School absenteeism data for surveillance purposes: a proxy for acute respiratory infection rates

BACKGROUND: School absenteeism data have potential as real-time public health surveillance tool to monitor infectious diseases. This study evaluates the validity and usefulness of school absenteeism data for application in acute respiratory illness (ARI) surveillance. METHODS: Five Dutch primary schools participated in a study (2022/23) to determine reasons for absence using illness surveys. Results were clustered into ARI and non-ARI illness. Spatiotemporal clustering of illness-related absence episodes within the five schools was assessed. A second dataset on absenteeism from 69 primary schools was used to examine longer term absenteeism trends (school years 2017/18, 2018/19, 2022/23) and school-level characteristics associated with absenteeism rates. We also assessed correlations between these absenteeism rates and the corresponding ARI-related primary care visit rates for children aged 5–9. RESULTS: In the five primary schools, ARI accounted for two-thirds of illness-related absenteeism and showed clear spatiotemporal clustering by school and class. Across the 69 primary schools, absenteeism rates exhibited a strong seasonal pattern and higher rates were significantly associated with lower neighbourhood socio-economic status. Post-pandemic absenteeism exceeded pre-pandemic levels by twofold with a mean rate of 2.2 absent days/100 school days in 2022/23 (equivalent to ~ 5 absent days per child/school year). Time trends in absenteeism rates correlated strongly with ARI primary care visit rates, with correlation coefficients > 0.8. CONCLUSION: School absenteeism is strongly linked to respiratory illness and children’s primary care visits for ARI. Absenteeism patterns reflect key characteristics of ARI trends including spatiotemporal clustering and seasonality. School absenteeism data is therefore suitable for ARI surveillance.