch, and 17.9 percent of students eligible for reduced-price lunch, missed three school days or more, compared with 15.4 percent of students who were not FRPL-eligible (that is, eligible for neither free lunch nor reduced-price lunch).
• Among students missing more than 10 days of school, the share of free-lunch-eligible students was more than twice as large as the share of non-FRPL-eligible students (2.3 percent vs. 1.1 percent). Similarly, the share of IEP students in this category was more than double the share of non-IEP students (3.2 percent vs. 1.5 percent).
• Perfect attendance rates were slightly higher among black and Hispanic non-ELL students than among white students, although all groups lagged substantially behind Asian students in this indicator.
• Hispanic ELL students and Asian ELL students were the most likely to have missed more than 10 school days, at 3.9 percent and 3.2 percent, respectively. These shares are significantly higher than the overall average rate of 1.7 percent and than the shares for their non-ELL counterparts (Hispanic non-ELL students, 1.6 percent; Asian non-ELL students, 0.6 percent).

Absenteeism varied by state. Some states had much higher absenteeism rates than others. Patterns within states remained fairly consistent over time.

• In 2015, California and Massachusetts were the states with the highest full-attendance rates: 51.1 and 51.0 percent, respectively, of their students did not miss any school days; they are closely followed by Virginia (48.4 percent) and Illinois and Indiana (48.3 percent).
• At the other end of the spectrum, Utah and Wyoming had the largest shares of students missing more than 10 days of school in the month prior to the 2015 assessment (4.6 and 3.5 percent, respectively).
• Five states and Washington, D.C., stood out for their high shares of students missing three or more days of school in 2015: in Utah, nearly two-thirds of students (63.5 percent) missed three or more days; in Alaska, nearly half (49.6 percent) did; and in the District of Columbia, Wyoming, New Mexico, and Montana, nearly three in 10 students were in this absenteeism category.
• In most states, overall absenteeism rates changed little between 2003 and 2015.

Prior research linking chronic absenteeism with lowered academic performance is confirmed by our results. As expected, and as states have long understood, missing school is negatively associated with academic performance (after controlling for factors including race, poverty status, gender, IEP status, and ELL status). As students miss school more frequently, their performance worsens.

• Overall performance gaps. The gaps in math scores between students who did not miss any school and those who missed three or more days of school varied from 0.3 standard deviations (for students who missed 3–4 days of school the month prior to when the assessment was taken) to close to two-thirds of a standard deviation (for those who missed more than 10 days of school). The gap between students who did not miss any school and those who missed just 1–2 days of school was 0.10 standard deviations, a statistically significant but relatively small difference in practice.
• For Hispanic non-ELL students, missing more than 10 days of school harmed their performance on the math assessment more strongly than for the average (0.74 standard deviations vs. 0.64 on average).
• For Asian non-ELL students, the penalty for missing school was smaller than the average (except for those missing 5–10 days).
• Missing school hindered performance similarly across the three poverty-status groups (nonpoor, somewhat poor, and poor). However, given that there are substantial differences in the frequency with which children miss school by poverty status (that is, poor students are more likely to be chronically absent than nonpoor students), absenteeism may in fact further widen income-based achievement gaps.

What do we already know about why children miss school and which children miss school? What do we add to this evidence?

Poor health, parents’ nonstandard work schedules, low socioeconomic status (SES), changes in adult household composition (e.g., adults moving into or out of the household), residential mobility, and extensive family responsibilities (e.g., children looking after siblings)—along with inadequate supports for students within the educational system (e.g., lack of adequate transportation, unsafe conditions, lack of medical services, harsh disciplinary measures, etc.)—are all associated with a greater likelihood of being absent, and particularly with being chronically absent (Ready 2010; U.S. Department of Education 2016). 8 Low-income students and families disproportionately face these challenges, and some of these challenges may be particularly acute in disadvantaged areas 9 ; residence in a disadvantaged area may therefore amplify or reinforce the distinct negative effects of absenteeism on educational outcomes for low-income students.

A detailed 2016 report by the U.S. Department of Education showed that students with disabilities were more likely to be chronically absent than students without disabilities; Native American and Pacific Islander students were more likely to be chronically absent than students of other races and ethnicities; and non-ELL students were more likely to be chronically absent than ELL students. 10 It also showed that students in high school were more likely to miss school than students in other grades, and that about 500 school districts reported that 30 percent or more of their students missed at least three weeks of school in 2013–2014 (U.S. Department of Education 2016).

Our analysis complements this evidence by adding several dimensions to the breakdown of who misses school—including absenteeism rates by poverty status and state—and by analyzing how missing school harms performance. We distinguish by the number of school days students report having missed in the month prior to the assessment (using five categories, from no days missed to more than 10 days missed over the month), 11 and we compare absenteeism rates across grades and across cohorts (between 2003 and 2015), as available in the NAEP data. 12

How much school are children missing? Are they missing more days than the previous generation?

In 2015, almost one in five, or 19.2 percent of, eighth-grade students missed three or more days of school in the month before they participated in NAEP testing. 13 About 13 percent missed 3–4 days, roughly 5 percent missed 5–10 days, and a small share—less than 2 percent—missed more than 10 days, or half or more of the instructional days that month ( Figure A , bottom panel). 14

How much school are children missing? : Share of eighth-grade students by attendance/absenteeism category, in the eighth-grade mathematics NAEP sample, 2003 and 2015

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Source: EPI analysis of National Assessment of Educational Progress microdata, 2003 and 2015

On average, however, students in 2015 did not miss any more days than students in the earlier period; by some measures, they missed less school than children in 2003 (Figure A, top panel). While the share of students with occasional absences (1–2 days) increased moderately between 2003 and 2015, the share of students who missed more than three days of school declined by roughly 3 percentage points between 2003 and 2015. This reduction was distributed about evenly (in absolute terms) across the shares of students missing 3–4, 5–10, and more than 10 days of school. But in relative terms, the reduction was much more significant in the share of students missing more than 10 days of school (the share decreased by nearly one-third). We find no significant differences by grade ( Appendix Figure A ) or by subject. Thus, we have chosen to focus our analyses below on the sample of eighth-graders taking the math assessment only.

Which groups miss school most often? Which groups suffer the most from chronic absenteeism?

Absenteeism by race/ethnicity and language status.

Hispanic ELLs and the group made up of Native Americans plus “all other races” (not white, black, Hispanic, or Asian) are the racial/ethnic and language status groups that missed school most frequently in 2015. Only 39.6 percent (Native American or other) and 41.2 percent (Hispanic ELL) did not miss any school in the month prior to the assessment (vs. 44.4 percent overall, 43.2 percent for white students, 43.5 percent for black students, and 44.1 percent for Hispanic non-ELL students; see Figure B1 ). 15

Which groups of students had the highest shares missing no school? : Share of eighth-graders with perfect attendance in the month prior to the 2015 NAEP mathematics assessment, by group

Notes: Students are grouped by gender, race/ethnicity and ELL status, FRPL eligibility, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines).

Source: EPI analysis of National Assessment of Educational Progress microdata, 2015

Asian students (both non-ELL and ELL) are the least likely among all racial/ethnic student groups to be absent from school at all. Two-thirds of Asian non-ELL students and almost as many (61.6 percent of) Asian ELL students did not miss any school. Among Asian non-ELL students, only 8.8 percent missed three or more days of school: 6.1 percent missed 3–4 days (12.7 percent on average), 2.1 percent missed 5–10 days (relative to 4.8 percent for the overall average), and only 0.6 percent missed more than 10 days of school (relative to 1.7 percent for the overall average). Among Asian ELL students, the share who missed three or more days of school was 13.3 percent.

As seen in Figure B2 , the differences in absenteeism rates between white students and Hispanic non-ELL students were relatively small, when looking at the shares of students missing three or more days of school (18.3 percent and 19.1 percent, respectively). The gaps are somewhat larger for black, Native American, and Hispanic ELL students relative to white students (with shares missing three or more days at 23.0, 24.0, and 24.1 percent, respectively, relative to 18.3 percent for white students).

Which groups of students had the highest shares missing three or more days? : Share of eighth-graders missing three or more days of school in the month prior to the 2015 NAEP mathematics assessment, by group

Notes: This chart represents the aggregate of data for students who missed 3–4 days, 5–10 days, and more than 10 days of school. Students are grouped by gender, race/ethnicity and ELL status, FRPL eligibility, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines).

Among students who missed a lot of school (more than 10 days), there were some more substantial differences by race and language status. About 3.9 percent of Hispanic ELL students and 3.2 percent of Asian ELL students missed more than 10 days of school, compared with 2.2 percent for Native American and other races, 2.0 percent for black students, 1.4 percent for white students, and only 0.6 percent for Asian non-ELL students (all relative to the overall average of 1.7 percent) (see Figure B3 ).

Which groups of students had the highest shares missing more than 10 days? : Share of eighth-graders missing more than 10 days of school in the month prior to the 2015 NAEP mathematics assessment, by group

Notes:  Students are grouped by gender, race/ethnicity and ELL status, FRPL status, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines).

Absenteeism by income status

The attendance gaps are even larger by income status than they are by race/ethnicity and language status (Figures B1–B3). Poor (free-lunch-eligible) students were 5.9 percentage points more likely to miss some school than nonpoor (non-FRPL-eligible) students, and they were 7.8 percentage points more likely to miss school three or more days (23.2 vs. 15.4 percent). 16 Among somewhat poor (reduced-price-lunch-eligible) students, 17.9 percent missed three or more days of school. The lowest-income (free-lunch-eligible) students were 4.1 percentage points more likely to miss school 3–4 days than non-FRPL-eligible students, and more than 2.4 percentage points more likely to miss school 5–10 days ( Appendix Figure B ). Finally, and most striking, free-lunch-eligible students—the most economically disadvantaged students—were more than twice as likely to be absent from school for more than 10 days as nonpoor students. In other words, they were much more likely to experience extreme chronic absenteeism. Figures B1–B3 show that the social-class gradient for the prevalence of absenteeism, proxied by eligibility for free or reduced-price lunch, is noticeable in all absenteeism categories, and especially when it comes to those students who missed the most school.

Absenteeism by disability status

Students with IEPs were by far the most likely to miss school relative to all other groups. 17 The share of IEP students missing school exceeded the share of non-IEP students missing school by 7.7 percentage points (Figure B1). More than one in four IEP students had missed school three days or more in the previous month (Figure B2). About 15.5 percent of students with IEPs missed school 3–4 days (vs. 12.4 percent among non-IEP students); 7.3 percent missed 5–10 days; and 3.2 percent missed more than 10 days of school in the month before being tested (Appendix Figure B; Figure B3).

Absenteeism by gender

The differences by gender are slightly surprising (Figures B1–B3). Boys showed a higher full-attendance rate than girls (46.6 vs. 42.1 percent did not miss any school), and boys were no more likely than girls to display extreme chronic absenteeism (1.7 percent of boys and 1.6 percent of girls missed more than 10 days of school). Boys (18.2 percent) were also slightly less likely than girls (20.2 percent) to be chronically absent (to miss three or more days of school, as per our definition).

Has there been any change over time in which groups of children are most often absent from school?

For students in several groups, absenteeism fell between 2003 and 2015 ( Figure C1 ), in keeping with the overall decline noted above. Hispanic students (both ELL and non-ELL), Asian non-ELL students, Native American and other race students, free-lunch-eligible (poor) students, reduced-priced-lunch-eligible (somewhat poor) students, non-FRPL-eligible (nonpoor) students, and IEP students were all less likely to miss school in 2015 than they were over a decade earlier. For non-IEP and white students, however, the share of students who did not miss any school days in the month prior to NAEP testing remained essentially unchanged, while it increased slightly for black students and Asian ELL students (by about 2 percentage points each).

How much have perfect attendance rates changed since 2003? : Percentage-point change in the share of eighth-graders who had perfect attendance in the month prior to the NAEP mathematics assessment, between 2003 and 2015, by group

Notes: Students are grouped by gender, race/ethnicity and ELL status, FRPL status, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines).

As seen in Figure C2 , we also note across-the-board reductions in the shares of students who missed three or more days of school (with the exception of the share of Asian ELL students, which increased by 1.7 percentage points over the time studied). The largest reductions occurred for students with disabilities (IEP students), Hispanic non-ELL students, Native American students or students of other races, free-lunch-eligible students, and non-FRPL-eligible students (each of these groups experienced a reduction of at least 4.4 percentage points). 18 For all groups except Asian ELL students, the share of students missing more than 10 days of school ( Figure C3 ) also decreased (for Asian ELL students, it increased by 1.3 percentage points).

How much have rates of students missing three or more days changed since 2003? : Percentage-point change in the share of eighth-graders who were absent from school three or more days in the month prior to the NAEP mathematics assessment, between 2003 and 2015, by group

Notes: This chart represents the aggregate of data for students who missed 3–4 days, 5–10 days, and more than 10 days of school. Students are grouped by gender, race/ethnicity and ELL status, FRPL status, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines).

How much have rates of students missing more than 10 days changed since 2003? : Percentage-point change in the share of eighth-graders who were absent from school more than 10 days in the month prior to the NAEP mathematics assessment, between 2003 and 2015, by group

Notes: Students are grouped by gender, race/ethnicity and ELL status, FRPL status, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines).

In order to get a full understanding of these comparisons, we need to look at both the absolute and relative differences. Overall, the data presented show modest absolute differences in the shares of students who are absent (at any level) in various groups when compared with the averages for all students (Figures B1–B3 and Appendix Figure B). The differences (both absolute and relative) among student groups missing a small amount of school (1–2 days) are minimal for most groups. However, while the differences among groups are very small in absolute terms for students missing a lot of school (more than 10 days), some of the differences are very large in relative terms. (And, taking into account the censoring problem mentioned earlier, they could potentially be even larger.)

The fact that the absolute differences are small is in marked contrast to differences seen in many other education indicators of outcomes and inputs, which tend to be much larger by race and income divisions (Carnoy and García 2017; García and Weiss 2017). Nevertheless, both the absolute and relative differences we find are revealing and important, and they add to the set of opportunity gaps that harm students’ performance.

Is absenteeism particularly high in certain states?

Share of students absent from school, by state and by number of days missed, 2015.

Notes: Based on the number of days eighth-graders in each state reported having missed in the month prior to the NAEP mathematics assessment. “Three or more days” represents the aggregate of data for students who missed 3–4 days, 5–10 days, and more than 10 days of school.

Over the 2003–2015 period, 22 states saw their share of students with perfect attendance grow. The number drops to 15 if we count only states in which the share of students not missing any school increased by more than 1 percentage point. In almost every state (44 states), the share of students who missed more than 10 school days decreased, and in 41 states, the share of students who missed three or more days of school also dropped, though it increased in the other 10. 19 Louisiana, Massachusetts, Nevada, Indiana, New Hampshire, and California were the states in which these shares decreased the most, by more than 6 percentage points, while Utah, Alaska, and North Dakota were the states where this indicator (three or more days missed) showed the worst trajectory over time (that is, the largest increases in chronic absenteeism).

Is absenteeism a problem for student performance?

