Introduction
Influenza surveillance utilizes multiple data sources, including
syndromic indicators, laboratory-confirmed cases, and deaths(1).
Non-clinical sources have the potential to complement clinical and
laboratory data and improve influenza prediction efforts(2). Student
absenteeism is a real-time school-based indicator for influenza
surveillance tool. It is advantageous for being widely available in
real-time, having minimal reporting delays, and being relatively low
cost and is a reasonable proxy for influenza infections since school-age
children (5-17 year olds) experience higher infections compared to other
age-groups(3) and contribute to household and community-level
transmission(4).
Previous studies used school-based surveillance (i.e. absence
duration(5) or causes(6, 7)) to identify patterns correlated with
influenza- or ILI-related cases, primarily at a city-level, but
usefulness of school absenteeism as a surveillance indicator in these
studies has been mixed. Using ILI-specific absence duration predicted
2005-2008 outbreaks well in Japan with high sensitivity and
specificity(5), but a similar approach using city-level all-cause
absences from 2005-2009 had low predictive ability when predicting
outbreaks in New York City(6). Absence patterns correlated well with
sentinel surveillance in Hong Kong showing similar peaks in absenteeism
and ILI consultation and influenza detection rates, but ILI-specific
absences had low specificity(8). The varied conclusions of these studies
could be from differing school- and absence-types captured, and short
surveillance periods, but other types of absence data could have
utility.
Grade-specific differences in absences have not been explored as a
predictor of influenza but may correlate better to high-risk
infections-groups. Given the variation of infection burden and
proportion of illness-related absences by age, particular individual
school levels and grades may serve as a proxy for these high-risk
infection groups. School absenteeism may also be useful for detecting
underlying viral changes in transmission. Unusual patterns of school
absences arising across different periods of time have also been
correlated to detecting changes in influenza A and B viruses(9) and have
been attributed to detecting the re-emergence of an influenza B/Victoria
antigenic group antigenic group(10). The varied study findings of school
absenteeism suggest further assessment is needed.
Here, we evaluated how school absences models predicted weekly confirmed
influenza cases in Allegheny County, Pennsylvania over multiple
influenza seasons. We compared predictions from all-cause absence models
for the 2010-2015 influenza seasons at varying administrative levels. We
also compared predictions for individual influenza seasons (2007-2008,
2012-2013, and 2015-2016) from models including all-cause and
ILI-related absences from three school-based cohort studies.