Results
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.
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<0.001) following winter breaks and 0.327 (95%
CI: 0.283-0.378; p<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.
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<0.001) for winter break and 0.488 (95% CI:
0.327-0.730; p<0.001) for spring break. The weekly community
MAI count was also strongly associated with a-ILI (p≤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
X2 statistic value of 125.9 on 3 degrees of freedom
(p<0.001) in the winter break analysis and 102.4
(p<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.
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
X2 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.
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
influenza24.
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
January25. 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 March25, 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 breaks20. 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 infection20. 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-ILI20. 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
pandemic26-33. 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
ILI34,35. 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
break34. Another study found school closures to
prevent or delay up to 42% of potential influenza cases among
school-age children35. 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
influenza36. Moreover, we have previously demonstrated
a significant association between a-ILI and influenza in
ORCHARDS20. Furthermore, several studies have proposed
that regular school closures may mitigate community impact by changing
social mixing patterns37-39.
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
pandemics29, as well as with the conclusions of
observational studies of school holidays’ effect on influenza
transmission in other countries12,40. 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.