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\begin{document}

\title{Effect of Planned School Breaks on Student Absenteeism due to
Influenza-like Illness in School Aged Children - Oregon School District,
Wisconsin September 2014-June 2019}



\author[1]{Cecilia He}%
\author[1]{Derek Norton}%
\author[2]{Jonathan Temte}%
\author[3]{Shari Barlow}%
\author[4]{Maureen Goss}%
\author[5]{Emily Temte}%
\author[1]{Cristalyne Bell}%
\author[1]{Guanhua Chen}%
\author[6]{Amra Uzicanin}%
\affil[1]{University of Wisconsin-Madison}%
\affil[2]{University of Wisconsin School of Medicine and Public Health}%
\affil[3]{University of Wisconsin}%
\affil[4]{University of Wisconsin Madison}%
\affil[5]{University of Wisconsin Madison Department of Family Medicine}%
\affil[6]{CDC}%


\vspace{-1em}



  
  \date{June 17, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Background School-aged children and school reopening dates have
important roles in community influenza transmission. Although many
studies evaluated the impact of reactive closures during seasonal and
pandemic influenza outbreaks on medically attended influenza in
surrounding communities, few assess the impact of planned breaks (i.e.,
school holidays) which coincide with influenza seasons, while accounting
for differences in seasonal peak timing. Here, we analyze the effects of
winter and spring breaks on influenza risk in school-aged children,
measured by student absenteeism due to influenza-like illness (a-ILI).
Methods We compared a-ILI counts in the two-week periods before and
after each winter and spring break over five consecutive years in a
single school district. We introduced a ``pseudo-break'' of 9 days'
duration between winter and spring break each year when school was still
in session to serve as a control. The same analysis was applied to each
pseudo-break to support any findings of true impact. Results We found
strong associations between winter and spring breaks and a reduction in
influenza risk, with a nearly 50\% reduction in a-ILI counts post-break
compared to the period before break, and the greatest impact when break
coincided with increased local influenza activity. Conclusions These
findings suggest that brief breaks of in-person schooling, such as
planned breaks lasting 9-16 calendar days, can effectively reduce
influenza in schools and community spread. Additional analyses
investigating the impact of well-timed shorter breaks on a-ILI may
determine an optimal duration for brief school closures to effectively
suppress community transmission of influenza.%
\end{abstract}%



\sloppy


\textbf{Title:} Effect of Planned School Breaks on Student Absenteeism
due to Influenza-like Illness in School Aged Children - Oregon School
District, Wisconsin September 2014---June 2019

\textbf{Running Title:} Effect of school breaks on absenteeism due to
ILI

\textbf{Authors:} Cecilia He, MPH\textsuperscript{1}; Derek Norton,
MS\textsuperscript{1}; Jonathan L Temte, MD/PhD\textsuperscript{1};
Shari Barlow, BS\textsuperscript{1}; Maureen Goss,
MPH\textsuperscript{1}; Emily Temte, BA\textsuperscript{1}; Cristalyne
Bell\textsuperscript{1}, BS; Guanhua Chen, PhD\textsuperscript{1}; Amra
Uzicanin, MD/MPH\textsuperscript{2}

\textbf{Author Affiliations}

\textsuperscript{1}University of Wisconsin, Madison, Wisconsin, USA

\textsuperscript{2}Centers for Disease Control and Prevention, Atlanta,
Georgia, USA

\textbf{Acknowledgments}

This study has been supported by Centers for Disease Control and
Prevention (\emph{www.cdc.gov}) through cooperative agreement \#
5U01CK000542-02-00. The findings and conclusions in this study are those
of the authors and do not necessarily represent the official position of
the Centers for Disease Control and Prevention.

\textbf{Conflicts of Interest}

Jonathan L. Temte, MD/PhD reports non-financial support from Quidel
Corporation during the conduct of the study.



\textbf{Background}

School-aged children and school reopening dates have important roles in
community influenza transmission. Although many studies evaluated the
impact of reactive closures during seasonal and pandemic influenza
outbreaks on medically attended influenza in surrounding communities,
few assess the impact of planned breaks (i.e., school holidays) which
coincide with influenza seasons, while accounting for differences in
seasonal peak timing. Here, we analyze the effects of winter and spring
breaks on influenza risk in school-aged children, measured by student
absenteeism due to influenza-like illness (a-ILI).

