ArticleInfluenza and other respiratory viruses2021
Predicting virologically confirmed influenza using school absences in Allegheny County, Pennsylvania, USA during the 2007-2015 influenza seasons.
Article in Influenza and other respiratory viruses, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Monitoring School Absenteeism for Influenza-Like Illness Surveillance: Systematic Review and Meta-analysis.JMIR public health and surveillance · 2023Pooled it
- Absences, Symptoms and Respiratory Viruses in a Swiss School: Longitudinal Study With Serial Saliva Sampling.Influenza and other respiratory viruses · 2025Article
- Evaluation of Kindergarten Through Grade 12 All-Cause Absenteeism Data as an Indicator and Predictor of Respiratory Disease, 2018-2022.Public health reports (Washington, D.C. : 1974) · 2025Article
- Predicting virologically confirmed influenza using school absences in Allegheny County, Pennsylvania, USA during the 2007-2015 influenza seasons.Influenza and other respiratory viruses · 2021Article
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Authors and funding
12 authors.
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Abstract
backgroundChildren are important in community-level influenza transmission. School-based monitoring may inform influenza surveillance.
methodsWe used reported weekly confirmed influenza in Allegheny County during the 2007 and 2010-2015 influenza seasons using Pennsylvania's Allegheny County Health Department all-age influenza cases from health facilities, and all-cause and influenza-like illness (ILI)-specific absences from nine county school districts. Negative binomial regression predicted influenza cases using all-cause and illness-specific absence rates, calendar week, average weekly temperature, and relative humidity, using four cross-validations.
resultsSchool districts reported 2 184 220 all-cause absences (2010-2015). Three one-season studies reported 19 577 all-cause and 3012 ILI-related absences (2007, 2012, 2015). Over seven seasons, 11 946 confirmed influenza cases were reported. Absences improved seasonal model fits and predictions. Multivariate models using elementary school absences outperformed middle and high school models (relative mean absolute error (relMAE) = 0.94, 0.98, 0.99). K-5 grade-specific absence models had lowest mean absolute errors (MAE) in cross-validations. ILI-specific absences performed marginally better than all-cause absences in two years, adjusting for other covariates, but markedly worse one year.
conclusionsOur findings suggest seasonal models including K-5th grade absences predict all-age-confirmed influenza and may serve as a useful surveillance tool.
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