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

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...

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Autores principales: Lai, Jennifer, Hughes, Helen E., Morbey, Roger, Loveridge, Paul, Lopez Bernal, Jamie, Saliba, Vanessa, Kissling, Esther, Lovelock-Wren, Alex, Mabbitt, Jeremy, Elliot, Alex J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8668399/
https://www.ncbi.nlm.nih.gov/pubmed/34749838
http://dx.doi.org/10.1017/S0950268821002399
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author Lai, Jennifer
Hughes, Helen E.
Morbey, Roger
Loveridge, Paul
Lopez Bernal, Jamie
Saliba, Vanessa
Kissling, Esther
Lovelock-Wren, Alex
Mabbitt, Jeremy
Elliot, Alex J.
author_facet Lai, Jennifer
Hughes, Helen E.
Morbey, Roger
Loveridge, Paul
Lopez Bernal, Jamie
Saliba, Vanessa
Kissling, Esther
Lovelock-Wren, Alex
Mabbitt, Jeremy
Elliot, Alex J.
author_sort Lai, Jennifer
collection PubMed
description 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.
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spelling pubmed-86683992021-12-16 Developing a sentinel syndromic surveillance system using school-absenteeism data, example monitoring absences over the 2020 COVID-19 pandemic Lai, Jennifer Hughes, Helen E. Morbey, Roger Loveridge, Paul Lopez Bernal, Jamie Saliba, Vanessa Kissling, Esther Lovelock-Wren, Alex Mabbitt, Jeremy Elliot, Alex J. Epidemiol Infect Short Paper 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. Cambridge University Press 2021-11-09 /pmc/articles/PMC8668399/ /pubmed/34749838 http://dx.doi.org/10.1017/S0950268821002399 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
spellingShingle Short Paper
Lai, Jennifer
Hughes, Helen E.
Morbey, Roger
Loveridge, Paul
Lopez Bernal, Jamie
Saliba, Vanessa
Kissling, Esther
Lovelock-Wren, Alex
Mabbitt, Jeremy
Elliot, Alex J.
Developing a sentinel syndromic surveillance system using school-absenteeism data, example monitoring absences over the 2020 COVID-19 pandemic
title Developing a sentinel syndromic surveillance system using school-absenteeism data, example monitoring absences over the 2020 COVID-19 pandemic
title_full Developing a sentinel syndromic surveillance system using school-absenteeism data, example monitoring absences over the 2020 COVID-19 pandemic
title_fullStr Developing a sentinel syndromic surveillance system using school-absenteeism data, example monitoring absences over the 2020 COVID-19 pandemic
title_full_unstemmed Developing a sentinel syndromic surveillance system using school-absenteeism data, example monitoring absences over the 2020 COVID-19 pandemic
title_short Developing a sentinel syndromic surveillance system using school-absenteeism data, example monitoring absences over the 2020 COVID-19 pandemic
title_sort developing a sentinel syndromic surveillance system using school-absenteeism data, example monitoring absences over the 2020 covid-19 pandemic
topic Short Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8668399/
https://www.ncbi.nlm.nih.gov/pubmed/34749838
http://dx.doi.org/10.1017/S0950268821002399
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