\textbf{Methods}

We compared a-ILI counts in the two-week periods before and after each
winter and spring break over five consecutive years in a single school
district. We introduced a ``pseudo-break'' of 9 days' duration between
winter and spring break each year when school was still in session to
serve as a control. The same analysis was applied to each pseudo-break
to support any findings of true impact.

\textbf{Results}

We found strong associations between winter and spring breaks and a
reduction in influenza risk, with a nearly 50\% reduction in a-ILI
counts post-break compared to the period before break, and the greatest
impact when break coincided with increased local influenza activity.

\textbf{Conclusions}

These findings suggest that brief breaks of in-person schooling, such as
planned breaks lasting 9-16 calendar days, can effectively reduce
influenza in schools and community spread. Additional analyses
investigating the impact of well-timed shorter breaks on a-ILI may
determine an optimal duration for brief school closures to effectively
suppress community transmission of influenza.

\textbf{Keywords:} influenza; respiratory infection; viral surveillance;
school breaks; K-12; student absenteeism

\textbf{Introduction}

School-aged children are often recognized as primary drivers of
influenza transmission within communities\textsuperscript{1}, and in the
fall of 2009 school reopening dates were associated with the local
surges of pandemic influenza\textsuperscript{2}. Children frequently
have larger social networks\textsuperscript{3,4}, experience prolonged
viral shedding\textsuperscript{5}, have lower coverage rates for
influenza vaccine\textsuperscript{6}, and may lack sufficient
preexisting immunity for herd effects\textsuperscript{7}. Although most
of the frequent influenza infections among school-aged children are mild
to moderate, some children can still develop serious influenza-related
complications following infection\textsuperscript{8}. During the 2017-18
influenza season, there were an estimated 11.5 million cases of
influenza in children and over 48,000 pediatric hospitalizations in the
U.S. alone\textsuperscript{8}.

The rapid evolution and wide variability of the influenza virus
contributes to the challenges of control. Normal efforts in disease
prevention, such as vaccination, are hampered as vaccines must be
updated and administered annually to account for changes in circulating
viruses\textsuperscript{9}, leading to varying levels of effectiveness
from year to year\textsuperscript{10}. Thus, it is important to consider
alternative strategies to control outbreaks, especially during seasons
when vaccine effectiveness is suboptimal, or when a well-matched vaccine
is not yet available (e.g., in early stages of a pandemic).

School closures include planned breaks in instruction for holidays or
teacher training, and unscheduled breaks due to weather, safety or other
emergencies. With regard to their anticipated effects on influenza
transmission, school closures are considered a nonpharmaceutical
intervention (NPI) only when implemented sufficiently early relative to
the start of an outbreak (i.e., before influenza becomes widespread in
schools and surrounding communities)\textsuperscript{11}. Effectiveness
of preemptive school closures has been extensively studied and
scrutinized in systematic literature reviews\textsuperscript{12,13}. In
contrast, reactive school closures---implemented only after influenza is
widespread in schools---are not considered NPI, but rather a consequence
of the disease\textsuperscript{11} because epidemiologic studies have
not found them to effectively reduce medically attended influenza (MAI)
in surrounding communities\textsuperscript{14-16}. Studies noted
reactive closures to have no statistically significant impact on overall
influenza-like illnesses (ILI) rates\textsuperscript{14,15}. In fact,
these unplanned closures often have socioeconomic consequences and may
further introduce challenges to households, such as making alternative
childcare arrangements and loss of access to school lunch
programs\textsuperscript{16}.

Schools close for regularly scheduled or planned breaks (holidays)
throughout the academic year (Figure 1). At least one earlier study
reported that such planned school breaks may interrupt the dynamics of
seasonal influenza by changing social contact patterns among
children\textsuperscript{17}. Such closures have been associated with a
reduction in the reproduction number relative to when school is in
session, leading to reduced transmission\textsuperscript{18,19}.
However, the precise impact of these breaks on seasonal influenza
remains unclear. Few studies have investigated the effect of planned
school closures on local transmission, and no studies currently assess
the impact of numerous breaks within an academic year to account for
potential seasonal differences in the timing of circulation.

