Objective
In the presented study, we examined the impact of school holidays (Autumn, Winter,
Summer, and Spring Breaks) and social events (Super Bowl, NBA Finals, World Series,
and Black Friday) for five age groups (<4, 5-24, 25-44, 45-64, >65 years) on
four health outcomes of influenza (total tested, all influenza positives, positives
for influenza A, and B) in Milwaukee, WI, in 2004-2009 using routine
surveillance.
Introduction
Influenza viral infection is contentious, has a short incubation period, yet
preventable if multiple barriers are employed. At some extend school holidays and
travel restrictions serve as a socially accepted control measure [1,2]. A
study of a spatiotemporal spread of influenza among school-aged children in Belgium
illustrated that changes in mixing patterns are responsible for altering disease
seasonality [3]. Stochastic numerical
simulations suggested that weekends and holidays can delay disease seasonal peaks,
mitigate the spread of infection, and slow down the epidemic by periodically
dampening transmission. While Christmas holidays had the largest impact on
transmission, other school breaks may also help in reducing an epidemic size.
Contrary to events reducing social mixing, sporting events and mass gatherings
facilitate the spread of infections [4]. A
study on county-level vital statistics of the US from 1974-2009 showed that Super
Bowl social mixing affects influenza dissemination by decreasing mortality rates in
older adults in Bowl-participating counties. The effect is most pronounced for
highly virulent influenza strains and when the Super Bowl occurs closer to the
influenza seasonal peak. Simulation studies exploring how social mixing affects
influenza spread [5] demonstrated that impact
of the public gathering on prevalence of influenza depends on time proximity to
epidemic peak. While the effects of holidays and social events on seasonal influenza
have been explored in surveillance time series and agent-based modeling studies, the
understanding of the differential effects across age groups is incomplete.
Methods
The City of Milwaukee Health Department Laboratory (MHDL), Wisconsin routinely
collect tests from residents of metropolitan areas and vicinities of the Marquette
University (MU). We obtained weekly counts of total tested, all influenza positives,
positives for influenza A and B, from MHDL between 5/16/04- 3/7/09 (before the surge
of tests associated with “swine flu”). Cases for <1 and 1-4 age
groups were combined. Meteorological data are routinely collected by a monitoring
station at the General Mitchell International airport located 7.5 miles from
Milwaukee. Daily dewpoint values representing the perceived ambient temperature
corrected for the air moisture content were downloaded from the open source website
[6] and aggregated to weekly averages with
Sunday designating the beginning of each week. School holidays were obtained from
academic calendars on the MU website with holiday weeks defined as having one or
more school holiday observed [7]. Selected
social events were retrieved from a public website [8]. As part of exploratory analysis,
average cases per week (c/w) for
each outcome for school holiday and non-holiday weeks were compared using a
non-parametric the Mann–Whitney U-test. We analyzed the association between
weekly cases and holiday effects using negative binomial regression with sets of
indicator variables for non-overlapping school holidays and social events and with
adjustments for weather fluctuations with harmonic terms (Model 1). Results are
presented as Relative Risk (RR) estimates along with their confidence intervals
(95%CI). Further analyses examined seasonal signatures (lead-lag structures) using
a
segmented regression approach for weekly counts and rates 5 academic weeks (aw)
before, 2-6 weeks during, and 5 weeks after select holidays (Model 2).
Results
Over 251 study weeks, 2282 tests were submitted, out of which 1098 cases were from
5-24 y.o. age group. 477 (21%) tests we positive, with 399 (84%) cases of influenza
A (73 tests were not subtyped) and 78 (16%) cases of influenza B. Figure 1 shows the
time series of weekly counts
of influenza tests and percent positives with superimposed information on school
holiday occurrences. Overall, during 135 weeks of the school period the average
number of tests was two times higher as compared to those during 116 holiday weeks
(11.9±10.3 vs 5.8±6.5 c/w, p<0.001). Similarly, the average weekly
number of positive tests was higher in non-holiday than during holiday periods
(2.9±5.7 vs 0.7±2.6 c/w, p<0.001). The reduction in tests during
holidays was confirmed by the regression model (RR=0.71; 95%CI=[0.60-0.86]). The
reduction in weekly tests was most pronounced during the Winter Break (15-19 aw) for
all age groups (4.8±3.0 c/w, p<0.001; RR=0.3; 95%CI=[0.23-0.41]) and
especially for school-aged children, young adults and adults (RR=0.14;
95%CI=[0.09-0.22] and RR=0.32; 95%CI=[0.16-0.62] for 5-24 and 25-44 age groups,
respectively). In contrast, during the Spring Break (27-30 aw) the number of tests
has almost doubled (20.4±10.4 c/w; p<0.001) as compared to the school
period, with the most noticeable increase in 5-24 and 25-44 age groups. Spring Break
differential effects were primarily due to later peaks in influenza B shown by
segmented regression results in Figure 2. The
seasonal increase in weekly rates is the steepest after the winter holidays. The
effects of the selected sporting and social events were inconclusive.
Figure 1
Weekly number tests and % of influenza positives tests and percent positives
with superimposed school holiday occurrences in Milwaukee, WI
(2004-2009)
Figure 2
Weekly number of tests, positive tests, influenza A and B (panels A-D,
respectively) for each study year with superimposed Winter and Spring school
holiday occurrences in Milwaukee, WI. Average and standard deviation values
for four outcomes and their weekly rates (in italic) for
five time periods: before (5 weeks), during (2-6 weeks), and after (5 weeks)
the Winter and Spring Breaks (time is shown in weeks starting September
1st); superscripts b and c indicate t-test significance for
during-after and before-after comparisons, respectively.
Conclusions
The differential effects of calendar events on seasonal influenza can be detected
by
routine surveillance and further explored with respect to lead-lag structures. We
recommend incorporating location-specific calendar effects in influenza near-term
forecasting models tailored to susceptible age groups to better predict and assess
targeted intervention measures.