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.