To account for multiple breaks and seasonal timing, we investigated the
role of regularly scheduled school breaks on ILI within a single school
district over the course of five academic years. We assessed rates of
ILI-related absenteeism (a-ILI) during two-week periods leading up to
and following scheduled winter and spring breaks.

\textbf{Methods}

\textbf{ORCHARDS} : The ORegon CHild Absenteeism due to Respiratory
Disease Study (ORCHARDS) is a prospective, observational study of
kindergarten through 12\textsuperscript{th} grade (K-12) student
absenteeism and influenza in the Oregon School District (OSD), Dane
County, located in southcentral Wisconsin. The primary goal of ORCHARDS
is to develop a system to monitor cause-specific, K-12 absenteeism on a
daily basis and to assess its usability for early detection of influenza
and ILI transmission in schools and in the community. The overall
methodology of ORCHARDS is detailed elsewhere\textsuperscript{20}.

\textbf{Population:} The OSD comprises six public schools with a growing
enrollment, estimated at 4,091 students (18\% of total population)
during the 2018-2019 school year\textsuperscript{21}. The district's
overall population is estimated at 23,000 and is less racially and
ethnically diverse, wealthier, and more educated than the average
community in the United States\textsuperscript{22}.

\textbf{Data Collection:} Parents/guardians are required to report
absences using an automated telephone system and are prompted to report
respiratory symptoms. The OSD records absenteeism in Infinite Campus\selectlanguage{ngerman}®
(https://www.infinitecampus.com), a commercially available electronic
student information system. For the purpose of this study, in 2014 the
OSD Information Technology staff added an option within the system that
allowed entry of student absenteeism characterized as a-ILI. We defined
absence as missing any part of the school day. We defined ILI as the
presence of fever and at least one of the following symptoms: cough,
sore throat, nasal congestion, or runny nose\textsuperscript{20}as
reported by a parent/guardian on the telephone system. The daily count
of a-ILI was the primary outcome measure for this study.

\textbf{Data Extraction:} The OSD developed an automated process to
extract daily counts of student absences by school, grade, and type of
absence. Data were sent on a daily basis to ORCHARDS researchers using a
secure file transfer (ftp) site. No personal identifiable information
was included, and the data were fully compliant with the Family
Educational Rights and Privacy Act (FERPA 20 U.S.C. § 1232g; 34 CFR Part
99).

\textbf{Community Risk:} The Wisconsin component of the Influenza
Incidence Surveillance Project (W-IISP) is a long-standing, independent
influenza surveillance system that assesses MAI in and around
OSD\textsuperscript{23}. The system has been in continuous operation
since October 2009 and is organized by the ORCHARDS research team.
W-IISP includes five primary care clinics, one of which is located in
the OSD and four that are located in communities surrounding OSD. The
clinics conduct active laboratory-supported surveillance for influenza
and other respiratory viruses in patients presenting with acute
respiratory illnesses. Weekly counts of laboratory-confirmed MAI served
as a proxy for underlying community influenza risk in this analysis.

\textbf{Timing of regularly scheduled major school breaks} : The winter
holiday (including Christmas and New Year's Day) at the OSD is
relatively fixed in time, occurring in late December and early January,
extending between 10 and 16 days, including weekend days (Table 1). The
timing of spring break is more variable depending upon year but is fixed
in length at 9 days (including weekend days).

\textbf{Pseudo-breaks as a control:} We introduced into the analysis
``pseudo-breaks'' of 9 days' duration between winter and spring break
each year and starting five weeks before the spring break, when school
was actually in session, to support any findings of the true impact of
the planned breaks. The timing of regularly scheduled winter and spring
breaks, along with pseudo-breaks, is presented in Figure 2 against the
backdrop of statewide laboratory-confirmed influenza detections.

