Developing a sentinel syndromic surveillance system using school-absenteeism data, example monitoring absences over the 2020 COVID-19 pandemic

Epidemiol Infect. 2021 Nov 9:149:e248. doi: 10.1017/S0950268821002399.

Abstract

This study describes the development of a pilot sentinel school absence syndromic surveillance system. Using data from a sample of schools in England the capability of this system to monitor the impact of disease on school absences in school-aged children is shown, using the coronavirus disease 2019 (COVID-19) pandemic period as an example. Data were obtained from an online app service used by schools and parents to report their children absent, including reasons/symptoms relating to absence. For 2019 and 2020, data were aggregated into daily counts of 'total' and 'cough' absence reports. There was a large increase in the number of absence reports in March 2020 compared to March 2019, corresponding to the first wave of the COVID-19 pandemic in England. Absence numbers then fell rapidly and remained low from late March 2020 until August 2020, while lockdown was in place in England. Compared to 2019, there was a large increase in the number of absence reports in September 2020 when schools re-opened in England, although the peak number of absences was smaller than in March 2020. This information can help provide context around the absence levels in schools associated with COVID-19. Also, the system has the potential for further development to monitor the impact of other conditions on school absence, e.g. gastrointestinal infections.

Keywords: Absence data; COVID-19; real-time surveillance; school-aged children; syndromic surveillance.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Absenteeism*
  • COVID-19 / epidemiology*
  • Child
  • Communicable Disease Control
  • Disease Outbreaks / prevention & control*
  • England / epidemiology
  • Epidemiological Monitoring*
  • Humans
  • Male
  • Pandemics
  • SARS-CoV-2
  • Schools
  • Sentinel Surveillance*
  • Students / statistics & numerical data