Previous research has focused mainly on two groups of students when estimating how much absenteeism influences performance: students who are chronically absent and all other students. This prior research has concluded that students who are chronically absent are at serious risk of falling behind in school, having lower grades and test scores, having behavioral issues, and, ultimately, dropping out (U.S. Department of Education 2016; see summary in Gottfried and Ehrlich 2018). Our analysis allows for a closer examination of the relationship between absenteeism and performance, as we look at the impact of absenteeism on student performance at five levels of absenteeism. This design allows us to test not only whether different levels of absenteeism have different impacts on performance (as measured by NAEP test scores), but also to identify the point at which the impact of absenteeism on performance becomes a concern. Specifically, we look at the relationship between student absenteeism and mathematics performance among eighth-graders at various numbers of school days missed. 20

The results shown in Figure D and Appendix Table 1 are obtained from regressions that assess the influence of absenteeism and other individual- and school-level determinants of performance. The latter include students’ race/ethnicity, gender, poverty status, ELL status, and IEP status, as well as the racial/ethnic composition of the school they attend and the share of students in their school who are eligible for FRPL (a proxy for the SES composition of the school). Our results thus identify the distinct association between absenteeism and performance, net of other factors that are known to influence performance. 21

In general, the more frequently children missed school, the worse their performance. Relative to students who didn’t miss any school, those who missed some school (1–2 school days) accrued, on average, an educationally small, though statistically significant, disadvantage of about 0.10 standard deviations (SD) in math scores (Figure D and Appendix Table 1, first row). Students who missed more school experienced much larger declines in performance. Those who missed 3–4 days or 5–10 days scored, respectively, 0.29 and 0.39 standard deviations below students who missed no school. As expected, the harm to performance was much greater for students who were absent half or more of the month. Students who missed more than 10 days of school scored nearly two-thirds (0.64) of a standard deviation below students who did not miss any school. All of the gaps are statistically significant, and together they identify a structural source of academic disadvantage.

The more frequently students miss school, the worse their performance : Performance disadvantage experienced by eighth-graders on the 2015 NAEP mathematics assessment, by number of school days missed in the month prior to the assessment, relative to students with perfect attendance in the prior month (standard deviations)

Notes: Estimates are obtained after controlling for race/ethnicity, poverty status, gender, IEP status, and ELL status; for the racial/ethnic composition of the student’s school; and for the share of students in the school who are eligible for FRPL (a proxy for school socioeconomic composition). All estimates are statistically significant at p < 0.01.

The results show that missing school has a negative effect on performance regardless of how many days are missed, with a moderate dent in performance for those missing 1–2 days and a troubling decline in performance for students who missed three or more days that becomes steeper as the number of missed days rises to 10 and beyond. The point at which the impact of absenteeism on performance becomes a concern, therefore, is when students miss any amount of school (vs. having perfect attendance); the level of concern grows as the number of missed days increases.

Gaps in performance associated with absenteeism are similar across all races/ethnicities, between boys and girls, between FRPL-eligible and noneligible students, and between students with and without IEPs. For example, relative to nonpoor (non-FRPL-eligible) students who did not miss any school, nonpoor children who missed school accrued a disadvantage of -0.09 SD (1–2 school days missed), -0.27 SD (3–4 school days missed), -0.36 SD (5–10 school days missed), and -0.63 SD (more than 10 days missed). For students eligible for reduced-price lunch (somewhat poor students) who missed school, compared with students eligible for reduced-price lunch who did not miss any school, the gaps are -0.16 SD (1–2 school days missed), -0.33 SD (3–4 school days missed), -0.45 SD (5–10 school days missed), and -0.76 SD (more than 10 days missed). For free-lunch-eligible (poor) students who missed school, relative to poor students who do not miss any school, the gaps are -0.11 SD (1–2 school days missed), -0.29 SD (3–4 school days missed), -0.39 SD (5–10 school days missed), and -0.63 SD (more than 10 days missed). By IEP status, relative to non-IEP students who did not miss any school, non-IEP students who missed school accrued a disadvantage of -0.11 SD (1–2 school days missed), -0.30 SD (3–4 school days missed), -0.40 SD (5–10 school days missed), and -0.66 SD (more than 10 days missed). And relative to IEP students who did not miss any school, IEP students who missed school accrued a disadvantage of -0.05 SD (1–2 school days missed), -0.21 SD (3–4 school days missed), -0.31 SD (5–10 school days missed), and -0.52 SD (more than 10 days missed). (For gaps by gender and by race/ethnicity, see Appendix Table 1).

Importantly, though the gradients of the influence of absenteeism on performance by race, poverty status, gender, and IEP status (Appendix Table 1) are generally similar to the gradients in the overall relationship between absenteeism and performance for all students, this does not mean that all groups of students are similarly disadvantaged when it comes to the full influence of absenteeism on performance. The overall performance disadvantage faced by any given group is influenced by multiple factors, including the size of the group’s gaps at each level of absenteeism (Appendix Table 1), the group’s rates of absenteeism (Figure B), and the relative performance of the group with respect to the other groups (Carnoy and García 2017). The total gap that results from adding these factors can thus become substantial.

To illustrate this, we look at Hispanic ELL, Asian non-ELL, Asian ELL, and FRPL-eligible students. The additional penalty associated with higher levels of absenteeism is smaller than average for Hispanic ELL students experiencing extreme chronic absenteeism; however, their performance is the lowest among all groups (Carnoy and García 2017) and they have among the highest absenteeism rates.

The absenteeism penalty is also smaller than average for Asian non-ELL students (except at 5-10 days); however, in contrast with the previous example, their performance is the highest among all groups (Carnoy and García 2017) and their absenteeism rate is the lowest.

The absenteeism penalty for Asian ELL students is larger than average, and the gradient is steeper. 22 Asian ELL students also have lower performance than most other groups (Carnoy and García 2017).

Finally, although there is essentially no difference in the absenteeism–performance relationship by FRPL eligibility, the higher rates of absenteeism (at every level) for students eligible for free or reduced-price lunch, relative to nonpoor (FRPL-ineligible) students, put low-income students at a greater risk of diminished performance due to absenteeism than their higher-income peers, widening the performance gap between these two groups.

Conclusions

Student absenteeism is a puzzle composed of multiple pieces that has a significant influence on education outcomes, including graduation and the probability of dropping out. The factors that contribute to it are complex and multifaceted, and likely vary from one school setting, district, and state to another. This analysis aims to shed additional light on some key features of absenteeism, including which students tend to miss school, how those profiles have changed over time, and how much missing school matters for performance.

Our results indicate that absenteeism rates were high and persistent over the period examined (2003–2015), although they did decrease modestly for most groups and in most states. Unlike findings for other factors that drive achievement gaps—from preschool attendance to economic and racial school segregation to unequal funding (Carnoy and García 2017; García 2015; García and Weiss 2017)—our findings here seem to show some positive news for black and Hispanic students: these students had slightly higher perfect attendance rates than their white peers; in addition, their perfect attendance rates have increased over time at least as much as rates for white students. But with respect to the absenteeism rates that matter the most (three or more days of school missed, and more than 10 days of school missed), black and Hispanic students still did worse (just as is the case with other opportunity gaps faced by these students). Particularly worrisome is the high share of Hispanic ELL students who missed more than 10 school days—nearly 4 percent. Combined with the share of Hispanic ELL students who missed 5–10 school days (nearly 6 percent), this suggests that one in 10 children in this group would miss school for at least a quarter of the instructional time.

The advantages that Asian students enjoy relative to white students and other racial/ethnic groups in academic settings is also confirmed here (especially among Asian non-ELL students): the Asian students in the sample missed the least school. And there is a substantial difference in rates of absenteeism by poverty (FRPL) and disability (IEP) status, with the difference growing as the number of school days missed increases. Students who were eligible for free lunch were twice as likely as nonpoor (FRPL-ineligible) students to be absent more than 10 days, and students with IEPs were more likely than any other group to be absent (one or more days, that is, to not have perfect attendance).

Missing school has a distinct negative influence on performance, even after the potential mediating influence of other factors is taken into account, and this is true at all rates of absenteeism. The bottom line is that the more days of school a student misses, the poorer his or her performance will be, irrespective of gender, race, ethnicity, disability, or poverty status.

These findings help establish the basis for an expanded analysis of absenteeism along two main, and related, lines of inquiry. One, given the marked and persistent patterns of school absenteeism, it is important to continue to explore and document why children miss school—to identify the full set of factors inside and outside of schools that influence absenteeism. Knowing whether (or to what degree) those absences are attributable to family circumstances, health, school-related factors, weather, or other factors, is critical to effectively designing and implementing policies and practices to reduce absenteeism, especially among students who chronically miss school. The second line of research could look at variations in the prevalence and influence of absenteeism among the states, and any changes over time in absenteeism rates within each state, to assess whether state differences in policy are reducing absenteeism and mitigating its negative impacts. For example, in recent years, Connecticut has made reducing absenteeism, especially chronic absenteeism, a top education policy priority, and has developed a set of strategies and resources that could be relevant to other states as well, especially as they begin to assess and respond to absenteeism as part of their ESSA plans. 23

The analyses in this report confirm the importance of looking closely into “other” education data, above and beyond performance (test scores) and individual and school demographic characteristics. The move in education policy toward widening accountability indicators to indicators of school quality, such as absenteeism, is important and useful, and could be expanded to include other similar data. Indicators of bullying, school safety, student tardiness, truancy, level of parental involvement, and other factors that are relevant to school climate, well-being, and student performance would also merit attention.

Acknowledgements

The authors gratefully acknowledge John Schmitt and Richard Rothstein for their insightful comments and advice on earlier drafts of the paper. We are also grateful to Krista Faries for editing this report, to Lora Engdahl for her help structuring it, and to Julia Wolfe for her work preparing the tables and figures included in the appendix. Finally, we appreciate the assistance of communications staff at the Economic Policy Institute who helped to disseminate the study, especially Dan Crawford and Kayla Blado.

About the authors

Emma García  is an education economist at the Economic Policy Institute, where she specializes in the economics of education and education policy. Her areas of research include analysis of the production of education, returns to education, program evaluation, international comparative education, human development, and cost-effectiveness and cost-benefit analysis in education. Prior to joining EPI, García was a researcher at the Center for Benefit-Cost Studies of Education, the National Center for the Study of Privatization in Education, and the Community College Research Center at Teachers College, Columbia University, and did consulting work for the National Institute for Early Education Research, MDRC, and the Inter-American Development Bank. García has a Ph.D. in economics and education from Teachers College, Columbia University.

Elaine Weiss  served as the national coordinator for the Broader, Bolder Approach to Education (BBA) from 2011 to 2017, in which capacity she worked with four co-chairs, a high-level task force, and multiple coalition partners to promote a comprehensive, evidence-based set of policies to allow all children to thrive. She is currently working on a book drawing on her BBA case studies, co-authored with Paul Reville, to be published by the Harvard Education Press. Weiss came to BBA from the Pew Charitable Trusts, where she served as project manager for Pew’s Partnership for America’s Economic Success campaign. Weiss was previously a member of the Centers for Disease Control and Prevention’s task force on child abuse and served as volunteer counsel for clients at the Washington Legal Clinic for the Homeless. She holds a Ph.D. in public policy from the George Washington University and a J.D. from Harvard Law School.

Appendix figures and tables

Are there significant differences in student absenteeism rates across grades and over time : shares of fourth-graders and eighth-graders who missed school no days, 1–2 days, 3–4 days, 5–10 days, and more than 10 days in the month before the naep mathematics assessment, 2003 and 2015, detailed absenteeism rates by group : shares of eighth-graders in each group who missed school no days, 1–2 days, 3–4 days, 5–10 days, and more than 10 days in the month before the naep mathematics assessment, 2015, the influence of absenteeism on eighth-graders' math achievement : performance disadvantage experienced by eighth-graders on the 2015 naep mathematics assessment, by group and by number of days missed in the month prior to the assessment, relative to students in the same group with perfect attendance in the prior month (standard deviations).

*** p < 0.01; ** p < 0.05; * p < 0.1

Notes: Students are grouped by gender, race/ethnicity and ELL status, FRPL eligibility, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines). Estimates for the “All students” sample are obtained after controlling for race/ethnicity, poverty status, gender, IEP status, and ELL status; for the racial/ethnic composition of the student’s school; and for the share of students in the school who are eligible for FRPL (a proxy for school socioeconomic composition). For each group, controls that are not used to identify the group are included (for example, for black students, estimates control for poverty status, gender, IEP status, and ELL status; for the racial/ethnic composition of the student’s school; and for the share of students in the school who are eligible for FRPL; etc.)

1. See García 2014 and García and Weiss 2016.

2. See ESSA 2015. According to ESSA, this nontraditional indicator should measure “school quality or student success.” (The other indicators at elementary/middle school include measures of academic achievement, e.g., performance or proficiency in reading/language arts and math; academic progress, or student growth; and progress in achieving English language proficiency.)

3. Thirty-six states and the District of Columbia have included student absenteeism as an accountability metric in their states’ ESSA plans. This metric meets all the requirements (as outlined in ESSA) to be considered a measure of school quality or student success (valid, reliable, calculated the same for all schools and school districts across the state, can be disaggregated by student subpopulation, is a proven indicator of school quality, and is a proven indicator of student success; see Education Week 2017). See FutureEd 2017 for differences among the states’ ESSA plans. See the web page “ ESSA Consolidated State Plans ” (on the Department of Education website) for the most up-to-date information on the status and content of the state plans.

4. There is no precise official definition that identifies how many missed days constitutes chronic absenteeism on a monthly basis. Definitions of chronic absenteeism are typically based on the number of days missed over an entire school year, and even these definitions vary. For the Department of Education, chronically absent students are those who “miss at least 15 days of school in a year” (U.S. Department of Education 2016). Elsewhere, chronic absenteeism is frequently defined as missing 10 percent or more of the total number of days the student is enrolled in school, or a month or more of school, in the previous year (Ehrlich et al. 2013; Balfanz and Byrnes 2012). Given that the school year can range in length from 180 to 220 days, and given that there are about 20–22 instructional days in a month of school, these latter two definitions imply that a student is chronically absent if he or she misses between 18 and 22 days per year (depending on the length of the school year) or more, or between 2.0 and about 2.5 days (or more) per month on average (assuming a nine-month school year). In our analysis, we define students as being chronically absent if they have missed three or more days of school in the last month (the aggregate of students missing “3–4,” “5–10,” or “more than 10 days”), and as experiencing extreme chronic absenteeism if they have missed “more than 10 days” of school in the last month. These categories are not directly comparable to categories used in studies of absenteeism on a per-year basis or that use alternative definitions or thresholds. We purposely analyze data for each of these “days absent” groups separately to identify their distinct characteristics and the influence of those differences on performance. (Appendix Figure B and Appendix Table 1 provide separate results for each of the absenteeism categories.)

5.  In our analysis, we define “poor” students as those who are eligible for free lunch; we define “somewhat poor” students as those who are eligible for reduced-price lunch; and we define “nonpoor” students as those who are not eligible for free or reduced-price lunch. We use “poverty status,” “income status,” “socioeconomic status” (“SES”), and “social class” interchangeably throughout our analysis. We use the free or reduced-price lunch status classification as a metric for individual poverty, and we use the proportion of students who are eligible for FRPL as a metric for school poverty (in our regression controls; see Figure D). The limitations of these variables to measure economic status are discussed in depth in Michelmore and Dynarski’s (2016) study. FRPL statuses are nevertheless valid and widely used proxies of low(er) SES, and students’ test scores are likely to reflect such disadvantage (Carnoy and García 2017).