\textbf{Statistical analysis:} Analyses were performed on absentee data
from five consecutive academic school years (September 2, 2014, to June
12, 2019). The primary outcome measure was the number of a-ILI days in
the two weeks before and after the regularly scheduled school break.
Absenteeism due to ILI has been validated as an acceptable marker for
influenza through the home visit component in
ORCHARDS\textsuperscript{20}.

The Cochran-Mantel-Haenszel (CMH) Test was used to measure the crude
association between a-ILI and the two-week period before versus after
each break period, stratified by school year. Exposure was defined as
whether the a-ILI count occurred during the two-week period before a
break (not exposed) or after a break (exposed). Cases were defined as
the sum of a-ILI counts for each pairing of school year and the two-week
time periods before versus after a break. The number of controls (not
absent due to ILI) in each of these two-week periods was defined as the
number of students enrolled in OSD for that school year, multiplied by
the number of school days in attendance during those two-week periods,
minus the number of a-ILI cases in that same period.

Generalized Linear Regression Models (GLM) were used to assess the
relationship between a-ILI counts and before- versus after-break
periods, while accounting for the community's underlying influenza risk.
A Poisson distribution was assumed with the outcome of daily a-ILI
counts and the canonical natural log link function used. The natural log
of the OSD enrollment number for that school year was used as an offset
to account for varying enrollment numbers. Time from break was accounted
for within these two-week periods, measured in days. For the period
before a break, days were counted going backwards in time from the first
date of the break. For the period after a break, days were counted going
forward in time from the last date of the break (i.e., break periods are
``day 0'', school days before a break are negative days, and school days
after a break are positive days). Covariates used in this model included
community-level influenza risk, linear effect of time, quadratic effect
of time, indicator of before or after break, an interaction between the
break indicator and linear time effect, and an interaction between the
break indicator and the quadratic time effect.

The community-level influenza risk was represented as a weekly measure
of influenza risk in the community. This was calculated by summing the
number of MAI instances in the community data set for the first 7-day
period (week 1) before and after break, and the second 7-day period
(week 2) before and after break, for a total of 20 such calculations at
the community level (4 weeks calculated for each of the 5 school years
analyzed), for each break type (winter and spring). To assess if there
was an association between a-ILI and the period indicator of before and
after break, a 3 degree-of-freedom Likelihood Ratio Test (LRT) was
performed. The results from the GLM were compared to a null model
without the period indicator and its interactions to assess the
significance of comparing before versus after breaks.

The analysis approach above for winter and spring break was also applied
to the pseudo-break period that was introduced five weeks prior to each
spring break when school remained in session. This pseudo-break serves
as a control to assess for time as a potential confounder. The estimates
predicted by the model for the pseudo-break were compared to the results
from the winter and spring break analyses to help assess the true impact
of school breaks on a-ILI.

\textbf{Results}

\textbf{Absenteeism due to ILI:} Across the 5 academic years, the mean
tallies of a-ILI for the two-week periods before winter and spring
breaks were 130.4 (range 51-262) and 151.4 (range 69-275), respectively.
The mean a-ILI tallies for the periods after winter and spring breaks
were 82 (range 33-152) and 49.6 (range 33-74), respectively.
Comparatively, the two weeks before pseudo-breaks had an a-ILI average
of 106 (range 43-200), and the two weeks after pseudo-breaks had a-ILI
average of 100.8 (range 71-131). The grade distributions of a-ILI are
displayed in Figure 3, showing higher levels of a-ILI reported among
students in 4K and elementary schools, in comparison to middle and high
schools.

\textbf{Crude association between school breaks and a-ILI:} The two-week
after-break period was associated with a statistically significant
decrease in the odds of a-ILI compared to the two-week before-break
period. The CMH test estimated an odds ratio of 0.679 (95\% CI:
0.600-0.769; p\textless{}0.001) following winter breaks and 0.327 (95\%
CI: 0.283-0.378; p\textless{}0.001) following spring breaks. The crude
a-ILI counts for each school year, occurring before versus after breaks,
are depicted in Table 2. Differences in a-ILI proportions in the two
weeks before and after each true break varied every school year (Figure
4). While several of the yearly school breaks had a clear difference in
the a-ILI proportions, not every yearly break displayed a difference.

\textbf{Adjusted association between school breaks and a-ILI:} In the
regression models, the estimated a-ILI over the two-week period after a
break was nearly half the amount of that in the period before a break.
The estimated proportional change following a break was 0.483 (95\% CI:
0.347-0.673; p\textless{}0.001) for winter break and 0.488 (95\% CI:
0.327-0.730; p\textless{}0.001) for spring break. The weekly community
MAI count was also strongly associated with a-ILI (p\selectlanguage{english}[?]0.001). No
statistical significance was detected in the change in linear or
quadratic time components for before vs. after breaks (Table 3).

The models produced estimates of daily mean a-ILI for the ten days
before and after each break, based on the mean weekly community MAI
counts and the mean student enrollment of 3,749 in OSD (Figure 5).
Although the behavior of time remained similar in the ten days before
and after each break, the model consistently estimated an overall
reduction in the amount of a-ILI in the periods following breaks
compared to the periods before breaks.

The assessment for the association between a-ILI in the periods before
and after breaks was found to be significant. The null model, which
consisted of a removal of the two-week period indicator and its
interactions with linear and quadratic time, yielded a
X\textsuperscript{2} statistic value of 125.9 on 3 degrees of freedom
(p\textless{}0.001) in the winter break analysis and 102.4
(p\textless{}0.001) in the spring break analysis. This indicated that
the inclusion of the period indicator in the model was associated with a
statistically significant amount of variation, after accounting for
linear and quadratic passage of time and the weekly community ILI count.

\textbf{Pseudo-breaks as a control:} There was consistently no
statistically significant difference observed in a-ILI in the two-week
periods before and after the pseudo-break when school was actually in
session. The unadjusted association between the two-week period after
the pseudo-break and the risk for change in a-ILI estimated an odds
ratio of 0.985 (95\% CI: 0.872-1.11; p=0.839). The changes in
proportions of a-ILI before and after each pseudo-break vary throughout
the five years (Figure 4). The LRT for removal of the two-week period
indicator and its interactions with linear and quadratic time yielded a
X\textsuperscript{2} statistic value of 4.8 on 3 degrees of freedom
(p=0.189), indicating that how the period indicator was included in the
model was not associated with a statistically significant amount of
variation in a-ILI. All covariates included in the pseudo- break model
were non-statistically significant (Table 3). In Figure 5, the estimated
daily ILI means predicted by the model displayed no clear level of
change in absenteeism counts for before versus after a pseudo-break.

\textbf{Conclusions} Over a 5-year period of enhanced monitoring of
cause-specific absenteeism, from September 2014 through June 2019, a
nearly 50\% reduction in a-ILI was observed consistently in the two-week
periods immediately following scheduled winter and spring breaks with
durations of 9 to 16 days, as compared to the two weeks immediately
preceding these breaks. We found a strong association between the period
indicator and a-ILI in regression models. This implies that the regular
scheduled school breaks produce a significant acute effect on a-ILI.
Such an effect has high biological plausibility: (a) if schools are
primary centers of influenza transmission and acceleration, and (b)
given that the time period spans approximately 2.8 to 4.4 serial
intervals for influenza\textsuperscript{24}.

The scale of the proportional differences in a-ILI associated with each
break in Figure 4 appears to reflect the timing of peak influenza
circulation and annual seasonal peak across Wisconsin (Figure 2). For
example, during the 2014/2015 and 2017/2018 school years, there was
relatively widespread circulation before the commencement of winter
break, with the seasonal peak occurring in late December and early
January\textsuperscript{25}. Thus, winter break appeared to have a
larger impact on reducing a-ILI than spring break in these years.
Conversely, in 2015/2016, 2016/2017, and 2018/2019, widespread
circulation occurred later in the season with the peak between February
and March\textsuperscript{25}, explaining the more profound difference
in a-ILI following spring break. This observation emphasizes the
importance of the timing of a school closure on the potential impact on
influenza risk.