6. Under the Individuals with Disabilities Education Act (IDEA), an IEP must be designed for each student with a disability. The IEP “guides the delivery of special education supports and services for the student” (U.S. Department of Education 2000). For more information about IDEA, see U.S. Department of Education n.d.

7. Students are grouped by gender, race/ethnicity and ELL status, FRPL eligibility, and IEP status.

8. The U.S. Department of Education (2016) defines “chronically absent” as “missing at least 15 days of school in a year.” Ready (2010) explains the difference between legitimate or illegitimate absences, which may respond to different circumstances and behaviors. Ready’s findings, pertaining to children at the beginning of school, indicate that, relative to high-SES students, low-SES children with good attendance rates experienced greater gains in literacy skills during kindergarten and first grade, narrowing the starting gaps with their high-SES peers. No differences in math skills gains were detected in kindergarten.

9. U.S. Department of Education 2016. This report uses data from the Department of Education’s Civil Rights Data Collection 2013–2014.

10. The analysis finds no differences in absenteeism by gender. It is notable that the Department of Education report finds that ELL students have lower absenteeism rates than their non-ELL peers, given that we find (as described later in the report) that Asian ELL students have higher absenteeism rates than Asian non-ELL students and that Hispanic ELL students have higher absenteeism rates than Hispanic non-ELL students. It is important to note, however, that the data the Department of Education analyze compared all ELL students to all non-ELL students (not only Asian and Hispanic students separated out by ELL status), and thus our estimates are not directly comparable.

11. Children in the fourth and eighth grades were asked, “How many days were you absent from school in the last month?” The possible answers are: none, 1–2 days, 3–4 days, 5–10 days, and more than 10 days. An important caveat concerning this indicator and results based on its utilization is that there is a potential inherent censoring problem: Children who are more likely to miss school are also likely to miss the assessment. In addition, some students may be inclined to underreport the number of days that they missed school, in an effort to be viewed more favorably (in social science research, this may introduce a source of response-bias referred to as “social desirability bias”). Although we do not have any way to ascertain the extent to which these might be problems in the NAEP data and for this question in particular, it is important to read our results and findings as a potential underestimate of what the rates of missingness are, as well as what their influence on performance is.

12. One reason to look at different grades is to explore the potential connection between early absenteeism and later absenteeism. Ideally, we would be able to include data on absenteeism from earlier grades in students’ academic careers since, as Nai-Lin Chang, Sundius, and Wiener (2017) explain, attendance habits are developed early and often set the stage for attendance patterns later on. These authors argue that detecting absenteeism early on can improve pre-K to K transitions, especially for low-income children, children with special needs, or children who experience other challenges at home; these are the students who most need the social, emotional, and academic supports that schools provide and whose skills are most likely to be negatively influenced by missing school. Gottfried (2014) finds reduced reading and math achievement outcomes, and lower educational and social engagement, among kindergartners who are chronically absent. Even though we do not have information on students’ attendance patterns at the earliest grades, looking at patterns in the fourth and eighth grades can be illuminating.

13. Students are excluded from our analyses if their absenteeism information and/or basic descriptive information (gender, race/ethnicity, poverty status, and IEP) are missing.

14. All categories combined, we note that in 2015, 49.5 percent of fourth-graders and 55.6 percent of eighth-graders missed at least one day of school in the month prior. Just over 30 percent of fourth-graders and 36.4 percent of eighth-graders missed 1–2 days of school during the month.

15. In the sample, 52.1 percent of students are white, 14.9 percent black, 4.5 percent Hispanic ELL, 19.4 percent Hispanic non-ELL, less than 1 percent Asian ELL, 4.7 percent Asian non-ELL, and 3.8 percent Native American or other.

16. Of the students in the sample, 47.8 percent are not eligible for FRPL, 5.2 percent are eligible for reduced-price lunch, and 47.0 percent are eligible for free lunch.

17. In the 2015 eighth-grade mathematics sample, 10.8 percent of students had an IEP.

18. For students who were eligible for reduced-price lunch (somewhat poor students), shares of students absent three or more days also decreased, but more modestly, by 3.3 percentage points.

19. Number of states is out of 51; the District of Columbia is included in the state data.

20. The results discussed below cannot be interpreted as causal, strictly speaking. They are obtained using regression models with controls for the relationship between performance and absenteeism (estimates are net of individual, home, and school factors known to influence performance and are potential sources of selection). However, the literature acknowledges a causal relationship between (high-quality) instructional time and performance, in discussions about the length of the school day (Kidronl and Lindsay 2014; Jin Jez and Wassmer 2013; among others) and the dip in performance children experience after being out of school for the summer (Peterson 2013, among others). These findings could be extrapolable to our absenteeism framework and support a more causal interpretation of the findings of this paper.

21. Observations with full information are used in the regressions. The absenteeism–performance relationship is only somewhat sensitive to including traditional covariates in the regression (not shown in the tables; results available upon request). The influence of absenteeism on performance is distinct and is not due to any mediating effect of the covariates that determine education performance.

22. Asian ELL students who miss more than 10 days of school are very far behind Asian ELL students with perfect attendance, with a gap of more than a standard deviation. This result needs to be interpreted with caution, however, as it is based on a very small fraction of students for whom selection may be a concern, too.

23. The data used in our analysis are for years prior to the implementation of measures intended to tackle absenteeism. See Education Week 2017. Data for future (or more recent) years will be required to analyze whether Connecticut’s policies have had an effect on absenteeism rates in the state.

Balfanz, Robert, and Vaughan Byrnes. 2012. The Importance of Being in School: A Report on Absenteeism in the Nation’s Public Schools . Johns Hopkins University Center for Social Organization of Schools, May 2012.

Carnoy, Martin, and Emma García. 2017. Five Key Trends in U.S. Student Performance: Progress by Blacks and Hispanics, the Takeoff of Asians, the Stall of Non-English Speakers, the Persistence of Socioeconomic Gaps, and the Damaging Effect of Highly Segregated Schools . Economic Policy Institute, January 2017.

Education Week. 2017. School Accountability, School Quality and Absenteeism under ESSA (Expert Presenters: Hedy Chang and Charlene Russell-Tucker) (webinar).

Ehrlich, Stacy B., Julia A. Gwynne, Amber Stitziel Pareja, and Elaine M. Allensworth with Paul Moore, Sanja Jagesic, and Elizabeth Sorice. 2013. Preschool Attendance in Chicago Public Schools: Relationships with Learning Outcomes and Reasons for Absences . The University of Chicago Consortium on Chicago School Research, September 2013.

ESSA. 2015. Every Student Succeeds Act of 2015 , Pub. L. No. 114-95 § 114 Stat. 1177 (2015–2016).

FutureEd. 2017. Chronic Absenteeism and the Fifth Indicator in State ESSA Plans . Georgetown University.

García, Emma. 2014. The Need to Address Noncognitive Skills in the Education Policy Agenda . Economic Policy Institute, December 2014.

García, Emma. 2015. Inequalities at the Starting Gate: Cognitive and Noncognitive Skills Gaps between 2010–2011 Kindergarten Classmates . Economic Policy Institute, June 2015.

García, Emma, and Elaine Weiss. 2016. Making Whole-Child Education the Norm. How Research and Policy Initiatives Can Make Social and Emotional Skills a Focal Point of Children’s Education . Economic Policy Institute, August 2016.

García, Emma, and Elaine Weiss. 2017. Education Inequalities at the School Starting Gate: Gaps, Trends, and Strategies to Address Them . Economic Policy Institute, September 2017.

Gottfried, Michael A. 2014. “Chronic Absenteeism and Its Effects on Students’ Academic and Socioemotional Outcomes.” Journal of Education for Students Placed at Risk 19, no. 2: 53–75. https://doi.org/10.1080/10824669.2014.962696 .

Gottfried, Michael A., and Stacy B. Ehrlich. 2018. “Introduction to the Special Issue: Combating Chronic Absence.” Journal of Education for Students Placed at Risk 23, no. 1–2: 1–4. https://doi.org/10.1080/10824669.2018.1439753 .

Jin Jez, Su, and Robert W. Wassmer. 2013. “The Impact of Learning Time on Academic Achievement.” Education and Urban Society 47, no. 3: 284–306. https://doi.org/10.1177/0013124513495275 .

Kidronl, Yael, and Jim Lindsay. 2014. The Effects of Increased Learning Time on Student Academic and Nonacademic Outcomes: Findings from a Meta-Analytic Review . REL 2014-015. Regional Educational Laboratory Appalachia.

Michelmore, K., and S. Dynarski. 2016.  The Gap within the Gap: Using Longitudinal Data to Understand Income Differences in Student Achievement . National Bureau of Economic Research Working Paper no. 22474.

Nai-Lin Chang, Hedy, Jane Sundius, and Louise Wiener. 2017. “ Using ESSA to Tackle Chronic Absence from Pre-K to K–12 ” (blog post). National Institute for Early Education Research website, May 23, 2017.

National Center for Education Statistics (NCES), National Assessment of Educational Progress (NAEP). Various years. NAEP microdata (unpublished data).

Peterson, T.K., ed. 2013. Expanding Minds and Opportunities: Leveraging the Power of Afterschool and Summer Learning for Student Success . Washington, D.C.: Collaborative Communications Group.

Ready, Douglas D. 2010. “Socioeconomic Disadvantage, School Attendance, and Early Cognitive Development: The Differential Effects of School Exposure.” Sociology of Education 83, no. 4: 271–286. https://doi.org/10.1177/0038040710383520 .

U.S. Department of Education. 2000. A Guide to the Individualized Education Program . Office of Special Education and Rehabilitative Services, July 2000.

U.S. Department of Education. 2016. Chronic Absenteeism in the Nation’s Schools: An Unprecedented Look at a Hidden Educational Crisis (online fact sheet).

U.S. Department of Education. n.d. “ About IDEA ” (webpage). IDEA (Individuals with Disabilities Education Act) website . Accessed September 19, 2018.

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Review article, school attendance and school absenteeism: a primer for the past, present, and theory of change for the future.

• 1 Department of Psychology, University of Nevada, Las Vegas, Las Vegas, NV, United States
• 2 Child Study Center, Yale School of Medicine, New Haven, CT, United States
• 3 Department of Developmental Psychology and Teaching, University of Alicante, San Vicente del Raspeig, Alicante, Spain
• 4 Research Group TOR, Department of Sociology, Vrije Universiteit Brussel and Research Foundation Flanders, Brussels, Belgium

School attendance and school absenteeism have been studied for over a century, leading to a rich and vast literature base. At the same time, powerful demographic, climate, social justice/equity, and technological/globalization forces are compelling disparate stakeholders worldwide to quickly adapt to rapidly changing conditions and to consider new visions of child education for the next century. These overarching forces are utilized within a theory of change approach to help develop such a vision of school attendance/absenteeism for this era. This approach adopts key long-range outcomes (readiness for adulthood for all students; synthesized systemic and analytic approaches to school attendance/absenteeism) derived from thematic outputs (reframing, social justice, and shared alliances) that are themselves derived from contemporary inputs (movement of educational agencies worldwide toward readiness for adulthood, technological advances, schools, and communities as one). As with theory of change approaches, the purpose of this discourse is not to provide a roadmap but rather a compass to develop multi-stakeholder partnerships that can leverage shared resources and expertise to achieve a final mutual goal.

Introduction

School attendance and school absenteeism were one of the first areas of study for emerging disciplines such as education, psychology, and criminal justice in the late 19th and early 20th centuries. With the advent of the labor rights movement, new employment laws, and the needs for an educated workforce and greater social order, children were increasingly moved from industrial and agricultural settings to more formalized school settings ( Rury and Tamura, 2019 ). School absenteeism thus became viewed as a legal as well as a societal problem in need of remediation, with a concurrent focus on illegal truancy as well as delinquency as a primary cause ( Williams, 1927 ; Kirkpatrick and Lodge, 1935 ; Gleeson, 1992 ). Around the mid-20th century, however, psychological approaches focused on other possible causal mechanisms of school absenteeism such as child fear/anxiety, problematic separation from caregivers, family dysfunction, and proximity to deviant peers (e.g., Johnson et al., 1941 ; Waldfogel et al., 1957 ; Kennedy, 1965 ). Many of these approaches centered on students and their families, a predominant focus of many professionals even today. Only later in the 20th century, and especially following the civil rights movement of the 1960s as well as a revival of Marxist theory via the emergence of social stratification research, did researchers and other stakeholders more intensely examine broader contexts of school absenteeism that included the school environment, the surrounding community, and economic, cultural, political, and other macro influences ( Bourdieu and Passeron, 1977 ; Willis, 1977 ; Weinberg, 1991 ; Sleeter, 2014 ).

Today, the study of school attendance/absenteeism comprises many disciplines such as child development, criminal and juvenile justice, economics, education, epidemiology, law, leadership, nursing, medicine, political science, program evaluation, psychiatry, psychology, public and educational policy, school counseling, social work, and sociology, among others. These approaches can be divided generally into systemic perspectives that focus on overarching contexts and structural concerns as well as analytic perspectives that focus on specific contexts and individual concerns ( Kearney, 2021 ). Together these approaches have produced a rich and vast repository of knowledge over the past century regarding the conceptualization of school attendance/absenteeism with respect to domains such as definition, classification, risk/protection, trajectory, measurement, and intervention. At the same time, however, the breadth and multifaceted nature of these varied systemic and analytic approaches has led to myriad avenues of investigation that are not always well-coordinated or integrated. In addition, geographical and cultural differences in systems of education, including areas where education does not exist at all, further complicate the current landscape of school attendance/absenteeism ( Porto, 2020 ).

On top of all of this are relatively recent revolutionary and fundamental changes in human communication and interaction that are spurred in part by climate change, demands for equity and social justice, demographic and migration shifts, globalization, health crises, political movements, and technological advancements ( Krishnamurthy et al., 2019 ; Mao et al., 2019 ; Cleveland-Innes, 2020 ; Rapanta et al., 2021 ). As such, the very nature of educating children is being radically altered and will continue to evolve (or devolve) quickly over the next decades. The challenge before us in the next century is thus not only to assimilate the different systemic/analytic and geographic/cultural approaches to school attendance/absenteeism but also to meld this assimilation process with rapidly changing undercurrents of essential human functioning.

The purpose of this article is to provide a primer for stakeholders in this area regarding the past and next century vis-à-vis school attendance/absenteeism. As such, broad strokes are emphasized at the expense of greater detail regarding specific investigations. The article is divided into three main sections. The first section outlines key conclusions that can be drawn from a century’s worth of study of school attendance/absenteeism. The second section outlines how some of the revolutionary and fundamental changes noted above are impacting child education as well as traditional notions of school attendance/absenteeism. The third section, a theory of change approach, outlines a potential mutual vision for what the study of school attendance/absenteeism could look like in the coming decades.

The past: What is known?

A more than century’s worth of study allows for several broad conclusions about what is known regarding school attendance/absenteeism. Six such conclusions are presented next that are drawn from communal themes across the many disciplines in this area. First, school attendance/absenteeism are global issues but ones that are studied primarily within geographically limited areas . Less than three-quarters of children worldwide complete at least a lower secondary school education ( UNESCO, 2019 ). This rate is particularly restricted for sub-Saharan Africa (38%), northern Africa and western Asia (72%), central and southern Asia (75%), and Latin America and the Caribbean (76%). Unfortunately, the vast majority of research regarding school attendance/absenteeism comes from continental areas that have the highest completion rates in this regard: Europe and North America (98%) and Oceania (92%). Although emerging research is emanating from places such as South America, Asia, and Africa (e.g., Momo et al., 2019 ; Gonzálvez et al., 2020 ), not nearly enough is known in these areas about the domains of school attendance/absenteeism noted earlier.

Second, rates of school attendance/absenteeism differ substantially and disproportionately affect vulnerable student groups . Approximately 17% of children worldwide do not attend school, and many of these students are deliberately deprived of an education on the basis of gender, disability, and/or ethnicity. Students in low-income countries also experience greater barriers to an education such as food and housing insecurity, lack of instructors and academic materials, large class sizes, long distances to school, poor infrastructure, and violence ( UNESCO, 2019 ). Health crises and limited economic opportunities in these regions also drive students out of school and into premature labor roles ( Mussida et al., 2019 ; Reimers, 2022 ). Even in developed countries, elevated school absenteeism and dropout rates occur among vulnerable groups such as impoverished students, migrant students, students of color, students with disabilities, and students less familiar with the dominant cultural language ( Garcia and Weiss, 2018 ; Koehler and Schneider, 2019 ; Sosu et al., 2021 ).

Third, school attendance is generally associated with student benefit and school absenteeism is generally associated with student harm . One could contend that formal schooling is one of the best interventions ever designed for children, or at least for many children. Regular school attendance and school completion have been linked to adaptive functioning in many child developmental domains (e.g., academic, behavioral, health, psychological, and social; Rocque et al., 2017 ; Ehrlich et al., 2018 ). These effects have both short-term (e.g., educational achievement) as well as long-term (e.g., enhanced lifetime earning potential) positive impacts. Conversely, school absenteeism and school dropout have been associated with less adaptive functioning in these domains, with both short-term and long-term negative impacts ( Ansari et al., 2020 ; Rumberger, 2020 ). Caveats apply to this general conclusion, however. For many students, particularly vulnerable students, school is an environment associated with biased exclusionary discipline, racism, oppression, systemic discrimination, and victimization ( Kohli et al., 2017 ; Sanders, 2022 ). In related fashion, many students miss school as a more adaptive choice, such as to support a family economically ( Chang et al., 2019 ; Ricking and Schulze, 2019 ).

Fourth, school attendance/absenteeism are complicated constructs that require innovative measurement strategies . School attendance/absenteeism represents more than just physical presence or absence in a brick-and-mortar building. Many forms of school attendance/absenteeism exist across multiple instructional formats, including virtual or distance learning formats, that demand new and broader metrics (e.g., log-ins, completed assignments, student-teacher interactions, and mastery of skills) for measuring these constructs ( National Forum on Education Statistics, 2021 ). In addition, school absenteeism comprises a spectrum of attendance problems that can include full or partial day absences, missing classes, tardiness, student/family problems in the morning, and distress, somatic complaints, and other psychological problems that interfere with school attendance ( Li et al., 2021 ; Kearney and Gonzálvez, 2022 ). This has led to broader definitions of school attendance/absenteeism that focus less on physical presence/absence and more on engagement ( Patrick and Chambers, 2020 ; Kearney, 2021 ). Greater sophistication with respect to systemic evaluation (e.g., early warning systems) and analytic assessment (e.g., clinical protocols) methods also allows for more sensitive data analytic strategies to define problematic school absenteeism for certain student groups and across geographical regions ( Balfanz and Byrnes, 2019 ; Gonzálvez et al., 2021 ; Kearney and Childs, 2022 ).

Fifth, school attendance/absenteeism remains associated with multiple risk and protective factors across ecological levels . One advantage of the contemporary era is that a historical, singular focus on either student/family or other narrow-band risk/protective factors or on school-related or other broad-band risk/protective factors is yielding to more integrated approaches for understanding the complex ecology of school attendance/absenteeism ( Kim, 2020 ; Singer et al., 2021 ). Stakeholders now understand that interconnected risk/protective factors in this area range from granular to immense levels; examples include disability/academic achievement (student level), psychopathology/academic involvement (caregiver level), residential movement/cohesion (family level), victimization/positive norms (peer level), negative/positive climate quality (school level), neighborhood violence/safe avenues to school (community level), and structural economic inequalities/well-financed educational agencies (macro level; e.g., Zaff et al., 2017 ; Gubbels et al., 2019 ). In addition, stakeholders increasingly view school attendance/absenteeism from a comprehensive Bronfenbrenner-like ecological approach; examples include linkages between student-caregiver interactions (microsystem), caregiver-school staff communications (mesosystem), educational policies (exosystem), transportation vulnerabilities (macrosystem), and changes in these systems as children move from preschool to elementary, middle, and high school and beyond (chronosystem; e.g., Melvin et al., 2019 ; Childs and Scanlon, 2022 ).

Sixth, positive interventions to enhance school attendance and to reduce school absenteeism are generally though perhaps only moderately effective . Positive interventions are defined here as those that are empirically supported, intentional, and designed to foster well-being ( Tejada-Gallardo et al., 2020 ). Systematic reviews and meta-analyses reveal that positive interventions from both systemic and analytic perspectives are modestly effective at boosting school attendance and reducing school absenteeism (refer to, for example, Maynard et al., 2018 ; Keppens and Spruyt, 2020 ; Eklund et al., 2022 ). Key limitations, however, include insufficient integration of these various intervention strategies as well as incomplete dissemination and implementation across schools, community support agencies, and student groups ( Heyne et al., 2020 ; Kearney and Benoit, 2022 ). In contrast, negative interventions, defined here as punitive measures to suppress certain behaviors, paradoxically exacerbate school absenteeism and are disproportionately and perniciously applied to vulnerable student groups ( Mireles-Rios et al., 2020 ; Weathers et al., 2021 ). Examples include exclusionary discipline (e.g., arrests, expulsion, and suspension) and zero tolerance laws that often focus on deprivation of resources (e.g., via fines or restrictions on financial assistance or licenses) for absenteeism ( Conry and Richards, 2018 ; Rubino et al., 2020 ).

A century of work has produced a prodigious amount of knowledge regarding school attendance/absenteeism. But, the world is changing fast. As mentioned, revolutionary and fundamental changes in human communication and interaction will alter the course of child education and thus the study of school attendance/absenteeism for decades to come. A complete summary of all possible future effects on education is beyond the scope of this paper. Instead, we concentrate on some of the broadest and perhaps most wide-ranging influences in this regard: demographic shifts, climate change, demands for social justice and equity, and technological advancements and globalization. These influences, discussed next, are naturally complex, often subsuming other themes, and are naturally interwoven with one another.

The present: What is changing?

As stakeholders develop new visions of child education and school attendance/absenteeism for the future, several key fundamental shifts must be considered. One key fundamental shift worldwide involves demographic changes such as uneven (rising and declining) birthrates, more frequent migration patterns between regional countries and especially from south to north, and increased urbanization. Population growth is expected to largely emanate from African and Indo-Pacific countries and population decline is expected to be most acute for European and eastern Asian countries ( United Nations Department of Economic and Social Affairs, Population Division, 2022 ). In addition, older age groups will grow fastest and will eventually outnumber children and adolescents. Migration is expected to expand considerably due to violence, persecution, deprivation, and natural disasters. Urbanization will increase from 55 to 68 percent of people by 2050, especially in Asia and Africa ( United Nations Department of Economic and Social Affairs, Population Division, 2018 ).

These demographic shifts have many ramifications for child education and the study of school attendance/absenteeism. First, school closures in areas of population decline, a phenomenon already present in many countries, would be expected to accelerate. School closures create interrupted learning and measurements of learning, lengthy distances to new schools, compromised nutrition, social isolation, economic costs for families, and burden on existing schools ( Hanushek and Woessmann, 2020 ). Learning losses due to school closures are particularly negatively impactful for disadvantaged students ( Maldonado and De Witte, 2022 ). Conversely, education infrastructure for fastest-growing areas, already a problematic situation in areas noted above, will need to be prioritized. Second, increased migration means the need to integrate different student groups into a dominant educational culture. Challenges with respect to interrupted schooling, language, seasonal work, community isolation, socioeconomic disadvantages, fears of deportation, stigma, discrimination, and family separation thus apply ( Martin et al., 2020 ; Osler, 2020 ; Rosenthal et al., 2020 ; Brault et al., 2022 ). Increased migration will also magnify brain drain of highly skilled educational professionals ( Docquier and Rapoport, 2012 ) that contributes to international student performance gaps ( Hanushek et al., 2019 ). Third, increased urbanization often means more concentrated economic disadvantage, racial segregation, affordable housing shortages, educational inequalities, and transportation vulnerabilities ( Shankar-Brown, 2015 ).

A second key fundamental shift worldwide involves climate change . Climate change affects migration, as noted above, forcing students to change schools, adapt to new curricula, and potentially experience greater trauma ( Prothero, 2022 ). Greater pressure to drop out of school to support families economically may occur as well ( Nordengren, 2021 ). Climate change can also affect the physical structure of schools with limited air conditioning or ventilation or ability to withstand extreme weather, forcing cancellation of school days and reducing the availability of safe water and school-based meals ( Sheffield et al., 2017 ). Schools in many parts of the world have closed for lengthy periods or been destroyed by cyclones, typhoons, floods, drought, landslides, and sea level rise. Related climate change risks include parent mortality, food insecurity, and increased air and water pollution in part due to lack of access to electricity and modern fuels ( UNICEF, 2019 ). Environmental activism appears to buffer climate change anxiety and may be a protective factor for mental health in the climate crisis ( Schwartz et al., 2022 ). Accordingly, students question the purpose of school attendance when their schools fail to provide curricular innovation regarding climate change, or to mitigate their environmental impact ( Benoit et al., 2022 ).

Such changes in climate, already rapidly accelerating, may demand abrupt shifts between in-person and distance learning, enhanced methods for student tracking and records transfer, and improvements in educational infrastructure ( Chalupka and Anderko, 2019 ). School buildings are also large energy consumers and may need to transition toward a reduced carbon footprint by shifting education to home- or community-based settings and/or by adopting energy efficient appliances, electric vehicles, and elimination of single use plastics, among other measures ( Bauld, 2021 ). Education will also need to shift to careers of the future that intersect with a changing climate, such as renewable energy, environmental engineering, and emergency management ( Kovacs, 2022 ). Basic education about the climate crisis, especially in developing countries, will need to be prioritized as well ( Rousell and Cutter-Mackenzie-Knowles, 2020 ). The transition to sustainable development starts with pedagogical strategy and teacher training involving an Education for Sustainable Development program that emphasizes a concordant balance between societal, economic, and environmental imperatives ( Ferguson et al., 2021 ).

A third key fundamental shift worldwide involves an increased demand for, as well as pushback against, social justice and equity in educational systems . Calls are growing to reduce or eliminate barriers to school attendance such as digital divides, disparities in school discipline, inequities in school funding, lack of access to school- and community-based care, oppressive school climates, transportation vulnerabilities, and victimization, all of which disproportionately impact vulnerable youth ( Kearney et al., 2022 ). In addition, efforts to integrate themes of social justice and equity into education include revising school curricula toward multiple perspectives, addressing personal biases, supporting vulnerable students with respect to school completion, and matching the demographic characteristics of school staff and students ( Spitzman and Balconi, 2019 ; Gottfried et al., 2021 ). Such efforts will also include a greater recognition that the surrounding community must be a target of intervention, especially in areas of high chronic absenteeism ( Grooms and Bohorquez, 2022 ; Kearney and Graczyk, 2022 ).

At the same time, however, an active global anti-science movement coupled with laws to restrict access to education, certain academic materials, and LGBTQ and gender rights in many countries serve as powerful counterweights to enhancing social justice and equity in educational systems ( Hotez, 2020 ; Horne et al., 2022 ). Political movements emphasizing meritocracy but simultaneously depriving the means for equitable educational and social mobility also remain active and influential ( Owens and de St Croix, 2020 ). Growing dissatisfaction with traditional educational settings and methods also means that many constituents are emboldened to attack educational system components such as school boards and curricula ( Borter et al., 2022 ). More caregivers are thus seeking alternative choices, including home-based education, and many schools are facing critical teacher and leadership shortages ( Eggleston and Fields, 2021 ; Wiggan et al., 2021 ).

A fourth key fundamental shift worldwide involves an ongoing modification of pedagogical goals and instructional formats for child education due to globalization and technological advancements . The pedagogical goals of education will depart from the historical Industrial Revolution model of memorization and standardization and toward a whole child/citizen approach where learning is accessible, collaborative, competency-based, inclusive, personalized, self-paced, and in part focused on student well-being. Such learning will emphasize skills needed for adult readiness that surround communication, creativity, innovation, and problem-solving ( World Economic Forum, 2020 ). In addition, such learning will extend into emerging adulthood and be lifelong in nature as necessary skills require continual upgrades ( Kim and Park, 2020 ).

Technological advancements also mean that the nature of education will be changing rapidly over the next decades. Some of these advancements will involve existing avenues such as cloud computing, hand-held devices and their applications, multi-touch surfaces, and social media ( Polly et al., 2021 ). Other advancements will involve currently nascent avenues such as artificial intelligence, augmented reality, biometrics, robots, and metaverse ( Aggarwal et al., 2022 ). As such, myriad alterations are expected with respect to instructional formats and settings, student-teacher communications, and strategies for learning ( Yang et al., 2021 ). Less distinction will be made between traditional schools and other home and community settings, and the classroom of tomorrow may represent more of a digital network than a physical space ( Kearney, 2016 ).

All of these changes demand consideration of new and more integrative visions for the future study of school attendance/absenteeism. Stakeholders in this area are often incentivized to pursue iterative processes or incremental changes; examples include researchers and clinicians beholden to outmoded conceptualization systems, granting agencies that reward piecemeal advancements, and policymakers searching for rapid and simple (and usually punitive) responses to a complex problem. Instead, a proactive approach is needed that integrates all stakeholders in part by establishing a mutual vision for the future. Such a vision would itself demand a focus on what is already known, what is changing, and what long-term goals must be pursued. One attempt to craft such a vision is presented next.

The future: What is the vision?

In this section, we make observations and recommendations for the future study of school attendance/absenteeism in light of the changing world and educational landscape noted in the previous section. We adopt two main perspectives in this regard. One perspective, a constructivist approach, means that stakeholders across the globe would be expected to view, develop, and apply these observations and recommendations quite differently based on their unique challenges, experiences, communities, viewpoints, and evolving life circumstances. In related fashion, areas of the world have vastly different systems, laws, and resources regarding education and thus school attendance/absenteeism. A second perspective, a theory of change approach, means that, despite these many global differences, a mutual vision could be developed to serve as a compass over the next decades for myriad global stakeholders. Such an approach toward a mutual vision may also be helpful for synthesizing systemic/analytic as well as geographic/cultural approaches to school attendance/absenteeism.

Theory of change

One avenue for integrating various approaches for a complex problem is the development of multi-stakeholder partnerships that leverage shared resources and expertise to achieve an eventual final goal in a postmodern era. Such partnerships involve establishing a mutual vision that sets the stage for ongoing interactions among the partner entities. Indeed, the sustainability of an alliance among partner entities is often enhanced by belief in a collective outlook, use of similar strategies, and some prior success working together ( D’Aunno et al., 2019 ). Key partner entities for school attendance/absenteeism that meet these criteria include those representing both systemic and analytic approaches, such as educators, health-based professionals, policymakers, researchers, students, caregivers, state agencies, and national and international organizations.