The absence of significant findings for the pseudo-breaks lends credence
to the true school breaks being an actual causal mechanism to reduce
a-ILI, particularly with the lack of association between pseudo-breaks
and reductions in a-ILI and weekly community MAI. Although the changes
in a-ILI after the pseudo-break for any given year in Figure 4 may
appear to be significant, the changes are inconsistent with three years
(2014/2015, 2015/2016, and 2018/2019) having higher a-ILI following the
pseudo-break and two years (2016/2017 and 2017/2018) having lower a-ILI
after the pseudo-break.

Other results from ORCHARDS---specifically data generated through home
visits to a subset of K-12 students who had to miss school due to an
acute respiratory illness---complement the findings from this analysis
on school breaks\textsuperscript{20}. Over the five school years
(2014-2019), 79\% of participants with acute respiratory infections
reported missing school because of their illness; 65\% of these students
who were absent tested positive for influenza or another non-influenza
respiratory viral infection, and more than half thought a classmate or
friend was the likely source of infection\textsuperscript{20}. Thus, the
ORCHARDS results support the concept that within-school transmission
drives community-wide outbreaks, and that well-timed school breaks (or,
alternatively, short-term transitions to distance learning of equivalent
duration as a winter or spring break) can reduce influenza or other
respiratory virus transmission.

This assessment has several limitations. First, findings based on the
models used are suggestive of an association, but do not necessarily
imply a causal relationship. The assessment periods occurring before and
after the planned breaks are---by definition---ordered through time;
therefore, any temporal effect during this same period that may impact
influenza may result in confounding. Second, there is some violation in
the assumption of independence of observations in both the adjusted and
unadjusted analyses. Since the data used in this assessment were
de-identified and a-ILI was measured by counts, it is likely that
individual students contributed multiple, sequential absences to the
a-ILI counts, thereby altering the independence of daily counts. Third,
because parents self-report absences through the absentee line, there is
potential that a-ILI numbers are underestimated because of
underreporting by parents. Fourth, results generated from OSD over five
influenza seasons (2014-2019) may not be generalizable to other
locations and populations, for markedly different influenza seasons, or
over different academic calendars in terms of school break timing
relative to local influenza outbreak peaks. Fifth, we used a-ILI as a
proxy for influenza. Whereas we have demonstrated a significant
association between influenza virus infection and a-ILI, we have also
shown that influenza type and subtype have differential effects on
a-ILI\textsuperscript{20}. Finally, although community data on MAI were
used in an attempt to represent the underlying community risk, the
models are imperfect as they do not capture the entirety of the
relationship between underlying community level risk and the risk in
schools. It is possible that the period indicator is representing
differential community-level risk behaviors during before- vs.
after-break periods.

Although reports documenting the effect of school closures on reduced
influenza transmission exist, there remains a lack of consensus on its
effectiveness. The majority of current literature has assessed the
impact of reactive school closures during an influenza
pandemic\textsuperscript{26-33}. Differences in the timing of
implementation and length of closure during the pandemic may explain why
studies have found variable results from reactionary closures.

Results from these analyses are consistent with findings from other
studies looking at the role of scheduled breaks on
ILI\textsuperscript{34,35}. A study in South Korea observed an immediate
27-39\% reduction in influenza transmission during the break period,
with a 6-23\% reduction in overall transmission following spring
break\textsuperscript{34}. Another study found school closures to
prevent or delay up to 42\% of potential influenza cases among
school-age children\textsuperscript{35}. Although we measured a-ILI as
the outcome in this analysis, previous studies have suggested that
observed a-ILI can adequately represent changes in community
influenza\textsuperscript{36}. Moreover, we have previously demonstrated
a significant association between a-ILI and influenza in
ORCHARDS\textsuperscript{20}. Furthermore, several studies have proposed
that regular school closures may mitigate community impact by changing
social mixing patterns\textsuperscript{37-39}.

Overall, the findings from these analyses support the hypothesis that
planned K-12 school breaks of moderate duration (9-16 days) reduce
influenza transmission. Our finding is consistent with the results of
the modeling studies which explored the impact of different timing and
durations of the school closures during influenza
pandemics\textsuperscript{29}, as well as with the conclusions of
observational studies of school holidays' effect on influenza
transmission in other countries\textsuperscript{12,40}. The identified
impact occurs in the short term and does not imply a long-term effect on
an annual seasonal influenza epidemic; however, such short-term effect
may be helpful for targeted suppression of influenza activity to reduce
pressures on local health care systems during the local disease surges.
Additional analyses investigating the impact of well-timed shorter
breaks, both planned and unplanned, on a-ILI may determine an optimal
duration for brief school closures to effectively suppress community
transmission of influenza.

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2. Chao DL, Halloran ME, Longini IM Jr. School opening dates predict
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3. Glass LM, Glass RJ. Social contact networks for the spread of
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\textbf{Table 1:} Length (in days) of winter, spring, and pseudo-breaks
from the 2014/2015 to 2018/2019 academic years. Specific dates of each
break are provided in parentheses.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\textbf{Year}\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\textbf{Winter break}\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\textbf{Spring break}\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\textbf{Pseudo-break}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
2014/2015\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
16 (12/20/2014---1/04/2015)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
9 (3/28/2015---4/05/2015)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
9 (2/21/2015---3/01/2015)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
2015/2016\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
12 (12/23/2015---1/03/2016)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
9 (3/19/2016---3/27/2016)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
9 (2/13/2016---2/21/2016)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
2016/2017\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
11 (12/23/2016---1/02/2017)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
9 (3/25/2017---4/02/2017)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
9 (2/18/2017---2/26/2017)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
2017/2018\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
10 (12/23/2017---1/01/2018)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
9 (3/24/2018---4/01/2018)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
9 (2/17/2018---2/25/2018)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
2018/2019\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
11 (12/22/2018---1/01/2019)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
9 (3/23/2019---3/31/2019)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
9 (2/16/2019---2/24/2019)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Table 2: Absenteeism counts before and after the winter, spring, and
pseudo-breaks over 5 academic years for influenza-like illness
(ILI)-associated absenteeism (a-ILI) and the complement of this number
(all other students in attendance or absent for other reasons). Odds
ratios (OR) comparing after break to before break counts calculated
using the Mantel-Haenszel test are provided along with 95\% confidence
intervals (CI).\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
& & \textbf{Winter break} & \textbf{Winter break} & \textbf{Spring
break} & \textbf{Spring break} & \textbf{Pseudo-break} &
\textbf{Pseudo-break}\tabularnewline
\midrule
\endhead
school year & ILI? & before break & after break & before break & after
break & before break & after break\tabularnewline
2014/15 & ILI absences & 262 & 152 & 74 & 33 & 66 & 88\tabularnewline
& Not ILI absences & 35618 & 32140 & 35806 & 35847 & 35814 &
32204\tabularnewline
2015/16 & ILI absences & 104 & 62 & 275 & 74 & 101 & 131\tabularnewline
& Not ILI absences & 37026 & 37068 & 36855 & 37056 & 37029 &
33286\tabularnewline
2016/17 & ILI absences & 80 & 77 & 115 & 33 & 200 & 108\tabularnewline
& Not ILI absences & 37410 & 33664 & 37375 & 33708 & 37290 &
33633\tabularnewline
2017/18 & ILI absences & 155 & 72 & 69 & 39 & 120 & 71\tabularnewline
& Not ILI absences & 38125 & 34380 & 34383 & 38241 & 34332 &
34381\tabularnewline
2018/19 & ILI absences & 51 & 47 & 224 & 69 & 43 & 116\tabularnewline
& Not ILI absences & 38619 & 38623 & 38446 & 38601 & 27026 &
38554\tabularnewline
OR estimate of a-ILI, after vs. before & OR estimate of a-ILI, after vs.
before & 0.679 & 0.679 & 0.327 & 0.327 & 0.985 & 0.985\tabularnewline
OR 95\% CI & OR 95\% CI & (0.600, 0.769) & (0.600, 0.769) & (0.283,
0.378) & (0.283, 0.378) & (0.872, 1.114) & (0.872, 1.114)\tabularnewline
\bottomrule
\end{longtable}