One mechanism for creating a mutual vision among disparate partner entities involves theory of change , which is a “participatory process whereby groups and stakeholders in a planning process articulate their long-term goals and identify the conditions they believe have to unfold for those goals to be met” (p. 2, Taplin and Clark, 2012 ). Theories of change are typically designed in backward fashion around desired long-term goals (outcomes), intermediate steps and interventions that can produce those outcomes (outputs), and current conditions and initiatives that serve as the impetus for the outputs (inputs; Guarneros-Meza et al., 2018 ). Theory of change helps inform overarching long-term vision and strategic planning by producing assumptions that can be tested by research. Theory of change is “method-neutral,” relying on many informational sources (e.g., grey/published literature, program/policy evaluation, stakeholder feedback), which makes the approach particularly amenable to the disparate area of school attendance/absenteeism ( Breuer et al., 2015 ).

The following sections introduce a futuristic, broad-strokes theory of change for school attendance/absenteeism that coalesces systemic and analytic approaches and assumes a mutual long-term (postmodern) goal of readiness for adulthood for all students . Although such a goal may pertain to quality of education more broadly, a specific focus on school attendance/absenteeism is chosen here because these constructs are better defined operationally, underpin education, and serve as a proxy for variables such as behavioral school engagement. Theory of change for a postmodern era seems particularly salient given substantial demographic, climate, social justice, pedagogical, technological, globalization, and other forces in the contemporary era that are compelling educators and other stakeholders to re-examine historical assumptions about instructional formats, equity of systems, and economic sustainability in adulthood ( Atiku and Boateng, 2020 ).

The theory of change framework introduced here is not a final blueprint but rather a starting point for discussion. All aspects of a theory of change framework, including its fundamental assumptions, are subject to debate, analysis, modification, and refutation. As such, the theory of change framework introduced here is a fundamental model of action and not an advanced log frame approach that articulates specific indicators for success, measurement milestones, and mechanisms for causal connections ( De Silva et al., 2014 ). The framework described here ( Figure 1 ) is instead presented in a flexible, constructivist format without a rigid, predefined structure in order to allow for multiple causal pathways and interlocking systems that may progress toward a mutual goal in various ways.

Figure 1 . Theory of change for school attendance and its roblems. This figure shows how contemporary inputs could lead to key outputs that could produce outcomes in a postmodern era.

The first step in designing a theory of change for a given issue is to define the primary long-term goals or outcomes. With respect to school attendance/absenteeism, the primary outcome utilized here is readiness for adulthood for all students. The secondary outcome is a synthesis of systemic/analytic and geographic/cultural approaches to school attendance/absenteeism to enhance multi-stakeholder partnerships that leverage shared resources and expertise to achieve full school attendance and thus readiness for adulthood for all students.

One overarching purpose of youth-based education, and thus school attendance, is to ensure readiness for adulthood for all students ( Pimentel, 2013 ). Readiness is a multifaceted construct that includes career and life skills necessary to be successful in postsecondary education and employment ( Mishkind, 2014 ). Career (or academic) readiness can include variables such as critical thinking, problem solving, learning strategies, and organizational/study skills, among others ( Monahan et al., 2018 ). Life skills (or nonacademic) readiness can include variables such as communication abilities, interpersonal skills, self-management, creativity/innovation, and conscientiousness, among others ( Morningstar et al., 2017 ). In addition, broader factors such as student motivation/engagement, growth mindset, understanding of postsecondary requirements, and opportunities and supports for post-high school development enhance career and life skills readiness ( Morningstar et al., 2018 ). All of these domains overlap considerably with one another, have been ensconced in educational policies, initiatives, and mandates (e.g., Common Core State Standards; Every Student Succeeds Act), and are considered crucial for employment in a globalized economy ( Malin et al., 2017 ).

Readiness for adulthood also hinges on evolving developmental theory that defines adolescence and emerging adulthood as overlapping, extended phases of growth that precede formal adulthood. Adolescence includes youth in pubertal years as well as youth up to age 24 years who have not yet assumed adult roles due to slower behavioral maturation (e.g., impulsivity; Hochberg and Konner, 2020 ). Emerging adulthood represents youth up to age 28 years who progress toward independence, complex interrelationships, and career trajectories within a volatile period of emotional, neurodevelopmental, and social development ( Wood et al., 2018 ). Evolving concepts of adolescence and emerging adulthood have important ramifications for K-12 educational systems, and thus school attendance, in that many students are not prepared to complete high school with respect to readiness at legally predefined ages (e.g., age 18 years; Duncheon, 2018 ). Instead, many students, and particularly those with disabilities, require extended time for school completion, transition services, and/or continuing academic and vocational training programs to successfully bridge adolescence, emerging adulthood, and formal adulthood ( Lombardi et al., 2020 ).

School attendance relevant to both K-12 and continuing education is a key cornerstone and positive consequence of readiness initiatives ( Hemelt et al., 2019 ). Unfortunately, as mentioned, school attendance problems remain stubbornly elevated among vulnerable student groups worldwide ( Garcia and Weiss, 2018 ). Key reasons for this include, from a systemic perspective, early structural disparities and achievement gaps that are exacerbated over time; and, from an analytic perspective, fewer home-based academic activities and greater mental health challenges and adverse experiences that impede learning. As such, large swaths of youth are ill-prepared for employment and have considerably lower lifetime earning potential than peers who at least completed high school ( Pfeffer, 2008 ; U.S. Department of Education, National Center for Education Statistics, 2020 ).

Readiness for adulthood for all students is the primary outcome chosen here for a theory of change regarding school attendance/absenteeism. Such an outcome will require ample resources, will, and creative educational efforts such as dual enrollment programs, reconfigured high school curricula, sectoral employment strategies, and revised graduation policies to essentially blur the line between completing high school and beginning the adult readiness process (e.g., via vocational training, community college, military service; Spangler and O'Sullivan, 2017 ). Such an outcome also requires a revised approach to understanding school attendance/absenteeism over the next decades. This revised approach involves viewing the readiness transition from adolescence to adulthood as a process and to ensure that this process is equitable for all students and informed by systemic and analytic perspectives.

As mentioned, a theory of change is typically designed in backward fashion; as such, the outputs, or intermediate steps and interventions that can produce identified outcomes, are discussed next. Outputs toward a vision of readiness for adulthood for all students, with specific reference to school attendance/absenteeism, intersect with the present changes described earlier and are arranged according to themes of reframing , social justice , and shared alliances . Each output involves a focus on transitional process, equity, and synthesis of systemic and analytic perspectives to school attendance/absenteeism.

Over the next decades, reframing with respect to school attendance/absenteeism will involve (1) focusing on attendance more than on absenteeism and (2) reconfiguring fundamental definitions of school attendance/absenteeism and school graduation/completion. Such reframing is necessary to accommodate an overall goal of readiness for adulthood for all students by emphasizing inclusivity and school engagement, allowing for an extended developmental period of preparatory education into emerging adulthood, and accounting for massive technological changes in instructional formats expected in the next decades ( Dimitrova and Wiium, 2021 ). Such reframing also requires synthesis of systemic and analytic approaches to school attendance.

The first aspect of reframing involves focusing on attendance more so than on absenteeism . Contemporary school and policy approaches often emphasize punitive measures for absenteeism such as exclusionary discipline (arrest, suspension, and expulsion) and referral to juvenile and criminal justice systems ( McNeely et al., 2021 ). In addition, as mentioned, absenteeism policies are often used to perniciously exclude students with behavioral and academic problems from the educational process ( Mireles-Rios et al., 2020 ). These policies thus derail an overall outcome of readiness for adulthood for many vulnerable students. A focus on absenteeism also tends to place burden for remediation on families and neglects more systemic reasons why many students cannot attend school, such as school closures, lack of timely bus and school assignments, limited access to educational technology, and health-based disparities in services ( U.S. Department of Education, 2018 ). Long-range early warning systems that focus more on absenteeism and dropout are also unstable across student groups and are unlinked to interventions to improve school attendance ( Newman et al., 2019 ).

In contrast, a focus on restorative practices and attendance augments connection and engagement with school. These efforts can do so via systemic school-family-community partnerships as well as analytic health-based strategies to enhance safety, academic growth, mental health, social relationships, family resources, and career development ( Gentle-Genitty et al., 2020 ). These efforts are further supported by large-scale data analytic/mining models in this area that often reveal greater specificity than sensitivity, meaning the models are better at predicting which students attend school rather than which students are absent from school ( Chung and Lee, 2019 ). As such, early warning systems can be designed in accordance with these models to provide a more nuanced, localized, and real-time analysis of attendance patterns. Such systems can be linked as well to attendance dashboards that absorb information from multiple agencies such as housing or public health to better track student attendance (refer also to the shared alliances section; Childs and Grooms, 2018 ; Kearney and Childs, 2022 ).

The second aspect of reframing involves reconfiguring fundamental definitions of school attendance/absenteeism as well as school graduation/completion by adopting broader and more flexible characterizations of these constructs to account for fast-moving changes in educational formats and to better synthesize systemic and analytic perspectives. Contemporary school and policy approaches in this area emphasize traditional metrics such as in-seat class time in a physical building and point-in-time graduation, which are becoming obsolete for many students given expansions in teaching and learning formats as well as evolving developmental theory regarding emerging adulthood. These approaches also rely on archaic, derogatory, and confusing terminologies. For example, the terms “truancy” and “unexcused absences” are rife with multiple and stigmatizing meanings that are applied disproportionately to vulnerable students and include negative connotations regarding delinquency and poverty ( Kearney et al., 2019a ; Martin et al., 2020 ; Pyne et al., 2021 ). In addition, school completion is often viewed more as a singular event (graduation) in adolescence rather than as an ongoing preparatory process into emerging adulthood, thus disenfranchising students who require additional supports. These approaches insufficiently promote an overall outcome of readiness for all students.

Broader and more flexible characterizations of school attendance/problems have been proposed. Patrick and Chambers (2020) redefined school attendance as time on task, participation or evidence of student work, and competency-based attainment with demonstrations of knowledge and skill-building. Kearney (2021) redefined school attendance/problems as involvement in teaching and learning practices that augments or subverts the prospect of school graduation or completion. Both revised definitions broaden school attendance toward engagement that can include cognitive, behavioral, and emotional investment in academic work and progression. The revised definitions also allow for growth metrics such as school achievement that focus on on-track instead of off-track status for students ( Bauer et al., 2018 ). The revised definitions further allow for greater understanding of whether engagement, or lack thereof, could be informed by impairment in school (e.g., academic achievement), social (e.g., interpersonal skills, relationships), and family (e.g., financial cost) domains ( Kearney, 2022 ). Both examples eschew traditional emphases on timeline and physical location and synthesize systemic and analytic perspectives by adopting a mutual language to define school attendance/ absenteeism, incorporating multiple instructional formats (e.g., in-person, hybrid, and online), and allowing for categorical distinctions better informed by dimensional aspects ( Kearney and Gonzálvez, 2022 ).

Broader and more flexible characterizations of school graduation will also be necessary for the next decades. In particular, graduation will need to be viewed more as a process extending potentially into emerging adulthood than as a singular event in adolescence and with an emphasis more on school completion without, necessarily, a predefined timeline. An analogy is the systemic conceptualization of school dropout as an elongated process of school disengagement, declining academic performance, and premature departure from school as opposed to a singular event ( Rumberger and Rotermund, 2012 ). As mentioned, systemic and flexible educational programs that blur the line between end of high school and beginning of adulthood are emerging ( Kearney, 2016 ). In addition, analytic health-based protocols for school attendance problems increasingly incorporate an extended developmental focus such as competencies for emerging adulthood (e.g., independent living skills) that may have been compromised by school absenteeism (e.g., Kearney and Albano, 2018 ). Extension of the school completion process allows for greater transition to readiness in emerging and later adulthood for a greater number of students and assimilates key systemic and analytic developments that emphasize flexibility for conceptualizing school attendance/absenteeism.

Social justice

Over the next century, social justice with respect to school attendance/problems will involve mechanisms and processes ensuring that all students have access to opportunities to achieve readiness for adulthood, in this case via school attendance. Such mechanisms and processes involve (1) removing structural barriers to school attendance, (2) utilizing disaggregated data regarding school attendance/absenteeism, (3) adopting a more inclusive and less deficit- and reductionistic-oriented approach to school attendance/absenteeism among key stakeholders, and (4) advocating for universal access to education. Such mechanisms and processes must involve a synthesis of systemic and analytic perspectives on school attendance/absenteeism.

The first aspect of social justice is removing structural barriers to school attendance , especially for vulnerable students. Recall that barriers in less developed countries include systematic deprivation of educational opportunity for all students often based on gender, ethnic status, poverty, and disability as well as limited qualified instructors and learning materials. Barriers in more developed countries include school closures, inequities in school funding, racial disparities in school discipline, oppressive school climate, victimization, lack of access to school counselors/nurses and mental health care, transportation vulnerability, and restricted access to technological supports for academic endeavors ( Kearney et al., 2022 ).

Over the next decades, efforts to remove structural barriers to school attendance will involve a coordinated effort among school officials, community partners, health professionals, and researchers from systemic and analytic perspectives to examine localized patterns of absenteeism and conditions that contribute most to that absenteeism. A key part of this effort will be to utilize sophisticated data analytic strategies for large data sets to pinpoint root causes of absenteeism for a given community, school, or student group ( Hough, 2019 ). These strategies include algorithm- and model-based strategies designed to reveal predictive patterns or outcomes.

Algorithm-based models establish predictive rules for a given outcome such as absenteeism that can also identify key barriers to attendance. These models have been used to identify specific barriers such as delays in assigning new schools following residential changes, safety concerns at school, lack of transportation, grade retention, teacher turnover, and lack of certain courses needed for graduation (e.g., Deitrick et al., 2015 ). These analyses can also be used to provide predictive information for certain developmental levels/grades, student groups, and schools and classrooms ( Newman et al., 2019 ). Model-based analyses identify relationships or clusters among variables related to absenteeism. Such approaches have also helped identify key barriers to school attendance in certain locations such as food and housing insecurity, elevated school suspension rates, and entry into juvenile/criminal justice systems (e.g., Coughenour et al., 2021 ).

The second aspect of social justice is focusing on disaggregated data regarding school attendance and absenteeism . Contemporary school and policy approaches emphasize aggregated data across various student groups to evaluate progress in a given area, such as overall graduation rates across schools or districts. A frequent tactic is to rely on cutoffs to determine acceptable levels of overall attendance rates for a school or district, such as 90% ( Durham et al., 2019 ). Reliance on aggregated data and cutoffs, however, discounts nuanced sources of information pertinent to targeted intervention efforts, such as timing of absences, information from other relevant agencies (e.g., housing and public health), qualitative data, and information on long-range attendance patterns ( Falissard et al., 2022 ; Kearney and Childs, 2022 ; Keppens, 2022 ). Reliance on aggregated data and cutoffs also discounts broader factors related to absenteeism such as lack of safe transportation to school, ignores attendance rates parsed by student group, and fails to inform effective interventions ( Hutt, 2018 ). Reliance on aggregated data also fails to capture important, nuanced, historical information for a given community that can be critical for addressing broader issues related to school attendance and absenteeism.

Over the next decades, efforts to address school attendance/absenteeism will focus on disaggregated data to better identify high-risk groups, focus on a continuum of school attendance/absenteeism, and include growth metrics to enhance school accountability efforts ( Bauer et al., 2018 ). Disaggregated data as opposed to cutoffs will help identify specific student groups, often those with intersecting risk factors, most in need of services. Examples include students of various racial and ethnic groups with certain health problems, students who are English language learners living in impoverished neighborhoods, students with disabilities without transportation to school, and migrant students with varying degrees of assimilation into a particular school ( Childs and Grooms, 2018 ). Alternatives to cutoffs will require synthesis of systemic and analytic approaches by adopting diverse disaggregation strategies such as conducting needs assessments, data system reconfigurations, and case studies in educational agencies ( National Forum on Education Statistics, 2016 ).

The use of disaggregated data also allows for greater consideration of a continuum of school attendance/absenteeism. Although many schools rely on full-day presence or absence from school, school attendance/absenteeism more accurately also includes partial absences (e.g., tardiness, skipped classes, or parts of a school day) and difficulties attending school (e.g., morning behavior problems to miss school and distress during a school day; Kearney et al., 2019a ). Reliance on full-day absences also penalizes students who are late to school due to transportation and other problems outside their control ( Chang, 2018 ). A focus on a continuum as opposed to full-day absences allows for more granular attendance coding, especially for online or hybrid learning environments and for vulnerable students, that supports a standards-based or competency-oriented progression with respect to academic progress and eventual school completion ( National Forum on Education Statistics, 2021 ).

A focus on disaggregated data also permits greater use of growth or on-track metrics to enhance school accountability regarding specific student groups ( Leventhal et al., 2022 ). Growth metrics can include school metrics related to climate and academic quality, achievement metrics related to academic progress (including attendance), and protective metrics related to school engagement and other variables that propel students toward school completion ( Zaff et al., 2017 ). These metrics are better suited for proactive practices to identify specific students drifting off track and in need of resources and moving away from reactive, punitive, and often discriminatory absenteeism policies that exclude students from the educational process ( Spruyt et al., 2017 ; Bauer et al., 2018 ). Growth metrics also synthesize systemic and analytic approaches in this area by emphasizing academic and non-academic variables.

The third aspect of social justice is adopting a more inclusive and less deficit- or reductionistic-oriented approaches among key stakeholders . Contemporary research, policy, and educational practices emphasize specific risk factors for school attendance problems involving youth and caregivers ( Conry and Richards, 2018 ). Examples include mental, behavioral, and learning challenges; caregiver strategies; and family dynamics (e.g., Roué et al., 2021 ). As such, researchers and other stakeholders disproportionately place blame and burden for remediating school attendance problems on students and their families, especially for vulnerable groups ( Grooms and Bohorquez, 2022 ). Less attention is paid to broader factors outside a family’s control such as structural barriers to school attendance or school and community factors ( Gubbels et al., 2019 ). Indeed, students often report that problems with the physical and social school environment impact their attendance more so than home-based experiences ( Corcoran and Kelly, 2022 ). School attendance/absenteeism constructs are instead, however, often framed within a deficit narrative.

Over the next decades, a more inclusive approach to school attendance/problems will include better recognition of broader contextual factors other than student and family variables that contribute to separation from the educational process. This will include consideration of various ecological levels associated with school attendance and absenteeism that involve both proximal and distal factors. Microsystem-level or proximal factors are often the focus of researchers and school personnel and are valid predictors of school absenteeism; examples include mental health challenges, limited parent involvement, and learning disorders. A more inclusive and less stigmatizing approach to school attendance/problems will involve greater analysis of, and integration with, broader ecological levels. Examples include interactions among microsystem variables such as caregiver-teacher communications (mesosystem), indirect influences of social structures such as caregiver unemployment and housing insecurity (exosystem), and cultural and policy influences such as neighborhood violence and exclusionary disciplinary practices (macrosystem; Singer et al., 2021 ). Developmental cascade models can also blend systemic/proximal and analytic/distal variables of causation for school attendance/absenteeism across multiple ecological levels ( Kearney, 2021 ).

Key stakeholders will also better recognize that missing school is often an adaptive option for many students. Examples include pursuing employment or caring for siblings to assist one’s family, avoiding victimizing or repressive school environments, or rejecting an academic system biased against certain student groups with respect to academic and social opportunities and disciplinary policies ( Kohli et al., 2017 ). Absence from school is thus not “disordered” in nature for many students. In related fashion, epistemic injustice in many educational institutions worldwide means that student knowledge and expression of local/indigenous contexts, practices, and culture are suppressed in favor of a dominant and oppressive orientation ( Elicor, 2020 ). Adopting an ecological, developmental, and equitable approach to school attendance/absenteeism thus requires synthesizing systemic and analytic perspectives with respect to racial inequality, implicit bias, and structural disadvantage.

The fourth aspect of social justice is advocating for universal access to education . Stakeholders in the next decades must pursue a more active advocacy agenda, in particular for vulnerable students worldwide who are deprived of an education. Such advocacy can occur at a systemic level, as when national and international organizations denounce educational oppression and promote the basic right to education. Such advocacy can also occur at the individual level, as when various professionals help students reconnect with the educational process after having been derailed by injustices and exclusionary and biased policies.

Shared alliances

Over the next decades, school absenteeism will be increasingly and accurately viewed as a wicked problem that is highly intertwined and relentless across communities and generations ( Childs and Lofton, 2021 ). Contemporary approaches to school attendance/problems are quite siloed across disciplines, but progression toward a postmodern era involves shared alliances among key agencies and stakeholders to address the complexities inherent in school attendance/absenteeism. Manifestations of these shared alliances include (1) multiagency tracking of students, (2) coordinated early warning and intervention systems, and (3) community asset mapping coupled with long-range intercession planning across generations. Shared alliances with respect to these manifestations necessarily involve partnerships among those from systemic and analytic perspectives, such as between policymakers who mandate school attendance practices and researchers and others who generate data to inform best practices in education and school attendance ( Iftimescu et al., 2020 ).

Multiagency tracking of students refers to collaboration among educational, governmental, public health, and other key community entities to better trace students who are separated from the educational process. Frequent reasons for separation include housing insecurity and residential mobility. Mechanisms of multiagency tracking include sharing data, liaisons, and office spaces among departments, meeting regularly to define appropriate metrics, and expanding criteria for those selected for assistance programs ( Welsh, 2018 ). Multiagency collaboration can also address key drivers of absenteeism related to housing insecurity via rental assistance and transportation to a previous school. Such collaboration can align with existing multiagency efforts for adult readiness ( Sambolt and Balestreri, 2013 ) and requires coalitions among those from systemic (e.g., public housing) and analytic (e.g., school counselor) perspectives.

Coordinated early warning and intervention systems refer to mechanisms by which students are identified as at-risk for short-range absenteeism or long-range school dropout, coupled with strategies to ameliorate risk and enhance school attendance for these students. Short-term risk for a given academic year can be quantified based on local conditions such as a particular school, whereas long-term risk over several years can be quantified for larger educational agencies across districts or states/provinces ( Balfanz and Byrnes, 2019 ). Risk factors thus often include broader variables such as school disengagement and academic progress as well as specific variables such as accommodation plans and newness to a school, thus blending systemic and analytic approaches ( Chu et al., 2019 ). Early warning/intervention systems can be also linked to adult readiness programs by incorporating readiness indicators such as enrollment in career/technical programs or dual high school/college courses ( National Forum on Education Statistics, 2018 ).

Community asset mapping with long-range intercession planning across generations refers to identifying and obtaining resources from businesses, individuals, and service and educational agencies to form family-school-community partnerships to enhance school attendance and adult readiness, particularly for vulnerable students ( Kearney and Graczyk, 2022 ). Key mechanisms include mentoring, tutoring, skills development, mental health support, and academic enrichment and adult readiness programs. Such partnerships are particularly useful for high-risk groups such as students who are homeless or those with disabilities ( Griffin and Farris, 2010 ) and can include support for families across generations. The partnerships blend systemic and analytic approaches to school attendance/absenteeism and support a developmental focus with respect to college and career readiness programs for underserved adolescents ( Gee et al., 2021 ).

As mentioned, a theory of change is typically designed in backward fashion; as such, the inputs, or current conditions and initiatives that can serve as the impetus for the outputs, are discussed next. Key inputs in the contemporary era include (1) movement of educational agencies worldwide toward readiness for adulthood, (2) technological advances, and (3) schools and communities as one. Each input directly supports avenues toward reframing, social justice, and shared alliances as well as increased synthesis of systemic and analytic perspectives with respect to school attendance/absenteeism.

Movement of educational agencies toward readiness for adulthood

The World Economic Forum Education 4.0 Framework emphasizes skills (global citizenship, innovation and creativity, technology, and interpersonal) and forms of learning (personalized and self-paced, accessible and inclusive, problem-based and collaborative, lifelong, and student-driven) necessary for adult readiness ( World Economic Forum, 2020 ). As mentioned, education and pedagogy are moving away from the Industrial Revolution model of memorization and standardization to a whole child/citizen education approach for postmodern globalization. Movement of educational agencies in this direction has implications for school attendance/absenteeism vis-à-vis the outputs described above.

With respect to reframing , school attendance is increasingly viewed as participation and engagement in instructional formats, including online and hybrid formats, that augment readiness for adulthood in more flexible and accessible ways. Alternative codes for attendance in this new context include number of hours per day; log-ins to virtual learning; student-teacher interactions; completion of assignments; measures of competency, mastery, and achievement (skills and knowledge); and meeting timelines for course objectives ( National Forum on Education Statistics, 2021 ). In addition, the proliferation of online, technical, skills training, and other nontraditional education programs available to those in emerging adulthood, including mechanisms to address the needs of students with disabilities and to simultaneously complete primary education while initiating these programs, propels a greater focus on participation/attendance than on absenteeism and set graduation times ( U.S. Department of Education, 2012 ). Moreover, ongoing educational disciplinary reforms recognizing the disparate punitive nature of truancy and related policies require a shift in emphasis from absenteeism to participation/ attendance ( Gentle-Genitty et al., 2020 ).

With respect to social justice , school attendance is increasingly framed as an access issue and as a key pathway to address entrenched inequalities. A key foundational principle in this regard is assuring the right to quality education throughout the lifespan, including the right to access and contribute to bodies of knowledge and to participate in discussions about education ( UNESCO, 2021 ). Learning frameworks are moving toward enhanced student agency to remove barriers to education, provide personalized learning environments to boost access to education, and ensure literacy and numeracy for as many as possible ( OECD, 2018 ). Researchers have also begun integrating global social justice variables in their models of school attendance/absenteeism, particularly with respect to migration, racial and income inequality, economic policies and opportunities, labor markets, violence, food insecurity, and healthcare ( Keppens and Spruyt, 2018 ; Kearney et al., 2019b ).

With respect to shared alliances , the emergence of family-school-community partnerships to address the needs of vulnerable students also allows for mechanisms to coordinate tracking, assessment, and early intervention services ( Benoit et al., 2018 ). Such partnerships often involve incorporating a set of community agencies into the school setting to reduce stigma, transportation problems, cost, wait time, and other barriers and thus draw students and their families. Such a process enhances the ability to identify families absent from this process, address family needs that supersede school attendance, and map community assets tailored best to a school’s jurisdiction ( Iftimescu et al., 2020 ).

Technological advances

As mentioned, myriad technological changes are occurring in education and include augmented reality, metaverse, artificial intelligence, social media, biometrics, cloud computing, multi-touch surfaces, 3D printing, hand-held devices, applications, blockchain, and gamification. Such changes obviously impact instructional formats and settings, learning strategies, communications, student-teacher relationships, and other core aspects of the educational process. These changes carry risks, such as unequal access to equipment and connectivity, as well as benefits such as reduced barriers and extension of education on a continuum from childhood to adolescence to emerging and later adulthood. Technological advances also have important ramifications for school attendance/absenteeism over the next decades.

With respect to reframing , technological advances that include remote learning are necessarily compelling educational agencies to reconfigure metrics for school attendance/absenteeism, as noted above. In addition, technological advances allow for enhanced attendance tracking, feedback to caregivers, and data accumulation for learning analytic practices, though privacy concerns remain relevant. The advances also allow for more nimble interventions and pinpointed root cause analyses of attendance and absenteeism patterns for a given jurisdiction ( Center for Education Policy Research, 2021 ). Various technologies also facilitate real-time communications between school counselors, caregivers, and mental health professionals at an analytic level or for designing proactive measures to boost school attendance at a systemic level ( Cook et al., 2017 ).

With respect to social justice , technological advances certainly have the potential to reinforce oppressive systems as well as a digital divide ( Elena-Bucea et al., 2021 ). Constructed properly, however, technological advances have the potential to increase access to education and reduce barriers to school attendance via mechanisms that provide students with multiple ways of engaging the same material, expressing academic work, and accessing options to learn a particular competency or skill, even into emerging and later adulthood ( U.S. Department of Education, 2017 ). Technological advances also enhance cross-cultural classrooms to build relationships and exchange skills while empowering and drawing more youth into the educational process ( Marx and Kim, 2019 ).

With respect to shared alliances , technological advances allow multiple agencies to better coordinate data systems by enhancing value and mitigating risk and friction that inhibit sharing. Advances in cloud computing, encryption, interoperability, data directories, execution environments, and artificial intelligence are used in this regard. Such developments will be particularly necessary for those agencies most pertinent to school attendance/absenteeism that have historically not collaborated and thus have quite disparate data sets, such as schools, medical centers, public housing agencies, legal systems, and developmental services ( Kearney and Benoit, 2022 ).

Schools and communities as one

As mentioned, the future of education will increasingly involve a blending and shifting of traditional school-based with home and community settings. Various mechanisms already exist for this process, sometimes derived from emergency and disaster contingency planning (such as following climate change events), that include formats for blended and self-learning, multiple learning modalities, online social networking, media broadcasts, and home- and nonprofit agency-based instruction ( Lennox et al., 2021 ). Other mechanisms include a greater reliance in education on community-based service and experiential learning, internships, practicum placements, portfolios, vocational and field work, and other applied demonstrations of academic competency that do not require traditional attendance in a physical building ( Filges et al., 2022 ).

Systemic and analytic approaches have also been moving toward school-based service delivery frameworks based on levels of support for different student needs that integrate school and community resources. Integrated multi-tiered systems of support (MTSS) models emphasize Tier 1 universal or primary prevention practices to promote adaptive behavior and deter maladaptive behavior; Tier 2 early, selective intervention or secondary prevention practices to address emerging and less severe problems; and Tier 3 intensive intervention or tertiary prevention practices to address chronic and severe problems. Strategies for school attendance/problems include systemic and analytic elements such as school dropout prevention and screening practices (Tier 1), mentoring and clinical practices (Tier 2), and alternative educational and specialized care practices (Tier 3; Kearney and Graczyk, 2020 ).

With respect to reframing , MTSS models themselves represent a transformative change by adopting sustainable school improvement practices and outcomes and eschewing “wait-to-fail” achievement-discrepancy frameworks to assess student growth. As such, interactive environmental factors (e.g., curricula and school responses) receive as much if not more emphasis than student factors for academic progress, behavior, and skills. Such an approach allows for a broader reframing of school absenteeism toward efforts to enhance school attendance via incentives, positive climate, and policy review as well as growth metrics for school accountability purposes. MTSS models are also amenable to long-term educational initiatives such as transition services that enhance readiness into emerging adulthood for all students ( Osgood et al., 2010 ).