Table 3: Summary statistics of the fitted regression model comparing
influenza-like illness absenteeism and community medically attended
influenza occurring after winter, spring, and pseudo-breaks to the
periods before breaks.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
\textbf{Break type} & \textbf{Coefficient} & \textbf{Estimate} &
\textbf{Std. Error} & \textbf{z value} & \textbf{p value} &
\textbf{Estimate 95\% CI} & \textbf{Proportional change (PC)} &
\textbf{PC 95\% CI}\tabularnewline
\midrule
\endhead
Winter & after winter break & -0.727 & 0.169 & -4.29 & 1.78E-05 & -1.06
--- -0.396 & 0.483 & 0.347 --- 0.673\tabularnewline
& weekly community ILI count & 0.0592 & 0.00733 & 8.07 & 6.83E-16 &
0.0448 --- 0.0736 & 1.06 & 1.05 --- 1.08\tabularnewline
Spring & after spring break & -0.717 & 0.205 & -3.5 & 0.000465 & -1.12
--- -0.315 & 0.488 & 0.327 --- 0.73\tabularnewline
& weekly community ILI count & 0.0453 & 0.00795 & 5.7 & 1.22E-08 &
0.0297 --- 0.0609 & 1.05 & 1.03 --- 1.06\tabularnewline
Pseudo & after pseudo-break & 0.0174 & 0.165 & 0.105 & 0.916 & -0.306
--- 0.341 & 1.02 & 0.736 --- 1.41\tabularnewline
& weekly community ILI count & 0.0102 & 0.00703 & 1.45 & 0.146 &
-0.00358 --- 0.024 & 1.01 & 0.996 --- 1.02\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure 1:} Representative kindergarten through
12\textsuperscript{th} grade academic year in the Oregon School District
(OSD: Dane County, WI) showing school days (yellow squares) and
non-school days for the 2016-2017 academic year. Longer-duration planned
school breaks for Thanksgiving, winter break and spring break are
demonstrated by empty boxes.

\textbf{Figure 2.} Weekly counts of Wisconsin influenza A and influenza
B detections combined for the 5 academic years in this study. Influenza
surveillance data were provided by the Wisconsin State Laboratory of
Hygiene. Lightly shaded vertical bands demonstrate the actual timing of
winter breaks (green) and spring breaks (orange) for each academic year
/ influenza season in this study. Nine-day pseudo-breaks between winter
and spring breaks each year were introduced in this analysis to support
any findings of impact from the planned breaks; they are shown as purple
bars. Dark shaded bands demonstrate the 2-week assessment periods before
and after winter breaks (green), spring breaks (orange) and
pseudo-breaks (purple).

\textbf{Figure 3.} Distribution of number of absences due to
influenza-like illness (a-ILI) per study year across all grade levels in
the Oregon School District. 0 = kindergarten. 13 = students eligible to
remain in the public school system past age 18 years.

\textbf{Figure 4.} Proportion of students absent due to influenza-like
illness in the 2-week periods before and after winter breaks (green --
upper panel), spring breaks (orange -- upper panel) and pseudo-breaks
(purple -- lower panel) in each of 5 academic years. Nine-day-long
pseudo-breaks between winter and spring breaks each year were included
in this analysis to act as control periods for comparison. The 95\%
confidence intervals are demonstrated by brackets.

\textbf{Figure 5:} Estimated mean absenteeism due to influenza-like
illness (a-ILI) counts for each of 10 school days before and after
winter breaks (green line -- upper panel), spring breaks (orange line --
upper panel) and pseudo-breaks (purple line -- lower panel) in each of 5
academic years (please see Table1 for precise dates and duration of real
and pseudo-breaks). Nine-day pseudo-breaks between winter and spring
breaks each year were introduced in this analysis to support any
findings of true impact from the planned breaks. The 95\% confidence
intervals are demonstrated by shading. Model estimated an overall
reduction in a-ILI in the periods following break compared to the period
before break.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure1/Figure1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure2/Figure2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure3/Figure3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure4/Figure4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure5/Figure5}

\end{center}
\end{figure}

\selectlanguage{english}
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