With respect to social justice , MTSS models can be a means to enhance equity among student groups because the models (1) rely on data-driven processes to drive continuous improvements to instruction and other outcomes, (2) include all students in a given school, and (3) specifically provide intensive services for at-risk students ( Fien et al., 2021 ). MTSS models are compatible with disaggregated data and learning analytic approaches to personalize learning experiences for individual students and include proactive preventative approaches instead of reactive, often punitive approaches. The models are also amenable to culturally responsive practices by welcoming traditionally marginalized students, validating student home cultures and communities, nurturing student cultural identities, promoting advocacy, and resisting deficit-oriented constructions of student performance ( Khalifa et al., 2016 ).

With respect to shared alliances , MTSS models depend on cross-system collaborations that include members of systemic and analytic approaches. Systems of care for youth and their families often include educational, primary care/community mental health, legal, and developmental systems. MTSS models utilize team-based approaches across these systems; examples include community mental health professionals within schools, hybrid truancy court practices, and linkage of preschool supports with early grade accommodations, especially for students with disabilities ( Kearney, 2016 ). Other key collaborators include researchers for expertise and technical support, external participating agencies for student tracking and progress monitoring (early warning) and service provision, and community stakeholders for asset mapping. Indeed, a key shared alliance for the future will involve partnerships between academia, industry, and other stakeholders (e.g., Heyne et al., 2020 ; Rocha et al., 2022 ).

Much is known about school attendance/absenteeism but we live in unprecedented times of rapid systemic shifts in basic human functioning. New visions are needed. The theory of change for school attendance/absenteeism presented here offers one possible compass that outlines how contemporary forces could shape key outputs that themselves could produce desirable long-range outcomes over the next decades. The theory is designed as a starting point for discussion among various stakeholders in this area, particularly those from disparate systemic and analytic perspectives. Agreement on long-term outcomes can help crystallize cohesive narratives that can then influence policy and educational and health-based practice. Such agreement also allows for frameworks specifically crafted to include all youth in the educational process. At the same time, the theory of change outlined here is designed to be flexible enough in a constructivist fashion to be fitted to jurisdictions worldwide that differ tremendously in their approaches to education, law, research, and child development. We invite commentary and input into the crystal ball.

Author contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: school attendance, school absenteeism, truancy, school dropout, theory of change, readiness for adulthood

Citation: Kearney CA, Benoit L, Gonzálvez C and Keppens G (2022) School attendance and school absenteeism: A primer for the past, present, and theory of change for the future. Front. Educ . 7:1044608. doi: 10.3389/feduc.2022.1044608

Received: 14 September 2022; Accepted: 17 October 2022; Published: 07 November 2022.

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Copyright © 2022 Kearney, Benoit, Gonzálvez and Keppens. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Christopher A. Kearney, [email protected]

This article is part of the Research Topic

The Unlearning of School Attendance: Ideas for Change

Absenteeism in Organizations

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Nonappearance; Shirking; Work attendance

Absenteeism is the failure to report for work as scheduled.

Introduction: The Problems and Consequences of Absenteeism

Absenteeism is a problem in many organizations. It has a number of negative consequences for society at large, organizations, colleagues, and employees.

Apart from the economic consequences such as sickness allowances, healthcare treatment, pay for temps or overtime pay, lost productivity, etc., there are also a number of personal costs. For the involved person, sickness absence may have major consequences such as a poorer life quality, loss of identity if away from work for long periods of time, lack of social contact to colleagues, worries about own health, loss of professional competences, and perhaps even discharge. There are also consequences for the colleagues of the absentee, such as increased workload when substituting the absent colleague.

The many negative consequences mean that absenteeism is on...

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How one school is trying to improve attendance of chronically absent students

Leigh Paterson

Elizabeth Miller

In 2023, about one in four students was chronically absent. Schools are going above and beyond to turn those numbers around. That often means having difficult conversations with students and families.

Copyright © 2024 NPR. All rights reserved. Visit our website terms of use and permissions pages at www.npr.org for further information.

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• Study Protocol
• Open access
• Published: 10 May 2024

Evaluating the impact of school-based influenza vaccination programme on absenteeism and outbreaks at schools in Hong Kong: a retrospective cohort study protocol

• Chuhan Miao 1 ,
• Qingyang Lu 1 ,
• Yuqian Wu 1 &
• Jianxun He 1 , 2  

Journal of Health, Population and Nutrition volume  43 , Article number:  62 ( 2024 ) Cite this article

Metrics details

Introduction

Seasonal influenza causes annual school breaks and student absenteeism in Hong Kong schools and kindergartens. This proposal aims to conduct a retrospective cohort study to evaluate the impact of a school-based influenza vaccination (SIV) programme on absenteeism and outbreaks at schools in Hong Kong.

The study will compare schools that implemented the SIV programme with schools that did not. The data will be sourced from school records, encompassing absenteeism records, outbreak reports, and vaccination rates. We will recruit 1000 students from 381 schools and kindergartens in 18 districts of Hong Kong starting June 2024. The primary outcome measures will include absenteeism rates due to influenza and school influenza outbreaks. Secondary outcomes will consist of vaccination coverage rates and the impact of the SIV programme on hospitalisations due to influenza-like illness. A t-test will be conducted to compare the outcomes between schools with and without the SIV programme.

Ethics and dissemination

The school completed signing the participants’ informed consent form before reporting the data to us. Our study has been approved by the Hospital Authority Hong Kong West Cluster IRB Committee (IRB No: UW 17–111) and was a subtopic of the research “The estimated age-group specific influenza vaccine coverage rates in Hong Kong and the impact of the school outreach vaccination program”.

Trial registration

This study will be retrospectively registered.

Influenza is a contagious respiratory tract infection caused by the influenza virus in humans [ 1 , 2 ]. In Hong Kong, seasonal influenza occurs more often from January to March/April and from July to August (online supplemental material 1). As of February 9, 2024, the flu hospitalisation rate for the 0-5-year-old group in Hong Kong is 1.432 per 10,000 people, and the flu hospitalisation rate for the 6-11-year-old group is 0.896 per 10,000 people. Among them, the 0-5-year-old group has the highest flu hospitalisation rate among all age groups in Hong Kong (online supplemental material 2). The paediatric population is generally at the most significant risk of influenza virus infection among all age groups, and influenza-related hospitalisation rates are high in school-age children [ 3 ]. The influenza vaccine is one of the most effective ways to prevent influenza. It is recommended by the Centre of Health Protection (CHP) that all people aged six months or above should receive the influenza vaccine for personal protection [ 3 ].

In the 2018/2019 winter season, the CHP reported 864 influenza-like-illness (ILI) institutional outbreaks, with 61% and 21% of outbreaks occurring in kindergartens (KG)/children care centres (CCC) and primary schools, respectively (online supplemental material 3). The influenza-associated hospital admission rate of children under 5 was the weekly peak, followed by elderly 65-year-old and 6-11-year-old children, demonstrating that young children are vulnerable to infection by seasonal influenza.

To increase the vaccination rates among primary school children, starting from the 2018/19 season, the Hong Kong Department of Health (DH) launched the School Outreach Vaccination Pilot Programme. All primary schools, including KGs, KG-cum-CCCs, CCCs and special CCCs, are eligible to apply and join the programme. 81.5% of primary schools and 72.7% of kindergartens have signed up for the free Seasonal Influenza Vaccination School Outreach Programme [ 4 ]. According to CHP data, the vaccination rate among children between 6 and 12 years has significantly increased by 205.1%, compared to the vaccination rate in 2017/18 [ 3 ].

The SIV programme is beneficial for preventing school absenteeism [ 5 ]. During the 2009 H1N1 pandemic, the Hong Kong government closed all KGs and primary schools for a prolonged period (online supplemental material 4), resulting in high rates of school absenteeism [ 6 ]. This plan interrupted student learning, and absenteeism negatively impacted teaching work productivity and pace [ 7 , 8 ]. Although there is no guideline for territory-wide school closure in Hong Kong, future influenza outbreaks at the school level are likely to result in higher rates of absenteeism, and individual school closures will likely be in large-scale outbreaks or those with severe health outcomes (online supplemental material 5).

There is limited literature investigating the impacts of a school-based influenza vaccination (SIV) programme on school absenteeism and outbreaks in Hong Kong. Additionally, this aspect of influenza vaccination in school children is paramount in limiting the negative impacts, like absenteeism during an influenza outbreak. It is critical to optimise the Seasonal Influenza Vaccination School Outreach Programmes.

In this sense, a retrospective cohort study is designed to examine the potential effects of SIV programs on participating primary schools and kindergartens in Hong Kong to address the gaps and provide valuable insights.

Several countries, including the US, Italy, Russia, and Japan, have conducted school-based immunisation programmes in primary schools [ 4 ]. Studies evaluating the impacts of school-based influenza in the US and Japan have concluded that increased influenza vaccination rates effectively reduce school absentee rates [ 9 , 10 ]. However, some studies have shown that school-based influenza moderately impacted absenteeism [ 11 , 12 ]. In Hong Kong, the existing school outreach programme successfully reduced the ILI rate and hospitalisations in primary school students from 2018 to 2019 [ 4 ]. Nevertheless, there are still limited studies examining the potential impact of the SIV programme on school outbreaks and absenteeism (online supplemental material 6) [ 4 ].

The expected outcomes of this retrospective observational study are as follows: Among 0-5-year-old children participating in the school flu vaccination program, flu-related hospitalisations are approximately 1.432 per 10,000 individuals; among 6-11-year-old children, the corresponding frequency is approximately 0.896 per 10,000. In each school participating in the flu vaccination program, the vaccination rate of students is at least 40%. Many students choose to be vaccinated with live-attenuated vaccines over inactivated ones. In schools participating in the flu vaccination program, the student attendance rate has increased by at least 1% compared to before. Students can wear masks consciously during flu-prone seasons and pay attention to hand hygiene. The parents of students participating in the school flu vaccination program can reduce the number of student absences and hospitalisations, thereby alleviating the health and financial burden on the family.

SPIRIT flow diagram

Literature review

Typically, hospitals would recommend seasonal flu vaccines. However, the high cost of vaccination is a significant concern. A double-blind, randomised, controlled study shows that children’s respiratory tract infections can directly impose a significant financial burden on the family, indicating that the medical expenses for respiratory tract infections in children are high. At the same time, the safety and effectiveness of vaccines are relatively low, which makes parents reluctant to vaccinate their children [ 13 ]. In the past, attempts to send text messages to pregnant women in middle- and high-income areas failed to increase their vaccination enthusiasm [ 14 ]. Analysing the factors that prevent parents from having their children vaccinated from the perspective of the vaccine type, although vaccinating children with the live attenuated influenza vaccine (LAIV) is more effective in reducing the incidence of flu per 1,000 person-days than the inactivated influenza vaccine (IIV) [ 15 ], parents generally prefer not to choose the live attenuated influenza vaccine (LAIV) for their children.

On the one hand, inactivated influenza vaccines are relatively cheaper than live attenuated influenza vaccines; on the other hand, parents need to manage their children’s vaccines carefully and receive regular booster vaccinations [ 16 ]. However, since 2016, how flu vaccines attract parents has gradually shifted from the cost of vaccination to vaccine education. A controlled trial for junior and senior high school students found that once parents receive good flu vaccine education, their willingness to vaccinate their children will be positively motivated, regardless of whether the flu vaccine requires additional expenses [ 17 ]. The results of a meta-analysis suggest that the main reason for voluntary and active flu vaccination is the vaccine’s effectiveness rather than forced or threatening behaviours [ 18 ]. A report suggests that different vaccine communication channels can be used for vaccinated people of different ages, effectively reducing the vaccine cost to balance vaccine costs [ 19 ]research and development, vaccination, and publicity [ 19 ]. This result means the top priority should be to develop more effective and safer vaccines to increase the flu vaccination rate of the 0-11-year-old group, which is only the case in some countries worldwide [ 20 ].

Another strategy is derived from the application of flu vaccines in adults. One of the targets was to find whether adults would directly benefit from getting flu vaccines at work or in life, thereby indirectly promoting the popularity of flu vaccine vaccinations. Vaccination can significantly reduce the incidence of flu-related diseases and the number of work absences [ 21 ], which means that vaccination has become a strategy for people to avoid getting sick and being absent from work. If not vaccinated, children should wear masks actively to prevent the spread of the disease through the air [ 22 ]. To further increase the coverage of flu vaccine vaccinations among teenagers, public health intervention measures have gradually attracted public attention. Public health commissioners enter the campus, distribute vaccine education booklets to students, and collaborate with campus leaders to carry out vaccine awareness-raising activities to increase the willingness and enthusiasm of students and parents to get flu vaccines. After the “campus vaccination program” matures gradually, public health commissioners also use communities as a unit and work with community hospitals and family doctors to continuously convey the concept of flu vaccine prevention to the public, thereby further increasing the flu vaccine coverage of teenagers [ 23 ]. Another target was to determine measures in the information-scarce and relatively underdeveloped suburban and rural areas to increase flu vaccine coverage in remote areas such as suburbs and rural areas. Unlike in the past, when public health commissioners educated students on campus, inviting parents to enter the campus in rural areas to receive vaccine education can increase parents’ willingness to consent to vaccination [ 24 ]. Vaccination has gradually been extended to school-aged children to develop primary health care, and there are different degrees of resistance during the promotion period. Implementing the school flu vaccination program is significantly correlated with the increase in the vaccination rate of students in suburban schools [ 25 ]. A third target was to find whether promoting school flu vaccination could drive the popularisation of student vaccinations. Surveys in American urban schools suggest that telephone and text message interventions cannot significantly increase the vaccination rate of flu vaccines [ 26 ]. When the UK promotes the national childhood flu vaccination program, it is believed that providing parents with behavioural-informed letters and email or text message reminders can increase the popularity of school vaccines [ 27 ].

The geometric titer (GMT) of the attenuated recombinant live influenza vaccine (Alice strain) reached 189.6, which initially endows this vaccine with antigenicity and safety [ 28 ]. It was not until 1985 that the trivalent inactivated influenza B vaccine could provide a 64% anti-infection protection rate for school-aged children aged 6–19 [ 29 ]. After school-aged children are vaccinated with seasonal influenza vaccines, the vaccine efficacy against diagnosed influenza A (H3N2) and influenza B infections is 31% (95% confidence interval: -138%, 80%) and 96% (95% confidence interval: 67%, 99%) respectively [ 30 ]. In a double-blind, randomised, controlled clinical trial in eastern China, the effectiveness and safety of the live-attenuated influenza vaccine for minors (3–17 years old) were verified, and the effectiveness of the vaccine against all types of influenza reached 62.5% (95% CI: 27.6–80.6) [ 31 ], which provides a theoretical basis for preventing influenza infections in Hong Kong children aged 0–11. With the early advocacy of the campus influenza vaccine program by the public health departments of many countries worldwide, in-depth research was required on topics including whether this program could be a blessing for children on campus, whether it could effectively improve the immunity of students against the flu, and whether it could guarantee the health and education of students and avoid absence due to flu hospitalisation. After researchers piloted the flu immunisation program in schools, it was found that student attendance increased significantly by 0.8–1.9% compared to before [ 32 ]. It is unknown whether all the reasons for student absences are from the flu. With the gradual enrichment of vaccine types, clinical observations have found that applying live-attenuated vaccines to the entire age group of elementary school students provides more significant influenza immune protection than inactivated vaccines [ 33 ]. In the past, it was generally believed that a single vaccination of influenza vaccine for children aged 4–6 could obtain influenza immunity. Combined vaccination was then found to achieve better cross-immune protection [ 34 ].

Once students can benefit from the flu vaccine, it can not only fundamentally promote the popularity of the flu vaccine but also, to a certain extent, guarantee children’s education. The United States has established a national strategic goal: the coverage rate of children’s flu vaccines in the entire United States should not be less than 80%. Compared with traditional vaccination in community hospitals, implementing the campus flu vaccination program achieves America’s strategic goal [ 35 , 36 , 37 ]. A study in New York believes that building a reasonable cost-benefit system for flu vaccines can increase the coverage of flu vaccines for school-aged children, increasing the flu vaccine coverage of first- and second-grade students in elementary schools by 11.2 and 12.0% points [ 38 ]. According to a cross-sectional observation report, 42,487,816 student days of absence were accumulated in Northern California from 2011 to 2018. However, the city-wide school flu vaccination can reduce the student flu absence rate [ 39 ]. In general, student vaccination against the flu can reduce the number of times they get sick and are hospitalised and prevent large flu outbreaks, thereby reducing the number of absences from class [ 40 ]. Further studies were required for questions, including whether vaccinations of students were against the flu and whether the reduction in the number of absences from class was an independent influencing factor.

Methods and analysis

Study objectives.

The main objective is to understand the impacts of the SIV programmes at the primary schools and kindergarten level, including absenteeism and outbreaks. This object is indicated by the steps involving recruitment of schools into different groups (SIV and non-SIV), data collection through self-reporting from the schools, and subsequent data analysis.

The research question was whether an SIV programme would be associated with reduced absenteeism and outbreaks for kindergartens and primary schools compared with schools that do not join the programme. We plan to recruit 1000 primary school and kindergarten students in Hong Kong.

Study design, setting and recruitment

The study will be conducted in Hong Kong primary schools and kindergartens, and it will include records of student absenteeism and reported outbreaks. Schools that participated in the influenza vaccination program and those that did not will be included to assess the program’s direct impact compared to the general population. The selection of schools will be based on the availability of complete data for the specified periods. School administrators will be contacted, and informed consent will be obtained for using their records in the study. The study is divided into two periods for comparison: the pre-intervention period (2016/17 and 2017/18 academic years) and the post-intervention period (2018/19 and 2019/20 academic years, Fig.  1 ). This design allows for examining trends and differences in absenteeism and outbreak rates before and after the implementation of the vaccination program.

Kindergartens and primary schools registered under the Education Bureau in Hong Kong will be selected by stratified random sampling in each of the 18 districts. The list of local kindergartens and primary schools that had joined and not joined the SIV Programs in 18 districts in two different periods was constructed.

Defining Strata: the strata are the 18 districts of Hong Kong. Each district serves as a separate stratum.

Random Sampling within Each Stratum: use a random sampling technique within each district (stratum) to select schools. This step can be done using a random number generator or a similar method to ensure that every school on the list has an equal chance of being selected.

Documentation and Reproducibility: Document the sampling process in detail to ensure the study’s reproducibility. This step includes recording the method used for random selection and any criteria for including or excluding certain schools.

Ethical Considerations: ensure that the selection process is fair and unbiased. Maintain confidentiality and adhere to ethical standards in research.

Selected schools will be invited to join the research by sending invitation letters and emails. Follow-up phone calls to the principals or their delegates of the schools will be made to ensure their receipt of the invitations, clarify details of the study, and recruit their schools further to participate in the study. The anonymous information on influenza vaccination records and data will be collected from the institutions included.

Outcome measures

Primary outcomes measures.

Absenteeism rates: Compare the difference in absenteeism rates due to influenza and school influenza outbreaks among students in schools with and without SIV programs. Schools participating in the study were required to provide sufficient supporting materials to provide detailed data reports on student absenteeism. However, schools were required to trace student absenteeism before participating in the SIV program to pass the data verification successfully.

Secondary outcomes measures

Hospitalisation rates: Compare the hospitalisation rates due to influenza and school influenza outbreaks among students in schools with and without SIV programs. Schools participating in this study must provide sufficient supporting materials to illustrate detailed data reported on student hospitalisations.

Vaccination rates: Compare the differences in vaccination rates among students in schools with SIV programs and schools without SIV programs.

Data collection

Retrospective data covering four academic years will be collected from schools that joined the SIV Programme (SIV group) and those that did not participate (non-SIV group). Data from 2016/17 to 2017/18 will be designated as “pre-SIV years” because the SIV Programme was not yet launched, while data from 2018/19 to 2019/20 will be designated as “SIV years”.

Three data sets from schools that agreed to join the study will be self-reported. First, data on half-day absences in each grade every month will be aggregated. As reasons for student absences can vary and may not be recorded by schools, all excused and unexcused absences will be counted. Second, the number of outbreaks per school year will be collected each month. As schools must report to CHP for respiratory tract infection outbreaks (i.e., three or more students in the same class developed symptoms), the school has such data on record. Third, basic information about the schools will be reported, including the number of students in each grade, the total number of school days per year and the year of joining the SIV programme, districts of school location, number of school days, grade of students, and healthcare access.

Sample size calculation

We hypothesize that the school-based influenza vaccination will result in a decrease in the rate of absenteeism from a baseline rate (prior to the intervention).

Assuming the baseline absenteeism rate is 5% (pre-intervention period) and anticipating a reduction to 3% post-intervention period, we will calculate the sample size required to detect this difference with sufficient statistical power.

The sample size for each group (schools with and without the vaccination program) can be calculated using the formula for comparing two proportions in cohort studies. This calculation will account for the expected absenteeism rates, the desired power of the study (typically 80% or 0.80), and the significance level (typically 5% or 0.05).

Using the standard formula:

n= ( Z α /2 ​+ Z β ) 2 × (p 1 (1 − p 1 ) + p 2 (1 − p 2 )) / (p 1  − p 2 ) 2 .

p 1  = 0.05 (baseline absenteeism rate without the intervention).

p 2  = 0.03 (expected absenteeism rate with the intervention).

Z α /2 ​ is the Z-score corresponding to the 95% confidence level (typically 1.96).

Z β ​ is the Z-score corresponding to the power of 80% (typically 0.84).

Plugging in the values:

n= (1.96 + 0.84) 2  × (0.05 × (1 − 0.05) + 0.03 × (1 − 0.03)) / (0.05 − 0.03) 2 .

After calculating, we determine the required sample size for each group. Suppose the calculation results in a sample size of 1,000 students per group; considering the design effect due to clustering within schools (e.g., design effect = 1.5 due to the intra-class correlation within schools), the adjusted sample size would be 1500 students per group.

Since schools vary in size, the number of schools needed to reach this sample size depends on the average number of students per school. For example, if the average school size is 300 students, five schools would be needed for each group of 10 schools.

However, the study aims to include a broader representation. Therefore, considering potential dropouts and missing data, we might aim to recruit more schools. If we estimate a 10% dropout or missing data rate, the target recruitment would be increased accordingly.

Therefore, for primary schools, if 272 schools participated and the remaining 109 did not (with a similar approach for kindergartens), we ensure the study has enough power to detect the anticipated differences in absenteeism rates while accounting for variations in school size and potential data loss.

Statistical analysis

Descriptive analysis.

Initially, a descriptive statistical analysis will be conducted to summarize the characteristics of the study population, including school demographics, student demographics (e.g., age, gender distribution), baseline absenteeism rates, and influenza vaccination coverage rates for both the pre-and post-intervention periods. This analysis will utilize means and standard deviations for continuous variables and frequencies and percentages for categorical variables.

Difference-in-differences (DiD) analysis

The core of the analysis will be a Difference-in-Differences (DiD) approach, comparing changes in absenteeism and outbreak rates from the pre- to the post-intervention period between schools that participated in the vaccination program and those that did not. This method helps to control for time-invariant unobserved heterogeneity between the treated and control groups and isolates the effect of the intervention by considering the differential effect over time.

The DiD estimator will be calculated using the formula:

DiD=(Y post, treated​ −Y pre, treated​ )−(Y post, control​ −Y pre, control ​).

where Y represents the outcome variable (absenteeism rate or outbreak rate).

Multivariable logistic regression models

Multivariable logistic regression models will be utilized to adjust for potential confounders and better understand the relationship between the influenza vaccination program and the outcomes (absenteeism and outbreaks). These models will include the intervention variable (vaccination program participation), time (pre- or post-intervention), and an interaction term between the intervention and period to estimate the DiD coefficient. Control variables will include school size, demographic characteristics, and other health interventions that might influence the outcomes.

Subgroup analyses

Subgroup analyses will be conducted to explore the differential effects of the influenza vaccination program across various demographic and school characteristics (e.g., primary vs. secondary schools, gender, and age groups). The regression models will include interaction terms between the intervention and these subgroup identifiers to test for significance.

Sensitivity analyses

Sensitivity analyses will be performed to assess the robustness of the study findings, which could include alternative specifications of the regression models, using different definitions of absenteeism and outbreaks and excluding schools with extreme values or missing data. These analyses help to identify whether the main findings are consistent across different assumptions and methodologies.

Power and sample size considerations in analysis

The statistical analysis plan incorporates the sample size calculations previously detailed, ensuring that the study is adequately powered to detect the hypothesised differences in outcomes. The analysis will account for data clustering within schools through cluster-adjusted standard errors or multilevel modelling techniques, as appropriate, to provide accurate confidence intervals and p values.

Handling missing data

The approach to handling missing data will be detailed, considering using multiple imputation techniques if the missingness is assumed at random (MAR) or conducting sensitivity analyses under different missing data assumptions if not MAR.

Additional details

According to the needs assessment in the introduction, most institutional outbreaks of ILI have been recorded in primary schools and kindergartens, and local evidence is necessary to understand whether it is appropriate to allocate more resources to implementing and promoting school-based vaccination programmes. Therefore, a retrospective cohort study evaluating the impact of SIV programmes on absenteeism and outbreaks at schools is proposed.

To further prevent and control the incidence and outbreaks of influenza among young children, researchers must conclude whether school-based vaccination programmes can offer indirect benefits in addition to reducing the ILI rate and hospitalisations. By providing local solid evidence, the government can understand whether allocating more resources to implement and promote school-based vaccination programmes is appropriate. With information from authorities, more schools can be encouraged to join the programme, and parents can be advised if more indirect benefits can be confirmed.

Our team believes this retrospective study could be a reference point for future similar studies and a preliminary guide for the Food and Health Bureau to optimise the school-based vaccination programme soon. We sincerely hope that our proposal will be taken into serious consideration.

Control of Bias and Confounders

Monitoring and evaluating the impact of the SIV programme is challenging due to the reliance on self-reported data from schools, which can be subject to biases or inaccuracies. The COVID-19 pandemic has affected Hong Kong since early 2020, and school closures have affected school days. The collected data would be inaccurate, incomprehensive, and unrepresentative if we conducted a prospective cohort study. Even if the face-to-face teaching mode is resumed, wearing masks and stringent hand hygiene practices at schools would also affect the transmission of infectious diseases, thus affecting the results. Therefore, our team chose to adopt a retrospective cohort-based approach instead of a prospective one to minimise the effects of the COVID-19 pandemic on the study results.

To control confounding variables that may affect absenteeism and the number of outbreaks at school, we use multiple linear regression models to adjust known confounders, including districts of school location, number of school days, and number of students in each grade. In addition to the confounders we have identified, here are more potential confounders that we might consider when analysing absenteeism and outbreak rates in schools, such as preexisting health conditions of students and school transportation mode since they might play a role in exposure to infectious agents. Subgroup analyses will also evaluate the program’s impact on different age groups and socioeconomic backgrounds.

Limitations

Potential limitations of the study include the retrospective design, reliance on school-reported data, and the potential for unmeasured confounding factors. Efforts will be made to mitigate these limitations through rigorous data validation processes and statistical adjustments for known confounders.

The observational nature of this study may not establish a definitive causal relationship between the SIV Programme on school-based influenza outbreaks and absenteeism. However, an observed relationship between school vaccination and the study parameters may still indicate an underlying impact, whether by direct effects of the SIV Programme or indirect effects of vaccination initiatives. The direct ones would be the reduced incidence of influenza among vaccinated students, causing fewer absenteeism and outbreaks directly attributable to the vaccination. Indirect ones occur when the vaccination of a portion of the population protects unvaccinated individuals. This situation happens as the disease’s overall prevalence decreases, reducing the likelihood of the disease spreading to those not vaccinated.

Odds and risk ratios may indicate the degree of associated difference in the odds and risks for outbreaks and absenteeism for schools that joined the SIV programme.

First, this observational study could not investigate whether the relationship between the SIV programme, absenteeism and school outbreaks is causal. This study would also suffer from other confounding factors that have not been controlled in the analysis, such as reasons for the absence and types of influenza vaccine administered. Research examining the causal relationship between them could be conducted in the future. Second, the study was conducted retrospectively, and data were collected by self-reporting from schools, which may lead to incomprehensive data and recall bias. Third, some students in non-SIV schools may have already been vaccinated outside school settings because they were parent-led, which could affect the result of the analysis. Finally, schools are recruited to join the study voluntarily. The study’s sample size is primarily influenced by the willingness of schools to participate, and a small sample size would affect the study’s external validity and statistical significance (Table  1 ).

Significance

Evidence gathered from this proposed study will provide valuable insights into the effectiveness of SIV programmes in reducing absenteeism and preventing school outbreaks. This study aims to have a fair representation of all kindergartens and primary schools in Hong Kong; as such, stratified random sampling within the 18 districts of Hong Kong will be adopted. Therefore, all the results will inform Hong Kong’s public health influenza prevention and control strategies. Furthermore, this proposed study is expected to contribute to the existing knowledge on the impact of SIV programs and provide evidence to support the implementation and expansion of such programs in Hong Kong and other similar places.

Despite challenges such as logistical hurdles, parental consent issues, and uneven vaccine uptake, preliminary data suggests a positive impact on reducing influenza-like illness rates and student hospitalisations. Future studies should focus on improving vaccine coverage and addressing barriers to access, particularly in underserved areas, to maximize the programme’s effectiveness. Continued evaluation and adaptation of the programme based on local needs and outcomes will be crucial for its success in enhancing public health among school-aged children.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

We thank the primary schools and kindergartens who will cooperate with our research.

Dr He received funding from Northwest Minzu University, grant number 31920170197. The funding source had no role in this research study.

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Chuhan Miao, Qingyang Lu, Yuqian Wu & Jianxun He

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Chuhan Miao coordinated the study, was the principal investigator, was responsible for the study’s design, and participated in the manuscript draft. Yuqian Wu and Qingyang Lu participated in the study’s design and coordination and the manuscript’s drafting. Yuqian Wu, Chuhan Miao, and Jianxun He are members of the research team and participated in the draft. All authors have read and approved the manuscript.

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Miao, C., Lu, Q., Wu, Y. et al. Evaluating the impact of school-based influenza vaccination programme on absenteeism and outbreaks at schools in Hong Kong: a retrospective cohort study protocol. J Health Popul Nutr 43 , 62 (2024). https://doi.org/10.1186/s41043-024-00561-z

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Received : 27 March 2024

Accepted : 03 May 2024

Published : 10 May 2024

DOI : https://doi.org/10.1186/s41043-024-00561-z

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